Displaying publications 1 - 20 of 453 in total

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  1. Lee JH, Gatera VA, Smith T, Panimbang F, Gonzalez A, Abdulah R, et al.
    New Solut, 2024 Feb;33(4):220-235.
    PMID: 38112404 DOI: 10.1177/10482911231218478
    Concerns about chemical exposure in the electronics manufacturing industry have long been recognized, but data are lacking in Southeast Asia. We conducted a study in Batam, Indonesia, to evaluate chemical exposures in electronics facilities, using participatory research and biological monitoring approaches. A convenience sample of 36 workers (28 exposed, 8 controls) was recruited, and urine samples were collected before and after shifts. Five solvents (acetone, methyl ethyl ketone, toluene, benzene, and xylenes) were found in 46%-97% of samples, and seven metals (arsenic, cadmium, cobalt, tin, antimony, lead, and vanadium) were detected in 60%-100% of samples. Biological monitoring and participatory research appeared to be useful in assessing workers' exposure when workplace air monitoring is not feasible due to a lack of cooperation from the employer. Several logistical challenges need to be addressed in future biomonitoring studies of electronics workers in Asia in factories where employers are reluctant to track workers' exposure and health.
    Matched MeSH terms: Air Pollutants, Occupational*
  2. Vilcins D, Christofferson RC, Yoon JH, Nazli SN, Sly PD, Cormier SA, et al.
    Ann Glob Health, 2024;90(1):9.
    PMID: 38312715 DOI: 10.5334/aogh.4363
    BACKGROUND: The United Nations has declared that humans have a right to clean air. Despite this, many deaths and disability-adjusted life years are attributed to air pollution exposure each year. We face both challenges to air quality and opportunities to improve, but several areas need to be addressed with urgency.

    OBJECTIVE: This paper summarises the recent research presented at the Pacific Basin Consortium for Environment and Health Symposium and focuses on three key areas of air pollution that are important to human health and require more research.

    FINDINGS AND CONCLUSION: Indoor spaces are commonly places of exposure to poor air quality and are difficult to monitor and regulate. Global climate change risks worsening air quality in a bi-directional fashion. The rising use of electric vehicles may offer opportunities to improve air quality, but it also presents new challenges. Government policies and initiatives could lead to improved air and environmental justice. Several populations, such as older people and children, face increased harm from air pollution and should become priority groups for action.

    Matched MeSH terms: Air Pollution, Indoor*
  3. Ravindiran G, Rajamanickam S, Kanagarathinam K, Hayder G, Janardhan G, Arunkumar P, et al.
    Environ Res, 2023 Dec 15;239(Pt 1):117354.
    PMID: 37821071 DOI: 10.1016/j.envres.2023.117354
    The impact of air pollution in Chennai metropolitan city, a southern Indian coastal city was examined to predict the Air Quality Index (AQI). Regular monitoring and prediction of the Air Quality Index (AQI) are critical for combating air pollution. The current study created machine learning models such as XGBoost, Random Forest, BaggingRegressor, and LGBMRegressor for the prediction of the AQI using the historical data available from 2017 to 2022. According to historical data, the AQI is highest in January, with a mean value of 104.6 g/gm, and the lowest in August, with a mean AQI value of 63.87 g/gm. Particulate matter, gaseous pollutants, and meteorological parameters were used to predict AQI, and the heat map generated showed that of all the parameters, PM2.5 has the greatest impact on AQI, with a value of 0.91. The log transformation method is used to normalize datasets and determine skewness and kurtosis. The XGBoost model demonstrated strong performance, achieving an R2 (correlation coefficient) of 0.9935, a mean absolute error (MAE) of 0.02, a mean square error (MSE) of 0.001, and a root mean square error (RMSE) of 0.04. In comparison, the LightGBM model's prediction was less effective, as it attained an R2 of 0.9748. According to the study, the AQI in Chennai has been increasing over the last two years, and if the same conditions persist, the city's air pollution will worsen in the future. Furthermore, accurate future air quality level predictions can be made using historical data and advanced machine learning algorithms.
