Emissions of VOCs at Urban Petrol Retail Distribution Centres in India (Delhi and Mumbai)

Air pollution has assumed gigantic proportion killing almost half a million Asians every year. Urban pollution mainly comprises of emissions from buses, trucks, motorcycle other forms of motorized transport and its supporting activities. As Asia's cities continue to expand the number of vehicles have risen resulting in greater pollution. Fugitive emissions from retail distribution center in urban area constitute a major source. Petrol vapours escape during refueling adding pollutants like benzene, toluene, ethylbenzene and xylene to ambient air. This paper discusses a study on fugitive emissions of Volatile Organic Compounds (VOC) at some refueling station in two metropolitan cities of India, i.e., Mumbai and Delhi. Concentration of VOCs in ambient air at petrol retail distribution center is estimated by using TO-17 method. Concentration of benzene in ambient air in Delhi clearly shows the effect of intervention in use of petroleum and diesel fuel and shift to CNG. Chemical Mass Balance (CMB) model is used to estimate source contributions. At Delhi besides diesel combustion engines, refueling emissions are also major sources. At Mumbai evaporative emissions are found to contribute maximum to Total VOC (TVOC) concentration in ambient air.     

泉州市大气PM2.5镧系元素组成特征及来源解析

2014年3月、4月和7月分别采集了泉州市5个采样点共49个PM_(2.5)样品,采用电感耦合等离子体质谱(ICP-MS)测定样品镧系元素(Loid)及其他微量金属元素浓度水平.分析了PM_(2.5)镧系元素组成特征和配分模式,利用La-Ce-V三元图和化学质量平衡(CMB)受体模型解析了泉州市大气PM_(2.5)污染来源.结果表明:(1)泉州市大气PM_(2.5)总镧系元素(Σ Loid)浓度为2.490~5.708 ng·m~(-3)(含量65.682~126.529μg·g~(-1)),轻重镧系元素比值(L/H)为12.086~14.319;(2)PM_(2.5)镧系元素配分模式与福建土壤相似,PM_(2.5)中Ce元素表现为正异常,而Eu元素表现为负异常;(3)城市扬尘、燃煤尘、汽车尾气尘和垃圾焚烧飞灰是泉州市大气PM_(2.5)的主要来源,贡献率分别为18.9%、10.9%、30.6%和30.2%.     

Determination of direct and fugitive PM emissions in a Mediterranean harbour by means of classic and novel tracer methods

Remodelling works are frequent in harbour areas, given that they must adapt to rapidly changing market requirements. Depending on their nature (construction, demolition, etc.), these works may be carried out during long periods of time and thus exert a significant impact on the air quality at the harbours and their surroundings. The air quality at the Valencia harbour was studied during an enlargement project. Four sampling stations aimed to cover the major dust-emitting activities in the harbour. In addition, a sampling campaign was carried out to collect dust materials at their emission sources (e.g., loading and unloading of clinker and petroleum coke, as well as the enlargement works). Differences obtained between PM levels at the monitoring sites were correlated with the distance between sampling stations and enlargement works and/or harbour operations, as well as with the preferential wind direction. In all cases, the days with the highest PM_10 levels were linked to wind directions coinciding with the location of the enlargement works or the clinker and petroleum coke stocks. Classic source apportionment methodologies (PCA and CMB) were applied to the data, but their interpretation was complex due to the similar chemical signatures of PM originating from direct and fugitive emissions from stocked materials (e.g., clinker), and the enlargement works. To overcome this difficulty, a novel non-statistical approach was used to obtain quantitative estimations of the contributions from sources (petroleum coke and clinker), based on the correlation between specific PM components (e.g., carbonaceous species) and source tracers (e.g., V). Finally, a qualitative test using phenolphthalein was devised to identify the presence of clinker on the filters, which provided positive results. This novel tracer approach is thus recommended for harbour authorities.     

