Chemical characteristics of size-resolved aerosols in winter in Beijing

Size-resolved aerosols were continuously collected by a Nano Sampler for 13 days at an urban site in Beijing during winter 2012 to measure the chemical composition of ambient aerosol particles. Data collected by the Nano Sampler and an ACSM(Aerodyne Aerosol Chemical Speciation Monitor) were compared. Between the data sets,similar trends and strong correlations were observed,demonstrating the validity of the Nano Sampler. PM10 and PM2.5concentrations during the measurement were 150.5 ± 96.0 μg/m3(mean ± standard variation)and 106.9 ± 71.6 μg/m3,respectively. The PM2.5/PM10 ratio was 0.70 ± 0.10,indicating that PM2.5dominated PM10. The aerosol size distributions showed that three size bins of 0.5–1,1–2.5 and 2.5–10 μm contributed 21.8%,23.3% and 26.0% to the total mass concentration(TMC),respectively. OM(organic matter) and SIA(secondary ionic aerosol,mainly SO42-,NO3-and NH4+) were major components of PM2.5. Secondary compounds(SIA and secondary organic carbon) accounted for half of TMC(about 49.8%) in PM2.5,and suggested that secondary aerosols significantly contributed to the serious particulate matter pollution observed in winter. Coal burning,biomass combustion,vehicle emissions and SIA were found to be the main sources of PM2.5. Mass concentrations of water-soluble ions and undetected materials,as well as their fractions in TMC,strikingly increased with deteriorating particle pollution conditions,while OM and EC(elemental carbon) exhibited different variations,with mass concentrations slightly increasing but fractions in TMC decreasing.     

基于SPAMS的太原市典型生活区停暖前后PM2.5来源及组成

为分析太原市采暖期和非采暖期PM_2.5的特征,利用单颗粒气溶胶质谱仪（SPAMS）分析太原市典型生活区采暖期（2016年3月11-18日）和非采暖期（2016年4月1-7日）PM_2.5的来源及组成.结果表明:① 采暖期（停暖前）颗粒物有机碳、硫酸盐和多环芳烃等信号强度大于非采暖期（停暖后）,而元素碳、硝酸盐、铵盐等反之.② 为了尽可能排除气象因素的影响,选取风向（东南风）、风级（二级）相同时段的颗粒物进行分析,停暖前后颗粒物主要化学组分为有机碳、混合碳和元素碳,采暖前有机碳占比（达51.9%）最高,非采暖期元素碳占比（32.6%）最高.采暖期有机碳、高分子有机物和左旋葡聚糖占比明显高于非采暖期,元素碳、矿物质和重金属反之.③ 停暖前后首要的两类污染源为燃煤和机动车尾气,二者贡献率之和分别高达70.1%和67.4%,可见本地主要受这两类源的影响.燃煤在采暖期为首要污染源,并且贡献比例高于非采暖期,而机动车尾气在非采暖期为首要污染源,且比例明显高于采暖期.研究显示,采暖和非采暖期虽然首要污染源有所差异,但在污染过程中,机动车尾气源的贡献比例均高于优良时段,说明无论是采暖期还是非采暖期,除燃煤排放的影响外,机动车尾气的影响也需得到重视,建议加强机动车燃油品质的升级,使用清洁煤,并在重污染时段采取相应的管控措施.      

