苏玲,高婵婵,曹闪闪,阎路宇,孟紫琪,田慧敏,刘敏.长三角地区空气质量国控环境监测点空间代表性评价——以PM2.5为例[J].环境科学学报,2021,41(11):4377-4387
长三角地区空气质量国控环境监测点空间代表性评价——以PM2.5为例
- Spatial representative evaluation of ambient air quality monitoring stations in the Yangtze River Delta: Taking PM2.5 as an example
- 基金项目：国家重点研发计划（No.2017YFC0505801-01）；国家自然科学基金（No.41977399）
- 苏玲
- 华东师范大学生态与环境科学学院, 上海市城市化生态过程与生态恢复重点实验室, 上海 200241
- 高婵婵
- 华东师范大学生态与环境科学学院, 上海市城市化生态过程与生态恢复重点实验室, 上海 200241
- 曹闪闪
- 华东师范大学生态与环境科学学院, 上海市城市化生态过程与生态恢复重点实验室, 上海 200241
- 孟紫琪
- 中国科学院地理科学与资源研究所, 北京 100101
- 田慧敏
- 华东师范大学生态与环境科学学院, 上海市城市化生态过程与生态恢复重点实验室, 上海 200241
- 刘敏
- 1. 华东师范大学生态与环境科学学院, 上海市城市化生态过程与生态恢复重点实验室, 上海 200241;2. 崇明生态研究院, 上海 200241
- 摘要：2013年我国正式开展了113个环境保护重点城市和国家环境保护模范城市细颗粒物等项目监测，目前已建成国家环境空气质量监测网络.为了较好地将空气质量国控环境监测点（简称国控点）监测结果提升到区域和全球水平，须了解当前空气质量环境监测网络的空间代表性.本文以长三角地区为研究区域，基于国控点空间分布信息，结合区域第二产业比重（POSI）、地区生产总值（GDP）、人口（POP）、风速（WDSP）、降水量（PRCP）、气温（TEMP）、增强型植被指数（EVI）和数字高程模型（DEM）8个影响细颗粒物（PM2.5）相关变量，在1 km×1 km空间分辨率下计算长三角地区129个国控点与研究区域中其他位置（像元）的多维欧氏距离，并结合K均值聚类方法开展区域国控点PM2.5监测的空间代表性评价及优化.结果表明：①长三角地区129个国控点代表区域面积差异明显，其中，上海市淀山湖站点的代表面积最大，为37933 km2，南京市迈皋桥站点的代表面积最小，仅为4 km2；②长三角地区国控点能较好地代表整个区域的PM2.5空间分布，现有国控点对PM2.5空间分布代表的有效范围（即像元与国控点多维欧氏距离低于全区平均值的区域）占总面积的63.23%；③难以被现有国控点代表性的区域主要集中在江苏省太湖、洪泽湖等水域、上海市中心城区与崇明沿海地区及浙江西南部山地地区；④在浙江省绍兴市与衢州市新增2个国控点，中东部丘陵地区的区域代表性可得到明显改善，长三角地区国控点可代表区域面积占比整体提高16.4%.
- Abstract：In 2013, China has officially launched the monitoring of fine particle (PM2.5) and other air pollutants in 113 key environmental protection cities and national environmental protection model cities. At present, the national ambient air quality monitoring network was established. To correctly upscale the observations obtained in these stations to the regional and global scale, it is necessary to understand the representativeness of the current monitoring network. Using the Yangtze River Delta as the study area, this paper calculated the multidimensional Euclidean distance between each pixel and the stations to determine the representativeness of the existing monitoring network. Eight variables related to PM2.5, including proportion of secondary industry (POSI), gross domestic product (GDP), population (POP), wind speed (WDSP), precipitation (PRCP), air temperature (TEMP), enhanced vegetation index (EVI) and digital elevation model (DEM) were used to indicate the environmental characteristics of PM2.5 with a spatial resolution of 1 km×1 km. In addition, the K-means clustering method was adopted to improve the spatial representativeness of the existing ambient air quality monitoring network. The results showed that:① there were obvious differences among the representative area of 129 stations in the Yangtze River Delta. The Dianshan Lake station in Shanghai had the largest representative area(37933 km2), and the representative area of the Maigaoqiao station in Nanjing was only 4 km2; ② the monitoring stations in the Yangtze River Delta could well represent the PM2.5 spatial distribution of the entire region, and the effective area (i.e. the area where the Euclidean distance between the pixels and the stations was lower than the average value) accounted for 63.23% of the total area; ③ the areas that were difficult to be represented mainly concentrated in the Taihu Lake and Hongze Lake, downtown area of Shanghai, the coastal areas of Chongming, and the southwestern mountainous areas in Zhejiang; ④ two additional stations were proposed in Shaoxing city and Quzhou city, which could improve the regional representativeness by 16.4%, especially in the hilly areas in Zhejiang Province.
