我国西南地区森林土壤中有机氯农药和多氯联苯残留的初步研究

对西南地区森林土壤中有机氯农药和多氯联苯的分布和剖面进行了区域尺度分析。相比较发达国家和地区,我国西南地区森林土壤中有机氯农药含量水平较高,多氯联苯则相对较低。这与我国此类持久性有机污染物历史使用情况吻合。多氯联苯和有机氯农药在腐殖质层的含量一般高于表层土壤中,说明淋溶作用导致污染物向下移动。DDTs和HCHs是土壤样品中有机氯农药的主要成分,说明我国历史上长期施用农药对森林土壤农药的组成有显著影响。环境参数（包括TOC、降雨量和海拔高度）对大多数有机氯农药的区域空间分布的作用并不显著,周边污染源是控制有机氯农药在腐殖质层土壤中分布的关键因素;尤其是DDTs,比值分析结果显示仍有新鲜的工业滴滴涕输入。多氯联苯在腐殖质层中的含量明显要高于其在表层土壤中的含量,多氯联苯在土壤中的空间分布和土层中的垂直分布主要受土壤TOC含量的控制。以大气干湿沉降来源贡献为主的多氯联苯与土壤中的有机质有效结合会降低它们的再挥发过程,表明西南地区山地森林对多氯联苯具有一定森林过滤效应。     

聊城冬季一重污染过程PM2.5污染特征及成因分析

北方秋冬季为重污染过程频发季节,为了解聊城市冬季重污染过程中PM_(2.5)及化学组分污染特征,于2016年1月7~11日在聊城市区开展PM_(2.5)样品采集并分析了其中水溶性离子、碳成分及无机金属元素这3种化学组分,并对污染特征及成因进行了分析.结果表明,此次污染过程PM_(2.5)浓度呈现明显的倒V字型,平均浓度为238.3μg·m~(-3),超过国家环境空气质量标准(GB 3095-2012)二级浓度限值2.2倍;NH_4~+、NO_3~-和SO_4~(2-)为PM_(2.5)的主要水溶性离子成分;随污染加重或减轻,NH_4~+、SO_4~(2-)、NO_3~-、Cl-和Mg~(2+)浓度呈现增加或降低趋势,而Ca~(2+)变化趋势与之相反.污染鼎盛时,NH_4~+、NO_3~-和SO_4~(2-)浓度分别为48.96、68.45和80.55μg·m~(-3),达到起始阶段的6.29、7.31和7.84倍;过程期间OC和EC的浓度为20.8~60.2μg·m~(-3)和3.0~7.5μg·m~(-3),OC浓度高于EC且变化幅度明显偏大;过程期间各日无机金属元素浓度和分别为10.2、22.4、16.0、19.6和8.2μg·m~(-3),富集因子(EF)结果显示,各元素EF均小于10,未被富集,表明污染过程中其主要来源于地壳等自然源;PM_(2.5)质量浓度重构结果表明,有机物(OM)、SO_4~(2-)和NO_3~-为PM_(2.5)的主要组分,其次为NH_4~+、地壳物质和其他离子,EC和微量元素含量相对较低.随着PM_(2.5)污染加重,二次无机盐(SO_4~(2-)、NO_3~-及NH_4~+)浓度及所占比例均随之增加,OM浓度随之增加但比例有所下降,而地壳物质浓度及比例均下降,表明二次无机转化是此次污染过程的主要原因,主要受燃煤和机动车排放影响.     

合肥市典型交通干道大气苯系物的特征分析

为研究合肥市交通干道大气苯系物污染状况,采用自主研制的差分吸收光谱(DOAS)系统,于2016年3月期间对合肥市交通主干道大气苯系物(苯、甲苯、间二甲苯和邻二甲苯)以及常规污染物NO_2、SO_2等进行了连续观测.观测结果显示,观测期间苯、甲苯、间二甲苯和邻二甲苯的平均浓度分别为:21.7、63.6、33.9和98.7μg·m~(-3).与国内外其它城市比较显示,合肥市交通干道大气苯和甲苯的污染处于中等水平,二甲苯的污染较为严重.结合观测期的间风速风向、T/B特征比值以及与CO等污染物的相关性,对上述苯系物来源进行了分析,结果显示观测期间T/B值为0.8~4.5,苯、甲苯与CO的相关性系数R分别为0.55和0.34.表明机动车尾气排放是观测区域苯和甲苯的主要排放源之一,同时也受到周边工业园区排放的影响,二甲苯的主要排放源为观测地点北偏东方向的涂料行业工业园区.苯和甲苯的夜间高浓度峰值分析结果表明,夜间的高浓度苯和甲苯可能主要来源于观测地点周边工业园区的排放.观测区域苯系物的臭氧生成潜势(OFP)表现为邻二甲苯间二甲苯甲苯苯,其中二甲苯的OFP占总OFP的85%,表明周边工业园区的排放对该地区臭氧生成的贡献较大.     