曹妃甸采暖期和非采暖期PM2.5中不同重金属元素污染特征及健康风险评价

为探讨曹妃甸采暖期和非采暖期PM_2.5中Cr、Pb、As和Cd元素的污染特征、来源及健康风险,以华北理工大学曹妃甸校区为研究地点,于2017年12月—2018年10月采集98份PM_2.5样品.利用重量法测定空气中PM_2.5浓度,使用电感耦合等离子体质谱仪分析PM_2.5中4种重金属元素(Cr、Pb、As和Cd)的浓度;采用Wilcoxon Mann-Whitney U检验比较采暖期与非采暖期,以及PM_2.5超标日与非超标日各元素含量的差异,利用Kruskal-Wallis H检验法比较不同PM_2.5浓度分级下4种金属元素浓度差异,用PMF(正定矩阵因子分解)模型对4种重金属元素的来源及贡献率进行解析,并用美国环境保护局推荐的人体暴露健康风险评价模型进行健康风险评估.结果表明:①曹妃甸采暖期PM_2.5及Pb、As和Cd浓度均高于非采暖期,而Cr浓度略低于非采暖期.②PM_2.5超标日Pb、As和Cd浓度均高于非超标日,不同PM_2.5浓度级别下Pb、As和Cd浓度有所差异,且Pb、As和Cd浓度随PM_2.5浓度的增加而增加.③PMF模型源解析表明,燃煤源及交通源是曹妃甸采暖期PM_2.5金属元素主要来源,二者贡献率分别为50.4%和31.7%;工业源及交通源是非采暖期PM_2.5金属元素的主要来源,二者贡献率分别为47.4%和37.0%.④健康风险评价结果表明,采暖期和非采暖期4种重金属元素的非致癌风险值均小于1.采暖期3种致癌性重金属(Cr、As和Cd)对成年男性、成年女性和儿童青少年的致癌风险均高于人类可接受风险水平(1×10-6);非采暖期Cr和As对成年男性、成年女性和儿童青少年的致癌风险均高于人类可接受风险水平;重金属非致癌风险(Cr、Pb、As和Cd)和致癌风险(Cr、As和Cd)指数高低均呈成年男性>成年女性>儿童青少年的特征.研究显示,在采暖期和非采暖期曹妃甸PM_2.5中Pb、As和Cd浓度随PM_2.5浓度的增加而增加,燃煤源和工业源是其主要来源,Cr、As和Cd对人群存在一定的致癌风险.      

基于重庆本地碳成分谱的PM2.5碳组分来源分析

为了解重庆主城PM2.5中碳组分特征和来源,2012-05-02~2012-05-10日在商业区、工业区和居民区进行了PM2.5采样.利用TOR方法分析了8种碳组分,对3个不同功能区大气环境PM2.5以及燃煤尘、尾气尘(机动车尾气、船舶尾气、施工机械尾气)、生物质燃烧尘、餐饮油烟尘这6类源PM2.5中的8种碳组分进行了特征分析.在源的碳成分谱基础上,利用化学质量平衡(CMB)模型得到重庆本地PM2.5的碳来源指示组分,利用因子分析法解析出各类源对不同功能区内PM2.5碳组分的贡献率.结果表明,重庆地区燃煤尘、机动车尾气尘、船舶尾气尘、施工机械尾气尘、生物质燃烧尘、餐饮油烟尘的OC/EC值分别为6.3、3.0、1.9、1.4、12.7和31.3.EC2、EC3的高载荷指示柴油车尾气排放,OC2、OC3、OC4、OPC的高载荷指示燃煤排放,OC1、OC2、OC3、OC4、EC1指示汽油车尾气排放,OC3指示餐饮业排放,OPC指示生物质燃烧排放.商业区OC/PM2.5为17.4%,EC/PM2.5为6.9%,估算得到,二次有机碳(SOC)/OC为40.0%;工业区OC/PM2.5为15.5%,EC/PM2.5为6.6%,SOC/OC为37.4%;居民区OC/PM2.5为14.6%,EC/PM2.5为5.6%,SOC/OC为42.8%.工业区PM2.5中碳组分的主要来源为燃煤和汽油车尾气、柴油车尾气;商业区PM2.5中碳组分的主要来源为汽油车尾气、柴油车尾气和餐饮业油烟;居住区PM2.5中碳组分的主要来源为汽油车尾气、餐饮业油烟、柴油车尾气.     