    Matched MeSH terms: Air Pollutants*; Air Pollution*
  4. Tian Y
    J Health Popul Nutr, 2023 Nov 08;42(1):125.
    PMID: 37941052 DOI: 10.1186/s41043-023-00465-4
    The creation of a welcoming hospital atmosphere is necessary to improve patient wellbeing and encourage healing. The goal of this study was to examine the variables affecting hospitalised patients' comfort. The study procedure included a thorough search of the Web of Science and Scopus databases, as well as the use of software analytic tools to graphically map enormous literature data, providing a deeper understanding of the linkages within the literature and its changing patterns. Insights from a range of disciplines, including engineering, psychology, immunology, microbiology, and environmental science, were included into our study using content analysis and clustering approaches. The physical environment and the social environment are two crucial factors that are related to patient comfort. The study stress the need of giving patient comfort a top priority as they heal, especially by tackling indoor air pollution. Our research also emphasises how important hospital care and food guidelines are for improving patient comfort. Prioritising patients who need specialised care and attention, especially those who have suffered trauma, should be the focus of future study. Future research in important fields including trauma, communication, hospital architecture, and nursing will be built on the findings of this study. To enhance research in these crucial areas, worldwide collaboration between experts from other nations is also advised. Although many studies stress the significance of patient comfort, few have drawn conclusions from a variety of disciplines, including medicine, engineering, immunology, microbiology, and environmental science, the most crucial issue of thoroughly researching the improvement of patient comfort has not been addressed. Healthcare workers, engineers, and other professions will benefit greatly from this study's investigation of the connection between hospital indoor environments and patient comfort.
    Matched MeSH terms: Air Pollution, Indoor*
  5. Kuah CT, Koh QY, Rajoo S, Wong KY
    Environ Sci Pollut Res Int, 2023 Jun;30(28):72074-72100.
    PMID: 35716302 DOI: 10.1007/s11356-022-21377-6
    Human usage of non-renewable energy resources has caused many environmental issues, which include air pollution, global warming, and climate irregularities. To counter these issues, researchers have been seeking after alternative renewable energy sources and ways to manage energy more efficiently. This is where energy recovery technologies such as waste heat recovery (WHR) come into play. WHR is a form of waste to energy conversion. Waste heat can be captured and converted into usable energy instead of dumping it into the environment. In the more recent years, the WHR research field has gained great attention in the scientific community as well as in some energy-intensive industries. This article presents a bibliometric overview of the academic research on WHR over the span of 30 years from 1991 to 2020. A total of 5682 documents from Web of Science (WoS) have been retrieved and analyzed using various bibliometric methods, including performance analysis and network analysis. The analyses were performed on different actors in the field, i.e., funding agencies, journals, authors, organizations, and countries. In addition, several network mappings were done based on co-citation, co-authorship, and co-occurrences of keywords analyses. The research identified the most productive and influential actors in the field, established and emergent research topics, as well as the interrelations and collaboration patterns between different actors. The findings can be a robust roadmap for further research in this field.
    Matched MeSH terms: Air Pollution*
  6. Wang C, Qi F, Liu P, Ibrahim H, Wang X
    Environ Sci Pollut Res Int, 2023 Jun;30(30):75454-75468.
    PMID: 37219774 DOI: 10.1007/s11356-023-27742-3
    Under the new development model, the digital economy has become a new engine to promote the green development of the economy and achieve the goal of "double carbon." Based on panel data from 30 Chinese provinces and cities from 2011 to 2021, the impact of the digital economy on carbon emissions was empirically studied by constructing a panel model and a mediation model. The results show that firstly, the effect of the digital economy on carbon emissions is a non-linear inverted "U" shaped relationship, and this conclusion still holds after a series of robustness tests; secondly, the results of the benchmark regression show that economic agglomeration is an essential mechanism through which the digital economy affects carbon emissions and that the digital economy can indirectly suppress carbon emissions through economic agglomeration. Finally, the results of the heterogeneity analysis show that the impact of the digital economy on carbon emissions varies according to the level of regional development, and its effect on carbon emissions is mainly in the eastern region, while its impact on the central and western regions is weaker, indicating that the impact effect is primarily in developed regions. Therefore, the government should accelerate the construction of new digital infrastructure and implement the development strategy of the digital economy according to local conditions to promote a more significant carbon emission reduction effect of the digital economy.
