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Abstract

The characteristics of fine particulate pollution (PM₁₀ and PM_2.5) were measured at urban and suburban sites in Jinan during the 2008–2009 heating and non-heating seasons. The results showed that PM₁₀ and PM_2.5 pollution was quite serious, and PM mass concentration was higher during the heating season than the non-heating season. PM was the highest in the chemical factory and lowest in the development zone. The mass concentrations of PM₁₀ and PM_2.5 were linearly related, and the mass concentration ratio of PM_2.5/PM₁₀ was up to 0.59 in urban areas. PM pollution in Jinan was related to local meteorological factors: PM_2.5 mass concentration and humidity were positively correlated, and PM_2.5 mass concentration was negatively correlated with both click on the temperature and wind speed, although wind speed varied more.     

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。     

2015年长三角地区城市PM2.5时空格局及影响因素分析

PM_2.5浓度值增加对大气能见度、人体健康和气候变化有着重要影响。采用2015年长三角地区监测数据，运用探索性空间数据分析法和相关系数法，分析长三角地区城市PM_2.5污染的时空格局和影响因素，结果表明：（1）2015年长三角地区城市PM_2.5年均浓度值为54.54 μg/m³，季节变化总体呈现春冬高夏秋低的季节性周期变化规律，1月和12月为一年中PM_2.5污染最严重的月份，污染范围最广，5~9月是PM_2.5浓度值优良时段，日均值春季和冬季的波动周期较短而剧烈，夏季和秋季波动周期相对较长而平缓。（2）2015年长三角地区城市PM_2.5年均浓度值整体上从江苏到浙江呈减少趋势，具有北高南低，局部突出的特征。（3）长三角地区城市PM_2.5浓度空间上存在集聚现象，低值集聚主要分布在浙江沿海地区，高值集聚主要分布在苏南地区。（4）燃烧排放的烟尘和前体物的二次转化对长三角地区PM_2.5浓度有显著影响。风速和降水量是影响PM_2.5浓度的两个重要气象因素。     

嘉兴双桥农场大气颗粒物的物理化学特征

为了对长三角地区大气污染进行防治和控制,了解长三角地区大气环境质量变化规律,有必要对其颗粒物的组成及特征进行分析,以揭示其形成机制。采用Partisal plus2025 型连续空气采样机在嘉兴双桥农场（长三角中心）进行采样,利用对采样样品化学分析的结果,分析了PM10、PM25的化学组成、质量浓度的分布特征及其相对关系。 PM25和PM10中19种无机元素质量浓度的总和约占其质量浓度的23%和25%,其中Al、Si、Ca是主要贡献元素;8种水溶性离子质量浓度总和约占PM25和PM10质量浓度的51%和43%,其中NO-3和SO2-4是主要贡献成分;有机碳的质量浓度约占PM25和PM10质量浓度的1612%和1743%,元素碳的质量浓度约占PM25和PM10质量浓度的1697%和1584%,可见该地区存在较严重的二次有机碳污染和元素碳污染。研究结果为揭示大气颗粒物的形成机制和对其污染进行防治和控制提供了基础性的研究数据。     

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

本文利用了1998—2012年中国241个城市的空间面板数据对中国雾霾污染和FDI的区域分布特征及空间溢出效应进行经验考察,结合系统广义矩估计(SGMM)方法构建了动态空间面板模型,采用了Moran’s I和Geary’s C指数对中国FDI与雾霾(PM_(2.5))污染空间自相关性进行了全域和局域分析。结果发现:(1)雾霾(PM_(2.5))污染与FDI存在显著的空间正相关性,证明了雾霾(PM_(2.5))污染空间的溢出效应以及FDI的辐射效应的存在。同时FDI高值集聚区域一般是雾霾(PM_(2.5))高值集聚区,FDI低值集聚区域一般是雾霾(PM_(2.5))低值集聚区,表明一个地区的引资效果和雾霾(PM_(2.5))污染在地理上的集聚密切相关。雾霾(PM_(2.5))污染表现出显著的"叠加效应"和"溢出效应",说明中国雾霾(PM_(2.5))污染在空间维度、时间维度以及时空维度上分别表现出交叉、累积、持续的演变特征。(2)全样本下,FDI对雾霾(PM_(2.5))浓度的影响表现出增促效应。FDI存量每升高1%,雾霾(PM_(2.5))浓度升高0.011%。(3)分地区样本下,东部城市FDI存量每升高1%,雾霾(PM_(2.5))浓度升高0.001 9%;中部城市FDI存量每升高1%,雾霾(PM_(2.5))浓度升高0.018 3%;而西部城市FDI存量对雾霾(PM_(2.5))浓度影响不显著。上述实证结果说明中国雾霾污染存在着显著的空间依赖性和区域异质性,FDI对中国大部分城市的雾霾污染存在显著的增促效应。     

