Bayesian geostatistical regression (GR) models estimate air pollution exposure at high spatial resolution, quantify the prediction uncertainty and provide probabilistic inference on the exceedance of air quality thresholds. However, due to high computational burden, previous GR models have provided gridded ambient nitrogen dioxide (NO2) concentrations at smaller areas of investigation. Here, we applied these models to estimate yearly averaged NO2 concentrations at 1 km2 spatial resolution across 44 European countries, integrating information from in situ monitoring stations, satellites and chemical transport model (CTM) simulations. The tropospheric values of NO2 derived from the ozone monitoring instrument (OMI) onboard the National Aeronautics and Space Administration's (NASA's) Aura satellite were converted to near ground NO2 concentration proxies using simulations from the 3-D global CTM (GEOS-Chem) at 0.5° × 0.625°spatial resolution and surface-to-column NO2 ratios. Simulations from the Ensemble of regional CTMs at spatial resolution of 0.1° × 0.1°were extracted from the Copernicus atmosphere monitoring service (CAMS). The contribution of these covariates to the predictive capability of geostatistical models was for the first time evaluated here through a rigorous model selection procedure along with additional continental high-resolution satellite-derived products, including novel data from the pan-European Copernicus land monitoring service (CLMS). The results have shown that the conversion of columnar NO2 values to surface quasi-observations yielded models with slightly better predictive ability and lower uncertainty. Nonetheless, the use of higher resolution CAMS-Ensemble simulations as covariates in GR models granted the most accurate surface NO2 estimates, showing that, in 2016, 16.17 (95% C.I. 6.34-29.96) million people in Europe, representing 2.97% (95% C.I. 1.16% - 5.50%) of the total population, were exposed to levels above the EU directive and WHO air quality guidelines threshold for NO2. Our estimates are readily available to policy makers and scientists assessing the burden of disease attributable to NO2 in 2016.

中文翻译：

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贝叶斯在欧洲高分辨率NO2暴露的地统计学模型，结合了来自监测器，卫星和化学运输模型的数据。

贝叶斯地统计回归（GR）模型以高空间分辨率估算空气污染暴露，量化预测不确定性并提供超过空气质量阈值的概率推断。但是，由于计算量大，以前的GR模型已经在较小的研究区域提供了网格化的环境二氧化氮（NO2）浓度。在这里，我们利用这些模型估算了欧洲44个国家/地区在1 km2空间分辨率下的年平均NO2浓度，并结合了来自现场监测站，卫星和化学传输模型（CTM）模拟的信息。由美国航空航天局（NASA）上的臭氧监测仪（OMI）得出的NO2对流层值 s）使用3-D全球CTM（GEOS-Chem）进行的模拟，以0.5°×0.625°的空间分辨率和地表NO2比率将Aura卫星转换为近地NO2浓度代理。从哥白尼大气监测服务（CAMS）中提取了空间分辨率为0.1°×0.1°的区域CTM集合的模拟。这些协变量对地统计学模型预测能力的贡献是首次通过严格的模型选择程序以及其他大陆高分辨率的卫星衍生产品进行了评估，其中包括来自泛欧洲哥白尼土地监测服务的新数据（ CLMS）。结果表明，将柱状NO2值转换为表面准观测值所产生的模型具有更好的预测能力和较低的不确定性。尽管如此，在GR模型中使用更高分辨率的CAMS-Ensemble模拟作为协变量仍能获得最准确的表面NO2估计值，显示出2016年，欧洲有16.17（95％CI 6.34-29.96）百万人口，占2.97％（95％） CI（总人口的1.16％-5.50％）暴露在高于欧盟指令和WHO的NO2空气质量指南阈值的水平。政策制定者和科学家可以轻松获得我们的估算值，以评估2016年可归因于NO2的疾病负担。在欧洲，有16.17万人（95％CI 6.34-29.96）占总人口的2.97％（95％CI 1.16％-5.50％），其暴露于欧盟指令和WHO空气质量准则中NO2阈值以上的水平。政策制定者和科学家可以轻松获得我们的估算值，以评估2016年可归因于NO2的疾病负担。在欧洲，有16.17万人（95％CI 6.34-29.96）占总人口的2.97％（95％CI 1.16％-5.50％），其暴露于欧盟指令和WHO空气质量准则中NO2阈值以上的水平。政策制定者和科学家可以轻松获得我们的估算值，以评估2016年可归因于NO2的疾病负担。