From smart sensing to adaptive remediation: artificial intelligence and Internet of Things for persistent organic pollutants.
5 affiliations
- 1Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hung Hom, Hong Kong. [email protected].
- 2Advanced Radiation Technology Institute (ARTI), Korea Atomic Energy Research Institute, Jeongeup, 56212, Republic of Korea.
- 3Department of Radiation Science, University of Science and Technology, Daejeon, 34113, Republic of Korea.
- 4Institute of Water Management and Treatment Technologies, Sustainability and Environment Sector, King Abdulaziz City for Science and Technology (KACST), 11442, Riyadh, Saudi Arabia.
- 5Environmental Engineering Department, Egypt-Japan University of Science and Technology, Alexandria, 21934, Egypt.
Abstract
Persistent organic pollutants (POPs) remain a major environmental challenge because of their persistence, long-range transport, bioaccumulation, and resistance to degradation. Conventional monitoring and remediation systems provide reliable analytical performance but are largely fragmented, laboratory dependent, and unable to deliver the continuous, predictive, and adaptive management required for complex, dynamic contamination scenarios. Recent literature reports substantial advances in biosensors, Internet of Things (IoT)-enabled sensing, machine learning (ML), and artificial intelligence (AI) for pollutant detection, forecasting, source identification, and remediation optimization. However, these developments often operate separately, with limited coordination between environmental sensing, data analytics, treatment processes, and decision-making. The literature further highlights persistent barriers to field deployment, including limited and geographically heterogeneous datasets, inconsistent sensor performance and calibration, restricted model transferability, insufficient independent validation, uncertainty in AI predictions, and the lack of mature regulatory frameworks for AI-assisted environmental assessment. From our perspective, the next advance in POP management should move beyond isolated AI applications toward integrated, closed-loop AI-IoT architectures that continuously connect sensing, prediction, remediation, and feedback control. Such systems could transform POP management from reactive assessment to proactive and adaptive environmental governance. Future progress will depend on standardized, Findable, Accessible, Interoperable, and Reusable (FAIR) environmental datasets, explainable and uncertainty-aware AI, robust field validation, and interoperable sensing infrastructures.
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BibTeX
@article{chawraba2026from,
title = {{From smart sensing to adaptive remediation: artificial intelligence and Internet of Things for persistent organic pollutants.}},
author = {Khaled Chawraba and Abrar Hussain and Obaid A Alharbi and Amal Abdelhaleem},
journal = {Environmental monitoring and assessment},
year = {2026},
month = oct,
doi = {10.1007/s10661-026-15975-x},
url = {https://europepmc.org/article/MED/42873333},
note = {CC BY},
}
RIS
TY - JOUR
TI - From smart sensing to adaptive remediation: artificial intelligence and Internet of Things for persistent organic pollutants.
AU - Khaled Chawraba
AU - Abrar Hussain
AU - Obaid A Alharbi
AU - Amal Abdelhaleem
PY - 2026
DA - 2026/10/14
JO - Environmental monitoring and assessment
DO - 10.1007/s10661-026-15975-x
UR - https://europepmc.org/article/MED/42873333
AB - Persistent organic pollutants (POPs) remain a major environmental challenge because of their persistence, long-range transport, bioaccumulation, and resistance to degradation. Conventional monitoring and remediation systems provide reliable analytical performance but are largely fragmented, laboratory dependent, and unable to deliver the continuous, predictive, and adaptive management required for complex, dynamic contamination scenarios. Recent literature reports substantial advances in biosensors, Internet of Things (IoT)-enabled sensing, machine learning (ML), and artificial intelligence (AI) for pollutant detection, forecasting, source identification, and remediation optimization. However, these developments often operate separately, with limited coordination between environmental sensing, data analytics, treatment processes, and decision-making. The literature further highlights persistent barriers to field deployment, including limited and geographically heterogeneous datasets, inconsistent sensor performance and calibration, restricted model transferability, insufficient independent validation, uncertainty in AI predictions, and the lack of mature regulatory frameworks for AI-assisted environmental assessment. From our perspective, the next advance in POP management should move beyond isolated AI applications toward integrated, closed-loop AI-IoT architectures that continuously connect sensing, prediction, remediation, and feedback control. Such systems could transform POP management from reactive assessment to proactive and adaptive environmental governance. Future progress will depend on standardized, Findable, Accessible, Interoperable, and Reusable (FAIR) environmental datasets, explainable and uncertainty-aware AI, robust field validation, and interoperable sensing infrastructures.
SN - 0167-6369
SN - 1573-2959
ER -