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A novel mobile monitoring approach to characterize spatial and temporal variation in traffic-related air pollutants in an urban community

机译:一种新颖的移动监测方法来表征城市社区中与交通有关的空气污染物的时空变化

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摘要

Air concentrations of traffic-related air pollutants (TRAPs) vary in space and time within urban communities, presenting challenges for estimating human exposure and potential health effects. Conventional stationary monitoring stationsetworks cannot effectively capture spatial characteristics. Alternatively, mobile monitoring approaches became popular to measure TRAPs along roadways or roadsides. However, these linear mobile monitoring approaches cannot thoroughly distinguish spatial variability from temporal variations in monitored TRAP concentrations. In this study, we used a novel mobile monitoring approach to simultaneously characterize spatial/temporal variations in roadside concentrations of TRAPs in urban settings. We evaluated the effectiveness of this mobile monitoring approach by performing concurrent measurements along two parallel paths perpendicular to a major roadway and/or along heavily trafficked roads at very narrow scale (one block away each other) within short time period (<30 min) in an urban community. Based on traffic and particulate matter (PM) source information, we selected 4 neighborhoods to study. The sampling activities utilized real-time monitors, including battery-operated PM2.5 monitor (SidePak), condensation particle counter (CPC 3007), black carbon (BC) monitor (Micro-Aethalometer), carbon monoxide (CO) monitor (Langan T15), and portable temperature/humidity data logger (HOBO U12), and a GPS-based tracker (Trackstick). Sampling was conducted for 3 h in the morning (7:30-10:30) in 7 separate days in March/April and 6 days in May/June 2012. Two simultaneous samplings were made at 5 spatially-distributed locations on parallel roads, usually distant one block each other, in each neighborhood. The 5-min averaged BC concentrations (AVG SD, [range]) were 2.53 +/- 2.47 [0.09-163] mu g/m(3), particle number concentrations (PNC) were 33,330 +/- 23,451 [2512-159,130] particles/cm(3), PM2.5 mass concentrations were 8.87 +/- 7.65 [0.27-46.5] mu g/m(3), and CO concentrations were 1.22 +/- 0.60 [0.22-6.29] ppm in the community. The traffic related air pollutants, BC and PNC, but not PM2.5 or CO, varied spatially depending on proximity to local stationary/mobile sources. Seasonal differences were observed for all four TRAPs, significantly higher in colder months than in warmer months. The coefficients of variation (CVs) in concurrent measurements from two parallel routes were calculated around 0.21 +/- 0.17, and variations were attributed by meteorological variation (25%), temporal variability (19%), concentration level (6%), and spatial variability (2%), respectively. Overall study findings suggest this mobile monitoring approach could effectively capture and distinguish spatial/temporal characteristics in TRAP concentrations for communities impacted by heavy motor vehicle traffic and mixed urban air pollution sources. (C) 2016 Elsevier Ltd. All rights reserved.
机译:在城市社区中,与交通有关的空气污染物(TRAP)的空气浓度在空间和时间上会发生变化,这对估算人类暴露量和潜在的健康影响提出了挑战。传统的固定监测站/网络无法有效捕获空间特征。另外,移动监视方法也变得流行起来,可以测量沿道路或路边的TRAP。但是,这些线性移动监测方法无法将被监测的TRAP浓度的时间变化与空间变化完全区分开。在这项研究中,我们使用一种新颖的移动监测方法来同时表征城市环境中TRAP的路边浓度的时空变化。我们通过在短时间内(<30分钟)内沿垂直于主要道路的两条平行路径和/或沿人流量大的道路以非常狭窄的比例(彼此相隔一个街区)进行并行测量来评估这种移动监控方法的有效性。一个城市社区。根据交通和颗粒物(PM)的来源信息,我们选择了4个社区进行研究。采样活动利用了实时监控器,包括电池供电的PM2.5监控器(SidePak),凝结颗粒计数器(CPC 3007),黑碳(BC)监控器(微烟度计),一氧化碳(CO)监控器(Langan T15) ),便携式温度/湿度数据记录器(HOBO U12)和基于GPS的跟踪器(Trackstick)。在3月/ 4月的7天和2012年5月/ 6月的6天的早晨(7:30-10:30)分别进行了3小时的采样。在平行道路上5个空间分布的位置同时进行了两次采样,通常在每个街区彼此相距很远。 5分钟的平均BC浓度(AVG SD,[范围])为2.53 +/- 2.47 [0.09-163] mu g / m(3),颗粒数浓度(PNC)为33,330 +/- 23,451 [2512-159,130 ]颗粒/cm(3)、PM2.5质量浓度为8.87 +/- 7.65 [0.27-46.5] mu g / m(3)和CO浓度为1.22 +/- 0.60 [0.22-6.29] ppm 。与交通有关的空气污染物BC和PNC,但PM2.5或CO却不随空间变化,具体取决于与当地固定/移动源的接近程度。在所有四个TRAP中均观察到季节差异,在寒冷月份比温暖月份明显更高。计算两条平行路线同时进行的测量中的变异系数(CVs)约为0.21 +/- 0.17,变异归因于气象变异(25%),时间变异(19%),浓度水平(6%)和空间变异性(分别为2%)。整体研究结果表明,这种移动监测方法可以有效捕获和区分TRAP浓度的时空特征,以应对受到重型机动车交通和城市混合空气污染源影响的社区。 (C)2016 Elsevier Ltd.保留所有权利。

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