Source Apportionment of Ambient PM_(10) in the Urban Area of Longyan City,China:a Comparative Study Based on Chemical Mass Balance Model and Factor Analysis Method

In order to identify the day and night pollution sources of PM10 in ambient air in Longyan City,the authors analyzed the elemental composition of respirable particulate matters in the day and night ambient air samples and various pollution sources which were collected in January 2010 in Longyan with inductivity coupled plasma-mass spectrometry（ICP-MS）.Then chemical mass balance（CMB） model and factor analysis（FA） method were applied to comparatively study the inorganic components in the sources and receptor samples.The results of factor analysis show that the major sources were road dust,waste incineration and mixed sources which contained automobile exhaust,soil dust/secondary dust and coal dust during the daytime in Longyan City,China.There are two major sources of pollution which are soil dust and mixture sources of automobile exhaust and secondary dust during the night in Longyan.The results of CMB show that the major sources are secondary dust,automobile exhaust and road dust during the daytime in Longyan.The major sources are secondary dust,soil dust and automobile exhaust during the night in Longyan.The results of the two methods are similar to each other and the results will guide us to plan to control the PM10 pollution sources in Longyan.

MASS BALANCES OF MAJOR CHEMICAL CONSTITUENTS IN BOHAI SEA WATER Ⅱ.REMOVAL PROCESSES

The removal processes of major seawater chemical constituents in the Bohai Sea include seawater ex-change between the Bohai Sea and the Northern Yellow Sea,cation exchange,interstitial water burialand spray,formation of illite,sepiolite,sulfate and sulfur,and carbonates.The mass balance calculationbased on these processes is very applicable to the Bohai Sea.

MASS BALANCES OF MAJOR CHEMICAL CONSTITUENTS IN BOHAI SEA WATER I. SOURCES

Because of the almast enclosed nature of the Bohai Sea, rivers are considered as the sources of the major seawater chemical constituents there. The annual quantities of river-derived solutes and solids in Bohai Sea water are accurately calculated in this paper.

基于改进CMB的石化企业VOCs源解析技术研究

石化企业是挥发性有机物(VOCs)的重点排放源,研究适用于石化企业复杂环境的VOCs源解析技术对开展针对性的污染治理具有重要意义。研究开发了基于特征组分的加权CMB改进算法,通过识别偏差平方和贡献度最大的若干特征组分并增加权重系数,实现源解析误差的有效控制,为证明算法效果,设计开展了模拟数据验证与企业现场溯源实验。结果表明:加权CMB改进算法具有更好的误差抑制效果,较常规CMB模型,贡献度解析结果的相对误差降低率达67.2%。

Elemental Compositions and Chemical Mass Closure of Fine Particulate in an Animal Feeding Operation Facility and Its Vicinity

Particulate matter (PM) emissions from animal feeding operations (AFOs) have been considered as an important contributor to ambient PM in rural areas. Investigation of the chemical compositions of PM2.5 inside and in the vicinity of AFOs can enhance our understanding of the AFO emissions impact on ambient PM characteristics. This year-long field study was conducted on a commercial egg production farm to investigate ambient PM chemical compositions as impacted by the air emissions from the production houses. The PM2.5 samples were collected from five sampling stations (one in-house station and four ambient locations in four wind directions). The trace elements, major ions, organic carbon (OC) and element carbon (EC) were analyzed by X-ray florescence (XRF), ion chromatography (IC), and thermo-optical analyzer, respectively. There were significant differences in elemental compositions between PM samples from in-house station (ST1) and ambient stations (ST2-ST5). The chemical mass balance analysis revealed that OC accounted for above 50% of PM2.5 mass at in-house and ambient stations;NH4+, SO42-, and NO3- accounted for about 40.0% of the total PM2.5 mass in ambient locations and for only 12% of the total PM2.5 mass in house. The measured PM2.5 masses agreed with the sums of the masses of chemical compositions at all stations except for the in-house station. Knowledge gained from this study, with additional consideration of NH3 concentrations and emissions, will lead to better understanding of PM2.5 source and formation, fate and transport, and their atmospheric dynamics.

