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.     

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.     

Characteristics of PM10, PM2.5, CO2 and CO monitored in interiors and platforms of subway train in Seoul, Korea

This study was performed to investigate the concentration of PM(10) and PM(2.5) inside trains and platforms on subway lines 1, 2, 4 and 5 in Seoul, KOREA. PM(10), PM(2.5), carbon dioxide (CO(2)) and carbon monoxide (CO) were monitored using real-time monitoring instruments in the afternoons (between 13:00 and 16:00). The concentrations of PM(10) and PM(2.5) inside trains were significantly higher than those measured on platforms and in ambient air reported by the Korea Ministry of Environment (Korea MOE). This study found that PM(10) levels inside subway lines 1, 2 and 4 exceeded the Korea indoor air quality (Korea IAQ) standard of 150 microg/m(3). The average percentage that exceeded the PM(10) standard was 83.3% on line 1, 37.9% on line 2 and 63.1% on line 4, respectively. PM(2.5) concentration ranged from 77.7 microg/m(3) to 158.2 microg/m(3), which were found to be much higher than the ambient air PM(2.5) standard promulgated by United States Environmental Protection Agency (US-EPA) (24 h arithmetic mean: 65 microg/m(3)). The reason for interior PM(10) and PM(2.5) being higher than those on platforms is due to subway trains in Korea not having mechanical ventilation systems to supply fresh air inside the train. This assumption was supported by the CO(2) concentration results monitored in tube of subway that ranged from 1153 ppm to 3377 ppm. The percentage of PM(2.5) in PM(10) was 86.2% on platforms, 81.7% inside trains, 80.2% underground and 90.2% at ground track. These results indicated that fine particles (PM(2.5)) accounted for most of PM(10) and polluted subway air. GLM statistical analysis indicated that two factors related to monitoring locations (underground and ground or inside trains and on platforms) significantly influence PM(10) (p<0.001, R(2)=0.230) and PM(2.5) concentrations (p<0.001, R(2)=0.172). Correlation analysis indicated that PM(10), PM(2.5), CO(2) and CO were significantly correlated at p<0.01 although correlation coefficients were different. The highest coefficient was 0.884 for the relationship between PM(10) and PM(2.5).     

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.     

Sudden high concentration of TSP affected by atmospheric boundary layer in Seoul metropolitan area during duststorm period

Hourly concentrations of TSP, PM(10), PM(2.5) near the surface at Seoul city were examined from March 20 to March 25, 2001 (duststorm event) in order to investigate the effect of a duststorm generated in China on the local aerosol concentration in Korea, The ratios of fine to coarse particles such as TSP to PM(10), TSP to PM(2.5) and PM(10)-PM(2.5) to PM(2.5) showed that a great amount of dust transported from the origin of the duststorm was remarkable with a maximum ratio of 9.77 between TSP and PM(2.5). Back trajectories every 6 h showed the movement of dust particles in the lower atmosphere near 500 m to 1500 m (atmospheric boundary layer), which implied transport from Baotou in inner Mongolia of northern China to the direction of Seoul city in Korea and then the back trajectories passed near the southern border of Mongolia and Baotou through Zengzhou in the midlevels (3000 m) and low levels (500 m) of China, finally reaching Seoul city. So, the TSP concentration at Seoul city was partially influenced by the duststorm, under the prevailing westerly wind and the transported aerosols could influence high concentrations of pollutants of TSP, PM(10) and PM(2.5) in Seoul. The sudden high concentrations of TSP and PM(10) were found for a few hours, especially at 1500 to 1800 LST, March 22. At 1200 LST, before the passage of a cold front through the Korean peninsula, the convective boundary layer (CBL) near Seoul was not shallow, but at 1500 LST, under the frontal passage, the CBL was remarkably thinner (less than 300 m), due to the compression of the boundary layer by the intrusion of cold air. This resulted in the increase of the TSP concentration, even though the mixed layer above maintained almost the same depth. At 1800 LST shortly after the frontal passage, that is, near sunset, the nocturnal cooling of the ground caused air parcels to cool, thereby enhancing the shallower nocturnal surface inversion layer and producing the maximum concentration of TSP of 1388 microg/m(3) near Seoul city.     

