南京江北新区大气单颗粒来源解析及混合状态

利用单颗粒气溶胶飞行时间质谱（SPAMS）于2015年12月1~31日对南京江北新区大气单颗粒进行了测量,共采集到同时含有正负离子谱图的颗粒747.8万个.结果表明,监测期间南京江北新区总体空气质量较差,污染天气占比为49.2%,SPAMS所捕获的颗粒数与PM_2.5质量浓度的相关性达到0.83,因此颗粒物数浓度在一定程度上能够用来反映大气污染状况,监测点主要污染源包括燃煤源以及机动车尾气源,工业工艺源污染占比居第3位,3种源的总贡献率达到63.5%.从整体上看,PM_2.5质量浓度的升高大多伴随着燃煤及机动车尾气占比的升高,EC、混合碳（ECOC）与OC在生物质燃烧、扬尘、汽车尾气排放、燃煤燃烧以及工业源中均与NO₂^-、NO₃^-以及SO₄^-有较高的混合程度.     

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.     

浙江金华秋季干气溶胶中主要化学组分的消光贡献解析

造成雾霾事件的主要原因是高浓度的大气细颗粒物污染.为了深入研究大气细颗粒物的消光来源,本研究采用高时间分辨率气溶胶观测仪器获得了浙江金华秋季PM1主要化学组分浓度及干气溶胶吸收系数和散射系数演变情况.结合有机气溶胶正矩阵因子解析模型(PMF)和多元线性回归方法,建立了拟合优度很高(R2=0.977)的细颗粒物中主要化学组分与干气溶胶消光系数间的定量关系模型.结果表明,观测期间消光贡献最大的是硫酸铵,贡献率为35.1%;其次是硝酸铵,贡献率为26.7%;二次有机气溶胶(SOA)、生物质燃烧有机气溶胶(BBOA)、黑碳(BC)及氯化铵的消光贡献率分别为14.3%、11.2%、8.7%、4.0%.在一些特定污染时段,BBOA具有最大的消光贡献,是导致此时大气能见度大幅度衰减的首要因子.     

中国秸杆焚烧及民用燃煤棕色碳排放的初步研究

近年来的研究表明,在BC(黑碳)和OC(有机碳)之间,还存在着一种有弱吸光能力的OC,因大多显棕黄色而被称为BrC(brown carbon,棕色碳). 广泛存在的秸杆焚烧和冬季大量民用燃煤的使用,使国内BrC排放严重,但鲜见对其排放量的测算. 采用七波段黑碳仪(aethalometer)方法,对夏季小麦秸杆焚烧过程及冬季民用炉燃煤过程产生的烟气进行现场监测,根据BrC与BC的光谱关联性差别,分化出R_BrC/BC(总光学衰减中BrC和BC的相对贡献). 结果表明:麦秆焚烧和民用燃煤烟气的R_BrC/BC分别为1.754±0.278和0.183±0.142. 借助R_BrC/BC值,结合现有的BC排放清单(2000年),初步推算出中国民用燃煤和秸杆田间焚烧BrC的排放总量(以BC当量计,下同)为(270.6±101.6)Gg,接近同期BC排放量的一半;其中秸杆焚烧的BrC排放量为(175.4±27.8)Gg,约占二者总量的65%;民用燃煤的BrC排放量为(95.2±73.7)Gg,约占35%. 该研究结果可为更全面的BrC排放测算奠定基础,并为研究BrC的大气化学及辐射强迫提供依据.      

Source apportionment of PM2.5 light extinction in an urban atmosphere in China

Haze in China is primarily caused by high pollution of atmospheric fine particulates (PM_2.5). However, the detailed source structures of PM_2.5 light extinction have not been well established, especially for the roles of various organic aerosols, which makes haze management lack specified targets. This study obtained the mass concentrations of the chemical compositions and the light extinction coefficients of fine particles in the winter in Dongguan, Guangdong Province, using high time resolution aerosol observation instruments. We combined the positive matrix factor (PMF) analysis model of organic aerosols and the multiple linear regression method to establish a quantitative relationship model between the main chemical components, in particular the different sources of organic aerosols and the extinction coefficients of fine particles with a high goodness of fit (R² = 0.953). The results show that the contribution rates of ammonium sulphate, ammonium nitrate, biomass burning organic aerosol (BBOA), secondary organic aerosol (SOA) and black carbon (BC) were 48.1%, 20.7%, 15.0%, 10.6%, and 5.6%, respectively. It can be seen that the contribution of the secondary aerosols is much higher than that of the primary aerosols (79.4% versus 20.6%) and are a major factor in the visibility decline. BBOA is found to have a high visibility destroying potential, with a high mass extinction coefficient, and was the largest contributor during some high pollution periods. A more detailed analysis indicates that the contribution of the enhanced absorption caused by BC mixing state was approximately 37.7% of the total particle absorption and should not be neglected.     

