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  1. Fulltext "Care and feeding": the Asian environmental tobacco smoke consultants programme
    Assunta M, Fields N, Knight J, Chapman S
    Tob Control, 2004 Dec;13 Suppl 2:ii4-12.
    PMID: 15564219
    STUDY OBJECTIVE: To review the tobacco industry's Asian environmental tobacco smoke (ETS) consultants programme, focusing on three key nations: China, Hong Kong, and Malaysia.
    METHODS: Systematic keyword and opportunistic website searches of formerly private internal industry documents.
    MAIN RESULTS: The release of the 1986 US Surgeon General's report on second hand smoke provoked tobacco companies to prepare for a major threat to their industry. Asian programme activities included conducting national/international symposiums, consultant "road shows" and extensive lobbying and media activities. The industry exploited confounding factors said to be unique to Asian societies such as diet, culture and urban pollution to downplay the health risks of ETS. The industry consultants were said to be "..prepared to do the kinds of things they were recruited to do".
    CONCLUSIONS: The programme was successful in blurring the science on ETS and keeping the controversy alive both nationally and internationally. For the duration of the project, it also successfully dissuaded national policy makers from instituting comprehensive bans on smoking in public places.
    Matched MeSH terms: Air Pollution, Indoor/adverse effects
  2. A Comparison of Various Imputation Methods for Missing Values in Air Quality Data
    Nuryazmin Ahmat Zainuri, Abdul Aziz Jemain, Nora Muda
    Sains Malaysiana, 2015;44:449-456.
    This paper presents various imputation methods for air quality data specifically in Malaysia. The main objective was to
    select the best method of imputation and to compare whether there was any difference in the methods used between stations
    in Peninsular Malaysia. Missing data for various cases are randomly simulated with 5, 10, 15, 20, 25 and 30% missing.
    Six methods used in this paper were mean and median substitution, expectation-maximization (EM) method, singular
    value decomposition (SVD), K-nearest neighbour (KNN) method and sequential K-nearest neighbour (SKNN) method. The
    performance of the imputations is compared using the performance indicator: The correlation coefficient (R), the index
    of agreement (d) and the mean absolute error (MAE). Based on the result obtained, it can be concluded that EM, KNN
    and SKNN are the three best methods. The same result are obtained for all the eight monitoring station used in this study.
    Matched MeSH terms: Air Pollution
  3. Fulltext A Review of Indoor Air Quality and Respiratory System among Schoolchildren in Malaysia
    Foo, Ming Hui
    Borneo Journal of Medical Sciences, 2018;12(101):51-0.
    MyJurnal
    In this era, most of us are suffering some level of respiratory problem. Respiratory system of our children is even more sensitive compare to adults. As our children spending an average of 8 hours in school, indoor air quality of the classroom become an important element. Many studies have shown that indoor air quality not only affecting the respiratory system of schoolchildren but their performance in academy as well.
    Matched MeSH terms: Air Pollution, Indoor
  4. Fulltext A case-control study on the association between environmental factors and the occurrence of acute leukemia among children in Klang Valley, Malaysia
    Abdul Rahman HI, Shah SA, Alias H, Ibrahim HM
    Asian Pac J Cancer Prev, 2008 Oct-Dec;9(4):649-52.
    PMID: 19256754
    BACKGROUND: In Malaysia, acute leukemia is the most common cancer among children below the age of 15. A case-control study was here conducted for cases from the Klang Valley, Malaysia, who received treatment at the National University of Malaysia Hospital (HUKM) and Kuala Lumpur General Hospital (GHKL). The main objective was to determine any association with environmental factors.

    METHODS: Case subjects were children aged below 15 years and diagnosed with acute leukemia in HUKM and GHKL between January 1, 2001 and May 30, 2007. Control subjects were children aged below 15 years who were diagnosed with any non-cancerous acute illnesses in these hospitals. A total of 128 case subjects and 128 control subjects were enrolled in this study. The information was collected using a structured questionnaire and a global positioning system (GPS) device. All factors were analyzed using unmatched logistic regression.

