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

6.2 初始博弈模型的构建 ································································· 67

6.3 不完全信息的博弈分析 ······························································ 68

6.4 博弈模型的拓展 ······································································· 70

第七章结论与建议 ············································································ 71

7.1 研究总结 ················································································ 71

7.2 相关建议 ················································································ 72

参考文献 ························································································· 74

附录 ······························································································· 79

附录 1 2014 年上海市空气质量指数及首要污染物汇总 ··························· 79

附录 2 2014 年上海市空气首要污染物分指数汇总 ································· 84

附录 3 2013-2014 年上海市 AQI 日报及污染物浓度汇总 ························· 89

附录 4 2014 年上海市 PM2.5 浓度及对应分指数级别和颜色汇总 ················ 99

附录 5 2014 年上海市 PM2.5 监测站点数据汇总 ···································· 108

在读期间公开发表的论文和承担科研项目及取得成果 ································ 127

致谢 ····························································································· 128

第一章绪论

1.1 研究背景及意义

大气为人类及各种动植物、微生物的生存和发展提供了理想的环境，它

的微小变动也将直接影响着人类的生存、生活和生产。随着温饱问题的解决，

人们也越来越关注空气的质量。同时，空气质量也始终是各个国家环境保护

部门和政府部门关注的热点问题。

PM2.5 也称细颗粒物，是指环境空气中空气动力学当量直径小于等于

2.5μm 的颗粒物。[1] 而颗粒直径小于 2.5μm 时，其直径大小仅相当于人体头

发直径的十分之一。

PM2.5 很容易被吸入体内，由于其体积细微，可能会直接进入人的支气管，

从而干扰肺部的一些重要功能，引发诸如支气管炎及哮喘等多种疾病。与此

同时，PM2.5 如果进入血管，就会影响血红蛋白输送氧气的能力，对患有这方

面疾病的病人来说，可能后果将不堪设想。而随着 PM2.5 细颗粒物进入人体血

液，里面所包含的有害气体和重金属等物质也会随之溶解在血液中，对人体

的伤害更是无法预计的。人体的生理结构一般不会进行任何过滤，这也就决

定了 PM2.5 对人体健康的重要危害。

与此同时，PM2.5 也是产生雾霾天气的重要原因之一。据中国新闻网在 2013

年7月12 日报道：“我国自 2013 年初以来，发生了大范围的、持续性的雾霾

天气，受到影响的区域波及华南北部、华北平原及江汉、江南、江淮、黄淮

等地区，受到影响的总面积约占我国整个国土面积的四分之一，受到影响的

总人口约为六亿人。”[2] 这种大规模、持续性的雾霾天气，严重降低了地面和

高空的空气的能见度，对地面及空中的交通安全构成严重的安全隐患，甚至

会造成重大事故，对人类的人身和财产造成严重威胁。

种种研究数据表明，PM2.5 是空气质量控制管理过程中比较重要的污染物，

这使 PM2.5 的相关研究被更为迫切的提上了日程。到目前为止，全球发达国家

及很多发展中国家均将 PM2.5 列入当地的空气质量标准。

通过借助大气颗粒物及其重要组成成分 PM2.5 的研究，对于控制和管理大

气中 PM2.5 等颗粒物的排放，提高大气环境的整体质量，进而改善人们所生活

和依赖的生存环境具有重大的意义。

上海理工大学硕士论文

1.2国内外研究现状

PM2.5 已成为全球大气环境研究热点，近年来许多国内外学者从 PM2.5 的来源、

时空分布特征、对能见度的影响、对人体健康危害、主要影响因素等方面开展了

研究。

（1）PM2.5 的来源

