长江三角洲城市群PM_(2.5)时空演变及影响因素

科学识别PM_(2.5)的空间分异及其驱动因素,是实现区域空气污染治理的关键。以国测点日均PM_(2.5)浓度为数据来源,基于多种空间分析方法,研究长江三角洲城市群PM_(2.5)浓度的时空演变及影响因素。结果发现:(1)2013～2017年,长江三角洲城市群的PM_(2.5)年平均浓度,处于不断下降的趋势;城市间的差异,呈现逐渐减少的趋势。(2)一年中,12月份的PM_(2.5)浓度最高,8月份的PM_(2.5)浓度最低。1～12月,PM_(2.5)浓度先减后增。(3)2013年,PM_(2.5)高浓度区域主要分布在江苏省;2017年,PM_(2.5)高浓度区域主要分布在安徽省。5年间,PM_(2.5)浓度的空间重心,向安徽省转移72 km。(4)长江三角洲城市群PM_(2.5)浓度存在明显的空间自相关。存在PM_(2.5)浓度高-高值区、低-低值区"扎堆"现象,且集聚程度趋于增大。(5)影响PM_(2.5)浓度的因素包括了自然因素和社会因素。自然因素中,降雨与PM_(2.5)浓度显著相关。社会因素主要来自工业排放、交通排放和能源消耗。其中,能源消耗的影响程度最大,工业排放次之,交通排放最后。     

京津冀PM_(2.5)的主要影响因素及内在关系研究

大气污染物的源排放是形成灰霾天气的内因,气象条件是形成灰霾天气的外因。本研究通过构建PM_(2.5)浓度的两段式分布滞后模型,结合自然环境因素及经济因素对PM_(2.5)的影响因素进行了综合分析。在第一段模型中构建了PM_(2.5)和大气污染物排放量的分布滞后模型,第二段模型中构建了不同的大气污染源对大气污染物排放量的影响因素模型。大气污染物排放源主要包括工业源、生活源、机动车源、集中式污染治理设施源。在工业源中,工业废气重度污染行业是大气污染物排放主要的贡献者;在生活源中,燃煤消费量对大气污染物排放影响很大,这也是冬季供暖期间PM_(2.5)剧增的原因;在机动车源中,尽管黄标车的保有量仅占汽车保有量的10%左右,但却占据了颗粒物排放量的绝大部分。利用京津冀代表性城市PM_(2.5)日度数据研究得出平均气温、平均风速、日照时数、平均气压、降雨量、平均相对湿度、沙尘暴等因素对PM_(2.5)浓度的负向与正向作用。研究发现,大气污染物排放量对PM_(2.5)浓度具有聚集的滞后效应,当期大气污染物排放量、滞后一期、滞后两期、滞后三期大气污染物对PM_(2.5)浓度具有显著的正向作用,且影响依次递减。构建的大气污染物排放量的污染源影响因素模型揭示一个地区煤炭消费量、工业废气重度污染行业工业增加值、黄标车保有量对该地区大气污染物排放量具有显著影响。本研究对优化能源消费结构和产业结构,减少空气污染物排放提出了对策建议。     

A statistical model to evaluate the effectiveness of PM2.5 emissions control during the Beijing 2008 Olympic Games

A statistical model was developed using satellite remote sensing data and meteorological parameters to evaluate the effectiveness of air pollution control measures during the 2008 Beijing Olympic Games. Custom satellite retrievals under hazy conditions were included in the modeling dataset to represent the air pollution levels more accurately. This model explained 70% of the PM_2.5 variability during the modeling period from June to October 2008. Using this tool, we estimate that the aggressive emission reduction measures alone effectively lowered PM_2.5 levels by 20–24 μg/m³ or 27–33% on average during the Games period, which is substantially greater than those reported previously. Since parameters required to develop this model are readily available in most cities of the world, it can be quickly applied after other major events to evaluate air pollution control policy.     