Particulate matter pollution in Kunshan High-Tech zone: Source apportionment with trace elements, plume evolution and its monitoring

Particulate matter(PM) in the Kunshan High-Tech zone is studied during a three-month campaign. PM and trace elements are measured by the online pollution monitoring, forecastwarning and source term retrieval system AS3. Hourly measured concentrations of PM_(10), PM_(2.5) and 16 trace elements in the PM_(2.5) section(Ca, Pb, Cu, Cl, V, Cr, Fe, Ti, Mn, Ni, Zn, Ga, As, Se, Sr, Ba)are focused. Source apportionment of trace elements by Positive Matrix Factorization modeling indicates that there are five major sources, including dust, industrial processing, traffic,combustion, and sea salt with contribution rate of 23.68%, 21.66%, 14.30%, 22.03%, and 6.89%,respectively. Prediction of plume dispersion from concrete plant and traffic emissions shows that PM_(10) pollution of concrete plant is three orders of magnitude more than that of the traffic. The influence range can extend to more than 3 km in 1 hr. Because the footprint of the industrial plumes is constantly moving according to the local meteorological conditions, the fixed monitoring sites scattered in a few hundred meters haven't captured the heaviest pollution plume at the local scale of a few km~2. As a more intensive monitoring network is not operationally possible, the use of online modeling gives accurate and quantitative information of plume location, which increases the spatial pollution monitoring capacity and improves the understanding of measurement data. These results indicate that the development of the AS3 system, which combines monitoring equipment and air pollution modeling systems, is beneficial to the real-time pollution monitoring in the industrial zone.     

Observation and analysis of atmospheric volatile organic compounds in a typical petrochemical area in Yangtze River Delta, China

Volatile organic compounds (VOCs) are a kind of important precursors for ozone photochemical formation. In this study, VOCs were measured from November 5th, 2013 to January 6th, 2014 at the Second Jinshan Industrial Area, Shanghai, China. The results showed that the measured VOCs were dominated by alkanes (41.8%), followed by aromatics (20.1%), alkenes (17.9%), and halo-hydrocarbons (12.5%). The daily trend of the VOC concentration showed a bimodal feature due to the rush-hour traffic in the morning and at nightfall. Based on the VOC concentration, a receptor model of Positive Matrix Factorization (PMF) coupled with the information related to VOC sources was applied to identify the major VOC emissions. The result showed five major VOC sources: solvent use and industrial processes were responsible for about 30% of the ambient VOCs, followed by rubber chemical industrial emissions (23%), refinery and petrochemical industrial emissions (21%), fuel evaporations (13%) and vehicular emissions (13%). The contribution of generalized industrial emissions was about 74% and significantly higher than that made by vehicle exhaust. Using a propylene-equivalent method, alkenes displayed the highest concentration, followed by aromatics and alkanes. Based on a maximum incremental reactivity (MIR) method, the average hourly ozone formation potential (OFP) of VOCs is 220.49?ppbv. The most significant source for ozone chemical formation was identified to be rubber chemical industrial emissions, following one by vehicular emission. The data shown herein may provide useful information to develop effective VOC pollution control strategies in industrialized area.     

Insights into the formation of secondary organic carbon in the summertime in urban Shanghai

To investigate formation mechanisms of secondary organic carbon(SOC) in Eastern China,measurements were conducted in an urban site in Shanghai in the summer of 2015. A period of high O_3 concentrations(daily peak 120 ppb) was observed, during which daily maximum SOC concentrations exceeding 9.0 μg/(C·m~3). Diurnal variations of SOC concentration and SOC/organic carbon(OC) ratio exhibited both daytime and nighttime peaks. The SOC concentrations correlated well with O_x(= O_3+ NO_2) and relative humidity in the daytime and nighttime, respectively, suggesting that secondary organic aerosol formation in Shanghai is driven by both photochemical production and aqueous phase reactions. Single particle mass spectrometry was used to examine the formation pathways of SOC. Along with the daytime increase of SOC, the number fraction of elemental carbon(EC) particles coated with OC quickly increased from 38.1% to 61.9% in the size range of 250–2000 nm, which was likely due to gas-to-particle partitioning of photochemically generated semi-volatile organic compounds onto EC particles. In the nighttime, particles rich in OC components were highly hygroscopic, and number fraction of these particles correlated well with relative humidity and SOC/OC nocturnal peaks. Meanwhile, as an aqueous-phase SOC tracer, particles that contained oxalate-Fe(III) complex also peaked at night. These observations suggested that aqueous-phase processes had an important contribution to the SOC nighttime formation. The influence of aerosol acidity on SOC formation was studied by both bulk and single particle level measurements, suggesting that the aqueous-phase formation of SOC was enhanced by particle acidity.     

