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

Chunxue Wu,Cheng Wang,Qiongli Wu,Sheng Xu,Qingfeng Fan,Neal N Xiong

doi:10.1109/access.2021.3051505

Abstract

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.

Highlights

The North American Great Lakes (Lakes Superior, Michigan, Huron, Erie, and Ontario) have been subjected to elevated pollutions due to intensive human activities [1], [2]
Venier et al reported that the residues of Persistent Organic Pollutants (POPs) in the Great Lakes air would not be removed promptly, and they mainly arose from human activities [3]
Similar trends were observed for CHIC, CLEV, Eagle Harbor (EH), Point Petre (PP), Sleeping Bear Dunes (SBD), and Sturgeon Point (STP) where Polycyclic Aromatic Hydrocarbons (PAHs) were significantly greater than the Polychlorinated Biphenyls (PCBs), while PESTs were the lowest

Summary

Introduction

The North American Great Lakes (Lakes Superior, Michigan, Huron, Erie, and Ontario) have been subjected to elevated pollutions due to intensive human activities [1], [2]. The United States Environmental Protection Agency (U.S EPA) implemented the Integrated Atmospheric Deposition Network (IADN) program to monitor air pollution over the Great Lakes. They applied the Internet of Things technology to the field of environment, widely collect data, and use intelligent technologies such as data mining to screen and refine the collected data, so as to provide researchers and decision makers with safe, reliable and effective data information. Venier et al reported that the residues of POPs in the Great Lakes air would not be removed promptly, and they mainly arose from human activities [3]. Though the concentrations of various contaminants have declined in the IADN samples since the implementation of the Stockholm convention in 2004, the decreasing trends in atmospheric levels of PCB-11 were not significant [4], [5]

Methods

Results

Conclusion