2013年1月宁波市PM_(2.5)污染的形成过程与传输规律

为了深入认识宁波市冬季细颗粒物(PM2.5)的污染特征和主要影响因素的作用规律,利用Models-3/CMAQ模式系统对2013年1月宁波市的PM2.5污染形成过程进行了模拟分析.结果表明,宁波市PM2.5的重点污染区主要分布在市区、北部地区及东部沿海,除了受到局地污染源排放的影响外,对比非污染的情况,大气输入和气溶胶生成作用的增强是引起PM2.5污染的主导因素,其中水平传输过程对PM2.5浓度升高的贡献最为突出.气溶胶过程的贡献在近地面(0~80 m)最显著,随着高度升高而逐渐减弱.硝酸盐在局地二次生产的细颗粒物中占主要份额(~70%).对于硫酸盐,局地二次生成所占的比例很低,主要来自宁波局地排放和宁波以外地区的大气传输(贡献比例分别为44%和40%).宁波市的PM2.5污染主要受到来自北向沿岸气团(占比54%)、西北向大陆气团(占比21%)和西向局地气团(占比25%)的传输影响.在西北方向短距离区域传输的作用下PM2.5浓度最高;在我国中东部大范围灰霾天气的影响下,西北向和北向的长距离传输作用也会导致宁波地区的PM2.5污染.     

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.     

北京冬季PM2.5中有机气溶胶的化学特征和来源解析

为探讨北京冬季大气细颗粒物（PM_2.5）中有机气溶胶的浓度水平、分布特征和来源变化,对2016年11月10日~12月10日采集的北京大气PM_2.5样品进行气相色谱-质谱测定,定量了129种颗粒有机物（POM）,约占有机物总量的（9.3±1.2）%.其中含量最高的是糖类,仅左旋葡聚糖即可占到定量有机物的18%,其次是正构烷酸、正构烷烃、二元羧酸和多环芳烃.根据POM示踪物的变化特征,分析了供暖和生物质燃烧传输对北京冬季有机气溶胶的影响.相比于非供暖期间,供暖期间化石燃料示踪物藿烷的质量浓度及在有机物中的占比都明显升高,各组分间的分布也更加趋向于燃煤排放的特征.正构烷烃主峰碳数和奇偶分布的变化,反映了化石燃料贡献增强的影响.生物质燃烧示踪物左旋葡聚糖的浓度权重轨迹（CWT）模型结果表明,北京周围区域的秸秆燃烧污染会通过传输影响北京的有机气溶胶组成.利用分子示踪-化学质量平衡（MM-CMB）模型对2016年北京冬季有机碳（OC）进行了来源解析,并与2006年的结果进行比较,以定量10年间各污染来源贡献发生的变化.2016年与2006年相比,机动车对有机气溶胶贡献明显增加,燃煤和木材燃烧的贡献则大幅度降低,餐饮排放的贡献也不容忽视.因此,控制机动车和餐饮源的排放对改善北京冬季PM_2.5污染问题至关重要.     

Characteristics, evolution, and regional differences of biomass burning particles in the Sichuan Basin, China

