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卡车终点站和驾驶室中潜在的空气有毒物质热点地区。

Potential air toxics hot spots in truck terminals and cabs.

作者信息

Smith Thomas J, Davis Mary E, Hart Jaime E, Blicharz Andrew, Laden Francine, Garshick Eric

机构信息

Exposure, Epidemiology, and Risk Program, Department of Environmental Health, Harvard School of Public Health, Boston, Massachusetts, USA.

出版信息

Res Rep Health Eff Inst. 2012 Dec(172):5-82.

Abstract

INTRODUCTION

Hot spots are areas where concentrations of one or more air toxics--organic vapors or particulate matter (PM)--are expected to be elevated. The U.S. Environmental Protection Agency's (EPA*) screening values for air toxics were used in our definition of hot spots. According to the EPA, a screening value "is used to indicate a concentration of a chemical in the air to which a person could be continually exposed for a lifetime ... and which would be unlikely to result in a deleterious effect (either cancer or noncancer health effects)" (U.S. EPA 2006). Our characterization of volatile organic compounds (VOCs; namely 18 hydrocarbons, methyl tert-butyl ether [MTBE], acetone, and aldehydes) was added onto our ongoing National Cancer Institute-funded study of lung cancer and particulate pollutant concentrations (PM with an aerodynamic diameter < or = 2.5 microm [PM2.5], elemental carbon [EC], and organic carbon [OC]) and source apportionment of the U.S. trucking industry. We focused on three possible hot spots within the trucking terminals: upwind background areas affected by nearby industrial parks; downwind areas affected by upwind and terminal sources; and the loading docks and mechanic shops within terminal as well as the interior of cabs of trucks being driven on city, suburban, and rural streets and on highways.

METHODS

In Phase 1 of our study, 15 truck terminals across the United States were each visited for five consecutive days. During these site visits, sorbent tubes were used to collect 12-hour integrated samples of hydrocarbons and aldehydes from upwind and downwind fence-line locations as well as inside truck cabs. Meteorologic data and extensive site information were collected with each sample. In Phase 2, repeat visits to six terminals were conducted to test the stability of concentrations across time and judge the representativeness of our previous measurements. During the repeat site visits, the sampling procedure was expanded to include real-time sampling for total hydrocarbon (HC) and PM2.5 at the terminal upwind and downwind sites and inside the truck cabs, two additional monitors in the yard for four-quadrant sampling to better characterize the influence of wind, and indoor sampling in the loading dock and mechanic shop work areas.

RESULTS

Mean and median concentrations of VOCs across the sampling locations in and around the truck terminals showed significant variability in the upwind concentrations as well as in the intensity of exposures for drivers, loading-dock workers, and mechanics. The area of highest concentrations varied, although the lowest concentrations were always found in the upwind background samples. However, the downwind samples, which included the terminal's contribution, were on average only modestly higher than the upwind samples. In the truck terminal, the mechanic-shop-area concentrations were consistently elevated for many of the VOCs (including the xylenes, alkanes, and acetone) and particulates; the loading-dock concentrations had relatively high concentrations of 1,3-butadiene, formaldehyde, and acetaldehyde; and nonsmoking driver exposures were elevated for benzene, MTBE, styrene, and hexane. Also, the loading dock and yard background concentrations for EC and PM2.5 were highly correlated with many of the VOCs (50% of pairs tested with Spearman r > 0.5 and 75% with r > 0.4); in the mechanic shop VOCs were correlated with EC but not PM2.5 (r = 0.4-0.9 where significant); and for driver exposures VOC correlations with EC and PM2.5 were relatively low, with the exception of a few aromatics, primarily benzene (r = 0.4-0.5). A principal component analysis of background source characteristics across the terminal locations that had repeat site visits identified three different groupings of variables (the "components"). This analysis suggested that a strong primary factor for hydrocarbons (alkanes and aromatics) was the major contributor to VOC variability in the yard upwind measurement. Aldehydes and acetone, which loaded onto the second and third components, were responsible for a smaller contribution to VOC variability. A multi-layer exposure model was constructed using structural equation modeling techniques that significantly predicted the yard upwind concentrations of individual VOCs as a function of wind speed, road proximity, and regional location (R2 = 0.5-0.9). This predicted value for the yard background concentration was then used to calculate concentrations for the loading dock and mechanic shop. Finally, we conducted a detailed descriptive analysis of the real-time data collected in the yard and in truck cabs during the six repeat site visits, which included more than 50 12-hour sessions at each sampling location. The real-time yard monitoring results suggested that under some conditions there was a clear upwind-to-downwind trend indicating a terminal contribution, which was not apparent in the integrated sampling data alone. They also suggested a nonlinear relationship with wind speed: calm conditions (wind speed < 2 mph) were associated with erratic upwind-downwind differences, lower wind speeds (2 to 10 mph) favored transport with little dilution, and higher wind speeds (> 10 mph) favored dilution and dispersal (more so for VOCs than for PM). Finally, an analysis of the real-time data for driver exposures in trucks with a global positioning system (GPS) matched with geographic information system (GIS) data suggested a clear influence of traffic and industrial sources along a given route with peaks in driver exposures. These peaks were largely associated with traffic, major intersections, idling at the terminals, and pickup and delivery (P&D) periods. However, VOCs and PM2.5 had different exposure patterns: VOCs exposures increased when the vehicle was stopped, and PM2.5 exposures increased during travel in traffic.

