Cities are hotspots for the emissions of air pollutants and greenhouse gases from traffic, industries, household heating and energy production. Air pollution impacts are episodic and often co-occur with heat waves and allergenic pollen release. Greenhouse gases are often co-emitted with air pollutants. Urban air quality and the effect of policy measures are a challenge to monitor with traditional fixed stations or with models, because of the extreme variability in the cities’ geometry and emission patterns.
This session intends to bring together researchers of urban air quality and greenhouse gases and will accept submissions of topics related to urban air quality, heat stress, and air pollution impacts including health. The presentations focus on new developments in the field of ground and satellite observations, process modelling, data merging and downscaling related to urban air quality. Topics include sensor networks, personal monitoring, observations from space and UAV’s, high spatial and temporal resolution model approaches, downscaling, source apportionment, optical properties, atmospheric processes, mechanisms for air quality deterioration, community and personal exposure quantification and air pollution effects. Air pollution species may include anthropogenic and biogenic ones, including greenhouse gases and allergenic pollen, their isotopes and concentration ratios.

Convener: Ulrike Dusek | Co-conveners: Dominik Brunner, Ru-Jin Huang, Michiel van der Molen, Felix Vogel
| Attendance Fri, 08 May, 08:30–12:30 (CEST), Attendance Fri, 08 May, 14:00–15:45 (CEST)

Files for download

Download all presentations (205MB)

Chat time: Friday, 8 May 2020, 08:30–10:15

Chairperson: Dominik Brunner, Ulrike Dusek
D2928 |
Zhiyuan Li, Steve Hung Lam Yim, and Kin-Fai Ho

Land use regression (LUR) models estimate air pollutant concentrations for areas without air quality measurements, which provides valuable information for exposure assessment and epidemiological studies. In the present study, we developed LUR models for ambient air pollutants in Hong Kong, China, a typical high-density and high-rise city. Air quality measurements at sixteen air quality monitoring stations, operated by the Hong Kong Environmental Protection Department, were collected. Moreover, five categories of predictor variables, including population distribution, traffic emissions, land use variables, urban/building morphology, and meteorological parameters, were employed to establish the LUR models of various air pollutants. Then the spatial distribution of air pollutant concentrations at 1 km × 1 km grid cells were plotted. Taking fine particle (PM2.5) as an example, the developed LUR model explained 89% of variability of PM2.5 concentrations, with a leave-one-out-cross-validation R2 of 0.64. LUR modelling results for other air pollutants will be presented. In addition, further improvements on the development of LUR models will be discussed. This study can help to assess long-term exposures to air pollutants for high-density and high-rise urban areas like Hong Kong.

How to cite: Li, Z., Yim, S. H. L., and Ho, K.-F.: Land use regression modelling of ambient air pollutants in Hong Kong , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12648, https://doi.org/10.5194/egusphere-egu2020-12648, 2020.

D2929 |
| Highlight
Patricia Tarín-Carrasco, Ulas Im, Laura Palacios-Peña, and Pedro Jiménez-Guerrero

Cities are hotspots for exposure to air pollution worldwide. The impact of atmospheric pollutants on human health is a main topic of concern related to health issues in urban areas; and there evidence that this problem will become worse under future climate change scenarios. One of the main anthropogenic pollutants released at cities that

impacts human mortality is particulate matter (PM). The riskiness of PM resides in both its composition and size. In particular, this study is focused on fine particles (particles with a diameter of 2.5μm or less, PM2.5). PM2.5 can reach lungs, pulmonary alveoli or even bloodstream being transported through the entire human body. In this sense, the emission of PM2.5 from combustion processes coming from energy production in cities can be a major health problem needing for mitigation policies regarding anthropogenic regulatory pollutants. In this sense, a bet for renewables energies can help the definition of mitigation strategies and can contribute to a better future urban air quality.

Henceforth, this study assesses the impacts of present (1991-2010) and future (RCP8.5,2031-2050) urban air pollution by fine particles on several Non-Communicable Diseases (NCD) mortality causes (Lung Cancer, Chronic Obstructive Pulmonary Disease, Ischaemic Heart Disease, Stroke, Lower Respiratory Infection and All diseases). Climate change scenarios were run by using the WRF-Chem online-coupled meteorological/chemistry model in framework of the Spanish REPAIR and ACEX projects, operated over an Euro-CORDEX compliant simulation domain. For the future scenarios, two alternatives under the RCP8.5 climate change scenarios are analysed: (1) business-as-usual energy production system and emissions, and (2) an scenario in which 80% of the European energy is obtained from renewable sources. The emission factors for energy production (g/GJ) were obtained from EMEP/EEA air pollutant emission inventory guidebook–2016.

The differences between both scenarios (future vs. present approach) provide the changes in future mortality caused by air pollution. We estimated the mortalities by using non-linear exposure-response functions. Furthermore, a novel contribution of this work is that changes in future population for the 2050 horizon have been taken into account. Different risk ratio and baseline mortalities for each pathology have been estimated in every age range (25-29, 30-34, 35-39, 40-44, 45-49, 50-54, 55-59, 60-64, 65-69, 70-74, 75-79, +80 and all ages). Data was obtained from Institute for Health Medicine.

The results obtained indicate that almost 900,000 deaths per year in Europe are caused by PM2.5 for the present scenario. Generally, the mortality will increase for both future scenarios. The total mortality on the future RCP8.5 scenario accounts for 1,500,000 deaths for the business-as-usual energy production scenario and 1,480,000 for the future scenario considering 80% of renewable energy production. Eastern Europe is the area most benefited with the change of energy production on the future because the number of deaths will be lower. Stroke is the cause which count with high of deaths in Europe.


Acknowledgments: Project ACEX (CGL-2017-87921-R) of the Spanish Ministry of Economy and Competitiveness, Fundación Biodiversidad of the Spanish Ministry for the Ecological Transition, and FEDER European program, for support to conduct this research.

How to cite: Tarín-Carrasco, P., Im, U., Palacios-Peña, L., and Jiménez-Guerrero, P.: Impacts of air pollution related to fine particulate matter on present and future European urban mortality: a renewable energy mitigation scenario, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5068, https://doi.org/10.5194/egusphere-egu2020-5068, 2020.

D2930 |
Min Xie, Tijian Wang, Jie Shi, Mengmeng Li, Da Gao, and Chenchao Zhan

Anthropogenic heat (AH) can affect regional meteorology and air quality. The spatial distributions of AH fluxes in the typical city clusters of China are estimated. Moreover, in order to study their impacts on regional atmospheric environment, these heat fluxes are incorporated into the modified WRF/Chem with the seasonal and the diurnal variation. The modeling results show that AH fluxes over YRD and PRD have been growing in recent years. The high values of AH can reach 113.5 W/m2 in YRD and 60 W/m2 in PRD, respectively. AH fluxes can significantly change the urban meteorology. In YRD, 2-m air temperature (T2) increases by 1.6 °С in January and 1.4°С in July, the planetary boundary layer height (PBLH) rises up by 140m in January and 160m in July, and 10-m wind speed (W10) is intensified by 0.7 m/s in January and 0.5 m/s in July. More moisture can be transported to higher levels, and increase the accumulative precipitation by 15-30% in July of YRD. In PRD, T2 rises up by 1.1°С in January and over 0.5°С in July, the PBLH increases by 120m in January and 90m in July, W10 is enhanced over 0.35 m/s in January and 0.3 m/s in July, and the accumulative precipitation is intensified by 20-40% in July. These changes in meteorology can influence the distribution of air pollutants as well. Due to the increase of PBLH, surface wind speed and upward movement, the concentrations of primary air pollutants decrease near surface and increase at the upper layers over the cities. Chemical effects can play a significant role in ozone changes over the urban areas of YRD, so ozone concentrations increase at surface and decrease at the upper layers. In PRD cities, however, the chemical effects play a significant role in ozone changes in winter, while the vertical movement can be the dominant effect in summer. Thus, ozone concentrations in big cities increase in January, but decrease at the lower layers and increase at the upper layers in July. In all, AH fluxes should not be ignored in urban meteorology and air quality assessments.

How to cite: Xie, M., Wang, T., Shi, J., Li, M., Gao, D., and Zhan, C.: The effects of anthropogenic heat fluxes on regional meteorology and air quality in typical city clusters of China, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13006, https://doi.org/10.5194/egusphere-egu2020-13006, 2020.

D2931 |
Giorgio Veratti, Sara Fabbi, Alessandro Bigi, Aurelia Lupascu, Gianni Tinarelli, Sergio Teggi, Giuseppe Brusasca, Tim M. Butler, and Grazia Ghermandi

In order to support environmental policies, epidemiological studies and urban mobility planning, a multi-scale modelling system was developed to provide hourly NO(NO + NO2) concentration fields at a building-resolving scale in the urban area of Modena, a city in the middle of the Po Valley (Italy). The modelling system relied on two different models: the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem), which is able to compute concentration fields over regional domain by considering specific emission scenarios, and Parallel Micro SWIFT SPRAY (PMSS), a Lagrangian particle model accounting for dispersion phenomena within the urban area. PMSS was used to simulate at building-scale resolution the NOdispersion produced by urban traffic flows in the city of Modena. Conversely, WRF-Chem was selected to estimate the NObackground concentrations over three nested domains with resolution of 15, 3 and 1 km in order to take into account emissions both at regional and local scale by excluding traffic emissions sources over the city of Modena. The estimation of traffic emissions in the urban area of Modena was based on a bottom-up approach relying on the Emission Factors suggested by the European Monitoring and Evaluation Programme (EMEP/EEA) and traffic fluxes estimated by the PTV VISUM model. By contrast, other anthropogenic emissions were taken from the TNO-MACC III inventory at the scales resolved by the WRF-Chem model.

Simulation was performed between 28 October and 8 November 2016, the same period whereby a direct vehicle flow measurement campaign was carried out continuously, with 4 Doppler radar counters in a four-lane road in Modena, to reproduce the hourly modulation rates of the emissions. The performances of the model chain were finally assessed by comparing modelled NOconcentrations with observations at two air quality monitoring stations located inside the urban domain.

Simulated and observed NOhourly concentrations exhibit a large agreement, in particular for urban traffic site where detailed traffic emissions estimation (real traffic modulation combined with a bottom-up approach) proved to be very successful in reproducing the observed NOpattern. At the urban background station, notwithstanding a general underestimation of the observed concentrations (more pronounced than at the urban traffic site), the analysis of hourly daily modelled concentrations shows that PMSS combined with WRF-Chem provided a daily pattern in line with observations. These features highlight the strength of this modelling chain in representing urban air quality, in particular at traffic sites, whose concentration levels make them the most critical area of the city; characteristics that chemical transport models alone cannot express, due to the coarser resolution to which they operate and to their inability to reproduce street canyons and urban structures.

How to cite: Veratti, G., Fabbi, S., Bigi, A., Lupascu, A., Tinarelli, G., Teggi, S., Brusasca, G., Butler, T. M., and Ghermandi, G.: The development of a multi-scale modelling system for evaluation of urban NOx levels in Modena (Italy), EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7379, https://doi.org/10.5194/egusphere-egu2020-7379, 2020.

D2932 |
Stefanie Kremser, Sara Mikaloff-Fletcher, Brian Nathan, Ethan Dale, Jordis Tradowsky, Leroy Bird, Greg Bodeker, Dongqi Lin, Guy Coulson, Marwan Katurji, Gustavo Olivares, Tim Mallett, Laura Revell, and Ian Longley

The growth of megacities from global urbanization has degraded urban air quality sufficient to impede economic growth and create a public health hazard. Emissions of particulate matter, photochemically reactive gases, and long-lived greenhouse gases, contribute to the urban environmental footprint with concomitant economic and social costs. Mitigation actions rely critically on knowing where these emissions occur. In response to this challenge, our team has developed a new method, MAPM (Mapping Air Pollution eMissions), to generate near real-time surface emissions maps of particulate matter pollution. Surface particulate matter (PM 2.5) emission maps will be derived from atmospheric measurements of particulate matter using an inverse model in conjunction with a state-of-the-art mesoscale atmospheric model.

The MAPM methodology is validated and refined using particulate matter measurements made during a field campaign that took place in Christchurch, New Zealand from June to September 2019. Key questions that MAPM aims to answer include:

  • How do uncertainties on the PM 2.5 measurements affect the quality of the emissions maps we extract from our inverse model.
  • How do uncertainties in the meteorological data affect the quality of the emissions maps we extract from our inverse model.
  • How does the spatial and temporal resolution of the air pollution concentration measurements affect the uncertainties in the retrieved pollution emissions maps?

Here we will not only present the measurements made during the winter field campaign but also present the first derived PM 2.5 emissions maps for the city of Christchurch.

How to cite: Kremser, S., Mikaloff-Fletcher, S., Nathan, B., Dale, E., Tradowsky, J., Bird, L., Bodeker, G., Lin, D., Coulson, G., Katurji, M., Olivares, G., Mallett, T., Revell, L., and Longley, I.: Mapping Air Pollution eMissions (MAPM), EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11611, https://doi.org/10.5194/egusphere-egu2020-11611, 2020.

D2933 |
Helen Pearce, Zhaoya Gong, Xiaoming Cai, and William Bloss

In most European cities, the key air pollutants driving adverse health outcomes are nitrogen dioxide (NO2) and fine particulate matter (PM2.5), with 64% of new paediatric asthma cases in urban centres attributed to elevated NO2 levels (Achakulwisut et al., 2019). In the complex landscape of a city, a synthesis of techniques to quantify air pollution is required to account for variations in traffic, meteorology, and urban geometry.

Here, we present the results from a comparison study between measured air pollutant data collected at Marylebone Road, London and the output from a three-stage modelling chain. This site was chosen due to the availability of road-side air quality data collected within a street canyon (aspect ratio approximately equal to 1) and daily traffic flow in excess of 70,000 motor vehicles. The modelling chain consists of: 1) real-time traffic information of vehicle journey times, 2) speed-related emission calculations, and 3) air quality box-model to simulate the interaction of pollutants within the environment.

While the transport sector accounts for much of the outdoor air pollution in UK cities, a limiting factor of current techniques is that traffic is approximated at coarse temporal and spatial resolutions. In this study, we present a novel technique that helps to ‘fill in’ the gaps in our traffic data by harnessing the power of real-time queries to Google Maps to obtain travel times between fixed locations, enabling the derivation of average vehicle speeds. This dataset can then be used to determine more accurate emission factors for NOx. Total emissions are then calculated with the aid of traffic flow data and vehicle fleet characteristics. The air quality box model simulates photochemical reactions that form NO2, the exchange of pollutants with the background air aloft, and advection of pollutants along the street.

Hourly travel times and total vehicle flow data were collected between July and October 2019, totalling 905 observations and calculated emissions values. Meteorological data from Heathrow airport and background air quality from the Kensington AURN site were used as supporting inputs to the air quality box model. Each observation was treated as a starting point of the box model, and the simulation was run for 1 hour, with mixing due to advection occurring every 60 seconds. Results are promising; when using the full model chain modelled and measured NO2 concentrations are significantly correlated (r = 0.467, p < 0.000). In comparison, when a constant speed of 30 mph is used to calculate total emissions, therefore excluding the impact of congestion, the strength of the correlation decreases (r = 0.362, p < 0.000) and the model underestimates pollutant concentrations.

The applications of this model chain are vast. For any street that is covered by a suitable mapping platform and has available data on vehicle numbers, it would be possible to provide a real-time estimation of pollutant concentrations at a high temporal resolution. This could be utilised in several ways, such as: assessing policy implementation, and providing a high resolution input for air quality modelling and health exposure studies.

How to cite: Pearce, H., Gong, Z., Cai, X., and Bloss, W.: From Google Maps to Air Quality: a big data approach to modelling real-time NO2 concentrations in an urban street canyon from road traffic data, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4768, https://doi.org/10.5194/egusphere-egu2020-4768, 2020.

D2934 |
Sally Jahn and Elke Hertig

Air pollution as well as high air temperature both pose a large risk to human health in Europe. High temperature levels are associated with an exceptionally high mortality rate, only representing the extreme end of a wide range of possible health effects. Tropospheric ozone, a secondary air pollutant, is primarily built by photochemical reactions under solar radiation with the involvement of precursor gases including nitrogen oxides, carbon monoxide, methane, and non-methane volatile organic compounds. Due to the specific characteristics of ozone formation, high levels of ozone and temperature often coincide, posing an even intensified threat to human health.

The current scientific work focuses on the co-occurrence of these two health stressors as well as their underlying meteorological conditions. A subset of European ozone (AirBase_v8, EEA) and temperature (ECA&D) stations is selected for analysis based on individual station locations and data coverage. Taking into account different settings of air substances concentrations (urban, outer conurbation area, rural regions), these stations are classified and grouped by station type and area type resulting in five distinct station classes: urban traffic, urban background, suburban background, rural background and rural industrial.

Maximum daily 8-hour average ozone values (MDA8O3, EEA), observed daily maximum air temperatures (TX, ECA&D) and meteorological variables (from ERA5, ECMWF) form the data basis for model building. Current thresholds and extreme definitions e.g. based on WHO air quality guidelines or high percentiles (75th and 90th) are examined and discussed to describe elevated levels of these variables and to finally define combined ozone-temperature events.

Possible regional patterns as well as disparities between urban and rural areas regarding the specific settings for ozone formation as well as varying meteorological mechanisms for the occurrence of combined ozone-temperature events are closely examined. The methodological focus is primary on statistical modelling, the application and comparison of varying multivariate statistical approaches and different machine learning methods, e.g. various regression analyses using shrinkage methods or random forests. Consequently, statistical models are generated to analyse the influence of meteorological conditions on the occurrence of combined ground-level ozone and temperature events along with the identification of primary key factors (e.g. ozone persistence or larger-scale air temperature and wind conditions) at each specific location.

Furthermore, frequency and intensity changes of combined ozone-temperate events in the scope of global warming are assessed. Thus, projections of these co-occurring events under the constraints of ongoing climate change until the end of the 21st century are analysed by integrating projections of general circulation models into the statistical modelling process.

How to cite: Jahn, S. and Hertig, E.: Statistical modelling of combined ozone-temperature events in Europe , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1314, https://doi.org/10.5194/egusphere-egu2020-1314, 2020.

D2935 |
Mark Wenig, Ying Zhu, Sheng Ye, Ka Lok Chan, Jia Chen, Florian Dietrich, Xiao Bi, and Gerrit Kuhlmann

In many cities around the world the NO2 concentration levels exceed WHO guideline limits. Urban air quality is typically monitored using a relatively small number or monitoring stations that follow certain guidelines in terms of inlet height and location relative to streets. However, the question remains how a limited number of point measurements can represent the city-wide air quality and capture spatial patterns. Measurement campaigns in Hong Kong and Munich were conducted, using a combination of mobile in-situ and stationary remote sensing differential optical absorption spectroscopy (DOAS) instruments. In order to separate spatial and temporal patterns, we developed an algorithm based on a combination of mobile and stationary data sets that corrects for the diurnal cycle in the mobile measurements.  We constructed pollution maps from the corrected measurements that represent daily average NO2 exposure. The maps have been used to identify pollution hot spots, determine the spatial dependency of long-term changes, and capture the weekly cycles of on-road NO2 levels in Hong Kong and Munich. Since our method can also be used to determine the spatial representativeness of the monitoring stations in cities, it is very valuable tool for identifying suitable locations for air quality monitoring stations.

