Identification of long-range transport pathways and potential sources of PM2.5 and PM10 in Beijing from 2014 to 2015

Trajectory clustering, potential source contribution function (PSCF) and concentration-weighted trajectory (CWT) methods were applied to investigate the transport pathways and identify potential sources of PM_2.5 and PM₁₀ in different seasons from June 2014 to May 2015 in Beijing. The cluster analyses showed that Beijing was affected by trajectories from the south and southeast in summer and autumn. In winter and spring, Beijing was not only affected by the trajectories from the south and southeast, but was also affected by trajectories from the north and northwest. In addition, the analyses of the pressure profile of backward trajectories showed that backward trajectories, which have important influence on Beijing, were mainly distributed above 970 hPa in summer and autumn and below 950 hPa in spring and winter. This indicates that PM_2.5 and PM₁₀ were strongly affected by the near surface air masses in summer and autumn and by high altitude air masses in winter and spring. Results of PSCF and CWT analyses showed that the largest potential source areas were identified in spring, followed by winter and autumn, then summer. In addition, potential source regions of PM₁₀ were similar to those of PM_2.5. There were a clear seasonal and spatial variation of the potential source areas of Beijing and the airflow in the horizontal and vertical directions. Therefore, more effective regional emission reduction measures in Beijing''s surrounding provinces should be implemented to reduce emissions of regional sources in different seasons.     

基于曲面响应建模的PM2.5可控人为源贡献解析

以东莞市PM_(2.5)重污染月份为例,使用强力法(Brute Force)和RSM/CMAQ曲面响应模型法分别解析了珠三角地区人为源排放对东莞PM_(2.5)的贡献,以及区域传输的可控人为源SO_2、NO_x和一次颗粒物(PM)在不同控制比例下(25%、50%、75%和100%)对东莞PM_(2.5)的累积浓度贡献.强力法研究结果表明,2014年1月珠三角地区人为源二次转化对东莞市PM_(2.5)的贡献(约58.10%)大于一次PM排放贡献(约41.90%),其中,人为源NH_3排放贡献最大,约占总量的21.66%.RSM/CMAQ动态源贡献结果显示,东莞市PM_(2.5)的人为可控源排放贡献(SO_2、NO_x和一次PM)占比为82.17%,受本地排放影响较大,且叠加区域排放的影响;一次PM减排对PM_(2.5)环境浓度的贡献高于仅减排SO_2和NO_x.在减排比例较低时,一次PM减排可有效削减东莞市PM_(2.5)浓度;随控制比例加大,二次前体物(SO_2和NO_x)减排对东莞市PM_(2.5)浓度削减率的影响加大.进一步使用HYSPLIT模式和轨迹聚类分析方法研究了2014年1月东莞市PM_(2.5)污染传输过程.结果显示,该时段共有6条长、短距离污染传输路径,污染物主要来自东莞市东、东北及东南方向,途经其上风向区域(惠州、深圳和广州等)传输至东莞;惠州是各主导上风向出现频率最高的城市,因而其区域传输对东莞PM_(2.5)的贡献也较大,深圳次之.     

双级虚拟撞击采样器应用于固定污染源PM10和PM2.5排放测量

为贯彻落实《大气污染防治行动计划》,环境保护部指导各地开展大气污染源排放清单编制工作,其中包括固定源PM_(10)和PM_(2.5)的排放清单.但目前国内尚无固定源PM_(10)和PM_(2.5)标准采样方法.本研究提出了适合我国固定源PM_(10)和PM_(2.5)测量的双级虚拟撞击采样方法,开发了相应的分级采样系统,并用该方法对各类固定源进行了现场测试.测试结果表明,所测试的煤粉炉电厂的烟囱入口PM_(2.5)质量浓度为(0.93±0.03)mg·m~(-3),PM_(10)质量浓度为(1.13±0.11)mg·m~(-3).所测试的垃圾焚烧电厂的烟囱排放口PM_(2.5)质量浓度为(3.3±0.65)mg·m~(-3),PM_(10)质量浓度为(6.9±0.86)mg·m~(-3).所测试的大型循环流化床发电厂的烟囱排放口PM_(2.5)质量浓度为(0.59±0.04)mg·m~(-3),PM_(10)质量浓度为(1.12±0.16)mg·m~(-3).所测试的钢铁转炉的烟囱排放口PM_(2.5)质量浓度为(0.15±0.04)mg·m~(-3),PM_(10)质量浓度为(0.43±0.15)mg·m~(-3).     

