Fine fractions of urban road dust represent an environmentally relevant and potentially hazardous component of particulate matter due to their high mobility, efficient atmospheric resuspension, and capacity to accumulate anthropogenic pollutants. This study provides an integrated mineralogical, physicochemical, magnetic, and organic components characterization of the finest fraction (<50 μm) of road dust collected from seven urban locations (V1-V7) in Vienna, Austria, with particular emphasis on sources of minerals and organic components of road dust.

Magnetic properties were assessed using mass-specific magnetic susceptibility (χ), frequency-dependent susceptibility (χfd%), anhysteretic remanent magnetization susceptibility (χARM), hysteresis loop parameters, and thermomagnetic κ(T) curves. Complementary mineralogical and physicochemical analysis included Scanning Electron Microscopy with Energy-Dispersive X-ray Spectroscopy (SEM-EDS), quantitative X-ray Diffraction (QXRD), Fourier Transform Infrared Spectroscopy (FTIR), Thermogravimetry coupled with Quadrupole Mass Spectrometry (TG-QMS), and elemental CHNS and TOC analyses, supported by multivariate statistics adapted for compositional data.

All samples showed a complete dominance (≈100%) of strongly magnetic material in the <50 μm fraction. Mass-specific magnetic susceptibility values ranged from 362 × 10⁻⁸ m³ kg⁻¹ (V4) to 911 × 10⁻⁸ m³ kg⁻¹ (V6), indicating substantial enrichment in ferrimagnetic particles. Frequency-dependent susceptibility values ranged from 4.1% to 6.5%, confirming a significant contribution of ultrafine superparamagnetic grains. Hysteresis loop parameters (Mrs/Ms = 0.057-0.089; Hcr/Hc = 4.44-6.18) and King plot relationships indicate a dominance of stable single-domain magnetite with a minor superparamagnetic fraction, characteristic of anthropogenic sources such as brake abrasion, fuel combustion, and industrial emissions. Thermomagnetic κ(T) curves revealed Curie temperatures consistent with magnetite and evidence of magnetic enhancement during heating, suggesting the formation of secondary magnetite from combustion-related precursors.

QXRD analysis showed a mineralogical composition dominated by carbonates and silicates, including quartz (24–46 wt%), dolomite (12-36 wt%), calcite (~10-15 wt%), feldspars (6-12 wt%), and muscovite (5-15 wt%), accounting for ~90 wt% of the samples. Minor phases included chlorite, kaolinite, amphiboles, and biotite (7-10 wt%), while iron oxides occurred below the quantitative detection limit of QXRD but were confirmed by XRD, FTIR, and SEM observations.

FTIR and TG-QMS analyses revealed abundant aliphatic C-H functional groups and hydrocarbon fragments indicative of organic matter derived from tire wear, asphalt binders, lubricants, fuel residues, and polymeric materials. TG-derived organic matter contents ranged from 3.0 to 8.6 wt%, closely matching TOC values (2.9-8.6 wt%). SEM-EDS provided direct evidence of microplastic particles, including carbon- and oxygen-rich fibers, irregular polymeric fragments, and significant amount of slag glasses (balls, rods).

The combined magnetic–chemical approach demonstrates that ultrafine road dust acts as an efficient carrier of strongly magnetic particles, C-H-rich organic pollutants, and microplastics. These findings highlight the environmental and health relevance of fine road dust as a vector for inhalable anthropogenic contaminants and emphasize the value of integrating magnetic indicators with mineralogical and organic analyses in urban pollution assessments.

This research was funded in whole by the National Science Centre, Poland under grant number 2021/43/D/ST10/00996.

How to cite: Lempart-Drozd, M. and Dytłow, S. K.: An Integrated Methodological Framework for the Characterization of Fine Urban Road Dust (<50 μm): Magnetic, Mineralogical, and Organic Components, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4442, https://doi.org/10.5194/egusphere-egu26-4442, 2026.

