Displaying publications 21 - 40 of 90 in total

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  1. Hybridization of hierarchical clustering with persistent homology in assessing haze episodes between air quality monitoring stations
    Zulkepli NFS, Noorani MSM, Razak FA, Ismail M, Alias MA
    J Environ Manage, 2022 Mar 15;306:114434.
    PMID: 35065362 DOI: 10.1016/j.jenvman.2022.114434
    Haze has been a major issue afflicting Southeast Asian countries, including Malaysia, for the past few decades. Hierarchical agglomerative cluster analysis (HACA) is commonly used to evaluate the spatial behavior between areas in which pollutants interact. Typically, using HACA, the Euclidean distance acts as the dissimilarity measure and air quality monitoring stations are grouped according to this measure, thus revealing the most polluted areas. In this study, a framework for the hybridization of the HACA technique is proposed by considering the topological similarity (Wasserstein distance) between stations to evaluate the spatial patterns of the affected areas by haze episodes. For this, a tool in the topological data analysis (TDA), namely, persistent homology, is used to extract essential topological features hidden in the dataset. The performance of the proposed method is compared with that of traditional HACA and evaluated based on its ability to categorize areas according to the exceedance level of the particulate matter (PM10). Results show that additional topological features have yielded better accuracy compared to without the case that does not consider topological features. The cluster validity indices are computed to verify the results, and the proposed method outperforms the traditional method, suggesting a practical alternative approach for assessing the similarity in air pollution behaviors based on topological characterizations.
    Matched MeSH terms: Particulate Matter/analysis
  2. Seasonal variation and size distribution of inorganic and carbonaceous components, source identification of size-fractioned urban air particles in Kuala Lumpur, Malaysia
    Jamhari AA, Latif MT, Wahab MIA, Hassan H, Othman M, Abd Hamid HH, et al.
    Chemosphere, 2022 Jan;287(Pt 4):132309.
    PMID: 34601373 DOI: 10.1016/j.chemosphere.2021.132309
    This study aims to determine the inorganic and carbonaceous components depending on the seasonal variation and size distribution of urban air particles in Kuala Lumpur. Different fractions of particulate matter (PM) were measured using a Nanosampler from 17 February 2017 until 27 November 2017. The water-soluble inorganic ions (WSIIs) and carbonaceous components in all samples were analysed using ion chromatography and carbon analyser thermal/optical reflectance, respectively. Total PM concentration reached its peak during the southwest (SW) season (70.99 ± 6.04 μg/m3), and the greatest accumulation were observed at PM0.5-1.0 (22%-30%, 9.55 ± 1.03 μg/m3) and PM2.5-10 (22%-25%, 10.34 ± 0.81 μg/m3). SO42-, NO3- and NH4+ were major contributors of WSIIs, and their formation was favoured mainly during SW season (80.5% of total ions). PM0.5-1.0 and PM2.5-10 exhibited the highest percentage of WSII size distribution, accounted for 28.4% and 13.5% of the total mass, respectively. The average contribution of carbonaceous species (OC + EC) to total carbonaceous concentrations were higher in PM0.5-1.0 (35.2%) and PM2.5-10 (26.6%). Ultrafine particles (PM<0.1) consistently indicated that the sources were from vehicle emission while the SW season was constantly dominated by biomass burning sources. Using the positive matrix factorization (PMF) model, secondary inorganic aerosol and biomass burning (30.3%) was known as a significant source of overall PM. As a conclusion, ratio and source apportionment indicate the mixture of biomass burning, secondary inorganic aerosols and motor vehicle contributed to the size-segregated PM and seasonal variation of inorganic and carbonaceous components of urban air particles.
    Matched MeSH terms: Particulate Matter/analysis
  3. 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
  4. Fulltext Towards Integrated Air Pollution Monitoring and Health Impact Assessment Using Federated Learning: A Systematic Review
    Neo EX, Hasikin K, Mokhtar MI, Lai KW, Azizan MM, Razak SA, et al.
    Front Public Health, 2022;10:851553.
