污灌区重金属和多环芳烃复合污染及其对农田土壤微生物数量的影响

污水灌溉作为一种污水再利用的方式已得到广泛的应用,但该方式产生的污染不仅危害植物生长,而且可对土壤微生物生长和代谢产生明显不良影响。为了解污水灌溉对农田产生的复合污染及其对微生物数量的影响,以太原市小店污灌区农田土壤为研究对象,采用R语言程序系统研究了土壤中重金属和多环芳烃的污染水平及分布特征,并对不同复合污染程度下农田土壤中微生物数量的分布进行了分析。研究表明,小店污灌区污染因子对土壤质量的影响大于肥力因子,土壤污染程度的评价处于首要地位。用综合污染指数对农田土壤重金属污染进行评价,结果显示,污灌区中部分土壤重金属污染等级为轻度污染或警戒等级;以土壤环境质量(GB15618—2008)国家二级标准值作为评价标准,对多环芳烃污染进行对比分析,结果表明,约有34%的点位超准。不同复合污染程度下,污染土壤中细菌、真菌的数量与对照相比均呈显著下降趋势,且随着重金属和PAHs含量的增加,细菌、真菌数量逐渐减少,而放线菌数量随污染程度的增加在4月和7月与对照相比呈下降趋势,在10月份高于对照;此外,微生物总数随时间延长呈现先上升后下降的变化趋势,7月份达最大值。因此,在一定程度上,微生物数量变化可反映污灌区土壤重金属和多环芳烃复合污染胁迫下的土壤质量变化。     

开封市污灌区土壤重金属污染评价

通过分析污水灌溉在农业生产中的利弊及我国土壤的污染现状,运用内梅罗指数法及潜在生态危害指数法,分别参照国家土壤环境质量二级标准和土壤背景值对开封市污灌区土壤重金属污染状况进行了评价.结果表明,以二级标准为评价标准,评价结果为安全级别,以土壤背景值为评价标准,评价结果为轻度污染.但污灌区重金属含量明显超过清灌区重金属含量,Pb污染已达到中等污染水平,当地应采取有效措施以保护土壤免受污染.     

停止污灌10年后上海川沙污水灌区老明渠两侧污染状况调查

本文调查了上海川沙县污水灌区停止污水灌溉10年后污水总明渠两侧土壤、稻米、水井及污泥中若干重金属污染物的含量。调查表明,停止污灌10年后,总明渠两侧土壤金属污染仍然严重,米中镉含量较1976年有所降低,污水明渠对其两侧水井影响不大。讨论了老明渠治理对策。     

污染水灌溉对土壤影响的研究进展及问题分析

污染水灌溉是缓解中国水资源短缺的重要途径之一。除了城市污水以外,污染水还包括污染的地表水、工业废水、养殖废水、矿区废水、垃圾渗滤液等。文章回顾了污染水灌溉对土壤的污染作用,分析了中国污水灌溉存在的主要问题。污染水灌溉对环境的污染,主要在土壤表层,具体表现为:(1)改变了土壤的p H值,不同地区、不同的污染水类型对土壤p H值的影响结果不同;(2)重金属在土壤表层富集,向深层土迁移的速率比较缓慢,不同种类的重金属在土壤中的累积迁移规律不同,重金属富集程度与污水灌溉时间密切相关,Cd、Pb等的活性较高,生态污染风险最大,应引起足够重视;(3)土壤表层氮、磷及有机质含量增加,长期进行污灌的土壤,易造成地下水中NO3--N污染,土壤全盐量也呈增加的趋势;(4)引起土壤微生物数量和活性的变化。中国的污染水灌溉存在:(1)缺乏整体监管与监测体系;(2)对环境和人类健康的不利影响没有引起足够的重视;(3)缺乏严格的环境影响评价体系等问题。加强污染水灌溉相关理论和技术的研究,制定全面的污染水灌溉规划,为污染水灌溉的应用提供必要的技术支持;通过宣传、设置缓冲区,加强监管力度等,有效避免病原菌传播风险;进行环境影响评价等措施,可以充分、高效地利用污染水,对实现可持续发展具有重要意义。     

