This study highlights the advantage of functional data approach in assessing and comparing the PM10 pollutant behaviour as an alternative statistical approach during and between the two extreme haze years (1997 and 2005) that have been reported in Selangor, state of Malaysia. The aim of the study was to improvise the current conventional methods used in air quality assessment so that any unforeseen implicit information can be revealed and the previous research findings can be justified. An analysis based on the daily diurnal curves in place of discrete point values was performed. The
analysis results provided evidences of the influence of the change in the climate (due to the El-Nino event), the different levels of different emission sources and meteorological conditions on the severity of the PM10 problem. By means of the cummulative exceedence index and the functional depth method, most of the monitoring stations for the year 2005 experienced the worst day of critical exceedences on the 10th of August, while for the year 1997 it occurred between 13th and 26th September inclusively at different dates among the stations.
Time series analysis and forecasting has become a major tool in many applications in air pollution and environmental management fields. Among the most effective approaches for analyzing time series data is the model introduced by Box and Jenkins, ARIMA (Autoregressive Integrated Moving Average). In this study we used Box-Jenkins methodology to build ARIMA model for monthly ozone data taken from an Automatic Air Quality Monitoring System in Kemaman station for the period from 1996 to 2007 with a total of 144 readings. Parametric seasonally adjusted ARIMA (0,1,1) (1,1,2)12 model was successfully applied to predict the long-term trend of ozone concentration. The detection of a steady statistical significant upward trend for ozone concentration in Kemaman is quite alarming. This is likely due to sources of ozone precursors related to industrial activities from nearby areas and the increase in road traffic volume.
In most research including environmental research, missing recorded data often exists and has become a common problem for data quality. In this study, several imputation methods that have been designed based on the techniques for functional data analysis are introduced and the capability of the methods for estimating missing values is investigated. Single imputation methods and iterative imputation methods are conducted by means of curve estimation using regression and roughness penalty smoothing approaches. The performance of the methods is compared using a reference data set, the real PM10 data from an air quality monitoring station namely the Petaling Jaya station located at the western part of Peninsular Malaysia. A hundred of the missing data sets that have been generated from a reference data set with six different patterns of missing values are used to investigate the performance of the considered methods. The patterns are simulated according to three percentages (5, 10 and 15) of missing values with respect to two different sizes (3 and 7) of maximum gap lengths (consecutive missing points). By means of the mean absolute error, the index of agreement and the coefficient of determination as the performance indicators, the results have showed that the iterative imputation method using the roughness penalty approach is more flexible and superior to other methods.
This paper presents various imputation methods for air quality data specifically in Malaysia. The main objective was to
select the best method of imputation and to compare whether there was any difference in the methods used between stations
in Peninsular Malaysia. Missing data for various cases are randomly simulated with 5, 10, 15, 20, 25 and 30% missing.
Six methods used in this paper were mean and median substitution, expectation-maximization (EM) method, singular
value decomposition (SVD), K-nearest neighbour (KNN) method and sequential K-nearest neighbour (SKNN) method. The
performance of the imputations is compared using the performance indicator: The correlation coefficient (R), the index
of agreement (d) and the mean absolute error (MAE). Based on the result obtained, it can be concluded that EM, KNN
and SKNN are the three best methods. The same result are obtained for all the eight monitoring station used in this study.
