Spatial patterns and influencing factors of intraurban particulate matter in the heating season based on taxi monitoring

Abstract

ABSTRACT Urbanization has introduced a series of environmental problems worldwide, and particulate matter (PM) is one of the main threats to human health. Due to the lack of high-resolution, large-scale monitoring data, few studies have analyzed the intraurban spatial distribution pattern of PM at a fine scale. In this study, portable air monitors carried by five taxis were used to collect the concentrations of PM1, PM2.5 and PM10 for five months in Shenyang during the heating season. The results showed that high concentrations of PM were distributed in the suburbs, while relatively low concentration areas were found in the central area. Agricultural, industrial and development zones had higher concentration values among the eight observed types. The PM concentration exhibited strong spatial autocorrelation based on Moran’s I index analysis. Meteorological factors were the most important influencing factors of the three pollutants, and their total contribution rate accounted for more than 80% among the 13 factors according to boosted regression trees analysis. The taxi monitoring method we proposed was a more efficient and feasible method for monitoring urban air pollution and could obtain higher spatial-temporal resolution data at a lower cost to elucidate the region’s dynamic air pollution distribution patterns.