中国城市群地区PM2.5时空演变格局及其影响因素

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王振波^,¹^,², 梁龙武¹^,², 王旭静³1. 中国科学院地理科学与资源研究所中国科学院区域可持续发展分析与模拟重点实验室,北京 100101
2. 中国科学院大学资源与环境学院,北京 100049
3. 山西师范大学地理科学学院,临汾 041004

Spatio-temporal evolution patterns and influencing factors of PM_2.5 in Chinese urban agglomerations

WANG Zhenbo^,¹^,², LIANG Longwu¹^,², WANG Xujing³1. Key Laboratory of Regional Sustainable Development Modeling, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
2. College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
3. School of Geography Science, Shanxi Normal University, Linfen 321004, Shanxi, China

通讯作者: 王振波(1980-), 男, 博士, 副研究员, 研究方向为城市与区域规划、城镇化与环境效应。E-mail:wangzb@igsnrr.ac.cn

收稿日期:2019-03-7修回日期:2019-11-23网络出版日期:2019-12-25

基金资助:

国家重点基础研究发展计划.2017YFC0505702
国家自然科学基金项目.41771181
清华大学新型城镇化研究院开放基金课题.TUCSU-K-17015-01

Received:2019-03-7Revised:2019-11-23Online:2019-12-25

Fund supported:

National Key Research and Development Plan.2017YFC0505702
National Natural Science Foundation of China.41771181
Open Fund Project of New Urbanization Research Institute of Tsinghua University.TUCSU-K-17015-01

摘要
城市群作为中国新型城镇化主体形态,是支撑全国经济增长、促进区域协调发展、参与国际分工合作的重要平台,也是空气污染的核心区域。本文选取2000-2015年NASA大气遥感影像反演PM_2.5数据,运用GIS空间分析和空间面板杜宾模型,揭示了中国城市群PM_2.5的时空演变特征与主控因素。结果显示：① 2000-2015年中国城市群PM_2.5浓度呈现波动增长趋势,2007年出现拐点,低浓度城市减少,高浓度城市增多。② 城市群PM_2.5浓度以胡焕庸线为界呈现东高西低的格局,城市群间空间差异性显著且不断扩大,东部、东北地区浓度提升更快。③ 城市群PM_2.5年均浓度空间集聚性显著,以胡焕庸线为界,热点区域集中东部,范围持续增加,冷点集中在西部,范围持续缩小。④ 城市群内各城市间PM_2.5浓度存在空间溢出效应。不同城市群影响要素差异显著,工业化和能源消耗对PM_2.5污染有正向影响;外商投资在东南沿海和边境城市群对PM_2.5污染具有负向影响;人口密度对本地区PM_2.5污染主要具有正向影响,对邻近地区则相反;城市化水平在国家级城市群对PM_2.5污染有负向影响,在区域性和地方性城市群则相反;产业结构高级度对本地区PM_2.5污染有负向影响,对邻近地区则相反;技术扶持度对PM_2.5污染的影响显著,但存在滞后性和回弹效应。
关键词： 城市群;PM_2.5;时空演变格局;影响因素;空间面板杜宾模型

Abstract

As the main form of China new urbanization, urban agglomerations are the important platform to support national economic growth, promote regional coordinated development and participate in international competition and cooperation, but they are also the core area of air pollution. This paper selects PM_2.5 data from NASA atmospheric remote sensing image inversion from 2000 to 2015, and uses GIS spatial analysis and Spatial Durbin Model to reveal the temporal and spatial evolution pattern characteristics and main controlling factors of PM_2.5 in China's urban agglomerations. The main conclusions are as follows: (1) From 2000 to 2015, the PM_2.5 concentration of China urban agglomerations showed a volatility growth trend. In 2007, there was an inflection point. The number of low-concentration cities declined, and the number of high-concentration cities increased. (2) The concentration of PM_2.5 in urban agglomerations was in the pattern of high in the east and and low in the west, with the "Hu Huanyong Line" as the boundary. The spatial difference between urban agglomerations is significant, and the difference is increasing. The concentration of PM_2.5 is growing faster in urban agglomerations in the eastern and northeastern regions. (3) The urban agglomeration of PM_2.5 has a significant spatial concentration. The hot spots are concentrated to the east of the "Hu Huanyong Line", and the number of cities continues to rise. The cold spots are concentrated to the west of the "Hu Huanyong Line", and the number of cities continues to decline. (4) There is a significant spatial spillover effect of PM_2.5 pollution among cities within urban agglomerations. The main controlling factors of PM_2.5 pollution in different urban agglomerations have significant differences. Industrialization and energy consumption have a significant positive impact on PM_2.5 pollution. Foreign direct investment has a significant negative impact on PM_2.5 pollution in the southeast coastal and border urban agglomerations. Population density has the significant positive impact on PM_2.5 pollution in the region, and has the opposite result in the neighbouring areas. Urbanization level has a negative impact on PM_2.5 pollution in national-level urban agglomerations, and has the opposite result in regional and local urban agglomerations. The high degree of industrial structure has a significant negative impact on PM_2.5 pollution in the region, and has the opposite result in the neighboring regions. Technical support has a significant impact on PM_2.5 pollution, but there are also lag effects and rebound effects.

Keywords：urban agglomeration;PM_2.5;spatial-temporal evolution;influencing factor;spatial Durbin Model

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本文引用格式
王振波, 梁龙武, 王旭静. 中国城市群地区PM_2.5时空演变格局及其影响因素. 地理学报[J], 2019, 74(12): 2614-2630 doi:10.11821/dlxb201912014
WANG Zhenbo. Spatio-temporal evolution patterns and influencing factors of PM_2.5 in Chinese urban agglomerations. Acta Geographica Sinice[J], 2019, 74(12): 2614-2630 doi:10.11821/dlxb201912014

1 引言

1978年以来,中国城镇化水平由1978年的17.92%提升至2017年的58.52%,仅用40年就完成了发达国家200多年的城镇化及工业化进程,中国城镇化与工业化发展取得了举世瞩目的成就^[1]。但是,长期的粗放式发展模式也加剧了中国生态环境的脆弱性,产生了一系列环境污染问题,其中空气污染问题尤为严峻^[2,3]。近年来中国中东部地区诸多城市空气质量指数“爆表”事件频发^[4],整个社会对于空气污染问题尤其是PM_2.5的关注程度日益升高。研究表明,PM_2.5已经对公民身体和心理健康造成严重的危害,对居民的哮喘等呼吸性疾病^[5]、脑血管疾病^[6]、精神性疾病^[7]等均具有重要影响。纵观世界,类似的雾霾污染事件在美国^[8]、德国^[9]、加拿大^[10]、印度^[11]等国家都爆发过,但是中国的雾霾污染问题更为严峻,中国如何应对如此严峻的空气污染事件已经引起了全球广泛关注。基于此,亟需对中国PM_2.5污染物的主要来源、时空演化格局、影响因素以及防控模式等方面进行深入研究。

2013年以来国内外学术界做了大量与PM_2.5相关的研究。在PM_2.5特征和性质方面,主要分析了化学特征^[12]、空间集聚性^[13]、空间变异性^[14]及雾霾污染期间人体可吸入的微生物^[15]等。在影响因素方面,PM_2.5浓度的经济社会因素主要有人均GDP和城市化率^[16]、人口密度和公共交通运输强度^[17]、外商直接投资^[18]、能源消耗^[19];自然地理因素主要有气压、温度、相对湿度、风速、降水量、日照时数以及SO₂、NO₂、CO、O₃浓度等^[20]。研究方法主要有灰色关联模型^[21]、地理探测器方法^[22]、土地利用回归^[23]、主成分分析^[24]、混合回归^[25]以及空间计量^[26]等模型。防控建议方面,主要提出分层跨区多向联动的大气污染治理模式^[27]、多元主体协同治理体系^[28]、多方承担雾霾治理成本的经济补偿机制^[29],以及气象科学与技术提升^[30]等。综合来看,上述文献主要是针对省域或城市地区展开分析,鲜有文献对比研究不同城市群地区的PM_2.5污染问题。

城市群是指在特定地域范围内,以1个特大城市为核心,由至少3个以上都市圈（区）或大中城市为基本构成单元,依托发达的交通通讯等基础设施网络,所形成的空间相对紧凑、经济联系紧密、并最终实现同城化和一体化的城市综合体,表现为空间形态高度发达、城市高度融合、群内要素向大城市高度集聚^[31]。目前,城市群已成为世界各国参与全球竞争与国际分工的全新地域单元^[32]。2013年中央城镇化会议首次将城市群作为推进国家新型城镇化的主体,党的“十七大”“十八大”“十九大”报告连续15年将城市群作为新的增长极。相比于城市地区,城市群地区城市化与环境保护之间的矛盾更为突出和严重,环境污染问题更为复杂和特殊^[33],城市群地区也是PM_2.5高污染城市聚集地^[34]。为此,迫切需要以中国城市群为对象,开展PM_2.5污染的时空演变格局与影响机制的定量化对比研究,研判中国不同城市群PM_2.5浓度的演变规律和主要影响因素,并进一步提出防控建议。

基于此,本文以城市群地区为研究单元,基于国家“十三五”规划纲要划定的19个城市群2000-2015年面板数据,深入揭示城市群地区PM_2.5污染的时空演变格局,采用空间自相关方法对城市群地区PM_2.5污染进行空间集聚性分析,运用空间面板杜宾模型厘定不同城市群PM_2.5的主控因素,剖析其影响机理,以期为中国空气污染防控工作提供借鉴。

2 研究区域、影响因素选取及数据来源

2.1 研究区域

本文以《国家新型城镇化规划（2014-2020）》提出的未来稳步建设的19个城市群为案例区（图1）,从市域尺度探讨PM_2.5浓度时空分布特征,并从人文要素维度解析其主要影响因素。19个城市群包括5个国家级的大城市群（京津冀城市群、长江三角洲城市群、珠江三角洲城市群、长江中游城市群、成渝城市群）,8个区域性中等城市群（辽中南城市群、山东半岛城市群、海峡西岸城市群、哈长城市群、中原城市群、江淮城市群、关中城市群、北部湾城市群、天山北坡城市群）和6个地区性小城市群（晋中城市群、呼包鄂榆城市群、滇中城市群、黔中城市群、兰西城市群、宁夏沿黄城市群）^[35]。

图1

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图1中国城市群规划区域示意图

注：该图基于国家测绘地理信息局标准地图服务网站下载的审图号为GS(2016)1593号的标准地图制作看,底图无修改。
Fig. 1Schematic diagram of China's urban agglomeration planning area

2.2 影响因素及数据来源

PM_2.5污染的时空差异主要受控于地理禀赋条件和人文经济要素等多种因素,本文借鉴EKC假说理论和相关研究成果^[36,37,38],选取PM_2.5污染的8个人文因素,即人均GDP（PGDP）、人口密度（PD）、城市化水平（UR）、工业化水平（IR）、产业结构高级度（ADIS）、外商直接投资（FDI）、技术扶持水平（TS）、能源消耗（EC）（图2）,以及5个自然要素包括风速、日照时数、空气湿度、气温、降水量,和2个政策要素政府研发投入、环境治理投资额作为控制变量,但由于文章篇幅受限,控制变量对PM_2.5污染的影响不作详细解析。工业化水平用工业产值与GDP比重表示,产业结构高级度用三次产业产值比重向量与对应单位向量之间的夹角大小表示,技术扶持水平用科学技术支出占GDP比重表示,能源消耗用人均供气量表示,由于煤炭数据较难获取,本文用人均供气量代替煤炭消耗量^[39]。中国城市群人文要素影响PM_2.5污染的机制可归纳为：单个城市内部人文要素系统综合影响“市内”PM_2.5污染;城市群内部多个城市交互耦合发展,人文要素复合系统综合影响“群内”PM_2.5污染;中国城市群巨系统内部多个城市群交互耦合发展,人文要素巨系统综合影响“国内”PM_2.5污染（图2）。

图2

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图2中国城市群人文要素系统交互耦合综合影响PM_2.5污染

Fig. 2Interactive coupling of human elements systems affecting PM_2.5 pollution comprehensively in China's urban agglomerations

研究数据包括城市群地市行政边界矢量数据、PM_2.5年均浓度栅格数据和影响因素数据。城市群行政边界矢量数据来源于国家基础地理信息中心提供的1∶400万中国基础地理信息数据。PM_2.5浓度数据为2000-2015年美国国家航空航天局的社会经济数据和应用中心（NASA Socioeconomic Data and Applications Center）的遥感反演栅格数据^[40]。影响因素指标数据主要来源于2001-2016年《中国城市统计年鉴》,部分缺失数据通过相应省级和地市年鉴及年报进行补充（不包含港澳台地区）。

2.3 指标的统计学意义检验

为了检查指标之间的共线性问题,在进行空间面板计量回归分析之前,本文运用SPSS软件对变量进行相关性分析。相关系数结果（表1）、方差膨胀因子以及条件指数表明,本文中变量之间的共线性问题基本不存在。

Tab. 1
表1
表1指标的共线性检验结果
Tab. 1The collinearity test results of indexes

	人均 GDP	人口密度	城市化水平	工业化水平	产业结构高级度	外贸依存度	技术扶持水平	能源消耗
人均GDP	1
人口密度	0.017	1
城市化水平	0.002	0.061^**	1
工业化水平	0.009	0.045^**	0.000	1
产业结构高级度	0.036^*	0.166^**	0.054^**	0.049^**	1
外贸依存度	0.019	0.226^**	0.049^**	0.036^*	0.183^**	1
技术扶持水平	0.161^**	0.096^**	-0.018	0.006	0.070^**	0.109^**	1
能源消耗	0.010	0.014	-0.007	0.002	0.031	0.003	0.019	1

注：^**表示在0.01水平（双侧）上显著相关;^*表示在0.05水平（双侧）上显著相关。条件指数（Condition Index）均值为43.47;各变量的方差膨胀因子均小于3,且均值为2.51。

