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  1. Fulltext Indoor Particulate Matters, Microbial Count Assessments, and Wheezing Symptoms among Toddlers in Urban Day Care Centers in the District of Seremban, Malaysia
    Khamal R, Isa ZM, Sutan R, Noraini NMR, Ghazi HF
    Ann Glob Health, 2019 01 22;85(1).
    PMID: 30741516 DOI: 10.5334/aogh.2425
    INTRODUCTION: Indoor air quality in day care centers (DCCs) is an emerging research topic nowadays. Indoor air pollutants such as particulate matter (PM) and microbes have been linked to respiratory health effects in children, particularly asthma-related symptoms such as night coughs and wheezing due to early exposure to indoor air contaminants.

    OBJECTIVE: The aim of this study was to determine the association between wheezing symptoms among toddlers attending DCCs and indoor particulate matter, PM10, PM2.5, and microbial count level in urban DCCs in the District of Seremban, Malaysia.

    METHODS: Data collection was carried out at 10 DCCs located in the urban area of Seremban. Modified validated questionnaires were distributed to parents to obtain their children's health symptoms. The parameters measured were indoor PM2.5, PM10, carbon monoxide, total bacteria count, total fungus count, temperature, air velocity, and relative humidity using the National Institute for Occupational Safety and Health analytical method.

    RESULTS: All 10 DCCs investigated had at least one indoor air quality parameter exceeding the acceptable level of standard guidelines. The prevalence of toddlers having wheezing symptoms was 18.9%. There was a significant different in mean concentration of PM2.5 and total bacteria count between those with and those without wheezing symptoms (P = 0.02, P = 0.006).

    CONCLUSIONS: Urban DCCs are exposed to many air pollutants that may enter their buildings from various adjacent sources. The particle concentrations and presence of microbes in DCCs might increase the risk of exposed children for respiratory diseases, particularly asthma, in their later life.

    Matched MeSH terms: Particulate Matter/analysis*
  2. Anionic Surfactants and Traffic Related Emission from an Urban Area of Perak, Malaysia
    Razak HA, Wahid NBA, Latif MT
    Arch Environ Contam Toxicol, 2019 Nov;77(4):587-593.
    PMID: 31359072 DOI: 10.1007/s00244-019-00656-3
    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 (
    Matched MeSH terms: Particulate Matter/analysis
  3. Relationship between vehicle count and particulate air pollution in Amman, Jordan
    Alnawaiseh NA, Hashim JH, Isa ZM
    Asia Pac J Public Health, 2015 Mar;27(2):NP1742-51.
    PMID: 22899706 DOI: 10.1177/1010539512455046
    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: Particulate Matter/analysis*
  4. Impact of Climate Change on Air Quality and Public Health in Urban Areas
    Hassan NA, Hashim Z, Hashim JH
    Asia Pac J Public Health, 2016 Mar;28(2 Suppl):38S-48S.
    PMID: 26141092 DOI: 10.1177/1010539515592951
    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.
    Matched MeSH terms: Particulate Matter/analysis
  5. Source contribution of PM₂.₅ at different locations on the Malaysian Peninsula
    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: Particulate Matter/analysis*
  6. Source identification of particulate matter in a semi-urban area of Malaysia using multivariate techniques
    Wahid NB, Latif MT, Suan LS, Dominick D, Sahani M, Jaafar SA, et al.
    Bull Environ Contam Toxicol, 2014 Mar;92(3):317-22.
    PMID: 24435135 DOI: 10.1007/s00128-014-1201-1
    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.
    Matched MeSH terms: Particulate Matter/analysis*
  7. Determination of trace metals in airborne particulate matter of Kuala Terengganu, Malaysia
    Mohd Tahir N, Poh SC, Suratman S, Ariffin MM, Shazali NA, Yunus K
    Bull Environ Contam Toxicol, 2009 Aug;83(2):199-203.
    PMID: 19436928 DOI: 10.1007/s00128-009-9751-3
    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)).
    Matched MeSH terms: Particulate Matter/analysis
  8. Fulltext Overviewing the air quality models on air pollution in Sichuan Basin, China
    Li X, Hussain SA, Sobri S, Md Said MS
    Chemosphere, 2021 May;271:129502.
