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首页> 美国卫生研究院文献>International Journal of Environmental Research and Public Health >The Impact of Activity-Based Mobility Pattern on Assessing Fine-Grained Traffic-Induced Air Pollution Exposure
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The Impact of Activity-Based Mobility Pattern on Assessing Fine-Grained Traffic-Induced Air Pollution Exposure

机译：基于活动的交通方式对细颗粒交通诱导的空气污染暴露评估的影响

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Quantifying the air pollution and health impacts of transportation plans provides decision makers with valuable information that can help to target interventions. However, a large number of environmental epidemiological research assumes exposures of static populations at residential locations and does not consider the human activity patterns, which may lead to significant estimation errors. This study uses an integrated modeling framework to predict fine-grained air pollution exposures occurring throughout residents’ activity spaces. We evaluate concentrations of fine particulate matter (PM2.5) under a regional transportation plan for Sacramento, California, using activity-based travel demand model outputs, vehicle emission, and air dispersion models. We use predicted air pollution exposures at the traffic analysis zone (TAZ) level to estimate residents’ exposure accounting for their movements throughout the day to assess the impact of activity-based mobility pattern on air pollution exposure. Results of PM2.5 exposures estimated statically (at residential locations) versus dynamically (over residents’ activity-based mobility) demonstrates that the two methods yield statistically significant different results (p < 0.05). In addition, the comparison conducted in different age groups shows that the difference between these two approaches is greater among youth and working age residents, whereas seniors show a similar pattern using both approaches due to their lower rates of travel activity.
机译：量化运输计划对空气污染和健康的影响，为决策者提供了宝贵的信息，可帮助确定干预措施。但是，大量的环境流行病学研究假设居住地区的静态人群处于暴露状态，并且没有考虑人类活动模式，这可能导致明显的估计误差。这项研究使用一个集成的建模框架来预测整个居民活动空间中发生的细粒度空气污染暴露。我们使用基于活动的旅行需求模型输出，车辆排放和空气扩散模型，根据加利福尼亚州萨克拉曼多的区域运输计划，评估了细颗粒物（PM2.5）的浓度。我们使用交通分析区（TAZ）级别的预测空气污染暴露量来估算居民全天的移动情况，从而评估基于活动的出行方式对空气污染暴露量的影响。静态（在居民区）与动态（在居民基于活动的移动性方面）动态估计的PM2.5暴露结果表明，两种方法在统计学上具有显着差异（p <0.05）。此外，在不同年龄组进行的比较显示，青年人和工作年龄的居民这两种方法之间的差异更大，而老年人由于旅行活动率较低而使用这两种方法的情况相似。

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