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3 研究方法

3.1 空间自相关方法

PM_2.5污染在大气的流通特性影响下呈现出更加显著的空间相关性,使得探索其内在规律具有较强的学术研究价值^[41]。为此,学术界通常采用空间自相关方法研究大气污染的空间集聚和变化规律^[42]。目前常用的空间自相关模型主要包括全局空间自相关和局部空间自相关。

3.1.1 全局空间自相关 在进行空间相关性检验时,通常采用全局Moran's I指数,基于该指数的大小判断空间邻近区域单元PM_2.5浓度的平均相似程度,其计算公式如下：

（1）

I = \frac{n}{S_{0}} \times \frac{\overset{n}{\sum_{i = 1}} \overset{n}{\sum_{j = 1}} w_{ij} z_{i} z_{j}}{\overset{n}{\sum_{i = 1}} z_{i}^{2}}, S_{0} = \overset{n}{\sum_{i = 1}} \overset{n}{\sum_{j = 1}} w_{ij}^{}, Z_{i} = Y_{i} - \overset{?}{Y}, Z_{j} = Y_{j} - \overset{?}{Y}

式中：I为全局空间自相关指数;Y_i、Y_j分别为城市i和j的空气质量观测值,

\overset{?}{Y}

为均值;

w_{ij}

为空间权重矩阵,通常取相邻单元为1,其他为0。I

\in

[-1, 1],且当I

\in

[-1, 0）时,表示区域单元之间具有负相关性;n为研究单元数量;当I = 0时,表示区域单元之间不具有相关性;当I

\in

（0, 1）时,表示区域单元之间具有正相关性。Moran's I指数越接近1,说明区域单元属性值之间关系越密切;越接近0,说明区域单元之间属性值不相关,越接近-1,则说明单元之间属性值差异越大。

3.1.2 局部空间自相关 局域自相关分析可以用来度量局部空间单元相对于整体研究范围空间自相关的影响程度,即一个区域单元的空气质量与邻近单元上空气质量特征的相关程度,计算公式如下：

（2）

LocalMoran's I = \frac{n (x_{i} - \overset{?}{x}) \overset{m}{\sum_{j = 1}} W_{ij} (x_{j} - \overset{?}{x})}{\overset{n}{\sum_{i = 1}} {(x_{i} - \overset{?}{x})}^{2}}, (i \neq j)

式中：

x_{i}

、

x_{j}

分别为城市

i

和

j

的空气质量观测值（

i = 1,2, \dots, n; j = 1,2, \dots, m

）;

W_{ij}

为空间权重;

n

为城市个数;

m

为与城市

i

地理上相邻接的城市个数。学术界通常采用标准化统计量Z来检验Moran's I指数是否存在空间自相关关系,其表达式如下：

（3）

Z_{i} = \frac{I - E [I]}{\sqrt[]{V [I]}}, E [I] = - 1 / (N - 1), [V [I] = E [I^{2}] - E {[I]}^{2}]

为了增强结论的准确性,本文采用0.01的显著性水平检验。在0.01显著性水平下,当

Z_{i} < 2.58

时,说明PM_2.5浓度的空间自相关性不显著,即PM_2.5浓度呈现出独立随机分布规律;当

Z_{i} < - 2.58

时,说明PM_2.5浓度在空间分布上具有负相关关系,且其属性值呈现分散分布,包括“高—低”关联和“低—高”关联;

Z_{i} > 2.58

,说明PM_2.5浓度在空间分布上具有正向自相关性,即相近的高值或者低值呈现空间集聚,即热点和冷点分布区。

3.2 空间计量模型

基于地理学空间差异性的核心思想,空间计量模型纳入空间权重矩阵,考虑了要素的空间相关性,相对于经典计量模型更贴近客观规律。城市PM_2.5污染现象作为一种区域空间行为,不是独立的测算值,相邻区域均能影响其变化趋势,具有较强的空间溢出性,为此,分析其人文要素驱动力时不能忽略其空间影响效应,应采用空间计量模型进行估计。空间计量模型按照数据结构类型可以分析空间截面计量模型和空间面板计量模型,空间截面计量模型仅仅采用某年的数据进行估计,忽视了要素的影响具有时间滞后效应,空间面板计量模型增加了指标数据的数量,满足了渐近性质对大样本的需求,同时充分利用了数据信息,模型准确性更高^[43]。常用的空间面板计量模型包括空间滞后模型（Spatial Lag Model, SLM）、空间误差模型（Spatial Error Model, SEM）和空间杜宾模型（Spatial Durbin Model, SDM）^[44],其中空间杜宾模型公式为：

（4）

\ln P M_{it} = αW \ln P M_{it} + ? \ln P M_{it - 1} + β_{0} + β_{i} X_{it} + θW Z_{it} + α_{i} + γ_{t} + μ_{it}

式中：lnPM_it、lnPM_it_-1、WlnPM_it分别表示城市地区PM_2.5浓度的对数值及其时间滞后项和空间滞后项;X_it为解释变量面板数据;WZ_it表示解释变量的空间滞后项;α_i、γ_t、μ_it分别表示个体效应、时间效应和误差项;?、α分别为被解释变量时间和空间滞后项系数;β₀、β_i是K×1阶待估参数向量;θ为解释变量空间滞后项系数。

当模型的误差项具有空间相关性时,采用空间误差模型;当被解释变量的空间依赖性对模型具有较为关键的作用,并存在显著的空间相关性时,采用空间滞后模型。空间杜宾模型则是空间误差和空间滞后模型的一般形式,SLM包含了被解释变量的内生交互效应,SEM包含了误差项的交互效应,SDM同时包含了内生交互效应（WY）和外生交互效应（WX）^[45],考虑到PM_2.5污染及其影响因素均具有较强的空间相关性,本文选用空间杜宾模型进行估计。在权重矩阵构建方面,本文选用邻接空间权重矩阵,即相邻的空间单元之间具有显著的相互影响,不相邻的空间单元基本不存在相互影响。空间杜宾模型使用的代码来自于Elhorst的空间计量经济Matlab工具箱。

4 结果分析

4.1 中国城市群地区PM_2.5时空演变格局

4.1.1 时序规律分析 2000-2015年,中国城市群地区PM_2.5浓度整体呈现波动增长趋势,平均浓度从21.50 ug/m³增长至33.23 ug/m³,增长率54.53%,空气质量堪忧（图3）。按城市群级别来看,国家级城市群污染浓度（珠三角城市群除外）>区域性城市群（山东半岛城市群和中原城市群除外）>地方性城市群。2000-2007年,城市群PM_2.5浓度均值呈上升趋势,2007-2015年波动变化。2007年也是京津冀、长江中游等9个城市群PM_2.5浓度的拐点,这与徐超等^[46]的研究结果一致。

图3

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图32000-2015年中国城市群地区PM_2.5浓度时序规律分析

Fig. 3Analysis of PM_2.5 concentration time series in China's urban agglomerations from 2000 to 2015

2000-2015年,哈长、辽中南城市群PM_2.5浓度增长率较高,京津冀、山东半岛和中原城市群PM_2.5污染最为严重。从该时段增长趋势来看,仅有兰西和宁夏沿黄城市群空气质量有所提升,哈长和辽中南城市群PM_2.5浓度上升超过150%,天山北坡、晋中、北部湾、山东半岛、珠三角、长江中游和京津冀城市群PM_2.5浓度上升率在50%~100%之间,其余城市群上升率低于50%。从浓度值高低来看,相比于《环境空气质量标准》（GB3095-2012）^[47]中PM_2.5浓度年均限值（35.00 ug/m³）,京津冀、山东半岛和中原城市群2000-2015年PM_2.5浓度均值都高于年均限值,长三角、珠三角、长江中游、成渝、辽中南、哈长、关中和晋中城市群部分年份均值高于年均限值,其余城市群所有年份均值都低于年均限值。

参考世界卫生组织和中国《环境空气质量标准》（GB3095-2012）中PM_2.5浓度标准值（表2）,将中国城市群PM_2.5浓度值划分为7个区间范围,分析2000-2015年各区间城市数量占比（图4）。结果显示：① PM_2.5年均浓度低于10 ug/m³（准则值）的城市数量比例由2000年的6.19%降至2015年的1.77%,低于15 ug/m³（过渡期目标3的年均限值）的城市数量比例由20.35%降至7.52%;② PM_2.5年均浓度高于35 ug/m³（中国年均限值）的城市数量比例由16.81%增至47.79%,增加近两倍,高于50 ug/m³（过渡期目标2的日均限值）的城市数量比例由4.42%增至32.74%,增幅超过6倍;③ PM_2.5年均浓度高于中国年均限值的城市数量占比超过50%的年份主要集中在2005-2010年,其次为2013-2014年。表明低于15 ug/m³和10 ug/m³的低和极低污染城市数量整体呈现下降趋势,高于35 ug/m³和50 ug/m³的污染和高污染城市数量具有快速上升的趋势。

Tab. 2
表2
表2世界卫生组织和中国政府所制定的PM_2.5浓度标准值
Tab. 2Standard values of PM_2.5 concentration established by the World Health Organization and the Chinese government

世界卫生组织(WHO)2005年发布的《空气质量准则》			中国2016年实施的《环境空气质量标准》
类别	年均值(ug/m³)	日均值(ug/m³)	类别	年均值(ug/m³)	日均值(ug/m³)
准则值	10	25	准则值	35	75
过渡期目标1	35	75	-	-	-
过渡期目标2	25	50	-	-	-
过渡期目标3	15	37.5	-	-	-

注：“-”表示没有此项类别。

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图4

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图42000-2015年中国城市群分区间PM_2.5浓度变化趋势

Fig. 4The trends of PM_2.5 concentration changes by range in China's urban agglomerations from 2000 to 2015

4.1.2 空间格局分析 以GB3095-2012年均限值35 ug/m³为分界点,将城市群分为污染浓度高值区和低值区。2000-2015年,中国城市群PM_2.5浓度的地区差异较大,整体上以胡焕庸线为界线,呈现由东南沿海向西北内陆递减的空间格局,区域差异不断扩大（图5）。具体来看,相比国家级和区域性城市群,地区性城市群PM_2.5浓度较低,低值区主要为天山北坡、呼包鄂榆、宁夏沿黄、兰西、滇中和海峡西岸城市群;高值区主要为哈长、辽中南、山东半岛、中原等以及长三角北部、京津冀东南部、长江中游城市群北部。为科学辨析PM_2.5浓度值的空间差异,本文选取具有相同时间间隔年份的PM_2.5浓度值进行分析,空间差异主要表现为：

图5

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图52000-2015年中国城市群地区PM_2.5浓度空间格局演变特征

注：该图基于国家测绘地理信息局标准地图服务网站下载的审图号为GS(2016)1593号的标准地图制作,底图无修改。
Fig. 5Evolution of spatial pattern of PM_2.5 concentration in China's urban agglomerations from 2000 to 2015

① PM_2.5浓度以胡焕庸线为界,呈现明显的两级分化,其中东南地区主要为PM_2.5浓度高值区,西北地区为低值区,空间格局与人口经济空间格局大体一致,表明人类社会经济活动对PM_2.5浓度具有显著影响。② 2000年PM_2.5浓度呈现由东部沿海向西部内陆地区递减态势,高值区空间集聚特征显著,黄河及长江下游沿线城市群为高值区。京津冀、山东半岛、中原、长中游、长三角城市群少部分城市为PM_2.5高污染聚集区,仅占所有城市群城市数量的16.8%。随着时间推移,高值区范围不断扩散,污染程度不断加剧。2015年高值区城市占比达47.3%,集中分布在山东半岛、中原、长三角、长中游城市群以及京津冀东南部。③ 东部、东北部城市群PM_2.5浓度增长速度较快。2000-2015年,仅有兰西、宁夏沿黄城市群PM_2.5浓度有所减少,其余城市群都有所增加,其中哈长城市群和辽中南城市群增幅最大。另外,天山北坡城市群PM_2.5浓度虽未超过35 ug/m³,空气质量较好,但污染浓度增加了近一倍,该区域生态环境较脆弱,亟需引起重视及关注。④ 2000-2015年,京津冀、山东半岛和中原城市群PM_2.5浓度始终位居前三,表现为典型的“高浓度、高集聚、高增加”特征,污染问题十分严峻。其中2002年之后,山东半岛城市群PM_2.5浓度均位居全国首位。

4.2 中国城市群地区PM_2.5空间集聚性特征分析

4.2.1 空间自相关指数分析 运用ArcGIS软件对中国18个城市群（因数据缺失,天山北坡城市群不参与影响因素分析）2000-2015年PM_2.5浓度的年均值进行空间自相关性检验（表3）。结果显示,全部城市群自相关检验时,Moran's I指数均为正值,且均高于0.700,通过了1%水平的显著性检验,表明2000-2015年中国城市群PM_2.5浓度具有相似的空间集聚性特征,可以进行“热点”和“冷点”的统计学分析。单个城市群进行自相关检验时,Moran's I指数有正有负,部分城市群指数值较低,少数城市群PM_2.5浓度水平相对分散,Moran's I指数长期为负值,但是2008年以后由负转正,空间集聚趋势逐渐增强,而且基本都通过了10%水平的显著性检验,为此,可以采用空间计量模型探究上述城市群地区PM_2.5污染的主控要素。

Tab. 3
表3
表32000-2015年中国城市群地区PM_2.5年均浓度空间自相关指数
Tab. 3Spatial autocorrelation index of PM_2.5 annual average concentration in China's urban agglomerations from 2000 to 2015