污灌区农田土壤多环芳烃污染及风险评价

污灌区农田土壤可能受到多环芳烃(PAHs)的严重污染.本文以山西省黎城污灌区为研究对象,对其中农田土壤中16种优控PAHs的含量水平、组成特征以及风险进行分析和评价.结果表明,该污灌区农田土壤中PAHs总含量范围为16.03—1320.95 ng·g~(-1),平均值为226.74 ng·g~(-1).土壤中PAHs以4—6环的PAHs为主,占总量的61%.风险评价结果显示该区域有40%的样品受到污染,其中重度、中度和轻微污染所占比例分别为2.9%、2.9%和34.3%.健康风险评价结果显示,该区域已具有潜在的致癌风险.从污染评价结果以及与山西省内其它区域农田土壤多环芳烃含量比较来看,研究区土壤已经受到PAHs不同程度的污染.在未来环境管理和控制过程中应采取有效措施,以保护人体健康和生态环境安全.     

污灌对农田土壤微生物特性影响研究

污灌对农田土壤特性和农作物生长有重要影响,通过分析污灌对农田土壤微生物特性影响,为农田污灌的实际应用和标准制定提供理论依据。分别采用平板计数法和最大可能数(Most-Probable-Number,MPN)法,研究了污水灌溉对农田土壤中普通微生物(细菌、放线菌、真菌)和功能微生物(亚硝化细菌、硝化细菌、氨氧化细菌、反硝化细菌、好气性自生固氮菌、好气性纤维素分解菌)的数量和分布特性的影响,并对不同类群微生物数量与土壤理化性状进行相关分析。结果表明:污灌减少农田土壤放线菌、真菌、亚硝化细菌、硝化细菌和反硝化细菌数量,增加细菌、氨氧化细菌和好气性纤维素分解菌的数量,其中细菌、好气性纤维素分解菌数量变化分别达显著水平(P0.05),氨氧化细菌变化达极显著水平(P0.01);污灌区大豆(Glycine max)、玉米(Zea mays)和水稻(Oryza sativa)根系土壤微生物数量分布差异很大,但都呈现真菌、好气性自生固氮菌和好气性纤维素分解菌数量变化显著的趋势(P0.05);污灌土壤中微生物含量与土壤理化性状相关分析表明,细菌、反硝化细菌和好气性纤维素分解菌与有机质显著正相关(r=0.843,P0.05;r=0.220,P0.05;r=0.843,P0.05),放线菌与全磷极显著负相关(r=-0.921,P0.01),亚硝化细菌和好气性自生固氮菌数量与氨氮含量显著正相关(r=0.973,P0.05;r=0.988,P0.05),氨氧化细菌和反硝化细菌与有效磷含量显著负相关(r=-0.967,P0.05;r=-0.988,P0.05)。这说明污灌对农田土壤微生物和理化特性影响显著,对农作物会产生一定的影响。     

白银市城郊周边农田土壤重金属污染特征及来源解析

为研究白银区周边农田土壤环境中重金属的污染及来源特征,以期为农田土壤重金属污染防治提供科学依据,于2021年7月在白银区周边农田采集67个表层土壤样品(0～20 cm),用微波消解-电感藕合等离子体质谱分析技术开展重金属(Cr、Ni、Co、Cu、Zn、Cd、As、Hg、Pb)污染特征及Pb的同位素指纹特征分析,并利用正定矩阵因子分析(PMF)和铅同位素指纹技术等方法手段解析重金属的污染来源。结果表明,研究区域农田土壤已受到不同程度的重金属污染,大部分重金属含量是相应土壤背景值的3.25～130.24倍;其中As、Cd、Cu、Pb和Zn含量均超过相应的农用地土壤污染风险筛选值,超标率为57.6%～100%。正定矩阵因子分析结果表明,该研究区农田土壤重金属污染共4个主要来源,其中,Cr、Ni、Co主要来自自然源,Hg主要来自煤燃烧释放;Zn、Cu和Pb主要来自铅锌矿的采选及冶炼等工业活动源,Cd和As主要来自于污水灌溉和农药化肥施用等农业活动源。Pb同位素源解析结果证实农田土壤Pb主要来源于矿产开采及冶炼活动等工业源。本研究表明,人为来源是当地农田土壤重金属的主要污染来源,不同重金属的污染...     

