Ozone and particulate nitrate, a key component of PM2.5, form through non-linear chemical interactions, with ozone formation governed by nitrogen oxides (NOx) and volatile organic compounds (VOCs), and particulate nitrate involving NOx (forming HNO3) and ammonia (NH3). This study utilise a multi-satellite observational approach to understand the formation response of ozone and particulate nitrate to their precursors over Delhi, world’s most polluted capital. We utilize Level-2 satellite data for NO2 and HCHO from TROPOMI (Sentinel-5P) and GEMS and NH3 from IASI (MetOp-B) over the spatial domain of 28°-29°N and 76.5°-77.8°E for 2023. Surface observations of ozone and PM2.5 were obtained from CPCB monitoring stations across Delhi after applying multi-level filtration. High resolution maps (1km × 1km) of NO2, HCHO, and NH3 along with their ratios (HCHO/NO2 and NH3/NO2) were generated, and their time series were extracted around each site locations ( 50 ppbv) during spring shows elevated NO2 (~0.1 DU) and HCHO (~0.2 DU) levels, confirming photochemical production. Diurnal variation in NO2 and HCHO from GEMS, highlights seasonal and meteorological influence, with HCHO bias indicating a predominantly VOC limited regime. The spatial gradient of NO2 (NO2/distance) highlights strong sinks in hotspot regions, particularly in winter and spring. A notable decline of 10-60% in ozone concentrations from spring to winter in these sink areas suggests substantial NOx-driven titration under low sunlight conditions. Site classification into urban, rural, and highway-proximal (within 500 m) categories shows consistently higher NO2 and HCHO levels in urban areas across all seasons, followed by highway sites. For particulate pollution, particulate nitrate, a secondary inorganic aerosol was found to significantly contribute to PM2.5 across seasons. PM2.5 levels peaked in autumn at all sites, followed by winter. Binned HCHO averages were higher in autumn, aligning with PM2.5 peaks and suggesting a biogenic contribution during extreme pollution events. Conversely, elevated NO2 during winter points towards a dominant inorganic and anthropogenic influence on PM2.5 enhancement in form of particulate nitrate.

Abstract. Nitrogen oxides (NOx = NO + NO2) are key pollutants that contribute to ozone and secondary aerosol formation, posing environmental and health risks. Accurate simulation and forecasting of NOx pollution is essential for developing mitigation strategies. Local inventories in Thailand are infrequently updated, leading researchers to use global inventories such as CAMS-GLOB-ANT for simulation. Global inventories carry uncertainties due to assumptions in emission factors, outdated activity data, and coarse temporal resolution. To address these limitations, this study applies a top-down approach to update NOx emissions in Thailand using the iterative finite difference mass balance (IFDMB) method. Tropospheric NO2 vertical column densities (VCDs) from the GEMS are integrated with the WRF-Chem to refine CAMS-GLOB-ANT emissions for September 2023. The simulations with posterior emissions are evaluated against TROPOMI NO2 VCDs and surface NOx concentration. Results show that the baseline simulation overestimates NO2 VCDs across Thailand compared with GEMS, except in North Thailand. Consequently, IFDMB reduces NOx emissions across most regions but increases in the North. These adjustments improve model bias and error relative to GEMS. However, when evaluated against TROPOMI, we find an increase in the bias for North Thailand, likely due to discrepancies between GEMS and TROPOMI retrievals. Discrepancies between GEMS and TROPOMI highlight the importance of future calibration across satellite products. Comparisons to surface observations indicate that IFDMB shifts the NOx peak to later than observations. This is because observations are strongly influenced by local transportation sources, which are hard to observe and simulate by GEMS and the model, respectively.