    RESULTS: The analysis showed that the occurrence of acute leukemia among children was strongly determined by the following factors: family income (odds ratio (OR) 0.19, 95% confidence interval (CI): 0.09-0.42), father with higher social contact (OR 7.61, 95% CI: 3.78-15.4), number of elder siblings (OR 0.36, 95% CI: 0.18-0.77), father who smokes (OR 2.78, 95% CI: 1.49-5.16), and the distance of the house from a power line (OR 2.30, 95% CI: 1.18-4.49).

    CONCLUSIONS: Some socioeconomic, demographic, and environmental factors are strong predictors of the occurrence of acute leukemia among children in Klang Valley, Malaysia. In terms of environmental factors, it is recommended that future housing areas should be developed at least 200 m away from power lines.
    Matched MeSH terms: Air Pollution, Indoor/statistics & numerical data*
  5. Fulltext A new hybrid fuzzy time series model with an application to predict PM10 concentration
    Alyousifi Y, Othman M, Husin A, Rathnayake U
    Ecotoxicol Environ Saf, 2021 Dec 20;227:112875.
    PMID: 34717219 DOI: 10.1016/j.ecoenv.2021.112875
    Fuzzy time series (FTS) forecasting models show a great performance in predicting time series, such as air pollution time series. However, they have caused major issues by utilizing random partitioning of the universe of discourse and ignoring repeated fuzzy sets. In this study, a novel hybrid forecasting model by integrating fuzzy time series to Markov chain and C-Means clustering techniques with an optimal number of clusters is presented. This hybridization contributes to generating effective lengths of intervals and thus, improving the model accuracy. The proposed model was verified and validated with real time series data sets, which are the benchmark data of actual trading of Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) and PM10 concentration data from Melaka, Malaysia. In addition, a comparison was made with some existing fuzzy time series models. Furthermore, the mean absolute percentage error, mean squared error and Theil's U statistic were calculated as evaluation criteria to illustrate the performance of the proposed model. The empirical analysis shows that the proposed model handles the time series data sets more efficiently and provides better overall forecasting results than existing FTS models. The results prove that the proposed model has greatly improved the prediction accuracy, for which it outperforms several fuzzy time series models. Therefore, it can be concluded that the proposed model is a better option for forecasting air pollution parameters and any kind of random parameters.
    Matched MeSH terms: Air Pollution*
  6. A review on factors related to patient comfort experience in hospitals
    Tian Y
    J Health Popul Nutr, 2023 Nov 08;42(1):125.
    PMID: 37941052 DOI: 10.1186/s41043-023-00465-4
    The creation of a welcoming hospital atmosphere is necessary to improve patient wellbeing and encourage healing. The goal of this study was to examine the variables affecting hospitalised patients' comfort. The study procedure included a thorough search of the Web of Science and Scopus databases, as well as the use of software analytic tools to graphically map enormous literature data, providing a deeper understanding of the linkages within the literature and its changing patterns. Insights from a range of disciplines, including engineering, psychology, immunology, microbiology, and environmental science, were included into our study using content analysis and clustering approaches. The physical environment and the social environment are two crucial factors that are related to patient comfort. The study stress the need of giving patient comfort a top priority as they heal, especially by tackling indoor air pollution. Our research also emphasises how important hospital care and food guidelines are for improving patient comfort. Prioritising patients who need specialised care and attention, especially those who have suffered trauma, should be the focus of future study. Future research in important fields including trauma, communication, hospital architecture, and nursing will be built on the findings of this study. To enhance research in these crucial areas, worldwide collaboration between experts from other nations is also advised. Although many studies stress the significance of patient comfort, few have drawn conclusions from a variety of disciplines, including medicine, engineering, immunology, microbiology, and environmental science, the most crucial issue of thoroughly researching the improvement of patient comfort has not been addressed. Healthcare workers, engineers, and other professions will benefit greatly from this study's investigation of the connection between hospital indoor environments and patient comfort.
