Air pollution is a critical issue, particularly in urban areas, making the monitoring and understanding of the air pollutant’s sources essential. Urban regions are often very heterogeneous due to the complexity of buildings, roadways, vegetation, and various emission sources. Governments prioritize these areas to implement intervention measures aimed at addressing air pollution, as urban regions are both major sources of pollution and densely populated. Therefore, simulating the dispersion of air pollutants at high spatial and temporal resolution is crucial for understanding pollution sources and evaluating different intervention measures.
In this context, we have set up the Large-Eddy Simulation (LES)-based air quality model, PALM-4U (Parallelized Large-Eddy Simulation Model for Urban), over Munich, Germany. The goal is to simulate meteorology and concentrations of air quality relevant species at high spatial (10 meters) and temporal (10 minutes) resolution across the city. The model uses high-resolution static parameters (e.g., building height, vegetation height), dynamic meteorological variables from the WRF model (with a 400-meter resolution) for boundary conditions, and a high-resolution emission inventory (100 meters). The boundary conditions for air quality species were obtained from CAMS model ensemble outputs.
We compared the simulated meteorology and air pollutant concentrations with data from an extensive measurement campaign conducted in August 2023 for selected days. The results showed good agreement for wind speed (Mean Bias (MB) = 0.23 m/s, Pearson correlation coefficient (R) = 0.86) and wind direction (MB = 35°, R = 0.83). The PALM-4U model overestimated NO2 concentrations by 0.82 ppb (+15.5 %), underestimated O3 concentrations by 4.5 ppb (-13.7 %) and overestimated PM10 concentrations by 1.4 µg m-3 (+11.7 %), although it accurately captured the diurnal variations. Sensitivity analysis revealed that the boundary conditions from the mesoscale model have a significant impact on the modeled air quality concentrations.
This model setup will be further utilized to evaluate the effectiveness of various measures, such as low-speed zones, low-emission zones, and the extent of electric vehicle adoption required to achieve safe air pollution levels within Munich.
How to cite: Balamurugan, V., Chen, J., Saathoff, H., Claus Holst, C., Wenzel, A., Abu-Hani, A., Li, Y., Li, Y., Abou-Rizk, S., and N Keutsch, F.: High-Resolution LES-Based Air Quality Modeling Over Munich: Evaluation of Model Performance, and Pollution Drivers, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16731, https://doi.org/10.5194/egusphere-egu25-16731, 2025.
In France, 97% of cities exceed the WHO guide values for PM2.5 (2022 data). The transport sector is responsible for about 10% of PM2.5 emissions. The development of green infrastructure along roads, among other ecosystem services, could enable air pollution removal by deposition and needs to be better quantified and monitored.
We studied the chemical composition of particle deposits and the temporal evolution of the quantity of deposits on tree leaves over a 9-month period in Toulouse (~500,000 inhabitants), France. Leaf samples were taken from 9 deciduous trees and 2 evergreen trees at three roadside sites. We analyzed the deposition of elemental carbon (EC) and magnetic minerals on the leaf surface of different tree species. The EC deposit was extracted from the leaves using double-deionized water for on-surface deposition and chloroform for in-wax deposition. EC deposit was then estimated using thermo-optical techniques. Total magnetic mineral deposition was analyzed directly on total leaves by acquiring saturation isothermal remanent magnetization (SIRM) using a JR6 magnetometer.
Our results show that the average on-surface EC and total magnetic minerals deposit were respectively 4 and 12 times higher for evergreen than for deciduous trees. On-surface EC for evergreen trees was 2 times lower than in-wax EC. In contrast, on-surface EC for deciduous trees was found 11 times higher than in-wax EC. The analyses enabled the interpretation of changes in particle deposition over time as a function of meteorological conditions. Our results highlighted the potential of leaves to be used as biosensors to monitor ambient air pollution.
How to cite: Delville, L., Léon, J.-F., Macouin, M., Dias Alves, M., Lenoir, O., and Drigo, L.: Particulate matter deposition on roadside trees in urban area: Evaluation of Elemental carbon and magnetic minerals., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11091, https://doi.org/10.5194/egusphere-egu25-11091, 2025.
Tropospheric ozone (O3) is a major air pollutant that negatively affects human health and vegetation, while also playing a central role in atmospheric chemistry and climate change. Dry deposition, a process by which gases are deposited on a surface by air turbulence and gravity, accounts for about 20–25% of tropospheric O3 removal. However, the mechanisms controlling the O₃ dry-deposition velocity (Vd,O₃) in urban areas are poorly understood, largely due to the scarcity of measurements in such environments.
We hypothesized that: (i) Combining direct O₃ flux measurements with source apportionment of factors controlling ozone levels (e.g., NO and volatile organic compounds [VOCs]) based on comprehensive field measurements in an urban environment is essential for disentangling the simultaneous effects of emission sources and meteorological conditions on Vd,O₃. (ii) Integrating atmospheric chemistry model simulations with flux measurements can elucidate how emissions and environmental conditions influence O₃ formation and removal, providing critical insights for air-quality assessment and urban planning.
Accordingly, we conducted direct eddy covariance measurements of O₃, VOCs (via Vocus PTR-TOF-MS), and NOx ([NO] + [NO₂]) fluxes at a height of 102 m on a meteorological tower in Beijing between April 28 and June 26, 2023. Vertical profiles of meteorological parameters were measured at 15 levels along the tower. Source apportionment analysis of VOCs and NOx was conducted using the positive matrix factorization (PMF) model. Additionally, the Weather Research and Forecasting model with Chemistry (WRF-Chem) was applied to evaluate the contributions of anthropogenic and biogenic emissions to O₃ formation. WRF-Chem model simulations were validated against data from nearby air-quality monitoring stations.
Our results show that Vd,O₃ in the urban environment was primarily controlled by chemical reactions, including O₃ titration by NO and contributions from anthropogenic VOCs. Surface wetness was identified as a key factor influencing Vd,O₃, consistent with our findings from measurements in vegetated environments [1,2]. Comparing urban and vegetated settings highlighted the influence of air turbulence, relative humidity, and chemical interactions on Vd,O₃. These findings provide valuable insights for improving urban O₃ simulations and for air-quality management and urban planning strategies.
1. Li, Qian, et al. "Measurement-based investigation of ozone deposition to vegetation under the effects of coastal and photochemical air pollution in the Eastern Mediterranean." Science of the Total Environment 645 (2018): 1579-1597.
2. Li, Qian, et al. "Investigation of ozone deposition to vegetation under warm and dry conditions near the Eastern Mediterranean coast." Science of the Total Environment 658 (2019): 1316-1333.
How to cite: Tas, E., Choi, D., Fredj, E., Yibo, H., Yuan, B., and Liu, H.: Ozone Formation and Dry Deposition in Urban Environments: Insights from WRF-Chem Modeling and Eddy Covariance Flux Measurements in Beijing, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4060, https://doi.org/10.5194/egusphere-egu25-4060, 2025.
Globally, about 14% of households have no option but to burn their waste. Open waste burning is a significant source of black carbon (BC) emissions, yet the exposure of those engaged in this practice has not been interrogated. This study provides the first quantification of personal exposure to BC emissions from open waste burning, revealing critical insights into the potential health risks faced by individuals engaged in this practice. Between November–December 2023, we conducted a comprehensive field study in Blantyre, Malawi, monitoring BC exposure among 46 volunteers from 23 households over approximately 20 hours on the day waste was burned at their household. Within each household, one individual responsible for burning waste and one non-burner wore MicroAeth MA200 monitors to capture personal exposure data. To summarize exposure, the average BC concentration was calculated for each a) monitoring period, b) for burning times, and c) for non-burning times. The median of these averages was then used to characterize exposure levels. Results showed that waste burners experienced significantly higher BC exposure than non-burners during both burning periods and the overall monitoring period (Wilcoxon signed rank test, p = 0.04). During burning, the median BC exposure for burners was 12.8 μg/m³, over four times higher than the median exposure of non-burners at 2.9 μg/m³. The median BC exposure for burners during the 20-hour monitoring period was 5.1 μg/m³, compared to 3.0 μg/m³ for non-burners. Notably, BC exposure levels during non-burning periods were statistically indistinguishable between burners and non-burners (Wilcoxon signed rank test, p = 0.44), with median exposures of 3.6 μg/m³ and 2.6 μg/m³, respectively. This study highlights the extreme BC exposure faced by individuals actively burning waste, and underscores the health risks associated with this practice and the need for interventions to mitigate exposure.
How to cite: Vijay, S., Khonje, L., Chatha, M. L., and Tilley, E.: Open waste burning leads to significantly high black carbon exposure amongst burners, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7939, https://doi.org/10.5194/egusphere-egu25-7939, 2025.
Equivalent black carbon (eBC) is generated from the partial combustion of fossil fuels and biomass. The scientific interest in eBC is large because its contribution to the PM2.5 fraction is high, especially in urban areas. It should be noted that combustion-related aerosols (including eBC) have been linked to adverse health effects and are considered more harmful than other aerosol components. In addition, eBC is considered the second most important component of global warming in terms of direct forcing. Until now. there is a lack of information on eBC concentrations in Poland, mostly due to lack of relevant instrumentation. The position of Poland in the center of Europe, as well as the presence of multiple combustion sources, make this type of measurements and data very relevant to the scientific community, both for health impact assessment and climate change studies. In the present study, the MABI (Multi-Wavelength Absorption Black Carbon Instrument), a new instrument measuring light transmission of particles collected on filters, was assessed. MABI was developed by the Australian Nuclear Science and Technology Organization (www.ansto.gov.au). The instrument consists of an optical assembly and electronic case. The instrument optics includes, among others, the multi-wavelength light source (7 LEDs), sampler holder, and photodetector. In the instrument, opaque glass is used to scatter the scattered light back through the filter to the detector. MABI offers the advantage of off-line measurements of aerosol light transmission, at seven fixed wavelengths (from 405 nm to 1050 nm). The performance of the instrument was assessed for different types of filters (Teflon and quartz fibre) collected at two distinct atmospheric environments, an urban background site in Krakow, Poland and an urban background site in Athens, Greece. Mass absorption coefficients provided by the manufacturer were used in order to calculate eBC from light transmission data (Ryś and Samek, Atmosphere, 2022). In addition, thermo-optical analysis (Lab OC-EC Aerosol Analyzer, Sunset Laboratory Inc.) was performed on the quartz fibre samples for the determination of elemental carbon (EC) concentrations. EC data were then used in order to calculate site-specific mass absorption cross section (MAC) values for the MABI. Finally, an aethalometer (AE33, Aerosol Magee Scientific) was used in parallel to PM sampling in Athens, in order to provide a reference eBC value, against which the performance of MABI was assessed.
Acknowledgments: This research project was supported by the program “Excellence initiative—research university” for the University of Science and Technology and ATMO ACCESS TNA project
How to cite: Samek, L., Diapouli, E., Ryś, A., Papagiannis, S., Vassilatou, V., Jakhar, R., and Eleftheriadis, K.: Determination of equivalent Black Carbon Concentrations by MABI (Multi-Wavelength Absorption Black Carbon Instrument) and of the respective mass absorption cross section (MAC): Case study for Krakow, Poland and Athens, Greece., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12815, https://doi.org/10.5194/egusphere-egu25-12815, 2025.
Over the past two decades, Bengaluru, a major city in South India, has undergone rapid urbanization and population growth, with significant increase in vehicular traffic, construction, and industrial activities. These changes can enhance local contributions from anthropogenic biomass burning and vehicular emissions, in addition to long-range transported pollutants, adversely impacting air quality, and reducing visibility. We investigated the chemical composition and sources of particulate matter (PM2.5) in Bengaluru using an Aerodyne Time-of-Flight Aerosol Chemical Speciation Monitor (ToF-ACSM) and two Aethalometers (AethLabs microAeth MA300 and Magee Scientific AE33). This study marks the first in-situ and high time resolution source apportionment of non-refractory PM2.5 (NR- PM2.5) in the city using the ToF-ACSM, commencing from post-monsoon sampling period (Aug 2024). Results thus far indicate that organic aerosols (OA) are the dominant (63%) NR-PM2.5 species, followed by sulphate (~20%) and ammonium (~9%). Positive Matrix Factorization (PMF) analysis identified two primary organic aerosol types namely hydrocarbon-like organic aerosols (HOA) and biomass-burning organic aerosols (BBOA), and two secondary organic aerosols namely less-oxidised oxygenated organic aerosols (LO-OOA) and more-oxidised oxygenated organic aerosols (MO-OOA). The air masses from the northeast (0-90°) direction were found to be associated with elevated levels of MO-OOA, which also correlated well with increased sulphate fraction. These findings highlight the role of local sources like vehicular emissions and waste burning, as well as regional sources including thermal power plant emissions, and oxidised and aged OA. Furthermore, the study includes Diwali and Kannada Rajyotsava celebrations, periods with extensive firework activity. Despite restrictions on conventional fireworks and recommended usage of green crackers, the city witnessed significant particle pollution, with hourly PM2.5 concentrations exceeding 100 µg/m³ and spikes up to 800 µg/m³. While NR-PM2.5 and black carbon (BC) increased during the firework periods, the rise in PM2.5 mass loading was much greater, resulting in incomplete mass closure (only ~16% from NR-PM2.5 + BC) in contrast to normal periods (~80% from NR-PM2.5 + BC). Further results and detailed analyses will be presented, including seasonal changes in PM2.5 composition and sources as we transition from the post-monsoon to the more polluted winter season.
How to cite: Narayana Kalkura, K., Chakraborty, M., Shekar, V., Varghese, E., and Ramachandran, S.: Chemical characterisation and source apportionment of PM2.5 in the cosmopolitan city of Bengaluru, India , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-787, https://doi.org/10.5194/egusphere-egu25-787, 2025.
Air pollutants (PM2.5, PM10, O3, NO2, SO2, and CO) have become a significant environmental concern, particularly in Asian metropolises. The Indian metropolis of Delhi serves as a prime example. A key challenge in addressing urban atmospheric pollution is the significant variation in pollution levels across short distances. Various factors, including nearby industries, vehicle traffic, and population density influence this heterogeneity. Thus, to accurately assess the urban pollution situation, installing multiple pollution monitoring stations that comprehensively cover an entire city, from its center to its periphery is essential. However, the number of stations monitoring entire urban areas increases gradually. For instance, the World Air Quality Historical Database currently lists only four stations in Delhi that have been operational since 2014. The number of stations in Delhi has gradually increased, and in 2024, the same database recorded 45 operational stations, providing more comprehensive coverage of the city. However, the time covered by the available pollution records varies, and, within these periods, there are numerous missing data points. The inconsistencies between stations introduce statistical artifacts when local pollution data are averaged to produce decade-long records that might be representative of the whole city area, making it difficult to assess the actual urban air quality and the effects of policies aimed at reducing urban air pollution. In this study, we propose statistical reconstructions of six daily atmospheric pollution concentrations for all 45 stations in the city of Delhi from 2014 to the present. These reconstructions aim to produce a more consistent database that could better represent the entire city area over 11 years (from January 1, 2014, to January 1, 2025). This reconstructed network is then used to evaluate an ensemble average record that could more realistically represent the daily evolution of air pollution concentration in the city of Delhi since 2014. To accomplish such network reconstruction, we apply the Regression Learner tool in MATLAB to assess 35 machine learning (ML) regression techniques. We select and use only those that perform better in modeling the available records to estimate the missing data. The ML regression models that demonstrated superior performance include: the Fine Tree (Regression Trees family), the Bagged Trees (Ensembles of Trees family), the Optimizable Ensemble (Ensembles of Trees family), the Fine Gaussian SVM (Support Vector Machine family), the Rational Quadratic (Gaussian Process Regressions family), and the Exponential (Gaussian Process Regressions family). In contrast, our analysis revealed that the commonly used multi-linear regression model underperforms compared to 20 other ML regression models. Generally, the proposed methodology can apply to all situations typically addressed in the literature using the multi-linear regression model only because its algorithm is readily available. However, the physical relationships between a given observable and its potential constructors are often nonlinear, rendering the multi-linear regression model suboptimal for such tasks. In the case of the city of Delhi, we demonstrate that the proposed analysis methodology corrects significant biases in the decadal trend for all six network pollution records, and show that from 2014 to 2024, air pollution quality has slightly improved.
How to cite: Scafetta, N. and Shafi, S.: Optimal reconstruction of incomplete urban pollution records with machine learning regression models: a case study for Delhi, India, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5078, https://doi.org/10.5194/egusphere-egu25-5078, 2025.
Being Germany’s 3rd largest city, Munich has the nation’s highest number of daily commuters leading to high traffic volumes and adding up to the urban air pollution through emissions from combustion and tire and brake wear. Although urban air quality is generally improving over the past years, all measurement stations by the Bavarian State Office for Environment (LfU) in Munich still exceed the critical NO2 level of 10 µg/m3 as suggested in the updated WHO guidelines 2021.
For quantifying air quality at high spatiotemporal resolution in the city, we developed a self-sufficient low-cost sensor system equipped with electrochemical cells (ECs) for measuring NO2, NO, CO and O3 as well as an optical particle counter.