CONCLUSIONS

All three types of testing sites--upwind and downwind fence-line locations and inside truck cabs while in heavy traffic--met the established definition for a hot spot by having periods with concentrations of pollutants that exceeded the EPA's screening values. Most frequently, the pollutants with concentrations exceeding the screening values were formaldehyde, acetaldehyde, and EC (which serves as a marker for diesel particulate); less frequently they were 1,3-butadiene and benzene. In the case of the downwind location of a single truck terminal without an aggregation of other sources, high concentrations of VOCs and PM were infrequent. Using structural equation modeling, a model was developed that could identify combinations of conditions and factors likely to produce hot spots. Source apportionment analyses showed that EC came predominantly from diesel emissions. As expected from the sites studied, organic vapors associated with vehicle emissions (C6-C8 alkanes and aromatics) were the predominant components of VOCs, followed by formaldehyde and acetaldehyde. For driver exposures, high VOC values were associated with stopped vehicles, and high PM2.5 values were associated with conditions during driving.

摘要

引言

热点区域是指预计一种或多种空气有毒物质(有机蒸气或颗粒物(PM))浓度会升高的地区。我们对热点区域的定义采用了美国环境保护局(EPA*)的空气有毒物质筛选值。根据EPA的定义,筛选值“用于表示空气中某种化学物质的浓度,人体在一生中可能持续暴露于该浓度下……且不太可能产生有害影响(癌症或非癌症健康影响)”(美国EPA,2006年)。我们在国立癌症研究所资助的肺癌与颗粒物污染物浓度(空气动力学直径≤2.5微米的颗粒物[PM2.5]、元素碳[EC]和有机碳[OC])及美国运输行业源解析的现有研究基础上,增加了对挥发性有机化合物(VOCs;即18种碳氢化合物、甲基叔丁基醚[MTBE]、丙酮和醛类)的特征分析。我们重点关注了运输枢纽内的三个可能热点区域:受附近工业园区影响的上风向背景区域;受上风向和枢纽源影响的下风向区域;以及枢纽内的装卸码头和维修车间,以及在城市、郊区、农村街道和高速公路上行驶的卡车驾驶室内。

方法

在研究的第一阶段,对美国各地的15个运输枢纽各进行了连续5天的走访。在这些实地考察期间,使用吸附管从围栏线的上风向和下风向位置以及卡车驾驶室内采集了12小时的碳氢化合物和醛类综合样本。每次采样时还收集了气象数据和详细的现场信息。在第二阶段,对6个枢纽进行了回访,以测试浓度随时间的稳定性,并判断我们之前测量结果的代表性。在回访期间,采样程序得到扩展,包括在枢纽的上风向和下风向位置以及卡车驾驶室内对总碳氢化合物(HC)和PM2.5进行实时采样,在场地内增设两个监测器进行四象限采样,以更好地描述风的影响,以及在装卸码头和维修车间工作区域进行室内采样。

