上海城区PM_(2.5)精细化空间分布插值方法研究
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摘要
PM_(2.5)是我国大中型城市的主要污染物之一,已成为多学科领域的研究热点.基于监测点的监测数据无法直接获取城市内部空气污染的高分辨率空间分布情况,以上海市中心为研究区,引入土地利用回归(LUR)模型模拟PM_(2.5)质量浓度的高分辨率空间分布情况.双变量相关分析表明,与PM_(2.5)质量浓度分布相关性最强的地理变量分别是国控点2 000 m缓冲区内的道路长度、2 500 m缓冲区内的建筑面积、2 500 m缓冲区内的绿地面积、500 m缓冲区内的水体面积以及人口密度.基于以上变量,用多元线性回归分析建立PM_(2.5)质量浓度空间分布的LUR模型.在研究区内建立1 km×1 km格网,用LUR模型模拟各格网交点的PM_(2.5)质量浓度,再通过空间插值分析得到上海市PM_(2.5)质量浓度的空间分布模拟图.结果表明,PM_(2.5)模拟质量浓度存在明显的空间梯度差异,整体呈现西部高东部低的格局,并由人口密集区域向四周递减.人类活动是影响PM_(2.5)质量浓度分布的主要原因,模拟结果与实际情况相符.
As the primary pollutant in large and medium size cities in China, PM_(2.5) has become research hotspot in multidisciplinary fields. As high resolution spatial distribution of inner city air pollution cannot be obtained from monitoring data in monitoring stations directly, the article takes central urban area of Shanghai as research area, introduces land ultilization return(LUR) model to stimulate high solution spatial distribution of PM_(2.5) mass concentration. Bivariate correlation statistics shows that, the strongest-correlated geographic variables for PM_(2.5) mass concentration distribution are the road length of 2 000 m buffering zone, the covered area of 2 500 m buffering zone, green land area of 2 500 m buffering zone, water area in 500 m buffering area and the population density of national controlling points. The article established PM_(2.5) mass concentration spatial distribution LUR model with the above variables and the multiple linear regression analysis. They prepared 1 km×1 km grid in research area, simulated the mass concentration of PM_(2.5) in each grid junction point with LUR model, and obtained spatial distribution simulated diagram of PM_(2.5) mass concentration of Shanghai with spatial interpolation analysis method. The result shows that, the simulated PM_(2.5) mass concentration shows distinct spatial gradient disparities, the integrally pattern of high in west area and low in east area, and the decrease progressively from densely populated area to surroundings area. The main cause of PM_(2.5) mass concentration distribution is human activities, and the simulated results match practical situation.
As the primary pollutant in large and medium size cities in China, PM_(2.5) has become research hotspot in multidisciplinary fields. As high resolution spatial distribution of inner city air pollution cannot be obtained from monitoring data in monitoring stations directly, the article takes central urban area of Shanghai as research area, introduces land ultilization return(LUR) model to stimulate high solution spatial distribution of PM_(2.5) mass concentration. Bivariate correlation statistics shows that, the strongest-correlated geographic variables for PM_(2.5) mass concentration distribution are the road length of 2 000 m buffering zone, the covered area of 2 500 m buffering zone, green land area of 2 500 m buffering zone, water area in 500 m buffering area and the population density of national controlling points. The article established PM_(2.5) mass concentration spatial distribution LUR model with the above variables and the multiple linear regression analysis. They prepared 1 km×1 km grid in research area, simulated the mass concentration of PM_(2.5) in each grid junction point with LUR model, and obtained spatial distribution simulated diagram of PM_(2.5) mass concentration of Shanghai with spatial interpolation analysis method. The result shows that, the simulated PM_(2.5) mass concentration shows distinct spatial gradient disparities, the integrally pattern of high in west area and low in east area, and the decrease progressively from densely populated area to surroundings area. The main cause of PM_(2.5) mass concentration distribution is human activities, and the simulated results match practical situation.
引文
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[4] 郭宇,邹滨,郑忠,等.高时空分辨率PM2.5浓度土地利用回归模拟与制图[J].遥感信息,2015,30(5):94-101.
[5] BRIGGS D J,COLLINS S,ELLIOT P,et a1.Mapping urban air pollution using GIS:a regression-based approach[J].International Journal of Geographical Information Science,1997,11(7):699-718.
[6] 吴健生,谢舞丹,李嘉诚.土地利用回归模型在大气污染时空分异研究中的应用[J].环境科学,2016,37(2):413-419.
[7] 吴健生,廖星,彭建,等.重庆市PM2.5浓度空间分异模拟及影响因子[J].环境科学,2015,36(3):759-767.
[8] 江曲图,何俊昱,王占山,等.基于LUR/BME的海岸带地区PM2.5时空特性研究[J].中国环境科学,2017,37(2):424-431.