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 (
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.
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.
Indoor air quality (IAQ) has been the object of several studies due to its adverse health effects on children. Methods. A cross-sectional comparative study was carried out among Malay children in Balakong (2 studied preschools) and Bangi (2 comparative preschools), Selangor, with the aims of determining IAQ and its association with respiratory health. 61 and 50 children aged 5-6 years were selected as studied and comparative groups. A questionnaire was used to obtain an exposure history and respiratory symptoms. Lung function test was carried out. IAQ parameters obtained include indoor concentration of particulate matter (PM), volatile organic compounds (VOCs), carbon monoxide (CO), carbon dioxide (CO2), temperature, air velocity (AV), and relative humidity. Results. There was a significant difference between IAQ in studied and comparative preschools for all parameters measured (P < 0.001) except for CO2 and AV. Studied preschools had higher PM and CO concentration. FVC, FEV1, FVC% and FEV1% predicted values were significantly lower among studied group. Exposures to PM, VOCs, and CO were associated with wheezing. Conclusion. The finding concluded that exposures to poor IAQ might increase the risk of getting lung function abnormality and respiratory problems among study respondents.
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.
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.
Results from the present study in Kuala Terengganu, Malaysia indicated a significant spatial variation but generally the total suspended particulate concentrations (mean = 17.2-148 microg/m(3)) recorded were below the recommended Malaysia guideline for total suspended particulate (mean of 24-h measurement = 260 microg/m(3)). Some of the elemental composition of particulate aerosol is clearly affected by non crustal sources, e.g. vehicular emission sources. Based on correlation and enrichment analyses, the elements could be grouped into two i.e. Pb, Cd and Zn group with sources from vehicular emission (r > 0.6; enrichment factor > 10) and Al, Fe, Mn and Cr group that appears to be of crustal origin (r > 0.6; enrichment factor < 10). It can also be concluded that the mean levels of Pb (1 ng/m(3)), Cd (0.02 ng/m(3)) and Zn (2 ng/m(3)) in the study area are generally lower than other urban areas in Malaysia (Pb < 181 ng/m(3); Cd < 6 ng/m(3); Zn < 192 ng/m(3)).
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.
High performance sorbents for flue gas desulfurization can be synthesized by hydration of coal fly ash, calcium sulfate, and calcium oxide. In general, higher desulfurization activity correlates with higher sorbent surface area. Consequently, a major aim in sorbent synthesis is to maximize the sorbent surface area by optimizing the hydration conditions. This work presents an integrated modeling and optimization approach to sorbent synthesis based on statistical experimental design and two artificial intelligence techniques: neural network and genetic algorithm. In the first step of the approach, the main and interactive effects of three hydration variables on sorbent surface area were evaluated using a full factorial design. The hydration variables of interest to this study were hydration time, amount of coal fly ash, and amount of calcium sulfate and the levels investigated were 4-32 h, 5-15 g, and 0-12 g, respectively. In the second step, a neural network was used to model the relationship between the three hydration variables and the sorbent surface area. A genetic algorithm was used in the last step to optimize the input space of the resulting neural network model. According to this integrated modeling and optimization approach, an optimum sorbent surface area of 62.2m(2)g(-1) could be obtained by mixing 13.1g of coal fly ash and 5.5 g of calcium sulfate in a hydration process containing 100ml of water and 5 g of calcium oxide for a fixed hydration time of 10 h.
This study aimed to determine the spatial distribution of PM2.5 and PM10 collected in four regions (North, Central, South and East Coast) of Peninsular Malaysia during the southwest monsoon. Concurrent measurements of PM2.5 and PM10 were performed using a high volume sampler (HVS) for 24 h (August to September 2018) collecting a total of 104 samples. All samples were then analysed for water soluble inorganic ions (WSII) using ion chromatography, trace metals using inductively coupled plasma-mass spectroscopy (ICP-MS) and polycyclic aromatic hydrocarbon (PAHs) using gas chromatography-mass spectroscopy (GC-MS). The results showed that the highest average PM2.5 concentration during the sampling campaign was in the North region (33.2 ± 5.3 μg m-3) while for PM10 the highest was in the Central region (38.6 ± 7.70 μg m-3). WSII recorded contributions of 22% for PM2.5 and 20% for PM10 mass, with SO42- the most abundant species with average concentrations of 1.83 ± 0.42 μg m-3 (PM2.5) and 2.19 ± 0.27 μg m-3 (PM10). Using a Positive Matrix Factorization (PMF) model, soil fertilizer (23%) was identified as the major source of PM2.5 while industrial activity (25%) was identified as the major source of PM10. Overall, the studied metals had hazard quotients (HQ) value of <1 indicating a very low risk of non-carcinogenic elements while the highest excess lifetime cancer risk (ELCR) was recorded for Cr VI in the South region with values of 8.4E-06 (PM2.5) and 6.6E-05 (PM10). The incremental lifetime cancer risk (ILCR) calculated from the PAH concentrations was within the acceptable range for all regions.
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.
