In spite of the important advances in the science of aerosols and air quality, important scientific and environmental challenges remain unsolved, especially those related to source apportionment of the specific components of atmospheric particulate matter (PM), atmospheric processes influencing aerosols, and the associated climate and health impacts. Moreover, ultrafine particle (UFP) studies are growing, they are still insufficient and much needed. Furthermore, there is a clear lack of information and guidance on UFP measurement, especially in smaller ranges. In addition, it is widely recognised that exposure to PM negatively impacts human health (WHO, 2021). In 2016, ambient air pollution accounted for almost seven million premature deaths per year (WHO, 2016), as derived from the aggravation of cardiovascular and respiratory diseases and cancers. Several studies have also shown that UFP can deeply penetrate the respiratory system, thus causing respiratory and cardiovascular diseases in humans (Cassee et al., 2019).

The 2017-2019 hourly particle number size distributions (PNSD) from 26 sites in Europe and 1 in the US were evaluated focusing on 16 urban background (UB) and 6 traffic (TR) sites in the framework of RI-URBANS project. The main objective was to describe the phenomenology of urban ultrafine particles in Europe with a significant air quality focus. The varying lower size detection limits made it difficult to compare PN concentrations (PNC), particularly PN_10-25, from different cities. PNCs follow a TR>UB>Suburban (SUB) order. PNC and Black Carbon (BC) progressively increase from Northern Europe to Southern Europe and from Western to Eastern Europe. At the UB sites, typical traffic rush hour PNC peaks are evident, many also showing midday-morning PNC peaks anti-correlated with BC. These peaks result from increased PN_10-25, suggesting significant PNC contributions from nucleation, fumigation and shipping.

Site types to be identified by daily and seasonal PNC and BC patterns are: (i) PNC mainly driven by traffic emissions, with marked correlations with BC on different time scales; (ii) marked midday/morning PNC peaks and a seasonal anti-correlation with PNC/BC; (iii) both traffic peaks and midday peaks without marked seasonal patterns. Groups (ii) and (iii) included cities with high insolation. PNC, especially PN_25-800, was positively correlated with BC, NO₂, CO and PM for several sites. The variable correlation of PNSD with different urban pollutants demonstrates that these do not reflect the variability of UFP in urban environments. Specific monitoring of PNSD is needed if nanoparticles and their associated health impacts are to be assessed. Implementation of the CEN-ACTRIS recommendations for PNSD measurements would provide comparable measurements, and measurements of <10 nm PNC are needed for full evaluation of the health effects of this size fraction.

WHO, 2021. Ambient (outdoor) air pollution. 22 September 2021, https://www.who.int/news-room/fact-sheets/detail/ambient-(outdoor)-air-quality-and-health

WHO, 2016. Ambient air pollution: a global assessment of exposure and burden of disease. World Health Organization., 121 pp, https://apps.who.int/iris/handle/10665/250141

Cassee F., Morawska L., Peters A. (Eds)., 2019. The White Paper on Ambient Ultrafine Particles: evidence for policy makers. ‘Thinking outside the box’ Team, October 2019, 23 pp, https://efca.net/files/WHITE%20PAPER-UFP%20evidence%20for%20policy%20makers%20(25%20OCT).pdf

How to cite: Trechera, P., Garcia-Marlès, M., Alaustey, A., and Querol, X. and the RI-URBANS collaborators: Phenomenology of ultrafine particle concentrations and size distribution across urban Europe, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16079, https://doi.org/10.5194/egusphere-egu23-16079, 2023.

Source apportionment by receptor modeling is used to determine the contributions of different emission sources to ambient levels of bulk Particulate Matter (PM), Black Carbon (BC), the Oxidative Potential (OP), among others. Source apportionment can be based e.g. on statistical approaches such as PMF (Positive Matrix Factorization), CMB (Chemical Mass Balance) or different properties such as absorption of particles originating from different sources (Aethalometer model). Often source apportionment analysis is time consuming and based on subjective decisions of an experienced user. 

