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

Shukla, Komal, Kumar, Prashant, Mann, Gaurav S and Khare, Mukesh (2020) Mapping spatial distribution of particulate matter using Kriging and Inverse Distance Weighting at supersites of megacity Delhi Sustainable Cities and Society, 54, 101997.

Shukla..Kumar (2020)_Kriging Delhi SCS.pdf - Accepted version Manuscript

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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.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Civil and Environmental Engineering
Authors :
Shukla, Komal
Mann, Gaurav S
Khare, Mukesh
Date : March 2020
Funders : Natural Environmental Research Council (NERC)
DOI : 10.1016/j.scs.2019.101997
Copyright Disclaimer : © 2019 Published by Elsevier Ltd.
Uncontrolled Keywords : Spatial interpolation; Discrete predictions; Ordinary kriging (OK); Inverse distance weighted (IDW); Prediction accuracy; ASAP-Delhi project
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Additional Information : This work has been supported by the University grant commission – Junior Research Fellowship, India, and also the Natural Environmental Research Council [grant number NE/P016510/1] through the project – An Integrated Study of Air Pollutant Sources in the Delhi National Capital Region (ASAP-Delhi) – under the UK-India NERC-MOES Programme on Air Quality and Health in Megacity Delhi.
Depositing User : Diane Maxfield
Date Deposited : 16 Dec 2019 11:10
Last Modified : 24 Dec 2020 02:08

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