Predicting of Daily PM Concentration Employing Wavelet Artificial Neural Networks Based on Meteorological Elements in Shanghai, China.

Anthropogenic sources of fine particulate matter (PM) threaten ecosystem security, human health and sustainable development. The accuracy prediction of daily PM concentration can give important information for people to reduce their exposure. Artificial neural networks (ANNs) and wavelet-ANNs (WANNs) are used to predict daily PM concentration in Shanghai. The PM concentration in Shanghai from 2014 to 2020 decreased by 39.3%. The serious COVID-19 epidemic had an unprecedented effect on PM concentration in Shanghai. The PM concentration during the lockdown in 2020 of Shanghai is significantly reduced compared to the period before the lockdown. First, the correlation analysis is utilized to identify the associations between PM and meteorological elements in Shanghai. Second, by estimating twelve training algorithms and twenty-one network structures for these models, the results show that the optimal input elements for daily PM concentration predicting models were the PM from the 3 previous days and fourteen meteorological elements. Finally, the activation function (tansig-purelin) for ANNs and WANNs in Shanghai is better than others in the training, validation and forecasting stages. Considering the correlation coefficients (R) between the PM in the next day and the input influence factors, the PM showed the closest relation with the PM 1 day lag and closer relationships with minimum atmospheric temperature, maximum atmospheric pressure, maximum atmospheric temperature, and PM 2 days lag. When Bayesian regularization (trainbr) was used to train, the ANN and WANN models precisely simulated the daily PM concentration in Shanghai during the training, calibration and predicting stages. It is emphasized that the WANN1 model obtained optimal predicting results in terms of R (0.9316). These results prove that WANNs are adept in daily PM concentration prediction because they can identify relationships between the input and output factors. Therefore, our research can offer a theoretical basis for air pollution control.