Displaying publications 41 - 60 of 152 in total

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  1. A case study on the source apportionment of indoor air quality at a kindergarten in eastern Peninsular Malaysia using cluster analysis
    Sidek SS, Yatim SRM, Abdullah S, Shafie FA, Ishak AR, Dom NC, et al.
    Med J Malaysia, 2024 Mar;79(Suppl 1):104-109.
    PMID: 38555893
    BACKGROUND: Indoor air quality is an important concern for kindergartener because young children are more vulnerable to the effects of poor air quality. Poor indoor air quality can cause respiratory problems and other health issues, which can negatively affect a child's ability to learn and grow. Aim of this study is to determine the trend and status of indoor air pollutants in study areas by using descriptive statistics and cluster analysis.

    MATERIALS AND METHODS: Air temperature (T), relative humidity (RH), air movement (AM), carbon dioxide (CO2), formaldehyde (HCHO), and particulate matter (PM) are the monitored parameters. Monitoring was carried out in the kindergarten for three consecutive days starting from 8.00am to 12.00pm.

    RESULTS: Indoor carbon dioxide readings were higher at 0800 when parents drove to kindergarten to drop off their children without turning off the engine. In addition to this, the PM10 reading at 1000 was high but still within the standard range according to ICOP-IAQ 2010.

    CONCLUSION: The findings highlight the importance of indoor air quality improvement measures for kindergarten buildings which can be used to improve indoor air quality in kindergarten environments.

    Matched MeSH terms: Particulate Matter/analysis
  2. Fulltext Data on sea surface biophysical parameters during different monsoon seasons
    Idris MS, Lee Siang H, Amin RM
    Data Brief, 2020 Feb;28:104982.
    PMID: 31890817 DOI: 10.1016/j.dib.2019.104982
    The biophysical data presented in this article were collected in the east coast of Peninsular Malaysia from May to November 2009. These monthly surface data were obtained from 32 stations along the coastal-offshore transect and were analyzed to understand the spatial and temporal distributions of biophysical parameters during different monsoon seasons. The data presented here include sea surface temperature (SST), sea surface salinity (SSS), Secchi disk depth (SDD), Chlorophyll-a (Chl-a), suspended particulate matter (SPM), mineral suspended solid (MSS) and chromophoric dissolved organic matter (CDOM).
    Matched MeSH terms: Particulate Matter
  3. Fulltext Machine learning methods to predict particulate matter PM 2.5
    Palanichamy N, Haw SC, S S, Murugan R, Govindasamy K
    F1000Res, 2022;11:406.
    PMID: 36531254 DOI: 10.12688/f1000research.73166.1
    Introduction Pollution of air in urban cities across the world has been steadily increasing in recent years. An increasing trend in particulate matter, PM 2.5, is a threat because it can lead to uncontrollable consequences like worsening of asthma and cardiovascular disease. The metric used to measure air quality is the air pollutant index (API). In Malaysia, machine learning (ML) techniques for PM 2.5 have received less attention as the concentration is on predicting other air pollutants. To fill the research gap, this study focuses on correctly predicting PM 2.5 concentrations in the smart cities of Malaysia by comparing supervised ML techniques, which helps to mitigate its adverse effects. Methods In this paper, ML models for forecasting PM 2.5 concentrations were investigated on Malaysian air quality data sets from 2017 to 2018. The dataset was preprocessed by data cleaning and a normalization process. Next, it was reduced into an informative dataset with location and time factors in the feature extraction process. The dataset was fed into three supervised ML classifiers, which include random forest (RF), artificial neural network (ANN) and long short-term memory (LSTM). Finally, their output was evaluated using the confusion matrix and compared to identify the best model for the accurate prediction of PM 2.5. Results Overall, the experimental result shows an accuracy of 97.7% was obtained by the RF model in comparison with the accuracy of ANN (61.14%) and LSTM (61.77%) in predicting PM 2.5. Discussion RF performed well when compared with ANN and LSTM for the given data with minimum features. RF was able to reach good accuracy as the model learns from the random samples by using decision tree with the maximum vote on the predictions.
    Matched MeSH terms: Particulate Matter
  4. Particulate matter air pollution components and risk for lung cancer
    Raaschou-Nielsen O, Beelen R, Wang M, Hoek G, Andersen ZJ, Hoffmann B, et al.
    Environ Int, 2016 Feb;87:66-73.