As a part of RI-URBANs project an automated Near real-time source apportionment (NRT-SA) of carbonaceous aerosols is piloted on 13 sites across Europe during 2023. In all cities real-time measurements of aerosol chemical composition are conducted with the Aerosol Chemical Speciation monitor (ACSM: organics, sulphate, ammonium, nitrate and chloride) and Aethalometer (AE33: Black carbon (BC) and Brown carbon (BrC)). The measurement sites include two traffic, nine urban, one regional and one background site. NRT-SA based on PMF (Gang et al., 2022) will be used to separate the different primary (e.g. traffic, cooking, biomass combustion, coffee roastery, coal combustion) and secondary sources of organics from the ACSM data and aethalometer model (Sandradewi et al., 2008) to separate the BClf (from liquid fuel combustion) and BrCwb (from solid fuel combustion).  Prior information about the sources of organics like number of factors and reference mass spectra of primary sources from previously conducted source apportionment studies (Chen et al., 2022) in pilot cities have been utilized. The results of the NRT-SA have been validated by comparison to the offline calculated source apportionment results. Based on these extensive measurements, the chemical composition and origins of the fine aerosol fraction will further be discussed regarding the different environments of the investigated pilot sites throughout the European continent.

The measurements and NRT-SA are conducted as a part of RI-URBANS’ project (Grant #101036245) , that aims to demonstrate how service tools from atmospheric research infrastructures can be adapted and enhanced in air quality monitoring networks in an interoperable and sustainable way.

Chen, G., Canonaco, F., Slowik, J. G., Daellenbach, K. R., Tobler, A., Petit, J.-E., Favez, O., Stavroulas, I., Mihalopoulos, N., Gerasopoulos, E., El Haddad, I., Baltensperger, U., and Prévôt, A. S. H.: Real-Time Source Apportionment of Organic Aerosols in Three European Cities, Environ. Sci. Technol., https://doi.org/10.1021/acs.est.2c02509, 2022.

Sandradewi, J., Prévôt, A. S. H., Szidat, S., Perron, N., Alfarra, M. R., Lanz, V. A., Weingartner, E., and Baltensperger, U.: Using Aerosol Light Absorption Measurements for the Quantitative Determination of Wood Burning and Traffic Emission Contributions to Particulate Matter, Environ. Sci. Technol., 42, 3316–3323, https://doi.org/10.1021/es702253m, 2008.

How to cite: Timonen, H. and Petit, J.-E. and the RI-URBAN NRT-SA pilot sites: Near real-time source apportionment of carbonaceous aerosols in 13 sites across Europe, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6361, https://doi.org/10.5194/egusphere-egu23-6361, 2023.

Road vehicles are typically the main source of ultrafine particles (UFP, Dp <100nm) in urban areas. A high spatio-temporal variation of UFP is found even in similar types of sites e.g roadside. We analyzed long-term particle datasets (number count or PNC, and number size distribution or NSD), and Black Carbon (BC) from two roadside sites in Europe, London Marylebone Road (LMR) in the UK, and Mäkelänkatu Street in Finland (Mäk). We evaluated 11 years (2010-2021) and 5 years (2015-2020) of particle data derived from SMPS (16.55-604.3nm) and DMPS (6-798.42nm) for LMR and Mäk, respectively.

The initial analysis shows that at LMR, PNC reduced by 64% over 2010-2021 (~20000#/cm3 to less than 10000#/cm3), while the BC declined by 86% (~9 µg/m3 to 1.2 µg/m3). Meanwhile, PNC at Mäk remained constant at ~15000#/cm3 during 2015-2018, then decreased to ~10000#/cm3 in 2020. BC decreased by 58% (1.3 to 0.6 µg/m3) during 2015-2020.

Using the same size range (16.5–604nm) from both sites during 2015-2019, the TNC at LMR were 1.4-1.6 times higher than that at Mäk, and 2.4-3.9 higher for BC. Particles less than 16.39 nm at Mäk account for approximately 50% of TNC over the full measured size range. When particles are classified into nucleation, Aitken and accumulation ranges, the nucleation mode contributes most at Mäk (68% of the full size range), while the Aitken mode is dominant at LMR (51%).

