上海市PM2.5的统计特征与污染评估研究

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摘要

空气质量问题始终是政府、环境保护部门和全国人民关注的热点问题。PM2.5

作为空气质量控制管理过程中比较重要的污染物，已成为全球大气环境研究的热

点。为了更科学有效的控制和有针对性治理空气中的 PM2.5，改善整体空气质量，

本文以上海市空气中的 PM2.5 为研究对象，首先对上海市现阶段整体空气质量状况

做出了相应分析，然后从相关因素、时空分布特征、污染评估、污染治理四个方

面对其空气中的 PM2.5 做了详细分析和探讨。主要研究内容与创新点如下：

（1）上海市的空气质量现状。收录整理了上海市最新空气质量的相关数据，

并对数据进行了统计分析，分别从上海市大气环境监测现状、上海市空气质量年

报、上海市空气质量全国排行及上海市空气中首要污染物的统计分析四个方面对

上海市现阶段空气质量的现状进行了分析。研究发现：2014 年上海市总体空气质

量良好，其空气污染的首要污染物为 PM2.5。

（2）上海市 PM2.5 与空气质量指数（AQI）监测指标的相关性分析。采用 Pearson

相关系数、Spearman 相关系数和 Kendall



相关系数，计算出 PM2.5 与AQI 其他监

测指标的相关系数，并以 AQI 监测指标为研究对象，分别建立了 PM2.5 与单一监

测指标的线性拟合模型、与主要监测指标的多元非线性回归模型及 BP 神经网络模

型。研究表明，BP 神经网络模型输出的 PM2.5 预测值与真实值误差很小，模型精

度高，利用该模型对上海市 PM2.5 指标进行有效的预测。同时 BP 神经网络模型与

多元非线性模型的分析结果高度一致，确保了研究结论的有效性和严谨性，从而

为控制 PM2.5 指标提供了科学依据。

（3）上海市 PM2.5 的时空分布特征。分别从上海市 PM2.5 的时间分布特征和上

海市 PM2.5 的区域分布特征两个部分进行分析。研究得出了上海市 PM2.5 的总体分

布特征、月份分布特征、季节分布特征，以及上海市 PM2.5 监测站点所在区域的日

变化分布特征、月份分布特征、季节分布特征，找出了上海市 PM2.5 污染的重点地

区，以便为上海市 PM2.5 的具体控制与治理提供科学依据。

（4）上海市 PM2.5 的污染评估。以 2014 年1月到 12 月为有效数据时间段，

基于主成分改进的分区聚类方法，对上海市现有的 10 个PM2.5 监测站点所在区域

进行聚类分区。并根据聚类结果，按照 PM2.5 分区污染评估标准进行污染评估。与

此同时，构建了模糊综合评判的污染评估模型，借助相关专家的知识，对上海现

阶段 PM2.5 的总体污染状况进行了评估，并得到一些有益的结果，为政府和相关部

门对上海市 PM2.5 的治理提供了一定的参考。

（5）上海市 PM2.5 污染治理的博弈分析。由于 PM2.5 的重要排放源为工业型企

业，故本文基于博弈论的方法论，就空气中 PM2.5 污染治理问题，建立了工业型企

业与政府监管机构的不完全信息的博弈模型。对比分析初始博弈模型和拓展模型，

得出 PM2.5 污染控制的相关举措，以使企业能够自主地对 PM2.5 主要来源污染物进

行控制和治理，促使社会总效益最大化。

（6）结论与建议。总结本文主要研究所得有效结论，并给出 PM2.5 相关治理

方向和建议。

关键词：PM2.5 空气质量相关分析时空分布污染评估

ABSTRACT

Air quality is always a hot issue concerned by government,environmental protection

departments and the whole nation.PM2.5 which is one of the most important pollutants

in process of air quality management and control has been a global research hotspot in

the atmospheric environment.In order to have a more scientific and effective control of

PM2.5 so that it can help to improve the overall air quality of China,we chose the PM2.5

in Shanghai as study subject in this paper. A statistical analysis about current situation of

air quality and its pollutants in Shanghai was first made and found that PM2.5 was the

primary pollutant at the present stage.And then,a more detailed analysis and discussion

of PM2.5 were made from four aspects including its correlation analysis, spatial and

temporal distribution,contamination assessment and governance.

