Source Apportionment and Analysis of Spatial Representativeness of Fine Particle Pollution for an Urban Residential Area in Lucknow, India

Land use changes in the wake of urbanization have arranged the urban residential sites to be encircled by anthropogenic activities, resulting in greater exposure to air pollution. There are limited studies carried out on source apportionment of urban landscape-induced air pollution and their spatial representativeness for urban residential sites, and the present study addressed this issue. Characterization, dissemination analysis, and source apportionment of particulate matter (PM)2.5 in a residential area, Lucknow have been carried out. Samples of PM2.5 were collected during mid-winter 2021–2022 at near-road-locations (viz. L1, L2, L3, and L4) of the study area. Samples of PM2.5 chemical speciation were done by inductively coupled plasma mass spectrometry and ion chromatography for 26-elements and 5-ions, respectively. Mean PM2.5 was recorded highest at L3 in the daytime and lowest at L4 in the nighttime with the values 166 μg/m3 and 76 μg/m3 respectively. ArcGIS-inverse distance weighing (IDW) simulations identified the representativeness of PM2.5 for the entire residential site. IDW mapping identified the PM2.5 propagation up to 200 m–500 m over the study area. The diversity in the concentration of PM2.5 observed within the residential area due to the influence of the respective day and night times, multiple sources mix and their spatial distribution. Chemical speciation data of PM2.5 applied to Positive Matrix Factorization version 5.0—as receptor model (PMF v5.0) run for individual sites separately and pool all four sites' data together as a single dataset, which ultimately confirmed seven factors. The PMF model outputs ascertained the mean apportionment of PM2.5 by the determined seven local sources that is, vehicular exhaust—21%, resuspended road dust—15%, cooking fuel emission—12%, waste burning—12%, diesel generator sets exhaust—10%, secondary aerosols—10%, and Construction and Demolition—7%. The remaining 13% mean contribution to PM2.5 was evidenced by unaccounted sources. The present study outcomes qualify the significance to undertake source apportionment studies in connection with urban landscape patterns for air pollution abatement specifically for rapidly changing cities.