    Matched MeSH terms: Air Pollution, Indoor*
  7. A systematic literature review of deep learning neural network for time series air quality forecasting
    Zaini N, Ean LW, Ahmed AN, Malek MA
    Environ Sci Pollut Res Int, 2022 Jan;29(4):4958-4990.
    PMID: 34807385 DOI: 10.1007/s11356-021-17442-1
    Rapid progress of industrial development, urbanization and traffic has caused air quality reduction that negatively affects human health and environmental sustainability, especially among developed countries. Numerous studies on the development of air quality forecasting model using machine learning have been conducted to control air pollution. As such, there are significant numbers of reviews on the application of machine learning in air quality forecasting. Shallow architectures of machine learning exhibit several limitations and yield lower forecasting accuracy than deep learning architecture. Deep learning is a new technology in computational intelligence; thus, its application in air quality forecasting is still limited. This study aims to investigate the deep learning applications in time series air quality forecasting. Owing to this, literature search is conducted thoroughly from all scientific databases to avoid unnecessary clutter. This study summarizes and discusses different types of deep learning algorithms applied in air quality forecasting, including the theoretical backgrounds, hyperparameters, applications and limitations. Hybrid deep learning with data decomposition, optimization algorithm and spatiotemporal models are also presented to highlight those techniques' effectiveness in tackling the drawbacks of individual deep learning models. It is clearly stated that hybrid deep learning was able to forecast future air quality with higher accuracy than individual models. At the end of the study, some possible research directions are suggested for future model development. The main objective of this review study is to provide a comprehensive literature summary of deep learning applications in time series air quality forecasting that may benefit interested researchers for subsequent research.
    Matched MeSH terms: Air Pollution*
  8. A systematic review of financial implications of air pollution on health in Asia
    Jaafar H, Razi NA, Azzeri A, Isahak M, Dahlui M
    Environ Sci Pollut Res Int, 2018 Oct;25(30):30009-30020.
    PMID: 30187406 DOI: 10.1007/s11356-018-3049-0
    Economic losses due to health-related implications of air pollution were huge and incurred significant burdens towards healthcare providers. The objective of this study is to systematically review published literature on the financial implications of air pollution on health in Asia. Four databases: PubMed, Scopus, NHS Economic Evaluation Database (NHS EED), and Web of Science (WoS) were used to identify all the relevant articles. It was limited to all articles that had been published in the respected databases from January 2007 until March 2017. Twenty-four articles were included in this review. Five of the 24 studies (20.8%) reported financial implications of air pollution-related disease through value of statistical life (VOSL) which ranged from USD180 million to USD2.2 billion, six (25%) studies used cost of illness (COI) to evaluate air pollution-related morbidity and found that the cost ranged from USD5.4 million to USD9.1 billion. Another six studies (25%) used a combination of VOSL and COI for both mortality and morbidity valuation and found that the financial implications ranging from USD253 million to USD2.9 billion. Thirteen (54.2%) studies reported healthcare cost associated with both hospital admission and outpatient visit, five (20.1%) on hospital admission only, and one (4.2%) on outpatient visit only. Economic impacts of air pollution can be huge with significant deterioration of health among the Asians.
    Matched MeSH terms: Air Pollution/analysis; Air Pollution/economics*
  9. Fulltext Accounting for black carbon lowers estimates of blue carbon storage services
    Chew ST, Gallagher JB
    Sci Rep, 2018 02 07;8(1):2553.
