Abstract
Air pollution is a considerable health danger to the environment. The objective of this study was to assess the characteristics of air quality and predict PM10 concentrations using boosted regression trees (BRTs). The maximum daily PM10 concentration data from 2002 to 2016 were obtained from the air quality monitoring station in Kuching, Sarawak. Eighty percent of the monitoring records were used for the training and twenty percent for the validation of the models. The best iteration of the BRT model was performed by optimizing the prediction performance, while the BRT algorithm model was constructed from multiple regression models. The two main parameters that were used were the learning rate (lr) and tree complexity (tc), which were fixed at 0.01 and 5, respectively. Meanwhile, the number of trees (nt) was determined by using an independent test set (test), a 5-fold cross validation (CV) and out-of-bag (OOB) estimation. The algorithm model for the BRT produced by using the CV was the best guide to be used compared with the OOB to test the predicted PM10 concentration. The performance indicators showed that the model was adequate for the next day’s prediction (PA=0.638, R2=0.427, IA=0.749, NAE=0.267, and RMSE=28.455).
Highlights
In Malaysia, air quality is monitored continuously throughout the country by the Department of Environment (DOE) at 65 stations
Afroz et al [1] discussed air pollution caused by open burning and forest fires in Malaysia, which has become harmful to the public health and the environment
The PM10, CO, NO2, and SO2 and relative humidity were highly skewed because their skewness coefficients were less than -1 or greater than +1, while the wind speed and temperature were moderately skewed since their skewness coefficients were between -1⁄2 and -1 or between +1⁄2 and +1
Summary
In Malaysia, air quality is monitored continuously throughout the country by the Department of Environment (DOE) at 65 stations. Afroz et al [1] discussed air pollution caused by open burning and forest fires in Malaysia, which has become harmful to the public health and the environment. According to the [2], PM10 and O3 are the major causes of unhealthy days recorded in Malaysia. PM10 is particulate matter with an aerodynamic diameter of less than 10 μm [3]. It is one of the main causes of pneumoconiosis, when it enters the bronchus, alveoli, and so on. The smaller the size of the dust particles, the deeper into the respiratory tract they enter
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
