A pruned feed-forward neural network (pruned-FNN) approach to measure air pollution exposure

Abstract

Environmental epidemiology studies require accurate estimation of exposure intensities to air pollution. The process from air pollutant emission to individual exposure is however complex and nonlinear, which poses signi cant modeling challenges. This study aims to develop an exposure assessment model that can strike a balance between accuracy, complexity, and usability. In this regard, neural networks offer one possible approach. This study employed a customdesigned pruned feed-forward neural network (pruned-FNN) approach to calculate the air pollution exposure index based on emission time and rates, terrain factors, meteorological conditions, and proximity measurements. The model performance was evaluated by cross validating the estimated exposure indexes with ground-based monitoring records. The pruned-FNN can predict pollution exposure indexes (PEIs) that are highly and stably correlated with the monitored air pollutant concentrations (Spearman rank correlation coe cients for 10-fold cross validation (mean ± standard deviation: 0.906 ± 0.028), for random cross validation (0.913 ± 0.024)). The predicted values are also close to the ground truth in most cases (95.5% of the predicted PEIs have relative errors smaller than 10%) when the training datasets are su ciently large and wellcovered. The pruned-FNN method can make accurate exposure estimations using a exible number of variables and less extensive data in a less money/time-consuming manner. Compared to other exposure assessment models, the pruned-FNN is an appropriate and effective approach for exposure assessment that covers a large geographic area over a long period of time.Table 1 Comparison of air pollution exposure assessment methods