“雾霾”在近年来已经成为了一个高频词汇，随着国家大气治理相关措施的颁布，国民对大气污染的关注也日益加强。PM_2.5与大气污染程度息息相关，而其能载带多种有害物质的特性使得它的危害性更加不容小觑。多环芳烃是PM_2.5载带的众多有害化学物质中危害较大的一种，能够引起鼻、咽、肺和生殖器官等的慢性甚至致癌疾病，因此对大气PM_2.5载带PAHs的研究具有十分重要的意义。鉴于目前的研究往往将目光放在单一地点，缺乏城郊不同地点的差异分析，本文于2018年1月至2019年3月，选择北京海淀和房山两个样点同期进行采样，分别代表城区和郊区。

首先通过前处理实验完成PM_2.5和PAHs从大气样品滤膜上的萃取和浓缩，再利用气相色谱质谱联用仪（GC-MS）测得其浓度，并从四季对比与城郊对比两方面分析PM_2.5和PAHs污染的时空分布特征，通过与气象参数的相关性，分析什么样的气象条件更容易导致污染。另一方面，了解污染物的排放源是控制污染的重要步骤。在排放源的判别方面，本文采取特征比值法与正定因子矩阵法（PMF）定性定量地判断大气PM_2.5载带PAHs的排放源，再通过聚类分析、潜在源因子贡献法（PSCF）和浓度权重轨迹分析法（CWT）结合探空气象数据，从远距离传输角度追溯污染的空间来源；采用条件概率函数（CPF）方法结合近地面的气象数据判断主要污染源向。在对大气PAHs污染导致的健康风险进行评价时，采用的是质量调整生命年（QALYs）将致癌与非致癌风险统一进行比较分析。基于排放源PMF浓度成分谱及居民暴露参数，计算出各排放源排放PAHs的潜在健康风险（QALYs损失），并据此得出排放源的风险排序。针对上述研究内容，本文得到的主要结论如下：

（1）2018年1月～2019年3月期间，海淀的PM_2.5平均浓度为77.01mg/m³，16种PAHs平均浓度为12.59ng/m³；房山的PM_2.5平均浓度为141.12mg/m³，16种PAHs平均浓度为26.95ng/m³。两地均是四环PAHs浓度占比最大，尤其是房山，四环PAHs浓度占比远大于其他环数PAHs，夏季二环PAHs贡献率相较其他季节在两地都有所增加。

（2）源解析结果发现，海淀大气PAHs可以解析为交通排放源（64.2%）、煤和天然气燃烧源（26.4%）和生物质燃烧、石油挥发和焦炉来源（9.3%）。房山的同样可以解析为交通排放源（54.7%）、煤和天然气燃烧源（32.1%）和生物质燃烧、石油挥发和焦炉来源（13.2%）。

（3）结合后向轨迹进行分析，从大尺度上来看，北京海淀的气流轨迹可以聚类成3条，受季风影响主要来源于西北方向的俄罗斯、蒙古国和内蒙古等地。房山的后向轨迹则可以聚类成7条，除了来源于西北方向俄罗斯、蒙古国、内蒙古的5条之外，还有来自山西和河北的两条聚类轨迹。两个地区四季聚类的季节特征分明，主要是受到季风气候的影响。两地都有来自河北、山西、山东等地的较短小的聚类轨迹，可能易造成较大的污染。

（4）PSCF和CWT从大尺度上通过不同的方法探究了不同污染的潜在源区。两者表现内容不同，但结果趋势大体一致。海淀各排放源的潜在区域均广于房山，海淀煤和天然气燃烧源的潜在污染源区为内蒙古中部偏西、山西大部、山西北部和河北西部，房山只零星分布在内蒙古、宁夏、山西等地。海淀交通排放源主要分布在内蒙古中西部、陕西北部和山西西部区域，房山与海淀分布区域相近，但面积较小。而海淀生物质燃烧、石油挥发和焦炉来源的潜在区域较分散，在内蒙古、蒙古国、山西和陕西等地均有分布，房山则是在内蒙古西部和俄罗斯等地有零散分布。

