Current Trends and Advances in Computer-Aided Intelligent Environmental Data Engineering

Gonçalo Marques holds a PhD in Computer Science Engineering and is member of the Portuguese Engineering Association (Ordem dos Engenheiros). He is currently working as Assistant Professor lecturing courses on programming, multimedia and database systems. His current research interests include Internet of Things, Enhanced Living Environments, machine learning, e-health, telemedicine, medical and healthcare systems, indoor air quality monitoring and assessment, and wireless sensor networks. He has more than 80 publications in international journals and conferences, is a frequent reviewer of journals and international conferences and is also involved in several edited books projects. Joshua O. Ighalo obtained his Bachelors' Degree in Chemical Engineering in 2015 from the University of Benin, Nigeria. He also received a Masters' Degree in Chemical Engineering in 2019 from the University of Ilorin, Nigeria. His research interests include computer-aided modelling and optimisation of chemical process systems, biofuel production, solid waste management, and environmental pollution control. He authored or co-authored over 25 papers in journals indexed in Scopus and Web of Scienc

• Section I: Data-centric and intelligent systems in air quality monitoring, assessment and mitigation1. Application of deep learning and machine learning in air quality modelling2. Case study of air quality prediction by deep learning and machine learning3. Considerations of particle dispersion modelling with data-centric and intelligent systems4. Data-centric modelling of air filters, HVAC and other industrial air quality control systems5. A review of recent developments and applications of data-centric systems in air quality monitoring, assessment and mitigationSection 2: Data-centric and intelligent systems in water quality monitoring, assessment and mitigation6. Application of deep learning and machine learning methods in water quality modelling and prediction7. Case studies of surface water, groundwater and rainwater quality prediction by data-centric and intelligent systems8. Application of deep learning and machine learning methods in contaminant hydrology9. Deep learning and machine learning methods in emerging contaminants and micro-pollutants research10. A review of recent developments and applications of data-centric systems in water quality monitoring, assessment and mitigationSection 3: Data-centric and intelligent systems inland pollution research11. Application of deep learning and machine learning methods in flow modelling of landfill leachate12. Case studies of evaluations and analysis of solid waste management techniques by deep learning and machine learning methods13. Application of deep learning and machine learning methods in soil quality assessment and remediation14. Establishing a nexus between non-biodegradable waste and data-centric systems15. A review of recent developments and applications of data-centric systems inland pollution researchSection 4: Data-centric and intelligent systems in noise pollution research16. Methods development for data-centric systems in noise pollution research17. Case studies of data-centric systems in noise pollution research18. A review of recent developments and applications of data-centric systems in noise pollution research