RI-URBANS (Research Infrastructures Services Reinforcing Air Quality (AQ) Monitoring Capacities in European Urban & Industrial AreaS) is a research project supported by the European Commission under the Horizon 2020 – Research and Innovation Framework Programme, H2020-GD-2020 (grant 10103624) that connects the atmospheric observation expertise from Aerosols, Clouds and Trace gases Research InfraStructure (ACTRIS), with the urban air quality observation capacities of the regulatory air quality monitoring networks. It is specifically connected to the new European AQ Directive (NAQD) 2024/2881/CE published on 20 November 2024.
RI-URBANS focuses on the infrastructures to measure emerging pollutants for AQ and the well-being of the citizens. Particularly, service tools (STs) for novel pollutants, such as ultrafine particles (UFP), UFP-number size distribution (PNSD), black carbon (BC), as well as ammonia (NH3) and numerous volatile organic compounds (VOCs), and measurements of tracers of potential toxicity of PM (oxidative potential (OP) of particulate matter PM), are provided for urban supersites in order to support scientific understanding of their effects on health and the environment. The NAQD has been introduced in Art. 10 the measurements of these new pollutants in a new network of AQ supersites.
In essence, these STs are guidance documents that RI-URBANS have reviewed, in some cases developed, tested, and recommended for advanced AQ assessment in urban areas. Two of these STs focus on the source apportionment of PM based on receptor modelling with offline and online PM speciation (ST10) and on UFP-PNSD, BC, VOCs and OP of PM. The electronic files of the guidance documents of ST10 and ST11 can be downloaded at https://riurbans.eu/project/#service-tools
PMF is used in most cases (PM, VOCs, UFP-PNSD), and it is coupled with multi-linear regression for (OP), and aethalometer source apportionment for BC.
Here we present the results from the application of these source apportionment tools to datasets of PM speciation, UFP-PNSD, VOCs, BC and OP data available in urban Europe. The results have a high interest for AQ policy (identifying major sources contributing to air quality impairment, but also identifying measures needed and evaluating the impact of policy actions), evaluation of the health outcomes, and identifying the source contributions with higher toxicity potential.
The creation of the European network of AQ supersites by the European NAQD will provide very valuable datasets for source apportionment receptor modelling of a number of pollutants, however the required PM speciation in the NAQD is quite limited for obtaining detailed source apportionment results. It is important to intensify the source apportionment studies to support the need of more tracers of PM to be included in the supersites in the next review of the Directive, which will take place in five years.
How to cite: Querol, X., Amato, F., Salameh, T., Uzu, G., Daellenbach, K., Via, M., Savadkooh, M., Garcia-Marlès, M., Petäjä, T., Timonen, H., Pandolfi, M., Alastuey, A., de la Rosa, J., Liu, X., and Hopke, P.: RI-URBANS: Source apportionment of different pollutants in urban Europe, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4111, https://doi.org/10.5194/egusphere-egu25-4111, 2025.
The Positive Matrix Factorisation (PMF) algorithm (Paatero and Tapper, 1994) has been the most widely used receptor model for a long time and has only recently been challenged with new methodologies. The novel Bayesian auto-correlated matrix factorisation method (BAMF, Rusanen et al. 2024) integrates an auto-correlation term emulating real-world pollutant sources time evolution has produced higher accuracy compared to PMF. However, both PMF and BAMF struggle to provide well-separated profiles manifested as mixed time series contributions.
A sparsity-handling algorithm named horseshoe (HS) regularisation has beenapplied to BAMF in order to improve profile determination. The horseshoe application pushes some parameters to be close to zero and others to have large values (Piironen and Vehtari, 2017). The BAMF+HS method reduces the dimensionality of the problem by suppressing the non-significant species for each profile. The resulting profiles are expected to be less noisy and better representing the nature of the atmospheric pollution sources. Figure 1 shows the effect of BAMF+HS (in orange) compared to the regular BAMF (in blue) and the PMF (in green) on a toy dataset, consisting on an oversimplified dataset with very sparse profiles. The BAMF+HS results show contributions pushed to zero, making the profiles closer to the truth (in black) with respect to the less sparse results of BAMF and PMF. This same comparison has been carried out on realistic synthetic datasets to show the effectiveness of sparsity introduction into source apportionment.
Figure 1. Comparison to truth of source apportionment profiles resulting from three different receptor models for a toy dataset.
Acknowledgement: This work is supported by the European Union's Horizon Europe research and innovation programme under the Marie Skłodowska-Curie Postdoctoral Fellowship Programme, SMASH co-funded under the grant agreement No. 101081355. The SMASH project is co-funded by the Republic of Slovenia and the European Union from the European Regional Development Fund. K.R.D. acknowledges support by SNSF Ambizione grant PZPGP2_201992.
References
Piironen, J., & Vehtari, A. (2017). Sparsity information and regularization in the horseshoe and other shrinkage priors.
Rusanen, A., Björklund, A., Manousakas, M., Jiang, J., Kulmala, M. T., Puolamäki, K., & Daellenbach, K. R. (2023). Atmospheric Measurement Techniques Discussions, 2023, 1-28.
Paatero, P., & Tapper, U. (1994). Environmetrics, 5(2), 111-126.
How to cite: Via, M., Demšar, J., Hao, Y., Močnik, G., and Daellenbach, K. R.: Sparsity introduction in Bayesian Autocorrelation Matrix factorization for organic aerosol source apportionment, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9207, https://doi.org/10.5194/egusphere-egu25-9207, 2025.
Source apportionment is a cornerstone in environmental sciences, providing essential insights into the contributions of diverse pollution sources to ambient air quality. Understanding these contributions is vital for designing effective regulatory and mitigation strategies. Among the most commonly employed techniques for source apportionment, Positive Matrix Factorization (PMF) has proven to be a powerful receptor model. PMF decomposes complex environmental datasets into source profiles and their corresponding contributions while accommodating measurement uncertainties Hopke [2016], Paatero and Tapper [1994]. Despite its advantages, PMF is not without limitations, such as rotational ambiguity, reliance on accurate input uncertainties, and potential biases in source attribution Reff et al. [2007].
In recent years, the emergence of artificial intelligence (AI)-based methodologies has opened new horizons for source apportionment. These approaches often build upon classical methods like PMF, aiming to enhance the interpretability and performance of source apportionment models Geng et al. [2020]. Machine learning techniques, including deep learning, leverage large datasets to identify patterns and relationships that may elude traditional approaches. Additionally, hybrid methods integrating classical models with AI frameworks have demonstrated potential for improved accuracy and robustness Wang et al. [2021].
A critical question remains unanswered: do the intrinsic limitations of PMF, such as biases and errors, propagate into these AI-driven alternatives? For instance, AI methods are theoretically capable of overcoming such challenges, their reliance on data-driven training may introduce new sources of bias or amplify existing uncertainties if the underlying data or assumptions are flawed.
Moreover, how do these biases compare to those in other receptor models, such as the Chemical Mass Balance (CMB) model ?
This work aims to look on the extent to which the errors and biases inherent to PMF are inherited by alternative methods, including AI-based approaches and other receptor models. By critically assessing these methods, we seek to provide a comprehensive understanding of the strengths and limitations of emerging tools for source apportionment and their potential to overcome traditional challenges.
Xiaoyu Geng, Lin Zhang, and Yu Wang. Ai-based approaches for source apportionment in atmospheric science: A review and perspective. Atmospheric Environment, 223: 117276, 2020. doi: 10.1016/j.atmosenv.2020.117276.
Philip K. Hopke. Review of receptor modeling methods for source apportionment. Journal of the Air Waste Management Association, 66(3):237–259, 2016. doi: 10.1080/10962247.2016.1140693.
Pentti Paatero and Unto Tapper. Positive matrix factorization: A non-negative factor model with optimal utilization of error estimates of data values. Environmetrics, 5(2): 111–126, 1994. doi: 10.1002/env.3170050203.
Adam Reff, Sierra I. Eberly, and Prakash V. Bhave. Receptor modeling of ambient particulate matter data using positive matrix factorization: Review of existing meth- ods. Journal of the Air Waste Management Association, 57(2):146–154, 2007. doi: 10.1080/10473289.2007.10465319.
Feifei Wang, Hui Zhang, and Xiaodong Li. Hybrid receptor models and machine learning for air pollution source apportionment: Advances and future directions. Environmental Research, 195:110827, 2021. doi: 10.1016/j.envres.2021.110827.
John G. Watson. Chemical mass balance receptor model methodology for assessing the sources of fine and coarse particulate matter. Environmental Software, 5(2):38–49, 1990. doi: 10.1016/0266-9838(90)90008-
How to cite: Gherras, M., Petit, J.-E., Gressent, A., Marchand, C., Colette, A., Gros, V., and Favez, O.: Traditional and new Approaches in Source Apportionment: A Critical Evaluation of Bias and Limitations, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15834, https://doi.org/10.5194/egusphere-egu25-15834, 2025.
Understanding urban air pollution at fine scales is essential for pinpointing emission sources that disproportionately impact vulnerable communities. Traditional emission inventories often suffer from insufficient spatial granularity and lack observational grounding, thus hampering effective source-specific interventions.