A downward trend was observed during 2015-2019 at both sites. However, the pollutant concentration reduced faster at LMR, apart from the Nucleation mode which slightly increased. BC showed the fastest decline (9.6%/yr and 14.2%/yr in Mäk and LMR, respectively). Further investigation using wind direction data shows that in LMR, the most significant reduction of pollutants mainly occurred from the southerly wind sector, which is associated with the emissions from road vehicles on the adjacent road, suggesting that interventions applied to the road vehicle fleet have effectively decreased pollutants concentration. In Mäk, pollutant concentrations decreased much faster on winds from the NW and SE sectors, corresponding to the alignment of the road. The Nucleation mode proportion has increased since 2014 from the southern and westerly sectors at LMR, and NW and west at the Mäk due to the rapid reduction of the Aitken or Accumulation mode, rather than an increase of Nucleation mode concentration, which has changed little.

The annual mean PNSD shows a bimodal pattern at both sites. A change was observed from a minor peak at ~60-70nm that gradually disappeared over the period seen more clearly at LMR.

We interpret the rapid reduction in BC and PNC as being largely attributable to the progressive uptake of diesel particle filters as Euro 5 and 6 standard vehicles have entered the fleet since 2011.  However, the greatest impact has been upon the BC and Aitken and Accumulation mode particles, with little change seen in the Nucleation mode particles, which are comprised largely of condensed lubricating oil, and form in the cooling exhaust after passage through the particle filter (Harrison et al., 2015).

How to cite: Harrison, R. M., Damayanti, S., Pope, F., and Niemi, J. V.: Ultrafine Particles and Black Carbon in Two Roadside Sites in Europe: Long-term Data Analysis, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5970, https://doi.org/10.5194/egusphere-egu23-5970, 2023.

Air pollution remains an outstanding issue due to its hazardous health and environmental impacts. For France, the number of premature deaths is estimated around 40,000 people per year mostly due to particulate matter (PM), while associated economical coast is estimated at 100 billion euros per year. In this context, action plans are implemented in order to reduce the PM mass concentrations in ambient air. However, considering mass concentration only can lead to an obvious bias: for instance, at equal mass concentrations, the exposure to fresh sea salts is assumed to have the same toxicity as soot particles containing toxic compounds. Therefore, accurate abatment measures need thorough knowledge on the PM chemical composition, which can then be used within receptor and/or chemical transport models to apportion their emission sources and secondary formation processes. The PM chemistry is also relevant in terms of adverse health effects. Based on the PM chemical species ability to generate oxidative stress through reactive oxygen species, the oxidative potential (OP) indicates the consumption of antioxidant per particles mass. This proxy estimates the imbalance between oxidants and antioxidants, responsible for inflammatory processes and chronic diseases. As a result, the aerosol’s oxidative potential has emerged as a promising indicator of PM adverse health impacts.

To better evaluate PM health effects, we set-up a strategy to implement OP in the state-of-art air quality model CHIMERE and to simulate particles OP over the whole French territory for the year 2013 and 2014. To do so, a measurement derived and source specific OP determined by Positive Matrix Factorization (PMF) receptor modelling approach is combined with particle sources apportionment in CHIMERE using a tagging method called Particulate Source Apportionment Technology (PSAT). Alternatively, a source specific OP is obtained by linear regression of observed OP and simulated sources. Both methods are used to simulate OP over France for the years 2013 and 2014, and to determine the most affected areas and responsible sources.

How to cite: Vida, M., Foret, G., Siour, G., Weber, S., Favez, O., Jaffrezo, J.-L., Uzu, G., and Beekmann, M.: Oxidative potential modelling of PM10 : an indicator of aerosol health risk studied in France with the CHIMERE model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5896, https://doi.org/10.5194/egusphere-egu23-5896, 2023.

Air pollution is the fourth cause of premature mortality (HEI, 2020) and in Europe, more than 0.3 million premature deaths are due to air pollution (EEA, 2021). In urban environments, people are exposed to a complex mixture of air pollutants with a large spatial variability. However, highly spatially resolved measurement data on air pollutants is lacking. These fine-grained data is needed to correctly assess personal exposure to air pollution for epidemiolocal studies and to support air quality management scenarios.

Within RI-URBANS different innovative approaches to get insights into novel air quality parameters, source contributions, exposure to air pollution and associated health effects will be developed and tested. One of the approaches relies on mobile measurements with citizens to derive spatial air pollution maps. Mobile measurements can contribute to understand spatial variability of short-living constituents of air pollution from a diversity of pollution sources.