Satellite observations for regional air quality assessment rely on comprehensive spatial coverage, and daily monitoring with reliable, cloud-free data quality. We investigated spatiotemporal variation and data quality of two global satellite Aerosol Optical Depth (AOD) products derived from MODIS and VIIRS imagery. AOD is considered an essential atmospheric parameter strongly related to ground Particulate Matter (PM) in Southeast Asia (SEA). We analyze seasonal variation, urban/rural area influence, and biomass burning effects on atmospheric pollution. Validation indicated a strong relationship between AERONET ground AOD and both MODIS AOD (R2 = 0.81) and VIIRS AOD (R2 = 0.68). The monthly variation of satellite AOD and AERONET AOD reflects two seasonal trends of air quality separately for mainland countries including Myanmar, Laos, Cambodia, Thailand, Vietnam, and Taiwan, Hong Kong, and for maritime countries consisting of Indonesia, Philippines, Malaysia, Brunei, Singapore, and Timor Leste. The mainland SEA has a pattern of monthly AOD variation in which AODs peak in March/April, decreasing during wet season from May-September, and increasing to the second peak in October. However, in maritime SEA, AOD concentration peaks in October. The three countries with the highest annual satellite AODs are Singapore, Hong Kong, and Vietnam. High urban population proportions in Singapore (40.7%) and Hong Kong (21.6%) were associated with high AOD concentrations as expected. AOD values in SEA urban areas were a factor of 1.4 higher than in rural areas, with respective averages of 0.477 and 0.336. The AOD values varied proportionately to the frequency of biomass burning in which both active fires and AOD peak in March/April and September/October. Peak AOD in September/October in some countries could be related to pollutant transport of Indonesia forest fires. This study analyzed satellite aerosol product quality in relation to AERONET in SEA countries and highlighted framework of air quality assessment over a large, complicated region.
Matched MeSH terms: Air Pollutants/analysis*; Air Pollution/analysis
The objective of this research is to examine the effects of stock market on air pollution in Malaysia during the period 1980-2017. To realize this aim, a nonlinear autoregressive distributed lag (ARDL) model is constructed. The short results in general revealed that the increase in stock markets will increase CO2 emissions and its significance increases in the long run. Moreover, the decline in stock market will reduce Malaysia's CO2 emissions but only in the long run. From the outcomes obtained, a number of policy recommendations were provided for the investigated country.
A membrane bioreactor enhances the overall biological performance of a conventional activated sludge system for wastewater treatment by producing high-quality effluent suitable for reuse. However, membrane fouling hinders the widespread application of membrane bioreactors by reducing the hydraulic performance, shortening membrane lifespan, and increasing the operational costs for membrane fouling management. This study assesses the combined effect of membrane surface corrugation and a tilted panel in enhancing the impact of air bubbling for membrane fouling control in activated sludge filtration, applicable for membrane bioreactors. The filterability performance of such a system was further tested under variable parameters: Filtration cycle, aeration rate, and intermittent aeration. Results show that a combination of surface corrugation and panel tilting enhances the impact of aeration and leads to 87% permeance increment. The results of the parametric study shows that the highest permeance was achieved under short filtration-relaxation cycle of 5 min, high aeration rate of 1.5 L/min, and short switching period of 2.5 min, to yield the permeances of 465 ± 18, 447 ± 2, and 369 ± 9 L/(m2h bar), respectively. The high permeances lead to higher operational flux that helps to lower the membrane area as well as energy consumption. Initial estimation of the fully aerated system yields the energy input of 0.152 kWh/m3, much lower than data from the full-scale references of <0.4 kWh/m3. Further energy savings and a lower system footprint can still be achieved by applying the two-sided panel with a switching system, which will be addressed in the future.
Attenuated backscatter profiles retrieved by the space borne active lidar CALIOP on-board CALIPSO satellite were used to measure the vertical distribution of smoke aerosols and to compare it against the ECMWF planetary boundary layer height (PBLH) over the smoke dominated region of Indo-Gangetic Plain (IGP), South Asia. Initially, the relative abundance of smoke aerosols was investigated considering multiple satellite retrieved aerosol optical properties. Only the upper IGP was selectively considered for CALIPSO retrieval based on prevalence of smoke aerosols. Smoke extinction was found to contribute 2-50% of the total aerosol extinction, with strong seasonal and altitudinal attributes. During winter (DJF), smoke aerosols contribute almost 50% of total aerosol extinction only near to the surface while in post-monsoon (ON) and monsoon (JJAS), relative contribution of smoke aerosols to total extinction was highest at about 8 km height. There was strong diurnal variation in smoke extinction, evident throughout the year, with frequent abundance of smoke particles at lower height (<4 km) during daytime compared to higher height during night (>4 km). Smoke injection height also varied considerably during rice (ON: 0.71 ± 0.65 km) and wheat (AM: 2.34 ± 1.34 km) residue burning period having a significant positive correlation with prevailing PBLH. Partitioning smoke AOD against PBLH into the free troposphere (FT) and boundary layer (BL) yield interesting results. BL contribute 36% (16%) of smoke AOD during daytime (nighttime) and the BL-FT distinction increased particularly at night. There was evidence that despite travelling efficiently to FT, major proportion of smoke AOD (50-80%) continue to remain close to the surface (<3 km) thereby, may have greater implications on regional climate, air quality, smoke transport and AOD-particulate modelling.
