Mapping spatial distribution of particulate matter using Kriging and Inverse Distance Weighting at supersites of megacity Delhi

Abstract

Anthropogenic airborne particulates are among the major contributors to urban air pollution and pose a significant health risk. Particulate matter has emerged as a serious pollution threat in India, specifically to the capital—New Delhi. The objective of this study is to map PM2.5 profile using two widely used spatial interpolation techniques (Kriging and IDW) by predicting their concentrations at distinct unmonitored locations. The implemented methodology has a wide-scoped utility in the field of air pollution; especially in Low-Middle Income Countries where setting up new monitoring stations include financial/logistical/location problems. The generated maps can help in policy formulation and decision making by providing aid in PM2.5 visualisation of spatial and temporal variability. First phase of study involves prediction of concentrations at two sites (reinforcing the need for sustainable development of the city) using concentrations for 2015–2017. In the second phase, pollutant mixing ratios were obtained for four winter months between Nov-2017 to Feb-2018 at 17 monitoring stations. In this phase, predictions were made for 11 supersites (zones of important land-use). The average error of Kriging and IDW (taking both phases) was ∼22 % and 24 %, respectively. The magnitude of change in the daily concentration was relatively negligible and annual trend can be identified.