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 × 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.

中文翻译：

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城市规模的行人空气污染暴露评估：巴塞罗那的案例研究


空气污染是公共卫生面临的紧迫环境风险，尤其是在人口密度和污染水平较高的城市。传统的暴露分析方法通常依赖于人口普查数据，但最近的研究强调了日常流动性对个人暴露的影响。在这里，我们开发了一种方法，通过将人行人流量与来自空气质量系统 CALIOPE-Urban 的偏差校正预测的高分辨率（25 m × 25 m）NO2 数据相结合，在城市范围内确定前所未有的行人暴露估计。应用于 2019 年的巴塞罗那（西班牙），我们表明行人流量和 NO2 水平表现出可以忽略不计的时间相关性。虽然短期（每小时）暴露是由行人流动性驱动的，但长期（每月）暴露是由 NO2 模式主导的。我们确定了强烈的暴露空间梯度，强调了在人行道尺度上高分辨率解决方案的重要性。最后，我们确定暴露缓解策略应根据他们的流动性和首选路线考虑不同的公民亚组，因为发现住宅和行人暴露之间存在显着差异。我们的结果为城市规划者和政策制定者提供了风险指标，有助于在最需要的地方和时间确定缓解措施的优先级。