四川盆地典型城市PM2.5污染过程组分特征和来源解析

为探究四川盆地典型城市PM_2.5污染特征和来源,利用成都市、绵阳市、自贡市超站数据分析2020年冬季典型污染过程PM_2.5组分特征,并采用CMB模型模拟获得研究期间PM_2.5来源及演变特征.结果表明,不同城市PM_2.5组分变化特征不尽相同,成都市污染过程整体呈现NO₃^-主导特征,但重度污染由OC主导.绵阳市污染期间呈现OC主导特征,是污染加重时增长最快的组分.EC是自贡市轻度污染增长最快的组分,NO₃^-、SO₄^2-、NH₄⁺是中度污染增长较快的组分,OC、EC是重度污染增长较快的组分.3个城市均是二次硝酸盐对PM_2.5贡献率最高.比较而言,成都市机动车、扬尘源贡献率均最高;绵阳市二次有机碳贡献率最高,是成都市的2倍;自贡市燃煤源和二次硫酸盐贡献率分别比成都市和绵阳市高出4%~6%和7%~9%.成都市由优良天气到中度污染,二次硝酸盐贡献率随着污染程度的加重而增加,轻度污染较优良天气上升6%,中度污染较轻度污染天气上升3%.中度到重度污染,二次有机碳、机动车贡献率分别上升2%和1%.绵阳市由轻度到重度污染,二次有机碳对PM_2.5的贡献率上升3%,机动车贡献率上升2%,是其污染加重的主要原因.自贡市由轻度到重度污染,各污染源贡献率变化幅度较小.     

城区与县区不同空气质量下碳组分污染特征对比分析

为对比城区与相邻县区不同空气质量下的碳组分污染特征,分别在成都市和仁寿县采集霾期及非霾期PM_(2.5)有效样品共计88个,确定其相应质量和各碳组分浓度[有机碳(OC)、元素碳(EC)和二次有机碳(SOC)等],并进行各碳组分之间的相关性及主成分分析.结果表明,不同空气质量下的城区污染物浓度均高于县区.OC和EC密切相关,非霾期的相关性系数较霾期大.与城区相比,霾期县区的SOC/PM_(2.5)较大,说明其受二次有机物污染更为明显;但城区非霾期二次气溶胶占比明显高于霾期,表明霾期的一次排放是城区大气污染的主要原因.燃煤、机动车排放和生物质燃烧均是两个区域PM_(2.5)的主要来源.     

Secondary organic carbon quantification and source apportionment of PM10 in Kaifeng, China

During 2005, the filter samples of ambient PM₁₀ from five sites and the source samples of particulate matter were collected in Kaifeng, Henan Province of China. Nineteen elements, water-soluble ions, total carbon (TC) and organic carbon (OC) contained in samples were analyzed. Seven contributive source types were identified and their contributions to ambient PM₁₀ were estimated by chemical mass balance (CMB) receptor model. Weak associations between the concentrations of organic carbon and element carbon (EC) were observed during the sampling periods, indicating that there was secondary organic aerosol pollution in the urban atmosphere. An indirect method of “OC/EC minimum ratio” was applied to estimate the concentration of secondary organic carbon (SOC). The results showed that SOC contributed 26.2%, 32.4% and 18.0% of TC in spring, summer-fall and winter, respectively, and the annual average SOC concentration was 7.07 μg/m³, accounting for 5.73% of the total mass in ambient PM₁₀. The carbon species concentrations in ambient PM₁₀ were recalculated by subtracting SOC concentrations from measured concentrations of TC and OC to increase the compatibility of source and receptor measurements for CMB model.     

