【24h】

Understanding Traffic-Related Air Pollution Exposures through Mobile Monitoring

机译:通过移动监控了解与交通有关的空气污染暴露

获取原文

摘要

Human exposures to air pollution can vary sharply in space and time. Carefully designed mobile sampling campaigns are now able to reveal patterns of long-term ambient air pollution concentrations at fine scales (« 100 m). Here, we report on a campaign where two specially equipped Google Street View cars mapped spatial patterns of air quality in the San Francisco Bay Area between May 2015 and December 2017. Cars were outfitted with reference-grade instruments to measure NO, N02, black carbon (BC) and ultrafine particle number count at ~ 1 Hz. During a 30-month campaign, measurements occured on most weekdays during daytime hours, resulting in a large dataset: ~~10M samples collected during > 4000 h, encompassing 100k km of driving. First, we sampled every road 20-50x within three neighborhoods (~~30 km2) in Oakland, CA, during the first year of measurements. We found persistent fine-scale variability in pollution exists within many neighborhoods. In some neighborhoods, prominent ~50-200 m sized hotspots of elevated primary pollutants were ubiquitous, with pollutant levels varying by 5-8× within many city blocks. Next, during 1.5 years of follow-up measurements, we mapped pollutant concentrations in rural, suburban, and dense urban neighborhoods throughout the SF Bay Area. These measurements reveal how within-neighborhood pollution structure is overlaid on top of regional spatial gradients in air quality. Repeated measurements over the 2.5-year period demonstrate persistent spatial variability over time. Patterns of NO, N02 and BC in Oakland had high correlation (r2 > 0.85) between the first and second years of measurement. Short-term measurement periods (~~1-2 months) were sufficient to reproduce overall spatial patterns, albeit with ±30% bias in mean concentrations relative to annual-average conditions. This presentation summarizes how routine mobile air pollution monitoring reveals new information about spatial variability in population exposure to air pollution.
机译:人类暴露在空气污染中的时空变化很大。现在,经过精心设计的移动采样活动能够以小规模(«100 m)揭示长期环境空气污染浓度的模式。在此,我们报道了一项运动,其中有两辆特别装备的Google Street View汽车绘制了2015年5月至2017年12月之间旧金山湾区空气质量的空间分布图。这些汽车配备了参考级仪器,用于测量NO,N02,黑碳(BC)和超细颗粒数在〜1 Hz处计数。在为期30个月的运动中,在白天的大多数工作日都进行了测量,从而形成了一个庞大的数据集:在> 4000小时内收集了约1000万个样本,涵盖了10万公里的行驶。首先,在测量的第一年,我们在加利福尼亚州奥克兰的三个街区(~~ 30 km2)中对20-50x的每条道路进行了采样。我们发现许多社区中都存在持续的精细尺度的污染变化。在一些社区中,普遍存在着约50-200 m大小的主要污染物升高热点,在许多城市街区,污染物水平变化幅度为5-8倍。接下来,在1.5年的跟踪测量中,我们绘制了整个SF湾地区农村,郊区和密集城市社区的污染物浓度图。这些测量结果揭示了在空气质量的区域空间梯度之上如何覆盖邻里内部污染结构。在2.5年的时间里重复进行的测量表明,随着时间的推移,空间会持续变化。在测量的第一年和第二年之间,奥克兰的NO,NO 2和BC的模式具有高度相关性(r2> 0.85)。短期测量期(~~ 1-2个月)足以重现整体空间格局,尽管相对于年平均条件,平均浓度有±30%的偏差。本演讲总结了常规的移动空气污染监测如何揭示有关人口暴露于空气污染中空间变异性的新信息。

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号