山西省空气质量时空格局及影响因素分析
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摘要
山西是我国的能源大省,同时也是空气污染较为严重的省份。为深入地分析山西省空气质量,继而给空气污染治理提供可靠的决策依据,本文采用空间自相关分析法、热点分析法、灰色关联分析法等方法对2014-2017年全省11个地市的空气质量时空分布特征及影响因素进行研究。结果表明:1.2014-2017年期间,山西省空气质量达标天数呈先增后减的趋势,高纬度地区空气质量优于中低纬度,两侧经度区域优于中间。2.2014-2016年空气质量空间聚集性较弱且不显著,2017年显著空间正相关,空间格局呈现从北到南为热点聚集、随机分布、冷点聚集的三层分布。3.山西省空气质量主要影响因素为第二产业占比和发电量。
Shanxi is a major energy province in China, but also a province with serious air pollution. In order to deeply analyze the air quality in Shanxi Province and then provide reliable decision-making basis for air pollution control, this paper uses spatial autocorrelation analysis, hot spot analysis and grey correlation analysis to study the Spatiotemporal distribution characteristics and influencing factors of air quality in 11 cities of Shanxi Province from 2014 to 2017. The results showed that: 1. From 2014 to 2017, the air quality of Shanxi Province increased first and then decreased. The air quality in high latitudes was better than that in middle and low latitudes, and the longitude areas on border sides are better than that in the middle. 2. From 2014 to 2016, the spatial aggregation of air quality was weak and not significant. In 2017, there was a significant positive spatial correlation. The spatial pattern showed three layers of hot spot aggregation, random distribution and cold point aggregation from north to south. 3. The main influencing factors of air quality in Shanxi Province were the proportion of secondary industry and power generation.
Shanxi is a major energy province in China, but also a province with serious air pollution. In order to deeply analyze the air quality in Shanxi Province and then provide reliable decision-making basis for air pollution control, this paper uses spatial autocorrelation analysis, hot spot analysis and grey correlation analysis to study the Spatiotemporal distribution characteristics and influencing factors of air quality in 11 cities of Shanxi Province from 2014 to 2017. The results showed that: 1. From 2014 to 2017, the air quality of Shanxi Province increased first and then decreased. The air quality in high latitudes was better than that in middle and low latitudes, and the longitude areas on border sides are better than that in the middle. 2. From 2014 to 2016, the spatial aggregation of air quality was weak and not significant. In 2017, there was a significant positive spatial correlation. The spatial pattern showed three layers of hot spot aggregation, random distribution and cold point aggregation from north to south. 3. The main influencing factors of air quality in Shanxi Province were the proportion of secondary industry and power generation.
引文
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[2]林长城,王宏,陈彬彬,赵卫红,洪荣林,林文.厦门市空气质量时空分布特征及其与气象条件的关系[J].福建农林大学学报(自然科学版),2010,39(01):79-83.
[3]盛永财,孜比布拉·司马义,王英鹏,王显,李颖.乌鲁木齐市空气污染时空分布特征及其与气象因素相关分析[J].地球环境学报,2018,9(04):323-333.
[4]苏维,张帅珺,赖新云,古新仁,赖胜男,黄国贤,张志坚,刘苑秋.南昌市空气PM2.5和PM10的时空动态及其影响因素[J].应用生态学报,2017,28(01):257-265.
[5]李婕,滕丽.珠三角城市空气质量的时空变化特征及影响因素[J].城市观察,2014(05):85-95.
[6]丁镭,刘超,黄亚林,陈昆仑,曾克峰.湖北省城市环境空气质量时空演化格局及影响因素[J].经济地理,2016,36(03):170-178.
[7]柏玲,姜磊,周海峰,陈忠升.长江经济带空气质量指数时空异质性及社会经济影响因素分析[J].水土保持研究,2019,26(02):312-319.
[8]杨冕,王银.长江经济带PM2.5时空特征及影响因素研究[J].中国人口.资源与环境,2017,27(01):91-100.
[9]贾晓红.山西省主要城市大气环境污染特征分析[J].山西能源与节能,2003(04):20-22.
[10]闫世明,孙鸿娉,王雁,张逢生,李莹.山西省1981—2010年大气能见度变化趋势研究[J].中国农学通报,2015,31(28):207-220.
[11]孙小燕,杨萍果,敖红,张鹏.山西省2015年细颗粒物的污染状况和空间分布[J].地球环境学报,2017,8(05):459-468.
[12]马杰.山西省城市空气质量指数地理特征及其与气象要素的关系[J].太原师范学院学报(自然科学版),2015,14(02):46-52.
[13]郭雪.太原市环境空气质量与气象条件关系的研究[D].兰州:兰州大学,2015.
[14]Epperson B K,Li T.Measurement of genetic structure within populations using Moran’s spatial autocorrelation statistics[J]. Proceedings of the National Academy of Sciences of the United States of Ameri ca,1996,93(19):10528-10532.
[15]Tiefelsdorf M. Modeling Spatial Process:The Identification and Analysis of Spatial Relationships in Regression Residuals by means of Moran’s I[J]. Lecture Notes in Earth Science Berlin Springer Verlag,2000,87(2):73-81.
[16]Anselin L, Getis O. Spatial statistical analysis and geographic information systems[J]. Annual Regression Science,1992,26:19-33.
[17]Shi J,Ding Z,Lee W J,et al. Hybrid Forecasting Model for VeryShort Term Wind Power Forecasting Based on Grey Relational Analysis and Wind Speed Distribution Feature[J]. IEEE Transactions onSmart Grid,2014,5(1):521-526
[18]孙玉刚.灰色关联分析及其应用的研究[D].南京航空航天大学,2007.
[19]刘湘南,黄方,王平.GIS空间分析原理与方法(第二版)[M].北京:科学出版社.2008.