Field Evaluation of the PAS III Passive Alcohol Sensor

Passive alcohol sensors (PAS) are screening devices designed to sample nonintrusively the ambient air around a driver's mouth to determine the presence of alcohol. Studies have shown that PAS devices can aid police officers in the identification of unpaired drivers, particularly at sobriety checkpoints. Data from a 1996 nationwide survey, in which 5,392 drivers were evaluated for alcohol using both the PAS III (a passive sensor housed in a flashlight) and evidential breath test devices, have allowed the determination of appropriate criteria at various blood alcohol concentrations (BAC) for detecting impaired drivers in the field. Using the appropriate criteria, the PAS III can identify about 75% of the drivers with BACs at or above 0.10%, and 70% at or above 0.08%. This is a vast improvement over the 40-50% detection rate currently achieved by police officers at checkpoints not using sensors. Using the PAS III few drivers would be identified inappropriately. At the criterion recommended for detecting BACs at or above 0.08%, about 14% of drivers with BACs of 0.02-0.05% would be incorrectly identified as having a higher BAC. Field studies have shown that when police officers rely on observation alone about 20% of drivers with low BACs are detained for further evaluation. More widespread use of passive sensors by police officers would aid in the detection of drinking drivers. Sensors also could provide an additional deterrent to the general public if they believe that when stopped by the police after drinking they will be detained for further evaluation.     

Assessment of ambient air quality in Eskişehir, Turkey

This paper presents an assessment of air quality of the city Eskişehir, located 230 km southwest to the capital of Turkey. Only five of the major air pollutants, most studied worldwide and available for the region, were considered for the assessment. Available sulphur dioxide (SO₂), particulate matter (PM), nitrogen dioxide (NO₂), ozone (O₃), and non-methane volatile organic carbons (NMVOCs) data from local emission inventory studies provided relative source contributions of the selected pollutants to the region.The contributions of these typical pollution parameters, selected for characterizing such an urban atmosphere, were compared with the data established for other cities in the nation and world countries. Additionally, regional ambient SO₂ and PM concentrations, determined by semi-automatic monitoring at two sites, were gathered from the National Ambient Air Monitoring Network (NAAMN). Regional data for ambient NO₂ (as a precursor of ozone as VOCs) and ozone concentrations, through the application of the passive sampling method, were provided by the still ongoing local air quality monitoring studies conducted at six different sites, as representatives of either the traffic-dense-, or coal/natural gas burning residential-, or industrial/rural-localities of the city. Passively sampled ozone data at a single rural site were also verified with the data from a continuous automatic ozone monitoring system located at that site. Effects of variations in seasonal-activities, newly established railway system, and switching to natural gas usage on the temporal changes of air quality were all considered for the assessment. Based on the comparisons with the national [AQCR (Air Quality Control Regulation). Ministry of Environment (MOE), Ankara. Official Newspaper 19269; 1986.] and a number of international [WHO (World Health Organization). Guidelines for Air Quality. Geneva; 2000. Downloaded in January 2006, website: http://www.who.int/peh/; EU (European Union). Council Directive 1999/30/EC relating to limit values for sulfur dioxide, nitrogen dioxide and lead in ambient air. Of J Eur Communities L 163: 14–30; 29.6.1999; EU (European Union). Council Directive 2002/3/EC relating to ozone in ambient air. Of J Eur Communities. L 67: 14–30; 9.3.2002.; USEPA (U.S. Environmental Protection Agency). National Ambient Air Quality Standards (NAAQS). Downloaded in January 2006, website: http://www.epa.gov/ttn/naaqs/] ambient air standards, among all the pollutants studied, only the annual average SO₂ concentration was found to exceed one specific limit value (EU limit for protection of the ecosystem). A part of the data (VOC/NO_x ratio), for determining the effects of photochemical interactions, indicated that VOC-limited regime was prevailing throughout the city.     

POCIS采样技术应用于九龙江流域水环境中雌激素的检测

极性有机物一体化采样器(POCIS)作为富集水体中有机物的新型采样技术,可以反映目标物在被测水体中的时间权重浓度,因此在环境监测中是对主动采样方式的重要补充.本研究采用POCIS进行了4种固醇类雌激素化合物(雌酮、雌二醇、雌三醇及雌炔醇)的富集动力学,以及温度和目标物浓度对POCIS富集雌激素影响的实验室模拟研究.结果表明,POCIS对目标化合物的富集放置时间7 d内呈现显著的线性关系(r2≥0.988 1,P<0.001).温度对目标物在POCIS上富集的影响比较显著(RSD>12%),随着温度的升高,目标化合物在POCIS上的采样速率减小.而目标物浓度对POCIS富集没有显著的影响(RSD<5%).将所建立的POCIS采样装置应用于九龙江流域的现场环境中,发现利用POCIS估算出的目标物时间权重浓度与主动采样测定的浓度具有显著的相关性(r2=0.720 9,P<0.001),验证了POCIS在实际环境中的可应用性.     

基于被动采样技术的垃圾焚烧厂及周边大气中PAHs分布研究

垃圾焚烧是大气PAHs污染的重要来源,为监测垃圾焚烧过程产生的PAHs,利用被动采样技术对垃圾焚烧厂及周边大气中的多环芳烃(PAHs)进行了定量分析.结果表明,PAHs总量为146.29～396.30 ng·d-1,其中气相中PAHs为128.03～377.05 ng·d-1,颗粒相中PAHs为10.698～19.251 ng·d-1.气相中PAHs组成以菲、荧蒽、芴等低环化合物为主,菲的含量高达55.1%.选定松针作为被动植物样品,测得松针中PAHs的浓度为651.88～1 044.43 ng·g-1;考察土壤中PAHs的分布特征,测得土壤中PAHs浓度为35.04～998.89 ng·g-1.被动采样和松针、土壤中所含的PAHs分布特征相似,说明被动采样能反映PAHs在环境中的真实积累情况.此外,通过比较主动采样与被动采样结果,表明两者对大气中PAHs的富集能达到基本一致的效果.     