Here, we introduce a novel application of receptor-oriented models (RMs) for the hyperlocal source apportionment of black carbon (BC). By integrating a rich dataset from both dense mobile monitoring and temporally detailed fixed-site measurements into a Bayesian inversion framework using the WRF-STILT model, we quantify BC contributions in the community of West Oakland, CA, USA from diverse urban sources including on-road vehicles (notably diesel trucks), locomotives, port cargo-handling equipment, and maritime vessels, with a high spatial resolution of 150 meters (~0.02 km2). We reveal a wide variety of uninventoried neighborhood-scale emissions sources that substantially impact this overburdened community.
Our method employs a data-driven spatiotemporal model that combines both mobile and fixed-site data within a factor analysis framework, providing robust observational constraints for Bayesian inference. The robustness of our method is particularly notable given the uncertainties in prior emissions inventories. Moreover, we demonstrate that with only 10 strategically placed stationary sensors within a 15 km2 area, supplemented by time-averaged mobile measurements, reliable source apportionment can be achieved.
This study advances the methodology of RMs by providing a scalable and adaptable approach for incorporating hyperlocal measurements, providing critical insights into the effectiveness of these models in real-world urban scenarios. Future applications of the method would support observationally constrained strategies for fine-scale urban emissions tracking and community-centered air quality improvements.
How to cite: Manchanda, C., Harley, R., Cohen, R., Alvarez, R., Thompson, T., Harris, M., Marshall, J., Turner, A., and Apte, J.: Connecting Urban Black Carbon Emissions and Measured Concentrations: A Fusion of Hyperlocal Monitoring and Bayesian Techniques, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7229, https://doi.org/10.5194/egusphere-egu25-7229, 2025.
Volatile Organic Compounds (VOCs), from anthropogenic or biogenic sources, participate in the formation of ozone and particulate matter (PM), impacting health, climate, and ecosystems. They can also be directly harmful, in particular anthropogenic VOCs such as vinyl chloride monomer (chloroethene), benzene or 1-3-butadiene, that are carcinogenic.1 In the southern region of France, the French thresholds for ozone (240 µg m³ hourly) and for PM2.5 (10 µg m³ annual) are regularly exceeded. A 2018-study showed an exceedance for O3 on 144 days and for PM on 28 days during a 14 month period.2 These exceedances contributed to over 1,800 hospitalizations in the region from 2010 to 2019.3
To better understand the drivers behind these exceedances and anticipate the population’s exposure, a detailed study in an impacted industrial area is conducted. The study area is located in the south of France, around a brackish lake called the Étang de Berre. This area includes three major industrial complexes (Berre l'Étang, Martigues Lavéra and Port-de-Bouc), where major metallurgical and petrochemical industries are located, known to be high emitters of VOCs. Notably, the region has a high industrial density, with 56 SEVESO high-threshold sites and the Fos/Berre zone presenting the second largest site concentration in France. Further, a petrochemical plant in Lavéra is particularly noteworthy, as it produces 25% of France's chlorine and 40% of its vinyl chloride monomer (VCM).4 The region's VOC dynamics are influenced by the orography and specific meteorological conditions, particularly a regime of land and sea breezes, that advects relatively clean marine air during the nighttime. In this complex region, three observation stations, run by the local air quality organization AtmoSud are monitoring continuously the concentrations of 68 VOCs since January 2022. The stations are installed in strategic locations close to the industrial complexes and in a residential area in proximity of school.
The observations reveal distinct behaviors for specific VOCs, that show a transient variability with very high intensity peaks. For example, at the Berre l’Etang station, cyclohexane exhibits baseline concentrations below 50 µg m-³, punctuated by peaks reaching up to 218 µg m-³. Similarly, VCM concentrations are typically below 20 µg m-³, with occasional spikes up to 600 µg m-³. For other VOCs, such as ethane and acetylene, more stable levels are observed following regional dynamics. We use a source-receptor approach to better characterize the sources of VOCs in this challenging area and developed an online uncertainty tool to ensure quality-controlled entry data for the Positive Matrix Factorization (PMF). Preliminary results reveal notable chemical signatures, including a pronounced cyclohexane profile and an aromatic profile, we attributed to emissions from the industrial platform northeast of the monitoring station. Further source apportionment results, including seasonal trends, will be discussed.
References:
(1) Report: 1,3-Butadiene, Ethylene Oxide and Vinyl Halides, (IARC, Lyon, 2008).
(2) Chazeau, B. et al.. Atmospheric Chem. Phys. 21, 7293–7319 (2021).
(3) Khaniabadi, Y. O. & Sicard, P. A Chemosphere 278, 130502 (2021).
(4) KEM ONE - Site KEM ONE de Lavéra. https://www.kemone.com/A-propos/Implantations/Lavera.
How to cite: Benmouhoub, Z., Tinel, L., Gille, G., Cancedda, C., Sauvage, S., and Locoge, N.: New approach to real-time analysis of multi-site Volatile Organic Compound (VOC) observation data from an industrial zone in the South of France, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17471, https://doi.org/10.5194/egusphere-egu25-17471, 2025.
The recent IPCC report highlights PM2.5 as one of the most important contributors to air pollution and its health impacts. Determining the sources of air pollution, quantifying their contributions to ambient pollutant levels, and characterizing their spatial and temporal patterns are all important steps in developing effective mitigation strategies (Hopke, 2016). Source apportionment studies, especially those using receptor models, are critical for this purpose. Of these, Positive Matrix Factorization (PMF) is a widely applied methodology. The current work evolves standard PMF (S-PMF) by Tapper and Paatero, 1994 based on the implementation of dispersion-normalized PMF (DN-PMF) suggested by Dai et al., 2020 that includes meteorologically-influenced enhancement towards increasing local source emissions resolution. DN-PMF preserves source information otherwise obscured by variable atmospheric conditions, yielding refined and distinct source profiles.
This research investigates the spatial and seasonal variability of PM2.5 sources across three Indian cities—Bhopal (MSL: 486 m), Mesra (MSL: 517 m), and Mysuru (MSL: 759 m)—as part of the COALESCE network in 2019. A Multiple Seasonal-Trend Decomposition using LOESS (MSTL) was applied to mixed layer height time series to decompose into their daily, weekly, and trend components. Stationarity tests were performed using the Augmented Dickey-Fuller with a p-value <0.05. Reconstructed mass (RCM) from PM2.5 chemical constituent was validated against measured PM2.5 gravimetric mass as suggested by Pullokaran et al., 2024. DN-PMF analysis was performed on chemically speciated datasets comprising organic and elemental carbon fractions (OC1, OC2 OC3,OC4, OP, EC1, EC2, EC3), water-soluble inorganic ions F-, Cl-, NO3- , SO4-2, Na+, NH4+, K+), elements (Al, Mg, Ca, Si, P, K, V, Ti, Co, Ni, Cu, Cd, Fe, Ni, Zn, Se, Ba, Hg, Pb), and non-water-soluble potassium (Knws).
A total of nine factors were resolved in Bhopal, the residential heating factor (23.1%) showed the highest contribution. A smelter source was also identified, due to the high explained variance in EC3 (40%), Zn (71%), Pb, Cu, and EC2. At Mysuru, seven factors were identified, with secondary sulfate (26.2%) identified as the dominant factor. Based on the high explained variance of F- (57%), SO4-2 (39%), and minor loadings of NO3-, Mn, Fe, and Si a brick kiln source was identified in Mysuru. Mesra revealed eight factors, with secondary sulfate (22.8%) and secondary nitrate (19.1%) as major contributors. The biomass burning emissions peaked during India’s stubble burning period (pre-monsoon and post-monsoon) at all three sites. Potential regional and local sources of PM2.5 sources were identified using the Potential Source Contribution Function (PSCF) analysis. The findings provide robust chemically speciated PM2.5 data and improved source apportionment through DN-PMF. These results offer actionable insights for policymakers and environmental agencies, facilitating effective air quality management and targeted mitigation strategies.
How to cite: Pullokaran, D., Bhardwaj, A., Haswani, D., Sunder Raman, R., Shukla, D., Dhandapani, A., Iqbal, J., Kumar R, N., Murthy BM, S., and Prasad, L.: Advancing PM2.5 Source Apportionment through Dispersion Normalized PMF: A Comprehensive Study across India, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1087, https://doi.org/10.5194/egusphere-egu25-1087, 2025.
Volatile Organic Compounds (VOCs) could contribute to the formation of secondary organic aerosols and ozone, both of which are detrimental to human health and the environment. There has been growing interest in developing more precise methods for analyzing VOC sources, and Positive Matrix Factorization (PMF) is widely used. However, overlapping chemical compositions of the retrieved factor profiles often pose challenges in accurately distinguishing sources, even with high-resolution VOC data. While the role of organic compounds (OC) in enhancing PMF-based source apportionment of PM2.5 has been well-documented, studies integrating VOCs with organic molecular tracers remain limited. This study aims to integrate VOCs and organic molecular tracers into PMF analysis to improve the accuracy of source identification and quantification.