How to cite: Wenig, M., Zhu, Y., Ye, S., Chan, K. L., Chen, J., Dietrich, F., Bi, X., and Kuhlmann, G.: Measuring Spatial and Temporal Patterns of Urban NO2 Concentrations by combining mobile and stationary DOAS instruments, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7523, https://doi.org/10.5194/egusphere-egu2020-7523, 2020.

D2936 |
Denis Pöhler, Oliver Fischer, Martin Weinreich, Sven Riedner, Martin Horbanski, Johannes Lampel, Stefan Schmitt, and Ulrich Platt

Nitrogen Dioxide (NO2) is currently the most critical air pollutant in Europe. The main source is traffic, especially diesel engines, and its concentration is highly variable. However, NO2 levels are only measured in larger cities at few measurement points. Passive samplers can provide a better spatial coverage but contain no temporal information about the NO2 variability at that location. Electrochemical sensors require a lot of manpower and additional parameters to be measured simultaneously to achieve sufficient accuracy and are thus not practical.

We apply the mobile, low power and high precision ICAD NO2 / NOx instrument (Airyx GmbH) to observe the distribution of NO2 concentration in a city or in industrial facilities. For example, smaller cities are of interest where so far no information about air pollution levels and possible hot spots are available. Measurements are conducted on a bicycle at ~1.6m height and beside the road-line (with a time resolution of 2s and 1ppb accuracy) to be comparable to data from permanent measurement stations. Along a predefined route through the city, covering different street types, repeated measurements at different days and times are performed.

We present results from measurements in multiple cities with focus on the small city of Walldorf in South-West Germany. An NO2 distribution map was derived from mobile bicycle measurements over a period of 3 months. Locations with increased air pollution levels are clearly identified. Additionally, extrapolated annual average NO2 level and its distribution were estimated by comparison with an urban air monitoring station in 6km distance. The method for this annual mean extrapolation will be described. For two hot spot locations the derived extrapolated annual mean concentration was validated in a second campaign with intensive stationary measurements using the same instrument in a small trailer. The annual mean concentrations agreed within ~10% and prove the mobile measurement results, not only for these locations, but also in general for this method. Due to the high time resolution of the data additional emission sources can be identified.

This example shows that it is possible to derive reliably annual mean NO2 air pollution distribution maps with few repeated mobile measurements and thus increase our understanding of real air pollution levels on a broad scale in a city.

Mobile measurements were also performed in industrial facilities like mines. An example of such measurements will be presented.

How to cite: Pöhler, D., Fischer, O., Weinreich, M., Riedner, S., Horbanski, M., Lampel, J., Schmitt, S., and Platt, U.: Observation of NO2 air pollution distribution maps in cities with mobile ICAD bicycle measurements, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11337, https://doi.org/10.5194/egusphere-egu2020-11337, 2020.

D2937 |
Vivien Voss, K. Heinke Schlünzen, and David Grawe

Air pollution is an important topic within urban areas.  Limit values as given in the European Guidelines are introduced to reduce negative effects on humans and vegetation.  Exceedances of the limit values are to be assessed using measurements.  In case of found exceedances of the limit values, the local authorities need to act to reduce pollution levels. Highest values are found for several pollutants (NOx, NO2, particles) within densely build-up urban areas with traffic emissions being the major source and dispersion being very much impacted by the urban structures.  The quality assured measuring network used by the authorities is often too coarse to determine the heterogeneity in the concentration field. Low cost sample devices as employed in several citizen science projects might help to overcome the data sparsity. Volunteers measure the air quality at many sites, contribute to the measurement networks and provide the data on the web. However, the questions arising are: a) Are these data of sufficient high quality to provide results comparable to those of the quality assured networks? b) Is the network density sufficient to determine concentration patterns within the urban canopy layer?
One-year data from a citizen science network, which measures particulate matter (PM10, PM2.5) were compared to measurements provided by the local environmental agency, using two hot-spot areas in the city of Hamburg as an example. To determine how well the measurements agree with each other, a regression analyses was performed dependent on seasonal and diurnal cycles. Additionally, model simulations with the microscale obstacle resolving model MITRAS were performed for two characteristic building structures and different meteorological situations. The model results were used to determine local hot spots as well as areas where measurements might represent the concentration of particles for the urban quarter. The low cost sensor measurements show a general agreement to the city’s measurements, however, the values per sensor differ. Moreover, the measurements of the low-cost-sensor show an unrealistic dependence on relative humidity, resulting in over- or underestimations in certain cases. The model results clearly show that only a few sites allow measurements to be representative for a city quarter. The measurements of the citizen science project can provide a good overview about the tendencies of the air quality, but are currently not of sufficient quality to provide measurements calling for legal action.

The model results were used for the project AtMoDat. AtMoDat is an attempt to create a data standard for obstacle resolving models based on the existing Climate and Forecast (CF) conventions. A web-based survey is developed to get information on the requirements for the data standard. The next step is to extend the collection of model characteristics and eventually to provide a generic scheme.

This work contributes to project “AtMoDat” funded by the Federal Ministry of Education and Research under the funding number 16QK02C. Responsibility for the content of this publication lies with the authors.

How to cite: Voss, V., Schlünzen, K. H., and Grawe, D.: Assessment of heterogenity of air pollution within an urban canopy, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-2822, https://doi.org/10.5194/egusphere-egu2020-2822, 2020.

D2938 |
Philipp Schneider, Nuria Castell, Paul Hamer, Sam-Erik Walker, and Alena Bartonova

One of the most promising applications of low-cost sensor systems for air quality is the possibility to deploy them in relatively dense networks and to use this information for mapping urban air quality at unprecedented spatial detail. More and more such dense sensor networks are being set up worldwide, particularly for relatively inexpensive nephelometers that provide PM2.5 observations with often quite reasonable accuracy. However, air pollutants typically exhibit significant spatial variability in urban areas, so using data from sensor networks alone tends to result in maps with unrealistic spatial patterns, unless the network density is extremely high. One solution is to use the output from an air quality model as an a priori field and as such to use the combined knowledge of both model and sensor network to provide improved maps of urban air quality. Here we present our latest work on combining the observations from low-cost sensor systems with data from urban-scale air quality models, with the goal of providing realistic, high-resolution, and up-to-date maps of urban air quality.

In previous years we have used a geostatistical approach for mapping air quality (Schneider et al., 2017), exploiting both low-cost sensors and model information. The system has now been upgraded to a data assimilation approach that integrates the observations from a heterogeneous sensor network into an urban-scale air quality model while considering the sensor-specific uncertainties. The approach further ensures that the spatial representativity of each observation is automatically derived as a combination of a model climatology and a function of distance. We demonstrate the methodology using examples from Oslo and other cities in Norway. Initial results indicate that the method is robust and provides realistic spatial patterns of air quality for the main air pollutants that were evaluated, even in areas where only limited observations are available. Conversely, the model output is constrained by the sensor data, thus adding value to both input datasets.

While several challenging issues remain, modern air quality sensor systems have reached a maturity level at which some of them can provide an intra-sensor consistency and robustness that makes it feasible to use networks of such systems as a data source for mapping urban air quality at high spatial resolution. We present our current approach for mapping urban air quality with the help of low-cost sensor networks and demonstrate both that it can provide realistic results and that the uncertainty of each individual sensor system can be taken into account in a robust and meaningful manner.


Schneider, P., Castell N., Vogt M., Dauge F. R., Lahoz W. A., and Bartonova A., 2017. Mapping urban air quality in near real-time using observations from low-cost sensors and model information. Environment international, 106, 234-247.

How to cite: Schneider, P., Castell, N., Hamer, P., Walker, S.-E., and Bartonova, A.: Networks of Air Quality Sensors and Their Use for High-resolution Mapping of Urban Air Quality, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9172, https://doi.org/10.5194/egusphere-egu2020-9172, 2020.

D2939 |
Simon Jirka, Eike Hinderk Jürrens, Benedikt Gräler, Carsten Hollmann, Alexander Kotsev, Michel Gerboles, Annette Borowiak, and Sven Schade

Over the last few years there have been many activities to evaluate and use air quality measurements gathered by lower cost devices. This is especially intended to complement the coverage of official air quality measurement networks that deliver authoritative air quality data. Examples of such activities include the AirSensEUR project of the Joint Research Centre (JRC), luftdaten.info, or hackAIR. Combining the data from multiple sources remains a challenge for utilising the full potential of those developments.

With this presentation we aim to introduce the development of an interoperable data platform that allows to integrate both authoritative as well as citizen science air quality measurements. Our presentation will cover especially the following aspects:

Interoperability: For sharing the collected data and to avoid the creation of isolated data silos, it is important to use open interfaces and data encodings. In case of the AQSens project, this comprises the provision of INSPIRE-compliant Download Services based on the SensorThings API (STA) and Sensor Observation Service (SOS) standards of the Open Geospatial Consortium.

Data analytics: Besides providing access to the raw data, different types of data analysis are necessary. On the one hand this comprises the validation of incoming citizen science data in conjunction with corresponding authoritative data sources. On the other hand, the aim is to provide a tool for further data analysis on top of the collected data. For this purpose we show, how the R programming language can be linked to the Sensor Web Server via a dedicated R package (sos4R).

Data visualisation: Finally, for enabling the visual exploration of the collected data, a Web-based client application will be provided. This allows users to connect to the published air quality Data Download Services (in this case the OGC SensorThings API) and to request graph-based time series visualisations combining data from potentially different sources.

In summary, our presentation will show how existing interoperability standards as well as Web technologies can be used for building a Cloud-ready data platform (i.e. relying on Docker) that enables the collection, management, analysis, and visualisation of both Citizen Science and authoritative air quality data.

How to cite: Jirka, S., Jürrens, E. H., Gräler, B., Hollmann, C., Kotsev, A., Gerboles, M., Borowiak, A., and Schade, S.: AQSens: An Interoperable Platform Integrating Citizen Science and Authoritative Air Quality Data, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-14358, https://doi.org/10.5194/egusphere-egu2020-14358, 2020.

D2940 |
Sensor network for traffic-related pollutant monitoring in urban public transport interchanges
Li Sun, Peng Wei, Jieqing He, Dane Westerdahl, and Zhi Ning
D2941 |
| Highlight
Ramachandran Subramanian, Matthias Beekmann, Carl Malings, Anais Feron, Paola Formenti, Michael Giordano, Beatrice Marticorena, Corinne Galy-Lacaux, Catherine Liousse, Joseph Adesina, Stuart Piketh, Kofi Amegah, Julien Bahino, Véronique Yoboué, Laouali Dungall, Rebecca M Garland, Jimmy Gasore, Vincent Madadi, and Jean-Louis Rajot

Ambient air pollution is a leading cause of premature mortality across the world, with an estimated 258,000 deaths in Africa (UNICEF/GBD 2017). These estimated impacts have large uncertainties as many major cities in Africa do not have any ground-based air quality monitoring. The lack of data is due in part to the high cost of traditional monitoring equipment and the lack of trained personnel. As part of the “Make Air Quality Great Again” project under the “Make Our Planet Great Again” framework (MOPGA), we propose filling this data gap with low-cost sensors carefully calibrated against reference monitors.

Fifteen real-time affordable multi-pollutant (RAMP) monitors have been deployed in Abidjan, Côte d'Ivoire; Accra, Ghana; Kigali, Rwanda; Nairobi, Kenya; Niamey, Niger; and Zamdela, South Africa (near Johannesburg). The RAMPs use Plantower optical nephelometers to measure fine particulate matter mass (PM2.5) and four Alphasense electrochemical sensors to detect pollutant gases including nitrogen dioxide (NO2) and ozone (O3).

Using a calibration developed in Créteil, France, the deployments thus far reveal morning and evening spikes in combustion-related air pollution. The median hourly NO2 in Accra and Nairobi for September-October 2019 was about 11 ppb; a similar value was observed across November-December 2019 in Zamdela. However, a previous long-term deployment of the RAMPs in Rwanda showed that, for robust data quality, low-cost sensors must be collocated with traditional reference monitors to develop localized calibration models. Hence, we acquired regulatory-grade PM2.5, NO2, and O3 monitors for Abidjan and Accra. We also collocated RAMPs with existing reference monitors in Zamdela, Kigali, Abidjan, and Lamto (a rural site in Côte d'Ivoire). In this talk, we will present results on spatio-temporal variability of collocation-based sensor calibrations across these different cities, source identification, and challenges and plans for future expansion.

How to cite: Subramanian, R., Beekmann, M., Malings, C., Feron, A., Formenti, P., Giordano, M., Marticorena, B., Galy-Lacaux, C., Liousse, C., Adesina, J., Piketh, S., Amegah, K., Bahino, J., Yoboué, V., Dungall, L., Garland, R. M., Gasore, J., Madadi, V., and Rajot, J.-L.: MOPGA/Make Air Quality Great Again: Filling in the air quality data gap in Africa using lower-cost RAMP monitors, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10942, https://doi.org/10.5194/egusphere-egu2020-10942, 2020.

D2942 |
Ting Wang, Rujin Huang, Lu Yang, Wei Yuan, and Yuquan Gong

Atmospheric brown carbon (BrC) has significant impact on Earth’s radiative budget. However, due to our very limited knowledge about the relationship between BrC light absorption and the associated sources, the estimation for radiative effects of BrC is still largely constrained. In this study, we combine ultraviolet−visible (UV−vis) spectroscopy measurements and chemical analyses of BrC samples collected from January to December 2015 in urban Beijing, to investigated the sources of atmospheric BrC. The multiple liner regression model was applied to apportion the contributions of individual primary and secondary organic aerosol (OA) source components to light absorption of BrC. Our results indicated that biomass burning emission and secondary formation are highly absorbing up to 500 nm, and their contributions increased with the wavelengths. In contrast, the contribution of traffic emission and coal combustion to total absorption decreased with the wavelength and the large contributions were mostly found at shorter wavelengths. Then the mass absorption efficiency (MAE) of major light-absorbing components were estimated, which can provide a support to estimate the impact of BrC from these sources on the climate. The positive matrix factorization model were also used to verify the contributions of different source components of BrC absorption at 365 nm. The results consistently demonstrate that the biomass burning and secondary formation contributes significantly to the overall absorption, followed by coal combustion and traffic emission.

How to cite: Wang, T., Huang, R., Yang, L., Yuan, W., and Gong, Y.: Brown carbon aerosol in urban Beijing: Significant contributions from biomass burning and secondary formation, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7241, https://doi.org/10.5194/egusphere-egu2020-7241, 2020.

D2943 |
Haiyan Ni, Ru-Jin Huang, and Ulrike Dusek

To investigate the sources and formation mechanisms of carbonaceous aerosols, a major contributor to severe particulate air pollution, radiocarbon (14C) measurements were conducted on aerosols sampled from November 2015 to November 2016 in Xi'an, China. Based on the 14C content in elemental carbon (EC), organic carbon (OC) and water-insoluble OC (WIOC), contributions of major sources to carbonaceous aerosols are estimated over a whole seasonal cycle: primary and secondary fossil sources, primary biomass burning, and other non-fossil carbon formed mainly from secondary processes. Primary fossil sources of EC were further sub-divided into coal and liquid fossil fuel combustion by complementing 14C data with stable carbon isotopic signatures.

The dominant EC source was liquid fossil fuel combustion (i.e., vehicle emissions), accounting for 64 % (median; 45 %–74 %, interquartile range) of EC in autumn, 60 % (41 %–72 %) in summer, 53 % (33 %–69 %) in spring and 46 % (29 %–59 %) in winter. An increased contribution from biomass burning to EC was observed in winter (∼28 %) compared to other seasons (warm period; ∼15 %). In winter, coal combustion (∼25 %) and biomass burning equally contributed to EC, whereas in the warm period, coal combustion accounted for a larger fraction of EC than biomass burning. The relative contribution of fossil sources to OC was consistently lower than that to EC, with an annual average of 47±4 %. Non-fossil OC of secondary origin was an important contributor to total OC (35±4 %) and accounted for more than half of non-fossil OC (67±6 %) throughout the year. Secondary fossil OC (SOCfossil) concentrations were higher than primary fossil OC (POCfossil) concentrations in winter but lower than POCfossil in the warm period.

Fossil WIOC and water-soluble OC (WSOC) have been widely used as proxies for POCfossil and SOCfossil, respectively. This assumption was evaluated by (1) comparing their mass concentrations with POCfossil and SOCfossil and (2) comparing ratios of fossil WIOC to fossil EC to typical primary OC-to-EC ratios from fossil sources including both coal combustion and vehicle emissions. The results suggest that fossil WIOC and fossil WSOC are probably a better approximation for primary and secondary fossil OC, respectively, than POCfossil and SOCfossil estimated using the EC tracer method.

How to cite: Ni, H., Huang, R.-J., and Dusek, U.: Sources and formation of carbonaceous aerosols in Xi'an, China: primary emissions and secondary formation constrained by radiocarbon, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12245, https://doi.org/10.5194/egusphere-egu2020-12245, 2020.

D2944 |
Provat Saha, Ellis Robinson, Wenwen Zhang, Steven Hankey, Allen Robinson, and Albert Presto

We measure highly spatially resolved primary organic aerosol (POA) concentrations in three North American cities (Oakland, Pittsburgh, and Baltimore) using an aerosol mass spectrometer deployed on a mobile laboratory. We conduct between 10 and 20 days of repeated mobile sampling in each city, covering a wide range of urban land use attributes. We derive two POA factors using positive matrix factorization of the measured organic mass spectra: cooking OA (COA) and traffic-related OA (hydrocarbon-like OA; HOA). Both the COA and HOA concentrations vary substantially within and between cities. The COA and HOA concentrations in Oakland are about a factor of 2-4 higher than Pittsburgh and Baltimore. Within a city, the concentrations vary by a factor of 2-5. The COA concentrations are higher than the HOA in each city, indicating that cooking is an important POA source in the US. In each city, the concentrations are higher in the downtown and near large sources, showing the linkage between land-use activities and POA concentrations. We develop land-use regression (LUR) models for COA and HOA using the measured concentrations and available land-use covariates. We find that a similar set of land-use covariates explain the variability of measured POA in each city. The LUR models are moderately transferable between sampling cities. An external validation effort using literature data shows that our models predict the previous point measurements in six North American cities reasonably well. We are applying our LUR models for a national prediction of the concentration surfaces of COA and HOA. We plan to apply the national estimates for the epidemiologic and environmental justice analysis of POA in the United States.

How to cite: Saha, P., Robinson, E., Zhang, W., Hankey, S., Robinson, A., and Presto, A.: Spatial Patterns and Spatial Modeling of Primary Organic Aerosol Concentrations in Three North American Cities, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19547, https://doi.org/10.5194/egusphere-egu2020-19547, 2020.

D2945 |
Jing Duan, Rujin Huang, Chunshui Lin, Haiyan Ni, and Meng Wang

Secondary aerosol constitutes a large fraction of fine particles in urban air of China. However, its formation mechanisms and atmospheric processes remain largely uncertain despite considerable studies in recent years. To elucidate the seasonal variations of fine particles composition and secondary aerosol formation, an Aerodyne quadrupole aerosol chemical speciation monitor (Q-ACSM) combined with other online instruments were used to characterize the submicron particulate matter (diameter < 1 μm, PM1) in Beijing during summer and winter 2015. Our results suggest that the photochemical oxidation was the major pathway for sulfate formation during summer, whereas aqueous-phase reaction became an important process for sulfate formation during winter. High concentration of nitrate (17% of the PM1 mass) was found during winter explained by enhanced gas-to-particle partitioning at low temperature, while high nitrate concentration (19%) was also observed under the conditions of high relative humidity (RH) during summer likely due to the hydrophilic property of NH4NO3 and hydrolysis of N2O5. As for SOA formation, photochemical oxidation perhaps played an important role for summertime oxygenated OA (OOA) formation and wintertime less oxidized OOA (LO-OOA) formation. The wintertime more oxidized OOA (MO-OOA) showed a good correlation with aerosol liquid water content (ALWC), indicating more important contribution of aqueous-phase processing than photochemical production to MO-OOA. Meanwhile, the dependence of LO-OOA and the mass ratio of LO-OOA to MO-OOA on atmospheric oxidative tracer (i.e., Ox) both degraded when RH were greater than 60%, suggesting that RH or aerosol liquid water may also affect the LO-OOA formation.