    PMID: 26641521 DOI: 10.1016/j.envint.2015.11.007
    Particulate matter (PM) air pollution is a human lung carcinogen; however, the components responsible have not been identified. We assessed the associations between PM components and lung cancer incidence.
    Matched MeSH terms: Particulate Matter
  5. Demystifying uncertainty in PM10 susceptibility mapping using variable drop-off in extreme-gradient boosting (XGB) and random forest (RF) algorithms
    AlThuwaynee OF, Kim SW, Najemaden MA, Aydda A, Balogun AL, Fayyadh MM, et al.
    Environ Sci Pollut Res Int, 2021 Aug;28(32):43544-43566.
    PMID: 33834339 DOI: 10.1007/s11356-021-13255-4
    This study investigates uncertainty in machine learning that can occur when there is significant variance in the prediction importance level of the independent variables, especially when the ROC fails to reflect the unbalanced effect of prediction variables. A variable drop-off loop function, based on the concept of early termination for reduction of model capacity, regularization, and generalization control, was tested. A susceptibility index for airborne particulate matter of less than 10 μm diameter (PM10) was modeled using monthly maximum values and spectral bands and indices from Landsat 8 imagery, and Open Street Maps were used to prepare a range of independent variables. Probability and classification index maps were prepared using extreme-gradient boosting (XGBOOST) and random forest (RF) algorithms. These were assessed against utility criteria such as a confusion matrix of overall accuracy, quantity of variables, processing delay, degree of overfitting, importance distribution, and area under the receiver operating characteristic curve (ROC).
    Matched MeSH terms: Particulate Matter
  6. Oil droplets and solid particles removal using circular separator with inclined coalescence mediums: comparison between co-current and counter-current flow
    Ngu LH, Law PL, Wong KK, Yusof AA
    Water Sci Technol, 2010;62(5):1129-35.
    PMID: 20818055 DOI: 10.2166/wst.2010.407
    This research investigated the effects of co- and counter-current flow patterns on oil-water-solid separation efficiencies of a circular separator with inclined coalescence mediums. Oil-water-solid separations were tested at different influent concentrations and flowrates. Removal efficiencies increased as influent flowrate decreased, and their correlationship can be represented by power equations. These equations were used to predict the required flowrate, Q(ss50), for a given influent suspended solids concentration C(iss) to achieve the desired effluent suspended solids concentration, C(ess) of 50 mg/L, to meet environmental discharge requirements. The circular separator with counter-current flow was found to attend removal efficiencies relatively higher as compared to the co-current flow. As compared with co-current flow, counter-current flow Q(ss50) was approximately 1.65 times higher than co-current flow. It also recorded 13.16% higher oil removal at influent oil concentration, C(io) of 100 mg/L, and approximately 5.89% higher TSS removal at all influent flowrates. Counter-current flow's better removal performances were due to its higher coalescing area and constant interval between coalescence plate layers.
    Matched MeSH terms: Particulate Matter/chemistry*
  7. Wavelet-based time series model to improve the forecast accuracy of PM10 concentrations in Peninsular Malaysia
    Yong NK, Awang N
    Environ Monit Assess, 2019 Jan 11;191(2):64.
    PMID: 30635772 DOI: 10.1007/s10661-019-7209-6
    This study presents the use of a wavelet-based time series model to forecast the daily average particulate matter with an aerodynamic diameter of less than 10 μm (PM10) in Peninsular Malaysia. The highlight of this study is the use of a discrete wavelet transform (DWT) in order to improve the forecast accuracy. The DWT was applied to convert the highly variable PM10 series into more stable approximations and details sub-series, and the ARIMA-GARCH time series models were developed for each sub-series. Two different forecast periods, one was during normal days, while the other was during haze episodes, were designed to justify the usefulness of DWT. The models' performance was evaluated by four indices, namely root mean square error, mean absolute percentage error, probability of detection and false alarm rate. The results showed that the model incorporated with DWT yielded more accurate forecasts than the conventional method without DWT for both the forecast periods, and the improvement was more prominent for the period during the haze episodes.
    Matched MeSH terms: Particulate Matter/analysis*
  8. Fulltext Exploring the linkage between PM2.5 levels and COVID-19 spread and its implications for socio-economic circles
    Ali SM, Malik F, Anjum MS, Siddiqui GF, Anwar MN, Lam SS, et al.
    Environ Res, 2021 02;193:110421.