Hourly VOC monitoring and 12-hour integrated filter sampling were conducted during a 21-day period in summer at an industrial complex in southern Taiwan. A two-stage PMF modeling approach was applied, with the first stage focusing on VOC analysis and the second stage incorporating both VOC and OC markers. In Stage 1, seven factors were identified, with the three highest contributors attributed to Vehicle Gasoline Combustion (18%), Industrial Emissions (17%), and Synthetic Resin and Paints (16%). In Stage 2, after incorporating OC data, additional sources were identified, including biogenic emissions, cooking sources and biomass burning, offering a more comprehensive source apportionment. These findings demonstrate that incorporating organic species can bridge gaps in VOC source apportionment, enhancing the resolution and accuracy of pollution source identification.
How to cite: Liu, Y.-H., Lai, K.-L., Chen, C.-Y., and Wu, C.-F.: Source Apportionment of Volatile Organic Compounds with Continuous Speciation Monitoring Data and Time-integrated measurements of Organic Markers, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7769, https://doi.org/10.5194/egusphere-egu25-7769, 2025.
Atmospheric fine particulate matter (PM2.5), a complex mixture of various chemical species, has emerged as a significant air quality issue in urban areas. PM2.5 is a key factor in harmful health effects, significantly contributes to the disease burden, plays a significant role in atmospheric visibility and climate change. These tiny particles can scatter and absorb sunlight, leading to haze and reduced visibility, especially in urban areas. Additionally, some components of PM2.5, like Black Carbon (BC), can contribute to global warming by absorbing heat in the atmosphere.
Central Asia is home to several republics that have been striving for independent development over the past 25 years. Kazakhstan is probably the most advanced of these countries as well as the largest in area. It is rich in mineral resources, particularly fossil fuels and thus, has relied primarily on coal for its heating, electricity generation, and industrial base operations. Its air quality is not well characterized to the rest of the world since governmental monitoring data are not publicly available and few prior studies of the amounts or sources of particulate matter have been published.
Currently, the capital Astana has become one of the most polluted cities. Thus, this work that provides almost two years of compositional data and source contributions provides an initial assessment of particulate air quality in Astana. This initial study thoroughly investigates the chemical compositions and source apportionment of PM2.5 based on elements, ions, BC, and estimated UMM, due to the lack of organic carbon data from the samples. Source apportionment was obtained by the U.S. EPA’s Positive Matrix Factorization (PMF) (v5.0) receptor model and Conditional Bivariate Probability Function (CBPF) analyses. Identified source types and average contribution to PM2.5 were ‘Spark Ignition’, ‘Coal Flyash’, ‘Secondary Nitrate’, ‘Primary Sulfate-Fuel Combustion’, ‘Secondary Sulfate-Coal Combustion’, ‘Soil/Road Dust’, ‘Diesel’, and ‘Local Power Plant(s)’. Carbonaceous matter, sulfates, and nitrates account for a significant PM2.5 fraction since power plants burn high-ash coal and fuel oil year-round in Astana city. The major contributions are heating power plants (CHPP-1 and CHPP-2), private residential chimney heating systems, autonomous boilers, vehicles, and local construction activities.
Kazakhstan recently declared its intent to decarbonize by 2030 and achieve carbon neutrality by 2060 that should substantially improve air quality. This study will thus provide baseline data against which future apportionment studies can be compared.
How to cite: Ormanova, G., Hopke, P., Shah, D., and Torkmahalleh, M. A.: Chemical Characterization and Source Apportionment of airborne PM2.5 at an urban site in Astana, Kazakhstan, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9466, https://doi.org/10.5194/egusphere-egu25-9466, 2025.
The evaluation of the sources of particulate matter (PM) is one of the most important topics in environmental science. Both natural and anthropogenic sources are involved in the overall PM pollution in both urban and rural areas. Mathematical methods, as Positive Matrix Factorization (PMF), applied to chemical data are the most powerful tools for the discrimination of PM sources.
In the present work, the results obtained from a three-year sampling campaign (between 2017 and 2019) are presented. 700 PM10 filters were collected in the framework of FRESA Project (Impact of dust-laden African air masses and of stratospheric air masses in the Iberian Peninsula. Role of the Atlas Mountains) from two sites in Andalusia, southern Spain: the first one is in the city of Granada, while the second one is in Sierra Nevada. Filters were analyzed by ion chromatography and Particle-Induced X-ray Emission (PIXE) for elemental analysis.
The two stations are relatively close to each other (around 20 km). However, the Sierra Nevada station is located at an altitude of 2550 m a.s.l, while the Granada station is at 738 m a.s.l. This altitude difference of almost 2000 m makes the two sites very different from a PM-sources point of view, as highlighted by the two parallel PMF models applied to chemical data. Indeed, Sierra Nevada samples showed the impact of frequent mineral dust intrusions from Sahara Desert, that greatly affected the overall PM composition; in Granada site, instead, samples showed the typical urban fingerprint, with lower evidences of Saharan dust intrusions, due to the different circulation as a function of height.
How to cite: Zappi, A., Brattich, E., Morozzi, P., Biondi, M., Masiol, M., Orza, J. A., and Tositti, L.: PM10 source apportionment in two sites of southern Spain by Positive Matrix Factorization. Evaluation of the relevance of sampling site altitude to the PM10 fingerprint., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4394, https://doi.org/10.5194/egusphere-egu25-4394, 2025.
Ultrafine particles (UFP) have gained increased attention during recent years due to their adverse health effects (WHO, 2021). Both the WHO and the EU have therefore recommended systematic measurements of particle number concentration (PNC) and particle number size distributions (PNSD) in cities.
The nPETS project (nanoparticles Emissions from the Transport Sector) aimed to study the lifecycle of sub-100 nm particles from different sources. The nPETS project in Stockholm included measurements of PNSD and PNC in urban background air on a rooftop. PNSDs were measured during two years with 16 channels between 10 and 410 nm and Positive Matrix Factorization (PMF) source apportionment was used to analyse these data.
Four different emission source profiles were attributed by the PMF analysis:
- The most important factor with on average 38 % of the PNC had a peak between 25 – 45 nm. It was most dominant during periods with high PNC, high NOx and low wind speed. It also followed the local rush hours and was identified as traffic with a typical diesel contribution.
- The second most important factor with 36 % of the PNC had at peak 60- 150 nm. It was dominating with northeasterly and higher wind speeds with strongest signal late afternoons and evening. The size distribution showed similarity with ship emissions from other nPETS measurements. Trajectories also showed influence from the Baltic Sea.
- The third factor was associated with a peak below 20 nm in size. It showed similarity to aircraft particles from other nPETS measurements and had also strongest impact during moderate northwesterly winds which corresponds to the direction of the local Bromma airport. This factor was contributing to 17 % of the PNC.
- The fourth factor had a peak in particles larger than 200 nm. It showed strong correlation with PM1 mass and air mass trajectories from easterly Europe. This factor was thought to be long-range transport and was contributing to 8 % of the PNC.
The PMF analysis was compared with dispersion modelling of PNC in the Stockholm region. The emission database (EDB) used was from Eastern Sweden Air Quality Association with inputs from the nPETS project. Based on the dispersion modelling the traffic is the largest source, while shipping is a minor source and aviation negligible. Long range transport was not included in the modelling. The magnitude of UFP emissions from aviation in Stockholm is still largely unknown with low emissions in the EDB. Shipping in central Stockholm is limited to passenger ships in port only during some hours per day which explains the relatively small emissions in the EDB.
The discrepancy between the PMF analysis and the dispersion modelling for shipping and aviation demands further investigations.
Reference.
WHO (2021). WHO global air quality guidelines
How to cite: Amato, F., Norman, M., Silvergren, S., Schlesinger, D., Broman, L., Bergseth, E., and Olofsson, U.: Source apportionment of aerosol particle number in background Stockholm: impact of aviation and shipping, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4263, https://doi.org/10.5194/egusphere-egu25-4263, 2025.
Tropospheric ozone (O3) is a typical secondary pollutant and produced by a series of chemical reactions of precursors such as nitrogen oxides (NOx) and volatile organic compounds (VOCs) under light conditions. The emission of precursors in industrial cities is large and complex, and the relationship between O3 and its precursors is not clear, making it is challenged to identify the key factors and source influencing O3 formation. This study used observation-based-model (OBM), based on the precursors’ observation data and chemical mechanism, to analyze O3 sensitivities to VOCs and NOx during summer in a typical industrial city in China. In our research, higher concentrations of O3 precursors were observed during O3 polluted periods in summertime indicating that precursor accumulation contributed to the higher max net (O3) (16.6 ppbv∙h-1) and HOx· concentrations. The important reactions in ROx· recycling was mainly dominated by the precursors of NO, NO2 and alkene, which were mainly discharged from sources caused by the developed industry. Analyses of relative incremental reactivity (RIR) indicated that O3 production during polluted period is in a chemical transition regime and was sensitive to both VOCs (RIR=0.39) and NOx (RIR=0.56), particularly emphasizing the crucial of phased control of O3 precursors. Results from PMF analysis indicated that gasoline vehicle emissions were the major contributor to VOCs (27.0%), followed by coal combustion (20.3%), diesel vehicle emissions (15.9%), industrial processes (15.1%). For NOx, coal combustion (44.0%) and diesel vehicle emissions (35.2%) had the largest contribution, followed by industrial processes (12.5%) and gasoline vehicle emissions (8.3%). Based on PMF results and OBM, O3 source was analyzed used RIR method in this study, and industrial process (36.7%) and biogenic source (24.6%) were the major sources of O3. The sensitivities of O3 formation to these sources depend on if both VOC and NOx sensitivities are considered. Previous studies only considered the influence of VOCs on O3 formation when analyzing the source of O3, but this study indicated that the influence of NOx in industrial cities on O3 formation should not be ignored. Meanwhile, considering only VOCs but not NOx in the analysis of O3 sources will underestimate the emission proportion of anthropogenic sources and overestimate the proportion of biogenic sources, resulting in inaccurate results. Industrial cities are typical cities in transition areas, and the sensitivity of O3 to both VOCs and NOx should also be taken into account when analyzing the source of O3 in transition areas, which can ensure the accuracy of source analysis results. In addition, emission reduction of VOCs and NOx simultaneously should be considered in the control of O3 in transition areas. This study provides an improved direction for the source analysis of O3 in industrial cities and even cities in transition areas.
