西安市夏季大气中生物二次有机气溶胶的分子组成与粒径分布

2011年夏季8月,在西安交通大学主楼顶（距离地面约100 m）运用MOUDI采样器采集了5套不同粒径大气气溶胶样品,经TMS硅烷化后运用GC/MS定量分析了生物二次有机气溶胶（BSOA）异戊二烯氧化产物（2-甲基丁四醇, C₅-烯三醇和2-甲基甘油酸）、单蒎烯类氧化产物（顺蒎酸,蒎酮酸,3-羟基戊二酸和3-甲基-1,2,3-丁三酸）以及倍半萜烯氧化产物（倍半萜烯酸）浓度。结果显示：西安城区大气BSOA中主要以异戊二烯类为主,其次是单萜烯和倍半萜烯类BSOA,其氧化所产生的生物二次有机碳（BSOC）浓度分别为0.39 μgC·m^-3,0.13 μgC·m^-3和0.10 μgC·m^-3。粒径分布研究表明：BSOA主要富集在细颗粒物上。MBTCA/CPA特征比值表明：采样期间西安城区BSOA主要来自本地源,老化程度较低。     

基于单颗粒质谱信息气溶胶分类方法的研究进展

气溶胶质谱仪在对气溶胶颗粒的监测过程中会产生大量单颗粒的化学成分和粒径信息,基于这些质谱数据能够推测颗粒的来源。而对气溶胶来源的分析,很大程度上是由颗粒的种类决定的,故对海量数据的分类就成为不可忽略的步骤。针对气溶胶质谱仪采集到的数据,概述了用于气溶胶分类的多种方法的理论核心、分类步骤及各自的优缺点等。据此可为今后气溶胶分类方法的研究提供建设性意见。     

气体停留时间对生物滤池去除恶臭和微生物气溶胶影响

气体停留时间是影响生物滤池去除恶臭和微生物气溶胶的重要因素之一。采用小试规模的生物滤池研究了气体停留时间对城市污水处理工艺恶臭和微生物气溶胶去除特性的影响。研究结果表明:随着气体停留时间的增加,硫化氢和氨的去除率随之增加,而异养细菌和真菌的去除率降低,低的气体停留时间利于微生物气溶胶的去除,保证硫化氢、氨和微生物气溶胶均能同时高效去除的气体停留时间为40 s。随着气体停留时间的增加,生物滤池出气中分布于stage1、stage2和stage3的大粒径微生物粒子所占比例减小,而分布于stage5和stage6的小粒径微生物粒子所占比例增加。在低的气体停留时间下,生物滤池出气微生物气溶胶潜在的健康风险更大。     

大气中臭氧行为研究进展

臭氧（O3）是一种重要的大气微量气体,是影响对流层——平流层大气动力、热力、辐射、化学等过程的关键成分,在气候和环境变化中扮演非常重要的角色。本文从臭氧在大气中的行为机理入手,研究影响臭氧源和汇的因子,了解臭氧含量变化机制,对当前臭氧研究热点进行概括和总结,展望未来研究重要问题。     

利用SPAMS研究华北乡村站点(曲周)夏季大气单颗粒物老化与混合状态

利用单颗粒气溶胶飞行时间质谱(SPAMS)于2013年6月30日~7月8日对华北地区乡村站点曲周大气单颗粒粒径及其化学组成进行了在线测量,共采集到同时含有正负离子谱图的颗粒230 152个,其粒径主要集中在0.2~2.0μm.结果表明,该地区的大气颗粒物主要分为8类:元素碳(EC,55.5%)、有机碳(OC,10.7%)、钠,钾等碱金属颗粒(alkalis,17.4%)、其他金属颗粒(other metals,1.7%)、富铁颗粒(Fe-rich,6.3%)、富铅颗粒物(Pb-rich,3.1%)、沙尘颗粒(dust,4.8%),other颗粒(0.8%),观测得到的8类气溶胶颗粒中绝大部分包含46NO-2、62NO-3、80SO-3、96SO-4、97HSO-4等二次组分离子,说明这些颗粒都经历了不同的老化过程或与二次组分进行了不同程度的混合.从气溶胶类型的谱分布看,各类型颗粒数浓度峰值基本出现在700~800 nm之间,dust、Fe颗粒主要集中在粗粒径段,EC颗粒子类研究表明随着表面不断吸附NH+4、NO-3、SO2-4等二次组分,EC颗粒逐步演化成老化程度较低的NO-3吸附型EC(ECN)和严重老化的SO2-4吸附型EC(ECS)混合态,两者日变化呈现明显的负相关性,也可能随着二次有机物在EC表面吸附,形成OC/EC混合态.     

