Addressing Biases in Ambient PM_2.5 Exposure and Associated Health Burden Estimates by Filling Satellite AOD Retrieval Gaps over India

Abstract

Ambient PM 2.5 exposure statistics in countries with limited ground monitors are derived from satellite aerosol optical depth (AOD) products that have spatial gaps. Here, we quantified the biases in PM 2.5 exposure and associated health burden in India due to the sampling gaps in AOD retrieved by a Moderate Resolution Imaging Spectroradiometer. We filled the sampling gaps and derived PM 2.5 in recent years (2017−2022) over India, which showed fivefold cross-validation R 2 of 0.92 and root mean square error (RMSE) of 11.8 μg m −3 on an annual scale against ground-based measurements. If the missing AOD values are not accounted for, the exposure would be overestimated by 19.1%, translating to an overestimation in the mortality burden by 93,986 (95% confidence interval: 78,638− 110,597) during these years. With the gap-filled data, we found that the rising ambient PM 2.5 trend in India has started showing a sign of stabilization in recent years. However, a reduction in population-weighted exposure balanced out the effect of the increasing population and maintained the mortality burden attributable to ambient PM 2.

Nationwide estimation of daily ambient PM2.5 from 2008 to 2020 at 1 km2 in India using an ensemble approach

“…Further, the model only relied on satellite- and reanalysis-based aerosol optical depth as a predictor of ambient PM 2.5 , which does not account for numerous other factors such as land use and meteorology, potentially affecting the model's ability to capture local and regional variations in PM 2.5 . Recently, an extension of this model incorporated an AOD filling algorithm and reported a fivefold cross-validation R 2 of 0.92 and RMSE of 11.8 μg/m 3 on an annual scale ( 51 ). In the modeling approach developed by Shaddick et al ( 18 ), 82% of the global ground monitoring data used to develop the model were from 2013 and 2014 with no rural or periurban representation which makes up the majority of India.…”

High ambient air pollution erodes the benefits of using clean cooking fuel in preventing low birth weight in India

“…AAP was assigned in terms of average ambient PM 2.5 exposure during pregnancy derived for each PSU. Due to inadequate coverage of India's ground monitoring network, particularly in rural areas, we used a satellite-derived PM 2.5 dataset from a published study Katoch et al [23]. This dataset was generated by converting daily aerosol optical depth (AOD) at 1 km 2 resolution, retrieved from the Moderate Resolution Imaging Spectroradiometer into surface PM 2.5 concentrations.…”

“…During the time period considered in this study, the surface PM 2.5 reported an R 2 of 0.85 and RMSE of 23.3 µg m −3 on a daily scale against measurements from the reference-grade monitors maintained by the CPCB (appendix A). More details on the distribution of CPCB ground-based monitors are given in Katoch et al [23].…”