    PMID: 33160973 DOI: 10.1016/j.envres.2020.110421
    A pneumonia-like disease of unknown origin caused a catastrophe in Wuhan city, China. This disease spread to 215 countries affecting a wide range of people. World health organization (WHO) called it a pandemic and it was officially named as Severe Acute Respiratory Syndrome Corona virus 2 (SARS CoV-2), also known as Corona virus disease (COVID-19). This pandemic compelled countries to enforce a socio-economic lockdown to prevent its widespread. This paper focuses on how the particulate matter pollution was reduced during the lockdown period (23 March to April 15, 2020) as compared to before lockdown. Both ground-based and satellite observations were used to identify the improvement in air quality of Pakistan with primary focus on four major cities of Lahore, Islamabad, Karachi and Peshawar. Both datasets have shown a substantial reduction in PM2.5 pollution levels (ranging from 13% to 33% in case of satellite observations, while 23%-58% in ground-based observations) across Pakistan. Result shows a higher rate of COVID-19 spread in major cities of Pakistan with poor air quality conditions. Yet more research is needed in order to establish linkage between COVID-19 spread and air pollution. However, it can be partially attributed to both higher rate of population density and frequent exposure of population to enhanced levels of PM2.5 concentrations before lockdown period.
    Matched MeSH terms: Particulate Matter/analysis
  9. Spatial identification and temporal prediction of air pollution sources using conditional bivariate probability function and time series signature
    Althuwaynee OF, Pokharel B, Aydda A, Balogun AL, Kim SW, Park HJ
    J Expo Sci Environ Epidemiol, 2021 07;31(4):709-726.
    PMID: 33159165 DOI: 10.1038/s41370-020-00271-8
    Accurate identification of distant, large, and frequent sources of emission in cities is a complex procedure due to the presence of large-sized pollutants and the existence of many land use types. This study aims to simplify and optimize the visualization mechanism of long time-series of air pollution data, particularly for urban areas, which is naturally correlated in time and spatially complicated to analyze. Also, we elaborate different sources of pollution that were hitherto undetectable using ordinary plot models by leveraging recent advances in ensemble statistical approaches. The high performing conditional bivariate probability function (CBPF) and time-series signature were integrated within the R programming environment to facilitate the study's analysis. Hourly air pollution data for the period between 2007 to 2016 is collected using four air quality stations, (ca0016, ca0058, ca0054, and ca0025), situated in highly urbanized locations that are characterized by complex land use and high pollution emitting activities. A conditional bivariate probability function (CBPF) was used to analyze the data, utilizing pollutant concentration values such as Sulfur dioxide (SO2), Nitrogen oxides (NO2), Carbon monoxide (CO) and Particulate Matter (PM10) as a third variable plotted on the radial axis, with wind direction and wind speed variables. Generalized linear model (GLM) and sensitivity analysis are applied to verify and visualize the relationship between Air Pollution Index (API) of PM10 and other significant pollutants of GML outputs based on quantile values. To address potential future challenges, we forecast 3 months PM10 values using a Time Series Signature statistical algorithm with time functions and validated the outcome in the 4 stations. Analysis of results reveals that sources emitting PM10 have similar activities producing other pollutants (SO2, CO, and NO2). Therefore, these pollutants can be detected by cross selection between the pollution sources in the affected city. The directional results of CBPF plot indicate that ca0058 and ca0054 enable easier detection of pollutants' sources in comparison to ca0016 and ca0025 due to being located on the edge of industrial areas. This study's CBPF technique and time series signature analysis' outcomes are promising, successfully elaborating different sources of pollution that were hitherto undetectable using ordinary plot models and thus contribute to existing air quality assessment and enhancement mechanisms.
    Matched MeSH terms: Particulate Matter/analysis
  10. Evaluation of the Relationship between Fractional Exhaled Nitric Oxide (FeNO) with Indoor PM10, PM2.5 and NO2 in Suburban and Urban Schools
    Mohd Isa KN, Jalaludin J, Mohd Elias S, Mohamed N, Hashim JH, Hashim Z
    Int J Environ Res Public Health, 2022 Apr 11;19(8).
