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Fulltext Occupancy implications on indoor air quality (IAQ) in selected primary school classrooms around Kuantan, Pahang

Hazrin AH, Maryam Z, Hizrri A, Mohd Shukri MA, Norhidayah A, Samsuddin N

Malaysian Journal of Public Health Medicine, 2017;17 Special(1): 95-105.

The effects of children’s exposure on high concentration of airborne pollutants at schools often associated with increased rate of absenteeism, low productivities and learning performances, and development of respiratory problems. Recent studies have found that the presence of occupants in the classroom seems to give major effect towards the elevation of concentration of airborne pollutants in indoors. In order to evaluate and further understand on the significance of occupancy factor on IAQ, this study has been designed to determine and compare the level of selected physical (particulate matter (PM)) and chemical (carbon dioxide (CO2) and temperature) IAQ parameters and biological contaminants via colony forming unit (CFUm-3 ) for bacteria and fungi inside the selected classrooms during occupied and non-occupied period (first objective). The second objective is to describe the possible sources of airborne pollutants inside the classrooms at the selected primary schools around Kuantan, Pahang. Assessments of physical and chemical IAQ were done by using instruments known as DustMate Environmental Dust Detector and VelociCalc® MultiFunction Ventilation Meter 9565.The data were recorded every 30 minutes for 8 hours during schooldays and weekend at the selected sampling point in the classrooms. For microbial sampling, Surface Air System Indoor Air Quality (SAS IAQ) was used to capture the bacteria and fungi. The data obtained were compared with the established standard reference known as the Industrial Code of Practice on Indoor Air Quality (2010) constructed by the Department of Occupational Safety and Health (DOSH), Malaysia. This study has found that some of the IAQ parameters in the selected classrooms were exceeding the established standards during occupied period in schooldays compared to non-occupied period during weekend. Findings of this study provide the insights for future research including the site selection of school, arrangement of the classrooms and numbers of students per class.

Matched MeSH terms: Air Pollution*
Fulltext Evaluation of the 2013 Southeast Asian Haze on Solar Generation Performance

Maghami M, Hizam H, Gomes C, Hajighorbani S, Rezaei N

PLoS One, 2015;10(8):e0135118.
PMID: 26275303 DOI: 10.1371/journal.pone.0135118

Pollution in Southeast Asia is a major public energy problem and the cause of energy losses. A significant problem with respect to this type of pollution is that it decreases energy yield. In this study, two types of photovoltaic (PV) solar arrays were used to evaluate the effect of air pollution. The performance of two types of solar arrays were analysed in this research, namely, two units of a 1 kWp tracking flat photovoltaic (TFP) and two units of a 1 kWp fixed flat photovoltaic arrays (FFP). Data analysis was conducted on 2,190 samples at 30 min intervals from 01st June 2013, when both arrays were washed, until 30th June 2013. The performance was evaluated by using environmental data (irradiation, temperature, dust thickness, and air pollution index), power output, and energy yield. Multiple regression models were predicted in view of the environmental data and PV array output. Results showed that the fixed flat system was more affected by air pollution than the tracking flat plate. The contribution of this work is that it considers two types of photovoltaic arrays under the Southeast Asian pollution 2013.

Matched MeSH terms: Air Pollution*
Fulltext Classifying Sources Influencing Indoor Air Quality (IAQ) Using Artificial Neural Network (ANN)

Saad SM, Andrew AM, Shakaff AY, Saad AR, Kamarudin AM, Zakaria A

Sensors (Basel), 2015;15(5):11665-84.
PMID: 26007724 DOI: 10.3390/s150511665

Monitoring indoor air quality (IAQ) is deemed important nowadays. A sophisticated IAQ monitoring system which could classify the source influencing the IAQ is definitely going to be very helpful to the users. Therefore, in this paper, an IAQ monitoring system has been proposed with a newly added feature which enables the system to identify the sources influencing the level of IAQ. In order to achieve this, the data collected has been trained with artificial neural network or ANN--a proven method for pattern recognition. Basically, the proposed system consists of sensor module cloud (SMC), base station and service-oriented client. The SMC contain collections of sensor modules that measure the air quality data and transmit the captured data to base station through wireless network. The IAQ monitoring system is also equipped with IAQ Index and thermal comfort index which could tell the users about the room's conditions. The results showed that the system is able to measure the level of air quality and successfully classify the sources influencing IAQ in various environments like ambient air, chemical presence, fragrance presence, foods and beverages and human activity.

Matched MeSH terms: Air Pollution; Air Pollution, Indoor
The cloud over Sarawak

Nature, 1997 Sep 25;389(6649):315.
PMID: 9311758
Matched MeSH terms: Air Pollution*
Fulltext Air filtration study to improve indoor air quality: a proposed study

Amir Abdullah, M.D., Abdullah, A.H., Leman, A.M.

