Title: Transforming CCTV cameras into NO$_2$ sensors at city scale for adaptive policymaking Show Chinese title

Title: 将城市规模的闭路电视摄像机转化为无$_2$传感器以实现适应性政策制定

Abstract: Air pollution in cities, especially NO\textsubscript{2}, is linked to numerous health problems, ranging from mortality to mental health challenges and attention deficits in children. While cities globally have initiated policies to curtail emissions, real-time monitoring remains challenging due to limited environmental sensors and their inconsistent distribution. This gap hinders the creation of adaptive urban policies that respond to the sequence of events and daily activities affecting pollution in cities. Here, we demonstrate how city CCTV cameras can act as a pseudo-NO\textsubscript{2} sensors. Using a predictive graph deep model, we utilised traffic flow from London's cameras in addition to environmental and spatial factors, generating NO\textsubscript{2} predictions from over 133 million frames. Our analysis of London's mobility patterns unveiled critical spatiotemporal connections, showing how specific traffic patterns affect NO\textsubscript{2} levels, sometimes with temporal lags of up to 6 hours. For instance, if trucks only drive at night, their effects on NO\textsubscript{2} levels are most likely to be seen in the morning when people commute. These findings cast doubt on the efficacy of some of the urban policies currently being implemented to reduce pollution. By leveraging existing camera infrastructure and our introduced methods, city planners and policymakers could cost-effectively monitor and mitigate the impact of NO\textsubscript{2} and other pollutants. Show Chinese abstract

Abstract: 城市中的空气污染，尤其是二氧化氮（NO\textsubscript{2}），与众多健康问题相关，从死亡率到心理健康挑战，再到儿童注意力缺陷。 尽管全球城市已开始实施政策以减少排放，但由于环境传感器的有限性和分布不均，实时监测仍然具有挑战性。 这一差距阻碍了制定能够应对影响城市污染的事件序列和日常活动的适应性城市政策。 在这里，我们展示了城市闭路电视（CCTV）摄像头如何充当伪二氧化氮（NO\textsubscript{2}）传感器。 利用预测图深度模型，我们使用了伦敦摄像头的交通流量数据，结合环境和空间因素，从超过1.33亿帧中生成了二氧化氮（NO\textsubscript{2}）预测。 我们对伦敦移动模式的分析揭示了关键的空间时间连接，表明特定的交通模式如何影响二氧化氮（NO\textsubscript{2}）水平，有时会有长达6小时的时间滞后。 例如，如果卡车仅在夜间行驶，其对二氧化氮（NO\textsubscript{2}）水平的影响最可能在人们通勤的早晨显现。 这些发现对当前一些正在实施的城市减排政策的有效性提出了质疑。 通过利用现有的摄像基础设施和我们提出的方法，城市规划者和政策制定者可以成本效益地监测和减轻二氧化氮（NO\textsubscript{2}）和其他污染物的影响。