Vehicular particulate matter emissions in road tunnels in Sao Paulo, Brazil

In the metropolitan area of S?o Paulo, Brazil, ozone and particulate matter (PM) are the air pollutants that pose the greatest threat to air quality, since the PM and the ozone precursors (nitrogen oxides and volatile organic compounds) are the main source of air pollution from vehicular emissions. Vehicular emissions can be measured inside road tunnels, and those measurements can provide information about emission factors of in-use vehicles. Emission factors are used to estimate vehicular emissions and are described as the amount of species emitted per vehicle distance driven or per volume of fuel consumed. This study presents emission factor data for fine particles, coarse particles, inhalable particulate matter and black carbon, as well as size distribution data for inhalable particulate matter, as measured in March and May of 2004, respectively, in the Janio Quadros and Maria Maluf road tunnels, both located in S?o Paulo. The Janio Quadros tunnel carries mainly light-duty vehicles, whereas the Maria Maluf tunnel carries light-duty and heavy-duty vehicles. In the Janio Quadros tunnel, the estimated light-duty vehicle emission factors for the trace elements copper and bromine were 261 and 220 microg km(-1), respectively, and 16, 197, 127 and 92 mg km(-1), respectively, for black carbon, inhalable particulate matter, coarse particles and fine particles. The mean contribution of heavy-duty vehicles to the emissions of black carbon, inhalable particulate matter, coarse particles and fine particles was, respectively 29, 4, 6 and 6 times higher than that of light-duty vehicles. The inhalable particulate matter emission factor for heavy-duty vehicles was 1.2 times higher than that found during dynamometer testing. In general, the particle emissions in S?o Paulo tunnels are higher than those found in other cities of the world.     

Regional and seasonal characteristics of emission sources of fine airborne particulate matter collected in the center and suburbs of Tokyo, Japan as determined by multielement analysis and source receptor models

Airborne particulate matter, suspected to induce adverse effects on human health, have been one of the most important concerns regarding recent air pollution issues in Japan. To characterize regional and seasonal variations in emission sources of fine airborne particulate matter (d < 2 microm), monthly samples (n = 36 for each site) were collected at urban (Tokyo), suburban (Maebashi), and mountainous (Akagi) sites in Japan from April 2003 to March 2006. Multielement analysis of chemical species (Na, Al, K, Ca, V, Cr, Mn, Fe, Ni, Cu, Zn, As, Sb, and Pb) was performed by inductively coupled plasma-atomic emission spectrometry and inductively coupled plasma-mass spectrometry. The combined source receptor model, which consists of positive matrix factorization and chemical mass balance, determined the contributions of nine emission sources (local and continental soils, road dust, coal and oil combustion, waste incineration, steel industry, brake wear, and diesel exhaust) to the observed elemental concentrations. Large regional differences were identified in the source contributions among the observational sites. Diesel exhaust was identified as the most significant source (70% of identified contributions) at the urban site. Local and continental soils, coal combustion, and diesel exhaust were intricately assigned (20-30% each) to the suburban site. Continental soil was the predominant source (65%) at the mountainous site. Respective significant source contributions dominated the seasonal variations of total elemental concentrations at each site. These results suggest that a better understanding of the regional and seasonal characteristics of impacting emission sources will be important for improving regional environments.     

北京地区PM10与秸秆燃烧排放单颗粒对比分析

采用场发射带能谱扫描电镜(FESEM/EDS)法分析北京怀柔地区PM10与秸秆燃烧排放颗粒的形貌特征和成分差异.结果显示:秸秆燃烧后排放颗粒物多为大粒径颗粒,成分上都含S、Cl和K元素.含有生物质燃烧标志元素K的PM10颗粒物多为含Si、Al和Na元素的燃煤飞灰和矿物颗粒,与秸秆燃烧排放颗粒组成化学元素差异明显.据此推...     