长江经济带PM_(2.5)时空特征及影响因素研究

大气细颗粒物(PM_(2.5))因其对空气环境质量乃至人类健康的巨大危害而逐渐引起学者们的关注。本文以我国综合实力最强、战略支撑作用最为突出的区域之一——长江经济带为研究对象,基于城市级空气质量监测数据,运用地理学时空分析与GIS可视化方法探索并呈现了2015年长江经济带PM_(2.5)的时空分布特征及其演变规律;在此基础上,结合空间回归模型考察了PM_(2.5)浓度与区域城市发展之间的内在关系。结果表明,就空间特征而言,长江中下游地区PM_(2.5)污染较长江上游地区更为严重,长江北岸地区比长江南岸地区更为严重;PM_(2.5)高浓度集聚地带主要位于鄂皖苏大部分地区,与空气质量较佳的云南及其周边地区呈"对角"分布状态。长江经济带内城市间PM_(2.5)浓度存在着显著的正向空间自相关,且自相关性随距离增大而不断减弱,其门槛尺度约为900 km;在这一范围内,PM_(2.5)空间集聚效应较为明显。就时间特征而言,冬季PM_(2.5)浓度相对较高,春秋两季次之,夏季空气质量最好;各地区浓度分布在年初相对离散,后有所趋同。此外,PM_(2.5)与其他类型的大气污染物(如SO2、NO2、O3)浓度两两之间均存在着显著的正相关性,暗示大气污染物从原发污染演变为二次污染,形成恶性循环。空间回归分析结果表明,PM_(2.5)污染随经济发展水平的提高呈现先上升后下降的趋势,在一定程度上支持了"环境库兹涅兹曲线"假说;且人口密度、公共交通运输强度均在不同程度上导致长江经济带PM_(2.5)浓度的升高。最后,从区域性联防联控、不同类型大气污染物协同治理、促进经济发展方式转型等方面为长江经济带的大气环境治理提出切实可行的政策建议。     

Long-term trend and spatial pattern of PM2.5 induced premature mortality in China

With rapid economic growth, China has witnessed increasingly frequent and severe haze and smog episodes over the past decade, posing serious health impacts to the Chinese population, especially those in densely populated city clusters. Quantification of the spatial and temporal variation of health impacts attributable to ambient fine particulate matter (PM_2.5) has important implications for China's policies on air pollution control. In this study, we evaluated the spatial distribution of premature deaths in China between 2000 and 2010 attributable to ambient PM_2.5 in accord with the Global Burden of Disease based on a high resolution population density map of China, satellite retrieved PM_2.5 concentrations, and provincial health data. Our results suggest that China's anthropogenic ambient PM_2.5 led to 1,255,400 premature deaths in 2010, 42% higher than the level in 2000. Besides increased PM_2.5 concentration, rapid urbanization has attracted large population migration into the more developed eastern coastal urban areas, intensifying the overall health impact. In addition, our analysis implies that health burdens were exacerbated in some developing inner provinces with high population density (e.g. Henan, Anhui, Sichuan) because of the relocation of more polluting and resource-intensive industries into these regions. In order to avoid such national level environmental inequities, China's regulations on PM_2.5 should not be loosened in inner provinces. Furthermore policies should create incentive mechanisms that can promote transfer of advanced production and emissions control technologies from the coastal regions to the interior regions.     

The temporal stability and developmental differences in the environmental impacts of militarism: the treadmill of destruction and consumption-based carbon emissions

This study examines the relationship between national-level militarism and consumption-based carbon dioxide emissions. We analyze panel data from 1990 to 2010 for 81 nations to determine whether the magnitude of the effects of (1) military expenditures as percent of total gross domestic product and (2) military personnel as percent of total labor force on carbon emissions change over time. Results of two-way fixed effects models highlight the temporal stability of the environmental impacts of both national-level military characteristics. The findings also reveal that the effect of military expenditures on emissions is larger in the more developed OECD nations than in the developing non-OECD nations. Overall, the results support the treadmill of destruction perspective, which suggests that the nations’ militaries are an important social institution to consider in sustainability science research on the human drivers of global environmental change.     

