Prafull P. Yadav

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.

Accurate representation of CO over India remains challenging because of large uncertainties in emissions, atmospheric transport, and sparse in‑situ measurements. This study examines the impact of assimilating MOPITT total column CO retrievals into the Weather Research and Forecasting model coupled with Chemistry (WRF‑Chem) using the Gridpoint Statistical Interpolation (GSI) system for October-December 2019, a period strongly influenced by post‑monsoon crop‑residue burning and stagnant meteorology over northern India. Model performance is evaluated against independent TROPOMI CO columns and MOPITT vertical profiles. The control simulation (WRF‑CNTL) shows a persistent positive bias, overestimating TROPOMI CO by about 0.4-0.6 × 10¹⁸ molecules cm⁻² across the Indo‑Gangetic Plain. Assimilation (WRF‑DA) markedly reduces these biases and better captures observed spatial and temporal variability. Normalized mean bias decreases from 33.5% to 14.4%, while the index of agreement increases from 0.45 in the control to 0.72 in the data assimilation run. Comparisons with MOPITT profiles indicate that assimilation lowers near‑surface CO by 40-100 ppbv and reduces profile errors by 40-60% in the lower troposphere. These results show that MOPITT CO assimilation effectively constrains regional CO distributions and substantially enhances WRF‑Chem performance over India.

This study investigates the impact of assimilating high resolution (770 m) Aerosol Optical Depth (AOD) retrieval derived from the Oceansat-3 Ocean Colour Monitor (OCM) sensor into the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) for the first time, aiming to improve fine particulate matter (PM₂.₅) forecasts over India. AOD assimilation, evaluated over the period 01–15 November 2023, leads to substantial improvements in model accuracy, reducing PM₂.₅ biases by 30–70% and lowering root mean square error (RMSE) across critical regions such as Delhi, Punjab, Bihar, and West Bengal. The assimilation substantially improves initial conditions of surface PM₂.₅ estimates by approximately 60 µg/m³. Forecast accuracy is the highest on the first day, with an RMSE of 21.35 µg/m³ and a correlation coefficient (R) of 0.75, followed by increasing RMSE values of 30.40 µg/m³ on Day 2 and 32 µg/m³ on Day 3, with correlations of 0.73 and 0.70, respectively, reflecting degradation of assimilation benefits by model uncertainties over time. With MODIS nearing phase-out, high-resolutionOCM AOD retrievals offer a reliable alternate choice for future AOD assimilation in the AIRWISE forecasting system over India.

The ability of a chemical transport model to simulate accurate meteorological and chemical processes depends upon the physical parametrizations and quality of meteorological input data such as initial/boundary conditions. In this study, weather research and forecasting model coupled with chemistry (WRF-Chem) is used to test the sensitivity of PM_2.5 predictions to planetary boundary layer (PBL) parameterization schemes (YSU, MYJ, MYNN, ACM2, and Boulac) and meteorological initial/boundary conditions (FNL, ERA-Interim, GDAS, and NCMRWF) over Indo-Gangetic Plain (Delhi, Punjab, Haryana, Uttar Pradesh, and Rajasthan) during the winter period (December 2017 to January 2018). The aim is to select the model configuration for simulating PM_2.5 which shows the lowest errors and best agreement with the observed data. The best results were achieved with initial/boundary conditions from ERA and GDAS datasets and local PBL parameterization (MYJ and MYNN). It was also found that PM_2.5 concentrations are relatively less sensitive to changes in initial/boundary conditions but in contrast show a stronger sensitivity to changes in the PBL scheme. Moreover, the sensitivity of the simulated PM_2.5 to the choice of PBL scheme is more during the polluted hours of the day (evening to early morning), while that to the choice of the meteorological input data is more uniform and subdued over the day. This work indicates the optimal model setup in terms of choice of initial/boundary conditions datasets and PBL parameterization schemes for future air quality simulations. It also highlights the importance of the choice of PBL scheme over the choice of meteorological data set to the simulated PM_2.5 by a chemical transport model.