北京市大气PM10源解析研究

于2004年在北京市定陵、车公庄、古城、亦庄、房山和奥体中心6个采样点采集大气PM10环境样品,针对北京市颗粒物主要排放源采集土壤尘、建筑水泥尘、燃煤等污染源PM10样品,分别对其中的无机元素、离子、有机碳(OC)和元素碳(EC)进行测定。采用代表北京市颗粒物主要排放源PM10组分特征的成分谱,利用CMB受体模型对PM10来源进行解析。结果表明,PM10的最大来源为土壤尘,其它贡献源类依次为燃煤排放、机动车/燃油排放、二次粒子(SO42-、NO3-和NH4 )、建筑水泥尘。污染源贡献具有明显的季节变化,并存在一定的地域变化。     

齐齐哈尔市春季大气中PM2.5的污染特征分析

2013年4—6月分析了齐齐哈尔市大气PM2.5的浓度特征、元素组成、水溶性离子及其来源,并利用单颗粒分析技术获得了单颗粒的形貌、化学组成及粒度分布。结果表明,监测周期PM2.5日均质量浓度为34μg/m3,受气象条件影响显著。PM2.5单颗粒类型主要为烟尘集合体、飞灰和矿物颗粒,分别来源于煤炭燃料的燃烧、机动车尾气排放和扬尘。其中约90%的PM2.5颗粒粒径小于1.0μm,属大气细粒子。全样分析表明,PM2.5主要组成元素是Al、Fe、Ca、K、Mg和Na,而Zn、Cu和Pb因受到人为污染富集度较高。SO42-、NO3-和Cl-为PM2.5主要监测到的水溶性阴离子,NO3-与SO42-的质量浓度比说明了固定排放源对齐齐哈尔市春季大气PM2.5的贡献大于移动排放源。     

石家庄市大气颗粒物元素组分特征分析

为研究石家庄市大气颗粒物的污染特征及其来源,于2013年4—5月在主城6区分别采集TSP、PM10和PM2.5颗粒物样品,利用ICP-MS分析其中的22种元素浓度。结果表明,石家庄市城区Ca、Fe元素在各粒径颗粒物中含量都较高,PM2.5中的S、K含量较高,PM10和TSP中Mg、Al的浓度相对较高。颗粒物的主要来源为燃煤尘、道路尘和建筑尘,TSP、PM10和PM2.5具有较好的统计相关性和同源性。     

京津冀典型城市采暖季颗粒物浓度与元素分布特征

选择京津冀地区3个典型城市和从南至北的4个国家大气背景站作为研究对象,收集采暖季空气颗粒物PM2.5、PM10样品,微波消解-ICP-MS法分析了样品中的68种元素。结果表明,北京、天津、石家庄PM2.5和PM10日均质量浓度均高于国家二级标准限值和背景点,一元线性回归分析结果表明,PM10与PM2.5质量浓度呈线性相关,Na、Mg、Al、S、K、Ca、Fe质量浓度为0.1~10μg/m3,Si、P、Ti、Mn、Ni、Cu、Zn、Ba、Pb质量浓度为10~100 ng/m3,其他元素质量浓度为0.01~10 ng/m3或未检出。在元素构成上,S、Na、Al、K、Fe、Mg、Ca、P、Si等是主要元素,元素含量均大于1%。其他微量元素每种元素含量为0.1%~1%。14种重点防控重金属在PM2.5中的吸附显著高于PM10,主要来源于燃煤、燃油、工业排放、机动车尾气等。     

上海市浦东新区大气细颗粒物中重金属污染特征及来源解析

于2017年对浦东城区和郊区大气PM2.5中的重金属特征和来源进行了分析。结果表明,K、Fe、Na、Ca、Mg、Al等矿物元素为浦东新区PM2.5中含量最高的金属元素,其中K的年均值为297.3 ng/m^3。浦东城区的不同元素在季节变化上呈现较为不同的变化规律,郊区的金属元素值大部分呈现春季先逐月下降,在夏、秋季有起伏波动,在10月之后逐渐上升;沙尘+道路源+建筑扬尘、煤燃烧、工业排放、金属冶炼、船舶排放、海盐+垃圾焚烧+生物质燃烧为浦东城区PM2.5中重金属元素的6大类主要来源。其中沙尘+道路源+建筑扬尘对Ca的贡献率为82.7%,煤燃烧对As的贡献率为86.6%,工业排放对SO4^2-的贡献率达到65.9%,金属冶炼对Cr的贡献率为75.7%,船舶排放对V的贡献率为97.5%、海盐+垃圾焚烧+生物质燃烧对Cl^-的贡献率为93.0%。煤燃烧和金属冶炼主要来自于西部方向。船舶排放分布在长江口及其延伸带。浦东新区PM2.5中重金属元素的质量浓度与本地源排放强度、外界传输和大气扩散条件均有密切关系。     