猪场废水污灌土壤的Cr和Ni空间变异及积累分析

为了解长期施用猪场废水对农田土壤Cr和Ni(以下简称重金属)的影响,采集了河北省京安猪场周边农田清洁区和灌溉8年猪场废水的污灌区耕层(0～20cm)共52个土壤样品,并测定了样品中pH、养分及重金属全量和有效态含量,应用GIS结合地统计学方法对重金属进行了空间结构和分布特征分析,探讨规模化猪场周边农田土壤重金属积累的影响因子及其贡献。结果表明,Cr和Ni全量和有效态含量变异函数理论模型均符合球状模型,具有中等程度自相关。根据背景调查,重金属与pH值和土壤养分的相关性分析以及重金属变异函数分析,研究区土壤Cr和Ni主要受成土母质的影响,受污灌猪场废水影响不大。富集因子分析表明,污灌猪场废水对土壤Cr和Ni几乎没有积累。灌溉猪场废水会降低土壤pH值,提高土壤Cr和Ni有效态含量。     

固定化毛霉对工业和农田污染土壤中多环芳烃的降解和生物有效性评价

为了探讨微生物修复不同类型多环芳烃污染土壤的可行性,应用固定化毛霉对多环芳烃污染工业土壤及农田土壤进行微生物修复,用羟丙基-β-环糊精(HPCD)提取模拟评价多环芳烃的微生物可利用性,并分析多环芳烃微生物降解和生物可利用性的相关关系.焦化厂污染土壤中多环芳烃的30 d降解率为77.6%,沈抚灌区污染土壤中多环芳烃的30 d降解率为54.2%,焦化厂土壤和污灌区农田土壤中多环芳烃降解差异明显.焦化厂土壤和污灌区土壤中多环芳烃的30 d降解量和多环芳烃的环糊精可提取量具有相关性,各环数多环芳烃的环糊精可提取量变化解释了焦化厂和污灌区土壤中多环芳烃降解的差异机制,说明可用环糊精提取量预测微生物降解土壤多环芳烃的情况.     

基于文献计量的关中地区农田土壤重金属污染特征及源解析

掌握关中地区农田土壤重金属污染特征和来源,对推进区域土壤环境风险管控、提升土壤环境管理水平具有重要意义。利用大量的文献检索数据,借助地累积指数法、潜在生态风险指数法和PMF源解析模型探究了关中地区农田土壤重金属的分布特征、污染程度、生态风险和可能来源。结果表明,关中地区农田土壤中8种重金属元素超过农用地土壤污染风险筛选值的比率,从高到低依次为Cd(43.3%)、Zn(13.7%)、Cu(10.9%)、Ni(8.1%)、Pb(7.2%)、As(6.1%)、Hg(5.4%)、Cr(4.0%)。Cd污染最重,部分地区达到重污染程度,Pb、Cu、Zn的污染程度次之,同时,Cd、Hg的生态风险最大,中风险以上的点位中Cd占76.51%,Hg占90.18%。PMF源解析模型计算结果表明,土壤中Cd污染主要来源于工业排放及农业活动,Hg污染主要来源于采金提炼和化石燃料燃烧,矿产资源开采和冶炼对土壤中Pb、Zn的富集影响较大,As、Cr在土壤中的累积与污水灌溉、化肥农药使用等农业活动有关,土壤中的Ni来源于成土母质。     

徐州市城市大气中的PAHs来源解析

采用化学质量平衡模型（CMB）对徐州市大气颗粒物中的多环芳烃（PAHs）进行来源分析,从而来确定各个源对大气的PAHs贡献值。主要通过利用大流量采样器配置PM10切割头在冬季和夏季对不同功能区,即生活区、工业区和旅游区采样大气中的可吸入颗粒物（PM10）样品,并用高效液相色谱法（HPLC）重点分析和研究了美国环保局（EPA）列出的16种PHAS优先污染物。研究结果表明：徐州市PM10污染比较严重,PM10污染质量浓度水平冬季是（288.81μg·m-3）大于夏季（276.34μg·m-3）,特别是工业区,污染数值达到393.13μg·m-3。夏季的总PAHs质量浓度为22.89 ng·m-3,分别是生活区28.35 ng·m-3、工业区21.75 ng·m-3和旅游区18.58 ng·m-3。冬季的总PAHs质量浓度为306.29 ng·m-3,分别是工业区388.03 ng·m-3、生活区276.29 ng·m-3和旅游区254.28 ng·m-3。夏季和冬季情况下,旅游区的污染相对来说都是最低的PM10中多环芳烃的源解析结果为,煤烟尘污染源的全年贡献率为64.00%,冬季煤烟尘污染源的贡献率为66.51%,夏季煤烟尘污染源的贡献率为57.21%,说明煤烟尘是PM10中多环芳烃的主要贡献源,土壤尘次之,全年贡献率为24.90%,冬季为25.48%,夏季为28.97%,因此,扬尘和烟煤尘的污染是徐州市的PM10中PAHs的最主要来源。     