Levels and major sources of PM2.5 and PM10 in Bangkok Metropolitan Region

This research was the first long-term attempt to concurrently measure and identify major sources of both PM(10) and PM(2.5) in Bangkok Metropolitan Region (BMR). Ambient PM(10) and PM(2.5) were evaluated at four monitoring stations and analyzed for elemental compositions, water-soluble ions, and total carbon during February 2002-January 2003. Fifteen chemical elements, four water-soluble ions, and total carbon were analyzed to assist major source identification by a receptor model approach, known as chemical mass balance. PM(10) and PM(2.5) were significantly different (p<0.05) at all sites and 24 h averages were high at traffic location while two separated residential sites were similar. Seasonal difference of PM(10) and PM(2.5) concentrations was distinct between dry and wet seasons. Major source of PM(10) at the traffic site indicated that automobile emissions and biomass burning-related sources contributed approximately 33% each. Automobiles contributed approximately 39 and 22% of PM(10) mass at two residential sites while biomass burning contributed about 36 and 28%. PM(10) from re-suspended soil and cooking sources accounted for 10 to 15% at a residential site. Major sources of PM(2.5) at traffic site were automobile and biomass burning, contributing approximately 32 and 26%, respectively. Biomass burning was the major source of PM(2.5) mass concentrations at residential sites. Meat cooking also accounted for 31% of PM(2.5) mass at a low impact site. Automobile, biomass burning, and road dust were less significant, contributed 10, 6, and 5%, respectively. Major sources identification at some location had difficulty to achieve performance criteria due to limited source profiles. Improved in characterize other sources profiles will help local authority to better air quality.     

Mutagenic properties of PM2.5 urban pollution in the Northern Italy: The nitro-compounds contribution

PM2.5 is the breathable fraction of the particulate matter and some adverse health effects, such as respiratory functionality, cardiological diseases and cancer, can be in some measure attributable to this risk factor exposure. Some of the most carcinogen compounds transported by PM2.5 are nitro-compounds. In this study, a strengthened in vitro bioassay — able to predict the mutagenic/carcinogenic activity of the environmental mixtures — was conducted on PM2.5 organic extracts to define the nitro-compounds burden. PM2.5 air pollution was daily monitored, during 2006, in three cities located in the Northern part of Italy (Torino, Pavia and Verona) and the mutagenic properties of the PM2.5 organic extracts were assessed with the Ames test. The bacterial used in this study were three Salmonella typhimurium strains: TA98, nitroreductase-less mutant TA98NR and YG1021 carrying a nitroreductase-producing plasmid. The annual PM2.5 mean level measured in Torino was 46.5 (± 31.6) μg/m³, in Pavia 34.8 (± 25.1) μg/m³, and in Verona 37.3 (± 27.8) μg/m³, while the mutagenicity expressed as TA98 net reverants/m³ was 28.0 (± 22.1), 28.3 (± 24.9), and 34.2 (± 30.9) respectively. Monthly pool bioassays, conducted with the three different strains, showed a greater mutagenic response of the YG1021 in each city. The relationship among the mutagenic answers for YG1021:TA98:TA98NR was about 6:3:1 (p < 0.001). Over nitroreductase activity enhanced the response of 2.2, 2.0 and 1.7 times for Torino, Pavia, and Verona (ANOVA Torino p < 0.05) respectively. Without nitroreductase activity the genotoxicity was limited. These biological findings are able to describe a relevant role played by the nitro compounds in the mutagenic properties of the urban PM2.5 in the Padana plain; moreover the bacterial nitroreductase plays a predominant role in DNA interaction primarily for Torino PM2.5 extracts.     