How to cite: Duan, J., Huang, R., Lin, C., Ni, H., and Wang, M.: Summertime and wintertime atmospheric processes of secondary aerosol in Beijing, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7440, https://doi.org/10.5194/egusphere-egu2020-7440, 2020.

D2946 |
Jia Chen, Florian Dietrich, Sebastian Lober, Konstantin Krämer, Graham Legget, Hugo Denier van der Gon, Ilona Velzeboer, Carina van der Veen, and Thomas Röckmann

Up to now, festivals have not been considered a significant methane (CH4) emission source and events with a limited duration were not included in the emission inventories. We have intensively investigated the Munich Oktoberfest, the world’s largest folk festival, for two consecutive years. Oktoberfest is a potential source for CH4 as a high amount of natural gas (about 200,000 m³) for cooking and heating is used.

The results from our 2018 investigation show that CH4 emissions at Oktoberfest not only come from human biogenic emissions. It is more likely that fossil-fuel related emissions are the major contributors to the Oktoberfest emissions (Chen et al. 2019). In 2019, our goal was to look closer into the source attribution. We used both a portable gas measurement system (LI-COR LI‑7810 Trace Gas Analyzer) in a backpack to measure the CH4 concentrations, and air sampling bags to examine the ethane/methane ratio and isotopic composition of the exhaust gas (δ13C, δD).

We walked around the perimeter of Oktoberfest to measure the CH4 concentration upwind and downwind of the Oktoberfest premises for several hours each day during the two-week festival. In addition, we entered the festival with our instrument to investigate the emission hotspots, i.e. tents and booths, thoroughly. The measurements were carried out both during and after the time of the festival to compare the differences in emission strength and distribution.

The backpack measurements around the Oktoberfest perimeter show enhancements up to several hundred ppb compared to background values and measurements performed after the festival. The concentration enhancements on the premises were even higher: up to 3,000 ppb for hotspot regions. The ethane/methane ratios and isotopic measurements show clear indications that the emission sources are thermogenic.

Furthermore, a CFD (Computational Fluid Dynamics) simulation was developed to simulate the gas dispersion within and around the terrain. The simulation uses Reynolds-Averaged Navier-Stokes equations and the k-ε turbulence model for the fluid flow. Wind speed and direction measurements taken close to the festival area were used as the boundary conditions. The dispersion of methane is solved afterwards using the unsteady convection-diffusion equation.

We will present the strengths and spatial/temporal distributions of the Oktoberfest emissions, assessed using the backpack measurements combined with a CFD model. Further, a comparison between the results of two consecutive years will be given. 


Chen, J., Dietrich, F., Maazallahi, H., Forstmaier, A., Winkler, D., Hofmann, M. E. G., Denier van der Gon, H., and Röckmann, T.: Methane Emissions from the Munich Oktoberfest, Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2019-709, in review, 2019

How to cite: Chen, J., Dietrich, F., Lober, S., Krämer, K., Legget, G., Denier van der Gon, H., Velzeboer, I., van der Veen, C., and Röckmann, T.: Methane Emission Source Attribution and Quantification for Munich Oktoberfest, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18919, https://doi.org/10.5194/egusphere-egu2020-18919, 2020.

D2947 |
Samuel Hammer, Christoph Rieß, Fabian Maier, Tobias Kneuer, Julian Della Coletta, Susanne Preunkert, Ute Karstens, and Ingeborg Levin

Reliable estimates of fossil fuel CO2 (ffCO2) emissions from high-emission regions or urban areas are currently in demand from a wide range of players. On the one hand, cities and municipalities themselves are interested in an independent validation of their ffCO2 emissions. On the other hand, there is an increased interest in atmospheric science to merge independent emission estimate methods over different scales [Pinty et al. 2019]. 14CO2 has become the gold standard when it comes to the experimental splitting of atmospheric CO2 concentration into its biogenic and fossil components [e.g. Levin et al. 2003; 2011 or Turnbull et al. 2009].

Here we report on the identification of ffCO2 emitted from the Mannheim/Ludwigshafen metropolitan region in the upper Rhine valley, Germany. Quantification of the regional ffCO2 component requires knowledge of the composition of the background air. Thus, the emission area has been sampled by an upwind and a downwind station. We will discuss the advantages and disadvantages of using local background measurements conducted at a dedicated upwind station of the emission area and compare this realisation of background estimate to regional background estimates derived from measurements at classical remote background sites. All CO2 and 14CO2 observations have been performed as part of the European RINGO project. Furthermore, we investigate the suitability of using the total-CO2 difference between the two stations as a proxy for fossil fuel CO2 and the seasonal applicability of such a surrogate tracer. Finally, the observations of the total-CO2 surrogate tracer will be compared with the predictions from STILT forward model runs.



Levin, I., B. Kromer, M. Schmidt and H. Sartorius, 2003. A novel approach for independent budgeting of fossil fuels CO2 over Europe by 14CO2 observations. Geophys. Res. Lett. 30(23), 2194, doi. 10.1029/2003GL018477.

Levin, I., S. Hammer, E. Eichelmann, F. Vogel, 2011. Verification of greenhouse gas emission reductions: The prospect of atmospheric monitoring in polluted areas. Philosophical Transactions A 369, 1906-1924, doi:10.1098/rsta.2010.0249.

Pinty B., P. Ciais, D. Dee, H. Dolman, M. Dowell, R. Engelen, K. Holmlund, G. Janssens-Maenhout, Y. Meijer, P. Palmer, M. Scholze, H. Denier van der Gon, M. Heimann, O. Juvyns, A. Kentarchos and H. Zunker (2019) An Operational Anthropogenic CO₂ Emissions Monitoring & Verification Support Capacity – Needs and high level requirements for in situ measurements, doi: 10.2760/182790, European Commission Joint Research Centre, EUR 29817 EN

Turnbull, J., Rayner, P., Miller, J., Naegler, T., Ciais, P., & Cozic, A. (2009). On the use of 14CO2 as a tracer for fossil fuel CO2: Quantifying uncertainties using an atmospheric transport model. Journal of Geophysical Research: Atmospheres, 114(D22).

How to cite: Hammer, S., Rieß, C., Maier, F., Kneuer, T., Della Coletta, J., Preunkert, S., Karstens, U., and Levin, I.: Monitoring ffCO2 emission hotspots using atmospheric 14CO2 measurements, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20377, https://doi.org/10.5194/egusphere-egu2020-20377, 2020.

D2948 |
Morten Hundt, Oleg Aseev, and Herbert Looser

Observation of air pollutants and greenhouse gases with high selectivity and sensitivity is of great importance for our understanding of their sources and sinks. For air pollution modelling and validation of emission inventories measurements at various spatial and temporal scales are required. Infrared laser absorption spectroscopy is often the method of choice, offering outstanding performance and reliability. Most frequently, however, this technology is used in a “one-species-one-instrument” solution because of the narrow spectral coverage of DFB-lasers. This can be overcome by combining several Quantum Cascade Lasers (QCLs), providing unique solutions in compact laser absorption spectrometers for environmental monitoring of multiple species in a single instrument.

We combined multiple DFB-QCLs into a single, compact laser absorption spectrometer to measure up to ten different compounds. We present simultaneous atmospheric measurements of the greenhouse gases CO2, N2O, H2O and CH4, and the pollutants CO, NO, NO2, O3, SO2 and NH3 with a single instrument. Furthermore, the instrument performance, first field results and comparison to standard air-quality and greenhouse gas monitoring instrumentation are discussed. The results demonstrate that spectrometers using QCLs can serve as an all-in-one solution for environmental monitoring stations replacing up to seven instruments at once. Furthermore, due to their reduced size and robustness, they can be used on mobile platforms, opening up new applications of air quality and greenhouse gas monitoring in cities.

How to cite: Hundt, M., Aseev, O., and Looser, H.: Single Instrument Solution for Air Quality and Greenhouse Gas Monitoring in Cities, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9944, https://doi.org/10.5194/egusphere-egu2020-9944, 2020.

D2949 |
Aiden Heeley-Hill

The air quality of indoor environments has increased in significance over the last few decades, particularly as contemporary estimates suggest that, on average, people spend 90% of their time indoors. This creates a growing requirement to understand the chemical composition of indoor atmospheres, and what factors influence absolute concentrations.  


Whole-air canister sampling techniques were used to collect three-day integrated air samples in a cohort of UK homes (n = 60). Each household was sampled three times in winter and three times in summer, plus a further three households were randomly selected each week to also collect an outdoor air sample. Sampling was performed over nine-week periods - between February and April 2019 and July and September 2019. Samples were subject to chemical analysis using a combination of GC-FID and GC-TOF-MS, allowing quantification of VOCs over the range C2 – C12, including nonmethane hydrocarbons, OVOCs, monoterpenes, siloxanes and halocarbons. A digitised survey (completed on iPad) was completed by each household, containing questions regarding property information, residence occupancy and demographics, and a daily log of solvent-containing product usage (including cosmetics and personal care, cleaning, decorative and hobby, smoking, fires, candles, and insecticides). Product usage was determined by how often residents recorded a given product category during the three-day sampling period.


Highest concentrations were seen in winter, with n-butane being the most abundant VOC (median value = 61.6 ppb), whilst tetrachloroethylene was the lowest concentration species quantified (median = <0.1 ppb) in this study. In terms of absolute concentration, VOCs derived from aerosol propellants were most significant, plus ethanol and acetone: general purpose solvents from many different product types. Median concentrations for terpenoids ranged from 0.17 ppb p-cymene to 0.65 ppb limonene.  A positive correlation existed internally between different terpenoid species and between alkene species. However, more generally, most individual VOCs did not correlate with each other, highlighting the wide range of uncorrelated sources that contribute to individual concentrations.


With the exception of aerosol usage, and n and i butane, indoor ambient VOC concentrations had no statistically significant relationship with product usage frequency, indicating that other factors, such as ventilation rates, VOCs released per dose, variability in product emissions, and other behavioural aspects were potentially more significant influencing factors. This highlights the great challenges in attempting to model indoor exposure to VOCs at a population scale, since it is not readily predictable based on behavioural use of solvents.

How to cite: Heeley-Hill, A.: Relating concentrations of indoor volatile organic compounds to occupant use of solvent-containing products, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-22486, https://doi.org/10.5194/egusphere-egu2020-22486, 2020.

D2950 |
Steven J. Campbell, Battist Utinger, Kate Wolfer, Joe Westwood, Sarah S. Steimer, Tuan V. Vu, Zongbo Shi, Nicholas Straw, Mark R. Miller, Steven Thomson, William J. Bloss, Roy M. Harrison, and Markus Kalberer

The negative effects of air pollution on human health has been subject to a number of epidemiological studies that consistently link respiratory and cardiovascular diseases to exposure to particulate matter (PM) (Englert, 2004). It is estimated that up to 0.3 million premature deaths per year in Europe and 2.1 million deaths worldwide are the result of exposure to particles with an aerodynamic diameter less than 2.5 μm (PM2.5) (Andersson, 2009). However, identifying the specific particle properties responsible for these health effects, such as their physical and physicochemical characteristics, as well as their chemical composition, remains a challenge.

One of the leading hypotheses for how particles cause harm is by inducing oxidative stress and inflammation, which can subsequently lead to disease (Øvrevik, 2015). In particular, reactive oxygen species (ROS), which typically refer to a range of species including hydrogen peroxide (H2O2) possibly including organic peroxides, the hydroxyl radical (.OH) and superoxide radical (O2.-), may substantially contribute to the oxidative potential (OP) of PM and hence influence their toxicity. An excess of ROS in the lung, introduced or generated via particle exposure, leads to an imbalance of the oxidant-antioxidant ratio in favour of the former, which can subsequently promote oxidative stress. There are a number of acellular methods used routinely to measure aerosol OP, including the dithiothreitol assay (DTT), ascorbic acid assay (AA), 2,7-dichlorofluoroscein/hydrogen peroxidase assay (DCFH/HRP), and electron paramagnetic resonance (EPR) spectroscopy.

In this work, the OP of aerosol collected in Beijing, China, in the winter 2016 and summer 2017 during the Atmospheric Pollution and Human Health in a Chinese Megacity (APHH) campaign is quantified, with 30 24-hr aerosol filter samples analysed for each season. We use the four aforementioned methods to measure OPAA, OPDTT, OPDCFH and OPEPR, and to extensively characterise the seasonal variation of aerosol OP in a megacity. All OP measurements show a significantly stronger correlation with PM2.5 mass in the winter compared to summer. Furthermore, the OPAA, OPDTT, OPDCFH and OPEPR were correlated using univariate and multivariate analysis with a variety of other measurements such as meteorological data, trace gas measurements and aerosol composition measurements including organic aerosol components and x-ray fluorescence elemental analysis.  

These results emphasise that the four OP methods applied in this study capture different aspects of aerosol OP between the seasons. As an example, OPAA normalised to account for aerosol mass show that aerosol OPAA in the winter is higher on average and more variable compared to the summer, whereas OPDCFH is more consistent between the winter and summer seasons. OPAA also showed a strong correlation with PM2.5 mass in the winter (r2 = 0.91) but correlated poorly in the summer months (r2 = 0.09), suggesting different aerosol components affect OPAA in summer and winter.

Englert, N. Toxicol. Lett. 149, 235–242 (2004).

Andersson, C., et al., Atmos. Environ. 43, 3614–3620 (2009).

Øvrevik, J., et al., Biomolecules 5, 1399–1440 (2015).

How to cite: Campbell, S. J., Utinger, B., Wolfer, K., Westwood, J., Steimer, S. S., Vu, T. V., Shi, Z., Straw, N., Miller, M. R., Thomson, S., Bloss, W. J., Harrison, R. M., and Kalberer, M.: Seasonal Variation of Aerosol Oxidative Potential in Beijing, China during the APHH Campaign , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-17119, https://doi.org/10.5194/egusphere-egu2020-17119, 2020.

D2951 |
Sara DAronco, Chiara Giorio, Federica Chiara, Roberta Seraglia, Valerio Di Marco, and Andrea Tapparo

Aerosol particle components can mix and interact with oxidants and organic compounds present in the atmosphere. How these chemical components interact and how the interactions affect the Earth’s climate, particle toxicity and human health is largely unknown. In the case of trace metals, the main focus so far has been the determination of the total amount while much less attention has been directed towards the metal speciation. Aqueous phase processing of aerosol can lead to substantial modifications of aerosol chemical and physical properties [1] by promoting the formation of metal-organic ligand complexes in atmospheric aqueous phases, like fog/cloud droplets and deliquescent aerosol. Such process can increase the solubility of metals, therefore their bioavailability [2], and affect their capability to generate reactive oxygen species.

We investigated the formation of metal-organic ligand complexes, especially those involving small dicarboxylic acids, in urban aerosol collected in the city centre of Padua (Italy), in the Po Valley. We assessed the effects of metal-ligand complexes formation on the solubility and solubilisation kinetic of metals from the particles to aqueous solutions simulating fog/cloud water. We found that solubilisation kinetics of many metals depended on the chemical form in which they were present in the aerosol and they were influenced by the environmental conditions during the campaign. Changes in oxidative potential (OP) and cytotoxicity of particles due to the formation of metal-ligand complexes were investigated by performing acellular and cellular in vitro tests, respectively. Preliminary results showed that metals and their complexed forms are both characterized by different OP and cellular toxicity.



[1] Decesari, S., Sowlat, M. H., Hasheminassab, S., Sandrini, S., Gilardoni, S., Facchini, M. C., Fuzzi, S., and Sioutas, C. Atmos. Chem. Phys., 17, 7721‑7731 (2017).

[2] Okochi, H., and Brimblecombe, P. Sci. World J., 2, 767–786(2002).

How to cite: DAronco, S., Giorio, C., Chiara, F., Seraglia, R., Di Marco, V., and Tapparo, A.: Effect of metal speciation on the oxidative potential and cytotoxicity of airborne particles, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19756, https://doi.org/10.5194/egusphere-egu2020-19756, 2020.

D2952 |
Patrice Coll, Mathieu Cazaunau, Jean-Francois Doussin, Edouard Pangui, Aline Gratien, Isabelle Coll, Gilles Foret, Cécile Gaimoz, Vincent Michoud, Claudia DiBiagio, Elie Al Marj, Marion Blayac, Zhuyi Lu, Audrey Der Vatanian, Stéphane Jamain, Geneviève Derumeaux, Maria Pini, Frédéric Relaix, Jorge Boczkowski, and Sophie Lanone


Using CESAM, an atmospheric simulation chamber (cesam.cnrs.fr), we have developed a totally innovative platform for exposing mice to realistic atmospheric conditions. Here we present the first toxicological analyses of the organs of these mice after 48 hours to several days of exposure, carried out as part of feasibility experiments aimed at testing this experimental concept. This platform has received funding from the European Union’s Horizon 2020 research and innovation programme through the EUROCHAMP-2020 Infrastructure Activity under grant agreement N° 730997, and is now supporting the new REMEDIA H-2020 project (call H2020 “Exposome”)



The World Health Organization (WHO) estimated that there were 3.7 million premature deaths due to air pollution in 2014, and confirmed that air pollution is the greatest environmental risk to health (responsible for a loss of more than 3% of productivity).

The studies conducted so far show that the effects of air pollution on health depend not only on the quality of the surrounding air, but also on the subjects exposed and their individual vulnerability (asthma, obesity, period of life, etc.). Despite the evidence on the adverse health effects of exposure to air micro-pollutants, there are still uncertainties about the nature of these effects, and progress need to be made on their quantification. This limitation of knowledge is mainly attributed to the complexity of the polluted atmospheres, and to the great difficulty to model the impact of realistic situations of exposure.



The innovative approach we set up is to realistically simulate, at the laboratory, the atmospheric mixture in all its complexity, thus keeping the ability to control, reproduce and carefully characterize the experimental conditions. We used the CESAM chamber (4.2 m3 stainless steel atmospheric simulation, evacuable down to a few 10-7 atm, temperature controlled between +15°C and +60°C) in order to study the myriad of products arising from the atmospheric oxidation of primary organic compounds.

The experimental protocol consists in the continuous injection of relevant mixtures of primary pollutants (mainly nitrogen oxides, organic compounds from a representative mix of anthropogenic emissions, sulphur dioxide, soot, inorganic salts and potentially mineral dust particles if needed - e.g. to simulate Beijing’s atmosphere) at low concentrations (ppb levels) in air in the CESAM simulation chamber operated as a slow flow reactor. The residence time of simulated air parcels in the experimental volume is fixed to 4 hours, in order to represent air masses of regional scale. During this time the synthetic mixture is exposed to an artificial solar irradiation, allowing secondary pollutants such as ozone, nitric acid, formaldehyde, peroxyacetyl nitrate as well as complex polyfunctional organics including SOA to be produced and to reach their chemical steady state. Mice are exposed to constant flows of such a mixture during time scales of week to address their effects on health.