年份	2000	2001	2002	2003	2004	2005	2006	2007	2008	2009	2010	2011	2012	2013	2014	2015
所有城市群	0.82^***	0.79^***	0.77^***	0.84^***	0.74^***	0.75^***	0.80^***	0.82^***	0.76^***	0.76^***	0.78^***	0.79^***	0.79^***	0.83^***	0.81^***	0.80^***
京津冀	0.33^**	0.41^**	0.41^**	0.39^**	0.36^**	0.37^**	0.36^**	0.36^**	0.33^**	0.33^**	0.33^**	0.33^**	0.34^**	0.35^**	0.35^**	0.34^**
长江三角洲	0.68^***	0.66^***	0.68^***	0.67^***	0.68^***	0.65^***	0.66^***	0.64^***	0.69^***	0.66^***	0.66^***	0.68^***	0.67^***	0.67^***	0.67^***	0.68^***
珠江三角洲	0.13^*	0.16^*	-0.10	0.20^*	0.18^*	-0.16^*	0.11	-0.14^*	0.13^*	0.15^*	0.17^*	0.12^*	0.14^*	0.17^*	0.25^*	0.23^*
长江中游	0.84^***	0.77^***	0.55^***	0.73^***	0.19^***	0.49^***	0.57^***	0.40^***	0.38^***	0.43^***	0.59^***	0.68^***	0.41^***	0.66^***	0.64^***	0.61^***
成渝	0.38^**	0.54^***	0.32^**	0.30^*	0.31^**	0.27^*	0.19	0.23	0.15	0.12	0.19	0.20	0.20	0.19	0.12	0.19
辽中南	0.11^*	0.11^*	0.12^*	0.28^***	0.15^*	0.15^*	0.14^*	0.21^**	0.15^*	0.21^**	0.10	0.15^*	0.20^**	0.18^**	0.12^*	0.13^*
山东半岛	0.44^***	0.49^***	0.46^***	0.51^***	0.48^***	0.54^***	0.53^***	0.50^***	0.49^***	0.51^***	0.58^***	0.53^***	0.53^***	0.52^***	0.53^***	0.47^***
海峡西岸	0.25^*	-0.16^*	-0.29	0.10^*	-0.14	0.11^*	0.24	0.12^*	0.10	0.24^*	0.11^*	0.10^*	0.17	0.11^*	0.13^*	0.21
哈长	0.55^**	0.67^***	0.57^***	0.62^***	0.74^***	0.74^***	0.71^***	0.75^***	0.81^***	0.72^***	0.68^***	0.77^***	0.75^***	0.61^***	0.61^***	0.52^***
中原	0.55^***	0.52^***	0.28^*	0.41^**	0.42^**	0.38^**	0.41^**	0.47^**	0.47^**	0.49^***	0.48^***	0.42^**	0.41^**	0.45^***	0.53^***	0.49^***
关中	-0.41^*	-0.31^*	-0.09^*	-0.13	-0.23^*	0.09^*	0.11^*	0.19^**	0.12^*	0.10	0.09^*	0.08	0.10^*	0.11	0.10^*	0.11^*
北部湾	0.79^***	0.76^***	0.85^***	0.92^***	0.93^***	0.93^***	0.93^***	0.87^***	0.97^***	0.95^***	0.96^***	0.96^***	0.97^***	0.92^***	0.93^***	0.90^***
晋中	0.23^*	-0.10^*	-0.44	-0.59^*	-0.52^*	-0.10	-0.34^*	-0.25^*	0.20	0.12^*	0.31^*	0.11^*	0.13^*	0.10^*	0.12	0.15^*
呼包鄂榆	-0.18^*	0.15	-0.43	-0.77^*	-0.78^*	-0.56^*	-0.47^*	-0.36	0.58^*	0.79^*	0.67^*	0.50^*	0.91^*	0.87^*	0.88^*	0.93^*
滇中	0.42^*	0.46^*	0.57^*	0.85^*	0.87^*	0.65^*	0.13^*	0.80^*	0.61^*	0.73^*	0.81^*	0.45^*	0.82^*	0.78^*	0.67^*	0.73^*
黔中	-0.45^*	0.10	-0.42^*	-0.17^*	-0.16^*	-0.16^*	-0.17^*	-0.10	0.11	0.25^*	0.26^*	0.24	0.23^*	0.38^*	0.19^*	0.29^*
兰西	0.81^**	0.81^**	0.83^**	0.79^**	0.76^**	0.90^**	0.87^**	0.84^**	0.77^**	0.67^*	0.59^*	0.74^**	0.66^*	0.68^*	0.75^**	0.76^**
宁夏沿黄	0.33^*	0.19^*	0.13	0.18^*	0.23^*	0.23^*	0.15^*	0.25^*	0.30^*	0.22	0.28^*	0.21^*	0.25^*	0.12	0.21^*	0.32^*

注：^*代表10%水平下显著,^**代表5%水平下显著,^***代表1%水平下显著。

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4.2.2 集聚性规律分析 整体来看,胡焕庸线为中国城市群PM_2.5浓度热点和冷点的分界线,热点区域集中在胡线以东地区,冷点区域集中在以西地区（图6）。2000年以来,热点区域城市数量占比持续上升,冷点区域持续下降,其中热点区域主要分布于京津冀、山东半岛、中原、长江中游和长三角等中东部城市群,尤其是北方地区,快速工业化和冬季燃煤恶化了空气质量^[34];冷点区域主要分布在天山北坡、兰西、呼包鄂榆、宁夏沿黄、滇中、海峡西岸,西部、西南和东南沿海城市群空气质量较好。值得注意的是,辽中南和哈长城市群西南部在2010年之前无特征点分布,2015年成为热点区,表明东北地区供暖期空气污染正在加重;珠三角和黔中城市群在2000年、2005年和2010年是无特征点分布区,而在2015年是冷点分布区,空气质量改善较为明显,表明该地区大气污染防治行动取得了显著成果。宁夏沿黄城市群和兰西城市群东部地区在2005年、2010年和2015年是冷点分布区,而在2000年是无特征点分布区,其空气质量相比于其他城市群较为良好。

图6

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图62000-2015年中国城市群地区PM_2.5浓度空间集聚特征

注：该图基于国家测绘地理信息局标准地图服务网站下载的审图号为GS(2016)1593号的标准地图制作,底图无修改。
Fig. 6Spatial agglomeration characteristics of PM_2.5 concentration in China's urban agglomerations from 2000 to 2015

4.3 中国城市群地区PM_2.5影响因素解析

4.3.1 要素估计值对比分析 式（4）中WInPM_it表示空间滞后项,兰西城市群WInPM_it不显著,中原和北部湾城市群在10%水平下显著,其余城市群都在1%水平下显著,表明城市群PM_2.5污染具有较强的空间内生性交互效应,即城市群内部城市间空气污染交互影响显著。其中京津冀、珠三角、长江中游、成渝、辽中南、山东半岛、海峡西岸、哈长、关中、晋中、呼包鄂榆、滇中、黔中、兰西（不显著）等14个城市群估计值超过0.600,邻近城市PM_2.5浓度升高1%,本城市PM_2.5浓度则上升0.6%以上,山东半岛、成渝和辽中南城市群位居估计值前三。影响因素结果如表4所示。

Tab. 4
表4
表42000-2015年中国城市群PM_2.5污染影响因素结果
Tab. 4Results of factors affecting PM_2.5 pollution in China's urban agglomerations from 2000 to 2015

城市群变量	京津冀	长江三角洲	珠江三角洲	长江中游	成渝	辽中南	山东半岛	海峡西岸	哈长	中原	关中	北部湾	晋中	呼包鄂榆	滇中	黔中	兰西	宁夏沿黄
Intercept	-2.128^***	3.366^***	-0.031	-0.073	-0.295	0.852^***	0.697^***	0.599^***	-1.010^***	-0.694	0.225	3.413^***	0.578	2.067	1.797^*	-1.699^*	0.583	0.655
lnPGDP	0.022	0.056^**	-0.006	0.108^***	0.066^*	0.083^***	0.074^***	0.064^***	0.116^***	-0.093	0.009	-0.048^***	0.046	-0.018	0.032	0.114	-0.002	-0.094
lnPD	0.495^***	-0.020	0.099^***	0.137^***	0.317^***	0.269^***	0.096^***	0.103^***	0.597^***	0.454^***	0.306^***	0.231^**	0.166^**	0.091^**	-0.058	-0.118	-0.041	0.123
UR	-0.218^**	-0.002^**	-0.075^***	0.024	-0.104	0.304^***	0.115^***	0.001	0.265^***	-0.024	0.206^***	0.143	-0.006	0.858^***	0.231^***	-0.091	0.232	0.693^**
IR	-0.818	0.605^**	0.742^***	-0.164	-0.237	-0.077	0.410^**	-0.001	0.083	0.139^*	0.351	0.063	-0.059	9.764^***	0.108^**	0.331	-0.105	1.577
ADIS	-1.300^**	0.114	-0.605^***	-0.371	-0.170	-0.583^***	-0.527^***	-0.205^*	-0.742^**	-0.042	0.040	0.042	-0.247	-8.500^***	0.233^**	-1.242^***	0.095	1.796
FDI	-0.232^***	-0.013^**	-0.002^**	-0.263^***	0.043	-0.011	-0.034	-0.382^***	-0.542^***	0.741	0.044	-0.104^**	0.722	0.171	-1.694^*	1.464	-0.422	0.325
TS	4.844	-6.201	-4.615^**	-7.039^**	-3.623	-0.973	0.704	3.952^*	-1.468	-5.727	-6.114	13.827	-18.973^**	0.389	-4.938^*	5.407^**	-0.611	-20.180
lnEC	-0.003	0.013^*	0.009^**	0.001	-0.001	0.032^***	0.016^***	0.004	0.046^***	0.047^***	0.026^***	-0.030	0.021^*	0.015	0.065^**	0.052^***	-0.008	0.005
W×lnPM_2.5	0.676^***	0.236^***	0.786^***	0.652^***	0.903^***	0.872^***	0.948^***	0.788^***	0.734^***	0.236^*	0.662^***	0.236^*	0.787^***	0.690^***	0.770^***	0.721^***	0.817	0.487^***
W×lnPGDP	0.066	0.037^***	0.070	0.002^***	0.034^**	0.060^**	0.133^***	0.056^***	0.178^***	0.222	-0.042	-0.230^***	0.083	0.185	-0.019	-0.225	0.188	0.214
W×lnPD	-0.203^***	-0.041	0.121	0.088	-0.247^***	-0.303^***	-0.066^*	-0.106^***	-0.329^***	-0.254^**	-0.280^***	-0.330^**	-0.353^**	-0.026	-0.298	-0.797^***	-0.168	-0.183
W×UR	0.147	-0.013^***	-0.003	-0.210	-0.611*	0.385^***	0.229^***	0.007	2.088^***	0.049	0.208^**	-0.197	-0.005	0.780^**	0.346^**	1.067^*	-0.507	-0.550
W×IR	2.615^*	-0.612	2.187^***	1.152^***	-0.377	0.660^***	0.169	0.001	0.003	0.282^***	1.206^***	0.383	0.071	6.792^**	0.182	-0.253	0.100	-0.744
W×ADIS	2.024	-0.566	2.296^***	-0.605	-0.077	-0.004	0.071	0.216*	0.455^**	-0.058	0.910^**	-0.885	0.161	5.740	0.266^*	1.842^***	-0.466	-1.28
W×FDI	0.204	-0.367^***	-0.034^***	-0.169	0.234	0.174	0.008	-0.058	-0.644^***	1.215	0.060	0.621	-1.640^**	-1.735^*	2.639	-2.607^*	0.991	0.610
W×TS	-8.491^*	5.481	2.973	7.609	3.475	0.298	-2.713^***	-3.932	-0.688	-15.23^**	10.358^*	-2.935	-21.698^*	20.184	0.717^***	-3.575	-0.402	3.257
W×lnEC	0.101^***	0.099^***	0.020^**	-0.016	0.024	0.058^***	0.034^***	-0.010	0.140^***	0.066^**	0.025^**	-0.141	0.043^*	0.152^***	-0.023	0.168^***	0.120^***	-0.035
R²	0.942	0.816	0.952	0.768	0.940	0.947	0.972	0.871	0.945	0.705	0.888	0.685	0.891	0.952	0.960	0.946	0.953	0.796
loglikehood	114.39	135.64	198.26	115.54	201.96	195.92	324.19	167.49	121.30	194.61	159.78	168.39	101.84	50.59	69.35	76.22	74.34	48.64

注：^*代表10%水平下显著,^**代表5%水平下显著,^***代表 1%水平下显著。天山北坡城市群数据缺失较大,故不进行分析。

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人均GDP对北部湾城市群PM_2.5污染具有显著负向影响和空间溢出效应,对长三角、长江中游、成渝、辽中南、山东半岛、海峡西岸、哈长等城市群PM_2.5污染具有显著正向影响和空间溢出效应,对京津冀、珠三角、中原、关中以及6个地区性城市群PM_2.5污染的影响和空间溢出效应都不显著。主要原因是各城市群的社会经济和工业化发展阶段不同,主导产业类型与生产效率差别较大。

人口密度对京津冀、珠江三角洲、长江中游、成渝、晋中、呼包鄂榆以及8个区域性城市群PM_2.5污染具有显著正向影响,对京津冀、成渝、辽中南、山东半岛、海峡西岸、哈长、中原、关中、北部湾、晋中和兰西等城市群PM_2.5污染具有显著负向空间溢出效应。人口密度对本地区PM_2.5污染主要具有显著正向影响,对邻近地区则反之,表明城市群核心城市人类活动强度加大,污染排放加大,但大城市阴影区则相反。

城市化水平对京津冀、长三角、珠三角PM_2.5污染具有显著负向影响,对辽中南、山东半岛、哈长、关中、呼包鄂榆、滇中、宁夏沿黄等城市群PM_2.5污染具有显著正向影响;对长三角、珠三角具有显著负向空间溢出效应,对辽中南、山东半岛、哈长、关中、呼包鄂榆、滇中和黔中城市群具有显著正向空间溢出效应。表明在城市化率较高的国家级城市群,城市化水平对城市群PM_2.5污染具有负向影响;在区域性和地方性城市群则相反。

