研究报告

  • 王延龙,李成,黄志炯,殷晓鸿,叶潇,王肖丽,郑君瑜.2013年中国海域船舶大气污染物排放对空气质量的影响[J].环境科学学报,2018,38(6):2157-2166

  • 2013年中国海域船舶大气污染物排放对空气质量的影响
  • Impact of ship emissions on air quality over Chinese waters in 2013
  • 基金项目:国家杰出青年科学基金项目(No.41325020);广东省省级科技计划项目(No.2016B050502005)
  • 作者
  • 单位
  • 王延龙
  • 华南理工大学环境与能源学院, 广州 510006
  • 李成
  • 暨南大学环境与气候研究院, 广州 511486
  • 黄志炯
  • 暨南大学环境与气候研究院, 广州 511486
  • 殷晓鸿
  • 华南理工大学环境与能源学院, 广州 510006
  • 叶潇
  • 华南理工大学环境与能源学院, 广州 510006
  • 王肖丽
  • 华南理工大学环境与能源学院, 广州 510006
  • 郑君瑜
  • 华南理工大学环境与能源学院, 广州 510006
  • 摘要:基于2013年中国海域船舶排放清单和空气质量数值模拟平台(WRF-SMOKE-CMAQ),利用敏感性分析方法定量识别了中国海域船舶排放对沿海地区空气质量的影响特征. 结果表明:船舶排放对不同污染物的贡献特征空间差异显著,就SO2、NO2和PM2.5而言,在沿海省份的年均贡献率分别为5%、7%、2%(1.1、1.7、0.9 μg·m-3),其中,珠三角和长三角地区受影响较大,SO2、NO2和PM2.5贡献分别可达30%、31%、8%(7.7、9.2、2.7 μg·m-3)和14%、13%、4%(3.7、5.3、1.9 μg·m-3).其次,船舶排放对空气质量影响季节性差异显著,尤其表现在PM2.5的空间分布上,三大城市群中,船舶排放对污染物贡献的季节间最大差异倍数为SO2(1.3~2.0),NO2(1.2~4.0),PM2.5(1.8~7.5).值得关注的是,船舶排放对PM2.5的浓度贡献表现出了明显的区域性(长距离传输)和复合性.本研究结果,一方面弥补了我国船舶排放对空气质量影响的量化特征认识不足,另一方面可为后续船舶排放的健康影响及控制费效分析等评估研究提供数据支撑.
  • Abstract:Ship emission is one of the major contributors to the anthropogenic emissions in China and therefore poses obvious impacts on air quality and human health. Previous studies had developed several sets of ship emissions in China but none of them quantified the impacts. In this paper, based on an air quality simulation platform (WRF-SMOKE-CMAQ), which covers all water areas of China, and AIS-based ship emissions in 2013,we used the Brute-force method (BFM) to quantify the impact of ship emissions on air quality in coastal areas of China. The temporal variation of impacts and its key driving factors were also investigated. Results show that 5% (1.1 μg·m-3) of SO2 concentrations, 7% (1.7 μg·m-3) of NO2 concentrations and 2% (0.9 μg·m-3) of PM2.5 concentrations in coastal provinces can be attributed to ship emissions. Particularly, these contributions increase in the Yangtze River Delta (YRD) and the Pearl River Delta (PRD) region, such as 14%, 13%,4% in YRD, and 30%, 31%,8% in PRD. The seasonal variation of impacts on air quality is obvious, especially in terms of the spatial distribution of PM2.5 concentrations. In the three urban agglomerations, the maximum difference among seasons can reach 1.3~2 for SO2, 1.2~4 for NO2 and 1.8~7.5 for PM2.5. The impact on PM2.5 formation is geographical variance regional and shows complex. This study provides robust support for health impact studies and cost-effectiveness analysis.

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