How to cite: Yan, Y., Niu, Y., Yue, K., Dong, J., Wu, J., Wang, Y., and Peng, L.: Formation sensitivity and source analysis of tropospheric ozone in a typical industrial city in China based on the observation data coupled with chemical mechanism, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6097, https://doi.org/10.5194/egusphere-egu25-6097, 2025.
Various air pollution problems ultimately have a serious negative impact on human health and welfare, damage to property, and adversely affect animals and plants. For this reason, people's interest in air pollution problems has increased, and researchers' study to solve air pollution problems has become more diverse and increased. In order to efficiently manage PM2.5 and prepare effective control measures, qualitative and quantitative analysis of pollutants emitting PM2.5 must be conducted first, and PM2.5 must be collected from the receptor and its characteristics analysed. Receptor methods that estimate and evaluate the contribution of pollutants are continuously implemented. The receptor model is a mathematical and statistical methodology that analyses the physical and chemical characteristics of air pollutants at the receptor, identifies sources that affect air quality, and quantitatively estimates the contribution of each source (source apportionment).
Since the release of PMF2 and ME, these programs have been successfully applied to assess ambient PM source contributions in many locations of virous countries. However, PMF and ME models are somewhat difficult to use because these models are DOS-base programs that require understanding of a special script language. Therefore, in order to provide a widely applicable PMF with a user-friendly and graphic user interface (GUI)-based program, the US Environmental Protection Agency (EPA) developed an EPA version of PMF. The US EPA continued to upgrade to the EPA-PMF model including the factor rotation functions. Therefore, the current version 5.0 model has been developed and widely used to source apportionment.
Pohang, the study area of this study, is where Korea's representative steel industry and steel-related industries are gathered, and a steel-related industrial complex is located there. In addition, it is one of the areas with severe air pollution that emits large amounts of particulate matter such as PM2.5 and various gaseous pollutants due to the frequent operation of heavy trucks to transport produced steel products. Therefore, in this study, we performed EPA-PMF modeling using data from the PM2.5 Pohang monitoring site, identified the PM2.5 sources, and then estimated the contribution of each source.
The PM2.5 samples were collected at Pohang air pollution monitoring site from January 2018 to August 2022 and 27 species (OC, EC, SO42-, NO3-, Cl-, Na+, NH4+, K+, Mg2+, Ca2+, As, Br, Ca, Cd, Cr, Cu, Fe, K, Mn, Ni, Pb, S, Se, Si, Ti, V, and Zn) were analyzed by XRF (X-ray fluorescence spectroscopy; Xact-series 620, USA), IC (ion chromatography; URG-9000D, USA), and TOT (thermal optical transmittance; 4F-semi continuous field analyzer, USA) methods. The EPA-PMF model was used to identify sources and estimate source apportionment in Pohang site. The source apportionment data for this study area, which is characterized by the location of a large-scale steel plant and related industrial complexes, can be said to be of great importance, unlike the results of source contribution studies for the study area that have been widely conducted in general. More detailed results of source apportionment for PM2.5 samples in Pohang site will be presented.
How to cite: Hwang, I. and Park, J.: Source Apportionment of Ambient PM2.5 Data using the EPA-PMF Model at Monitoring site near the steel mill, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-39, https://doi.org/10.5194/egusphere-egu25-39, 2025.
With the increase in ozone (O3) concentrations in multiple cities or regions worldwide in recent years, accurate source apportionment methods of its key precursor VOCs have been acquired increasing attention. Chemical reactive losses of ambient VOCs have been a long-term issue yet to be resolved in the VOC source analyses research. Thus, we systematically assessed the common methods and existing issues in ways to reduce losses and loss impacts in source apportionment studies by reviewing relevant publications and suggests research directions for improved VOC source apportionments. Compared to any other mathematical models, positive matrix factorization (PMF) is now a main VOC source apportionment approach. The issue in using any apportionment tool is the processing of the data to be analyzed to reduce the impacts of reactive losses. Calculating the initial concentrations of VOC species based on photochemical age has become a major method to reduce reactive loss effects in PMF, except for selecting low-reactivity species or nighttime data into the analysis. The initial concentration method only considers daytime reactions with hydroxyl (•OH) radicals at present. However, the •OH rate constants vary with temperature, and that has not been considered. Losses from reactions with O3 and NO3 radicals remain to be included. Therefore, the accuracy of the currently photochemical age estimation is uncertain. Beyond developing accurate quantitative methods for chemical losses, source apportionment methods of the consumed VOCs and the accurate quantification of source contributions to O3 and secondary organic aerosol (SOA) are important directions for future studies.
How to cite: Liu, B., Gu, Y., Wu, Y., Dai, Q., Song, S., Feng, Y., and Hopke, P. K.: Methods and issues of reducing reactive loss impacts in ambient VOC source apportionments, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2700, https://doi.org/10.5194/egusphere-egu25-2700, 2025.
Rural background sites, representative of a wider area, are important for investigating the influence of regional and long-range transport as well as long-term trends in PM concentrations (Putaud et al., 2010). Dispersion normalisation using the ventilation coefficient has recently been shown to be an effective approach that provides improved source apportionment results with clearer diel and seasonal patterns for speciated PM datasets (Dai et al., 2020). The focus of this study was to assess the influence of dispersion conditions represented by ventilation coefficient on speciated PM10 concentrations and their origins at the National Atmospheric Observatory Košetice (NAOK, 49°35'N, 15°05'E), a rural background site in Central Europe (LRI ACTRIS ERIC, https://www.actris.eu/).
PM10 elemental composition was measured every 4-h for three years (2021 – 2023) by the Xact625i (Cooper Environmental Services, USA), an online ED-XRF ambient multi-metals monitor. Ventilation coefficient was calculated with 1-h resolution by the numerical weather prediction model ALADIN for subsequent dispersion normalisation of highly time-resolved and speciated PM10 data. Advanced receptor modelling (US EPA PMF 5.0) was applied to both non-dispersion and dispersion normalised highly time-resolved PM10 elemental datasets.
PMF resolved five factors of PM10 (secondary sulphate, residential heating – biomass and coal, soil/re-suspended dust, sea/road salt, industry) for both datasets with almost identical chemical profiles. The factor contributions were positively (lower contributions – secondary sulphate, residential heating, and salt) and negatively (higher contributions – soil/re-suspended dust and industry) influenced by dispersion conditions. Dispersion normalisation provided improved source apportionment results with clearer diel and seasonal patterns, primarily for secondary sulphate and residential heating, the main sources of elemental PM10.
This conference contribution was supported by the Ministry of Education, Youth and Sports of the Czech Republic under grant ACTRIS-CZ (LM2023030).
Putaud, J.P., et al., 2010. A European aerosol phenomenology — 3: physical and chemical characteristics of particulate matter from 60 rural, urban, and kerbside sites across Europe. Atmos. Environ. 44, 1308–1320.
Dai, Q. at al., 2020. Dispersion Normalized PMF Provides Insights into the Significant Changes in Source Contributions to PM2.5 after the COVID-19 Outbreak. Environ. Sci. Tech. 54, 16, 9917–9927.
How to cite: Pokorná, P., Windell, L., Holubová Šmejkalová, A., Vlček, O., Zíková, N., Lhotka, R., Schwarz, J., Ondráček, J., and Ždímal, V.: Effect of dispersion normalisation on PM10 elemental sources at a rural background site in Central Europe , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3840, https://doi.org/10.5194/egusphere-egu25-3840, 2025.
China's dual strategy to mitigate climate change and air pollution is constrained by insufficient data on the distinct sources of carbon emissions and associated health damages. This study utilizes adjoint emission sensitivity modeling with the CMAQ Adjoint model, alongside an exposure-response model and a multiregional input-output model, to perform high-resolution source attribution across 53 production sectors and fuel/process combinations, as well as 42 consumption economic sectors. Our analysis uncovers significant discrepancies between sources of CO2 emissions and PM2.5-related premature mortality, with monetized health damages surpassing climate impacts in over half of the subsectors examined. Additionally, more than one third of the CO2 emissions and health damages are outsourced from well-developed coastal provinces to less-developed inland provinces, though the regions absorbing these burdens differ geographically. CO2 emissions are primarily shifted to the northwestern region, which relies heavily on coal as an energy source, while PM2.5-related deaths are concentrated in the central region, the heavy industrial hub of China with high population densities. These findings demonstrate that high population densities and lower adoption of control technologies exacerbate health damages, particularly in downstream provinces that bear the majority of emission leakages. The CMAQ Adjoint model’s capability to evaluate the marginal benefits of emission reductions at a granular level enabled precise source attribution by incorporating emission profiles, population density, and atmospheric conditions. This research underscores the critical advantage of adjoint modeling in integrating health and climate impacts, advocating for tailored mitigation strategies that address emissions from both production and consumption sides to achieve balanced decarbonization and effective health risk mitigation in China.
