Key Research Institute of Yellow River Civilization and Sustainable Development & Collaborative Innovation Center on Yellow River Civilization of Henan Province, Henan University, Kaifeng, 475004, China; College of Geography and Environmental Science, Henan University, Kaifeng, 475004, China; College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China.
Key Research Institute of Yellow River Civilization and Sustainable Development & Collaborative Innovation Center on Yellow River Civilization of Henan Province, Henan University, Kaifeng, 475004, China; College of Geography and Environmental Science, Henan University, Kaifeng, 475004, China; Institute of Urban Big Data, College of Geography and Environmental Science, Henan University, Kaifeng, 475004, China.
Environ Pollut. 2022 Dec 15;315:120392. doi: 10.1016/j.envpol.2022.120392. Epub 2022 Oct 14.
Elucidating the characteristics and influencing mechanisms of PM concentrations is the premise and key to the precise prevention and control of air pollution. However, the temporal and spatial heterogeneity of PM concentrations and its driving mechanism are complex and need to be further analyzed. We analyzed the temporal and spatial variations of PM concentrations in the "2 + 26" cities from 2015 to 2021, and quantified the influence of meteorological factors and anthropogenic emissions and their interactions on PM concentrations based on geographic detector model. We find the inter-annual and inter-season PM concentrations show downward trend from 2015 to 2021, and the inter-month PM concentrations present a U-shaped distribution. The PM concentrations in the "2 + 26" cities manifest a spatial distribution pattern of high in the south and low in the north, and high in the middle and low in the surroundings. Meteorological conditions have stronger effects on PM concentrations than anthropogenic emissions, and planetary boundary layer height and temperature are the two main driving factors at the annual scale. On the seasonal scale, sunshine duration is the dominant factor of PM concentrations in summer and autumn, and planetary boundary layer height is the dominant factor of PM concentrations in winter. The effect of anthropogenic emissions on PM concentration is higher in winter and spring than in summer and autumn, and ammonia and ozone have stronger effects on PM concentrations than other anthropogenic emissions. Interactions between the factors significantly enhance the PM concentrations. The interactions between planetary boundary layer height and other impacting factors play dominant roles on PM concentrations at annual scale and in winter. Our results not only provide crucial information for further developing air quality policies of the "2 + 26" cities, but also bear out several important implications for clean air policies in China and other regions of the world.
阐明 PM 浓度的特征和影响机制是精准防控空气污染的前提和关键。然而，PM 浓度的时空异质性及其驱动机制较为复杂，需要进一步分析。我们分析了“2+26”城市 2015-2021 年 PM 浓度的时空变化，并基于地理探测器模型定量分析了气象因素和人为排放及其相互作用对 PM 浓度的影响。我们发现，2015-2021 年 PM 浓度表现出逐年和逐季下降的趋势，而逐月 PM 浓度呈 U 型分布。“2+26”城市 PM 浓度呈现南高北低、中间高四周低的空间分布格局。气象条件对 PM 浓度的影响大于人为排放，边界层高度和温度是年际尺度的两个主要驱动因素。在季节尺度上，日照时数是夏秋季 PM 浓度的主导因素，边界层高度是冬季 PM 浓度的主导因素。人为排放对 PM 浓度的影响在冬春季高于夏秋季，氨和臭氧对 PM 浓度的影响大于其他人为排放。各因素之间的相互作用显著增强了 PM 浓度。边界层高度与其他影响因素之间的相互作用在年际尺度和冬季对 PM 浓度的影响较大。我们的研究结果不仅为进一步制定“2+26”城市的空气质量政策提供了重要信息，而且对中国和世界其他地区的清洁空气政策也具有重要意义。