In summer 2023, we started setting up a dense sensor network of 25 sites in the inner city and since then, we have gathered 22 million data points. Prior to installation at their final locations, we mounted each sensor system for several weeks at an automated measurement station operated by the LfU in order to acquire high-accuracy reference data for calibrating our system. During operation of the sensor network, several nodes are occasionally returned to the reference site and three sensor nodes have been mounted there continuously ever since. In total, 8 million data points have been gathered during these co-location measurements. The combination of frequent rotation of sensor nodes between network locations and reference site as well as long-term co-location nodes yields a unique dataset for a novel calibration approach. Here, we analyze the calibration performance of NO2; other pollutants will follow.
Firstly, we analyzed the correlation of the EC’s raw hourly signal to the reference station by assessing their coefficient of determination (R2). Remarkably, highest R2 values occurred in fall and winter time with temperatures in the range of -5 to +15 °C. Lowest and even negative R2 values occurred during summer and during long-term co-locations facing seasonal changes.
Secondly, for assessing a real-world scenario, we analyzed the performance of one node with long-term co-location at the reference station. The raw EC data yields an R2 of 0.35 over 27 weeks. By applying a Random Forest Regressor using the first 30 % of the data points for training and including temperature, humidity and NO as features, R2 could be increased to 0.7. Currently, we are developing a novel calibration strategy that leverages this extensive co-location data set with advanced machine learning methods to increase the calibration performance and to map air quality within our sensor network at high resolution and accuracy.
How to cite: Wenzel, A., Chen, J., Klama, T., Böhm, F., Angleitner, M., and Lobmaier, R.: Towards high-resolution air pollutants sensing through dense low-cost sensor networks – a case study in Munich, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16784, https://doi.org/10.5194/egusphere-egu25-16784, 2025.
In the last decades, the EU has observed an increase in the use of biomass for energy, and for space heating in particular. Climate change mitigation strategies have favoured the use of wood-based biomass for heat and power supply, to reduce dependency on fossil fuels. However, it can pose risks for ambient air quality and public health, especially when used in small-scale building heating systems in cities. To leverage synergies and avoid trade-offs between climate change mitigation and ambient air quality, policymaking should be supported by quantitative analyses and robust evidence.
This paper presents a framework to assess potential life-cycle greenhouse gas (GHG) emissions and fine particulate matter (PM2.5) impacts on health of wood-based biomass use in small-scale residential heating systems. The framework draws on a life-cycle assessment (LCA) model, and it is applied to 87 EU cities in 23 countries (i) to quantify current GHG and PM2.5 impacts associated wood-based biomass heating, and (ii) to two scenario analyses that evaluate potential trade-offs and co-benefits of climate change and air pollution mitigation actions. A complementary analysis is also performed to provide insight on the robustness of PM2.5 results, comparing health effects associated with PM2.5 using the LCA framework and city-specific emission and effect factors.
Results confirm strong correlation between GHG and PM2.5 impacts. Scenarios for GHG mitigation increased PM2.5 impacts of small-scale biomass heating per capita up to 14%, while those for PM2.5 mitigation reduced PM impacts up to 63%, with GHG mitigation co-benefits (reduction up to 50%) or trade-offs (increase up to 125%). The framework is widely applicable and provides robust results; and the scenario analyses demonstrate the importance of context-specific assessments to inform policymaking.
How to cite: Zauli Sajani, S., Bastos, J., Giuntoli, J., and Pisoni, E.: Assessing synergies and trade-offs between climate change mitigation and air quality effects of small-scale biomass heating in the residential sector, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10545, https://doi.org/10.5194/egusphere-egu25-10545, 2025.
Volatile organic compounds (VOCs) released into the atmosphere by natural and anthropogenic sources play a key role in atmospheric processes. They can react with atmospheric oxidants leading to secondary organic aerosols and tropospheric ozone, with effects on air pollution, human health and climate.
In Europe, the improvement of air quality policies in the last decades has caused some pollutants’ concentrations to decrease. This is the case for nitrogen dioxide and particulate matter concentrations that decreased between 30-50% during 2000-2010, leading to a decreasing trend of the associated health effects on people exposed to them. Yet, 70% of EU citizens lives in urban areas, and 99-97% of this population is exposed to concentrations of ozone and fine particulate matter above the guidelines recommended by WHO in 2021 for protecting public health (EEA, 2021). The Po Valley, located in the North of Italy is one of the areas in Europe suffering the worst air pollution, with several air quality threshold exceedances throughout the years. A recent example was recorded in the city of Milan in winter 2024, when, during several days of high atmospheric pressure conditions, PM 2.5 concentration was above 100 μg/m3, while the WHO recommended threshold is 15 μg/m3 on a 24-hour averaging time.
Within the EU-funded H2020 project RI-URBANS and ACTRIS, we conducted two field campaigns at two urban areas of different size, 200 km apart in the Italian Po Valley: Milano and Bologna. In both cases, VOCs were measured with a Vocus CI-ToF (chemical ionization time of flight) 2R mass spectrometer (Tofwerk, Switzerland) that was deployed first in Milan from January 2023 during one year, then in Bologna in September 2024 for one month. The analysis of our study focuses on sixteen VOCs common to both measuring sites, identified and quantified with a certified VOC mixture and covering the measured mass range 42-371 amu. The absolute concentration, the diel and seasonal variability of the VOC measured in Milan and Bologna are discussed and compared. In particular, we determined the effect of atmospheric dilution and atmospheric reactivity on the measured concentrations. We also discuss the results in terms of potential formation of ozone and secondary organic aerosols.
How to cite: Marinoni, A., Zannoni, N., Cristofanelli, P., Paglione, M., Rapuano, M., Perfetti, C., Bracci, A., Bellini, A., Barnaba, F., Colombi, C., and Rinaldi, M.: Volatile organic compounds at two urban areas in the Italian Po Valley: Milan and Bologna, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19635, https://doi.org/10.5194/egusphere-egu25-19635, 2025.
Nitrogen dioxide (NO2) and volatile organic compounds (VOCs) are of concern in urban environments as primary pollutants and as main precursors of tropospheric O3, which has adverse effects on both human health and vegetation. Furthermore, as a secondary pollutant, its complex nature due to its non-linear chemistry makes it difficult to reduce with the reduction in the precursors.
This study is carried out to spatially characterise the urban air pollution, using NO2 and VOCs, and to evaluate the recent temporal trends of these compounds, which are closely related to the O3 formation in the metropolitan area of Valencia. Being the third largest Spanish city, with one of the Mediterranean's largest ports, the complex emission sources contribute to high ozone levels in the surrounding areas.
A total of 97 passive samplers for NO2 were used in the study area, covering an area of 11 km x 10 km including the urban centre with a resolution of about 1 km x 1 km. The temporal resolution of the measurement covers the winter and summer seasons (one exposure week every February and July, respectively) for the last 8 years, from 2017 to 2024. Meanwhile, the passive samplers for VOCs were installed at 10 selected points through the urban centre among the 97 points, following the main wind direction of the region. The measurement period is two years shorter than those for NO2 from 2019 to 2024 but covers the same seasons. The measured NO2 levels were determined using ultraviolet-visible (UV-VIS) spectroscopy, while the VOCs levels were analysed through gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS).
To characterise the spatial patterns of the city, the k-mean clustering is used to group all points where NO2 levels are measured. As a result, the city is divided into overall two clusters. Cluster 1 is close to road traffic emissions and follows the prevailing wind direction, resulting in relatively high levels of NO2. Cluster 2 represents the rest of the points, which have lower levels. As for VOCs, the analysis is performed at two specific points, where NO2 has the highest and the lowest among the given 10 points, in the city centre and in the harbour area, representing Cluster 1 and Cluster 2, respectively. In the city centre, the aromatic hydrocarbons are more abundant, while in the harbour area, the contribution of the aldehydes is greater.
The Theil-Sen method is used for the temporal analysis of each cluster. NO2 shows a decreasing trend in both clusters. The reduction is more pronounced in Cluster 1 where the levels tend to be greater than the other cluster, especially in winter. However, total VOCs levels seem to be increasing overall. In particular, there is a tendency to increase in winter, while VOCs decrease slightly in summer.
This result shows that the ozone formation regime of this area has been changing as NO2 levels are decreasing while VOCs are generally increasing. Therefore, ozone levels may be locally increasing.
How to cite: Jung, D., Mantilla, E., Borrás, E., Vera, T., Gómez, T., and Muñoz, A.: Spatial-temporal analysis of recent trends in nitrogen dioxide (NO2) and volatile organic compounds (VOCs) in the Mediterranean metropolitan area of Valencia city, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18113, https://doi.org/10.5194/egusphere-egu25-18113, 2025.
The influence of traffic regulations on urban air quality has been discussed for years, especially during the COVID-19 lockdowns, when significant shifts in urban air quality were observed, particularly in the concentration of nitrogen dioxide (NO₂), a key pollutant linked to vehicular emissions and industrial activities. This study provides a long-term analyzes the variation of NO₂ levels in an urban environment, and an investigation of the interplay of various influencing factors during the lockdown periods in Munich, Germany, including traffic volume, wind speed, radiation, boundary layer height, humidity, and precipitation.
Using a combination of ground-based stationary and mobile NO₂ measurements in Munich coupled with traffic flow records, we apply statistical and machine learning techniques to identify the primary drivers of NO₂ concentration variability. The analysis reveals the extent to which reductions in traffic during the lockdown contributed to NO₂ declines, while highlighting the modulating effects of meteorological conditions such as wind dispersion and atmospheric stability.
Our findings provide insights into the complex dynamics of urban air pollution and its sensitivity to human activity and weather patterns. By comparing pre-lockdown, lockdown, and post-lockdown scenarios, the study underscores the potential for targeted interventions to achieve sustained improvements in air quality and offers valuable guidance in designing evidence-based strategies to mitigate urban air pollution and its health impacts.
How to cite: Wenig, M. and Ye, S.: Long-Term analysis of the impact of traffic volume and other influencing factors on urban NO2 levels for interpreting the COVID-19 lockdown effects in Munich, Germany, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9698, https://doi.org/10.5194/egusphere-egu25-9698, 2025.
Exposure to air pollution contributes to chronic cardiovascular and respiratory diseases as well as mortality, particularly in urban areas. For assessing the health impacts of air pollution, integrated mobility – emissions – air quality – exposure modelling chains have been developed in recent years (Gurram, Stuart, et Pinjari 2019). Numerous studies have highlighted the importance of considering daily mobility when assessing air pollution exposure (Dias et Tchepel 2018). However, the question of uncertainties associated with these modelling chains remains little studied, in particular uncertainties related to models’ spatiotemporal resolution. This work aims to perform a sensitivity analysis of individual exposure to ambient air pollution with an agent-based mobility model coupled with emission, air quality and exposure models.
This study is based on a modelling chain to assess individuals’ exposure with an agent-based approach. Individuals’ daily mobility and car traffic within the region are simulated with MATSim. As urban air quality is both affected by long-range pollution transport and local pollution sources within the urban canyon layer, spatial resolution of air quality was addressed. To this end, we developed novel methodology to generate a disaggregated car fleet attributing car types (i.e. fuel and Euro norm) to households depending on their socioeconomic characteristics, instead of the state-of-the-art average emitting car. This car fleet model aims to better represent spatial heterogeneities in car traffic emissions. Moreover, air quality is simulated at the street scale with the MUNICH street-network model (Kim et al. 2022) while urban background concentrations are simulated with the Polair3D Eulerian chemical transport model (CTM). The exposure model, at last, combines individual travel patterns and street-level pollution concentrations to assess individuals’ exposure, taking into account ambient air pollution infiltration and exposure in transportation.
To study the modelling chain sensitivity, three scenarios comparisons will be performed to assess the impact of the spatiotemporal resolution of car emissions, air quality and individual activities. First, we compare individuals’ exposure when implementing emissions based on a disaggregated car fleet versus a homogenous car fleet composed of an average emitting car. Secondly, we explore the impact of air quality spatial representation on exposure by comparing the use of the background CTM model alone (Polair3D) and the combined CTM and street air quality model. The third test will compare an exposure model incorporating mobility with a traditional static approach, where individuals stay at home.
References
Dias, Daniela, et Oxana Tchepel. 2018. « Spatial and Temporal Dynamics in Air Pollution Exposure Assessment ». International Journal of Environmental Research and Public Health 15 (3): 558. https://doi.org/10.3390/ijerph15030558.
Gurram, Sashikanth, Amy Lynette Stuart, et Abdul Rawoof Pinjari. 2019. « Agent-Based Modeling to Estimate Exposures to Urban Air Pollution from Transportation: Exposure Disparities and Impacts of High-Resolution Data ». Computers, Environment and Urban Systems 75 (mai):22‑34. https://doi.org/10.1016/j.compenvurbsys.2019.01.002.
Kim, Youngseob, Lya Lugon, Alice Maison, Thibaud Sarica, Yelva Roustan, Myrto Valari, Yang Zhang, Michel André, et Karine Sartelet. 2022. « MUNICH v2.0: A Street-Network Model Coupled with SSH-Aerosol (v1.2) for Multi-Pollutant Modelling ». Geoscientific Model Development 15 (19): 7371‑96. https://doi.org/10.5194/gmd-15-7371-2022.
How to cite: Lannes, M., Roustan, Y., and Coulombel, N.: Sensitivity analysis of a mobility – emissions – air quality – exposure modelling chain to assess individuals’ exposure in metropolitan areas, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20635, https://doi.org/10.5194/egusphere-egu25-20635, 2025.
Urban air quality monitoring presents unique challenges due to complex emission patterns and operational difficulties. Our work and research combine multiple approaches to address these challenges, focusing particularly on three areas: improving quality of life for vulnerable population groups in cities, managing a sensor network on a construction-site, and leakage monitoring of fugitive greenhouse gas emissions.
Through the DivAirCity project, implemented across five European cities, we developed an integrated monitoring framework that combines quantitative air quality measurements with social equity considerations, specifically addressing the disproportionate impact of pollution on vulnerable communities. Our monitoring framework combines quantitative air quality measurements with social equity considerations, requiring careful attention to sensor placement, data quality assurance, and long-term reliability.
As part of the Green Construction Site of the Future project, we gained valuable experience in deploying and maintaining sensor networks in challenging and dynamic construction environments. Over a two-year period, we successfully operated a continuous monitoring system, investigating dust mitigation strategies and PM2.5 dispersion patterns from source points. This implementation provided valuable insights into the practical challenges of maintaining long-term sensor networks in harsh urban environments.
For our environmental monitoring project MONICO, we demonstrate and evaluate the integration of low-cost sensors, satellite observations, inverse modelling, and drone measurements for quantifying fugitive emissions in the CO2 infrastructure. Through controlled release experiments, we validated and tested methodologies for selecting and deploying sensor networks around point sources, while addressing challenges in weather influence and data reliability.
Together, these projects contribute to the practical aspects of operating sensor networks across different contexts, contributing to environmentally conscious cities and establishing best practices for effective monitoring strategies.
How to cite: Stoltenberg, M., Ottosen, T.-B., Koust, S., Cappelluti, F., Møller, S., Jørgensen, S., Cox, S., Villadsen, N., Overgaard, L., Simaitis, G., Karoff, C., Vara-Vela, A., Eikeland, A., Knudsen, J., Alberti, R., and Engedal, A. S.: Field Applications with Air Quality Monitoring: From Social Equity to CO2 Infrastructure, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15945, https://doi.org/10.5194/egusphere-egu25-15945, 2025.
Despite recent advancements in technology and purification techniques, industrial pollution continues to pose significant challenges in terms of human exposure and monitoring of air quality close to sources. Optimizing air quality networks and integrating them with advanced spatiotemporal statistical methods is thus essential for effective monitoring of atmospheric contamination.
This study addresses the problem of optimizing the placement of sensors for measuring air pollution at urban and regional scales. Several global optimization techniques, including the GlobalSearch Algorithm, Genetic Algorithm, and Particle Swarm Optimization, are applied to this problem.
Two interpolation methods are used to estimate contamination levels at control points: the standard triangulation-based Natural Neighbour interpolation method for scattered data and Gaussian Process Regression (GPR), which employs covariances derived from a dynamic pollution transfer model. The GPR technique is particularly suitable for simulating smoke-like, narrowly directed industrial pollution at distances of less than a few tens of kilometres from the source.
Numerical experiments were conducted using two pollution datasets: aerosol (PM10) concentrations simulated by the ADMS model for the Dunkirk region in northern France and sulphur dioxide (SO2) concentrations simulated by the CALPUFF model for the Dnipropetrovsk region in Ukraine. The first dataset involves diffuse pollution from multiple anthropogenic and natural sources, while the second involves emissions from industrial point sources.
Optimal sensor placements are identified, and estimation errors are evaluated for the interpolation methods and datasets. The described method could allow the construction of effective air quality networks for different types of atmospheric pollution and provide a means to estimate their effectiveness.
How to cite: Sokolov, A., Delbarre, H., and Karroum, K.: Optimizing Air Quality Sensor Networks Using Gaussian Process Regression and Global Optimization Techniques, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11481, https://doi.org/10.5194/egusphere-egu25-11481, 2025.
In recent years, the impact of air quality on health has drawn significant attention. The World Health Organization has highlighted that prolonged exposure to high concentrations of PM2.5 can increase the incidence and mortality rates of cardiovascular and respiratory diseases, as well as elevate the risk of premature death, particularly among highly sensitive populations. Therefore, reducing air pollution levels and developing air quality policies require collaborative efforts from both the government and the public.