Comparative overview of indoor air quality in Antwerp, Belgium

This comprehensive study, a first in Belgium, aimed at characterizing the residential and school indoor air quality of subgroups that took part in the European Community Respiratory Health Survey and the International Study of Asthma and Allergy in Childhood [Masoli M, Fabian D, Holt S, Beasley R. Global Burden of Asthma, Medical Research Institute of New Zealand, University of Southampton; 2004.] questionnaire-based asthma and related illnesses studies. The principal aim was to perform a base-line study to assess the indoor air quality in Antwerp in terms of various gaseous and particulate pollutants. Secondly, it aimed to establish correlations between these pollutants investigated, the pollutant levels in the indoor and outdoor micro-environments, findings of the previous questionnaire-based studies and an epidemiological study which ran in conjunction with this study. Lastly, these results were compared and evaluated with current indoor and ambient guidelines in various countries This paper presents selected results on PM1, PM2.5 and PM10 mass concentrations and elemental C estimates as black smoke, as well as gaseous NO(2), SO(2), O(3) and BTEX concentrations of 18 residences and 27 schools. These are related to current guidelines of Flanders, Germany, Norway, China and Canada and evaluated with reference to selected similar studies. It was found that indoor sources such as tobacco smoking and carpets, the latter causing re-suspension of dust, are responsible for elevated indoor respirable particulate matter and place school children and residents at risk. Both PM2.5 and PM10 equalled or exceeded the current guidelines adopted by Flanders, noting that 12-h and 24-h PM2.5 were compared with an annual limit value. Indoor and ambient NO(2) concentrations in the school campaign were higher than the annual EU ambient norm. The other studied pollutant levels were below the current guidelines.     

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。     

Particulate air pollution in Lanzhou China

Concentrations of total suspended particles (TSP) and PM(10) in Lanzhou China have been kept high for the past two decades. Data collected during the intensive observational period from October 1999 to April 2001 show high TSP and PM(10) concentrations. Starting from November, the PM(10) pollution intensifies, and reaches mid to high alert level of air pollution, continues until April next year, and is at low alert level in the summer. In the winter and spring, the TSP concentration is 2-10 times higher than the third-level criterion of air quality (severe pollution). Effects of intrinsic factors (sources of pollution) and remote preconditions (propagation of dust storms) for severe PM(10) and TSP pollution in Lanzhou are analyzed.     

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。     

Source apportionment of fine carbonaceous particles by positive matrix factorization at Gosan background site in East Asia

Fine particle (aerodynamic diameter <2.5 microm) samples were collected during six intensive measurement periods from November 2001 to August 2003 at Gosan, Jeju Island, Korea, which is one of the representative background sites in East Asia. Chemical composition of these aerosol samples including major ion components, trace elements, organic and elemental carbon (OC and EC), and particulate polycyclic aromatic hydrocarbons (PAHs) were analyzed to study the impact of long-range transport of anthropogenic aerosol. Aerosol chemical composition data were then analyzed using the positive matrix factorization (PMF) technique in order to identify the possible sources and estimate their contribution to particulate matter mass. Fourteen sources were then resolved including soil dust, fresh sea salt, transformed natural source, ammonium sulfate, ammonium nitrate, secondary organic carbon, diesel vehicle, gasoline vehicle, fuel oil combustion, biomass burning, coal combustion, municipal incineration, metallurgical emission source, and volcanic emission. The PMF analysis results of source contributions showed that the natural sources including soil dust, fresh and aged sea salt, and volcanic emission contributed to about 20% of the measured PM(2.5) mass. Other primary anthropogenic sources such as diesel and gasoline vehicle, coal and fuel oil combustion, biomass burning, municipal incineration, metallurgical source contributed about 34% of PM(2.5) mass. Especially, the secondary aerosol mainly involved with sulfate, nitrate, ammonium, and organic carbon contributed to about 39% of the PM(2.5) mass.     

