Artificial intelligence and IoT driven technologies for environmental pollution monitoring and management - Popescu, Simona Mariana; Mansoor, Sheikh; Wani, Owais Ali; Kumar, Shamal Shasang; Sharma, Vikas; Sharma, Arpita; Arya, Vivak M.; Kirkham, M. B.; Hou, Deyi; Bolan, Nanthi; Chung, Yong Suk (Frontiers in Environmental Science, 2024)
Detecting hazardous substances in the environment is crucial for protecting human well-being and ecosystems. As technology continues to advance, artificial intelligence (AI) has emerged as a promising tool for creating sensors that can effectively detect and analyze these hazardous substances. The increasing advancements in information technology have led to a growing interest in utilizing this technology for environmental pollution detection. AI-driven sensor systems, AI and Internet of Things (IoT) can be efficiently used for environmental monitoring, such as those for detecting air pollutants, water contaminants, and soil toxins. With the increasing concerns about the detrimental impact of legacy and emerging hazardous substances on ecosystems and human health, it is necessary to develop advanced monitoring systems that can efficiently detect, analyze, and respond to potential risks. Therefore, this review aims to explore recent advancements in using AI, sensors and IOTs for environmental pollution monitoring, taking into account the complexities of predicting and tracking pollution changes due to the dynamic nature of the environment. Integrating machine learning (ML) methods has the potential to revolutionize environmental science, but it also poses challenges. Important considerations include balancing model performance and interpretability, understanding ML model requirements, selecting appropriate models, and addressing concerns related to data sharing. Through examining these issues, this study seeks to highlight the latest trends in leveraging AI and IOT for environmental pollution monitoring.