    PMID: 29416101 DOI: 10.1038/s41598-018-20644-2
    The canopies and roots of seagrass, mangrove, and saltmarsh protect a legacy of buried sedimentary organic carbon from resuspension and remineralisation. This legacy's value, in terms of mitigating anthropogenic emissions of CO2, is based on total organic carbon (TOC) inventories to a depth likely to be disturbed. However, failure to subtract allochthonous recalcitrant carbon overvalues the storage service. Simply put, burial of oxidation-resistant organics formed outside of the ecosystem provides no additional protection from remineralisation. Here, we assess whether black carbon (BC), an allochthonous and recalcitrant form of organic carbon, is contributing to a significant overestimation of blue carbon stocks. To test this supposition, BC and TOC contents were measured in different types of seagrass and mangrove sediment cores across tropical and temperate regimes, with different histories of air pollution and fire together with a reanalysis of published data from a subtropical system. The results suggest current carbon stock estimates are positively biased, particularly for low-organic-content sandy seagrass environs, by 18 ± 3% (±95% confidence interval) and 43 ± 21% (±95% CI) for the temperate and tropical regions respectively. The higher BC fractions appear to originate from atmospheric deposition and substantially enrich the relatively low TOC fraction within these environs.
    Matched MeSH terms: Air Pollution
  10. Fulltext Air filtration study to improve indoor air quality: a proposed study
    Amir Abdullah, M.D., Abdullah, A.H., Leman, A.M.
    Journal of Occupational Safety and Health, 2015;12(1):203-207.
    MyJurnal
    Indoor air quality has been a major public concern recently. Several health effects are related to this problem.
    Findings from several studies have shown MVAC system as the main contributor for IAQ problem. Good practice of
    maintenance and servicing is important to maintain MVAC system, especially the filter. Good air filtration for MVAC
    system is needed to make sure adequate air is received by the occupants. This paper illustrated a recent study of air
    filtration for MVAC system especially for several industries that used MVAC system in their premises. This paper also
    proposed an air filtration study for a better air quality. Several Acts and Regulations related to Safety and Health were
    identified to create the framework for the proposed study. Air filtration technique was used in this preliminary study
    to set up guidelines to create safe and clean indoor spaces for workers and occupants.
    Matched MeSH terms: Air Pollution; Air Pollution, Indoor
  11. Air pollution and cardiorespiratory hospitalization, predictive modeling, and analysis using artificial intelligence techniques
    Usmani RSA, Pillai TR, Hashem IAT, Marjani M, Shaharudin R, Latif MT
    Environ Sci Pollut Res Int, 2021 Oct;28(40):56759-56771.
    PMID: 34075501 DOI: 10.1007/s11356-021-14305-7
    Air pollution has a serious and adverse effect on human health, and it has become a risk to human welfare and health throughout the globe. One of the major effects of air pollution on health is hospitalizations associated with air pollution. Recently, the estimation and prediction of air pollution-based hospitalization is carried out using artificial intelligence (AI) and machine learning (ML) techniques, i.e., deep learning and long short-term memory (LSTM). However, there is ample room for improvement in the available applied methodologies to estimate and predict air pollution-based hospital admissions. In this paper, we present the modeling and analysis of air pollution and cardiorespiratory hospitalization. This study aims to investigate the association between cardiorespiratory hospitalization and air pollution, and predict cardiorespiratory hospitalization based on air pollution using the artificial intelligence (AI) techniques. We propose the enhanced long short-term memory (ELSTM) model and provide a comparison with other AI techniques, i.e., LSTM, DL, and vector autoregressive (VAR). This study was conducted at seven study locations in Klang Valley, Malaysia. The utilized dataset contains the data from January 2006 to December 2016 for five study locations, i.e., Klang (KLN), Shah Alam (SA), Putrajaya (PUJ), Petaling Jaya (PJ), and Cheras, Kuala Lumpur (CKL). The dataset for Banting contains data from April 2010 to December 2016, and the data for Batu Muda, Kuala Lumpur, contains data from January 2009 to December 2016. The prediction results show that the ELSTM model performed significantly better than other models in all study locations, with the best RMSE scores in Klang study location (ELSTM: 0.002, LSTM: 0.013, DL: 0.006, VAR: 0.066). The results also indicated that the proposed ELSTM model was able to detect and predict the trends of monthly hospitalization significantly better than the LSTM and other models in the study. Hence, we can conclude that we can utilize AI techniques to accurately predict cardiorespiratory hospitalization based on air pollution in Klang Valley, Malaysia.
