Displaying publications 1 - 20 of 90 in total

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  1. 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*
  2. Classification of air quality using fuzzy synthetic multiplication
    Abdullah L, Khalid ND
    Environ Monit Assess, 2012 Nov;184(11):6957-65.
    PMID: 22160435 DOI: 10.1007/s10661-011-2472-1
    Proper identification of environment's air quality based on limited observations is an essential task to meet the goals of environmental management. Various classification methods have been used to estimate the change of air quality status and health. However, discrepancies frequently arise from the lack of clear distinction between each air quality, the uncertainty in the quality criteria employed and the vagueness or fuzziness embedded in the decision-making output values. Owing to inherent imprecision, difficulties always exist in some conventional methodologies when describing integrated air quality conditions with respect to various pollutants. Therefore, this paper presents two fuzzy multiplication synthetic techniques to establish classification of air quality. The fuzzy multiplication technique empowers the max-min operations in "or" and "and" in executing the fuzzy arithmetic operations. Based on a set of air pollutants data carbon monoxide, sulfur dioxide, nitrogen dioxide, ozone, and particulate matter (PM(10)) collected from a network of 51 stations in Klang Valley, East Malaysia, Sabah, and Sarawak were utilized in this evaluation. The two fuzzy multiplication techniques consistently classified Malaysia's air quality as "good." The findings indicated that the techniques may have successfully harmonized inherent discrepancies and interpret complex conditions. It was demonstrated that fuzzy synthetic multiplication techniques are quite appropriate techniques for air quality management.
    Matched MeSH terms: Particulate Matter/analysis
  3. Associations between air pollutants and peak expiratory flow and fractional exhaled nitric oxide in students
    Adman MA, Hashim JH, Manaf MRA, Norback D
    Int J Tuberc Lung Dis, 2020 02 01;24(2):189-195.
    PMID: 32127103 DOI: 10.5588/ijtld.19.0096
    BACKGROUND: Studies on the effects of outdoor air pollution on the respiratory health of students in tropical countries such as Malaysia are limited.OBJECTIVE: To assess associations between outdoor air pollutants and peak expiratory flow (PEF) and fractional exhaled nitric oxide (FeNO).METHOD: PEF and FeNO levels of 487 students recruited in Melaka and Putrajaya, Malaysia, were measured in April and June 2014. Multiple linear regression with mutual adjustment was used to analyse the associations between exposure to air pollution and health.RESULTS: PEF was significantly associated with ozone for 1-day exposure (β = -13.3 l/min, 95% CI -22.7 to -3.8), carbon monoxide for 2-day exposure (β = -57.2 l/min, 95% CI -90.7 to -23.7) and particulate matter ≦10 μm in diameter for 3-day exposure (β = -6.0 l/min, 95% CI -9.2 to -2.8) and 7-day exposure (β = -8.6 l/min, 95% CI -13.0 to -4.1). Stratified analysis showed that associations between PEF and outdoor air pollutant exposures were similar in students with and without elevated FeNO levels.CONCLUSION: Outdoor air pollution in Malaysia may cause airway obstruction unrelated to eosinophilic airway inflammation among students as measured using FeNO.
    Matched MeSH terms: Particulate Matter/analysis
  4. 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
  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. 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
  7. 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
  8. Relationship between vehicle count and particulate air pollution in Amman, Jordan
    Alnawaiseh NA, Hashim JH, Isa ZM
    Asia Pac J Public Health, 2015 Mar;27(2):NP1742-51.
    PMID: 22899706 DOI: 10.1177/1010539512455046
    The main objective of this cross-sectional comparative study is to observe the relationship between traffic-related air pollutants, particularly particulate matter (PM) of total suspended particulate (TSP) and PM of size 10 µm (PM10), and vehicle traffic in Amman, Jordan. Two study areas were chosen randomly as a high-polluted area (HPA) and low-polluted area (LPA). The findings indicate that TSP and PM10 were still significantly correlated with traffic count even after controlling for confounding factors (temperature, relative humidity, and wind speed): TSP, r = 0.726, P < .001; PM10, r = 0.719, P < .001). There was a significant positive relationship between traffic count and PM level: TSP, P < .001; PM10, P < .001. Moreover, there was a significant negative relationship between temperature and PM10 level (P = .018). Traffic volume contributed greatly to high concentrations of TSP and PM10 in areas with high traffic count, in addition to the effect of temperature.