Geostationary Earth Orbit (GEO) satellites offer unique capabilities for capturing diurnal variations and providing valuable insights into aerosol cycles. However, publicly available hourly aerosol products with sufficient accuracy remain scarce across Europe, Africa, and West Asia, primarily due to the lack of shorter-wavelength (< 0.6 µm) channels on the Meteosat Second Generation (MSG) satellite series. Therefore, we developed a novel deep learning framework to retrieve hourly aerosol optical depth (AOD) at 550 nm over land in 2021 from MSG-1/SEVIRI imagery, which offers wider spatial coverage through Indian Ocean Data Coverage (IODC). This framework integrates an advanced time-sequence Transformer architecture with transfer learning, utilizing pre-training and fine-tuning techniques. The eXplainable Artificial Intelligence (XAI) analysis revealed that satellite observations across multiple wavelengths contribute 38% to the AOD retrieval, followed by viewing geometry (34%). In comparison with ground-based AOD measurements, our model achieves high accuracy, with an average ten-fold cross-validation (CV) R² of 0.88 and a root mean square error (RMSE) of 0.079. Additionally, our model maintains strong predictive performance in areas and periods lacking ground-based measurements, as evidenced by strong spatial- and temporal-based CV-R² values ranging from 0.71 to 0.86. The model performance is significantly improved when withholding each continent, showing marked increases in R (0.71–0.78) compared to models trained without transfer learning (0.23–0.47). Using the generated reliable 3-km-resolution AOD datasets, we capture pronounced diurnal aerosol variations, characterized by a gradual increase after sunrise, a peak around 10:00 UTC, and a decline by late afternoon, with average magnitude changes of approximately 26% ± 15% relative to the daily mean level (0.22 ± 0.14) on an annual scale, especially during the Northern Hemisphere summer, reaching 30% ± 19%. More importantly, we successfully tracked the rapid dispersion of aerosols and their transport process throughout the day during highly polluted events, driven by both natural and anthropogenic emissions, including dust storms, wildfires, and urban haze. These findings emphasize the unique value of our study for advancing aerosol research over under-monitored regions, particularly focusing on diurnal variations during extreme events.

How to cite: Fan, Y., Li, Z., Sun, L., Dubovik, O., and Wei, J.:   Enhancing Hourly AOD Retrieval from MSG-1/SEVIRI Imagery Integrating Deep and Transfer Learning, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3824, https://doi.org/10.5194/egusphere-egu26-3824, 2026.

Exposure to pollutants is closely linked to respiratory illness, cardiovascular disease, and premature mortality. Accurate full-field prediction of air pollutant concentrations is essential for assessing exposure to pollution and guide sustainable urban planning. However, the intrinsic interaction among pollutants, hard-to-predict weather patterns, along with limited and randomly placed monitoring stations make this a complex task. While the domain has shifted from traditional interpolation methods towards machine learning algorithms, generation of high-resolution maps remain challenging. In this study, we use hourly available sparse data and apply data-driven techniques to provide faster and accurate reconstruction of four key pollutants - NO₂, O₃, PM_2.5 and PM₁₀. Models are trained on full-field simulation data and evaluated on real-world observations collected from 20-25 monitoring stations in the city of Paris. We propose multi-pollutant modelling using both discriminative and ensemble-based generative approaches, investigate the impact of incorporating historical data into discriminative models, and introduce stochastic modelling via diffusion techniques to capture the variability in spatial distribution. Despite observing anomalies in spatial map and recording noisy observations, the proposed ML models achieve high structural similarity (SSIM) in field reconstruction. By utilizing noise-based augmentation strategy, we facilitate prediction of real-world data without model retraining. The models exhibit superior generalization ability on real-data by predicting realistic pollution patterns on time periods that lie outside the training period. These findings highlight the potential of ML-models for reliable real-world deployment in reconstruction tasks.

How to cite: Sabnis, A. A., Mitrea, M., Lugon, L., Sartelet, K., Bocquet, M., and Cheng, S.: Predicting Air Pollution from sparse and movable observation points using machine learning techniques, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5146, https://doi.org/10.5194/egusphere-egu26-5146, 2026.