因子分析法解析北京市大气颗粒物PM10的来源

2004年10月份在北京市6个采样点采集了大气PM10样品,分析了大气颗粒物的质量浓度、元素组成、离子、有机碳(OC)和元素碳(EC)的浓度,并用因子分析模型对颗粒物的来源进行了研究。结果显示,北京市大气颗粒物的来源主要有6类:建筑水泥尘/机动车尾气尘/燃煤尘、土壤风沙尘、二次粒子尘、工业粉尘、生物质燃烧尘和燃油尘。用模型计算得到的各源对PM10的贡献率分别为建筑水泥尘/机动车尾气尘/燃煤尘占36.57%、土壤风沙尘占16.07%、二次粒子尘占12.33%、工业粉尘占10.29%、生物质燃烧尘占6.07%、燃油尘占3.84%、其它占14.84%。其中建筑水泥/机动车尾气尘/燃煤尘、土壤风沙尘、二次粒子尘、工业粉尘是大气颗粒物PM10的主要来源。实验表明,在缺少源成分谱时可以用因子分析模型来分析大气颗粒物的来源及其相对贡献。     

利用SPAMS研究长沙市秋季PM2.5化学组成及来源

2019年10月12日—11月25日,使用单颗粒气溶胶飞行时间质谱仪(SPAMS)在位于长沙市的湖南省生态环境厅点位进行了为期45 d的定点监测。结果表明,监测期间长沙市总体空气质量小时级别优、良天气占比为80.3%。长沙市首要污染物为PM_(2.5),其主要来源为机动车尾气源,二次无机源次之,工业工艺源排在第三位,占比分别为27.4%,21.5%和17.4%。整体来看,监测期间PM_(2.5)质量浓度的升高大多伴随着以上3种污染源颗粒物的同步升高。机动车尾气源具有明显的早高峰,工业工艺源、生物质燃烧源和餐饮源夜间占比增加。在偏东方向气团主导下,工业工艺源和燃煤源贡献最大;在东北方向气团主导下,PM_(2.5)质量浓度最高,且机动车尾气源占比最高。     

宁波市颗粒物中多环芳烃浓度水平、分布及来源分析

讨论了2003年宁波市颗粒物中多环芳烃浓度水平、分布及来源,结果表明,PM10中PAHS占TSP中总量的83%,PM2.5中的PAHS占TSP总量的54%,颗粒物中多环芳烃主要存在于小于10μm的颗粒中。颗粒物中多环芳烃季节变化特征明显,夏季最低,冬季最高。汽车尾气对PM10中多环芳烃的贡献率达56%,汽车尾气是颗粒物中多环芳烃的主要来源。     

合肥市冬季灰霾天气PM_(2.5)源解析

于2016年12月30日—2017年2月4日,利用单颗粒气溶胶飞行时间质谱仪(SPAMS),对合肥市PM_(2.5)开展来源解析连续监测,共捕捉到4次较为明显的灰霾过程,对颗粒物种类及质谱特征进行了分析。结果显示,监测期间合肥市主要颗粒物成分为元素碳(EC)(31. 9%)、富钾(K)(16. 6%)、有机碳(OC)(16. 0%)及混合碳颗粒(ECOC)(15. 0%)等。主要污染源为机动车尾气源(24. 5%)、工业工艺源(22. 7%)、燃煤源(14. 1%)、二次无机源(13. 5%)等。污染天气发生时,工业工艺源占比上升2. 2个百分点,生物质燃烧和燃煤源占比分别下降1. 7和2. 7个百分点,机动车尾气和扬尘源基本持平,表明此次污染过程主要受到工业工艺源的累积影响。     

Characterization of ionic composition of TSP and PM10 during the Middle Eastern Dust (MED) storms in Ahvaz, Iran

