Sachin D. Ghude

Accurate prediction of winter fog over Delhi remains challenging because operational numerical models inadequately represent aerosol–fog interactions. To address this, a modified Thompson–Eidhammer (TE) aerosol‐aware microphysics scheme, coupled with a diagnostic visibility parameterization accounting for extinction by hydrometeors, aerosols, and gases, was implemented within the Weather Research and Forecasting model. The framework includes a dynamic aerosol activation mechanism linking cloud condensation nuclei responds to thermodynamic cooling rates and water vapor tendencies. Using Winter Fog EXperiment observations, the modified configuration (Experimental Run [EXP]) was evaluated against the default TE scheme (Control Run [CTRL]) for a cloud base lowering (CBL) and a radiation (RAD) fog event over Delhi during January 2025. During CBL event, EXP preserved realistic nocturnal cooling and near‐surface stratification, enabling elevated layers near 400 m to reach saturation and descend in a top‐down manner, whereas CTRL incorrectly simulated a bottom‐up fog structure. EXP also reproduced the observed fog lifecycle more accurately, limiting shallow surface fog duration to ∼9 hr, while CTRL overestimated fog duration and onset by ∼15 and ∼6 hr, respectively. For RAD event, EXP more accurately captured the vertical fog structure through its dynamic aerosol–microphysics coupling. Variable fog droplet number concentrations (N _d ~ 400–1,200 cm ⁻³ ), compared to the fixed N _d (~300 cm ⁻³ ) in CTRL, were crucial for representing fog layering and vertical growth. Consequently, EXP maintained a much smaller nocturnal temperature bias (∼0.5°C) than CTRL (~3–4°C). The results demonstrate importance of aerosol–fog–radiation feedback and realistic aerosol fields for improving fog prediction over polluted urban regions.

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 (PM_2.5) 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 %. PM_2.5 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.

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 (PM_2.5) 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 %. PM_2.5 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.

Post‐monsoon air pollution over the Indo‐Gangetic Plain (IGP) is strongly influenced by crop‐residue burning, but how much transported, aging smoke adds particle mass and contributes to forecast underestimation remains poorly quantified. We report continuous high‐resolution time‐of‐flight aerosol mass spectrometer (HR‐ToF‐AMS) measurements in New Delhi from September–December 2024, spanning a pre‐event baseline, an intense biomass‐burning episode (12–19 November), and a winter post‐event period. During the event, non‐refractory PM _2.5 increased about four‐fold to 203.6 μg m ⁻³ , with organic aerosol (OA) contributing 75.3% of the non‐refractory mass. Observed PM _2.5 averaged 358.9 μg m ⁻³ , compared with an operational forecast mean of 182.4 μg m ⁻³ , giving a mean forecast underestimation of 176.5 μg m ⁻³ . Positive matrix factorization resolved biomass‐burning OA (BBOA, 43.4%), oxygenated OA (OOA, 37.8%), and hydrocarbon‐like OA (18.8%). Diagnostic m/z 60/K ⁺ and m/z 60/OC ratios, combined with 72 hr concentration‐weighted trajectories, identified agricultural‐residue burning over the upwind Punjab–Haryana corridor as the dominant external source. Although period‐mean O:C decreased from 0.76 to 0.65 and carbon oxidation state from +0.10 to −0.34, low‐volatility OOA increased from 9.9 to 46.6 μg m ⁻³ , indicating that fresh smoke diluted bulk oxidation metrics while chemically processed OA remained substantial. An organic‐mass‐to‐organic‐carbon (OM/OC) mass balance gives secondary organic enhancement of 33–40 μg m ⁻³ and nitrate enhancement of 12 μg m ⁻³ , together accounting for approximately 25%–30% of the mean forecast underestimation. Secondary formation during smoke aging therefore contributed appreciably to the transported pollution not captured by the operational forecast.

This study presents the design and evaluation of a high-resolution Air Quality Early Warning and Decision Support System (AQEWS–DSS) for Jaipur, India, developed to provide short-term (1–5 day) forecasts and real-time source attribution of particulate matter (PM₂.₅). The system employs the WRF-Chem model configured at 2 km resolution with daily data assimilation of satellite aerosol optical depth (AOD) and surface observations from RSPCB (Rajasthan State Pollution Control Board) stations. Model evaluation during October 2024–January 2025 demonstrates robust forecast skill, with Root Mean Square Error (RMSE) values of 26.2 µg/m³ for PM₂.₅ and 48.8 µg/m³ for PM₁₀, Index of Agreement (IOA) of 0.78 and 0.75, and Performance Index (PI) scores of 85.1 and 82.5, for PM_2.5 and PM₁₀ respectively. Over 90% of model predictions fall within a factor of two of observations, and diurnal patterns are also well captured. Although peak events such as Diwali are slightly underestimated (by ~ 35%), the model effectively reproduces seasonal transitions and day-to-day variability. DSS-based source apportionment reveals that Jaipur city contributes 27–28% of its PM₂.₅ burden, with the remainder dominated by external sources. In October, dust transport from the Thar Desert accounts for ~ 28% of PM₂.₅, while in November, biomass burning and regional transport become more influential. These results emphasize that Jaipur’s air quality is shaped by both persistent local emissions and seasonally varying regional sources. The findings support the need for integrated regional control strategies and operational use of high-resolution DSS tools to guide air quality management across northern India.