应用CMB模型和FA法对区域大气颗粒物的综合源解析研究

利用受体模型CMB法和FA法的综合解析技术,对武汉某区域的14个TSP(总悬浮颗粒物)监测点及4种主要尘源的元素组分进行定性和定量分析,识别TSP的主要污染源及各源对环境污染的贡献率。将两种方法所得解析结果相互验证,得到和实际情况比较符合的结论。CMB模型分析结果为:A区域TSP主要污染源为土壤尘源(贡献率为54.09%)、交通道路尘源(30.47%)和煤烟尘源(9.33%);B区域主要污染尘源为土壤尘源(41.65%)和建筑尘源(19.05%)。对TSP元素组分数据进行FA法分析,结果为:该区域TSP污染源约有5种,分别为建筑尘源(57.524%)、土壤尘源(19.46%)、煤烟尘源(14.50%)、交通道路尘源(6.74%)和钢铁尘源(0.95%)。CMB法和FA法综合解析结果与研究区域的实际情况基本吻合,主要污染源为土壤尘源、建筑尘源和交通道路尘源。根据解析结果及研究区域的TSP实际污染特征,有针对性地提出各污染源控制对策。

采用CMB模型对济宁市大气污染颗粒物的源解析及控制对策研究

目前,环境空气质量是最重要的民生问题之一,而济宁又是全国著名的煤化工产业基地,支柱产业均是以煤炭为原料,因此,环境空气质量更是大问题。通过深入的走访调查,济宁市的大气污染颗粒物主要是土壤风沙尘、煤尘、建筑尘、机动车尾气油烟飞灰、燃煤飞灰五种。本文采用CMB模型解析了这五种大气污染颗粒物的贡献值和分担率,其中,煤尘和燃煤飞灰是影响济宁市环境空气质量的主要原因,为此,提出济宁市的产业结构急需调整,煤化工产业需要向高附加值、低污染的精细化工产品和清洁能源产品的方向发展。

中纬度造山带地区的长期化学风化——中亚和北美的对比研究

长期化学风化速率(LCWR)和化学风化剥蚀率(CDF)是表征区域化学风化程度和揭示地貌和气候系统演变的重要依据。全球尺度上,中纬度地区的长期化学风化研究远比低纬地区争议更多,原因之一是其风化机制在理论认识上仍存在不确定性,特别是造山带地区的长期化学风化与物理侵蚀、构造、气候之间的关系等。本文利用地球化学质量平衡方法,针对北半球中纬度典型造山带(中亚黑河流域和北美内华达山脉)开展了相关数据和资料的系统整理与再分析,估算了黑河流域的LCWR值和物理侵蚀速率(E)值并与内华达山脉进行了对比研究。分析结果表明,黑河流域、内华达山脉的LCWR分别为17.4~895t/km^(2)/a、1~173t/km^(2)/a,CDF分别为0.17~0.81、0.02~0.61,LCWR与E均显著正相关,与海拔高度、年均温度、年均降水量等仅局部相关。研究区的长期化学风化主要表现为“供应受限型”风化,但黑河流域局部地区已处于“供应受限”与“动力学受限”过渡的风化阶段。结合回归分析结果和已有成果的综合分析,中纬度造山带地区的长期化学风化速率受到地质、气候等因素的协同影响,但主控因素为地质因素,而化学风化剥蚀率则主要受控于其他因素。

耦合PMF、CMB模型对大气颗粒物源解析的研究

化学质量平衡法是目前应用最为广泛的大气颗粒物源解析受体模型之一。针对利用化学质量平衡法进行源解析时,需建立大量的污染源成分谱,耗财耗力,同时选择不同的污染源及特征元素拟合时易导致源解析结果不确定性的问题,结合正矩阵因子分解法模型,提出了化学质量平衡法与正矩阵因子分解法的耦合模型,利用正矩阵因子分解法模型解析出各污染源及其特征元素,确定化学质量平衡法模型拟合参数及污染源清单。对比各污染源的理论贡献量与模拟贡献量,结果表明,耦合模型拟合结果与理论值差异不大,验证了耦合模型的可行性。耦合模型缩减了化学质量平衡法模型建立污染源成分谱的工作量,提高了化学质量平衡法模型进行源解析工作的高效性、准确性。