How to cite: Chen, Y., Shen, H., Shen, G., Ma, J., Chen, Y., Russell, A., Zhao, S., Hakami, A., Tao, S., Du, J., and Meng, J.: Substantial Differences in Source Contributions to Carbon Emissions and Health Damage Revealed by Adjoint Modeling, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4799, https://doi.org/10.5194/egusphere-egu25-4799, 2025.
Mass concentrations of ambient particulate matter (PM) have been extensively monitored in urban areas worldwide. Despite the widespread availability of such data, it has rarely been utilized in receptor-based source apportionment studies, which predominantly rely on PM chemical speciation data. In this study, we used over one million data points of PM concentrations from more than 100 monitoring sites within a Chinese megacity to perform spatial source apportionment of coarse particles (PM2.5-10). These particles are believed to primarily originate from local emissions and are often characterized by significant spatial heterogeneity. We employed an enhanced positive matrix factorization (PMF) approach, designed to handle large datasets, in combination with a Bayesian multivariate receptor model to determine spatial source impacts. Four primary sources were successfully identified: residential burning, industrial processes, road dust, and meteorologically-related sources. The combined methodology demonstrates substantial potential for broader application in other regions.
How to cite: Dai, Q., Dai, T., Yin, J., Chen, J., Liu, B., Bi, X., Wu, J., Zhang, Y., and Feng, Y.: PMF-Bayesian Modeling for Spatial Source Apportionment of Airborne Particulate Matter, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4796, https://doi.org/10.5194/egusphere-egu25-4796, 2025.
Air quality is significantly influenced by weather patterns, such as wind direction, wind speed, and atmospheric dispersion, which play a direct role in driving fine particulate matter (PM2.5) concentrations. This study employed three online monitoring instruments to conduct assessments of the chemical species in PM2.5 during the winter season of 2020–2021. By applying the positive matrix factorization (PMF) model, pollution sources and their contributions were identified within Taipei City. To further explore the relationship between meteorological conditions and pollution, clustering techniques were employed to classify weather patterns associated with PM2.5 levels exceeding 25 µg/m³.
The analysis revealed three distinct high-concentration weather patterns, each linked to specific pollution sources: (1) Low wind speed and poor dispersion conditions were associated with elevated traffic-related emissions, peaking at 7.4 µg/m³; (2) Strong northeast monsoon patterns resulted in relatively lower PM2.5 levels due to reduced pollutant accumulation in the basin; and (3) Northwest wind patterns were characterized by significant contributions from coal and fuel combustion, industrial sources, and secondary aerosols, with PM2.5 concentrations reaching up to 56 µg/m³.
This study is the first to combine source apportionment results from individual receptor sites with nonparametric trajectory analysis (NTA). Under weak wind conditions, traffic-related pollutants were observed to accumulate predominantly south of the receptor site, with maximum concentrations of 14 µg/m³. In contrast, northwest wind patterns showed notable accumulation of pollutants from civil construction and metalwork northwest of the receptor site, with concentrations reaching 8 µg/m³.
These findings highlight the critical role of weather pattern classification in understanding PM2.5 pollution sources, offering valuable guidance for policymakers to implement effective air quality controls. Building on this foundation, future research will adapt these methodologies to explore the pollution sources of ozone and its precursors. Specifically, the new approach will integrate data from multiple monitoring stations with NTA to achieve spatial mapping of pollution source contributions. This advancement aims to provide a more comprehensive understanding of the distribution and impact of ozone and its precursors, deepening insights into the complex interactions between meteorology, atmospheric chemistry, and air quality management.
How to cite: Hsieh, P.-Y. and Wu, C.-F.: Integrating Weather Patterns with PMF Modeling: Insights into PM2.5 Pollution Sources and Future Applications to Ozone, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4893, https://doi.org/10.5194/egusphere-egu25-4893, 2025.
In environmental data analysis, source apportionment can be an important approach to extract useful information that might otherwise be hidden within the data. The United States Environmental Protection Agency (EPA) has developed an open-source python package, the Environmental Source Apportionment Toolkit (ESAT), which enables source apportionment modeling and error estimation workflows. ESAT is intended to replace Positive Matrix Factorization v5 (PMF5) that has substantial data size limitations. ESAT is currently in alpha testing with development plans for enhanced functionality and support of large datasets, High-performance Computing (HPC) execution through a command line interface (CLI), and a standalone desktop graphical user interface (GUI). The alpha product of ESAT is publicly available and offers a complete application programming interface (API) to replicate the workflows and functionality of PMF5, with examples provided through Jupyter Notebooks. The ESAT computing module currently contains two non-negative matrix factorization (NMF) algorithms for model training, with the module designed for other algorithms to be easily added. The two algorithms currently available are the least-squares NMF (LS-NMF) and weighted-semi NMF (WS-NMF). Each algorithm offers different benefits depending on project or data requirements. The ESAT python codebase has been optimized to run in a highly parallelized manner, with most of the numerical computations implemented in Rust, a low-level language comparable in performance to C. ESAT replicates the model error estimation methods of PMF5, namely bootstrap, displacement, and a hybrid method. To facilitate experimentation and testing, ESAT contains a synthetic dataset generator and model simulator that can evaluate how well ESAT can recreate synthetic factors and contributions. Continuous development of new features are tested and added to the python package on a regular basis. One such feature is the addition of an uncertainty perturbation workflow, which will run a collection of models while slightly perturbing the uncertainty matrix, and then evaluating the impact on the solution profiles and contributions. The alpha version of the ESAT python package is available for installation from pypi at https://pypi.org/project/esat/. Further testing and development of the alpha version will proceed to a full release in late 2025. The development of a GUI desktop application is currently planned to begin after the ESAT full release.
How to cite: Hopke, P., Smith, D., Cyterski, M., Johnston, J., Wolfe, K., and Parmar, R.: Alpha Release of the US Environmental Protection Agency’s Environmental Source Apportionment Toolkit (ESAT), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6691, https://doi.org/10.5194/egusphere-egu25-6691, 2025.
Light-absorbing carbonaceous aerosols (LACs) are key contributors to climate change due to their ability to absorb solar radiation and reduce surface albedo. These aerosols primarily comprise black carbon (BC) and brown carbon (BrC). The optical properties of LACs are influenced by a complex interplay of factors, including emission sources, atmospheric conditions, and secondary formation processes. However, clearly distinguishing the light-absorption characteristics of various sources remains a significant challenge. Instruments such as the Aethalometer, for example, can differentiate between BC from fossil fuel combustion and biomass burning based on the frequency dependency of absorption strength. Yet, the reliance on empirical values for the Absorption Ångström Exponent (AAE) introduces uncertainties in these estimates. Source apportionment models, such as Positive Matrix Factorization (PMF) and the Multilinear Engine (ME-2), help address these uncertainties by directly attributing sources through the integration of optical and chemical composition data (Wang et al., ACP, 2020). In this study, we applied the PMF model to quantify and compare the source-specific optical properties of carbonaceous aerosols in Chuncheon, a rural area surrounded by forests, and Seoul, a representative urban area in Republic of Korea, during the spring of 2022. The input data included ionic, carbonaceous, and elemental components, as well as light absorption coefficients measured using an Aethalometer (model AE33, Magee Scientific, United States) from March 14 to April 13, 2022. The PMF analysis identified four major sources; mineral dust, biomass burning, industrial emissions, and traffic emissions. Biomass burning was the largest contributor to light absorption in Chuncheon, whereas traffic emissions were the dominant contributor in Seoul. This trend was consistently reflected in the AAE and Mass Absorption Cross-section (MAC) values calculated for each source. This study provides new insights into the quantification of source-specific optical properties of aerosols using PMF, offering a robust approach to understanding their impacts on atmospheric radiative forcing.
How to cite: Oh, S. M., Kwon, J., Lee, S. H., Kim, Y. P., Jung, C. H., and Lee, J. Y.: Source Apportionment of Optical Properties of Carbonaceous Aerosols between Urban and Rural areas in Republic of Korea, spring 2022, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7495, https://doi.org/10.5194/egusphere-egu25-7495, 2025.
