Methane (CH₄) is one of the most relevant greenhouse gases and it has a global warming potential 25 times greater than that of carbon dioxide (CO₂), risking human health and the environment. Microbial CH₄ oxidation in landfill cover soils may constitute a means of controlling CH₄ emissions. The study was intended to quantify CH₄ and CO₂ emissions rates at the Sungai Sedu open dumping landfill during the dry season, characterize their spatial and temporal variations, and measure the CH₄ oxidation associated with the landfill cover soil using a homemade static flux chamber. Concentrations of the gases were analyzed by a Micro-GC CP-4900. Two methods, kriging values and inverse distance weighting (IDW), were found almost identical. The findings of the proposed method show that the ratio of CH₄ to CO₂ emissions was 25.4 %, indicating higher CO₂ emissions than CH₄ emissions. Also, the average CH₄ oxidation in the landfill cover soil was 52.5 %. The CH₄ and CO₂ emissions did not show fixed-pattern temporal variation based on daytime measurements. Statistically, a negative relationship was found between CH₄ emissions and oxidation (R(2) = 0.46). It can be concluded that the variation in the CH₄ oxidation was mainly attributed to the properties of the landfill cover soil.
This study was conducted to determine the composition and source apportionment of surfactant in atmospheric aerosols around urban and semi-urban areas in Malaysia based on ionic compositions. Colorimetric analysis was undertaken to determine the concentrations of anionic surfactants as Methylene Blue Active Substances (MBAS) and cationic surfactants as Disulphine Blue Active Substances (DBAS) using a UV spectrophotometer. Ionic compositions were determined using ion chromatography for cations (Na(+), NH4(+), K(+), Mg(2+), Ca(2+)) and anions (F(-), Cl(-), NO3(-), SO4(2-)). Principle component analysis (PCA) combined with multiple linear regression (MLR) were used to identify the source apportionment of MBAS and DBAS. Results indicated that the concentrations of surfactants at both sampling sites were dominated by MBAS rather than DBAS especially in fine mode aerosols during the southwest monsoon. Three main sources of surfactants were identified from PCA-MLR analysis for MBAS in fine mode samples particularly in Kuala Lumpur, dominated by motor vehicles, followed by soil/road dust and sea spray. Besides, for MBAS in coarse mode, biomass burning/sea spray were the dominant source followed by motor vehicles/road dust and building material.
Asthma is a chronic condition of great public health concern globally. The associated morbidity, mortality and healthcare utilisation place an enormous burden on healthcare infrastructure and services. This study demonstrates a multistage quantile regression approach to predicting excess demand for health care services in the form of asthma daily admissions in London, using retrospective data from the Hospital Episode Statistics, weather and air quality. Trivariate quantile regression models (QRM) of asthma daily admissions were fitted to a 14-day range of lags of environmental factors, accounting for seasonality in a hold-in sample of the data. Representative lags were pooled to form multivariate predictive models, selected through a systematic backward stepwise reduction approach. Models were cross-validated using a hold-out sample of the data, and their respective root mean square error measures, sensitivity, specificity and predictive values compared. Two of the predictive models were able to detect extreme number of daily asthma admissions at sensitivity levels of 76 % and 62 %, as well as specificities of 66 % and 76 %. Their positive predictive values were slightly higher for the hold-out sample (29 % and 28 %) than for the hold-in model development sample (16 % and 18 %). QRMs can be used in multistage to select suitable variables to forecast extreme asthma events. The associations between asthma and environmental factors, including temperature, ozone and carbon monoxide can be exploited in predicting future events using QRMs.
The objective of this study is to identify spatial and temporal patterns in the air quality at three selected Malaysian air monitoring stations based on an eleven-year database (January 2000-December 2010). Four statistical methods, Discriminant Analysis (DA), Hierarchical Agglomerative Cluster Analysis (HACA), Principal Component Analysis (PCA) and Artificial Neural Networks (ANNs), were selected to analyze the datasets of five air quality parameters, namely: SO2, NO2, O3, CO and particulate matter with a diameter size of below 10 μm (PM10). The three selected air monitoring stations share the characteristic of being located in highly urbanized areas and are surrounded by a number of industries. The DA results show that spatial characterizations allow successful discrimination between the three stations, while HACA shows the temporal pattern from the monthly and yearly factor analysis which correlates with severe haze episodes that have happened in this country at certain periods of time. The PCA results show that the major source of air pollution is mostly due to the combustion of fossil fuel in motor vehicles and industrial activities. The spatial pattern recognition (S-ANN) results show a better prediction performance in discriminating between the regions, with an excellent percentage of correct classification compared to DA. This study presents the necessity and usefulness of environmetric techniques for the interpretation of large datasets aiming to obtain better information about air quality patterns based on spatial and temporal characterizations at the selected air monitoring stations.