    PMID: 35664109 DOI: 10.3389/fpubh.2022.851553
    Environmental issues such as environmental pollutions and climate change are the impacts of globalization and become debatable issues among academics and industry key players. One of the environmental issues which is air pollution has been catching attention among industrialists, researchers, and communities around the world. However, it has always neglected until the impacts on human health become worse, and at times, irreversible. Human exposure to air pollutant such as particulate matters, sulfur dioxide, ozone and carbon monoxide contributed to adverse health hazards which result in respiratory diseases, cardiorespiratory diseases, cancers, and worst, can lead to death. This has led to a spike increase of hospitalization and emergency department visits especially at areas with worse pollution cases that seriously impacting human life and health. To address this alarming issue, a predictive model of air pollution is crucial in assessing the impacts of health due to air pollution. It is also critical in predicting the air quality index when assessing the risk contributed by air pollutant exposure. Hence, this systemic review explores the existing studies on anticipating air quality impact to human health using the advancement of Artificial Intelligence (AI). From the extensive review, we highlighted research gaps in this field that are worth to inquire. Our study proposes to develop an AI-based integrated environmental and health impact assessment system using federated learning. This is specifically aims to identify the association of health impact and pollution based on socio-economic activities and predict the Air Quality Index (AQI) for impact assessment. The output of the system will be utilized for hospitals and healthcare services management and planning. The proposed solution is expected to accommodate the needs of the critical and prioritization of sensitive group of publics during pollution seasons. Our finding will bring positive impacts to the society in terms of improved healthcare services quality, environmental and health sustainability. The findings are beneficial to local authorities either in healthcare or environmental monitoring institutions especially in the developing countries.
    Matched MeSH terms: Particulate Matter/analysis
  5. 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
  6. Integration of complete ensemble empirical mode decomposition with deep long short-term memory model for particulate matter concentration prediction
    Fu M, Le C, Fan T, Prakapovich R, Manko D, Dmytrenko O, et al.
    Environ Sci Pollut Res Int, 2021 Dec;28(45):64818-64829.
    PMID: 34318419 DOI: 10.1007/s11356-021-15574-y
    The atmospheric particulate matter (PM) with a diameter of 2.5 μm or less (PM2.5) is one of the key indicators of air pollutants. Accurate prediction of PM2.5 concentration is very important for air pollution monitoring and public health management. However, the presence of noise in PM2.5 data series is a major challenge of its accurate prediction. A novel hybrid PM2.5 concentration prediction model is proposed in this study by combining complete ensemble empirical mode decomposition (CEEMD) method, Pearson's correlation analysis, and a deep long short-term memory (LSTM) method. CEEMD was employed to decompose historical PM2.5 concentration data to different frequencies in order to enhance the timing characteristics of data. Pearson's correlation was used to screen the different frequency intrinsic-mode functions of decomposed data. Finally, the filtered enhancement data were inputted to a deep LSTM network with multiple hidden layers for training and prediction. The results evidenced the potential of the CEEMD-LSTM hybrid model with a prediction accuracy of approximately 80% and model convergence after 700 training epochs. The secondary screening of Pearson's correlation test improved the model (CEEMD-Pearson) accuracy up to 87% but model convergence after 800 epochs. The hybrid model combining CEEMD-Pearson with the deep LSTM neural network showed a prediction accuracy of nearly 90% and model convergence after 650 interactions. The results provide a clear indication of higher prediction accuracy of PM2.5 with less computation time through hybridization of CEEMD-Pearson with deep LSTM models and its potential to be employed for air pollution monitoring.
    Matched MeSH terms: Particulate Matter/analysis
  7. Fulltext Metagenomic characterization of indoor dust fungal associated with allergy and lung inflammation among school children
    Isa KNM, Jalaludin J, Elias SM, Than LTL, Jabbar MA, Saudi ASM, et al.
    Ecotoxicol Environ Saf, 2021 Sep 15;221:112430.
