TITLE:
Use of a Land Use Regression Model Methodology for the Estimation of Individual Long-Term PM2.5 Exposure Profiles of Urban Residents in Jiujiang City, China
AUTHORS:
Weiye Wang, Sisi Hu
KEYWORDS:
Land Use Regression, Fine Particulate Matter, PM2.5, Individual Exposure, Long-Term Exposure
JOURNAL NAME:
Journal of Geoscience and Environment Protection,
Vol.13 No.1,
January
21,
2025
ABSTRACT: The purpose of this study was to establish a method able to accurately estimate the long-term exposure levels of individuals to fine particulate matter (PM2.5) in Jiujiang City (China) by constructing land use regression (LUR) models. Subsequently, the accuracy of models was further verified. PM2.5 concentrations were continuously collected daily from seven monitoring stations for the construction of daily LUR models from September 1 to 14, 2023. The constructed models used PM2.5 concentrations as the dependent variable, while land use, elevation, population density and road length were used as the predictive variables. Subsequently, twenty volunteers were invited to participate, with their daily PM2.5 exposure estimated based on their work address and home address, allowing their average exposure levels to be calculated. Furthermore, volunteers wore portable PM2.5 detectors continuously for a 14-day period and the average measured PM2.5 level was used as a comparative standard. Results showed that the adjusted R2 values for the 14 daily models ranged from 0.85 to 0.94, with the R2 values generated from leave-one-out-cross-validation tests all greater than 0.61, indicating good prediction accuracy. No significant differences were observed in the measurement accuracy of the LUR modeling method and measurements using a portable PM2.5 detector (p > 0.05). This study aimed to develop a novel method for the accurate and convenient measurement of individual long-term PM2.5 exposure levels for epidemiological studies in urban environments comparable to that of Jiujiang city.