The main research contents and innovations are as follows:

1)The current situation of air quality in Shanghai.Acoording the latest air quality

reports of Shanghai, it was analyzed and summarized from four aspects including its

atmospheric environmental monitoring status, its air quality annual reports, national

ranking of its air quality and statistical analysis of its primary pollutants.The research

has shown that the overall air quality of Shanghai in 2014 was good and PM2.5 was the

primary pollutant in the air of Shanghai at the present stage.

2)The correlation analysis between Air Quality Index (AQI) monitoring indicators

and PM2.5 in Shanghai. The correlation coefficients between PM2.5 and the other AQI

monitoring indicators were calculated based on Pearson, Spearman and Kendall



.With

AQI monitoring indicators as study subject, a single linear fit model was established

with PM2.5 and a single monitoring indicator. In order to have a further study, a

multivariate nonlinear regression model and BP neural network model were established

respectively with PM2.5 and the other key monitoring indicators.The research has shown

that mean square error between PM2.5 prediction value which was the output of BP

neural network model and true value is very small. Due to high accuracy of the model,

we could use it to predict the value of PM2.5 in Shanghai effectively. Meanwhile, the

analysis results of BP neural network model and multivariate nonlinear models were

highly consistent which could help to ensure the validity of research conclusions and

provide a scientific basis for an effective control of PM2.5.

3)The temporal and spatial distribution of PM2.5 in Shanghai.The study in this section

could be analyzed separately from the two portions:the time distribution characteristics

and the regional distribution characteristics. Based on the analysis, the distribution

characteristics of PM2.5 in Shanghai were derived including its whole distribution,

month distribution and seasonal distribution.And the regional distribution characteristics

were also derived including its geographical distribution, diurnal changes distribution,

month distribution and seasonal distribution.On the basis of the result,we could find out

the key pollution areas of PM2.5 in Shanghai and provide a scientific basis for the

specific control and treatment of PM2.5 in Shanghai at the same time.

4)The contamination assessments of PM2.5 in Shanghai.According to daily air quality

reports of ten monitoring stations from January to December in 2014, a cluster analysis

improve by principal component was employed to classify monitoring stations.And

based on the clustering results, contamination assessments were conducted respectively

in the light of ‘Ambient Air Quality Standards’.At the same time,a pollution assessment

model based on fuzzy comprehensive evaluation were built to assess the overall

pollution situation of PM2.5 in Shanghai with the knowledge of the relevant experts.

And some beneficial results were derived finally which could provide a reference for

the control of air quality in Shanghai.

5)The game analysis on contamination governance of PM2.5 in Shanghai.Since

industrial enterprises is one of the most important emission sources of PM2.5,a game

model with incomplete information was made between industrial enterprises and

regulatory agencies of government in this paper.According comparison between the

initial game model and its improved model, related pollution control initiatives of PM2.5

were put forward so that the industrial companies could control and govern its major

source of PM2.5 autonomously and maximize the total social benefits at the same time.

6)Conclusions and recommendations.In this section,valid conclusions of the main

research were summarized and related governance direction and suggestions of PM2.5

were proposed.