    PMID: 33465622 DOI: 10.1016/j.chemosphere.2020.129502
    Most developing countries in the world face the common challenges of reducing air pollution and advancing the process of sustainable development, especially in China. Air pollution research is a complex system and one of the main methods is through numerical simulation. The air quality model is an important technical method, it allows researchers to better analyze air pollutants in different regions. In addition, the SCB is a high-humidity and foggy area, and the concentration of atmospheric pollutants is always high. However, research on this region, one of the four most polluted regions in China, is still lacking. Reviewing the application of air quality models in the SCB air pollution has not been reported thoroughly. To fill these gaps, this review provides a comprehensive narration about i) The status of air pollution in SCB; ii) The application of air quality models in SCB; iii) The problems and application prospects of air quality models in the research of air pollution. This paper may provide a theoretical reference for the prevention and control of air pollution in the SCB and other heavily polluted areas in China and give some1inspirations for air pollution forecast in other countries with complex terrain.
    Matched MeSH terms: Particulate Matter/analysis
  9. Spatial distribution of fine and coarse particulate matter during a southwest monsoon in Peninsular Malaysia
    Othman M, Latif MT, Jamhari AA, Abd Hamid HH, Uning R, Khan MF, et al.
    Chemosphere, 2021 Jan;262:127767.
    PMID: 32763576 DOI: 10.1016/j.chemosphere.2020.127767
    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.
    Matched MeSH terms: Particulate Matter/analysis*
  10. Indoor generated PM2.5 compositions and volatile organic compounds: Potential sources and health risk implications
    Idris SA', Hanafiah MM, Khan MF, Hamid HHA
    Chemosphere, 2020 Sep;255:126932.
    PMID: 32402880 DOI: 10.1016/j.chemosphere.2020.126932
    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.
    Matched MeSH terms: Particulate Matter/analysis*
  11. Seasonal variation and size distribution of inorganic and carbonaceous components, source identification of size-fractioned urban air particles in Kuala Lumpur, Malaysia
    Jamhari AA, Latif MT, Wahab MIA, Hassan H, Othman M, Abd Hamid HH, et al.
    Chemosphere, 2022 Jan;287(Pt 4):132309.
    PMID: 34601373 DOI: 10.1016/j.chemosphere.2021.132309
    This study aims to determine the inorganic and carbonaceous components depending on the seasonal variation and size distribution of urban air particles in Kuala Lumpur. Different fractions of particulate matter (PM) were measured using a Nanosampler from 17 February 2017 until 27 November 2017. The water-soluble inorganic ions (WSIIs) and carbonaceous components in all samples were analysed using ion chromatography and carbon analyser thermal/optical reflectance, respectively. Total PM concentration reached its peak during the southwest (SW) season (70.99 ± 6.04 μg/m3), and the greatest accumulation were observed at PM0.5-1.0 (22%-30%, 9.55 ± 1.03 μg/m3) and PM2.5-10 (22%-25%, 10.34 ± 0.81 μg/m3). SO42-, NO3- and NH4+ were major contributors of WSIIs, and their formation was favoured mainly during SW season (80.5% of total ions). PM0.5-1.0 and PM2.5-10 exhibited the highest percentage of WSII size distribution, accounted for 28.4% and 13.5% of the total mass, respectively. The average contribution of carbonaceous species (OC + EC) to total carbonaceous concentrations were higher in PM0.5-1.0 (35.2%) and PM2.5-10 (26.6%). Ultrafine particles (PM<0.1) consistently indicated that the sources were from vehicle emission while the SW season was constantly dominated by biomass burning sources. Using the positive matrix factorization (PMF) model, secondary inorganic aerosol and biomass burning (30.3%) was known as a significant source of overall PM. As a conclusion, ratio and source apportionment indicate the mixture of biomass burning, secondary inorganic aerosols and motor vehicle contributed to the size-segregated PM and seasonal variation of inorganic and carbonaceous components of urban air particles.
    Matched MeSH terms: Particulate Matter/analysis
  12. A performance comparison study on PM2.5 prediction at industrial areas using different training algorithms of feedforward-backpropagation neural network (FBNN)
    Chinatamby P, Jewaratnam J
    Chemosphere, 2023 Mar;317:137788.
    PMID: 36642141 DOI: 10.1016/j.chemosphere.2023.137788
    Presence of particulate matters with aerodynamic diameter of less than 2.5 μm (PM2.5) in the atmosphere is fast increasing in Malaysia due to industrialization and urbanization. Prolonged exposure of PM2.5 can cause serious health effects to human. This research is aimed to identify the most reliable model to predict the PM2.5 pollution using multi-layered feedforward-backpropagation neural network (FBNN). Air quality and meteorological data were collected from Department of Environment (DOE) Malaysia. Six different training algorithms consisting of thirteen various training functions were trained and compared. FBNN model with the highest coefficient correlation (R2) and lowest root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) were selected as the best performing model. Levenberg Marquardt (trainlm) is the best performing algorithms compared to other algorithms with R2 value of 0.9834 and the lowest error values for RMSE (2.3981), MAE (1.7843) and MAPE (0.1063).