This study uses the WRF-CMAQ model to evaluate the inter-regional interactions of PM2.5 and to assess the emission reduction benefits of the State Implementation Plan (SIP) implemented by Taiwan's six major cities. Taipei City, New Taipei City, Taoyuan City, Taichung City, Tainan City, and Kaohsiung City are Taiwan's primary urban centers, and their air pollution control efforts are essential for improving environmental quality nationwide.
The SIP measures in the six metropolitan focus on three major sources of pollution. For stationary sources, the policies include stricter standards for large emitters such as factories and coal-fired power plants, along with the promotion of low-pollution technologies. For mobile sources, the measures involve phasing out high-polluting vehicles, increasing the adoption of electric vehicles, and improving public transportation systems. For non-stationary sources, efforts are directed at strengthening the monitoring and control of construction dust and agricultural burning.
The policy's effectiveness is reflected in significant emission reductions: Taipei reduced PM2.5 emissions by 283 tons; New Taipei, by 72 tons; Taoyuan, by 599.4 tons; Taichung, by 73.3 tons; Tainan, by 2,150 tons; and Kaohsiung, by 2,043 tons. For instance, in Taichung, the latest SIP measures are expected to reduce PM2.5 concentrations from 16.8 μg/m³ to 15.7 μg/m³, a 7% improvement.
Despite these achievements, inter-regional pollutant transport continues to affect the six metropolitan, particularly in southern Taiwan. Future policies must balance regional pollutant transport, climate change, and economic development needs. Moreover, collaboration with neighboring countries will be essential to reduce transboundary pollution.
Overall, the SIP policies implemented in Taiwan's six metropolitan areas have successfully improved air quality and offer valuable insights for reducing air pollution. However, continuous adjustments and strategic refinements will be necessary to address emerging challenges and ensure the long-term effectiveness of these policies.
How to cite: Chang, H.-L., Lai, H.-C., Hsiao, M.-C., and Lai, L.-W.: The impact of metropolitan's Air Quality Policies on air pollution improvement in Taiwan, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14898, https://doi.org/10.5194/egusphere-egu25-14898, 2025.
Vehicle emissions are a significant source of greenhouse gases and air quality pollutants in urban areas, yet current city-scale CO and CO2 emission inventories may not accurately reflect real-world conditions. In Toronto, Canada, traffic emissions contribute to approximately one third of the city’s total CO2 emissions. As a part of the Toronto Atmospheric Monitoring of Emissions (TAME) project, the goal of this work is to investigate the emission signatures of the vehicle fleet in the Greater Toronto Area (GTA) under different engine operating conditions and seasons. Specifically, we are interested in the role of ‘cold-start’ emissions (i.e. emissions during the engine and catalyst warm-up period) on the distribution of CO emissions in the GTA. As exhaust aftertreatment technologies improve for gasoline engines, air quality emissions that occur before the catalytic converter has warmed are expected to contribute an increasing, and possibly dominant, proportion of non-CO2 vehicle emissions. Simultaneous measurements of CO and CO2 were collected by deploying calibrated low-cost sensors in parking garages at the University of Toronto over several months. To calculate the CO and CO2 enhancements from each vehicle emission plume, a peak-finding algorithm was developed. The ΔCO/ΔCO2 ratio of the enhancements is used as an emission signature for each vehicle. The results from this study are compared to mobile and stationary measurement campaigns conducted by Environment and Climate Change Canada using a high precision analyzer (cavity ring-down spectrometer (CRDS)). We assess the impact of choices made about data collection, peak-finding, and cold-start definitions. Using one approach, the range of ΔCO/ΔCO2 ratios observed is 0.1 to 779 ppb CO/ ppm CO2, with a median of 14 ppb CO/ ppm CO2. On average, the cold-start emission ratios are observed to be at least 2 times greater than those of warm vehicles. These results can be used to update CO and CO2 emission inventories to more accurately capture activity patterns and independently verify emission reductions as urban vehicle fleets transition to electric.
How to cite: Corapi, A., Murphy, J., Panas, M., Ward, E., Wunch, D., Ars, S., and Vogel, F.: Investigating the impact of cold-starts on the distribution of vehicle emissions in the Greater Toronto Area, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12094, https://doi.org/10.5194/egusphere-egu25-12094, 2025.
Urban air pollution and greenhouse gas (GHG) emissions stem from diverse sources, including transportation, heating, buildings, waste management, industrial and agricultural activities, and natural events like forest fires. Simultaneous monitoring of air pollutants and GHGs with high selectivity and sensitivity is crucial for identifying and evaluating their sources and sinks, as well as understanding their interactions. Accurate measurements across various spatial and temporal scales are essential for modeling and validating emission inventories or satellite observations.
Traditionally, solutions for monitoring air pollutants or GHGs with high precision and temporal resolution have been "one-gas-one-instrument," resulting in large, stationary setups with high energy consumption. MIRO Analytical’s compact laser absorption spectrometer that integrates multiple mid-IR lasers enables simultaneous high-precision measurements of greenhouse gases (CO2, N2O, H2O, CH4), pollutants (CO, NO, NO2, O3, SO2, NH3), and trace gases (OCS, HONO, CH2O) within a single instrument. With a time-resolution of up to 10Hz, it is well-suited for detecting the relationships between co-emitted pollutants and GHGs as well as eddy-covariance flux studies.
In our presentation, we will showcase examples of our instrument's applications for mobile monitoring of 10 GHGs and air pollutants in urban areas, as well as airborne measurements using airships or planes. Additionally, we will present results from parallel monitoring with our instrument and conventional gas analyzers used for GHG and air pollutant measurements. This demonstrates our instrument's capability to serve as an all-in-one solution, replacing up to seven standard gas analyzers and enabling a wide range of new mobile multi-compound gas monitoring applications, such as in airplanes or cars.
How to cite: Hundt, M., Bruckhuisen, J., and Aseev, O.: Advanced Mobile Monitoring of Greenhouse Gases and Air Pollutants Using a Compact Spectrometer, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17440, https://doi.org/10.5194/egusphere-egu25-17440, 2025.
The NOAA Chemical Sciences Laboratory has led a series of recent airborne campaigns in the U.S. to investigate urban air quality, emissions and greenhouse gases. The Atmospheric Emissions and Reactivity Observed from Megacities to Marine Areas (AEROMMA) flew the three largest U.S. cities (New York, Chicago and Los Angeles) in 2023 on the NASA DC-8 with a comprehensive suite of trace gases, aerosols, radiation and meteorology. The Airborne and Remote Sensing Methane and Air Pollutant Surveys (AiRMAPS) is a series of campaigns sampling urban areas and oil and gas basins over 3 years. Airborne measurements during AEROMMA probed nonlinear O3 photochemistry in New York, Chicago and Los Angeles. The mean ozone production efficiency (OPE), the ratio of Ox (O3 + NO2) to NOx oxidation product enhancements, were 9 ± 4 (1 s), 6 ± 3 and 6 ± 3 ppbv ppbv-1, respectively. OPE exhibited a nonlinear, inverse dependence on total reactive nitrogen (NOy, a proxy for initial NOx) and a positive correlation with ∆VOC/∆NOy. A zero-dimensional photochemical model supports these observed OPE dependences on NOx and VOCs and shows that OPE is a distinct metric from total O3 production that may be informative to the development of O3 pollution control strategies. AEROMMA flights also quantified the magnitude and sources of urban methane (CH4) emission from in-situ measurements of CH4, CO2, CO, and C2-C5 alkanes in Los Angeles. Using the CA Air Resources Board CO emissions inventory alongside CH4/CO enhancement ratios, the analysis determines summertime CH4 and C2–C5 alkanes emissions. Roughly half of Los Angeles CH4emissions are from natural gas sources and half from sources such as landfills and dairies. Comparison to historical aircraft campaigns from 2010-2023 shows declining CH4 but increasing ethane emissions, with the latter due to changes in pipeline natural gas ethane content.
How to cite: Brown, S., Chace, W., Woamck, C., Schafer, N., and Peischl, J. and the The AEROMMA and AiRMAPS Teams: Urban Air Quality and Greenhouse Gases from Recent Airborne Field Campaigns in the United States, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14289, https://doi.org/10.5194/egusphere-egu25-14289, 2025.
As Asian urban areas continue to expand, so will their contribution to global greenhouse gas (GHG) emissions, driven primarily by increases in fossil fuel combustion. Left unchecked, these emissions will negatively impact air quality and climate, therefore it is imperative that emission sources are properly identified and accounted for in emission inventories. Enhancement ratios of GHGs have been used to characterize regional emissions as either dominated by fossil fuel combustion or biomass burning. In particular, airborne assessment of short-term continuous emission ratios has proven useful to quantify relative contributions of fossil fuel and biomass burning to GHG emissions. The 2024 Airborne and Satellite Investigation of Asian Air Quality (ASIA-AQ) campaign, flying over the Philippines, Korea, Thailand, and Taiwan, sampled a variety of emissions including significant biomass burning, local urban pollution, and transport events. This work will explore the impact of GHG emissions on the distinct pollution of cities including Manila, Bangkok, Chiang Mai, Seoul, and Kaohsiung. The incorporation of missed approaches within urban areas allowed us to sample boundary layer pollution within these cities. As airborne GHG measurements over Southeast Asia are scarce if not nonexistent, this work provides a crucial link between established ground-based measurements and state-of-the-art satellite observations such as those from South Korea’s Geostationary Environment Monitoring Spectrometer (GEMS). Analysis of GHG enhancement ratios in Asia will lead to more accurate emission inventories which can be used to implement more effective GHG control measures leading to improved air quality and minimizing the effect on climate.
How to cite: Miech, J., DiGangi, J., Diskin, G., Choi, Y., Moore, R., Ziemba, L., Gallo, F., Jordan, C., Shook, M., Winstead, E., Wiggins, E., Roy, S., and Gatebe, C.: Using airborne greenhouse gas enhancement ratios for source apportionment in Asia during ASIA-AQ, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3684, https://doi.org/10.5194/egusphere-egu25-3684, 2025.
Continuous measurements of carbon dioxide and black carbon emissions in Kwabia Ghana are presented. The community is discrete and dominated by wood burning. Here a mass balance array is deployed to measure upstream and downstream concentrations of black carbon, PM2.5, and carbon dioxide. A report on the 2022 deployment demonstrates that top-down flux measurements using a plume superposition modeling technique is innovative and comprehensive. Accurate measurements of carbon dioxide and black carbon provides a combined quantification of the dynamics of green house gas emissions, particle/air-quality emissions, and the devastating impact of deforestation. Results show that morning and evening cooking time introduce dangerous levels on air-quality and the subsequent emission of carbon dioxide to the atmosphere.
How to cite: McGillis, W., Apraku, C., Chillrud, S., and Culligan, P.: In situ measurements of emissions from Kwabia Ghana, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14294, https://doi.org/10.5194/egusphere-egu25-14294, 2025.
The ICOS-Cities project aims at evaluating different observational approaches to determine CO2 emissions from large cities, such as Paris (France). A developing approach consists in building a network of three (half-)autonomous FTIR spectrometers used for CO2 total column retrieval: two EM27/SUN placed up and down the prevailing wind axis (Gonesse and Saclay) and one IFS 125 HR placed in Paris Center (Jussieu). These measurements are complementary to the surface measurements carried out in Paris for a decade in the framework of the ICOS European research infrastructure.
When the wind blows in the instrument location axis, the spatial gradient between stations is considered to be representative of the enhancement due to the surface fluxes in the area in between. It is therefore the signal assimilated by inverse modelling for surface fluxes calculations. The columns are driven not only by the geophysical variations, but also by measurement-specific effects such as solar zenith angle) and measurement noise. Part of our work consists in characterizing these effects so as to be able to make the best use of gradients in calculating fluxes. The expected XCO2 gradients (Saclay-Gonesse) are derived from WRF-GHG modelling that accounts for prior inventory of the Paris area emissions, large scale transport of CO2 constrained by a global inversion, and atmospheric transport constrained by ECMWF meteorological parameters. The ratio of the measured and modelled gradients is used to estimate a correction to the prior inventory leading to new estimates of the Paris area emissions. The aim is to analyse the potential contribution of total column estimates, with respect to surface measurements, to estimate urban emissions.
How to cite: Doc, J., Ramonet, M., Lopez, M., Bréon, F.-M., Maquart, B., Cassagne, M., Leuridan, H., Jeseck, P., and Té, Y.: Greenhouse gases total column measurements from ground-based FTIR spectrometers in the Paris' region for emission inventory optimization., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5924, https://doi.org/10.5194/egusphere-egu25-5924, 2025.
Accurate and timely monitoring of urban CO₂ emissions is essential for tracking progress toward climate goals and enabling effective policy interventions. In the Netherlands (NL), emissions arise from industrial, agricultural, and transportation sources. Traditionally, emission reporting in Europe has relied on annual bottom-up inventories based on activity data and emission factors, aggregating emissions from sectors such as energy, transport, industry, and agriculture. While these methods have contributed to reducing anthropogenic emissions, they lack the granularity and timeliness required for real-time decision-making or for tracking progress toward more immediate climate targets. This highlights the urgent need for enhanced spatial and temporal resolution of urban CO₂ emissions data.
This study seeks to address this gap by leveraging a novel approach: using tropospheric NO₂ columns from TROPOMI as a proxy for urban CO₂ emissions. In recent years, a general consensus has been reached that satellite-derived NO₂ from instruments like OMI and TROPOMI are indicative of surface NO₂ concentrations and can be used to estimate top-down NOx emissions. We hypothesize that combining TROPOMI tropospheric NO₂ data with advanced deep learning (DL) methods will enable near real-time estimation of urban CO₂ emissions, offering a high-resolution, dynamic approach to emission monitoring.
Our research focused on the Netherlands, covering the period from 2018 to 2023 for model training and 2024 for validation. Various DL architectures to process TROPOMI data and predict local emissions were evaluated, incorporating ground-based emission inventories and additional metadata. Our goal is to identify the most effective DL models for improving emission estimation accuracy, reducing uncertainty, and enhancing the timeliness of reporting.
Although the initial focus is on the Netherlands, with its well-established monitoring systems (the "brownfields" effect), our methodology has broader applicability for regions with limited emissions data, such as those in developing areas (the "greenfields" effect).
A key aspect of this research is the development of trustworthy AI, ensuring the deep learning models used are transparent, reliable, and interpretable. By combining cutting-edge AI techniques with Earth observation data and validating the results against ground-based inventories, we created a robust framework for scaling emissions monitoring, especially in regions with limited infrastructure.
How to cite: Curier, R. L., Ham, S., Stoorvogel, J. J., and Bromuri, S.: Bridging the Gap in Emission Reporting: Synergistic Use of AI and Earth Observation for Real-Time Insight on Urban CO₂ Emissions., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9108, https://doi.org/10.5194/egusphere-egu25-9108, 2025.
This study presents a comprehensive analysis of CO₂ emissions in Glasgow, utilizing a dense network of Berkeley Environmental Air quality and CO2 Network (BEACON) CO2 sensors for the year 2022. The research employs a sophisticated model setup, integrating high-resolution meteorological data from the Weather Research and Forecasting (WRF) model with a Lagrangian Particle Dispersion model for footprint modeling. A Bayesian inversion framework developed by University of California, Berkeley refines a prior emission inventory using observed CO₂ concentrations and sensitivity footprints. The analysis reveals a 23% increase in overall mean anthropogenic emission for the year 2022 compared to available prior inventory estimate with significant seasonal variations. Winter fluxes were 70% higher than prior estimates, driven by increased heating demands and diminished biospheric uptake. Summer showed a 29% reduction, a combined impact of less energy demand for domestic heating and CO₂ uptake . A moderate negative correlation (R² = 0.58) between winter emission episodes and minimum daily temperatures was observed, highlighting the impact of domestic heating on CO₂ emissions. The study also found a 9.8% increase in total posterior emissions on weekends compared to weekdays, a smaller gap than the 21.8% difference in prior values. Our inverse model actively adjusts the emission values based on the real time CO2 measurement from sensors and high-resolution meteorology driven transport model at finer temporal scale, which is very valuable in making adjustments and validating local authority inventory data. Spatial analysis revealed that the most substantial emission changes were concentrated in areas corresponding to the 95th percentile of the posterior-prior emission difference. These regions, consistently exhibiting higher emissions throughout the year, reflect the combined impact of transport and heating sources in the city. These results highlight the urgent necessity for both enhancing building energy efficiency and targeted strategies to reduce street level vehicular emission. Furthermore, the results point out the importance of continuous, sensor-based measurements for achieving a more precise representation of urban emission sources. This study also examines the impact of Glasgow's Low Emission Zone (LEZ) implemented in June 2023. A comparative analysis of CO₂ emissions and concentrations before and after the LEZ implementation provides insights into its effectiveness in reducing urban emissions. The findings underscore the importance of seasonal variability in emission patterns and the need to account for both anthropogenic activities and natural processes when analysing CO₂ fluxes at finer temporal and spatial scales.
How to cite: Kodoli, S., Michie, C., Davison, C., Tachtatzis, C., Asimow, N., and Cohen, R.: High-Resolution Urban Emission Mapping: Sensor-Driven CO2 Inverse Modeling in Glasgow, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9568, https://doi.org/10.5194/egusphere-egu25-9568, 2025.