Matched MeSH terms: Air Pollutants/analysis*; Air Pollution/analysis*
It is important to have good indoor air quality, especially in indoor office environments, in order to enhance productivity and maintain good work performance. This study investigated the effects of indoor office activities on particulate matter of less than 2.5 μm (PM2.5) and ozone (O3) concentrations, assessing their potential impact on human health. Measurements of indoor PM2.5 and O3 concentrations were taken every 24 h during the working days in five office environments located in a semi-urban area. As a comparison, the outdoor concentrations were derived from the nearest Continuous Air Quality Monitoring Station. The results showed that the average 24 h of indoor and outdoor PM2.5 concentrations were 3.24 ± 0.82 μg m-3 and 17.4 ± 3.58 μg m-3 respectively, while for O3 they were 4.75 ± 4.52 ppb and 21.5 ± 5.22 ppb respectively. During working hours, the range of PM2.5 concentrations were 1.00 μg m-3 to 6.10 μg m-3 while for O3 they were 0.10 ppb to 38.0 ppb. The indoor to outdoor ratio (I/O) for PM2.5 and O3 was <1, thus indicating a low infiltration of outdoor sources. The value of the hazard quotient (HQ) for all sampling buildings was <1 for both chronic and acute exposures, indicating that the non-carcinogenic risks are negligible. Higher total cancer risk (CR) value for outdoors (2.67E-03) was observed compared to indoors (4.95E-04) under chronic exposure while the CR value for acute exposure exceeded 1.0E-04, thus suggesting a carcinogenic PM2.5 risk for both the indoor and outdoor environments. The results of this study suggest that office activities, such as printing and photocopying, affect indoor O3 concentrations while PM2.5 concentrations are impacted by indoor-related contributions.
Matched MeSH terms: Air Pollutants, Occupational/analysis*; Air Pollution, Indoor/analysis*
Particulate matter with an aerodynamic diameter of 10 μm or less (PM10) pollution poses a considerable threat to human health, and the first step in quantifying health impacts of human exposure to PM10 pollution is exposure assessment. Population-weighted exposure level (PWEL) estimation is one of the methods that provide a more refined exposure assessment as it includes the spatiotemporal distribution of the population into the pollution concentration estimation. This study assessed the population weighting effects on the estimated PM10 concentrations in Malaysia for years 2000, 2008, and 2013. Estimated PM10 annual mean concentrations with a spatial resolution of 5 kilometres retrieved from satellite data and population count obtained from the Gridded Population of the World version 4 (GPWv4) from the Centre for International Earth Science Information Network (CIESIN) were overlaid to generate the PWEL of PM10 for each state. The calculated PWEL of PM10 concentrations were then classified based on the World Health Organization (WHO) and the national Air Quality Guidelines (AQG) and interim targets (IT) for comparison. Results revealed that the annual mean PM10 concentrations in Malaysia ranged from 31 to 73 µg/m3 but became generally lower, ranging from 20 to 72 µg/m3 after population weighting, suggesting that the PM10 population exposure in Malaysia might have been overestimated. PWEL of PM10 distribution showed that the majority of the population lived in areas that complied with the national AQG, but were vulnerable to exposure level 3 according to the WHO AQG and IT, indicating that the population was nevertheless potentially exposed to significant health effects from long-term exposure to PM10 pollution.