    Matched MeSH terms: Air Pollution*
  7. Arsad FS, Hod R, Ahmad N, Baharom M, Ja'afar MH
    Environ Sci Pollut Res Int, 2023 Jun;30(29):73137-73149.
    PMID: 37211568 DOI: 10.1007/s11356-023-27089-9
    Thermal comfort is linked to our health, well-being, and productivity. The thermal environment is one of the main factors that influence thermal comfort and, consequently, the productivity of occupants inside buildings. Meanwhile, behavioural adaptation is well known to be the most critical contributor to the adaptive thermal comfort model. This systematic review aims to provide evidence regarding indoor thermal comfort temperature and related behavioural adaptation. Studies published between 2010 and 2022 examining indoor thermal comfort temperature and behavioural adaptations were considered. In this review, the indoor thermal comfort temperature ranges from 15.0 to 33.8 °C. The thermal comfort temperature range varied depending on several factors, such as climatic features, ventilation mode, type of buildings, and age of the study population. Elderly and younger children have distinctive thermal acceptability. Clothing adjustment, fan usage, AC usage, and open window were the most common adaptive behaviour performed. Evidence shows that behavioural adaptations were also influenced by climatic features, ventilation mode, type of buildings, and age of the study population. Building designs should incorporate all factors that affect the thermal comfort of the occupants. Awareness of practical behavioural adaptations is crucial to ensure occupants' optimal thermal comfort.
    Matched MeSH terms: Air Conditioning*; Air Pollution, Indoor*
  8. Negash YT, Hassan AM, Tseng ML, Ali MH, Lim MK
    Environ Sci Pollut Res Int, 2023 May;30(25):67303-67325.
    PMID: 37103710 DOI: 10.1007/s11356-023-27060-8
    This study contributes to develop a hierarchical framework for assessing the strategic effectiveness of waste management in the construction industry. This study identifies a valid set of strategic effectiveness attributes of sustainable waste management (SWM) in construction. Prior studies have neglected to develop a strategic effectiveness assessment framework for SWM to identify reduce, reuse, and recycle policy initiatives that ensure waste minimization and resource recovery programs. This study utilizes the fuzzy Delphi method to screen out nonessential attributes in qualitative information. This study initially proposes a set of 75 criteria; after two rounds of assessment, consensus regarding 28 criteria is achieved among experts, and the 28 criteria are validated. Fuzzy interpretive structural modeling divides the attributes into various elements. The modeling constructs a six-level model that depicts the interrelationships among the 28 validated criteria as a hierarchical framework, and it finds and ranks the optimal drivers for practical improvement. This study integrates the best-worst method to measure the weights of different criteria in the hierarchical strategic effectiveness framework. The findings reveal that waste management operational strategy, construction site waste management performance, and the mutual coordination level are the top aspects for assessing strategic effectiveness in the hierarchical framework. In practice, the waste reduction rate, the recycling rate, water and land usage, the reuse rate, and noise and air pollution levels are identified to assist policymakers in evaluation. The theoretical and managerial implications are discussed.
    Matched MeSH terms: Air Pollution*
  9. Çitil M, İlbasmış M, Olanrewaju VO, Barut A, Karaoğlan S, Ali M
    Environ Sci Pollut Res Int, 2023 Apr;30(18):53962-53976.