Ultrafine dust research is being conducted using the Positive Matrix Factorization (PMF) model to identify receptor-centered pollutants. Among the various chemical components used in the PMF model, trace elements such as heavy metals serve as indicators of pollutants emitted by industries. ICP-MS and XRF are used to analyze these trace elements. ICP-MS is the most widely used method for analyzing heavy metals and has a low detection limit, allowing it to analyze even trace concentrations. However, there are limits to reanalysis because it requires a complex sample pretreatment process and consumes samples. On the other hand, XRF has the advantage of being able to analyze samples without pretreatment and that reanalysis is possible at any time because the samples are not consumed. However, compared to ICP-MS, the detection limit is relatively high and the uncertainty increases when the amount is small. In this study, we sought to determine whether these differences in analysis methods affect the identification of industrial pollutants. The PMF model was performed by analyzing 107 PM2.5 filters collected at 4-day intervals from November 2021 to December 2022. The same analysis method was used for carbon and ion components, excluding trace elements. Through this study, we will be able to find out what differences the model results obtained through different analysis methods have in deriving industrial pollution sources. Additionally, it is expected that more reliable results will be obtained in accurate pollutant source estimation and weight determination.
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: Kim, M., Shin, H., and Lee, S.-M.: A comparative study of PM2.5 source apportionment and toxicological effects based on differences in heavy metal analysis methods, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7532, https://doi.org/10.5194/egusphere-egu25-7532, 2025.
Accurate quantification secondary organic aerosols (SOA) in ambient PM2.5 is crucial for addressing the current challenges in visibility improvement and further exploring the climate impacts of SOA. In this study, we conducted synchronous measurements of PM2.5 components in Shenzhen from November 1 to December 15, 2022, utilizing a suite of multiple online instruments, including high-resolution time-of-flight aerosol mass spectrometer (HR-ToF-AMS), semi-continuous OC/EC carbon aerosol analyzer, a monitor for aerosols and gases (MARGA), and a continuous multi-metals monitor (Xact-625). Three methods were employed for SOA in PM2.5 quantification, including AMS-PMF, Online-PMF, and the EC (elemental carbon) tracer method. The three methods yielded generally consistent SOA mass concentrations of 4.2 ± 3.2 μg m-3, 3.6 ± 2.6 μg m-3 and 3.5 ± 2.4 μg m-3, respectively. A strong correlation (r = 0.95) was found between AMS-PMF and Online-PMF results. While the EC tracer method showed lower consistency with AMS-PMF and Online-PMF, with correlation coefficients r of only 0.82 and 0.79, respectively. The AMS-PMF method determined that SOA accounted for 61.3 % of the organic mass (33.7% for more oxygenated organic aerosols, MO-OOA, and 27.6% for less oxygenated organic aerosols, LO-OOA), and the Online-PMF method estimated 57.0 %. However, the EC tracer method estimated only 50.5 %, primarily due to the higher uncertainty associated with SOA quantification in this method. The daily SOA variations from all three methods showed consistent peaks in the afternoon (14:00 ~ 15:00), and a significant rise at night. These patterns were attributed to increased photochemical activity in the afternoon and changes in boundary layer height at night. These analyses further support the reliability of the SOA quantification results in this study, encompassing both MO-OOA and LO-OOA.
How to cite: Peng, X., Wei, F.-H., and Huang, X.-F.: Comparative Evaluation of Secondary Organic Aerosols in PM2.5 in Shenzhen Using Multiple Methodologies, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7712, https://doi.org/10.5194/egusphere-egu25-7712, 2025.
Ambient fine particulate matter (PM2.5) poses a significant risk to health in Europe, where many cities are exposed to levels above World Health Organization guidelines. To support the implementation of optimal PM2.5 reduction policies, air quality models are necessary. In such context, source-receptor relationships (SRRs) are models that can be used to replace fully-fledged Chemical Transport models, and save significant computation time when simulating various emission reduction scenarios. They allow calculating the relative potential of a given source at a receptor, i.e. the share of PM2.5 concentration at a given receptor that results from the complete removal of the emissions from that source. Here, we use the SRR model SHERPA, based on the EMEP Chemical Transport Model for four different meteorological years (2015, 2017, 2019 and 2021), to evaluate relative potentials for 150 European cities. These potentials are evaluated for five different emission precursors, twelve emission sectors, and four reduction scopes (city core, commuting zone, remaining national territory and rest of Europe). Results show that relative potentials vary little between meteorological years for most cities. The industry, transport and residential sectors generally bear the highest values of relative potential for most cities through emissions of primary particulate matter. Cities near important ports where the shipping sector exhibits high values through sulfur oxides. High potentials are observed for agriculture at the national and international scales. Cities in Southern Europe have low reduction potentials due to high PM2.5 levels originating from natural sources. Smaller cities have a higher relative potential for national and international emissions, while larger cities have high relative potentials for city core emissions. Relative potentials are generally low for commuting zones. These results underline the reliability of SRRs in guiding targeted air quality interventions, thereby helping to reduce PM2.5 exposure effectively across diverse urban settings in Europe.
How to cite: Rey-Pommier, A., Pisoni, E., Thunis, P., Zauli-Sajani, S., and de Meij, A.: Source attribution of fine particulate matter in European cities for different meteorological years, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10883, https://doi.org/10.5194/egusphere-egu25-10883, 2025.
PM2.5 species, PAHs and VOCs were sampled between 2013 and 2019 once every three or six days for a period of 24 hours in an industrialized city in Ontario, Canada, and analyzed to apportion their common sources. The consequences of using these species jointly for receptor modelling were assessed via combined-phase source apportionment that used the data as is, and in an approach that considered the potential for photochemical losses of gas-phase species. Thus, initial concentrations corrected for photochemistry, called PIC were calculated. The data were then analyzed either with positive matrix factorization or its dispersion-normalized variant (DN-PMF). Comparisons of applying PMF to the originally observed input data (BASE) and DN-PMF on data with PIC corrections were made. When the combined phase input data were analyzed, nine factors were resolved for both BASE and DN-PIC PMF. These factors were: particulate sulphate, secondary organic aerosol (SOA), particulate nitrate (pNO3), biomass burning with natural gas, crustal matter, winter blend of gasoline, coking/coal combustion, steelmaking, and summer blend/light duty vehicular emissions. On comparison of the BASE and DN-PIC PMF results, the average PM mass contribution of the summer gasoline fuel factor increased from 2% in BASE case to 5%, suggesting severe underestimation of this source’s initial contributions without DN-PIC. Also, substantial increases of reactive VOCs in the SOA factor, and PAHs with ≥four rings in the pNO3 and steelmaking factors were observed with DN-PIC PMF compared to the BASE PMF case, indicating that for the SOA factor, reactive VOCs at the location of study contributed to its sources.
How to cite: Sofowote, U., Dabek-Zlotorzynska, E., Yassine, M., Mooibroek, D., Siu, M., Celo, V., and Hopke, P.: The inclusion of photochemical initial concentrations in the combined-phase source apportionment of PM2.5, PAHs and VOCs from an industrialized environment, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11663, https://doi.org/10.5194/egusphere-egu25-11663, 2025.
Industrial and mining activities are important anthropogenic sources of metals and metalloids in the air. Traditionally, 24-h offline sampling of particulate matter (PM) is considered and chemical composition is determined by ICP-MS and ICP-OES analysis. In recent years, near real-time techniques have been developed that allow high time resolution (1-h) studies to be carried out. An example is the Xact 625i Ambient Metals Monitor, based on reel-to-reel filter tape sampling followed by nondestructive X-ray fluorescence analysis.
Our study presents the results of metals and metalloids sampling and analysis with Xact 625i over three years at three stations in a southwestern region of Europe with urban-industrial (Campus, Pérez-Vizcaíno et al., 2025), industrial (La Rabida) and mining (La Dehesa de Riotinto) influence. The high concentrations were associated with channeled and fugitive emissions from different sources: copper smelter, Port of Huelva, oil refinery, and/or open-pit mining. At the three stations, the daily, weekly and annual variation patterns of each element were obtained. Hourly As peaks in PM10 of up to 311 ng m-3 in the city of Huelva, 292 ng m-3 in La Dehesa de Riotinto and in PM2.5 of up to 578 ng m-3 in La Rabida were measured. The application of the Positive Matrix Factorization (PMF v5.0 EPA) model made it possible to identify sources and the contribution of each of them, showing the relevance of industrial and mining activities throughout the day.
This work highlights the need to conduct high time resolution studies to understand the hourly behavior of different pollutants and their correlation with meteorological parameters, complement air quality models, and better understand the impacts of atmospheric pollution on public health.
References
Pérez-Vizcaíno, P., Sánchez de la Campa, A.M., Sánchez-Rodas, D., de la Rosa, J.D., 2025. Application of a near real-time technique for the assessment of atmospheric arsenic and metals emissions from a copper smelter in an urban area of SW Europe. Environmental Pollution 367, 125602. https://doi.org/10.1016/j.envpol.2024.125602.
How to cite: Pérez-Vizcaíno, P., Sánchez de la Campa, A. M., Sánchez-Rodas, D., de la Rosa, J. D., Alastuey, A., and Querol, X.: Hourly chemical composition and source apportionment of PM in industrial and mining areas of SW Europe using a near real-time technique, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13996, https://doi.org/10.5194/egusphere-egu25-13996, 2025.
Assessing the site-to-site variability of source contributions to the ambient PM2.5 concentrations plays an important role in estimation of exposure misclassification in epidemiological studies. Exposure misclassification error may be substantially lowered when accounting for the heterogeneity of source contributions resulting in a lower relative risk evaluation.
