Study of Particular Air Quality and Meteorological Parameters at a Construction Site

The construction industry is a major contributor to dust, greenhouse gases, and other air pollutants. Implementing effective sustainable practices in managing site operations can greatly mitigate the environmental effects of project. To achieve this, collaboration between scientific research institution company enabled real-time monitoring quality parameters at using Internet Things (IoT) technologies. They implemented an IoT-based system framework that integrated distributed sensor network collect data from site. Various sensors were utilized gather on concentration NO2 particulate matter (PM2.5 PM10), as well meteorological such wind speed, direction, humidity, pressure, temperature. measurements yielded insights into level pollution its association with earth excavation, primary activity. This information be manage excavation work reduce levels polluting gases (NO2) suspended particles. By conducting on-site these three pollutants, study discovered dust resulting activities relatively high. When comparing direction PM concentrations, it was concluded significantly influenced area. However, terms factors affecting analysis revealed did not exhibit significant correlation multiple linear regression (MLR) artificial neural (ANN) models for predicting PM2.5, PM10 predictors applied. ANN model showed greater accordance measured concentrations than MLR model.