合肥市PM_(2.5)浓度时空分布特征及影响因素分析

利用2017年合肥市污染监测站点PM_(2.5)浓度数据、气象数据以及土地利用类型数据,结合随机森林算法(RF)与土地利用回归模型(LUR),模拟合肥市PM_(2.5)浓度空间分布,并利用主成分分析法对PM_(2.5)影响因素进行分析。结果表明:(1)合肥市PM_(2.5)浓度日变化特征大致呈双峰变化,春季、夏季及秋季的峰值多出现在8∶00～9∶00,而冬季的峰值则出现在10∶00～11∶00。低谷值大致都出现在15∶00～17∶00。全年PM_(2.5)浓度变化趋势与春季类似。夏季PM_(2.5)浓度变化最为平稳。(2)2017年合肥市PM_(2.5)浓度分布由城市中心向外减弱,形成北高南低,西高东低的空间分布格局。(3)影响因素方面,PM_(2.5)浓度变化与降水、风速以及相对湿度等呈负相关关系,日照对PM_(2.5)浓度的影响较大,气压及其他污染物与PM_(2.5)浓度呈正相关关系,其中NO_2对PM_(2.5)浓度的影响力度较大。     

北京、上海两地2004和2005年大气污染特征对比分析

为了解北京、上海两市大气污染的变化及其影响因素之间的关系，根据2004、2005年大气质量监测数据，分析了北京、上海两地大气污染特征和两地主要大气污染物的变化规律。结果表明，2004、2005年北京市的PM10最大值是最小值的637倍，北京市的变化幅度较上海市的变化幅度大；2004、2005年北京市的SO2最大值是最小值的467倍，上海市的SO2最大值是最小值的131倍，上海市的SO2污染的因素变化幅度较小；两市的NO2浓度都较高，且季节变化不太明显；两市NO2浓度年平均值满足国家空气质量二级标准，但质量浓度都较高，这与两市的机动车尾气污染有关；两市SO2浓度年平均值除上海市2005年外均满足国家空气质量二级标准，上海市SO2污染严重与上海市消耗大量煤炭有关，北京市SO2污染物浓度年度变化剧烈与冬季取暖烧煤有关；两市可吸入颗粒物污染比较严重，北京市PM10浓度年平均值超过国家空气质量二级标准，上海市PM10浓度年平均值达到国家空气质量二级标准。总体上上海市的大气质量要好于北京市。两地大气主要污染物随时间的变化规律与两地的污染物来源、地理、气候等条件有关。     

The study of Central Taiwan particles concentration variations during earthquake period

Ambient particle concentration was taken on the traffic sampling site over the Chung-Chi Road over the bridge (CCROB) in front of Hungkuang Institute of Technology (HKIT). The sampling time was from August 1999 to December 1999. During the sampling period, Taiwan's biggest earthquake in more than a century registered 7.3 on the Richter scale (Taiwan Chi-Chi Earthquake). Besides, there are more than 20,000 aftershocks following the Taiwan Chi-Chi Earthquake within 3 months. Thus, the mass concentration of particles with aerodynamic diameters smaller than 2.5 microm (PM2.5) and PM2.5-10 was also collected then compared with the total mass concentration of suspended particles (TSP) in this study. The average TSP, PM2.5-10, and PM2.5 concentrations are 106, 24.6, and 58.0 microg/m3, respectively, after the Taiwan Chi-Chi Earthquake. The average TSP concentrations before and after Taiwan Chi-Chi Earthquake were 69.6 and 127 microg/ m3, respectively. In addition, statistical analysis of the PM10 data from this study and EPA in 1999 yielded a Tstatistic of 0.147, which is smaller than t(0.975,18) = 2.101. It is indicated that there was no significant difference. So, the PM10 concentrations measured after Taiwan Chi-Chi Earthquake in this study were also greater than those data previously obtained from Taiwan EPA in the same region of this area. The relationships between TSP, PM10, PM2.5-10, and PM2.5 particle concentrations and wind speed (R2) are .77, .59, .58, .58, respectively. And the ratios of PM2.5/PM25-10, PM2.5/PM10, and PM10/TSP are 221%, 67.2%, 58.0%, respectively. The average ratios of PM2.5/PM2.5-10 and PM2.5/PM10 increase by about 120% and 17%. It indicated that the fine-particles concentration increases compared to the coarse-particles concentration after 921 Taiwan Chi-Chi Earthquake. And the proposed reasons are that local motor vehicle emissions combined the fine particles transported from the Chi-Chi epicenter. More importantly, the wind direction was mainly blown from southeastern part. These two main factors enhance the fine-particles concentration in this area.     