Methane (CH₄) emissions and oxidation were measured at the Air Hitam sanitary landfill in Malaysia and were modeled using the Intergovernmental Panel on Climate Change waste model to estimate the CH₄ generation rate constant, k. The emissions were measured at several locations using a fabricated static flux chamber. A combination of gas concentrations in soil profiles and surface CH₄ and carbon dioxide (CO₂) emissions at four monitoring locations were used to estimate the CH₄ oxidation capacity. The temporal variations in CH₄ and CO₂ emissions were also investigated in this study. Geospatial means using point kriging and inverse distance weight (IDW), as well as arithmetic and geometric means, were used to estimate total CH₄ emissions. The point kriging, IDW, and arithmetic means were almost identical and were two times higher than the geometric mean. The CH₄ emission geospatial means estimated using the kriging and IDW methods were 30.81 and 30.49 gm(−2) day(−1), respectively. The total CH₄ emissions from the studied area were 53.8 kg day(−1). The mean of the CH₄ oxidation capacity was 27.5 %. The estimated value of k is 0.138 year(−1). Special consideration must be given to the CH₄ oxidation in the wet tropical climate for enhancing CH₄ emission reduction.
Nineteen pairs of gaseous and surface seawater samples were collected along the cruise from Malaysia to the south of Bay of Bengal passing by Sri Lanka between April 12 and May 4, 2011 on the Chinese research vessel Shiyan I to investigate the latest OCP pollution status over the equatorial Indian Ocean. Significant decrease of α-HCH and γ-HCH was found in the air and dissolved water phase owing to global restriction for decades. Substantially high levels of p,p'-DDT, o,p'-DDT, trans-chlordane (TC), and cis-chlordane (CC) were observed in the water samples collected near Sri Lanka, indicating fresh continental riverine input of these compounds. Fugacity fractions suggest equilibrium of α-HCH at most sampling sites, while net volatilization for DDT isomers, TC and CC in most cases. Enantiomer fractions (EFs) of α-HCH and o,p'-DDT in the air and water samples were determined to trace the source of these compounds in the air. Racemic or close to racemic composition was found for atmospheric α-HCH and o,p'-DDT, while significant depletion of (+) enantiomer was found in the water phase, especially for o,p'-DDT (EFs = 0.310 ± 0.178). 24% of α-HCH in the lower air over the open sea of the equatorial Indian Ocean is estimated to be volatilized from local seawater, indicating that long-range transport is the main source.
Forecasting higher than expected numbers of health events provides potentially valuable insights in its own right, and may contribute to health services management and syndromic surveillance. This study investigates the use of quantile regression to predict higher than expected respiratory deaths. Data taken from 70,830 deaths occurring in New York were used. Temporal, weather and air quality measures were fitted using quantile regression at the 90th-percentile with half the data (in-sample). Four QR models were fitted: an unconditional model predicting the 90th-percentile of deaths (Model 1), a seasonal/temporal (Model 2), a seasonal, temporal plus lags of weather and air quality (Model 3), and a seasonal, temporal model with 7-day moving averages of weather and air quality. Models were cross-validated with the out of sample data. Performance was measured as proportionate reduction in weighted sum of absolute deviations by a conditional, over unconditional models; i.e., the coefficient of determination (R1). The coefficient of determination showed an improvement over the unconditional model between 0.16 and 0.19. The greatest improvement in predictive and forecasting accuracy of daily mortality was associated with the inclusion of seasonal and temporal predictors (Model 2). No gains were made in the predictive models with the addition of weather and air quality predictors (Models 3 and 4). However, forecasting models that included weather and air quality predictors performed slightly better than the seasonal and temporal model alone (i.e., Model 3 > Model 4 > Model 2) This study provided a new approach to predict higher than expected numbers of respiratory related-deaths. The approach, while promising, has limitations and should be treated at this stage as a proof of concept.
A short-term investigation on the chemical composition of rainwater was carried out at five selected sampling stations in Kuantan district, Pahang, Malaysia. Sampling of rainwater was conducted by event basis between September and November 2011. Rainwater samples were collected using polyethylene containers and the parameters measured were cations (sodium, potassium, ammonium, calcium and magnesium) and anions (chlorides, nitrates and sulphates). The average pH value for rainwater samples was 6.0 ± 0.57 in which most of the sampling sites exhibited pH values >5.6. Calcium and sulphate were the most abundant cation and anion, respectively, whilst the concentrations of other major ions varied according to sampling location.
