A Novel Organic-Inorganic-Nanocomposite-Based Reduced Graphene Oxide as an Efficient Nanosensor for NO Detection.

There are three components to every environmental protection system: monitoring, estimation, and control. One of the main toxic gases with considerable effects on human health is NO, which is released into the atmosphere by industrial activities and the transportation network. In the present research, a NO sensor is designed based on FeO piperidine-4-sulfonic acid grafted onto a reduced graphene oxide FeO@rGO-N-(piperidine-4-SOH) nanocomposite, due to the highly efficient detection of pollution in the air.

Air quality prediction and control systems using machine learning and adaptive neuro-fuzzy inference system.

Accurately predicting air quality concentrations is a challenging task due to the complex interactions of pollutants and their reliance on nonlinear processes. This study introduces an innovative approach in environmental engineering, employing artificial intelligence techniques to forecast air quality in Semnan, Iran. Comprehensive data on seven different pollutants was initially collected and analyzed.

A risk-based soft sensor for failure rate monitoring in water distribution network via adaptive neuro-fuzzy interference systems.

Water Distribution Networks (WDNs) are considered one of the most important water infrastructures, and their study is of great importance. In the meantime, it seems necessary to investigate the factors involved in the failure of the urban water distribution network to optimally manage water resources and the environment. This study investigated the impact of influential factors on the failure rate of the water distribution network in Birjand, Iran.