降低PM2.5健康风险的行为选择及支付意愿——以北京市居民为例

本文运用权变评价法(CVM)调查了北京市居民对大气细颗粒物(PM2.5)健康风险的认知状况、行为选择及降低健康风险的支付意愿。结果表明:182.5%的居民认为北京市空气污染较为严重,52.4%的居民知道雾霾天气是由PM2.5引起的并认为其会对自己和家人的健康带来影响,92%的居民认为政府应该承担更多降低PM2.5健康风险的责任。2在降低风险的行为中,能够节省生活成本、有益于身心健康的生活方式和符合日常生活习惯的行为得到了最积极的响应,而会降低居民生活舒适性和增加生活成本的行为支持率相对较低。3在降低30%的PM2.5浓度情景下,居民的支付意愿平均为22.78元/月;在降低60%的PM2.5浓度情景下,居民的支付意愿平均为39.82元/月。而且,居民的个体特征、经济条件、居住位置、交通方式、认知水平及风险沟通等因素,都对降低PM2.5健康风险的支付意愿产生显著影响。     

PM_(2.5)污染对京津冀地区人群健康影响和经济影响

京津冀地区是中国工业最为发达的地区之一和空气污染最严重的地区之一,也是国家控制空气污染的重点区域。空气污染导致的健康影响不仅会增加额外健康支出,还会导致过早死亡和工作时间减少,进而影响宏观经济发展。为了评估该地区PM_(2.5)污染引起的健康问题对宏观经济的影响,以及控制空气污染后带来的经济效益和福利的影响,本研究结合可计算一般均衡模型(Computable General Equilibrium)、温室气体与大气污染物协同效益模型(The Greenhouse Gas and Air Pollution Interactions and Synergies-Model,GAINS-Model)和健康影响模型对2020年京津冀地区PM_(2.5)污染引起的健康影响和经济影响进行评估。模型结果表明,2020年Wo Pol情景下PM_(2.5)污染引起的额外健康支出分别为北京44.2亿元、天津27.5亿元、河北97.5亿元。PM_(2.5)污染引起人均每年劳动时间损失分别为北京81.3小时、天津89.6小时、河北73.1小时。而劳动力供给和劳动时间减少所造成GDP和福利损失依次为天津(GDP和福利损失分别为2.79%和8.11%),其次为北京(2.46%和5.10%)、河北(2.15%和3.44%)。如果采取积极的控制空气污染物排放政策,在2020年WPol情景下,PM_(2.5)污染引起的额外健康支出分别为北京8.8亿元、天津4.9亿元、河北2.0亿元,较Wo Pol情景下显著下降。PM_(2.5)污染引起人均劳动时间损失分别下降为北京22.0小时、天津23.2小时、河北22.4小时。空气污染物控制政策给北京、天津和河北带来的经济效益分别相当于GDP的1.75%、2.02%和1.46%。因此,本研究显示控制京津冀地区PM_(2.5)污染带来的经济效益非常可观,其中天津效益最高,其次为北京,河北最低。空气污染物的迁移扩散会影响周边省市的空气质量,因此京津冀地区联合控制空气污染效果更好。     

中美大气污染的空间交互影响--来自国家和城市层面PM2.5的经验证据

准确识别中美大气污染的空间交互影响可以为两国在大气污染治理领域开展双边合作提供科学依据。以PM 2.5浓度表征大气污染程度,采用收敛交叉映射(Convergent Cross Mapping,CCM)方法,在采集伯克利地球发布的小时数据的基础上通过算数平均得到2018年1月1日至2018年12月31日的PM 2.5浓度日均数据,从国家和城市两个层面识别了中美大气污染的空间交互影响。研究发现,中美两国的大气污染是空间交互影响的。其中,国家层面,在CCM因果检验基础上的广义同步检验表明,在1%显著性水平上,大气污染仅存在由美国指向中国的单向因果关系。城市层面,中美两国10个样本城市之间理论上共存在50个可能的因果关系(5×5×2)。研究发现,在1%显著性水平上,美国城市指向中国城市的因果关系有12个,而中国城市指向美国城市的因果关系仅有5个。在影响强度上,美国城市的大气污染对中国城市的影响强度高于中国城市的大气污染对美国城市的影响强度,如重庆的大气污染对华盛顿的影响强度为0.21,而华盛顿的大气污染对重庆的影响强度为0.35。面对大气污染的空间交互影响,中美两国可通过积极开展联合科研攻关,厘清大气污染的传输路径及其驱动因素,进行污染物的联合监测与数据管理;通过共享大气污染治理技术和建立大气污染防治基金等方式,加强在大气污染治理领域的合作。一旦中美两国成功建立起大气污染双边合作治理体系,依靠两国的国际地位和国际影响力,必将吸引越来越多的国家参与进来共同行动,大气污染全球治理体系的构建将非常值得期待。     

