Pollution characteristics and health risk assessment of polycyclic aromatic hydrocarbons and nitrated polycyclic aromatic hydrocarbons during heating season in Beijing

Polycyclic aromatic hydrocarbons(PAHs) and their nitrated derivatives(NPAHs) attract continuous attention due to their outstanding carcinogenicity and mutagenicity. In order to investigate the diurnal variations, sources, formation mechanism, and health risk assessment of them in heating season, particulate matter(PM) were collected in Beijing urban area from December 26, 2017 to January 17, 2018. PAHs and NPAHs in PM were quantitatively analyzed via gas chromatography-mass spectrometry(GC-MS). ...     

奥运期间北京交通环境细颗粒物中多环芳烃特征研究

采用GC/MS测定了奥运空气质量保障措施实施期间(2008年8月)及非奥运时段(2008年6月、2009年8月)北京市北四环道路边PM2.5中12种优控PAHs含量,并应用特征化合物比值法对PAHs来源进行了识别.研究表明,奥运空气质量保障措施实施期问PAHs总浓度平均为4.77 ng·m-3,较非奥运时段下降了59%...     

南京市大气细颗粒物(PM2.5)中硝基多环芳烃污染特征与风险评估

硝基多环芳烃是大气细颗粒物中具有致癌效应的一类重要污染物,为探明硝基多环芳烃污染特征与来源,采集南京市14个大气细颗粒样品,利用气相色谱-质谱联用仪（GC-MS）测定硝基多环芳烃浓度,进行分布特征分析,来源识别和健康风险评估.结果表明,南京市大气细颗粒物中2,8-二硝基二苯并噻吩（743 pg·m^-3）、2,7-二硝基芴（331 pg·m^-3）、9-硝基蒽（326 pg·m^-3）、3-硝基荧蒽（217 pg·m^-3）和1,8-二硝基芘（193 pg·m^-3）为主要的硝基多环芳烃;硝基多环芳烃检出浓度具有明显的季节变化,冬季最高（3082 pg·m^-3）,秋季其次（1553 pg·m^-3）,春季最低（1218 pg·m^-3）.南京市大气细颗粒物中硝基多环芳烃主要来自多环芳烃大气光氧化反应与生物质燃烧,二次生成是硝基多环芳烃的重要来源.当前南京PM_2.5中硝基多环芳烃的致癌风险可控,二硝基多环芳烃是致癌风险的主要来源.     

聊城市冬季PM2.5中水溶性化合物的昼夜变化特征及来源解析

为探究聊城市冬季PM_(2.5)中水溶性物质的昼夜变化特征及其来源,于2017年1～2月进行PM_(2.5)样品采集,对其水溶性无机离子、乙二酸和左旋葡聚糖等水溶性化合物进行分析,并采用主成分分析-多元线性回归模型(PCA-MLR)对其来源进行解析.结果表明,采样期间聊城市PM_(2.5)平均质量浓度为(132. 6±65. 4)μg·m-3,是国家二级标准的1. 8倍,且夜晚PM_(2.5)的污染程度略高于白天. SNA(SO24-、NO3-和NH4+)是聊城市PM_(2.5)中最主要的水溶性离子,在白天与夜晚占总离子的质量分数为73. 4%和77. 1%,说明聊城市冬季二次污染较严重.白天与夜晚阴阳离子平衡当量比值(AE/CE)都小于1,说明PM_(2.5)呈碱性,且夜晚PM_(2.5)的酸性比白天强.无论在白天还是晚上,NH4+的主要存在形态均为NH4HSO4和NH4NO3.通过相关性分析,证实了乙二酸是在液相中经酸催化的二次氧化反应形成的,且受生物质燃烧的影响很强.通过PCA-MLR模型分析可知,聊城市冬季PM_(2.5)中的水溶性化合物主要来自机动车尾气及其二次氧化、生物质燃烧,而受矿物粉尘与煤炭燃烧的影响较小.     