重庆主城区春季大气PM10及PM2.5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、PM_2.5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、PM_2.5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、PM_2.5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、PM_2.5中ρ(PAHs)较低,ρ(PAHs)分别为22.03~31.71、19.02~29.92 ng/m3,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PM_2.5中,说明PAHs主要富集在PM_2.5中. 重庆主城区大气PM₁₀、PM_2.5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PM_2.5中,机动车尾气尘的贡献率较高,可达62%左右.      

A case study of development and application of a streamlined control and response modeling system for PM2.5 attainment assessment in China

This article describes the development and application of a streamlined air control and response modeling system with a novel response surface modeling-linear coupled fitting method and a new module to provide streamlined model data for PM_(2.5) attainment assessment in China.This method is capable of significantly reducing the dimensions required to establish a response surface model,as well as capturing more realistic response of PM_(2.5) to emission changes with a limited number of model simulations.The newly developed module establishes a data link between the system and the Software for Model Attainment Test—Community Edition(SMAT-CE),and has the ability to rapidly provide model responses to emission control scenarios for SMAT-CE using a simple interface.The performance of this streamlined system is demonstrated through a case study of the Yangtze River Delta(YRD) in China.Our results show that this system is capable of reproducing the Community Multi-Scale Air Quality(CMAQ) model simulation results with maximum mean normalized error 3.5%.It is also demonstrated that primary emissions make a major contribution to ambient levels of PM_(2.5) in January and August(e.g.,more than50%contributed by primary emissions in Shanghai),and Shanghai needs to have regional emission control both locally and in its neighboring provinces to meet China's annual PM_(2.5)National Ambient Air Quality Standard.The streamlined system provides a real-time control/response assessment to identify the contributions of major emission sources to ambient PM_(2.5)(and potentially O_3 as well) and streamline air quality data for SMAT-CE to perform attainment assessments.     

Soil emissions of NO, N2O and CO2 from croplands in the savanna region of central Brazil

In the last 40 years, a large area of savanna vegetation in Central Brazil (Cerrado) has been converted to agriculture, with intensive use of fertilizers, irrigation and management practices. Currently, the Cerrado is the main region for beef and grain production in Brazil. However, the consequences of these agricultural practices on NO, N₂O and CO₂ emissions from soil to atmosphere are still poorly investigated. The objectives of this study were to quantify soil emissions of NO-N, N₂O-N and CO₂-C in different no-till cultivation systems in comparison with native savanna vegetation. The agricultural areas included: (a) the maize and Brachiaria ruzizienses intercropping system followed by irrigated bean in rotation; (b) soybean followed by natural fallow; and (c) cotton planting over B. ruzizienses straw. The study was performed from August 2003 to October 2005 and fluxes were measured before and after planting, after fertilizations, during the growing season, before and after harvesting. NO-N fluxes in the soybean field were similar to those measured in the native vegetation. In the cornfield, higher NO-N fluxes were measured before planting than after planting and pulses were observed after broadcast fertilizations. During Brachiaria cultivation NO-N fluxes were lower than in native vegetation. In the irrigated area (bean cultivation), NO-N fluxes were also significantly higher after broadcast fertilizations. Most of the soil N₂O-N fluxes measured under cultivated and native vegetation were very low (<0.6 ng N₂O-N cm⁻² h⁻¹) except during bean cultivation when N₂O-N fluxes increased after the first and second broadcast fertilization with irrigation and during nodule senescence in the soybean field. Soil respiration values from the soybean field were similar to those in native vegetation. The CO₂-C fluxes during cultivation of maize and irrigated bean were twice as high as in the native vegetation. During bean cultivation with irrigation, an increase in CO₂-C fluxes was observed after broadcast fertilization followed by a decrease after the harvest. Significantly lower soil C stocks (0-30 cm depth) were determined under no-tillage agricultural systems in comparison with the stocks under savanna vegetation. Fertilizer-induced emission factors of N oxides calculated from the data were lower than those indicated by the IPCC as default.     