Chemical Transport Models (CTMs) are widely used to simulate aerosol mass concentrations, composition, and optical properties at regional to global scales and are fundamental tools for assessing aerosol impacts on climate and air quality. Despite continued advances in CTMs, their performance remains strongly dependent on the accuracy of meteorological inputs, chemical mechanisms, and emission inventories. Differences among biomass burning emission inventories can arise from several factors, such as fire detection methods, fuel consumption estimates from fire radiative power (top-down) or burn area (bottom-up) approaches, assumptions regarding fuel type, combustion completeness, emission factors, and temporal allocation assumptions. Systematic evaluation of biomass burning emission datasets against ground-based observations is therefore essential for identifying region-specific aerosol representation in CTMs. In this study, we use the GEOS-Chem chemical transport model to quantify the sensitivity of simulated Aerosol Optical Depth (AOD) to biomass burning emission inventories during the 2019 wildfire season over North America. We perform simulations driven by two recently developed biomass burning emission products from Environment and Climate Change Canada (ECCC): the Canadian Forest Fire Emissions Prediction System (CFFEPS) and the Global Forest Fire Emissions Prediction System (GFFEPS). We compare these against simulations driven by three commonly used global inventories (the Global Fire Emissions Database version 4 (GFED4), the Global Fire Assimilation System (GFAS), and the Quick Fire Emissions Dataset version 2 (QFED2)). Model output is evaluated against quality-assured Level 2 AOD observations from 138 NASA Aerosol Robotic Network (AERONET) stations across Canada and the United States. The evaluation reveals substantial regional variability in model performance across emission inventories. Over Canada, simulations driven by CFFEPS and GFFEPS exhibit the strongest agreement with observations, particularly in northern and western regions, where correlations reach values of up to ~0.88 and normalized mean errors are as low as ~30%–49%, while simulations using other global inventories generally show larger normalized errors. Across the United States, GFAS-driven simulations achieve correlations of approximately 0.6–0.7 in the western and eastern regions, while all inventories exhibit reduced skill over the central United States. Overall, these results demonstrate the strong sensitivity of simulated AOD to biomass burning emission datasets and emphasize the importance of regionally optimized fire emissions for accurately representing aerosols in chemical transport models.

How to cite: Ashraf, S., Hayes, P., Payette, T., Stevens, R., and Chen, J.: Sensitivity of Simulated Aerosol Optical Depth to Biomass Burning Emission Inventories over North America, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8664, https://doi.org/10.5194/egusphere-egu26-8664, 2026.

Accelerating the energy transition toward a new electricity system dominated by renewable energy, with coal power serving as a flexible backup, is a critical strategy to synergize pollution control and carbon reduction. This study couples a power system model with WRF-CMAQ to reveal the power system's response to renewable energy droughts and assess associated environmental impacts. Our findings reveal that while large-scale development of renewable energy will contribute to the reduction of anthropogenic emissions and overall air quality improvement across China, it also introduces regional increasing pollution. The annual average PM_2.5 concentrations exhibit significant increases in the Border Area of Jiangsu, Anhui, Shandong, and the Twain-Hu Basin, accompanied by a rise in the frequency of mildly polluted days in the Twain-Hu Basin and the Sichuan Basin. Moreover, the Twain-Hu Basin experiences a nearly tenfold surge in short-term severe air pollution episodes compared to the baseline scenario. These regional pollution spikes are linked to renewable energy droughts triggered by extreme low-wind, extreme low-radiation, and compound low-wind-low-solar events. Our research underscores that while advancing the integration of wind and solar, it is essential to conduct regional environmental risk assessments across multiple time scales and enhance extreme weather early warning and emergency response mechanisms.

How to cite: Xin, X. and Tong, D.: Renewable energy droughts shape the air pollution patterns through power system response, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6336, https://doi.org/10.5194/egusphere-egu26-6336, 2026.

Solar ultraviolet (UV) radiation at Earth’s surface poses a well documented risk for human health [1]. The World Health Organization has defined the UV-
Index to quantify the amount of UV radiation as integer numbers in a range of typically 1 to 10 [2].

The UV-Index is typically forecasted on the scale of days to warn the public in the case of high UV-Index values. In Germany this is done by Deutscher Wetterdienst (DWD) who use their weather model ICON (ICOsahedral Nonhydrostatic model) [3] in combination with external datasets for Ozone forecasts and UV radiation calculations [4].

In order to make the UV-Index forecast more self-consistent, we present a setup that provides atmospheric ozone via the LINearized OZone (LINOZ) scheme [5] that is used for UV radiation calculations via the Cloud-J scheme [6] from within ICON and the coupled Aerosols and Reactive Trace gases (ART) module [7].

