While plastic recycling has gained conclusive acceptance by various stakeholders as a preferable products’ end-of-life management, plastic waste processing industries may contribute to serious air pollutants emission and impair human health, especially if it is in uncontrolled conditions. Apart from toxic gas pollutants, this industry may also emit significant concentration of particulates matter or dust, notably via physical (shredding, sorting, and washing) and melt (re-granulation and reprocessing) processes. Meanwhile, in Sungai Petani, Kedah, Malaysia, public anguish is increasing in recent years due to mushrooming plastic waste recycling industries in its residential area. Thus, a study was conducted to analyse the day- and night-time ambient air PMio levels and their relationship with selected meteorological parameters (ambient temperature, relative humidity and wind speed) at two different locations (Cinta Sayang Resort Villa, CSRV and Metro Specialist Hospital, HM) in Sungai Petani, Kedah, Malaysia. The mean ambient PMio levels of Cinta Sayang Resort Villa (CSRV) and Metro Specialist Hospital (HM) were found exceeding the New Malaysia Ambient Air Quality Standard at 150 (μg/m3 (24-hours), which were 568.082 + 266.441 (μg/m3 615.046 + 355.672 (μg/m3, respectively. Distribution of PMio concentrations betwen day and night-time were found to be statistically insignificant at both sites. Meteorological parameters have also contributed to the trend of PMio concentrations at both sites especially at HM. Inverse correlation with PMio at CSRV was explained by the absence of moisture (or rain) at the site while the positive correlation observed at HM was due to the hot temperature-strong wind association at the site. Temperature was found to be the manipulating factor for PMio at HM, via linear regression model developed at PMouth = - 4352.426 + 170.557 x Th with F(1, 8) = 15.224 at p < .005, accounting for 65.6% of the variation Thus, proper attention should be given to the particulates matter emitted in Sungai Petani, believed to be influenced by the uncontrolled emission from the plastic recycling industries.
Malaysia has been facing transboundary haze events repeatedly, in which the air contains extremely high particulate matter, particularly PM10, which affects human health and the environment. Therefore, it is crucial to understand the characteristics of PM10 concentration and develop a reliable PM10 forecasting model for early information and warning alerts to the responsible parties in order for them to mitigate and plan precautionary measures during such events. This study aims to analyze PM10 variation and investigate the performance of quantile regression in predicting the next-day, the next two days, and the next three days of PM10 levels during a high particulate event. Hourly secondary data of trace gases and the weather parameters at Pasir Gudang, Melaka, and Petaling Jaya during historical haze events in 1997, 2005, 2013, and 2015. The Pearson correlation was calculated to find the correlation between PM10 level and other parameters. Moderate correlated parameters (r > 0.3) with PM10 concentration were used to develop a Pearson–QR model with percentiles of 0.25, 0.50, and 0.75 and were compared using quantile regression (QR) and multiple linear regression (MLR). Several performance indicators, namely mean absolute error (MAE), root mean squared error (RMSE), coefficient of determination (R2), and index of agreement (IA), were calculated to evaluate and compare the performances of the predictive model. The highest daily average of PM10 concentration was monitored in Melaka within the range of 69.7 and 83.3 µg/m3. CO and temperature were the most significant parameters associated with PM10 level during haze conditions. Quantile regression at p = 0.75 shows high efficiency in predicting PM10 level during haze events, especially for the short-term prediction in Melaka and Petaling Jaya, with an R2 value of >0.85. Thus, the QR model has high potential to be developed as an effective method for forecasting air pollutant levels, especially during unusual atmospheric conditions when the overall mean of the air pollutant level is not suitable for use as a model.
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