A prediction model of occupational manganese exposure based on artificial neural network
Yanning Li, Luo Fu-tian, Yue‐Ming Jiang, Yourong Lu, Jinli Huang, Zai-Biao Zhang · Toxicology Mechanisms and Methods · 2009
Application of two statistical models to reconstruct occupational exposure to manganese (Mn) is discussed. Air monitoring of 635 samples were analyzed by a back-propagation artificial neural network (back-propagation ANN) in comparison with a multiple linear regression (MLR). The stepwise MLR yielded significant results with five selected variables for predicting airborne manganese dioxide (MnO(2)). However, a 6-12-1 back-propagation ANN was superior to the data from MLR. Statistical parameters and non-parametric paired tests indicated that back-propagation ANN represents the more useful and accurate tool. ANN was used to predict missing MnO(2) concentrations in the present study. The median of MnO(2) was 0.445 mg/m(3) (IQR 0.131-1.342). The MnO(2) characteristics of time, distance, and exposure site were defined. Airborne MnO(2) for three previous periods (1978-1988, 1989-1998, and 1998-2007) were 1.228 mg/m(3), 0.664 mg/m(3), and 0.501 mg/m(3), respectively. The medians were 0.350 mg/m(3), 0.281 mg/m(3), and 0.190 mg/m(3) at distances of 5, 10, and 25 m away from the site of exposure. Compared with levels encountered in other studies, mine concentrator sites were more seriously polluted, due to the practices of direct ore processing.