The result is a setup that can forecast ozone and UV-Index fields for a time frame of January to April 2025 with a precision of ±1 for 94.9% of the data points in comparison to ground measurement stations. Ozone columns stay within 5% agreement for a time frame of four months in the northern hemisphere in comparison to Ozonewatch satellite data.

We use this setup for an analysis of the influencing factors on UV radiation that finds that the solar zenith angle is the quantity that introduces most variability on the UV-Index. Aerosol optical depth, cloud cover and overhead ozone introduce smaller variabilites while the effect of surface albedo and altitude is even less pronounced. A comparison of the novel setup to the operational forecast by DWD agrees within ±2 units of UV-Index for almost all data points with the exception of larger differences in mountainous areas.

References:
[1] Mohammed Ahmed Sadeq et al. Causes of death among patients with cutaneous melanoma: a US population-based study. Scientific Reports 2023 13:1, 13(1):1–11, 6 2023.

[2] Report of the WMO-WHO Meeting of Experts on Standardization of UV Indices and their Dissemination to the Public. Technical report, 1997.

[3] Günther Zängl et al. The ICON(ICOsahedral Non-hydrostatic) modelling framework of DWD and MPI-M : Description of the non-hydrostatic dynamical core. Quarterly Journal of the Royal Meteorological Society, 141(687):563–579, 1 2015.

[4] Henning Staiger et al. UV index forecasting on a global scale. Meteorologische Zeitschrift, 14(2):259–270, 4 2005.

[5] C. A. McLinden et al. Stratospheric ozone in 3-D models: A simple chemistry and the cross-tropopause flux. Journal of Geophysical Research: Atmospheres, 105(D11):14653–14665, 6 2000.

[6] M. J. Prather. Photolysis rates in correlated overlapping cloud fields: Cloud-J 7.3c. Geoscientific Model Development, 8(8):2587–2595, 8 2015.

[7] Jennifer Schröter et al. ICON-ART 2.1: A flexible tracer framework and its application for composition studies in numerical weather forecasting and climate simulations. Geoscientific Model Development, 11(10):4043–4068, 10 2018.

How to cite: Hanft, V., Ruhnke, R., Seifert, A., and Braesicke, P.: Forecasting the UV-Index and Analyzing its Dependence on Influencing Factors, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11413, https://doi.org/10.5194/egusphere-egu26-11413, 2026.

Oxidative potential (OP) is a critical metric for assessing the health effects of particulate matter (PM) pollution. Among the various OP assays, the dithiothreitol (DTT) assay is the most widely used and several online systems have been developed. However, existing online systems either lack sufficient reliability for field deployment or offer relatively low temporal resolution, typically around 1 hour. This study presents an automated online DTT measurement system designed for minute-scale monitoring of PM_2.5 OP with enhanced sensitivity. The system consists of three main components: a Particle-Into-Liquid Sampler (PILS) for PM_2.5 collection, a dual-path incubation module with precisely controlled reaction temperatures (37°C) and durations (~5 and ~10 minutes), and an absorbance-based detector utilizing a liquid waveguide capillary cell (LWCC). This dual-path design enables DTT consumption measurement at two time points within a 15-minute cycle. Assay optimization was conducted to improve the sensitivity of the system. System calibration demonstrated strong correlation (R² > 0.99) and repeatability for DTT detection (0-10 μM) with rapid response. Validation using standards 9,10-phenanthraquinone and Cu²⁺ solutions showed excellent correlations between species concentrations and DTT consumption (R² > 0.99), with strong agreement between online and offline methods. Field deployment further confirmed the system's capability for real-time monitoring and long-term atmospheric OP observation

How to cite: Liu, J. and Fang, T.: Development of an Automated Online System for Minute-Resolution Measurement of PM2.5 Oxidative Potential Using the DTT Assay, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10311, https://doi.org/10.5194/egusphere-egu26-10311, 2026.

CO₂ sequestration technologies (CSTs) allow for increased CO₂ emissions without exceeding a chosen temperature limit by creating additional carbon budgets. While these technologies offer low-cost routes to net-zero emissions (i.e., CST benefits), they impede progress toward the Sustainable Development Goals (i.e., CST disbenefits). Focusing on China, we assess both the disbenefits and benefits of CSTs across the climate-energy-air-health cascade using an integrated modeling framework. We show that CSTs can save 4.98–15.65 trillion CNY in achieving net-zero emissions but compromise sustainability in non-fossil energy penetration, air quality, and public health improvement, resulting in a total loss of up to 7.82 trillion CNY during 2020–2060. Given the high likelihood of large-scale CST deployment in the future, pursuing policy coherence to balance trade-offs between disbenefits and benefits is vital. To that end, CSTs should be prioritized in the power sector, and stringent end-of-pipe equipment should be retrofitted in non-power sectors before CST allocation.