    PMID: 36869955 DOI: 10.1007/s11356-023-26016-2
    As the negative repercussions of environmental devastation, such as global warming and climate change, become more apparent, environmental consciousness is growing across the world, forcing nations to take steps to mitigate the damage. Thus, the current study assesses the effect of green investments, institutional quality, and political stability on air quality in the G-20 countries for the period 2004-2020. The stationarity of the variables was examined with the Pesaran (J Appl Econ 22:265-312, 2007) CADF, the long-term relationship between the variables by Westerlund (Oxf Bull Econ Stat 69(6):709-748, 2007), the long-run relationship coefficients with the MMQR method proposed by Machado and Silva (Econ 213(1):145-173, 2019), and the causality relationship between the variables by Dumitrescu and Hurlin (Econ Model 29(4):1450-1460, 2012) panel causality. The study findings revealed that green finance investments, institutional quality and political stability increased the air quality, while total output and energy consumption decreased air quality. The panel causality reveals a unidirectional causality from green finance investments, total output, energy consumption and political stability to air quality, and a bidirectional causality between institutional quality and air quality. According to these findings, it has been found that in the long term, green finance investments, total output, energy consumption, political stability, and institutional quality affect air quality. Based on these results, policies implications were proposed.
    Matched MeSH terms: Air Pollution*
  10. Yu H, Zahidi I
    Sci Total Environ, 2023 Mar 15;864:161135.
    PMID: 36566867 DOI: 10.1016/j.scitotenv.2022.161135
    The over-exploitation of mineral resources has led to increasingly serious dust pollution in mines, resulting in a series of negative impacts on the environment, mine workers (occupational health) and nearby residents (public health). For the environment, mine dust pollution is considered a major threat on surface vegetation, landscapes, weather conditions and air quality, leading to serious environmental damage such as vegetation reduction and air pollution; for occupational health, mine dust from the mining process is also regarded as a major threat to mine workers' health, leading to occupational diseases such as pneumoconiosis and silicosis; for public health, the pollutants contained in mine dust may pollute surrounding rivers, farmlands and crops, which poses a serious risk to the domestic water and food security of nearby residents who are also susceptible to respiratory diseases from exposure to mine dust. Therefore, the second section of this paper combines literature research, statistical studies, and meta analysis to introduce the public mainly to the severity of mine dust pollution and its hazards to the environment, mine workers (occupational health), and residents (public health), as well as to present an outlook on the management of mine dust pollution. At the same time, in order to propose a method for monitoring mine dust pollution on a regional scale, based on the Dense Dark Vegetation (DDV) algorithm, the third section of this paper analysed the aerosol optical depth (AOD) change in Dexing City of China using the data of 2010, 2014, 2018 and 2021 from the NASA MCD19A2 Dataset to explore the mine dust pollution situation and the progress of pollution treatment in Dexing City from 2010 to 2021. As a discussion article, this paper aims to review the environmental and health risks caused by mine dust pollution, to remind the public to take mine dust pollution seriously, and to propose the use of remote sensing technologies to monitor mine dust pollution, providing suggestions for local governments as well as mines on mine dust monitoring measures.
    Matched MeSH terms: Air Pollution*
  11. Gohari A, Gohari A, Ahmad AB
    Environ Sci Pollut Res Int, 2023 Jan;30(2):3707-3725.
    PMID: 35953748 DOI: 10.1007/s11356-022-22472-4
    Megacities recently are experiencing a shortage of green spaces basically due to the rapid growth of urbanization and increasing demand for different building types. Consideration of sustainable urban development is essential since the expansion of city facilities should be in line with social, economic, and environmental aspects. In this regard, green roof technology has been recommended as an effective solution for the growth of green spaces per capita and improving sustainability means of urban developments due to its diverse advantages. This study thus aimed at prioritizing sustainability indicators and relative sub-criteria of adopting green roof technology for residential and governmental buildings in the city of Mashhad, Iran, which has a dry climate. For this purpose, thirteen sub-criteria, which are extracted from the existing literature, are classified into three main sustainability indicators (environmental, economic, and social). Also, the best-worth method (BWM) as a multi-criteria decision-making technique was implemented to prioritize indicators and sub-criteria by analyzing the expert's opinion. The results indicated that respective economic and environmental indicators attract the highest priority in residential and governmental buildings. Additionally, the most important sub-criteria in environmental, economic, and social groups are air quality, roof longevity, and public health in both building types, respectively. However, when all criteria were considered, the respective highest priorities belong to roof longevity and air quality in residential and governmental buildings, while biodiversity conservation is the least important one in both building types. The results of this research can be beneficial in other cities with similar economic and climate conditions.