Cities are hot spots on greenhouse gas (GHG) emissions, yet green infrastructure (GI) such as green spaces and parks provides potential solution for reducing urban carbon footprints through photosynthetic uptake and carbon sequestration. Studies have shown that the offset of urban vegetation uptake on local anthropogenic CO2 emissions varies between 2% and 100%, underscoring the complexity associated with this solution. Quantifying CO2 capture by GI is challenging due to the interplay of photosynthetic uptake and respiration, seasonal variability, the heterogeneous distribution of GI, urban climate, and soil conditions. While biosphere models have been used to quantify carbon exchange processes, they are often employed at the ecosystem level and at coarse spatial resolutions(10-100km), making them insufficient for capturing biospheric signals at the urban scale(10m-1km). Therefore, high-resolution quantification of biogenic CO2 fluxes is essential for understanding their role on urban GHG budget.
This study estimates biogenic CO2 fluxes for 2023 in the Metropolitan Area of Barcelona (AMB) at a 10 m resolution using the Vegetation Photosynthesis and Respiration Model (VPRM). Our approach integrates vegetation indices derived from Sentinel-2, a detailed vegetation land cover dataset constructed by merging local land cover and tree maps, and meteorological inputs (temperature and shortwave radiation) from the Weather Research and Forecasting (WRF) model coupled with an urban canopy scheme that better represents atmosphere exchanges inside the urban canyons. A sensitivity analysis is conducted comparing different VPRM configurations including flux parameterization, input satellite-derived vegetation indices and modifications to land cover map. To constrain the modelled biogenic CO2 emissions and determine their uncertainties, the estimated biogenic fluxes are evaluated with atmospheric CO2 mixing ratios observations from the AMB GHG monitoring network using an atmospheric transport model (WRF-Chem) in a passive tracer approach. This research presents an improved method to estimate the urban biogenic CO2 fluxes and provides guidance for improving and creating more robust ways of accounting for the contribution of urban green to aid policy and urban planners in the design and implementation of GI.
How to cite: Luo, Q., Segura-Barrero, R., Badia, A., Lauvaux, T., Li, J., Chen, J., and Villalba, G.: High-Resolution Quantification of Biogenic CO2 Fluxes over a Metropolitan Area, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11711, https://doi.org/10.5194/egusphere-egu25-11711, 2025.
Urbanisation and climate change are global megatrends. Currently, more than half of the world's population lives in urban areas and this number is expected to increase in the future. Urban areas are vulnerable to climate change-induced extreme weather events due to urban characteristics such as the urban heat island (UHI), but are also large sources of anthropogenic greenhouse gas emissions. Urban green spaces are increasingly being explored as a solution to offset these emissions and adapt to climate change in cities. Our knowledge of urban carbon sequestration is mainly derived from research in natural ecosystems and is lacking in the urban context. Urban environments are characterized by a high degree of complexity due to their fragmented and heterogeneous nature and intensive anthropogenic management and modification. More knowledge on carbon sequestration in different urban vegetation types and the influence of management is needed to guide the planning of resilient and climate-smart urban green spaces.
Here, the JSBACH, a process-based land surface model, was used to understand how carbon sequestration in different Nordic urban vegetation types responds to possible future climates in Finnish cities. JSBACH, previously tested for urban conditions in Helsinki, was used to simulate seven urban vegetation types in 20 Finnish cities between the years 2006 and 2100. The urban vegetation types used were urban lawn, park site with Tilia cordata, urban birch-dominated forest, mesic meadow and dry meadow. In addition, irrigated versions of urban lawn and park with Tilia cordata were also simulated. JSBACH was driven by daily EURO-CORDEX data from global models CanESM2, MIROC5 and CNMR-CM5 using RCP4.5 and RCP8.5 emission pathways downscaled to the EUR-44 domain.
Based on these simulations, urban ecosystems with trees were more consistent carbon sinks and less sensitive to future weather conditions than vegetation types dominated by grasses. Drought decreased primary production in some vegetation types during summertime, but on an annual scale, productivity was mainly driven by the length of the growing season. In these simulations irrigation caused a decrease in Net Ecosystem Production (NEP) compared to their non-irrigated counterparts, highlighting the role of moisture as a driver of respiration.
How to cite: Koiso-Kanttila, A., Backman, L., and Kulmala, L.: Carbon Sequestration across Urban Vegetation Types in Changing Climate in Finnish Cities, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14947, https://doi.org/10.5194/egusphere-egu25-14947, 2025.
Emissions of greenhouse gases (GHGs) are known drivers of climate change and related effects; however, they continue to increase every year despite current reduction efforts. Rising populations worldwide as well as changes in land use and in anthropogenic activities contribute significantly to this observed, unmitigated increase in GHG emissions. Cities are clear hotspots for anthropogenic sources of GHGs, and a regional or national emission reduction plan is not enough to effectively target their complex and unique source compositions or relative contributions. To push local or city-level action plans forward, GHG flux towers in three pilot cities (Zurich, Munich, and Paris) were established for long-term eddy-covariance measurements as part of the H2020 ICOS Cities/PAUL project (https://www.icos-cp.eu/projects/icos-cities). The cities were chosen to offer insight into how city size, topography, and source mixture affect GHG and trace gas emissions.
We present results from emission source attribution of carbon dioxide (CO2), carbon monoxide (CO), and methane (CH4) using a combination of flux measurements, footprint modelling, and local emission inventories. Turbulence measurements observed at or derived from information at each tower were implemented in the Flux Footprint Prediction (FFP) model (Kljun et al., 2015) to develop highly spatially- and temporally-resolved flux footprints for each site. These footprints were subsequently combined with (1) annual emission inventories at high spatial resolution and (2) emission sector-specific hourly temporal profiles for the aforementioned trace gases to estimate the relative contributions of emission sectors to the flux signal at each tower. We also incorporate outputs of the Vegetation Photosynthesis and Respiration Model (VPRM; Mahadevan et al., 2008) to quantify biogenic contributions to the CO2 flux signal. The presented results highlight seasonal and diurnal trends as well as spatial hotspots within the flux footprint of sector-separated CO2, CH4 and CO fluxes in cities with diverse characteristics, all of which is valuable for source attribution and for supporting localized and targeted emission reduction plans.
How to cite: Molinier, B., Kljun, N., Aigner, P., Brunner, D., Chen, J., Christen, A., Constantin, L., Denier van der Gon, H., Hilland, R., Holst, C., Kühbacher, D., Li, J., Maiwald, R., Stagakis, S., Super, I., and Vardag, S.: Identifying Greenhouse Gas Emission Trends and Validating Hotspot Locations via Flux Measurements and Footprints in Three Pilot Cities, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2888, https://doi.org/10.5194/egusphere-egu25-2888, 2025.
Accurate quantification and monitoring of urban fossil-fuel CO2 (FFCO2) emissions at improved spatial granularity are critical to emission control and climate change mitigation policies. In this work, we use a top-down Bayesian inversion method and Eulerian transport modeling to constrain FFCO2 emissions from the Xiamen-Zhangzhou-Quanzhou metropolitan area, China, based on high-resolution areal snapshots of total column CO2 (XCO2) from OCO-3 Snapshot Area Maps (SAMs) during September 2019 to July 2023. The emissions from point sources and different areas are constrianed simultaneously, including the area sources Xiamen, local power plants in Xiamen, and other adjacent urban areas. Observed XCO2 enhancements range from 0.70±0.53 ppm to 2.29±1.16 ppm, indicating potential capability of OCO-3 SAMs on detecting emission signatures. We show the mean posterior emission from Xiamen of about 7.79 ± 0.92 MtC/yr, being within the spread of different inventories and 34 % higher than their average. Several challenges hampering the inversion performance are revealed, including the spatial displacements of the modeled and observed enhancements, the limited representation of local power plants, and data availability. The results provide insights on inversely disentangling imprints of emission sources based on dense space-borne observations, facilitating applications of future missions with improved XCO2 mapping coverage and frequency.
How to cite: Ye, X., Li, W., Lauvaux, T., Lin, S., Zhang, Z., Lin, Y., Hua, J., and Lin, J.: Constraining Anthropogenic CO2 Emissions using XCO2 Observations from OCO-3 over Xiamen-Zhangzhou-Quanzhou Metropolitan Area, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15751, https://doi.org/10.5194/egusphere-egu25-15751, 2025.
The Munich Urban Carbon Column network (MUCCnet) consists of five solar-tracking Fourier Transform spectrometers (EM27/SUN) measuring column-averaged mole fractions of carbon dioxide (XCO2), methane (XCH4), and carbon monoxide (XCO). They are strategically placed in the center of Munich and in every cardinal direction. Starting with one instrument in 2015, MUCCnet has been collecting data continuously with five instruments since 2020, allowing a detailed analysis of Munich's urban greenhouse gas emissions based on inverse methods. To this end, we use the Bayesian inversion approach: We compute the observed enhancements in dependence of the wind direction using one network site as background (observed background) and derive spatially resolved emissions. The forward model uses a Munich-specific inventory (100×100 m2 resolution) for anthropogenic fluxes and the Vegetation Photosynthesis and Respiration Model (VPRM) for biogenic fluxes. The transport is modeled with the Lagrangian particle dispersion model STILT.
A critical aspect of our analysis is the estimation of uncertainties within the inversion framework. Balancing the confidence in transport and measurements against prior information (inventories) is of great importance. Furthermore, we investigate the number and spatial distribution of state vector parameters based on the available degrees of freedom for signal. The choice of an appropriate background is crucial, since the urban enhancements for Munich are typically below 1 ppm, which is small (< 1%) compared to the background XCO2 concentrations (> 400 ppm). In addition to the observed background approach, we investigate background approaches derived from models (e.g. ICON-ART), and fitted, a posteriori backgrounds from the inversion approach.
Our inversion results represent spatially resolved, long-term, top-down CO2 emission estimates for Munich. Over a comprehensive measurement period of five years, we highlight seasonal and annual trends, providing valuable insights into Munich's CO2 emissions.
How to cite: Stauber, J., Chen, J., Klappenbach, F., Li, J., Luther, A., Makowski, M., Tang, H., Ponomarev, N., and Brunner, D.: Assessment of Munich’s CO2 emissions via Bayesian inversion using MUCCnet data from 2020-2025, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17841, https://doi.org/10.5194/egusphere-egu25-17841, 2025.
Northern Italy is one of the most polluted and densely populated areas in Europe. The diversity of land use in the Po basin makes this region an important contributor to greenhouse gas emissions from different sources. Medium and large cities, as well as industrialised areas, contribute significantly to emissions from industrial processes, combustion, waste management and natural gas distribution.
As part of the H2020 RI-URBANS project (https://riurbans.eu/) and in synergy with PNRR “ITINERIS” Project, a pilot study has been carried out in the Milan urban area with the aim of supporting the local authorities to evaluate the effectiveness of air quality policies and the effects of pollutants on human health. A one-year long measurement campaign was carried out using a mobile platform in the premises of the CNR facility (AdRMi1, 45°28’47"N 9°13’54"E; 120 m a.s.l.), located in in the urban area of Milan, in the University district. The mobile platform has been equipped with a suite of instruments (ACSM, Vocus Chemical Ionization TOF-MS, Aethalometer, NOx CLD) to provide near real time (NRT) information on aerosol source partitioning and to characterise nanoparticle contributions from urban hot spots. From July 2023 to March 2024, the mobile platform has been equipped with a Cavity Ring Down Analyser for continuous observations of carbon dioxide (CO2) and methane (CH4).
In this work we will provide a first characterization of the diel cycles and seasonal variations (from late summer to early spring) of atmospheric CO2 and CH4 in the urban area of Milan. Thanks to the combined analysis of other atmospheric tracers, first insights about the influence by atmospheric processes (i.e. PBL dynamics) and emission sources (fossil fuel and biomass burning combustion, biogenic, waste management) will be discussed. Finally, we provided a preliminary “top-down” estimate of CH4 emission for the Milan urban area by using the interspecies correlation approach: the obtained values were higher (but still within the range of uncertainties) than emissions provided by statistical “bottom-up” inventories (e.g. INEMAR).
Acknowledgments: RI-URBANS t has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101036245. This reserach was partially supported by the European Unione- Next Generation EU, Missione 4 Componente 2 - CUP B53C22002150006 - IR0000032 – ITINERIS, Italian Integrated Environmental Research Infrastructures System
How to cite: Cristofanelli, P., Zannoni, N., Apadula, F., Barnaba, F., Bracci, A., Bellini, A., Calzolari, F., Diliberto, L., Manca, G., Mardonez, V., Magnani, C., Montaguti, S., Renzi, L., Zazzeri, G., and Marinoni, A.: Urban CO2 and CH4 atmospheric measurements in the Milan city area (northern Italy), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17963, https://doi.org/10.5194/egusphere-egu25-17963, 2025.
Quantifying road traffic CO2 emissions is critical for urban climate and sustainability studies. However, detailed modeling often requires high-resolution input data that is unavailable in many regions. To address this gap, we present a simplified regression-based model that quantifies traffic-related CO2 emissions within Local Climate Zones (LCZs) using readily available data such as building surface area, asphalt surface area, population, traffic lights, and road type. This approach minimizes computational requirements and circumvents the need for traffic data, offering a practical alternative for regions with limited resources.
Our results show that road type and asphalt surface area are the most influential variables in describing CO2 emissions. Median CO2 emissions from built LCZs are 1.8 times higher than those from land cover LCZs. The generalized model can explain up to 69% of the emissions for some LCZ. Based on this model, we introduce a look-up table for LCZ-specific traffic CO2 emissions, providing a user-friendly tool to estimate emissions in data-scarce regions. This simplified methodology emphasizes accessibility and efficiency while maintaining robust results, making it an invaluable resource for urban emission studies.
How to cite: Al-Jaghbeer, O., Järvi, L., Fung, P. L., and Paunu, V.-V.: Mapping and Modeling CO2 traffic emissions within local climate zones in Helsinki, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1581, https://doi.org/10.5194/egusphere-egu25-1581, 2025.
Cities around the globe are aiming to reduce their carbon dioxide emissions, but monitoring and validating urban CO2 emissions is a major challenge. This motivates the ICOS cities PAUL project to use a combination of different measurement and modelling techniques to provide observation-based emission estimates in three pilot cities: Zurich, Paris, and Munich. The challenge comes due to large variations in emissions and concentration gradients, high uncertainties in prior estimates, and inherent modeling errors. Here we present the results of an inverse modeling study for the city of Paris, which builds on the insights gained from similar simulations conducted for Zurich. Our approach employs the state-of-the-art atmospheric mesoscale model ICON-ART, which we ran in conjunction with an ensemble Kalman smoother to optimize CO2 fluxes based on simulated and measured concentration differences.
Paris offers advantages for mesoscale model simulations due to its flat terrain and large size, unlike Zurich, where simulations were challenged by the city's complex topography. Furthermore, the CO2 measurements in Paris, which were collected from a network of 2 tall towers inside and 7 towers outside the city, were easier to represent by the model due to their larger spatial representativeness compared to the more locally influenced rooftops measurements in Zurich.
The ICON-ART model simulations were performed for two offline nested model domains. The outer domain covers Central Europe with a spatial resolution of 6.5 km and was chosen large enough to serve as initial and boundary conditions for the simulations over both Zurich and Paris. The inner, high-resolution domain is centered on the Île-de-France region with a spatial resolution of 1 km. According to our previous experience with Zurich simulations, the atmospheric transport is well simulated by ICON-ART in most weather situations with the exception of low wind conditions, where relative errors in wind speeds and the corresponding dilution of CO2 emitted from the city are the largest. The prior anthropogenic CO2 fluxes were based on the anthropogenic inventory data prepared by AIRPARIF for the Île-de-France area at 0.5 km spatial resolution and on TNOGHGco 2018 data (1 km) for the rest of Europe. Biogenic fluxes were computed online using the Vegetation Photosynthesis and Respiration Model (VPRM), integrated online into ICON-ART.
In this presentation, we analyze the performance of ICON-ART model against meteorological and CO₂ observations in and around Paris, and demonstrate initial results from emission inversion experiments. Furthermore, we contrast the results with those obtained for Zurich to emphasize the different challenges and modelling capabilities in the two cities.
How to cite: Ponomarev, N., Rubli, P., Stuart, G., Ramonet, M., David, L., Emmenegger, L., and Brunner, D.: Estimation of carbon dioxide fluxes in the city of Paris using the ICON-ART-CTDAS model, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8467, https://doi.org/10.5194/egusphere-egu25-8467, 2025.
The insufficient density of the air quality monitoring network is a long-standing issue that, at least in the Czech Republic, has yet to be satisfactorily resolved. In practice, greater emphasis is placed on monitoring emissions rather than immission (ambient emissions). Air quality measurements are, in most cases, conducted at the sources of polluting gases (CO, CO₂, SO₂, NOₓ). Monitoring stations are often located in areas with heavy industry. However, more detailed information on air quality is missing in places where air interacts directly with the respiratory system, such as urban agglomerations of various sizes. An illustrative example is that the Czech Hydrometeorological Institute (ČHMÚ) does not operate monitoring stations in all regional cities. This results in a critical deficiency of objective information on air pollution across the entire territory.
Insufficient information on long-term pollutant concentrations in the air is also a significant issue in locations where climatic conditions have, or could have, therapeutic benefits—specifically, in spa locations. Some areas with favorable climatic conditions (e.g., those with clinically proven benefits for cardiac patients) are certified as climatic spas under the Spa Act. However, stringent air quality standards are also required for all other spa locations. The absence of direct monitoring of tropospheric air pollution in the centres of most spa locations poses a challenge because pollution levels are estimated using mathematical models based on data collected from nearby or more distant surroundings.