    Matched MeSH terms: Air Pollution*
  12. Fulltext Air pollution and incidence of cancers of the stomach and the upper aerodigestive tract in the European Study of Cohorts for Air Pollution Effects (ESCAPE)
    Nagel G, Stafoggia M, Pedersen M, Andersen ZJ, Galassi C, Munkenast J, et al.
    Int J Cancer, 2018 10 01;143(7):1632-1643.
    PMID: 29696642 DOI: 10.1002/ijc.31564
    Air pollution has been classified as carcinogenic to humans. However, to date little is known about the relevance for cancers of the stomach and upper aerodigestive tract (UADT). We investigated the association of long-term exposure to ambient air pollution with incidence of gastric and UADT cancer in 11 European cohorts. Air pollution exposure was assigned by land-use regression models for particulate matter (PM) below 10 µm (PM10 ), below 2.5 µm (PM2.5 ), between 2.5 and 10 µm (PMcoarse ), PM2.5 absorbance and nitrogen oxides (NO2 and NOX ) as well as approximated by traffic indicators. Cox regression models with adjustment for potential confounders were used for cohort-specific analyses. Combined estimates were determined with random effects meta-analyses. During average follow-up of 14.1 years of 305,551 individuals, 744 incident cases of gastric cancer and 933 of UADT cancer occurred. The hazard ratio for an increase of 5 µg/m3 of PM2.5 was 1.38 (95% CI 0.99; 1.92) for gastric and 1.05 (95% CI 0.62; 1.77) for UADT cancers. No associations were found for any of the other exposures considered. Adjustment for additional confounders and restriction to study participants with stable addresses did not influence markedly the effect estimate for PM2.5 and gastric cancer. Higher estimated risks of gastric cancer associated with PM2.5 was found in men (HR 1.98 [1.30; 3.01]) as compared to women (HR 0.85 [0.5; 1.45]). This large multicentre cohort study shows an association between long-term exposure to PM2.5 and gastric cancer, but not UADT cancers, suggesting that air pollution may contribute to gastric cancer risk.
    Matched MeSH terms: Air Pollution/adverse effects*
  13. Air pollution effects on adult mortality rate in developing countries
    Ahmad NA, Ismail NW, Ahmad Sidique SF, Mazlan NS
    Environ Sci Pollut Res Int, 2021 Feb;28(7):8709-8721.
    PMID: 33068244 DOI: 10.1007/s11356-020-11191-3
    Although industrialisation is a crucial aspect of economic growth across developing nations, through the release of air contaminants, industrial activities may also create adverse environmental health consequences. Noting that continuous production and other economic activities are crucial for continued survival, this study explores this issue by including the role of governance that is deemed essential but the literature is relatively sparse particularly in the context of developing countries. This research empirically analyses the relationship between air pollution and adult mortality rates from 72 developing countries from the period of 2010 until 2017. Particulate matter (PM2.5) and carbon dioxide (CO2) are used as indicators of air pollution. From the generalized method of moments (GMM) estimations, the results reveal that air pollution negatively affects adult mortality rate. The result reveals that a 10% increase in the PM2.5 level induces the adult mortality rates to increase between 0.04% and 0.06%. In addition, the government significantly moderates the negative effect of air pollution on adult mortality, whereby a one-unit enhancement in governance quality index reduces mortality among the adults in the developing countries by 0.01%. On the other hand, CO2 emission also appears to be positive, but not statistically significant. The results suggest that governance and public health interplay in the sense of a transition towards economic development for improved living and health states can be achievable with improved governance quality.
