State Key Laboratory of Remote Sensing Science, College of Global and Earth System Sciences, Beijing Normal University, 19 Xinjiekou Street, Haidian, Beijing 100875, China; Joint Center for Global Change Studies, Beijing 100875, China.
State Key Laboratory of Remote Sensing Science, College of Global and Earth System Sciences, Beijing Normal University, 19 Xinjiekou Street, Haidian, Beijing 100875, China.
Environ Int. 2020 Jun;139:105558. doi: 10.1016/j.envint.2020.105558. Epub 2020 Apr 8.
Air pollution over China has attracted wide interest from public and academic community. PM is the primary air pollutant across China. Quantifying interactions between meteorological conditions and PM concentrations are essential to understand the variability of PM and seek methods to control PM. Since 2013, the measurement of PM has been widely made at 1436 stations across the country and more than 300 papers focusing on PM-meteorology interactions have been published. This article is a comprehensive review on the meteorological impact on PM concentrations We start with an introduction of general meteorological conditions and PM concentrations across China, and then seasonal and spatial variations of meteorological influences on PM concentrations. Next, major methods used to quantify meteorological influences on PM concentrations are checked and compared. We find that causality analysis methods are more suitable for extracting the influence of individual meteorological factors whilst statistical models are good at quantifying the overall effect of multiple meteorological factors on PM concentrations. Chemical Transport Models (CTMs) have the potential to provide dynamic estimation of PM concentrations by considering anthropogenic emissions and the transport and evolution of pollutants. We then comprehensively examine the mechanisms how major meteorological factors may impact the PM concentrations, including the dispersion, growth, chemical production, photolysis, and deposition of PM. The feedback effects of PM concentrations on meteorological factors are also carefully examined. Based on this review, suggestions on future research and major meteorological approaches for mitigating PM pollution are made finally.
中国的空气污染引起了公众和学术界的广泛关注。颗粒物（PM）是中国各地的主要空气污染物。量化气象条件和 PM 浓度之间的相互作用对于理解 PM 的变化规律以及寻求控制 PM 的方法至关重要。自 2013 年以来，全国已有 1436 个站点广泛开展 PM 测量工作，发表了 300 多篇关于 PM-气象相互作用的论文。本文对气象条件对 PM 浓度的影响进行了全面综述。我们首先介绍中国的一般气象条件和 PM 浓度概况，然后探讨气象条件对 PM 浓度的季节性和空间变化的影响。接下来，我们检查和比较了用于量化气象对 PM 浓度影响的主要方法。我们发现因果分析方法更适合提取个别气象因素的影响，而统计模型则擅长量化多个气象因素对 PM 浓度的综合影响。化学输送模型（CTMs）通过考虑人为排放以及污染物的传输和演化，有潜力提供 PM 浓度的动态估计。然后，我们全面研究了主要气象因素可能影响 PM 浓度的机制，包括 PM 的扩散、增长、化学产生、光解和沉积。PM 浓度对气象因素的反馈效应也进行了仔细研究。基于此综述，我们最后提出了未来研究和缓解 PM 污染的主要气象方法的建议。
