• 张成影,廖婷婷,孙扬,韩琳,孟祥来,张琛.基于空间密集传感器解析城市本地排放和区域传输贡献方法初探[J].环境科学学报,2021,41(9):3683-3695

  • 基于空间密集传感器解析城市本地排放和区域传输贡献方法初探
  • Preliminary study on the method of analyzing the contribution of urban local emission and regional transmission based on space-dense sensor
  • 基金项目:国家重点研发计划(No.2018YFC0214003,2016YFA0602004)
  • 作者
  • 单位
  • 张成影
  • 1. 成都信息工程大学大气科学学院, 高原大气与环境四川省重点实验室, 成都 610225;2. 中国科学院大气物理研究所, 创新转化基地, 淮南 232000
  • 廖婷婷
  • 成都信息工程大学大气科学学院, 高原大气与环境四川省重点实验室, 成都 610225
  • 孙扬
  • 中国科学院大气物理研究所, 创新转化基地, 淮南 232000
  • 韩琳
  • 成都信息工程大学大气科学学院, 高原大气与环境四川省重点实验室, 成都 610225
  • 孟祥来
  • 中国科学院大气物理研究所, 创新转化基地, 淮南 232000
  • 张琛
  • 中国科学院大气物理研究所, 创新转化基地, 淮南 232000
  • 摘要:为了对城市污染物进行详细区域来源解析,基于长沙市低成本传感器监测网络,收集了2019年10月PM2.5、PM10、SO2、NO2的高空间分辨率监测数据,对污染特征进行分析.同时,根据本地排放和背景浓度变化的不同相对频率,基于小波分析提取了污染物背景浓度并结合空间密集监测量化了城市环境中监测点的近场、远场及区域传输贡献.结果显示,2019年10月长沙市4项常规污染物中,PM2.5浓度较高,SO2浓度较低.小波分析提取各监测点背景浓度结果表明,部署在乡村的监测点PM2.5、PM10和NO2背景浓度平均水平较低,而城市总体数据分布更分散,存在明显的本地排放源.估计近场、远场及区域传输对城市监测点总污染水平贡献发现,研究期间,区域传输对监测点污染贡献最大.其中,PM2.5的区域贡献、远场贡献和近场贡献占比分别为43%、24%和17%;PM10的区域贡献占比较高为59%,远场贡献和近场贡献分别占比14%和16%;NO2的区域贡献、远场贡献和近场贡献占比分别为45%、24%和19%;而SO2主要以区域贡献为主,占比达78%.
  • Abstract:To analyze the source attribution of urban pollutants, based on the low-cost sensor monitoring network in Changsha, we collected the high spatiotemporal resolution monitoring data of PM2.5, PM10, SO2, and NO2 in October 2019 to analyze the characteristics of the local pollution. According to the different relative frequencies of local and background pollution variations, the wavelet analysis method was used to extract the background concentration of pollutants-combined with monitoring data with high spatial resolution to quantify the near-field, far-field and of monitoring sites in the urban and regional transportation contributions. The result showed that among the four conventional pollutants in October 2019, the concentration of PM2.5 was high while the SO2 was relatively low. Using the wavelet analysis method, we extracted the background concentration of each monitoring site, finding out that the background concentration of PM2.5, PM10 and NO2 in rural was low. Compared with the background concentration in rural, the overall distribution of the urban data was more scattered. Therefore, obvious local emission sources were observed. In estimating the contribution of different scale pollution sources to the total pollution level, the regional contribution was the largest contributor to air pollution in the monitoring sites during the study period. The contribution of regional transportation, far-field and near-field ratio of PM2.5 were 43%, 24% and 17%, respectively; the regional contribution ratio of PM10 was 59%, which was considerably higher than the others, while the contribution of far-field and the near-field of PM10 accounted for 14% and 16%, respectively; and for NO2, that was 45%, 24% and 19% respectively. For SO2, the source attribution of SO2 was mainly based on its regional transportation, which accounted for 78% of the total contribution.

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