    PMID: 35457448 DOI: 10.3390/ijerph19084580
    Numerous epidemiological studies have evaluated the association of fractional exhaled nitric oxide (FeNO) and indoor air pollutants, but limited information available of the risks between schools located in suburban and urban areas. We therefore investigated the association of FeNO levels with indoor particulate matter (PM10 and PM2.5), and nitrogen dioxide (NO2) exposure in suburban and urban school areas. A comparative cross-sectional study was undertaken among secondary school students in eight schools located in the suburban and urban areas in the district of Hulu Langat, Selangor, Malaysia. A total of 470 school children (aged 14 years old) were randomly selected, their FeNO levels were measured, and allergic skin prick tests were conducted. The PM2.5, PM10, NO2, and carbon dioxide (CO2), temperature, and relative humidity were measured inside the classrooms. We found that the median of FeNO in the school children from urban areas (22.0 ppb, IQR = 32.0) were slightly higher as compared to the suburban group (19.5 ppb, IQR = 24.0). After adjustment of potential confounders, the two-level hierarchical multiple logistic regression models showed that the concentrations of PM2.5 were significantly associated with elevated of FeNO (>20 ppb) in school children from suburban (OR = 1.42, 95% CI = 1.17−1.72) and urban (OR = 1.30, 95% CI = 1.10−1.91) areas. Despite the concentrations of NO2 being below the local and international recommendation guidelines, NO2 was found to be significantly associated with the elevated FeNO levels among school children from suburban areas (OR = 1.11, 95% CI = 1.06−1.17). The findings of this study support the evidence of indoor pollutants in the school micro-environment associated with FeNO levels among school children from suburban and urban areas.
    Matched MeSH terms: Particulate Matter/analysis
  11. Characterization and source apportionment of particle number concentration at a semi-urban tropical environment
    Khan MF, Latif MT, Amil N, Juneng L, Mohamad N, Nadzir MS, et al.
    Environ Sci Pollut Res Int, 2015 Sep;22(17):13111-26.
    PMID: 25925145 DOI: 10.1007/s11356-015-4541-4
    Principal component analysis (PCA) and correlation have been used to study the variability of particle mass and particle number concentrations (PNC) in a tropical semi-urban environment. PNC and mass concentration (diameter in the range of 0.25->32.0 μm) have been measured from 1 February to 26 February 2013 using an in situ Grimm aerosol sampler. We found that the 24-h average total suspended particulates (TSP), particulate matter ≤10 μm (PM10), particulate matter ≤2.5 μm (PM2.5) and particulate matter ≤1 μm (PM1) were 14.37 ± 4.43, 14.11 ± 4.39, 12.53 ± 4.13 and 10.53 ± 3.98 μg m(-3), respectively. PNC in the accumulation mode (<500 nm) was the most abundant (at about 99 %). Five principal components (PCs) resulted from the PCA analysis where PC1 (43.8 % variance) predominates with PNC in the fine and sub-microme tre range. PC2, PC3, PC4 and PC5 explain 16.5, 12.4, 6.0 and 5.6 % of the variance to address the coarse, coarser, accumulation and giant fraction of PNC, respectively. Our particle distribution results show good agreement with the moderate resolution imaging spectroradiometer (MODIS) distribution.
    Matched MeSH terms: Particulate Matter/analysis*
  12. Fulltext Cardiovascular, respiratory and all-cause (natural) health endpoint estimation using a spatial approach in Malaysia
    Mazeli MI, Pahrol MA, Abdul Shakor AS, Kanniah KD, Omar MA
    Sci Total Environ, 2023 May 20;874:162130.
    PMID: 36804978 DOI: 10.1016/j.scitotenv.2023.162130
    In 2016, the World Health Organization (WHO) estimated that approximately 4.2 million premature deaths worldwide were attributable to exposure to particulate matter 2.5 μm (PM2.5). This study assessed the environmental burden of disease attributable to PM2.5 at the national level in Malaysia. We estimated the population-weighted exposure level (PWEL) of PM10 concentrations in Malaysia for 2000, 2008, and 2013 using aerosol optical density (AOD) data from publicly available remote sensing satellite data (MODIS Terra). The PWEL was then converted to PM2.5 using Malaysia's WHO ambient air conversion factor. We used AirQ+ 2.0 software to calculate all-cause (natural), ischemic heart disease (IHD), stroke, chronic obstructive pulmonary disease (COPD), lung cancer (LC), and acute lower respiratory infection (ALRI) excess deaths from the National Burden of Disease data for 2000, 2008 and 2013. The average PWELs for annual PM2.5 for 2000, 2008, and 2013 were 22 μg m-3, 18 μg m-3 and 24 μg m-3, respectively. Using the WHO 2005 Air Quality Guideline cut-off point of PM2.5 of 10 μg m-3, the estimated excess deaths for 2000, 2008, and 2013 from all-cause (natural) mortality were between 5893 and 9781 (95 % CI: 3347-12,791), COPD was between 164 and 957 (95 % CI: 95-1411), lung cancer was between 109 and 307 (95 % CI: 63-437), IHD was between 3 and 163 deaths, according to age groups (95 % CI: 2-394) and stroke was between 6 and 155 deaths, according to age groups (95 % CI: 3-261). An increase in estimated health endpoints was associated with increased estimated PWEL PM2.5 for 2013 compared to 2000 and 2008. Adhering the ambient PM2.5 level to the Malaysian Air Quality Standard IT-2 would reduce the national health endpoints mortality.