The Sichuan Basin has experienced serious air pollution from fine particulate matter (PM_2.5) in the past few years with biomass burning has been identified as a major source of PM_2.5 in this region. We used single particle aerosol mass spectrometer to investigate the characteristics of biomass burning particles in three interacting cities representing different types of urban environment in the Sichuan Basin. A total of 739,794, 279,610, and 380,636 biomass burning particles were detected at Ya'an, Guang'an, and Chengdu, which represented 42%, 69%, and 61%, respectively, of the total number of particles. We analyzed the chemical composition, transportation, and evolution of biomass burning particles. The contribution of K-elemental carbon and K-secondary inorganic particles was highest in Ya'an (36%) and Guang'an (47%), respectively, reflecting the important role of fresh biomass burning particles and long-distance transport in these two cities. Air masses originating from different directions corresponded to different levels of PM_2.5 and the contributions of polluted clusters increased significantly on polluted days. Fresh and secondary inorganic biomass burning particles increased pollution at Ya'an and Guang'an, respectively, but dominated different stages of pollution in Chengdu. K-nitrate particles were formed by photochemical reactions, whereas K-sulfate particles were formed by both photochemical and liquid-phase reactions. Investigation of the degree of particle aging showed that there were more fresh particles at Ya'an and more aged particles at Guang'an. These results are useful in helping our understanding of the characteristics of biomass burning particles and evaluating their role in PM_2.5 pollution in the Sichuan Basin.     

合肥市PM2.5和PM10中元素组成特征及重污染成因分析

通过对合肥市2018年大气PM_(2.5)和PM_(10)样品的采集,结合对样品中无机元素的测试分析,研究了PM_(2.5)和PM_(10)中的元素组成特征,并根据合肥市重污染天气和非污染天气下大气颗粒物中元素组成的差异性分析,探讨了合肥市重污染天气的主要污染物来源与成因.结果表明,本研究所检测的16种元素中,Si、Al、Mg和Ca这4种地壳元素在重污染天气的质量浓度较非污染天气低,可能是重污染状况下的静风天气引起的地面扬尘减少所造成的; S、Na、K、Cl、Ti、Fe、P、Cu和Ni等元素在重污染天气的质量浓度较非污染天气高,其中S元素的增幅最大,在重污染天气所占的比重和富集因子也大大提升,表明燃煤排放源是合肥地区重污染天气的主要污染成因;重金属元素含量低、富集因子大,其主要来源可能为垃圾焚烧、机动车和工业排放.     

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.     

东南亚生物质燃烧输送影响我国西南气溶胶辐射特性研究

利用2013年地基CE-318太阳光度计观测数据,结合中分辨率成像光谱仪（MODIS）遥感产品和HYSPLIT后向轨迹分析,研究了一次东南亚生物质燃烧污染长距离输送至中国西南昆明站点过程（2013年4月5~8日）中,气溶胶光学特性和辐射特性的变化及其可能来源.结果表明,此次污染过程期间,我国西南地区生物质燃烧活动较少,而中南半岛地区生物质燃烧活动显著.4月5~7日,昆明站点气溶胶光学厚度（AOD）升高,消光波长指数（EAE）和吸收波长指数（AAE）均增大.此外,依据EAE和AAE分类方法,5~6日昆明站点以城市工业气溶胶为主,7~8日以生物质燃烧气溶胶为主,其细模态峰值半径（0.11μm）小于5~6日（0.15μm）,7日细模态粒子体积浓度峰值（约为0.16μm³/μm²）是5日的2倍.气溶胶直接辐射强迫（ARF）日变化结果表明4月7日气溶胶对地表的降温效应达到最大,对大气的加热作用最强.气溶胶直接辐射强迫效率值（ARFE）的变化表明生物质燃烧气溶胶对大气顶的降温作用减弱.MODIS遥感以及HYSPLIT模式后向轨迹表明,此次昆明站点生物质燃烧气溶胶主要来源于东南亚地区（主要是印度北部、印缅北部和不丹地区）.     

基于遥感资料的北京大气污染治理投资对降低PM_(2.5)的效能分析

采用北京市环境监测中心35个站点的PM2.5监测数据及MODIS Terra的大气气溶胶光学厚度L3 C051产品数据,分季度建立北京市PM2.5历史浓度遥感估算模型.结合北京大气污染物(PM10、PM2.5、SO2、NO2)年均浓度数据,对北京市2001—2012年间用于工业废气污染治理投资累计额进行了效能分析.研究表明,北京市工业废气污染治理投资对于改善大气PM10、SO2、NO2均有显著贡献,但其对于大气PM2.5污染的治理效果并不明显.可能原因包括PM2.5排放源的复杂性、相关治理措施对PM2.5的针对性、经济增长导致的区域PM2.5源排放持续增长及区域外排放的持续影响等.因此,需要采取专门的有针对性的治理措施,建立健全大气污染治理技术和激励机制,控制工业燃煤及城市交通排放,削减本地及周边源排放,以有效改善北京地区大气PM2.5污染状况.     