Here we present the first toxicological analyses related to organs/tissue of these mice after exposure of 48h to several day, carried out with a representative atmosphere of Beijing or a representative atmosphere of Paris.



Coll P. et al., 2018, WIT Transactions on Ecology and the Environment, 230.

How to cite: Coll, P., Cazaunau, M., Doussin, J.-F., Pangui, E., Gratien, A., Coll, I., Foret, G., Gaimoz, C., Michoud, V., DiBiagio, C., Al Marj, E., Blayac, M., Lu, Z., Der Vatanian, A., Jamain, S., Derumeaux, G., Pini, M., Relaix, F., Boczkowski, J., and Lanone, S.: How atmospheric simulation chambers can help to investigate the impact of air quality on health, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21812, https://doi.org/10.5194/egusphere-egu2020-21812, 2020.

Chat time: Friday, 8 May 2020, 10:45–12:30

Chairperson: Ru-Jin Huang, Uli Dusek
D2953 |
Lorenza Gilardi, Thilo Erbertseder, Frank Baier, and Michael Bittner

Several World Health Organization (WHO) studies have shown that air pollution is likely associated with an increased rate of premature mortality and morbidity, mainly attributable to respiratory and cardiovascular diseases [1]. The species normally considered for the evaluation are: PM10, PM2.5, O3 and NO2. All these compounds are typical sub products of processes of combustion and other anthropogenic activities and their presence in highly densely populated areas is commonly observed. As a result, a significant proportion of the European population is exposed to annual average concentrations of these pollutants exceeding the WHO Air Quality Guidelines (WHO-AQG), as the European Environment Agency (EEA)  reports for the year 2016 [1].  Data of air pollutants concentrations at high temporal resolution and on a large spatial scale are currently available from satellite remote sensing and air quality models. The data from the multi-year reanalysis of the Copernicus Atmospheric Monitoring Service (CAMS) and from the DLR / POLYPHEMUS, together with the health Relative Risk (RR) values provided by the WHO, are used as input-source for the method developed by Sicard [2] to estimate an overall increase in health risk due to short-term exposure to air pollution. With this operation it is possible to obtain a geographical representation of the Aggregate Risk Index (ARI). This approach allows for various estimates of the spatial distribution of the increased health risk for several health endpoints, in terms of ARI, and its temporal behavior. The Sicard’s method is tested by (spatial) correlation to a real-world health data base. We especially investigate the validity of the linear additive approach for different mixtures of pollutants.


[1] European Environmental Agency, 2019, Air quality in Europe 2019 report, pages 61-70.

[2] Sicard, P.,et al., 2012. The Aggregate Risk Index: An intuitive tool providing the health risks of air pollution to health care community. Atm Env, 46, 11-16.

How to cite: Gilardi, L., Erbertseder, T., Baier, F., and Bittner, M.: Assessment of the human health risk due to the exposure to air pollution using air quality ensemble modelling data, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-16202, https://doi.org/10.5194/egusphere-egu2020-16202, 2020.

D2954 |
Martin Otto Paul Ramacher, Matthias Karl, Eleni Athanasopolou, Anastasia Kakouri, Orestis Speyer, and Volker Matthias

Population exposure estimates are used in epidemiological studies to evaluate health risks associated with impacts of air pollution on human health. Traditional approaches in exposure modelling assume that air pollutants' concentrations at the residential address of the study population are representative of overall exposure. This approach is acknowledged to introducing bias in the quantification of human health effects, as individual and population-level mobility is non-existent. To sufficiently model population numbers for exposure estimates, the dynamic population activity (DPA) must be known. Information on DPA is mostly derived from national or municipal surveys and is scarce.

We developed a generic approach to model DPA integrating the Copernicus Urban Atlas land use and land cover product with literature based and microenvironment-specific diurnal activity data (Ramacher et al. 2019) and moreover taking into account gridded monthly day- and night-time populations as derived in the ENACT project (https://ghsl.jrc.ec.europa.eu/enact.php), while also considering indoor and outdoor environments. This approach produces maps with distribution of citizens in various microenvironments (MEs) and hours of the day. These maps can consequently be used to calculate population-level outdoor exposure when paired with consistent spatio-temporal air pollution concentration fields. In this study, we applied the generic DPA approach to the cities of Hamburg (DE) and Athens (GR). Hourly, urban-scale pollutant concentrations were produced by the Chemistry Transport Model (CTM) EPISODE-CityChem (Karl et al. 2019) driven by detailed local emission inventories, 4D meteorological fields and regional pollutant boundary conditions for 2015. Both the concentrations for NOx, O3 and PM2.5 as well as the DPA maps for Hamburg and Athens were simulated on a 100 m horizontal resolution grid, and were then combined to calculate population exposure. We additionally used gridded population densities (based on residential addresses) to calculate population exposure, i.e. following a static population approach. We compared the exposures from the two approaches to capture the effect of a population moving in space and time. 

The presented approach to account for dynamic instead of static population activity in urban population exposure calculations is beneficial for cities in European regions where relevant population data are missing. It is found that by taking into account movement of population through different urban environments as well as commuting per se, the overall exposure estimates are elevated when compared to a static approach. Furthermore, we have shown that by considering infiltration of outdoor concentrations to indoor environments there are substantial decreases in population exposure estimates. This approach and the implications on exposure estimates is believed to be of interest to air pollution health studies.

How to cite: Ramacher, M. O. P., Karl, M., Athanasopolou, E., Kakouri, A., Speyer, O., and Matthias, V.: A novel approach for dynamic population activity in urban-scale exposure estimates, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-2576, https://doi.org/10.5194/egusphere-egu2020-2576, 2020.

D2955 |
Sheng Ye and Mark Wenig

Air pollution has been gaining increasing global attention. The public is concerned about urban pollution levels including both in- and outdoor air quality. A handheld Air Quality Inspection Box (Airquix) was developed in order to monitor air pollutants in real-time, and determine individual exposure to different pollutants in different environments. The Airquix is equipped with air quality sensors: electrical chemical NO2, O3, NO sensors, NDIR CO2 sensor, OPC-N3 PM sensor; environment sensor (T, RH, P), GPS sensor and a raspberry pi for data logging, processing and display. To achieve a relatively high accuracy, e.g. +/- 5ppb at 5 seconds time resolution for the NO2 concentration, the pre- and post- calibration for the Airquix were performed by comparison with high-end air monitoring instruments. In this study, several Airquixes were distributed to different persons to assess individual exposure. The daily activities were distinguished by different commutes, in- and outdoor behaviors, the personal habits and potential episodes. The resulting data set can be used for the assessment of health impacts.

How to cite: Ye, S. and Wenig, M.: Human exposure assessment to air pollutants: application of a new portable air monitoring instrument, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9075, https://doi.org/10.5194/egusphere-egu2020-9075, 2020.

D2956 |
Eleni Liakakou, Anastasia Panopoulou, Georgios Grivas, Stéphane Sauvage, Theodora Kritikou, Evangelos Gerasopoulos, and Nikolaos Mihalopoulos

VOCs are key atmospheric constituents for both health and climate issues and further knowledge is still needed about their sources and fate. The presence of volatile organic compounds in ambient air is strongly dependent on the site characteristics and a harbor area undergoes many source typologies such as road transport, ship emissions and contaminants of commercial activities, the shipbuilding zone and other operating facilities. The current work was implemented at the recently established Atmospheric Pollution Monitoring Station of the Municipality of Keratsini-Drapetsona located in the close vicinity of the Piraeus port. Since December 2018 an automatic gas chromatograph with flame ionization detector (FID) continuously monitors at a 30 minutes time resolution non methane hydrocarbons (NMHCs) focusing on hazardous compounds (aromatics) and strong precursors (aromatics, monoterpenes) of secondary pollutants like ozone and secondary organic aerosols. High levels of benzene were observed, especially during the morning to noon period, and the mean concentration of both benzene and toluene were two-folded in summer (July and August 2019) compared to winter (January and February 2019). Ethylbenzene follows the same pattern, whereas xylenes presented comparable levels during the cold and warm periods. Preliminary results based on source apportionment techniques are presented. In general terms the NMHC levels present their maximum under the impact of low wind speed, addressing thus the role of local emission sources, which are further investigated by the ratios used as tracking tools of processes of different origin (e.g. the traffic related ratio of toluene/benzene).

How to cite: Liakakou, E., Panopoulou, A., Grivas, G., Sauvage, S., Kritikou, T., Gerasopoulos, E., and Mihalopoulos, N.: Volatile Organic Compounds in the atmosphere of the Great Athens area: The case of a port site close to Piraeus, Greece, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20087, https://doi.org/10.5194/egusphere-egu2020-20087, 2020.

D2957 |
Dina Gubanova, Andrey Skorokhod, Nikolai Elansky, Vyacheslav Minashkin, and Mikhail Iordanskii

In recent years, interest in studying the physicochemical parameters of atmospheric aerosols, which is associated with their active influence on the air pollution, optical characteristics of the atmosphere and the Earth’s climate has increased. Climatic changes cause the occurrence of atypical weather conditions and dangerous meteorological phenomena that affect changes in the properties of aerosol particles. In large industrial megacities frequent atypical meteorological situations change aerosols behavior and complicate the predictive model assessment of the air quality and thermal regime of the atmosphere.

We consider the results of studies of the daily and seasonal variability of the chemical composition, microphysical parameters, and mass concentration of surface aerosols in Moscow under atypical weather conditions prevailing in summer (June 10-July 10) and in autumn (October 10-November 7) of 2019. In the second half of June 2019, strong cyclonic activity was observed, and air masses of Arctic origin dominated, bringing intense rainfall, cleansing the atmosphere of contaminants and significant decrease in air temperature. October was characterized by air temperature above the climatic norm, insignificant precipitation and frequent strong gusts of wind of western direction.

Under such conditions, abnormally low aerosol PM2.5 and PM10 concentrations were found, and in the summer average monthly concentrations were 1.5 times lower than in the autumn period what is untypical for aerosols. Usually aerosols annual course is characterized by broad maximum in summer, and by minimum in October-November. In addition, comparison with the results of observations of previous years showed that in 2019 aerosol concentration was 3-5 times lower during both summer and autumn. In particular, the average monthly calculated concentration of  submicron fraction in the study period was: 273 particles/cm3 in the summer and 405 particles/cm3 in the autumn; mass concentration of PM2.5 particles: 3.9 and 5.4 μg/m3, respectively. For comparison, multiyear average mass concentrations of PM2.5 are 15-30 μg/m3. The day-to-day variability and weekly cyclicity of atmospheric aerosols also underwent changes as a result of synoptic and meteorological factors. Under these conditions, the contribution of urban anthropogenic sources, including traffic leveled, in particular, due to such intensive processes as leaching and weathering of aerosols from the atmosphere.

Simultaneously with the measurement of microphysical parameters, the elemental composition of aerosol samples was determined by inductively coupled plasma mass spectrometry. It showed that atmospheric aerosol particles are characterized by a high content of sulfur, heavy metals (Cd, Cu, Zn, Mo, W, Ti, Au, Hg, Pb, Ag, Mn, Fe, Co, As), and metalloids (Bi, Sb, B, P, As, Sn), mainly of anthropogenic nature. Such harmful substances are accumulated in the fine fractions of particles that are part of the PM2.5 aerosol and most dangerous for human health.

Large amplitudes of variations in the disperse composition and concentration of aerosol particles in the atmospheric surface layer, recorded during seasonal observations under atypical weather conditions, characterize strong inhomogeneities of aerosol parameters in space and time, which can significantly affect the chemical and optical properties of aerosols, as well as lower atmosphere state in general.

The reported study was funded by RFBR, projects ## 05-19-00352 and 05-19-50088.

How to cite: Gubanova, D., Skorokhod, A., Elansky, N., Minashkin, V., and Iordanskii, M.: Study of the variability of physicochemical characteristics of surface aerosol in Moscow under atypical weather conditions in 2019, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-15266, https://doi.org/10.5194/egusphere-egu2020-15266, 2020.

D2958 |
Constantin Rosu, Dumitru Mihaila, and Petrut-ionel Bistricean

The analysis of the air quality represents an essential component of the tourist resources evaluation in a city declared a tourist resort of national interest, as Piatra Neamt is, also called the Pearl of Moldova. The chemical particularities of the urban atmosphere are being influenced, in different extents, by the size of the city, the anthropic activities and are modulated by the geographical peculiarity of the place. Piatra Neamt is located in the NE of Romania in an area of depressive widening of Bistrita valley, at 345 m altitude. The coniferous vegetation, the presence of the water reservoirs along Bistrita river, the climate of shelter and so on are elements that contribute to the self-purification of the air. The city had 85,055 inhabitants according to the 2011 census, being an important economic and tourist center of the NE region of Romania.

The aims of the study conducted has as goal the evaluation of the air quality in the Municipality of Piatra Neamt, on the basis of the hourly data from the station NT1 (urban background) from the interval January 2009 - October 2019, relying on five chemical indicators: Nitrogen dioxide (NO2), Sulfur dioxide (SO2), Carbon monoxide (CO), Ozone (O3) and PM10 sedimentable particles.

The main objectives aimed at the identification of the variations in time of the average hourly or daily concentrations of these pollutants, with the outlining of their daily or annual progress, with the explanation of their causality and with the identification of some episodes of pollution, but also at the releasing of some accurate assessments based on data, observations and findings of duration which to include the air inhaled by the inhabitants of the city and by the tourists in different intervals of quality.

The results obtained show that in Piatra Neamt the concentrations of NO2 (with 94.36% of the hours of observations with indices of air quality evaluated as excellent), SO2 (with 99.59% of the hours of observations with excellent indices of air quality) and CO (with 99.78% of the hours of observations with indices of air quality also excellent) do not cause real problems to human health. For the O3 in 0.8% of the hours of observation from NT1, the concentration of this gas has exceeded the threshold of 120 μg / mc which, according to the European directives, is the target value for the protection of human health. Neither the concentration of PM10 sedimentable particles causes problems (the amount of time with exceedings of the daily limit value for the protection of human health being on average 2.8 days a year-1). The October-March interval, with thermal inversions, with radiation fog and persistent stratiform clouds is more favorable for keeping this pollutant in suspension.

Conclusion.. The quality of the air from the city of Piatra Neamt atmosphere is excellent. The sedative-indifferent bioclimate and the sanogenous atmosphere of this city at the contact between the Eastern Carpathians - Subcarpathians of Moldova increase the tourist assets of the municipality.

How to cite: Rosu, C., Mihaila, D., and Bistricean, P.: The air quality in the Municipality of Piatra Neamt from the North Eastern Region, Romania, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-402, https://doi.org/10.5194/egusphere-egu2020-402, 2020.

D2959 |
Dumitru Mihăilă, Petruț Ionel Bistricean, Alin Prisacariu, and Mihaela Țiculeanu – Ciurlică

In cities the chemical parameters of the urban atmosphere are being influenced, mainly negatively, by the daily human activities. The urban agglomeration of Suceava (from the NE of Romania) amounted to 116404 inhabitants as per the census from 2011. Their quality of life depends directly on the quality of the air inhaled, and this is being affected by the variable emissions of the transport and industrial sectors and by the household activities. The Municipality of Suceava is an important commercial center and, at the same time, a tourist city.

The general objective of the study consists in the evaluation of the air quality of Suceava Municipality, on the basis of the hourly data from the stations SV1 (urban background) and SV2 (industrial background) from the interval January 2009 - October 2019, on the basis of five chemical indicators: NO2, SO2, CO, O3 and PM10. The main objectives are: i) the identification of the fluctuations in time of the daily or hourly average concentrations of these emissions with the outlining of their daily or annual regime; ii) the comparison of the air quality in the neighbourhoods with residential function from the central and central-southern areas (Zamca, Marasesti, George Enescu, Areni, Obcini and so on) with the one from the industrial platform vicinity, and iii) the releasing of some accurate evaluations based on data from monitoring, which to classify in different levels of quality the air breathed in by humans.

Results. In Suceava the concentrations of NO2 (with hourly indices of quality evaluated as being excellent in 96,51% of cases at SV1 and 93,51% of cases at SV2), SO2 (with hourly indices of quality evaluated as being excellent in 99,79% of cases at SV1 and 99,03% of cases at SV2) and CO (with indices of excellent quality of the air in 99,78% of the hours of observations at SV1 and 97,32% at SV2) are not capable to raise real problems from the perspective of their impact on human health. In the case of O3, in 1,67% of the hours of observations from SV1 the concentration of this gas exceeded the target value for the protection of human health (120 μg/mc). The situation is not alarming due to the reduce percentage held by these situations and to the limitation of the areal to a single monitoring point. In the case of PM10 the concentration does not raise problems at SV1 station where the proportion of time with exceedings of the daily limit value for human health protection is on average 1,3 days/year-1, but at SV2 the daily limit values are being exceeded in 35 day/year1. The interval October - March, with thermal inversions, persistent fog and low stratiform clouds, is the critical one related to this pollutant.

Conclusions. On the background of the industrial decline that followed after 1989, the quality of the air from the atmosphere of Suceava has increased. The problem of the particles in the areal of the industrial platform and Burdujeni neighbourhood stays a current one. 

How to cite: Mihăilă, D., Bistricean, P. I., Prisacariu, A., and Țiculeanu – Ciurlică, M.: Evaluation of air quality in Suceava, Romania, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1268, https://doi.org/10.5194/egusphere-egu2020-1268, 2020.

D2960 |
Anastasios Alimissis, Chris G. Tzanis, Constantinos Cartalis, Kostas Philippopoulos, and Ioannis Koutsogiannis

Urban climate change affects important aspects of urban life (health, urban environment and infrastructure) through considerable fluctuations in the values of both climatic and air quality parameters. At the same time, in recent years, the networks of atmospheric pollution and climatic parameters monitoring stations have become denser, leading to more information which, if presented correctly, can guide policy makers to achieve sustainable solutions. Compοsite environmental classifications are a credible tool to describe in an easily comprehensible manner the complex interactions of gaseous and particulate pollutants with climatic parameters in different land use types and urban topography. The aim of this study is the development and implementation of a composite climate - air quality classification in order to describe and study their combined effects on living conditions and quality of life in urban environments. By employing pollutant observations from surface stations and climatic gridded data from reanalysis databases, the available data will be converted into groups of cases through a process which is based on a non-linear method of clustering and categorization. An artificial neural network methodology and in particular, self-organizing maps will be used to convert non-linear statistical associations of input data into simple geometric relationships of points in a low dimensional map. This method can create classifications of air pollutants and climatic parameters that group days which follow specific patterns, hidden due to non-linear interactions. The results can contribute to finding a relationship between ambient air quality and climatic variables and subsequently gaining important knowledge in this field.

How to cite: Alimissis, A., Tzanis, C. G., Cartalis, C., Philippopoulos, K., and Koutsogiannis, I.: A self-organizing maps methodology for developing a composite air quality and climatic parameters classification, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1566, https://doi.org/10.5194/egusphere-egu2020-1566, 2020.

