In the last few years, air within homes have been indicates by various and emerging body as more serious polluted than those outdoor. Prevalence of respiratory inflammation among school children aged 8 and 10 years old attending national primary schools in urban and rural area were conducted in Klang Valley. Two population studies drawn from the questionnaires were used to investigate the association between indoor particulate matter (PM2.5 & PM10) in a home environment and respiratory implication through the understanding of biological responses. Approximately 430 healthy school children of Standard 2 and Standard 5 were selected. Indication of respiratory symptoms using adaptation questionnaire from American Thoracic Society (1978). Sputum sample collection taken for biological analysis. IL-6 then was analyse by using ELISA techniques. Indoor PM2.5 and PM10 were measured using Dust Trak Aerosol Monitor. The mean concentration of PM2.5 (45.38 µg/m3) and PM10 (80.07 µg/m3) in urban home environment is significantly higher compared to those in rural residential area (p=0.001). Similar trend also shows by the prevalence of respiratory symptom. Association were found with PM2.5 and PM10 with the level of IL-6 among school children. A greater exposure to PM2.5 and PM10 are associated with higher expression of IL-6 level suggesting that the concentration of indoor particulate in urban density area significantly influence the health of children.
Rapid urbanisation in Malaysian cities poses risks to the health of residents. This study aims to estimate the relative risk (RR) of major air pollutants on cardiovascular and respiratory hospitalisations in Kuala Lumpur. Daily hospitalisations due to cardiovascular and respiratory diseases from 2010 to 2014 were obtained from the Hospital Canselor Tuanku Muhriz (HCTM). The trace gases, PM10 and weather variables were obtained from the Department of Environment (DOE) Malaysia in consistent with the hospitalisation data. The RR was estimated using a Generalised Additive Model (GAM) based on Poisson regression. A "lag" concept was used where the analysis was segregated into risks of immediate exposure (lag 0) until exposure after 5 days (lag 5). The results showed that the gases could pose significant risks towards cardiovascular and respiratory hospitalisations. However, the RR value of PM10 was not significant in this study. Immediate effects on cardiovascular hospitalisations were observed for NO2 and O3 but no immediate effect was found on respiratory hospitalisations. Delayed effects on cardiovascular and respiratory hospitalisations were found with SO2 and NO2. The highest RR value was observed at lag 4 for respiratory admissions with SO2 (RR = 1.123, 95% CI = 1.045-1.207), followed by NO2 at lag 5 for cardiovascular admissions (RR = 1.025, 95% CI = 1.005-1.046). For the multi-pollutant model, NO2 at lag 5 showed the highest risks towards cardiovascular hospitalisations after controlling for O3 8 h mean lag 1 (RR = 1.026, 95% CI = 1.006-1.047), while SO2 at lag 4 showed highest risks towards respiratory hospitalisations after controlling for NO2 lag 3 (RR = 1.132, 95% CI = 1.053-1.216). This study indicated that exposure to trace gases in Kuala Lumpur could lead to both immediate and delayed effects on cardiovascular and respiratory hospitalisations.
There have been few but timely studies examining the role of air pollution in lung cancer and survival. The Southeast Asia haze is a geopolitical problem that has occurred annually since 1997 in countries such as Malaysia, Singapore and Indonesia. To date, there has been no study examining the impact of the annual haze in the presentation of lung cancer. Data on all lung cancers and respiratory admissions to Universiti Kebangsaan Malaysia Medical Centre (UKMMC) from 1st January 2010 to 31th October 2015 were retrospectively collected and categorized as presentation during the haze and non-haze periods defined by the Department of Environment Malaysia. We report a lung cancer incidence rate per week of 4.5 cases during the haze compared to 1.8 cases during the non-haze period (p<0.01). The median survival for subjects presenting during the haze was 5.2 months compared to 8.1 months for the non-haze period (p<0.05). The majority of subjects diagnosed during the haze period initially presented with acute symptoms. Although this study could not suggest a cause and effect relationship of the annual haze with the incidence of lung cancer, this is the first study reporting a local air pollution-related modifiable determinant contributing to the increase in presentation of lung cancer in Southeast Asia.
