Air Pollution Source Identification by Using Neural Network with Bayesian Optimization. To accurately locate pollution sources, in this research, we first create an airpollution identification system, called Air Pollution Source Identification System (APSIS), which adopts tensorflow to establish three neuralnetwork based analytical models with which to find the sources of air pollution. The APSIS collects environmental data in a relatively smaller grid area. Next, collected data are tuned when necessary to prevent the APSIS built by collected data from being seriously affected by outlier and other unstable factors, like wind direction. The purpose is to identify possible distribution of pollution and then more accurately find out the sources. After that, the APSIS is applied to identify the sources of air pollution in a wide area. Source identification accuracies of these neural networks are compared with other air diffusion models, aiming to develop one which is suitable for identifying the air pollution sources.

Predicting concentration levels of air pollutants by transfer learning and recurrent neural network Abstract   Air pollution (AP) poses a great threat to human health, and people are paying more attention than ever to its prediction.xc2xa0Accurate prediction of AP helps people to plan for theirxc2xa0outdoor activities and aids protecting human health. In this paper, longshort term memory (LSTM) recurrent neural networks (RNNs) have been used toxc2xa0predict the future concentration of air pollutants (APS) in Macau. Additionally, meteorological data and data on the concentration of APS have been utilized. Moreover, in Macau, some air qualityxc2xa0monitoring stations (AQMSs) have less observed data in quantity,xc2xa0and, at the same time, some AQMSs recorded less observed data of certain types of APS. Therefore, the transfer learning and pretrained neuralxc2xa0networks have been employed to assist AQMSs with less observed data toxc2xa0build a neural network with high prediction accuracy. The experimental sample covers a period longer than 12year and includes daily measurements from several APS as well as other more classical meteorological values. Records from five stations, four out of them are AQMSs and the remaining one is an automatic weather station, have been prepared from the aforesaid period and eventually underwent to computational intelligence techniques to build and extract a prediction knowledgebased system. As shownxc2xa0by experimentation, LSTM RNNs initialized with transfer learning methods have higher prediction accuracy; it incurred shorterxc2xa0training time than randomly initialized recurrent neural networks.