长三角大气颗粒物的污染特征与影响因素分析毕业论文
2021-12-24 16:19:46
论文总字数：24187字
摘 要
以整个长三角地区为研究区域，收集区域内代表性城市近二十年的PM10浓度数据。利用Spearman秩相关系数法分析颗粒污染物的长期变化趋势；收集区域内所有城市2019年的月均浓度，利用图表分析颗粒污染物短期内的时空分布特征。使用Pearson相关性系数法分析自然因素对颗粒污染物的影响，以及灰色关联度法分析社会因素对颗粒污染物的影响。
得到的结果为长三角地区颗粒污染物浓度与时间之间为负相关关系，并且相关性较强，其中四个城市spearman系数分别为-0.954、-0.916、-0.568、-0.557。颗粒污染物的时空分布特征为夏季颗粒污染物浓度处于一个比较低的状态，冬季颗粒污染物浓度处于一个比较高的状态。PM2.5、PM10最高值分别为53 μg/m3、79 μg/m3。PM2.5、PM10浓度最低值分别为24 μg/m3、45 μg/m3。从空间分布上来看沿海地区浓度最低，其中PM2.5、PM10最低浓度值出现在浙江省舟山市为20 μg/m3、36 μg/m3，安徽省和江苏省北部地区颗粒污染物浓度较高，PM2.5、PM10最低浓度值分别出现在安徽省滁州市和铜陵市为48 μg/m3、76 μg/m3。
从气象因素方面分析得知，温度是颗粒污染物浓度的重要影响因素，二者为正相关关系，其pearson系数-0.758到-0.949之间相关性较强。相对湿度和风速与其关系较为复杂。考虑与相对湿度的关系时需要同时考虑到降水，降水量的大小直接影响到了颗粒污染物的浓度。考虑与风速的关系时，需要同步考虑到气温和相对湿度的影响。
从社会因素的分析得知人口数、能源消耗量（煤炭）、汽车拥有量、烟尘排放总量与颗粒污染物的浓度具有较高的关联度，其中人口数是大气颗粒物主要的社会影响因素。颗粒污染物与人口数、能源消耗量（煤炭）、烟尘排放总量的关联度在0.42到0.67中间，属于中等关联到较高关联之间；所以要着重在这几个方面开展大气环境治理项目。
关键词：颗粒污染物 时空分布特征 spearman系数 pearson系数 灰色关联度
Analysis of pollution characteristics and influencing factors of atmospheric particulates in the Yangtze River Delta
Abstract
This paper takes the whole Yangtze River Delta as the research area. First collect PM10 concentration data of representative cities in the region for the past 20 years, and use the spearman rank correlation coefficient method to analyze the long-term change trend of particulate pollutants. Second, collect the monthly average concentration of all cities in the region in 2019, and use the icon to analyze the temporal and spatial distribution characteristics of particulate pollutants.
The results showed that there is a negative correlation between particle pollutant concentration and time in the Yangtze river delta region, and the correlation was strong. The four cities' spearman coefficients are -0.954, -0.916, -0.568 and -0.557. The temporal and spatial distribution of particulate pollutants is characterized by low concentration in summer and high concentration in winter. The maximum PM2.5 and PM10 values were 53 μg/m3 and 79 μg/m3 respectively. The minimum concentrations of PM2.5 and PM10 are 24 μg/m3and 45 μg/m3 respectively. In terms of spatial distribution, the lowest concentrations of PM2.5 and PM10 were found in Zhoushan city, Zhejiang province, where the concentrations of PM2.5 and PM10 were 20 μg/m3 and 36 μg/m3 respectively. In the northern part of Anhui province and Jiangsu province, the concentrations of particulate pollutants were relatively high. The lowest concentrations of PM2.5 and PM10 were found in Chuzhou city and Tongling city, Anhui province, where the concentrations were 48 μg/m3 and 76 μg/m3 respectively.
From the analysis of meteorological factors, it is known that temperature is an important factor influencing the concentration of particulate pollutants. There was a strong correlation between the pearson coefficient of -0.758 and -0.949. And the relationship between the relative humidity and concentration of particulate pollutants is complex, so is the wind speed. When considering the relationship with relative humidity, precipitation should also be considered, and the magnitude of precipitation directly affects the concentration of particulate pollutants. When considering the relationship with wind speed, it is necessary to simultaneously consider the influence of air temperature and relative humidity.
From the analysis of social factors, we know that population, energy consumption (coal), car ownership, total smoke emission and particulate pollutant concentration have a high degree of correlation, and population is the main social impact factor of atmospheric particulate matter. The degree of correlation between particulate pollutants and population, energy consumption (coal), and the total amount of soot emissions is between 0.42 and 0.67, which is a medium to high correlation; therefore, it is important to carry out atmospheric environmental governance projects in these areas.
Key words: Particulate contaminants; Spatiotemporal distribution characteristic; Spearman coefficient; Pearson coefficient; grey relational degree
目 录
摘 要 I
Abstract II
第一章 引言 1
1.1 研究背景 1
1.2 研究内容 1
1.3 技术路线 2
第二章 国内外的相关研究进展 3
2.1 不同因素对大气中颗粒污染物浓度的影响 3
2.1.1 自然因素对颗粒污染物时空分布特征的影响 3
2.1.2 人为因素对颗粒污染物时空分布特征的影响 4
2.2 大气中颗粒污染物的空间分布特征 4
2.2.1 大气中颗粒污染物在全国范围内的分布特征 5
2.2.2 大气中颗粒污染物在区域上的分布特征（以长三角地区为例） 5
2.2.3 大气中颗粒污染物在单个城市中的分布特征 5
2.3 本章小结 6
第三章 数据与方法 7
3.1 数据来源 7
3.2 研究方法 7
3.2.1 长三角地区颗粒污染物时空分布特征分析方法 7
3.2.2 长三角地区颗粒污染物影响因素分析方法 7
第四章 大气中颗粒污染物的时空分布特征 9
4.1 长三角地区颗粒污染物的时间污染分布特征 9
4.1.1 上海、合肥、南京、杭州四个地区PM10的年度变化特征 9
4.1.2 长三角地区PM10、PM2.5的月均、季均污染特征 10
4.2 长三角地区所有城市颗粒污染物的空间分布特征 11
4.3 大气颗粒物浓度相关性分析 13
4.4 本章小结 13
4.4.1 PM2.5和PM10的时间污染特征 14
4.4.2 PM2.5和PM10的空间污染特征 14
4.4.3 PM2.5和PM10的治理建议 14
第五章 大气中颗粒污染物影响因素的分析 15
5.1 长三角地区自然因素对大气中颗粒污染物的影响 15
5.1.1 长三角地区气象因素概况 15
5.1.2 气象因素对PM2.5、PM10月均浓度变化的影响 16
5.1.3 气象因素对PM2.5、PM10季均浓度变化的影响 18
5.2 长三角地区社会因素对大气中颗粒污染物的影响 19
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