    Matched MeSH terms: Air Pollution
  14. Fulltext Air quality changes during the COVID-19 lockdown over the Yangtze River Delta Region: An insight into the impact of human activity pattern changes on air pollution variation
    Li L, Li Q, Huang L, Wang Q, Zhu A, Xu J, et al.
    Sci Total Environ, 2020 Aug 25;732:139282.
    PMID: 32413621 DOI: 10.1016/j.scitotenv.2020.139282
    The outbreak of COVID-19 has spreaded rapidly across the world. To control the rapid dispersion of the virus, China has imposed national lockdown policies to practise social distancing. This has led to reduced human activities and hence primary air pollutant emissions, which caused improvement of air quality as a side-product. To investigate the air quality changes during the COVID-19 lockdown over the YRD Region, we apply the WRF-CAMx modelling system together with monitoring data to investigate the impact of human activity pattern changes on air quality. Results show that human activities were lowered significantly during the period: industrial operations, VKT, constructions in operation, etc. were significantly reduced, leading to lowered SO2, NOx, PM2.5 and VOCs emissions by approximately 16-26%, 29-47%, 27-46% and 37-57% during the Level I and Level II response periods respectively. These emission reduction has played a significant role in the improvement of air quality. Concentrations of PM2.5, NO2 and SO2 decreased by 31.8%, 45.1% and 20.4% during the Level I period; and 33.2%, 27.2% and 7.6% during the Level II period compared with 2019. However, ozone did not show any reduction and increased greatly. Our results also show that even during the lockdown, with primary emissions reduction of 15%-61%, the daily average PM2.5 concentrations range between 15 and 79 μg m-3, which shows that background and residual pollutions are still high. Source apportionment results indicate that the residual pollution of PM2.5 comes from industry (32.2-61.1%), mobile (3.9-8.1%), dust (2.6-7.7%), residential sources (2.1-28.5%) in YRD and 14.0-28.6% contribution from long-range transport coming from northern China. This indicates that in spite of the extreme reductions in primary emissions, it cannot fully tackle the current air pollution. Re-organisation of the energy and industrial strategy together with trans-regional joint-control for a full long-term air pollution plan need to be further taken into account.
    Matched MeSH terms: Air Pollution*
  15. Fulltext Air quality status during 2020 Malaysia Movement Control Order (MCO) due to 2019 novel coronavirus (2019-nCoV) pandemic
    Abdullah S, Mansor AA, Napi NNLM, Mansor WNW, Ahmed AN, Ismail M, et al.
    Sci Total Environ, 2020 Aug 10;729:139022.
    PMID: 32353722 DOI: 10.1016/j.scitotenv.2020.139022
    An outbreak of respiratory illness which is proven to be infected by a 2019 novel coronavirus (2019-nCoV) officially named as Coronavirus Disease 2019 (COVID-19) was first detected in Wuhan, China and has spread rapidly in other parts of China as well as other countries around the world, including Malaysia. The first case in Malaysia was identified on 25 January 2020 and the number of cases continue to rise since March 2020. Therefore, 2020 Malaysia Movement Control Order (MCO) was implemented with the aim to isolate the source of the COVID-19 outbreak. As a result, there were fewer number of motor vehicles on the road and the operation of industries was suspended, ergo reducing emissions of hazardous air pollutants in the atmosphere. We had acquired the Air Pollutant Index (API) data from the Department of Environment Malaysia on hourly basis before and during the MCO with the aim to track the changes of fine particulate matter (PM2.5) at 68 air quality monitoring stations. It was found that the PM2.5 concentrations showed a high reduction of up to 58.4% during the MCO. Several red zone areas (>41 confirmed COVID-19 cases) had also reduced of up to 28.3% in the PM2.5 concentrations variation. The reduction did not solely depend on MCO, thus the researchers suggest a further study considering the influencing factors that need to be adhered to in the future.