基于环境承载力的京津冀雾霾治理政策效果评估

雾霾污染治理是京津冀协同发展需要解决的重大问题。2013年9月颁布的"大气污染防治行动计划(大气国十条)"明确提出了京津冀地区雾霾治理目标,各地区也制定了雾霾污染治理的政策措施。本文旨在环境承载力分析的基础上评估雾霾治理的政策效果。首先,分析了京津冀地区大气环境污染特征,并结合相关文献确定京津冀地区雾霾治理的主要影响因素为污染物排放、风力以及相邻地区的传输效应等;其次,将影响PM_(2.5)浓度主要因素进行统计建模,并采用分位数回归模型进行矫正,大大提高模型的拟合精度;再次,基于大气国十条规定的京津冀各地区的PM_(2.5)年均浓度目标计算各地区的大气环境承载力;最后,在假定风力等气象条件不变的情况下,根据大气国十条规定的京津冀地区的污染物排放量利用统计模型模拟2017年的雾霾污染水平,模拟除张家口、承德和秦皇岛以外其余10个地区年均浓度60μg/m~3和70μg/m~3目标下PM_(2.5)日均浓度发生频率的变化情况,评估和讨论大气国十条提出的京津冀雾霾治理目标。结果表明:按照大气国十条减排计划的京津冀地区污染物排放量普遍高于其PM_(2.5)浓度目标下的大气环境容量(邯郸市除外),即大气国十条所规定的减排措施难以实现既定的PM_(2.5)浓度目标;PM_(2.5)年均浓度目标从60μg/m~3上升到70μg/m~3,重污染天气发生频率上升有限,大气污染物的减排量却显著下降。因此,要实现既定的雾霾浓度控制目标,天津和河北需要进一步加大污染物减排力度;雾霾治理应注重减少重污染天气的发生频率,治理重点应转向重度雾霾发生频率较高的冬季污染物排放控制;在科学确定环境承载力的基础上,确定切实可行的PM_(2.5)浓度控制目标,制定具有可操作性的污染物减排计划。     

上海崇明岛近三年PM_(2.5)浓度变化特征与气象条件的关系

利用2011年1月~2014年2月上海崇明岛地区颗粒物(PM_(2.5)、PM_(10))的连续监测资料,研究了PM_(2.5)总体分布、季节变化、日变化及浓度频率分布规律,初步分析了逆温、相对湿度、风向风速等气象要素对颗粒物浓度的影响。结果表明:2011~2013年该地区PM_(2.5)平均值分别为24.7,33.6和28.3μg/m~3,均低于PM2.5的年平均浓度限值35μg/m~3,细粒子污染程度较轻。PM_(2.5)浓度日变化幅度不大,呈微弱的单峰型分布,9∶00左右达到一天中的最大值,15∶00左右达到最小值。PM_(2.5)浓度的季节分布特征明显,呈现出冬季春季秋季夏季,一般情况下5月份PM_(2.5)月均浓度值最高,8月份浓度最低。PM_(2.5)日平均浓度有57.9%达到国家空气质量一级标准,有93.4%达到国家空气质量二级标准,超标率为6.6%。对PM_(2.5)与各气象要素进行分析后发现:PM_(2.5)质量浓度在逆温层结稳定、风速小、高湿以及近地面盛行西北到西风这样的静稳天气条件配合高空西北方向上的外来污染物输送,容易造成高浓度的PM_(2.5)污染。     