The aims of this study were to identify the common pollution sources and their contributions from the PM2.5 compositional data collected during the two sampling campaigns (2012/13 and 2018/19) of the Multiple Air Toxics Study (MATES) at ten sites across the South Coast Air Basin using positive matrix factorization and to characterize the spatial variations among the source contributions by coefficients of determination and divergence.
The results of the study showed that the major common contributor to the PM2.5 mass at all sampling sites was the “gasoline vehicle” source followed by “aged sea salt”, “biomass burning”, “secondary nitrate”, “secondary sulfate”, “diesel vehicles”, “soil/road dust” and “OP-rich”. The contribution patterns of all eight sources were highly heterogeneous over time. Among them, the highest spatial variability was found for the contributions from “OP-rich” source in both MATES campaigns suggesting the different wildfire contributions that occurred in the region. Alternatively, the smallest spatial site diversities were observed for the highly correlated contributions of the “secondary sulfate” and “aged sea salt” sources obtained from the MATES data collected in 2012/13 and for the “soil/road dust” sources from 2018/19 campaign. Overall, the source contributions obtained for Inland Valley and Rubidoux were the most different in comparison to the other sites likely due to their distant location from the Pacific Ocean and the major industrial region in Los Angeles.
How to cite: Stanimirova, I. and Hopke, P. K.: Assessment of spatial variability in PM2.5 source contributions during two sampling campaigns (2012/13 and 2018/19) across ten sites in the South Coast Air Basin, California, the USA., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14163, https://doi.org/10.5194/egusphere-egu25-14163, 2025.
The air pollutant compounds BTEX (Benzene, Toluene, Ethylbenzene, and Xylenes) have emerged as significant urban air pollutants, raising increasing environmental and health concerns. This study presents a comparative analysis of BTEX concentrations across three major metropolitan cities in Western India; Pune, Mumbai, and Ahmedabad. A clear seasonal pattern has been observed in BTEX concentrations across all three cities, with peak levels observed during winter and post-monsoon seasons. The spatial distribution revealed that Mumbai and Pune exhibited the highest concentrations of Benzene, Ethylbenzene, and Xylenes, while Toluene was highest in Ahmedabad. Source apportionment using interspecies ratios identified vehicular emissions as the primary contributor of BTEX at all locations. Notably, Mumbai's higher Benzene/Toluene ratios (>0.5) suggested long-range transport of these pollutants. Further analysis using interspecies correlations showed strong relationships (correlation coefficient >0.7) between all BTEX parameters in Pune and Mumbai, supporting common emission sources. The health risk assessment quantified through Lifetime Cancer Risk (LCR) calculations indicated that BTEX exposure levels were below both the prescribed cancer risk threshold of 10⁻⁴ and the US-EPA recommended limit of 10⁻⁶ across all study locations. These findings provide valuable insights into the distribution patterns, potential sources, and health implications of BTEX pollutants in Western Indian urban environments.
How to cite: Anand, V., P Sreevalsam, A., Katre, B., Panicker, A. S., and Ghude, S. D.: BTEX in Urban Air: Source Apportionment, Seasonal Trends, and Health Risk Assessment Across Three Western Indian Metropolitan Cities , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15427, https://doi.org/10.5194/egusphere-egu25-15427, 2025.
This study focuses on PM2.5 pollution in the Taichung Metropolitan Area, Taiwan, with an emphasis on the application of an advanced PMFxPMF method for detailed source identification. Initial Positive Matrix Factorization (PMF) analysis identified six major pollution sources, including regional secondary pollution (41%), carbonaceous aerosols (24%), and Heavy metal-rich industrial processes (8%). To refine the attribution of industrial sources, a second-phase PMFxPMF analysis was employed, specifically targeting heavy metals within the industry factor. By integrating source fingerprints from chimney samples of coal combustion and sintering furnaces, the analysis revealed that these sources contributed 18.4% and 4.9%, respectively, to the total heavy metals in the industry factor. When excluding Fe, the contributions increased to 35.5% and 8.4% for non-Fe heavy metals. The application of the PMFxPMF method was crucial in accurately linking specific industrial activities to heavy metal emissions, offering a more precise understanding of pollution sources. These insights are essential for developing targeted strategies to reduce PM2.5 levels and mitigate the associated health risks in the Taichung region, particularly through stricter control of emissions from coal combustion and metal processing industries.
How to cite: Sun, S. S.-E. and Chou, C. C.-K.: Observation-based investigation unveils major local sources of heavy metals associated to fine particulate matters (PM2.5) in an urban area, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15558, https://doi.org/10.5194/egusphere-egu25-15558, 2025.
PM2.5 consists of various chemical constituents originating from multiple sources and is associated with adverse health effects, including cardiovascular and respiratory diseases. Effective source specific management strategies are essential for mitigating these impacts. Positive Matrix Factorization (PMF) has been widely used as a receptor model to identify sources and quantify their contributions. However, conventional PMF (C-PMF) often overestimates or underestimates source contributions due to meteorological influences. To address this limitation, the Dispersion-Normalized PMF (DN-PMF) model has been introduced. This advanced approach accounts for meteorological conditions, providing more accurate source contributions.
In this study, hourly data for 28 chemical constituents of PM2.5, measured from 2019 to 2022 at National Intensive Monitoring Stations (NIMS) in Daejeon and Gwangju, South Korea, were used as input data for both C-PMF and DN-PMF. The study aimed to identify sources whose contributions are significantly influenced by meteorological factors and to compare regional variations. Ten sources were resolved by both models in each city, and differences in source contributions between the two approaches were calculated. Seasonal and temporal variations were also examined to determine meteorologically influenced sources and regional differences.
In Daejeon, a significant difference in secondary nitrate contributions was observed between the models, particularly during winter, when the atmospheric conditions favor its formation. In contrast, contributions of secondary sulfate showed minimal differences, suggesting it is primarily affected by long-range transport and less sensitive to local meteorological conditions. In Gwangju, secondary nitrate and sulfate contributions showed relatively small differences, indicating lower sensitivity to local meteorological factors. Additionally, differences in contributions were observed for sources influenced by local emissions, highlighting variations between the two regions, for the same sources. These regional differences are likely attributable to the specific emission characteristics, meteorological conditions, and sources locations of each city. Supporting data, including emission inventories, meteorological parameters, and the Conditional bivariate probability function (CBPF) were used to explain the observed variations. This study underscores the influence of local meteorological conditions on source contributions and provide valuable insights for developing region-specific PM2.5 management strategies.
Acknowledgement
This research was supported by “Study on the analysis of medium- and long-term factors affecting PM2.5 emission changes” funded by National Air Emission Inventory and Research Center of the Ministry of Environment under grant, South Korea. This work was supported by the National Institute of Environmental Research (NIER) of the Ministry of Environment under grant, South Korea No. NIER-2021-03-03-001. 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, T., Han, S., Ryoo, I., Rim, D., Kim, M., and Yi, S.-M.: Influence of local meteorological conditions on source contributions of PM2.5: A comparison of Conventional Positive Matrix Factorization (C-PMF) and Dispersion Normalized PMF (DN-PMF) models in Daejeon and Gwangju, South Korea. , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16131, https://doi.org/10.5194/egusphere-egu25-16131, 2025.
PM2.5, identified as a health-hazardous substance and classified as a Group 1 carcinogen by the International Agency for Research on Cancer in 2013, poses significant risks to public health. To combat these risks, the South Korean government established the ambient air quality standard for PM2.5 mass concentration through the Air Quality Preservation Act in 2013, initially implemented in Seoul and expanded nationwide in 2015. Since December 2019, the Seasonal Management Program has targeted major PM2.5 sources during winter (December–March), a period of frequent high concentration events. While these measures initially achieved notable reductions, the downward trend in PM2.5 mass concentration has slowed in recent years. In 2022, South Korea recorded an annual average PM2.5 mass concentration of 18.3 μg/m³, exceeding the national air quality standard of 15 μg/m³.
This study aimed to analyze long-term trends in PM2.5 source contributions using the meteorologically adjusted Dispersion-Normalized Positive Matrix Factorization (DN-PMF) model. Hourly monitoring data from 2016 to 2022, provided by the National Institute of Environmental Research (NIER), were analyzed for two sites: Seoul, the capital city, and Ulsan, an industrial hub. The dataset included PM2.5 mass concentrations, carbonaceous components (OC, EC), ionic species (NO3-, SO42-, Cl-, NH4+, Na+, K+), and trace elements (S, K, Si, Al, Ca, Ti, V, Cr, Mn, Fe, Ni, Cu, Zn, As, Se, Br, Ba, Pb). The Conditional Bivariate Probability Function (CBPF) model was utilized to identify the direction of local sources. Comprehensive trend analyses, including Theil-Sen regression, Seasonal Trend Decomposition based on LOESS (STL), and Piecewise Regression, were conducted to assess variations in source contributions over time.
This study resolved 10 sources through PMF modeling at each site: secondary sulfate, secondary nitrate, motor vehicles, biomass burning, incineration, oil combustion, coal combustion, soil, industry, and sea salt. Temporal variations revealed differing trends between the two sites. In Seoul, PM2.5 mass concentrations consistently decreased, with significant reductions in contributions from incineration, oil combustion, and industry sources. In contrast, Ulsan exhibited a more rapid decline in PM2.5 mass concentrations particularly for biomass burning and oil combustion sources. Addressing secondary sulfate and mobile sources remains critical for further air quality improvements.