Composition, source, mass closure of PM2:5 aerosols for four forests in eastern China

PM2.5 aerosols were collected in forests along north latitude in boreal-temperate, temperate, subtropical and tropical climatic zones in eastern China, i.e., Changbai Mountain Nature Reserve (CB), Dongping National Forest Park in Chongming Island (CM), Dinghu Mountain Nature Reserve (DH), Jianfengling Nature Reserve in Hainan Island (HN). The mass concentrations of PM2.5, organic carbon (OC), elemental carbon (EC), water soluble organic carbon (WSOC) as well as concentrations of ten inorganic ions (F-, Cl-, NO3-, SO42-, C2O42-, NH4+, Na+, K+, Ca2+, Mg2+) were determined. Aerosol chemical mass closures were achieved. The 24-hr average concentrations of PM2.5 were 38.8, 89.2, 30.4, 18 μg/m3 at CB, CM, DH and HN, respectively. Organic matter and EC accounted for 21%-33% and 1.3%-2.3% of PM2.5 mass, respectively. The sum of three dominant secondary ions (SO42-, NO3-, NH4+) accounted for 44%, 50%, 45% and 16% of local PM2.5 mass at CB, CM, DH and HN, respectively. WSOC comprised 35%-65% of OC. The sources of PM2.5 include especially important regional anthropogenic pollutions at Chinese forest areas.     

天津市春季道路降尘PM2.5和PM10中碳组分特征

为研究天津市春季道路降尘PM_2.5和PM₁₀中碳组分特征,丰富道路降尘的成分谱库,于2015年3月22日-5月23日在天津市主干道、次干道、支路、快速路和环线5种道路类型道路两侧采集道路降尘样品,通过再悬浮装置得到PM_2.5和PM₁₀的滤膜样品,并用热光碳分析仪测定PM_2.5和PM₁₀中OC（有机碳）和EC（元素碳）的百分含量,利用两相关样本非参数检验、OC/EC比值法以及相关分析法,定性分析天津市春季道路降尘PM_2.5和PM₁₀的碳组分的特征及其主要来源;利用因子分析法,进一步分析道路降尘PM_2.5和PM₁₀的主要来源.结果表明:道路降尘PM_2.5中w（OC）为10.27%（主干道）~13.94%（快速路）、w（EC）为1.24%（支路）~1.77%（环线）,PM₁₀中w（OC）为8.48%（主干道）~12.56%（快速路）、w（EC）为1.01%（次干道）~1.59%（快速路）,可见快速路中碳组分含量相对较高,这可能与其车流量较大,导致道路扬尘和机动车尾气排放量较大有关,也可能与其路面保养及保洁状况有关.对于大部分碳组分而言,其在PM_2.5中的百分含量均高于PM₁₀;除EC2,其他碳组分在PM_2.5和PM₁₀间均无显著性差异.不同道路类型PM_2.5和PM₁₀中OC/EC的大小顺序基本相同,与其车质量变化趋势相反.道路降尘中PM_2.5中碳组分主要来源于道路扬尘、机动车尾气、生物质燃烧以及燃煤源的混合源,PM₁₀主要受道路扬尘、燃煤和柴油车尾气等污染源的影响.      

北京市典型排放源PM_(2.5)成分谱研究

为了建立和完善北京市PM_(2.5)本地化源谱,对北京市11类排放源PM_(2.5)进行采集,并测定其26种组分,分析了不同排放源源谱的组分特征.结果表明,在有组织排放源中,燃煤电厂PM_(2.5)中OC和Si含量很高,占PM_(2.5)的质量分数分别为8.56%和6.19%(平均值),而供热/工业锅炉排放PM_(2.5)中则是SO_4~(2-)(占48.38%)和OC(11.0%)比例最高,水泥窑炉PM_(2.5)中OC(7.12%)、Ca(4.81)和Si(4.41%)占有较大比例;垃圾焚烧排放的PM_(2.5)中Si、Ca、K和SO_4~(2-)均较高,分别占8.15%、9.36%、7.17%和6.79%,且Cl~-含量(2.5%)高于其他所有源,生物质燃烧源PM_(2.5)中OC(21.7%)、Si(6.75%)、Ca(6.15%)较为丰富,餐饮源PM_(2.5)中OC(19.44%)、SO_4~(2-)(5.76%)和K(3.11%)含量均较高;无组织开放源中,道路扬尘和土壤风沙PM_(2.5)化学组分含量变化较为一致,均是Si(分别为16.8%和9.3%)和OC(分别为8.89%和6.61%)最高,建筑水泥尘PM_(2.5)中Ca(17.46%)含量高于其他源;流动排放源PM_(2.5)中OC、EC比例最高,其中,重型柴油车的OC(29.79%)与EC(26.5%)排放比例相当,而轻型汽油车OC排放占有绝对优势(占75%).本文通过对比国内外部分排放源PM_(2.5)成分谱的差异,指出不同区域相同源类排放的PM_(2.5)化学组分差异较大,在应用受体模型中的化学质量平衡模型(CMB)判断受体颗粒物来源时,应基于本地的排放源成分谱,以避免较大的误差.     

