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Industrialization and population growth have resulted in significant environ mental implications such as global warming, waste disposal, ocean acidification, deforestation, etc. A sustainable clean energy future requires systems with zero carbon and water footprints, which requires advanced materials and autonomous processes. The Digital revolution has fast-forwarded the transition from fossil fuel to a renewable civilization, which can reverse the damage caused by human activities to the environment. Sensors are the pillars of the digital revolution which generates data for developing advanced mathematical models and autonomous processes. Novel sensing technologies and advancements in data processing are our greatest tools to fight against various ways that humans have affected the environment such as overpopulation, pollution, burning fossil fuels, deforestation, etc. Using these technologies, we can find ways to reduce climate change, soil erosion, poor air and water quality, and keep our planet “green” for the next generations. In the last decade, considerable research works have been carried out on developing sensitive, low-powered, and durable sensors for environmental sensing. Artificial intelligence (AI) and big data processing techniques and algorithms made it possible to create continuous monitoring systems to minimize the effect of human activities on the environment. We devoted this book to exploring new opportunities and possibilities in using advanced devices and AI for various environment sensing applications which will be published in the name of “Artificial Intelligence and Data Science in Environmental Sensing.”
Preface
Smart sensing technologies for wastewater treatment plants
Advancements and artificial intelligence approaches in antennas for environmental sensing
Intelligent geo-sensing for moving toward smart, resilient, low emission, and less carbon transport
Language of response surface methodology as an experimental strategy for electrochemical
wastewater treatment process optimization
Artificial intelligence and sustainability: solutions to social and environmental challenges
Application of multi-criteria decision-making tools for a site analysis of offshore wind turbines
Recent advances of image processing techniques in agriculture
Tuning swarm behavior for environmental sensing tasks represented as coverage problems
Machine learning applications for developing sustainable construction materials
The AI-assisted removal and sensor-based detection of contaminants in the aquatic environment
Recent progress in biosensors for wastewater monitoring and surveillance
Machine learning in surface plasmon resonance for environmental monitoring