## 2 Related Work  
There is a plethora of research work on air pollution in Port areas that can be found in [17] and references therein. In this section, the evolutionary computation models of air pollution will be addressed in order to show the efficiency of our proposed work. [PERSON] et al. [18], suggested a spatiotemporal approach, which is based on multiobjective evolutionary algorithms for air pollution forecasting. These algorithms' aim was to identify multiple linear regression models and their combination in an ensemble learning model. Moreover, the Pareto of the found solutions is utilized to construct forecast models, which are of geographic distribution in the area that is examined. The method under study was tested for NO\({}_{2}\) prediction and their results were promising. The system was applied for short-term forecasting and specific metrics showed good estimations. However, longer-term forecasting is not shown. In [19], the authors utilized measured vaporized fine particulate matter (PM2.5) information, direct determination imaging spectroradiometer (MODIS) vaporized optical profundity (AOD) information, and meteorological parameters (temperature, wind speed, wind course, boundary layer stature, and relative stickiness) from the Chinese national control checking organization, to consider regular and territorial contrasts within the relationship between AOD and PM2.5. They suggested a two-stage combined estimation showing PM2.5 concentrations based on the \(e\)-support vector relapse and the Intellect Developmental Computation-BP neural organizing (MEC-BP) for analyzing spatiotemporal varieties in PM2.5 concentrations in China between 2000 and 2017. It appeared that the two-stage combined estimation demonstrated a dependable estimation of the month-to-month ground-level PM2.5 concentrations at a spatial determination of \(1^{\circ}\times 1^{\circ}\) from 2000-2017 in China. In [20], the authors suggested daily air quality index prediction models in Northern Thailand. The models were drawn from linear regression, neural networks, and genetic programming. In the case of no pollution, the accuracy of all three models was significantly good. When air pollution was present, only two models derived from linear regression and genetic programming provided results that were acceptable. The genetic programming model was slightly more accurate than the linear regression model. [PERSON] et al. [21], proposed the prediction of air pollution using a heterogeneous ensemble of differential evolution and the random forest approach. This is different from existing work, as a method was proposed to combine state-of-the-art differential evolution strategies with the random forest method instead of focusing on an existing single technique. When the existing strategy, which uses independent and multilabel classifiers, was compared to the proposed approach, it was clear that the suggested approach outperformed the existing approach. Continuous ambient air quality data of two cities, Delhi and Patna, were taken, from which seven pollutant datasets, including CO, were collected with daily average concentration. [PERSON] et al. [22] suggested checking how different input factors affected the training of several air quality indicators utilizing fuzzy logic and two metaheuristic optimization techniques: simulated annealing (SA) and particle swarm optimization (PSO). NO\({}_{2}\) and CO concentrations were predicted using five resistivities from multisensor devices and three meteorological factors in this study (temperature, relative humidity, and absolute humidity). Several indicators were derived to validate the results, including the correlation coefficient and the mean absolute error. Overall, PSO was shown to be the most effective. This part provides an overview of various works that have utilized machine learning (ML) models to monitor air pollution in ports and surrounding areas. Ports often contribute to air pollution, and truck traffic in large ports can be a significant source of pollution.