    Matched MeSH terms: Particulate Matter/analysis
  13. A review of personal exposure studies in selected Asian countries' public transport microenvironments: lessons learned and future directions
    Otuyo MK, Nadzir MSM, Latif MT, Din SAM
    Environ Sci Pollut Res Int, 2023 Dec;30(58):121306-121337.
    PMID: 37993649 DOI: 10.1007/s11356-023-30923-9
    This comprehensive paper conducts an in-depth review of personal exposure and air pollutant levels within the microenvironments of Asian city transportation. Our methodology involved a systematic analysis of an extensive body of literature from diverse sources, encompassing a substantial quantity of studies conducted across multiple Asian cities. The investigation scrutinizes exposure to various pollutants, including particulate matters (PM10, PM2.5, and PM1), carbon dioxide (CO2), formaldehyde (CH2O), and total volatile organic compounds (TVOC), during transportation modes such as car travel, bus commuting, walking, and train rides. Notably, our review reveals a predominant focus on PM2.5, followed by PM10, PM1, CO2, and TVOC, with limited attention given to CH2O exposure. Across the spectrum of Asian cities and transportation modes, exposure concentrations exhibited considerable variability, a phenomenon attributed to a multitude of factors. Primary sources of exposure encompass motor vehicle emissions, traffic dynamics, road dust, and open bus doors. Furthermore, our findings illuminate the influence of external environments, particularly in proximity to train stations, on pollutant levels inside trains. Crucial factors affecting exposure encompass ventilation conditions, travel-specific variables, seat locations, vehicle types, and meteorological influences. The culmination of this rigorous review underscores the need for standardized measurements, enhanced ventilation systems, air filtration mechanisms, the adoption of clean energy sources, and comprehensive public education initiatives aimed at reducing pollutant exposure within city transportation microenvironments. Importantly, our study contributes to the growing body of knowledge surrounding this subject, offering valuable insights for policymakers and researchers dedicated to advancing air quality standards and safeguarding public health.
    Matched MeSH terms: Particulate Matter/analysis
  14. Fulltext Real-Time In-Vehicle Air Quality Monitoring System Using Machine Learning Prediction Algorithm
    Goh CC, Kamarudin LM, Zakaria A, Nishizaki H, Ramli N, Mao X, et al.
    Sensors (Basel), 2021 Jul 21;21(15).
    PMID: 34372192 DOI: 10.3390/s21154956
    This paper presents the development of a real-time cloud-based in-vehicle air quality monitoring system that enables the prediction of the current and future cabin air quality. The designed system provides predictive analytics using machine learning algorithms that can measure the drivers' drowsiness and fatigue based on the air quality presented in the cabin car. It consists of five sensors that measure the level of CO2, particulate matter, vehicle speed, temperature, and humidity. Data from these sensors were collected in real-time from the vehicle cabin and stored in the cloud database. A predictive model using multilayer perceptron, support vector regression, and linear regression was developed to analyze the data and predict the future condition of in-vehicle air quality. The performance of these models was evaluated using the Root Mean Square Error, Mean Squared Error, Mean Absolute Error, and coefficient of determination (R2). The results showed that the support vector regression achieved excellent performance with the highest linearity between the predicted and actual data with an R2 of 0.9981.
    Matched MeSH terms: Particulate Matter
  15. 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; Particulate Matter/chemistry*
  16. Fulltext Effects of Population Weighting on PM10 Concentration Estimation
    Abdul Shakor AS, Pahrol MA, Mazeli MI
    J Environ Public Health, 2020;2020:1561823.