常州市大气PM2.5中PAHs污染特征及来源解析

2016年1~8月期间,在常州市采集到55个大气细颗粒物PM_(2.5)样品,采用气相色谱-质谱联用仪测定其中17种PAHs的含量.结果表明,冬、春、夏季PAHs的季均浓度分别为140.24、41.42和2.96 ng·m~(-3),冬季污染较严重,且以4~6环中高分子量化合物为主.Ba P日均浓度平均值3.64 ng·m~(-3),超标日占总采样天数的41%.PAHs浓度与气温(相关系数-0.643)和能见度(相关系数-0.466)显著负相关,与大气压呈显著正相关(相关系数0.544),而与风速、相对湿度相关性较差.受昼夜温差、大气层结和污染源变化等因素影响,夜间PAHs浓度高于白天.气团后向轨迹模型分析表明,常州PM_(2.5)中PAHs主要受当地排放源和短距离传输的影响,长距离传输影响小(仅占11%).特征比值法分析发现,PAHs主要来源于燃煤、机动车尾气和生物质燃烧.利用超额终生致癌风险(ILCR)模型评估PAHs通过呼吸暴露途径对人体健康的影响,结果表明:成人的ILCR值高于儿童,冬季和春季人群的ILCR值略高于风险阈值,夏季则不明显.     

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.      

二级热脱附结合气相色谱-质谱联用分析大气细颗粒物中多环芳烃类污染物

化学组分是影响大气细颗粒物(即PM_(2.5))健康危害的重要因素,但目前流行病学研究对于颗粒物组分的暴露评价受到了传统分析方法的限制.为探索高效的颗粒物组分测定方法,本研究建立并优化了二级热脱附结合气相色谱-质谱联用(TD-GC-MS)方法,以多环芳烃(PAHs)为目标污染物开展研究.结果表明,该方法具有极高的灵敏度,当使用0.28 m~3PM_(2.5)样品时,该方法的检出限为0.018~0.26 ng·m~(-3).对于标准参考物质的测量显示,该方法具有较好的准确性和精密度.同时,分析了北京2012年3月—2013年3月PM_(2.5)样品并与索氏提取结果进行对比,发现两种方法测量3~5环PAHs的一致性较好;部分物种的差异较大,热脱附因减少前处理步骤,结果可能更为准确.北京PM_(2.5)中∑_(12)PAHs浓度为4.27~340 ng·m~(-3),采暖季比非采暖季高一个数量级.基于正矩阵因子分解法(PMF)的源解析显示,燃煤是采暖季的主要污染源,非采暖季则为交通排放.最后,估算了成年北京居民暴露于PAHs的终生致癌风险,结果表明,可重点控制交通源及煤炭源以降低其潜在危害.     

中国东部冷锋推进中的PM2.5三维结构变化特征

利用观测资料和中尺度天气-化学模式（WRF-Chem）对一次冷锋南下天气过程导致的我国东部大范围空气污染开展研究,强调了冷锋过境前后的边界层结构及其对PM_2.5三维结构和变化的影响.观测发现,地面重污染区域位于冷锋前部均压场或等压线稀疏区域,在冷锋由北向南快速移动过程中,途经各站点PM_2.5浓度峰值伴随锋前而至.WRF-Chem模式可以较好地模拟中国东部地面和高空气象要素以及PM_2.5浓度的时空变化.模拟结果表明,处于该移动冷锋天气系统相同位置的沿途各站点的边界层结构以及PM_2.5垂直廓线表现出相似的特征.即:当冷锋开始入侵时,锋前污染物从地面被抬升到高空,PM_2.5浓度的增加和高空风速的增大导致高空PM_2.5通量增大,且PM_2.5浓度高值区随着高度升高向暖气团一侧倾斜.夜间冷锋过境引发边界层内对流性不稳定增加,边界层高度可达1 km以上,打破了边界层昼夜演变特征.本研究表明,垂直观测和精细模拟的结合可以有效地解释天气过程对空气污染的...     