Matched MeSH terms: Air Pollutants/analysis*; Air Pollution/analysis
Activated carbons have been reported to be useful for adsorptive removal of the volatile anaesthetic sevoflurane from a vapour stream. The surface functionalities on activated carbons could be modified through aqueous oxidation using oxidising solutions to enhance the sevoflurane adsorption. In this study, an attempt to oxidise the surface of a commercial activated carbon to improve its adsorption capacity for sevoflurane was conducted using 6 mol/L nitric acid, 2 mol/L ammonium persulfate, and 30 wt per cent (wt%) of hydrogen peroxide (H2O2). The adsorption tests at fixed conditions (bed depth: 10 cm, inlet concentration: 528 mg/L, and flow rate: 3 L/min) revealed that H2O2 oxidation gave desirable sevoflurane adsorption (0.510 ± 0.005 mg/m2). A parametric study was conducted with H2O2 to investigate the effect of oxidation conditions to the changes in surface oxygen functionalities by varying the concentration, oxidation duration, and temperature, and the Conductor-like Screening Model for Real Solvents (COSMO-RS) was applied to predict the interactions between oxygen functionalities and sevoflurane. The H2O2 oxidation incorporated varying degrees of both surface oxygen functionalities with hydrogen bond (HB) acceptor and HB donor characters under the studied conditions. Oxidised samples with enriched oxygen functionalities with HB acceptor character and fewer HB donor character exhibited better adsorption capacity for sevoflurane. The presence of a high amount of oxygen functional groups with HB donor character adversely affected the sevoflurane adsorption despite the enrichment of oxygen functional groups with HB acceptor character that have a higher tendency to adsorb sevoflurane.
Matched MeSH terms: Air Pollutants/analysis*; Air Pollution/prevention & control*
In the early days of abundant resources and minimal development pressures, little attention was paid to growing environmental concerns in Malaysia. The haze episodes in Southeast Asia in 1983, 1984, 1991, 1994, and 1997 imposed threats to the environmental management of Malaysia and increased awareness of the environment. As a consequence, the government established Malaysian Air Quality Guidelines, the Air Pollution Index, and the Haze Action Plan to improve air quality. Air quality monitoring is part of the initial strategy in the pollution prevention program in Malaysia. Review of air pollution in Malaysia is based on the reports of the air quality monitoring in several large cities in Malaysia, which cover air pollutants such as Carbon monoxide (CO), Sulphur Dioxide (SO2), Nitrogen Dioxide (NO2), Ozone (O3), and Suspended Particulate Matter (SPM). The results of the monitoring indicate that Suspended Particulate Matter (SPM) and Nitrogen Dioxide (NO2) are the predominant pollutants. Other pollutants such as CO, O(x), SO2, and Pb are also observed in several big cities in Malaysia. The air pollution comes mainly from land transportation, industrial emissions, and open burning sources. Among them, land transportation contributes the most to air pollution. This paper reviews the results of the ambient air quality monitoring and studies related to air pollution and health impacts.
Matched MeSH terms: Air Pollutants/adverse effects*; Air Pollution/adverse effects*
This paper discuss thermal comfort studies of an under air conditioning in hot and humid climate which at one of the higher institution in East Coast of Malaysia. Indoor thermal environment is important as it affects the health and productivity of building occupants. The paper reports on an experimental investigation of indoor thermal comfort characteristics under the control of air conditioning. Firstly, the well known Fanger’s thermal comfort model was simplified for the current experimental investigation. This is followed by reporting the experimental results of indoor thermal comfort characteristics under the control of temperature, with eight different of temperatures which are 22oC to 29oC. Finally, indoor thermal comfort was merely affected by the increment ventilation and outdoor climate. PMV value was higher when near from the window because of the effects of the wall radiations and the metabolic heat.
The indoor and outdoor radon/thoron progenies concentrations and natural background radiation levels throughout Sarawak and Sabah were measured. The measurements were carried out at 234 locations in 40 towns in Sarawak and Sabah. The mean indoor and outdoor radon equilibrium equivalent concentrations (EEC) in Sarawak were found to be 1.2 Bqm-3 and 1.5 Bqm-3 respectively. In Sabah, the mean indoor and outdoor radon equilibrium equivalent concentrations were 1.7 Bqm-3. The mean indoor and outdoor thoron equilibrium equivalent concentrations of 0.4 Bqm-3 and 0.3Bqm-3 respectively, were the same for Sarawak and Sabah. The mean indoor and outdoor radiation levels of 46 nGyh-1 and 42 nGyh-1 in Sarawak were slightly lower than the respective values in Sabah, i.e. 53 nGyh-1 and 46 nGy h-1.