To address this issue, a mobile air quality station will be procured as part of the SRC (Spa Research Centre) transformation project. The station will be capable of analyzing concentrations of various pollutants, including CO, SO₂, NOₓ, suspended particles (PM2.5, PM10), selected volatile organic compounds (e.g., benzene as a representative of VOC), selected heavy metals (e.g., Be, Cr, Cd, Ni, Pb, As, Zn), and polycyclic aromatic hydrocarbons. Moreover, the station will be equipped to transmit the collected data online in real time. The project aims to establish baseline air quality values for the most significant spa locations. These values will serve as the foundation for defining the parameters of the so-called "spa therapeutic landscape." This definition will facilitate the specification of air quality limits necessary to ensure the sustainable preservation of the favorable climate in spa locations and its associated therapeutic effects.
Initial results from measurements in the spa towns of Karlovy Vary and Lázně Kynžvart indicate that air quality in the centers of spa towns is significantly better than in areas without spa functions. This improvement can be attributed to factors such as restrictions on passenger and freight transport and other anthropogenic activities implemented to maintain the protective regime for spa clients. These findings align with observations that locations with high levels of vehicular traffic experience increased concentrations of pollutants such as NOₓ.
The primary goal of the ongoing project is to enhance detailed air quality monitoring and facilitate the establishment of air quality limits for locations where the therapeutic use of favorable climatic conditions supports the treatment of the human body.
Research within the SRC project is funded by JTF CZ 10.01.01/00/22_001/0000261.
How to cite: Kalina, P., Landa, D.-A., Vylita, T., Schořová, E., and Walterová, J.: Air quality issues in therapeutically exposed locations, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11082, https://doi.org/10.5194/egusphere-egu25-11082, 2025.
Cities are one of the main sources of greenhouse gases, accounting for over 70% of global CO2 emissions. Accurate quantification of these emissions through direct observations is crucial for developing and assessing the effectiveness of adopted mitigation strategies.
As part of the European project ICOS Cities (https://www.icos-cp.eu/projects/icos-cities), three eddy covariance towers were installed in the Paris area to capture the variability of surface-atmosphere CO2 fluxes as a function of an urbanization gradient. Specifically, the selected sites were chosen to be representative of a highly urbanised and densely built-up area (Jussieu), an urban forest (Vincennes), and a heterogeneous area combining highly urbanised areas with areas of vegetation (Romainville). The observations from the urban sites were also integrated with the EC flux measurements conducted on the ICOS atmosphere tower of Saclay and the observations from the ecosystem sites of Fontainebleau (FR-FON, forest) and Grignon (FR-GRI, crop).
Long-term measurements of CO2 fluxes (2 years for the sites of Romainville and Jussieu and 1 year for the site of Vincennes) showed seasonal dynamics that reflected their respective degrees of urbanisation. The Jussieu site, in the city center, was constantly dominated by anthropogenic CO2 emissions, with maximum emission (up to 15 µmol m-2 s-1) during the winter months (November-February) and low absorptions (up to -2.5 µmol m-2 s-1) during the summer (July-August) in the central hours of the day. On the other hand, the mixed urban forest of Vincennes showed a strong biogenic signature, characterized by a predominant CO2 absorption in the central hours of the day (up to -10 µmol m-2 s-1 in the months of May, June and July). The 100 m-tall tower of Romainville showed instead the coexistence of anthropogenic and biogenic fluxes, each contributing its own seasonal and daily variations to the measured flux. A comparison between our observations and the emissions of the City of Paris will be included in the presentation.
How to cite: Laura, B., Depuydt, J., Herig-Coimbra, P.-H., Fortineau, A., Feron, A., Stella, P., Buysse, P., Kalalian, C., Nief, G., Ramonet, M., and Loubet, B.: Eddy covariance measurements of CO2 fluxes along an urban-to-rural gradient in the Paris area, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18076, https://doi.org/10.5194/egusphere-egu25-18076, 2025.
Within the research project DE-CIST (“Developing Energy Communities with Intelligent and Sustainable Technologies”) a novel, AI-based Energy Demand Simulator has been developed by our project partners. The Energy Demand Simulator determines the energy efficiency and energy saving potential of the residential building stock in Rotterdam (The Netherlands). By adding socio-economic features to the Energy Demand Simulator, the tool will guide policy makers in developing building renovation strategies on different scales, allowing to design household-level renovation packages which consider social, financial, and physical requirements.
From such building renovation scenarios, we calculated greenhouse gas emissions and the emission reduction potential of the renovation actions. This insight will allow policymakers to optimize the renovation strategies also for climate change mitigation. We conducted a series of high-resolution (100 m²) simulations of CO2 emissions using the Dutch Large Eddy Simulation (DALES) model for the city of Rotterdam. DALES is particularly advantageous due to its explicit simulation of boundary-layer turbulence, enhancing the accuracy of atmospheric transport and dispersion of chemical species in the urban environment. The emission inputs for DALES were refined from the Dutch national emission inventory by incorporating statistical data on household gas consumption specific to Rotterdam. This involves adjusting the emission estimates to reflect high-resolution local consumption patterns and spatial distribution, which improves the spatial accuracy of the modeled emissions. This approach will be extended to reactive gases to gain insights into the exposure of citizens to air pollution in combination with other socio-economic conditions.
How to cite: Los, A. and Doyennel, A.: Greenhouse gas emission reduction from residential building renovations in Rotterdam, The Netherlands, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10364, https://doi.org/10.5194/egusphere-egu25-10364, 2025.
The health effects of particulate matter (PM) are well-documented, with long-term exposure to elevated concentrations of respirable PM linked to increased risks of respiratory conditions such as allergic reactions, lung inflammation, and asthma. A key contributor to these health effects is the oxidative stress induced by PM, stemming from heavy metals and the generation of reactive oxygen species (ROS).
In this study, we measured the oxidative potential (OP) of respirable dust and inhalable PM collected from households in Slovenia, Sweden, and the UK as part of the international INQUIRE project on air quality. Samples were collected using active samplers, and OP was assessed using a simulated epithelial lung fluid (SELF) model, following established protocols. Quantitative mass spectrometry was employed to determine the depletion rates of key lung antioxidants, including glutathione, cysteine, and ascorbic acid, alongside the accumulation of glutathione dimer.
Our results revealed statistically significant higher antioxidant depletion rates in experiments with PM compared to control samples. To elucidate the underlying mechanisms, we measured the concentrations of soluble heavy metals and analyzed water-soluble organic matter (WSOM) from both coarse and fine PM fractions using high-resolution mass spectrometry. Correlations between the relative abundance of organic constituents and antioxidant depletion rates highlighted the role of specific organic compounds in driving oxidative potential.
These findings underscore the need for targeted intervention strategies to mitigate the health risks associated with PM-induced oxidative stress in indoor environments.
How to cite: Zherebker, A., Williams, M., and Giorio, C.: Oxidative Potential of Indoor Particulate Matter Collected During Campaigns in the UK, Slovenia, and Sweden, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19806, https://doi.org/10.5194/egusphere-egu25-19806, 2025.
Ensuring good air quality in children's environments is recognized as a critical public health issue, which raises the question of monitoring indoor air quality (IAQ) in classrooms as poor air quality affects children's health and academic performance. To better understand the dynamics that affect classroom air quality in urban environments, we examined some physical characteristics of the classroom, including ventilation and occupancy in contrasting contexts. A combined set of low-cost optical devices, CO2, humidity and temperature sensors as well as passive biosensors (Tillandsia usneoides, tree barks) was implemented in 5 classrooms in the urban context of the medium-sized Toulouse city (France), 3 classrooms in the West African capital city of Abidjan (Ivory Coast) and 2 classrooms in the megacity of Sao Paulo (Brazil). Concentrations of particulate matter (PM), CO2 and comfort data (humidity and temperature) were monitored every 2 minutes over more than 6 months in 2024. Processing the data according to whether the class is occupied or not allows to assess the impact of the presence of children and their activities on IAQ. Besides, the elemental composition of PM deposited on biocaptors exposed in the classroom, analyzed by ICP-MS for Toulouse and Abidjan, allows the identification of PM sources within classrooms. The results are part of the Coop’Air participatory research experiment designed by an interdisciplinary team to co-construct with the children appropriate measures to improve indoor air quality in their classroom.
How to cite: Rousse, S., Calas, A., Belleville, V., Gauthier, H., Avellan, A., Gnamien, S., de Miranda, R., Drigo, L., Labelle, V., Bakayoko, A., de Andrade, F., and Laffont, L.: Coop'Air : a participatory research initiative to monitor classrooms indoor air quality in classrooms through combined measurements of active devices and biosensors (France, Ivory Coast and Brazil)., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13794, https://doi.org/10.5194/egusphere-egu25-13794, 2025.
In developed countries, people spend nearly 90% of their time indoors, where activities such as cooking and cleaning are significant sources of air pollution, with consequent impacts on occupant health. Volatile organic compounds (VOCs), emitted indoors, can also escape into the urban outdoor environment and contribute to secondary pollution, e.g. via production of ozone and particulate matter formation. This comes at a time when successful regulation has led to gradual decreases in emission from some traditional emission sources such as traffic. Despite their importance, VOC from indoor sources remain understudied, with limited understanding of their emission patterns and broader environmental impacts. In this work, we used selected ion flow tube mass spectrometry (SIFT-MS) to examine VOC emissions from common indoor activities under controlled laboratory and semi-realistic domestic conditions. The speciated chemical signatures and emission rates derived from real-time measurements provide valuable insights into the sources and dynamics of these indoor emissions and help identify the tracer molecules (like nonanal, chloroform, carbon tetrachloride, nonane, etc) that can also be used to assess the contribution of indoor activities to the urban ambient air. These findings provide a valuable framework for understanding and designing comprehensive intervention strategies to address both indoor and outdoor air quality challenges.
How to cite: Kumar, A., O'Leary, C., Winkless, R., Thompson, M., Dighriri, W., Davies, H., Shaw, D., Budisulistiorini, S., Shaw, M., Carslaw, N., Carslaw, D., and Dillon, T.: Living with VOCs: Understanding indoor emissions and their implications beyond four walls, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6270, https://doi.org/10.5194/egusphere-egu25-6270, 2025.
In typical Korean-style barbecue restaurants, either customers or employees grill meat directly at the table using charcoal or natural gas as fuel. During the direct grilling process, significant amounts of odor and cooking fumes, including particulate matter (PMs) and volatile organic compounds (VOCs), are generated, resulting in numerous complaints from neighboring residents. To address this issue, this study developed an abatement system that can simultaneously remove PMs, odors, and VOCs using a filtration device and an advanced oxidation agent. A 50 m³/min capacity abatement device, capable of treating polluted air emitted from approximately 20 barbecue tables, was installed in a restaurant, and its performance was evaluated over 18 months.
The abatement system comprises (1) a dry advanced composite oxidation and adsorption layer, (2) a membrane filter utilizing advanced oxidants and adsorbent powders as filter aids, and (3) a real-time monitoring system for odor, VOCs, and PMs at the inlet and outlet.
During the entire operation period, the average concentration of complex odor in the exhaust gas, expressed as air dilution ratio, was 606±811. After treatment by the abatement system, the complex odor concentration was reduced to 50±96, meeting the odor management standards stipulated by the Korean Odor Prevention Act. Additionally, the system demonstrated stable reduction efficiencies of 86.6±15.0% for PM10 and 86.2±14.5% for VOCs.
Seasonal variations in emission characteristics were observed. The highest complex odor concentration occurred in winter (865±962 OU), followed by autumn (689±811 OU), summer (456±715 OU), and spring (444±757 OU). PM10 concentration peaked in autumn (2,060±8,957 μg/m³), followed by summer (1,335±7,736 μg/m³), spring (638±5,228 μg/m³), and winter (462±3,328 μg/m³). The VOC concentration was highest in autumn (0.35±1.13 ppm) and similar in summer (0.25±0.72 ppm) and winter (0.25±0.90 ppm).
Despite significant seasonal fluctuations in pollutant emissions, the abatement system provided stable operation and reduction performance. Regardless of the season, the average complex odor concentration at the outlet was maintained at 50±95, PM10 concentration was reduced by over 90% to an average of 215±621 μg/m³, and VOC removal efficiency was stably maintained at 88.0±15.7%.
The impact of installing the abatement system on air quality improvement around the restaurant was analyzed using the CALPUFF modeling system. The results confirmed that the system effectively reduced the diffusion of odors and cooking fumes, significantly improving the air quality in the surrounding area. In conclusion, this study suggests that a dry advanced oxidation-based system offers a practical and scalable solution for stable performance under various operating conditions and seasonal factors, contributing to air quality improvement and the protection of public health.
Acknowledgements
This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education (2020R1A6A1A03044977)
How to cite: Oh, H., Yoo, S.-Y., Cho, K.-S., and Ryu, H.-W.: Advanced Oxidation-Based System for Odor and Cooking Fume Reduction in Korean Barbecue Restaurants: Long-Term Evaluation and Impact Analysis, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14270, https://doi.org/10.5194/egusphere-egu25-14270, 2025.
Indoor air quality (IAQ) in workplaces significantly impacts occupational health and productivity, necessitating comparative evaluations across diverse environments. This study investigates particulate matter (PM) concentrations in indoor academic workspaces in Chennai, India, and Canberra, Australia, using the GRIMM 11D aerosol spectrometer. A 12-hour monitoring campaign was done in IIT Madras, India, and ANU, Australia for 3 days during the post-winter (Spring) season. It measured the PM1, PM2.5, PM4, and PM10 levels in the indoor workspaces offering insights into PM concentrations.
The average PM concentrations in Chennai were significantly higher than in Canberra. Indian workplaces recorded PM1, PM2.5, PM4, and PM10 levels of 4.03±1.09, 8.28±2.15, 12.36±4.26, and 15.83±6.43 (µg/m³), respectively. Corresponding Australian values were notably lower, at 1.53±0.47, 2.82±1.02, 3.52±1.69, and 4.14±2.72 (µg/m³), respectively. Spikes in PM10 levels in both regions suggest occasional localized pollution events or episodic pollutant intrusions, influencing PM concentrations. Additionally, the fine PM fractions (PM1 and PM2.5) were more prominent in Canberra, indicating potential variations in pollutant sources and infiltration rates.
Health risk assessments were performed by simulating lung deposition dosages for males and females using the ‘Symmetric Lung’ configuration within the Multiple Path Dosimetry Model (MPPD). The model revealed stark contrasts in PM lung deposition doses between the two regions, with Indian workplaces presenting significantly higher health risks. In Chennai, male dosages for PM1, PM2.5, PM4, and PM10 were 4.23, 22.16, 38.98, and 56.41 µg, respectively, while females experienced slightly lower dosages of 2.79, 12.99, 23.33, and 34.70 µg. In Canberra, the respective values for males were 1.61, 5.80, 9.14, and 12.33 µg, and for females, 1.05, 3.40, 5.41, and 7.46 µg. These findings highlight a significantly higher health risk for workers in Chennai, with females in both locations receiving lower doses due to smaller lung capacities and breathing rates.
This pilot study brings out substantial regional differences in IAQ, shaped by environmental factors, building ventilation standards, and external pollutant sources and infiltration rates. Elevated PM concentrations in Chennai signal a pressing need for interventions to enhance workplace air quality, such as improved filtration and ventilation systems and awareness campaigns. Meanwhile, the finer PM fraction in Canberra warrants attention due to its deeper penetration into the respiratory tract and long-term health implications.
Further research should address long-term exposure risks, seasonal variability, and effective mitigation strategies to improve IAQ and safeguard academic workforce health in diverse geographical settings.
How to cite: Das, S., Indu, G., Nagendra SM, S., and Vardoulakis, S.: Comparative Analysis of Indoor Air Quality and Health Risk Assessment in Academic Workspaces in India and Australia, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13038, https://doi.org/10.5194/egusphere-egu25-13038, 2025.
Air pollution, primarily driven by particulate matter (PM), is a major global health challenge, contributing to respiratory, cardiovascular, and neurological diseases, as well as premature mortality. Traditional ambient monitoring often fails to capture the spatial variability and individualized exposure patterns critical to understanding PM's health impacts. Personal exposure monitoring has emerged as a transformative tool, as it include diverse microenvironments. Studies reveal that personal exposure data reduce misclassification, improve exposure-health relationship modeling, and provide insights into source-specific toxicity, thereby enabling more targeted regulatory and public health interventions.
Traffic policemen, due to their constant presence in traffic-dense environments, are uniquely vulnerable to PM exposure. Despite advancements in wearable and low-cost monitoring technologies, limited research addresses occupational exposure in this high-risk group. This study aims to bridge this gap by implementing cutting-edge monitoring technologies to quantify and characterize the exposure of traffic policemen to PM. The personal exposure (PM5 & PM2.5) samples for traffic policemen standing on roads of an industrial area were collected for 15 days along with ambient air quality data for PM (PM100, PM10, PM2.5, and PM1) and gaseous pollutants (NO2, CO, and O3). The personal exposure samples were collected using an SKC personal monitor and Envirotech handy sampler, whereas the ambient air quality data was collected using a sensor-based instrument (Make: Oizom Pvt. Ltd). The collected samples were analyzed for PM concentration using gravimetric methods.PM deposits in human lungs as they enter in to the respiratory system by inhalation. The deposition of PM in different regions of the lungs was also estimated by using Multiple-Path Particle Dosimetry 3.02 model.