Estimation of PM10 in the traffic-related atmosphere for three road types in Beijing and Guangzhou, China

The levels of roadside PM10 in Beijing, China, were investigated in 2011 and 2012 on a seasonal basis to estimate the population exposure to particulates for three road types. The measurements of PM10 were also conducted in the southern Chinese megacity of Guangzhou for comparison purposes. The results showed that roadside PMlo in Beijing correlated strongly with the PM10 background in the urban atmosphere. The levels of PM10 in street canyons were markedly higher than those along the open roads and in crossroad areas because of limited ventilation. An elevation of PM10 was observed in April, which was possibly due to the sand storms that frequently occur in the spring. Based on these observations, roadside PM10 in Beijing could have multiple origins and was to some extent dispersion- governed. In Guangzhou, the roadside PM10 did not closely relate to the background values. The PM10 pollution was greatly affected by local traffic conditions. The simulation of PM10 for different road types was completed during the study period using the Motor Vehicle Emissions Factor Model （MOBILE6.2） as an emission model and the California Line Source Dispersion Model （CALINE4） and Operational Street Pollution Model （OSPM） as dispersion models. The MOBILE6.2/CALINE4 software package was demonstrated to be sufficient for the simulation of PM10 in the open roads and crossroad areas in both Beijing and Guangzhou, and the simulation results of roadside PM10 in the street canyons by the MOBILE6.2/OSPM package were in close agreement with those of the measurements.     

Nitro-PAH exposures of occupationally-exposed traffic workers and associated urinary 1-nitropyrene metabolite concentrations

The assessment of occupational exposure to diesel exhaust(DE) is important from an epidemiological perspective. Urinary biomarkers of exposure have been proposed as a novel approach for measuring exposure to DE. In this study, we measured the concentrations of two urinary metabolites of 1-nitropyrene(1NP), a nitrated polycyclic aromatic hydrocarbon that has been suggested as a molecular marker of diesel particulate matter. These two metabolites, 6-hydroxy-1-nitropyrene and 8-hydroxy-1-nitropyrene, were determined in urine samples(10 m L) from a small group of workers who were occupationally-exposed to vehicle exhaust in Trujillo, Peru, before and after their workshifts. Workshift exposures to1 NP, as well as PM_(2.5), 2-nitropyrene and 2-nitrofluoranthene, were also measured.Exposures to 1NP were similar in all studied workers, averaging 105 ± 57.9 pg/m~3(±standard deviation). Median urinary concentrations of the average of the pre- and post-exposure samples for 6-hydroxy-1-nitropyrene and 8-hydroxy-1-nitropyrene, were found to be 3.9 and 2.3 pg metabolite/mg creatinine, respectively in the group of occupationally-exposed subjects(n = 17) studied. A direct relationship between workshift exposure to 1NP and urinary 1NP metabolites concentrations was not observed. However,the 1NP exposures and the creatinine-corrected urinary concentrations of the hydroxynitropyrene metabolites in these Peruvian traffic workers were similar to occupationally-exposed taxi drivers in Shenyang, China, and were higher than biomarker levels in office workers from Trujillo without occupational exposure to vehicle exhaust.This study provides further evidence that urinary metabolites of 1NP are associated with exposure to DE and may serve as a useful exposure biomarker.     

Source apportionment of PAHs and n-alkanes bound to PM1 collected near the Venice highway

n-Alkanes and polycyclic aromatic hydrocarbons (PAHs) bound to atmospheric particulate matter (PM₁) were investigated in a traffic site located in an urban area of Venice Province (Eastern Po Valley, Italy) during the cold season. Considering the critical situation affecting the Veneto Region concerning the atmospheric pollution and the general lack of information on PM₁ composition and emission in this area, this experimental study aims at determining the source profile, their relative contributions and the dispersion of finer particles. Four sources were identified and quantified using the Positive Matrix Factorization receptor model: (1) mixed combustions related to the residential activities, (2) agricultural biomass burning in addition to the resuspension of anthropogenic and natural debris carried by the wind, (3) gasoline and (4) diesel traffic-related combustions. The role of local atmospheric circulation was also investigated to identify the pollutant sources.     