Climate change and global warming as the key human societies' threats are essentially associated with energy consumption and CO2 emissions. A system dynamic model was developed in this study to model the energy consumption and CO2 emission trends for Iran over 2000-2025. Energy policy factors are considered in analyzing the impact of different energy consumption factors on environmental quality. The simulation results show that the total energy consumption is predicted to reach 2150 by 2025, while that value in 2010 is 1910, which increased by 4.3% yearly. Accordingly, the total CO2 emissions in 2025 will reach 985million tonnes, which shows about 5% increase yearly. Furthermore, we constructed policy scenarios based on energy intensity reduction. The analysis show that CO2 emissions will decrease by 12.14% in 2025 compared to 2010 in the scenario of 5% energy intensity reduction, and 17.8% in the 10% energy intensity reduction scenario. The results obtained in this study provide substantial awareness regarding Irans future energy and CO2 emission outlines.
Matched MeSH terms: Air Pollutants/analysis*; Air Pollution/statistics & numerical data*
This study aims to determine PM2.5concentrations and their composition during haze and non-haze episodes in Kuala Lumpur. In order to investigate the origin of the measured air masses, the Numerical Atmospheric-dispersion Modelling Environment (NAME) and Global Fire Assimilation System (GFAS) were applied. Source apportionment of PM2.5was determined using Positive Matrix Factorization (PMF). The carcinogenic and non-carcinogenic health risks were estimated using the United State Environmental Protection Agency (USEPA) method. PM2.5samples were collected from the centre of the city using a high-volume air sampler (HVS). The results showed that the mean PM2.5concentrations collected during pre-haze, haze and post-haze periods were 24.5±12.0μgm-3, 72.3±38.0μgm-3and 14.3±3.58μgm-3, respectively. The highest concentration of PM2.5during haze episode was five times higher than World Health Organisation (WHO) guidelines. Inorganic compositions of PM2.5, including trace elements and water soluble ions were determined using inductively coupled plasma-mass spectrometry (ICP-MS) and ion chromatography (IC), respectively. The major trace elements identified were K, Al, Ca, Mg and Fe which accounted for approximately 93%, 91% and 92% of the overall metals' portions recorded during pre-haze, haze and post-haze periods, respectively. For water-soluble ions, secondary inorganic aerosols (SO42-, NO3-and NH4+) contributed around 12%, 43% and 16% of the overall PM2.5mass during pre-haze, haze and post-haze periods, respectively. During haze periods, the predominant source identified using PMF was secondary inorganic aerosol (SIA) and biomass burning where the NAME simulations indicate the importance of fires in Sumatra, Indonesia. The main source during pre-haze and post-haze were mix SIA and road dust as well as mineral dust, respectively. The highest non-carcinogenic health risk during haze episode was estimated among the infant group (HI=1.06) while the highest carcinogenic health risk was estimated among the adult group (2.27×10-5).
Matched MeSH terms: Air Pollutants/analysis*; Air Pollution/statistics & numerical data*
Air pollution is a substantial environmental threat to children and acts as acute and chronic disease risk factors alike. Several studies have previously evaluated epigenetic modifications concerning its exposure across various life stages. However, findings on epigenetic modifications as the consequences of air pollution during childhood are rather minimal. This review evaluated highly relevant studies in the field to analyze the existing literature regarding exposure to air pollution, with a focus on epigenetic alterations during childhood and their connections with respiratory health effects. The search was conducted using readily available electronic databases (PubMed and ScienceDirect) to screen for children's studies on epigenetic mechanisms following either pre- or post-natal exposure to air pollutants. Studies relevant enough and matched the predetermined criteria were chosen to be reviewed. Non-English articles and studies that did not report both air monitoring and epigenetic outcomes in the same article were excluded. The review found that epigenetic changes have been linked with exposure to air pollutants during early life with evidence and reports of how they may deregulate the epigenome balance, thus inducing disease progression in the future. Epigenetic studies evolve as a promising new approach in deciphering the underlying impacts of air pollution on deoxyribonucleic acid (DNA) due to links established between some of these epigenetic mechanisms and illnesses.