工业化对长三角、珠三角、山东半岛、中原、呼包鄂榆、滇中等城市群PM_2.5污染具有显著正向影响,对京津冀、珠三角、长三角、辽中南、中原、关中、呼包鄂榆等城市群具有显著正向空间溢出效应。工业化对城市群PM_2.5污染具有显著正向影响,表明工业“三废”及烟粉尘等污染物加重了PM_2.5污染。

产业结构高级度对京津冀、珠三角、辽中南、山东半岛、海峡西岸、哈长、呼包鄂榆、滇中和黔中城市群PM_2.5污染具有负向影响,对珠三角、海峡西岸、哈长、关中、滇中和黔中城市群PM_2.5污染具有显著正向空间溢出效应。产业结构优化升级有效降低了本地区PM_2.5污染,但因污染型企业转移到邻近地区,导致邻近地区PM_2.5污染加重。

外商直接投资（FDI）对京津冀、长三角、珠三角、长江中游、海峡西岸、北部湾等沿海城市群和哈长、滇中等沿边城市群PM_2.5污染具有负向影响,对长三角、珠三角、哈长、晋中、呼包鄂榆和黔中城市群PM_2.5污染具有显著负向空间溢出效应。FDI对环境污染的影响包括“污染避难所”假说和“污染光环”假说^[48]。FDI对环境污染具有负向影响,说明“污染避难所”假说在中国城市群尺度并不成立,与许和连等^[49]、姜磊等^[50]的研究结论一致。

技术扶持度对珠三角、长三角、晋中和滇中城市群PM_2.5污染具有负向影响,对海峡西岸和黔中城市群具有正向影响;对关中和呼包鄂榆城市群具有显著正向溢出效应,对京津冀、山东半岛、中原和晋中城市群具有显著负向溢出效应。技术创新有助于PM_2.5治理和防控,改善空气质量;科技成果的市场化能促进新技术跨越式发展,技术不成熟导致工业企业粗放式快速发展,则引发能源消耗的回弹效应,加重PM_2.5污染,这与程中华等^[51]的研究结论相符;技术创新的滞后性决定科技成果的作用短时间较难显现,对PM_2.5污染的影响不显著。

能源消耗对长三角、珠三角、辽中南、山东半岛、哈长、中原、关中、晋中、滇中和黔中城市群PM_2.5污染具有正向影响,对京津冀、长三角、珠三角、辽中南、山东半岛、哈长、中原、关中、晋中、呼包鄂榆、黔中和兰西城市群具有显著正向溢出效应。能源消耗加重了地区PM_2.5污染,加之城市之间大气流动,也加重了邻近地区PM_2.5污染。

4.3.2 城市群主控要素分析 整体来看,国家级城市群降低PM_2.5污染的主控要素为技术扶持度,加剧PM_2.5污染的主控要素为工业化水平;区域性城市群降低污染的主控要素以技术扶持水平、产业高级度和人口密度为主,加剧污染的主控要素以邻近地区PM_2.5污染为主;地方性城市群降低污染的主控要素为技术扶持水平,加剧PM_2.5污染的主控要素为邻近地区PM_2.5污染（表4）。因此,加强研发投入、深化产业结构调整,实施新型绿色工业化,优化人口空间布局及实现跨区域联动治理,对解决城市群空气污染问题尤为重要。

分城市群来看,技术扶持度对京津冀、珠三角、长中游、山东半岛、中原、晋中和滇中城市群降低PM_2.5浓度影响显著,表明各级政府的技术创新扶持政策对降低空气污染起到了关键作用。产业结构高级度对辽中南和哈长城市群降低PM_2.5浓度影响显著,表明东北地区应加快推进产业结构的高级化、合理化发展,优化和淘汰污染型产业。外商投资对长三角、海峡西岸、呼包鄂榆和黔中城市群降低PM_2.5浓度影响显著,应该着重完善外商投资环境、布局和保障激励措施。

邻近地区PM_2.5浓度是成渝、辽中南、山东半岛、晋中和滇中城市群PM_2.5浓度提升的主控因素,亟需跨区域联防联控联动。工业化是京津冀、长三角和长中游城市群PM_2.5浓度提升的主控因素,以上城市群工业化程度较高,亟需产业转型升级和控制污染排放。技术扶持度是海峡西岸和关中城市群PM_2.5浓度提升的主控因素,表明该城市群存在技术利用成熟度低且快速市场化的问题。城市化是哈长和宁夏沿黄城市群PM_2.5浓度提升的主控因素,提升城市化效率迫在眉睫。人口密度、邻近地区产业高级度、能源消耗分别是中原、珠三角和兰西城市群的主控要素,应采取相应措施调控。

5 结论与讨论

5.1 结论

（1）2000-2015年中国城市群PM_2.5浓度整体呈现波动增长趋势,增长率为54.53%,空气质量堪忧;2000-2007年上升趋势显著,2007-2015年波动变化,2007年也是京津冀、长江中游等9个城市群PM_2.5浓度的拐点。具体来看,2000-2015年兰西和宁夏沿黄城市群空气质量有所提升,哈长、辽中南城市群PM_2.5浓度增长率超过150%,京津冀、山东半岛和中原城市群PM_2.5污染最为严重。PM_2.5浓度低于15 ug/m³的城市数量比例由2000年的20.35%降至2015年的7.52%,呈现快速下降趋势;高于35 ug/m³的城市数量比例由2000年的16.81%增至2015年的47.79%,呈现快速上升趋势。

（2）2000-2015年中国城市群PM_2.5浓度地区差异较大,整体上以胡焕庸线为界线,呈现由东部沿海向西部内陆地区递减的空间差异,且区域差异不断扩大,东部、东北部城市群PM_2.5浓度增长速度较快。具体来看,PM_2.5浓度低值区主要为天山北坡、呼包鄂榆、宁夏沿黄、兰西、滇中和海峡西岸城市群;PM_2.5浓度高值区主要为哈长、辽中南、山东半岛、中原等以及长三角城市群北部、京津冀东南部、长江中游城市群北部。京津冀、山东半岛和中原城市群PM_2.5浓度始终位居前三,其大力发展社会经济的同时亟需重视生态环境保护。

（3）中国19个城市群2000-2015年Moran's I指数均高于0.700,具有显著空间集聚性特征。以胡焕庸线为界,热点区域主要集中在以东地区,冷点区域主要集中在以西地区。整体来看,热点区域城市数量占比持续上升,冷点区域持续下降,其中热点区域主要分布于京津冀、山东半岛、中原、长江中游和长江三角洲城市群,东部城市群空气污染较为严重,尤其是北方地区;冷点区域主要分布于空气质量较好的天山北坡、兰西、呼包鄂榆、宁夏沿黄、滇中、海峡西岸,西部、西南部城市群和东南部沿海城市群空气质量较好。

（4）中国城市群PM_2.5污染具有较强的空间内生交互效应,亟需创建污染治理的跨区域联防联控联动机制。工业化和能源消耗对PM_2.5污染具有显著正向影响;外商投资在东南沿海和邻近国界城市群对PM_2.5污染具有显著负向影响,符合“污染光环”假说。其余要素对城市群地区PM_2.5污染的影响具有显著的空间差异,具体表现为：人口密度对本地区PM_2.5污染主要具有显著正向影响,对邻近地区则相反;城市化水平在国家级城市群对PM_2.5污染具有负向影响,在区域性和地方性城市群则相反;产业结构高级度对本地区PM_2.5污染具有显著负向影响,对邻近地区则相反;技术扶持度对PM_2.5污染的影响具有较大不确定性,但是影响程度较高,需要加大对技术创新的投入,严格规范新型技术的快速市场化行为。

5.2 讨论

1978年以来,中国城市群快速城镇化与工业化进程产生了一系列环境污染问题,其中雾霾污染尤为严峻,城市群地区已成为PM_2.5的高污染城市聚集地。本文以中国城市群为对象,揭示了中国城市群地区PM_2.5污染及其空间集聚性的演变格局,从人文要素方面厘清了PM_2.5污染的主控因素,并进一步剖析了其影响机理,可以为城市群地区PM_2.5的精准寻因、专项防控、情景模拟和风险预警以及城市群内部产业规划布局和城市健康发展提供科学依据。科学辨析中国城市群地区PM_2.5污染的时空演变格局及其主控因素,有效防控雾霾污染,切实改善空气质量,提升城市宜居环境水平是一场需要我国政府、企业、****和广大人民共同参与的攻坚战和持久战。本文主要是从人文要素方面探究中国不同城市群地区PM_2.5污染主控因素,但是自然要素对PM_2.5污染同样具有显著的影响效应,后续还需要深入分析PM_2.5污染的自然影响因素和揭示其主要污染源,以及在区域范围内模拟PM_2.5传输路径,挖掘和规划PM_2.5流通廊道。PM_2.5污染的防控工作是一项需要多部门、多学科、多组织、多领域交叉合作,开展多要素、多尺度、多维度、多模型交叉研究的复杂巨工程。本文从宏观视角揭示长时间尺度下不同城市群地区PM_2.5污染的时空演变格局及其主控因素只是第一步,还需要从微观视角科学地深入剖析城市群、省域、城市、县域以及乡镇尺度下PM_2.5污染的复杂机理机制,进一步精准研判PM_2.5的污染源头,严格控制污染物排放,以及实现PM_2.5污染治理的“因群施策”“因城施策”和具体要素分类指导。

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被引期刊影响因子

[1]

Fang

Chuanglin

. Important progress and prospects of China's urbanization and urban agglomeration in the past 40 years of reform and opening-up
Economic Geography, 2018,38(9):1-9.

DOI:10.2307/142321 URL [本文引用: 1]

[ 方创琳 . 改革开放40年来中国城镇化与城市群取得的重要进展与展望
经济地理, 2018,38(9):1-9.]

DOI:10.2307/142321 URL [本文引用: 1]

[2]

Wang Z

, Liang L

, Sun

, et al. Spatiotemporal differentiation and the factors influencing urbanization and ecological environment synergistic effects within the Beijing-Tianjin-Hebei urban agglomeration
Journal of Environmental Management, 2019,243:227-239.

DOI:10.1016/j.jenvman.2019.04.088 URLPMID:31096175 [本文引用: 1]

Numerous environmental problems have been seen due to the &quot;high energy consumption, high pollution, high emissions&quot; economic model in the Beijing-Tianjin-Hebei urban agglomeration (BTHUA). The coupling coordination degree model is applied to provide a coordination of urbanization and ecological environment composite system (CUECS) value while a geographic detector is applied to explore the dominant factors controlling it. This study reached the following conclusions. (1)The CUECS types are mainly low coordination, but which generally exhibit positive evolutionary trend. The change trends can be characterized as urbanization lags followed by system equilibrium followed by ecological environmental lags. (2)The CUECS conforms to a core-edge distributional pattern that comprises plain high mountain low, inland high coastal low. Industrialization played a key role in the development of BTHUA, the landform type was the important factor controlling CUECS. (3) Social consumer goods, gross domestic product, the disposable income of urban residents (all per capita) are the core factors controlling CUECS within different spatial units. Urbanization rate, per capita social consumer goods, the proportion of tertiary industrial population are the core factors controlling CUECS during different urbanization development stages. (4)The relative impacts of urbanization and ecological environmental subsystems on CUECS are (in decreasing order of importance) population urbanization, economic urbanization, social urbanization, ecological environment subsystem. Therefore, green urbanization remains the primary path for sustainable development within the urban agglomeration. It is unsuitable for rapid urbanization development model in the mountainous areas that encapsulate ecological and environmental security as their main functions, so the government urgently needs to amend its 'one size fits all' policy system.

[3]

Wang

Zhenbo

, Liang

Longwu

, Fang

Chuanglin

. Temporal and spatial evolution characteristics of ecological security patterns of Beijing-Tianjin-Hebei mega-urban group and its influencing factors
Journal of Ecology, 2018,38(12):4132-4144.

[本文引用: 1]

[ 王振波, 梁龙武, 方创琳 . 京津冀特大城市群生态安全格局时空演变特征及其影响因素
生态学报, 2018,38(12):4132-4144.]

[本文引用: 1]

[4]

Tian

, Pan

, Liu

, et al. Size-resolved aerosol chemical analysis of extreme haze pollution events during early 2013 in urban Beijing, China
Journal of Hazardous Materials, 2014,279:452-460.

DOI:10.1016/j.jhazmat.2014.07.023 URL [本文引用: 1]

Using size-resolved filter sampling and chemical characterization, high concentrations of water-soluble ions, carbonaceous species and heavy metals were found in both fine (PM2.1) and coarse (PM2.1-9) particles in Beijing during haze events in early 2013. Even on clear days, average mass concentration of submicron particles (PM1.1) was several times higher than that previously measured in most of abroad urban areas. A high concentration of particulate matter on haze days weakens the incident solar radiation, which reduces the generation rate of secondary organic carbon in PM1.1. We show that the peak mass concentration of particles shifted from 0.43-0.65 mu m on clear days to 0.65-1.1 mu m on lightly polluted days and to 1.1-2.1 mu m on heavily polluted days. The peak shifts were also found for the following species: organic carbon, elemental carbon, NH4+, SO42-, NO3-, K, Cu, Zn, Cd and Pb. Our findings demonstrate that secondary inorganic aerosols (36%) and organic matter (26%) dominated the fine particle mass on heavily polluted days, while their contribution reduced to 29% and 18%, respectively, on clear days. Besides fine particles, anthropogenic chemical species also substantially accumulated in the coarse mode, which suggests that particles with aerodynamic diameter larger than 2.1 mu m cannot be neglected during severe haze events. (C) 2014 Elsevier B.V.

[5]

Tecer L

, Alagha

, Karaca

. Particulate matter (PM_2.5, PM_10-2.5, and PM₁₀) and children's hospital admissions for asthma and respiratory diseases: A bidirectional case-crossover study
Journal of Toxicology and Environmental Health (Part A), 2008,71(8):512-520.

