Abstract—Coal-ablaze Brick Kiln industries are the major contributors of Particulate Matter (PM_2.5, PM₁₀) emissions that endanger the environment and pose a variety of health risks to all the living beings. Current static ambient pollutant monitoring stations are sparsely located due to their expensive deployment. Recent advancements in Internet of Things (IoT) technology tends to have portable sensors which could be easily deployed at any location to monitor the quality of air. Calibration for these portable sensors requires training data from static reference monitoring stations. In this study, Brick Kiln industry, which are usually remotely located from the reference stations, is chosen to monitor its emission through the IoT devices, and the calibration for the portable sensors are performed using data from a reference sensor. Calibration of the sensor reading is performed using proposed Meta Learning based Transfer Learning (MLTL) and its performance is evaluated utilizing evaluation metrics of various Machine Learning (ML) and Deep Learning (DL) based regression models. The proposed model shows the most significant scores 0.992236, 0.0002, 0.0048 for the evaluation metrics, R-squared, Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE), respectively, as compared to other ML models while calibrating the Particulate Matter (PM) pollutant’s emission rate obtained from the industry.
 
Keywords—brick kiln industry, meta learning-based transfer learning, machine learning, deep learning


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