As in many nations, air pollution linked to rapid industrialization is a public health and environmental concern in Malaysia, especially in cities. Understanding awareness of air pollution and support for environmental protection from the general public is essential for informing governmental approaches to dealing with this problem. This study presents a cross-sectional survey conducted in the Klang Valley and Iskandar conurbations to examine urban Malaysians' perception, awareness and opinions of air pollution. The survey was conducted in two languages, English and Malay, and administered through the online survey research software, Qualtrics. The survey consisted of three sections, where we collected sociodemographic information, information on the public perception of air quality and the causes of air pollution, information on public awareness of air pollution and its related impacts, and information on attitudes towards environmental protection. Of 214 respondents, over 60% were positive towards the air quality at both study sites despite the presence of harmful levels of air pollution. The air in the Klang Valley was perceived to be slightly more polluted and causing greater health issues. Overall, the majority of respondents were aware that motor vehicles represent the primary pollution source, yet private transport was still the preferred choice of transportation mode. A generally positive approach towards environmental protection emerged from the data. However, participants showed stronger agreement with protection actions that do not involve individual effort. Nonetheless, we found that certain segments of the sample (people owning more than three vehicles per household and those with relatives who suffered from respiratory diseases) were significantly more willing to personally pay for environmental protection compared to others. Implications point to the need for actions for spreading awareness of air pollution to the overall population, especially with regards to its health risks, as well as strategies for increasing the perception of behavioural control, especially with regards to motor vehicles' usage.
The Sub-Saharan Africa (SSA) is far lag behind the sustainable targets that set out in the United Nation's Sustainable Development Goals (SDGs), which is highly needed to embark the priorities by their member countries to devise sustainable policies for accessing clean technologies, energy demand, finance, and food production to mitigate high-mass carbon emissions and conserve environmental agenda in the national policy agenda. The study evaluated United Nation's SDGs for environmental conservation and emission reduction in the panel of 35 selected SSA countries, during a period of 1995-2016. The study further analyzed the variable's relationship in inter-temporal forecasting framework for the next 10 years' time period, i.e., 2017-2026. The parameter estimates for the two models, i.e., CO2 model and PM2.5 models are analyzed by Generalized Method of Moment (GMM) estimator that handle possible endogeneity issue from the given models. The results rejected the inverted U-shaped Environmental Kuznets Curve (EKC) for CO2 emissions, while it supported for PM2.5 emissions with a turning point of US$5540 GDP per capita in constant 2010 US$. The results supported the "pollution haven hypothesis" for CO2 emissions, while this hypothesis is not verified for PM2.5 emissions. The major detrimental factors are technologies, FDI inflows, and food deficit that largely increase carbon emissions in a panel of SSA countries. The IPAT hypothesis is not verified in both the emissions; however, population density will largely influenced CO2 emissions in the next 10 years' time period. The PM2.5 emissions will largely be influenced by high per capita income, followed by trade openness, and technologies, over a time horizon. Thus, the United Nation's sustainable development agenda is highly influenced by socio-economic and environmental factors that need sound action plans by their member countries to coordinate and collaborate with each other and work for Africa's green growth agenda.
The air pollution index (API) is an important figure used for measuring the quality of air in the environment. The API is determined based on the highest average value of individual indices for all the variables which include sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), ozone (O3), and suspended particulate matter (PM10) at a particular hour. API values that exceed the limit of 100 units indicate an unhealthy status for the exposed environment. This study investigates the risk of occurrences of API values greater than 100 units for eight urban areas in Peninsular Malaysia for the period of January 2004 to December 2014. An extreme value model, known as the generalized Pareto distribution (GPD), has been fitted to the API values found. Based on the fitted model, return period for describing the occurrences of API exceeding 100 in the different cities has been computed as the indicator of risk. The results obtained indicated that most of the urban areas considered have a very small risk of occurrence of the unhealthy events, except for Kuala Lumpur, Malacca, and Klang. However, among these three cities, it is found that Klang has the highest risk. Based on all the results obtained, the air quality standard in urban areas of Peninsular Malaysia falls within healthy limits to human beings.
This study determined the source contribution of PM2.5 (particulate matter <2.5 μm) in air at three locations on the Malaysian Peninsula. PM2.5 samples were collected using a high volume sampler equipped with quartz filters. Ion chromatography was used to determine the ionic composition of the samples and inductively coupled plasma mass spectrometry was used to determine the concentrations of heavy metals. Principal component analysis with multilinear regressions were used to identify the possible sources of PM2.5. The range of PM2.5 was between 10 ± 3 and 30 ± 7 µg m(-3). Sulfate (SO4 (2-)) was the major ionic compound detected and zinc was found to dominate the heavy metals. Source apportionment analysis revealed that motor vehicle and soil dust dominated the composition of PM2.5 in the urban area. Domestic waste combustion dominated in the suburban area, while biomass burning dominated in the rural area.