Chemical composition, mass closure and sources of atmospheric PM10 from industrial sites in Shenzhen, China

Concentrations of atmospheric PM10 and chemical components (including twenty-one elements, nine ions, organic carbon (OC) and elemental carbon (EC)) were measured at five sites in a heavily industrial region of Shenzhen, China in 2005. Results showed that PM10 concentrations exhibited the highest values at 264 μg/m3 at the site near a harbor with the influence of harbor activities. Sulfur exhibited the highest concentrations (from 2419 to 3995 ng/m3) of all the studied elements, which may be related to the influence of coal used as fuel in this area for industrial plants. This was verified by the high mass percentages of SO42-, which accounted for 34.3%-39.7% of the total ions. NO3-/SO42- ratios varied from 0.64-0.71, which implies coal combustion was predominant compared with vehicle emission. The anion/cation ratios range was close to 0.95, indicating anion deficiency in this region. The harbor site showed the highest OC and EC concentrations, with the influence of emission from vessels. Secondary organic carbon accounted for about 22.6%-38.7% of OC, with the highest percentage occurring at the site adjacent to a coal-fired power plant and wood plant. The mass closure model performed well in this heavily industrial region, with significant correlation obtained between chemically determined and gravimetrically measured PM10 mass. The main constituents of PM10 were found to be organic materials (30.9%-69.5%), followed by secondary inorganic aerosol (7.9%-25.0%), crustal materials (6.7%-13.8%), elemental carbon (3.5%-10.8%), sea salt (2.4%-6.2%) and trace elements (2.0%-4.9%) in this heavily industrialized region. Principal component analysis indicated that the main sources for particulate matter in this industrial region were crustal materials and coal/wood combustion, oil combustion, secondary aerosols, industrial processes and vehicle emission.     

长春市典型高架公路大气环境颗粒物中重金属污染特征

于2014年4—11月对长春市典型高架公路大气颗粒物PM_(2.5)和PM_(10)进行采样,对颗粒物中主要重金属元素的浓度进行了分析,并采用变异系数法、地积累指数法和富集因子法对大气颗粒物重金属污染情况进行评价.研究表明,道路施工期间,PM_(2.5)和PM_(10)中重金属的浓度普遍低于道路运行通车后的浓度,而采暖期重金属的浓度又高于非采暖期的浓度.重金属元素在PM_(2.5)中的含量占PM_(10)中总含量的比例均超过50%,说明颗粒物越细越容易富集重金属.Zn、Fe和Mn的变异系数较小,说明来源稳定,自然源占主导地位.富集因子法表明,Zn的富集程度极强,Mn无富集.Cr和Cd的变异系数较大,受人为活动干扰严重,在道路施工期间二者富集程度较轻,道路通行加之采暖后富集程度加强,主要来源为汽油和煤的燃烧.Pb、Cu和Ni的变异系数在0.56~0.76之间,有一定的人为干扰,Pb和Ni在高架公路施工期富集程度较轻,道路运行及取暖期富集程度显著,主要来源是交通源;Cu富集程度强,主要来源可能是油燃烧、建筑扬尘.     