Abstract Accurate characterization of aerosol optical depth (AOD) uncertainties is critical for air quality assessment, data assimilation (DA), and environmental studies. In this study, we evaluate two sets of AOD products retrieved from Suomi‐NPP VIIRS over Africa. Specifically, we compare the products of NASA's Dark Target (DT) and Deep Blue (DB) algorithms with co‐located AERONET observations from 2020 to 2024 over Africa. AERONET visible–near‐IR Angstrom Exponents (AE) shows a bimodal distribution and strong monthly variability, with fine‐mode dominance in August–September and coarse‐mode dominance in March–April in this region. When the VIIRS retrievals are collocated with AERONET over Africa, DB shows a slight overestimation with DT showing a slight underestimation. When examined by AOD value range, DB shows a low bias under heavier aerosol loading, whereas DT exhibits a wider data spread and a less pronounced low bias at higher AOD values. Overall, DB demonstrates a higher correlation and a smaller expected error (EE) envelope compared to DT. Analysis of monthly uncertainty indicates that fine‐mode‐dominated months, particularly August, September, and October, which also contain the largest number of moderate to heavy aerosol loading cases, exhibit the lowest uncertainty in the DB retrievals, highlighting the improved performance of the updated algorithm. Our analysis shows that, for both DT and DB, AOD retrieval uncertainties are related to the observed AE, suggesting mismatches between algorithm assumptions and the actual dominant aerosol mode, particularly for coarse and mixed‐mode aerosols.

This study presents a landslide susceptibility assessment using multiple parameters by integrating remote sensing, GIS, and field observations in the Jammu and Kashmir region. Eight contributing variables were selected for landslide susceptibility analysis: Land Use and Land Cover (LULC), proximity to roads, streams, slope gradient, slope orientation (aspect), geology, geomorphology, and elevation. In addition, an extensive landslide inventory consisting of 669 landslide events was developed using the Field Landslide Inventory Mapping (FLIM) application, LISS-IV satellite data, and field observations, covering an area of 42,950.43 km². Landslide susceptibility mapping (LSM) was carried out using the Analytical Hierarchy Process (AHP) approach and validated with MaxEnt software and field-generated landslide data. The resulting landslide susceptibility map was classified into five categories: very high, high, medium, low, and very low susceptibility zones. Based on the AHP approach, these zones cover 3.65% (1,569.0483 km²), 24.43% (10,492.8912 km²), 51.56% (22,147.2369 km²), 18.81% (8,079.2199 km²), and 1.54% (662.0301 km²) of the study area, respectively. The weighted overlay and MaxEnt models proved effective for landslide vulnerability mapping, with MaxEnt achieving AUC values of 0.82 for training data and 0.807 for testing data, indicating good predictive performance. Field validation further showed that 87% of landslides occurred within high and very high susceptibility zones. The Jackknife test identified road proximity, slope, and stream proximity as the most significant independent variables influencing landslide occurrence. The generated landslide susceptibility map provides important insights for reducing landslide risk and serves as a valuable tool for infrastructure planning, community development, and disaster management in the region.

Abstract. A computationally inexpensive, observation-constrained parameterization for Secondary Organic Aerosols (SOA) formation is implemented and tested in the default MOZART–GOCART (MOZCART) chemical mechanism of the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem). The outcomes were evaluated against hourly observations of SOA, Organic Aerosols (OA) and Fine particulate matter (PM2.5) over Delhi during November 2024. Sector-specific Emissions ratios (ERs) derived from in-situ Volatile Organic Compounds (VOC) measurements in Delhi were used, with values of 77 ± 5, 130 ± 13, and 60 ± 9 ppbv VOC/ppmv CO for transportation, biomass burning, and industry-dominated plumes, respectively, to represent SOA formation from anthropogenic and open biomass burning precursors. The modified MOZCART scheme reproduces the temporal variability, with a monthly mean simulated concentration of 53 ± 24 µg/m3 compared to an observed mean of 83 ± 43 µg/m3 (RMSE = 58.2 µg/m3; MFB = −0.53). Inclusion of SOA parameterization substantially improves total organic aerosol (OA), increasing mean OA from 48 to 101 µg/m3 and reducing normalized mean bias from −57.8 % to −19.1 %. PM2.5 predictions also improve, with mean concentrations increasing from 151 to 203 µg m⁻³ and mean bias reduced by ~54 %, alongside better reproduction peak pollution events. Intercomparison with other WRF-Chem mechanisms shows that MOZCART achieves SOA performance comparable to the more complex MOZART–MOSAIC scheme (RMSE = 58.2 vs. 54.8 µg/m3) and substantially better than RADM2–SORGAM, while being ~5.3 times faster than MOZART–MOSAIC. These results demonstrate that the proposed simplified SOA parameterization provides an effective balance between accuracy and computational efficiency and can be effectively used in operational air quality forecasting over highly polluted urban regions like Delhi.