机动车排放颗粒物成分谱对比研究

对深圳、无锡、济南和美国EPA建立的机动车排放颗粒物成分谱进行对比研究,建立的成分谱中各组分含量存在较大差别原因为使用了不同的采样方法。     

洛阳市大气细颗粒物化学组分特征及溯源分析

为研究洛阳市大气细颗粒物(PM2.5)的化学组分及来源的时空分布特征,对汾渭平原地区较为欠缺的PM2.5相关研究进行补充,在2018年4月至2019年1月在洛阳市高新和林校2个点位进行了样品采集,对P(PM2.5)、化学组分(水溶性离子、碳质组分、元素)和来源进行分析.2个点位的年均ρ(PM2.5)分别为(76.6±37.9)μg-m-3和(83.2±38.9)μg·m-3,季节变化由高到低均为:冬季、春季、秋季和夏季.高新和林校的9种水溶性离子浓度分别占PM2.5的 55.1％和54.2％,林校的二次离子(NO3-、SO42-和NH4+)年均浓度之和高于高新.高新和林校的ρ[有机碳(OC)]、P[元素碳(EC)]分别为(12.4±7.7)μg·m-3、(1.2±0.5)μg·m-3和(13.4±7.7)μg·m-3、(1.3±0.5)μg·m-3,林校的含碳组分在各季节均高于高新;高新和林校冬季的二次有机碳(SOC)在OC中质量分数分别为67.8％和77.3％,远高于其他季节.化学质量平衡结果表明,高新和林校的主要贡献源均为二次硝酸盐(26.9％和27.1％)、二次硫酸盐(14.5％和14.8％)、燃煤(12.6％和11.6％)、SOA(10.8％和12.2％),高新的生物质源贡献较高,而林校的扬尘源和机动车源贡献较高.后向轨迹和潜在源贡献因子分析表明,洛阳市春季不仅受到来自西北方向的传输,来自西南地区的污染传输也不能忽略;夏季既受到正东方向的季风影响,又有来自正南方向的潜在污染;秋季污染物主要来自东南方向,同时也存在西北方向的潜在来源;冬季受到的传输影响则主要来自周边区域,污染来源较为集中.     

杭州市灰霾与非灰霾日不同粒径大气颗粒物来源解析

在2011年典型灰霾和非灰霾天气下,采集了杭州市不同粒径的大气颗粒物样品,测定并分析各粒径段颗粒物的质量浓度及其化学成分;同时采集并分析了主要污染源排放的颗粒物样品,通过CMB(化学质量平衡)模型进行源解析. 结果表明:灰霾天气下,二次粒子是杭州市各粒径段颗粒物的首要贡献源,其对≤1.1、>1.1~3.3、>3.3~5.8和>5.8~10μm粒径段的颗粒物贡献率分别为60.4%、62.2%、54.8%和46.5%. 在一次排放源中,机动车尾气是≤1.1和>1.1~3.3μm粒径段颗粒物的重要来源,贡献率分别为13.8%和12.2%;城市扬尘是>3.3~5.8μm粒径段颗粒物的重要来源,贡献率达到16.0%;而建筑施工尘是>5.8~10μm粒径段颗粒物的重要来源,贡献率为14.2%. 非灰霾天气下,随着颗粒物粒径的增加,二次粒子的贡献率显著下降,对≤1.1μm粒径段颗粒物的贡献率为42.7%,而对>5.8~10μm粒径段颗粒物的贡献率仅为15.5%;机动车是各粒径段颗粒物的重要贡献源,贡献率均在20%以上;煤烟尘是≤3.3μm细粒径段颗粒物的重要贡献源类,贡献率为22.0%;城市扬尘是>3.3~5.8μm粒径段颗粒物的重要来源,贡献率为18.3%;建筑施工尘依然是>5.8~10μm粒径段颗粒物的重要来源,贡献率为21.4%.      