北京市大气环境PM2.5和PM1及其碳质组分季节变化特征及来源分析

大气颗粒物是影响我国大多数城市环境空气质量的首要污染物,近年来随着监测技术的进步和采样设备的改进,相关研究对象逐渐从大粒径的PM_10、PM_(2.5)转移到更小粒径的PM_1上.碳质组分是大气颗粒物的重要组成部分.以北京市为研究区域,选取2016年7月、10月及2017年1月、4月作为4个季节的代表月,对大气环境中的PM_(2.5)和PM_1进行采集,分析了二者的质量浓度和季节变化特征.采用两层嵌套气象-空气质量模型系统(WRF-CMAQ)耦合模型对采样时段进行了模拟,分析观测期间PM_(2.5)和PM_1的来源贡献,并使用因子分析法解析了碳质组分的来源.结果表明,PM_(2.5)和PM_1的质量浓度均呈现春、夏、秋、冬这4个季节递增的趋势;PM_1是PM_(2.5)中的主要组成,而且秋冬季节随着灰霾发生频率的增加,PM_1质量浓度占PM_(2.5)的比值明显升高;北京市大气环境中存在明显的二次污染,且SOC更容易在粒径更小的PM_1中积聚.散煤燃烧、机动车尾气排放、居民面源及生物质燃烧排放是北京市大气颗粒物的重要贡献来源;汽油车尾气、柴油车尾气、生物质燃烧和燃煤排放是北京市大气颗粒物中碳质组分的主要来源.     

基于16S rRNA基因测序法分析北京霾污染过程中PM2.5和PM10细菌群落特征

2013年1月8~14日,北京出现了严重的霾污染.霾污染时高浓度的大气颗粒物增加了暴露人群的健康风险,而大气中的微生物也可能带来一些风险,但目前对霾污染时大气中微生物组成了解较少.本研究选取了2013年1月8~14日北京7d的PM2.5和PM10采样样本,通过对细菌16S rRNA基因V3区扩增和Mi Seq测序,得到PM2.5和PM10中的细菌群落结构特征,并将结果与相同采样样本的宏基因组测序结果及三项国外基于16S rRNA基因测序方法的大气中细菌研究结果进行了比较.研究发现7 d连续采样条件下,PM2.5中细菌群落结构在门和属的水平上均差别不大.在属级别上,节杆菌属(Arthrobacter)和弗兰克氏菌属(Frankia)是北京冬季大气中细菌群落的主要类群.16S rRNA基因测序与宏基因组测序结果对比分析发现,在属级别上,两种分析方法中有39个相同的属类群(两种分析方法丰度前50的细菌类群合并所得),弗兰克氏菌属(Frankia)和副球菌属(Paracoccus)在16S rRNA基因测序分析结果中相对含量较多,而考克氏菌属(Kocuria)和地嗜皮菌属(Geodermatophilus)在宏基因组测序结果中相对含量较高.在门和属的水平上,PM2.5和PM10中细菌群落结构特征呈现出相似的规律.在门水平上,放线菌门(Actinobacteria)在PM2.5中的相对百分比较大,而厚壁菌门(Firmicutes)在PM10中的相对百分比较大.在属水平上,梭菌属(Clostridium)在PM10中的相对百分比较大.与三项国外基于16S rRNA基因测序研究结果对比发现,尽管在采样地点和采样时间上有较大差异,大气中普遍存在一些相同的细菌类群,且近地面大气细菌群落结构特征相似度较高,区别于高空对流层中细菌群落结构特征.     

基于虚拟撞击原理的固定源PM10/PM2.5采样器的研制

目前我国尚无固定源PM2.5采样标准方法,现有商业化的固定源PM2.5采样器在使用中存在明显不足,因此本研究开发了一种固定源PM10/PM2.5双级虚拟撞击采样器.经实验室标定,该采样器切割效率曲线优于国际标准ISO 7708:1995对采样器的规定,采样器横截面直径为74 mm,满足我国固定源采样口尺寸要求.采样器既可以安装滤膜,也可以安装滤筒,适用于不同浓度的烟尘采样.虚拟撞击器的切割点与次流所占比值呈负相关,比值减小时,切割点增大.为降低颗粒物损失,虚拟撞击器喷嘴距收口的距离至少应为喷嘴直径的1.5~2倍.     