福州市区冬春季PM2.5污染特征与来源差异性分析

基于福州市区2015年2月—2016年1月间的大气PM_(2.5)监测数据,综合运用HYSPLIT后向轨迹模式、潜在源贡献因子法(WPSCF)与浓度权重轨迹分析(WCWT)等方法,探讨了福州市区冬、春季PM_(2.5)污染特征和典型污染过程成因,总结了气象因子和污染来源的季节性差异.研究期间,冬、春季是福州市区PM_(2.5)污染的主要季节,福州市区不同类型站点的PM_(2.5)浓度在冬、春季污染发生时均呈现出整体升高的特点,但浓度日变化却存在季节性差异,冬季无显著日变化,春季则表现为单峰单谷特征.福州市区春季主要受锋前暖区和高压后部等天气系统影响,大气扩散条件差,PM_(2.5)极易在不利的气象条件下累积,福建沿海地区是其PM_(2.5)污染的主要潜在源区;冬季污染易受高压天气系统作用,盛行偏北风,长江三角洲地区的污染物输入会对福州市区空气质量产生较大影响,长江三角洲、浙江东南沿海、福建北部是其PM_(2.5)污染的主要潜在源区.     

北京地区不同天气条件下气溶胶数浓度粒径分布特征研究

2012~2014年,在北京城区利用宽范围粒径谱仪(WPS-1000XP)对气溶胶数粒径分布特征进行观测,进而分析了不同季节与不同天气条件下气溶胶粒径分布的变化特征.结果表明,春季爱根核模态气溶胶日均数浓度值最高,秋季最低;春季和冬季积聚模态下日均数浓度较高,夏季最低;粗模态气溶胶日均浓度在冬季最高.爱根核模态粒子数浓度日变化特征最为显著,受交通源及夏季中午前后的光化学作用影响明显.春、秋、冬季积聚模态状态气溶胶数浓度夜晚高于白天,粗模态粒子没有明显的日变化特征.重污染过程中,积聚模态气溶胶对于PM_(2.5)质量浓度起到决定作用,通常需通过北风的清除才能有效降低PM_(2.5)浓度;降雨及降雪对粗模态粒子的清除效果较为明显,而小风和静风状态下,降水对积聚模态的气溶胶没有明显的清除作用;沙尘过程中,粗模态粒子浓度显著增加,而积聚模态气溶胶却被明显清除.     

兰州大气细颗粒物中多环芳烃污染特征及来源分析

多环芳烃(polycyclic aromatic hydrocarbons,PAHs)是环境空气中一类重要的有毒化合物,为探究兰州市大气细颗粒物(fine particulate matter,PM_(2.5))中PAHs的污染特征,于2012年冬季和2013年夏季采集兰州市PM_(2.5)样品共60个,并进行了GC/MS分析.结果表明,16种PAHs的冬、夏季平均总质量浓度分别为(191.79±88.29)ng·m~(-3)和(8.94±4.34)ng·m~(-3),冬季污染程度明显严重;降雪是导致兰州冬季大气PM_(2.5)中PAHs质量浓度降低最主要的气象因素;冬、夏季PAHs的环数分布均以4环比例最大,分别为51.40%和49.94%,5~6环比例夏季41.04%,高于冬季24.94%,2~3环比例冬季23.67%,高于夏季9.03%;通过PAHs的特征比值分析,兰州大气PM_(2.5)中PAHs的来源冬季以燃煤源和机动车尾气为主,其中柴油车比例较大;夏季汽油车对PAHs的相对贡献较大.     