DOI:10.1080/15287390801907459 URLPMID:18338286 [本文引用: 1]

Epidemiological studies reported adverse effects of air pollution on the prevalence of respiratory diseases in children. The purpose of this study was to examine the association between air pollution and admissions for asthma and other respiratory diseases among children who were younger than 15 yr of age. The study used data on respiratory hospital admissions and air pollutant concentrations, including thoracic particulate matter (PM(10)), fine (PM(2.5)), and coarse (PM(10-2.5)) particulate matter in Zonguldak, Turkey. A bidirectional case-crossover design was used to calculate odds ratios for the admissions adjusted for daily meteorological parameters. Significant increases were observed for hospital admissions in children for asthma, allergic rhinitis (AR), and upper (UPRD) and lower (LWRD) respiratory diseases. All fraction of PM in children showed significant positive associations with asthma admissions. The highest association noted was 18% rise in asthma admissions correlated with a 10-microg/m(3) increase in PM(10-2.5) on the same day of admissions. The adjusted odds ratios for exposure to PM(2.5) with an increment of 10 microg/m(3) were 1.15 and 1.21 for asthma and allergic rhinitis with asthma, respectively. PM(10) exerted significant effects on hospital admissions for all outcomes, including asthma, AR, UPRD, and LWRD. Our study suggested a greater effect of fine and coarse PM on asthma hospital admissions compared with PM(10) in children.

[6]

Santiba?ez D

, Ibarra

, Matus

. A five-year study of particulate matter (PM_2.5) and cerebrovascular diseases
Environmental Pollution, 2013,181:1-6.

DOI:10.1016/j.envpol.2013.05.057 URL [本文引用: 1]

Cerebrovascular accidents, or strokes, are the second leading cause of mortality and the leading cause of morbidity in both Chile and the rest of the world. However, the relationship between particulate matter pollution and strokes is not well characterized. The association between fine particle concentration and stroke admissions was studied. Data on hospital admissions due to cerebrovascular accidents were collected from the Ministry of Health. Air quality and meteorological data were taken from the Air Quality database of the Santiago Metropolitan Area. Santiago reported 33,624 stroke admissions between January 1, 2002 and December 30, 2006. PM2.5 concentration was markedly seasonal, increasing during the winter. This study found an association between PM2.5 exposure and hospital admissions for stroke; for every PM2.5 concentration increase of 10 mu g m(-3), the risk of emergency hospital admissions for cerebrovascular causes increased by 1.29% (95% CI 0.552%-2.03%). (C) 2013 Elsevier Ltd.

[7]

Massimiliano

, Silvia

, Alice

, et al. Is there a link between air pollution and mental disorders
Environment International, 2018,118:154-168.

DOI:10.1016/j.envint.2018.05.044 URLPMID:29883762 [本文引用: 1]

Several studies have demonstrated the association between air pollution and different medical conditions including respiratory and cardiovascular diseases. Air pollutants might have a role also in the etiology of mental disorders in the light of their toxicity on central nervous system. Purpose of the present manuscript was to review and summarize available data about an association between psychiatric disorders and air pollution. A research in the main database sources has been conducted to identify relevant papers about the topic. Different air pollutants and in particular PM and nitric oxides have been associated with poor mental health; long exposition to PM_2.5 has been associated with an increased risk of new onset of depressive symptoms (Cohen's effect size d: 0.05-0.81), while increased concentration of nitric dioxide in summer with worsening of existing depressive conditions (Cohen's effect size d: 0.05-1.77). However, the interpretation of these finding should take into account the retrospective design of most of studies, different periods of observations, confounding factors such as advanced age or medical comorbidity. Further studies with rigorous methodology are needed to confirm the results of available literature about this topic.

[8]

Franklin

, Zeka

, Schwartz

. Association between PM_2.5 and all-cause and specific-cause mortality in 27 US communities
Journal of Exposure Science and Environmental Epidemiology, 2007,17(3):279.

DOI:10.1038/sj.jes.7500530 URLPMID:17006435 [本文引用: 1]

While fine mode particulate matter (PM(2.5)) forms the basis for regulating particles in the US and other countries, there is a serious paucity of large population-based studies of its acute effect on mortality. To address this issue, we examined the association between PM(2.5) and both all-cause and specific-cause mortality using over 1.3 million deaths in 27 US communities between 1997 and 2002. A two-stage approach was used. First, the association between PM(2.5) and mortality in each community was quantified using a case-crossover design. Second, meta-analysis was used to estimate a summary effect over all 27 communities. Effect modification of age and gender was examined using interaction terms in the case-crossover model, while effect modification of community-specific characteristics including geographic location, annual PM(2.5) concentration above 15 microg/m(3) and central air conditioning prevalence was examined using meta-regression. We observed a 1.21% (95% CI 0.29, 2.14%) increase in all-cause mortality, a 1.78% (95% CI 0.20, 3.36%) increase in respiratory related mortality and a 1.03% (95% CI 0.02, 2.04%) increase in stroke related mortality with a 10 microg/m(3) increase in previous day's PM(2.5). The magnitude of these associations is more than triple that recently reported for PM(10), suggesting that combustion and traffic related particles are more toxic than larger sized particles. Effect modification occurred in all-cause and specific-cause deaths with greater effects in subjects &gt;or=75 years of age. There was suggestive evidence that women may be more susceptible to PM(2.5) effects than men, and that effects were larger in the East than in the West. Increased prevalence of central air conditioning was associated with a decreased effect of PM(2.5). Our findings describe the magnitude of the effect on all-cause and specific-cause mortality, the modifiers of this association, and suggest that PM(2.5) may pose a public health risk even at or below current ambient levels.

[9]

Kourtchev

, Warnke

, Maenhaut

, et al. Polar organic marker compounds in PM_2.5 aerosol from a mixed forest site in western Germany
Chemosphere, 2008,73(8):1308-1314.

DOI:10.1016/j.chemosphere.2008.07.011 URL [本文引用: 1]

Abstract

The molecular composition of PM2.5 (particulate matter with an aerodynamic diameter <2.5 μm) aerosol samples collected during a very warm and dry 2003 summer period at a mixed forest site in Jülich, Germany, was determined by gas chromatography/mass spectrometry in an effort to evaluate photooxidation products of biogenic volatile organic compounds (BVOCs) and other markers for aerosol source characterization. Six major classes of compounds represented by twenty-four individual organic species were identified and measured, comprising tracers for biomass combustion, short-chain acids, fatty acids, sugars/sugar alcohols, and tracers for the photooxidation of isoprene and α-/β-pinene. The tracers for the photooxidation of α-/β-pinene include two compounds, 3-hydroxyglutaric acid and 3-methyl-1,2,3-butanetricarboxylic acid, which have only recently been elucidated. The characteristic molecular distribution of the fatty acids with a strong even/odd number carbon preference indicates a biological origin, while the presence of isoprene and terpene secondary organic aerosol products suggests that the photooxidation of BVOCs contributes to aerosol formation at this site. The sum of the median concentrations of the isoprene oxidation products was 21.2 ng m⁻³, while that of the terpene oxidation products was 19.8 ng m⁻³. On the other hand, the high median concentration of malic acid (37 ng m⁻³) implies that photooxidation of unsaturated fatty acids should also be considered as an important aerosol source process. In addition, the occurrence of levoglucosan and pyrogallol indicates that the site is affected by biomass combustion. Their median concentrations were 30 and 8.9 ng m⁻³, respectively.

[10]

Lee P K

, Brook J

, Dabek-Zlotorzynska

, et al. Identification of the major sources contributing to PM_2.5 observed in Toronto
Environmental Science & Technology, 2003,37(21):4831-4840.

DOI:10.1088/1361-6560/ab63b6 URLPMID:31851954 [本文引用: 1]

Radiotherapy-induced lymphopenia has increasingly been shown to reduce cancer survivorship. We developed a novel hybrid deep learning model to efficiently integrate an entire set of dosimetric parameters of a radiation treatment plan with a patient's pre- and mid-treatment information to improve the prediction of grade 4 radiotherapy-induced lymphopenia.

[11]

Chowdhury

, Zheng

, Schauer J

, et al. Speciation of ambient fine organic carbon particles and source apportionment of PM_2.5 in Indian cities
Journal of Geophysical Research: Atmospheres, 2007,112(D15).

[本文引用: 1]

[12]

Sun

, Zhuang

, Tang

, et al. Chemical characteristics of PM_2.5 and PM₁₀ in haze fog episodes in Beijing
Environmental Science & Technology, 2006,40(10):3148-3155.

DOI:10.1088/1361-6560/ab63b6 URLPMID:31851954 [本文引用: 1]

Xiong

Huanhuan

, Liang

Longwu

. Dynamic comparative analysis of temporal and spatial distribution of PM_2.5 in Chinese cities
Resources Science, 2017,39(1):136-146.

DOI:10.18402/resci.2017.01.14 URL [本文引用: 1]

Based on PM_2.5 concentration observation data for 2014 and 2015 from 190 medium and large cities in China we look at changes in PM_2.5 concentration and trends in spatial agglomeration. We found that in 2015,the average PM_2.5 concentrations in China decreased by 10% compared with 2014,the number of days of compliance enhanced by 4.4%,and the air quality improved obviously. Spring improved the most,followed by summer,autumn and winter. In December 2015,the pollution was worse than the same period in 2014. The other months were improved,with the largest decrease in June （21.48%）,and a decline of less than 10% in February,August and September. In 2015,the pollution area of PM_2.5 was less than that in 2014,and the pollution core area spread from Beijing,Tianjin and Hebei to the northwest and north of Henan. The areas where there were large declines in PM_2.5 annual average concentration were mainly in the Beijing-Tianjin-Hebei Region,Yangtze River Delta,middle reaches and other urban agglomerations; cities with rapid growth in the average daily standard days were mainly concentrated in the Yangtze River Delta,Pearl River Delta and Chengyu urban agglomerations. In contrast to PM_2.5 in 2014,the concentration of PM_2.5 was more obvious in 2015,the concentration of high-value areas decreased,and the hot spots were more densely distributed in the North China Region centered around the Beijing-Tianjin-Hebei Region. Hot agglomeration regions showed a multi-center cluster phenomenon. Based on the above situation,it is necessary to speed up the construction of institution-supported multi-center supervision and governance mode. In the North China Region we should build a management-supported multi-regional linkage governance mode,construct public environmental consciousness governance supported by environmental ethics,and incorporate these into the Chinese cultural rejuvenation system.

[ 熊欢欢, 梁龙武 . 中国城市PM_2.5时空分布的动态比较分析
资源科学, 2017,39(1):136-146.]

DOI:10.18402/resci.2017.01.14 URL [本文引用: 1]

Pinto J

, Lefohn A

, Shadwick D

. Spatial variability of PM_2.5 in urban areas in the United States
Journal of the Air & Waste Management Association, 2004,54(4):440-449.

DOI:10.1088/1361-6560/ab63b5 URLPMID:31851952 [本文引用: 1]

Proton neutron gamma-x detection (PNGXD) is a novel imaging concept being investigated for tumor localization during proton therapy that uses secondary neutron interactions with a gadolinium contrast agent (GDCA) to produce characteristic photons within the 40-200 keV energy region. The purpose of this study is to experimentally investigate the feasibility of implementing this procedure by performing experimental measurements on a passive double scattering proton treatment unit. Five experimental measurements were performed with varying concentrations and irradiation conditions. Photon spectra were measured with a 25 mm2, 1 mm thick uncollimated X-123 CdTe spectrometer. For a 10.4 Gy administration on a 100 mL volume phantom with 10 mg/g Gd solution placed in a water phantom, 1129 ± 184 K-shell Gd counts were detected. For an administered dose of 21 Gy and the same Gd solution measured in air, resulted in 3296 ± 256 counts. A total of 1094 ± 171, 421 ± 150 and 23 ± 141 K-shell Gd counts were measured for Gd concentrations of 10 mg/g, 1 mg/g and 0 mg/g for 7 Gy dose in air. The signal to noise ratio for these five measurements were: 7, 15, 6, 3, and 0.2, respectively. The spectrum showed 43 keV Kα and 49 keV Kβ peaks, however a small amount of 79.5 and 181.9 keV prompt gamma rays were detected from gadolinium neutron capture. This discrepancy is due to a drop in the intrinsic detection efficiency of the CdTe spectrometer over this energy range. The measurements were compared with Monte-Carlo simulation to determine the contributions of Gd neutron capture from internal and external neutrons on a passive scattering proton therapy unit and to investigate the discrepancy in detected characteristic x-rays versus prompt gamma rays.

[15]

Cao

, Jiang

, Wang

, et al. Inhalable microorganisms in Beijing's PM_2.5 and PM₁₀ pollutants during a severe smog event
Environmental Science & Technology, 2014,48(3):1499-1507.

DOI:10.1088/1361-6560/ab63b6 URLPMID:31851954 [本文引用: 1]

Wang Z

, Fang C

. Spatial-temporal characteristics and determinants of PM_2.5 in the Bohai Rim Urban Agglomeration
Chemosphere, 2016,148:148-162.

DOI:10.1016/j.chemosphere.2015.12.118 URLPMID:26802272 [本文引用: 1]

Ambient particulate matter (PM) pollution of China has become a global concern and has great impact on air quality and human health. This paper adopts the PM2.5 concentration data obtained from 241 newly located observation points in the Bohai Rim Urban Agglomeration (BRUA), as well as economic, urban and industrial working population data in the study area, revealing the spatio-temporal distribution of PM2.5 and its determinants with the help of a spatial data model. The results indicate that: 1) The BRUA was the core area of PM2.5 pollution in China in 2014, the average PM2.5 concentration of which reached 74 μg/m(3), which is 13 μg/m(3) higher than the country average (61 μg/m(3)); 2) The PM2.5 concentration distribution had a characteristic of high in winter and autumn but low in spring and summer, presenting a U-shaped monthly profile and a U-impulse type daily profile; 3) The urban PM2.5 concentrations showed obvious spatial variation and agglomeration. The highest hot-spot was observed in spring, while the lowest was in summer. High concentration cities were mainly located in southern Hebei and western Shandong, and low concentration cities were in the coastal area around the Bohai Sea and the mountainous areas in northern Hebei. High hot-spot areas demonstrated an M-shaped change, with two cycles of advance and retreat from west to east. 4) The Geographically weighted regression (GWR) model shows that the GDP per capita, urbanization rate and construction of the cities were closely related to PM2.5 concentrations in the BRUA.