The monitoring campaign is performed with volunteers, who are all employees of DCMR or the city of Rotterdam. They are asked to measure during their daily bicycle commutes. Before the measurement campaign, a training session was organized for the volunteers. Measurements were performed in winter (November 2022 – February 2023) and will be repeated in spring 2023.

Measurements are based on the airQmap approach; more information on the approach and previous studies can be found on https://www.airQmap.com. Measurements of Black Carbon (BC) are performed using a microaethalometer (microAeth®, AE51, AethLabs) and a GPS.  BC is measured at 1s temporal resolution and a flow rate of 150 mL min^-1. To reduce the noise in BC measurements, the ONA (Optimized Noise-reduction Averaging, Hagler et al., 2011) algorithm was used with an attenuation threshold of 0.05. The geo-tagged measurements were aggregated (trimmed mean) and attributed to fixed points 20 m apart from each other along the cycling route.

The dataset will be used to test different data processing techniques (a.o. temporal aggregation, background correction approaches) to construct representative BC maps.  The collected spatiotemporal BC measurements will be analysed to identify main sources of BC in the area. The pilot study will result in guidance on best practices for mobile air quality monitoring involving citizens.

This paper will present the results of the winter campaign.

____________________

EEA, 2021. HI of Air Pollution in EU

Hagler, G.S., Yelverton, T.L., Vedantham, R., Hansen, A.D., Turner, J.R., 2011. Postprocessing method to reduce noise while preserving high time resolution in Aethalometer real-time black carbon data. Aerosol Air Qual. Res. 11, 539-546.

HEI, 2020. State of Global Air

How to cite: Van Poppel, M., Hofman, J., Peters, J., Hoek, G., Kerckhoffs, J., Willers, S., and Özdemir, E.: Mobile Air Quality monitoring with daily commuters in Rotterdam, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7684, https://doi.org/10.5194/egusphere-egu23-7684, 2023.

Accelerating growth of urban populations has become a driving force of human development, especially in developing countries. Crowded cities are centres of creativity and economic progress; however, extreme weather conditions, flooding, water quality, air pollution and other hazards create substantial vulnerability and challenges in the urban environment.

The third United Nations Conference on Housing and Sustainable Urban Development (HABITAT III) in October 2016 adopted the New Urban Agenda (United Nations, 2017), which brings into focus urban resilience, climate and environment sustainability, and disaster risk management. Following the event at the United Nations Economic and Social Council, efforts are required from WMO to consolidate its input to the revision of the New Urban Agenda (NUA) and support urban related activities in a comprehensive manner. Urban development is now a cornerstone of the United Nations 2030 Sustainable Development Goals. It has its own sustainable development goal (SDG 11): Make cities inclusive, safe, resilient and sustainable.

To support implementation of urban activities the WMO inter-programme Urban Expert Team under the Commission for Atmospheric Sciences and Commission for Basic Systems (2018) supported by a dedicated team of urban focal points in the Secretariat developed the Guidance on Integrated Urban Hydro-Meteorological, Climate and Environmental Services (IUS). The needs for integrated urban services (IUS) include information for short-term preparedness (e.g. hazard response and early warning systems), longer-term planning (e.g. adaptation and mitigation to climate change) and support for day-to-day operations (e.g. water resources). The aim is to build urban systems and services that meet the special needs of cities through a combination of dense observation networks, high-resolution forecasts, multi-hazard early warning systems, disaster management plans and climate services. This approach gives cities the tools they need to reduce emissions, build thriving and resilient communities and implement the UN Sustainable Development Goals.

WMO with its urban cross-cutting approach is involved in joint UN urban activities for development of and implementation of NUA and SDG 11 with a number of external partners, e.g. UN-Habitat, WHO, ITU, GEO, International Association for Urban Climate (IAUC), etc. The IUS methodology is integrated into more broad Multi-Agency UN system U4SSC: United for Smart Sustainable Cities and its key performance indicators (KPIs) for smart sustainable cities.

This presentation provides an overview of the current efforts towards future IUSs on urbanization under climate change undertaken by the WMO and UN international initiatives for building climate smart, sustainable and resilient cities.

How to cite: Baklanov, A. and the WMO IUS and U4SSC Expert Teams: Toward Climate Smart and Sustainable cities: Integrated Urban System Methodology and Key Performance Indicators, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9843, https://doi.org/10.5194/egusphere-egu23-9843, 2023.