Present-day road transportation is largely fueled by traditional fossil fuels and has unintended adverse effects through emissions of greenhouse gases, anthropogenic heat, and air pollutants, thereby affecting heat, air quality and public health. A thorough assessment of current environmental and health burdens helps clarify how future transport strategies could improve outcomes. Here, four case studies are presented – greenhouse gas emissions in the UK, anthropogenic heat emissions in Chicago (US), air pollutant emissions in Indian megacities, and air pollutant concentrations and associated health burden in the West Midlands (a major metropolitan area in the UK). By examining diverse regions worldwide, this chapter highlights that while mitigating road transportation impacts is a global challenge, its influence on emissions, air quality, heat waves, and public health is shaped uniquely by regional challenges and policy outcomes, and so addressing this requires region-specific targeted interventions.

Mineral dust is a major atmospheric aerosol influencing climate, air quality, and human health through radiative and microphysical processes. The Iberian Peninsula is frequently affected by North African dust intrusions, leading to episodic PM10 exceedances that challenge air quality forecasting. However, accurate representation of dust emissions remains limited by uncertainties in soil erodibility, land surface properties, and meteorological forcing. This study evaluates the impact of two high-resolution soil erodibility datasets on dust simulations using WRF-Chem with the GOCART scheme. The first dataset, EROD-HR, integrates fine-resolution topography to improve dust source representation at 0.0625° (about 5 km) globally and 1 km over the Iberian Peninsula. The second dataset, SOILHD, further refines dust source characterization by incorporating high-resolution soil texture (sand, silt, and clay fractions) and removing misclassified bare-soil areas, resulting in a 1 km global resolution. Both datasets aim to better capture the spatial heterogeneity of dust sources. Simulations are conducted for five dust episodes between 2022 and 2025, covering local and long-range transport conditions. Model performance is evaluated against PM10 observations from the SINQLAIR network in the Region of Murcia. Results show improved representation of dust emissions, with better agreement in magnitude and timing of PM10 peaks at inland stations. Improvements are more limited at coastal and anthropogenically influenced sites, although statistical metrics (correlation, bias, RMSE) indicate consistent gains. Overall, high-resolution erodibility datasets enhance dust simulations by reducing biases and improving variability representation, highlighting the importance of detailed land-surface information for regional dust forecasting systems.

Systematic and random errors remain in operational predictions of fine particulate matter (PM2.5) and ozone (O3) from the Community Multiscale Air Quality (CMAQ) model. Current post-processing methods at the National Air Quality Forecast Capability (NAQFC) apply the analog ensemble (AnEn) technique only at observation sites, followed by spatial interpolation to produce gridded bias-corrected fields. This approach, however, can degrade performance in regions with sparse monitoring or discontinuous bias patterns, such as during wildfire smoke events.This study evaluates a novel framework in which AnEn is applied directly at every CMAQ grid point, leveraging bias-corrected “ground truth” fields derived by merging AirNow observations with either Copernicus Atmosphere Monitoring Service (CAMS) analyses or CMAQ analyses using the Satellite-Enhanced Data Interpolation (SEDI) technique. Year-long evaluations for 2021 across the contiguous United States demonstrate that all bias-corrected methods substantially improve PM2.5 and O3 forecasts relative to raw CMAQ. Among them, AnEn driven by bias-corrected CMAQ analyses consistently yields the best overall performance across metrics, while AnEn with CAMS inputs performs best during extreme PM2.5 episodes associated with wildfire smoke. For ozone, bias correction improves daytime forecasts, though challenges remain at night due to nonlinear chemistry and sparse observations. These findings highlight the effectiveness of gridded AnEn post-processing for improving operational CMAQ forecasts, particularly for PM2.5, and emphasize the need for pollutant-specific strategies. The proposed framework provides a pathway toward more accurate and spatially complete air quality information to better support public health protection