The findings from the study indicate that the average personal exposure concentrations for PM2.5 and PM5 were 102.96 ± 38.20 µg/m³ and 138.18 ± 30.41 µg/m³, respectively. The personal exposure level for PM2.5 was notably six times higher than the 24-hour average air quality standard set by the World Health Organization (WHO).
Analysis of ambient air quality data revealed that PM2.5 levels varied from 69.79 µg/m³ to 127.23 µg/m³, with an average concentration of 99.96 µg/m³, which, while still significantly above the WHO guidelines, was lower than the personal exposure levels. This discrepancy highlights that the population under study experienced elevated exposure to PM concentrations compared to ambient air conditions, suggesting that individual exposure conditions are influenced by specific situational and occupational factors. The identified primary sources of particulate matter include industrial fuel combustion, traffic emissions, and resuspension of road dust. Additionally, the proximity of traffic policemen to vehicle exhausts due to the low height of their standing platforms was identified as a contributing factor to the elevated personal exposure levels. This occupational setup positions individuals directly in the pathway of high concentrations of vehicular emissions, exacerbating their risk.
The study found the highest particulate matter deposition in the head region of the respiratory tract, highlighting health risks from prolonged exposure to high PM levels. These findings emphasize the need for mitigation measures, such as enhanced occupational safeguards and stricter emission controls, to protect vulnerable populations
How to cite: Chakraborty, M., Panda, S., and Christian, R.: Unveiling Occupational Exposure: The Impact of Particulate Matter on Traffic Policemen in Industrial Zones, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-666, https://doi.org/10.5194/egusphere-egu25-666, 2025.
The rapid urbanization and population growth of megacities like Delhi, India, have led to significant changes in land use and land cover (LULC), adversely impacting environmental conditions, including forest biomass and air quality. This study inspects the complex relationships between LULC, forest biomass, and air pollution in Delhi, a city grappling with severe environmental degradation and some of the world’s highest air pollution levels. Landsat-8 satellite imagery was used to analyze LULC changes between 2021 and 2023, focusing on urbanization and its impact on vegetation cover. Air quality data were collected from Central Pollution Control Board (CPCB) monitoring stations across four locations in the city. Two high-traffic, sparsely vegetated zones (Anand Vihar and ITO) and two densely vegetated areas (Sri Aurobindo Marg and Mandir Marg) were selected for a comparative analysis. Additionally, forest biomass was quantified through direct sampling in two major green zones: Sanjay Van near Sri Aurobindo Marg and the Ridge Forest near Mandir Marg CPCB station. LULC analysis revealed a decline in vegetative cover in urban areas due to infrastructure expansion and the conversion of green spaces into residential and commercial zones. CPCB data over six years (2018 – 2023) indicated notable differences in air quality between densely vegetated and sparsely vegetated zones. PM2.5 levels in high-traffic areas (Anand Vihar and ITO) were 24.20% and 23.19% higher than in densely vegetated areas (Sri Aurobindo Marg and Mandir Marg). Similarly, SO2 concentrations were 1.52 times greater, and NH3 levels were 1.69 times higher in regions with sparse vegetation. The biomass at Sanjay Van (112.57 tons) and the Ridge Forest (91.17 tons) significantly contributes to pollutant absorption, capturing considerable quantities of particulate matter (PM10 and PM2.5) and gaseous pollutants such as SO2, NOX, and NH3, while also mitigating the impacts of land use and land cover changes by serving as essential green lungs in an urbanized environment. These forests collectively offer an estimated pollutant absorption capacity of many tons annually, underscoring their vital role in alleviating air pollution and preserving natural equilibrium amid fast urbanization. In brief, the study highlights the essential function of forested regions in Delhi in mitigating air pollution and promoting environmental sustainability.
Keywords: Air Quality; Vegetation; LULC; Biomass; Megacity
How to cite: Rani, A. and Kumar, M.: Interlinking Land Use Land Cover, Forest Biomass, and Air Quality in the Megacity Delhi, India, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-669, https://doi.org/10.5194/egusphere-egu25-669, 2025.
Particulate matter (PM) is a Group 1 carcinogen and a significant environmental and public health concern globally. In South Korea, concerns over the reliability of conventional air quality monitoring stations, often installed at heights above 10 meters, have led to the deployment of low-cost air quality monitoring systems positioned closer to human breathing zones (2–3 meters). This study uses data from these systems, focusing on a case study in Anyang City, to analyze PM and PM-2.5 concentrations at roadside (bus stops) and riverside/park locations.
To ensure data reliability, the top 10% and bottom 10% of extreme values were excluded, and the remaining 80% of the dataset was analyzed. The results reveal significantly higher PM and PM-2.5 concentrations at roadside locations, emphasizing the need for mitigation strategies to address public health risks. This study also proposes policy recommendations to reduce PM exposure at roadside locations, demonstrating the applicability of such approaches to urban environments in South Korea and beyond.
Acknowledgments
This research was supported by Particulate Matter Management Specialized Graduate Program through the Korea Environmental Industry & Technology Institute (KEITI) funded by the Ministry of Environment (MOE).
How to cite: Park, M., Lim, H., and Yun, H.-Y.: Analysis of Roadside PM Concentrations Using Low-Cost Air Quality Monitoring Systems in South Korea, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2269, https://doi.org/10.5194/egusphere-egu25-2269, 2025.
South Korea's large-scale municipal waste incineration facilities have played a vital role since their introduction in the mid-1980s, particularly amidst rapid economic growth and urbanization. Initially designed to focus on waste volume reduction, these facilities soon became a source of concern for environmental and public health due to emissions of air pollutants, including dioxins, heavy metals, and particulate matter(PM). Particulate matter, especially PM10 and PM2.5, is a critical pollutant with severe health impacts, prompting the strengthening of emission regulations for incineration facilities.
This study investigates the evolution of air pollutant emission standards in South Korea, with a specific focus on particulate matter, through a chronological comparison with those of the European Union (EU). By analyzing major policy developments from the mid-1980s to the present, the study highlights South Korea's progress in adopting international standards and identifies key areas for improvement in future air quality policies.
The findings reveal that South Korea introduced dioxin emission standards (5ng-TEQ/m3) in the 1990s and significantly strengthened regulations in the 2000s under the influence of EU directives, reducing dioxin concentrations to 0.1ng-TEQ/m3. Standards for heavy metals and particulate matter emissions were established, alongside enhanced monitoring systems. Specifically, regulations for particulate matter (PM10 and PM2.5) have been increasingly stringent, with ongoing efforts to reduce emissions in areas surrounding incineration facilities. Meanwhile, the EU has implemented stringent standards through Best Available Techniques (BAT), carbon neutrality, and greenhouse gas reduction policies. South Korea is aligning with these trends by enhancing regulations to improve air quality and adopting localized strategies for particulate matter management.
This study confirms that South Korea's emission standards for incineration facilities have reached levels comparable to those of the EU. It emphasizes the need for tailored policies that account for local characteristics and technological constraints, particularly concerning particulate matter. These findings offer practical insights for reducing air pollution and achieving carbon neutrality both nationally and globally.
[Acknowledgment] This research was supported by Particulate Matter Management Specialized Graduate Program through the Korea Environmental Industry & Technology Institute(KEITI) funded by the Ministry of Environment(MOE)
How to cite: Kim, Y.-K., Kim, S.-H., Park, Y.-K., and Yun, H.-Y.: Evolution of Air Pollutant Emission Standards in South Korea’s Municipal Waste Incineration Facilities : A Focus on Particulate Matter, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3011, https://doi.org/10.5194/egusphere-egu25-3011, 2025.
Municipal solid waste incineration (MSWI) facilities play a critical role in managing increasing waste volumes driven by urbanization and population growth, serving an essential function in environmental protection. However, many existing facilities face challenges due to aging infrastructure, struggling to meet stricter air pollution regulations or process waste efficiently. In particular, discrepancies between the design specifications for waste calorific values or composition and the current heterogeneous characteristics of waste have led to reduced operational efficiency.
Building new incineration facilities is often constrained by economic and social barriers. Consequently, the revamping of existing facilities has emerged as a practical solution to enhance operational performance. Revamping typically involves the integration of advanced air pollution control technologies and process optimization, aiming to reduce air pollutant emissions while improving waste treatment efficiency.
This study examines the impact of revamping on air pollutant emissions at municipal solid waste incineration facilities in major cities across South Korea. Using data collected between 2011 and 2023, we analyzed changes in the concentrations of key air pollutants, including particulate matter (PM), nitrogen oxides (NOX), and hydrogen chloride (HCl), before and after refurbishments. Additionally, we evaluated the technological improvements implemented during the refurbishment process and their overall environmental benefits.
The analysis indicates that air pollutant concentrations decreased consistently after revamping, reflecting compliance with stricter environmental regulations and improved process efficiency. These findings provide critical insights for enhancing the operational efficiency of municipal solid waste incineration facilities and developing strategies to mitigate air pollution.
This study offers empirical evidence on the effectiveness of revamping in reducing air pollutant emissions, contributing to sustainable waste management practices and the development of informed environmental policies.
How to cite: Kim, S.-H., Kim, Y.-K., Park, Y.-K., and Yun, H.-Y.: Impact of Revamping on PM, NOX, and HCl Emissions in Municipal Solid Waste Incineration Facilities, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3361, https://doi.org/10.5194/egusphere-egu25-3361, 2025.
Accurate NOx emission estimates are required to better understand air pollution, investigate the effectiveness of emission restrictions, and develop effective emission control strategies. This study investigates and demonstrates the ability and uncertainty of the superposition column model in combination with the TROPOspheric Monitoring Instrument (TROPOMI) tropospheric NO2 column data to estimate city-scale NOx emissions and chemical lifetimes and their variabilities. Using the recently improved TROPOMI tropospheric NO2 column product (v2.4–2.6), we derive daily NOx emissions and chemical lifetimes over the city of Wuhan for 372 full-NO2-coverage days between May 2018 and December 2023, and validate the results with bottom-up emission inventories. We find insignificant weekly cycle of NOx emissions for Wuhan. We estimate a summer-to-winter emission ratio of 0.77, which is overestimated to some extent, though it is even higher provided by the bottom-up inventories. We calculate a steady decline of NOx emissions from 2019 to 2023 (except for the valley in 2020 caused by the COVID-19 lockdown), indicating the success of the emission control strategy. The superposition model method results in ~15% lower estimation of NOx emissions when the wind direction is from distinct upwind NO2 hotspots compared to other wind directions, indicating the need to improve the approach for cities that are not relatively isolated pollution hotspots. The method tends to underestimate NOx emissions and lifetimes when the wind speed is > 5－7 m s-1, the estimation for Wuhan is ~4% for the emissions and ~8% for the chemical lifetime. The results of this work nevertheless confirm the strength of the superposition column model in estimating urban NOx emissions with reasonable accuracy.
How to cite: Zhang, Q.: Estimating the variability of NOx emissions from the city of Wuhan with TROPOMI NO2 data during 2018 to 2023, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3368, https://doi.org/10.5194/egusphere-egu25-3368, 2025.
Air pollutant emissions originate from diverse and complex sources, making immediate reductions challenging. In South Korea, fossil fuel-based power plants are a major source of air pollution, particularly in the form of fine particulate matter (PM10, PM2.5), which significantly impacts air quality. To address this issue, the South Korean government has prioritized sustainable power supply and air quality improvement, focusing on expanding renewable energy and periodically revising the Basic Plan for Electricity Supply and Demand.
This study analyzes the changes in South Korea's power supply policies from 2014 to 2023 and examines the correlation between these policies and variations in air pollutant emissions during this period. Using government-provided data, we investigated changes in installed capacities and annual power generation by energy sources (nuclear, coal, LNG, renewables, and pumped storage), as well as regional air pollutant emissions, to assess the relationship between policy implementation and air quality improvements.
The results indicate a steady increase in renewable energy capacity and generation during the study period. This transition was accompanied by a decline in fossil fuel-based power generation and noticeable improvements in PM10 and PM2.5 concentrations in key regions.
This study highlights the potential for policy frameworks to reduce air pollutants through the expansion of renewable energy and the reduction of fossil fuel power generation. The findings serve as valuable references for future policy development aimed at improving air quality and achieving sustainable energy goals.
Keywords: Korea's electricity supply policy, fossil fuels, Renewable Energy, Air Pollutants, Fine Dust
[Acknowledgement]
"This research was supported by Particulate Matter Management Specialized Graduate Program through the Korea Environmental Industry & Technology Institute(KEITI) funded by the Ministry of Environment(MOE)“
How to cite: Park, Y.-K., Kim, S.-H., Kim, Y.-K., and Yun, H.-Y.: The impact of changes in Korea's electricity supply policy on air pollutants: Focusing on fine dust (PM10, PM2.5), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3375, https://doi.org/10.5194/egusphere-egu25-3375, 2025.
There are about 800 air pollution measurement networks (urban, roadside, rural and so on) in Korea, however air pollution in agricultural area are not measured. Thus it is necessary to identify air pollution in agricultural area which possess a relatively larger area than urban area. In this study, we established eight air pollution monitoring stations in agricultural area, and we conducted to comparatively analyze the air quality in agricultural and urban areas, during the periods for 1) the entire measurement data, 2) high PM episodes, and 3) non-high PM episodes. Considering the spatial distribution by region of Korea, air pollution monitoring stations were established in in agricultural area (Yeoju, Nonsan, Naju, Gimhae, Hongcheon, Danyang, Muan, and Sangju). PM-10 and PM-2.5 (ß-Ray attenuation method), SO2 (Ultraviolet fluorescent method), NOx and NH3 (Chemiluminescence method) were measured in real-time. Meteorological data such as temperature, humidity, wind direction, and wind speed were also measured. One-year measurement data from October 2023 to September 2024 were used, and high-PM episodes was defined as the period when the PM-2.5 concentration exceeds 24-hour air quality standard (35 μg/m3) of Korea and persists for 24 hours or more. The air quality data of urban area (near large cities: Suwon, Daejeon, Gwangju, Busan, Chuncheon, Cheongju, and Daegu) during the same period were used to compare to the air quality of agricultural area. During the entire period, the average concentrations in urban and agricultural areas were found to be similar for both particulate and gaseous compounds. During non- high PM episodes, agricultural and urban areas also showed similar levels. During high PM episodes (a total of 17 days), while the concentration of air pollutants in urban areas was obviously higher than those in agricultural area, but higher concentrations of particulate matter in agricultural areas were observed during certain periods (PM-10 in the morning and PM-2.5 in the afternoon). Gaseous concentrations in agricultural areas were found to be lower than those in urban area during the high PM episodes. In the future, it is necessary to analyze the characteristics of the variation of air pollution according to concentration changes by period (diurnal variation), solar intensity, and meteorological factors to clarify the differences in characteristics of air quality between agricultural and urban areas.
Acknowledgments
"This research was supported by Particulate Matter Management Specialized Graduate Program through the Korea Environmental Industry & Technology Institute(KEITI) funded by the Ministry of Environment(MOE)"
How to cite: Baek, J., Kim, J., and Joo, H.: Characteristics of air pollution of agricultural region in high PM episodes using air quality measurement data of megacity and agricultural sites from 2023 to 2024, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3404, https://doi.org/10.5194/egusphere-egu25-3404, 2025.
Despite ongoing efforts to mitigate air pollution, the effectiveness of policies often varies across regions due to the differing spatial scales of air pollution variability, which arise from the characteristics of pollution sources as well as geographical and meteorological factors. Understanding air pollution by isolating its components at different spatial scales is crucial for designing effective mitigation strategies. In this study, we propose a simple and intuitive method for the scalable spatial decomposition of spatiotemporal air pollution data into intercity-scale (tens of kilometers) and neighborhood-scale (several kilometers) components. To separate the intercity-scale from the neighborhood-scale component, we introduce a spatially varying ‘effective range’ for intercity-scale variability, based on the distance-decaying spatial autocorrelation of background-removed components. This effective range is influenced by emissions and geographical features. We applied this method to hourly PM2.5 data from 535 air quality monitoring stations (AQMSs) across South Korea for 2021–2022. Our findings reveal that the intercity-scale component contributes most significantly to PM2.5 concentrations in urbanized and industrial regions, such as the Seoul metropolitan area. In contrast, the neighborhood-scale component is more prominent near emission hotspots, such as industrial complexes. These results suggest that in regions where intercity-scale contributions are significant, effective air pollution mitigation strategies should prioritize intercity-scale regulations, which should be managed by the central government or through inter-local agreements, rather than focusing solely on local hotspots. This study provides a robust approach for quantifying both intercity-scale and neighborhood-scale air pollution contributions using ground-based AQMS data, facilitating the development of multi-spatial-scale strategies for air quality management.
How to cite: Seo, J., Lee, A., Park, D.-S. R., Youn, D., Kim, K. H., Park, C.-E., and Kim, J. Y.: A scalable spatial decomposition of air pollution data into intercity- and neighborhood-scale components: Application to PM2.5 in South Korea, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3478, https://doi.org/10.5194/egusphere-egu25-3478, 2025.
