Fig 1.
Workflow diagram of the IoT monitoring system.
Fig 2.
Schematic diagram of the wireless environmental monitoring device integrating PMS5003, DHT22, and MQ135 sensors with NodeMCU V3 (ESP8266).
Fig 3.
Location of the experimental facility in Ait Melloul.
Map data (C) OpenStreetMap contributors, under the Open Database License (ODbL). This map was created using the folium library in Python.
Table 1.
Basic statistics of pollutants and meteorological data of monitoring points in industrial zone.
Fig 4.
Temporal fluctuations of pollution and meteorological parameters.
The data were observed at station 1 during the initial days of October.
Fig 5.
Wind rose of the wind speed and direction in Ait Melloul.
Map data (C) OpenStreetMap contributors, under the Open Database License (ODbL). This map was created using the folium library in Python.
Fig 6.
Hourly Concentration of PM2.5 in Ait Melloul.
The figure depicts the hourly concentrations of PM2.5 at two monitoring sites: S_RH (blue) and S_ZI (red).
Fig 7.
Average hourly concentration of PM2.5 at two monitoring stations in Ait Melloul.
Fig 8.
Correlation between PM2.5 levels and environmental parameters at two monitoring sites.
Fig 9.
Boxplot of hourly mean air pollution levels in S1 and S2.
Fig 10.
Comparison of imputed data: Actual vs. Imputed dataset in S1 and S2.
Table 2.
Feature sets from ZI dataset.
Table 3.
Performance of machine learning models on IZ dataset (Primary metrics).
Table 4.
Performance of machine learning models on IZ dataset (Additional metrics).
Fig 11.
Observed and predicted PM2.5 concentrations.
Fig 12.
Fit curve of PM2.5 real value and predicted value using the LightGBM model.
Fig 13.
Feature ranking using the SHAP values for the LightGBM model.
Fig 14.
Feature importance plot derived from the LightGBM model.