Four-dimensional spatiotemporal analysis of PM2.5 and seasonal climate interactions across Pakistan’s topographic gradient using 4D-GTWR

Abstract

The fine particulate matter (PM2.5) is a serious environmental and public health problem in Pakistan as it is determined by multiple interactions between meteorological conditions, seasonal variations, and topographic heterogeneities. In this study, a four-dimensional (spatial, temporal and altitudinal) geographically and temporally weighted regression (4D-GTWR) framework is used to explore the spatial, temporal and altitudinal variations of PM2.5 concentrations with respect to precipitation, temperature, relative humidity, wind speed, surface pressure and aerosol optical depth (AOD) in 161 districts of Pakistan for the period 1998–2021. The proposed approach differs from traditional GTWR in that it explicitly accounts for the elevation effect as part of the spatiotemporal weighting function to reflect the variation in local regression relationships with elevation. The outcomes indicate significant seasonal and regional variation in the PM2.5 and meteorological relationships, with different responses in the plains, mountainous and monsoon influenced parts of Pakistan. Model validation results showed that 4D-GTWR was able to capture the seasonal R² of 70.28%, 80.70%, 80.87% and 72.41% for DJF, MAM, JJA and SON seasons, respectively and had a lower level of residual error as well as spatial autocorrelation (Moran’s I) compared to both OLS and GTWR models. The results show that the use of elevation increases the ability to capture the spatiotemporal variability in PM2.5, and offers a more comprehensive framework for understanding the seasonal variability in air pollution in Pakistan. The proposed framework provides an important framework in support of air quality management and mitigation for different geographic regions in various seasons.