Status and characteristics of ambient PM2.5 pollution in global megacities

Ambient PM_2.5 pollution is a substantial threat to public health in global megacities. This paper reviews the PM_2.5 pollution of 45 global megacities in 2013, based on mass concentration from official monitoring networks and composition data reported in the literature. The results showed that the five most polluted megacities were Delhi, Cairo, Xi'an, Tianjin and Chengdu, all of which had an annual average concentration of PM_2.5 greater than 89 μg/m³. The five cleanest megacities were Miami, Toronto, New York, Madrid and Philadelphia, the annual averages of which were less than 10 μg/m³. Spatial distribution indicated that the highly polluted megacities are concentrated in east-central China and the Indo-Gangetic Plain. Organic matter and SNA (sum of sulfate, nitrate and ammonium) contributed 30% and 36%, respectively, of the average PM_2.5 mass for all megacities. Notable seasonal variation of PM_2.5 polluted days was observed, especially for the polluted megacities of China and India, resulting in frequent heavy pollution episodes occurring during more polluted seasons such as winter. Marked differences in PM_2.5 pollution between developing and developed megacities require more effort on local emissions reduction as well as global cooperation to address the PM_2.5 pollution of those megacities mainly in Asia.     

安徽省冬季PM2.5污染来源及其成因分析

随着中国城市化和工业化的加速发展,大气污染的问题日益突出,严重危害公众身体健康。基于安徽省逐小时PM2.5浓度监测数据,采用后向轨迹模式、潜在源因子分析法（PSCF）和权重浓度分析法（CWT）,构建PM2.5来源分析模型,分析了安徽省PM2.5的来源,并结合地理探测器辨析了影响PM2.5本底贡献浓度的驱动因子。结果表明：（1）本底贡献、本底外溢和外地输送这3个动态过程对安徽省PM2.5浓度的时空变化有重要的影响;（2）PM2.5月累计逐小时测量浓度、总浓度、外地输送浓度、本底贡献浓度、本底外溢浓度和月均PM2.5本底排放贡献率,均在整体呈现出西南高、东北低的分布趋势,但前3项在安徽西北部的阜阳、亳州和淮北等地出现高值区;（3）安徽省约97.5%的面积外地输送贡献率>50%,下辖市PM2.5本底排放贡献率在30%~50%,说明1月污染以外地输送为主;（4）工厂密度、车辆保有量密度和人口密度对PM2.5月累计本底贡献浓度的解释力q值分别为0.33、0.47和0.61,通过与PM2.5月累计测量浓度地理探测分析结果的比较,表明人为要素与PM2.5月累计本底贡献浓度的关系更加密切。研究结果可为区域大气污染治理提供科学的参考依据。     

Primary and secondary components of PM2.5 in Milan (Italy)

In sampling campaigns--carried out by means of a high-volume gravimetric sampler--performed between August 2002 and December 2003, 24-h PM2.5 samples have been collected at an urban background site in downtown Milan and analyzed for elemental and organic carbon, ionic species (i.e., chloride, nitrates, sulfates and ammonium) and some elemental species. Chemical speciation data are evaluated also in terms of primary and secondary components of fine particulate matter: in particular, the contribution of secondary organic aerosols (SOA) and of the primary contribution from traffic to observed PM2.5 concentration levels are evaluated by means of the EC tracer method.     

中国城市环境规制对PM2.5污染的影响效应

基于2005～2015年中国城市环境规制与PM2.5污染的面板数据,利用面板灰色关联、空间计量和中介效应模型探究环境规制对PM2.5污染的时空关联影响、直接效应及作用机制.研究发现:(1)环境规制与PM2.5污染具有显著为负的时空关联性,其时序关联强度均值为负,呈"W"型波动变化,且空间关联强度负值城市占比较高.(2)环境规制对PM2.5污染起到明显的抑制作用,而邻近城市环境规制的溢出效应并未发挥促降本地PM2.5污染的积极作用.(3)环境规制促降PM2.5污染的重要中介变量分别是工业生态效率和技术水平,环境规制通过提升工业生态效率降低PM2.5污染的间接效应占比达55.69％,而通过提高技术水平降低PM2.5污染的间接效应占比为4.85％.(4)环境规制对PM2.5污染的影响呈现空间异质性,其并不能有效降低东部、中部和东北地区城市的PM2.5污染,而对西部城市PM2.5污染起到明显的改善作用.     