结果

运输枢纽及其周边采样地点的VOCs平均浓度和中位数浓度显示,上风向浓度以及司机、装卸码头工人和机械师的暴露强度存在显著差异。尽管上风向背景样本中的浓度始终最低,但浓度最高的区域各不相同。然而,包括枢纽贡献的下风向样本平均仅略高于上风向样本。在运输枢纽内,许多VOCs(包括二甲苯、烷烃和丙酮)和颗粒物在维修车间区域的浓度持续升高;装卸码头的1,3 - 丁二烯、甲醛和乙醛浓度相对较高;不吸烟司机接触的苯、MTBE、苯乙烯和己烷浓度升高。此外,装卸码头和场地背景的EC和PM2.5浓度与许多VOCs高度相关(50%的测试对Spearman相关系数r>0.5,75%的测试对r>0.4);在维修车间,VOCs与EC相关,但与PM2.5不相关(显著时r = 0.4 - 0.9);对于司机接触情况,VOCs与EC和PM2.5的相关性相对较低,少数芳烃除外,主要是苯(r = 0.4 - 0.5)。对有回访的枢纽地点的背景源特征进行主成分分析,确定了三个不同的变量分组(“成分”)。该分析表明,碳氢化合物(烷烃和芳烃)的一个主要因素是场地围栏线上风向测量中VOCs变异性的主要贡献者。醛类和丙酮分别属于第二和第三成分,对VOCs变异性的贡献较小。使用结构方程建模技术构建了一个多层暴露模型,该模型显著预测了围栏线上风向单个VOCs的浓度是风速、道路距离和区域位置的函数(R2 = 0.5 - 0.9)。然后,利用该预测的场地背景浓度值来计算装卸码头和维修车间的浓度。最后,我们对在6次回访期间在场地和卡车驾驶室内收集的实时数据进行了详细的描述性分析,每个采样地点包括50多个12小时的时段。实时场地监测结果表明,在某些条件下,存在明显的上风向到下风向趋势,表明枢纽有贡献,而这在单独的综合采样数据中并不明显。结果还表明与风速存在非线性关系:平静条件(风速<2英里/小时)与不稳定的上风向 - 下风向差异相关,较低风速(2至10英里/小时)有利于传输且稀释较少,较高风速(>10英里/小时)有利于稀释和扩散(VOCs比PM更明显)。最后,对配备全球定位系统(GPS)的卡车中司机接触情况的实时数据与地理信息系统(GIS)数据进行分析,结果表明给定路线上交通和工业源对司机接触情况有明显影响,且司机接触情况存在峰值。这些峰值主要与交通、主要十字路口、在枢纽处怠速以及装卸货(P&D)时段有关。然而,VOCs和PM2.5有不同暴露模式:车辆停止时VOCs暴露增加,行驶时PM2.5暴露增加。

结论

所有三种测试地点——围栏线的上风向和下风向位置以及交通繁忙时的卡车驾驶室内——在某些时段污染物浓度超过了EPA的筛选值,符合热点区域的既定定义。最常出现浓度超过筛选值情况的污染物是甲醛、乙醛和EC(用作柴油颗粒物的标志物);较少出现的是1,3 - 丁二烯和苯(在单个无其他源聚集的运输枢纽的下风向位置,VOCs和PM的高浓度情况较少见)。利用结构方程建模,开发了一个模型,该模型可以识别可能产生热点区域的条件和因素组合。源解析分析表明,EC主要来自柴油排放。正如在所研究的地点所预期的那样,与车辆排放相关的有机蒸气(C6 - C8烷烃和芳烃)是VOCs的主要成分,其次是甲醛和乙醛。对于司机接触情况,高VOC值与车辆停止有关,高PM2.5值与驾驶时的条件有关。

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