Using Machine Learning Methodology to Model Nutrient Discharges from Ports: A Case Study of a Fertilizer Terminal

Abstract

Marine eutrophication is a pervasive and growing threat to global sustainability. Thereby, nutrient discharges to the marine environment should be reduced to a minimum. When fertilizers are loaded to the vessels in ports, a significant amount of nutrients are released into the sea, but so far these actions have received little attention. Here, we employed the Boosted Regression Trees modeling (BRT) to define the relationships between fertilizer loading, the loading area, rain intensity, nutrient discharge, and the marine environment, and then used the established relationships to predict the daily nutrient discharge due to fertilizer loading. The studied subject was a port in the Gulf of Finland, where significant amounts of both nitrogen and phosphorus are loaded to vessels. BRT models accounted for a significant proportion of the variability of nutrient discharge. As expected, the nutrient discharge increased with the number of fertilizers loaded and the intensity of rain. On the other hand, with the increasing loading area, the total nitrogen discharge increased, but the total phosphorus discharge decreased. The latter result may be due to the different characteristics of the loading areas of different terminals. The model predicted that at the studied port, the total nitrogen and phosphorus discharge to the marine environment due to fertilizer loading was 272,906 and 196 kg per year, respectively. Importantly, the developed model can be used to predict the nutrient loads for different future scenarios in order to propose the best mitigation methods for nutrient discharges to the sea.