Ultrafine particles (UFP, particles sized <100 nm)  significantly impact health and the environment. However, their study is still a challenge, and a specific regulation is required. The measurement of UFP-PSD (Ultrafine Particles – Particle Size Distribution) and its application in air quality assessment is a significant goal of RI-URBANS. 2017-2019 hourly UFP-PSD data from 26 sites in urban Europe and one site in the US have been compiled and evaluated according to the instrumental and methodological approaches implemented; the comparison of urban concentrations across Europe; the identification of similarities and major differences; and the evaluation of relationships with other pollutants, such as BC, PM_x and gaseous pollutants (SO₂, NO_x, O₃, CO), and with meteorological parameters. The results of this study have recently been published by Trechera et al. (2023).

To continue the study by Trechera et al. (2023) in UFP, source apportionment for the previous 27 urban sites is being analyzed with an extended period from 2009 to 2019. This study aims to identify and quantify sources contributing to UFP-PSD using Positive Matrix Factorization (PMF), comparing the differences between the sites. PMF is a widely used multivariate factor analysis tool to identify the source types that may be contributing to the sample using measured source profile information. According to a recent review on UFP source apportionment based on UFP-PSD measurements by Hopke et al. (2022), the typically reported sources of UFP include nucleation, several traffic sources (fresh to aged), domestic and residential heating, regional secondary inorganic aerosols (i.e., regional nitrate and sulfate), particles associated with oxidants as represented by O₃ (i.e., regional secondary organic and inorganic aerosols) and other sources (such as biomass burning, urban background sources, industrial emissions, diverse sources, dust and unknown sources). In the near future we expect to publish the results of source apportionment for the datasets mentioned above.

Cassee F., Morawska L., Peters A. (Eds)., 2019. The White Paper on Ambient Ultrafine Particles: evidence for policy makers. ‘Thinking outside the box’ Team, October 2019, 23 pp, https://efca.net/files/WHITE%20PAPER-UFP%20evidence%20for%20policy%20makers%20(25%20OCT).pdf

Hopke, P.K., Feng, Y., Dai, Q., 2022. Source apportionment of particle number concentrations: A global review. Sci. Total Environ. 819, 153104. https://doi.org/10.1016/j.scitotenv.2022.153104

Paatero, P., and U. Tapper, 1994. Positive matrix factorization: A non-negative factor model with optimal utilization of error estimates of data values., Environmetrics, 5, 111–126, doi:10.1002/env.3170050203

Rivas, I., Vicens, L., Basagaña, X., Tobías, A., Katsouyanni, K., Walton, H., Hüglin, C., Alastuey, A., Kulmala, M., Harrison, R.M., Pekkanen, J., Querol, X., Sunyer, J., Kelly, F.J., 2021. Associations between sources of particle number and mortality in four European cities. Environ. Int. 155. https://doi.org/10.1016/j.envint.2021.106662

Trechera, P., Garcia-Marlès, M., Liu, X., et al., 2023. Phenomenology of ultrafine particle concentrations and size distribution across urban Europe. Environ. Int. Accepted (Publication in progress).

How to cite: Garcia Marlès, M., Alastuey, A., Querol, X., and Hopke, P. K.: Source apportionment of ultrafine particle size distributions in urban Europe, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15235, https://doi.org/10.5194/egusphere-egu23-15235, 2023.