D2961 |
Wenjun Meng, Qirui Zhong, Yilin Chen, Huizhong Shen, and Shu Tao

In addition to many recent actions taken to reduce emissions from energy production, industry, and transportation, a new campaign substituting residential solid fuels with electricity or natural gas has been launched in Beijing, Tianjin, and other 26 municipalities in northern China, aiming at solving severe ambient air pollution in the region. Quantitative analysis shows that the campaign can accelerate residential energy transition significantly, and if the planned target can be achieved, more than 60% of households are projected to remove solid fuels by 2021, compared with less than 20% without the campaign. Emissions of major air pollutants will be reduced substantially. With 60% substitution realized, emission of primary PM2.5 and contribution to ambient PM2.5 concentration in 2021 are projected to be 30% and 41% of those without the campaign. With 60% substitution, average indoor PM2.5 concentrations in living rooms in winter are projected to be reduced from 209 (190-230) μg/m3 to 125 (99-150) μg/m3. The population-weighted PM2.5 concentrations can be reduced from 140 μg/m3 in 2014 to 78 μg/m3 or 61 μg/m3 in 2021 given that 60% or 100% substitution can be accomplished. Although the original focus of the campaign was to address ambient air quality, exposure reduction comes more from improved indoor air quality because approximately 90% of daily exposure of the population is attributable to indoor air pollution. Women benefit more than men.

How to cite: Meng, W., Zhong, Q., Chen, Y., Shen, H., and Tao, S.: Energy and air pollution benefits of household fuel policies in northern China, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-2708, https://doi.org/10.5194/egusphere-egu2020-2708, 2020.

D2962 |
Rahul Chaurasia and Manju Mohan

The megacities of the world are experiencing a punishing level of air pollution where primary sources of emissions are industrial, residential and transportation. Delhi is also no exception and had been worst performing in terms of air quality and air pollution. In this backdrop, a high-resolution emission inventory becomes an essential tool to predict and forecast pollutant concentration along with the assessment of the impact of various government policies. This study aims to prepare a high-resolution gridded emission inventory (1km*1km) of criteria air pollutants (PM10, PM2.5, NO2, SO2 and CO) for Delhi-NCT (National Capital Territory).  The bottom-up gridded emission inventory has been prepared taking account of population density, land use pattern and socio-economic status. The emission from all the primary sectors has been taken into accounts such as transport, residential burning, industries, power plants, and municipal solid waste burning.  The emissions are estimated using emission factors and activity data for each sector. The emission factor for various fuel type burning is taken from CPCB (Central Pollution Control Board) reports and previous literature. Data corresponding to various sectors such as the amount of fuel consumed, population density, road density, traffic congestion points, industrial location, unauthorized colonies, slums, and total solid waste generation has been acquired from various government bodies, reports, and literature. The result reveals that the total estimated emissions from transportation, industries and domestic sector contribute nearly 72%, 60%, 52% of NOx, SO2 and PM10 emission respectively.  The transport sector has been found as the bulk contributor towards CO and NOx emissions. Domestic sector and Power plant emission have been found to be a bulk contributor of CO and SO2. Later, the spatial distribution of the emission is done using GIS technique (Arc-GIS). For spatial distribution of emission, district-wise population data, road density data, power plant location and digitization of the road network was carried out.

How to cite: Chaurasia, R. and Mohan, M.: Gridded Emission Inventory of Criteria Air Pollutants for National Capital Territory, Delhi, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-22571, https://doi.org/10.5194/egusphere-egu2020-22571, 2020.

D2963 |
Willem W. Verstraeten, Nicolas Bruffaerts, Rostislav Kouznetsov, Marijke Hendrickx, Mikhail Sofiev, and Andy W. Delcloo

Air pollution has tremendous effects on mortality and the quality of life. Air pollution is not restricted to anthropogenic contaminants only, since also natural sources (soils, lakes, marshes, vegetation, volcanoes, etc) may emit substantial amounts of unhealthy pollutants (VOCs, SOX, NOX, aerosols, etc). Releases of biogenic aerosols such as pollen affect the public health badly, putting additional distress on people already suffering from cardiovascular and respiratory diseases. In Belgium, ~10% of the people is estimated to suffer from allergies due to pollen released by the birch family trees and ~15% due to pollen emitted by grasses. In some European countries the prevalence is up to 40%.

To date, the only available airborne pollen level data in Belgium are retrieved by Sciensano at five stations that monitor off-line daily concentrations of grass and birch pollen among other species. Patients suffering from rhinitis have therefore no access to detailed real-time spatial information and warnings on forthcoming exposures.

Chemistry Transport Models (CTM) can both quantify as well as forecast the spatial and temporal distribution of airborne birch and grass pollen concentrations if accurate and updated maps of birch and grass pollen emission sources are available, and if the large inter-seasonal variability in emissions is considered.

Here we show the results of the modelled spatio-temporal distributions of grass and birch pollen over Brussels and other locations in Belgium using the CTM SILAM. This CTM is driven by ERA5 meteorological reanalysis data from ECMWF, an updated MACC-III birch tree fraction map, based on local information, and a grass pollen emission map showing the spatial distribution of the potential pollen sources. Pollen release is based on the temperature degree days approach. Inter-seasonal variability in birch pollen release was taken into account by using spaceborne MODIS vegetation activity (Gross Primary Productivity, GPP). For grass pollen this approach does not fit, therefore we use average temperature and precipitation of the previous year in a first approach.

SILAM modelled and observed time series of daily birch pollen levels of 50 birch pollen seasons at multiple sites in Belgium correlate well for the period 2008-2018 with an increased R² of up to ~50% compared to the reference run. What is more, SILAM is able to capture the allergy thresholds of 50 and 80 pollen grains m-³ exposure from the observations for birch trees. Grass pollen simulations are in progress.

How to cite: Verstraeten, W. W., Bruffaerts, N., Kouznetsov, R., Hendrickx, M., Sofiev, M., and Delcloo, A. W.: Timely information on birch and grass pollen levels in Belgium, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4026, https://doi.org/10.5194/egusphere-egu2020-4026, 2020.

D2964 |
| Highlight
Keunmin Lee, Je-Woo Hong, Jeongwon Kim, and Jinkyu Hong

Urban parks provide a wide range of services for healthcare and social welfare. In particular, they function as a key link connected to urban ecosystems, mitigating various environmental problems such as urban heat island effect and greenhouse emission. It is, therefore, urgent to improve our understanding of the role of the urban park in regulating their surrounding environment. We observed surface turbulent fluxes at an artificially constructed Seoul Forest Park (SFP) in Seoul, for two years from June 2013 to May 2015. The objectives of the study are to 1) quantify water, energy and CO2 exchanges from urban park into the atmosphere and 2) quantify the effect of the urban park on microclimate through comparison before and after the park construction and comparison with urbanized surrounding areas and 3) identify abiotic and biotic factors controlling the temperature reduction and CO2 offset.

Our analysis shows that SFP in summer daytime has cooler surface air temperature up to -0.6 ℃ with a Bowen ratio of 0.15 than surrounding commercial-residential zone in Seoul. SFP also acts as local sinks, and its carbon uptake gives economic benefits in terms of a carbon tax and reduction of heatwaves. Our findings indicate the important role of urban parks in mitigating urban heat island intensity and CO2 emission from urban areas and in providing eco-social benefits with us.

How to cite: Lee, K., Hong, J.-W., Kim, J., and Hong, J.: Contribution of urban park to thermal comfort and CO2 mitigation in a hot-humid environment, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21074, https://doi.org/10.5194/egusphere-egu2020-21074, 2020.

D2965 |
Vidit Parkar, Savita Datta, Haseeb Hakkim, Ashish Kumar, Muhammed Shabin, Vinayak Sinha, and Baerbel Sinha

Tropospheric ozone is a major pollutant and it is harmful for humans at sustained exposures of 40 ppb or more in ambient air. In this study we calibrate the deposition of ozone for stomatal exchange (DO3SE) model for Polyalthia longifolia (False Ashoka), a tree that accounts for 5-20% of the urban plantations in Indian cities and subsequently use the model to estimate not only the stomatal O3 uptake by this tree but also its capability to sequester other criteria air pollutants. We discuss the impact of planting this tree on ozone precursors NOx and VOCs in a roadside plantation scenario for mitigating air pollution.

Stomatal conductance of Polyalthia longifolia was measured, using a SC-1 Leaf Porometer, at IISER Mohali-Punjab in the NW-IGP (Northwest Indo-Gangetic Plane) which has a sub-tropical dry climate. Stomatal conductance was measured during all the four (Summer, Monsoon, Post-Monsoon, Winter) seasons, while BVOC emission fluxes were quantified using a dynamic plant cuvette during post monsoon, winter and summer season. We use ambient mixing ratios of ozone, NO, NO2, SO2 and O3 in combination with the meteorological parameters such as temperature, RH, soil moisture and photosynthetically active radiation (PAR) from the IISER Mohali Atmospheric chemistry facility to quantify Polyalthia longifolia roadside plantations’ impact on urban air quality through stomatal uptake of air pollutants (primarily NO, NO2 and O3) and BVOC emissions. Polyalthia longifolia displays a number of very interesting characteristics that include being a low isoprene and monoterpene emitter, having an extremely high leaf area index thanks to its height, straight shape and dense canopy. It displays extreme resistance to drought and high vapour pressure deficits in summer allowing stomatal uptake of pollutants and evaporative cooling to continue even under unfavourable meteorological conditions.

How to cite: Parkar, V., Datta, S., Hakkim, H., Kumar, A., Shabin, M., Sinha, V., and Sinha, B.: Polyalthia longifolia (False Ashoka) is an ideal choice for better air quality at kerbside locations, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-922, https://doi.org/10.5194/egusphere-egu2020-922, 2020.

D2966 |
Olga Gavrichkova, Kristina Ivashchenko, Pavel Konstantinov, Maria Korneykova, Claudia Mattioni, Andrej Novikov, Paola Pollegioni, Olesya Sazonova, Gregorio Sgrigna, Anna Vetrova, Alexej Yaroslavtsev, and Viacheslav Vasenev

Particulate matter (PM) is recognized among the most harmful pollutants for the human health in cities and mega cities with particles smaller than 10 µm being considered as the most dangerous. European Environmental Agency attributes up to 1150 premature deaths per millions of habitants to harmful exposure to PM. Given that, the causes of the toxicity of PM exposure are not well addressed till now. Recently, biogenic fraction connected to airborn PM was proposed among one of the potential causes alongside physico-chemical composition. Among this fraction could be named pathogenic and allergenic bacteria, viruses, fungi and pollens. Few available results suggest that particulate in the air is characterized by a big biological specific richness and possess a considerable variability in space and time. Environmental factors involved in shaping the airborne microbial community are many and necessitates ulterior evaluation.

The project aims to conduct a comprehensive multidisciplinary study of the biological and physico-chemical characteristics of PM in urban environments characterized by different climatic characteristics. Particularly, MicroAir will address how chemical and biological characteristics of PM are linked to each other, to the distance from the source of pollution and presence of the green infrastructure and to the climatic particularities of the city and mircoclimate of the sampling place. Competencies in the field of microbiology, molecular biology, chemistry, climatology, urban ecology, plant and soil biology will be involved in the project realization with effective combination of modern and classical experimental approaches.  

According to the proposed aims, to be involved in the project there were chosen three cities situated in different climatic regions:  Murmansk (Subarctic, av. annual temperature 0.6°C), Moscow (Temperate, av. annual temperature 5.8°C), Turin (Mediterranean, av. annual temperature 12.5°C). In each city a network of samplers will be collecting the PM in the locations, different in terms of the distance to the pollution source (traffic roads) and in control non contaminated area. In each site will be characterized the seasonal variation of PM sampled in the air, on leaf surfaces and sealed surfaces in terms of quantity and quality with detailed physico-chemical and biological characterization. 

MicroAir is in its initial stage and will have a 3 year duration. The obtained data will serve to characterize the role of the green infrastructure, anthropogenic load, climate and seasonality in shaping chemical and microbiological characteristic of particulate matter and hence in determining the quality of the air in the cities. Will be identified the distribution and activity of potentially-pathogenic and allergenic agents and evaluated whether their presence in PM is linked to the typical seasonal peaks in the registration of certain health disturbances. These knowledge will serve to develop measures for the improvement of the air quality in urban environment and to support decision-making in the field of environmental design, planning and sustainable development of the cities.  

The project was funded by RFBR, project number 19-05-50112.

How to cite: Gavrichkova, O., Ivashchenko, K., Konstantinov, P., Korneykova, M., Mattioni, C., Novikov, A., Pollegioni, P., Sazonova, O., Sgrigna, G., Vetrova, A., Yaroslavtsev, A., and Vasenev, V.: Biogenic characteristics of microparticles in big cities: structure of microbial community, pathogenicity and driving factors (MicroAir)., EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11776, https://doi.org/10.5194/egusphere-egu2020-11776, 2020.

D2967 |
Ahmed Alhuseen

The main objective of this research work is to demonstrate the potential of using Geographic Information System (GIS) in exposure, risk assessment and emission predictions for Khartoum state. One of the objectives of this study is to develop a digital GIS model; that can evaluate, predict and visualize Carbon Monoxide (CO) levels in Khartoum state. To achieve these aims, sample data had been collected, processed and managed to generate a dynamic GIS model of Carbon Monoxide levels in the study area.

  GIS and geostatistical models were found to be valuable tools for creating interactive and dynamic models to enhance the visualization and analysis of Carbon Monoxide emissions in Khartoum state. Parametric data collected from the field and analysis carried throughout this study show that (CO) emissions were much lower than the allowable ambient air quality standards released by National Environment Protection Council (NEPC) for 1998. However, this pilot study has found emissions of (CO) to be unevenly distributed geographically as well as temporally; with Omdurman city exhibiting highest (CO) levels. This pilot study shows that GIS and geostatistical modeling can be used as a powerful tool to produce maps of exposure to enhance exposure assessment in environmental studies.               

How to cite: Alhuseen, A.: Geostatistical Modeling of Carbon Monoxide Levels in Khartoum State-GIS Based Study, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20495, https://doi.org/10.5194/egusphere-egu2020-20495, 2020.

D2968 |
Ed Bannister, Xiaoming Cai, Jian Zhong, and Rob MacKenzie

Cities intimately intermingle people and air pollution. However, is very difficult to assess the efficacy of air pollution policy. Permanent in-situ observations are usually too sparsely spaced to monitor transport processes within a city. The post-processing and maintenance costs associated with calibrated low-cost sensors remains too high for them simply to fill the gaps in permanent networks. The behaviour of pollutants around the scale of a neighbourhood (1-2km) remains particularly difficult to interpret and model. This gap in our understanding is unfortunate because neighbourhood-scale processes disperse pollutants from peaks beside busy roads to levels treated as the ‘urban background’, and may link urban pollution models with weather forecasts.

Urban areas can be treated as patches of porous media to which the wind adjusts by changing its mean and turbulent components. Most cities around the world are made up of lots of neighbourhoods of differing form, density and land use – e.g. commercial centres interspaced with low-rise residential neighbourhoods. For cities whose urban form varies in this way, we formulated two neighbourhood-scale flow regimes, based on the size and density of the different neighbourhood patches.

We used large-eddy simulation to investigate how these two dynamical regimes emerge in patchy neighbourhoods, and their implications for pollution policy and research. We found that these flow regimes distribute pollutants in counter-intuitive ways, such as producing pollution ‘hot spots’ in less dense patches. The flow regimes also provide: (a) a quantitative definition of the ‘urban background’, which can be used for more precisely targeted pollution monitoring; and (b) a conceptual basis for neighbourhood-scale air pollution problems and transport of fluid constituents in other porous media.

How to cite: Bannister, E., Cai, X., Zhong, J., and MacKenzie, R.: Neighbourhood-scale flow regimes and pollution transport in cities, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5111, https://doi.org/10.5194/egusphere-egu2020-5111, 2020.

D2969 |
Carlo Camporeale, Matteo Bertagni, Massimo Marro, and Pietro Salizzoni

The dispersion dynamics of a contaminant released in the atmosphere is crucial for risk assessments and environmental analyses. Yet, because of the unsolved problem of turbulence, analytical solutions physically-derived are currently limited to the Gaussian models for the mean concentration field. In this work, we have obtained simple solutions for the concentration statistics of a passive scalar released from a punctual source. The main result is a novel analytical solution for the passive scalar variance, which is obtained from the contaminant transport equation. We have verified this solution against wind-tunnel data, and further adopted it in a simple stochastic model to provide closed relationships for the temporal statistics of concentration (e.g., the mean duration and occurrence of the peak events). These results may serve as a rapid and practical way to estimate the intensity and duration of the concentration fluctuations of a pollutant released in the atmosphere.

How to cite: Camporeale, C., Bertagni, M., Marro, M., and Salizzoni, P.: Simple solutions for the concentration fluctuations of a passive scalar, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7878, https://doi.org/10.5194/egusphere-egu2020-7878, 2020.

D2970 |
Woo-Sik Jung and Woo-Gon Do

With increasing interest in air pollution, the installation of air quality monitoring networks for regular measurement is considered a very important task in many countries. However, operation of air quality monitoring networks requires much time and money. Therefore, the representativeness of the locations of air quality monitoring networks is an important issue that has been studied by many groups worldwide. Most such studies are based on statistical analysis or the use of geographic information systems (GIS) in existing air quality monitoring network data. These methods are useful for identifying the representativeness of existing measuring networks, but they cannot verify the need to add new monitoring stations. With the development of computer technology, numerical air quality models such as CMAQ have become increasingly important in analyzing and diagnosing air pollution. In this study, PM2.5 distributions in Busan were reproduced with 1-km grid spacing by the CMAQ model. The model results reflected actual PM2.5 changes relatively well. A cluster analysis, which is a statistical method that groups similar objects together, was then applied to the hourly PM2.5 concentration for all grids in the model domain. Similarities and differences between objects can be measured in several ways. K-means clustering uses a non-hierarchical cluster analysis method featuring an advantageously low calculation time for the fast processing of large amounts of data. K-means clustering was highly prevalent in existing studies that grouped air quality data according to the same characteristics. As a result of the cluster analysis, PM2.5 pollution in Busan was successfully divided into groups with the same concentration change characteristics. Finally, the redundancy of the monitoring stations and the need for additional sites were analyzed by comparing the clusters of PM2.5 with the locations of the air quality monitoring networks currently in operation.

This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education(2017R1D1A3B03036152).

How to cite: Jung, W.-S. and Do, W.-G.: A Cluster Analysis of PM2.5 Using CMAQ Model Results for Representativeness of Air Quality Monitoring Networks in Busan, Korea, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3189, https://doi.org/10.5194/egusphere-egu2020-3189, 2020.

D2971 |
Xinxu Zhao, Jia Chen, Julia Marshall, Michal Galkowski, Christoph Gerbig, Stephan Hachinger, Florian Dietrich, Lijuan Lan, Christoph Knote, and Hugo Denier van der Gon

Since the establishment of the firstly fully-automatic urban greenhouse gas (GHG) measurement network in Munich in 2019 [1], we are building a high-resolution modeling infrastructure which will be the basis for a quantitative understanding of the processes responsible for the emission and consumption of CO2, CH4, and CO in Munich. The results of our near-real-time modeling are expected to also provide guidance for local emission reduction strategies.

The precision of our transport modeling framework is assessed through comparison with surface and column measurements in August and October 2018, and the contributions from different emissions tracers are quantified to understand the sources and sinks of atmospheric carbon in the Munich area. A differential column approach [3] is applied for comparing models to observations independently of the biases from background concentration fields (Zhao, X. et al, 2019). Various tracers are separately included in the simulation to further analyze the contribution from different emission processes (e.g., biogenic emissions from wetlands, fossil fuel emissions, and biofuel emissions). Surface emissions are taken from TNO-GHGco v1.1 emission inventory at a resolution of 1 km (cf. Super, I. et al, 2019). Biogenic fluxes of CO2 are calculated online using the diagnostic VPRM model driven by MODIS indices. The initial and lateral tracer boundary conditions are implemented using Copernicus Atmosphere Monitoring Service (CAMS) data, with a spatial resolution of 0.8° on 137 vertical levels, with a temporal resolution of 6 hours.