Journal of Occupational Safety and Health, 2015;12(1):203-207.
MyJurnal

Indoor air quality has been a major public concern recently. Several health effects are related to this problem.
Findings from several studies have shown MVAC system as the main contributor for IAQ problem. Good practice of
maintenance and servicing is important to maintain MVAC system, especially the filter. Good air filtration for MVAC
system is needed to make sure adequate air is received by the occupants. This paper illustrated a recent study of air
filtration for MVAC system especially for several industries that used MVAC system in their premises. This paper also
proposed an air filtration study for a better air quality. Several Acts and Regulations related to Safety and Health were
identified to create the framework for the proposed study. Air filtration technique was used in this preliminary study
to set up guidelines to create safe and clean indoor spaces for workers and occupants.

Matched MeSH terms: Air Pollution; Air Pollution, Indoor
A systematic literature review of deep learning neural network for time series air quality forecasting

Zaini N, Ean LW, Ahmed AN, Malek MA

Environ Sci Pollut Res Int, 2022 Jan;29(4):4958-4990.
PMID: 34807385 DOI: 10.1007/s11356-021-17442-1

Rapid progress of industrial development, urbanization and traffic has caused air quality reduction that negatively affects human health and environmental sustainability, especially among developed countries. Numerous studies on the development of air quality forecasting model using machine learning have been conducted to control air pollution. As such, there are significant numbers of reviews on the application of machine learning in air quality forecasting. Shallow architectures of machine learning exhibit several limitations and yield lower forecasting accuracy than deep learning architecture. Deep learning is a new technology in computational intelligence; thus, its application in air quality forecasting is still limited. This study aims to investigate the deep learning applications in time series air quality forecasting. Owing to this, literature search is conducted thoroughly from all scientific databases to avoid unnecessary clutter. This study summarizes and discusses different types of deep learning algorithms applied in air quality forecasting, including the theoretical backgrounds, hyperparameters, applications and limitations. Hybrid deep learning with data decomposition, optimization algorithm and spatiotemporal models are also presented to highlight those techniques' effectiveness in tackling the drawbacks of individual deep learning models. It is clearly stated that hybrid deep learning was able to forecast future air quality with higher accuracy than individual models. At the end of the study, some possible research directions are suggested for future model development. The main objective of this review study is to provide a comprehensive literature summary of deep learning applications in time series air quality forecasting that may benefit interested researchers for subsequent research.

Matched MeSH terms: Air Pollution*
Importance of green roof criteria for residential and governmental buildings: a multi-criteria decision analysis

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*
Developing a hierarchical framework for assessing the strategic effectiveness of sustainable waste management in the Somaliland construction industry

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*
Integration of chemical health risk assessment (CHRA) and indoor air quality (IAQ) assessment: from a Malaysian perspective

Abdullah F, Jaafar MH, Ahmad MI, Ismail ZS

Int J Environ Health Res, 2024 May;34(5):2280-2298.
PMID: 37552824 DOI: 10.1080/09603123.2023.2243843

In Malaysia, chemical management in workplaces is managed under the Occupational Safety and Health Act 1994. Hence, the introduction of the Occupational Safety and Health (Use and Standards of Exposure of Chemicals Hazardous to Health) Regulations 2000 has strengthened the chemical management level in workplaces, including higher academic institutions. The introduction of chemical health risk assessment through the regulation required management to conduct the assessment at workplaces. Poor levels of Indoor Air Quality (IAQ) in chemical laboratories may also cause discomfort among workers when there is sick building syndrome in laboratories. IAQ is managed through the Industry Code of Practice on Indoor Air Quality 2010. Although both are different in method and approach, both are meant to ensure the workers' safety and comfort. This study is aimed to investigate the need to integrate both chemical health risk assessment and IAQ assessment in laboratories to ensure optimum safety levels among workers.

Matched MeSH terms: Air Pollution, Indoor*
Fulltext Updates in Air Pollution: Current Research and Future Challenges

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*
Fulltext Real-Time In-Vehicle Air Quality Monitoring System Using Machine Learning Prediction Algorithm

Goh CC, Kamarudin LM, Zakaria A, Nishizaki H, Ramli N, Mao X, et al.

Sensors (Basel), 2021 Jul 21;21(15).
PMID: 34372192 DOI: 10.3390/s21154956

This paper presents the development of a real-time cloud-based in-vehicle air quality monitoring system that enables the prediction of the current and future cabin air quality. The designed system provides predictive analytics using machine learning algorithms that can measure the drivers' drowsiness and fatigue based on the air quality presented in the cabin car. It consists of five sensors that measure the level of CO2, particulate matter, vehicle speed, temperature, and humidity. Data from these sensors were collected in real-time from the vehicle cabin and stored in the cloud database. A predictive model using multilayer perceptron, support vector regression, and linear regression was developed to analyze the data and predict the future condition of in-vehicle air quality. The performance of these models was evaluated using the Root Mean Square Error, Mean Squared Error, Mean Absolute Error, and coefficient of determination (R2). The results showed that the support vector regression achieved excellent performance with the highest linearity between the predicted and actual data with an R2 of 0.9981.

Matched MeSH terms: Air Pollution*
Every breath you take: how does air pollution affect the international traveller?

