Contribution of aerosol species to the 2019 smoke episodes over the east coast of peninsular Malaysia.

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 re-analyses 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

Spatio-Temporal Modelling of Noise Pollution

An undesired or hazardous outdoor sound produced by human activities is referred to as environmental noise. For example, the noise emitted through industrial activities and transportation networks such as road, rail and air traffic. In Malaysia, most of the schools located very close to the roadside and near busy places such as cities, shops, and residential areas. This study aims to analyze the environmental noise in terms of spatial and temporal analysis in two primary schools in Terengganu State.  The noise monitoring had conducted in two (2) primary schools with different land use; residential area (Batu Rakit Primary School) and commercial area (Paya Bunga Primary School) on the school and non-school days by using Sound Level Meter (SLM). The spatial mapping had constructed by using SketchUp® 2018 and Surfer® version 11 software. The noise level between both study areas was significantly different based on a p-value of less than 0.05. It also surpassed the Department of Environment (DOE) of Malaysia's permitted limit, with the Equivalent Noise Level (LAeq) in residential areas being greater than in commercial areas due to traffic volume and noise from nearby activities. Lastly, the area near the roadside has higher critical noise pollution compared with the location that furthers from the roadside. In conclusion, this study is useful in creating awareness to the public about the noise pollution effect on primary school students and is also can be used for mitigation measures to have a better place for students to study

Different Approaches of Multiple Linear Regression (MLR) Model in Predicting Ozone (O3) Concentration in Industrial Area

Meteorological conditions and other gaseous pollutants generally impacted the development of ozone (O3) in the atmosphere. The purpose of this study was to create the best O3 model for forecasting O3 concentrations in the industrial area and to determine the variables that affect O3 concentrations. Five-year data of meteorological and gaseous pollutants were used to analyze and develop the prediction model. Based on three distinct techniques, three separate multiple linear regression (MLR) prediction models of O3 concentration were developed. MLR3 had the highest correlation coefficient of 0.792 during development as compared to models MLR1 and MLR2. MLR2 was deemed the best O3 prediction model, however, since it had the lowest error values of root mean square error (3.976) and mean absolute error (3.548) when compared to other models. The establishment of an O3 prediction model can offer local governments with early information that could help them reduce and manage air pollution emissions

Different Approaches of Multiple Linear Regression (MLR) Model in Predicting Ozone (O3) Concentration in Industrial Area

Meteorological conditions and other gaseous pollutants generally impacted the development of ozone (O3) in the atmosphere. The purpose of this study was to create the best O3 model for forecasting O3 concentrations in the industrial area and to determine the variables that affect O3 concentrations. Five-year data of meteorological and gaseous pollutants were used to analyze and develop the prediction model. Based on three distinct techniques, three separate multiple linear regression (MLR) prediction models of O3 concentration were developed. MLR3 had the highest correlation coefficient of 0.792 during development as compared to models MLR1 and MLR2. MLR2 was deemed the best O3 prediction model, however, since it had the lowest error values of root mean square error (3.976) and mean absolute error (3.548) when compared to other models. The establishment of an O3 prediction model can offer local governments with early information that could help them reduce and manage air pollution emissions

Development of multiple linear regression for particulate matter (PM10) forecasting during episodic transboundary haze event in Malaysia

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 PM10, t+1 model was the best MLR model to predict PM10 during transboundary haze events compared to PM10,.t+2 and PM10,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