How to cite: Wang, F.:  Assessment of Climate-Energy-Air-Health Co-benefits and Trade-offs of CO₂ Sequestration Technologies in China, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13970, https://doi.org/10.5194/egusphere-egu26-13970, 2026.

Air pollution, particularly fine particulate matter (PM2.5), presents a significant environmental health risk in Central Asia, where scientific understanding and monitoring systems remain limited. This study provides a comprehensive analysis of PM2.5 concentrations and their associated health impacts in the capital cities of Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan, and Uzbekistan – Astana, Bishkek, Dushanbe, Ashgabat, and Tashkent – during the period 2010-2019. A fused dataset combining satellite-derived estimates with CAMS reanalysis outputs was developed to produce daily, spatially refined PM2.5 concentrations for the region.

All five cities exceeded the WHO annual guideline of 5 µg/m³, with Ashgabat and Tashkent recording the highest annual averages, surpassing 25 µg/m³. Using the AirQ+ model, a Health Impact Assessment (HIA) was performed for chronic obstructive pulmonary disease (COPD) and lung cancer (LC) in adults aged 25 and older. Attributable COPD mortality under the background pollution scenario (2.4 µg/m³) reached up to 34 cases per 100,000 in Bishkek, with attributable proportions ranging from 9% to 21% across cities. LC burdens, although lower in absolute numbers, showed attributable fractions between 9% and 20% in Ashgabat and Tashkent, corresponding to 2 - 3 deaths per 100,000 – values comparable to estimates from studies in similarly polluted urban areas.

Scenario analysis revealed that reducing PM2.5 to the WHO guideline of 5 µg/m³ would cut COPD and LC mortality by up to 80% in the most polluted cities. Notably, even moderate reductions, such as reaching the WHO Interim Target-3 of 15 µg/m³, already yielded substantial health benefits. These findings emphasize the urgent need for targeted air quality interventions and stricter regulatory standards across Central Asia.

How to cite: Vega Garcia, L., Caseiro, A., Schmitz, S., Lawrence, M., and von Schneidemesser, E.: A Comparative Analysis of Air Quality (PM2.5) and Its Health Impact in Central Asia, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17411, https://doi.org/10.5194/egusphere-egu26-17411, 2026.

The generation of reactive oxygen species (ROS), particularly superoxide radical anion (·O₂^-), is a primary driver of particulate matter (PM) toxicity. However, traditional toxicological assessments are often limited by static endpoint measurements and high experimental doses, failing to capture the dynamic reaction kinetics relevant to real-world ambient exposure. To bridge this gap, we developed a high-temporal-resolution machine learning model based on XGBoost, trained on a comprehensive dataset comprising over 60,000 kinetic data points of ·O₂^- generation. The model demonstrated robust predictive performance (R² > 0.8) on the testing set, proving its capability to capture complex, non-linear kinetic patterns. Specifically, the model successfully reproduced the non-monotonic inverted V-shaped dose-response of Isoprene SOA and 9,10-Phenanthrenequinone (PQN) and accurately captured the antagonistic effects in PQN-Fe²⁺ mixtures, distinguishing these complex interactions from simple additive effects. Ongoing work focuses on applying this validated model to extrapolate cell-based kinetic data to environmentally relevant scenarios in human respiratory tract. We will first calculate the pollutant burden across different respiratory regions (e.g., trachea, bronchi, alveoli) by integrating ambient PM concentration data with a lung deposition model. We will then simulate region-specific ·O₂^- generation profiles by incorporating varying cell densities and analyzing kinetic parameters. Ultimately, this study aims to develop a model that translates ambient data into physiologically relevant oxidative profiles, providing a precise and cost-effective strategy for screening region-specific respiratory health risks.

