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Fine particulate matter (PM2.5) trends in China, 2013–2018: separating contributions from anthropogenic emissions and meteorology

机译:中国的细颗粒物(PM2.5)趋势,2013-2018:分离人为排放和气象的贡献

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Fine particulate matter (PM2.5) is a severe air pollution problem in China. Observations of PM2.5 have been available since 2013 from a large network operated by the China National Environmental Monitoring Center (CNEMC). The data show a general 30%–50% decrease in annual mean PM2.5 across China over the 2013–2018 period, averaging at ?5.2μgm?3a?1. Trends in the five megacity cluster regions targeted by the government for air quality control are -9.3±1.8μgm?3a?1 (±95% confidence interval) for Beijing–Tianjin–Hebei, -6.1±1.1μgm?3a?1 for the Yangtze River Delta, -2.7±0.8μgm?3a?1 for the Pearl River Delta, -6.7±1.3μgm?3a?1 for the Sichuan Basin, and -6.5±2.5μgm?3a?1 for the Fenwei Plain (Xi'an). Concurrent 2013–2018 observations of sulfur dioxide (SO2) and carbon monoxide (CO) show that the declines in PM2.5 are qualitatively consistent with drastic controls of emissions from coal combustion. However, there is also a large meteorologically driven interannual variability in PM2.5 that complicates trend attribution. We used a stepwise multiple linear regression (MLR) model to quantify this meteorological contribution to the PM2.5 trends across China. The MLR model correlates the 10d PM2.5 anomalies to wind speed, precipitation, relative humidity, temperature, and 850hPa meridional wind velocity (V850). The meteorology-corrected PM2.5 trends after removal of the MLR meteorological contribution can be viewed as being driven by trends in anthropogenic emissions. The mean PM2.5 decrease across China is ?4.6μgm?3a?1 in the meteorology-corrected data, 12% weaker than in the original data, meaning that 12% of the PM2.5 decrease in the original data is attributable to meteorology. The trends in the meteorology-corrected data for the five megacity clusters are -8.0±1.1μgm?3a?1 for Beijing–Tianjin–Hebei (14% weaker than in the original data), -6.3±0.9μgm?3a?1 for the Yangtze River Delta (3% stronger), -2.2±0.5μgm?3a?1 for the Pearl River Delta (19% weaker), -4.9±0.9μgm?3a?1 for the Sichuan Basin (27% weaker), and -5.0±1.9μgm?3a?1 for the Fenwei Plain (Xi'an; 23% weaker); 2015–2017 observations of flattening PM2.5 in the Pearl River Delta and increases in the Fenwei Plain can be attributed to meteorology rather than to relaxation of emission controls.
机译:细颗粒物质(PM2.5)是中国的严重空气污染问题。自2013年从中国国家环境监测中心(CNEMC)运营的大型网络以来,PM2.5的观察已获得。该数据显示,2013 - 2018年期间,中国每年平均PM2.5减少30%-50%,平均在?5.2μgm?3a?1。北京 - 天津 - 河北的政府瞄准的五个巨型群集区域的趋势为-9.3±1.8μgm?3a?1(±95%的置信区间),-6.1±1.1μgm?3a?1长江三角洲,-2.7±0.8μgm?3a?1为珠江三角洲,-6.7±1.3μgm?3a?1为四川盆地,和-6.5±2.5μgm?3a?1为佛前普(xi')一个)。同时2013-2018二氧化硫(SO2)和一氧化碳(CO)的观察结果表明,PM2.5的下降与来自煤燃烧的排放的激烈控制定性一致。但是,在PM2.5中也存在大的气象导向续际变异,使趋势归因复杂化。我们使用逐步多个线性回归(MLR)模型来量化对中国的PM2.5趋势的这种气象贡献。 MLR模型将10D PM2.5异常与风速,沉淀,相对湿度,温度和850HPa的经络速度(V850)相关联。在去除MLR气象贡献后,气象纠正的PM2.5趋势可被视为受到人为排放趋势的推动。中国的平均PM2.5减少了?3A?3在气象校正的数据中,比原始数据较弱12%,这意味着原始数据的12%的PM2.5减少是归因于气象学。五兆型簇的气象校正数据的趋势是-8.0±1.1μgm?3a?1用于北京 - 天津 - 河北(比原有数据弱14%),-6.3±0.9μgm?3a?1长江三角洲(3%强),-2.2±0.5μgm?3a?1用于珠江三角洲(弱19%),-4.9±0.9μgm?3a?1为四川盆地(弱27%),和-5.0±1.9μgm?3a?1为汾威平原(西安; 23%弱); 2015-2017珠江三角洲平整PM2.5的观测,汾威平原增加可归因于气象学,而不是放松排放控制。

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