Because of the recent frequent observations of major dust storms in southwestern cities in Iran such as Ahvaz, and the importance of the ionic composition of particulate matters regarding their health effects, source apportionment, etc., the present work was conducted aiming at characterizing the ionic composition of total suspended particles (TSP) and particles on the order of ～10?μm or less (PM(10)) during dust storms in Ahvaz in April-September 2010. TSP and PM(10) samples were collected and their ionic compositions were determined using an ion chromatography. Mean concentrations of TSP and PM(10) were 1,481.5 and 1,072.9?μg/m(3), respectively. Particle concentrations during the Middle Eastern Dust (MED) days were up to four times higher than those in normal days. Ionic components contributed to only 9.5% and 11.3% of the total mass of TSP and PM(10), respectively. Crustal ions were most abundant during dust days, while secondary ions were dominant during non-dust days. Ca(2+)/Na(+) and Cl(-)/Na(+) ratios can be considered as the indicators for identification of the MED occurrence. It was found that possible chemical forms of NaCl, (NH(4))(2)SO(4), KCl, K(2)SO(4), CaCl(2), Ca(NO(3))(2), and CaSO(4) may exist in TSP. Correlation between the anionic and cationic components suggests slight anion and cation deficiencies in TSP and PM(10) samples, though the deficiencies were negligible.     

Source identification and apportionment of PM2.5 and PM2.5−10 in iron and steel scrap smelting factory environment using PMF,PCFA and UNMIX receptor models

To identify the potential sources responsible for the particulate matter emission from secondary iron and steel smelting factory environment, PM_2.5 and PM_2.5?10 particles were collected using the low-volume air samplers twice a week for a year. The samples were analyzed for the elemental and black carbon content using x-ray fluorescence spectrometer and optical transmissometer, respectively. The average mass concentrations were 216.26, 151.68, and 138. 62 μg/m³ for PM_2.5 and 331.36, 190.01, and 184.60 μg/m³ for PM_2.5?10 for the production, outside M1 and outside M2 sites, respectively. The same size resolved data set were used as input for the positive matrix factorization (PMF), principal component factor analysis (PCFA), and Unmix (UNMIX) receptor modeling in order to identify the possible sources of particulate matter and their contribution. The PMF resolved four sources with their respective contributions were metal processing (33 %), e-waste (33 %), diesel emission (22 %) and soil (12 %) for PM_2.5, and coking (50 %), soil (29 %), metal processing (16 %) and diesel combustion (5 %) for PM_2.5?10. PCFA identified soil, metal processing, Pb source, and diesel combustion contributing 45, 41, 9, and 5 %, respectively to PM_2.5 while metal processing, soil, coal combustion and open burning contributed 43, 38, 12, and 7 %, respectively to the PM_2.5?10. Also, UNMIX identified metal processing, soil, and diesel emission with 43, 42 and 15 % contributions, respectively for the fine fraction, and metal processing (71 %), soil (21 %) and unidentified source (1 %) for the coarse fraction. The study concluded that metal processing and e-waste are the major sources contributing to the fine fraction while coking and soil contributed to the coarse fraction within the factory environment. The application of PMF, PCFA and UNMIX receptor models improved the source identification and apportionment of particulate matter drive in the study area.     

Chemical compositions and sources of atmospheric PM10 in heating, non-heating and sand periods at a coal-based city in northeastern China