    Matched MeSH terms: Particulate Matter/analysis
  13. Fulltext Air quality prediction by machine learning models: A predictive study on the indian coastal city of Visakhapatnam
    Ravindiran G, Hayder G, Kanagarathinam K, Alagumalai A, Sonne C
    Chemosphere, 2023 Oct;338:139518.
    PMID: 37454985 DOI: 10.1016/j.chemosphere.2023.139518
    Clean air is critical component for health and survival of human and wildlife, as atmospheric pollution is associated with a number of significant diseases including cancer. However, due to rapid industrialization and population growth, activities such as transportation, household, agricultural, and industrial processes contribute to air pollution. As a result, air pollution has become a significant problem in many cities, especially in emerging countries like India. To maintain ambient air quality, regular monitoring and forecasting of air pollution is necessary. For that purpose, machine learning has emerged as a promising technique for predicting the Air Quality Index (AQI) compared to conventional methods. Here we apply the AQI to the city of Visakhapatnam, Andhra Pradesh, India, focusing on 12 contaminants and 10 meteorological parameters from July 2017 to September 2022. For this purpose, we employed several machine learning models, including LightGBM, Random Forest, Catboost, Adaboost, and XGBoost. The results show that the Catboost model outperformed other models with an R2 correlation coefficient of 0.9998, a mean absolute error (MAE) of 0.60, a mean square error (MSE) of 0.58, and a root mean square error (RMSE) of 0.76. The Adaboost model had the least effective prediction with an R2 correlation coefficient of 0.9753. In summary, machine learning is a promising technique for predicting AQI with Catboost being the best-performing model for AQI prediction. Moreover, by leveraging historical data and machine learning algorithms enables accurate predictions of future urban air quality levels on a global scale.
    Matched MeSH terms: Particulate Matter/analysis
  14. Recent progress on phytoremediation of urban air pollution
    Duan X, Gu H, Lam SS, Sonne C, Lu W, Li H, et al.
    Chemosphere, 2024 Feb;349:140821.
    PMID: 38042424 DOI: 10.1016/j.chemosphere.2023.140821
    The rapid growth of population and economy has led to an increase in urban air pollutants, greenhouse gases, energy shortages, environmental degradation, and species extinction, all of which affect ecosystems, biodiversity, and human health. Atmospheric pollution sources are divided into direct and indirect pollutants. Through analysis of the sources of pollutants, the self-functioning of different plants can be utilized to purify the air quality more effectively. Here, we explore the absorption of greenhouse gases and particulate matter in cities as well as the reduction of urban temperatures by plants based on international scientific literature on plant air pollution mitigation, according to the adsorption, dust retention, and transpiration functions of plants. At the same time, it can also reduce the occurrence of extreme weather. It is necessary to select suitable tree species for planting according to different plant functions and environmental needs. In the context of tight urban land use, the combination of vertical greening and urban architecture, through the rational use of plants, has comprehensively addressed urban air pollution. In the future, in urban construction, attention should be paid to the use of heavy plants and the protection and development of green spaces. Our review provides necessary references for future urban planning and research.
    Matched MeSH terms: Particulate Matter/analysis
  15. Fulltext Air Quality Prediction Using the Fractional Gradient-Based Recurrent Neural Network
    Arora S, Sawaran Singh NS, Singh D, Rakesh Shrivastava R, Mathur T, Tiwari K, et al.
    Comput Intell Neurosci, 2022;2022:9755422.
    PMID: 36531923 DOI: 10.1155/2022/9755422
    In this study, the air quality index (AQI) of Indian cities of different tiers is predicted by using the vanilla recurrent neural network (RNN). AQI is used to measure the air quality of any region which is calculated on the basis of the concentration of ground-level ozone, particle pollution, carbon monoxide, and sulphur dioxide in air. Thus, the present air quality of an area is dependent on current weather conditions, vehicle traffic in that area, or anything that increases air pollution. Also, the current air quality is dependent on the climate conditions and industrialization in that area. Thus, the AQI is history-dependent. To capture this dependency, the memory property of fractional derivatives is exploited in this algorithm and the fractional gradient descent algorithm involving Caputo's derivative has been used in the backpropagation algorithm for training of the RNN. Due to the availability of a large amount of data and high computation support, deep neural networks are capable of giving state-of-the-art results in the time series prediction. But, in this study, the basic vanilla RNN has been chosen to check the effectiveness of fractional derivatives. The AQI and gases affecting AQI prediction results for different cities show that the proposed algorithm leads to higher accuracy. It has been observed that the results of the vanilla RNN with fractional derivatives are comparable to long short-term memory (LSTM).