Analysis of daily and seasonal variation of fine particulate matter (PM2.5) for five cities of China

Environment, Development and Sustainability - Monitoring of air quality is demanding, especially in poor air quality regions. China has been suffering from PM2.5 pollution associated with the fast...     

Cause-specific premature death from ambient PM2.5 exposure in India: Estimate adjusted for baseline mortality

In India, more than a billion population is at risk of exposure to ambient fine particulate matter (PM_2.5) concentration exceeding World Health Organization air quality guideline, posing a serious threat to health. Cause-specific premature death from ambient PM_2.5 exposure is poorly known for India. Here we develop a non-linear power law (NLP) function to estimate the relative risk associated with ambient PM_2.5 exposure using satellite-based PM_2.5 concentration (2001 − 2010) that is bias-corrected against coincident direct measurements. We show that estimate of annual premature death in India is lower by 14.7% (19.2%) using NLP (integrated exposure risk function, IER) for assumption of uniform baseline mortality across India (as considered in the global burden of disease study) relative to the estimate obtained by adjusting for state-specific baseline mortality using GDP as a proxy. 486,100 (811,000) annual premature death in India is estimated using NLP (IER) risk functions after baseline mortality adjustment. 54.5% of premature death estimated using NLP risk function is attributed to chronic obstructive pulmonary disease (COPD), 24.0% to ischemic heart disease (IHD), 18.5% to stroke and the remaining 3.0% to lung cancer (LC). 44,900 (5900–173,300) less premature death is expected annually, if India achieves its present annual air quality target of 40 μg m^− 3. Our results identify the worst affected districts in terms of ambient PM_2.5 exposure and resulting annual premature death and call for initiation of long-term measures through a systematic framework of pollution and health data archive.     

Driving factors and the implications of work choices of rural laborers in ecologically vulnerable areas in North China

Using rural survey data for the year 2009,we provide evidence of work choices of rural laborers in ecologically vulnerable areas in North China,and model these choices as a function of characteristics of individuals,households,and land endowments.The findings reveal large differences between males and females,and between non-farm employment and part-time farming engagement Land scarcity is still an important driving factor in non-farming participation of rural laborers in the study area;and in the current case of land productivity and the total amount of land,there are still surplus rural labor forces in the study area.Diversification of livelihood reduces the pressure on land,thus facilitating ecosystem conservation.So,the paper concludes with the argument that establishing coordination mechanisms among non-farm employment of rural laborers,agricultural production,and restoration of ecosystems are essential for mitigating the conflicts of ecological security,agricultural production,and rural development in mis area.     

1998~2016年中国大气PM2.5污染浓度空间格局演化 ——基于339个城市的实证研究

摘要： 近年来,与PM2.5相关的大气环境质量状况和改善问题一直是社会和公众关注的热点话题。通过选取中国339个地级市为研究对象,采用重心模型和局部空间自相关的研究方法,对中国339个城市大气PM2.5污染浓度空间格局演化进行实证分析。结果显示：（1）从重心分析来看,1998~2016年内的中国大气PM2.5污染浓度几何重心位于渭河平原附近以及中国东部和北方的大气PM2.5污染程度分别相对高于西部和南方地区。（2）从局部空间自相关分析来看,1998~2016年期间“高-高”类型的空间正相关的地区主要集中在华北地区、华中地区、华东地区、长江中下游沿岸城市及四川盆地;“低-低”类型的空间正相关的地区主要集中在西南和西北地区,也零星出现在东北和福建的部分城市中。最后对中国大气PM2.5污染空间格局进行初步成因探析。