Does planned retreat matter? Investigating land use change under the impacts of flooding induced by sea level rise

Coastal regions worldwide are during the process of rapid urban expansion. However, expanded urban settlements in land-sea interfaces have been faced with unprecedented threats from climate change related hazards. Adaptation to coastal hazards has received increasing attention from city managers and planners. Adaptation and land management practices are largely informed by remote sensing and land change modeling. This paper establishes a framework that integrates land change analysis, coastal flooding, and sea level rise adaptation. Multilayer perceptron neural network, similarity learning, and binary logistic regression were applied to analyze spatiotemporal changes of residential, commercial, and other built-up areas in Bay County, Florida, USA. The prediction maps of 2030 were produced by three models under four policy scenarios that included the population relocation strategy. Validation results reveal that three models return overall acceptable accuracies but generate distinct landscape patterns. Predictions indicate that planned retreat of residents can greatly reduce urban vulnerability to sea level rise induced flooding. While managed realignment of the coast brings large benefits, the paper recommends different mixes of adaptation strategies for different parts of the globe, and advocates the application of reflective land use planning to foster a more disaster resilient coastal community.     

多级AO耦合流离生化工艺流量分配比优化及脱氮机制

为探究进水流量分配比对低温城市污水脱氮效率的影响,并解析多级AO耦合流离生化工艺脱氮规律,实验采用三级AO耦合流离生化工艺,在温度为(10±1)℃、水力停留时间8 h、气水比恒定的条件下处理模拟低C/N值城市污水.系统依次在进水比5∶4∶4(等容积负荷)、3∶2∶1(等停留时间)和25∶15∶6(等污泥负荷)这3种工况下运行.结果表明,该工艺对低温低C/N值污水的处理效果较好,其中在进水比为3∶2∶1的工况下脱氮效率最高,COD、NH+4-N和TN平均去除率分别为87. 44%、96. 63%和76. 81%.进一步对氮的迁移转化规律进行研究发现,制约工艺低温脱氮的主要因素为各级硝化效率,3∶2∶1的进水比合理地分配了进水负荷,各级硝化率均超过85%,为反硝化创造了有利条件,最终获得了较高的脱氮效率,此时系统也具有最高的总生物量.研究结果丰富了多级AO耦合工艺低温脱氮理论,同时为工程设计应用提供参考.     

基于IVE模型和大数据分析的杭州市道路移动源主要温室气体排放清单研究

利用IVE模型和对杭州市机动车排放管理数据库大数据的分析,得到杭州市2015年各类机动车主要温室气体高分辨率排放清单,分析了排放分担情况及时间变化特征,并利用Arc GIS及杭州市路网信息建立了1 km×1 km网格化空间分布.结果表明,杭州市道路移动源温室气体排放中CO_2、CH_4和N_2O的年排放量分别为818.11×10~4、0.85×10~4和0.07×10~4t,合计856.79×10~4t(以CO2当量计).从温室气体种类来看,CO_2占道路移动源温室气体排放总量的绝大部分,为95.5%;从机动车类型来看,小微型客车对道路移动源温室气体排放的贡献率最大,占72.8%;从道路类型的排放情况来看,杭州市市中心、城区、城郊和郊区中温室气体合计CO_2当量贡献率最高的均为主干路,分别为43.4%、61.8%、58.0%和42.4%.杭州市道路移动源温室气体排放强度均呈现由城市中心向城市边缘递减的趋势,同时温室气体排放量日变化特征明显,均出现弱双峰现象.     

上海中心城区二氧化氮在线测量比对研究

对比了基于腔衰减相移光谱技术(CAPS)、化学荧光法(CL)和非相干宽带腔增强吸收光谱技术(IBBCEAS)3种不同方法在线测量NO_2浓度的仪器,并于2017年5月10日—6月10日采用3台仪器对上海中心城区大气环境NO_2进行监测,验证了IBBCEAS实验装置的稳定性、灵敏度、检测下限等关键特性,证明可以实现复杂环境下大气痕量气体高精度在线监测.IBBCEAS装置与其它两种仪器监测数据的一致性较好,监测过程中上海市中心NO_2日浓度变化范围为3~63 ppbv.各仪器测量结果之间的线性关系图表明,3种方式对NO_2浓度监测较为准确,偏差普遍在10 ppbv.根据早晚高峰车流量增加情况分析可以看出,NO_2高浓度污染主要来自于车辆排放.另外发现,受局地污染源的影响,NO_2浓度随时间推移而增加.