Combined use of principal component analysis and artificial neural network approach to improve estimates of PM2.5 personal exposure: A case study on older adults.,Science of the Total Environment

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Combined use of principal component analysis and artificial neural network approach to improve estimates of PM2.5 personal exposure: A case study on older adults.
Science of the Total Environment ( IF 9.8 ) Pub Date : 2020-04-07 , DOI: 10.1016/j.scitotenv.2020.138533
Shuang Gao ₁ , Hong Zhao ₂ , Zhipeng Bai ₃ , Bin Han ₄ , Jia Xu ₄ , Ruojie Zhao ₄ , Nan Zhang ₄ , Li Chen ₅ , Xiang Lei ₆ , Wendong Shi ₆ , Liwen Zhang ₇ , Penghui Li ₈ , Hai Yu ₉

Affiliation

College of Computer Science, Nankai University, Tianjin, China; School of Geographic and Environmental Sciences, Tianjin Normal University, Tianjin, China; Ministry of Education Key Laboratory of Pollution Processes and Environmental Criteria, Nankai University, Tianjin 300350, China; Postdoctoral Innovation Practice Base, Huafa Industrial Share Co., Ltd, Zhuhai, China.
College of Computer Science, Nankai University, Tianjin, China.
School of Geographic and Environmental Sciences, Tianjin Normal University, Tianjin, China; State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, China.
State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, China.
School of Geographic and Environmental Sciences, Tianjin Normal University, Tianjin, China.
Postdoctoral Innovation Practice Base, Huafa Industrial Share Co., Ltd, Zhuhai, China.
Collage of Public Health, Tianjin Medical University, Tianjin, China.
School of Environmental Science and Safety Engineering, Tianjin University of Technology, Tianjin, China.
Commonwealth Scientific and Industrial Research Organization (CSIRO) Energy, North Ryde, Australia.

Accurate exposure estimate of the air pollutant PM2.5 is required to evaluate its health impacts in epidemiological studies, due to its adverse effects on human's respiratory and cardiovascular systems. However, traditional personal sampling is time and cost consuming. Thus, modeling techniques are needed to accurately predict the personal exposure level to PM2.5. In this study, a total of 117 older adults over 60 were recruited in Tianjin, a heavily polluted city in northern China, for indoor, outdoor and personal PM2.5 sampling. Eighteen variables which may increase the exposure level of older adults were recorded for artificial neural network (ANN) simulation. Four modeling techniques, including time-integrated activity modeling, Monte Carlo simulation, ANN modeling, and combined use of principal component analysis (PCA) and ANN model, were used to evaluate their ability for predicting real exposure values of PM2.5. The results of traditional time-weighted activity modeling showed the lowest correlation with measured values with R2 of 0.57 and 0.42 in winter and summer, respectively. For Monte Carlo simulation, high correlation was obtained (R2 of 0.93 and 0.92 in winter and summer, respectively) between percentiles of the predicted and the real exposure values. Compared with the simple ANN models, the combined use of PCA and ANN produced the most accurate results with R2 of 0.99 and RMSE lower than 15. Since the information of the input variables for the PCA-ANN model can be obtained from the questionnaire and fixed air quality monitoring sites, this technique shows a great potential in predicting personal exposure level to the air pollutant because no additional concentration measurement is needed.

更新日期：2020-04-08

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