Mass concentrations and chemical components (18 elements, 9 ions, organic carbon [OC] and elemental carbon [EC]) in atmospheric PM(10) were measured at five sites in Fushun during heating, non-heating and sand periods in 2006-2007. PM(10) mass concentrations varied from 62.0 to 226.3 μg m(-3), with 21% of the total samples' mass concentrations exceeding the Chinese national secondary standard value of 150 μg m(-3), mainly concentrated in heating and sand periods. Crustal elements, trace elements, water-soluble ions, OC and EC represented 20-47%, 2-9%, 13-34%, 15-34% and 13-25% of the particulate matter mass concentrations, respectively. OC and crustal elements exhibited the highest mass percentages, at 27-34% and 30-47% during heating and sand period. Local agricultural residuals burning may contribute to EC and ion concentrations, as shown by ion temporal variation and OC and EC correlation analysis. Heavy metals (Cr, Ni, Zn, Cu and Mn) from coal combustion and industrial processes should be paid attention to in heating and sand periods. The anion/cation ratios exhibited their highest values for the background site with the influence of stationary sources on its upper wind direction during the sand period. Secondary organic carbon were 1.6-21.7, 1.5-23.0, 0.4-17.0, 0.2-33.0 and 0.2-21.1 μg m(-3), accounting for 20-77%, 44-88%, 4-77%, 8-69% and 4-73% of OC for the five sampling sites ZQ, DZ, XH, WH and SK, respectively. From the temporal and spatial variation analysis of major species, coal combustion, agricultural residual burning and industrial emission including dust re-suspended from raw material storage piles were important sources for atmospheric PM(10) in Fushun at heating, non-heating and sand periods, respectively. It was confirmed by principal component analysis that coal combustion, vehicle emission, industrial activities, soil dust, cement and construction dust and biomass burning were the main sources for PM(10) in this coal-based city.     

沈阳市大气中PM2.5来源解析研究

通过2015年在沈阳市采集PM2.5样品及源类样品,分析样品的质量浓度和化学组成,用化学质量平衡(CMB)模型对该市PM2.5来源进行解析。结果表明:沈阳市大气中PM2.5浓度时空变化特征明显;各主要源类对沈阳市PM2.5的分担率依次为煤烟尘(28.03%)、二次无机离子(22.63%)、机动车尾气尘(17.27%)、城市扬尘(13.28%)、建筑尘(5.94%)、土壤风沙尘(5.82%)、道路尘(3.04%)、生物质燃烧尘(2.74%)和冶金尘(1.25%)。燃煤和机动车的有效控制既能降低本类源的贡献,也能降低二次无机离子,体现了多源类综合治理原则。     

Estimation of air pollutant emission loads from construction and operational activities of a port and harbour in Mumbai, India

Port causes environmental and health concerns in coastal cities if its operation and development are not made environmentally compatible and sustainable. An emission inventory is necessary to assess the impact of port projects or growth in marine activity as well as to plan mitigation strategies. In this study, a detailed emission inventory of total suspended particulate (TSP) matter, respirable particulate matter (PM₁₀), sulphur dioxide (SO₂) and oxides of nitrogen (NO_x) for a port having operation and construction activities in parallel is compiled. The study has been done for 1 year. Results show that the maximum contribution of emission of air pollutants in the port area was from TSP (68.5%) and the minimum was from SO₂ (5.3%) to the total pollutants considered in this study. Total TSP emission from all activities of the port was 4,452 tyr^???1 and PM₁₀ emission was 903 tyr^???1 in the year 2006. Re-suspension of dust from paved roads was the major contributor of TSP and PM₁₀ in the road transport sector. Construction activities of the port had contributed 3.9% of TSP and 7.4% of PM₁₀ to total emission of particulate matter. Of the total particulate emissions from various port activities approximately 20% of TSP could be attributed to PM₁₀. The sectoral composition indicates that major contribution of SO₂ emission in the port was from maritime sector and major contribution of NO_x was from road transport sector.     

区域大气细颗粒物化学组分及来源年变化趋势

利用单颗粒气溶胶质谱仪(SPAMS)于2018年1月1日—2019年12月31日对上海市浦东新区环境空气PM_2.5开展高时间分辨化学成分监测。结果表明，2019年监测点空气质量总体优于2018年，AQI达标率由74.8%升高至86.6%。通过对PM_2.5成分分类，最终确定了8类颗粒物，相较于2018年，2019年富钾颗粒物升幅较为明显，左旋葡聚糖、重金属和元素碳有小幅增加，其余各组分相对减少。对PM_2.5排放源分类分析显示，机动车尾气源占比>25%，其中2018年3月、2018年7月、2019年2和3月贡献超过40%；二次无机源和燃煤源呈现一定的季节变化特点，整体秋冬季高于春夏季，2019年燃煤源占比较2018年下降了41%；工业排放源2018年5和10月、2019年1和5月占比相对较高，其余各月份占比相对较为稳定。     