    Matched MeSH terms: Air Pollution*
  12. Aliero MS, Pasha MF, Toosi AN, Ghani I
    Environ Sci Pollut Res Int, 2022 Dec;29(57):85727-85741.
    PMID: 35001275 DOI: 10.1007/s11356-021-17862-z
    The enforcement of the Movement Control Order to curtail the spread of COVID-19 has affected home energy consumption, especially HVAC systems. Occupancy detection and estimation have been recognized as key contributors to improving building energy efficiency. Several solutions have been proposed for the past decade to improve the precision performance of occupancy detection and estimation in the building. Environmental sensing is one of the practical solutions to detect and estimate occupants in the building during uncertain behavior. However, the literature reveals that the performance of environmental sensing is relatively poor due to the poor quality of the training dataset used in the model. This study proposed a smart sensing framework that combined camera-based and environmental sensing approaches using supervised learning to gather standard and robust datasets related to indoor occupancy that can be used for cross-validation of different machine learning algorithms in formal research. The proposed solution is tested in the living room with a prototype system integrated with various sensors using a random forest regressor, although other techniques could be easily integrated within the proposed framework. The primary implication of this study is to predict the room occupation through the use of sensors providing inputs into a model to lower energy consumption. The results indicate that the proposed solution can obtain data, process, and predict occupant presence and number with 99.3% accuracy. Additionally, to demonstrate the impact of occupant number in energy saving, one room with two zones is modeled each zone with air condition with different thermostat controller. The first zone uses IoFClime and the second zone uses modified IoFClime using a design-builder. The simulation is conducted using EnergyPlus software with the random simulation of 10 occupants and local climate data under three scenarios. The Fanger model's thermal comfort analysis shows that up to 50% and 25% energy can be saved under the first and third scenarios.
    Matched MeSH terms: Air Conditioning
  13. Kazemi Shariat Panahi H, Dehhaghi M, Lam SS, Peng W, Aghbashlo M, Tabatabaei M, et al.
    Semin Cancer Biol, 2022 Nov;86(Pt 3):1122-1142.
    PMID: 34004331 DOI: 10.1016/j.semcancer.2021.05.013
    Human livelihood highly depends on applying different sources of energy whose utilization is associated with air pollution. On the other hand, air pollution may be associated with glioblastoma multiforme (GBM) development. Unlike other environmental causes of cancer (e.g., irradiation), air pollution cannot efficiently be controlled by geographical borders, regulations, and policies. The unavoidable exposure to air pollution can modify cancer incidence and mortality. GBM treatment with chemotherapy or even its surgical removal has proven insufficient (100% recurrence rate; patient's survival mean of 15 months; 90% fatality within five years) due to glioma infiltrative and migratory capacities. Given the barrage of attention and research investments currently plowed into next-generation cancer therapy, oncolytic viruses are perhaps the most vigorously pursued. Provision of an insight into the current state of the research and future direction is essential for stimulating new ideas with the potentials of filling research gaps. This review manuscript aims to overview types of brain cancer, their burden, and different causative agents. It also describes why air pollution is becoming a concerning factor. The different opinions on the association of air pollution with brain cancer are reviewed. It tries to address the significant controversy in this field by hypothesizing the air-pollution-brain-cancer association via inflammation and atopic conditions. The last section of this review deals with the oncolytic viruses, which have been used in, or are still under clinical trials for GBM treatment. Engineered adenoviruses (i.e., DNX-2401, DNX-2440, CRAd8-S-pk7 loaded Neural stem cells), herpes simplex virus type 1 (i.e., HSV-1 C134, HSV-1 rQNestin34.5v.2, HSV-1 G207, HSV-1 M032), measles virus (i.e., MV-CEA), parvovirus (i.e., ParvOryx), poliovirus (i.e., Poliovirus PVSRIPO), reovirus (i.e., pelareorep), moloney murine leukemia virus (i.e., Toca 511 vector), and vaccinia virus (i.e., vaccinia virus TG6002) as possible life-changing alleviations for GBM have been discussed. To the best of our knowledge, this review is the first review that comprehensively discusses both (i) the negative/positive association of air pollution with GBM; and (ii) the application of oncolytic viruses for GBM, including the most recent advances and clinical trials. It is also the first review that addresses the controversies over air pollution and brain cancer association. We believe that the article will significantly appeal to a broad readership of virologists, oncologists, neurologists, environmentalists, and those who work in the field of (bio)energy. Policymakers may also use it to establish better health policies and regulations about air pollution and (bio)fuels exploration, production, and consumption.
