Sustainable global expansion is dependent on a plethora of elements, including the economy, agriculture, industries, and others; however, the environment is one of the most crucial. Health constitutes a critical component of humanity’s long-term viability and any country’s progress, both of which are dependent on a clean, pollution-free, and hazardous-free environment. As a result, monitoring is necessary to ensure that the inhabitants of any country can live a healthy life. Environment monitoring (EM) entails disaster planning and management, pollution control, and successfully resolving difficulties that develop as a result of harmful external conditions. Water pollution, air pollution, dangerous radiation, weather changes, and other environmental issues are all dealt with by EM [1]. Several factors lead to pollution, some of which are of human origin and others due to natural causes, and the duty of EM is to handle the difficulties in order to safeguard the environment for a healthy society and planet [2].

Among the environmental issues is air pollution. Environmental protection agencies, as well as governments, have made significant efforts to reduce the effects of air pollution on the community. Researchers, policymakers, and developers can use accurate information about air pollution levels to regulate and improve the living environment. Typically, traditional air pollution monitoring stations are used to assess air quality. These monitoring stations have a high level of data accuracy and can measure a wide range of contaminants [3]. Here, environmental stations are encapsulated that monitor specific environmental parameters, and the CO in the air is among them.

Monitoring can be undertaken using Internet of Things (IoT) devices and wireless sensor networks (WSN)s. There are different sensors and architectures that can be employed to perform the task [4]. Environmental monitoring can be performed using a number of technologies, including Zigbee [5], Wi-Fi [6], GSM [7], and other telecommunications [8]. The significance of IoT and WSNs lies in the fact that they can be distributed in nature and measure local microclimate as opposed to other solutions that are centralized and based on an entire area. In such a way, the locality of the readings can be compared and an investigation of potential patterns may be identified.

Artificial intelligence and machine learning are often encapsulated in order to identify patterns and classify them. An appropriate and good method constructs classification programs in a human-readable format using the technique of grammatical evolution [9]. Grammatical evolution is an evolutionary process that has been applied with success in many areas, such as music composition [10], economics [11], symbolic regression [12], robot control [13], and combinatorial optimization [14]. 

2. The Evolutionary Computation Models of Air Pollution

There is a plethora of research work on air pollution in Port areas that can be found in ^[15][17] and references therein.  Espinosa et al. ^[16][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 NO₂ 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 ^[17][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 𝜖-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° × 1° from 2000–2017 in China. In ^[18][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. Kumar et al. ^[19][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. Ly et al. ^[20][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). NO₂ 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. In the future, data from trucks will be incorporated into the proposed forecasting model to improve the accuracy of carbon monoxide (CO) pollution prediction. One work by ^[21][23] suggested the use of wireless sensors equipped with mini low-power artificial neural networks (ANNs), which are trained from their local environment, rather than from a base station. This work also considered the impact of microclimates on prediction accuracy and developed a prototype chip to reduce the computational complexity of the ANNs. Another work by ^[22][24] used ML models to predict air quality in Barcelona, gathering weather and pollutant concentration data using networks. The ML tool exhibited better predictive capabilities than the CALIOPE Urban v1.0 platform and showed that time of the year, daytime, and intensity of road traffic are the most significant factors impacting pollutant-level variability. In ^[23][25], the authors aimed to reduce CO2 emissions from the crane of the Casablanca Port of Morocco, studying the daily energy of eleven RTG cranes collected for two years. The energy consumption was analyzed using regression analysis to discover the factors that impact production, and the authors introduced inexpensive strategies for clean air. Another work by ^[24][26] provided a comprehensive review of the state-of-the-art ML models and their applications to international freight transportation management, including demand forecasting, vehicle trajectory, procedure and asset maintenance, and on-time performance prediction. Finally, Ref. ^[25][27] described two kernel-based supervised ML models for daily truck traffic in port terminals, which were compared with a multilayer feedforward neural network model. The Gaussian processes and 𝜖-support vector machine models performed well and required less effort in model fitting compared to the MLFNN model. These models are good candidates to substitute for the MLFNN in port-generated truck traffic.