Design and Analysis of an Data-Driven Intelligent Model for Persistent Organic Pollutants in the Internet of Things Environments

The targeted compounds included Polychlorinated Biphenyls (PCBs), Pesticides (PESTs), Polycyclic Aromatic Hydrocarbons (PAHs) and so on in the Great Lakes Integrated Atmospheric Deposition Network (IADN), which is a platform based on the IoT (Internet of Things) technology to collect environmental pollutants data. While previous studies usually employed traditional statistical approaches to analyze the IADN results, we performed a complete modeling workflow of the total concentrations of PCBs, PESTs, and PAHs (which is referred to as $\sum $ PCBs, $\sum $ PEST s and $\sum $ PAHs orderly) in 1990–2016 samples by using a machine learning algorithm combined with data-driven research method, which lets the model fit the data, so as to change the model to achieve the effect. The main results of this article are as follows, 1) identifying the spatial and temporal trends of POPs (Persistent Organic Pollutants) in the air of the Great Lakes; 2) An appropriate data-driven intelligent model was constructed for the data at EH (Eagle Harbor) and STP(Sturgeon Point) sampling sites, via which we estimated their $\sum $ PCBs, $\sum $ PESTs, and $\sum $ PAHs in the following 4–5 years, showing the concentrations will continue declining with slight fluctuations; 3) The important role which IoT played in smart environmental protection was pointed out.