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。     

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。     

Cancer risk assessment of selected hazardous air pollutants in Seattle

The risk estimates calculated from the conventional risk assessment method usually are compound specific and provide limited information for source-specific air quality control. We used a risk apportionment approach, which is a combination of receptor modeling and risk assessment, to estimate source-specific lifetime excess cancer risks of selected hazardous air pollutants. We analyzed the speciated PM(2.5) and VOCs data collected at the Beacon Hill in Seattle, WA between 2000 and 2004 with the Multilinear Engine to first quantify source contributions to the mixture of hazardous air pollutants (HAPs) in terms of mass concentrations. The cancer risk from exposure to each source was then calculated as the sum of all available species' cancer risks in the source feature. We also adopted the bootstrapping technique for the uncertainty analysis. The results showed that the overall cancer risk was 6.09 x 10(-5), with the background (1.61 x 10(-5)), diesel (9.82 x 10(-6)) and wood burning (9.45 x 10(-6)) sources being the primary risk sources. The PM(2.5) mass concentration contributed 20% of the total risk. The 5th percentile of the risk estimates of all sources other than marine and soil were higher than 110(-6). It was also found that the diesel and wood burning sources presented similar cancer risks although the diesel exhaust contributed less to the PM(2.5) mass concentration than the wood burning. This highlights the additional value from such a risk apportionment approach that could be utilized for prioritizing control strategies to reduce the highest population health risks from exposure to HAPs.     

Distribution of PBDEs in air particles from an electronic waste recycling site compared with Guangzhou and Hong Kong, South China

Air samples of total suspended particles (TSP, particles less than 30-60 microm), and particles with aerodynamic diameter smaller than 2.5 microm (PM(2.5)) were collected simultaneously at Guiyu (an electronic waste recycling site), three urban sites in Hong Kong and two urban sites in Guangzhou, South China from 16 August to 17 September 2004. Twenty-two PBDE congeners (BDE-3, -7, -15, -17, -28, -49, -71, -47, -66, -77, -100, -119, -99, -85, -126, -154, -153, -138, -156, -184, -183, -191) in TSP and PM(2.5) were measured. The results showed that the overall average concentrations of TSP and PM(2.5) collected at Guiyu were 124 and 62.1 microg m(-3), respectively. The monthly concentrations of the sum of 22 BDE congeners contained in TSP and PM(2.5) at Guiyu were 21.5 and 16.6 ng m(-3), with 74.5 and 84.3%, contributed by nine congeners (BDE-28, -47, -66, -100, -99, -154, -153, -183 and -191 respectively). This pattern was similar to Tsuen Wan site of Hong Kong. Two urban sites of Guangzhou had the same congener pattern, but were different from Yuen Long and Hok Tsui sites of Hong Kong. The results also showed that the amount of mono to penta brominated congeners, which are more toxic, accounted for 79.4-95.6% of Sigma(22)PBDEs from all sites. All congeners tested in Guiyu were up to 58-691 times higher than the other urban sites and more than 100 times higher than other studies reported elsewhere. The higher concentration in the air was due to heating or opening burning of electronic waste since PBDEs are formed when plastics containing brominated flame retardants are heated.     

Levels of household particulate matter and environmental tobacco smoke exposure in the first year of life for a cohort at risk for asthma in urban Syracuse, NY

The Syracuse, NY, AUDIT (Assessment of Urban Dwellings for Indoor Toxics) study was designed to quantify asthma agent levels in the inner-city homes of a birth cohort whose mothers had a diagnosis of asthma. Risk of exposure to particulate matter (PM), particle number and tobacco smoke was assessed in 103 infants' homes. Repeat measurements were made in 44% of the homes. Infants also were examined on a quarterly basis during the first year of life to monitor their respiratory health and urine cotinine levels. Overall geometric mean (GM) values for PM(2.5) of 21.2 μg/m(3) and for PM(10) of 31.8 μg/m(3) were recorded in homes at visit 1. GM values for PM(2.5) and PM(10) in smoking homes were higher at 26.3 and 37.7 μg/m(3), while values in non-smoking homes were 12.7 and 21.2 μg/m(3) respectively. Fifty-four percent of mothers (55/103) smoked at some point in pregnancy (39% smoked throughout pregnancy). Environmental tobacco smoke (ETS) exposure occurred in 68% of homes during the infants' first year. Significant to this study was the size- and time-resolved monitoring of PM at 140 home visits and the classification of PM count data. PM number counts ranged from continuously low levels (little indoor activity) to continuously high counts (constant indoor activity), and recorded apparent instances of prolonged repeated cigarette smoking. Wheezing in the first year of life was recorded for 38% of the infants (39/103). Adjusted logistic regression modeling demonstrated that elevated levels of indoor PM(2.5) (≥ 15 μg/m(3)) were a significant risk factor for infant wheezing after controlling for infant gender, mothers' age and education level, season of home visit and presence of carpeting (OR 4.21; 95% CI 1.36-13.03; p=0.013). An elevated level of the nicotine metabolite cotinine in infant urine also was associated with infant wheezing after adjusting for infant gender, mothers' age and education level (OR 5.10; 95% CI 0.96-27.24; p=0.057). ETS exposure was pervasive in the AUDIT cohort and a risk for developing infants in this urban population.     