路边微环境PM2.5化学组分特征及来源解析

采集并分析了武汉市机动车尾气源PM_2.5样品,并于2019年10月18~27日采集了武汉市不同路边微环境（市区路边、环线路边、环境背景点）PM_2.5样品并分析其化学组分特征,利用化学质量平衡模型（CMB）解析评估了机动车尾气对城市不同路边微环境PM_2.5的贡献.结果表明,机动车尾气成分谱以OC和EC为主,汽油车OC质量分数约为柴油车的1.14倍,柴油车EC质量分数是汽油车的1.08倍.路边碳组分主要来源于机动车尾气,其中OC浓度在市区路边最高,EC浓度在环线路边最高;市区路边NO₃^-和NH₄⁺浓度较高,与二次转化有关;环线路边Fe、Si、Al质量浓度高于市区路边.CMB来源解析结果显示,机动车尾气源是环线路边、市区路边微环境的主要来源,分担率为35.20%和38.89%,是环境背景点的2倍左右.不同路边微环境污染源贡献差异明显,与环线路边相比,市区路边机动车尾气源与二次来源均相对较高,而扬尘源贡献低于环线路边.     

大气颗粒物“二重源解析”技术的误差分析

"二重源解析"模型计算结果的误差是采样误差、样品处理误差、化学组分分析误差、数据处理误差以及数学模型误差等所有误差的积累。提出了"二重源解析"解析结果的相对误差和标准偏差表达式,并用之计算了某市利用"二重源解析"模型计算的源贡献值的相对误差和标准偏差,还针对从源排放出来的初始态颗粒物在传输过程中发生的扬尘态变化提出了扬尘转化率的概念和计算方法。     

大气颗粒物中多环芳烃的污染特征及来源识别

研究了北京市2000年采暖期和非采暖期2个典型代表月(6月和12月)不同粒径颗粒物的质量浓度特征以及不同粒径颗粒物中ρ(PAHs)分布特征,并同时利用比值法和化学质量平衡(CMB)受体模型对可吸入颗粒物(PM₁₀)中PAHs的来源进行识别和解析.研究结果表明:北京市采暖期ρ(颗粒物)明显高于非采暖期;采暖期和非采暖期不同粒径颗粒物的比例有差别,采暖期、非采暖期ρ(PM₁₀)分别约占ρ(TSP)的0.662和0.734;PAHs具有更明显富集于细颗粒物中的特征;源解析结果表明燃煤污染和机动车污染是PM₁₀中PAHs的最主要来源.      

银川市冬季PM2.5重污染特征、来源与成因分析

近年来银川市冬季重污染过程频发,为明确银川市冬季PM_2.5重污染的特征,分析其主要来源及成因,于2016年12月-2017年1月在银川市选取3个采样点开展PM_2.5的样品采集与化学组分分析,利用CMB（化学质量平衡）模型对银川市冬季PM_2.5进行来源解析,对比分析了重污染日与非重污染日污染特征的差异.结果表明:①银川市冬季重污染日ρ（PM_2.5）[（181±33.6）μg/m3]是非重污染日的2.1倍;重污染日和非重污染日的ρ（NO₃-）/ρ（SO₄2-）均小于1,表明燃煤仍是银川市冬季PM_2.5的重要来源.银川市冬季PM_2.5中ρ（SOC）为（14.4±7.34）μg/m3,约占ρ（OC）的65.2%.②与非重污染日相比,重污染日人为源无机元素As、Pb、Cd和Zn质量浓度在ρ（PM_2.5）中的占比分别升高33.2%、18.4%、9.8%和2.9%,表明银川市冬季重污染主要受人为源贡献影响.③源解析结果表明,燃煤源、机动车尾气源、二次离子源和扬尘源是银川市PM_2.5的主要污染源,与非重污染日相比,重污染日机动车尾气源的贡献率明显降低.研究显示,银川市冬季重污染受人为源污染物排放的影响较大,燃煤源是银川市冬季PM_2.5的重要来源.