基于LMDI分解方法的河北省PM2.5排放驱动因素分析

研究了河北省1990—2015年PM_(2.5)的排放总量及各个产业部门的排放量分布,并应用LMDI模型对五大部门PM_(2.5)排放变化的驱动因素进行了较全面的分解分析,以找出每个部门中PM_(2.5)排放变化的关键驱动因素.同时,通过对原始数据的整理分析和模型计算,结果发现:①工业、生活消费、农业部门是河北省PM_(2.5)排放的三大来源.其中,工业部门是PM_(2.5)排放的最主要贡献部门,占PM_(2.5)总排放量的70%;生活消费部门的贡献位居第二,占比约24%.②对于生产端,经济规模总量扩张是PM_(2.5)排放量增加的最主要原因;能源利用效率的提高和污染治理工艺水平的改进使得各部门的排放强度减弱,明显缓解了PM_(2.5)的排放增长,部门经济结构的优化也可带来明显的减排效应.③对于消费端,人均收入的提高促进了PM_(2.5)排放的增加,倡导绿色消费模式可以有效减缓PM_(2.5)排放.根据以上结论,本研究通过找到各部门存在的问题,为河北省大气污染治理提供参考和借鉴.     

基于中欧对比视角的货运机动车尾气排放PM2.5分析研究

基于中欧对比视角,本研究运用扩展的Kaya模型以及LMDI(Logarithmic-Mean Divisia Index)对货运机动车尾气排放PM_(2.5)的影响因素进行分析.分析表明:尽管中欧货运机动车PM_(2.5)排放近年来持续下降,但中国的排放强度远高于欧盟15国(EU15)平均水平.从分解因素来看,货运量增加是中国货运机动车PM_(2.5)排放增加的最主要影响因素,但该因素的作用自2013年以来极为微小.而EU15货运机动车PM_(2.5)排放量增加最主要的因素是较高的用车次数.此外,越来越严格的排放标准为中国和EU15降低PM_(2.5)排放都做出了突出的贡献.     

餐饮行业细颗粒物(PM2.5)排放测算方法:以上海市为例

以上海市餐饮企业为例,研究了餐饮企业PM_(2.5)排放特征以及排放测算方法.按照单位灶头、单位时间、单位就餐人次这3种计算基准,获得了不同类型餐饮企业PM_(2.5)的排放因子,并在此基础上结合2014年上海市餐饮企业活动水平测算了PM_(2.5)的排放清单.结果表明,餐饮企业排放PM_(2.5)的浓度范围0.1~1.8 mg·m~(-3),甚至超过国家饮食业标准中关于油烟1mg·m~(-3)的排放限值;PM_(2.5)中OC质量贡献超过50%,OC/EC比值的变化范围为58.8~752.3,平均值为128.4,可作为餐饮排放的示踪特征.企业规模是影响餐饮企业PM_(2.5)排放因子的重要因素.按照灶头活动、餐饮作业时间以及就餐人次这3种方法计算得出的餐饮企业PM_(2.5)排放因子均表明,大型、中型企业明显高于小型和微型企业(食堂、快餐).基于上述3种排放因子,计算2014年上海PM_(2.5)排放量相对一致,表明本研究获得基于3种活动水平的排放因子比较可靠,未来可应用于其他城市餐饮企业排放清单的核算.     

临沂市PM2.5和PM10中元素分布特征及来源解析

为探究临沂市PM_(2.5)和PM_(10)中元素的污染特征及来源,于2016年12月至2017年10月对临沂市环境空气中PM_(2.5)和PM_(10)进行了同步采样.利用电感耦合等离子体质谱仪(ICP-MS)和电感耦合等离子体发射光谱仪(ICP-OES)测定了其中的23种元素,并采用富集因子法和PMF法分析其来源.结果表明,采样期间临沂市PM_(2.5)和PM_(10)中主要元素为Si、Ca、Al、Fe、K、Na和Mg,分别占所测元素的质量分数为92.93%和94.61%. 18种元素(除Ti、Ni、Mo、Cd和Mg)的浓度水平在冬春季最高,夏秋季最低.其中Si、Al、Ca、K和Na表现为春季浓度最高,主要分布在粗颗粒中;Cu、Zn、Pb和Sb表现为冬季浓度最高,主要分布在细颗粒中.富集因子结果表明Cd、Sb和Bi元素富集程度显著,主要受燃煤、工业生产、垃圾焚烧等人为源共同影响.PMF源解析结果表明,临沂市PM_(2.5)中元素来源主要有燃煤和铜冶炼的混合源、市政垃圾焚烧源、扬尘源、机动车排放和工业源,贡献率分别为22.64%、 7.49%、 41.22%、 14.71%和13.94%.PM_(10)中元素来源主要有扬尘源、燃煤和铜冶炼的混合源、机动车排放和工业源,贡献率分别为55.47%、 19.80%、 7.48%和12.83%.由此可见,扬尘源和燃煤与铜冶炼的混合源是临沂市颗粒物污染形成过程中的重要源类.     

