摘要：AbstractThe methodology discussed in Lekinwala et al., 2020, hereinafter referred to as the ‘parent article’, is used to setup a nation-wide network for background PM2.5measurement at strategic locations, optimally placing sites to obtain maximum regionally representative PM2.5concentrations with minimum number of sites. Traditionally, in-situ PM2.5measurements are obtained for several potential sites and compared to identify the most regionally representative sites , Wongphatarakul et al., 1998) at the location. The ‘parent article’ proposes the use of satellite-derived proxy for aerosol (Aerosol Optical Depth, AOD) data in the absence of in-situ PM2.5 measurements. This article focuses on the details about satellite-data processing which forms part of the methodology discussed in the ‘parent article’. Following are some relevant aspects:•High resolution AOD is retrieved from Moderate Resolution Imaging Spectroradiometer (MODIS) instruments aboard NASA's Aqua and Terra satellite using Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm. The data is stored as grids of size 1200 × 1200 and a total of seven such grids cover the Indian land mass. These grids were merged, regridded and multiplied by conversion factors from GEOS-Chem Chemical Transport Model to obtain PM2.5values. Standard set of tools like CDO and NCL are used to manipulate the satellite-data (*.nc files).•The PM2.5values are subjected to various statistical analysis using metrics like coefficient of divergence (CoD), Pearson correlation coefficient (PCC) and mutual information (MI).•Computations for CoD, MI are performed using Python codes developed in-house while a function inNumPymodule of Python was used for PCC calculations.Graphical abstractDisplay Omitted
关键词：MODIS;MAIAC Algorithm;Aerosol Optical Depth;Coefficient of Divergence;Pearson Correlation Coefficient;Mutual Information;Python;CDO
