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, PM_2.5 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.

摘要

城市化在全球范围内引入了一系列环境问题，颗粒物（PM）是对人类健康的主要威胁之一。由于缺乏高分辨率、大规模的监测数据，很少有研究对城市内PM的空间分布格局进行精细的分析。在这项研究中，由五辆出租车携带的便携式空气监测仪用于收集 PM ₁、 PM _2.5和 PM _10的浓度采暖季在沈阳呆了五个月。结果表明，PM高浓度区分布在郊区，而相对低浓度区集中在中心区。在八种观测类型中，农业、工业和开发区的集中度值较高。基于 Moran's I 指数分析，PM 浓度表现出很强的空间自相关性。气象因子是3种污染物最重要的影响因子，在boosted回归树分析的13个因子中，其总贡献率占80%以上。