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 PM₁₀ and PM_2.5 in Bangkok Metropolitan Region (BMR). Ambient PM₁₀ 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. PM₁₀ 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 PM₁₀ and PM_2.5 concentrations was distinct between dry and wet seasons. Major source of PM₁₀ at the traffic site indicated that automobile emissions and biomass burning-related sources contributed approximately 33% each. Automobiles contributed approximately 39 and 22% of PM₁₀ mass at two residential sites while biomass burning contributed about 36 and 28%. PM₁₀ 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.     

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.     

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.     

Wintertime indoor air levels of PM10, PM2.5 and PM1 at public places and their contributions to TSP

From 26 October 2002 to 8 March 2003, particulate matter (PM) concentrations (total suspended particles [TSP], PM10, PM2.5 and PM1) were measured at 49 public places representing different environments in the urban area of Beijing. The objectives of this study were (1) to characterize the indoor PM concentrations in public places, (2) to evaluate the potential indoor sources and (3) to investigate the contribution of PM10 to TSP and the contributions of PM2.5 and PM1 to PM10. Additionally, The indoor and outdoor particle concentrations in the same type of indoor environment were employed to investigate the I/O level, and comparison was made between I/O levels in different types of indoor environment. Construction activities and traffic condition were the major outdoor sources to influence the indoor particle levels. The contribution of PM10 to TSP was even up to 68.8%, while the contributions of PM2.5 and PM1 to PM10 were not as much as that of PM10 to TSP.     

Spatial variation of PM elemental composition between and within 20 European study areas — Results of the ESCAPE project

An increasing number of epidemiological studies suggest that adverse health effects of air pollution may be related to particulate matter (PM) composition, particularly trace metals. However, we lack comprehensive data on the spatial distribution of these elements.We measured PM_2.5 and PM₁₀ in twenty study areas across Europe in three seasonal two-week periods over a year using Harvard impactors and standardized protocols. In each area, we selected street (ST), urban (UB) and regional background (RB) sites (totaling 20) to characterize local spatial variability. Elemental composition was determined by energy-dispersive X-ray fluorescence analysis of all PM_2.5 and PM₁₀ filters. We selected a priori eight (Cu, Fe, K, Ni, S, Si, V, Zn) well-detected elements of health interest, which also roughly represented different sources including traffic, industry, ports, and wood burning.PM elemental composition varied greatly across Europe, indicating different regional influences. Average street to urban background ratios ranged from 0.90 (V) to 1.60 (Cu) for PM_2.5 and from 0.93 (V) to 2.28 (Cu) for PM₁₀.Our selected PM elements were variably correlated with the main pollutants (PM_2.5, PM₁₀, PM_2.5 absorbance, NO₂ and NO_x) across Europe: in general, Cu and Fe in all size fractions were highly correlated (Pearson correlations above 0.75); Si and Zn in the coarse fractions were modestly correlated (between 0.5 and 0.75); and the remaining elements in the various size fractions had lower correlations (around 0.5 or below). This variability in correlation demonstrated the distinctly different spatial distributions of most of the elements. Variability of PM₁₀_Cu and Fe was mostly due to within-study area differences (67% and 64% of overall variance, respectively) versus between-study area and exceeded that of most other traffic-related pollutants, including NO₂ and soot, signaling the importance of non-tailpipe (e.g., brake wear) emissions in PM.     

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。     

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.     

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.     

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.     

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。     

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.     

Fine Particulate Pollution Characteristics in Jinan City

The characteristics of fine particulate pollution(PM10and PM25)were measured at urban and suburban sites in Jinan during the 2008-2009 heating and non-heating seasons.The results showed that PM10 and PM2.5 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 PM2.5 were linearly related,and the mass concentration ratio of PM2.5/PM10 was up to 0.59 in urban areas.PM pollution in Jinan was related to local meteorological factors: PM2.5 mass concentration and humidity were positively correlated,and PM2.5mass concentration was negatively correlated with both click on the temperature and wind speed,although wind speed varied more.     