How to cite: Miao, Y., Shen, Y., and Fang, T.: A Kinetic Machine Learning Model to Simulate PM-Induced Cellular ROS Generation Across the Human Respiratory Tract, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15556, https://doi.org/10.5194/egusphere-egu26-15556, 2026.

Rural environments play a critical role in shaping regional air quality and atmospheric chemistry due to their distinct emission sources, seasonal activities, and meteorological conditions. This study investigates the seasonal variability of PM_2.5 mass concentrations and their associated major and trace elements in a rural region of Uttar Pradesh, India, based on day- and nighttime sampling conducted from July 2023 to May 2024. A total of 135 samples were collected and analysed for elemental composition. In total, 31 elements (Li, B, Na, Mg, Al, P, K, Ca, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Se, Rb, Sr, Y, Zr, Mo, Ag, Cd, Sn, Sb, Ba, Pb, Bi) were quantified using ICP–MS.

Seasonal analysis revealed that nighttime PM2.5 levels were consistently higher than daytime concentrations (1.3–1.5 times higher), with the highest mass loadings observed during the post-monsoon season, followed by the winter, pre-monsoon, and monsoon seasons. In contrast, elemental concentrations peaked in winter despite lower PM_2.5 mass relative to the post-monsoon period, indicating stronger influences from combustion and industrial emissions along with reduced atmospheric dispersion. Enrichment factor (EF) analysis revealed a strong enrichment of anthropogenic elements (K, Cu, Zn, Pb, Sb, Bi), while crustal elements (Ca, Al, Fe, Mg, Ti) exhibited low EF values, confirming a significant contribution from soil and resuspended dust. FTIR analysis further revealed seasonal shifts in functional groups, with higher contributions of organic and carbonyl species in winter and post-monsoon periods.

Principal Component Analysis (PCA) identified major source categories—including biomass burning, industrial emissions, and traffic-related sources in agreement with EF-derived source signatures. HYSPLIT back-trajectory analysis further demonstrated the influence of regional long-range transport on seasonal aerosol composition. SEM–EDX morphological analysis also revealed clear seasonal differences in particle size and structure, with substantially higher particle loading during the winter and post-monsoon seasons.

Health risk assessment indicated substantially elevated risks during winter, with carcinogenic risk increasing by ~1.8-fold relative to post-monsoon and ~2.3-fold relative to monsoon, and non-carcinogenic hazards rising by 0.5–7.6-fold across seasons. Elements such as Pb, Cr, V, and Mn were the dominant contributors to both carcinogenic and non-carcinogenic risks.

Overall, the findings highlight the significant influence of seasonal emission patterns, combustion activities, and atmospheric dynamics on shaping the composition of rural aerosols and their associated health impacts.

Keywords: Particulate matter; Elements Analysis: Principal component analysis; Enrichment factor; Health risk assessment.

How to cite: Sharma, R., Puttaswamy, H., and Tyagi, S.: Influence of Seasonal Emissions, Regional Transport, and Particle Morphology on PM2.5-Bound Elements in Rural Northern India, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21118, https://doi.org/10.5194/egusphere-egu26-21118, 2026.

Ambient fine particulate matter (PM_2.5) exposure is a leading global health risk, requiring integrative frameworks that link atmospheric science, epidemiology, and socioeconomic analysis to enable effective mitigation. Despite this global attention, Central Asia faces a knowledge gap regarding the dominant sources of PM_2.5 pollution due to the scarcity of related research. This study applies advanced source apportionment, health impact modeling, and multi-criteria decision-making analysis to quantify the health and economic burdens and mitigation strategies resulting from persistent PM_2.5 pollution in Kazakhstan’s two largest urban centers, Almaty and Astana.

The study employed Positive Matrix Factorization (PMF), HYSPLIT trajectory, and Conditional Probability Function analysis to characterize PM_2.5 sources [1]. An extensive year-long sampling campaign (August 2022–July 2023) confirmed that annual PM_2.5 concentrations exceeded the World Health Organization (WHO) guideline (5 µg/m³) by 7.1 times in Almaty and 3.9 times in Astana. PMF identified five distinct sources in both cities, with coal and biomass combustion emerging as the overall predominant contributors to PM_2.5. In Almaty, sources included the urban atmosphere (20%), power plants (18%), residential heating (16%), and exhaust emissions (14%), with the valley topography exacerbating pollutant accumulation. In Astana, contributions were distributed among heating, regional/local power plants, and traffic emissions (exhaust/non-exhaust), each contributed 20%, and industrial emissions (18%), with HYSPLIT analysis confirming the influence of regional industrial emissions originating from areas such as Karagandy and Pavlodar.