Urban areas with industry focused on oil refining activities face significant challenges in air quality management due to complex interactions between local emissions, meteorology, and regional transport. The oil extraction and petrochemical industry in Ploiesti, Romania, played a pivotal role into the development of the country since 19th century. This study investigates air pollution in Ploiesti area focusing on particulate matter (PM10, PM2.5) and key gaseous pollutants (NO, NO2, SO2, CO, VOCs, O3) using hourly data sets from the city AQ Monitoring Network, local meteorological observations, and boundary layer data from the ERA5 reanalysis. For the medium term in-depth analysis of air pollutants, data were examined for a four year period (01.01.2018 - 31.12.2021), while for the long-term analysis statistical analysis was performed on a 16 year period (01.01.2007 - 31.12.2023).
The specific questions were targeted to the intra-urban variability of air pollutants versus the identification of city areas with similar pollution pattern, in relation with the meteorological observations; identification of emission sources (by source categories anthropogenic/natural); temporal patterns (daily, weekly, seasonal, annual) and trend quantifications; a check on the causes of pollution episodes (with respect to local sources or transport from medium to distant sources).
- The anthropogenic activity in Ploiesti strongly affects atmospheric pollution levels on daily timescales (PM2.5/PM10 = 0.71). However, high-resolution measurements reveal the specificity of the surroundings of the monitoring stations.
- The general temporal pattern of major pollutants (except O3) with lower mass concentrations during warm periods and higher levels during colder seasons follows the general annual pattern of particulate emissions and is modulated by the meteorological seasonal variations (atmospheric mixing layer height).
- Particulate matter diurnal cycle indicates a peak during the morning rush hours (about 08:00-10:00) but no peak is clear in the afternoon/evening. This could possibly be related to the people social behavior in Ploiesti (i.e. social activities in parks and cafés, as well as various times for the job ending or shift works).
- We identified that the temporal patterns of O3 precursors (elevated VOCs despite reduced NOx during weekends) in this mid-size city lead to the so-called O3 weekday-weekend effect.
- From January 2018 to December 2021 a total of 36 pollution episodes were identified. It was found that local anthropogenic emissions coupled with boundary layer dynamics determined the occurrence of 42% of events, while the remaining (58%) are divided in almost equal parts between regional-scale events (27%) and events when dust advections coming from long distances (31%). The trends in the occurrence of pollution events are opposite: decreasing for local events and increasing for regional and LRT-determined events. This suggests that the large-scale air circulation patterns influenced by the higher energy in the atmosphere due to climate changes might alter the distribution and concentration of pollutants leading to fugitive air pollution events.
Acknowledgment: BM work was supported by the University of Bucharest, PhD research grant. Climate and meteorology data were extracted from https://cds.climate.copernicus.eu. Ground-level air pollution were extracted from Romanian National Air Quality Database, www.calitateaer.ro.
How to cite: Mihalache, B., Stefan, S., Colt, M., and Iorga, G.: Air Quality Challenges in a Petrochemical Urban Area: Signatures of Pollution Sources and Atmospheric Conditions, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3538, https://doi.org/10.5194/egusphere-egu25-3538, 2025.
Urban air pollution has consistently captured social and scientific attention due to its significant health and climatic impacts. Among the short-lived climate pollutants (SLCPs) targeted for reduction, black carbon (BC) stands out as a critical component. BC is a carbonaceous primary aerosol emitted from fossil fuel and biomass combustion, with an atmospheric lifetime of approximately five days.
This study involved a tower-based field campaign conducted in late spring in Incheon, a South Korean city adjacent to the Yellow Sea. Using a Single-Particle Soot Photometer (SP2; Droplet Measurement Technology, Boulder, CO, USA), refractory black carbon (rBC) properties, including concentrations, size distribution, and mixing state, were monitored for two weeks in May 2023 at the 303-meter-high Posco Tower-Songdo (PT).
The average ± standard deviation mass concentration of rBC was 0.2±0.1 μg m⁻³, with the mass median diameter (MMD) ranging from 133 to 227 nm. The highest mass concentration and the lowest MMD and Rshell/core (the ratio of shell-to-core diameter of rBC) were observed at 10 a.m. daily, indicating the arrival of freshly emitted local rBC particles. During pollution events characterized by elevated PM2.5 and O3 levels, Rshell/core increased to 1.4–2.0. The mass absorption cross-section (MAC) at 550 nm, estimated using the BHCOAT implementation of Mie theory with input of measured diameter and coating thickness of individual rBC particle, was enhanced by a factor of 1.7 (Eabs). Eabs was positively correlated with Ox (NO2+O3) and aerosol liquid water content (ALWC). Notably, the highest Eabs coincided with relative humidity (RH) exceeding 70% and ALWC reaching ~30 μg m⁻³. These results suggest that under high atmospheric oxidation states, coating formation on the rBC surface is enhanced, promoting the development of hygroscopic aerosols on BC particles in this urban area. More detailed analysis will be presented in the meeting.
This research was supported by the National Institute of Environ- mental Research (NIER) grants funded by the Korean government (NIER-2023-01-02-083) and the National Research Foun- dation of Korea (NRF) from the Ministry of Science and ICT (NRF- 2021R1C1C2011543 & RS-2023-00249553). We thank POSCO International for establishing and maintaining the site.
How to cite: Lim, S., Choi, Y., Lee, J., Gil, J., Cho, I. S., Kim, J. Y., and Kim, S.: Physical and optical properties of black carbon observed at the 303-meter-high Tower in the urban environment. , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7547, https://doi.org/10.5194/egusphere-egu25-7547, 2025.
The dense roadway network leads to high emissions of air pollutants per unit area, causing adverse environmental and health impacts. Various approaches to mitigating these air pollutions have been proposed, and one of the notable strategies is the use of vegetation for air purification. Vegetation is known to remove particulate and gaseous pollutants through various processes such as stomatal uptake, physical impaction and adsorption, and blocking by surfaces. Additionally, urban forests can contribute to improving air quality by reducing the urban heat island effect, potentially leading to slower chemical reactivities for secondary pollutants. However, air pollutant removal efficiencies of urban forests can be controlled by various factors such as vegetation type, leaf density, size, location, and seasonal and meteorological conditions. Moreover, the complex urban canopy and built environments can lead to spatiotemporally heterogeneous distributions of air pollutants, adding uncertainty to the assessment of the air purification capacity of urban forests.
In this study, we conducted high-resolution measurements with an air quality sensor network to evaluate air pollution mitigation capacity of an urban forest that sits in a densely road-networked area. Air quality sensors were installed inside and outside the urban forests to monitor concentrations of both gaseous (CO, NO, NO2, O3) and particulate pollutants (PM2.5 and number density) across different seasons. Here, we present the preliminary results of our findings obtained from four intensive measurement campaigns in different seasons.
Acknowledgments
This research was supported by Particulate Matter Management Specialized Graduate Program through the Korea Environmental Industry & Technology Institute(KEITI) funded by the Ministry of Environment(MOE)
Keywords: urban forest, air pollution mitigation, sensor network, field measurements, seasonal variations
How to cite: Ryu, J. W., Lee, J. Y., Park, Y. K., and Choi, W. S.: Air Pollution Mitigation Capacity of an Urban Forest in Densely Road-Networked Area: Findings from High-Resolution Sensor Monitoring, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7874, https://doi.org/10.5194/egusphere-egu25-7874, 2025.
Since the mid-1990s, ozone concentrations in the United States and Europe have steadily declined, whereas South Korea has experienced an increase to this day. Identifying the scientific causes of the ozone increase is essential, which necessitates an accurate understanding of the ozone budget equation. To quantify the ozone budget equation, it also requires measurements of dry deposition velocity. However, the dry deposition velocity is highly uncertain due to surface conditions and turbulence. Eddy covariance (EC) methods are used to measure ozone dry deposition velocities. However, the ozone flux studies using the EC method in Korea have not been conducted to our knowledge. In this study, ozone flux was directly measured using the EC method over approximately one month in the coastal megacities, Busan and Ulsan, in Korea, and here, we present the preliminary results of ozone fluxes and dry deposition velocities in the urban surface of coastal cities.
The preprocessing steps included despike, double rotation, time lag calculation, ozone concentration correction, and detrending. Diurnal variations in ozone concentrations showed a unimodal distribution in Ulsan, a typical pattern for photochemical products, whereas a bimodal distribution was observed in Busan due to a combination of local production and transport from upwind regions. The average daytime ozone flux was -3.1 nmol·m-2·s-1 in Busan and -1.3 nmol·m-2·s-1 in Ulsan. A more detailed discussion, including comparisons with previous studies, will be presented.
Acknowledgments
This work was funded by the Korea Meteorological Administration Research and Development Program under Grant RS-2024-00404042.
How to cite: Park, Y., Park, Y., Han, S., Ryu, J., and Choi, W.: Ozone Flux Measurements and Data Correction in Coastal Megacities in South Korea Busan and Ulsan, South Korea, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8920, https://doi.org/10.5194/egusphere-egu25-8920, 2025.
As climate change leads to more frequent and intense high-temperature events, elevated O3 episodes during periods of extreme heat have raised widespread concerns. This research investigates how O3's chemical response to elevated temperatures varies between urban and rural areas, particularly focusing on different emission conditions defined by ozone precursor regimes.
Simulations are carried out using the UKCA Box Model under idealised meteorological conditions. It runs chemistry-only zero-dimensional experiments in a single grid cell with chemistry relevant to the troposphere and the stratosphere. Using the UKCA Box Model, we simulated conditions typical of summer 2022 at three global hotspots (Yangtze River Delta, England and California), analysing scenarios where O3 precursors are either limited by VOCs in urban environments or by NOx in rural settings. The simulations were conducted across a temperature range of 20°C to 40°C while controlling for relevant factors such as photolysis, humidity, emissions, and initial concentrations. To determine the O3 precursors regimes, photochemical indicators such as NOy, H2O2/HNO3, H2O2/(O3+NO2) and HCHO/NO2 were employed.
The results suggest a significant diversity of O3’s chemical response to temperature in urban and rural areas. In urban areas characterised by VOC-limited conditions, O3 levels exhibited a nearly linear increase with rising temperatures. In contrast, rural areas, where O3 is typically NOx-limited, displayed a more complex relationship where negative correlations were found. Additionally, humidity emerged as a critical factor influencing these chemical dynamics. The mechanism by which O3 responds chemically to temperature changes will be examined by analysing O3 production and destruction budgets.
Our findings highlight that the O3 precursor regimes are crucial in evaluating the impact of temperature responses on ozone from a chemical perspective. This research contributes valuable insights into the mechanisms driving O3 responses to temperature changes during extreme heat events. It underscores the importance of considering urban and rural differences in ozone studies and can inform future emission control strategies aimed at mitigating ozone pollution under varying temperature conditions.
Keywords: Surface ozone; Temperature response; UKCA Box model; O3 precursor regimes; Urban and rural
How to cite: Su, C., Stevenson, D., and Bollasina, M.: Mechanisms of Surface Ozone's Chemical Response to High Temperatures: Differences Between Urban and Rural Areas, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12146, https://doi.org/10.5194/egusphere-egu25-12146, 2025.
Ozone pollution remains a significant environmental challenge in urban areas, with elevated ground-level ozone posing risks to public health, ecosystems, and climate stability. In Taichung City, Taiwan, rapid urbanization and industrial activities have contributed to deteriorating air quality, making it crucial to identify the key factors driving ozone formation for effective mitigation strategies. This study employs machine learning (ML) models, including Random Forest (RF) and eXtreme Gradient Boosting (XGBoost), to analyze ozone pollution in Taichung. Moreover, feature importance analysis is used to identify the key factors driving ozone variability, including volatile organic compounds (VOCs), nitrogen oxides (NO and NO₂), and meteorological variables such as temperature, humidity, wind speed, and solar radiation. The models were trained and tested on the hourly observational data collected from the Urban Air Pollution Research Station (UAPRS) in Taichung City from January to December 2023. To enhance the models’ accuracy, GridSearchCV is utilized to select optimal parameters and reduce the risk of overfitting. Preliminary results indicated that the number of predictors impacts ML performance—RF outperforms XGBoost when fewer predictors are used. However, with a more comprehensive set of predictors, XGBoost demonstrated superior performance, achieving determination coefficients of 0.945 and 0.886 for the training and test datasets, respectively. Feature importance analysis revealed that the top three contributors to ozone variability in 2023 were NO (44%), humidity (19%), and NO₂ (12%). For high ozone episodes, NO, humidity, and solar radiation were identified as the key drivers. By combining the predictive power of ensemble ML techniques with feature importance analysis, this study provides valuable insights into the interactions between chemical and meteorological factors driving ozone formation. The results highlight the relative significance of these factors in influencing ozone levels and provide actionable insights for air quality management in Taichung. Additionally, the study demonstrates the potential of ML models as powerful tools for advancing urban air quality research, with implications for policy interventions and future environmental studies. Future work will focus on refining the models to predict ozone episodes in real time and exploring their applicability to other rapidly urbanizing cities facing similar air quality challenges.
How to cite: Madriaga, J. and Chou, C.: Determining Key Factors Influencing Ozone Formation in Taichung City, Taiwan Using Machine Learning Models, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16129, https://doi.org/10.5194/egusphere-egu25-16129, 2025.
India, a rapidly developing economy with the world’s largest population, has set an ambitious target of achieving net-zero carbon emissions by 2070. Road transport, contributing to 12% of India’s energy-related CO2 emissions, plays a significant role in exacerbating urban air pollution. Given the country’s swift urbanization and the expansion of road transport to meet mobility demands, CO2 emissions from this sector could potentially double by 2050, risking the achievement of long-term climate objectives. Our study presents a comprehensive analysis of traffic emissions across India, leveraging high-resolution mobility data at street level, including vehicle count, types, and speeds for 100 different Indian cities. We use statistical and machine learning methods to improve data quality and extrapolate mobility data to all city traffic using city-level vehicle registration data. Here, we focus on understanding traffic and congestion patterns within and between cities, using additional data on population, urban structure, road network, public transport supply, and socio-economic variables. Finally, we simulate hourly CO2 and pollutant emissions at a street level using the COPERT model, which includes speed and vehicle-type dependent emission factors. With this study, we also aim to explore scenarios for reducing pollution, a critical issue for Indian metropolises. The findings from this study will provide valuable insights into the environmental impact of road traffic in India and inform strategies for pollution reduction. This work is part of the CHETNA project (City-wise High-resolution carbon Emissions Tracking and Nationwide Analysis), which leverages artificial intelligence and advanced datasets to deliver high-resolution, near real-time daily CO2 and air pollutant emissions data for over 100 Indian cities.
How to cite: Mittakola, R. T., Ciais, P., Barthelemy, M., Shi, Q., Bonnemaizon, X., Megel, N., Phuleria, H., and Zhou, C.: CHETNA-Traffic: Street level CO2 and pollutant emission analysis from road traffic in Indian cities, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12873, https://doi.org/10.5194/egusphere-egu25-12873, 2025.
The influence of greenhouse gas (GHG) emissions on climate change represents a critical global issue. This study investigates the dynamics of three key greenhouse gases-carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O)-from 1990 to 2021 across Romania's development regions, including a focused analysis of emissions in the urban area of Bucharest and the surrounding Ilfov region. Data for the analysis were sourced from the EDGAR database (Emissions Database for Global Atmospheric Research), with emissions categorized by major activity sectors, including transport, waste management, biomass burning, manufacturing and construction, fossil fuel usage, and agricultural practices such as rice cultivation and livestock. This categorization allows for a comprehensive examination of sectoral contributions to overall GHG emissions.
To capture the evolution of GHG emissions, the study applies advanced statistical tools. Temporal variations in the GHG time series were analyzed using Change Point Analysis, identifying both major and minor change points, all statistically significant at the 99% confidence level. Monotonic annual trends in emissions were further assessed using the non-parametric Mann-Kendall test in combination with Sen’s method, providing a nuanced understanding of long-term emission patterns.
The findings reveal substantial regional disparities in CO2, CH4, and N2O emissions, with distinct periods of increase or reduction. These variations correlate with factors such as industrial development, shifts in agricultural practices, and the implementation of environmental regulations. By analyzing both national and regional trends, the study sheds light on the sectoral drivers of emissions and their long-term behavior.
This research enhances understanding of Romania's GHG emission trends over the past three decades, emphasizing the influence of regional variations and sectoral contributions to shaping the country's overall emissions profile.
How to cite: Scarlat, A., Tudor, A., and Iorga, G.: Understanding GHG Emission Trends in Romania: Sectoral and Regional Perspectives, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15353, https://doi.org/10.5194/egusphere-egu25-15353, 2025.
Methane (CH₄) is a significant greenhouse gas, playing a critical role in air quality and climate change. In situ measurements of CH4 in Romania are very scarce. This study presents results from a ground-level measurement campaign of atmospheric methane concentrations in Bucharest, Romania. One of the main objectives was to map the spatial distribution of methane concentrations. The campaign was performed using a mobile laboratory, between August 6 and 27, 2024 and covered over 1500 km throughout Bucharest. Methane concentrations were measured using a Sniffer4D TDLAS analyzer with a resolution of 1 ppm. Bucharest was divided into three zones: northwest, northeast, and south. A designated route was established for each zone and crossed a total of 4 times: once at night and during the daytime over 3 different days. This data collection approach ensured statistical consistency and captured temporal and spatial variations in methane concentrations.
The results are presented as grid maps to highlight significant diurnal and spatial variations more clearly, indicating anthropogenic contributions from traffic and industrial activities during the day. At night, concentrations exhibited a more uniform distribution, suggesting relatively constant background emissions and a reduced influence of local activities.