西安市城市降尘和土壤尘PM10和PM2.5中碳组分特征

为研究西安市城市降尘和土壤尘PM₁₀和PM_2.5中碳组分污染特征,丰富大气降尘的成分谱库,于2015年4~5月收集了西安市城区5个点位的城市降尘和周边16个点位的土壤尘样品,通过ZDA-CY01颗粒物再悬浮采样器获得PM₁₀和PM_2.5的滤膜样品,使用Model5L-NDIR型OC和EC分析仪测定了样品中的有机碳（OC）和元素碳（EC）,定量分析了西安市城市降尘和土壤尘PM₁₀和PM_2.5中碳组分特征及其主要来源.结果表明,不同站点降尘PM₁₀和PM_2.5中OC的占比差异较大,分别为6.0%~19.4%和7.6%~29.8%.不同站点降尘PM₁₀和PM_2.5中EC的占比较小,在城市站点的占比分别为0.6%~2.2%和0.2%~3.6%,而在多数外围土壤尘中几乎检测不到EC的存在.PM₁₀中含碳组分的占比为：城市降尘>外部对照>河滩土>土壤尘,PM_2.5中含碳组分的占比为：城市降尘>土壤尘>外部对照>河滩土.不同站点降尘含碳气溶胶均以OC为主,在城市降尘中相对较低,在PM₁₀和PM_2.5中OC占总碳（TC）的比值分别为85.2%~95.3%和87.9%~98.9%;在土壤尘中OC的占比较高,均超过99%.含碳物质主要集中在细颗粒物中.不同城市站点降尘中碳组分的分布具有一致性,不同土壤尘中碳组分的差异较大.城市和土壤降尘中碳组分主要受生物质燃烧、燃煤、汽油车和柴油车尾气等污染源的影响,PM₁₀和PM_2.5中含碳气溶胶的来源贡献率存在差异.     

北京市典型绿化灌木阻滞吸附PM2.5能力研究

选取北京市典型绿化灌木物种大叶黄杨、小叶黄杨、紫叶小檗、矮紫杉,结合气室模拟与实地观测的方法,综合测定不同树种对PM2.5的吸附能力.同时,收集2012年12月~2013年5月间北京市区PM2.5浓度值,分析了北京市冬春季PM2.5污染特征.结果表明,由气室实验得到的4种植物对PM2.5阻滞吸附能力排序为:紫叶小檗>小叶黄杨>矮紫衫>大叶黄杨,其原因主要为叶片特征差异所致;室外测量结果表明,4种物种吸附能力排序为:小叶黄杨>紫叶小檗>矮紫衫>大叶黄杨.气室模拟与室外实测结果均表明,小叶黄杨和紫叶小檗具有较强的阻滞吸附PM2.5的能力;气室模拟与室外观测实验中植物阻滞吸附PM2.5能力的大小略有差异,其原因应与植物结构相关.同时,通过分析北京市PM2.5浓度的季节性变化,发现北京市冬季的PM2.5浓度值尤为高,且常绿灌木植物仍能表现出较好的阻滞吸附PM2.5的能力.     