We present MATCHA (Model for Atmospheric Transport and Chemistry in Asia), a 17-year (2003–2019) regional hydroclimate-chemical reanalysis for Asia (58–140° E, 4– 40° N) at 12 km resolution. MATCHA couples the Weather Research and Forecasting model with Chemistry (WRF-Chem), Community Land Model (CLM), and SNow, Ice, and Aerosol Radiative (SNICAR) model, and assimilates aerosol optical depth (AOD) from the Moderate Resolution Imaging Spectroradiometer (MODIS) and carbon monoxide (CO) profiles from the Measurement of Pollution in the Troposphere (MOPITT) every three hours, to explicitly represent interactions between atmospheric composition and regional hydroclimate (including aerosol-snowpack interactions) across High Mountain Asia (HMA). MATCHA comprises hourly surface and column-integrated fields and 3-hourly three-dimensional fields across different light-absorbing aerosol species, e.g., black carbon (BC), dust, and brown carbon (BrC), trace gases, and a broad set of meteorological, hydrological, and land-surface variables over the region. We evaluate 12 key variables in the reanalysis against in-situ and satellite observations. Surface and upper-air meteorology is reproduced well, with Kling-Gupta efficiencies (KGEs) of 0.65 to 1, although high-elevation regions show a persistent winter cold and dry bias and too-strong surface winds. Snow cover fraction seasonality is captured across the major glacier regions, with a slight underestimation during snowmelt, and daily precipitation agrees most closely during the monsoon (KGE up to 0.6). MATCHA reproduces the spatial and seasonal patterns of AOD and single scattering albedo (SSA) at 550 nm but overestimates summer AOD over India and Southeast Asia; surface PM2.5 and PM10 are biased high and surface CO is underestimated relative to observations. A distinguishing feature of MATCHA is a set of tagged BC tracers that attribute concentrations to specific emission sectors and source regions. These tracers show anthropogenic BC peaking in winter (Chinese sources over eastern and northern HMA, Indian sources over the west and center) and biomass-burning BC dominating in March-April. MATCHA is the first high-resolution reanalysis over HMA to fully couple aerosols, radiation, and snow. It supports research on aerosol-cryosphere feedbacks, air quality, and hydroclimate over a region where observations are sparse, and its resolution and 17-year length make it suitable as a training set for statistical and machine-learning models. The dataset is publicly available at https://doi.org/10.5067/CG4OT8DJX2Z7 (Kumar et al., 2024).

Africa is increasingly being exposed to the negative impacts of climate and environmental change, while having less capacity to respond compared to other continents. The vulnerability partially results from unprecedented demographic growth, urbanization, and industrialization. However, the continent has still largely been underserved by the broader Earth system science (ESS) community, as evidenced by the limited amount of ESS data and research that cover Africa compared to other areas of the world. Here, we present the recent University Corporation for Atmospheric Research (UCAR) Africa Initiative that aims to enhance environmental sustainability in Africa by fostering international collaborative research partnerships coled by African scientists. Specifically, we outline urgent challenges and opportunities identified through an international workshop in six areas of ESS, namely, 1) air quality and health, 2) weather, 3) climate, 4) land and water, 5) social science perspectives, and 6) developing equitable collaboration and sustainable infrastructure. We highlight examples of successful partnerships and conclude with recommendations to advance collaborative, actionable ESS research that addresses Africa's critical environmental challenges.