    Matched MeSH terms: Air Pollution*
  16. Airborne particles in the city center of Kuala Lumpur: Origin, potential driving factors, and deposition flux in human respiratory airways
    Khan MF, Hamid AH, Bari MA, Tajudin ABA, Latif MT, Nadzir MSM, et al.
    Sci Total Environ, 2019 Feb 10;650(Pt 1):1195-1206.
    PMID: 30308807 DOI: 10.1016/j.scitotenv.2018.09.072
    Equatorial warming conditions in urban areas can influence the particle number concentrations (PNCs), but studies assessing such factors are limited. The aim of this study was to evaluate the level of size-resolved PNCs, their potential deposition rate in the human respiratory system, and probable local and transboundary inputs of PNCs in Kuala Lumpur. Particle size distributions of a 0.34 to 9.02 μm optical-equivalent size range were monitored at a frequency of 60 s between December 2016 and January 2017 using an optical-based compact scanning mobility particle sizer (SMPS). Diurnal and correlation analysis showed that traffic emissions and meteorological confounding factors were potential driving factors for changes in the PNCs (Dp ≤1 μm) at the modeling site. Trajectory modeling showed that a PNC <100/cm3 was influenced mainly by Indo-China region air masses. On the other hand, a PNC >100/cm3 was influenced by air masses originating from the Indian Ocean and Indochina regions. Receptor models extracted five potential sources of PNCs: industrial emissions, transportation, aged traffic emissions, miscellaneous sources, and a source of secondary origin coupled with meteorological factors. A respiratory deposition model for male and female receptors predicted that the deposition flux of PM1 (particle mass ≤1 μm) into the alveolar (AL) region was higher (0.30 and 0.25 μg/h, respectively) than the upper airway (UA) (0.29 and 0.24 μg/h, respectively) and tracheobronchial (TB) regions (0.02 μg/h for each). However, the PM2.5 deposition flux was higher in the UA (2.02 and 1.68 μg/h, respectively) than in the TB (0.18 and 0.15 μg/h, respectively) and the AL regions (1.09 and 0.91 μg/h, respectively); a similar pattern was also observed for PM10.
    Matched MeSH terms: Air Pollution/statistics & numerical data*
  17. Analysis of Southeast Asian pollution episode during June 2013 using satellite remote sensing datasets
    Vadrevu KP, Lasko K, Giglio L, Justice C
    Environ Pollut, 2014 Dec;195:245-56.
    PMID: 25087199 DOI: 10.1016/j.envpol.2014.06.017
    In this study, we assess the intense pollution episode of June 2013, in Riau province, Indonesia from land clearing. We relied on satellite retrievals of aerosols and Carbon monoxide (CO) due to lack of ground measurements. We used both the yearly and daily data for aerosol optical depth (AOD), fine mode fraction (FMF), aerosol absorption optical depth (AAOD) and UV aerosol index (UVAI) for characterizing variations. We found significant enhancement in aerosols and CO during the pollution episode. Compared to mean (2008-2012) June AOD of 0.40, FMF-0.39, AAOD-0.45, UVAI-1.77 and CO of 200 ppbv, June 2013 values reached 0.8, 0.573, 0.672, 1.77 and 978 ppbv respectively. Correlations of fire counts with AAOD and UVAI were stronger compared to AOD and FMF. Results from a trajectory model suggested transport of air masses from Indonesia towards Malaysia, Singapore and southern Thailand. Our results highlight satellite-based mapping and monitoring of pollution episodes in Southeast Asia.
    Matched MeSH terms: Air Pollution/analysis; Air Pollution/statistics & numerical data*
  18. Fulltext Analysis of indoor air pollutants checklist using environmetric technique for health risk assessment of sick building complaint in nonindustrial workplace
    Syazwan A, Rafee BM, Juahir H, Azman A, Nizar A, Izwyn Z, et al.
    Drug Healthc Patient Saf, 2012;4:107-26.