The precision of our transport modeling framework is assessed through comparison with surface and column measurements in August and October 2018, and the contributions from different emissions tracers are quantified to understand the sources and sinks of atmospheric carbon in the Munich area. A differential column approach [3] is applied for comparing models to observations independently of the biases from background concentration fields (Zhao, X. et al, 2019).

[1] Dietrich, F. et al.: First fully-automated differential column network for measuring GHG emissions tested in Munich. In Geophysical Research Abstracts. 2019.

[2] Zhao, X. et al.: Analysis of total column CO2 and CH4 measurements in Berlin with WRF-GHG, Atmos. Chem. Phys., 19, 11279–11302, https://doi.org/10.5194/acp-19-11279-2019, 2019. 

[3] Chen, J. et al: Differential column measurements using compact solar-tracking spectrometers, Atmos. Chem. Phys., 16, 8479–8498, https://doi.org/10.5194/acp-16-8479-2016, 2016.

How to cite: Zhao, X., Chen, J., Marshall, J., Galkowski, M., Gerbig, C., Hachinger, S., Dietrich, F., Lan, L., Knote, C., and Denier van der Gon, H.: A semi-operational near-real-time Modelling Infrastructure for assessing GHG emissions in Munich using WRF-GHG, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13164, https://doi.org/10.5194/egusphere-egu2020-13164, 2020.

D2972 |
Fritz Nieborowski

Improper ventilation of buildings may lead to an accumulation of pollutants indoors. In the case of a room with forced air ventilation and external air intake like most centralized and some home air conditioning units, this study will show CFD simulations of various indoor air quality conditions based on different forced ventilation AC unit intake conditions like common in housing situations like Hong Kong. Especially when close to roadways or other external pollution sources, the positioning of the air intake shows up to have a high significance for the infiltration rate resulting as influence for the indoor air quality as previous research shows (e.g. Zheming Tong et al., 2016). The same is the case for a forced ventilation case like air conditioning units with outside air intake. Research like earlier referenced paper has not been conducted with higher buildings or forced air intake yet. Parametrized CFD-based air quality models with using OpenFoam will be employed to quantify the impact of the air intake location and rate in a 2-dimensional interface on the indoor air quality of a forced ventilated section of a building. The findings of the CFD simulation will be simplified as average indoor air pollution and other external factors. As an approach to predict the estimate indoor infiltration rate, an ANN (Artificial Neuronal Network) will be used, trained and validated with said data. The neural network is supposed to predict the pollutant intake based on fewer and as easier to obtain meteorological parameters and air pollution data. Finally, the ANN predictions of the models will be verified with real life data from other papers. Results will show that a major part of indoor pollutants may emerge indoors and cannot be neglected. In comparison with real life data, it seems the model lacks significant input to predict with high accuracy. 

How to cite: Nieborowski, F.: Coupled Computational Fluid Dynamics (CFD) and Artificial Neural Network (ANN) for Prediction of Traffic-related Air Pollution Infiltration Effects in Hong Kong, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8169, https://doi.org/10.5194/egusphere-egu2020-8169, 2020.

D2973 |
Renate Forkel, Basit Khan, Johannes Werhahn, Sabine Banzhaf, Edward C. Chan, Farah Kanani-Sühring, Klaus Ketelsen, Björn Maronga, Matthias Mauder, Siegfried Raasch, and Matthias Sühring

Large-Eddy Simulation (LES) allow to simulate pollutant dispersion at a fine-scale turbulence-resolving scale with explicitly resolved turbulent transport around building structures and in street canyons. The microscale urban climate model with atmospheric chemistry PALM-4U (i.e. PALM for Urban applications; Maronga et al., 2019, Met. Z., https://doi.org/10.1127/metz/2019/0909) has been developed within the collaborative project MOSAIK (Model-based city planning and application in climate change). With such a large-eddy simulation (LES) model, pollutant dispersion around buildings and within street canyons can be simulated, with explicitly resolving the turbulent transport in urban environments.

Cyclic boundaries are frequently applied in LES in order to obtain lateral boundary conditions for the turbulent quantities. In addition to the default cyclic boundary conditions, PALM-4U allows also time-dependent boundary conditions from regional models to account for variable weather conditions and regional scale pollutant transport. Turbulent fluctuations, which are not included in the boundary conditions from the regional simulation but are needed as additional boundary conditions for the LES model are produced by a turbulence generator (Maronga et al, 2019, GMDD, https://doi.org/10.5194/gmd-2019-103).

PALM-4U simulations with and without time dependent boundary conditions from regional simulations with WRF-Chem are performed for different setups in order to test the impact of the domain configuration. The simulations indicate that cyclic boundary conditions can lead to unrealistic accumulation of pollutants over urban areas with strong sources, which is not the case when time-dependent boundary conditions are applied. However, even though a turbulence generator is applied, explicit setting of time-dependent boundary conditions requires large model domains, in order to obtain fully developed turbulence within the domain of interest, increasing the computational demand of the simulation.

How to cite: Forkel, R., Khan, B., Werhahn, J., Banzhaf, S., Chan, E. C., Kanani-Sühring, F., Ketelsen, K., Maronga, B., Mauder, M., Raasch, S., and Sühring, M.: Test of chemistry boundary conditions large-eddy simulations in urban areas, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8795, https://doi.org/10.5194/egusphere-egu2020-8795, 2020.

D2974 |
Irene Zyrichidou, Stavros Solomos, Stylianos Kotsopoulos, Panagiota Syropoulou, and Evangelos Kosmidis

Air pollution models play an important role in science because of their capability to give a description of the air quality problem including an analysis of factors and causes (emission sources, meteorological processes, and physical and chemical changes). Real-time forecast of urban air quality is highly important to the public as advanced information for both air quality and safety assessment. This study presents the development of a regional scale high-resolution modeling system for simulating air quality and forecasting changes in urban pollution levels. The air quality system based on the state-of-the-art Weather Research and Forecasting model coupled with chemistry (WRF-Chem) has been applied over the greater area of Thessaloniki, Greece. The model performance, in terms of simulated surface major air pollutants’ concentrations, is evaluated using ground-based measurements during the operational implementation period in winter-spring 2020. Our study highlights the importance of resolving local scale atmospheric conditions such as surface wind flow and boundary layer properties for describing the pollutants’ concentrations and the importance of constraining emissions over the study area.


How to cite: Zyrichidou, I., Solomos, S., Kotsopoulos, S., Syropoulou, P., and Kosmidis, E.: Development of a high-resolution air quality forecasting system, based on WRF-Chem model, for the greater area of Thessaloniki, Greece, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21296, https://doi.org/10.5194/egusphere-egu2020-21296, 2020.

D2975 |
Yumi Kim

Along with the development of new cities, the construction of LNG cogeneration plant in urban areas is being promoted, and the facility has been pointed out as one of the major air pollution sources along with many vehicles in urban areas. For example, the construction of a new administrative city in Korea has led to the relocation of major government buildings and the influx of more than 300,000 people. The city has a 530 MW power plant + 391 Gcal/h district heating facility. The facility released 294,835 kg and 325,381 kg of NOx annually in 2017 and 2018, respectively. When examining the impact, we analyzed the impact of air pollutants (PM2.5, O3, NO2, etc.) through CMAQ modeling. In addition, the impact prediction using AERMOD related to the release of carcinogenic air pollutants is estimated to be no more than 10-5 (risk level), but measurement and verification are required. In addition to concentration-based risk assessments, health impact assessments are needed that consider the number of populations exposed. In this study, QGIS was used to calculate population. In conclusion, even if the same LNG power plant is constructed, the LNG cogeneration plant located adjacent to a large residential facility requires air pollutant management measures according to the exposure population by radius of influence


How to cite: Kim, Y.: Status of Air Pollutant Emissions and Health Impact of LNG Cogeneration Plant in Administrative City, Republic of Korea, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7626, https://doi.org/10.5194/egusphere-egu2020-7626, 2020.

D2976 |
Jing Zhao and Haikun Wang

As the largest carbon emitter in the world, China shoulders weighty responsibility for carbon emission reduction. Recent evidence shows that carbon emission in China has the potential to peak ahead of schedule (2030). Analysis of both economic and environmental outcomes of the acceleration of carbon emission reduction will provide important implications for the better design and implementation of climate policies in China and other countries. However, there is a lack of research.

In this study, we focus on carbon emission paths and assess health impacts related to the synergistic emission reduction of atmospheric pollutants and conduct a cost-benefit assessment. We adopte the simulated emission of different Shared Socioeconomic Pathways and climate policy scenarios based on Global Change Assessment Model (GCAM) to quantify co-reduction of air pollution. Using the WRF-Chem chemical transport model and MEIC emission inventory, we simulate the change of air pollutant concentrations and calculate the related health benefit using the IER model and monetize it using the VSL model.

Our analysis shows that carbon emission in China could peak before 2030 in stringent climate policy. Meanwhile, cleaner developing pathways or stricter climate strategies can help to alleviate the pressure of carbon emission reduction. Climate policy will bring additional emission mitigations to atmospheric pollutants, especially for SO2, and NOx, but NH3 emission increases. Climate policies aimed at reducing carbon emissions will also bring different degrees of improvements in air quality and health benefits across China in 2030 and 2050, respectively.

How to cite: Zhao, J. and Wang, H.: Healthy synergies of carbon mitigation under climate policy in China, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6574, https://doi.org/10.5194/egusphere-egu2020-6574, 2020.

Chat time: Friday, 8 May 2020, 14:00–15:45

Chairperson: Michiel van der Molen, Felix Vogel
D2977 |
Li Peng, Yanling Shen, and Jing Cai

The impact of microenvironment on public health has received increasing attention, especially in the traffic and living microenvironment. This study shows a comparative research of PM2.5 exposure concentrations associated with five commuting modes (i.e., walking, bicycling, car, bus and subway) in haze and non-haze periods in Shanghai, China. On the days of observation, the experimenter carried portable instruments to measure personal PM2.5 exposure concentrations, commuting by different transport modes, following designated routes round Century Park in Shanghai. Fixed observations of indoor and background concentrations of PM2.5 were also taken for comparison in a three-story building nearby. We found that the choice of different commuting modes will result in different personal PM2.5 exposure levels. During the haze periods in winter, cyclists followed by pedestrians had the highest PM2.5 exposure than those who commuted by subway, bus and car with controlled ventilation settings. During the non-haze periods, subway commuters had the highest PM2.5 exposure. By contrast with personal exposure, the hourly inhaled dose of PM2.5 was higher among active commuters than among commuters who used motorised transport such as subway, bus and car. Our results may provide information to help develop exposure mitigation strategies for public health protection.

How to cite: Peng, L., Shen, Y., and Cai, J.: Personal exposure to PM2.5 during commuting in Shanghai, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-22364, https://doi.org/10.5194/egusphere-egu2020-22364, 2020.

D2978 |
Yifan Liu, Xiaojing He, Zixiao Zhao, Ge Zhu, Clive Sabel, Zongwei Ma, Ziheng Jiao, Jing Zhao, and Haikun Wang

Ambient PM2.5 (fine particulate matter) pollution in China has been greatly reduced in recent years, especially since the implementation of Clean Air Action in 2013. Analysis of variations in the pollution related health burden and the driving factors has important implications for the policymakers to further improve the health benefit of air pollution controls. Here we adopted an annual population distribution estimate, disaggregated by age structure, together with PM2.5 concentration and incidence data, to better estimate total PM2.5 attributable mortality considering the effect of changing population size and age structure. We then quantified the contribution of each factor to the total variation of PM2.5 attributable mortality both nationally and regionally. Our analysis showed that national PM2.5 attributable mortality generally increased from 861,140 (95% confidence interval: 525,860~1,161,550) in 2004 to 932,500 (546,590~1,300,160) in 2017. In most 2nd- and higher-tier cities in China, which stand for highly developed cities like Beijing, Shanghai, Guangzhou, etc., the PM2.5 health burden increased. Meanwhile, the decrease in city-level PM2.5 health burden mainly happened in 3rd- and lower-tier cities, where local developments were relatively smaller. The effect of exposure to PM2.5 on air pollution-related mortality has altered from aggravating to mitigating since 2012, and the abated PM2.5 exposure resulted in a reduction of 19.7% of PM2.5 attributable mortality between 2012 and 2017. However, such benefit was almost masked by the effect of the population aging, which brought an increase of 18.4% to the health burden. Our results implied that the increasing trend in China’s PM2.5 health burden since 2006 was halted after 2012 due to the pollution control policies, and population aging impeded it from declining further. For future air pollution control and public health affairs, growing cities in China should focus attention on old-age care, where the growth of attributable mortality might occur.

How to cite: Liu, Y., He, X., Zhao, Z., Zhu, G., Sabel, C., Ma, Z., Jiao, Z., Zhao, J., and Wang, H.: Population aging might mask the health benefit from China’s 2013 Clean Air Action, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12322, https://doi.org/10.5194/egusphere-egu2020-12322, 2020.

D2979 |
Stephanie Koller, Christa Meisinger, Markus Wehler, and Elke Hertig

For a long time it has been known that exceptionally strong and long-lasting heat waves have negative health effects on the population, which is expressed in an intensification of existing diseases and over-mortality of certain risk groups (Kampa, Castanas 2008). Often associated with heat are stagnant airflow conditions that cause a large increase in the concentration of certain air substances (Ebi, McGregor 2008). Many of these air substances have a strong adverse effect on the human organism (Kampa, Castanas 2008).

The aim of the project is to investigate the actual hazard potential of health-relevant air pollution- and climatological variables by quantifying the effects on human health of increased exposure to air constituents and temperature extremes. Different multivariate statistical methods such as correlation analysis, regression models and random forests, extreme value analysis and individual case studies are used.

As a medical data basis for this purpose, the emergency department data of the University Hospital Augsburg are regarded. In addition to the diagnosis, supplementary information such as age, gender, place of residence and pre-existing conditions of the patients are used. Among the air constituents, the focus is on ozone, nitrogen dioxide and particulate matter. In the meteorological part, the focus is primarily on temperature, which is not only a direct burden but, as in the case of ozone, also has a decisive influence on the formation of ozone molecules. However, a large number of other meteorological parameters such as precipitation, relative humidity and wind speed as well as the synoptic situation also play a major role in the formation, decomposition process and the distribution of pollutants (Ebi, McGregor 2008).

The first major question to answer is whether air-pollution and meteorological stress situations are visible in the emergency department data. Further in-depth questions are which factors have the greatest negative impact, what is the most common environment-related disease, which weather conditions carry a higher than average risk and what are the health risks of climate change.

Ideally, the analysis may also provide a short-term forecast from which to derive whether or not there will be an above or below average number of visits to the emergency department.

The project is funded by the German Federal Foundation for Environment (DBU) and the German Research Foundation (DFG) - project number 408057478.


Ebi K., McGregor G. (2008): Climate Change, Tropospheric Ozone and Particulate Matter, and Health Impacts. doi: 10.1289/ehp.11463

Kampa M., Castanas E. (2008): Human health effects of air pollution. In: Environmental Pollution 151(2): 362-367. doi: 10.1016/j.envpol.2007.06.012

How to cite: Koller, S., Meisinger, C., Wehler, M., and Hertig, E.: Health-relevant influences of air substances and meteorological conditions, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-2669, https://doi.org/10.5194/egusphere-egu2020-2669, 2020.

D2980 |
Ohad Zivan, Alessandro Bigi, Giorgio Veratti, José Antonio Souto González, Lorena Marrodán, Chiara Bachechi, Sara Fabbi, David Cartelle Fernández, Xabier Diz Gerpe, Grazia Ghermandi, Giovanni Gualtieri, Raquel Trillo Lado, Javier Cacheiro López, Ángel Rodríguez López, Paolo Nesi, Michela Paolucci, Stefano Bilotta, José Ramón Rı́os Viqueira, Alessandro Zaldei, and Laura Po

Most of worldwide population lives in urban areas, demanding for air quality information with a high spatio-temporal resolution. The most promising approaches for estimating urban air quality within the complex urban topography are small sensor networks and simulation models.

The TRAFAIR project focuses on understanding the role of traffic emissions on urban air quality by the combination of dispersion modelling, space- and time-resolved gas monitoring by lower cost sensors and realistic traffic flow rates by dynamic traffic model based on real time traffic data. Test cities of TRAFAIR are Modena, Florence, Pisa, Livorno, Zaragoza and Santiago de Compostela.

Depending on the size of the urban area, from 6 to 13 sensors units are deployed across each city since August 2019, providing estimates of NO, NO2, CO and O3, along with RH and temperature. Metal oxide sensors are deployed in Tuscany (Florence, Pisa, Livorno) and electrochemical cells are used elsewhere. The units are calibrated on a regular basis by co-location at the air quality regulatory stations and subsequently deployed across the town to monitor several representative locations (e.g. Low Emission Zones, hospital surroundings). For each sensor the raw readings (e.g. mV for electrochemical cells) are collected and a regression model (e.g. Random Forest) is applied to derive a calibration function, exploiting the data from the regulatory stations during co-location periods; for instance in Modena, the first short-term calibration provided a model with a Mean Absolute Error between 5 – 6 ppb and 2 – 4 ppb for NO and NO2 respectively.

The sensors are used for both real-time urban air quality mapping and to test and validate the 24hr forecast service of NOx by the microscale lagrangian dispersion model GRAL. The simulation domains, covering the urban area of each TRAFAIR city, have a horizontal resolution of 4 m and allow to account for the presence of buildings. The dispersion model mainly focuses on NOx by traffic emissions, although domestic heating will be also included in the analysis. Vehicular emissions are based either upon historical traffic data (e.g. induction loops), or upon previously available traffic flow simulation, or upon traffic pattern reconstruction using a traffic flow model followed by a cluster analysis to group streets with similar pattern.

The final goal of the project is the development of a tool to support local policymakers and to inform citizenship about the quality of air and the impact of urban emission sources, particularly traffic. A secondary goal of the project is the development of a valuable QA/QC protocol for small sensor units and the optimization of the modelling chain for the forecast of traffic and domestic heating impact on local air quality at the urban scale.

How to cite: Zivan, O., Bigi, A., Veratti, G., Souto González, J. A., Marrodán, L., Bachechi, C., Fabbi, S., Cartelle Fernández, D., Diz Gerpe, X., Ghermandi, G., Gualtieri, G., Trillo Lado, R., Cacheiro López, J., Rodríguez López, Á., Nesi, P., Paolucci, M., Bilotta, S., Rı́os Viqueira, J. R., Zaldei, A., and Po, L.: Analysis of urban air quality in 6 European cities by lower cost sensors, Lagrangian urban dispersion modelling and traffic flow modelling: the TRAFAIR project , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13601, https://doi.org/10.5194/egusphere-egu2020-13601, 2020.

D2981 |
Yunsong Liu, Jean-Daniel Paris, Mihalis Vrekoussis, Panayiota Antoniou, Marios Argyrides, Christos Constantinides, Dylan Desbree, Neoclis Hadjigeorgiou, Christos Keleshis, Olivier Laurent, Andreas Leonidou, Carole Philippon, Panagiotis Vouterakos, Pierre-Yves Quehe, Philippe Bousquet, and Jean Sciare

Unmanned Aerial Vehicles (UAVs) have the potential to fill in gaps in greenhouse gases (GHG) observations by providing high-resolution vertical profiling, horizontal mapping of fluxes and 3D measurements close to the ground. UAVs can ultimately allow better characterizing the spatial distribution of various GHG sources and sinks. To achieve these goals, important efforts are currently put towards the development of compact, lightweight, low powered and highly accurate GHG sensors on UAVs.

