Regression models (e.g. Land-Use Regression) are currently the most popular way to estimate retrospective exposures to air pollution. However, these models lack important features of atmospheric dispersion. We developed a new non-linear air quality regression model which is based on the physical grounds of the well-established and commonly applied Gaussian dispersion model. This was achieved through parametrization of the basic Gaussian model (including its standard deviations) and optimizing the parameters to provide a least-squares fit with ambient measurements at each individual time-point. The new model (GaussODM) outperformed both a simpler regression model and a benchmark interpolation model in predicting spatial ambient nitrogen oxides (NO_x) concentrations. The GaussODM enables a deeper understanding of the relationship between air pollution and adverse health effects. This is partly because it is better adapted at incorporating meteorological data and the effects of elevated emissions compared with previously available air pollution regression models.

回归模型（例如土地利用回归）是目前估算追溯性空气污染暴露的最流行方法。但是，这些模型缺乏大气弥散的重要特征。我们建立了一个新的非线性空气质量回归模型，该模型基于已建立且普遍应用的高斯色散模型的物理基础。这是通过对基本高斯模型（包括其标准偏差）进行参数化并优化参数以在每个单独的时间点提供与环境测量值的最小二乘拟合来实现的。新模型（GaussODM）在预测空间环境氮氧化物（NO _x）浓度。通过GaussODM，可以更深入地了解空气污染与不良健康影响之间的关系。部分原因是与以前可用的空气污染回归模型相比，它更适合合并气象数据和排放增加的影响。