This study aims to determine the composition and sources of particulate matter with an aerodynamic diameter of 10 μm or less (PM10) in a semi-urban area. PM10 samples were collected using a high volume sampler. Heavy metals (Fe, Zn, Pb, Mn, Cu, Cd and Ni) and cations (Na(+), K(+), Ca(2+) and Mg(2+)) were detected using inductively coupled plasma mass spectrometry, while anions (SO4 (2-), NO3 (-), Cl(-) and F(-)) were analysed using Ion Chromatography. Principle component analysis and multiple linear regressions were used to identify the source apportionment of PM10. Results showed the average concentration of PM10 was 29.5 ± 5.1 μg/m(3). The heavy metals found were dominated by Fe, followed by Zn, Pb, Cu, Mn, Cd and Ni. Na(+) was the dominant cation, followed by Ca(2+), K(+) and Mg(2+), whereas SO4 (2-) was the dominant anion, followed by NO3 (-), Cl(-) and F(-). The main sources of PM10 were the Earth's crust/road dust, followed by vehicle emissions, industrial emissions/road activity, and construction/biomass burning.
Nineteen pairs of air and seawater samples collected from the equatorial Indian Ocean onboard the Shiyan I from 4/2011 to 5/2011 were analyzed for PCBs and HCB. Gaseous concentrations of ∑(ICES)PCBs (ICES: International Council for the Exploration of the Seas) and HCB were lower than previous data over the study area. Air samples collected near the coast had higher levels of PCBs relative to those collected in the open ocean, which may be influenced by proximity to source regions and air mass origins. Dissolved concentrations of ∑(ICES)PCBs and HCB were 1.4-14 pg L⁻¹ and 0.94-13 pg L⁻¹, with the highest concentrations in the sample collected from Strait of Malacca. Fugacity fractions suggest volatilization of PCBs and HCB from the seawater to air during the cruise, with fluxes of 0.45-34 ng m⁻² d⁻¹ and 0.36-18 ng m⁻² d⁻¹, respectively.
This study aims to determine the source apportionment of surfactants in marine aerosols at two selected stations along the Malacca Straits. The aerosol samples were collected using a high volume sampler equipped with an impactor to separate coarse- and fine-mode aerosols. The concentrations of surfactants, as methylene blue active substance and disulphine blue active substance, were analysed using colorimetric method. Ion chromatography was employed to determine the ionic compositions. Principal component analysis combined with multiple linear regression was used to identify and quantify the sources of atmospheric surfactants. The results showed that the surfactants in tropical coastal environments are actively generated from natural and anthropogenic origins. Sea spray (generated from sea-surface microlayers) was found to be a major contributor to surfactants in both aerosol sizes. Meanwhile, the anthropogenic sources (motor vehicles/biomass burning) were predominant contributors to atmospheric surfactants in fine-mode aerosols.
This study assessed the economic value of health impacts of transboundary smoke haze pollution in Kuala Lumpur and adjacent areas in the state of Selangor, Malaysia. Daily inpatient data from 2005, 2006, 2008, and 2009 for 14 haze-related illnesses were collected from four hospitals. On average, there were 19 hazy days each year during which the air pollution levels were within the Lower Moderate to Hazardous categories. No seasonal variation in inpatient cases was observed. A smoke haze occurrence was associated with an increase in inpatient cases by 2.4 per 10,000 populations each year, representing an increase of 31 percent from normal days. The average annual economic loss due to the inpatient health impact of haze was valued at MYR273,000 ($91,000 USD).
Rural background stations provide insight into seasonal variations in pollutant concentrations and allow for comparisons to be made with stations closer to anthropogenic emissions. In Malaysia, the designated background station is located in Jerantut, Pahang. A fifteen-year data set focusing on ten major air pollutants and four meteorological variables from this station were analysed. Diurnal, monthly and yearly pollutant concentrations were derived from hourly continuous monitoring data. Statistical methods employed included principal component regression (PCR) and sensitivity analysis. Although only one of the yearly concentrations of the pollutants studied exceeded national and World Health Organisation (WHO) guideline standards, namely PM10, seven of the pollutants (NO, NO2, NOx, O3, PM10, THC and CH4) showed a positive upward trend over the 15-year period. High concentrations of PM10 were recorded during severe haze episodes in this region. Whilst, monthly concentrations of most air pollutants, such as: PM10, O3, NOx, NO2, CO and NmHC were recorded at higher concentrations between June and September, during the southwest monsoon. Such results correspond with the mid-range transport of pollutants from more urbanised and industrial areas. Diurnal patterns, rationed between major air pollutants and sensitivity analysis, indicate the influence of local traffic emissions on air quality at the Jerantut background station. Although the pollutant concentrations have not shown a rapid increase, an alternative background station will need to be assigned within the next decade if development projects in the surrounding area are not halted.
