Displaying publications 1 - 20 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. 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
  3. 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
  4. Fulltext A review study on Diesel and Natural Gas and its impact on CI Engine Emissions
    Hayder A. Alrazen, Ahmad, K.A.
    Pertanika Journal of Science & Technology, 2017;25(3):983-994.
    MyJurnal
    Diesel engines produce high emissions of nitrogen oxide, smoke and particulate matter. The challenge is to reduce exhaust emissions but without making changing their mechanical configuration. This paper is an overview of the effect of natural gas on the diesel engine emissions. Literature review suggests that engine load, air-fuel ratio, and engine speed play a key role in reducing the pollutants in the diesel engine emissions with natural gas enrichment. It is found that increasing the percentage of natural gas (CNG) will affect emissions. Nitrogen oxide (NOx) is decreased and increased at part loads and high loads respectively when adding CNG. The reduction in carbon dioxide (CO2), particulate matter (PM) and smoke are observed when adding CNG. However, carbon monoxide (CO) and unburned hydrocarbon (HC) are increased when CNG is added.
    Matched MeSH terms: Particulate Matter
  5. Fulltext A two-year assessment of particulate air pollution and sources in Kuwait
    Alahmad B, Al-Hemoud A, Kang CM, Almarri F, Kommula V, Wolfson JM, et al.
    Environ Pollut, 2021 Aug 01;282:117016.
    PMID: 33848912 DOI: 10.1016/j.envpol.2021.117016
    BACKGROUND: Kuwait and the Gulf region have a desert, hyper-arid and hot climate that makes outdoor air sampling challenging. The region is also affected by intense dust storms. Monitoring challenges from the harsh climate have limited data needed to inform appropriate regulatory actions to address air pollution in the region.

    OBJECTIVES: To compare gravimetric measurements with existing networks that rely on beta-attenuation measurements in a desert climate; determine the annual levels of PM2.5 and PM10 over a two-year period in Kuwait; assess compliance with air quality standards; and identify and quantify PM2.5 sources.

    METHODS: We custom-designed particle samplers that can withstand large quantities of dust without their inlet becoming overloaded. The samplers were placed in two populated residential locations, one in Kuwait City and another near industrial and petrochemical facilities in Ali Sabah Al-Salem (ASAS) to collect PM2.5 and PM10 samples for mass and elemental analysis. We used positive matrix factorization to identify PM2.5 sources and apportion their contributions.

    RESULTS: We collected 2339 samples during the period October 2017 through October 2019. The beta-attenuation method in measuring PM2.5 consistently exceeded gravimetric measurements, especially during dust events. The annual levels for PM2.5 in Kuwait City and ASAS were 41.6 ± 29.0 and 47.5 ± 27.6 μg/m3, respectively. Annual PM2.5 levels in Kuwait were nearly four times higher than the U.S. National Ambient Air Quality Standard. Regional pollution was a major contributor to PM2.5 levels in both locations accounting for 44% in Kuwait City and 46% in ASAS. Dust storms and re-suspended road dust were the second and third largest contributors to PM2.5, respectively.

    CONCLUSIONS: The premise that frequent and extreme dust storms make air quality regulation futile is dubious. In this comprehensive particulate pollution analysis, we show that the sizeable regional anthropogenic particulate sources warrant national and regional mitigation strategies to ensure compliance with air quality standards.

    Matched MeSH terms: Particulate Matter/analysis
  6. Access to clean technologies, energy, finance, and food: environmental sustainability agenda and its implications on Sub-Saharan African countries
    Hishan SS, Sasmoko, Khan A, Ahmad J, Hassan ZB, Zaman K, et al.
    Environ Sci Pollut Res Int, 2019 Jun;26(16):16503-16518.