质谱直接测量法解析盐城市大气细颗粒物来源

为全面了解盐城市大气颗粒物的组成,摸清以PM2.5为首要污染物的来源,说清其化学组分和源贡献率,于2014年12月16日00:00—2014年12月21日09:00,利用在线单颗粒气溶胶质谱仪,对盐城市细颗粒物进行实时在线源解析。结果表明,盐城首要污染物为燃煤,占比为23.7%,其次是机动车尾气,占比为18.3%,第三位是扬尘,占总颗粒数的15.7%,生物质燃烧占比为14.8%位列第四,工业工艺源、二次无机源和其他源贡献率相对较小。     

Chemical Composition and Sources of PM10 and PM2.5 Aerosols in Guangzhou, China

Aerosol samples of PM10 and PM2.5 are collected in summertime at four monitoring sites in Guangzhou, China. The concentrations of organic and elemental carbons (OC/EC), inorganic ions, and elements in PM10 and PM2.5 are also quantified. Our study aims to: (1) characterize the particulate concentrations and associated chemical species in urban atmosphere (2) identify the potential sources and estimate their apportionment. The results show that average concentration of PM2.5 (97.54 μg m⁻³) in Guangzhou significantly exceeds the National Ambient Air Quality Standard (NAAQS) 24-h average of 65 μg m⁻³. OC, EC, Sulfate, ammonium, K, V, Ni, Cu, Zn, Pb, As, Cd and Se are mainly in PM2.5 fraction of particles, while chloride, nitrate, Na, Mg, Al, Fe, Ca, Ti and Mn are mainly in PM2.5-10 fraction. The major components such as sulfate, OC and EC account for about 70–90% of the particulate mass. Enrichment factors (EF) for elements are calculated to indicate that elements of anthropogenic origins (Zn, Pb, As, Se, V, Ni, Cu and Cd) are highly enriched with respect to crustal composition (Al, Fe, Ca, Ti and Mn). Ambient and source data are used in the multi-variable linearly regression analysis for source identification and apportionment, indicating that major sources and their apportionments of ambient particulate aerosols in Guangzhou are vehicle exhaust by 38.4% and coal combustion by 26.0%, respetively.     

工业链条炉燃用型煤与原煤颗粒物排放特征

选取燃烧型煤和原煤的典型链条炉,应用自行设计的固定源烟气颗粒物稀释采样系统,现场测试细颗粒物PM_(2.5)、PM_(10)和金属元素的排放特征。结果表明,型煤燃烧细颗粒物的排放比例高于原煤,型煤燃烧除尘器进口、出口PM_(2.5)质量比原煤燃烧分别增加715%和708%。燃烧型煤时,As和Pb在各粒径段的质量比均比原煤大。同时,由于型煤燃烧可吸入颗粒物的排放比例增加,包含或附着在烟尘上的金属元素排放比例也相应增加。     

Morphology,microstructure and chemical composition of single inhalable particles in Shanghai,China

The morphology, microstructure, and chemical composition of a variety of particles emitted from coal-fired power plants, steel plants, and vehicle exhausts, which are possible sources of particulate matter (PM) in the atmosphere, were investigated by scanning electron microscopy (SEM) and transmission electron microscopy (TEM) and compared with particle samples collected from urban atmosphere to identify the best footprint or the suitable indicator relating the existence of studied particles and their possible emitters by the morphology, microstructure, and chemical composition of the particles. The investigation indicated that the particles from these three sources are different in morphology, microstructure, and chemical composition. Sphere aggregates were generally the most abundant components, with silicon and aluminum as major elements. The urban air particulate contained particles similar to those observed in the power plant, steel plant, and vehicle exhaust samples suggesting that all three sources are contributing to the pollution in the city.     

