| 成渝城市群PM2.5的时空分布及其影响因素研究 |
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| 引用本文： | 曾德珩,陈春江.成渝城市群PM2.5的时空分布及其影响因素研究[J].环境科学研究,2019,32(11):1834-1843. |
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| 作者姓名： | 曾德珩 陈春江 |
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| 作者单位： | 重庆大学建设管理与房地产学院,重庆,400044;重庆大学建设管理与房地产学院,重庆,400044 |
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| 基金项目： | 国家社科基金重大项目（No.17ZDA063）；中央高校项目（No.2017CDJSK03XK02）；重庆市研究生科研创新项目（No.CYS18053） |
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| 摘 要： | 随着工业化与城镇化的深入推进，成渝城市群的PM2.5污染不断加剧，呈明显的区域性与复合性特征.该研究以2015—2017年成渝城市群空气质量监测站的日均ρ（PM2.5）数据为基础，结合区域气象、遥感与统计年鉴等多源数据，采用反距离插值法分析了ρ（PM2.5）的时空分布差异，采用Moran's I指数与LISA指数探索了ρ（PM2.5）的全局和局部空间自相关性，并利用空间回归模型研究了自然、经济社会等因素对ρ（PM2.5）的影响.结果表明:①成渝城市群ρ（PM2.5）分布存在明显的时空差异.时间上，2015年PM2.5污染最严重，ρ（PM2.5）年均值为54.38 μg/m3，2016年、2017年PM2.5污染状况逐年减轻，ρ（PM2.5）年均值分别为53.68与47.56 μg/m3；空间上，成渝城市群东北部ρ（PM2.5）较低，而南部ρ（PM2.5）较高.②空间自相关分析结果表明，PM2.5污染在成渝城市群存在显著的空间聚集性，成渝城市群南部ρ（PM2.5）呈高值-高值聚集，成渝城市群北部ρ（PM2.5）则呈低值-低值聚集.③空间回归结果表明，成渝城市群范围内某一地区邻近区域的ρ（PM2.5）平均值增加1%时，该地区ρ（PM2.5）将上升至少0.38%.城镇化率对ρ（PM2.5）的影响最大，其次是第一产业增加值，再次是工业增加值占比和降水量.城镇化率、降水量与ρ（PM2.5）呈负相关，而第一产业增加值、工业增加值占比与ρ（PM2.5）呈正相关.研究显示，加快城镇化进程、减少第一产业排放、降低工业增加值占比（尤其是重污染工业）是有效解决成渝城市群PM2.5污染的重要手段.
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| 关 键 词： | PM2.5 成渝城市群 时空分布 空间自相关性 空间回归模型 |
| 收稿时间： | 2018/7/31 0:00:00 |
| 修稿时间： | 2019/5/10 0:00:00 |
Spatial-Temporal Characteristics and Influence Factors of PM2.5 Concentrations in Chengdu-Chongqing Urban Agglomeration |
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| ZENG Deheng and CHEN Chunjiang.Spatial-Temporal Characteristics and Influence Factors of PM2.5 Concentrations in Chengdu-Chongqing Urban Agglomeration[J].Research of Environmental Sciences,2019,32(11):1834-1843. |
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| Authors: | ZENG Deheng and CHEN Chunjiang |
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| Affiliation: | School of Construction Management and Real Estate, Chongqing University, Chongqing 400044, China |
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| Abstract: | In line with the development of industrialization and urbanization, PM2.5 pollution in Chengdu-Chongqing urban agglomeration (CCUA) had regional and sophisticated characteristics. The paper presented the spatial-temporal distributions of PM2.5 concentrations in CCUA using Inverse Distance Weighted Interpolation. The global and local spatial auto-correlation of PM2.5 concentrations was analyzed by Moran's I and LISA. The contribution of natural, economic and social factors to PM2.5 concentrations was explored with spatial regression model. Multi-source data (including meteorology data, remote-sensing data, national statistical data) and daily PM2.5 data from air quality monitoring stations in CCUA were collected for analysis in the research during 2015 and 2017. The research findings were summarized as follows:(1) The PM2.5 distributions had significant spatial-temporal differences in CCUA. From time dimension, PM2.5 pollution was most serious in 2015 at 54.38 μg/m3, but improved in 2016 and 2017 at 53.68 and 47.56 μg/m3. From spatial dimension, the northern and north-eastern CCUA had lower PM2.5 concentrations, while this index was higher in southern part. (2) There were spatial interdependence and spatial clustering of PM2.5 concentrations. In general, the southern CCUA was high-high aggregation region, and the northern part was low-low aggregation region. (3) Regression results show that the PM2.5 concentration in the local area increased by more than 0.38% net for every 1% increase in the average PM2.5 concentrations of its neighboring areas. Urbanization rate had the largest negative effect on PM2.5 concentrations, and the added value of the primary industry had the largest positive effect, while precipitation and the proportion of industrial added value also had significant effects. Thus, accelerating urbanization, reducing emissions from the primary industry and reducing the proportion of industrial added value (especially heavily polluted industries) are important measures to solve the PM2.5 problem effectively in CCUA. |
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| Keywords: | PM2 5 Chengdu-Chongqing urban agglomeration spatial-temporal distribution spatial auto-correlation spatial regression model |
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