This study provided essential receptor-based evidence to support the development of future air quality management strategies, addressing both local and transboundary PM₂.₅ sources effectively.
Acknowledgment
This research was supported by “Study on the analysis of medium- and long-term factors affecting PM2.5 emission changes” funded by the National Air Emission Inventory and Research Center of the Ministry of Environment under grant, South Korea. This work was supported by the National Institute of Environmental Research (NIER) of the Ministry of Environment under grant, South Korea No. NIER-2021-03-03-001. This research was supported by the Particulate Matter Management Specialized Graduate Program through the Korea Environmental Industry & Technology Institute (KEITI) funded by the Ministry of Environment (MOE).
How to cite: Yi, S.-M., Kim, T., Han, S., Ryoo, I., and Kim, M.: Trend Analysis of PM2.5 Source Contributions in Seoul and Ulsan, South Korea (2016-2022), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16605, https://doi.org/10.5194/egusphere-egu25-16605, 2025.
Air pollution was responsible for 8.1 million deaths globally in 2021, making it the second leading risk factor for mortality (5th edition of the SoGA report, 2021). Among air pollutants, PM2.5, airborne particles with an aerodynamic diameter of 2.5 μm or less, is particularly harmful, as it penetrates deeply into the alveoli and can reach the respiratory and cardiovascular system, exacerbating diseases such as asthma, lung cancer, and heart arrhythmia. In Southeast Asia, PM2.5 mass concentrations have risen over the decades due to rapid urbanization, industrial activities, and biomass burning. This study aims to identify the sources of PM2.5 and quantify their contributions in Bangkok, Thailand, and Jakarta, Indonesia.
PM₂.₅ samples were collected from rooftop sites in both cities over 24-hour intervals every third day from September 2023 to December 2024, using three types of filters (Teflon, Quartz, Nylon). PM2.5 mass concentrations were measured using Teflon filters with an automatic weighing system. Trace elements, including Cl, Al, Ca, Cr, Cu, Fe, K, Mg, Mn, Pb, Si, Ti, V, Zn, Ni, As, S, Se, Ba, and Br, were analyzed using Energy Dispersive X-ray Fluorescence (ED-XRF). Carbonaceous components (OC, EC) were measured with an OC/EC analyzer on Quartz filters, while ionic species (NO3-, SO42-, Cl-, NH4+, Na+, and K+) were analyzed using Ion Chromatography on Nylon filters.
The Positive Matrix Factorization (PMF) model was applied to identify and quantify PM2.5 sources and the Conditional Bivariate Probability Function (CBPF) model was used for the directional analysis of PM2.5 sources. Seasonal variations in PM2.5 mass concentrations were examined by comparing wet and dry seasons, providing insights into seasonal differences. Additionally, chemical constituent concentrations and proportions were analyzed at each site to identify site-specific characteristics. The contributions of sources were quantified, and the study further explored the directions of local sources and geographical influences. The findings of this study provide critical scientific evidence for the development of policies to manage ambient PM2.5, aiming to improve air quality while balancing economic and social considerations.
Acknowledgment
This research was supported by “Clean Air for Sustainable ASEAN (CASA)” funded by the ASEAN Korea Cooperation Fund (AKCF). This research was supported by the Particulate Matter Management Specialized Graduate Program through the Korea Environmental Industry & Technology Institute (KEITI) funded by the Ministry of Environment (MOE).
How to cite: Han, S., Kim, T., Kang, M., Hwang, J., Kim, Y., Tantrakarnapa, K., Muhayatun, S., Rim, D., Kim, M., and Yi, S.-M.: Source Apportionment of PM2.5 in Southeast Asia: Bangkok, Thailand, and Jakarta, Indonesia, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16932, https://doi.org/10.5194/egusphere-egu25-16932, 2025.
Positive Matrix Factorization (PMF) is a powerful method for the apportionment of aerosol sources. Traditionally applied to chemical datasets, it has more recently been extended to physical datasets, focusing predominantly on size distributions of ultra-fine particles (Hopke et al., 2022). In this study, aerosol physical properties (particle size distributions, from ultrafine to coarse mode, and spectral aerosol absorption) were used as input to PMF (EPA 5.0) for identifying emission sources of two distinct sites, and results compared with those from chemical PMF.
Located at the two extremes of the Italian territory, the sites of Aosta and Lecce represent two markedly different environments. The first lies in the mountainous area of the northwestern Alps, the second in a flat area at the southeastern edge of the peninsula, in a typical Mediterranean context. In previous studies, PMF of aerosol chemical composition was performed using chemical speciation of PM over the period 2019-2021 (Aosta, e.g. Diemoz et al., 2019) and 2017 (Lecce, Giannossa et al., 2022).
The PMF input dataset at the urban-background site in Aosta included aerosol size distributions measured by an Optical Particle Counter (OPC) across the 0.18–18 µm range and wavelength-dependent aerosol absorption from an AE33-aethalometer. At the urban-background observatory in Lecce, an aerosol size distribution range (0.02–10 µm) was available using both a Scanning Mobility Particle Sizer (SMPS) and an OPC, complemented by single-wavelength aerosol absorption (Multi-Angle Absorption Photometer, MAAP) providing eBC concentrations. Ancillary data included meteorological parameters and trace-gases concentration. Although with differences in the physical properties used, at both sites the physical PMF allowed the identification of 6 aerosol sources. In Aosta, the aerosol sources included fossil fuel combustion, biomass burning, secondary droplet mode, secondary condensation, dust, and coarse particles. In Lecce, the sources were nucleation (thanks to the additional use of the SMPS), traffic emissions, secondary nitrate, secondary sulfate, regional transport (sea spray and dust), and local resuspension (coarse particles).
The work will present a comparison of the two PMF approaches (Chemistry- and Physics-based PMF) and advantages and limits of the different physical input datasets used at the two sites. For example, at the Lecce site, the information on ultrafine and fine particle distribution well captured features of the nucleation and traffic factors, while the absence of wavelength-dependent absorption coefficients limited the ability to distinguish biomass burning from fossil fuel sources, which was key for Aosta. Overall, the use of physical aerosol data as input to PMF proved to be an effective method for source apportionment and could usefully complement the chemical-PMF analysis. In fact, this approach offers significant advantages, such as the capability for quasi real-time monitoring and the relative ease of instruments use and data analysis compared to the ‘traditional’ chemical analysis of PM.
The authors acknowledge the MUR for funding the research through the CIR01_00015-PER-ACTRIS-IT.
Diémoz, H.et al., https://doi.org/10.5194/acp-19-10129-2019, 2019.
Giannossa, L. et al., http://doi.10.1016/j.jenvman.2022.115752, 2022.
Hopke, P. K. et al., https://doi.org/10.1016/j.scitotenv.2022.153104, 2022.
How to cite: Mapelli, C., Contini, D., Diémoz, H., Dinoi, A., Cesari, D., and Barnaba, F.: Aerosol source apportionment in two contrasting Italian sites: a comparison between physical and chemical PMF in Aosta and Lecce , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19384, https://doi.org/10.5194/egusphere-egu25-19384, 2025.
The air quality in the Indian subcontinent has been a growing concern in recent years, with particulate matter (PM) being one of the major pollutants. PM2.5, in particular, has a significant impact on human health and the environment as it can penetrate deep into the respiratory system. PM2.5 has been linked to several health issues including respiratory and cardiovascular disease, while Water-Soluble inorganic Ions (WSII) contribute to the acidity and salinity of the air. In this study, we aim to investigate the seasonal variation and contributing sources of PM2.5 and 9 associated WSII (Na+, NH4+, K+, Mg2+, Ca2+, F-, Cl−, NO3− and SO42−) in two non-attainment Indian cities, Alwar and Amritsar in the states of Rajasthan and Punjab respectively. The study regions are selected owing to the unique meteorological conditions, population density and industrial activities. The study employs a combination of field measurements and comparative analysis to understand the sources and seasonal patterns of PM2.5 and WSII in these cities. Initial analysis of PM2.5 winter samples shows Nitrate (22.078 μg/m3) and Sulphate (17.408 μg/m3) to be the dominant anionic species and Ammonium (13.046 μg/m3) and Sodium (5.452 μg/m3) to be dominant cationic species for both day and night respectively in Alwar city. The total average day anionic concentrations for the same period were observed to be 31.37 μg/m3 and night concentrations to be 52.52 μg/m3 with total observed average day cationic concentrations to be 15.23 μg/m3 and night concentrations to be 23.29μg/m3.
This study provides valuable insights into the seasonal patterns of PM2.5 and WSII and help understand the contributing factors and sources. This information can be used by policymakers to develop strategies for mitigating air pollution and improving the air quality in the region.
How to cite: Jain, Y., Kota, S. H., and Kumar, V.: Seasonal Variation and Source Apportionment of PM2.5 bound Water-Soluble Inorganic Ions (WSII) in Tier 2 and 3 Non-Attainment cities of India using PMF 5.0, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20150, https://doi.org/10.5194/egusphere-egu25-20150, 2025.