The inhalational anaesthetic agent - sevoflurane is widely employed for the induction and maintenance of surgical anaesthesia. Sevoflurane possesses a high global warming potential that imposes negative impact to the environment. The only way to resolve the issue is to remove sevoflurane from the medical waste gas before it reaches the atmosphere. A continuous adsorption study with a fixed-bed column was conducted using two commercial granular activated carbons (E-GAC and H-GAC), to selectively remove sevoflurane. The effect of bed depth (Z, 5-15 cm), gas flow rate (Q, 0.5-6.0 L/min) and inlet sevoflurane concentration (C0, ∼55-700 mg/L) was investigated. E-GAC demonstrated ∼60% higher adsorption capacity than H-GAC under the same operating conditions. Varying the levels of Z, Q and C0 showed significant differences in the adsorption capacities of E-GAC, whereas only changing the C0 level had significant differences for H-GAC. Three breakthrough models (Adams-Bohart, Thomas, and Yoon-Nelson) and Bed-depth/service time (BDST) analysis were applied to predict the breakthrough characteristics of the adsorption tests and determine the characteristic parameters of the column. The Yoon-Nelson and Thomas model-predicted breakthrough curves were in good agreement with the experimental values. In the case of the Adams-Bohart model, a low correlation was observed. The predicted breakthrough time (tb) based on kinetic constant (kBDST) in BDST analysis showed satisfactory agreement with the measured values. The results suggest the possibility of designing, scaling up and optimising an adsorption system for removing sevoflurane with the aid of the models and BDST analysis.
The aim of the present study was to investigate the potential sources of heavy metals in fine air particles (PM2.5) and benzene, toluene, ethylbenzene, and isomeric xylenes (BTEX) in gas phase indoor air. PM2.5 samples were collected using a low volume sampler. BTEX samples were collected using passive sampling onto sorbent tubes and analyzed using gas chromatography-mass spectrometry (GC-MS). For the lower and upper floors of the evaluated building, the concentrations of PM2.5 were 96.4 ± 2.70 μg/m3 and 80.2 ± 3.11 μg/m3, respectively. The compositions of heavy metals in PM2.5 were predominated by iron (Fe), zinc (Zn), and aluminum (Al) with concentration of 500 ± 50.07 ng/m3, 466 ± 77.38 ng/m3, and 422 ± 147.38 ng/m3. A principal component analysis (PCA) showed that the main sources of BTEX were originated from vehicle emissions and exacerbate because of temperature variations. Hazard quotient results for BTEX showed that the compounds were below acceptable limits and thus did not possess potential carcinogenic risks. However, a measured output of lifetime cancer probability revealed that benzene and ethylbenzene posed definite carcinogenic risks. Pollutants that originated from heavy traffic next to the sampling site contributed to the indoor pollution.
Due to the rapid development of economy and society around the world, the most urban city is experiencing tropospheric ozone or commonly known as ground-level air pollutants. The concentration of air pollutants must be identified as an early precaution step by the local environmental or health agencies. This work aims to apply the artificial neural network (ANN) in estimating the ozone concentration forecast in Bangi. It consists of input variables such as temperature, relative humidity, concentration of nitrogen dioxide, time, UVA and UVB rays obtained from routine monitoring, and data recorded. Ten hidden layer is utilized to obtain the optimized ozone concentration, which is the output layer of the ANN framework. The finding showed that the meteorology condition and emission patterns play an important part in influencing the ozone concentration. However, a single network is sufficient enough to estimate the concentration despite any circumstances. Thus, it can be concluded that ANN is able to give reliable and satisfactory estimations of ozone concentration for the following day.
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.
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.
It is important to assess indoor air quality in school classrooms where the air quality may significantly influence school children's health and performance. This study aims to determine the concentrations of PM2.5 and dust chemical compositions in indoor and outdoor school classroom located in Kuala Lumpur City Centre. The PM2.5 concentration was measured from 19th September 2017-16th February 2018 using an optical PM2.5 sensor. Indoor and outdoor dust was also collected from the school classrooms and ion and trace metal concentrations were analysed using ion chromatography (IC) and inductively couple plasma-mass spectrometry (ICP-MS) respectively. This study showed that the average indoor and outdoor 24 h PM2.5 was 11.2 ± 0.45 µg m-3 and 11.4 ± 0.44 µg m-3 respectively. The 8 h PM2.5 concentration ranged between 3.2 and 28 µg m-3 for indoor and 3.2 and 19 µg m-3 for outdoor classrooms. The highest ion concentration in indoor dust was Ca2+ with an average concentration of 38.5 ± 35.0 µg g-1 while for outdoor dust SO42- recorded the highest ion concentration with an average concentration of 30.6 ± 9.37 µg g-1. Dominant trace metals in both indoor and outdoor dust were Al, Fe and Zn. Principle component analysis-multiple linear regression (PCA-MLR) demonstrated that the major source of indoor dust was road dust (69%), while soil dominated the outdoor dust (74%). Health risk assessment showed that the hazard quotient (HQ) value for non-carcinogenic trace metals was
This study intends to determine the health impacts from two office life cycles (St.1 and St.2) using life cycle assessment (LCA) and health risk assessment of indoor metals in coarse particulates (particulate matter with diameters of less than 10µm). The first building (St.1) is located in the city centre and the second building (St.2) is located within a new development 7km away from the city centre. All life cycle stages are considered and was analysed using SimaPro software. The trace metal concentrations were determined by inductively couple plasma-mass spectrometry (ICP-MS). Particle deposition in the human lung was estimated using the multiple-path particle dosimetry model (MPPD). The results showed that the total human health impact for St.1 (0.027 DALY m-2) was higher than St.2 (0.005 DALY m-2) for a 50-year lifespan, with the highest contribution from the operational phase. The potential health risk to indoor workers was quantified as a hazard quotient (HQ) for non-carcinogenic elements, where the total values for ingestion contact were 4.38E-08 (St.1) and 2.59E-08 (St.2) while for dermal contact the values were 5.12E-09 (St.1) and 2.58E-09 (St.2). For the carcinogenic risk, the values for dermal and ingestion routes for both St.1 and St.2 were lower than the acceptable limit which indicated no carcinogenic risk. Particle deposition for coarse particles in indoor workers was concentrated in the head, followed by the pulmonary region and tracheobronchial tract deposition. The results from this study showed that human health can be significantly affected by all the processes in office building life cycle, thus the minimisation of energy consumption and pollutant exposures are crucially required.
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.