Ultrafine particles (UFPs with a diameter less than 100 nm) are tiny and respirable particles. Because of their small sizes, UFPs can penetrate cells and tissue, accumulate in lungs, and cause health effects. Although UFPs are currently not regulated in the same way as mass concentrations for large particles such as PM_2.5 and PM₁₀, the 2021 WHO global air quality guidelines have highlighted the pressing issue of UFPs with a good practice statement. Particle number concentrations (PNC) are the most common measure for UFPs with tiny mass. UFPs often dominate the total ambient PNC in urban environments. There is a strong need to quantify the PNC in the ambient air through measurements and modelling. This study simulates the dispersion of particle number concentrations in the West Midlands (WM), UK using the local scale ADMS-Urban model, which is an advanced quasi-Gaussian plume dispersion modelling system. ADMS-Urban implements a physics-based approach to represent the characteristics of the atmospheric boundary layer. It can represent a variety of source types (such as road and grid emissions) occurring in urban environments and requires a range of input data. Grid sources of PNC for SNAP (Selected Nomenclature for Air Pollution) sectors across the WM were obtained from TNO. Road sources were derived based on the local traffic activity maps (from Transport for West Midlands and Birmingham City Council) and PNC emission factors available in the literature. Meteorological data for Birmingham Airport was used to drive the dispersion. Particle number was used as a passive scalar, with no inclusion of aerosol microphysics. Background data from the rural Chilbolton air quality site was downloaded from Defra UK-Air website. Advanced canyon and urban canopy parameters were derived based on the building data and road network shapefiles using ArcGIS tools. The model was run on the University of Birmingham’s BlueBEAR HPC. Model evaluation was conducted by comparing the modelled (from a receptor run) and measured data at the Birmingham Air Quality Supersite. Overall, the model performed well. Based on the modelling output from a contour run, street scale resolution maps for annual PNC were generated, which could be linked to local population and health data for potential epidemiological studies.        

How to cite: Zhong, J. and Harrison, R.: Modelling the dispersion of particle number concentrations in the West Midlands, UK, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3202, https://doi.org/10.5194/egusphere-egu23-3202, 2023.

Source apportionment (SA) techniques allow matching the measured ambient pollutants with their potential source origin. Hence, they are a powerful tool for assessing air pollution mitigation strategies. The Positive Matrix factorization (PMF) model is one of the most widely used SA approaches, and its multi-time resolution add-on (MTR-PMF), which enables mixing different instrument data in their original time resolution, is the focus of this study.

Co-located one-year measurements of non-refractory submicronic particulate matter (NR-PM₁), black carbon (BC) and metals, obtained respectively, by a Q-ACSM (Aerodyne Research Inc.), an Aethalometer (Aerosol d.o.o.) and offline fine PM samples collected on quartz-fibre filters, were combined in a single PMF in two different resolutions (30 minutes for the NR-PM₁ and BC, and 24h every 4 days for the offline samples). The multi-time resolution PMF (MTR-PMF) was run varying both the time resolution (averaging the dataset) and the uncertainty weightings of both datasets in order to assess the impact of these variations on the model output. The resolution assessment revealed that averaging the high-resolution data was disadvantageous in terms of model residuals and environmental feasibility. Regarding uncertainty weightings, overweighting the uncertainties of the 24-h dataset dividing them by two provided the most optimal scaled residuals adjustment.

The MTR-PMF was run with the optimised time resolution and uncertainty weightings retrieving eight PM₁ sources: ammonium sulphate (AS) + heavy oil combustion (24%), ammonium nitrate (AN) and ammonium chloride (15%), fresh SOA (15%), traffic (14%), biomass burning (11%), aged SOA + mineral dust (8%), urban mix (7%) and cooking-like organic aerosol + industry (6%). The MTR-PMF technique allowed the identification of two more sources respect a dataset containing the same species at a 24h time resolution (base case) and four more respect to the conventional offline and PMF, proving that the combination of both high and low time resolution data through MTR-PMF is significantly beneficial for SA. This is especially true for those sources which have been disentangled with respect to the conventional and base case PMFs.

How to cite: Via, M., Yus-Díez, J., Canonaco, F., Petit, J.-E., Hopke, P., Reche, C., Pandolfi, M., Ivančič, M., Rigler, M., Querol, X., Alastuey, A., and Minguillón, M. C.: Towards a better understanding of fine PM sources: online and offline datasets combination in a single PMF, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13319, https://doi.org/10.5194/egusphere-egu23-13319, 2023.

Traffic remains a key source of PM_2.5 and PM₁₀ in urban environments and contributes to PM in the form of exhaust related particles and non-exhaust related particles (Grigoratos and Martini, 2014). It is expected that due to traffic fleet changes and stricter exhaust emissions controls the relative contribution of non-exhaust traffic related particles, such as brake and tyre wear, but also of other urban sources, such as wood smoke and cooking aerosols, will become more significant. To understand these changes in relative contribution, sources need to be identified and quantified in different urban environments. High time resolution measurements of PM composition, such as those from the Xact instrument allow for a more accurate identification and quantification of sources as the high time resolution reflects short term changes due to emission and atmospheric processing. Here we present a comparison of positive matrix factorisation (PMF) analysis at an urban background site, a roadside site, and of the resulting roadside increment. This work aims to improve the identification of non-exhaust traffic emissions.

