Air pollution regulation is central to urban public health governance, but estimating its effects is difficult because policies are implemented non-randomly and pollution trajectories are shaped by meteorology, socioeconomic change, temporal trends, and overlapping interventions. This study develops an uncertainty-aware Bayesian deep learning framework to estimate the aggregate effect of air pollution regulations on PM$_{2.5}$ concentrations in London from 2010 to 2020. The framework integrates daily PM$_{2.5}$ observations from Inner London monitoring stations, meteorological covariates, annual socioeconomic indicators, month-of-year and day-of-week indicators, and daily regulation status data for 32 policy measures. A Bayesian LSTM captures temporal dependencies in environmental and socioeconomic covariates, Bayesian embedding layers represent temporal and regulation status inputs, and a regulation status prediction branch supports propensity score-based adjustment for non-random policy implementation. Regulatory effects are estimated by comparing observed PM$_{2.5}$ concentrations with counterfactual predictions under a hypothetical no-regulation scenario, with uncertainty summarized across repeated Bayesian training runs and bootstrap resampling. Results show that London's regulations were associated with an average PM$_{2.5}$ reduction of 1.88 $μ$g/m$^3$, a relative reduction of 12.35%, with a 95% confidence interval of 1.64-2.12 $μ$g/m$^3$. Estimated effects were limited before 2013, became clearer from 2013 to 2017, and were strongest in 2018 and 2019. The findings suggest that sustained and cumulative regulatory interventions contributed to measurable improvements in London's air quality. This study demonstrates how uncertainty-aware causal AI can support environmental accountability, public health protection, and evidence-based governance for environmental decision-making.

翻译：空气污染监管是城市公共卫生治理的核心，但评估其效果存在困难，因为政策实施并非随机选择，且污染轨迹受气象、社会经济变化、时间趋势及重叠干预措施的共同影响。本研究构建了一种不确定性感知的贝叶斯深度学习框架，用于评估2010至2020年间伦敦空气污染监管对PM$_{2.5}$浓度的总体影响。该框架整合了来自伦敦内城区监测站的每日PM$_{2.5}$观测数据、气象协变量、年度社会经济指标、年月与周天指标，以及32项政策措施的每日监管状态数据。贝叶斯LSTM用于捕捉环境与社会经济协变量的时间依赖性，贝叶斯嵌入层表示时间与监管状态输入，监管状态预测分支则支持基于倾向性评分的方法对非随机政策实施进行校正。通过将观测到的PM$_{2.5}$浓度与假设无监管情景下的反事实预测值进行比较，并结合重复贝叶斯训练与自助重采样过程中的不确定性汇总，评估了监管效应。结果显示，伦敦的监管政策与PM$_{2.5}$平均下降1.88 $μ$g/m$^3$（相对降幅12.35%），95%置信区间为1.64-2.12 $μ$g/m$^3$。估计效应在2013年前较为有限，2013至2017年间逐渐清晰，并在2018至2019年达到最强。研究结果表明，持续且累积的监管干预对改善伦敦空气质量产生了可测量的贡献。本研究展示了不确定性感知的因果AI如何支持环境问责、公共卫生保护及基于证据的环境决策治理。