This study aimed to predict monthly columnar ozone (O3) in Peninsular Malaysia by using data on the concentration of environmental pollutants. Data (2003-2008) on five atmospheric pollutant gases (CO2, O3, CH4, NO2, and H2O vapor) retrieved from the satellite Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) were employed to develop a model that predicts columnar ozone through multiple linear regression. In the entire period, the pollutants were highly correlated (R = 0.811 for the southwest monsoon, R = 0.803 for the northeast monsoon) with predicted columnar ozone. The results of the validation of columnar ozone with column ozone from SCIAMACHY showed a high correlation coefficient (R = 0.752-0.802), indicating the model's accuracy and efficiency. Statistical analysis was utilized to determine the effects of each atmospheric pollutant on columnar ozone. A model that can retrieve columnar ozone in Peninsular Malaysia was developed to provide air quality information. These results are encouraging and accurate and can be used in early warning of the population to comply with air quality standards.
The Proposed New Environmental Quality (Clean Air) Regulation 201X (Draft), which replaces the Malaysia Environmental Quality (Clean Air) 1978, specifies limits to additional pollutants from power generation using fossil fuel. The new pollutants include Hg, HCl, and HF with limits of 0.03, 100, and 15 mg/N-m3 at 6% O2, respectively. These pollutants are normally present in very small concentrations (known as trace elements [TEs]), and hence are often neglected in environmental air quality monitoring in Malaysia. Following the enactment of the new regulation, it is now imperative to understand the TEs behavior and to assess the capability of the existing abatement technologies to comply with the new emission limits. This paper presents the comparison of TEs behavior of the most volatile (Hg, Cl, F) and less volatile (As, Be, Cd, Cr, Ni, Se, Pb) elements in subbituminous and bituminous coal and coal combustion products (CCP) (i.e., fly ash and bottom ash) from separate firing of subbituminous and bituminous coal in a coal-fired power plant in Malaysia. The effect of air pollution control devices configuration in removal of TEs was also investigated to evaluate the effectiveness of abatement technologies used in the plant. This study showed that subbituminous and bituminous coals and their CCPs have different TEs behavior. It is speculated that ash content could be a factor for such diverse behavior In addition, the type of coal and the concentrations of TEs in feed coal were to some extent influenced by the emission of TEs in flue gas. The electrostatic precipitator (ESP) and seawater flue gas desulfurization (FGD) used in the studied coal-fired power plant were found effective in removing TEs in particulate and vapor form, respectively, as well as complying with the new specified emission limits. Implications: Coals used by power plants in Peninsular Malaysia come from the same supplier (Tenaga Nasional Berhad Fuel Services), which is a subsidiary of the Malaysia electricity provider (Tenaga Nasional Berhad). Therefore, this study on trace elements behavior in a coal-fired power plant in Malaysia could represent emission from other plants in Peninsular Malaysia. By adhering to the current coal specifications and installation of electrostatic precipitator (ESP) and flue gas desulfurization, the plants could comply with the limits specified in the Malaysian Department of Environment (DOE) Scheduled Waste Guideline for bottom ash and fly ash and the Proposed New Environmental Quality (Clean Air) Regulation 201X (Draft).
Waste management can be regarded as a cross-cutting environmental 'mega-issue'. Sound waste management practices support the provision of basic needs for general health, such as clean air, clean water and safe supply of food. In addition, climate change mitigation efforts can be achieved through reduction of greenhouse gas emissions from waste management operations, such as landfills. Landfills generate landfill gas, especially methane, as a result of anaerobic degradation of the degradable components of municipal solid waste. Evaluating the mode of generation and collection of landfill gas has posted a challenge over time. Scientifically, landfill gas generation rates are presently estimated using numerical models. In this study the Intergovernmental Panel on Climate Change's Waste Model is used to estimate the methane generated from a Malaysian sanitary landfill. Key parameters of the model, which are the decay rate and degradable organic carbon, are analysed in two different approaches; the bulk waste approach and waste composition approach. The model is later validated using error function analysis and optimum decay rate, and degradable organic carbon for both approaches were also obtained. The best fitting values for the bulk waste approach are a decay rate of 0.08 y(-1) and degradable organic carbon value of 0.12; and for the waste composition approach the decay rate was found to be 0.09 y(-1) and degradable organic carbon value of 0.08. From this validation exercise, the estimated error was reduced by 81% and 69% for the bulk waste and waste composition approach, respectively. In conclusion, this type of modelling could constitute a sensible starting point for landfills to introduce careful planning for efficient gas recovery in individual landfills.