    PMID: 34147866 DOI: 10.1016/j.ecoenv.2021.112430
    The exposure of school children to indoor air pollutants has increased allergy and respiratory diseases. The objective of this study were to determine the toxicodynamic interaction of indoor pollutants exposure, biological and chemical with expression of adhesion molecules on eosinophil and neutrophil. A self-administered questionnaire, allergy skin test, and fractional exhaled nitric oxide (FeNO) analyser were used to collect information on health status, sensitization to allergens and respiratory inflammation, respectively among school children at age of 14 years. The sputum induced were analysed to determine the expression of CD11b, CD35, CD63 and CD66b on eosinophil and neutrophil by using flow cytometry technique. The particulate matter (PM2.5 and PM10), NO2, CO2, and formaldehyde, temperature, and relative humidity were measured inside the classrooms. The fungal DNA were extracted from settled dust collected from classrooms and evaluated using metagenomic techniques. We applied chemometric and regression in statistical analysis. A total of 1869 unique of operational taxonomic units (OTUs) of fungi were identified with dominated at genus level by Aspergillus (15.8%), Verrucoconiothyrium (5.5%), and Ganoderma (4.6%). Chemometric and regression results revealed that relative abundance of T. asahii were associated with down regulation of CD66b expressed on eosinophil, and elevation of FeNO levels in predicting asthmatic children with model accuracy of 63.6%. Meanwhile, upregulation of CD11b expressed on eosinophil were associated with relative abundance of A. clavatus and regulated by PM2.5. There were significant association of P. bandonii with upregulation of CD63 expressed on neutrophil and exposure to NO2. Our findings indicate that exposure to PM2.5, NO2, T. asahii, P.bandonii and A.clavatus are likely interrelated with upregulation of activation and degranulation markers on both eosinophil and neutrophil.
    Matched MeSH terms: Particulate Matter/analysis
  8. Fulltext On the investigation of COVID-19 lockdown influence on air pollution concentration: regional investigation over eighteen provinces in Iraq
    Hashim BM, Al-Naseri SK, Al Maliki A, Sa'adi Z, Malik A, Yaseen ZM
    Environ Sci Pollut Res Int, 2021 Sep;28(36):50344-50362.
    PMID: 33956319 DOI: 10.1007/s11356-021-13812-x
    At the end of 2019, a novel coronavirus COVID-19 emerged in Wuhan, China, and later spread throughout the world, including Iraq. To control the rapid dispersion of the virus, Iraq, like other countries, has imposed national lockdown measures, such as social distancing, restriction of automobile traffic, and industrial enterprises. This has led to reduced human activities and air pollutant emissions, which caused improvement in air quality. This study focused on the analysis of the impact of the six partial, total, and post-lockdown periods (1st partial lockdown from March 1 to16, 2020, 1st total lockdown from March 17 to April 21, 2nd partial lockdown from April 22 to May 23, 2nd total lockdown from May 24 to June 13, 3rd partial lockdown from June 14 to August 19, and end partial lockdown from August 20 to 31) on the average of daily NO2, O3, PM2.5, and PM10 concentrations, as well as air quality index (AQI) in 18 Iraqi provinces during these periods (from March 1st to August 31st, 2020). The analysis showed a decline in the average of daily PM2.5, PM10, and NO2 concentrations by 24%, 15%, and 8%, respectively from March 17 to April 21, 2020 (first phase of total lockdown) in comparison to the 1st phase of partial lockdown (March 1 to March 16, 2020). Furthermore, the O3 increased by 10% over the same period. The 2nd phase of total lockdown, the 3rd partial lockdown, and the post-lockdown periods witnessed declines in PM2.5 by 8%, 11%, and 21%, respectively, while the PM10 increases over the same period. Iraqi also witnessed improvement in the AQI by 8% during the 1st phase of total lockdown compared to the 1st phase of partial lockdown. The level of air pollutants in Iraq declined significantly during the six lockdown periods as a result of reduced human activities. This study gives confidence that when strict measures are implemented, air quality can improve.
    Matched MeSH terms: Particulate Matter/analysis
  9. Fulltext Modeling the Characteristics of Unhealthy Air Pollution Events: A Copula Approach
    Masseran N
    Int J Environ Res Public Health, 2021 08 19;18(16).