Particulate matter and black smoke concentration levels in central Athens, Greece

This study presents the statistical analysis of PM(10) and PM(2.5) concentrations (measured at a central site, in the Athens area), along with black smoke (BS) data, for a 2-year period. The biennial average concentrations of PM(10) and PM(2.5) were 75 and 40 microg m(-3). The respective average concentration of BS, as estimated by the OECD method, was 108 microg m(-3). Severe exceedances of the PM(10) air quality standards were recorded. The seasonal variation of PM(10) and BS was less pronounced than the variation of PM(2.5), which concentration was elevated by 14.2% during the cold period. Concentrations of all three pollutants were significantly lower during weekends; however, PM(2.5) and BS displayed a more uniform weekly distribution pattern. PM(10) particles were found to be almost equally comprised by PM(2.5) and PM(10-2.5) particles (PM(2.5)/PM(10) ratio=0.53+/-0.09 microg/m(3)). The average PM(10)/BS value was found lower than unity revealing the inappropriateness of the used reflectance conversion method, for the estimation of mass-equivalent BS concentrations, in the study area, where diesel-powered vehicles mainly control emissions of light-absorbing substances. Important reductions in concentrations were observed during days when drivers of diesel-powered taxies and transportation buses went on strike (reaching 40% for BS). Calm wind conditions were found to have an incremental effect on particle concentrations and were also associated with the appearance of persistent episodic events. Increased PM levels were also observed during southern-southwestern wind flows while significantly lower-than-average concentrations were measured during precipitation events. Separate regression analyses were performed for PM(10), PM(2.5) with BS and NO(x) as independent variables, in an attempt to estimate the relative contribution of specific source types (diesel-powered vehicles) to measured particle levels. The contribution of the diesel-exhaust component to PM(10) mass was estimated at 49.9%, while the corresponding contributions to PM(2.5) mass concentrations was 53.8%. These results may have important implications with the oncoming decision of national authorities to allow the purchase of diesel-powered private cars to the residents of the Greater Athens Area, which was forbidden up to this day.     

长江中游城市群PM2.5时空特征及影响因素研究

近年来，伴随着工业化和城市化进程的加快，长江中游城市群灰霾天气持续增多，空气污染问题日益突出。基于2015年1月至2016年2月长江中游城市群189个空气质量监测站点的PM2.5逐时监测数据，采用普通克里金插值、探索性空间数据分析法和相关系数法，从年、季、月尺度上分析了PM2.5的空间分布格局及其影响因素。结果表明：（1）在年尺度上，长江中游城市群PM2.5浓度空间分布总体呈现出明显的北部高南部低，局部地区略有突出的特征，该区PM2.5浓度年均值为55.28 μg/m3，其中湖北省PM2.5的年均值为三省市最高，为68.17 μg/m3；其次为湖南省，年均值为53.66 μg/m3；江西省PM2.5的年均值较小，为44.01 μg/m3。（2）在季节尺度上，长江中游城市群PM2.5浓度表现出冬春季高，夏秋季低的现势性，这与区域内夏季高温多雨、冬季低温少雨的气候条件密切相关。（3）长江中游城市群PM2.5月浓度变化大致呈U形分布，1月份PM2.5浓度最高， 1~6月份，PM2.5浓度呈逐步下降趋势， 6~8月份，区域PM2.5浓度处于“U”字的谷底。（4）NO2、CO是影响PM2.5浓度的两项主控大气污染物，而降水量和相对湿度则是影响PM2.5浓度的两个重要气象因素。 关键词： PM2.5浓度；时空特征；气象因素；长江中游城市群     