[17]

Yang

Mian

, Wang

Yin

. Temporal and spatial characteristics and influencing factors of PM_2.5 in the Yangtze River Economic Belt. China's Population,
Resources and Environment, 2017,27(1):91-100.

[本文引用: 1]

[ 杨冕, 王银 . 长江经济带PM_2.5时空特征及影响因素研究
中国人口·资源与环境, 2017,27(1):91-100.]

[本文引用: 1]

[18]

Yan

Yaxue

, Qi

Shaozhou

. Time and space effects of foreign direct investment on China's urban haze (PM_2.5) pollution. China's Population,
Resources and Environment, 2017,27(4):68-77.

[本文引用: 1]

[ 严雅雪, 齐绍洲 . 外商直接投资对中国城市雾霾(PM_2.5)污染的时空效应检验
中国人口·资源与环境, 2017,27(4):68-77.]

[本文引用: 1]

[19]

Jiansheng

, Wang

Qian

, Li

Jiangcheng

, et al. Comparison of spatial differentiation simulation models of PM_2.5 concentration: Taking the Beijing-Tianjin-Hebei region as an example
Environmental Science, 2017,38(6):2191-2201.

[本文引用: 1]

[ 吴健生, 王茜, 李嘉诚 , 等. PM_2.5浓度空间分异模拟模型对比: 以京津冀地区为例
环境科学, 2017,38(6):2191-2201.]

[本文引用: 1]

[20]

Xiang

, Lin

Zhenshan

. Analysis of the influence of the interaction of influencing factors on the change of PM_2.5 concentration based on GAM model
Environmental Science, 2017(1):22-32.

DOI:10.1039/c6em00593d URLPMID:28092384 [本文引用: 1]

To improve understanding of long-range transport of perfluoroalkyl substances to the High Arctic, samples were collected from a snow pit on the Devon Ice Cap in spring 2008. Snow was analyzed for perfluoroalkyl acids (PFAAs), including perfluoroalkyl carboxylic acids (PFCAs) and perfluoroalkyl sulfonic acids (PFSAs), as well as perfluorooctane sulfonamide (FOSA). PFAAs were detected in all samples dated from 1993 to 2007. PFAA fluxes ranged from <1 to hundreds of ng per m² per year. Flux ratios of even-odd PFCA homologues were mostly between 0.5 and 2, corresponding to molar ratios expected from atmospheric oxidation of fluorotelomer compounds. Concentrations of perfluorobutanoic acid (PFBA) were much higher than other PFCAs, suggesting PFBA loading on the Devon Ice Cap is influenced by additional sources, such as the oxidation of heat transfer fluids. All PFCA fluxes increased with time, while PFSA fluxes generally decreased with time. No correlations were observed between PFAAs and the marine aerosol tracer, sodium. Perfluoro-4-ethylcyclohexanesulfonate (PFECHS) was detected for the first time in an atmospherically - derived sample, and its presence may be attributed to aircraft hydraulic system leakage. Observations of PFAAs from these samples provide further evidence that atmospheric oxidation of volatile precursors is an important source of PFAAs to the Arctic environment.

[ 贺祥, 林振山 . 基于GAM模型分析影响因素交互作用对PM_2.5浓度变化的影响
环境科学, 2017(1):22-32.]

DOI:10.1039/c6em00593d URLPMID:28092384 [本文引用: 1]

Xiang

, Lin

Zhenshan

, Liu

Huiyu

. Analysis of factors affecting PM_2.5 concentration in Jiangsu Province based on grey correlation model
Acta Geographica Sinica, 2016,71(7):1119-1129.

DOI:10.11821/dlxb201607003 URL [本文引用: 1]

In this paper, the Kriging interpolation method was introduced to analyze the spatial distribution characteristics of PM_2.5 in Jiangsu province in 2014, and then the evaluation index system for the PM_2.5 was constructed, which consists of three index layers and 27 indexes. The grey correlation analysis method was used to explore the correlation between PM_2.5 and its influencing factors. Finally, the relationship between the spatial distribution of PM_2.5 and the main influencing factors was analyzed. The conclusions can be drawn as follows: (1) The PM_2.5 in the coastal areas and the north is lower, while it is higher in the inland areas and the south. (2) The weight of PM_2.5 pollution sources index layer is the largest (w_i = 0.4691), the weight of the air quality index and meteorological elements layer is larger (w_i = 0.2866), and the weight value of urbanization and industrial structure index layer is the minimum (w_i = 0.2453). (3) In the 27 indexes, the volume of highway freight, housing construction area, garden green space area and population density have moderate correlation degrees. The other indexes have strong correlation degrees, among which, the correlation degree of the PM₁₀, O₃, total road freight volume and gross industrial output value are relatively high. (4) The synthetic correlation degree values between the PM_2.5 pollution sources index layer and PM_2.5 are much higher in cities of Nanjing, Wuxi, Changzhou, Nantong and Taizhou. The synthetic correlation degree values between urbanization and industrial structure index layer and PM_2.5 are much higher in cities of Xuzhou, Suzhou, Yancheng and Changzhou. The synthetic correlation degree values between the air quality index and meteorological elements layer and PM_2.5 are much higher in cities of Yancheng, Yangzhou, Changzhou and Nantong. Our results demonstrate that the grey correlation degrees of the evaluation indexes system are closely related with spatial distribution of PM_2.5 in Jiangsu province. Therefore, the grey correlation analysis model can be employed to analyze and evaluate the spatial distribution of PM_2.5.

[ 贺祥, 林振山, 刘会玉 . 基于灰色关联模型对江苏省PM_2.5浓度影响因素的分析
地理学报, 2016,71(7):1119-1129.]

DOI:10.11821/dlxb201607003 URL [本文引用: 1]

Zhou

Liang

, Zhou

Chenghu

, Yang

Fan

. Analysis of temporal and spatial evolution characteristics and driving factors of PM_2.5 in China from 2000 to 2011
Acta Geographica Sinica, 2017,72(11):161-174.

[本文引用: 1]

[ 周亮, 周成虎, 杨帆 . 2000-2011年中国PM_2.5时空演化特征及驱动因素解析
地理学报, 2017,72(11):161-174.]

[本文引用: 1]

[23]

Eeftens

, Beelen

, de Hoogh

, et al. Development of land use regression models for PM_2.5, PM_2.5 absorbance, PM₁₀ and PMcoarse in 20 European study areas: Results of the ESCAPE project
Environmental Science & Technology, 2012,46(20):11195-11205.

DOI:10.1088/1361-6560/ab63b6 URLPMID:31851954 [本文引用: 1]

Song

, Xie

, Zhang

, et al. Source apportionment of PM_2.5 in Beijing using principal component analysis absolute principal component scores and UNMIX
Science of the Total Environment, 2006,372(1):278-286.

DOI:10.1016/j.scitotenv.2006.08.041 URLPMID:17097135 [本文引用: 1]

Source apportionment of fine particulate matter (PM2.5, i.e., particles with an aerodynamic diameter of 2.5 microm or less) in Beijing, China, was determined using two eigenvector models, principal component analysis/absolute principal component scores (PCA/APCS) and UNMIX. The data used in this study were from the chemical analysis of 24-h samples, which were collected at 6-day intervals in January, April, July, and October 2000 in the Beijing metropolitan area. Both models identified five sources of PM2.5: secondary sulfate and secondary nitrate, a mixed source of coal combustion and biomass burning, industrial emission, motor vehicles exhaust, and road dust. On average, the PCA/APCS and UNMIX models resolved 73% and 85% of the PM2.5 mass concentrations, respectively. The results were comparable to previous estimate using the positive matrix factorization (PMF) and chemical mass balance (CMB) receptor models. Secondary products and the emissions from coal combustion and biomass burning dominated PM2.5. Such comparison among various receptor models, which contain different physical constraints, is important for better understanding PM2.5 sources.

[25]

Kloog

, Nordio

, Coull B

, et al. Incorporating local land use regression and satellite aerosol optical depth in a hybrid model of spatio-temporal PM_2.5 exposures in the Mid-Atlantic states
Environmental Science & Technology, 2012,46(21):11913-11921.

DOI:10.1088/1361-6560/ab63b6 URLPMID:31851954 [本文引用: 1]

Liu

, Fang

, Zhang

, et al. The effect of natural and anthropogenic factors on haze pollution in Chinese cities: A spatial econometrics approach
Journal of Cleaner Production, 2017,165:323-333.

DOI:10.1016/j.jclepro.2017.07.127 URL [本文引用: 1]

[27]

Wang

Zhenbo

, Liang

Longwu

, Lin

Xiongbin

. Mode summary of air pollution in Beijing-Tianjin-Hebei urban agglomeration and evaluation of treatment effects
Environmental Science, 2017,38(10):4005-4014.

DOI:10.13227/j.hjkx.201701007 URLPMID:29965182 [本文引用: 1]

Controlling air pollution in the Jing-jin-ji urban agglomeration (JJJUA), the most seriously polluted area in China, is related to the integrated development strategy for the region. Based on the national and regional implementation of air pollution control measures in recent years, the hierarchical cross-regional multi-directional linkage (HCML) air pollution prevention and control model was applied in this study. The effect of air pollution control was evaluated by monitoring the pollutants, SO₂, NO₂, PM₁₀, PM_2.5, O₃, and CO, at 112 monitoring sites in 13 cities in 2014-2015. The results can be summarized as follows:① The HCML model is an interrelated framework at the horizontal and vertical level. Under the efforts provided by the central, urban agglomeration, and city governments, this multi-level governance model serves as an effective tool to resolve the issues related to air pollution beyond the borders of municipalities. Environmental regulations on certain industries, energy consumption structure, car ownership and usage, and air quality supervision and warning systems are well established under this governance model. ② The air quality of the JJJUA has improved significantly in the past two years. The concentrations of air pollutants significantly decreased, with the exception of O₃, and high pollution ranges significantly reduced from north to south. The annual average concentrations of PM_2.5, PM₁₀, SO₂, NO₂, and CO decreased by 17.84%, 14.61%, 37.55%, 4.82%, and 16.18%, respectively. The number of days meeting the air quality standards increased for all pollutants except NO₂. Based on the current situation and unsolved problems of air pollution, the JJJUA area needs certain measures including diversifying the governance subjects, joint legislation (beyond municipalities) on air pollution to regulate pollution discharge, enhancing public awareness on air pollution and its health impacts, carefully examining sources of air pollution in winter to reduce pollution, and to better understand the sources of ozone and adopt effective control measures.

[ 王振波, 梁龙武, 林雄斌 . 京津冀城市群空气污染的模式总结与治理效果评估
环境科学, 2017,38(10):4005-4014.]

DOI:10.13227/j.hjkx.201701007 URLPMID:29965182 [本文引用: 1]

Liu

Huajun

, Lei

Mingyu

. The dilemma of coordinated management of smog pollution areas in China and its solution. China's Population,
Resources and Environment, 2018,28(10):88-95.

[本文引用: 1]

[ 刘华军, 雷名雨 . 中国雾霾污染区域协同治理困境及其破解思路
中国人口·资源与环境, 2018,28(10):88-95.]

[本文引用: 1]

[29]

Zhou

Zhen

, Xing

Yaoyao

, Sun

Hongxia

. Interval game analysis of government subsidy to the control strategy of Beijing-Tianjin-Hebei smog
System Engineering Theory and Practice, 2017,37(10):2640-2648.

[本文引用: 1]

[ 周珍, 邢瑶瑶, 孙红霞 . 政府补贴对京津冀雾霾防控策略的区间博弈分析
系统工程理论与实践, 2017,37(10):2640-2648.]

[本文引用: 1]

[30]

, Zhang

Renhe

. Dealing with smog weather: Meteorological science and technology are promising
Science China: Earth Science, 2014,44(1):1-2.

DOI:10.1007/BF02882066 URLPMID:18763082 [本文引用: 1]

The Russian wheat aphid (RWA),Diuraphis noxia (Mordvilko), exists with holocyclic life cycle in Tacheng, Xinjiang in Northwest China. It produces males and oviparae to mate and oviposit for overwintering by eggs. Under laboratory conditions with 14 h/d photophase and temperature not lower than 15 degrees C, RWA occurred in parthenogenesis and produced no males. The laboratory populations of Russian wheat aphid, which were kept under natural conditions in fall by 15th, 49th and 81st generation while wild populations produced males and oviparae for mating, produced males and oviparae with their number decreased gradually, but viviparae and nymphs increased sequentially. As a result, it produced a small amount of oviparae and no males emerged in fields by 49 generations' reproduction in laboratory. After development of 81 generations, oviparae happened occasionally and no eggs occurred for overwintering instead of viviparae and nymphs. A hypothesis of RWA disastrous process was proposed. The life cycle of RWA can be changed from holocycly to anholocycly in its long-term spread and evolution. Anholocycly is more dangerous than holocycly to small grains for its strong adaptability and dispersal ability.

[ 穆穆, 张人禾 . 应对雾霾天气: 气象科学与技术大有可为
中国科学: 地球科学, 2014,44(1):1-2.]

DOI:10.1007/BF02882066 URLPMID:18763082 [本文引用: 1]

Fang

. Progress and the future direction of research into urban agglomeration in China
Acta Geographica Sinica, 2014,69(8):1130-1144.