    Matched MeSH terms: Air Pollution*
  14. Tan H, Wong KY, Nyakuma BB, Kamar HM, Chong WT, Wong SL, et al.
    Environ Sci Pollut Res Int, 2022 Jan;29(5):6710-6721.
    PMID: 34458973 DOI: 10.1007/s11356-021-16171-9
    In this study, a systematic procedure for establishing the relationship between particulate matter (PM) and microbial counts in four operating rooms (ORs) was developed. The ORs are located in a private hospital on the western coast of Peninsular Malaysia. The objective of developing the systematic procedure is to ensure that the correlation between the PMs and microbial counts are valid. Each of the procedures is conducted based on the ISO, IEST, and NEBB standards. The procedures involved verifying the operating parameters are air change rate, room differential pressure, relative humidity, and air temperature. Upon verifying that the OR parameters are in the recommended operating range, the measurements of the PMs and sampling of the microbes were conducted. The TSI 9510-02 particle counter was used to measure three different sizes of PMs: PM 0.5, PM 5, and PM 10. The MAS-100ECO air sampler was used to quantify the microbial counts. The present study confirms that PM 0.5 does not have an apparent positive correlation with the microbial count. However, the evident correlation of 7% and 15% were identified for both PM 5 and PM 10, respectively. Therefore, it is suggested that frequent monitoring of both PM 5 and PM 10 should be practised in an OR before each surgical procedure. This correlation approach could provide an instantaneous estimation of the microbial counts present in the OR.
    Matched MeSH terms: Air Microbiology; Air Pollutants*
  15. Zaini N, Ean LW, Ahmed AN, Malek MA
    Environ Sci Pollut Res Int, 2022 Jan;29(4):4958-4990.
    PMID: 34807385 DOI: 10.1007/s11356-021-17442-1
    Rapid progress of industrial development, urbanization and traffic has caused air quality reduction that negatively affects human health and environmental sustainability, especially among developed countries. Numerous studies on the development of air quality forecasting model using machine learning have been conducted to control air pollution. As such, there are significant numbers of reviews on the application of machine learning in air quality forecasting. Shallow architectures of machine learning exhibit several limitations and yield lower forecasting accuracy than deep learning architecture. Deep learning is a new technology in computational intelligence; thus, its application in air quality forecasting is still limited. This study aims to investigate the deep learning applications in time series air quality forecasting. Owing to this, literature search is conducted thoroughly from all scientific databases to avoid unnecessary clutter. This study summarizes and discusses different types of deep learning algorithms applied in air quality forecasting, including the theoretical backgrounds, hyperparameters, applications and limitations. Hybrid deep learning with data decomposition, optimization algorithm and spatiotemporal models are also presented to highlight those techniques' effectiveness in tackling the drawbacks of individual deep learning models. It is clearly stated that hybrid deep learning was able to forecast future air quality with higher accuracy than individual models. At the end of the study, some possible research directions are suggested for future model development. The main objective of this review study is to provide a comprehensive literature summary of deep learning applications in time series air quality forecasting that may benefit interested researchers for subsequent research.