平顶山市大气PM10、PM2.5 污染调查

于2003年12月-2004年11月对平顶山市城区大气PM10、PM2.5污染进行了调查.结果表明,2004年大气PM10、PM2.5质量浓度分别为0.031 mg/m3～0.862 mg/m3、0.019 mg/m3～0.438 mg/m3;年均值分别为0.174 mg/m3、0.114 mg/m3,超标0.74倍、6.60倍.PM10、PM2.5污染的季节变化趋势是以冬季、春季高,秋季次之,夏季最低,细颗粒(PM2.5)约占PM10 65%;As、Pb、Cd、S、Zn、Cu、Mn、Ca等元素是颗粒物中主要污染元素,易在PM2.5中富集.平顶山市大气颗粒物污染的主要来源有煤炭燃烧、汽车尾气、城市基础建设和有色金属冶炼行业.     

北京市机动车尾气排放PM10组分特征研究

为提高机动车尾气排放可吸入颗粒物PM10成分谱的代表性和准确性,提出并采用在机动车尾气检测线上采用稀释通道采样器随机采集机动车排出PM10的采样方法。采集了591辆轻、重型汽油车和柴油车尾气排放PM10,测试并分析了颗粒物的27种组分。数据表明:机动车排放颗粒物PM10中含量丰富的组分为OC、EC、NH+4、NO-3和SO2-4;柴油车排放PM10中OC和EC的质量分数为49.08%,是汽油车(38.38%)的1.3倍,汽油车的OC/EC(2.36)是柴油车(0.78)的3倍;汽油车排放PM10中的二次转化产物(SO42-+NH4++NO3-)的质量分数为19.37%,是柴油车(3.57%)的5.4倍;汽油车排放NH+4是柴油车的5.3倍。     

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。     

张家口市沙尘天气过程气溶胶单颗粒特征研究

利用单颗粒气溶胶质谱仪对张家口市2015年春季的一次典型沙尘过程进行了监测,分析了沙尘过程对当地大气颗粒物成分的影响。结果表明,监测期间的颗粒物类型主要分为8种:矿物质(MD)、左旋葡聚糖(LEV)、元素碳(EC)、有机碳(OC)、混合碳(ECOC)、重金属(HM)、富钾(K)、其他(Other)。对比沙尘天气来临前、中、后3个时段,随着沙尘天气的来临,本地大气颗粒物成分发生较大变化,矿物质、左旋葡聚糖等成分含量升高,而有机碳、重金属等成分含量下降,其中矿物质在PM10峰值时段小时比例高达27.8%;沙尘天气期间,由于矿物质颗粒占比增加,使得总颗粒物的粒径分布向0.9μm以上的粗粒径段偏移;此外,沙尘天气期间的颗粒物各成分与二次组分的混合程度相较非沙尘天气时段的低,说明其老化程度相对较低。     

桂林市细颗粒物部分典型排放源的粒径谱及成分分析

采用在线单颗粒气溶胶质谱技术源解析方法,对桂林市PM2.5典型排放源的粒径和化学成分进行质谱分析,采集燃煤/燃气源、工业工艺源、扬尘源、油烟源4类共计7个典型排放源。结果表明,桂林市4类排放源细颗粒物的粒径分布为0.25~1.25μm,80%以上的细颗粒分布在0.2~1.0μm的小粒径范围,峰值约0.68μm。细颗粒物离子成分含有Na~+、Mg~+、K~+、NH~+4、Fe~+、Pb~+、Cd~+、V~+、Mn~+、Li~+、Al~+、Ca~+、Cu~+、Zn~+、Cr~+、CN~-、PO_3~-、NO_2~-、NO_3~-、Cl~-、SO_4~(2-)、SiO_3~-等成分,桂林市细颗粒物为元素碳、有机碳元素碳、有机碳、富锰颗粒、富铁颗粒、富钾颗粒、矿物质、左旋葡聚糖以及其他金属等9类。     

秸秆焚烧对空气质量影响特征及判别方法的研究

利用南京空气自动监测数据及PM_(2.5)组分监测结果,分析了2011年夏收秸秆焚烧期间大气污染特征,并探寻快速判别秸秆焚烧影响的指标及方法。结果表明:秸秆焚烧期间PM_(2.5)污染特征显著,其组分中K~+、EC、OC等浓度相对偏高。基于离子组分及碳元素在线监测数据,可选取K~+作为快速判别指标,并根据K~+与PM_(2.5)的相关性,计算秸秆焚烧对PM_(2.5)的贡献。同时结合OC、EC浓度变化,综合判别秸秆焚烧对空气质量的影响程度。     