基于PMF模型的PM2.5中金属元素污染及来源的区域特征分析

金属元素是大气PM_2.5的重要组成成分,对人群危害性极强且兼具源特异性,分析不同经济模式地区大气细颗粒物中金属污染状况及来源差异,可以为科学规划城市产业布局和保护大气环境提供参考.通过霾/非霾期大气PM_2.5采样,使用电感耦合等离子体发射光谱仪（ICP-OES）测定成都市及仁寿县样品中18种金属元素质量浓度,分析其污染水平,并基于正定矩阵因子分解模型（PMF）解析两地大气PM_2.5中金属元素的来源.结果表明,成都市扬尘源、移动源和燃煤源特征元素占元素总和的比值大于仁寿县,而仁寿县生物质燃烧源、工业源以及燃油源特征元素占比则较高.两地Cr、Cd和As元素浓度均超标,表明PM_2.5中重金属污染严重.随着霾污染加剧,两地PM_2.5中金属元素总量上升,但增幅远低于PM_2.5浓度增长.此外,不同元素在霾期和非霾期浓度比值存在差异,成都市变化范围为0.7（Al）~2.8（Ba）,仁寿县介于0.8（Al）~3.1（Mn）之间,但总的来说两地大致呈现出燃煤和工业活动排放元素增幅较大,机动车污染源次之,扬尘源增幅较缓的状况.受地区产业布局、经济规模和发展模式的影响,大气PM_2.5中金属元素污染水平及来源呈现出不同的区域特征.在重点发展第三产业的大型城市,如成都,更易遭受交通运输和城市建设等带来的大气污染,而在仁寿等第二产业占比不断增加的郊县,其污染主要受化石燃料燃烧和工业过程排放的影响.     

烟花爆竹集中燃放的大气细颗粒物(PM2.5)成分图谱

烟花爆竹燃放是大气细颗粒物（PM_2.5）来源的途径之一.以泉州城区春节期间为例,研究烟花爆竹燃放对大气细颗粒物的影响,服务大气污染的特殊污染源管理.结果表明,烟花爆竹集中燃放时段,SO₂、PM₁₀和PM_2.5浓度明显升高,尤以PM_2.5的升高最为显著,城区PM_2.5日均浓度峰值约为年均值的4倍,涂山街点位PM_2.5小时浓度峰值约为城区年均值的21倍;燃放高峰期Al、Mg、Ba、Cu、Sr等烟花爆竹的特征元素占比迅速上升,Al⁺、Mg⁺、Ba⁺、Cu⁺间的小时数浓度高度相关;监测期间泉州城区细颗粒物主要污染源是烟花爆竹燃放和生物质燃烧,贡献占总颗粒物的一半以上,燃煤和工业工艺源的比例相对较低,均低于10.0%;集中燃放时段大气细颗粒物浓度高达0.578 mg·m^-3,此时的烟花源的贡献比例也提升到58.2%;污染过程分析表明PM_2.5浓度与烟花源的占比、数浓度的变化趋势具有趋同性.以上结果说明烟花爆竹的集中燃放是春节期间泉州大气环境恶化的主要原因.     

基于多种新型受体模型的PM2.5来源解析对比

为了解多种新型受体模型的适用性,利用正定矩阵分解/多元线性引擎2-物种比值（PMF/ME2-SR）、偏目标转换-正定矩阵分解（PTT-PMF）、正定矩阵分解（PMF）和化学质量平衡（CMB）这4种受体模型对我国北方典型城市细颗粒物(PM_2.5)数据进行同步解析并互相验证.结果发现,燃煤源（25%～26%）、扬尘源（19%～21%）、二次硝酸盐（17%～19%）、二次硫酸盐（16%）、机动车源（13%～15%）、生物质燃烧源（4%～7%）和钢铁源（1%～2%）这7种主要污染源对研究地区PM_2.5有贡献.通过比较不同模型获得的源成分谱和源贡献以及计算各源的差异系数（CD）和平均绝对误差（AAE）,发现4种模型的解析结果具有较高的一致性（平均CD值在0.6～0.7之间）,但不同模型对各污染源中组分的识别存在差异.相比于传统PMF模型,PMF/ME2-SR模型由于纳入一次源类的特征比值,能够更好地区分源谱特征较为相似的源类,如扬尘源的CD和AAE分别比PMF模型低15%和54%; PTT-PMF模型以实测一次源谱和虚拟二次源谱为约束目标,计算的二次硫...     

