Nano-enabled solutions: Advancing pesticide detection and remediation in wastewater

• Nanotechnology key role in detecting/eliminating pesticides from wastewater was explored. • Adsorbents & catalytic nanomaterial are effective strategies for pesticide removal • Nanotechnology showed remarkable pesticide removal efficiency in the range 93.6% - 99.7%. • Machine Learning (ML) accurately model pesticide removal • Nanotechnology has potential to mitigate pesticide pollution in highly contaminated regions Nanotechnology is an emerging field that deals with the nanoscale. It is characterized by certain optical, electrical, and mechanical properties, being applicable to wastewater treatment. Nanomaterials aid in eliminating contaminants, notably pesticides, by enabling pesticide detection and remediation via nano-sensors and nanocomposites. Even though nanotechnology has a promising impact on the remediation of pesticides, there still remain issues with its toxicity, biological impacts, and scalability. Additionally, there is limited use of nanotechnology in certain regions with high levels of water contamination, such as the Middle East. Therefore, it is essential to investigate the use of nanotechnology in the region while upscaling the process to remediate pesticides at an industrial and municipal level. This review shows that nanomaterials can be modified to form nanoadsorbents, nanocatalysts, and nanomembranes, having removal efficiencies of 96.5%, 98%, and 93.6%, respectively. These nanomaterials can be combined to form an adsorptive membrane, increasing the removal rate to 99.7%. The removal of pesticides can also be modeled via machine learning (ML) and response surface methodology (RSM). These computational techniques make it possible to distinguish between different types of pesticides, with an accuracy rate of 92.46%, and optimize the process parameters for an enhanced removal rate. These techniques show the potential of using nanotechnology in the detection and removal of pesticides, although challenges persist regarding data collection and quality, large-scale implementation, and toxicity levels of nanoparticles.