    Matched MeSH terms: Air Pollution*
  16. Ibrahim F, Samsudin EZ, Ishak AR, Sathasivam J
    Front Public Health, 2022;10:1067764.
    PMID: 36424957 DOI: 10.3389/fpubh.2022.1067764
    Indoor air quality (IAQ) has recently gained substantial traction as the airborne transmission of infectious respiratory disease becomes an increasing public health concern. Hospital indoor environments are complex ecosystems and strategies to improve hospital IAQ require greater appreciation of its potentially modifiable determinants, evidence of which are currently limited. This mini-review updates and integrates findings of previous literature to outline the current scientific evidence on the relationship between hospital IAQ and building design, building operation, and occupant-related factors. Emerging evidence has linked aspects of building design (dimensional, ventilation, and building envelope designs, construction and finishing materials, furnishing), building operation (ventilation operation and maintenance, hygiene maintenance, access control for hospital users), and occupants' characteristics (occupant activities, medical activities, adaptive behavior) to hospital IAQ. Despite the growing pool of IAQ literature, some important areas within hospitals (outpatient departments) and several key IAQ elements (dimensional aspects, room configurations, building materials, ventilation practices, adaptive behavior) remain understudied. Ventilation for hospitals continues to be challenging, as elevated levels of carbon monoxide, bioaerosols, and chemical compounds persist in indoor air despite having mechanical ventilation systems in place. To curb this public health issue, policy makers should champion implementing hospital IAQ surveillance system for all areas of the hospital building, applying interdisciplinary knowledge during the hospital design, construction and operation phase, and training of hospital staff with regards to operation, maintenance, and building control manipulation. Multipronged strategies targeting these important determinants are believed to be a viable strategy for the future control and improvement of hospital IAQ.
    Matched MeSH terms: Air Pollution, Indoor*
  17. Liu Y, Abdul Karim Z, Khalid N, Said FF
    J Environ Public Health, 2022;2022:5635853.
    PMID: 35719856 DOI: 10.1155/2022/5635853
    Wind is a renewable energy source. Overall, using wind to produce energy has fewer effects on the environment than many other energy sources. Wind and solar energy provide public health and environmental benefits to the social. Wind turbines may also reduce the amount of electricity generation from fossil fuels, which results in lower total air pollution and carbon dioxide emissions. In order to better optimize the effect of social energy economic management and facilitate the multiobjective decision making of coordinated development of energy and socioeconomic environment, a modeling and analysis method of economic benefits of wind power generation based on deep learning is proposed. In this paper, based on the principle of deep learning, the evaluation indicators of wind power economic benefits are excavated, a scientific and reasonable economic benefit evaluation system is constructed, a wind power economic benefit analysis model supported by public management is constructed, and the steps of wind power economic benefit analysis are simplified. It is concluded that the modeling and analysis method of wind power economic benefits based on deep learning has high practicability in the actual application process, which is convenient for the prediction and analysis of energy demand for social and economic development.
    Matched MeSH terms: Air Pollution*
  18. Alyousifi Y, Othman M, Husin A, Rathnayake U
    Ecotoxicol Environ Saf, 2021 Dec 20;227:112875.
    PMID: 34717219 DOI: 10.1016/j.ecoenv.2021.112875
    Fuzzy time series (FTS) forecasting models show a great performance in predicting time series, such as air pollution time series. However, they have caused major issues by utilizing random partitioning of the universe of discourse and ignoring repeated fuzzy sets. In this study, a novel hybrid forecasting model by integrating fuzzy time series to Markov chain and C-Means clustering techniques with an optimal number of clusters is presented. This hybridization contributes to generating effective lengths of intervals and thus, improving the model accuracy. The proposed model was verified and validated with real time series data sets, which are the benchmark data of actual trading of Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) and PM10 concentration data from Melaka, Malaysia. In addition, a comparison was made with some existing fuzzy time series models. Furthermore, the mean absolute percentage error, mean squared error and Theil's U statistic were calculated as evaluation criteria to illustrate the performance of the proposed model. The empirical analysis shows that the proposed model handles the time series data sets more efficiently and provides better overall forecasting results than existing FTS models. The results prove that the proposed model has greatly improved the prediction accuracy, for which it outperforms several fuzzy time series models. Therefore, it can be concluded that the proposed model is a better option for forecasting air pollution parameters and any kind of random parameters.