Matched MeSH terms: Air Pollutants/adverse effects*; Air Pollution/adverse effects
Airline industry is one of the largest industries in the world of transport because it is the most important transport in the global transport system. The airline industry has played a very important role in the economic development in Malaysia. Due to the increase in its operating business, the demand for air travel increases day by day. Hence, this study focused on the number of passengers using air transport in Malaysia. The monthly data from January 2005 to December 2015 were obtained from Malaysia Airport Holdings Berhad (MAHB) in Sepang, Selangor. The data is divided into 2 parts, which are in sample data from January 2005 to December 2014 and out sample data from January 2015 to December 2015. The study was conducted to predict airline passengers in Malaysia using the Box-Jenkins model and Artificial Neural Network (ANN) model. Both models were studied to choose the best model. Mean Absolute Percentage Error (MAPE) and Mean Squared Error (MSE) were used to measure the performance of both models. SARIMA was selected as the best model for Box-Jenkins with MAPE and MSE were 7.3458388 and 2.67011 respectively while Multilayer Feed Forward Neural Network (MFFNN) with seven input variables, with MAPE and MSE, 7.251 and 0.0006 respectively were selected as the best model for Multilayer Feed Forward Neural Network (FFNN). In conclusion, these studies have proven that the Multilayer Feed Forward Neural Network (FFNN) model is the best model for considering airplanes in Malaysia compared to the SARIMA model.
The main objective of this cross-sectional comparative study is to observe the relationship between traffic-related air pollutants, particularly particulate matter (PM) of total suspended particulate (TSP) and PM of size 10 µm (PM10), and vehicle traffic in Amman, Jordan. Two study areas were chosen randomly as a high-polluted area (HPA) and low-polluted area (LPA). The findings indicate that TSP and PM10 were still significantly correlated with traffic count even after controlling for confounding factors (temperature, relative humidity, and wind speed): TSP, r = 0.726, P < .001; PM10, r = 0.719, P < .001). There was a significant positive relationship between traffic count and PM level: TSP, P < .001; PM10, P < .001. Moreover, there was a significant negative relationship between temperature and PM10 level (P = .018). Traffic volume contributed greatly to high concentrations of TSP and PM10 in areas with high traffic count, in addition to the effect of temperature.
Matched MeSH terms: Air Pollutants/analysis*; Air Pollution/analysis*
Proper identification of environment's air quality based on limited observations is an essential task to meet the goals of environmental management. Various classification methods have been used to estimate the change of air quality status and health. However, discrepancies frequently arise from the lack of clear distinction between each air quality, the uncertainty in the quality criteria employed and the vagueness or fuzziness embedded in the decision-making output values. Owing to inherent imprecision, difficulties always exist in some conventional methodologies when describing integrated air quality conditions with respect to various pollutants. Therefore, this paper presents two fuzzy multiplication synthetic techniques to establish classification of air quality. The fuzzy multiplication technique empowers the max-min operations in "or" and "and" in executing the fuzzy arithmetic operations. Based on a set of air pollutants data carbon monoxide, sulfur dioxide, nitrogen dioxide, ozone, and particulate matter (PM(10)) collected from a network of 51 stations in Klang Valley, East Malaysia, Sabah, and Sarawak were utilized in this evaluation. The two fuzzy multiplication techniques consistently classified Malaysia's air quality as "good." The findings indicated that the techniques may have successfully harmonized inherent discrepancies and interpret complex conditions. It was demonstrated that fuzzy synthetic multiplication techniques are quite appropriate techniques for air quality management.
Matched MeSH terms: Air Pollutants/analysis*; Air Pollution/statistics & numerical data*