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。     

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。     

空气污染、空间外溢与公共健康——以中国珠江三角洲9个城市为例

空气污染对居民公共健康的影响,引起了人们高度的关注。但大多数学者研究从样本的独立性出发且不考虑内生性问题,忽视区域之间空间相关性,所得结论和政策建议需谨慎对待。为了弥补上述不足,本文基于Grossman中国宏观健康生产函数,选取2001—2014年中国广东省珠江三角洲9个城市作为样本,选择以PM_(10)和PM_(2.5)作为空气污染的代理指标,在充分考虑空间效应和严格假设检验的基础上选择合适的空间计量经济学模型,对此进行实证研究。主要研究结果显示:空气污染对居民的公共健康带来了负面影响,即PM_(10)和PM_(2.5)每增加1%,导致哮喘疾病和内科门诊等疾病人数不断上升,且影响都比较大,尤其是对哮喘疾病的影响分别为0.2236%和0.2272%。经济增长对公共健康均有显著的促进作用,影响最大;其它财政医疗支出、卫生技术人员和人口密度等要素对居民公共健康的影响较小。由于空气污染的负外部性,研究还发现,区域之间空气污染的"溢出效应"对领域居民公共健康存在显著的影响,说明忽视空间自相关性的存在,会使得空气污染对公众健康的估计产生偏差。从长期看,空气污染对本地居民公共健康的直接效应都显著为正,PM_(2.5)间接效应显著为负,但PM_(10)间接效应并不显著。因此,各级政府除了在源头上治理污染物的排放,提高公共健康水平外,还应该打破各自为阵的行政垄断,应该作为一个整体,实现跨区域环保合作,共同治理和制定公共卫生政策等。这对区域之间协同减排和保护居民公共健康具有重要的理论和现实意义。     

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.     

Modeling indoor air pollution of outdoor origin in homes of SAPALDIA subjects in Switzerland

Given the shrinking spatial contrasts in outdoor air pollution in Switzerland and the trends toward tightly insulated buildings, the Swiss Cohort Study on Air Pollution and Lung and Heart Diseases in Adults (SAPALDIA) needs to understand to what extent outdoor air pollution remains a determinant for residential indoor exposure. The objectives of this paper are to identify determining factors for indoor air pollution concentrations of particulate matter (PM), ultrafine particles in the size range from 15 to 300 nm, black smoke measured as light absorbance of PM (PM_absorbance) and nitrogen dioxide (NO₂) and to develop predictive indoor models for SAPALDIA. Multivariable regression models were developed based on indoor and outdoor measurements among homes of selected SAPALDIA participants in three urban (Basel, Geneva, Lugano) and one rural region (Wald ZH) in Switzerland, various home characteristics and reported indoor sources such as cooking. Outdoor levels of air pollutants were important predictors for indoor air pollutants, except for the coarse particle fraction. The fractions of outdoor concentrations infiltrating indoors were between 30% and 66%, the highest one was observed for PM_absorbance. A modifying effect of open windows was found for NO₂ and the ultrafine particle number concentration. Cooking was associated with increased particle and NO₂ levels. This study shows that outdoor air pollution remains an important determinant of residential indoor air pollution in Switzerland.