Displaying all 4 publications

  1. Othman M, Latif MT, Matsumi Y
    Ecotoxicol Environ Saf, 2019 Apr 15;170:739-749.
    PMID: 30583285 DOI: 10.1016/j.ecoenv.2018.12.042
    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
    Matched MeSH terms: Sulfates/analysis
  2. Ee-Ling O, Mustaffa NI, Amil N, Khan MF, Latif MT
    Bull Environ Contam Toxicol, 2015 Apr;94(4):537-42.
    PMID: 25652682 DOI: 10.1007/s00128-015-1477-9
    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.
    Matched MeSH terms: Sulfates/analysis
  3. Tao H, Bobaker AM, Ramal MM, Yaseen ZM, Hossain MS, Shahid S
    Environ Sci Pollut Res Int, 2019 Jan;26(1):923-937.
    PMID: 30421367 DOI: 10.1007/s11356-018-3663-x
    Surface and ground water resources are highly sensitive aquatic systems to contaminants due to their accessibility to multiple-point and non-point sources of pollutions. Determination of water quality variables using mathematical models instead of laboratory experiments can have venerable significance in term of the environmental prospective. In this research, application of a new developed hybrid response surface method (HRSM) which is a modified model of the existing response surface model (RSM) is proposed for the first time to predict biochemical oxygen demand (BOD) and dissolved oxygen (DO) in Euphrates River, Iraq. The model was constructed using various physical and chemical variables including water temperature (T), turbidity, power of hydrogen (pH), electrical conductivity (EC), alkalinity, calcium (Ca), chemical oxygen demand (COD), sulfate (SO4), total dissolved solids (TDS), and total suspended solids (TSS) as input attributes. The monthly water quality sampling data for the period 2004-2013 was considered for structuring the input-output pattern required for the development of the models. An advance analysis was conducted to comprehend the correlation between the predictors and predictand. The prediction performances of HRSM were compared with that of support vector regression (SVR) model which is one of the most predominate applied machine learning approaches of the state-of-the-art for water quality prediction. The results indicated a very optimistic modeling accuracy of the proposed HRSM model to predict BOD and DO. Furthermore, the results showed a robust alternative mathematical model for determining water quality particularly in a data scarce region like Iraq.
    Matched MeSH terms: Sulfates/analysis
  4. Beckmann S, Luk AWS, Gutierrez-Zamora ML, Chong NHH, Thomas T, Lee M, et al.
    ISME J, 2019 03;13(3):632-650.
    PMID: 30323265 DOI: 10.1038/s41396-018-0296-5
    Despite the significance of biogenic methane generation in coal beds, there has never been a systematic long-term evaluation of the ecological response to biostimulation for enhanced methanogenesis in situ. Biostimulation tests in a gas-free coal seam were analysed over 1.5 years encompassing methane production, cell abundance, planktonic and surface associated community composition and chemical parameters of the coal formation water. Evidence is presented that sulfate reducing bacteria are energy limited whilst methanogenic archaea are nutrient limited. Methane production was highest in a nutrient amended well after an oxic preincubation phase to enhance coal biofragmentation (calcium peroxide amendment). Compound-specific isotope analyses indicated the predominance of acetoclastic methanogenesis. Acetoclastic methanogenic archaea of the Methanosaeta and Methanosarcina genera increased with methane concentration. Acetate was the main precursor for methanogenesis, however more acetate was consumed than methane produced in an acetate amended well. DNA stable isotope probing showed incorporation of 13C-labelled acetate into methanogenic archaea, Geobacter species and sulfate reducing bacteria. Community characterisation of coal surfaces confirmed that methanogenic archaea make up a substantial proportion of coal associated biofilm communities. Ultimately, methane production from a gas-free subbituminous coal seam was stimulated despite high concentrations of sulfate and sulfate-reducing bacteria in the coal formation water. These findings provide a new conceptual framework for understanding the coal reservoir biosphere.
    Matched MeSH terms: Sulfates/analysis
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