基于快速聚类方法分析常州市区PM2.5的统计特性

运用统计方法研究常州市区2013~2014年6个国控点六项基本污染物(SO_2、NO_2、CO、O_3、PM_(2.5)和PM_(10))月平均浓度变化,结果表明,除O_3外,其它五项污染物月平均浓度夏季较低冬季较高.颗粒物与风速之间的关系为PM_(2.5)浓度随风速的升高一直降低,PM_(10)随风速的升高浓度先降低后升高.采用快速聚类分析(k-means)并运用SWV和DIV指数对六项基本污染物进行分类,得到4个样本分类.与依据颗粒物化学成分或粒径谱对PM进行源解析方法不同,本研究更多是从PM_(2.5)与其它污染物相关关系以及污染程度等角度按照欧式距离进行分类.不同类中PM_(2.5)来源明显不同,类1中PM_(2.5)与化石燃料燃烧排放密切相关,类2与O_3密切相关,类3与城市不完全燃烧排放、区域灰霾污染密切相关,类4可以归类于城市"背景"类.快速聚类分析结果也表明常州市区PM_(2.5)有着复杂的来源.     

海口市PM_(2.5)和PM_(10)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.     

北京市PM2.5时空分布特征及其与PM10关系的时空变异特征

PM_(2.5)时空分布特征及其与其它污染物的相关关系是PM_(2.5)时空统计分析的主要研究内容.然而,现有的方法直接从监测站点的角度对时空分布特征进行分析,难以有效地揭示PM_(2.5)浓度的聚集分布特征;同时,常用的地理加权回归在对PM_(2.5)与其它污染物间关系进行建模的过程中,缺乏同时考虑时间异质性与空间异质性,从而不能准确地描述依赖关系的时空变异特征.为此,首先借助于空间聚类分析技术,对北京市2014年PM_(2.5)浓度的聚集结构进行探测,在此基础上,通过聚集结构来分析PM_(2.5)季节性时空分布特征.然后,利用地理时空加权回归对北京市PM_(2.5)与PM_(10)季节平均浓度间关系进行建模,依据回归结果分析PM_(2.5)-PM_(10)间关系的时空变异特征.实验结果表明,春夏季节PM_(2.5)污染程度及空间变异程度均低于秋冬季节,各季节PM_(2.5)浓度均表现为北部浓度低、南部浓度高的空间分布特征;地理时空加权回归具有更好的拟合效果,由回归系数进一步可发现,春夏季PM_(2.5)-PM_(10)相关性低于秋冬季PM_(2.5)-PM_(10)相关性;各季节均表现为西北部PM_(2.5)-PM_(10)的相关性高于东南部PM_(2.5)-PM_(10)的相关性.     

Seasonal variation and source apportionment of organic and inorganic compounds in PM2.5 and PM10 particulates in Beijing, China

The distribution and source of the solvent-extractable organic and inorganic components in PM 2.5(aerodynamics equivalent diameter below 2.5 microns),and PM 10(aerodynamics equivalent diameter below 10 microns) fractions of airborne particles were studied weekly from September 2006 to August 2007 in Beijing.The extracted organic and inorganic compounds identified in both particle size ranges consisted of n-alkanes,PAHs(polycyclic aromatic hydrocarbons),fatty acids and water soluble ions.The potential emission sources of these organic compounds were reconciled by combining the values of n-alkane carbon preference index(CPI),%waxC n,selected diagnostic ratios of PAHs and principal component analysis in both size ranges.The mean cumulative concentrations of n-alkanes reached 1128.65ng/m3 in Beijing,74% of which(i.e.,831.7ng/m3) was in the PM 2.5 fraction,PAHs reached 136.45ng/m3(113.44ng/m3 or 83% in PM 2.5),and fatty acids reached 436.99ng/m3(324.41ng/m3 or 74% in PM 2.5),which resulted in overall enrichment in the fine particles.The average concentrations of SO42-,NO3-,and NH4+ were 21.3±15.2,6.1±1.8,12.5±6.1μg/m3 in PM 2.5,and 25.8±15.5,8.9±2.6,16.9±9.5μg/m3 in PM 10,respectively.These three secondary ions primarily existed as ammonium sulfate((NH4)2SO4),ammonium bisulfate(NH4HSO4) and ammonium nitrate(NH4NO3).The characteristic ratios of PAHs revealed that the primary sources of PAHs were coal combustion,followed by gasoline combustion.The ratios of stearic/palmitic acid indicated the major contribution of vehicle emissions to fatty acids in airborne particles.The major alkane sources were biogenic sources and fossil fuel combustion.The major sources of PAHs were vehicular emission and coal combustion.     

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.