    Matched MeSH terms: Particulate Matter/analysis*
  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. Fulltext Chronic airflow obstruction and ambient particulate air pollution
    Amaral AFS, Burney PGJ, Patel J, Minelli C, Mejza F, Mannino DM, et al.
    Thorax, 2021 12;76(12):1236-1241.
    PMID: 33975927 DOI: 10.1136/thoraxjnl-2020-216223
    Smoking is the most well-established cause of chronic airflow obstruction (CAO) but particulate air pollution and poverty have also been implicated. We regressed sex-specific prevalence of CAO from 41 Burden of Obstructive Lung Disease study sites against smoking prevalence from the same study, the gross national income per capita and the local annual mean level of ambient particulate matter (PM2.5) using negative binomial regression. The prevalence of CAO was not independently associated with PM2.5 but was strongly associated with smoking and was also associated with poverty. Strengthening tobacco control and improved understanding of the link between CAO and poverty should be prioritised.
    Matched MeSH terms: Particulate Matter/analysis
  11. Fulltext Effect of large-scale social restriction (PSBB) during COVID-19 on outdoor air quality: Evidence from five cities in DKI Jakarta Province, Indonesia
    Anugerah AR, Muttaqin PS, Purnama DA
    Environ Res, 2021 06;197:111164.
    PMID: 33872645 DOI: 10.1016/j.envres.2021.111164
    The variation in the concentration of outdoor air pollutants during the COVID-19 lockdown was studied in Jakarta, Indonesia. The term lockdown was replaced by large-scale social restrictions (PSBB) in Indonesia by more flexible regulations to save the economy. Data on five air pollutants, namely, PM10, SO2, CO, O3, and NO2, from five monitoring stations located in five regions in Jakarta (West, East, Central, North, and South Jakarta) were utilized. We analyzed the changes in the concentrations of outdoor air pollutants before lockdown from January 1 to April 9, 2020, and during lockdown from April 10 to June 4, 2020. Overall, the CO concentration (39.9%) demonstrated the most significant reduction during lockdown, followed by NO2 (7.5%) and then SO2 (5.7%). However, we unexpectedly found that during lockdown, the PM10 concentration in Jakarta increased by 10.9% due to the southwest monsoon during the seasonal change in Jakarta. Among the five cities in Jakarta, East and Central Jakarta experienced the maximum improvement in their air quality, whereas North Jakarta had the least air quality improvement. To the best of our knowledge, this research is the first to study the effect of lockdown on outdoor air quality improvement in Indonesia using ground-level measurement data. The findings of the study provide additional strategies to the regulatory bodies for the reduction of temporal air pollutants in Jakarta, Indonesia, by restricting people mobility as a supplementary initiative.
    Matched MeSH terms: Particulate Matter/analysis
  12. Fulltext Long-term exposure to outdoor and household air pollution and blood pressure in the Prospective Urban and Rural Epidemiological (PURE) study
    Arku RE, Brauer M, Ahmed SH, AlHabib KF, Avezum Á, Bo J, et al.
    Environ Pollut, 2020 Jul;262:114197.
