Particulate Matter is an air pollutant that has resulted in tremendous health effects to the exposed populace. Air quality forecasting is an established process where air pollutants particularly, Particulate Matter (PM10) concentration is predicted in advance, so that adequate measures are implemented to reduce the health effect of PM10 to the barest level. The present study used daily average PM10 concentration and meteorological parameters (temperature, humidity, wind speed and wind direction) for five years (2006)(2007)(2008)(2009)(2010) from three industrial air quality monitoring stations in Malaysia (Balok Baru, Tasek and Paka). Time series plot was used to assess PM10 pollution trend in the industrial areas. Additionally, Step Wise Regression (SWR) analysis was used to predict next day PM10 concentrations for the three industrial areas. The SWR method was compared with a Persistence model to assess its predictive capabilities. The results for the trend analysis showed that, Balok Baru (BB) had higher PM10 concentration levels, having high values in 2006, 2007 and 2009. These values were higher than the Malaysian Ambient Air Quality Guideline (MAAQG) of 150 µg/m 3 . Subsequently, the other two industrial areas Tasek (TK) and Paka (PK) had no record of violating the MAAQG. The results for the SWR analysis had significant R 2 values of 0.64, 0.66 and 0.60, respectively. The model performance results for Variance Inflation Factor (VIF) were less than 5 and Durbin Watson test (DW) had value of 2 for each of the study areas, which were significant. The comparative analysis between SWR and Persistence model showed that the SWR had better capabilities, having lower errors for the BB, TK and PK areas. Using Root Mean Square Error (RMSE), the results showed error differences of 7, 12 and 16%, and higher predictability using Index of Agreement (IA), having a difference of 17, 19 and 16% for BB, TK and PK areas, respectively. The results showed that SWR can be used in predicting PM10 next day average concentration, while the extreme event detection results showed that 100 mg/m 3 were better detected than the 150 mg/m 3 bench marked levels.

The devastating health effects of particulate matter (PM) exposure by susceptible populace has made it necessary to evaluate PM pollution. Meteorological parameters and seasonal variation increases PM concentration levels, especially in areas that have multiple anthropogenic activities. Hence, stepwise regression (SR), multiple linear regression (MLR) and principal component regression (PCR) analyses were used to analyse daily average PM concentration levels. The analyses were carried out using daily average PM concentration, temperature, humidity, wind speed and wind direction data from 2006 to 2010. The data was from an industrial air quality monitoring station in Malaysia. The SR analysis established that meteorological parameters had less influence on PM concentration levels having coefficient of determination (R ) result from 23 to 29% based on seasoned and unseasoned analysis. While, the result of the prediction analysis showed that PCR models had a better R result than MLR methods. The results for the analyses based on both seasoned and unseasoned data established that MLR models had R result from 0.50 to 0.60. While, PCR models had R result from 0.66 to 0.89. In addition, the validation analysis using 2016 data also recognised that the PCR model outperformed the MLR model, with the PCR model for the seasoned analysis having the best result. These analyses will aid in achieving sustainable air quality management strategies.

Over the years, anthropogenic activities have led to the increase in air pollution concentration levels in the atmosphere, this persistent increase in pollution levels can be influenced by meteorological parameters. These parameters assist in the formation and transportation of air pollutants in the atmosphere. Hence, this study aims at evaluating the association between meteorological parameters and air pollutants. The analysis was carried out using Ozone (O3), Particulate matter (PM10), Nitrogen dioxide (NO2), temperature, humidity, wind speed, and wind direction data from 2006 to 2010, from two industrial air quality monitoring stations. Stepwise regression (SR) analysis was used to assess the influence of meteorological parameters in accounting for the variability of O3 concentration levels. The SR analysis showed that meteorological parameters accounted for more than 50 % of O3 variability. It can be concluded that different relationship between meteorological parameters and O3 can exist in different locations in the same region.

No abstract

Prediction of Particulate Matter (PM10) episode in advance enables for better preparation to avert and reduce the impact of air pollution ahead of time. This is possible with proper understanding of air pollutants and the parameters that influence its pattern. Hence, this study analyzed daily average PM10, temperature (T), humidity (H), wind speed (WS) and wind direction (WD) data for five years (2006)(2007)(2008)(2009)(2010), from two industrial air quality monitoring stations. This data was used to evaluate the impact of meteorological parameters and PM10 in two peculiar seasons; Southwest Monsoon (SWM) and Northeast Monsoon (NEM) seasons, using Principal Component Analysis (PCA). Subsequently, Lognormal Regression (LR), Multiple Linear Regression (MLR) and Principal Component Regression (PCR) methods were used to forecast next day average PM10 concentration level. The PCA result (seasonal variability) showed that peculiar relationship exist between PM10 pollutants and meteorological parameters. For the prediction models, the three methods gave significant results in terms of performance indicators. However, PCR had better predictability, having a higher coefficient of determination (R 2 ) and better performance indicator results than LR and MLR methods. The outcomes of study signify that PCR models can be effectively used as a suitable format in predicting next day average PM10 concentration levels.