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。     

基于LUR模型探究城市景观格局对PM2.5浓度的影响——以长株潭城市群为例

随着城市化进程的加快，空气污染问题已成为中国最主要城市问题之一，严重影响公众健康。当前微观尺度下空气监测点周围景观格局对PM25浓度影响的研究较少，以长株潭城市群为例，选取地形、污染源、人口、道路交通、土地利用与城市景观格局6大类预测变量，其中城市景观格局选取边缘密度、连续度、形状指数、斑块平均面积、蔓延度、均匀度指数7个景观指数，运用逐步线性回归模型，探究城市景观格局对PM25浓度的影响。研究结果显示：（1）所选取的土地景观格局指数可以解释研究区PM25浓度的732%的变异，模型拟合较好；（2）影响PM25浓度的土地利用类型包括建设用地、林地、草地与水体。微观尺度下城市各类型景观格局中连续度和形状指数对PM25影响显著，建设用地连续度越高，分布越集聚，PM25浓度越高；水体形状指数越小，形状越简单规则，越易降低PM25浓度；（3）城市整体景观格局中，景观聚集程度与景观多样性等因素对PM25浓度产生重要影响。减少景观内各类型斑块的离散分布，使各景观类型均匀分布于整体景观内，有助于降低PM25浓度。研究结果可为未来大气防治与城市规划提供参考依据。〖HJ1〗〖HJ〗〖JP+1〗     

北京、上海两地2004和2005年大气污染特征对比分析

为了解北京、上海两市大气污染的变化及其影响因素之间的关系,根据2004、2005年大气质量监测数据,分析了北京、上海两地大气污染特征和两地主要大气污染物的变化规律.结果表明,2004、2005年北京市的PM10最大值是最小值的63.7倍,北京市的变化幅度较上海市的变化幅度大;2004、2005年北京市的SO2最大值是最小值的46.7倍,上海市的SO2最大值是最小值的13.1倍,上海市的SO2污染的因素变化幅度较小;两市的NO2浓度都较高,且季节变化不太明显;两市NO2浓度年平均值满足国家空气质量二级标准,但质量浓度都较高,这与两市的机动车尾气污染有关;两市SO2浓度年平均值除上海市2005年外均满足国家空气质量二级标准,上海市SO2污染严重与上海市消耗大量煤炭有关,北京市SO2污染物浓度年度变化剧烈与冬季取暖烧煤有关;两市可吸入颗粒物污染比较严重,北京市PM10浓度年平均值超过国家空气质量二级标准,上海市PM10浓度年平均值达到国家空气质量二级标准.总体上上海市的大气质量要好于北京市.两地大气主要污染物随时间的变化规律与两地的污染物来源、地理、气候等条件有关.     

城市土地利用类型与PM2.5浓度相关性研究——以武汉市为例

随着我国工业化的不断发展,在我国的主要经济发展地区的雾霾天气不断爆发,使我国的大气环境日益恶化,严重影响了人们的日常生活和身体健康。PM2.5作为雾霾的重要组成成分,也日渐成为环境领域的研究热点问题。随着全球性变化研究领域逐渐加强了对土地利用与生态环境的相关研究,因此无论从法律和社会经济发展的角度,还是从生态资源保护与环境可持续发展的角度,土地利用与PM2.5的相关研究都显得相当重要。研究目的:分析武汉市各类用地类型与PM2.5浓度的相关性程度。研究方法:使用ENVI与ArcGIS对武汉市2013年MODIS气溶胶产品进行空间分析与插值处理,再应用SPSS将其与武汉市2013年10个观测点的PM2.5浓度数据作相关性分析,以证实MODIS气溶胶厚度与PM2.5浓度的相关性,并建立两者的线性回归方程,然后利用计算后的武汉市整体PM2.5浓度分布与各土地利用类型进行相关性研究。研究结果:武汉市PM2.5浓度有明显的空间分布特征,绿化面积比例与PM2.5浓度呈显著负相关,建设用地面积比例与PM2.5浓度呈显著正相关,未利用地面积比例虽然与PM2.5浓度呈正相关,但相关性较低,而耕地与水体对PM2.5浓度没有显著影响。研究结论:土地利用类型对武汉市PM2.5浓度的分布有显著的影响,其与搭载MODIS传感器的遥感卫星监测方式的结合能成为研究大范围特定区域PM2.5浓度空间格局的新方法,并且增加城市绿化面积,控制建设用地规模能有效减少PM2.5浓度。     

