AI 中文总结
本研究构建贝叶斯时空模型预测罗马1公里分辨率的NO₂、PM₁₀和PM₂.₅日浓度，通过病例交叉法分析发现这些污染物暴露与自然原因死亡率正相关，模型可用于城市空气污染健康研究。
AI 中文摘要
评估城市环境中精细尺度的时空空气污染差异是环境流行病学的一项重大挑战。我们提出了一种贝叶斯时空模型，用于预测2011-2022年意大利罗马1公里精细网格尺度上二氧化氮（NO₂）、PM₁₀和PM₂.₅的日浓度，根据污染物类型，使用8至13个监测站的数据。该模型包含气象和时间（工作日/周末）固定效应，以及旨在捕捉空间和日依赖关系的滞后1阶自回归时空随机效应。通过留一站点交叉验证评估预测性能，在高交通站点误差较大，整体预测性能良好。随后将估计的暴露与罗马2012-2019年的地理定位病因别死亡率数据关联，并采用病例交叉时间分层方法研究急性效应。估计的暴露（滞后0-5天均值）与自然原因死亡率呈正相关，每增加10μg/m³，PM₁₀的风险增加百分比为1.3（95%置信区间：0.6-2.0），NO₂为2.1（1.4-2.9），PM₂.₅为2.4（1.4-3.3）。使用城市特定日平均暴露而非我们的1公里分辨率模型时，相应估计值量级相当。所提出的贝叶斯时空框架可为流行病学应用提供可靠的精细尺度暴露估计，且在独立暴露估计中结果一致，支持其在城市空气污染健康研究中的应用。
英文摘要
Assessing fine-scale spatio-temporal air pollution contrasts in urban contexts is a major challenge for environmental epidemiology. We propose a Bayesian spatio-temporal model to predict daily concentrations of NO$_2$, PM$_{10}$, and PM$_{2.5}$ in Rome (Italy), over 2011-2022, on a fine grid scale (1 km), using data from 8 to 13 monitoring stations, depending on pollutant. The model includes meteorological and temporal (working day/weekend) fixed effects together with a lag-1 autoregressive spatio-temporal random effect aimed at capturing spatial and daily dependence. Predictive performance was assessed by leave-one-site-out cross-validation. Estimated exposures were then linked to geolocated cause-specific mortality data for Rome (2012-2019), and a case-crossover time-stratified approach was adopted to investigate acute effects. Cross-validation showed good overall predictive performance, with larger errors at high-traffic sites. Estimated exposure (mean lag 0-5) was positively associated with natural-cause mortality, with percent increase in risk of 1.3 (95% CI: 0.6-2.0) for PM$_{10}$, 2.1 for NO$_2$ (1.4-2.9), and 2.4 for PM$_{2.5}$ (1.4-3.3) per 10 $μ$g/m$^3$ increase. Corresponding estimates when using the city-specific daily average exposure, instead of our 1 km resolution model, were of comparable magnitude. The proposed Bayesian spatio-temporal framework provides reliable fine-scale exposure estimates for epidemiological use, with results consistent across independent exposure estimates, supporting its application in urban air-pollution health studies.