二级热脱附结合气相色谱-质谱联用分析大气细颗粒物中多环芳烃类污染物

化学组分是影响大气细颗粒物(即PM_(2.5))健康危害的重要因素,但目前流行病学研究对于颗粒物组分的暴露评价受到了传统分析方法的限制.为探索高效的颗粒物组分测定方法,本研究建立并优化了二级热脱附结合气相色谱-质谱联用(TD-GC-MS)方法,以多环芳烃(PAHs)为目标污染物开展研究.结果表明,该方法具有极高的灵敏度,当使用0.28 m~3PM_(2.5)样品时,该方法的检出限为0.018~0.26 ng·m~(-3).对于标准参考物质的测量显示,该方法具有较好的准确性和精密度.同时,分析了北京2012年3月—2013年3月PM_(2.5)样品并与索氏提取结果进行对比,发现两种方法测量3~5环PAHs的一致性较好;部分物种的差异较大,热脱附因减少前处理步骤,结果可能更为准确.北京PM_(2.5)中∑_(12)PAHs浓度为4.27~340 ng·m~(-3),采暖季比非采暖季高一个数量级.基于正矩阵因子分解法(PMF)的源解析显示,燃煤是采暖季的主要污染源,非采暖季则为交通排放.最后,估算了成年北京居民暴露于PAHs的终生致癌风险,结果表明,可重点控制交通源及煤炭源以降低其潜在危害.     

Particle number concentration, size distribution and chemical composition during haze and photochemical smog episodes in Shanghai

The aerosol number concentration and size distribution as well as size-resolved particle chemical composition were measured during haze and photochemical smog episodes in Shanghai in 2009. The number of haze days accounted for 43%, of which 30% was severe （visibility 〈 2 km） and moderate （2 km 〈 visibility 〈 3 km） haze, mainly distributed in winter and spring. The mean particle number concentration was about 17,000/cm3 in haze, more than 2 times that in clean days. The greatest increase of particle number concentration was in 0.5-1μm and 1-10 μm size fractions during haze events, about 17.78 times and 8.78 times those of clean days. The largest increase of particle number concentration was within 50-100 nm and 100-200 nm fractions during photochemical smog episodes, about 5.89 times and 4.29 times those of clean days. The particle volume concentration and surface concentration in haze, photochemical smog and clean days were 102, 49, 15 μm3/cm3 and 949, 649, 206 μm2/cm3, respectively. As haze events got more severe, the number concentration of particles smaller than 50 nm decreased, but the particles of 50-200 nm and 0.5-1μm increased. The diurnal variation of particle number concentration showed a bimodal pattern in haze days. All soluble ions were increased during haze events, of which NH4, SO24- and NO3 increased great/y, followed by Na＋, IC, Ca2＋ and CI-. These ions were very different in size-resolved particles during haze and photochemical smog episodes.     

Aerosol scattering coefficients and major chemical compositions of fine particles observed at a rural site in the central Pearl River Delta, South China

During November-December 2010 aerosol scattering coefficients were monitored using a single-waved (525 nm) Nephelometer at a regional monitoring station in the central Pearl River Delta region and 24-hr fine particle (PM_2.5) samples were also collected during the period using quartz filters for the analysis of major chemical components including organic carbon (OC), elemental carbon (EC), sulfate, nitrate and ammonium. In average, these five components accounted for about 85% of PM_2.5 mass and contributed 42% (OC), 19% (SO₄^2-), 12% (NO₃^-), 8.4% (NH₄⁺) and 3.7% (EC), to PM_2.5 mass. A relatively higher mass scattering efficiency of 5.3 m²/g was obtained for fine particles based on the linear regression between scattering coefficients and PM_2.5 mass concentrations. Chemical extinction budget based on IMPROVE approach revealed that ammonium sulfate, particulate organic matter, ammonium nitrate and EC in average contributed about 32%, 28%, 20% and 6% to the light extinction coefficients, respectively.     