贵阳市主城区空气质量指数时空分布特征

为了解贵阳市主城区空气质量指数的时间分布和空间分布特征，采用时间序列法和插值法统计分析了2013~2015年贵阳市9个空气自动监测点SO2、NO2、PM10、PM25、CO的监测数据。结果表明：2013~2015年期间，贵阳市主城区空气质量整体良好,优于Ⅱ级空气质量标准，IAQISO2、IAQINO2、IAQIPM10、IAQIPM25、IAQICO的年均值呈现降低趋势, 空气污染物得到一定的控制；季节变化和月变化分析表现出冬季空气质量最差，夏季最好的特征。春节半月ISO2、IAQINO2、IAQIPM10、IAQIPM25的均值好于冬季均值，反映了工业和交通污染源的主控作用。空间分布上，IAQISO2、IAQINO2、IAQIPM10、IAQIPM25、IAQICO呈现“市区高，郊区低”的分布特征，空气质量指数较高的区域集中在贵阳市主城区中部和东北部     

Characteristics and health implications of fine and coarse particulates at roadside, urban background and rural sites in UK

Recent studies have pointed to evidence that fine particles in the air could be significant contributors to respiratory and cardiovascular diseases and mortality. Epidemiologists looking at the health effects of particulate pollution need more information from various receptor locations to improve the understanding of this problem. Detailed information on temporal, spatial and size distributions of particulate pollution in urban areas is also important for air quality modellers as well as being an aid to decision and policy makers of local authorities. This paper presents a detailed analysis of temporal and seasonal variation of PM(10) and PM(2.5) levels at one urban roadside, one urban background and one rural monitoring location. Levels of PM(10), PM(2.5) and coarse fraction of particulates are compared. In addition, particulate levels are compared with NO(2) and CO concentrations. The study concludes that PM(10) and PM(2.5) are closely related at urban locations. Diurnal variation in PM(2.5)/PM(10) ratio shows the influence of vehicular emission and movement on size distribution. This ratio is higher in winter than in summer, indicating a build-up or longer residence time of finer particulates or washout due to wet weather in winter. In the second part of this study, a disease burden analysis is carried out based on the dose-response relationships recommended by the UK Committee on the Medical Effects of Air Pollution. The disease burden analysis indicates that if Marylebone Road (MR) levels of PM(10) were prevalent all over London, it will result in around 2.5% increase in death rates due to all causes. Whereas, if Bloomsbury (BB) levels were prevalent in London, which is more likely to occur as this is more representative of the urban background environment to which people in London are likely to be exposed, the corresponding increase would be around 1.7%. Considering this, in London, at Bloomsbury levels, 973 deaths and 1515 respiratory hospital admissions (RHA) are attributable to PM(10) while 2140 RHA are attributable to NO(2). After deducting the disease burden due to background levels at Rochester (RC), PM(10) emission caused by anthropogenic activities in London equates to 273 additional deaths and 410 additional RHA, while NO(2) account for additional 1205 incidences of RHA.     

Mutagenic properties of PM2.5 air pollution in the Padana Plain (Italy) before and in the course of XX Winter Olympic Games of "Torino 2006"

PM2.5 is one of the most important aspects of environmental health. This air pollutant is breathable and it is implicated in several chronic adverse health effects such as the decrease of respiratory functionality and cancer. Several in vitro bioassays are able to predict the mutagenic/carcinogenic activity of the environmental pollutants and mixtures of them. In this study PM2.5 air pollution was daily monitored in three cities located in the Northern part of Italy and the mutagenic properties of the PM2.5 organic extracts were also assessed. Samplings lasted 14 months and cover the period of the Winter Olympic Games of "Torino 2006". In this work, the levels of PM2.5, its mutagenic properties (detected with Salmonella typhimurium assay), the role of the Olympic Games as environmental factor and some meteorological data are discussed. The mean concentration of PM2.5 measured in Torino was 45.4 (+/-30.6) microg/m(3), in Pavia 37.6 (+/-25.6) microg/m(3), in Verona 43.1 (+/-28.5) microg/m(3). Findings of the monthly pool bioassay were in Torino 107 (+/-104) net revertans/m(3), in Pavia 108 (+/-89) net revertans/m(3), in Verona 128 (+/-109) net revertans/m(3). The Olympic Games period data show that PM2.5 pollution and its load of mutagenic potential are different and partially independent phenomena. The Olympic Games had not a great impact on the PM2.5 pollution. The exclusive PM2.5 gravimetric analysis shows a potential human risk if compared with the latest international guide values but it does not describe exhaustively the human health risk associated to the presence of this particular air pollutant. Moreover, the chemical and biological activity qualification of the PM organic extracts as a whole, can instead improve the knowledge.     

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。