Improving satellite aerosol optical Depth-PM<sub>2.5</sub> correlations using land use regression with microscale geographic predictors in a high-density urban context

Shi, Yuan ORCID: 0000-0003-4011-8735, Ho, Hung Chak, Xu, Yong and Ng, Edward (2018) Improving satellite aerosol optical Depth-PM<sub>2.5</sub> correlations using land use regression with microscale geographic predictors in a high-density urban context. ATMOSPHERIC ENVIRONMENT, 190. pp. 23-34.

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Official URL: http://dx.doi.org/10.1016/j.atmosenv.2018.07.021

Abstract

Estimating the spatiotemporal variability of ground-level PM2.5 is essential to urban air quality management and human exposure assessments. However, it is difficult in a high-density and highly heterogeneous urban context as ground-level monitoring stations are most likely sparsely distributed. Satellite-derived Aerosol Optical Depth (AOD) observation has made it possible to overcome such difficulty due to its advantage of spatial coverage. In this study, we improve the AOD-PM2.5 correlations by combining land use regression (LUR) modelling and incorporating microscale geographic predictors and atmospheric sounding indices in Hong Kong. The spatiotemporal variations of ground-level PM2.5 over Hong Kong were estimated using MODerate resolution Imaging Spectroradiometer (MODIS) AOD remote sensing images for the period of 2003–2015. An extensive LUR variable database containing 294 variables was adopted to develop AOD-LUR models by seasons. Compared to the baseline models (fixed effect models include only basic weather parameters), the prediction performance of all annual and seasonal AOD-LUR fixed effect models were significantly enhanced with approximately 20–30% increases in the model adjusted R2. On top of that, a mixed effect model covers time-dependent random effects and a group of geographically and temporally weighted regression (GTWR) models were also developed to further improve the model performance. As the results, compared to the uncalibrated AOD-PM2.5 spatiotemporal correlation (adjusted R2 = 0.07, annual fixed effect AOD-only model), the calibrated AOD-PM2.5 correlation (the GTWR piecewise model) has a significantly improved model fitting adjusted R2 of 0.72 (LOOCV adjusted R2 of 0.65) and thus becomes a ready reference for spatiotemporal PM2.5 estimation.

Item Type:	Article
Uncontrolled Keywords:	Land use regression, Aerosol optical depth, PM2.5, Spatial mapping
Divisions:	Faculty of Science and Engineering > School of Environmental Sciences
Depositing User:	Symplectic Admin
Date Deposited:	25 Jul 2022 14:59
Last Modified:	09 Oct 2023 09:52
DOI:	10.1016/j.atmosenv.2018.07.021
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3159426