    Matched MeSH terms: Particulate Matter/analysis
  16. Source apportionment and health risk assessment of PM10 in a naturally ventilated school in a tropical environment
    Mohamad N, Latif MT, Khan MF
    Ecotoxicol Environ Saf, 2016 Feb;124:351-362.
    PMID: 26590697 DOI: 10.1016/j.ecoenv.2015.11.002
    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.
    Matched MeSH terms: Particulate Matter/analysis*
  17. Risk of concentrations of major air pollutants on the prevalence of cardiovascular and respiratory diseases in urbanized area of Kuala Lumpur, Malaysia
    Tajudin MABA, Khan MF, Mahiyuddin WRW, Hod R, Latif MT, Hamid AH, et al.
    Ecotoxicol Environ Saf, 2019 Apr 30;171:290-300.
    PMID: 30612017 DOI: 10.1016/j.ecoenv.2018.12.057
    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.
    Matched MeSH terms: Particulate Matter/analysis
  18. Fulltext Metagenomic characterization of indoor dust fungal associated with allergy and lung inflammation among school children
    Isa KNM, Jalaludin J, Elias SM, Than LTL, Jabbar MA, Saudi ASM, et al.
    Ecotoxicol Environ Saf, 2021 Sep 15;221:112430.
    PMID: 34147866 DOI: 10.1016/j.ecoenv.2021.112430
    The exposure of school children to indoor air pollutants has increased allergy and respiratory diseases. The objective of this study were to determine the toxicodynamic interaction of indoor pollutants exposure, biological and chemical with expression of adhesion molecules on eosinophil and neutrophil. A self-administered questionnaire, allergy skin test, and fractional exhaled nitric oxide (FeNO) analyser were used to collect information on health status, sensitization to allergens and respiratory inflammation, respectively among school children at age of 14 years. The sputum induced were analysed to determine the expression of CD11b, CD35, CD63 and CD66b on eosinophil and neutrophil by using flow cytometry technique. The particulate matter (PM2.5 and PM10), NO2, CO2, and formaldehyde, temperature, and relative humidity were measured inside the classrooms. The fungal DNA were extracted from settled dust collected from classrooms and evaluated using metagenomic techniques. We applied chemometric and regression in statistical analysis. A total of 1869 unique of operational taxonomic units (OTUs) of fungi were identified with dominated at genus level by Aspergillus (15.8%), Verrucoconiothyrium (5.5%), and Ganoderma (4.6%). Chemometric and regression results revealed that relative abundance of T. asahii were associated with down regulation of CD66b expressed on eosinophil, and elevation of FeNO levels in predicting asthmatic children with model accuracy of 63.6%. Meanwhile, upregulation of CD11b expressed on eosinophil were associated with relative abundance of A. clavatus and regulated by PM2.5. There were significant association of P. bandonii with upregulation of CD63 expressed on neutrophil and exposure to NO2. Our findings indicate that exposure to PM2.5, NO2, T. asahii, P.bandonii and A.clavatus are likely interrelated with upregulation of activation and degranulation markers on both eosinophil and neutrophil.
    Matched MeSH terms: Particulate Matter/analysis
  19. PM2.5 and ozone in office environments and their potential impact on human health
    Othman M, Latif MT, Yee CZ, Norshariffudin LK, Azhari A, Halim NDA, et al.
    Ecotoxicol Environ Saf, 2020 May;194:110432.
    PMID: 32169727 DOI: 10.1016/j.ecoenv.2020.110432
    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: Particulate Matter/analysis*
  20. Health impact assessment from building life cycles and trace metals in coarse particulate matter in urban office environments
    Othman M, Latif MT, Mohamed AF
    Ecotoxicol Environ Saf, 2018 Feb;148:293-302.
    PMID: 29080527 DOI: 10.1016/j.ecoenv.2017.10.034
    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.
    Matched MeSH terms: Particulate Matter/analysis*
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