The subsequent health risk assessment, quantified using the Global Exposure Mortality Model (GEMM), showed that PM_2.5-attributable excess mortality was 2108±144 deaths annually in Almaty and 676±41 deaths in Astana (2022-2024) [2]. These fatality rates significantly exceeded those from road traffic accidents and HIV/AIDS in both cities. The corresponding economic losses, quantified using the Value of Statistical Life (VSL) approach, were estimated at USD 2.8-4.6 billion per year for Almaty and USD 0.9-1.5 billion for Astana. Achieving the WHO limit could prevent 1642-2195 deaths and yield annual economic savings exceeding USD 3.8 billion.

The DEMATEL-ANP analysis, based on responses from 20 international experts, assessed the interaction among key mitigation measures [2]. It identified that effective air quality policies must prioritize pollution-purification efficiency, manage capital costs, and minimize the risks of secondary pollution, identified as the primary criteria driving systemic improvements. The findings emphasize the urgent need for comprehensive air quality management, particularly fossil fuel phase-out initiatives. High-capital interventions, such as the planned modernization of Almaty's CHPP-2 to gas in 2026, are critical, as the resulting economic savings from reduced health burdens (USD 1066-6300 million) significantly exceed the modernization cost (USD 703.6 million).

Acknowledgments

This research was funded by the Science Committee of the Ministry of Higher Education and Science of the Republic of Kazakhstan (Grant No. AP23486720, 2024-2026).

References

 [1]   K. Tursun et al. Dominant sources of PM_2.5 in Kazakhstan’s urban cities: A PMF and HYSPLIT-based study for air quality management in Central Asia, Urban Clim 64 (2025) 102706.

[2]   A. Muratuly et al. Urban PM_2.5 pollution in Kazakhstan: health burden and economic costs, Environ. Sci: Adv. (2025).

How to cite: Baimatova, N., Radelyuk, I., Ibragimova, O. P., and Tursun, K.: The Health and Economic Cost of PM2.5 in Kazakhstan: Identifying Source-Driven Mitigation Priorities, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1958, https://doi.org/10.5194/egusphere-egu26-1958, 2026.

The rising frequency and severity of wildfires have intensified concerns regarding their adverse impacts on public health. However, accurately quantifying acute wildfire smoke exposure and its associated health impacts remains challenging due to limitations in existing high-resolution exposure data. Here, we develop a novel Cascade-Machine-Learning framework to generate unprecedented hourly wildfire-specific PM_2.5 concentrations at a 1 km × 1 km resolution across California, achieving substantial accuracy improvements over traditional chemical transport models and satellite-derived datasets. Leveraging this high-resolution dataset with health records from the University of California, we identify critical relationships between short-term wildfire smoke exposure and acute pneumonia-related health risks. Notably, we introduce a new exposure metric, P_max, capturing the intensity of hourly peak exposures relative to daily accumulated exposure, and reveal that short-lived, pulse-type wildfire smoke events are associated with nearly tenfold higher pneumonia-related medical risks compared to sustained exposure at equivalent daily average concentrations. Our results further highlight heightened vulnerability among individuals younger than 18 years and the African American populations. This work underscores the urgent need for temporally detailed exposure assessment in wildfire health studies and provides a robust scientific foundation for targeted public health interventions and emergency preparedness in an era of intensifying wildfire risks.

How to cite: Wang, L., Chen, X., and Jiao, A.: Cascade-Machine-learning quantifies hourly wildfire smoke exposure and acute health risks in California, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-947, https://doi.org/10.5194/egusphere-egu26-947, 2026.

 In recent years, air pollution control in China has advanced substantially. While these efforts have led to pronounced reductions in particulate matter concentrations, ozone (O₃) levels have increased significantly. Many previous studies have suggested that reductions in nitrogen oxides (NOₓ) may be a major driver of the observed increase in O₃, thereby highlighting the importance of controlling volatile organic compounds (VOCs). Using Beijing, a Chinese megacity, as a case study, this work analyzes long-term observational data from 2009 to 2024 to investigate the temporal evolution of O₃ and its precursors (NOₓ and VOCs) and their interrelated response characteristics.