Sergio Rodriguez 研究发现，城市及工业密集地的吸入颗粒物的主要来源

为汽车尾气、工业粉尘及海洋的气溶胶等。Fumo Yang 等研究发现碳元素和

土壤尘粒是颗粒物 PM2.5 的主要来源物。Sinan Yatkin 则研究发现，矿产排放

物、交通工具废气排放和化石燃料燃烧物是 PM2.5 和PM10 等空气中颗粒物的

主要来源。[3,4] 与此同时，张艺耀等[5]数据处理结果表明 PM2.5 的主要来源物为

施工扬尘、燃料煤及机动车的尾气排放。

以水泥行业为例，早在 1994 年时，美国就已经公布了《空气污染物排放

技术手册 AP-42》[6]，研究和分析水泥中 PM2.5 的污染物来源。与此对应，

Ehrilich 等[7]则研究分析了德国的水泥行业对于各种以工业源为主要来源物的

颗粒物的贡献率。在国内，张强、雷宇、张楚莹等[8-10]均通过相关数量模型

的构建和计算，发现 PM2.5 最大的排放来源于水泥行业。

（2）PM2.5 的时空分布特征

Chan 等[11]通过对香港大气颗粒物的垂直分布分布的研究，发现 PM2.5 的质量

浓度随高度呈指数下降的趋势。潘纯珍等[12]选取公共道路附近空气中的 PM2.5 浓度

为研究对象，进行垂直分布特征分析。研究发现其空气中的 PM2.5 浓度在水平方向

无明显变化特征，在垂直方向上，以 30m 和90m 为分界点，随地理的高度呈现先

下降，后平缓，再明显下降的趋势。

徐敬、刘君霞等[13,14]对北京市近年来大气颗粒物，尤其是颗粒物 PM2.5，进行

了观测和统计，分析其污染水平变化规律。研究表明，北京市 PM2.5 污染程度具有

明显的季节变化特征，冬季最为严重，春季次之，夏季最低。

欧阳琐等[15]则过引入气溶胶模块，运用城市空气质量数值预报模式系统

(NJU-CAQPS)，对南京市空气中 PM2.5 浓度的时间变化特征进行分析。实验发现，

南京市空气中 PM2.5 浓度具有明显的日变化特征，在清晨和半夜最高，午后至傍晚

浓度较低。

徐衡等[16]利用 E-YAM 粒子监测仪连续监测了宝鸡市建成区 2012 年4月到

2013 年3月的大气中 PM2.5 浓度。数据分析表明，宝鸡市大气中 PM2.5 浓度在非供

暖时期（4月-10 月）的污染程度较轻，而在集中供暖时期（11 月-翌年 3月）的污

第一章绪论

染程度较重，这也说明了集中供暖期是治理 PM2.5 污染问题的关键时期。

（3）PM2.5 对能见度的影响

雾霾天气的频繁出现，给人们的日常生活和身体健康带来诸多不利影响，如

何改善环境空气质量成为人们日益关注的焦点。而造成雾霾天气、降低能见度的

主要因素就是空气中的 PM2.5。

Sloane 等[17]分析了空气中颗粒物对于能见度的影响，他认为主要是由于大气

中的颗粒物和气状的污染物，能通过散射和吸收光减弱其信号，从而使空气中的

能见度降低。Appel 等[18]则具体研究了空气中的颗粒物对光的散射和吸收作用。

有研究表明[19]，颗粒物 PM2.5 与大气能见度之间的线性相关系数最高，影响最

大。王贝贝[20]分析了北京市空气中 PM2.5 浓度与气象因子的相关关系，得出 PM2.5

与大气能见度表现出较好的负相关性。同时，张婷婷[21]也得到了此结论。

刘岩磊等[21]则从颗粒物 PM2.5 的含有成分，具体的解释了形成雾霾天气和影响

能见度的原因。此外，国内外的一些专家研究也均表明，空气中的 PM2.5 的含量会

大大影响能见度。[23-25]

（4）PM2.5 对人类健康危害

空气中颗粒物与健康方面的研究，开始于 19 世纪 80 年代末的大量流行病学

研究。Cao MX 等[26]发现室内空气中的污染物含量高于室外，并且对人类健康影响

更为显著。Pellizzari 和Geller[27,28]对室内外的 PM2.5 的浓度进行对比测试，结果也

显示室内 PM2.5 的浓度高于室外。在国内，赵厚银等[29]具体分析了室内 PM2.5 来源

及影响室内 PM2.5 的控制因素，并提出了 PM2.5 的发展趋势。

Bellina、Dockery DW、McCreanor 等[30-32]分别在相关研究中表明，空气中的

细小颗粒物容易诱发气道神经炎、急性呼吸道及哮喘等疾病。

Fang、Berger 和Dvonch[33-35]等分别通过实验数据，证明了人体心血管系统、

心脏等器发生病变的危险度，随环境中诸如 PM2.5 等的超细颗粒物浓度升高而明显

增加，对人类的健康极为不利。Linares、Lee 和Halonen [36-38]则具体研究了空气中

PM2.5 含量对儿童和老人的健康影响，研究也得到了相同的结论。

Mehta M 和李朋昆等[39,40]均采用体外实验的方法，证明了空气中的 PM2.5 含量

能够影响和诱导 DNA 修复出错。

Schwartz J 等[41,42]实验发现，人类日死亡率与空气中的颗粒物 PM2.5 含量密切

相关。Dockery 和Pope 等[43,44]以美国城市居民为例进行分析研究，同样也证明了

空气中的颗粒物 PM2.5 含量会导致重大疾病死亡率的增加。

在国内也以张文丽、杨书申和时宗波为代表的学者在研究 PM2.5 对人体健康危

害。[45-47]

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