（5）从小范围结合近地面风速风向数据的源向分析发现，海淀交通排放源的主要浓度贡献方向为东北（NE）方向，煤和天然气燃烧源的为西北（NW）方向而生物质燃烧、石油挥发和焦炉来源的为东北（NE）风向。对于房山来说，交通排放源的主要浓度贡献方向为西南（SW）风向，煤和天然气燃烧源的和生物质燃烧、石油挥发和焦炉来源的都为西向（W）、西偏南（WSW）方向。

（6）海淀不同污染源的QALYs损失大小为交通排放源（2943.39/QALYs）>煤和天然气燃烧源（180.04/QALYs）>生物质燃烧、石油挥发和焦炉来源（98.17/QALYs），房山的为交通排放源（6360.58/QALYs）>生物质燃烧、石油挥发和焦炉来源（746.65/QALYs）>煤和天然气燃烧源（532.70/QALYs）。与PMF源解析浓度贡献排序相比，两地排序均是交通排放源占比最高。房山郊区其他两种排放源的QALYs贡献排序不同于质量贡献排序。另外，发现COPD、间质性疾病、肺炎和肺癌这几种肺部疾病在两地各排放源中均会造成较大的QALYs损失。

（7）采用QALYs可比较健康风险评价方法与传统健康风险评价方法计算的不同排放源的健康风险结果趋势一致，但QALYs可以将致癌与非致癌风险统一比较，更利于风险的总和评估与排序。QALYs与文献相关DALYs可比较健康风险评价指标的对比发现，两者结果差异不大。但评估角度不同，QALYs更能反映患者的生理和心理健康，使健康风险评估更富有人文关怀。

"Haze" has always been mentioned these years. With the promulgation of national air control measures, people gradually pay more and more attention to air pollution. PM_2.5 plays a very important role in air pollution, not only because it can reduce visibility in our daliy life, but also it can carry harmful and toxic substances. The harm of PM_2.5 can not be underestimated. Polycyclic aromatic hydrocarbons (PAHs) are a group of PM_2.5 carrying harmful chemicals, which can cause chronic and even carcinogenic diseases in nose, pharynx, lung and reproductive organs. Therefore, the study of PAHs carrying PM_2.5 in the atmosphere is of great significance. In view of the fact that most researches generally focused on a single location and lack of spatial comparative analysis, this thesis collected samples at two sites from January 2018 to March 2019, one in Haidian District and one in Fangshan District of Beijing to represent urban and suburban areas respectively.

PM_2.5 and PAHs were extracted from the filter membrane of atmospheric samples and concentrated through pre-treatment experiments, and then their concentrations were measured by gas chromatography mass spectrometry (GC-MS). Through the differences of four seasons and the comparison between the urban and suburban sites to learn about the spatial and temporal distribution characteristics of PM_2.5 and PAHs pollution, and using the correlation analysis of meteorological parameters to determine the meteorological conditions leading to pollution. On the other hand, understanding the sources of pollution is also an important step in pollution control.In terms of the identification of emission sources, the characteristic ratio method and the positive matrix factorization method (PMF) were used to qualitatively and quantitatively judge the emission sources of PAHs in the airborne PM_2.5.Then cluster analysis, potential source factor contribution method (PSCF) and concentration weight trajectory analysis (CWT) were used to trace the spatial sources of pollution from upper air data at regional scale and conditional probability function (CPF) method was used to determine the direction of main pollution sources from near-surface meteorological data.In the study of the health risks caused by atmospheric PAHs pollution, this paper used QALYs to compare and analyze the carcinogenic and non-carcinogenic risks.Based on PMF concentration component profile and exposure parameters of local people, the potential health risk of emission sources was calculated as QALYs loss, and the risk ranking of emission sources was determined. Concerning the above research contents, this paper can draw the following conclusions:

(1) During the period from January 2018 to March 2019, the average PM_2.5 concentration in Haidian District was 77.01mg/m³, and the average concentration of 16 PAHs was 12.59 ng/m³.The average concentration of PM_2.5 in Fangshan was 141.12 mg/m³, and the average concentration of 16 PAHs was 26.95 ng/m³.The concentration of four-rings PAHs was the largest in both places, especially in Fangshan, where the concentration of four-rings PAHs was much higher than that of other PAHs. The contribution of two-rings PAHs in summer was higher than that in other seasons in both places.

