Review

20260 citationspeer-reviewgold Open Access

Authors

Jat, Rajmal · Indian Institute of Tropical Meteorology

Vispute, Akash Sagar · Indian Institute of Tropical Meteorology

Ghude, Sachin D. · Indian Institute of Tropical Meteorology

Kumar, Rajesh · NSF National Center for Atmospheric Research

Sinha, Vinayak · Indian Institute of Science Education and Research Mohali

Sinha, Baerbel · Indian Institute of Science Education and Research Mohali

Govardhan, Gaurav · Indian Institute of Tropical Meteorology

Abstract

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.

Topics & Keywords

Publication Details

DOI: 10.5194/egusphere-2026-1302-rc1

Command Palette

Review

Authors

Abstract

Topics & Keywords

Publication Details