Weight-of-evidence approach to identify regionally representative sites for air-quality monitoring network: Satellite data-based analysis

The methodology discussed in Lekinwala et al., 2020, hereinafter referred to as the ‘parent article’, is used to setup a nation-wide network for background PM(2.5) measurement at strategic locations, optimally placing sites to obtain maximum regionally representative PM(2.5) concentrations with minimum number of sites. Traditionally, in-situ PM(2.5) measurements are obtained for several potential sites and compared to identify the most regionally representative sites [4], 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 PM(2.5) values. Standard set of tools like CDO and NCL are used to manipulate the satellite-data (*.nc files). • The PM(2.5) values 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 in NumPy module of Python was used for PCC calculations.