The comparison of source contributions from residential coal and low-smoke fuels,using CMB modeling,in South Africa

D-grade residential coal is being widely used as a fuel source for heating and cooking by most of the lower-income urban communities in South Africa. Emissions from residential coal combustion have been a major cause of elevated air pollution levels in the industrialized areas of South Africa. The adverse health effects resulting from exposure to residential coal combustion emissions have been a major public concern for many years. To address this, the Department of Minerals and Energy of South Africa conducted a macro-scale experiment in the township of Qalabotjha during the winter of 1997 to assess the technical and social benefits of combusting low-smoke fuels.This paper reports the PM_2.5 and PM₁₀ chemical mass-balance (CMB) source apportionment results from Qalabotjha during a 30-day sampling period, including a 10-day period when a large proportion of low-smoke fuels was combusted. Though emission rates of D-grade coal and low-smoke fuels may vary, their chemical abundances are too similar to be separated in CMB calculations. The source apportionment study confirmed that residential coal combustion is by far the greatest source of air pollution, accounting for 62.1% of PM_2.5 and 42.6% of PM₁₀ at the three Qalabotjha sites. Biomass burning is also a major source, accounting for 13.8% of PM_2.5 and 19.9% of PM₁₀. Fugitive dust is only significant in the coarse particle fraction, accounting for 11.3% of PM₁₀. Contributions from secondary ammonium sulfate are three–four times greater than from ammonium nitrate, accounting for 5–6% of PM mass. Minor contributions (less than 1%) were found for power plant fly ash, motor vehicle exhaust, and agricultural lime. Average PM_2.5 and PM₁₀ mass decreased by 20 and 25%, respectively, from the D-grade coal combustion period (days 1–10) to the majority of the low-smoke fuel period (days 11–20). Relative source contribution estimates (SCE) were quite similar among the three sampling periods for PM_2.5, and were quite different for PM₁₀ during the second period when 14% higher residential coal combustion and 9% lower biomass burning source contributions were found.     

太原市空气颗粒物中正构烷烃分布特征及来源解析

为明确城市空气颗粒物中正构烷烃分布特征及污染来源,采集采暖和非采暖季环境空气PM10样品和典型排放源(高等植物、燃煤和机动车)样品,利用GC-MS测定正构烷烃,选取诊断参数并结合污染源排放特征讨论PM10中正构烷烃分布和来源,采用主成分分析法定量解析源贡献率.结果表明,环境空气PM10中正构烷烃含量呈较强时空变化,采暖和非采暖季浓度分别为213.74~573.32 ng·m-3和22.69~150.82 ng·m-3,前者总浓度最高是后者的18倍;采暖季郊区点位(JY、JCP、XD和SL)浓度均高于市区,以JY最高(577.32 ng·m-3),非采暖季工业区(JS)总烷烃量(150.82 ng·m-3)明显高于其它点位,是SL总量的7倍.采暖季化石燃料来源烷烃(C n≤C24)与总烷烃量相关性优于植物来源烷烃(C n≥C25),非采暖季相反,表明前者化石燃料输入较后者高.CPI和%WNA指示非采暖季植物贡献率较采暖季高,且植物蜡烷烃随环境压力的增大总产率增加;C max和OEP表明非采暖季PM10中有机质成熟度低于采暖季;两季样品TIC图均存在UCM鼓包,机动车尾气是该城市的重要污染源.PCA解析结果表明太原市环境空气PM10中正构烷烃首要排放源为机动车尾气和高等植物,约占51.28%;其次为煤烟尘,贡献率为43.14%.煤烟尘污染控制协同机动车尾气净化措施的完善将成为降低城市空气颗粒物中正构烷烃浓度的有效途径.     