Reliable estimates of black carbon (BC) emissions over the Third Pole, also referred to as High Mountain Asia (HMA), remain challenging due to sparse observations, complex terrain, and uncertainties in emission inventories. We present a hierarchical Bayesian synthesis inversion framework that integrates surface BC observations from 91 sites with MATCHA, a 12 km regional chemical reanalysis based on WRF-Chem spanning 2003–2019 with tagged anthropogenic BC tracers from 10 Asian regions. We perform inversions using both daily and monthly observations to optimize regional BC emissions and quantify errors in both priors and observations. Monthly inversions provide the best performance, resolving 8–9 source tags and reducing biases at pollution hotspots and high-altitude sites. Posterior emissions show up to a fourfold underestimation for Tibetan Plateau and Bangladesh, moderate adjustments for China and India (±20%), and underestimated biomass-burning contributions over Southeast Asia. Sensitivity analysis reveals that site density and strategic location, particularly remote, high-altitude stations, strongly influence inversion performance, more so than observation duration. Transport errors dominate model–observation mismatches, particularly in vertical mixing and long-range horizontal advection across complex terrain. A machine learning (ML) surrogate of the model sensitivity matrix confirms underrepresentation of source-receptor pathways in WRF-Chem, especially over complex terrain. Our results underscore the importance of improving emission inventories in underrepresented regions, enhancing vertical transport and deposition processes in models, and expanding observational networks. The integrated Bayesian-ML approach provides a robust framework for refining BC emissions that can contribute to more accurate climate impact assessments over HMA.

Workshop on Surface Process Coupling and Its Interactions with the Atmosphere What: This paper reports on the Workshop on surface process coupling and its interactions with the atmosphere that took place within the ECMWF 50th-year celebrations, alongside three other workshops and its Annual Seminar. The purpose of this workshop was to improve the representation of coupled processes to enhance future implementations of global numerical weather prediction models, focusing on coupling strategies and tools, conservation and consistency of the coupling, and differences in coupling strategies between physics-based and machine-learned models. See https:// events.ecmwf.int/event/432/ and https://events.ecmwf.int/event/427/ for more information on these events. When: 9-10 April 2025 Where: Bonn, Germany

Surface ozone (O3) exhibits spatiotemporal variability in the New York City metropolitan area (NYCMA) under the influence of complex mesoscale flows which transport O3 and its precursors. Fine-scale ambient air quality monitoring is critical for estimating air pollution exposure and assessing whether mitigation strategies are sufficient to attain the National Ambient Air Quality Standards. To improve air quality monitoring in the NYCMA, 38 New York State Mesonet (NYSM) sites were outfitted with well-calibrated low-cost O3 sensors. This study applies the Satellite Enhanced Data Interpolation (SEDI) method to bias-correct 1-year of gridded output from the Weather Research and Forecasting with Chemistry (WRF-Chem) through fusion with surface O3 observations from the NYSM low-cost sensor sites and instruments from the U.S. EPA's AirNow monitoring network. Prior to bias correction, WRF-Chem overestimated O3 concentrations at 11 NYCMA AirNow sites. Constraining WRF-Chem using the NYSM low-cost sensor sites alone reduced mean bias error by around 6 ppb at the AirNow sites. At NYSM sites, the gridded O3 dataset constrained by observations from NYSM and AirNow together resulted in a better-performing dataset compared to the dataset constrained using observations from AirNow alone. These results highlight the value added by low-cost sensors in filling observational gaps in existing regulatory monitoring networks. Additionally, the SEDI algorithm is a computationally inexpensive post-processing technique that effectively reduces error and bias and is enhanced by the increase in spatial resolution of air quality monitoring provided by integrating the NYSM low-cost sensor sites with the AirNow network.

