A three-dimensional LUR framework for PM2.5 exposure assessment based on mobile unmanned aerial vehicle monitoring

Xiangyu Xu; Ning Qin; Wenjing Zhao; Qi Tian; Qi Si; Weiqi Wu; Nursiya Iskander; Zhenchun Yang; Yawei Zhang; Xiaoli Duan

doi:10.1016/j.envpol.2022.118997

A three-dimensional LUR framework for PM_2.5 exposure assessment based on mobile unmanned aerial vehicle monitoring

Environ Pollut. 2022 May 15:301:118997. doi: 10.1016/j.envpol.2022.118997. Epub 2022 Feb 14.

Authors

Xiangyu Xu¹, Ning Qin¹, Wenjing Zhao¹, Qi Tian¹, Qi Si¹, Weiqi Wu¹, Nursiya Iskander¹, Zhenchun Yang², Yawei Zhang³, Xiaoli Duan⁴

Affiliations

¹ School of Energy and Environmental Engineering, University of Science and Technology of Beijing, Beijing 100083, China.
² Duke Global Health Institute, Duke University, Durham, NC 27708, United States.
³ National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China.
⁴ School of Energy and Environmental Engineering, University of Science and Technology of Beijing, Beijing 100083, China. Electronic address: jasmine@ustb.edu.cn.

PMID: 35176409
DOI: 10.1016/j.envpol.2022.118997

Abstract

Land use regression (LUR) models have been widely used in epidemiological studies and risk assessments related to air pollution. Although efforts have been made to improve the performance of LUR models so that they capture the spatial heterogeneity of fine particulate matter (PM_2.5) in high-density cities, few studies have revealed the vertical differences in PM_2.5 exposure. This study proposes a three-dimensional LUR (3-D LUR) assessment framework for PM_2.5 exposure that combines a high-resolution LUR model with a vertical PM_2.5 variation model to investigate the results of horizontal and vertical mobile PM_2.5 monitoring campaigns. High-resolution LUR models that were developed independently for daytime and nighttime were found to explain 51% and 60% of the PM_2.5 variation, respectively. Vertical measurements of PM_2.5 from three regions were first parameterized to produce a coefficient of variation for the concentration (CVC) to define the rate at which PM_2.5 changes at a certain height relative to the ground. The vertical variation model for PM_2.5 was developed based on a spline smoothing function in a generalized additive model (GAM) framework with an adjusted R² of 0.91 and explained 92.8% of the variance. PM_2.5 exposure levels for the population in the study area were estimated based on both the LUR models and the 3-D LUR framework. The 3-D LUR framework was found to improve the accuracy of exposure estimation in the vertical direction by avoiding exposure estimation errors of up to 5%. Although the 3-D LUR-based assessment did not indicate significant variation in estimates of premature mortality that could be attributed to PM_2.5, exposure to this pollutant was found to differ in the vertical direction. The 3-D LUR framework has the potential to provide accurate exposure estimates for use in future epidemiological studies and health risk assessments.

Keywords: High-density cities; Land use regression; Three-dimensional model; Vertical variation modellings.

MeSH terms

Air Pollutants* / analysis
Air Pollution* / analysis
Environmental Monitoring / methods
Particulate Matter / analysis
Unmanned Aerial Devices

Substances

Air Pollutants
Particulate Matter