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.

翻译：城市空气污染，尤其是NO\textsubscript{2}，与从死亡率到心理健康问题乃至儿童注意力缺陷等多种健康问题相关。尽管全球各城市已出台政策以限制排放，但由于环境传感器数量有限且分布不均，实时监测仍面临挑战。这一缺口阻碍了能够响应影响城市污染的事件序列与日常活动的自适应城市政策的制定。本文展示了城市闭路电视（CCTV）摄像头如何充当准NO\textsubscript{2}传感器。通过使用预测性图深度模型，我们结合了伦敦摄像头的交通流量数据以及环境与空间因素，从超过1.33亿帧图像中生成了NO\textsubscript{2}预测。我们对伦敦移动模式的分析揭示了关键的时空关联，展示了特定交通模式如何影响NO\textsubscript{2}水平，有时其影响存在长达6小时的时滞。例如，若卡车仅在夜间行驶，其对NO\textsubscript{2}水平的影响最可能在早晨通勤时段显现。这些发现对当前为减少污染而实施的某些城市政策的有效性提出了质疑。通过利用现有摄像头基础设施及我们提出的方法，城市规划者和政策制定者能够以经济高效的方式监测并减轻NO\textsubscript{2}及其他污染物的影响。