    PMID: 32146361 DOI: 10.1016/j.envpol.2020.114197
    Exposure to air pollution has been linked to elevated blood pressure (BP) and hypertension, but most research has focused on short-term (hours, days, or months) exposures at relatively low concentrations. We examined the associations between long-term (3-year average) concentrations of outdoor PM2.5 and household air pollution (HAP) from cooking with solid fuels with BP and hypertension in the Prospective Urban and Rural Epidemiology (PURE) study. Outdoor PM2.5 exposures were estimated at year of enrollment for 137,809 adults aged 35-70 years from 640 urban and rural communities in 21 countries using satellite and ground-based methods. Primary use of solid fuel for cooking was used as an indicator of HAP exposure, with analyses restricted to rural participants (n = 43,313) in 27 study centers in 10 countries. BP was measured following a standardized procedure and associations with air pollution examined with mixed-effect regression models, after adjustment for a comprehensive set of potential confounding factors. Baseline outdoor PM2.5 exposure ranged from 3 to 97 μg/m3 across study communities and was associated with an increased odds ratio (OR) of 1.04 (95% CI: 1.01, 1.07) for hypertension, per 10 μg/m3 increase in concentration. This association demonstrated non-linearity and was strongest for the fourth (PM2.5 > 62 μg/m3) compared to the first (PM2.5 
    Matched MeSH terms: Particulate Matter/analysis
  13. 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
  14. Fulltext Determination of lead, cations, and anions concentration in indoor and outdoor air at the primary schools in Kuala Lumpur
    Awang N, Jamaluddin F
    J Environ Public Health, 2014;2014:408275.
    PMID: 25136371 DOI: 10.1155/2014/408275
    This study was carried out to determine the concentration of lead (Pb), anions, and cations at six primary schools located around Kuala Lumpur. Low volume sampler (MiniVol PM10) was used to collect the suspended particulates in indoor and outdoor air. Results showed that the concentration of Pb in indoor air was in the range of 5.18 ± 1.08 μg/g-7.01 ± 0.08 μg/g. All the concentrations of Pb in indoor air were higher than in outdoor air at all sampling stations. The concentrations of cations and anions were higher in outdoor air than in indoor air. The concentration of Ca(2+) (39.51 ± 5.01 mg/g-65.13 ± 9.42 mg/g) was the highest because the cation existed naturally in soil dusts, while the concentrations of NO3 (-) and SO4 (2-) were higher in outdoor air because there were more sources of exposure for anions in outdoor air, such as highly congested traffic and motor vehicles emissions. In comparison, the concentration of NO3 (-) (29.72 ± 0.31 μg/g-32.00 ± 0.75 μg/g) was slightly higher than SO4 (2-). The concentrations of most of the parameters in this study, such as Mg(2+), Ca(2+), NO3 (-), SO4 (2-), and Pb(2+), were higher in outdoor air than in indoor air at all sampling stations.
    Matched MeSH terms: Particulate Matter/analysis
  15. 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
  16. Fulltext Assessment and valuation of health impacts of fine particulate matter during COVID-19 lockdown: a comprehensive study of tropical and sub tropical countries
    Bherwani H, Kumar S, Musugu K, Nair M, Gautam S, Gupta A, et al.
    Environ Sci Pollut Res Int, 2021 Aug;28(32):44522-44537.
    PMID: 33852112 DOI: 10.1007/s11356-021-13813-w
    A novel coronavirus disease (COVID-19) continues to challenge the whole world. The disease has claimed many fatalities as it has transcended from one country to another since it was first discovered in China in late 2019. To prevent further morbidity and mortality associated with COVID-19, most of the countries initiated a countrywide lockdown. While physical distancing and lockdowns helped in curbing the spread of this novel coronavirus, it led to massive economic losses for the nations. Positive impacts have been observed due to lockdown in terms of improved air quality of the nations. In the current research, ten tropical and subtropical countries have been analysed from multiple angles, including air pollution, assessment and valuation of health impacts and economic loss of countries during COVID-19 lockdown. Countries include Brazil, India, Iran, Kenya, Malaysia, Mexico, Pakistan, Peru, Sri Lanka, and Thailand. Validated Simplified Aerosol Retrieval Algorithm (SARA) binning model is used on data collated from moderate resolution imaging spectroradiometer (MODIS) for particulate matters with a diameter of less than 2.5 μm (PM2.5) for all the countries for the month of January to May 2019 and 2020. The concentration results of PM2.5 show that air pollution has drastically reduced in 2020 post lockdown for all countries. The highest average concentration obtained by converting aerosol optical depth (AOD) for 2020 is observed for Thailand as 121.9 μg/m3 and the lowest for Mexico as 36.27 μg/m3. As air pollution is found to decrease in the April and May months of 2020 for nearly all countries, they are compared with respective previous year values for the same duration to calculate the reduced health burden due to lockdown. The present study estimates that cumulative about 100.9 Billion US$ are saved due to reduced air pollution externalities, which are about 25% of the cumulative economic loss of 435.9 Billion US$.