PAH mass concentrations measured in PM10 particle fraction

This paper presents daily, monthly and yearly variations of PAH mass concentrations measured in PM(10) particle fraction, collected at one measuring site in Zagreb air between 2001 and 2004, and seasonal differences in PAH mass concentrations in PM(10) samples collected from 21 March 2003 to 20 March 2004. Twenty-four hour samples were taken in the northern residential part of Zagreb using a low-volume (50 m(3)) sampler and glass or quartz filters. The analysis was performed using a high-performance liquid chromatograph (HPLC) and fluorescence detector with changeable excitation and emission wavelength. The annual average mass concentrations over the four-year measuring period for BaP ranged from 1.17 ng/m(3) in 2004 to 1.87 ng/m(3) in 2003 and were below the limit value (2 ng/m(3)) set by the Ordinance on Recommended and Limit Air Quality Values in Croatia. The highest concentrations of all PAHs measured in PM(10) samples collected from 21 March 2003 to 20 March 2004 were found in the winter and the lowest in the summer. Winter average of BaP was 2.94 ng/m(3) and summer average 0.12 ng/m(3). Autumn average was 2.76 ng/m(3) and was very similar to winter concentrations. Spring average of 0.58 ng/m(3) was higher than the summer average (0.12 ng/m(3)). Mass concentrations of all measured PAHs were much higher in the autumn than in the spring. Although annual averages for BaP did not exceed the limit value, autumn and winter BaP mass concentrations did, which calls for measures for reducing PAH emissions in the autumn and winter.     

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。     

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。     

Modeling and source apportionment of diesel particulate matter

The fine and ultra fine sizes of diesel particulate matter (DPM) are of greatest health concern. The composition of these primary and secondary fine and ultra fine particles is principally elemental carbon (EC) with adsorbed organic compounds, sulfate, nitrate, ammonia, metals, and other trace elements. The purpose of this study was to use an advanced air quality modeling technique to predict and analyze the emissions and the primary and secondary aerosols concentrations that come from diesel-fueled sources (DFS). The National Emissions Inventory for 1999 and a severe southeast ozone episode that occurred between August and September 1999 were used as reference. Five urban areas and one rural area in the Southeastern US were selected to compare the main results. For urban emissions, results showed that DFS contributed (77.9%+/-8.0) of EC, (16.8%+/-8.2) of organic aerosols, (14.3%+/-6.2) of nitrate, and (8.3%+/-6.6) of sulfate during the selected episodes. For the rural site, these contributions were lower. The highest DFS contribution on EC emissions was allocated in Memphis, due mainly to diesel non-road sources (60.9%). For ambient concentrations, DFS contributed (69.5%+/-6.5) of EC and (10.8%+/-2.4) of primary anthropogenic organic aerosols, where the highest DFS contributions on EC were allocated in Nashville and Memphis on that episode. The DFS contributed (8.3%+/-1.2) of the total ambient PM(2.5) at the analyzed sites. The maximum primary DPM concentration occurred in Atlanta (1.44 microg/m(3)), which was 3.8 times higher than that from the rural site. Non-linearity issues were encountered and recommendations were made for further research. The results indicated significant geographic variability in the EC contribution from DFS, and the main DPM sources in the Southeastern U.S. were the non-road DFS. The results of this work will be helpful in addressing policy issues targeted at designing control strategies on DFS in the Southeastern U.S.     

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。     

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.     

Airborne reduced nitrogen: ammonia emissions from agriculture and other sources

Ammonia is a basic gas and one of the most abundant nitrogen-containing compounds in the atmosphere. When emitted, ammonia reacts with oxides of nitrogen and sulfur to form particles, typically in the fine particle size range. Roughly half of the PM(2.5) mass in eastern United States is ammonium sulfate, according to the US EPA. Results from recent studies of PM(2.5) show that these fine particles are typically deposited deep in the lungs and may lead to increased morbidity and/or mortality. Also, these particles are in the size range that will degrade visibility. Ammonia emission inventories are usually constructed by multiplying an activity level by an experimentally determined emission factor for each source category. Typical sources of ammonia include livestock, fertilizer, soils, forest fires and slash burning, industry, vehicles, the oceans, humans, pets, wild animals, and waste disposal and recycling activities. Livestock is the largest source category in the United States, with waste from livestock responsible for about 3x10(9) kg of ammonia in 1995. Volatilization of ammonia from livestock waste is dependent on many parameters, and thus emission factors are difficult to predict. Despite a seasonal variation in these values, the emission factors for general livestock categories are usually annually averaged in current inventories. Activity levels for livestock are from the USDA Census of Agriculture, which does not give information about animal raising practices such as housing types and grazing times, waste handling systems, and approximate animal slurry spreading times or methods. Ammonia emissions in the United States in 1995 from sources other than livestock are much lower; for example, annual emissions are roughly 8x10(8) kg from fertilizer, 7x10(7) kg from industry, 5x10(7) kg from vehicles and 1x10(8) kg from humans. There is considerable uncertainty in the emissions from soil and vegetation, although this category may also be significant. Recommendations for future directions in ammonia research include designing experiments to improve emission factors and their resolution in all significant source categories, developing mass balance models, and refining of the livestock activity level data by eliciting judgment from experts in this field.