深圳市大气中全氟化合物的残留特征

为探究深圳市大气中全氟化合物(perfluorinated compounds,PFCs)的残留特征,基于负载XAD-4的PUF膜被动采样器采集了2014年11月1日至2015年1月31日深圳市12个位点的大气样品,分析了大气中7种挥发性PFCs和15种离子型PFCs的含量及分布特征.结果表明,深圳市大气中ΣPFCs平均浓度为79.0 pg·m-3(23.7~157 pg·m-3),以挥发性PFCs为主.8∶2氟调聚醇(fluorotelomer alcohol,FTOH)、6∶2 FTOH和全氟戊酸(perfluoropentanoic acid,PFPe A)、全氟辛酸(perfluorooctane acid,PFOA)分别是挥发性和离子型PFCs的主要组分.深圳市大气中挥发性和离子型PFCs的空间分布相同,均呈西北高东南低的态势.此外,6∶2 FTOHs、8∶2 FTOHs、PFPe A、PFHx A和PFOA与PM_(2.5)、PM_(10)呈正相关(P0.05,P0.01),而与PM_(10)的相关性更显著.     

硅胶腕带被动采集多环芳烃的可行性分析

多环芳烃（polycyclic aromatic hydrocarbons,PAHs）对人体危害巨大,目前关于人体暴露于大气环境PAHs研究主要以主动采样方式开展。硅胶腕带作为一种新的大气被动采样装置,具有体积小、价格低、耐磨耐热、无毒无味等优点,适合人体环境暴露监测。国际上研究刚起步,尚未建立完善的腕带被动采样与主动采样技术之间的定量关系。本研究于2019年秋冬季和2021年冬季在西安市城区,同时开展硅胶腕带被动采样与大气主动采样对低分子量（MW＜203 g·mol^?1）PAHs的对比实验,力求建立两者的联系。2019年和2021年西安冬季大气中低分子量PAHs浓度分别为(52.81±14.76) ng·m^?3和(134.59±39.48) ng·m^?3,与前人研究相当。相对应的腕带浓度为(183.07±40.43) ng·d^?1和(524.99±150.32) ng·d^?1。Pearson相关分析发现：萘、苊烯、1-甲基萘、2-甲基萘、荧蒽以及总PAHs的主、被动采样之间显著相关（P＜0.05）,相关系数均大于0.4,证明硅胶腕带被动采样方法适用于低分子量PAHs的人体暴露监测。     

天津-长岛区域有机氯污染物的相对化学组成与大气传输

为了研究持久性有机污染物(POPs)的区域分布、大气传输与来源,采用被动采样技术于2006年7月—2008年6月在山东长岛与天津同步采集大气样品.色谱/高分辨质谱(GC/HRMS)分析数据表明,有机氯污染物化学组成季节变化明显.天津-长岛区域的大气样品主要按照季节聚类,表现出有机氯污染的区域性特征.天津和长岛相距约400 km,两地化学组成的相似性归因于有效的大气扩散和长距离传输.同时,天津点位的源区特征,长岛渔港和船厂含DDTs船舶防污漆的使用作为一种重要的DDTs排放,在化学组成和聚类结果中,均得到了客观的反映.有机氯污染物相对化学组成可以作为一种“指纹”和“探针”技术,研究大气污染物的传输和区域分布.      

LDPE膜被动采样技术预测模型的建立及其应用

选用3种厚度的低密度聚乙烯(LDPE)膜(76,56和25μm)作为研究对象,对辛醇-水分配系数的对数(logKow)>2的有机污染物采用被动采样技术进行膜-水分配系数(Kpew)实测实验和动力学实验,首次建立考虑时间(t),膜厚度(d)和动力学3个因素的LDPE膜 Kpew预测模型.实验结果表明,预测模型得到的Kpew与实测Kpew的相对误差为±0.03,证明了预测模型的准确性和可靠性.Kpew预测模型的建立避免了实验监测Kpew的繁琐实验过程,从而极大地提高了有机污染物的监测效率.将预测模型Kpew值应用于浑河与东洲河有机污染物质的监测,监测结果进一步表明了预测模型的准确性和实用性.此外本文首次提出了苯系物的Kpew值,对LDPE膜被动采样技术应用的延伸属于突破性进展.     

被动桩-土相互作用的简化分析

首先简要地分析了地面堆载作用下处于被动状态下的桩及桩侧土的反应,基于桩和土的变形协调条件,确定了桩侧土压力表达式。在此基础上,建立了桩身挠曲的控制微分方程,并联合采用非线性p y关系曲线与有限差分法进行求解。通过算例分析,验证了所建议分析方法的合理性和实用性。最后对影响被动桩—土相互作用体系工作性能的各种因素进行了系统的对比计算与分析。     

被动采样-UV法测定环境空气中的臭氧浓度

研究被动采样-UV法测定臭氧浓度.以NaNO2作为诱捕试剂,用被动采样器吸收O3,然后在紫外分光光度计上进行测定.通过测定所生成的NO-3浓度,间接计算出臭氧浓度.结果表明,测得臭氧浓度范围在20～100 μg/m3,NO-3浓度线性范围在0～0.6 mmol/L.在测定NO-3浓度时,NO-2会造成很大的干扰,但在实验中,NO-2浓度在0～0.8 mmol/L,氨基磺酸都能有效地去除其干扰.该方法是简单、灵敏、可靠的测定环境空气中臭氧浓度的良好方法.