Traffic emission factors of ultrafine particles: effects from ambient air

Ultrafine particles have a significant detrimental effect on both human health and climate. In order to abate this problem, it is necessary to identify the sources of ultrafine particles. A parameterisation method is presented for estimating the levels of traffic-emitted ultrafine particles in terms of variables describing the ambient conditions. The method is versatile and could easily be applied to similar datasets in other environments. The data used were collected during a four-week period in February 2005, in Gothenburg, as part of the G?te-2005 campaign. The specific variables tested were temperature (T), relative humidity (RH), carbon monoxide concentration (CO), and the concentration of particles up to 10 μm diameter (PM(10)); all indicators are of importance for aerosol processes such as coagulation and gas-particle partitioning. These variables were selected because of their direct effect on aerosol processes (T and RH) or as proxies for aerosol surface area (CO and PM(10)) and because of their availability in local monitoring programmes, increasing the usability of the parameterization. Emission factors are presented for 10-100 nm particles (ultrafine particles; EF(ufp)), for 10-40 nm particles (EF(10-40)), and for 40-100 nm particles (EF(40-100)). For EF(40-100) no effect of ambient conditions was found. The emission factor equations are calculated based on an emission factor for NO(x) of 1 g km(-1), thus the particle emission factors are easily expressed in units of particles per gram of NO(x) emitted. For 10-100 nm particles the emission factor is EF(ufp) = 1.8×10(15)×(1 - 0.095×CO - 3.2×10(-3)×T) particles km(-1). Alternative equations for the EFs in terms of T and PM(10) concentration are also presented.     

The Effect of Traction Sanding on Urban Suspended Particles in Finland

Springtime urban road dust forms one of the most serious problems regarding air pollution in Finland. The composition and origin of springtime dust was studied in southern Finland with two different methods. Suspended particles (PM10 and TSP) were collected with high volume particle samplers and particle deposition was collected with moss bags. The composition of the PM(1.5-10) fraction was studied using individual particle analysis with SEM/EDX. The deposition in the moss bags was analysed with ICP-MS. The results showed that during the study period, approximately 10% of both PM(1.5-10) particles and the deposition originated from sanding. Other sources in the springtime PM(1.5-10) were e.g. asphalt aggregate or soil and combustion processes. It can be concluded that sanding produced a relatively small amount of particulate matter under the investigated circumstances.     

Study of size and mass distribution of particulate matter due to crop residue burning with seasonal variation in rural area of Punjab, India

Emission from field burning of agricultural crop residue is a common environmental hazard observed in northern India. It has a significant potential health risk for the rural population due to respirable suspended particulate matter (RSPM). A study on eight stage size segregated mass distribution of RSPM was done for 2 wheat and 3 rice crop seasons. The study was undertaken at rural and agricultural sites of Patiala (India) where the RSPM levels remained close to the National Ambient Air quality standards (NAAQS). Fine particulate matter (PM(2.5)) contributed almost 55% to 64% of the RSPM, showing that, in general, the smaller particles dominated during the whole study period with more contribution during the rice crop as compared to that of wheat crop residue burning. Fine particulate matter content in the total RSPM increased with decrease in temperature. Concentration levels of PM(10) and PM(2.5) were higher during the winter months as compared to that in the summer months. Background concentration levels of PM(10), PM(2.5) and PM(10-2.5) were found to be around 97 ± 21, 57 ± 15 and 40 ± 6 μg m(-3), respectively. The levels increased up to 66, 78 and 71% during rice season and 51, 43 and 61% during wheat crop residue burning, respectively. Extensive statistical analysis of the data was done by using pair t-test. Overall results show that the concentration levels of different size particulate matter are greatly affected by agricultural crop residue burning but the total distribution of the particulate matter remains almost constant.     

乌鲁木齐市可吸入颗粒物水溶性离子特征及来源解析

采暖期时在乌鲁木齐市采集了环境空气中的可吸入颗粒物,对可吸入颗粒物质量浓度及8种水溶性离子的特征和来源进行了分析。结果表明,细粒子和粗粒子的月平均质量浓度分别是53.5～233.3μg/m3和38.9～60.9μg/m3;细粒子和粗粒子中水溶性离子主要由SO24-、NH4+和NO3-组成;粗粒子中NH4+与NO3-和SO24-的相关性分别是0.70和0.66,细粒子中NH4+与NO3-和SO24-的相关性分别是0.89和0.93,铵盐是乌鲁木齐可吸入颗粒物主要存在形式;煤烟尘是乌鲁木齐市采暖期可吸入颗粒物的主要来源。     