Key Words：PM2.5, Air Quality, Correlation Analysis, Temporal and

Spatial Distribution, Contamination Assessment

摘要

ABSTRACT

第一章绪论 ······················································································· 1

1.1 研究背景及意义 ········································································· 1

1.2 国内外研究现状 ········································································· 2

1.3 主要研究内容 ············································································ 4

1.4 主要创新点 ··············································································· 5

第二章上海市的空气质量现状 ······························································· 7

2.1 上海市大气环境监测现状 ····························································· 7

2.2 上海市空气质量（AQI）年报 ························································ 8

2.3 上海市空气质量全国排行 ··························································· 10

2.4 上海市空气中首要污染物 ··························································· 11

第三章上海市 PM2.5 与AQI 监测指标的相关性分析 ·································· 13

3.1 原始数据的预处理方法 ······························································ 13

3.2 PM2.5 与监测指标的交叉相关系数 ················································· 15

3.2.1 相关系数的计算方法 ··························································· 15

3.2.2 具体数据的选取与计算 ························································ 17

3.3 PM2.5 与单一监测指标的线性拟合 ················································· 17

3.3.1 单一监测指标（分指数）的拟合模型 ······································ 18

3.3.2 单一监测指标（含量）的拟合模型 ········································· 19

3.4 PM2.5 与多种监测指标的多元非线性回归模型 ·································· 22

3.4.1 多元非线性模型的建立 ························································ 22

3.4.2 多元非线性模型的求解 ························································ 25

3.4.3 模型结果及分析 ································································· 26

3.5 PM2.5 与多种监测指标的 BP 神经网络的模型 ··································· 27

3.5.1 BP 神经网络模型及特点 ······················································· 28

3.5.2 BP 神经网络模型的建立 ······················································· 29

3.5.3 模型的求解及分析 ······························································ 31

3.6 相关因素分析小结 ···································································· 32

第四章上海市 PM2.5 的时空分布特征 ····················································· 33

4.1 上海市 PM2.5 时间分布特征 ························································· 33

4.1.1 上海市 PM2.5 总体分布特征 ··················································· 33

4.1.2 上海市 PM2.5 月份分布特征 ··················································· 35

4.1.3 上海市 PM2.5 季节分布特征 ··················································· 36

4.2 上海市 PM2.5 区域分布特征 ························································· 37

4.2.1 上海市 PM2.5 监测站点的地理分布 ·········································· 37

4.2.2 上海市 PM2.5 监测站点日变化分布特征 ···································· 38

4.2.3 上海市 PM2.5 监测站点月份分布特征 ······································· 41

4.2.4 上海市 PM2.5 监测站点季节分布特征 ······································· 42

4.3 时空分布特征小结 ···································································· 44

第五章上海市 PM2.5 的污染评估 ··························································· 46

5.1 PM2.5 污染评估标准及要求 ·························································· 46

5.1.1 污染评估标准 ···································································· 46

5.1.2 数据统计的有效性 ······························································ 46

5.2 基于描述分析的上海市 PM2.5 的分区污染评估 ································· 47

5.3 基于聚类分析的上海市 PM2.5 的分区污染评估 ································· 47

5.3.1 具体聚类方法的选取 ··························································· 47

5.3.2 系统聚类的数学模型 ··························································· 49

5.3.3 分类数据的收录及处理 ························································ 51

5.3.4 系统聚类分析 ···································································· 52

5.4 基于主成分改进的分区聚类污染评估 ············································ 53

5.4.1 主成分分析的数学模型 ························································ 53

5.4.2 主成分分析的基本步骤 ························································ 54

5.4.3 主成分模型结果及分析 ························································ 55

5.4.4 改进聚类分析 ···································································· 58

5.4.5 聚类污染评估结果 ······························································ 59

5.5 基于模糊综合评判的上海市 PM2.5 的污染评估 ································· 59

5.5.1 模糊综合评判的数学模型 ····················································· 59

5.5.2 模糊综合评判模型的建立 ····················································· 62

5.5.3 综合评判评价结果 ······························································ 64

5.6 污染评估小结 ·········································································· 64

第六章上海市 PM2.5 污染治理的博弈分析 ··············································· 66

6.1 博弈论的基本理论 ···································································· 66

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