Work

Real-time IoT-powered AI system for monitoring and forecasting of air pollution in industrial environment

Year: 2024

Type: article

Abstract: , PM2.5, and PM10, and provides real-time data on pollutant concentration levels. Data collected by the sensors are processed using LSTM, Random Forest, and Linear Regression models to predict pollution levels. The LSTM model achieved a coefficient of variation (R²) of 99 % and a mean absolute percentage error (MAE) of 0.33 for temperature and humidity forecasting. For PM2.5, the Random Forest model outperformed others, achieving an R² of 84 % and an MAE of 10.11. The system activates factory exhaust fans to circulate air when high pollution levels are predicted to occur in the next hours, allowing for proactive measures to improve air quality before issues arise. This innovative approach demonstrates significant advancements in industrial environmental monitoring, enabling dynamic responses to pollution and improving air quality in industrial settings.

Source: Ecotoxicology and Environmental Safety

DOI: 10.1016/j.ecoenv.2024.116856

Authors: Montaser N.A. Ramadan, Mohammed A.H. Ali, Shin Yee Khoo, Mohammad Alkhedher, Mohammad Alherbawi

Institutions: University of Malaya, Abu Dhabi University, Hamad bin Khalifa University

Language: English

Topic: Air Quality Monitoring and Forecasting

Subfield: Environmental Engineering

Field: Environmental Science

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Responsible consumption and production, Clean water and sanitation

Open Access status: gold

Estimated APC paid: $2,990