The spatial distribution of 19 polycyclic aromatic hydrocarbons (tPAHs) was quantified in aquacultures located in intertidal mudflats of the west coast of Peninsular Malaysia in order to investigate bioaccumulation of PAH in blood cockles, Anadara granosa (A. granosa). Fifty-four samples from environmental matrices and A. granosa were collected. The sampling locations were representative of a remote area as well as PAH-polluted areas. The relationship of increased background levels of PAH to anthropogenic PAH sources in the environment and their effects on bioaccumulation levels of A. granosa are investigated in this study. The levels of PAH in the most polluted station were found to be up to ten-fold higher than in remote areas in blood cockle. These high concentrations of PAHs reflected background contamination, which originates from distant airborne and waterborne transportation of contaminated particles. The fraction and source identification of PAHs, based on fate and transport considerations, showed a mix of petrogenic and pyrogenic sources. The relative biota-sediment accumulation factors (RBSAF), relative bioaccumulation factors from filtered water (RBAFw), and from suspended particulate matter (SPM) (RBAFSP) showed higher bioaccumulations of the lower molecular weight of PAHs (LMWs) in all stations, except Kuala Juru, which showed higher bioaccumulation of the higher molecular weight of PAHs (HMWs). Calculations of bioaccumulation factors showed that blood cockle can accumulate PAHs from sediment as well as water samples, based on the physico-chemical characteristics of habitat and behaviour of blood cockles. Correlations among concentrations of PAHs in water, SPM, sediment and A. granosa at the same sites were also found. Identification of PAH levels in different matrices showed that A. granosa can be used as a good biomonitor for LMW of PAHs and tPAHs in mudflats. Considering the toxicity and carcinogenicity of PAHs, the bioaccumulation by blood cockles are a potential hazard for both blood cockles and their consumers.
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.
This review discusses how climate undergo changes and the effect of climate change on air quality as well as public health. It also covers the inter relationship between climate and air quality. The air quality discussed here are in relation to the 5 criteria pollutants; ozone (O3), carbon dioxide (CO2), nitrogen dioxide (NO2), sulfur dioxide (SO2), and particulate matter (PM). Urban air pollution is the main concern due to higher anthropogenic activities in urban areas. The implications on health are also discussed. Mitigating measures are presented with the final conclusion.
Anionic surfactants are one of the pollutants derived from particulate matter (PM) and adversely affect the health of living organisms. In this study, the compositions of surfactants extracted from PM and vehicle soot collected in an urban area were investigated. A high-volume air sampler was used to collect PM sample at urban area based on coarse (> 1.5 µm) and fine (
Long-term exposure to air pollution has been associated with several adverse health effects including cardiovascular, respiratory diseases and cancers. However, underlying molecular alterations remain to be further investigated. The aim of this study is to investigate the effects of long-term exposure to air pollutants on (a) average DNA methylation at functional regions and, (b) individual differentially methylated CpG sites. An assumption is that omic measurements, including the methylome, are more sensitive to low doses than hard health outcomes. This study included blood-derived DNA methylation (Illumina-HM450 methylation) for 454 Italian and 159 Dutch participants from the European Prospective Investigation into Cancer and Nutrition (EPIC). Long-term air pollution exposure levels, including NO2, NOx, PM2.5, PMcoarse, PM10, PM2.5 absorbance (soot) were estimated using models developed within the ESCAPE project, and back-extrapolated to the time of sampling when possible. We meta-analysed the associations between the air pollutants and global DNA methylation, methylation in functional regions and epigenome-wide methylation. CpG sites found differentially methylated with air pollution were further investigated for functional interpretation in an independent population (EnviroGenoMarkers project), where (N=613) participants had both methylation and gene expression data available. Exposure to NO2 was associated with a significant global somatic hypomethylation (p-value=0.014). Hypomethylation of CpG island's shores and shelves and gene bodies was significantly associated with higher exposures to NO2 and NOx. Meta-analysing the epigenome-wide findings of the 2 cohorts did not show genome-wide significant associations at single CpG site level. However, several significant CpG were found if the analyses were separated by countries. By regressing gene expression levels against methylation levels of the exposure-related CpG sites, we identified several significant CpG-transcript pairs and highlighted 5 enriched pathways for NO2 and 9 for NOx mainly related to the immune system and its regulation. Our findings support results on global hypomethylation associated with air pollution, and suggest that the shores and shelves of CpG islands and gene bodies are mostly affected by higher exposure to NO2 and NOx. Functional differences in the immune system were suggested by transcriptome analyses.
