High-Spatial-Resolution Population Exposure to PM2.5 Pollution Based on Multi-Satellite Retrievals: A Case Study of Seasonal Variation in the Yangtze River Delta, China in 2013

Abstract

To assess the health risk of PM2.5, it is necessary to accurately estimate the actual exposure level of the population to PM2.5. However, the spatial distribution of PM2.5 may be inconsistent with that of the population, making it necessary for a high-spatial-resolution and refined assessment of the population exposure to air pollution. This study takes the Yangtze River Delta (YRD) Region as an example since it has a high-density population and a high pollution level. The brightness reflectance of night-time light, and MODIS-based (Moderate Resolution Imaging Spectroradiometer) vegetation index, elevation, and slope information are used as independent variables to construct a random-forest (RF) model for the estimation of the population spatial distribution, before any combination with the PM2.5 data retrieved from MODIS. This enables assessment of the population exposure to PM2.5 (i.e., intensity of population exposure to PM2.5 and population-weighted PM2.5 concentration) at a 3-km resolution, using the year 2013 as an example. Results show that the variance explained for the RF-model-estimated population density reaches over 80%, while the estimated errors in half of counties are < 20%, indicating the high accuracy of the estimated population. The spatial distribution of population exposure to PM2.5 exhibits an obvious urban–suburban–rural difference consistent with the population distribution but inconsistent with the PM2.5 concentration. High and low PM2.5 concentrations are mainly distributed in the northern and southern YRD Region, respectively, with the mean proportions of the population exposed to PM2.5 concentrations > 35μg/m3 close to 100% in all four seasons. A high-level population exposure to PM2.5 is mainly found in Shanghai, most of the Jiangsu Province, the central Anhui Province, and some coastal cities of the Zhejiang Province. The highest risk of population exposure to PM2.5 occurs in winter, followed by spring and autumn, and the lowest in summer, consistent with the PM2.5 seasonal variation. Seasonal-averaged population-weighted PM2.5 concentrations are different from PM2.5 concentrations in the region, which are closely related to the urban-exposed population density and pollution levels. This work provides a novel assessment of the proposed population-density exposure to PM2.5 by using multi-satellite retrievals to determine the high-spatial-resolution risk of air pollution and detailed regional differences in the population exposure to PM2.5.

Daily Global Solar Radiation in China Estimated From High‐Density Meteorological Observations: A Random Forest Model Framework

“…To evaluate the performance of the models and for consistency with previous validation studies ( Zou et al, 2019;Yang, Ye, et al, 2019;Wang et al, 2019), the tenfold CV method is used in our study and four statistical metrics-the coefficient of determination (R 2 ), root-mean-square error (RMSE), mean fractional bias (MB), and correlation coefficient (R)-are used to measure the prediction performance. The four statistical metrics are calculated at each radiation site using the following equations (1)-(4), respectively:…”

“…The RF model is a popular and highly flexible machine learning algorithm and is capable of analyzing the characteristic of the complex interaction of classification with good robustness for data with noise or missing values (Chen et al, ; Wang et al, ; Yang, Ye, et al, ; Ye et al, ). In particular, the RF model has been widely used as a feature selection tool for high‐dimensional data for identifying variable importance (Xiao et al, ; Wang et al, ). Several previous studies using RF to predict DGSR mainly focused on solar radiation at a single site or in a case study within a certain area of China (Sun et al, ).…”

“…Based on a high‐density ground‐based meteorological observation data set, we use the random forest (RF) model, a popular and highly flexible machine learning algorithm that can identify variable importance (Wang et al, ; Yang, Ye, et al, ; Ye et al, ), to construct a high‐density DGSR network and estimate the spatiotemporal distributions of DGSR across China. Following this introduction, the solar radiation and meteorological observation data sets are introduced in section .…”

PM2.5 Pollution Modulates Wintertime Urban Heat Island Intensity in the Beijing‐Tianjin‐Hebei Megalopolis, China

“…Fine particulate matter, that is, particulate matter with aerodynamic diameter equal to or less than 2.5 μm (PM 2.5 ), is considered as one of the most important air pollutants degrading air quality (Kim et al, ; Pongpiachan et al, ; Tiwari et al, ; Xing et al, ; Zheng, Xu, et al, ). It endangers the urban environment and the health of residents due to its complicated composition and properties (Gu et al, ; Jiang et al, ; Liang et al, ; Turpin & Huntzikcker, ; Wang, Gao, et al, ; Wang, Li, ; Zhao et al, ). The relationship between air pollutants and the urban heat island (UHI, i.e., elevated urban air/surface temperatures compared to those in rural surroundings, most prominently present during nighttime) has also attracted attention from the scientific community (Cao et al, ; Crutzen, ; Jonsson et al, ; Luo & Lau, ; Wu et al, ; Zheng, Ren, et al, ).…”

A Validation Approach Considering the Uneven Distribution of Ground Stations for Satellite-Based PM2.5 Estimation

“…As reported in a study conducted by the World Health Organization [1], public health has been heavily influenced by air pollution during the 21st century. Therein, fine particulate matter (PM 2.5 , particulate matter with an aerodynamic diameter of less than 2.5 µm) is one of the main air pollutants [2]- [8].…”