The results show that during 2009–2015, both the maximum daily 8-hour average ozone (MDA8 O₃) concentration and total oxidant (Oₓ) increased rapidly, at rates of 8.9% yr⁻¹ and 5.4% yr⁻¹, respectively. After 2015, the growth rates slowed markedly and both metrics exhibited high-level fluctuations (O₃: 2.9% yr⁻¹; Oₓ: −0.9% yr⁻¹). Further stratification by pollution level and temperature reveals that, since 2015, the 90th percentile of O₃ and O₃ concentrations under high-temperature conditions (≥25 °C) have shown declining trends (90th percentile: −0.47% yr⁻¹; ≥25 °C: −0.76% yr⁻¹), with the decreases mainly occurring during midday high-ozone periods.

A combined analysis of the response relationships between O₃ and NOₓ, together with photochemical reactivity indicators, indicates that 2015 represents a turning point at which O₃ formation sensitivity in Beijing shifted from a VOC-limited regime toward a transitional, co-limited regime. Looking ahead, further reductions in NOₓ emissions from natural gas–fired power plants and mobile sources—particularly diesel vehicles and non-road mobile machinery—will be critical for effective O₃ pollution mitigation in Beijing. This study provides valuable insights and practical experience for photochemical air pollution control in megacities worldwide.

How to cite: kang, Y. and tang, G.: Sixteen Years of Ozone Changes: Photochemical Pollution Control Experience in Beijing, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3217, https://doi.org/10.5194/egusphere-egu26-3217, 2026.

The global premature mortality attributable to air pollution and heatwaves is substantial, yet determining which driver exerts a larger impact remains a complex task. Here, mortalities associated with heatwaves, fine particulate matter (PM_2.5), and ozone (O₃) are estimated from storylines of air pollution with constant anthropogenic emissions, comparing a factual scenario for 2018-2019 to two warmer scenarios at +2K and +2.75K above pre-industrial levels. The mortality evaluation reveals regional disparities: in Asia and Africa, air pollution far outweighs heatwaves. Conversely, in Europe, heatwave effects dominate. In warmer worlds, heatwaves control changes in aggregated mortality but are partially offset by improved air quality, especially in a +2.75K scenario. The shape of the air quality indices-population-exposure curve reveals the industrialization level, indicating the degree of population exposure risk. These findings highlight the need for region-specific adaptation strategies that address both air pollution and climate change exposures to effectively reduce the global mortality burden.

How to cite: Shen, F., Hegglin, M., Emmerichs, T., Taraborrelli, D., and Zhao, Q.: How the global health burden changes due to heatwaves and air pollution in a warmer world, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11717, https://doi.org/10.5194/egusphere-egu26-11717, 2026.

Ground-level ozone pollution poses significant public health risks globally, necessitating spatially-resolved governance strategies. Utilizing real-time monitoring data from China's National Environmental Monitoring Network (2017–2021), this study establishes localized exposure-response relationships for Shenzhen through meta-analytic synthesis and BenMAP-CE modeling, quantifying spatial-temporal health burdens. The results found that: (1) the ozone concentration in Shenzhen exhibited fluctuating patterns between seasons and years, indicating an overarching decline between 2017 and 2021 and a peak annual average was observed in 2019; (2) the disease- risk hierarchy was found that cardiovascular mortality with the highest susceptibility (RR=1.0092), followed by respiratory diseases (RR=1.0063), and all-cause non-accidental mortality (RR=1.0046); and (3) severe health burdens were mainly concentrated in western industrial zones in Shenzhen. The study provides insights into the spatial-temporal distribution of ozone pollution and its health impacts in Shenzhen, and the results confirm that ozone control must prioritize megacity emission hotspots and seasonal peaks. Future research should integrate microenvironmental exposure assessment, toxicological mechanisms, and demographic stratification to advance spatially-precise governance in megacities.

How to cite: Wang, H., Fu, J., Huang, H., Zhang, J., Tan, X., Ni, H., and Wu, J.: Spatiotemporal variations of ground-level ozone on public health in megacities: a continuous analysis of Shenzhen, China, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21844, https://doi.org/10.5194/egusphere-egu26-21844, 2026.