Accurate aerosol prediction remains challenging due to uncertainties in atmospheric composition arising from imperfect initial conditions, errors in emission inventories, and our limited understanding of aerosol processes and properties interacting with atmospheric variables. Due to their short lifetime and strong spatial/temporal variability, global observations of aerosols and clouds rely heavily on satellite remote sensing. The U.S. National Science Foundation (NSF) National Center for Atmospheric Research (NCAR) has recently developed the atmospheric Model for Prediction Across Scales (MPAS-A; Skamarock et al. 2012) coupled with the next-generation Goddard Chemistry Aerosol Radiation and Transport model (GOCART-2G; Collow et al. 2024) and interfaced with the Joint Effort for Data Assimilation Integration (JEDI) system. This integrated framework enables online-coupled data assimilation of multi-sensor, hyperspectral satellite aerosol retrievals and all-sky radiances across a wide spectral range within a unified atmosphere-aerosol analysis and forecasting system. This talk introduces the new MPAS-GOCART2G-JEDI system, with an emphasis on the assimilation of NASA Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) Aerosol Optical Depth (AOD) retrievals and their systematic evaluation against legacy AOD products such as MODIS, VIIRS, and AERONET.

Glacial Lake Outburst Floods (GLOFs) represent a growing hazard in the Himalaya, driven by climate-induced glacier retreat and the expansion of glacial lakes. While most GLOF assessments rely on satellite-derived indicators, they often lack the field-level precision required for comprehensive hazard assessment and for designing effective, site-specific mitigation strategies. This study presents one of the first multi-disciplinary multi-lake Comprehensive Field-Based Hazard Assessments (CFBHA) undertaken in the Indian Eastern Himalaya, covering nine high-risk glacial lakes in Sikkim. Through bathymetric investigations, geophysical assessments, morphometric studies and hydro-meteorological monitoring, we obtained lake depth profiles, identified saturated moraine, located subsurface seepage, assessed the integrity of moraines, and proposed data-driven mitigation planning. These findings significantly enrich susceptibility rankings derived from satellite imagery alone. Based on the CFBHA, a four-step operational framework for GLOF risk mitigation is proposed, linking satellite-based prioritisation to field surveys and intervention planning. This study presents a replicable model for assessing Himalayan GLOF risk and informing mitigation strategies under a changing climate, thereby bridging the gap between science, engineering, and policy.

We evaluate regional and interannual variations in tropospheric ozone in five global and regional chemical reanalyses, consisting of the Copernicus Atmosphere Monitoring Service reanalysis (CAMSRA), the second-generation Tropospheric Chemistry Reanalysis (TCR-2), the GEOS-Chem reanalysis, the Community Multiscale Air Quality (CMAQ) regional analysis, and the Chinese air quality reanalysis (CAQRA). We find that there are large regional differences (about 10-15 nmol mol(-1)) in mean surface ozone between the reanalyses. GEOS-Chem has high ozone relative to the ensemble mean across most continental regions, whereas CAMSRA has low ozone. Comparison with surface ozone observations shows that the reanalyses are biased high relative to the observations, with surface ozone biases exceeding 10 nmol mol(-1) in GEOS-Chem. We find that CAMSRA has the smallest bias with respect to the observations, with negative biases in Europe, and in the central and western US, and positive biases everywhere else. In the free troposphere the reanalyses are in good agreement, and the mean bias between the reanalyses and ozonesonde observations are small, less than 4 nmol mol(-1) at 500 hPa. In addition, the correlations between the ozonesondes and the reanalyses are as high as 0.8 and 0.9 in the southern and northern midlatitudes respectively. The results suggest that chemical reanalyses should provide valuable information for quantifying variations in ozone in the free troposphere. However, to enhance the utility of the surface ozone analyses, improvements in the reanalyses are needed to better exploit assimilated observations to mitigate the impact of discrepancies in the model chemistry and ozone precursor emissions.