    PMID: 30980369 DOI: 10.1007/s11356-019-05056-7
    The Sub-Saharan Africa (SSA) is far lag behind the sustainable targets that set out in the United Nation's Sustainable Development Goals (SDGs), which is highly needed to embark the priorities by their member countries to devise sustainable policies for accessing clean technologies, energy demand, finance, and food production to mitigate high-mass carbon emissions and conserve environmental agenda in the national policy agenda. The study evaluated United Nation's SDGs for environmental conservation and emission reduction in the panel of 35 selected SSA countries, during a period of 1995-2016. The study further analyzed the variable's relationship in inter-temporal forecasting framework for the next 10 years' time period, i.e., 2017-2026. The parameter estimates for the two models, i.e., CO2 model and PM2.5 models are analyzed by Generalized Method of Moment (GMM) estimator that handle possible endogeneity issue from the given models. The results rejected the inverted U-shaped Environmental Kuznets Curve (EKC) for CO2 emissions, while it supported for PM2.5 emissions with a turning point of US$5540 GDP per capita in constant 2010 US$. The results supported the "pollution haven hypothesis" for CO2 emissions, while this hypothesis is not verified for PM2.5 emissions. The major detrimental factors are technologies, FDI inflows, and food deficit that largely increase carbon emissions in a panel of SSA countries. The IPAT hypothesis is not verified in both the emissions; however, population density will largely influenced CO2 emissions in the next 10 years' time period. The PM2.5 emissions will largely be influenced by high per capita income, followed by trade openness, and technologies, over a time horizon. Thus, the United Nation's sustainable development agenda is highly influenced by socio-economic and environmental factors that need sound action plans by their member countries to coordinate and collaborate with each other and work for Africa's green growth agenda.
    Matched MeSH terms: Particulate Matter/analysis
  7. 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
  8. 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: Particulate Matter
  9. Air pollution monitoring and tree and forest decline in East Asia: A review
    Takahashi M, Feng Z, Mikhailova TA, Kalugina OV, Shergina OV, Afanasieva LV, et al.
    Sci Total Environ, 2020 Nov 10;742:140288.
    PMID: 32721711 DOI: 10.1016/j.scitotenv.2020.140288
    Air pollution and atmospheric deposition have adverse effects on tree and forest health. We reviewed studies on tree and forest decline in Northeast and Southeast Asia, Siberia, and the Russian Far East (hereafter referred to as East Asia). This included studies published in domestic journals and languages. We identified information about the locations, causes, periods, and tree species exhibiting decline. Past air pollution was also reviewed. Most East Asian countries show declining trends in SO2 concentration in recent years, although Mongolia and Russia show increasing trends. Ozone (O3) concentrations are stable or gradually increasing in the East Asia region, with high maxima. Wet nitrogen (N) deposition was high in China and tropical countries, but low in Russia. The decline of trees and forests primarily occurred in the mid-latitudes of Japan, Korea, China, and Russia. Long-term large N deposition resulted in the N saturation phenomenon in Japan and China, but no clear forest health response was observed. Thereafter, forest decline symptoms, suspected to be caused by O3, were observed in Japan and China. In East Russia, tree decline occurred around industrial centers in Siberia. Haze events have been increasing in tropical and boreal forests, and particulate matter inhibits photosynthesis. In recent years, chronically high O3 concentrations, in conjunction with climate change, are likely have adverse effects on tree physiology. The effects of air pollution and related factors on tree decline are summarized. Recently, the effects of air pollution on tree decline have not been apparent under the changing climate, however, monitoring air pollution is indispensable for identifying the cause of tree decline. Further economic growth is projected in Southeast Asia and therefore, the monitoring network should be expanded to tropical and boreal forest zones. Countermeasures such as restoring urban trees and rural forests are important for ensuring future ecosystem services.
    Matched MeSH terms: Particulate Matter
  10. Air pollution, governance quality, and health outcomes: evidence from developing countries
    Ahmad NA, Ismail NW, Sidique SFA, Mazlan NS
    Environ Sci Pollut Res Int, 2023 Mar;30(14):41060-41072.
    PMID: 36630041 DOI: 10.1007/s11356-023-25183-6
    While studies have demonstrated that air pollution can be catastrophic to the population's health, few empirical studies are found in the economic literature because a considerable proportion of the evidence comes from epidemiological studies. Because of the crucial role of governance in the health community, good governance has been a contentious issue in public sector management in recent years. Therefore, the aim of this study is to examine the effects of air pollution and the role of governance on health outcomes. This study employed the generalized method of moment (GMM) estimation techniques to analyse panel data for 72 developing countries from 2010 to 2017. The empirical results confirm that higher PM2.5 and CO2 levels have a detrimental influence on life expectancy and healthy life expectancy, whereas the role of governance has a positive impact on life expectancy and healthy life expectancy. Furthermore, the findings show governance quality plays a role in moderating the negative effect of PM2.5 on health outcomes. The ongoing rise in air pollution has had a significant impact on the health of developing countries. It appears that governance quality has improved health outcomes. The findings have important policy implications, such that strengthening governance can reduce air pollution emissions in developing countries. However, to reduce the health effects of air pollution, developing countries must implement effective environmental development policies and track the implementation and enforcement of such policies.