六类餐饮源排放PM2.5化学成分谱

大气颗粒物源成分谱可以表征源排放颗粒物的理化特征,为受体模型开展来源解析研究提供基础数据.餐饮油烟排放是室内外环境大气污染的来源之一,当前餐饮源排放PM_2.5的化学成分谱仍然缺乏.该研究分别在成都市、武汉市和天津市采集了29组6种餐饮源（居民烹饪、火锅店、烧烤店、职工食堂、中餐馆、商场综合餐饮）排放的PM_2.5样品,分析无机元素、离子、碳、多环芳烃（PAHs）等化学组分,并构建了餐饮源排放颗粒物化学成分谱.结果表明:①餐饮源排放PM_2.5化学成分中的主要组分为OC（有机碳）、EC（元素碳）、Ca、Al、Fe、NH₄+、SO₄2-、NO₃-、Na+、K+、Mg2+和Cl-,其中w（OC）最高,为41.67%~57.91%.②餐饮源排放PM_2.5的PAHs中,3环和4环占比较高,其中芴（Flu）、菲（Phe）、荧蒽（Fla）、芘（Pyr）的质量分数相对其他物质较高.研究显示:餐饮源排放PM_2.5中OC/EC约为15.99~67.61,在一定程度上可以用来表征餐饮源排放;Fla/（Fla+Pyr）和InP/（InP+BghiP）多集中在0.45~0.55之间,或可作为标识餐饮源的特征比值.      

基于K-means聚类分析法的肇庆市干季PM2.5污染天气分型研究

通过分析肇庆市2013—2018年国控大气环境监测站的PM_(2.5)连续监测数据,发现肇庆市区PM_(2.5)浓度在干季(10月—次年4月)明显高于其余月份,轻度以上污染基本发生在干季,且PM_(2.5)浓度对年总浓度贡献达70.8%.基于Era-interim再分析资料采用K-means聚类分析法对2013—2018年干季逐日的海平面气压和10 m水平风进行分型,揭示了肇庆市易出现PM_(2.5)污染的6种大气环流形势,包括冷锋前部(CF)、变性高压脊(THR)、脊后槽前型(BRFT)、高压底后部(HSW)、弱冷高压脊(HR)和台风外围型(TP).2013—2016年易污染天气型影响天数呈明显减少趋势,2017—2018年呈增加趋势.不同天气型PM_(2.5)浓度与局地气象要素相关性不一致,其中CF、HR、HSW、TP天气型与湿度相关性最好,THR与风速、BRFT与气压相关性最好.PM_(2.5)污染除BRFT天气型主要以本地排放累积影响为主,其余易污染天气型存在不同尺度的外来输送影响,HSW、HR主要来自广州、清远、韶关, CF主要来自佛山、中山,THR来自广州、清远、佛山.同一污染天气型在不同月份的污染影响差异较大,其中HSW、THR污染型主要影响1月和10月,CF为1月和12月,HR为2月和12月,TP为10月,BRFT为1月和10—11月.不同年份的同一月份造成不同程度的PM_(2.5)污染除了排放影响,还与天气环流类型和同一天气型下的局地气象要素密切相关.     

Effects of building aspect ratio, diurnal heating scenario, and wind speed on reactive pollutant dispersion in urban street canyons

A photochemistry coupled computational fluid dynamics (CFD) based numerical model has been developed to model the reactive pollutant dispersion within urban street canyons, particularly integrating the interrelationship among diurnal heating scenario (solar radiation affections in nighttime, daytime, and sun-rise/set), wind speed, building aspect ratio (building-height-to-street-width), and dispersion of reactive gases, specifically nitric oxide (NO), nitrogen dioxide (NO₂) and ozone (O₃) such that a higher standard of air quality in metropolitan cities can be achieved. Validation has been done with both experimental and numerical results on flow and temperature fields in a street canyon with bottom heating, which justifies the accuracy of the current model. The model was applied to idealized street canyons of different aspect ratios from 0.5 to 8 with two different ambient wind speeds under different diurnal heating scenarios to estimate the influences of different aforementioned parameters on the chemical evolution of NO, NO₂ and O₃. Detailed analyses of vertical profiles of pollutant concentrations showed that different diurnal heating scenarios could substantially affect the reactive gases exchange between the street canyon and air aloft, followed by respective dispersion and reaction. Higher building aspect ratio and stronger ambient wind speed were revealed to be, in general, responsible for enhanced entrainment of O₃ concentrations into the street canyons along windward walls under all diurnal heating scenarios. Comparatively, particular attention can be paid on the windward wall heating and nighttime uniform surface heating scenarios.     