Vertical distribution and environmental significance of PAHs in soil profiles in Beijing,China

Vertical distribution of both the concentration and composition of polycyclic aromatic hydrocarbons (PAHs) in ten profiles in Beijing has been investigated. The results showed that PAH concentrations and compositions in topsoil from different sampling sites were different. PAH concentrations were much higher in topsoil of the investigated urban area, industrial region, and paddy field with wastewater irrigation than in other areas. Moreover, PAH concentrations in topsoil were much higher than those at greater depth, where the concentrations were relatively consistent in most soil profiles. The fingerprints of PAHs in the samples from topsoil (0–30 cm) in the same profiles were similar and were obviously different from those at greater depth, suggesting that PAH sources were consistent in topsoil samples and were discriminating between topsoil and deeper soils. PAHs in topsoil mainly arose from mixed sources of combustion of liquid fuel, coal, and/or wood, as well as wastewater irrigation, while those at greater depth were derived from soil genesis and the process of soil formation.     

Particle-bound polycyclic aromatic hydrocarbons in typical urban of Yunnan-Guizhou Plateau: Characterization,sources and risk assessment

• The sampling was conducted in city on the Yunnan-Guizhou Plateau for one year. • The groups of PAHs revealed their different environmental fates and migration paths. • Seasonal biomass burning could affect the concentration by long-distance transport. • Industrial sources and traffic emissions were the main contributor of PAHs. • Living in industrial areas or winter had higher health risk by exposure PAHs in PM_2.5. Monthly particle-phase ambient samples collected at six sampling locations in Yuxi, a high-altitude city on the edge of Southeast Asia, were measured for particle-associated PAHs. As trace substances, polycyclic aromatic hydrocarbons (PAHs) are susceptible to the influences of meteorological conditions, emissions, and gas-particulate partitioning and it is challenging job to precise quantify the source and define the transmission path. The daily concentrations of total PM_2.5-bound PAHs ranged from 0.65 to 80.76 ng/m³, with an annual mean of 11.94 ng/m³. Here, we found that the concentration of PM_2.5-bound PAHs in winter was significantly higher than that in summer, which was mainly due to source and meteorology influence. The increase of fossil combustion and biomass burning in cold season became the main contributors of PAHs, while precipitation and low temperature exacerbated this difference. According to the concentration variation trend of PM_2.5-bound PAHs and their relationship with meteorological conditions, a new grouping of PAHs is applied, which suggested that PAHs have different environmental fates and migration paths. A combination of source analysis and trajectory model supported local sources from combustion of fossil fuel and vehicle exhaust contributed to the major portion on PAHs in particle, but on the Indochina Peninsula the large number of pollutants emitted by biomass burning during the fire season would affect the composition of PAHs through long-range transporting. Risk assessment in spatial and temporal variability suggested that citizens living in industrial areas were higher health risk caused by exposure the PM_2.5-bound PAHs than that in other regions, and the risk in winter was three times than in summer.     

粤港澳地区多环芳烃的多介质归趋

以广东省及港澳两地为研究区域,在完全混合和稳态假设下采用多介质逸度模型估算了目前状况下16种优控多环芳烃(PAH16)在环境各相中的质量浓度及跨界面迁移通量,并采用实测质量浓度数据进行模型验证。除个别数据外,预测质量浓度与验证质量浓度的差别在一个数量级以下,且多数在半个量级之内。模型计算结果表明:化石燃料燃烧是当地PAH16的主要来源,大气和土壤中的降解是其消失的主要途径,PAH16在该地区的最重要界面迁移过程是气地(水)交换。     