Chemical and optical properties of aerosols and their interrelationship in winter in the megacity Shanghai of China

A field campaign on air quality was carried out in Shanghai in winter of 2012. The concentrations of NO, NO₂, NO_x, SO₂, CO, and PM_2.5 increased during haze formation. The average masses of SO₄^2-, NO₃^- and NH₄⁺ were 10.3, 11.7 and 6.7 μg/m³ during the haze episodes, which exceeded the average (9.2, 7.9, and 3.4 μg/m³) of these components in the non-haze days. The mean values for the aerosol scattering coefficient (b_sp), aerosol absorption coefficient (b_ap) and single scattering albedo (SSA) were 288.7, 27.7 and 0.91 Mm^-1, respectively. A bi-peak distribution was observed for the mass concentrations of CO, NO, NO₂, and NO_x. More sulfate was produced during daytime than that in the evening due to photochemical reactions. The mass concentration of NH₄⁺ achieved a small peak at noontime. NO₃^- showed lower concentrations in the afternoon and higher concentrations in the early morning. There were obvious bi-peak diurnal patterns for bsp and bap as well as SSA. bsp and bap showed a positive correlation with PM_2.5 mass concentration. (NH₄)₂SO₄, NH₄NO₃, organic mass, elemental carbon and coarse mass accounted for 21.7%, 19.3%, 31.0%, 9.3% and 12.3% of the total extinction coefficient during non-haze days, and 25.6%, 24.3%, 30.1%, 8.1% and 8.2% during hazy days. Organic matter was the largest contributor to light extinction. The contribution proportions of ammonium sulfate and ammonium nitrate to light extinction were significantly higher during the hazy time than during the non-haze days.     

晋城城市扬尘化学组成特征及来源解析

采集晋城市城市扬尘及其他污染源样品,分析其中元素、离子、碳含量,选取富集因子分析法、潜在生态风险评价法、化学质量平衡模型分析城市扬尘化学组成及来源,为制定有效的城市扬尘污染防治工作方案提供科学依据.结果表明,晋城市城市扬尘中主量成分包括Si、TC、Ca、OC、Al、Mg、Na、Fe、K和SO_4~(2-),质量分数总和为61.14%.地壳元素在城市扬尘中含量最丰富,离子更易在细颗粒上富集.OC在PM_(2.5)上的质量分数较高,EC在PM_(10)上的质量分数较高,说明二次有机污染物主要集中在细颗粒上.城市扬尘PM_(2.5)和PM_(10)潜在生态风险指数均为极强,且PM_(2.5)比PM_(10)具有更强的生态危害性.城市扬尘中Pb的富集因子最大,在PM_(2.5)中达196.97,其次为As、Cr、Ni、V、Zn、Cu,且这些重金属元素的富集因子均在10以上,表明这几种元素显著富集,受人类活动影响较大.土壤风沙尘、建筑水泥尘、机动车尾气尘、煤烟尘是城市扬尘的主要来源.     

钢铁工业排放颗粒物中碳组分的特征

为探究钢铁工业排放颗粒物中碳组分的特征,使用荷电低压颗粒物撞击器(ELPI)采样,采集到3组不同载荷和除污设施的烧结炉和1组炼铁高炉排放的颗粒物样品,利用热-光反射法,分析颗粒物中的有机碳(OC)和元素碳(EC)以及按温度划分的7种碳组分物质.结果表明,烧结工艺排放颗粒物中OC的质量分数高于炼铁工艺,OC在PM_(10)和PM_(2.5)质量分数分别是(5.3±2.3)%和(7.1±3.0)%,说明OC易在细粒径段颗粒富集,炼铁工艺排放颗粒物中OC在PM_(10)和PM_(2.5)质量分数分别是2.5%和2.0%;4组样品的7种碳组分相对比例相似,OC2和OC3在7种碳组分中质量分数最高,EC1、EC2、EC3的质量分数依次递减,OC1的质量分数可能与锅炉规模和脱硫设施有关;另外,烧结工艺排放各粒径段的颗粒物中OC和EC表现出较高的相关性,一次排放的PM_(2.5)中的OC/EC为4.7±0.7,远高于受体中估算二次碳组分的指标值.钢铁工业排放颗粒物中碳质组分的深入分析,可以为受体碳质气溶胶的来源解析提供基础数据,也有助于钢铁工业后续的除污管理.