Dear Colleagues,

This Special Issue represents the applications of artificial intelligence (AI) technologies, including artificial neural network, deep neural network, support vector machine, genetic algorithm, fuzzy logic, and adaptive-neuro fuzzy systems, to the field of environmental engineering. These AI techniques have been employed in wastewater degradation processes for predicting the pollutants’ removal efficiencies, optimizing the systems’ operating conditions, and controlling the aeration, flowrates, and tank temperature. Moreover, AI-based digital tools have been utilized for deploying real-time monitors and early warning systems to detect possible contamination in aquatic, terrestrial, and atmospheric environments. AI-aided soft-sensors are also used for collecting, monitoring, and analyzing remote data related to environmental pollution. Furthermore, AI is used to address inputs/outputs nonlinear correlations in chemical and biological environmental-associated processes. Intelligent control systems are employed for smart and wireless monitoring and collection of solid waste management in green cities. Regarding atmospheric environment sustainability, AI-based techniques are used to diagnose, manage, and forecast air-pollution-related diseases. Different artificial intelligence algorithms are utilized to create maps, showing the noise distribution in the area. These advantages are beneficial for fulfilling the sustainable development goals accompanied by pollution control and reduction, protection of human health, and climate change mitigation and adaptation.

1. AI applications in risk management and assessment related to human health impacts from trace elements, micropollutants, heavy metals, and metalloids;
2. AI utilization to manage and control several water engineering and water-quality-associated issues;
3. Environmental sustainability and automated monitoring techniques;
4. Low-cost and reliable AI-based smart sensors for mitigation of atmospheric pollution;
5. AI-based methods for the control of urban noise pollution and health effects;
6. Evaluation of AI-based decision support systems for maintaining socio-economic development and environmental conservation;
7. Artificial intelligence applications for sustainable solid waste management practices;
8. Information storage, annotation, and management related to pollutants in water, air, and subsurface environments, using AI based on a digital data collection framework;
9. Overview of hazardous waste management solutions based on IoT and AI;
10. Employing AI based on the value chain of green economy, complying with federal and state regulation.

Dr. Mahmoud Nasr
Guest Editor

Manuscript Submission Information

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• air quality
• artificial neural network
• early-warning
• intelligent control
• modeling and forecasting
• noise pollution
• soft measurement
• solid waste