Characteristics of Hazardous Airborne Dust Around an Indian Surface Coal Mining Area

Surface coal mining creates more air pollution problems with respect to dust than underground mining . An investigation was conducted to evaluate the characteristics of the airborne dust created by surface coal mining in the Jharia Coalfield. Work zone air quality monitoring was conducted at six locations, and ambient air quality monitoring was conducted at five locations, for a period of 1 year. Total suspended particulate matter (TSP) concentration was found to be as high as 3,723 μg/m³, respirable particulate matter (PM10) 780 μg/m³, and benzene soluble matter was up to 32% in TSP in work zone air. In ambient air, the average maximum level of TSP was 837 μg/m³, PM10 170 μg/m³ and benzene soluble matter was up to 30%. Particle size analysis of TSP revealed that they were more respirable in nature and the median diameter was around 20 μm. Work zone air was found to have higher levels of TSP, PM10 and benzene soluble materials than ambient air. Variations in weight percentages for different size particles are discussed on the basis of mining activities. Anionic concentration in TSP was also determined. This paper concludes that more stringent air quality standards should be adopted for coal mining areas and due consideration should be given on particle size distribution of the air-borne dust while designing control equipment.     

Airborne particulate matter and BTEX in office environments

Total suspended particulate (TSP), PM(2.5) and BTEX were collected in nine offices in the province of Antwerp, Belgium. Both indoor and outdoor aerosol samples were analysed for their weight, elemental composition, and water-soluble fraction. Indoor TSP and PM(2.5) concentrations ranged from 7-31 microg m(-3) and 5-28 microg m(-3), with an average of 18 and 11 microg m(-3), respectively. Of all the elements analysed in indoor TSP, more than 95% was represented by Al, Si, K, Ca, Fe, Cl and S, accounting for 12% of the TSP by mass. The other elements showed significant enrichment relative to the earth's crust. The water-soluble ionic fraction accounted for almost 30% of the sampled indoor TSP by weight, and was enriched by anthropogenic activities. It was shown that the indoor PM levels varied among the offices, depending on the ventilation pattern, location, and occupation density of the office. Indoor BTEX levels ranged together from 5-47 microg m(-3) and were considerably higher than the corresponding outdoor levels. It was observed that some recently constructed and renovated buildings were clearly burdened with elevated levels for toluene, ethyl benzene, and xylenes, while outdoor air was found to be the main source for BTEX levels at the 'older' offices.     

蚌埠市夏季大气细颗粒物在线源解析

为了解蚌埠市环境空气中PM_(2.5)的来源,于2017年8月18日—9月18日,在百货大楼和高新区站点,利用单颗粒物气溶胶飞行时间质谱仪开展PM_(2.5)在线源解析。结果表明,百货大楼点位ρ(PM_(2.5))高于高新区点位,轻度污染比例(4.2%)明显高于高新区点位(0.8%),出现了中度污染(0.3%);SPAMS的PM_(2.5)质谱图显示百货大楼点位PM_(2.5)中K~+、Na~+特征明显,高新区点位HSO_4~-、NO_3~-、NO_2~-等无机信号较为明显;2个点位NO_3~-、NO_2~-、NH_4~+离子颗粒数占总颗粒数的百分比明显较高,且高新区点位NO_3~-、HSO_4~-离子数占比要明显高于百货大楼点位,燃料燃烧、工业工艺源、农田氮肥施用是其主要的人为污染源;2个点位PM_(2.5)成分主要为元素碳,分别占比42.4%,40.6%;污染时段,ρ(PM_(2.5))快速上升,除受本地机动车尾气源和燃煤源累积影响外,百货大楼点位扬尘源排放增加,高新区点位扬尘源和工业工艺排放源增加;2个点位机动车尾气源均为首要污染源,分别占比29.5%和30.9%,其次为燃煤源(24.3%和24.7%),扬尘源占比分别为22.9%和20.8%。