基于卫星遥感与CMB模型的济南市冬季重污染过程PM_(2.5)溯源分析

济南市作为京津冀大气污染传输通道“2+26”城市之一,冬季易出现以PM_(2.5)为首要污染物的重污染天气。为探究济南市冬季PM_(2.5)重污染过程的污染成因和主要来源,以济南市冬季一次重污染过程(2020年1月1日—6日)为研究对象,基于卫星遥感、化学质量平衡(CMB)模型、潜在源贡献因子分析(PSCF)和浓度权重轨迹分析(CWT),同时结合气态污染物和PM_(2.5)组分小时数据以及各项气象要素等资料,全面和综合地对济南市冬季重污染过程污染特征、本地来源、区域传输和时空演变过程进行了分析。结果表明,济南市本次重污染过程以PM_(2.5)为首要污染物,随着湿度升高和大气边界层高度(PBL)的降低,PM_(2.5)日均质量浓度达到最高211μg·m^(−3),其中PM_(2.5)小时质量浓度最高达到333μg·m^(−3);NO_(3)^(−)、SO_(4)^(2−)、NH4+和有机物(OM)在PM_(2.5)中占比分别为30.7%、11.4%、13.9%和13.7%;重污染时段硫氧化率(SOR)和氮氧化率(NOR)平均值分别达到0.6和0.5,说明此次重污染过程主要来自于气态前体物的二次转化。CMB来源解析结果表明,二次源(硝酸盐、硫酸盐、二次有机碳)和机动车源是济南市冬季PM_(2.5)重污染过程的主要来源;与清洁天相比,硝酸盐的分担率和质量浓度均显著升高,分担率是清洁天的2.2倍,质量浓度是清洁天的10.5倍。本次重污染过程除济南本地污染源以外,还受山东省内德州东部、滨州西部以及济南市的商河县和济阳区等地污染气团近距离传输,以及来自西南方向的河南省东北部和安徽省西部地区的跨省输送的污染气团,途径济南市南部、泰安和济宁地区以及济南市的长清区和平阴县等地的共同影响。

伊犁河谷城市群夏季不同粒径大气颗粒物组分特征及来源解析

为研究伊犁河谷城市群不同城市不同粒径大气颗粒物的组分特征和来源,于2021年7月19—29日,在伊犁河谷城市群的伊宁市、伊宁县、察布查尔锡伯自治县(简称察县)和霍城县布设6个采样点采集大气PM_(2.5)和PM_(10)样品,对样品中的化学组分(无机元素、水溶性离子和碳组分)进行分析,并使用化学质量平衡模型对其来源进行解析。结果表明:研究期间伊犁河谷核心区城市群PM_(2.5)和PM_(10)浓度均处于较低水平,分别为(22.81±2.79)和(58.81±6.95)μg/m^(3);从空间分布来看,伊宁市和伊宁县的颗粒物浓度相对较高,霍城县和察县的浓度相对较低。化学组分质量重构结果表明,地壳元素是研究期间PM_(2.5)和PM_(10)的主要组分,占比分别为39.8%和54.1%;其次为有机物,占比分别为33.2%和19.8%;二次无机离子在PM_(2.5)和PM_(10)中也有一定占比,分别为20.2%和10.7%。源解析结果表明,PM_(2.5)主要来自二次颗粒物(29.1%)和扬尘源(28.3%)的贡献,工业源(16.1%)、机动车(10.5%)、燃煤源(9.3%)也均有一定贡献;PM_(10)中则以扬尘源的贡献最大(42.3%),远高于二次颗粒物(14.7%)、工业源(14.1%)、机动车(8.4%)和燃煤源(7.3%)。

基于分区水质监测的污水管网收集效能评估

为评估城镇污水管网收集效能,提出了污水管网分区水质监测与化学质量平衡模型相结合的污水管网诊断评估方法,并联合贝叶斯-马尔科夫链蒙特卡洛模拟算法解决了溯源解析的不确定问题,对污水处理厂进水有机污染指标质量浓度偏低和可生化性低的原因进行了解析。以武汉市某污水处理厂服务区域为研究对象,建立了生活污水、工业废水和地下水的属地化水质特征因子数据库,并开展了污水干管分区水质特征因子监测。结果表明:通过耦合贝叶斯-马尔科夫链蒙特卡洛模拟算法与化学质量平衡模型,实现了基于水质监测的水量来源占比合理解析;研究区污水干管中电子工业废水量占比高达53.2%~74.4%,地下水入渗量占比为14.3%~29.5%,污水处理厂进水可生化性低的主要原因是大量无机工业废水排入污水管网;应加强对工业废水排入污水管网的监管,重点对研究区域上游污水管段的破损状况开展排查,通过精准化手段提高污水治理效能。

An integrated chemical mass balance and source emission inventory model for the source apportionment of PM2.5 in typical coastal areas