This study contributes to understanding methane levels and sources in the urban environment of Bucharest and provides a critical reference for future monitoring efforts. The findings emphasize the importance of continuous methane concentration surveillance to support air quality management strategies and climate change mitigation.
How to cite: Tudor, A., Scarlat, A., and Iorga, G.: Measurement of Methane Concentrations at Ground Level in Bucharest during August 2024 Using a Mobile Laboratory, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15891, https://doi.org/10.5194/egusphere-egu25-15891, 2025.
India's power sector plays a pivotal role in the country's greenhouse gas (GHG) mitigation efforts, contributing 45% of national CO2 emissions, with coal-fired power plants responsible for 72% of CO2 emissions from fuel combustion in 2022. The sector's dependence on coal and rising electricity demand pose significant challenges to achieving India's targets of reducing GDP emission intensity by 45% and transitioning to 50% non-fossil fuel installed capacity by 2030. Monitoring emissions at the city level is especially critical, as urban areas concentrate electricity demand, with the residential and industrial sectors accounting for 25.77% and 41.16% of total consumption, respectively. High-resolution, city-specific data is essential for identifying emission hotspots, optimizing renewable energy deployment, and prioritizing energy efficiency improvements.
To address the current dataset gaps in India, we conducted a high spatial-temporal resolution analysis of CO2 and air pollutant emissions for 100 Indian cities. This analysis integrates diverse open-source datasets, including power plant locations, capacities, and fuel types from the Global Energy Monitor (GEM) and OpenStreetMap (OSM); transmission grid data from OSM; industrial factory data from Indian statistical databases and OSM; emission factors from the Central Electricity Authority (CEA) of India; power generation and outage data from the Indian National Power Portal (NPP); and gridded population and land-use data from the Global Human Settlement Layer and Copernicus Global Land Cover Layers.
The power generation time series was first completed using a machine learning model to address missing data. Then, we developed a grid gravity-based power distribution model to analyze power consumption and emissions. This model evaluates the relative "attractiveness" of power consumption for each grid by incorporating large industrial factories, grid population, and cropland areas. An optimization algorithm was employed to allocate power generation, constrained by transmission grid capacity and minimizing the losses. Parameters were fine-tuned using regional monthly electricity consumption data, establishing a robust framework for spatial emission mapping while excluding electricity imports and exports. Using this gridded power distribution model, we generated high-resolution maps of both CO2 and air pollutant emissions for each city, offering valuable insights into their spatial distribution across urban areas.
This work, part of the CHETNA project (City-wise High-resolution Carbon Emissions Tracking and Nationwide Analysis), leverages artificial intelligence and advanced datasets to deliver near real-time, high-resolution emissions data for over 100 Indian cities.
How to cite: Sharma, A., Zhou, C., Ciais, P., Sarkar, A., Jana, A., and Phuleria, H.: CHETNA-Power Sector: High-Resolution Mapping of Power Sector Emissions in Indian Cities: Bridging Data Gaps for Effective GHG Mitigation and Urban Energy Planning, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15602, https://doi.org/10.5194/egusphere-egu25-15602, 2025.
In the face of the complex composition of atmospheric pollutants, our laboratory has developed a Thermo Desorption Unit (TD) for capturing and concentrating trace-level volatile organic compounds (VOCs) in air samples. Because of the humid climate, we added a Dewater Unit (DW) before the TD to remove excess moisture from air samples while retaining polar and non-polar species to keep sample integrity. This setup has been successfully utilized in the past by connecting the DW-TD units with gas chromatography (GC) equipped with flame ionization detection (FID) and mass spectrometry (MS). While the data quality from GC-FID was extremely stable and robust, the drift and, thus, instability in MS is significant by comparison. In this research, we attempted to use electron capture detection (ECD) to test the stability of the DW-TD units by exploiting ECD’s high sensitivity, stability, and ease of operation. Another prominent advantage of ECD is that it only needs high-purity nitrogen gas as both the carrier and make-up gas. We exploited ECD's highly sensitive and selective properties to measure trace-level atmospheric chlorofluorocarbons (CFCs) and halocarbons to demonstrate the performance of the self-built DW-TD apparatuses. Since CFCs have extremely long atmospheric lifetimes and are well-mixed in the atmosphere due to the Montreal Protocol banning them from most applications, they exhibit certain background mixing levels during a relatively short period of time, e.g., weeks, with variability smaller than most GC’s analytical precisions. We then utilized this property to assess the stability of our homemade instrument. During the one-month continuous online analysis of DW-TD/GC-ECD at an industrial park known for semiconductor and electronics manufacturing, the mole fractions of CFC-12 was found to be 485.19±0.22 ppt (parts per trillion), with RSD (Relative Standard Deviations) = 0.06%. Although CFC-11, CFC-113, and CCl4 have long been phased out, abrupt rises in signal were still detected, suggesting emissions still existed in this industrial complex. By filtering out data with relatively stable mole fractions in between events, the RSD for CFC-11, CFC-113, and CCl4 was found to be 0.12%, 0.36%, and 0.30%, respectively. To further validate the high-value events observed in the industrial park, we conducted an additional one-month continuous online analysis of DW-TD/GC-ECD at a university campus in Taipei as a contrast of environment. This comparative study yielded stable background mole fractions for CFC-12, CFC-11, CFC-113, and CCl4, with RSD of 0.05%, 0.10%, 0.32%, and 0.29%, respectively. These results will be compared with the variability from AGAGE's online data using Medusa/GC-MS and the offline data of NOAA by GC-MS. The occurrence of the high-value events during the month-long measurements can be traced back to emission sources by utilizing backward trajectories to the potential sources for further investigation.
How to cite: Ou-Yang, C.-F., Wang, J.-L., Hsu, C.-Y., Wang, C.-H., Chang, C.-C., and Lin, N.-H.: Continuous Monitoring of Atmospheric Halocarbons with a Dewater-Thermo Desorption Unit and GC-ECD: Insights from Industrial and Urban Environments, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5706, https://doi.org/10.5194/egusphere-egu25-5706, 2025.
The research takes place within the EU Horizon 2020 program, and the ICOS Cities project, that aims to support cities in formulating climate action plans, through the delivery of data on fossil fuel emissions from urban areas. The EC station is located on the roof of a building in Sassari (Sardinia, Italy N 40° 43' 0.4836 E 8° 34' 32.88, 254 m asl). Measurement height is fixed at 23 m from ground. Instruments include a Gill HS-50 sonic anemometer and a closed-path LI-7200 gas analyzer, for H2O and CO2 fluxes. The Eddy Covariance approach for environmental studies is a powerful technique that is used in many applications in the study of urban ecosystems and fluxes. The post-processing phase of data consisted in energy and carbon budgets calculations, together with the flux footprint land mapping, at the ICOS urban EC site ‘ITSas’, which were realized using the software ‘Tovi’ (from LI-COR®). The daily variation of the energy components revealed that the heat storage reaches high values in the morning, while drops out later in the evening. The correction of the regression model revealed the action of GHGs in delaying the daily heat flux peak. The seasonal variations of energy terms revealed that the latent heat flux, the evapotranspiration and the water vapor flux varied at the same rate and correlated positively with high air temperatures and strong radiation values. The energy balance residuals seemed to correlate well with energy availability and heat storage, while they kept a fairly constant variation, with only little deviations falling along the main wind directions (NW and SW winds). The daily C cycle was made up of two main daily maximum peaks, associated with traffic peaks and urban emissions. The footprint-based land mapping of flux contributions demonstrated the urban typology of the flux data since most of the contributions (about 60%) to footprint climatology came from urban areas, while a minor input was delegated to vegetated surfaces.
How to cite: Piroddu, F. and Sirca, C.: Short-term energy and carbon balance calculation and footprint-based land cover classification at an urban site, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19788, https://doi.org/10.5194/egusphere-egu25-19788, 2025.
Posters virtual: Wed, 30 Apr, 14:00–15:45 | vPoster spot 5
EGU25-5323 | ECS | Posters virtual | VPS3
Assessing black carbon dynamics in Iran: the role of urban growth and land use changes in long-term trends (1980–2023)Wed, 30 Apr, 14:00–15:45 (CEST) vPoster spot 5 | vP5.21
Black carbon (BC), the primary light-absorbing aerosol, has significant implications for atmospheric heating and climate change, with far-reaching effects on regional air quality and public health. In Iran, BC concentrations, primarily resulting from combustion processes such as industrial emissions, vehicular exhaust, and biomass burning, constitute a significant air quality challenge, particularly in urban regions with high levels of anthropogenic activity. However, there is a lack of studies on the long-term trends of BC in Iran, particularly regarding the effects of urban growth and land use changes on air quality and human health. This study systematically analyzes trends in BC concentrations from 1980 to 2023, both on a national and regional scales, using high-resolution data from the Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2). The analysis includes temporal and spatial variations to evaluate the impact of anthropogenic and natural factors on BC levels over this period. A substantial increase in BC concentrations was observed from 1980 to 2023, followed by a decline after 2010. Regional analysis revealed higher BC levels in western Iran, driven by concentrated anthropogenic and industrial activities, compared to the sparsely populated, desert-dominated eastern regions, characterized by arid landscapes. Seasonal variations in BC concentrations were observed nationwide, with peak levels occurring in Tehran and Ahvaz during the winter. Trend analysis across various land use and land cover (LULC) types indicated that urban and agricultural expansion were the primarily drivers of increasing BC concentrations. Positive correlations were observed between the aforementioned factors and aerosol emissions, while water and grassland coverage were associated with reduced emissions in most regions. These findings underscore the necessity of expanding natural land use, such as forest coverage, and promoting sustainable urbanization as strategies to mitigate BC emissions.
How to cite: Shaheen, A., Yousefi, R., Wang, F., Kaveh-Firouz, A., and Ge, Q.: Assessing black carbon dynamics in Iran: the role of urban growth and land use changes in long-term trends (1980–2023), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5323, https://doi.org/10.5194/egusphere-egu25-5323, 2025.
EGU25-9328 | Posters virtual | VPS3
Using Sentinel 5P satellite and vehicle flow data to map NO2 air pollution near highways in the Metropolitan Region of Campinas, BrazilWed, 30 Apr, 14:00–15:45 (CEST) | vP5.22
Nitrogen dioxide (NO2), an atmospheric pollutant produced by fossil fuel combustion in vehicles and industrial processes, is harmful to human health, worsening respiratory and cardiovascular diseases. The main effects of NO2 pollution on human health are respiratory infections, airway inflammation, asthma, and low birth weight, among others. Vehicle traffic in cities is one of the main sources of NO2, affecting the health of the population living near highways. The highest NO2 concentration occurs at distances between 200 and 500 meters from high-traffic highways. The study area, the metropolitan region of Campinas (MRC), Brazil, is a technological, industrial and economic hub with 3.3 million inhabitants and busy transport corridors that connect the southeast and central-west regions of the country. It is composed of 20 municipalities and is located in São Paulo state, the most developed and populated Brazilian state. The aims of this work are to map atmospheric NO2 pollution and estimate NO2 concentrations near the highways in the MRC using average daily vehicle flow (DVF) and NO2 concentrations estimated from satellite images. Data on the tropospheric vertical column of nitrogen dioxide (in mol/cm2) values from 32 daily images from the Sentinel 5P satellite TROPOMI spectrometer that were collected from April 15 to May 20, 2024, were used. During that period, there was no rain, and the sky remained clear and cloudless. The images were processed to produce NO2 median images during the study period. The NO2 pollution map was produced by the spline interpolation algorithm method. To estimate the concentration of NO2 near the MRC highways, a road map was used, and a 500 m buffer was drawn around the highways. The NO2 pollution map was combined with the buffer map, and the median NO2 concentration within the 500 m buffer around the highways was estimated. Pearson regression analysis was performed between the average DVF and the NO2 concentration. The results revealed a positive and significant correlation (r=0.692; p= 0.004) between the DVF and NO2 concentration near the highways estimated from satellite data. The highest NO2 concentrations were observed near highways SP-083 (1.5591 mol/cm2; 45,000 vehicles/day), SP-330 (1.521 mol/cm2; 38,815 vehicles/day), and SP-075 (1.485 mol/cm2; 37,813 vehicles/day). The results of this study can be used in epidemiological research to identify neighborhoods and populations that live near high NO2 concentration highways and are exposed to respiratory and cardiovascular disease risks. In the next step of this research, the NO2 concentration values estimated from Sentinel 5P images in mol/cm2 units will be converted to µg/m3 units using data from ground-based measurement stations located in the MRC. In the future, this methodology can be used to produce highway NO2 pollution maps for areas in which ground measurement station data are unavailable.
How to cite: Ferreira, M.: Using Sentinel 5P satellite and vehicle flow data to map NO2 air pollution near highways in the Metropolitan Region of Campinas, Brazil, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9328, https://doi.org/10.5194/egusphere-egu25-9328, 2025.
EGU25-13295 | Posters virtual | VPS3
Multiple lines of evidence help identify the sources and formation mechanisms of Nitrogen and Carbon in Particulate Matter sampled in the historical center of Naples (Italy)Wed, 30 Apr, 14:00–15:45 (CEST) | vP5.23
The aim of this study is to investigate Particulate Matter (PM) sources and mechanisms of formations over the city of Naples (Italy) and their seasonal and day-to-day variations.
We have sampled fine particles with diameter < 2.5 μm (PM2.5) and < 10 μm (PM10) daily on pre-cleaned (700 °C for 2 h) quartz filters, during the months of May and November 2016-January 2017, on top of the historical building complex in Largo San Marcellino, Naples. We have measured the concentrations of total N/C together with their isotopic composition (δ15N and δ13C). We have also measured the concentration of major ions and interpreted the results with data of gaseous compounds, as well as consideration of the meteorology, using data and state of the art models of atmospheric circulation (Hysplit). Our point was to show that the uncertainty associated with quantification of sources contribution with an apportionment model decreases when the model is constrained with information derived from different methods.
Seasonal differences: the results show that the concentrations of total PM10/PM2.5, N/C measured in autumn are more variable than those measured in spring. This is related to a different wind regime, whereby in spring air masses mostly originated from West and South (the “clean” Mediterranean sea), whereas in autumn the wind blew air from North (over the highly urbanized and “dirty” European continent). This interpretation is supported by the concentration of major ions showing more scattered values in autumn for species typically originating from land (K+, NH4+, NO3-), with high values on the 9th and the 26-27th of December and the 2nd of January 2017. However, neither the monthly mean δ15N and δ13C, nor the daily values corresponding to the spikes show significant changes, suggesting that the isotopic composition of total N/C has limited power in identifying changes of mean monthly sources or for the spikes.
Day-to-day variations: a significant change of the main species measured is found around the middle of May. This event is associated with a change in weather pattern going from a typical land-sea breeze wind regime (typically causing poor air circulation and stagnation of air masses) to an intense synoptic with winds originating mostly from South/South-West (the sea). Correspondingly, there is a peak in the concentration of major ions originating mostly from land (NO3-, SO42-, Ca2+, C2O42-, K+) towards the end of the land-sea breeze regime (9-11th May), followed (10-15th May) by an increase of the concentration of major ions originating mostly from the sea (Na+, Mg2+, Cl-). The entire period (9-14th) is characterized by a concurrent variation of total N, C, δ15N and δ13C. While the changes of δ15N are caused mainly by isotope fractionations, associated with the dissociation of NH4Cl producing NH3 and HCl, the changes of δ13C are caused mostly by a change of the source of total C, associated with carbonate (CO32-) apportion.
We conclude that the concentrations and isotopic compositions of N/C in PM are useful tools only when coupled with other tools like the analysis of the meteorology and the concentration of major ions.
How to cite: Rubino, M., Sirignano, C., Chianese, E., Hernández-Ceballos, M. Á., Angyal, A., Marzaioli, F., Di Rosa, D., Caso, G., and Riccio, A.: Multiple lines of evidence help identify the sources and formation mechanisms of Nitrogen and Carbon in Particulate Matter sampled in the historical center of Naples (Italy), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13295, https://doi.org/10.5194/egusphere-egu25-13295, 2025.
EGU25-14892 | Posters virtual | VPS3
Assessing the Impact of Urban Development and Land Use Changes on Dhaka's Hazardous Air QualityWed, 30 Apr, 14:00–15:45 (CEST) | vP5.24
Dhaka, the capital of Bangladesh, is currently experiencing critically alarming levels of air pollution, with its Air Quality Index (AQI) exceeding 200, indicating hazardous conditions. This study investigates the factors contributing to Dhaka's deteriorating air quality over the past two decades by integrating AQI data with Land Use and Land Cover (LULC) analyses. Particular attention is given to the impacts of major development projects, including the Metro Rail, Elevated Expressway, and International Airport Terminal 3, on the city’s air quality. Comparative assessments of AQI before and after the completion of these projects reveal a significant worsening of air quality, attributed to increased construction activity and subsequent urbanization. The rapid expansion of impervious surfaces is identified as another critical factor exacerbating the AQI. The findings emphasize the urgent need for sustainable urban planning and air quality management strategies to mitigate the adverse effects of development on public health and the environment in Dhaka.
How to cite: Akhter, J. and Rayhan, M.: Assessing the Impact of Urban Development and Land Use Changes on Dhaka's Hazardous Air Quality, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14892, https://doi.org/10.5194/egusphere-egu25-14892, 2025.