DOI:10.11821/dlxb201408009 URL [本文引用: 1]

Urban agglomeration has been the inevitable result of China's rapid industrialization and urbanization over the last 30 years. Since the early 2000s, urban agglomeration has become the new regional unit participating in international competition and the division of labor. China has declared urban agglomeration the main spatial component of new types of urbanization over the next decade as clarified at the first Central Urbanization Working Conference and in the National New-type Urbanization Plan (2014?2020). However, research on urban agglomeration remains weak and needs to be strengthened. From 1934 to 2013, only 19 papers published in Acta Geographica Sinica contained the theme of urban agglomeration (0.55% of the total number of articles published) and the first paper on urban agglomeration appeared less than 10 years ago. Despite a small number of divergent studies, this work has contributed to and guided the formation of the overall pattern of urban agglomeration in China. For example, spatial analyses have promoted the formation of the fundamental framework of China's urban agglomeration spatial structure and guided the National New-type Urbanization Plan; spatial identification standards and technical processes have played an important role in identifying the scope and extent of urban agglomeration; serial studies have facilitated pragmatic research; and problems with the formation and development of urban agglomeration have provided a warning for future choices and Chinese development. Future research into urban agglomeration in China should (1) review and examine new problems in China's urban agglomeration options and cultivation; (2) critically consider urban agglomeration when promoting the formation of the 5+9+6 spatial pattern; (3) rely on urban agglomeration to construct new urbanization patterns such as 'stringing the agglomerations with the axis, supporting the axis with the agglomerations'; and (4) deepen national awareness about resources, environment effects and environmental carrying capacity in high density urban agglomerations, management and government coordination innovation, the construction of public finance and fiscal reserve mechanisms, the technical regulation of urban agglomeration planning, and standards for identifying the scope and extent of urban agglomeration.

[32]

Wang

Jing

, Fang

Chuanglin

. A new driving force for the development of urban agglomerations in China
Geographical Research, 2011,30(2):335-347.

[本文引用: 1]

[ 王婧, 方创琳 . 中国城市群发育的新型驱动力研究
地理研究, 2011,30(2):335-347.]

[本文引用: 1]

[33]

Fang C

, Liu

, Li G

. International progress and evaluation on interactive coupling effects between urbanization and the eco-environment
Journal of Geographical Sciences, 2016,26(8):1081-1116.

DOI:10.1007/s11442-016-1317-9 URL [本文引用: 1]

[34]

Wang

Zhenbo

, Fang

Chuanglin

, Xu

Guang

, et al. Temporal and spatial variation of PM_2.5 concentration in Chinese cities in 2014
Acta Geographica Sinica, 2015,70(11):1720-1734.

DOI:10.11821/dlxb201511003 URL [本文引用: 2]

Haze pollution in China has become a severe environmental problem for people’s daily life as well as their health, among which PM_2.5 makes significant contribution to poor air quality. Satellite observations played a leading role in the recognition in the spatio-temporal variation of PM_2.5 nationally. However, based on the information and data obtained by satellites, the inversion method has limitations to truly reflect the spatio-temporal variation of PM_2.5 concentrations near ground level. Based on the observed PM_2.5 concentration data from 945 newly set-up air monitoring sites in 2014, our research reveals the spatio-temporal variations of PM_2.5 concentrations in China by using spatial statistical model. The results show that (1) in 2014, the average PM_2.5 concentration in China was 61 μg/m³. It had a periodical U-impulse type daily variation as well as a U-shaped monthly variation with a higher level in autumn and winter while a lower one in spring and summer. (2) Concentration of PM_2.5 in urban China shows a significant spatial differentiation and clustering pattern with spatial-periodic occurrences in north and south China. (3) The Hu-line (Hu Population Line) and Yangtze River are respectively the east-west and north-south boundaries which separate the high-value zone and the low-value zone of PM_2.5 concentrations in China. In 2014, the highly polluted cities by PM_2.5 were mainly distributed in the urban agglomerations (Central Henan, Harbin-Changchun, the Bohai Rim Region, the Yangtze River Delta, and the Middle Yangtze River), east of the Hu-line and north of the Yangtze River. The Beijing-Tianjin-Hebei urban agglomeration was the most severely polluted region all the year round. The southeast coastal region centered on the Pearl River Delta had good air quality in a stable manner.

[ 王振波, 方创琳, 许光 , 等. 2014年中国城市PM_2.5浓度的时空变化规律
地理学报, 2015,70(11):1720-1734.]

DOI:10.11821/dlxb201511003 URL [本文引用: 2]

Fang

Chuanglin

, Mao

Qizhi

, Ni

Pengfei

. The controversy and exploration of scientific selection and grading development of Chinese urban agglomeration
Acta Geographica Sinica, 2015,70(4):515-527.

DOI:10.11821/dlxb201504001 URL [本文引用: 1]

As a country's brand-new geographical unit in global competition and international division of labor, urban agglomeration is the product of China's new industrialization and urbanization in a higher stage, as well as the main battlefield of the "One Belt and One Road" project. Meanwhile, the development of urban agglomerations dominates not only the economic arteries, but also the future of China's new urbanization. Hence, it is of great strategic significance to promoting China's new urbanization and socio-economic development. However, a series of problems have emerged in the selection and cultivation process of China's urban agglomerations, which needs appropriate technological paths and plans to facilitate the healthy development of China's urban agglomerations from the scientific point of view. Therefore, the "High-Level Forum on China's Urban Agglomeration Development", jointly organized by the Geographical Society of China, the China's City Forum of Hundred Experts, and the Institute of Geographic Science and Natural Resources Research, CAS, was held in Beijing on December 20, 2014. After a series of heated debates, contention and discussion, nearly 100 experts attending the forum agreed that: (1) urban agglomeration plays an important role and dominates China's new urbanization. The research and development of urban agglomerations is a complex scientific issue and a very long process, as well as a natural process which cannot go against the objective laws; (2) there is a fierce debate and discussion on the basic connotation and standards of spatial recognition range, the benefit and value orientation are different at the policy level and academic level; (3) during the selection and cultivation process, "urban agglomeration disease", such as indiscriminating enclosure, unlimited sprawl, spoiling cities by excessive enthusiasm, create something out of nothing and throwing cities together in groups, should be solved as soon as possible; (4) different organization plans for the future spatial pattern of China's urban agglomerations are forming; (5) distinctive development patterns and problems do exist among urban agglomerations with different development levels, for example, the collaborative development optimization mode of Beijing-Tianjin-Hebei urban agglomeration, the expansion mode of the Yangtze River Delta urban agglomeration, the parallel development mode of the Pearl River Delta urban agglomeration, the spatial integration mode of Central and Southern Liaoning urban agglomeration, the intersecting parallels ("＃") space pattern of Harbin-Changchun urban agglomeration, the strategic integration mode of Central Plains urban agglomeration, and the balanced organization mode of Guanzhong urban agglomeration.

[ 方创琳, 毛其智, 倪鹏飞 . 中国城市群科学选择与分级发展的争鸣及探索
地理学报, 2015,70(4):515-527.]

DOI:10.11821/dlxb201504001 URL [本文引用: 1]

Liu

, Fang

, Zhang

, et al. The effect of natural and anthropogenic factors on haze pollution in Chinese cities: A spatial econometrics approach
Journal of Cleaner Production, 2017,165:323-333.

DOI:10.1016/j.jclepro.2017.07.127 URL [本文引用: 1]

[37]

Guang

Wei

, Xu

Shuting

. Spatial characteristics and coupling relationship of energy efficiency and industrial Structure in Liaoning Province
Acta Geographica Sinica, 2014,69(4):520-530.

DOI:10.11821/dlxb201404008 URL [本文引用: 1]

Taking 14 prefecture-level cities in Liaoning Province as the source of samples in the study area, this paper first explored the spatial hierarchy and structural characteristics of energy efficiency from the following three aspects: comprehensive energy efficiency by means of DEA, energy consumption per unit of GDP, and the energy efficiency in combination of the former two indexes. After measuring and analyzing the degrees of advancement, rationality and concentration of industrial structure in each city, we generalized the coupling features between energy efficiency and industrial structure in Liaoning by using the coupling degree rating model. Some conclusions can be drawn as follows. (1) The 14 cities differ significantly in their energy efficiency, among which, four cities (Shenyang, Dalian, Anshan and Jinzhou) enjoy the highest energy efficiency, but the northwestern part and other heavy industry cities such as Fushun and Benxi belong to low efficiency and high consumption area. (2) In areas with higher efficiency, the spatial patterns of the comprehensive energy efficiency, the energy consumption for GDP per unit, and the energy utilization efficiency present patterns of ‘π’, ‘II’, and ‘H’, respectively. Geographically, from east to west and from north to south, the energy utilization efficiency shows different trend; the binuclear structure of economic development and other factors have a major effect on the formation of this kind of spatial pattern of energy efficiency. (3) The southeastern part of Liaoning enjoys higher degree of advancement of industrial structure. With Shenyang and Dalian holding two ends, the areas with higher degree of rationality of industrial structure presents an‘H’-shaped pattern. The urban agglomerations in the central and southern Liaoning enjoy higher degree of concentration of industrial structure. (4) The overall coupling degree between energy efficiency and industrial structure is low in the province, but at both ends of Shenyang and Dalian, the coupling degree between the advancement of industrial structure and energy efficiency is relatively high than that of other cities.

[ 关伟, 许淑婷 . 辽宁省能源效率与产业结构的空间特征及耦合关系
地理学报, 2014,69(4):520-530.]

DOI:10.11821/dlxb201404008 URL [本文引用: 1]

, Hardy

, Zhang

, et al. Chemical characterization and source apportionment of PM_2.5 in a Nonattainment Rocky Mountain Valley
Journal of Environmental Quality, 2018,47(2):238-245.

DOI:10.2134/jeq2017.07.0262 URLPMID:29634806 [本文引用: 1]

Severe air pollution has significant adverse health effects and poses a threat to public health in many communities, including nonattainment areas in the Unites States. To develop effective control strategies to reduce air pollution with minimum economic cost, one of the biggest challenges is to quantify the contributions from different sources. By combining chemical analyses, Positive Matrix Factorization modeling, and emission inventory development, this study identified primary and secondary sources of particulate matter with a diameter of &lt;2.5 μm (PM) in a nonattainment Rocky Mountain valley (i.e., West Silver Valley [WSV]) in Idaho. The results show that biomass burning is the dominant source and contributes ～84% of the PM concentration in the valley. The study also identified influences on the WSV PM concentrations from traffic (7.4%), soil dust (3.4%), and secondary aerosols (4.8%). The results of this paper represent the first report on the chemical composition and source apportionment of PM in mountain valleys of northern Idaho and have been used to develop effective strategies to reduce the PM concentrations in the WSV. Moreover, this study provides detailed equations and methods in PM speciation, accounting for artifacts of the chemical analysis, Positive Matrix Factorization modeling, and emission inventory development, which can be used for source apportionment of severe air pollution in other regions.

[39]

Huang

Xiaogang

, Zhao

Jingbo

, Cao

Junyu

, et al. Temporal and spatial variation characteristics and driving factors of O₃ concentration in Chinese cities
Environmental Science, 2019,40(3):1120-1131.

DOI:10.1021/es051636z URL [本文引用: 1]

[ 黄小刚, 赵景波, 曹军骥 , 等. 中国城市O₃浓度时空变化特征及驱动因素
环境科学, 2019,40(3):1120-1131.]

DOI:10.1021/es051636z URL [本文引用: 1]

[40]

Lee H

, Liu

, Coull B

, et al. A novel calibration approach of MODIS AOD data to predict PM_2.5 concentrations
Atmospheric Chemistry and Physics, 2011,11(15):7991.

DOI:10.5194/acp-11-7991-2011 URL [本文引用: 1]

Epidemiological studies investigating the human health effects of PM2.5 are susceptible to exposure measurement errors, a form of bias in exposure estimates, since they rely on data from a limited number of PM2.5 monitors within their study area. Satellite data can be used to expand spatial coverage, potentially enhancing our ability to estimate location-or subject-specific exposures to PM2.5, but some have reported poor predictive power. A new methodology was developed to calibrate aerosol optical depth (AOD) data obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS). Subsequently, this method was used to predict ground daily PM2.5 concentrations in the New England region. 2003 MODIS AOD data corresponding to the New England region were retrieved, and PM2.5 concentrations measured at 26 US Environmental Protection Agency (EPA) PM2.5 monitoring sites were used to calibrate the AOD data. A mixed effects model which allows day-today variability in daily PM(2.)5-AOD relationships was used to predict location-specific PM2.5 levels. PM2.5 concentrations measured at the monitoring sites were compared to those predicted for the corresponding grid cells. Both cross-sectional and longitudinal comparisons between the observed and predicted concentrations suggested that the proposed new calibration approach renders MODIS AOD data a potentially useful predictor of PM2.5 concentrations. Furthermore, the estimated PM2.5 levels within the study domain were examined in relation to air pollution sources. Our approach made it possible to investigate the spatial patterns of PM2.5 concentrations within the study domain.

[41]

Fang

Chuanglin

, Wang

Zhenbo

, Xu

Guang

. The spatial distribution of PM_2.5 in urban agglomerations in China
Journal of Geographical Sciences, 2016,26(11):1519-1532.

DOI:10.1016/j.scitotenv.2018.11.105 URLPMID:30469058 [本文引用: 1]

China experiences severe particulate matter pollution associated with rapid economic growth and accelerated urbanization. In this study, concentrations of PM_2.5 (fine particulate matter with an aerodynamic diameter?≤?2.5?μm) throughout China, and specifically in nine typical urban agglomerations and one economic region, were statistically analyzed using high-resolution ground-based PM_2.5 observations from June 2014 to May 2018. The spatial variation of PM_2.5 was also explored via spatial autocorrelation analysis. High annual mean PM_2.5 concentrations were predominantly concentrated in the Beijing-Tianjin-Hebei, Central Plain, Northern Slope of Tianshan Mountain, and Cheng-Yu urban agglomerations, as well as the Huaihai Economic Region. The proportion of air quality nationwide monitoring sites where annual average PM_2.5 concentrations exceeded the Chinese Ambient Air Quality Standards (CAAQS) Grade II annual standard were 82.8%, 77.1%, and 70.8% in 2015, 2016, and 2017, respectively. Moreover, the frequency of PM_2.5 concentrations meeting the CAAQS Grade I 24-h standard increased in five national-level urban agglomerations, and the average annual PM_2.5 decreased from 2015 to 2017 with a reduction rate of over 20%. The southern Beijing-Tianjin-Hebei agglomeration and surrounding areas revealed the highest PM_2.5 pollution in four seasons. Monthly mean PM_2.5 typically exhibited a characteristic "U" shape. Diurnal mean PM_2.5 concentrations were generally consistent with typical urban agglomerations, with maximum and minimum PM_2.5 values occurring at approximately 08:00-12:00 and 15:00-17:00, respectively, except for the Northern Slope of Tianshan Mountain urban agglomeration (NSTM-UA) (14:00 and 08:00, respectively). A positive spatial autocorrelation of PM_2.5 concentrations was observed in all urban agglomerations (except NSTM-UA); high-high agglomeration centers of PM_2.5 pollution were located far inland with a circular distribution, and low-low agglomeration centers formed at the periphery of the high-high agglomeration region. This study is key for understanding the difference in PM_2.5 concentrations among urban agglomerations and region-oriented air pollution control strategies are highly suggested.

