基于GIS的贵阳PM_(2.5)质量浓度城乡过渡特征及影响因素研究

Study on Characteristics of Urban and Rural PM_(2.5) Concentrations Alternation and Main Influencing Factors Using GIS in Guiyang

PM2.5是影响城市空气质量和身体健康的主要污染物,也是气候和环境问题的热点研究问题之一。PM2.5即大气污染细颗粒物,是大气污染的主要物质来源,通过选取具有明显城乡过渡趋势的环保局、太慈桥、小河、花溪、马鞍山、金阳和桐木岭监测点,采集2013年12月20日到2014年2月27日的PM2.5日均质量浓度数据,以及2014年1月20日到2月18日的时均质量浓度数据,分析研究PM2.5质量浓度的时空变化特征和浓度变化的影响因素。PM2.5数据覆盖了优良中差多种污染类型,在数据平均抽样误差分析的基础上,参考世界卫生组织的空气质量准则,探索性地利用遥感、GIS技术和统计分析方法,分析贵阳市PM2.5质量浓度的城乡变化特征,以及与气象因素、土地利用信息和城市区域之间的关系。1973、1990年的贵阳城区信息分别提取自1973年12月30日的LANDSAT MSS影像(辅以1:50000地形图)、1990年10月16日的LANDSAT 5影像,土地利用现状信息和2010年贵阳建成区信息提取自2010年9月21日的LANDSAT 5。以监测点为原点生成监测点500 m缓冲区,以资源1号02C星遥感影像为数据源,采用目视解译方法提取土地利用信息,分析PM2.5质量浓度与土地利用类型的关系。利用GIS量取监测点与1973年贵阳市主城区边缘的最短距离,分析其与监测点PM2.5质量浓度的关系。结果表明,1PM2.5日均质量浓度值呈现由农村向城市递增的趋势,并随着监测时间的推移形成明显的递减趋势,7个监测点日均数据均值是77μg·m-3,农村监测点桐木岭的监测值是56μg·m-3,马鞍山74μg·m-3,和金阳73μg·m-3,花溪81μg·m-3,小河86μg·m-3,太慈桥86μg·m-3,环保局85μg·m-3。质量浓度>100μg·m-3的总时数,桐木岭为13 h,金阳81 h,环保局、马鞍山106 h,花溪118 h,小河154 h,太慈桥157 h。2PM2.5日均质量浓度总体上呈下降的趋势,除夕以后PM2.5浓度显著下降,平均浓度相差47μg·m-3。PM2.5时均浓度在总体下降的趋势下,还表现出明显的24小时周期性变化,并有明显的城乡差异。3PM2.5质量浓度和气象因素间表现出复杂的非线性关系。PM2.5质量浓度与主城区距离的相关系数高达-0.89,与建筑用地密度的相关系数为-0.69。

PM2.5 reduces urban atmospheric quality and physical health. Additionally, PM2.5 is a hot issue of climatic and environmental problems. PM2.5, a fine particulate matter, is the main atmospheric pollutant. To study the spatiotemporal variation characteristics of PM2.5 and its influencing factors, both daily and hourly average concentrations of PM2.5 were captured. It involves such monitoring points as Tongmuling, Manshan, Jinyang, Huaxi, Xiaohe, Taiciqiao and Huanbaoju in Guiyang, covering from December 20, 2013 to February 27, 2014, and January 20 to February 18, 2014 respectively. On sampling mean error basis, this paper studied variation characteristics of PM2.5 concentration using remote sensing, GIS technology and statistical analysis method referred to Air Quality Criteria of the World Health Organization. Consequentially, relationship of PM2.5 concentrations and related factors, including meteorological elements and land use, was analyzed. The spatial range of Guiyang urban in 1973, 1990 and 2010 was extracted from LANDSAT MSS and topographic map （1973） and LANDSAT 5（1990 and 2010）. In order to analyze the relationship of concentration and land use, this paper interpreted land use within circles 250 m around these monitoring points utilizing 02C image on January 1, 2014. The minimum distances between PM2.5 monitoring points and main urban area in 1973 were calculated by using GIS. The findings were as follows： ①there was a complex relationship between PM2.5 concentrations and related influencing factors, and there was an obvious spatial downward tendency of PM2.5 concentrations from urban to rural, and a temporal downward tendency. Coefficient between density of urban built-up land and PM2.5 concentration was-0.69, and one of concentration and the minimum distance was-0.89. The average daily concentration value was 77μg·m^-3 among all points. Daily concentrations were 56 μg·m^-3, 74 μg·m^-3, 73 μg·m^-3, 81 μg·m^-3, 86 μg·m^-3, 86 μg·m^-3 and 85 μg·m^-3, respectively. The hours of hourly average concentrations above 100 μg·m^-3 were 13, 106, 81, 118, 154, 157 and 106. PM2.5 daily average concentration showed an obvious downward trend within the study period. There was a significant change around the Spring Festival’s Eve, and the difference was 47μg·m^-3. PM2.5 hourly average concentrations showed an obvious downturn and a daily periodic variation during the study period, and there is an obvious difference between urban and rural.