基于2000-2006七年的气溶胶外环境、2004-2005两年的气溶胶个体暴露和环境暴露人群生物指标的监测数据，综合ICP-AES、IC和HPLC等多种化学分析手段，采用富集系数、元素/离子特征比、图像分析、后项轨迹包括频数分析、相关性分析等在内的多种统计学分析方法，系统全面地研究了以北京为代表的中国典型地区大气颗粒物的环境污染特征及个体暴露水平及它们之间的相互影响，研究了环境暴露下特定人群如交通警察等的个体暴露PM2.5及其共存污染物水平，并寻找合适的生物标志物对污染物在人体内的内暴露水平进行分析。外环境气溶胶的污染研究包括颗粒物的年际变化、季节变化特征等长期变化规律及与气象条件和源排放特征之间相互关系的短期影响因素，分析了气溶胶的粒径分布、化学组成、来源、时空分布、转化机制及其与气象因素之间的相互影响。此外，还对中国北方沙尘暴尤其是2006年4月一次特大降尘进行了系统的监测和研究，并对两次不同类型的沙尘暴—强度弱但是污染性强的DS1（4月8-10日）和强度大但是沙尘特性强的DS2（4月16-18日），从其粒径分布、化学成分、组成来源特征、元素比值等多方面进行了分析，不同侧面均表明了DS1为“污染暴”，而DS2是典型的“沙尘暴”，并通过后向轨迹对于两次沙尘暴的差异给予了很好的解释，DS1由于在高、低空来源途径不一致，高空来自西部沙尘区而低空来自污染重的南部地区使其污染元素浓度增加较多，而DS2高、低空均来自西北和北部的沙尘区，属典型沙尘源区，地壳元素浓度较高。研究了细颗粒物PM2.5及其共存多环芳烃PAHs、挥发性有机污染物VOCs的人群个体暴露水平，并与相应的外环境水平进行比较，结果表明北京交通环境暴露下人群的个体暴露PM2.5污染较重，且受外环境浓度的影响变化较大，一般要高于相应的环境水平约20-30％，但不具有统计学差异，可以用环境浓度代替个体暴露水平，但是其共存PAHs的个体暴露水平要显著高于外环境水平，不能用外环境浓度来代替。PAHs中芘与BaP和总TPAH之间较好的相关性也表明了用芘的代谢产物尿中1-OHP作为PAHs生物标志物具有可行性，而结果也表明了这点。为更好的了解污染物的内暴露情况，选择合适的生物标志物针对环境暴露下特殊职业人群如交通警察、保安、交通协管员等进行研究，选择了尿中1-OHP和t,t-MA作为污染物PM2.5及其共存PAHs及挥发性有机污染物苯的低剂量暴露的接触生物标志物进行了分析，结果表明尿中1-OHP表现出了灵敏的生物标志物特性，而t,t-MA则未观察到较高的灵敏度，这可能是由于仪器检测限不够低和其他混杂因素如吸烟和食品添加剂等的影响，也为今后进一步寻找其它合适的苯系物标志物提供了有益的信息。

The ambient level and personal exposure of atmospheric particulate matter, and the biomarkers of environmental exposure in Beijing, a typical metropolitan city of China were systematically studied. A seven-year long term aerosol samples from 2000 to 2006, a two-year personal exposure samples from 2004 to 2005 were collected, and then analyzed by ICP-AES, IC, and HPLC. The data were analyzed by combining enrichment factors, elemental/ionic/PAHs signatures, graphical techniques, back trajectory analysis, and multi-statistical analysis including spectral-density analysis and correlation analysis. A comprehensive study of particulate matter pollution, including particulate seasonal and short-term variation, size distributions, composition, and sources and its relation with meteorology and source emission were discussed in Beijing. In addition, the two dust storms in northern China in 2006 was systematically monitored and its composition, sources, and transformation were investigated. Two different kinds of dust storms were observed during the sampling time: weaker but polluted DS1 (8-10 April), and stronger and mineral DS2 (16-18 April). DS1 showed more pollution characteristics such as higher pollution elements and ions, higher pollutant elements’ EFs, and higher accumulations to fine particles but relatively lower concentration in coarse particles and crustal elements, and was named “pollution storm”. DS2 showed the contrary characteristics, i.e. the accumulation in coarse particles and higher crustal constitutes but lower pollution species and PAHs, and was named “dust storm”. The backward trajectory analysis gave the interesting explanation for the pollution of DS1, and mineral of DS2, i.e. the west direction in higher altitude for dust and south or southwest direction in lower altitude for pollution in DS1, the northwest and north transport pathways for DS2 in lower and higher altitude for pure dust in DS2. The personal exposure of PM2.5 and co-pollutants PAHs, and the volatile organic compounds were studied, and compared the personal exposure with relatively ambient level. Those who worked under ambient environments such as traffic policemen and safeguards outdoor were also selected to study the urine samples for biomarker analysis. Results showed that personal exposure of PM2.5 didn’t show statistically difference with the ambient level, which may be instituted by the ambient PM2.5 level. However, the personal exposure of PAHs showed significantly higher level than those ambient concentration, which may implied that the personal exposure of PAHs could not be instituted by the ambient level. Both the ambient concentration and the personal exposure of pyrene, BaP, and the total TPAH in PM2.5 showed good correlations, suggesting the great value of 1-OHP, the major urinary metabolite of pyrene, to be the biomarker of BaP and TPAH. As the major metabolites of atmospheric pyrene and benzene in urine, the urinary 1-hydroxypyrene (1-OHP) and trans, trans-muconic acid (t,t-MA) were selected to be the biomarker of them. Results showed that 1-OHP is a relatively coarse biomarker, and did not respond sensitively enough to the scale of differences in personal exposures to PAHs between the exposed and control group (30% or so), which implies that future studies need to build in bigger differences in exposures. Furthermore, smoking provided as much exposure to PAHs as the ambient aerosol did, and so represented a major confounding factor, and need to be controlled very carefully in the future. Urinary t,t-MA seems not so sensitively enough to be the biomarker of benzene, as smoke and the food additives sorbic acid, the limitation of instrumental analysis may impacts the urinary t,t-MA detection. This may imply that the better biomarkers of benzene, s-phenylmercapturic acid need to be studied more in the future.