[42]

, Lin

, Wang

, et al. Spatial and temporal characteristics of particulate matter in Beijing, China using the empirical mode decomposition method
Science of the Total Environment, 2013,458:70-80.

DOI:10.1016/j.scitotenv.2013.04.005 URLPMID:23644281 [本文引用: 1]

Air pollution has become a serious problem in Beijing, China. Daily PM10 mass concentration measurements were collected at 27 stations in Beijing over a 5-year period from January 1, 2008 to October 31, 2012. We used a new clustering method (kernel K-means) and a new period and trend decomposition method (Empirical Mode Decomposition, EMD) to explore the spatial and temporal characteristics of the PM10 mass concentration in the City. The temporal period and trend of each cluster center were decomposed using the EMD method, which is an adaptive data analysis method that requires no prior information. The daily PM10 mass concentrations varied greatly from 5 μg/m(3) to more than 600 μg/m(3). All of the stations were partitioned into three clusters by the kernel K-means method, and which represent the low-, middle- and high-pollution stations, respectively. The first cluster contained nine stations, mainly located in the north suburban area. The second cluster, whose degree of pollution was much more serious than the first cluster, contained 13 stations distributed in urban and peri-urban areas. The pollution level in the southern part of Beijing was much more serious than in the northern part of the City. The third cluster contained five stations located outside the second-cluster stations. The total decreased amplitudes of the three clusters during the whole period were 19 μg/m(3), 10 μg/m(3) and 4 μg/m(3), respectively. Although the global trend of the PM10 mass concentration decreased in general, it was not the same for each season and station. The trends in summer and winter declined, while in spring, it has been increasing in recent years. Five types of trends can be found for stations, including monotonic decreasing, rise fall, fall rise fall, fall rise and rise. The rising trend of the regional background air pollution monitoring station, Miyun-reservoir, indicates an increase in the City's background PM10 mass concentration.

[43]

Qiangmin

, Li

Guoping

. Spatial division of labor and spillover effects of production service industry in Beijing-Tianjin-Hebei region
Acta Geographica Sinica, 2015,70(12):1926-1938.

DOI:10.11821/dlxb201512006 URL [本文引用: 1]

The rational space division of producer services in the Beijing-Tianjin-Hebei metropolitan region plays an extremely important role in promoting coordinated and integrated regional development. In this research, we use the prefecture-level panel data of the Beijing-Tianjin-Hebei metropolitan region for the period of 2003-2012 to estimate the spatial characteristics and industrial features of the producer service division in the region. We also estimate the spatial spillover effects of various producer service industries among the cities on the basis of a spatial panel econometric model. The main conclusions can be drawn as follows. (1) Beijing and Tianjin exhibit diversification in the division system of their producer services, whereas 11 cities in Hebei Province show specialization, mostly focusing on the development of their financial service and transportation sectors. (2) The division index of the producer services between Beijing and Tianjin is relatively low, and the producer service structures of these cities show a convergence trend. (3) The division indexes of the financial, business, and information service sectors in the region are relatively high, whereas those of the technology and real estate service sectors are significantly low. (4) The spillover effects of the real estate and technology service sectors with a low division index are remarkable. However, as a result of the limitation of transaction cost and the demand for "face-to-face" contact, the spatial spillovers perform best at a distance of around 150 km. When the distance goes beyond this specified range, the spatial spillovers are evidently reduced. The spillover effects of the transportation, finance service, and business service sectors with a high division index are not significant and should improve the industrial linkage between cities in the future. The scope of the spatial effect of the information service sector is exceedingly limited, as it only reflects the correlation among neighboring cities. Results of this study demonstrate that spatial spillover performs satisfactorily, whereas the localization of the service sector is poor.

[ 席强敏, 李国平 . 京津冀生产性服务业空间分工特征及溢出效应
地理学报, 2015,70(12):1926-1938.]

DOI:10.11821/dlxb201512006 URL [本文引用: 1]

Cheng

, Wang

, Ye

, et al. Spatiotemporal dynamics of carbon intensity from energy consumption in China
Journal of Geographical Sciences, 2014,24(4):631-650.

DOI:10.1007/s11442-014-1110-6 URL [本文引用: 1]

The sustainable development has been seriously challenged by global climate change due to carbon emissions. As a developing country, China promised to reduce 40%-45% below the level of the year 2005 on its carbon intensity by 2020. The realization of this target depends on not only the substantive transition of society and economy at the national scale, but also the action and share of energy saving and emissions reduction at the provincial scale. Based on the method provided by the IPCC, this paper examines the spatiotemporal dynamics and dominating factors of China’s carbon intensity from energy consumption in 1997-2010. The aim is to provide scientific basis for policy making on energy conservation and carbon emission reduction in China. The results are shown as follows. Firstly, China’s carbon emissions increased from 4.16 Gt to 11.29 Gt from 1997 to 2010, with an annual growth rate of 7.15%, which was much lower than that of GDP (11.72%). Secondly, the trend of Moran’s I indicated that China’s carbon intensity has a growing spatial agglomeration at the provincial scale. The provinces with either high or low values appeared to be path-dependent or space-locked to some extent. Third, according to spatial panel econometric model, energy intensity, energy structure, industrial structure and urbanization rate were the dominating factors shaping the spatiotemporal patterns of China’s carbon intensity from energy consumption. Therefore, in order to realize the targets of energy conservation and emission reduction, China should improve the efficiency of energy utilization, optimize energy and industrial structure, choose the low-carbon urbanization approach and implement regional cooperation strategy of energy conservation and emissions reduction.

[45]

Elhorst, Paul

. Applied spatial econometrics: Raising the bar
Spatial Economic Analysis, 2010,5(1):9-28.

DOI:10.1080/17421770903541772 URL [本文引用: 1]

[46]

Chao

, Wang

Yunpeng

, Li

Lili

. Study on the relationship between space-time distribution and total energy consumption of PM_2.5 in China from 1998 to 2012
Ecological Science, 2018,37(1):108-120.

[本文引用: 1]

[ 徐超, 王云鹏, 黎丽莉 . 中国1998-2012年PM_2.5时空分布与能源消耗总量关系研究
生态科学, 2018,37(1):108-120.]

[本文引用: 1]

[47]

Ministry of Environmental Protection of the People's Republic of China. Ambient Air Quality Standards (on trial)
National Environmental Protection Standards of the People's Republic of China (GB3095-2012), 2012-02-29.

[本文引用: 1]

[ 国家环境保护部. 环境空气质量标准(试行)
中华人民共和国国家环境保护标准(GB3095-2012), 2012-02-29.]

[本文引用: 1]

[48]

Walter I

, Ugelow

. Environmental policies in developing countries
Ambio, 1979,8(2/3):102-109.

DOI:10.1002/(sici)1097-0193(1999)8:2/3&lt;102::aid-hbm6&gt;3.0.co;2-j URLPMID:10524600 [本文引用: 1]

We present an overview of the types of imaging experiments that can be performed on psychologically impaired patients. The critical observation from such studies is a differential pattern of activation in the patients and normals. Underactivity is interpretable only when the patients make normal responses. In this context, a failure to activate a component region of the normal system implies that this region was not necessary for task performance. Overactivity indicates either cognitive or neuronal reorganisation. Neuronal reorganisation is indicated only if the patient performs the task using the same set of cognitive operations as normal subjects. Cognitive reorganisation can be demonstrated if the same activation pattern is elicited by normals when they are co-erced into using the same cognitive implementation as the patient. We conclude that the interpretation of neuroimaging studies of psychologically impaired patients depends on intact task performance and a detailed task analysis. When these criteria are met, patient studies can be used to identify: (1) necessary and sufficient brain systems, (2) dysfunction at sites distant to damage, (3) peri-damage activation, and (4) compensation either at a neuronal level when pre-existing cognitive strategies are re-instantiated using duplicated neuronal systems (degeneracy), or at a cognitive level when alternative cognitive strategies (and their corresponding brain systems) are adopted.

[49]

Helian

, Deng

Yuping

. Does foreign direct investment lead to environmental pollution in China? Spatial measurement based on Chinese provincial panel data
Management World, 2012(2):30-43.

[本文引用: 1]

[ 许和连, 邓玉萍 . 外商直接投资导致了中国的环境污染吗? 基于中国省际面板数据的空间计量研究
管理世界, 2012(2):30-43.]

[本文引用: 1]

[50]

Jiang

Lei

, Zhou

Haifeng

, Bo

Ling

. Spatial heterogeneity analysis of the impact of foreign direct investment on air pollution: A case study of China's 150 cities' air quality index (AQI)
Scientia Geographica Sinica, 2018,38(3):351-360.

DOI:10.13249/j.cnki.sgs.2018.03.004 URL [本文引用: 1]

The issue of air pollution in China has already surged in recent years, attracting public concerns, even great anxiety. This paper employs a unique cross-sectional data set of 150 Chinese cities in 2014 and adopts air quality index (AQI), an indicator newly-developed by the ministry of environmental protection of China, to measure air quality. Then, from a spatial heterogeneity perspective it applies ordinary least square (OLS) technique and geographically weighted regression (GWR) model to analyze the impact of foreign direct investment (FDI) on air pollution controlling for other five explanatory variables, namely, per capita GDP, population density, sulfur dioxide emissions, PM_2.5 concentration, and the number of vehicles. Meanwhile, it also investigates if there is an inverted U-shaped curve between per capita GDP and air quality index, specifically environmental Kuznets curve (EKC). The findings are the following. Firstly, there is no evidence of EKC, but an increasing linear relationship between income and air pollution. Secondly, the variable of FDI is found to be significant and negative, indicating that an increase of foreign capital may reduce air quality index, in other words, improve air quality. This is because foreign capital brings advanced technologies, improving air quality effectively. Besides, sulfur dioxide emissions and PM_2.5 concentration are found to have significant and positive impacts on AQI while population density, as an indicator of environmental awareness, is found to have a negative impact on AQI. However, from the results of the OLS model, the variable of private cars is not statistically significant, even found to be positive. It hence was deleted from the model. Thirdly, the GWR model results suggest that the impacts of FDI of 150 cities on air pollution greatly vary from city to city. Specifically speaking, foreign capitals of the northeastern cities, Guangzhong plain urban cities and cities of the middle reaches of the Yangtze River play a more important role in reducing air pollution than those of other cities. On the contrary, the contribution of foreign capitals of Shandong peninsula cities and Sichuan-Chongqing urban cluster is found to be insignificant. Finally, from the above analysis, a series of policy handles and suggestions are given.

[ 姜磊, 周海峰, 柏玲 . 外商直接投资对空气污染影响的空间异质性分析: 以中国150个城市空气质量指数(AQI)为例
地理科学, 2018,38(3):351-360.]

DOI:10.13249/j.cnki.sgs.2018.03.004 URL [本文引用: 1]

Cheng

Zhonghua

, Liu

Jun

, Li

Lianshui

. Research on the influence of industrial structure adjustment and technological progress on haze reduction
China Soft Science, 2019(1):146-154.

DOI:10.1007/s12011-017-0973-7 URLPMID:28281223 [本文引用: 1]

This study was conducted to investigate the effects of excess dietary fluoride (F) on serum biochemical indices, egg quality, and concentrations of F in soft tissues, eggs, and serum of laying hens. Commercial laying hens (n?=?576, 51?weeks of age) were randomly allotted to 6 treatments with 6 replicates of 16 birds. The basal diets contained fluorine inclusions at a level of 16?mg/kg, and graded sodium fluoride was added to the basal diet to achieve fluorine inclusions, respectively, at a level of 200, 400, 600, 800, and 1000?mg/kg in the experimental diets. Dietary F levels at 600, 800, and 1000?mg/kg decreased (P?&lt;?0.05) albumin height and yolk color, while eggshell strength and eggshell thickness significantly decreased at 800 and 1000?mg/kg, respectively, compared with the control group. Fluoride concentrations in eggshell, albumin, yolk, liver, kidney, ovary, and oviduct responded to dietary F levels positively, and F concentrations in eggshell were the highest. Fluorine concentrations in albumin and yolk increased with the feeding time at the same dietary F levels (P?&lt;?0.05). Dietary F level at 400?mg/kg increased serum calcium level and activity of glutamic oxalacetic transaminase (P?&lt;?0.05). In conclusion, dietary F levels at 600?mg/kg decreased albumin height and yolk color, while eggshell strength and eggshell thickness significantly decreased at 800 and 1000?mg/kg, respectively. F concentrations in soft tissues, albumin, yolk, and eggshell of layers had a positive correlation with dietary F levels. By disturbing Ca and phosphorus metabolism, dietary F levels affected the formation of eggshell, reducing eggshell strength and eggshell thickness.

[ 程中华, 刘军, 李廉水 . 产业结构调整与技术进步对雾霾减排的影响效应研究
中国软科学, 2019(1):146-154.]

DOI:10.1007/s12011-017-0973-7 URLPMID:28281223 [本文引用: 1]