The purpose of this article is to provide a broad summary of recent discoveries, identify important constraints, and suggest future lines of inquiry. In recent years, pollution has surpassed the environment's bearing capacity. There hasn't been much progress despite the installation of multiple pollution-control initiatives. Natural resources are occasionally exploited by unavoidable waste discharges, despite our government's best attempts to safeguard them. For instance, the water-intensive and polluting sectors of textiles, leather, sugar, and paper have altered in recent decades due to their large-scale water extraction and untreated effluent discharge. To address this, better pollution-linked databases and ecosystem-balancing technologies are needed. There are several pollution tracking technologies in the literature, however they are all based on traditional databases. The integration of IoT-based technologies enables real-time monitoring of critical parameters, such as temperature and humidity, in industrial waste and air management systems. Continuous assessment of environmental pollution is essential for preserving biodiversity, preventing ecosystem degradation, and safeguarding human health. While traditional methods for evaluating air and water quality are scientifically reliable, they often suffer from limitations including high operational costs, low spatiotemporal resolution, slow data processing, and restricted scalability. Recent advancements in machine learning have facilitated the development of more efficient and robust solutions to address these challenges. Supervised learning paradigms like random forests, support vector machines, and deep neural networks are especially notable since they have shown great success in low-cost sensor array calibration, anomaly detection, and pollution forecasting. The groundwork for future intelligent stewardship systems that will be transparent, scalable, and successful after 2025.

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1. Chemical gas sensor drifts compensation using classifier ensembles Actuat. B (2012)
2. Pereira et al.Detection and quantification of temperature sensor drift using probabilistic neural networks Expert Syst. Appl. (2023)
3. Kamyab et al.The latest innovative avenues for the utilization of artificial Intelligence and big data analytics in water resource management Results Eng. (2023)
4. Hähnel et al.Using deep learning to extend the range of air pollution monitoring and forecasting J. Comput. Phys. (2020)
5. Peng et al.Application of machine learning in atmospheric pollution research: a state-of-art review Sci. Total Environ. (2024)
6. Gladkova et al.Applying machine learning techniques in air quality prediction
7. Gorelick et al.Google Earth Engine: planetary-scale geospatial analysis for everyone
8. Nandy et al.Audacity of huge: overcoming challenges of data scarcity and data

9. Geetha, S.Gowthami - A review of IoT enabled real time water quality monitoring system
10. Octavian, Postolache, J.M. Dias Pereira - A review of Smart Sensors network for air quality monitoring system
11. Vennam Madhavireddy, Bonagiri Koteswarrao - A review of Smart water quality monitoring system using IoT technology
12. Pavan Kumar, S.Praveen Kumar - A review of Air and Water quality monitoring through IoT by using aquatic surface drone
13. Aditya Pradeep, Pooja Pramod Pataki, Swasthika Anal Jadhav - A review of water and air quality monitoring system
14. Yiheng Chen, dawei Han - A review of Water quality monitoring in smart city
15. Nikhil, R.Rajendar, GR.Dushyantha - A review of Smart Water quality monitoring system using IoT environment
16. Adamo, F.Attivissimo, CGC.Carducci - A review of Smart sensor network for sea water quality monitoring.

The purpose of this article is to provide a broad summary of recent discoveries, identify important constraints, and suggest future lines of inquiry. In recent years, pollution has surpassed the environment's bearing capacity. There hasn't been much progress despite the installation of multiple pollution-control initiatives. Natural resources are occasionally exploited by unavoidable waste discharges, despite our government's best attempts to safeguard them. For instance, the water-intensive and polluting sectors of textiles, leather, sugar, and paper have altered in recent decades due to their large-scale water extraction and untreated effluent discharge. To address this, better pollution-linked databases and ecosystem-balancing technologies are needed. There are several pollution tracking technologies in the literature, however they are all based on traditional databases. The integration of IoT-based technologies enables real-time monitoring of critical parameters, such as temperature and humidity, in industrial waste and air management systems. Continuous assessment of environmental pollution is essential for preserving biodiversity, preventing ecosystem degradation, and safeguarding human health. While traditional methods for evaluating air and water quality are scientifically reliable, they often suffer from limitations including high operational costs, low spatiotemporal resolution, slow data processing, and restricted scalability. Recent advancements in machine learning have facilitated the development of more efficient and robust solutions to address these challenges. Supervised learning paradigms like random forests, support vector machines, and deep neural networks are especially notable since they have shown great success in low-cost sensor array calibration, anomaly detection, and pollution forecasting. The groundwork for future intelligent stewardship systems that will be transparent, scalable, and successful after 2025.

🔒 This is a subscription article

Full text is available to subscribers and institutional members. Please choose an option below to access it.

1. Chemical gas sensor drifts compensation using classifier ensembles Actuat. B (2012)
2. Pereira et al.Detection and quantification of temperature sensor drift using probabilistic neural networks Expert Syst. Appl. (2023)
3. Kamyab et al.The latest innovative avenues for the utilization of artificial Intelligence and big data analytics in water resource management Results Eng. (2023)
4. Hähnel et al.Using deep learning to extend the range of air pollution monitoring and forecasting J. Comput. Phys. (2020)
5. Peng et al.Application of machine learning in atmospheric pollution research: a state-of-art review Sci. Total Environ. (2024)
6. Gladkova et al.Applying machine learning techniques in air quality prediction
7. Gorelick et al.Google Earth Engine: planetary-scale geospatial analysis for everyone
8. Nandy et al.Audacity of huge: overcoming challenges of data scarcity and data

9. Geetha, S.Gowthami - A review of IoT enabled real time water quality monitoring system
10. Octavian, Postolache, J.M. Dias Pereira - A review of Smart Sensors network for air quality monitoring system
11. Vennam Madhavireddy, Bonagiri Koteswarrao - A review of Smart water quality monitoring system using IoT technology
12. Pavan Kumar, S.Praveen Kumar - A review of Air and Water quality monitoring through IoT by using aquatic surface drone
13. Aditya Pradeep, Pooja Pramod Pataki, Swasthika Anal Jadhav - A review of water and air quality monitoring system
14. Yiheng Chen, dawei Han - A review of Water quality monitoring in smart city
15. Nikhil, R.Rajendar, GR.Dushyantha - A review of Smart Water quality monitoring system using IoT environment
16. Adamo, F.Attivissimo, CGC.Carducci - A review of Smart sensor network for sea water quality monitoring.