    PMID: 32351580 DOI: 10.1155/2020/1561823
    Particulate matter with an aerodynamic diameter of 10 μm or less (PM10) pollution poses a considerable threat to human health, and the first step in quantifying health impacts of human exposure to PM10 pollution is exposure assessment. Population-weighted exposure level (PWEL) estimation is one of the methods that provide a more refined exposure assessment as it includes the spatiotemporal distribution of the population into the pollution concentration estimation. This study assessed the population weighting effects on the estimated PM10 concentrations in Malaysia for years 2000, 2008, and 2013. Estimated PM10 annual mean concentrations with a spatial resolution of 5 kilometres retrieved from satellite data and population count obtained from the Gridded Population of the World version 4 (GPWv4) from the Centre for International Earth Science Information Network (CIESIN) were overlaid to generate the PWEL of PM10 for each state. The calculated PWEL of PM10 concentrations were then classified based on the World Health Organization (WHO) and the national Air Quality Guidelines (AQG) and interim targets (IT) for comparison. Results revealed that the annual mean PM10 concentrations in Malaysia ranged from 31 to 73 µg/m3 but became generally lower, ranging from 20 to 72 µg/m3 after population weighting, suggesting that the PM10 population exposure in Malaysia might have been overestimated. PWEL of PM10 distribution showed that the majority of the population lived in areas that complied with the national AQG, but were vulnerable to exposure level 3 according to the WHO AQG and IT, indicating that the population was nevertheless potentially exposed to significant health effects from long-term exposure to PM10 pollution.
    Matched MeSH terms: Particulate Matter/analysis*; Particulate Matter/chemistry
  17. 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*; Particulate Matter/chemistry
  18. The effects of air pollution on human mortality: does gender difference matter in African countries?
    Aliyu AJ, Ismail NW
    Environ Sci Pollut Res Int, 2016 Nov;23(21):21288-21298.
    PMID: 27497851
    The relationship between environmental factors and human health has long been a concern among academic researchers. We use two indicators of environmental pollution, namely particulate matter (PM10) and carbon dioxide (CO2) to examine the effects of poor air quality on human mortality. This study explores an issue that has largely been ignored, particularly in the African literature, where the effect of air pollution on human mortality could be influenced by gender specification. We analyse a panel data from 35 African countries and our result suggests that the elevated levels of PM10 and CO2 have a significant effect on the increasing mortality rates in infants, under-five children and adults. Although the effect of poor air quality on adults is found to differ between genders, such difference is not statistically significant. We conclude that the air pollution effects, on average, are similar between genders in the African countries.
    Matched MeSH terms: Particulate Matter/analysis; Particulate Matter/pharmacology
  19. Evaluation of the potential bioaccumulation ability of the blood cockle (Anadara granosa L.) for assessment of environmental matrices of mudflats
    Mirsadeghi SA, Zakaria MP, Yap CK, Gobas F
    Sci Total Environ, 2013 Jun 1;454-455:584-97.
    PMID: 23583984 DOI: 10.1016/j.scitotenv.2013.03.001
    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.
    Matched MeSH terms: Particulate Matter/analysis; Particulate Matter/metabolism*
  20. Fulltext Fine Particle Emissions From Tropical Peat Fires Decrease Rapidly With Time Since Ignition
    Roulston C, Paton-Walsh C, Smith TEL, Guérette ÉA, Evers S, Yule CM, et al.
    J Geophys Res Atmos, 2018 May 27;123(10):5607-5617.
    PMID: 30167349 DOI: 10.1029/2017JD027827
    Southeast Asia experiences frequent fires in fuel-rich tropical peatlands, leading to extreme episodes of regional haze with high concentrations of fine particulate matter (PM2.5) impacting human health. In a study published recently, the first field measurements of PM2.5 emission factors for tropical peat fires showed larger emissions than from other fuel types. Here we report even higher PM2.5 emission factors, measured at newly ignited peat fires in Malaysia, suggesting that current estimates of fine particulate emissions from peat fires may be underestimated by a factor of 3 or more. In addition, we use both field and laboratory measurements of burning peat to provide the first mechanistic explanation for the high variability in PM2.5 emission factors, demonstrating that buildup of a surface ash layer causes the emissions of PM2.5 to decrease as the peat fire progresses. This finding implies that peat fires are more hazardous (in terms of aerosol emissions) when first ignited than when still burning many days later. Varying emission factors for PM2.5 also have implications for our ability to correctly model the climate and air quality impacts downwind of the peat fires. For modelers able to implement a time-varying emission factor, we recommend an emission factor for PM2.5 from newly ignited tropical peat fires of 58 g of PM2.5 per kilogram of dry fuel consumed (g/kg), reducing exponentially at a rate of 9%/day. If the age of the fire is unknown or only a single value may be used, we recommend an average value of 24 g/kg.
    Matched MeSH terms: Particulate Matter
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