Risk assessment of heavy metals in road and soil dusts within PM2.5, PM10 and PM100 fractions in Dongying city, Shandong Province, China

15 road and 14 soil dust samples were collected from an oilfield city, Dongying, from 11/2009-4/2010 and analyzed by inductively coupled plasma-mass spectroscopy (ICP-MS) for V, Cr, Mn, Co, Ni, Cu, Zn, As, Cd and Pb within PM(2.5), PM(10) and PM(100) fractions synchronously. Metal concentrations, sources and human health risk were studied. Results showed that both soil and road dust exhibited higher values for Mn and Zn and lower values for Co and Cd for the three fractions. Mass concentration ratios of PM(2.5)/PM(10) and PM(10)/PM(100) for metals in road and soil dust indicate that most of the heavy metals tend to concentrate in fine particles. Geoaccumulation index and enrichment factors analysis showed that Cu, Zn and Cd exhibited moderate or heavy contamination and significant enrichment, indicating the influence of anthropogenic sources. Vanadium, Cr, Mn and Co were mostly not enriched and were mainly influenced by crustal sources. For Ni, As and Pb, they ranged from not enriched to moderately enriched and were influenced by both crustal materials and anthropogenic sources. The conclusions were confirmed by multivariate analysis methods. Principle component analysis revealed that the major sources were vehicle emission, industrial activities, coal combustion, agricultural activities and crustal materials. The risk assessment results indicated that metal ingestion appeared to be the main exposure route followed by dermal contact. The most likely cause for cancer and other health risks are both the fine particles of soil and road dusts.     

Dynamics and origin of PM2.5 during a three-year sampling period in Beijing, China

Systematic sampling and analysis were performed to investigate the dynamics and the origin of suspended particulate matter smaller than 2.5 μm in diameter (PM(2.5)), in Beijing, China from 2005 to 2008. Identifying the source of PM(2.5) was the main goal of this project, which was funded by the German Research Foundation (DFG). The concentrations of 19 elements, black carbon (BC) and the total mass in 158 weekly PM(2.5) samples were measured. The statistical evaluation of the data from factor analysis (FA) identifies four main sources responsible for PM(2.5) in Beijing: (1) a combination of long-range transport geogenic soil particles, geogenic-like particles from construction sites and the anthropogenic emissions from steel factories; (2) road traffic, industry emissions and domestic heating; (3) local re-suspended soil particles; (4) re-suspended particles from refuse disposal/landfills and uncontrolled dumped waste. Special attention has been paid to seven high concentration "episodes", which were further analyzed by FA, enrichment factor analysis (EF), elemental signatures and backward-trajectory analysis. These results suggest that long-range transport soil particles contribute much to the high concentration of PM(2.5) during dust days. This is supported by mineral analysis which showed a clear imprint of component in PM(2.5). Furthermore, the ratios of Mg/Al have been proved to be a good signature to trace back different source areas. The Pb/Ti ratio allows the distinction between periods of predominant anthropogenic and geogenic sources during high concentration episodes. Backward-trajectory analysis clearly shows the origins of these episodes, which partly corroborate the FA and EF results. This study is only a small contribution to the understanding of the meteorological and source driven dynamics of PM(2.5) concentrations.     

北京市冬季大气细粒子数浓度的粒径分布特征

考虑到对人体的健康危害,大气颗粒物的数浓度值可能比质量浓度值更重要.通过对北京市交通道路边、生活区和远郊背景点大气细粒子数浓度的监测,对北京市大气细粒子数浓度的主要来源、浓度和粒径分布特征进行研究.文章认为交通源是城市大气细粒子数浓度的主要来源.城市生活区的大气细粒子主要是污染源稀释后扩散而来.远郊区既可能存在气象污染物光化学成核生成的超细颗粒物,也存在外部运移而来的细粒子.与国外其他城市相比,北京市大气细粒子数浓度在道路边处于中等偏下水平,但生活区和背景点处于相当或偏高的水平.     

杭州市燃煤废气中重金属排放清单建立

采用基于燃料消耗的排放因子法,以污染源普查动态更新数据为基础,建立了2010年杭州市燃煤废气中重金属(汞、砷、铅、镉、总铬、镍、锑等7种)排放清单。结果表明,2010年杭州市燃煤废气中汞、砷、铅、镉、总铬、镍、锑的年排放量分别为194.2、252.9、1 915.7、53.9、3 390.4、1 465.4、101.0 kg。燃煤废气中重金属的排放主要集中在燃煤消耗较高的拱墅区和江干区,其次是上城区,这3个区燃煤废气中重金属的排放量之和超过全市的95%。燃煤废气中重金属的排放量与燃煤量密切相关,但锅炉燃烧方式、除尘脱硫设施对重金属排放也起到了决定性作用。