A Study Estimating the Contribution of Organic Contaminants to Ocean Pollution Using AI Techniques

The majority of ocean pollution stems from terrestrial sources, with more than 80% attributed to chemicals, industrial waste, toxic metals, plastics, sewage, and other land-based materials. Therefore, the management of ocean pollution must initiate from terrestrial interventions, necessitating the control of pollutants discharged from land sources and the crucial pursuit of identifying the roots of ocean pollution. Identifying the causes of ocean pollution enables the exploration of mitigation strategies for the entities responsible for pollution generation and facilitates the formulation of relevant policies. Furthermore, it can contribute to imposing costs for purification and raising awareness about the seriousness of ocean pollution. However, Rivers and streams that lead to the ocean are influenced by various changing factors as they pass through the land, making it a globally challenging task to pinpoint the sources of pollution.

 

Accordingly, in this study, we collected water quality observation data measured at five points in a specific estuary area and water quality data from five areas near the source of pollution, and conducted a study to calculate the contribution of pollution from the source. We performed statistical analysis and machine learning-based pollution source analysis, and developed an improved artificial intelligence model proposed in this study that complements the limitations of existing analysis methods. The variables used in the analysis were POC average concentration, POC δ13C, PN average concentration, PN δ15N, average concentration, and DOC δ13C. For basic data analysis, data distribution analysis, similarity/discrimination analysis, and clustering analysis of variables by branch were performed. Basic data analysis allowed for dividing the data's characteristics into four groups, but discrepancies in similarities emerged among items based on each analysis method, limiting the meaningfulness of the data analysis. For this, we analyzed which pollutants contaminated the five points in the river estuary using machine learning techniques such as XGBoost and a deep learning neural network, an artificial neural network model. The XGBoost analysis categorized pollution sources for each location into 1 to 2 categories, showing accuracies ranging from 51.04% to 99.92%. However, due to the intrinsic nature of machine learning, predicted values tend to maximize similarity to the most similar pollutant source, resulting in extreme values exceeding 99%. The artificial neural network analysis resulted in the classification of 2 or more pollution sources for each location, with accuracies ranging from 33.23% to 62.45%. This is considered a result of relatively lower accuracy due to the unique characteristics of each location.

 

To overcome the limitations of each model, this study created an integrated model that aggregates results from multiple models to determine the similarity. The analysis using the integrated model effectively identified pollution sources excellently without encountering extreme accuracy issues. To ensure the reliability of future pollution contribution assessment models, determining pollution contribution through isotopic fraction analysis will be necessary.