The source apportionment of PM2.5 is essential for pollution prevention.In view of the weaknesses of individual models,we proposed an integrated chemical mass balancesource emission inventory(CMB-SEI)model to acquire more accurate results.First,the SEI of secondary component precursors(SO2,NOx,NH3,and VOCs)was compiled to acquire the emission ratios of these sources for the precursors.Then,a regular CMB simulation was executed to obtain the contributions of primary particle sources and secondary components(SO4^2-,NO3^-3,NH4^+,and SOC).Afterwards,the contributions of secondary components were apportioned into primary sources according to the source emission ratios.The final source apportionment results combined the contributions of primary sources by CMB and SEI.This integrated approach was carried out via a case study of three coastal cities(Zhoushan,Taizhou,and Wenzhou;abbreviated WZ,TZ,and ZS)in Zhejiang Province,China.The regular CMB simulation results showed that PM2.5 pollution was mainly affected by secondary components and mobile sources.The SEI results indicated that electricity,industrial production and mobile sources were the largest contributors to the emission of PM2.5 gaseous precursors.The simulation results of the CMB-SEI model showed that PM2.5 pollution in the coastal areas of Zhejiang Province presented complex pollution characteristics dominated by mobile sources,electricity production sources and industrial production sources.Compared to the results of the CMB and SEI models alone,the CMB-SEI model completely apportioned PM2.5 to primary sources and simultaneously made the results more accurate and reliable in accordance with local industrial characteristics.

Third-order unconditional positivity-preserving schemes for reactive flows keeping both mass and mole balance

In this paper,the previously proposed second-order process-based modified Patankar Runge-Kutta schemes are extended to the third order of accuracy.Owing to the process-based implicit handling of reactive source terms,the mass conservation,mole balance and energy conservation are kept simultaneously while the positivity for the density and pressure is preserved unconditionally even with stiff reaction networks.It is proved that the first-order truncation terms for the Patankar coefficients must be zero to achieve a prior third order of accuracy for most cases.A twostage Patankar procedure for each Runge-Kutta step is designed to eliminate the first-order truncation terms,accomplish the prior third order of accuracy and maximize the Courant number which the total variational diminishing property requires.With the same approach as the second-order schemes,the third-order ones are applied to Euler equations with chemical reactive source terms.Numerical studies including both 1D and 2D ordinary and partial differential equations are conducted to affirm both the prior order of accuracy and the positivity-preserving property for the density and pressure.

杭州市大气PM_(2.5)和PM_(10)污染特征及来源解析

2006年在杭州市两个环境受体点位采集不同季节大气中PM2.5和PM10样品,同时采集了多种颗粒物源类样品,分析了其质量浓度和多种化学成分,包括21种无机元素、5种无机水溶性离子以及有机碳和元素碳等,并据此构建了杭州市PM2.5和PM10的源与受体化学成分谱;用化学质量平衡(CMB)受体模型解析其来源。结果表明,杭州市PM2.5和PM10污染较严重,其年均浓度分别为77.5μg/m3和111.0μg/m3;各主要源类对PM2.5的贡献率依次为机动车尾气尘21.6%、硫酸盐18.8%、煤烟尘16.7%、燃油尘10.2%、硝酸盐9.9%、土壤尘8.2%、建筑水泥尘4.0%、海盐粒子1.5%。各主要源类对PM10贡献率依次为土壤尘17.0%、机动车尾气尘16.9%、硫酸盐14.3%、煤烟尘13.9%、硝酸盐粒8.2%、建筑水泥尘8.0%、燃油尘5.5%、海盐粒子3.4%、冶金尘3.2%。

济南市环境空气中TSP和PM_(10)来源解析研究

于1999—2000年在济南市5个采样点分别采集了环境空气中的总悬浮颗粒物(TSP)和可吸入颗粒物(PM10),用化学质量平衡(CMB)受体模型和二重源解析技术解析了TSP和PM10的来源。结果表明,各主要源类对TSP的贡献率依次为:扬尘34%,煤烟尘25%,土壤风沙尘18%,机动车尾气尘6%,建筑水泥尘2%,其他15%;对PM10的贡献率依次为:扬尘30%,煤烟尘27%,土壤风沙尘15%,机动车尾气尘9%,建筑水泥尘3%,其他16%。