    PMID: 34444503 DOI: 10.3390/ijerph18168751
    This study proposes the concept of duration (D) and severity (S) measures, which were derived from unhealthy air pollution events. In parallel with that, the application of a copula model is proposed to evaluate unhealthy air pollution events with respect to their duration and severity characteristics. The bivariate criteria represented by duration and severity indicate their structural dependency, long-tail, and non-identically marginal distributions. A copula approach can provide a good statistical tool to deal with these issues and enable the extraction of valuable information from air pollution data. Based on the copula model, several statistical measurements are proposed for describing the characteristics of unhealthy air pollution events, including the Kendall's τ correlation of the copula, the conditional probability of air pollution severity based on a given duration, the joint OR/AND return period, and the conditional D|S and conditional S|D return periods. A case study based on air pollution data indices was conducted in Klang, Malaysia. The results indicate that a copula approach is beneficial for deriving valuable information for planning and mitigating the risks of unhealthy air pollution events.
    Matched MeSH terms: Particulate Matter/analysis
  10. Fulltext Chemical, Biological and Morphological Properties of Fine Particles during Local Rice Straw Burning Activities
    Ramli NA, Md Yusof NFF, Zarkasi KZ, Suroto A
    Int J Environ Res Public Health, 2021 08 02;18(15).
    PMID: 34360485 DOI: 10.3390/ijerph18158192
    Rice straw is commonly burned openly after harvesting in Malaysia and many other Asian countries where rice is the main crop. This operation emits a significant amount of air pollution, which can have severe consequences for indoor air quality, public health, and climate change. Therefore, this study focuses on determining the compositions of trace elements and the morphological properties of fine particles. Furthermore, the species of bacteria found in bioaerosol from rice burning activities were discovered in this study. For morphological observation of fine particles, FESEM-EDX was used in this study. Two main categories of particles were found, which were natural particles and anthropogenic particles. The zinc element was found during the morphological observation and was assumed to come from the fertilizer used by the farmers. ICP-OES identifies the concentration of trace elements in the fine particle samples. A cultured method was used in this study by using nutrient agar. From this study, several bacteria were identified: Exiguobavterium indicum, Bacillus amyloliquefaciens, Desulfonema limicola str. Jadabusan, Exiguobacterium acetylicum, Lysinibacillus macrolides, and Bacillus proteolyticus. This study is important, especially for human health, and further research on the biological composition of aerosols should be conducted to understand the effect of microorganisms on human health.
    Matched MeSH terms: Particulate Matter/analysis
  11. 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
  12. 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
  13. Fulltext The Influence of Traffic-Related Air Pollution (TRAP) in Primary Schools and Residential Proximity to Traffic Sources on Histone H3 Level in Selected Malaysian Children
    Suhaimi NF, Jalaludin J, Abu Bakar S
    Int J Environ Res Public Health, 2021 07 28;18(15).
    PMID: 34360284 DOI: 10.3390/ijerph18157995
    This study aimed to investigate the association between traffic-related air pollution (TRAP) exposure and histone H3 modification among school children in high-traffic (HT) and low-traffic (LT) areas in Malaysia. Respondents' background information and personal exposure to traffic sources were obtained from questionnaires distributed to randomly selected school children. Real-time monitoring instruments were used for 6-h measurements of PM10, PM2.5, PM1, NO2, SO2, O3, CO, and total volatile organic compounds (TVOC). Meanwhile, 24-h measurements of PM2.5-bound black carbon (BC) were performed using air sampling pumps. The salivary histone H3 level was captured using an enzyme-linked immunosorbent assay (ELISA). HT schools had significantly higher PM10, PM2.5, PM1, BC, NO2, SO2, O3, CO, and TVOC than LT schools, all at p < 0.001. Children in the HT area were more likely to get higher histone H3 levels (z = -5.13). There were positive weak correlations between histone H3 level and concentrations of NO2 (r = 0.37), CO (r = 0.36), PM1 (r = 0.35), PM2.5 (r = 0.34), SO2 (r = 0.34), PM10 (r = 0.33), O3 (r = 0.33), TVOC (r = 0.25), and BC (r = 0.19). Overall, this study proposes the possible role of histone H3 modification in interpreting the effects of TRAP exposure via non-genotoxic mechanisms.