Characterization and sources of PAHs in an urban river system in Beijing,China

Water samples from 20 locations on rivers in the Tongzhou District of Beijing were collected four times from July 2005 to March 2006. In addition, sediment samples were collected in July 2005. All samples were analyzed for 16 US Environmental Protection Agency (EPA) priority pollutants polycyclic aromatic hydrocarbons (PAHs). The concentration, distribution, seasonal variation, and sources of the 16 PAH compounds identified in the water samples, suspended particles, and surface sediments were then evaluated. The concentrations of PAHs in the water and suspended particle and surface sediment samples ranged from 87.3 to 1,890 ng l⁻¹, 1,330 to 27,700 ng g⁻¹, and 156 to 8,650 ng g⁻¹, respectively. These results demonstrated that rivers in the Tongzhou District of Beijing had a high level of PAH pollution, especially in the suspended particles. The highest and lowest concentrations of PAHs in the water samples were observed in summer and spring. However, the seasonal variations in the concentration of PAHs in the suspended particles were more complicated. The dominant compounds in the water, suspended particle, and surface sediment samples were two-, three- and four-ring PAH compounds, respectively. Ratio analysis illustrated that fuel-burning was the primary source of PAHs in the study area. Gasoline, diesel, coal, and coke oven sources were identified and the contributions of the different fuel-burning sources were then calculated using factor analysis and multiple linear regression. These analyses revealed that coal combustion, gasoline combustion plus coke oven emission, and diesel combustion accounted for 38.8%, 38.5%, and 22.7% of the PAHs in suspended particles, respectively.     

北京地区表层土壤中多环芳烃的分布特征及污染源分析

根据北京地区不同环境功能区62个样品的分析结果,讨论了研究区表层土壤中多环芳烃的分布特征及污染源类型。结果表明：（1）研究区表层土壤中检测到的多环芳烃主要包括萘、苊、菲、惹烯、三芴、荧蒽、芘、、苯并蒽、苯并[b]荧蒽、苯并[k]荧蒽、苯并[e]芘、苯并[a]芘、苝、二苯并[a,h]蒽、茚并[1,2,3–cd]芘、苯并[g,h,i]苝及其同系物;（2）不同环境功能区表层土壤中多环芳烃的组成及质量分数均存在一定的差别,16种优先控制的多环芳烃质量分数为175.1~10 344 ng.g-1,其中城市中心区表层土壤中多环芳烃的质量分数最高,交通干线附近、工矿企业附近表层土壤中PAHs的质量分数较高,林地、果园和农田表层土壤中PAHs的质量分数较低;（3）表层土壤中PAHs既有来源于石油源,也有来源于化石燃料燃烧产物的,但不同功能区二者贡献存在差别,其中农业用地（林地、果园、农田）中PAHs主要来源于石油源（或部分来源于土壤母岩中的有机质）,城区、交通干线附近及工矿企业附近表层土壤中PAHs污染源以化石燃料燃烧产物输入为主。     

Distribution,source apportionment and health risk assessment of polycyclic aromatic hydrocarbons (PAHs) in intertidal sediment of Asaluyeh,Persian Gulf

Surface sediment samples were collected from intertidal zone of Asaluyeh, Persian Gulf, to investigate distribution, sources and health risk of sixteen polycyclic aromatic hydrocarbons (PAHs). Total PAH concentrations ranged from 1.8 to 81.2 μg kg^?1 dry weight, which can be categorized as low level of pollution. Qualitative and quantitative assessments showed that PAHs originated from both petrogenic and pyrogenic sources with slight pyrogenic dominance. Source apportionment using principal component analysis indicated that the main sources of PAHs were fossil fuel combustion (33.59%), traffic-related PAHs (32.77%), biomass and coal combustion (18.54%) and petrogenic PAHs (9.31%). According to the results from the sediment quality guidelines, mean effects range-median quotient (M-ERM-Q) and benzo[a]pyrene toxic equivalents (BaP_eq), low negative ecological risks related to PAH compounds would occur in the intertidal zone of Asaluyeh. The total benzo[a]pyrene (BaP) toxic equivalent quotient (TEQ^carc) for carcinogenic compounds ranged from 0.01 to 7 μg kg^?1-BaP_eq, indicating low carcinogenic risk. The human health risk assessment of PAH compounds via ingestion and dermal pathways suggests low and moderate potential risk to human health, respectively.     

