Malaysia has been facing transboundary haze events every year in which the air contains particulate matter, particularly PM₁₀, which affects human health and the environment. Therefore, it is crucial to develop a 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. Therefore, this study aimed to develop and compare the best-fitted model for PM₁₀ prediction from the first hour until the next three hours during transboundary haze events. The air pollution data acquired from the Malaysian Department of Environment spanned from the years 2005 until 2014 (excluding years 2007–2009), which included particulate matter (PM₁₀), ozone (O₃), nitrogen oxide (NO), nitrogen dioxide (NO), carbon monoxide (CO), sulfur dioxide (SO₂), wind speed (WS), ambient temperature (T), and relative humidity (RH) on an hourly basis. Three different stepwise Multiple Linear Regression (MLR) models for predicting the PM₁₀ concentration were then developed based on three different prediction hours, namely t+1, t+2, and t+3. The PM_{10, t+1} model was the best MLR model to predict PM₁₀ during transboundary haze events compared to PM_10,.t+2 and PM_10,t+3 models, having the lowest percentage of total error (28%) and the highest accuracy of 46%. A better prediction and explanation of PM₁₀ concentration will help the authorities in getting early information for preserving the air quality, especially during transboundary haze episodes.

Rapid economic growth has led to an increase in ozone (O₃) concentration which significantly affecting human health and environment. The prediction of O₃ is complicated due to the redundancy of influencing parameters which introduce the multicollinearity problem. The aim of this study is to assess the best prediction model for O₃ concentration which is Multiple Linear Regression (MLR) and Principle Component Regression (PCR). Data from 2012 to 2014 were used including O₃, nitrogen dioxide (NO₂), nitrogen oxide (O₂), temperature, relative humidity and wind speed on hourly basis. Principle Component Analysis (PCA) was used in order to reduce multicollinearity problem, prior to the implementation of MLR. The hybrid model of PCR was selected as best -fitted models as it had higher correlation coefficient, R² values compared with MLR model. In conclusion, the information from best-fitted prediction model can be used by local authorities to plan the precaution measure in combating and preserve the better air quality level.

Large-scale biomass burning (BB) emits large amounts of aerosols that lead to transboundary smoke events and adversely impacts human health, whilst causing societal and environmental issues. High ambient PM2.5 concentration in the year 2019 based on New Malaysia Ambient Air Quality Standard (NMAAQS) was identified as high pollution episodes, HP1 and HP2 on the east coast Peninsular Malaysia (ECPM). Meanwhile, the low PM2.5 concentration episodes are known as LP1 and LP2. The transboundary smoke events in Indochina and Indonesia are linked to HP1 (March-April) and HP2(August-September), respectively from backward trajectory and MERRA-2 model reanalysed weather data. The correlation analysis showed a significantly strong positive correlation (r) of black carbon (HP1: 0.91; HP2: 0.96), organic carbon (HP1: 0.90; HP2: 0.94), and sulphate (HP1: 0.80; HP2: 0.61) with the aerosol optical depth (AOD) levels during high pollution episodes. The synoptic weather condition and inter- monsoon in HP1 and southwest monsoon in HP2 introduce strong wind speed and favourable wind pattern that can initiate the long-range transport of high AOD and PM2.5 to the ECPM region. In conclusion, this study demystified the sources of BB emissions, the transport route of transboundary smoke events, their influence factors during different high pollution periods, and the links between aerosol species from local and non-local emissions with AOD levels and PM2.5 concentrations along the ECPM, which altogether provide crucial information on climate variability signal and can help in developing a corresponding strategy for high pollution episodes.

Malaysia is a developing country that facing air pollution issues due to industrialization, urbanization activities and population growth. Ozone (O3) is one of the pollutants that can give adverse effects to human health either in long- term or short-term effect. Moreover, it also can interrupt the normal ecosystem and cause extinction in species. This study aims to analyse the annual trends of the concentration of ozone (O3) level for 3-year and the diurnal trends of ozone at Kemaman, Terengganu. One-way Analysis of Variance (ANOVA) revealed that there exists statistically significant different (p<0.05) of O3 concentrations during year 2012-2014. Meanwhile, diurnal trend showed the lowest O3 concentration values in the morning and the highest average concentration values of O3 in the noon, but it starts to decrease gradually in evening and night. The understanding of the ozone trend, yearly and hourly, can helps in tackling the atmospheric pollution problems due to industrialization in Malaysia.

The increase of air pollutants emission through anthropogenic activities and natural phenomena in the atmosphere can give an adverse impact on human health especially to some groups of people such as children, the elderly, and people that have cardiovascular problems. Multiple Linear Regression (MLR) model establishments for the particulate matter (PM10) forecasting can be useful, as it provides early warning information to the local authorities and the communities. We aim to develop MLR models for PM10 forecasting in Peninsular Malaysia, specifically in the southern part. In this study, the hourly data of PM10, meteorological factors, and gaseous pollutants from the year 2009-2011 had been used. As a result, the next first hour of the MLR prediction model, PM10,t+1 has been selected as the best-fitted model as compared to the second and third prediction hour models, PM10,t+2, and PM10,t+3, respectively. The PM10,t+1 model was explained 61.4% (R2=0.614) variance in the data which is higher compared to model PM10,t+2 and PM10,t+3 with 42.3% (R2=0.423) and 34.7% (R2=0.347), respectively. Thus, the validation of PM10, t+1 model also has a high accuracy value of R2 (55.1%) as compared to the other two models. We conclude that the development of MLR models is adequate for PM10 forecasting in the industrial area.