海口市PM2.5和PM10来源解析

以海口市为例,研究了我国典型热带沿海城市——海口市环境空气颗粒物的污染特征和主要来源.2012年春季和冬季在海口市区4个采样点同步采集了环境空气中PM10和PM2.5样品,同时采集了多种颗粒物源样品,并使用多种仪器分析方法分析了源与受体样品的化学组成,建立了源化学成分谱.使用CMB(化学质量平衡)模型对海口市大气颗粒物进行源解析.结果表明:污染源贡献具有明显的季节特点,并存在一定的空间变化.冬季城市扬尘、机动车尾气尘、二次硫酸盐和煤烟尘是海口市PM10和PM2.5中贡献较大的源,在PM10和PM2.5中贡献率分别为23.6%、16.7%,17.5%、29.8%,13.3%、15.7%和13.0%、15.3%;春季机动车尾气尘、城市扬尘、建筑水泥尘和二次硫酸盐是海口市PM10和PM2.5中贡献较大的源,在PM10和PM2.5中贡献率分别为27.5%、35.0%,20.2%、14.9%,12.8%、6.0%和9.5%、10.5%.冬季较重的颗粒物污染可能来自于华南内陆地区的区域输送,特别是,本地排放极少的煤烟尘和二次硫酸盐受区域输送的影响更为显著.

无锡市区环境空气中PM10来源解析

于2005年采集了无锡市区PM10源和受体样品,并测定了无机元素、水溶性离子和碳等组分的含量.采用OC/EC〔即ρ(OC)/ρ(EC)〕最小比值法确定了二次有机碳(Secondary Organic Carbon)对PM10的贡献,并据此重新构建了受体化学成分谱,使用化学质量平衡受体模型(CMB)和二重源解析技术对无锡市区的PM10来源进行了解析.结果表明:城市扬尘是无锡市环境空气中PM10的主要来源,其分担率达50.49%;煤烟尘和机动车尾气尘的分担率也分别为13.97%和7.80%;其他重要源类按分担率依次为二次有机碳,SO42-,建筑水泥尘,NO3-,土壤尘和钢铁尘等,其中二次有机碳年贡献值为6.94μg/m3,年分担率为6.18%.

重庆主城区春季大气PM_(10)及PM_(2.5)中多环芳烃来源解析

于2012年春季采集了重庆主城区和缙云山共6个环境采样点的大气PM10、PM2.5样品,同步采集了燃煤尘、机动车尾气尘、施工机械尾气尘、船舶尾气尘、餐饮油烟尘、生物质燃烧尘及土壤尘等7类污染源,采集到有效受体样品139个、有效源样品233个,使用GC-MS分析样品中18种PAHs的质量浓度（ρ）,分析了PM10、PM2.5上载带PAHs的污染特征,并分别运用比值法、主成分分析法及CMB（化学质量平衡）受体模型法对PM10、PM2.5中的PAHs进行来源解析,所得源解析结果较为一致.结果表明：重庆主城区大气PM10、PM2.5中ρ（PAHs）较低,ρ（PAHs）分别为22.03~31.71、19.02~29.92 ng/m^3,其中位于工业区新山村采样点的ρ（PAHs）最高.PM10载带的PAHs有86%~99%集中在PM2.5中,说明PAHs主要富集在PM2.5中.重庆主城区大气PM10、PM2.5载带的PAHs主要来自机动车尾气尘和燃煤尘的贡献,这2类源对PM10的贡献率分别为25.89%、32.80%;而在PM2.5中,机动车尾气尘的贡献率较高,可达62%左右.

南京市PM2.5物理化学特性及来源解析

在夏、冬两季,分别在南京市4个站点进行为期7天的气溶胶PM2.5采样,同步采集并分离主要排放源的PM2.5样品,用X射线荧光光谱仪(XRF)分析得到气样及源样中PM2.5的化学成分,对南京市PM2.5的物理化学特性、富集因子进行了分析,并应用化学质量平衡法(CMB)计算各类源对气溶胶PM2.5的贡献。结果表明,南京市PM2.5的夏、冬平均值分别为69.1、139.5μg.m-3,PM2.5/PM10的全年平均值为63.9%;富集成分中,S、As、Zn、Pb等主要来源于人为污染源,Na则主要来源于海洋。来源解析的结果表明,各类污染源对南京市气溶胶PM2.5的贡献率分别为:扬尘37.28%、煤烟尘30.34%、硫酸盐9.87%、建筑尘7.95%、汽车尘2.98%、冶炼尘2.57%、其他源9.01%。作者还对扬尘中的PM2.5进行了来源解析。