Quantification of emission reduction potentials of primary air pollutants from residential solid fuel combustion by adopting cleaner fuels in China

Residential low efficient fuel burning is a major source of many air pollutants produced during incomplete combustions, and household air pollution has been identified as one of the top environmental risk factors. Here we compiled literature-reported emission factors of pollutants including carbon monoxide(CO), total suspended particles(TSPs), PM2.5, organic carbon(OC),elemental carbon(EC) and polycyclic aromatic hydrocarbons(PAHs) for different household energy sources, and quantified the potential for emission reduction by clean fuel adoption. The burning of crop straws, firewood and coal chunks in residential stoves had high emissions per unit fuel mass but lower thermal efficiencies, resulting in high levels of pollution emissions per unit of useful energy, whereas pelletized biofuels and coal briquettes had lower pollutant emissions and higher thermal efficiencies. Briquetting coal may lead to 82%–88% CO, 74%–99%TSP, 73%–76% PM2.5, 64%–98% OC, 92%–99% EC and 80%–83% PAH reductions compared to raw chunk coal. Biomass pelletizing technology would achieve 88%–97% CO, 73%–87% TSP, 79%–88%PM2.5, 94%–96% OC, 91%–99% EC and 63%–96% PAH reduction compared to biomass burning. The adoption of gas fuels(i.e., liquid petroleum gas, natural gas) would achieve significant pollutant reduction, nearly 96% for targeted pollutants. The reduction is related not only to fuel change, but also to the usage of high efficiency stoves.     

Assessing the influence of environmental conditions on secondary organic aerosol formation from a typical biomass burning compound

The atmospheric chemistry in complex air pollution remains poorly understood. In order to probe how environmental conditions can impact the secondary organic aerosol (SOA) formation from biomass burning emissions, we investigated the photooxidation of 2,5-dimethylfuran (DMF) under different environmental conditions in a smog chamber. It was found that SO₂ could promote the formation of SOA and increase the amounts of inorganic salts produced during the photooxidation. The formation rate of SOA and the corresponding SOA mass concentration increased gradually with the increasing DMF/OH ratio. The addition of (NH₄)₂SO₄ seed aerosol accelerated the SOA formation rate and significantly shortened the time for the reaction to reach equilibrium. Additionally, a relatively high illumination intensity promoted the formation of OH radicals and, correspondingly, enhanced the photooxidation of DMF. However, the enhancement of light intensity accelerated the aging of SOA, which led to a gradual decrease of the SOA mass concentration. This work shows that by having varying influence on atmospheric chemical reactions, the same environmental factor can affect SOA formation in different ways. The present study is helpful for us to better understand atmospheric complex pollution.     

京津冀地区主要排放源减排对PM2.5污染改善贡献评估

研究选取2012年1月和7月作为冬夏两季代表时段,利用CMAQ/2D-VBS模型分析了冬夏两季京津冀地区主要排放源减排30%对改善区域PM_(2.5)污染的效果.结果表明,工业源对PM_(2.5)污染的贡献最大,其次是民用源,但工业源单位减排量贡献低于民用源,交通源和电厂源的整体贡献和单位减排量贡献均较小.工业部门内贡献最大的为钢铁冶金行业,其次是水泥、工业锅炉、炼焦、石灰砖瓦和化工行业.与各部门各物种排放量的比较反映出各排放源贡献大小与其一次PM_(2.5)排放水平高度相关.因京津冀地区冬季NO_x减排对PM_(2.5)形成的促进作用,以及冬季较弱的大气垂直扩散作用,各排放源夏季减排比冬季普遍更有效,交通源、电厂源以及工业源中的水泥、工业锅炉和石灰砖瓦行业夏季减排效果相比冬季优势明显.民用源由于采暖季排放较高而冬季贡献更明显,农业源因秸秆开放燃烧量大,冬季单位减排量贡献十分显著.从同等幅度减排考虑,应将工业源作为控制重点,优先控制其一次PM_(2.5)排放,在部门内进一步重点控制钢铁冶金行业的NO_x和SO_2排放、水泥行业的夏季NO_x排放以及炼焦行业的SO_2和NMVOC排放.民用源排放应着重在冬季采暖期控制.     

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.