Hourly PM₁₀ measurements of a range of elements were made with the Xact at an urban background and a roadside location in London between Aug. 2019 and Aug. 2020. Source contributions were determined with PMF using the Source Finder software (SoFi) (Canonaco et al., 2013). PMF is carried out in steps: i) initial factor profiles are established for the roadside and background location; ii) factor profiles of the roadside increment are established; iii) the roadside increment profiles are used to improve the factor identification at the roadside and background and results evaluated in comparison with the initial profiles. Black carbon, PM₁₀, PM_2.5 mass concentrations and NO_x were used to verify PMF results.

PMF analysis was initially performed on roadside increment data. The profiles identified were then used to improve the combined PMF for both sites. In this novel approach, the advantages of utilizing the roadside increment are explored to investigate the identification of factors which can be used to quantify traffic impact more accurately at roadside and background locations.

This work was supported by the Natural Environment Research Council (NERC) under grant NE/T001909/2.

References

Canonaco et al, (2013). AMT 6, 3649-2013

• Grigoratos and G. Martini, 2014; Non-exhaust traffic-related emissions - Brake and tyre wear PM. Report no. Report EUR 26648 EN

How to cite: Tremper, A. H., Hicks, W. A., Priestman, M., Chen, G., Manousakas, M.-I., Prevot, A. S. H., and Green, D.: Source apportionment analysis at an urban background site, a roadside site, and the resulting roadside increment in London, UK, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-944, https://doi.org/10.5194/egusphere-egu23-944, 2023.

Improving air quality in urban areas is essential since 97% of European cities exceed the annual PM_2.5 value of the WHO guidelines (10 μg/m³). Therefore, in order to mitigate adverse health outcomes of air pollutants, it is extremely important to understand the long-term chemical compositions and organic aerosol (OA) sources in the largest European city, London. North Kensington (50.52° N, 0.21° W) is an urban background monitoring station located in a residential area of west London. A Quadrupole Aerosol Chemical Speciation Monitors, Q-ACSM (Aerodyne Research Inc., MA, USA), was deployed to continuously monitor submicron non-refractory particulate matter (NR-PM₁) from March 2013 until May 2018. The knowledge of OA sources is crucial not only because OA is the main constituent of particulate matter (PM), but also because different sources of OA are known to have different toxicities. Positive matrix factorization (PMF) on the OA matrix of ACSM data remains the most common technique to conduct source apportionment (SA) analyses. By following the standardized protocol developed by Chen et al. (2022), we have retrieved high-quality SA results in these 5 years using the most advanced SA techniques (i.e., rolling PMF (Parworth et al., 2015), the ME-2 solver (Paatero, 1999), and bootstrap resampling (Erfon, 1979)). Additionally, back trajectory analysis has helped identify the geographical origin of OA sources. A variety of auxiliary measurements have been conducted on the same site, which has been used to validate our SA results. Overall, this study provided a rare opportunity to investigate the long-term trends of PM composition and OA sources in a European megacity and provided valuable information for policymakers to understand the effects of mitigation strategies and design more efficient policies.

How to cite: Chen, G., Priestman, M., Font, A., Tremper, A., and Green, D.: Five-year PM Chemical Composition and Organic Aerosol (OA) Sources in a European Megacity, London, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-943, https://doi.org/10.5194/egusphere-egu23-943, 2023.

Air quality is the leading environmental factor for public health globally. Source apportionment of air pollution is a key aspect of understand and ameliorating air quality problems. However, its use has been rather limited primarily due to the high cost that comes with instrument deployment. The emergence of low-cost sensors provides a new tool for assessing air quality and assigning sources within outdoor and indoor environments.

We have developed low-cost source apportionment techniques that can be used with single and multiple point measurements in outdoor environments. We have demonstrated its applicability for understanding sources at urban background sites, as well as assessing the pollution footprint of several industrial activities. The application of our methodology greatly improved our understanding of major pollution sources, by pinpointing and quantifying their effect in the surrounding area, at only a fraction of the cost of regulatory approaches. The information of the effect of any polluting activity, as well as the conditions that enhance or reduce it, is crucial information for the remediation of air quality problems.

