Spatial Variation of the Relationship between PM_2.5Concentrations and Meteorological Parameters in China

Abstract

Epidemiological studies around the world have reported that fine particulate matter (PM2.5) is closely associated with human health. The distribution of PM2.5 concentrations is influenced by multiple geographic and socioeconomic factors. Using a remote-sensing-derived PM2.5 dataset, this paper explores the relationship between PM2.5 concentrations and meteorological parameters and their spatial variance in China for the period 2001–2010. The spatial variations of the relationships between the annual average PM2.5, the annual average precipitation (AAP), and the annual average temperature (AAT) were evaluated using the Geographically Weighted Regression (GWR) model. The results indicated that PM2.5 had a strong and stable correlation with meteorological parameters. In particular, PM2.5 had a negative correlation with precipitation and a positive correlation with temperature. In addition, the relationship between the variables changed over space, and the strong negative correlation between PM2.5 and the AAP mainly appeared in the warm temperate semihumid region and northern subtropical humid region in 2001 and 2010, with some localized differences. The strong positive correlation between the PM2.5 and the AAT mainly occurred in the mid-temperate semiarid region, the humid, semihumid, and semiarid warm temperate regions, and the northern subtropical humid region in 2001 and 2010.

Association between Atmospheric Fine Particulate Matter and Hospital Admissions for Chronic Obstructive Pulmonary Disease in Southwestern Taiwan: A Population-Based Study

“…In addition, the results indicated that PM 2.5 -induced effects varied by season. The observed seasonal differences on PM 2.5 concentrations and effect estimates might be explained by variations in meteorological parameters, the chemical composition of PM 2.5 and exposure patterns [ 23 , 33 , 34 ]. Sea-land breezes (SLBs) that prevail in summer bring abundant moisture, which could reduce the concentrations of atmospheric particles in southwestern Taiwan.…”

Mechanism of Spatiotemporal Air Quality Response to Meteorological Parameters: A National-Scale Analysis in China

“…Correlations between air pollution and meteorological parameters have been reported in cities worldwide, such as New York [17], Paris [18], Amsterdam [19], Beijing [10,20], Delhi [21], Nagasaki [22], and Seoul [23]. In China, the North China Plain (NCP), Yangtze River Delta (YRD), and Pearl River Delta (PRD) are air pollution research hotspots due to their dense pollution, developed economies, and deteriorated air quality, whereas vast northwestern regions have attracted less attention [24][25][26].…”

Long-Term Atmospheric Visibility Trends and Characteristics of 31 Provincial Capital Cities in China during 1957–2016

“…Shenyang, Harbin, Lanzhou, Jinan, Tianjin, and Changsha were regions that were industrialized relatively early and have suffered from air pollution prior to the 1950s [52]. Throughout the process of rapid urbanization and industrialization, the governments of these cities established environmental management policies earlier than other cities, thus the AV of these cities did not deteriorate significantly during the period of our study [46]. Shenyang and Harbin, as developed industrial cities, are the important industrial cities in northern China and have been centers for heavy industry in China since the 1930s.…”

“…Seasonal variations reflect most of the effects of meteorological conditions, such as those impacted by emissions from the global desert and residents' activities. During winter and spring, AV is relatively low due to two factors: stagnant meteorological conditions characterized by slow winds and shallow mixing layers occur more frequently, and there is a higher concentration of air pollutants due to the trapping of pollutants near the atmospheric surface [45][46][47]. Furthermore, emissions from the global desert are also a reason for low AV during spring and winter in northern and western China [48].…”