Winter fog over Delhi develops within one of the most polluted boundary‐layer environments in the Indo‐Gangetic Plain, where stagnant meteorology and high aerosol loading often evolve together. Under such conditions, it is difficult to determine whether aerosol changes arise from fog itself or simply from weak winter ventilation. We investigated aerosol‐fog interactions during a persistent fog‐haze spell over Delhi from 1 to 11 January 2023 using high‐resolution aerosol mass spectrometry, ceilometer backscatter, liquid water measurements, and surface micrometeorology. The event sequence comprised nine fog episodes and one haze episode within a shallow, weakly ventilated boundary layer. To minimize first‐order dynamical confounding, each fog event was paired with a dry non‐fog window of equal duration under similar low‐level dynamic conditions. Relative to those matched dry periods, fog showed stronger depletion of organics, nitrate, and primary organic aerosol, whereas sulfate did not follow the same dominant depletion pattern. A phase‐wise analysis further showed that median non‐refractory submicron particulate matter, organics, nitrate, ammonium, chloride, and especially primary organic aerosol (POA) declined from pre‐fog to in‐fog conditions, whereas sulfate remained stable to slightly enhanced. The secondary organic aerosol (SOA) fraction increased from 32.9% before fog to 55.9% during fog and 61.5% after fog because POA decreased much more sharply than SOA. Bulk organic aerosol became less oxidized during fog and regained oxygenation after dissipation. These results show that winter fog over Delhi selectively reorganizes aerosol composition through coupled removal, repartitioning, and transformation, with the response varying by species class and pre‐existing aerosol loading.

Frequent dust storms in the Middle East cause major health and economic impacts. The Qatar Meteorology Department (QMD), Doha, in collaboration with the Indian Institute of Tropical Meteorology (IITM), Pune, and the Integrated Land Ecosystem‐Atmosphere Processes Study (iLEAPS) developed a dust forecast system in experimental mode to provide dust forecasts during the 2022 FIFA World Cup in Doha, Qatar. This system provided PM ₁₀ (particulate matter with an aerodynamic diameter ≤10 μm) forecasts up to 3 days in advance from November–December 2022. It satisfactorily captured a dust storm on 9 November 2022 with observed and forecasted peaks of PM ₁₀ at ∼1,600 μg/m ³ and ∼1,200 μg/m ³ , respectively. While the system exhibited reasonable performance (Mean Fractional Bias (MFB): −0.02, Mean Fractional Error: 0.71, Root Mean Square Error: 198 μg/m ³ , Normalized Mean Bias: 15%), occasional false alarms were noted. The analysis of false alarm events revealed that the combined effect of wind speed overestimation and uncertainties in the dust source representation over the broader Doha region, including northwestern Qatar, appears to be the primary cause of these false alarm events. Diagnostic sensitivity experiments over this region suggested that a 50% reduction in the erodibility reduced false alarms and closely matched the true dust storm event. The findings emphasize the need for higher resolution modeling, improved region specific dust source data set, and refined surface physics representation to enhance operational dust forecast reliability over the Middle East.

Ambient air pollution is a critical environmental and public health issue, particularly in Indian metropolitan cities. To address this, the System of Air Quality and Weather Forecasting and Research (SAFAR) was developed by the Ministry of Earth Sciences, Government of India, in 2010 during the Commonwealth Games in Delhi, providing high-resolution air quality monitoring and forecasting. The program was later expanded to Pune, Mumbai, and Ahmedabad to offer air quality forecasts. SAFAR’s unique feature lies in its network of monitoring stations situated in diverse microenvironments across urban, semiurban, residential, and industrial areas. Complementing this, the Modeling Air Pollution and Networking (MAPAN) system was established to integrate air pollution data from across India for improved forecasting. This paper reviews SAFAR and MAPAN, focusing on long-term data of particulate matter and air quality index (AQI) variations across metropolitan cities, and highlights the integration with the Air Quality Warning and Integrated Decision Support System for Emissions (AIRWISE) for high-resolution forecasting and decision-making. Long-term observations show a decline (3.03 ± 0.39 μg m−3 yr−1) in PM2.5 in Delhi, reflecting the positive impact of monitoring on policy implementation. The increasing frequency of “good” and “satisfactory” AQI days in Delhi suggests an improvement in air quality. However, the average carbon dioxide (CO2) concentration across Indian metropolitan regions shows an upward trend from 2014 to 2023 (an increase of 9 ppm yr−1). Following the success of these initiatives, the MAPAN program is expanding to its second phase to include air quality monitoring and modeling in the Himalayan and oceanic outflow regions. Significance Statement Air pollution has emerged as a critical public health concern across Indian metropolitan areas, affecting a substantial portion of the population. Implementing the System of Air Quality and Weather Forecasting and Research (SAFAR) and Modeling Air Pollution and Networking (MAPAN) programs enabled city-specific, real-time air quality monitoring and forecasting. These networks have not only increased public and governmental awareness of air pollution concerns but have also directly informed policy action—such as graded response action plans and targeted mitigation measures—especially in critical urban centers like Delhi. The integration of SAFAR with the Air Quality Warning and Integrated Decision Support System for Emissions (AIRWISE) has strengthened the ability of authorities to enact timely interventions, such as school closures and traffic restrictions during severe pollution episodes. Collectively, these air quality programs are now central to air quality management strategies in India and have provided robust scientific insights into the sources and dynamics of regional air pollution.

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 (PM_2.5) 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 %. PM_2.5 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.