Continuing our successful work on apportioning pollution in outdoor environments, we now focus on the indoor environment. Indoor environments can be particularly important because of the duration of time spent within them, and hence the potential for high exposure. These environments can be highly heterogenous, with different sources and concentrations varying from room to room.  In our latest study, four low-cost sensors were deployed both inside and outside a typical family house close to Birmingham, UK. While the average PM concentrations in all rooms were within the latest World Health Organisation (WHO) guidelines, great variation was found on the PM concentrations among the rooms. Using the source apportionment methods the effect of the indoor and outdoor sources of particles was quantified. Up to 95% of the PM₁ was found to be from outdoor sources in all the rooms. This effect was reduced as particle size increased, though the outdoor sources were still contributing more than 65% of the PM_2.5 and up to 50% of the PM₁₀, depending on the room studied. These measurements allowed for the estimation of the average exposure of a work-at-home day. The implications on increased working from home will be discussed with respect to total exposure.

This study reveals new insights into the how different indoor and outdoor sources combine within households to contribute to total air pollution exposures.  It highlights that total exposure is a function of the geographic situation of the household, the physical infrastructure of the household including filtration and appliances. This methodology will also be tested and used within the RI-URBANS pilot project to assess the air quality of the area surrounding the University of Birmingham. The presentation will finish with a roadmap on how low cost source apportionment can help to improve indoor and outdoor air quality.

How to cite: Bousiotis, D., Alconcel, L.-N., Harrison, R. M., Beddows, D. C. S., and Pope, F.: Monitoring and apportioning sources of outdoor and indoor air quality using low-cost particulate matter sensors., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1011, https://doi.org/10.5194/egusphere-egu23-1011, 2023.

Cycling activity has benefits for both health and the environment. However, there are also health risks for cyclists due to their direct exposure to air pollutants from on-road vehicle emissions. Higher breathing rates and the proximity to the emission sources while cycling could also increase the health risk for the cyclist. Airborne particle exposure, particularly ultrafine particles (UFP, Dp smaller than 100nm) has been considered to have a detrimental effect on human health. The assessment of exposure to UFP is essential for UFP management, which is also included in a Good Practice Statement by WHO.

In this study, we estimated the personal exposure to UFP using mobile measurements (bicycling) between 17 October and 18 November 2022, in Birmingham, UK. The measurements were conducted at three (3) different times (morning, afternoon, and evening) on weekdays and weekends. A miniature particle counter (DiscMini) that measures particle number concentration (PNC), and average diameter with 1s resolution was used. Besides, a similar instrument was also deployed at an urban background site which was passed during the bicycling measurements.

A total of 34 trips (~1.5 hours, and ~12km per trip) were completed. Overall, the result revealed that the exposure to PNC varied substantially both spatially and temporally. Relatively higher PNC exposure was found during the morning (MWD) and evening (EWD) trips on weekdays, while the lowest was during morning weekend (MWE) trips. The mean concentration of MWD, EWD, and MWE was 16664#/cm3, 15255 #/cm3, and 4004#/cm3, respectively. A moderate level was observed during the afternoon on weekdays and weekend (AWD, AWE), and evening weekend (EWE) ranging from 8321-9676 #/cm3.

During weekend trips, the average geometric mean diameter was observed to be larger (43-52.3 nm), suggesting a greater relative background contribution during the weekend, especially in the morning. All the average PNC from bicycling measurements were 2.3-2.6x higher than that measured at the background site due to more emission sources being present along the route. However, PNC and average diameter measured at the background site revealed similar behaviour to the mobile measurement with high concentrations during morning and evening on  weekdays.

Spatial analysis indicated some hotspots that were at intersections and traffic lights. A construction area and shopping park area also exhibited high concentrations, especially during afternoon and evening weekend trips. These results may be of value in support of strategies to mitigate UFP personal exposure, particularly for cyclists who commute daily in this area and on similar routes elsewhere.

How to cite: Damayanti, S., Harrison, R. M., and Pope, F.: Personal exposure to UFP from bicycling measurements in Birmingham, UK, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5899, https://doi.org/10.5194/egusphere-egu23-5899, 2023.