    PMID: 23055779 DOI: 10.2147/DHPS.S33400
    To analyze and characterize a multidisciplinary, integrated indoor air quality checklist for evaluating the health risk of building occupants in a nonindustrial workplace setting.
    Matched MeSH terms: Air Pollution, Indoor
  19. Fulltext Annual Southeast Asia haze increases respiratory admissions: A 2-year large single institution experience
    Ming CR, Ban Yu-Lin A, Abdul Hamid MF, Latif MT, Mohammad N, Hassan T
    Respirology, 2018 10;23(10):914-920.
    PMID: 29923364 DOI: 10.1111/resp.13325
    BACKGROUND AND OBJECTIVE: The Southeast Asia (SEA) haze is an annual problem and at its worst could produce respirable particles of concentrations up to 500 μg/m3 which is five times the level considered as 'unhealthy'. However, there are limited reports examining the direct clinical impact of the annual haze. This study examines the effects of the SEA haze on respiratory admissions.

    METHODS: Data from all respiratory admissions in Universiti Kebangsaan Malaysia Medical Centre (UKMMC) from 1st January 2014 to 31st December 2015 were collected retrospectively from chart and electronic database. A total of 16 weeks of haze period had been formally dated by the Department of Environment using the definition of weather phenomenon leading to atmospheric visibility of less than 10 km. Multivariable regression analyses were performed to estimate rate ratios and 95% CI.

    RESULTS: There were 1968 subjects admitted for respiratory admissions in UKMMC during the study period. Incidence rates per week were significantly different between the two groups with 27.6 ± 9.2 cases per week during the haze versus 15.7 ± 6.7 cases per week during the non-haze period (P < 0.01). A total of 4% versus 2% was admitted to the intensive care unit in the haze and the non-haze groups, respectively (P = 0.02). The mean ± SD lengths of stay was 12.1 ± 5.2 days; the haze group had a longer stay (18.2 ± 9.7 days) compared to the non-haze groups (9.7 ± 3.9) (P < 0.001).

    CONCLUSION: The annual SEA haze is associated with increased respiratory admissions.

    Matched MeSH terms: Air Pollution*
  20. Anthropogenic emissions of atomic chlorine precursors in the Yangtze River Delta region, China
    Yi X, Yin S, Huang L, Li H, Wang Y, Wang Q, et al.
    Sci Total Environ, 2021 Jun 01;771:144644.
    PMID: 33736175 DOI: 10.1016/j.scitotenv.2020.144644
    Chlorine radical plays an important role in the formation of ozone and secondary aerosols in the troposphere. It is hence important to develop comprehensive emissions inventory of chlorine precursors in order to enhance our understanding of the role of chlorine chemistry in ozone and secondary pollution issues. Based on a bottom-up methodology, this study presents a comprehensive emission inventory for major atomic chlorine precursors in the Yangtze River Delta (YRD) region of China for the year 2017. Four primary chlorine precursors are considered in this study: hydrogen chloride (HCl), fine particulate chloride (Cl-) (Cl- in PM2.5), chlorine gas (Cl2), and hypochlorous acid (HClO) with emissions estimated for twelve source categories. The total emissions of these four species in the YRD region are estimated to be 20,424 t, 15,719 t, 1556 and 9331 t, respectively. The emissions of HCl are substantial, with major emissions from biomass burning and coal combustion, together accounting for 68% of the total HCl emissions. Fine particulate Cl- is mainly emitted from industrial processing, biomass burning and waste incineration. The emissions of Cl2 and HClO are mainly associated with usage of chlorine-containing disinfectants, for example, water treatment, wastewater treatment, and swimming pools. Emissions of each chlorine precursor are spatially allocated based on the characteristics of individual source category. This study provides important basic dataset for further studies with respect to the effects of chlorine chemistry on the formation of air pollution complex in the YRD region.
    Matched MeSH terms: Air Pollution
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