This study aims to develop and validate a UAV-CO2 sensor system to map specific source emissions close to the ground. The CO2 sensor used here is the High-Performance Platform (HPP 3.2, SenseAir AB) of a total weight 1058g including battery. Prior to its integration in the UAV, the CO2 sensor accuracy and linearity tests were performed in the laboratory. Allan Deviation showed the sensor precision to be within ±1ppm at 1 Hz. Corrections due to temperature and pressure changes were performed using specific formulas obtained from chamber experiments. Field (manned aircraft) tests were performed, where the P/T correction equations were evaluated for two CO2 sensors which were compared against an airborne reference instrument (Picarro G2401-m). After laboratory tests and field deployment, the HPP CO2 sensor was integrated into a small fixed-wing UAV with a wingspan of 1.83m and customized avionics and payload developed by the Unmanned Systems Research Laboratory of the Cyprus Institute performed successful atmospheric profiling below/above the boundary layer, at an agricultural site in Cyprus. This HPP CO2 sensor is also to be integrated in a quad-copter for vertical take-off & landing (VTOL) in urban environments to execute intensive (every 20-min) atmospheric profiling (0-1km altitude) over the city of Nicosia (Cyprus). These flights provide us with useful insights into the CO2 vertical distribution within the planetary boundary layer (and above) for different (remote/urban) regions in Cyprus.

How to cite: Liu, Y., Paris, J.-D., Vrekoussis, M., Antoniou, P., Argyrides, M., Constantinides, C., Desbree, D., Hadjigeorgiou, N., Keleshis, C., Laurent, O., Leonidou, A., Philippon, C., Vouterakos, P., Quehe, P.-Y., Bousquet, P., and Sciare, J.: Improvement of a low-cost CO2 commercial NDIR sensor for UAV atmospheric profiling applications, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8891, https://doi.org/10.5194/egusphere-egu2020-8891, 2020.

D2982 |
J. William Munger, Shuxiao Wang, Chris Nielsen, and Michael B. McElroy

CO2 and CH4 are radiatively important trace gases closely associated with human activity particularly in urban emission hotspots. Through rapid development and economic growth China has become a major source of CO2. CO2 emission inventories for China are becoming increasingly accurate. CH4 emissions in China are not as well characterized, though for various reasons, including; Chinese policies mandating conversion from coal to natural gas for district heating, intensification of agriculture, and the large volumes of urban waste that must be managed, it is likely CH4 emissions from Chinese urban centers could be significant. As part of an ongoing Tsinghua – Harvard collaboration we have set up a pair of atmospheric observatories to the north and south of Beijing. The northern site (Miyun) is 75km NNE and the southern site, Dashiwo, is 63 km SSW of the center of Beijing. Miyun has been in semi continuous operation since 2005. Miyun was located to sample Beijing urban outflow as well as clean airmasses depending on wind direction. Dashiwo is located primarily to capture the polluted air coming into Beijing from Hebei province, though it will also be influenced at times by cleaner airmasses coming over the mountains on the western edge of the basin. The high accuracy and precision measurements of CO2 and CH4 that are the focus of this presentation started in May 2018. Observations at Dahsiwo started in November 2019.For this presentation we focus on quantifying the magnitudes of CO2 and CH4 in urban-influenced air masses and their enhancements relative to clean background air. The correlations between CO2 and CH4 and their relationships to other air pollutant tracers including SO2, NOx/NOy, and CO provide constraints on potential sources for these gases. Through back trajectory analysis the source regions can be distinguished. As expected, both sites have enhanced mixing ratios of CO2 and CH4. Median CO2 during the overlapping period Nov. Dec. 2019 is 430, and 459 ppm at Miyun and Dashiwo. Median CH4 is 2036 and 2228 ppb. Outside the growing season when CO2 is influenced by vegetation uptake the CH4:CO2 ratio is 6.1 ppb:ppm. The Dashiwo data are bounded by the same slope, but have more scatter due to periods with elevated CH4 but not CO2. A tight correlation for CO2 and CH4 at Miyun suggests a single predominant combustion or respiration source type, while variability in the Dashiwo observations suggests multiple sources including some rich in CH4 that are not combustion or respiration. Identification of major CH4 sources is a starting point for choosing mitigation options.

How to cite: Munger, J. W., Wang, S., Nielsen, C., and McElroy, M. B.: CO2 and CH4 observations surrounding Beijing to to distinguish possible emission processes and locations, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11858, https://doi.org/10.5194/egusphere-egu2020-11858, 2020.

D2983 |
Bryce F.J. Kelly, Xinyi (Lexie) Lu, Zoë M. Loh, and Rebecca E. Fisher

Methane (CH4) is the second most important anthropogenic greenhouse gas after carbon dioxide (CO2) in the atmosphere1. Human activities are estimated to contribute ~50% of total CH4 emissions globally2. With 68% of the population projected to live in urban areas by 20503, there is a need to better quantify CH4 emissions from urban sources and to develop mitigation plans. Major potential urban sources include: the gas distribution network, landfills, the sewerage network, appliances in houses (heaters, stoves, hot-water systems), wood burning heaters, and urban wetlands.

This study aimed to determine the major CH4 sources in Melbourne (Australia’s second largest city with a population approaching 5 million people). Melbourne has grown rapidly since it was founded ~200 years ago and this has left legacy potential CH4 sources. For example, the gas distribution system has piping ranging from modern to 100 years old (common in unrenovated houses from early last century); landfills that use to be on the city fringe are now surrounded by new housing developments. 

To map the location of major CH4 sources throughout Melbourne we conducted a mobile survey, measuring the CH4 mole fraction ([CH4]) at a height of 3 m using a Los Gatos Research ultra-portable greenhouse gas analyser. An air inlet was attached to the roof of the car and the location of the measurements were georeferenced using a Hemisphere GPS system as we drove around the city. The day and night-time surveys were undertaken from the 26th – 27th July 2019 (winter).  When a major CH4 plume was detected 10 air samples were collected and stored 3 litre FlexFoil bags. These samples were analysed for [CH4], δ13C-CH4, [CO2], δ13C-CO2 using a Picarro G2201-i cavity ring-down spectrometer (CRDS). To determine the δ13C-CH4 signature of the plume each set of bags was analysed using a Miller-Tans plot and Bayesian regression. The combination of potential observable sources and the δ13C-CH4 signature was then used to attribute the source of the CH4 plume. We show that the δ13C-CH4 signatures of the CH4 plumes are needed to reduce the risk of attributing a plume to the wrong source. We present an example of separating domestic wood fires from a ‘super-emitter’ leak from the gas distribution system, and we also show how we traced a landfill plume for a distance of over 5 km using δ13C-CH4 measurements.

Our research demonstrates that mobile [CH4] surveys coupled with δ13C-CH4 analyses is a cost-effective workflow for mapping both diffuse CH4 emissions and ‘super-emitters’. Such surveys could be systematically undertaken in cities worldwide to delineate and prioritise targets for CH4 emission reduction. 



(1)      Myhre, G. et al. Cambridge University Press, 2013; Vol. 9781107057, pp 659–740.

(2)      Saunois, M. et al. Earth Syst. Sci. Data Discuss. 2019, 1–138.

(3)      United Nations. World Urbanization Prospects The 2018 Revision (ST/ESA/SER.A/420). 2019.

How to cite: Kelly, B. F. J., Lu, X. (., Loh, Z. M., and Fisher, R. E.: Urban Methane Surveys – A Case Study Where Isotope Measurements Guided Source Attribution, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12475, https://doi.org/10.5194/egusphere-egu2020-12475, 2020.

D2984 |
Conner Daube, Christoph Dyroff, Edward Fortner, Jordan Krechmer, Francesca Majluf, Tara Yacovitch, and Scott Herndon

During late 2019, the Aerodyne Mobile Laboratory sampled numerous industrial areas primarily in the County of Los Angeles, California, USA. Commercial and laboratory-grade instruments were used to analyze the gaseous and particulate composition of ambient air samples while operating in mobile and stationary modes. Measurements of CO2, CH4, and N2O were collected in addition to several specific hazardous air pollutants. Short-lived plumes from a wide variety of industries and broader regional trends were observed. Multi-day measurements at identified sources and overnight sampling added depth and context to these findings. Results from this characterization of industrial emission sources, including analysis of both greenhouse gases and pollutants in the urban environment, will be presented.

How to cite: Daube, C., Dyroff, C., Fortner, E., Krechmer, J., Majluf, F., Yacovitch, T., and Herndon, S.: Using the Aerodyne Mobile Laboratory to characterize industrial emissions in Southern California, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12030, https://doi.org/10.5194/egusphere-egu2020-12030, 2020.

D2985 |
Tilman Leo Hohenberger

Urban air pollution remains a key pressure on public health. With the megatrend of urbanization and its forcing on emissions and exposure, effective monitoring tools in cities are at the center of prevention efforts.

Air Quality Monitoring Stations (AQMS) are traditionally used for regulatory efforts and, increasingly, as publicly available information sources. Facing high levels of air pollution heterogeneity in complex urban environments, a simple spatial approach is often misleading when choosing an AQMS that represents local street-level conditions the best. Model-based calculation of representativeness areas are rare for the urban scale (e.g. Rodriguez et al., 2019), and suffer from short model times, low model correlations and a lack of external validation by observation data. Moreover, as both health impacts and air-pollution episodes are influenced by environmental factors, the sensitivity of representativeness areas to wind impacts and during different seasons are a further point of interest not covered well by previous literature.

For the high-density environment of geographically complex Hong Kong, we used a full year (2019) of high-resolution air quality modelling (ADMS-Urban) data to establish representativeness areas for the territory’s 16 AQMS. We constructed representativeness areas for key air-pollutants for the full period and based on season and wind speed. We parameterized the effects of wind and geography on the size and shape of the representativeness areas. Furthermore, we validated our findings by a series of week-long outdoor measurements aimed to cover the whole territory of Hong Kong.

Our results show that Hong Kong’s AQMS network covering the territory well for a PM2.5, PM10 and O3, where the mean CSF (hourly Concentration Similarity Frequency with a target of ±20%) of each grid-cell to the best matching AQMS lies at around 60%. Both NO2 and SO2 are less well represented, with a CSF of around 30%. Moreover, we show that representativeness areas calculated from similarity-based metrices as CSF and percentage difference represent the impact of geographical features on pollution dispersion better than correlation-based metrices (R2 and ioa). It was further found that AQMS represent upwind areas better than downwind areas, especially in areas exposed to open wind-flow, and that the represented areas change strongly over the course of a year.

In this study, we showcase the ability of high-resolution urban air-pollution modelling to guide the public with information on AQMS representativeness. Furthermore, we report that representativeness areas are non-static, changing with seasons and under the influence of wind. High-resolution urban modelling can further be used to gauge the quality of AQMS networks and assess the need and location of additions to an existing network.


Rodriguez, D., Valari, M., Payan, S., & Eymard, L. (2019). On the spatial representativeness of NOX and PM10 monitoring-sites in Paris, France. Atmospheric Environment: X, 1, 100010.

How to cite: Hohenberger, T. L.: Influences of Season and Geography on Urban Air Quality Monitoring Stations (AQMS) Representativeness Areas by High Resolution Air Quality Monitoring, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13652, https://doi.org/10.5194/egusphere-egu2020-13652, 2020.

D2986 |
Rodrigo Carbajales, Massimiliano Iurcev, and Paolo Diviacco

Low cost sensors and crowd-sourcing data could potentially revolutionise the way air pollution measurements are collected providing high density geolocated data. In fact, so far data have been collected mostly using dedicated fixed position monitoring stations. These latter rely on high quality instrumentation, well established practices and well trained personnel, which means that, due to its costs, this paradigm entails limitations in the resolution and extension of geographic sampling of an area.

The combination of low costs sensors and volunteer-based or opportunistic acquisition of data can, instead, possibly turn the cost issue into an advantage. This approach, however, introduces other limitations since low cost sensors provide less reliable data and crowd source acquisition are subjects to data gaps in space and time.

In order to overcome these issues redundant data from multiple platforms have to be made available. On one hand this allows statistics to be applied to identify and remove anomalous values, and on the other hand when multiple platforms are used, the chances to have a better coverage and more reliable data  increases.

To implement this approach OGS developed the full suite of tools that has been named COCAL that allow to follow the full path from the acquisition, transmission, storage, integration and real time visualization of the crowdsourced data.

Low cost sensors for the detection of suspended particulate matter size 2.5 and 10 µm, together with atmospheric pressure, humidity and temperature, have been combined with GPS positioning and transmission (being able to opt for GSM, WiFi or LoRaWAN transmission) unit in a black box that can be attached to any moving vehicle travelling in an area. This way large areas can be sampled with high geographic resolution.

Atmospheric data are collected in an InfluxDB database, which allows easy integration with TheThingsNetwork for LoRaWAN network management and directly with GSM and WiFi connections. Public users are provided with a real-time web interface based on OpenLayers for map visualization. Server based processing and conversion scripts generate both filtered data and aggregate data, by computing averages on a spatial and temporal grid.. Finally, automatic interpolation techniques like Inverse Distance Weighting or Natural Neighbours may provide detailed online maps with contouring and boundary definition. All products are available in near real-time through OGC compliant web services, suited for an easy integration with other repositories and services.

How to cite: Carbajales, R., Iurcev, M., and Diviacco, P.: Low cost sensors and crowd-sourced data to map air pollution in urban areas, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18946, https://doi.org/10.5194/egusphere-egu2020-18946, 2020.

D2987 |
Daniel Zollitsch, Jia Chen, Florian Dietrich, Benno Voggenreiter, Luca Setili, and Mark Wenig

As the number of official monitoring stations for measuring urban air pollutants such as nitrogen oxides (NOx), particulate matter (PM) or ozone (O3) in most cities is quite small, it is difficult to determine the real human exposure to those pollutants. Therefore, several groups have established spatially higher resolved monitoring networks using low-cost sensors to create a finer concentration map [1-3].

We are currently establishing a low-cost, but high-accuracy network in Munich to measure the concentrations of NOx, PM, O3, CO and additional environmental parameters. For that, we developed a compact stand-alone sensor systems that requires low power, automatically measures the respective parameters every minute and sends the data to our server. There the raw data is transferred into concentration values by applying the respective sensitivity function for each sensor. These functions are determined by calibration measurements prior to the distribution of the sensors.

In contrast to the other existing networks, we will apply a recurring calibration method using a mobile high precision calibration unit (reference sensor) and machine learning algorithms. The results will be used to update the sensitivity function of each single sensor twice a week.  With the help of this approach, we will be able to create a calibrated real-time concentration map of air pollutants in Munich.

[1] Bigi et al.: Performance of NO, NO2 low cost sensors and three calibration approaches within a real world application, Atmos. Meas. Tech., 11, 3717–3735, 2018

[2] Popoola et al., “Use of networks of low cost air quality sensors to quantify air quality in urban settings,” Atmos. Environ., 194, 58–70, 2018

[3] Schneider et al.: Mapping urban air quality in near real-time using observations from low-cost sensors and model information, Environ. Int., 106, 234–247, 2017

How to cite: Zollitsch, D., Chen, J., Dietrich, F., Voggenreiter, B., Setili, L., and Wenig, M.: Low-Cost Air Quality Sensor Network in Munich, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19276, https://doi.org/10.5194/egusphere-egu2020-19276, 2020.

D2988 |
Yongmi Park, Ho-Sun Park, and Wonsik Choi


As urbanization has spread, increased energy consumption, complicated built environments, and dense road networks cause spatiotemporal heterogeneity of air pollutant distributions even in an intra-community scale. High spatiotemporal heterogeneity of air pollutant distributions can affect pedestrian and/or traffic users’ exposure to air pollutants according to where and when they are, potentially forming air pollution hotspots. Thus, it is important to understand the characteristics of spatiotemporal distributions in air pollutants in various micro-built environments in populated urban areas. However, current air quality monitoring performed by the government cannot capture these highly heterogeneous distributions of air pollutants due to the limitations of financial and human resources. In this respect, cost-effective sensors have great potential to build highly spatially dense air quality monitoring networks to address the low spatial resolution issue of conventional air quality monitoring stations.

In this study, we built a highly dense air quality monitoring network consisting of 30 sets of sensor nodes in an 800 m ´ 800 m spatial domain to understand the characteristics of air pollutant distributions in various urban microenvironments. The domain includes urban street canyon with moderate traffic, a mixture of high and low buildings with high traffic, an open space with minimal traffic, and others. The sensor node consists of sensors (for CO, NO2, O3, PM2.5, and PM10, temperature, and humidity) and communication/data storage parts (wifi, interface for smartphone connection, and SD card). We also conducted inter-sensor comparison among sensor nodes and intercomparison tests between the sensor node and conventional reference instruments.

Intra-community air quality monitoring with a sensor network was conducted for a couple of weeks in two distinct weather conditions (humid and hot summer and dry and cold winter) in 2017 and 2018. During the observation periods, the concentration distribution analyses for air pollutants (except CO, PM) showed significant heterogeneity in their distributions in space. In addition, the correlation analysis with the meteorological factors showed that CO concentrations were affected by wind speed (winter, R2=0.22-0.25), but the other air pollutants were not directly correlated. We also examined the effects of land-use and building configuration on air pollution distributions. More details concerning these results are presented.

Keywords: Sensor network, low-cost sensor, spatial heterogeneity, micro-built environments

How to cite: Park, Y., Park, H.-S., and Choi, W.: Intra-community air quality monitoring in various urban microenvironments in South Korea: based on observations from highly dense cost-effective sensor network, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19324, https://doi.org/10.5194/egusphere-egu2020-19324, 2020.

D2989 |
Jiun-Horng Tsai and Hsiao-Hsuan Tsai

This research investigated the hazardous air pollutants (HAPs), also known as air toxics, emission profiles and the potential health risks in Tainan City in Taiwan. Emission profiles of HAPs were derived by source test data and speciation data bank. Emissions from stationary source, mobile source, and area source were estimated in this study. Airborne concentration of target HAPs had been simulated by Models-3/CMAQ simulation and followed by cancer risk assessments for control priority assessment.

Five species of air toxics were selected as target component in this study, which included benzene, formaldehyde, acetaldehyde, acrolein, and 1,3-butadiene, by weighting the emissions and toxicity factors. Emission estimation indicated that these target air toxics were released by stationary sources with 34.5, 35.0, 5.0, 72.3, 94.5 %, respectively. Emissions of these 5 air toxics from mobile sources were 62.8, 45.4, 94.7. 27.5, 3.5 %, respectively. Area sources contributed less fraction in the city. The simulated annual average concentrations of target air toxics indicated the hot zone of various HAPs present in different location in the city. The airborne concentration of benzene and acetaldehyde in hot zone were mainly caused by mobile source emissions. Concentrations of formaldehyde in hot zone was caused by various sources. Airborne concentrations of acrolein and 1,3-butadiene in hot zone were mainly caused by area sources. The potential health risk assessment imposed by these target air toxics were evaluated by simulated exposure concentrations and with inhalation unit risk factor (cancer risk) and reference concentration level (non-cancer risk), respectively. The results showed 1,3-butadiene would pose the highest carcinogenic potential in the city which were mainly released by area sources. Acrolein had the highest non-carcinogenic potential. The cancer burden, by considering population density and exposure concentration, was higher in downtown area. Formaldehyde was the critical HAP which would impose the highest impacts on people caused by dense emission from mobile sources.

