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首页> 外文期刊>The Science of the Total Environment >The impact of anthropogenic emissions and meteorological conditions on the spatial variation of ambient SO_2 concentrations: A panel study of 113 Chinese cities
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The impact of anthropogenic emissions and meteorological conditions on the spatial variation of ambient SO_2 concentrations: A panel study of 113 Chinese cities

机译:人为排放和气象条件对环境中SO_2浓度空间变化的影响:一项对113个中国城市的面板研究

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摘要

China has received increased international criticism in recent years in relation to its air pollution levels, both in terms of the transmission of pollutants across international borders and the attendant adverse health effects being witnessed. Whilst existing research has examined the factors influencing ambient air pollutant concentrations, previous studies have failed to adequately explore the determinants of such concentrations from either a source or diffusion perspective. This study addressed both source (specifically, anthropogenic emissions) and diffusion (namely, meteorological conditions) indicators, in order to detect their respective impacts on the spatial variations seen in the distribution of air pollution. Spatial panel data for 113 major cities in China was processed using a range of global regression models—the ordinary least square model, the spatial lag model, and the spatial error model—as well as a local, geographic weighted regression (GWR) model. Results from the study suggest that in 2014, average SO_2 concentrations exceeded China's first-level target. The most polluted cities were found to be predominantly located in northern China, while less polluted cities were located in southern China. Global regression results indicated that precipitation exerts a significant effect on SO_2 reduction (p < 0.001) and that a regional increase of 1 mm in precipitation can reduce SO_2 concentrations by 0.026 μg/m~3. Both emission and temperature factors were found to aggravate SO_2 concentrations, although no such significant correlation was found in relation to wind speed. GWR results suggest that the association between SO_2 and its factors varied over space. Increased emissions were found to be able to produce more pollution in the northwest than in other parts of the country. Higher wind speeds and temperatures in northwestern areas were shown to reinforce SO_2 pollution, while in southern regions, they had the opposite effect. Further, increased precipitation was found to exert a greater inhibitory effect on SO_2 pollution in the country's northeast than that in other areas. Our findings could provide a detailed reference for formulating regionally specific emission reduction policies in China.
机译:近年来,在空气污染水平方面,无论是在跨国际边界的污染物传播方面,还是在随之而来的不利健康影响方面,中国都受到了国际上越来越多的批评。尽管现有研究已经检查了影响环境空气污染物浓度的因素,但先前的研究未能从源头或扩散角度充分探讨此类浓度的决定因素。这项研究研究了源指标(具体来说是人为排放量)和扩散指标(即气象条件),以便发现它们各自对空气污染分布中空间变化的影响。使用一系列全球回归模型(普通最小二乘模型,空间滞后模型和空间误差模型)以及局部地理加权回归(GWR)模型处理了中国113个主要城市的空间面板数据。研究结果表明,2014年,SO_2的平均浓度超过了中国的一级目标。发现污染最严重的城市主要位于中国北方,而污染较轻的城市则位于中国南部。全球回归结果表明,降水对SO_2的减少有显着影响(p <0.001),降水增加1mm可以使SO_2的浓度降低0.026μg/ m〜3。尽管没有发现与风速相关的显着相关性,但排放因子和温度因子都加剧了SO_2浓度。 GWR结果表明,SO_2及其因子之间的关联随空间变化。人们发现,增加的排放量在西北部比该国其他地区产生的污染更大。西北地区较高的风速和温度显示出可以加强SO_2污染,而南部地区则相反。此外,发现增加的降水量对该国东北地区的SO_2污染具有比其他地区更大的抑制作用。我们的发现可以为制定中国特定区域的减排政策提供详细参考。

著录项

  • 来源
    《The Science of the Total Environment》 |2017年第15期|318-328|共11页
  • 作者单位

    Key Laboratory of Regional Sustainable Development Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China,University of Chinese Academy of Sciences, Beijing, China;

    Guangdong Provincial Key Laboratory of Urbanization and Geo-simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou, China;

    Key Laboratory of Regional Sustainable Development Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China;

    Key Laboratory of Regional Sustainable Development Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China,University of Chinese Academy of Sciences, Beijing, China;

    Key Laboratory of Regional Sustainable Development Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Anthropogenic emissions; China; Meteorological conditions; SO_2 concentration; Spatial variation;

    机译:人为排放物;中国;气象条件;SO_2浓度;空间变异;

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