Modeling Spatial Distribution and Determinant of PM2.5 at Micro-Level Using Geographically Weighted Regression (GWR) to Inform Sustainable Mobility Policies in Campus Based on Evidence from King Abdulaziz University, Jeddah, Saudi Arabia

Abstract

Air pollution is fatal. Fine particles, such as PM2.5, in ambient air might be the cause of many physical and psychological disorders, including cognitive decline. This is why educational policymakers are adopting sustainable mobility, and other policy measures, to make their campuses carbon-neutral; however, car-dependent cities and their university campuses are still lagging behind in this area. This study attempts to model the spatial heterogeneity and determinants of PM2.5 at the King Abdulaziz University campus in Jeddah, which is ranked first among the Saudi Arabian universities, as well as in the MENA region. We developed four OLS and GWR models of different peak and off-peak periods during weekdays in order to estimate the determinants of the PM2.5 concentration. The number of cars, humidity, temperature, windspeed, distance from trees, and construction sites were the estimators in our analysis. Because of a lack of secondary data at a finer scale, we collected the samples of all dependent and independent variables at 51 locations on the KAU campus. Model selection was based on RSS, log-likelihood, adjusted R2, and AICc, and a modal comparison shows that the GWR variant of Model-2 outperformed the other models. The results of the GWR model demonstrate the geographical variability of the PM2.5 concentration on the KAU campus, to which the volume of car traffic is the key contributor. Hence, we recommend using the results of this study to support the development of a car-free and zero-carbon campus at KAU; furthermore, this study could be exploited by other campuses in Saudi Arabia and the Gulf region.

Wind-urban structure interplay in PM2.5 variation: Insights from multi-seasonal mobile air quality campaign

“…This research offers a unique insight into PM 2.5 variation at a very high spatial-temporal resolutions on a university campus through multi-seasonal mobile monitoring, which is still relatively rare in current studies (Liu et al, 2020; Song et al, 2022; Tiwari and Aljoufie, 2021). We examined the impacts of wind-dynamics at both the micro scale and the meso scale, and evaluated the statistical efficacy of 3D built environment variables obtained through four novel wind-based buffer methods in the OLS models.…”

“…Local pollution sources. Machinery movement and dust uplift have been found to elevate PM 2.5 levels (Charron et al, 2005;Gilliland et al, 2018); yet little attention has been paid to microscale hotspot events (Tiwari and Aljoufie, 2021). During the study period, two significant emission hotspots were identified along or near the monitored route: a bustling construction project and a campus dining hall.…”

Understanding the shared working spaces: a Geography of Co-working supply in Delhi

“…GWR proved useful in detecting spatial variability of parameters to estimate an outcome variable. Researchers have used it frequently in detecting determinants of a spatial event in several cases (Aljoufie & Tiwari, 2021 ; Tiwari & Aljoufie, 2021 ; Zhang et al, 2019 ).…”

Quantification of the Seasonality and Vertical Dispersion Environment of PM2.5 Variation: A Comparative Analysis of Micro-Scale Wind-Based Buffer Methods

“…The purpose of these models is to 1) explain the variation of PM2.5 in the context of multiple variables with interdependent relationships and 2) correlate PM2.5 variation with these variables using specialized GIS methodologies and other spatial autocorrelation techniques (Dadvand et al, 2014;Messier et al, 2018;Fu et al, 2017;Hankey et al, 2015;Tian et al, 2019). One approach to accomplish this is to tie land-use with PM2.5 variation through spatial autocorrelation methods like the spatial autoregressive model, geographically weighted regression model, kriging, land use regression, or another similar method (Hankey et al, 2015;Messier et al, 2018;Shi et al, 2019;Tiwari & Aljoufie, 2021;Xu et al, 2022;Zhou & Lin, 2022). Others have used ordinary least squares regression or multiple linear regression to statistically tie predictors to PM2.5 variation (Tiwari & Aljoufie, 2021Stavroulas et al, 2020).…”

“…Wind is rarely considered for its interactive relationship with the micro-scale vertical dispersion environment, a point that is often overlooked in previous studies focusing on macro-scale, multi-city analysis or street canyon framework (Shi et al, 2019;Song et al, 2022;Miao et al, 2020;Wang et al, 2022;Weisert et al, 2019). This research offers a unique insight into PM2.5 variation and wind-dynamics at the micro-scale, capturing extraordinarily high spatial and temporal resolutions at an urban college university in a mid-sized city (Liu et al, 2020;Tiwari & Aljoufie, 2021;Song et al, 2022). All analysis is performed on unique seasonal datasets with unique meteorological conditions, using Ordinary Least Squares Regression to evaluate the statistical significance of wind-informed buffers and other variables (Apte et al, 2017;Tang et al, 2013;Tiwari & Aljoufie, 2019).…”