北京典型道路交通环境机动车黑碳排放与浓度特征研究

本研究对2009年北京市典型道路(北四环中路西段)进行实际交通流监测和调研,分析了总车流量、车型构成和平均速度的日变化规律.应用北京机动车排放因子模型(EMBEV模型)和颗粒物黑碳排放的研究数据,计算该路段的黑碳平均排放因子和排放强度.根据同期观测的气象数据,应用AERMOD模型对道路黑碳排放进行了扩散模拟,并根据城市背景站点和道路边站点的监测数据对模拟结果进行了验证.研究表明,该路段黑碳平均排放因子与重型柴油车在总车流中所占比例呈现出极强的相关性,由于北京市实行货车区域限行制度,日间时段总车流的平均黑碳排放因子为(9.3±1.2)mg·km-1·veh-1,而夜间时段上升至(29.5±11.1)mg·km-1·veh-1.全天时均黑碳排放强度为17.9~115.3g·km-1·h-1,其中早(7:00—9:00)晚(17:00—19:00)高峰时段的黑碳排放强度分别为(106.1±13.0)g·km-1·h-1和(102.6±6.2)g·km-1·h-1.基于同期监测数据验证,AERMOD模型的模拟效果较好.模拟时段的道路黑碳排放对道路边监测点的平均浓度贡献为(2.8±3.5)μg·m-3.由于局地气象条件差异,日间和夜间的机动车排放对道路边黑碳的模拟浓度存在显著差异.日间时段,小型客车排放对道路边站点的黑碳浓度贡献最高,达(1.07±1.57)μg·m-3;其次为公交车,达(0.58±0.85)μg·m-3.夜间时段货车比例明显上升,其黑碳排放占主导地位,贡献浓度(2.44±2.31)μg·m-3.     

Reduction and characterization of bioaerosols in a wastewater treatment station via ventilation

Bioaerosols from wastewater treatment processes are a significant subgroup of atmospheric aerosols. In the present study,airborne microorganisms generated from a wastewater treatment station(WWTS) that uses an oxidation ditch process were diminished by ventilation.Conventional sampling and detection methods combined with cloning/sequencing techniques were applied to determine the groups,concentrations,size distributions,and species diversity of airborne microorganisms before and after ventilation. There were 3021 ± 537 CFU/m3 of airborne bacteria and 926 ± 132 CFU/m3 of airborne fungi present in the WWTS bioaerosol.Results showed that the ventilation reduced airborne microorganisms significantly compared to the air in the WWTS. Over 60% of airborne bacteria and airborne fungi could be reduced after4 hr of air exchange. The highest removal(92.1% for airborne bacteria and 89.1% for fungi) was achieved for 0.65–1.1 μm sized particles. The bioaerosol particles over 4.7 μm were also reduced effectively. Large particles tended to be lost by gravitational settling and small particles were generally carried away,which led to the relatively easy reduction of bioaerosol particles0.65–1.1 μm and over 4.7 μm in size. An obvious variation occurred in the structure of the bacterial communities when ventilation was applied to control the airborne microorganisms in enclosed spaces.     

亚微米气溶胶高效过滤装置的设计

针对亚微米气溶胶颗粒进入大气中危害环境及人类的健康的问题,设计了一种移动式亚微米气溶胶高效过滤装置,采用最易穿透粒径法(MPPS)测试了装置的过滤效率,结果表明:该装置具有较高的过滤效率,在0.1~0.25μm粒径范围内,装置的过滤效率在99.999%以上,该设备可用于亚微米气溶胶颗粒的过滤净化。     

苏州地区黑碳气溶胶季节变化研究

通过对2011年7月至2012年4月苏州地区黑碳气溶胶浓度的季节变化与气象条件的关系进行分析研究,主要得出以下结论:水平风场、大气环流形势决定黑碳扩散条件;秸秆焚烧是秋季黑碳浓度增加的主要来源;降水是导致冬季黑碳浓度偏低的主要原因。     

Compositions and sources of organic acids in fine particles (PM2.5) over the Pearl River Delta region, south China

Organic acids as important constituents of organic aerosols not only influence the aerosols' hygroscopic property, but also enhance the formation of new particles and secondary organic aerosols. This study reported organic acids including C14–C32fatty acids, C4–C9dicarboxylic acids and aromatic acids in PM2.5collected during winter 2009 at six typical urban, suburban and rural sites in the Pearl River Delta region. Averaged concentrations of C14–C32fatty acids, aromatic acids and C4– C9 dicarboxylic acids were 157, 72.5 and 50.7 ng/m3, respectively. They totally accounted for 1.7% of measured organic carbon. C20–C32fatty acids mainly deriving from higher plant wax showed the highest concentration at the upwind rural site with more vegetation around, while C14–C18fatty acids were more abundant at urban and suburban sites, and dicarboxylic acids and aromatic acids except 1,4-phthalic acid peaked at the downwind rural site. Succinic and azelaic acid were the most abundant among C4–C9dicarboxylic acids, and 1,2-phthalic and 1,4-phthalic acid were dominant aromatic acids. Dicarboxylic acids and aromatic acids exhibited significant mutual correlations except for 1,4-phthalic acid, which was probably primarily emitted from combustion of solid wastes containing polyethylene terephthalate plastics. Spatial patterns and correlations with typical source tracers suggested that C14–C32fatty acids were mainly primary while dicarboxylic and aromatic acids were largely secondary. Principal component analysis resolved six sources including biomass burning, natural higher plant wax, two mixed anthropogenic and two secondary sources; further multiple linear regression revealed their contributions to individual organic acids. It turned out that more than 70% of C14–C18fatty acids were attributed to anthropogenic sources, about 50%–85% of the C20–C32fatty acids were attributed to natural sources, 80%–95% of dicarboxylic acids and 1,2-phthalic acid were secondary in contrast with that 81% of 1,4-phthalic acid was primary.