    Matched MeSH terms: Air Pollution*
  19. Yap HS, Roberts AC, Luo C, Tan Z, Lee EH, Thach TQ, et al.
    Indoor Air, 2021 11;31(6):2239-2251.
    PMID: 34096640 DOI: 10.1111/ina.12863
    Space is a resource that is constantly being depleted, especially in mega-cities. Underground workspaces (UGS) are increasingly being included in urban plans and have emerged as an essential component of vertical cities. While progress had been made on the engineering aspects associated with the development of high-quality UGS, public attitudes toward UGS as work environments (ie, the public's design concerns with UGS) are relatively unknown. Here, we present the first large-scale study examining preferences and attitudes toward UGS, surveying close to 2000 participants from four cities in three continents (Singapore, Shanghai, London, and Montreal). Contrary to previous beliefs, air quality (and not lack of windows) is the major concern of prospective occupants. Windows, temperature, and lighting emerged as additional important building performance aspects for UGS. Early adopters (ie, individuals more willing to accept UGS and thus more likely to be the first occupants) across all cities prioritized air quality. Present results suggest that (perceived) air quality is a key building performance aspect for UGS that needs to be communicated to prospective occupants as this will improve their attitudes and views toward UGS. This study highlights the importance of indoor air quality for the public.
    Matched MeSH terms: Air Pollution, Indoor*
  20. Usmani RSA, Pillai TR, Hashem IAT, Marjani M, Shaharudin R, Latif MT
    Environ Sci Pollut Res Int, 2021 Oct;28(40):56759-56771.
    PMID: 34075501 DOI: 10.1007/s11356-021-14305-7
    Air pollution has a serious and adverse effect on human health, and it has become a risk to human welfare and health throughout the globe. One of the major effects of air pollution on health is hospitalizations associated with air pollution. Recently, the estimation and prediction of air pollution-based hospitalization is carried out using artificial intelligence (AI) and machine learning (ML) techniques, i.e., deep learning and long short-term memory (LSTM). However, there is ample room for improvement in the available applied methodologies to estimate and predict air pollution-based hospital admissions. In this paper, we present the modeling and analysis of air pollution and cardiorespiratory hospitalization. This study aims to investigate the association between cardiorespiratory hospitalization and air pollution, and predict cardiorespiratory hospitalization based on air pollution using the artificial intelligence (AI) techniques. We propose the enhanced long short-term memory (ELSTM) model and provide a comparison with other AI techniques, i.e., LSTM, DL, and vector autoregressive (VAR). This study was conducted at seven study locations in Klang Valley, Malaysia. The utilized dataset contains the data from January 2006 to December 2016 for five study locations, i.e., Klang (KLN), Shah Alam (SA), Putrajaya (PUJ), Petaling Jaya (PJ), and Cheras, Kuala Lumpur (CKL). The dataset for Banting contains data from April 2010 to December 2016, and the data for Batu Muda, Kuala Lumpur, contains data from January 2009 to December 2016. The prediction results show that the ELSTM model performed significantly better than other models in all study locations, with the best RMSE scores in Klang study location (ELSTM: 0.002, LSTM: 0.013, DL: 0.006, VAR: 0.066). The results also indicated that the proposed ELSTM model was able to detect and predict the trends of monthly hospitalization significantly better than the LSTM and other models in the study. Hence, we can conclude that we can utilize AI techniques to accurately predict cardiorespiratory hospitalization based on air pollution in Klang Valley, Malaysia.
    Matched MeSH terms: Air Pollution*
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