上海市大气污染物时空分布及其相关性因子分析

doi:10.16258/j.cnki.1674-5906.2021.06.013

生态环境学报 ›› 2021, Vol. 30 ›› Issue (6): 1220-1228.DOI: 10.16258/j.cnki.1674-5906.2021.06.013

上海市大气污染物时空分布及其相关性因子分析

侯素霞¹^,^*(), 张鉴达², 李静³

1.河北科技工程职业技术大学资源与环境工程系,河北邢台 054000
2.河北师范大学资源与环境科学学院/河北省环境演变与生态建设省级重点实验室,河北石家庄 050024
3.河北交通职业技术学院党政办公室,河北石家庄 050011

收稿日期:2021-03-23 出版日期:2021-06-18 发布日期:2021-09-10
通讯作者: *
作者简介:侯素霞（1981年生）,女,副教授,硕士,主要从事废水与污泥处理、大气污染防治技术、土壤污染防治等方面的教学和研究。E-mail: housxhb@foxmail.com
基金资助:
河北省科技支撑计划项目(17274206)

Analysis of Spatiotemporal Distribution and Correlation Factors of Atmospheric Pollutants in Shanghai City

HOU Suxia¹^,^*(), ZHANG Jianda², LI Jing³

1. Department of Resource and Environmental Engineering, Hebei Vocational University of Technology and Engineering, Xingtai 054000, China
2. College of Resources and Environmental Science, Hebei Normal University/Key laboratory of Environment Evolution and Ecological Construction of Hebei Province, Shijiazhuang 050024, China
3. Party and Government Office, Hebei Jiaotong Vocatioinal and Technical College, Shijiazhuang 050011, China

Received:2021-03-23 Online:2021-06-18 Published:2021-09-10

摘要/Abstract

摘要：

利用2016—2020年上海市PM₁₀、PM_2.5、SO₂、NO₂、O₃的质量浓度和温度、相对湿度、平均风速、水平能见度气象条件,分析了上海市PM₁₀、PM_2.5、SO₂、NO₂、O₃污染物的时间变化趋势。同时,利用多元线性回归模型及BP神经网络建立污染物与气象因素之间的相关关系,对其质量浓度进行预测,分析对比不同模型的预测结果。研究表明：2016—2020年上海市大气污染物质量浓度随时间变化整体呈现下降趋势;污染物质量浓度季节性差异显著,PM_2.5及PM₁₀质量浓度呈现“冬高夏低”,而O₃质量浓度呈现“冬低夏高”;可吸入颗粒物质量浓度（PM_2.5、PM₁₀）与SO₂、NO₂质量浓度,O₃质量浓度与NO₂的质量浓度之间存在显著相关性;多元线性回归分析表明相对湿度、平均风速及水平能见度3个气象因素对上海市PM_2.5、PM₁₀质量浓度产生显著影响;温度、相对湿度、平均风速及水平能见度4个气象因素对上海市O₃质量浓度产生显著影响;多元线性回归分析表明上海市PM₁₀质量浓度与温度之间显著性水平为0.303,意味着温度对上海市大气PM₁₀质量浓度并没有产生显著影响;PM₁₀质量浓度随相对湿度的增加、平均气压及水平能见度的增大而减小;O₃质量浓度则与温度和平均风速呈正相关,与相对湿度和水平能见度呈负相关。相比多元线性回归,BP神经网络在预测上海市气象污染物质量浓度表现出强大的泛化能力,PM_2.5、PM₁₀、NO₂与O₃的真实值与预测值相关系数（r²）分别为98.6%,97.4%,97.6%和98.3%。

关键词: 上海市, 大气污染物, 分布特征, BP神经网络, 多元线性回归, 特征分析

Abstract:

Based on the mass concentrations of PM₁₀, PM_2.5, SO₂, NO₂, O₃and meteorological parameters of temperature, relative humidity, average wind speed, horizontal visibility in Shanghai from 2016 to 2020, this study analyzed the temporal variation trends of the concentrations of PM₁₀, PM_2.5, SO₂, NO₂ and O₃. Furthermore, the correlation between atmospheric pollutants and meteorological factors was analyzed by using the models of multiple linear regression and BP neural network. The two models were used to predict the concentration of atmospheric pollutants and its prediction results were also analyzed and compared in the paper. The results indicated that the concentrations of PM_2.5, PM₁₀, SO₂, NO₂, and O₃ showed an overall downward trend in Shanghai from 2016 to 2020. Meanwhile, significant seasonal variation of pollution concentrations was found, that the concentrations of PM_2.5 and PM₁₀ always were “higher in winter and lower in summer”, while the concentrations of O₃showed a opposite trend compared with PM_2.5 and PM₁₀ which was “higher in summer and lower in winter”. In addition, there was a significant correlation between the concentration of inhalable particulate matter (PM_2.5, PM₁₀) and the concentration of SO₂ and NO₂. The relationship between concentration of O₃ and NO₂ should be emphasized. According to the results of multiple linear regression relative humidity, average wind speed and horizontal visibility had a significant impact on the concentration of PM_2.5 and PM₁₀ in Shanghai city, while four meteorological factors including temperature, relative humidity, average wind speed and horizontal visibility played an important role in the concentration of O₃. Multiple linear regression analysis showed that the significance level between the concentration of PM₁₀ and temperature was 0.303, which indicated that temperature had no significant effect on the concentration of PM₁₀; The concentration of PM₁₀ decreased with the increase of relative humidity, average air pressure and horizontal visibility. While concentration of O₃ was positively correlated with temperature and average wind speed, but negatively correlated with relative humidity and horizontal visibility. Compared with multiple linear regression model, BP neural network showed an outstanding generalization capability to predict the concentrations of PM_2.5, PM₁₀, NO₂and O₃, which the correlation coefficient (r²) between the true values and predicted values of PM_2.5, PM₁₀, NO₂ and O₃ were 98.6 %, 97.4 %, 97.6 % and 98.3 %, respectively.

Key words: Shanghai city, atmospheric pollutants, distribution characteristics, BP neural network, multiple linear regression, characteristic analysis

中图分类号:

侯素霞, 张鉴达, 李静. 上海市大气污染物时空分布及其相关性因子分析[J]. 生态环境学报, 2021, 30(6): 1220-1228.

HOU Suxia, ZHANG Jianda, LI Jing. Analysis of Spatiotemporal Distribution and Correlation Factors of Atmospheric Pollutants in Shanghai City[J]. Ecology and Environment, 2021, 30(6): 1220-1228.

图/表 9

图1 人工神经网络（a）及神经元工作机理（b）示意图

Fig. 1 Artificial neural network inversion (a) and mechanism of neural unit (b)

图2 2016—2020年上海市PM2.5、PM10、SO2、NO2及O3质量浓度年变化

Fig. 2 Annual variation of the concentrations of PM2.5, PM10, SO2, NO2 and O3 in Shanghai from 2016 to 2020

图3 2016—2020上海市PM2.5、PM10、SO2、NO2及O3质量浓度月度变化

Fig. 3 Month variation of the concentrations of PM2.5, PM10, SO2, NO2 and O3 in Shanghai from 2016 to 2020

图4 2016—2020上海市PM2.5、PM10、SO2、NO2及O3质量浓度周变化

Fig. 4 Weekly variation of the concentrations of PM2.5, PM10, SO2, NO2 and O3 in Shanghai from 2016 to 2020

图5 层次热力图：各参数相关性分析

Fig. 5 Correlation analysis of various parameters among the heatmap

表1 多元线性回归模型

Table 1 Multiple linear regression models

Pollutant	Variable	VIF	Non-normalized coefficients B	t Value	Significance level
PM_2.5	Constant Term		131.444	45.612	0.000
	C_T	1.151	-0.090	-1.947	0.052
	C_RH	1.195	-0.706	-21.102	0.000
	C_AWS	1.059	-3.916	-8.836	0.000
	C_HV	1.278	-2.255	-39.550	0.000
PM₁₀	Constant Term		195.938	47.554	0.000
	C_T	1.151	0.068	1.029	0.303
	C_RH	1.195	-1.317	-27.509	0.000
	C_AWS	1.059	-4.801	-7.577	0.000
	C_HV	1.278	-2.446	-30.008	0.000
NO₂	Constant Term		113.473	48.524	0.000
	C_T	1.151	-0.626	-16.739	0.000
	C_RH	1.195	-0.301	-11.074	0.000
	C_AWS	1.059	-10.112	-28.120	0.000
	C_HV	1.278	-0.979	-21.166	0.000
O₃	Constant Term		115.874	24.398	0.000
	C_T	1.151	1.716	22.576	0.000
	C_RH	1.195	-1.000	-18.120	0.000
	C_AWS	1.059	2.935	4.019	0.000
	C_HV	1.278	-0.588	-6.261	0.000

图6 多元线性回归模型实测值和预测值线性拟合图

Fig. 6 Linear fitting diagram of the measured and predicted values with the multiple linear regression model

图7 BP神经网络预测污染物过程中迭代次数与均方误差关系

Fig. 7 The relationship between the iteration times and the MSE in the prediction process of air pollutions using BP neural network

图8 BP神经网络预测PM2.5、PM10、NO2、O3性能表现

Fig. 8 Performance of concentration prediction of PM2.5, PM10, NO2, and O3 with BP neural network

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上海市大气污染物时空分布及其相关性因子分析

Analysis of Spatiotemporal Distribution and Correlation Factors of Atmospheric Pollutants in Shanghai City

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