    Matched MeSH terms: Particulate Matter/analysis
  11. 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: Particulate Matter
  12. 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
  13. 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: Particulate Matter/analysis*
  14. Fulltext Algae-Derived Anti-Inflammatory Compounds against Particulate Matters-Induced Respiratory Diseases: A Systematic Review
    Tan PX, Thiyagarasaiyar K, Tan CY, Jeon YJ, Nadzir MSM, Wu YJ, et al.
    Mar Drugs, 2021 May 30;19(6).
    PMID: 34070821 DOI: 10.3390/md19060317
    Air pollution has recently become a subject of increasing concern in many parts of the world. The World Health Organization (WHO) estimated that nearly 4.2 million early deaths are due to exposure to fine particles in polluted air, which causes multiple respiratory diseases. Algae, as a natural product, can be an alternative treatment due to potential biofunctional properties and advantages. This systematic review aims to summarize and evaluate the evidence of metabolites derived from algae as potential anti-inflammatory agents against respiratory disorders induced by atmospheric particulate matter (PM). Databases such as Scopus, Web of Science, and PubMed were systematically searched for relevant published full articles from 2016 to 2020. The main key search terms were limited to "algae", "anti-inflammation", and "air pollutant". The search activity resulted in the retrieval of a total of 36 publications. Nine publications are eligible for inclusion in this systematic review. A total of four brown algae (Ecklonia cava, Ishige okamurae, Sargassum binderi and Sargassum horneri) with phytosterol, polysaccharides and polyphenols were reported in the nine studies. The review sheds light on the pathways of particulate matter travelling into respiratory systems and causing inflammation, and on the mechanisms of actions of algae in inhibiting inflammation. Limitations and future directions are also discussed. More research is needed to investigate the potential of algae as anti-inflammatory agents against PM in in vivo and in vitro experimental models, as well as clinically.
    Matched MeSH terms: Particulate Matter/adverse effects*
  15. An Integrated Source Apportionment Methodology and Its Application over the Yangtze River Delta Region, China
    Li L, An J, Zhou M, Qiao L, Zhu S, Yan R, et al.
    Environ Sci Technol, 2018 Dec 18;52(24):14216-14227.
    PMID: 30288976 DOI: 10.1021/acs.est.8b01211
    An integrated source apportionment methodology is developed by amalgamating the receptor-oriented model (ROM) and source-oriented numerical simulations (SOM) together to eliminate the weaknesses of individual SA methods. This approach attempts to apportion and dissect the PM2.5 sources in the Yangtze River Delta region during winter. First, three ROM models (CMB, PMF, ME2) are applied and compared for the preliminary SA results, with information from PM2.5 sampling and lab analysis during the winter seasons. The detailed source category contribution of SOM to PM2.5 is further simulated using the WRF-CAMx model. The two pieces of information from both ROM and SOM are then stitched together to give a comprehensive information on the PM2.5 sources over the region. With the integrated approach, the detailed contributing sources of the ambient PM2.5 at different receptors including rural and urban, coastal and in-land, northern and southern receptors are analyzed. The results are compared with previous data and shows good agreement. This integrative approach is more comprehensive and is able to produce a more profound and detailed understanding between the sources and receptors, compared with single models.
    Matched MeSH terms: Particulate Matter
  16. An assessment of the spatio-temporal dynamics of Landsat-derived aerosol concentration in relation with land cover and road networks in the Lagos megacity
    Ayodele E, Okolie C, Akinnusi S, Mbu-Ogar E, Alani R, Daramola O, et al.
    Environ Sci Pollut Res Int, 2023 Mar;30(15):43279-43299.
