ORBIT: A new, reduced-complexity air quality model for South Asia
Abstract
Exposure to outdoor air pollution is the most important environmental health risk worldwide, associated with over a million deaths each year in South Asia alone. To understand how to improve air quality, it is necessary to model how changes in emissions affect changes in pollutant concentrations and human health. However, air quality modeling typically demands substantial computation and expertise, limiting the capacity to inform policy. Reduced complexity air quality models (RCMs) have been developed, but for South Asia many models are unavailable or have poor performance. Here, I present ORBIT, a new reduced complexity model currently implemented for South Asia at 0.5° x 0.625° resolution. ORBIT can natively report health damages, and has many features that distinguish it from other RCMs. Its predictions of annual PM2.5 concentrations (R: 0.76, normalized mean error (NME): 21.6%, normalized mean bias (NMB): -4.2%) meet the goals for published benchmarks. I have used ORBIT to produce marginal health damage estimates per 1,000 kg pollution for each primary pollutant, month, and time of day, for each subdistrict in South Asia.
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BibTeX
@misc{thakrar2026orbit,
title = {{ORBIT: A new, reduced-complexity air quality model for South Asia}},
author = {Sumil Thakrar},
howpublished = {EarthArXiv},
year = {2026},
month = oct,
doi = {10.31223/x5vf8p},
url = {https://eartharxiv.org/repository/view/15460/},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - ORBIT: A new, reduced-complexity air quality model for South Asia
AU - Sumil Thakrar
PY - 2026
DA - 2026/10/11
PB - EarthArXiv
DO - 10.31223/x5vf8p
UR - https://eartharxiv.org/repository/view/15460/
AB - Exposure to outdoor air pollution is the most important environmental health risk worldwide, associated with over a million deaths each year in South Asia alone. To understand how to improve air quality, it is necessary to model how changes in emissions affect changes in pollutant concentrations and human health. However, air quality modeling typically demands substantial computation and expertise, limiting the capacity to inform policy. Reduced complexity air quality models (RCMs) have been developed, but for South Asia many models are unavailable or have poor performance. Here, I present ORBIT, a new reduced complexity model currently implemented for South Asia at 0.5° x 0.625° resolution. ORBIT can natively report health damages, and has many features that distinguish it from other RCMs. Its predictions of annual PM2.5 concentrations (R: 0.76, normalized mean error (NME): 21.6%, normalized mean bias (NMB): -4.2%) meet the goals for published benchmarks. I have used ORBIT to produce marginal health damage estimates per 1,000 kg pollution for each primary pollutant, month, and time of day, for each subdistrict in South Asia.
ER -