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

Abstract

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

Keywords

• construction pollution
• PM₁₀
• PM_2.5
• NO₂
• meteorology
• prediction model