(2) The results of source apportionment showed that the sources of PM_2.5 carrying PAHs in Haidian District could be identified as traffic emission source (64.2%), coal and natural gas combustion source (26.4%), and biomass combustion, oil volatilization and coke oven source (9.3%).In Fangshan, the sources also could be analyzed as traffic emission source (54.7%), coal and natural gas combustion source (32.1%) and biomass combustion, oil volatilization and coke oven source (13.2%).

(3) In a large-scale perspective, the airflow trajectory of Beijing Haidian can be grouped into three categories combined with the backward trajectory analysis. The main sources of PAHs are from northwest region such as Russia, Mongolia and Inner Mongolia, etc., under the influence of monsoon. The backward trajectories of Fangshan can be clustered into 7 clusters. In addition to the 5 clusters from northwest region like Russia, Mongolia and Inner Mongolia, there are also two cluster trajectories from Shanxi and Hebei respectively. The seasonal characteristics of seasonal clustering in the two regions are distinct, which is mainly influenced by monsoon climate.There are several short clustering tracks from Hebei, Shanxi, Shandong and other places in both places, which may easily cause greater pollution.

(4) Both PSCF and CWT explore potential sources of pollution at large spatial scale but the methods they used and the contents they showed are different. It can be found that the results of the two methods are generally the same. The potential pollution sources of Haidian’s coal and natural gas combustion are distributed in the western part of Mongolia, most of Shanxi, and western part of Hebei, but Fangshan’s is only scattered in Inner Mongolia, Ningxia and Shanxi.Haidian’s traffic emission sources are mainly distributed in the central and western regions of Inner Mongolia, northern Shanxi and western Shanxi. Fangshan’s distribution is similar to Haidian’s, but Fangshan’s distribution area is smaller. However, the potential sources of biomass combustion, oil volatilization and coke oven in Haidian are scattered in Inner Mongolia, Mongolia, Shanxi and Shaanxi, while Fangshan’s are scattered in the west of Inner Mongolia and Russia.

(5) The source direction was analyzed based on wind speed and direction data from a small scale. It was found that the main concentration contribution direction of traffic emission sources in Haidian is northeast direction, coal combustion and natural gas and natural gas source is northwest direction, while biomass combustion, oil volatilization and coke oven sources are northeast direction. For Fangshan, the main concentration contribution direction of traffic emission sources is southwest direction, coal combustion and natural gas sources, biomass combustion, oil volatilization and coke oven sources are all west and west by south directions.

(6) The QALYs loss of different pollution sources in Haidian was traffic emission source (2943.39/QALYs) > coal and natural gas combustion source (180.04/QALYs) > biomass combustion, oil volatilization and coke oven source (98.17/QALYs).In Fangshan, the order of QALYs loss was the traffic emission source (6360.58/QALYs) > biomass combustion, oil volatilization and coke oven source (746.65/QALYs) > coal and natural gas combustion source (532.70/QALYs). Compared with the order of PMF analysis, the proportion of traffic emission sources is the highest in both places and both sortord. For Fangshan, the sequence of two other emission sources is different between QALYs loss base and mass concentration base. Lung diseases such as COPD, interstitial disease, pneumonia and lung cancer were found to cause significant QALYs loss in all emission sources.

（7）The trend of health risk results of different emission sources calculated by QALYs was identical with that by traditional health risk assessment methods. However, QALYs can compare the carcinogenic and non-carcinogenic risks uniformly, which is comprehensive and thus more conducive to risk ranking. The comparison of our QALYs results with the relative literature reported DALYs values indicated that the two values are basically consistent. However, the perspectives of two assessments are different. QALYs can better reflect the physical and mental health of patients, which makes health risk assessment more humane.