    Matched MeSH terms: Particulate Matter/analysis
  14. 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
  15. Fulltext Mixed POT-BM Approach for Modeling Unhealthy Air Pollution Events
    Masseran N, Safari MAM
    Int J Environ Res Public Health, 2021 06 23;18(13).
    PMID: 34201763 DOI: 10.3390/ijerph18136754
    This article proposes a novel data selection technique called the mixed peak-over-threshold-block-maxima (POT-BM) approach for modeling unhealthy air pollution events. The POT technique is employed to obtain a group of blocks containing data points satisfying extreme-event criteria that are greater than a particular threshold u. The selected groups are defined as POT blocks. In parallel with that, a declustering technique is used to overcome the problem of dependency behaviors that occurs among adjacent POT blocks. Finally, the BM concept is integrated to determine the maximum data points for each POT block. Results show that the extreme data points determined by the mixed POT-BM approach satisfy the independent properties of extreme events, with satisfactory fitted model precision results. Overall, this study concludes that the mixed POT-BM approach provides a balanced tradeoff between bias and variance in the statistical modeling of extreme-value events. A case study was conducted by modeling an extreme event based on unhealthy air pollution events with a threshold u > 100 in Klang, Malaysia.
    Matched MeSH terms: Particulate Matter/analysis
  16. 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
  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 Overviewing the air quality models on air pollution in Sichuan Basin, China
    Li X, Hussain SA, Sobri S, Md Said MS
    Chemosphere, 2021 May;271:129502.
    PMID: 33465622 DOI: 10.1016/j.chemosphere.2020.129502
    Most developing countries in the world face the common challenges of reducing air pollution and advancing the process of sustainable development, especially in China. Air pollution research is a complex system and one of the main methods is through numerical simulation. The air quality model is an important technical method, it allows researchers to better analyze air pollutants in different regions. In addition, the SCB is a high-humidity and foggy area, and the concentration of atmospheric pollutants is always high. However, research on this region, one of the four most polluted regions in China, is still lacking. Reviewing the application of air quality models in the SCB air pollution has not been reported thoroughly. To fill these gaps, this review provides a comprehensive narration about i) The status of air pollution in SCB; ii) The application of air quality models in SCB; iii) The problems and application prospects of air quality models in the research of air pollution. This paper may provide a theoretical reference for the prevention and control of air pollution in the SCB and other heavily polluted areas in China and give some1inspirations for air pollution forecast in other countries with complex terrain.
    Matched MeSH terms: Particulate Matter/analysis
  19. Fulltext Exposure to Particulate PAHs on Potential Genotoxicity and Cancer Risk among School Children Living Near the Petrochemical Industry
    Sopian NA, Jalaludin J, Abu Bakar S, Hamedon TR, Latif MT
    Int J Environ Res Public Health, 2021 03 04;18(5).
    PMID: 33806616 DOI: 10.3390/ijerph18052575
    This study aimed to assess the association of exposure to particle-bound (PM2.5) polycyclic aromatic hydrocarbons (PAHs) with potential genotoxicity and cancer risk among children living near the petrochemical industry and comparative populations in Malaysia. PM2.5 samples were collected using a low-volume sampler for 24 h at three primary schools located within 5 km of the industrial area and three comparative schools more than 20 km away from any industrial activity. A gas chromatography-mass spectrometer was used to determine the analysis of 16 United States Environmental Protection Agency (USEPA) priority PAHs. A total of 205 children were randomly selected to assess the DNA damage in buccal cells, employing the comet assay. Total PAHs measured in exposed and comparative schools varied, respectively, from 61.60 to 64.64 ng m-3 and from 5.93 to 35.06 ng m-3. The PAH emission in exposed schools was contributed mainly by traffic and industrial emissions, dependent on the source apportionment. The 95th percentiles of the incremental lifetime cancer risk estimated using Monte Carlo simulation revealed that the inhalation risk for the exposed children and comparative populations was 2.22 × 10-6 and 2.95 × 10-7, respectively. The degree of DNA injury was substantially more severe among the exposed children relative to the comparative community. This study reveals that higher exposure to PAHs increases the risk of genotoxic effects and cancer among children.
    Matched MeSH terms: Particulate Matter/analysis
  20. 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
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