How to cite: Tsai, J.-H. and Tsai, H.-H.: Characteristics and Control Priority of Hazardous Air Pollutants in the Metropolitan Areas : A Case Study in Tainan, Taiwan, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-22492, https://doi.org/10.5194/egusphere-egu2020-22492, 2020.

D2990 |
Jun Zou, Jianning Sun, Zixuan Xiang, Xiaomen Han, and Qiuji Ding

At the end of November 2018, a heavy air pollution event was recorded by many meteorological stations in the Yangtze River Delta (YRD), China. The local PM2.5 concentration exceeding to 200 µg m-3. This is the heaviest, longest and most widespread heavy-polluted weather in Jiangsu Province since 2018. Meanwhile, there has been severe foggy weather in Jiangsu Province, with visibility less than 200 meters in most parts of the province. In order to study the interaction between PM2.5 concentration and boundary layer height in the haze event, and the effect of fog on pollutant aggregation, the boundary layer structure of the continuous haze process was analyzed by using the SORPES Observation of Nanjing University's Xianlin Campus. The results of the analysis show that:
1, The PM2.5 concentration in the boundary layer is inversely correlated with the boundary layer height, the higher the PM2.5 concentration, the lower the boundary layer height during the day. By absorbing and scattering solar radiation, atmospheric aerosols affect the balance of surface energy and reduce the sensitive heat flux, thereby inhibiting the development of the boundary layer. While inhibited development of the boundary layer will limit the diffusion of atmospheric aerosols, thereby increasing the concentration of atmospheric aerosols in the boundary layer. In addition, nocturnal atmospheric aerosols absorb heat, leading to strong grounding inversion temperature the next day, further inhibiting the development of the daytime boundary layer. 
2, The fog-top inversion is very strong, far stronger than the inversion caused by atmospheric aerosols. Therefore, the heights of the boundary layer of fog days are much lower than that of non-fog days under the same pollution conditions.
3, During the fog, the PM2.5 concentration significantly reduced. And after the fog dissipated, due to the sun, the air moisture evaporation, PM2.5 concentration quickly reverted to the pre-fog state. Fog has limited wet removal of PM2.5.
4, Fog can inhibit the development of the boundary layer, with the continuation of the fog process, the pollution in the boundary layer continues to increase. At the same time, due to the inhibition of the development of the boundary layer, the diffusion of water vapor in the air is also affected, resulting in the boundary layer water vapor content is always in a high state, thus promoting the production of fog.

How to cite: Zou, J., Sun, J., Xiang, Z., Han, X., and Ding, Q.: The vertical distribution of PM2.5 and boundary-layer structure during winter haze in Nanjing, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-2198, https://doi.org/10.5194/egusphere-egu2020-2198, 2020.

D2991 |
Veronika S. Brand, Thiago Nogueira, Prashant Kumar, and Maria de Fatima Andrade

Commuters are vulnerable to traffic air pollutants, especially to fine particulate matter (PM2.5) and black carbon (BC) because of their proximity to on-road vehicles. Both pollutants have been extensively associated to adverse health effects (i.e., stroke, diabetes, cardiovascular and respiratory diseases, and cancer). Therefore, this work aims to investigate the extreme concentrations of PM2.5 and BC occurrence in commuters in the megacity of São Paulo, Brazil. We carried out a field campaign measuring the commuter exposure to PM2.5 and BC concentrations inside buses, cars and undergrounds in São Paulo during morning and evening peak-hours. We fitted an Extreme Value Distribution to the collected data to investigate the behavior of the extreme values in the different transport modes and periods of the day. The results suggest that higher concentrations of PM2.5 and BC occur more frequently inside buses, followed by cars and undergrounds. Extreme concentrations for both pollutants are more likely to happen during morning peak-hours when compared to evening peak-hours. Our findings add further evidence that the transport mode and period of the day affect substantially the PM2.5 and BC exposure in commuters. Furthermore, the results are quite useful for supporting urban policies that consider the improvement of the efficiency of air filtering systems inside public transport and private cars.

How to cite: S. Brand, V., Nogueira, T., Kumar, P., and Andrade, M. D. F.: Extreme concentrations of fine particulate matter and black carbon during commute, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21032, https://doi.org/10.5194/egusphere-egu2020-21032, 2020.

D2992 |
Wei Yuan, Rujin Huang, and Lu Yang

Nitrated aromatic compounds (NACs) are an important chromophore component of atmospheric brown carbon (BrC) and can affect both the urban air quality and global climate. However, the compositions, sources of NACs in atmospheric particulate matter and its impact on the optical properties of BrC are still of very limited understood. In this study, the concentrations of 10 NACs and their light absorption contributions to BrC were investigated based on daily aerosol fine particle filter samples collected in Xi’an, Northwest China from 2015 to 2016. Both the concentrations and constitutions of NACs show distinct seasonal differences with average concentration of 2.05, 1.06, 12.90 and 56.32 ng m-3 in spring, summer, fall and winter, respectively, which could be ascribed to the differences in emission sources and formation processes. The contributions of NACs to light absorption of BrC at wavelength between 300 to 500 nm was wavelength dependent and varied greatly over different seasons, with high contribution even at wavelength > 350 nm in fall and winter and contribution mainly at wavelength < 350 nm in spring and summer, which could be related to the differences in the composition of NACs and the specific light absorbing properties of each NAC. The mean contributions of NACs to the light absorption of BrC at wavelength of 365 nm were 0.14, 0.09, 0.36 and 0.91% during spring, summer, fall and winter, respectively, about 3-5 times higher than their corresponding mass fraction in total organic carbon. Further, the sources of NACs were achieved by positive matrix factorization (PMF) receptor model. The results show that the sources of NACs varies among different seasons. Specifically, vehicular emission and secondary formation are dominant sources in summer (~80%), while biomass burning and coal combustion are major sources in winter (~75%).

How to cite: Yuan, W., Huang, R., and Yang, L.: Seasonal composition and source apportionment of nitrated aromatic compounds in atmospheric fine particulate matter in northern China over one year, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7517, https://doi.org/10.5194/egusphere-egu2020-7517, 2020.

D2993 |
Junwei Song, Linyu Gao, and Harald Saathoff

Aerosol particles have significant impacts on climate, air quality, and human health. Their characteristics are especially important in urban atmospheres during heat waves. Therefore, we conducted a 4-week measurement campaign at an urban kerbside in the city of Karlsruhe in southwest Germany during a heat wave period in July 2019. A high resolution time-of-flight aerosol mass spectrometer (HR-ToF-AMS) was deployed in the container to measure non-refractory aerosol compositions of PM2.5 online. Filter samples were also collected during the campaign, and characterized for oxygenated organic molecular compounds using a chemical ionization mass spectrometer (FIGAERO-CIMS). In addition, a small box with low cost particle sensors and meteorological sensors (solar radiation, temperature, and humidity) was used for spatial resolved measurements employing a bicycle. During our measurement, the total organics, sulfate, nitrate, ammonium, chloride and black carbon contributed on average 58.9%, 17.3%, 5.9%, 5.5%, 0.2% and 12.3% to the particle mass comprising non-refractory components plus black carbon. Positive matrix factorization (PMF) analysis for the AMS organic aerosol (OA) data resolved three factors including hydrocarbon-like OA (HOA), semi-volatile oxygenated OA (SV-OOA) and low-volatility oxygenated OA (LV-OOA). Meteorological effects on aerosol compositions were investigated. Low wind speeds during the whole campaign correspond to major contributions from local emissions. During heat waves, high temperature and low humidity suppressed the formation of nitrate, but facilitated the formation of sulfate and organics. In particular, SV-OOA and LV-OOA showed positive correlations with temperature. The ratios of LV-OOA to SV-OOA strongly correlated with temperature and odd oxygen (Ox = O3 + NO2), suggesting fast photochemical transformation of SV-OOA to LV-OOA during heat waves. Furthermore, the relationships between organic aerosol factors and typical organic markers were investigated to study the relative influences of biogenic and anthropogenic emissions on OA formation. Besides, bicycle measurements point to important hot spots of particle pollution. This contribution will discuss the interaction of urban air pollution and heat islands.

How to cite: Song, J., Gao, L., and Saathoff, H.: Characteristics of PM2.5 aerosol particles during a heat wave in an urban atmosphere in southwest Germany, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7257, https://doi.org/10.5194/egusphere-egu2020-7257, 2020.

D2994 |
Yuying Wang, Zhanqing Li, Renyi Zhang, Xiaoai Jin, and Qiuyan Wang

In this study, we report a phenomenon of fast changing in aerosol hygroscopicity between clean and pollution periods observed frequently in urban Beijing during winter using a hygroscopicity tandem mobility analyzer (H-TDMA). The cause of this phenomenon and the formation process of particles in different modes are discussed. During clean periods, ultrafine mode particles (Nucleation and Aitken modes) mainly stem from nucleation events with subsequent growth. During heavy pollution periods, they originate chiefly from primary emissions. Coarser-mode particles like accumulation mode particles are mainly from primary emission during clean periods and aqueous reactions during pollution periods. This finding based on H-TDMA measurement can make up the deficiency of mass-dependent instruments in analyzing sources and chemical processes of ultrafine mode particles.

How to cite: Wang, Y., Li, Z., Zhang, R., Jin, X., and Wang, Q.: Distinct particle properties between ultrafine and accumulation modes under clean and polluted urban environments, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-2146, https://doi.org/10.5194/egusphere-egu2020-2146, 2020.

D2995 |
Lady Mateus, Kelly Burbano, Rodrigo Jimenez, and Nestor Rojas

Elemental and Organic Carbon (EC/OC) make up a significant fraction of particulate matter emitted by combustion process and water-soluble ions provide an important information about the origin of ambient aerosols. The sized-segregated chemical characterization of ambient aerosol is useful to understand its sources and formation mechanisms and complements well the information obtained from the bulk aerosol composition. Previous studies in Bogota determined the chemical composition and source contribution of PM10 in Bogota, as well as the temporal and spatial variability of polycyclic aromatic hydrocarbons (PAH) in the same city. However, the size-segregated chemical composition of ambient particles has not been studied in Colombian cities. This work aims to better understand the variability of size-segregated PM chemical composition in Bogota, one of the main Latin American megacities. Eight sets of samples were collected using an Andersen 8-stage cascade impactor in the southwest area of the city, where the highest concentrations of PM2.5 usually occur, over two periods in 2018. The concentration of OC/EC and ions (ammonium, sodium, potassium, magnesium, calcium, chloride, nitrate, sulfate and oxalate) were quantified. The average PM1 concentration was 30.3 mg/m3 (75% of PM2.5). The mass size distribution was bimodal, with a coarse mode between 5.8 and 4.7 mm aerodynamic diameter and an accumulation mode between 0.43 and 0.65mm. Most of the mass (75%) of PM1 consists of carbonaceous species, being EC the main constituent. The main inorganic ions in PM1 were sulfate, nitrate and ammonium. These and other results from this work will contribute to the validation of models within the PAPILA (Prediction of Air Pollution In Latin America and the Caribbean) project, funded by the EU MSCA action for research and innovation staff exchange (GA 777544).

How to cite: Mateus, L., Burbano, K., Jimenez, R., and Rojas, N.: Size-segregated ions and carbonaceous fractions of ambient aerosol in Bogota, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12478, https://doi.org/10.5194/egusphere-egu2020-12478, 2020.

D2996 |
Yang Chen

Brc Carbon is a class of light-absorbing organic species, playing important roles on solar radiation budget and therefore influences climate forcing over regional and even global scales. We analyzed and evaluated the light absorption and radiative forcing of BrC in Chongqing, Wanzhou (Three Gorges Reservoir region), and Chengdu in the Sichuan Basin of Southwest China. The light-absorbing properties were evaluated, including mass absorption efficiency, absorption Ångström exponent, and contributions to radiative forcing. The sources of BrC are also identified, including the contribution of secondary aerosol formation and primary emissions. This study contributes to the understandings of sources and the impact of brown carbon in the Sichuan Basin, southwestern China.

How to cite: Chen, Y.: Characterization and light absorption of Brown Carbon in Sichuan Basin, Southwestern China: Impacts of biomass burning and secondary formation, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12517, https://doi.org/10.5194/egusphere-egu2020-12517, 2020.

D2997 |
Ho-young Ku, Baek-min Kim, and Wonsik Choi

In this study, we investigated precursory regional weather patterns prior to the high PM10 events over Korean Peninsula. The criterion for high-concentration PM10 events was set at 150 ug/m3 per day, referring to the “bad” among air environmental standards. In order to examine the regional weather pattern prior to the high PM10 events, the pressure fields of upper-level and lower-level were simply synthesized expecting the existence of clear signature of stagnant weather pattern. However, the resulting patterns were statistically insignificant around East Asia. We further investigated a possibility of existence of multiple precursory patterns partly offsetting each other.  Through the synoptic analysis of each case, we found that precursory weather patterns can be easily partitioned as two groups: 1) pre-existing persistent ridge and 2) Decaying east Asian cold-surge. In the case 1), persistent ridge embedded in an overall positive AO pattern sustains over East Asia both before and after the high PM10 event causing long-term accumulation of fine dusts over Korean Peninsula. In this case, warm surface temperature dominates before and after the high PM 10 event. In the case 2), upper-level trough over east Asia rapidly moves eastward along with cold-surge evolution and stagnant high pressure system sits in over Korean peninsula just after the timing of high PM event. Surface temperature suddenly changes from cold to warm dramatically.

How to cite: Ku, H., Kim, B., and Choi, W.: Classification of precursory weather patterns prior to high PM10 events over the Korean Peninsula, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12845, https://doi.org/10.5194/egusphere-egu2020-12845, 2020.

D2998 |
Andrius Garbaras, Inga Garbariene, Agne Masalaite-Nalivaike, Darius Ceburnis, Edvinas Krugly, Vidmantas Remeikis, and Dainius Martuzevicius

The main idea of this research was to combine carbon stable isotope ratio (δ13C) analysis and polycyclic aromatic hydrocarbons (PAH) diagnostic ratios for the identification of pollution sources in Kaunas city, Lithuania. Aerosol particle sampling was performed in wintertime simultaneously in outdoor and indoor environments using cascade impactors.

Due too low mass not all impactors stages were analysed, especially in the indoor environment. It was determined that total carbon concentrations were higher in outdoor samples in the most cases. The outdoor δ13C values varied from -27.5 to -24.5 ‰. The indoor δ13C values varied from -28.5 to -25.8 ‰ and were close to δ13C values reported for biomass burning [1].

δ13C and PAH analysis revealed that main aerosol sources were biomass combustion and traffic emissions. Also coal combustion was identified as common source for aerosol particles in one location.


[1] A. Garbaras et al., “Stable carbon fractionation in size-segregated aerosol particles produced by controlled biomass burning,” J. Aerosol Sci., 2015.

How to cite: Garbaras, A., Garbariene, I., Masalaite-Nalivaike, A., Ceburnis, D., Krugly, E., Remeikis, V., and Martuzevicius, D.: Aerosol sources identification in Kaunas city using stable carbon isotope and polycyclic aromatic hydrocarbons analysis, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18479, https://doi.org/10.5194/egusphere-egu2020-18479, 2020.

D2999 |
James Matthews, Panida Navasumrit, Krittinee Chaisatra, Chalida Chompoobut, Matthew Wright, Mathuros Ruchirawat, and Dudley Shallcross

Airborne particulate matter is known to be deleterious to human health and exceeds exposure limits in many large cities. Some heavy metals and metalloids are known carcinogens and have been measured as constituents of PM in Bangkok air. There is growing interest in the sub-micron and ultrafine (< 100 nm) fractions due to their deeper penetration in the lung. Identifying distribution of metals over the size range can provide information on the metals source as well as providing information on the likely exposure to those particles.

Three sites, owned and managed by the Thailand Pollution Control Department, were identified to provide contrasting particulate samples in a measurement campaign during 2018. The Ayutthaya site was located within the grounds of a school, 80 km to the north of Bangkok. The site was chosen as concentrations due to city traffic would be lower and could be considered a reference site. The Bang Phli site was situated in an industrial area 50 km to the south-east of Bangkok, in an area near industry. The Chok Chai site was in central Bangkok near to a busy road.

At each site, three 3-day weekend and 3-day weekday gravimetric samples of size differentiated mass were drawn using an Electrical Low Pressure Impactor (ELPI; Dekati, Finland) over 12 size fractions in six different study visits. These were chosen to enable three measurements over the rainy season and three in the dry season. Each size fraction was weighed and then analysed by inductively coupled plasma mass spectroscopy to find the concentration of 17 elements (Mg, Al, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Se, Mo, Cd, Sb, Ba and Pb). The ELPI also measured particle number concentration at 1 Hz. 

The number concentration of aerosol was highest in the Chok Chai roadside site, and lowest in the Ayutthaya background site. Al concentration was the highest in all three locations, with an average concentration over all measurements of 1909, 1012 and 1576 ng m-3 in Ayutthaya, Bang Phli and Chok Chai respectively. Concentrations of Al, Cr, Mg and Fe were typically higher than 100 ng m-3 in all sites, Cu and Zn higher than 10 ng m-3 and the rest lower.

The shape of the metal distributions was consistent across all three sites for specific metals. Mg, Al, Cr, Mo, Ni and Cu could be described as having a flat distribution across all measured size distributions V, Mn, Cd, Sb, Pb, Zn As and Se had a peak in the sub-micron range, while Fe, Ba and Co peaked above 1 µm.

Some seasonal effects could be seen across all three locations, these included an increase in Al, Cr and Fe during four measurements in dry season (November, 2018). This was particularly observed at Ayutthaya, where total measurements of Al were between 4862 and 5961 ng m-3, when all other measurements were between 98 and 264 ng m-3, suggesting a strong local source.

How to cite: Matthews, J., Navasumrit, P., Chaisatra, K., Chompoobut, C., Wright, M., Ruchirawat, M., and Shallcross, D.: Aerosol size distribution and metal constituents in three sites in Bangkok , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19816, https://doi.org/10.5194/egusphere-egu2020-19816, 2020.

D3000 |
Meng Gao, Kaili Lin, Shiqing Zhang, and Ken kin lam Yung

Severe wintertime PM2.5 pollution in Beijing has been receiving increasing worldwide attention, yet the decadal variations remain relatively unexplored. Combining field measurements and model simulations, we quantified the relative influences of anthropogenic emissions and meteorological conditions on PM2.5 concentrations in Beijing overwinters of 2002-2016. Between the winters of 2011 and 2016, stringent emission control measures resulted in a 21% decrease in mean mass concentrations of PM2.5 in Beijing, with 7 fewer haze days per winter on average. Given the overestimation of PM2.5 by model, the effectiveness of stringent emission control measures might have been slightly overstated. With fixed emissions, meteorological conditions over the study period would have led to an increase of haze in Beijing, but the strict emission control measures have suppressed the unfavorable influences of recent climate. The unfavorable meteorological conditions are attributed to the weakening of the East Asia Winter Monsoon associated particularly with an increase in pressure associated with the Aleutian low.

How to cite: Gao, M., Lin, K., Zhang, S., and Yung, K. K. L.: China’s emission control strategies have suppressed unfavorable influences of climate on wintertime PM2.5 concentrations in Beijing since 2002, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6424, https://doi.org/10.5194/egusphere-egu2020-6424, 2020.