城市尺度行人空气污染暴露评估：巴塞罗那案例研究
City-Scale Assessment of Pedestrian Exposure to Air Pollution: A Case Study in Barcelona
AI总结：
本研究结合巴塞罗那人行道行人流量与CALIOPE-Urban高分辨率NO2数据，评估城市尺度行人污染暴露，揭示其短长期驱动机制及空间与群体差异。
摘要：
空气污染是公共健康面临的紧迫环境风险，在人口密度和污染水平均较高的城市中尤其如此。传统暴露分析方法通常依赖人口普查数据，但近期研究强调了日常出行对个体暴露的影响。在此，我们通过将人行道行人流量与来自空气质量系统CALIOPE-Urban偏差校正预测的高分辨率（25米×25米）NO2数据相结合，开发了一种方法，用于确定城市尺度上前所未有的行人暴露估算。将该方法应用于2019年西班牙巴塞罗那，我们发现行人流量与NO2水平在时间上的相关性可忽略不计。短期（每小时）暴露由行人出行驱动，而长期（每月）暴露则由NO2模式主导。我们识别出强烈的暴露空间梯度，凸显了在人行道尺度采用高分辨率方案的重要性。最后，我们确定，暴露缓解策略应根据不同市民群体的出行方式和偏好路线加以考虑，因为居住暴露与行人暴露之间存在显著差异。我们的结果提供了为城市规划者和政策制定者设计的暴露指标，有助于在最需要的地点和时间优先安排缓解措施。
英文摘要：
Air pollution is a pressing environmental risk to public health, particularly in cities where population density and pollution levels are high. Traditional methods for exposure analysis often rely on census data, but recent studies highlight the impact of daily mobility on individuals' exposure. Here, we develop a methodology to determine unprecedented pedestrian exposure estimates at the city scale by combining sidewalk pedestrian flows with high-resolution (25 m x 25 m) NO2 data from bias-corrected predictions of the air quality system CALIOPE-Urban. Applied to Barcelona (Spain) for the year 2019, we show that pedestrian flow and NO2 levels exhibit negligible temporal correlation. While short-term (hourly) exposure is driven by pedestrian mobility, long-term (monthly) exposure is dominated by NO2 patterns. We identify strong spatial gradients of exposure, highlighting the importance for high-resolution solutions at the sidewalks scale. Finally, we determine that exposure mitigation strategies should consider different citizen subgroups based on their mobility and preferred routes, as significant differences were found between residential and pedestrian exposure. Our results provide exposure indicators designed for city planners and policymakers, helping to prioritize mitigation measures where and when they are most needed.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