In this study, we assess the intense pollution episode of June 2013, in Riau province, Indonesia from land clearing. We relied on satellite retrievals of aerosols and Carbon monoxide (CO) due to lack of ground measurements. We used both the yearly and daily data for aerosol optical depth (AOD), fine mode fraction (FMF), aerosol absorption optical depth (AAOD) and UV aerosol index (UVAI) for characterizing variations. We found significant enhancement in aerosols and CO during the pollution episode. Compared to mean (2008-2012) June AOD of 0.40, FMF-0.39, AAOD-0.45, UVAI-1.77 and CO of 200 ppbv, June 2013 values reached 0.8, 0.573, 0.672, 1.77 and 978 ppbv respectively. Correlations of fire counts with AAOD and UVAI were stronger compared to AOD and FMF. Results from a trajectory model suggested transport of air masses from Indonesia towards Malaysia, Singapore and southern Thailand. Our results highlight satellite-based mapping and monitoring of pollution episodes in Southeast Asia.
This study was carried out to determine the concentration of lead (Pb), anions, and cations at six primary schools located around Kuala Lumpur. Low volume sampler (MiniVol PM10) was used to collect the suspended particulates in indoor and outdoor air. Results showed that the concentration of Pb in indoor air was in the range of 5.18 ± 1.08 μg/g-7.01 ± 0.08 μg/g. All the concentrations of Pb in indoor air were higher than in outdoor air at all sampling stations. The concentrations of cations and anions were higher in outdoor air than in indoor air. The concentration of Ca(2+) (39.51 ± 5.01 mg/g-65.13 ± 9.42 mg/g) was the highest because the cation existed naturally in soil dusts, while the concentrations of NO3 (-) and SO4 (2-) were higher in outdoor air because there were more sources of exposure for anions in outdoor air, such as highly congested traffic and motor vehicles emissions. In comparison, the concentration of NO3 (-) (29.72 ± 0.31 μg/g-32.00 ± 0.75 μg/g) was slightly higher than SO4 (2-). The concentrations of most of the parameters in this study, such as Mg(2+), Ca(2+), NO3 (-), SO4 (2-), and Pb(2+), were higher in outdoor air than in indoor air at all sampling stations.
There are few studies on associations between respiratory health and allergens, fungal and bacterial compounds in schools in tropical countries. The aim was to study associations between respiratory symptoms in pupils and ethnicity, chemical microbial markers, allergens and fungal DNA in settled dust in schools in Malaysia. Totally 462 pupils (96%) from 8 randomly selected secondary schools in Johor Bahru, Malaysia, participated. Dust was vacuumed from 32 classrooms and analysed for levels of different types of endotoxin as 3-hydroxy fatty acids (3-OH), muramic acid, ergosterol, allergens and five fungal DNA sequences. Multiple logistic regression was applied. Totally 13.1% pupils reported doctor's diagnosed asthma, 10.3% wheeze and 21.1% pollen or pet allergy. Indian and Chinese children had less atopy and asthma than Malay. Carbon dioxide levels were low (380-690 ppm). No cat (Fel d1), dog (Can f 1) or horse allergens (Ecu cx) were detected. The levels of Bloomia tropicalis (Blo t), house dust mite allergens (Der p 1, Der f 1, Der m 1) and cockroach allergens (Per a 1 and Bla g 1) were low. There were positive associations between levels of Aspergillus versicolor DNA and daytime breathlessness, between C14 3-OH and respiratory infections and between ergosterol and doctors diagnosed asthma. There were negative (protective) associations between levels of C10 3-OH and wheeze, between C16 3-OH and day time and night time breathlessness, between cockroach allergens and doctors diagnosed asthma. Moreover there were negative associations between amount of fine dust, total endotoxin (LPS) and respiratory infections. In conclusion, endotoxin at school seems to be mainly protective for respiratory illness but different types of endotoxin could have different effects. Fungal contamination measured as ergosterol and Aspergillus versicolor DNA can be risk factors for respiratory illness. The ethnical differences for atopy and asthma deserve further attention.