## 2 Related Work  
This section essentially summarises similar works that utilised ML models to accomplish air pollution monitoring in ports and surrounding areas. A number of cases have been identified that show the necessity of the monitoring procedure in this particular part of a city because ports often reside within city limits and can contribute to air pollution. Truck traffic is also given because in large ports it can be a source of pollution due to the fact that they load and unload in the port, often carrying containers and other goods. In the future, data from trucks will be added to the proposed forecasting model to provide a multivariate approach of CO pollution prediction. In [28], the authors suggest the use of wireless sensors to perform air pollution monitoring. In addition, the system is given prediction capabilities, which consider conditions of the local climate within a city. An investigation is undertaken which shows that there is a difference in measurements of the microclimate at a street level, which imposes a change in the prediction accuracy. Thus, the sensors are equipped with mini low-power artificial neural networks (ANNs), which are trained from their local environment, rather than doing so from a base station. In [29] the authors have used a machine-learning (ML) model, in order to proceed with the prediction of air quality in the city of Barcelona. To this end, weather and pollutant concentrations were gathered by the use of networks in the area of the city. The tool which is described has been proven to exhibit better performance than the CALIOPE Urban v1.0 platform, with respect to predictive capabilities of local NO\({}_{2}\) concentration levels. This paper proposed the utilisation of metrics including the mean squared and absolute errors to show the predictive accuracy. For the dataset that was used and for all stations and pollutants, the best performance was shown to be accomplished by using the gradient boosting machine (GBM). In terms of the importance of pollution factors, the research showed that one of the least important predictors was the number of cruise ships in the port, among the work-week day, precipitation, and relative humidity. The most important factors that impacted the pollutant level variability were the time of the year, the time of day, and the intensity of the road traffic. The ML tool also has been used to distinguish and calculate the air-quality level with respect to the overall port activity and due to cruise liners. This task took place using the difference between the observed and predicted pollutant concentrations for several difference values with respect to the actual number of vessels. Results have shown that the liners' contributions to the worsened air quality around the port of Barcelona is limited compared to that of the overall port operation. In [30], the authors present a research work, which aims to show the reduction of unclean production of CO\({}_{2}\) emissions of the crane of the port and contributed to clean air gains. Towards achieving this, they studied the Casablanca port of Morocco, and utilised its data regarding the daily energy of eleven RTG cranes that have been collected for a period of two years. The energy consumption has been performed by analysing the data by using a machine-learning tool, namely the regression analysis statistical method, in order to discover the factors impacting the production as well as the degree of the impact. Thereafter the authors treated the factors of high impact by introducing inexpensive strategies towards large investments for clean air. Finally, the authors showed a significant reduction of energy consumption and the reduction of a quite large amount of CO\({}_{2}\) emissions per year for the port of Casablanca. In [31] the authors provide a complete review of the state-of-the-art ML models and their applications to a number of cases of international freight transportation management (IFTM). Initially, the ML methods are described regarding their functionality.