    Matched MeSH terms: Particulate Matter/analysis
  17. Recent progress in research on PM2.5 in subways
    Chang L, Chong WT, Wang X, Pei F, Zhang X, Wang T, et al.
    Environ Sci Process Impacts, 2021 May 26;23(5):642-663.
    PMID: 33889885 DOI: 10.1039/d1em00002k
    Nowadays, PM2.5 concentrations greatly influence indoor air quality in subways and threaten passenger and staff health because PM2.5 not only contains heavy metal elements, but can also carry toxic and harmful substances due to its small size and large specific surface area. Exploring the physicochemical and distribution characteristics of PM2.5 in subways is necessary to limit its concentration and remove it. At present, there are numerous studies on PM2.5 in subways around the world, yet, there is no comprehensive and well-organized review available on this topic. This paper reviews the nearly twenty years of research and over 130 published studies on PM2.5 in subway stations, including aspects such as concentration levels and their influencing factors, physicochemical properties, sources, impacts on health, and mitigation measures. Although many determinants of station PM2.5 concentration have been reported in current studies, e.g., the season, outdoor environment, and station depth, their relative influence is uncertain. The sources of subway PM2.5 include those from the exterior (e.g., road traffic and fuel oil) and the interior (e.g., steel wheels and rails and metallic brake pads), but the proportion of these sources is also unknown. Control strategies of PM mainly include adequate ventilation and filtration, but these measures are often inefficient in removing PM2.5. The impacts of PM2.5 from subways on human health are still poorly understood. Further research should focus on long-term data collection, influencing factors, the mechanism of health impacts, and PM2.5 standards or regulations.
    Matched MeSH terms: Particulate Matter/analysis
  18. 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
  19. Fulltext Public awareness and support for environmental protection-A focus on air pollution in peninsular Malaysia
    Chin YSJ, De Pretto L, Thuppil V, Ashfold MJ
    PLoS One, 2019;14(3):e0212206.
    PMID: 30870439 DOI: 10.1371/journal.pone.0212206
    As in many nations, air pollution linked to rapid industrialization is a public health and environmental concern in Malaysia, especially in cities. Understanding awareness of air pollution and support for environmental protection from the general public is essential for informing governmental approaches to dealing with this problem. This study presents a cross-sectional survey conducted in the Klang Valley and Iskandar conurbations to examine urban Malaysians' perception, awareness and opinions of air pollution. The survey was conducted in two languages, English and Malay, and administered through the online survey research software, Qualtrics. The survey consisted of three sections, where we collected sociodemographic information, information on the public perception of air quality and the causes of air pollution, information on public awareness of air pollution and its related impacts, and information on attitudes towards environmental protection. Of 214 respondents, over 60% were positive towards the air quality at both study sites despite the presence of harmful levels of air pollution. The air in the Klang Valley was perceived to be slightly more polluted and causing greater health issues. Overall, the majority of respondents were aware that motor vehicles represent the primary pollution source, yet private transport was still the preferred choice of transportation mode. A generally positive approach towards environmental protection emerged from the data. However, participants showed stronger agreement with protection actions that do not involve individual effort. Nonetheless, we found that certain segments of the sample (people owning more than three vehicles per household and those with relatives who suffered from respiratory diseases) were significantly more willing to personally pay for environmental protection compared to others. Implications point to the need for actions for spreading awareness of air pollution to the overall population, especially with regards to its health risks, as well as strategies for increasing the perception of behavioural control, especially with regards to motor vehicles' usage.
    Matched MeSH terms: Particulate Matter/analysis
  20. 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
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