PAHs in surface sediments from coastal and estuarine areas of the northern Bohai and Yellow Seas, China

Polycyclic aromatic hydrocarbon (PAH) concentrations and their risks in surface sediments (n = 35) collected from coastal and estuarine areas of the northern Bohai and Yellow Seas, China, were investigated in 2008. Total concentrations of PAHs ranged from 52.3 to 1,870.6 ng/g dry weight. The greatest concentrations were observed in the Dou River of Tangshan where waste water from small factories is discharged into the river without treatment. At other locations, municipal sewage was the primary contributor of PAHs. Regional differences in concentrations of PAHs in sediments are related to human activities. Concentrations of PAHs were significantly correlated with concentrations of organic carbon in sediments. The patterns of relative concentrations and types of PAHs observed and knowledge of the potential sources, as well as the results of a principal component analysis, are consistent with the primary sources of PAHs in sediments of the northern Bohai Sea and Yellow Sea, being derived from the high-temperature pyrolytic processes such as combustion of fossil fuel. While concentrations of PAHs at most locations did not exceed the effects range median stated by the numerical effect-based sediment quality guidelines of the United States, concentrations of PAHs at some locations were similar to or greater than the effects range low.     

青藏高原东缘冬季PM2.5中碳组分特征和来源解析

于2017年冬季12月13—21日在青藏高原东缘理塘地区分昼夜采集PM_2.5样品,并用DRI2001A热光碳分析仪测定了有机碳(OC)和元素碳(EC)的质量浓度,研究青藏高原PM_2.5中碳组分的化学特征及主要来源,以期为理塘地区制定污染排放政策提供参考。结果表明,2017年冬季青藏高原东缘理塘地区PM_2.5平均质量浓度为44.34μg·m^?3,OC和EC的质量浓度为12.72μg·m^?3和3.85μg·m^?3,分别占PM_2.5质量浓度的29.61%和8.96%。通过经验公式,计算得到总碳气溶胶(TCA)质量浓度为24.20μg·m^?3,占PM_2.5的54.84%,说明碳质气溶胶对青藏高原东缘理塘地区PM_2.5有着十分重要的贡献。OC和EC在白天和夜间都有较高的相关性(相关系数分别为0.74和0.91),表明OC和EC的来源基本一致,受燃烧源影响较大。其中白天的相关系数低于夜间,说明青藏高原东缘理塘地区白天碳组分来源相对复杂。昼夜浓度对比显示,青藏高原东缘理塘地区PM_2.5白天和夜间的质量浓度分别为53.88μg·m^?3和33.44μg·m^?3,OC和EC浓度白天高于夜间,表明白天人为排放相对较高。冬季观测期间,PM_2.5中二次有机碳(SOC)昼夜浓度分别为1.11μg·m^?3和3.03μg·m^?3,分别占OC质量浓度的7.09%、26.59%,表明青藏高原东缘理塘城区白天碳组分主要为一次源。利用PMF 5.0软件对理塘城区碳组分进行进一步的解析,结果显示燃煤和生物质燃烧的混合源对总碳(TC)的贡献高达47.84%,占比最高;其次是汽车尾气和柴油车尾气源,贡献率分别为28.62%和23.54%。     

Urban fractionation of polycyclic aromatic hydrocarbons from Dalian soils

This report evidences the fractionation of polycyclic aromatic hydrocarbons (PAHs) from urban to rural areas, and a higher contribution of coal and wood combustion in rural areas. PAHs are persistent semi-volatile organic pollutants in the environment. PAHs originate from the incompleted combustion of fossil fuel and biomass. Cities are usually considered as primary sources of PAHs. Due to different types and loads of fuel consumption in various functional areas of a city, the levels and composition profiles of PAHs are expected to be different. We, therefore, studied the mechanisms ruling PAH distributions in soils from a major Chinese city. Seventeen soil samples were collected in urban traffic areas, residential and park areas, suburban areas and rural areas of Dalian, northeastern China. PAHs were analyzed using a high-performance liquid chromatography. The composition profiles and seasonal variation of PAHs were investigated. Results show that the proportions of low-weight molecular PAHs to total PAHs increased with urban-suburban-rural gradient. This trend is explained by the “urban fractionation” of PAHs. Furthermore, the spring/autumn ratios of PAH concentrations were higher than 1. Specifically, the spring/autumn ratio was 1.79 for two ring PAHs, 1.42 for three ring PAHs, and lower than 1.20 for five and six ring PAHs. The spring/autumn ratios of phenanthrene were higher than 1 and increased with increasing distance from the urban areas. The results imply that the contribution of coal and wood combustion PAHs increases with the urban-suburban-rural gradient.