    PMID: 36652079 DOI: 10.1007/s11356-022-25042-w
    The interrelationships between air quality, land cover change, and road networks in the Lagos megacity have not been explored. Globally, there are knowledge gaps in understanding these dynamics, especially using remote sensing data. This study used multi-temporal and multi-spectral Landsat imageries at four epochs (2002, 2013, 2015, and 2020) to evaluate the aerosol optical thickness (AOT) levels in relation to land cover and road networks in the Lagos megacity. A look-up table (LUT) was generated using Py6S, a python-based 6S module, to simulate the AOT using land surface reflectance and top of atmosphere reflectance. A comparative assessment of the method against in situ measurements of particulate matter (PM) at different locations shows a strong positive correlation between the imagery-derived AOT values and the PMs. The AOT concentration across the land cover and road networks showed an increasing trend from 2002 to 2020, which could be explained by urbanization in the megacity. The higher concentration of AOT along the major roads is attributed to the high air pollutants released from vehicles, including home/office generators and industries along the road corridors. The continuous rise in pollutant values requires urgent intervention and mitigation efforts. Remote sensing-based AOT monitoring is a possible solution.
    Matched MeSH terms: Particulate Matter/analysis
  17. 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
  18. 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: Particulate Matter*
  19. Applied chemometric approach in identification sources of air quality pattern in Selangor, Malaysia
    Ang KH
    Sains Malaysiana, 2018;47:471-479.
    In recent years, Malaysia has experienced quite a few number of chronic air pollution problems and it has become a
    major contributor to the deterioration of human health and ecosystems. This study aimed to assess the air quality data
    and identify the pattern of air pollution sources using chemometric analysis through hierarchical cluster analysis (HCA),
    discriminant analysis (DA), principal component analysis (PCA) and multiple linear regression analysis (MLR). The air
    quality data from January 2016 until December 2016 was obtained from the Department of Environment Malaysia. Air
    quality data from eight sampling stations in Selangor include the selected variables of nitrogen dioxide (NO2
    ), ozone (O3
    ),
    sulfur dioxide (SO2
    ), carbon monoxide (CO) and particulate matter (PM10). The HCA resulted in three clusters, namely low
    pollution source (LPS), moderate pollution source (MPS) and slightly high pollution source (SHPS). Meanwhile, DA resulted
    in two and four variables for the forward stepwise mode and the backward stepwise mode, respectively. Through PCA,
    it was identified that the main pollutants of LPS, MPS and SHPS came from industrial and vehicle emissions, agricultural
    systems, residential factors and natural emission sources. Among the three models yielded from the MLR analysis, it was
    found that SHPS is the most suitable model to be used for the prediction of Air Pollution Index. This study concluded that
    a clearer review and practical design of air quality monitoring network would be beneficial for better management of
    air pollution. The study also suggested that chemometric techniques have the ability to show significant information on
    spatial variability for large and complex air quality data.
    Matched MeSH terms: Particulate Matter
  20. Fulltext Assessing temporal correlation in environmental risk factors to design efficient area-specific COVID-19 regulations: Delhi based case study
    Chaudhary V, Bhadola P, Kaushik A, Khalid M, Furukawa H, Khosla A
    Sci Rep, 2022 07 28;12(1):12949.
    PMID: 35902653 DOI: 10.1038/s41598-022-16781-4
    Amid ongoing devastation due to Serve-Acute-Respiratory-Coronavirus2 (SARS-CoV-2), the global spatial and temporal variation in the pandemic spread has strongly anticipated the requirement of designing area-specific preventive strategies based on geographic and meteorological state-of-affairs. Epidemiological and regression models have strongly projected particulate matter (PM) as leading environmental-risk factor for the COVID-19 outbreak. Understanding the role of secondary environmental-factors like ammonia (NH3) and relative humidity (RH), latency of missing data structuring, monotonous correlation remains obstacles to scheme conclusive outcomes. We mapped hotspots of airborne PM2.5, PM10, NH3, and RH concentrations, and COVID-19 cases and mortalities for January, 2021-July,2021 from combined data of 17 ground-monitoring stations across Delhi. Spearmen and Pearson coefficient correlation show strong association (p-value  0.60) and PM10 (r > 0.40), respectively. Interestingly, the COVID-19 spread shows significant dependence on RH (r > 0.5) and NH3 (r = 0.4), anticipating their potential role in SARS-CoV-2 outbreak. We found systematic lockdown as a successful measure in combatting SARS-CoV-2 outbreak. These outcomes strongly demonstrate regional and temporal differences in COVID-19 severity with environmental-risk factors. The study lays the groundwork for designing and implementing regulatory strategies, and proper urban and transportation planning based on area-specific environmental conditions to control future infectious public health emergencies.
    Matched MeSH terms: Particulate Matter/analysis
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