The molecular composition and distribution of sterols were investigated in the East China Sea to identify the origins of suspended particulate matter (SPM) in offshore waters influenced by Changjiang River Diluted Water (CRDW). Total sterol concentrations ranged from 3200 to 31,900pgL(-1) and 663 to 5690pgL(-1) in the particulate and dissolved phases, respectively. Marine sterols dominated representing 71% and 66% in the particulate and dissolved phases, respectively. Typical sewage markers, such as coprostanol, were usually absent at ~250km offshore. However, sterols from allochthonous terrestrial plants were still detected at these sites. A negative relationship was observed between salinity and concentrations of terrestrial sterols in SPM, suggesting that significant amounts of terrestrial particulate matter traveled long distance offshore in the East China Sea, and the Changjiang River Diluted Water (CRDW) was an effective carrier of land-derived particulate organic matter to the offshore East China Sea.
This study aimed to investigate the chemical composition and potential sources of PM10 as well as assess the potential health hazards it posed to school children. PM10 samples were taken from classrooms at a school in Kuala Lumpur's city centre (S1) and one in the suburban city of Putrajaya (S2) over a period of eight hours using a low volume sampler (LVS). The composition of the major ions and trace metals in PM10 were then analysed using ion chromatography (IC) and inductively coupled plasma-mass spectrometry (ICP-MS), respectively. The results showed that the average PM10 concentration inside the classroom at the city centre school (82µg/m(3)) was higher than that from the suburban school (77µg/m(3)). Principal component analysis-absolute principal component scores (PCA-APCS) revealed that road dust was the major source of indoor PM10 at both school in the city centre (36%) and the suburban location (55%). The total hazard quotient (HQ) calculated, based on the formula suggested by the United States Environmental Protection Agency (USEPA), was found to be slightly higher than the acceptable level of 1, indicating that inhalation exposure to particle-bound non-carcinogenic metals of PM10, particularly Cr exposure by children and adults occupying the school environment, was far from negligible.
Current study presents RSM based optimized production of biodiesel from palm oil using chemical and enzymatic transesterification. The emission behavior of biodiesel and its blends, namely, POB-5, POB-20, POB-40, POB-50, POB-80, and POB-100 was examined using diesel engine (equipped with tube well). Optimized palm oil fatty acid methyl esters (POFAMEs) yields were depicted to be 47.6 ± 1.5, 92.7 ± 2.5, and 95.4 ± 2.0% for chemical transesterification catalyzed by NaOH, KOH, and NaOCH3, respectively, whereas for enzymatic transesterification reactions catalyzed by NOVOZYME-435 and A. n. lipase optimized biodiesel yields were 94.2 ± 3.1 and 62.8 ± 2.4%, respectively. Distinct decrease in particulate matter (PM) and carbon monoxide (CO) levels was experienced in exhaust emissions from engine operating on biodiesel blends POB-5, POB-20, POB-40, POB-50, POB-80, and POB-100 comparative to conventional petroleum diesel. Percentage change in CO and PM emissions for different biodiesel blends ranged from -2.1 to -68.7% and -6.2 to -58.4%, respectively, relative to conventional diesel, whereas an irregular trend was observed for NOx emissions. Only POB-5 and POB-20 showed notable reductions, whereas all other blends (POB-40 to POB-100) showed slight increase in NOx emission levels from 2.6 to 5.5% comparative to petroleum diesel.
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.
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.
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.
This study aims at developing a novel effluent removal management tool for septic sludge treatment plants (SSTP) using a clonal selection algorithm (CSA). The proposed CSA articulates the idea of utilizing an artificial immune system (AIS) to identify the behaviour of the SSTP, that is, using a sequence batch reactor (SBR) technology for treatment processes. The novelty of this study is the development of a predictive SSTP model for effluent discharge adopting the human immune system. Septic sludge from the individual septic tanks and package plants will be desuldged and treated in SSTP before discharging the wastewater into a waterway. The Borneo Island of Sarawak is selected as the case study. Currently, there are only two SSTPs in Sarawak, namely the Matang SSTP and the Sibu SSTP, and they are both using SBR technology. Monthly effluent discharges from 2007 to 2011 in the Matang SSTP are used in this study. Cross-validation is performed using data from the Sibu SSTP from April 2011 to July 2012. Both chemical oxygen demand (COD) and total suspended solids (TSS) in the effluent were analysed in this study. The model was validated and tested before forecasting the future effluent performance. The CSA-based SSTP model was simulated using MATLAB 7.10. The root mean square error (RMSE), mean absolute percentage error (MAPE), and correction coefficient (R) were used as performance indexes. In this study, it was found that the proposed prediction model was successful up to 84 months for the COD and 109 months for the TSS. In conclusion, the proposed CSA-based SSTP prediction model is indeed beneficial as an engineering tool to forecast the long-run performance of the SSTP and in turn, prevents infringement of future environmental balance in other towns in Sarawak.
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.