新疆自然植被对非对称性昼夜增温的时滞响应

为探究新疆地区植被活动与昼夜增温之间的复杂关系,本文利用2000~2015年归一化植被指数（NDVI）及气象数据,定量研究了新疆北疆、南疆及伊犁地区生长季日间最高气温（T_max）、夜间最低气温（T_min）变化趋势及植被对非对称性累积昼夜增温的时滞响应,通过二阶偏相关分析了T_max、T_min对新疆地区植被的影响.结果表明：北疆、南疆及伊犁地区昼夜增温均呈非对称性,昼夜增温空间异质性显著,北疆地区T_min增温速率为T_max增温速率的1.67倍,南疆地区T_min增温速率为T_max增温速率的1.59倍,伊犁地区T_min增温速率为T_max增温速率的2.67倍,伊犁地区昼夜温差缩小趋势显著.植被对非对称性昼夜累积增温时滞性呈现明显的空间差异,T_max、T_min对新疆地区植被平均滞后时间为（0.97±0.93）个月和（0.91±0.95）个月.南疆、北疆及伊犁地区针叶林、阔叶林及草甸草原植被受日间增温的影响,荒漠草原、典型草原受夜间增温影响显著,夜间增温影响范围更广.     

辽河口芦苇湿地净生态系统CO2交换及其环境调控

采用涡度相关法,对2018年下半年辽河口国家级自然保护区内滨海芦苇湿地的净生态系统CO₂交换（NEE）、总初级生产力（GPP）和生态系统呼吸（R_eco）进行测量分析,研究海洋保护区内典型芦苇湿地生态系统-大气CO₂交换的变化规律及其环境调控。结果表明,有芦苇生长的7月－10月,各月NEE日变化曲线呈相似的“U”形,但变化幅度存在较大差异;非芦苇生长季的11月－12月,生态系统表现为微弱的净CO₂释放,NEE日变化轨迹与温度波动一致。该芦苇湿地生态系统的GPP、R_eco和NEE均呈现7月－10月数值较大、11月－12月数值较小的规律。7月－10月,白天CO₂交换主要受芦苇光合作用的影响,各月白天NEE与光合有效辐射（PAR）之间呈直角双曲线关系,PAR可以解释白天NEE变化的45%～54%。7月－12月,夜间R_eco与气温呈指数关系,气温可以解释R_eco变化的62%,生态系统呼吸敏感性（Q₁₀）为2.20。2018年下半年,辽河口国家级自然保护区内芦苇湿地生态系统累计GPP总值达到359.67 g C/m2,累计R_eco达到278.29 g C/m2,总净固碳量为81.38 g C/m2。     

基于长时间序列的北京PM2.5浓度日变化及气象条件影响分析

基于2005~2014年北京宝联(城区)和上甸子大气成分本底站(郊区)监测结果得到了PM_(2.5)质量浓度日变化特征,并且讨论了气象条件的影响.结果表明,北京城区PM_(2.5)质量浓度10年平均值的日变化呈双峰分布,对应早晚出行高峰.但是,该特征在2007年以后才比较明显.月际(季节)变化表现为单峰与双峰之间、日最大值在早高峰与晚高峰之间的转换.一年中早峰值浓度在5~8月最高,与地面风速小、相对湿度以及水汽压(空气绝对含水量)较高有关,该季节晚高峰期间浓度变化相对较小主要受混合层较厚、地面风速大、降雨天气发生频繁的影响.11~12月和1~2月的16:00以后浓度明显升高,混合层顶高度在14:00~17:00大幅度下降是重要的影响因素.此外,严重和重度污染日09:00以后污染加重,与年平均值和中度污染日不同.边界层偏南风对来自周边地区高架源的污染物输送是其重要的影响机制.PM_(2.5)质量浓度日变化幅度随污染加重而增大.日最大风速和相对湿度日变化对浓度日变化幅度有影响,而且午后地面出现4~6 m·s-1的偏南风也会加大日变化幅度.北京郊区PM_(2.5)质量浓度多年平均值日变化呈单峰分布,日最大值超前于城区.而且,昼间的浓度在5~7月高于冬季.本研究结果将有助于细化不利扩散条件下的污染减排方案.