Guindi MN, Flaherty GT, Byrne M

J Travel Med, 2018 01 01;25(1).
PMID: 29718402 DOI: 10.1093/jtm/tay021
Matched MeSH terms: Air Pollution*
Fulltext A new hybrid fuzzy time series model with an application to predict PM10 concentration

Alyousifi Y, Othman M, Husin A, Rathnayake U

Ecotoxicol Environ Saf, 2021 Dec 20;227:112875.
PMID: 34717219 DOI: 10.1016/j.ecoenv.2021.112875

Fuzzy time series (FTS) forecasting models show a great performance in predicting time series, such as air pollution time series. However, they have caused major issues by utilizing random partitioning of the universe of discourse and ignoring repeated fuzzy sets. In this study, a novel hybrid forecasting model by integrating fuzzy time series to Markov chain and C-Means clustering techniques with an optimal number of clusters is presented. This hybridization contributes to generating effective lengths of intervals and thus, improving the model accuracy. The proposed model was verified and validated with real time series data sets, which are the benchmark data of actual trading of Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) and PM10 concentration data from Melaka, Malaysia. In addition, a comparison was made with some existing fuzzy time series models. Furthermore, the mean absolute percentage error, mean squared error and Theil's U statistic were calculated as evaluation criteria to illustrate the performance of the proposed model. The empirical analysis shows that the proposed model handles the time series data sets more efficiently and provides better overall forecasting results than existing FTS models. The results prove that the proposed model has greatly improved the prediction accuracy, for which it outperforms several fuzzy time series models. Therefore, it can be concluded that the proposed model is a better option for forecasting air pollution parameters and any kind of random parameters.

Matched MeSH terms: Air Pollution*
Air pollution and cardiorespiratory hospitalization, predictive modeling, and analysis using artificial intelligence techniques

Usmani RSA, Pillai TR, Hashem IAT, Marjani M, Shaharudin R, Latif MT

Environ Sci Pollut Res Int, 2021 Oct;28(40):56759-56771.
PMID: 34075501 DOI: 10.1007/s11356-021-14305-7

Air pollution has a serious and adverse effect on human health, and it has become a risk to human welfare and health throughout the globe. One of the major effects of air pollution on health is hospitalizations associated with air pollution. Recently, the estimation and prediction of air pollution-based hospitalization is carried out using artificial intelligence (AI) and machine learning (ML) techniques, i.e., deep learning and long short-term memory (LSTM). However, there is ample room for improvement in the available applied methodologies to estimate and predict air pollution-based hospital admissions. In this paper, we present the modeling and analysis of air pollution and cardiorespiratory hospitalization. This study aims to investigate the association between cardiorespiratory hospitalization and air pollution, and predict cardiorespiratory hospitalization based on air pollution using the artificial intelligence (AI) techniques. We propose the enhanced long short-term memory (ELSTM) model and provide a comparison with other AI techniques, i.e., LSTM, DL, and vector autoregressive (VAR). This study was conducted at seven study locations in Klang Valley, Malaysia. The utilized dataset contains the data from January 2006 to December 2016 for five study locations, i.e., Klang (KLN), Shah Alam (SA), Putrajaya (PUJ), Petaling Jaya (PJ), and Cheras, Kuala Lumpur (CKL). The dataset for Banting contains data from April 2010 to December 2016, and the data for Batu Muda, Kuala Lumpur, contains data from January 2009 to December 2016. The prediction results show that the ELSTM model performed significantly better than other models in all study locations, with the best RMSE scores in Klang study location (ELSTM: 0.002, LSTM: 0.013, DL: 0.006, VAR: 0.066). The results also indicated that the proposed ELSTM model was able to detect and predict the trends of monthly hospitalization significantly better than the LSTM and other models in the study. Hence, we can conclude that we can utilize AI techniques to accurately predict cardiorespiratory hospitalization based on air pollution in Klang Valley, Malaysia.

Matched MeSH terms: Air Pollution*
Impact of air pollutants on climate change and prediction of air quality index using machine learning models

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 Pollution*
Fulltext Waste heat recovery research - a systematic bibliometric analysis (1991 to 2020)

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*
Fulltext Hospital indoor air quality and its relationships with building design, building operation, and occupant-related factors: A mini-review

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*
Fulltext Benefit Modeling and Analysis of Wind Power Generation under Social Energy Economy and Public Health

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*
Fulltext Effects of temperature, humidity, air quality and anthropic activities on the transmission of SARS-CoV-2: a systematic review protocol

Jeevananthan C, Muhamad NA, Jaafar MH, Hod R, Ab Ghani RM, Md Isa Z, et al.

BMJ Open, 2020 11 04;10(11):e039623.
PMID: 33148753 DOI: 10.1136/bmjopen-2020-039623

INTRODUCTION: The current global pandemic of the virus that emerged from Hubei province in China has caused coronavirus disease in 2019 (COVID-19), which has affected a total number of 900 036 people globally, involving 206 countries and resulted in a cumulative of 45 693 deaths worldwide as of 3 April 2020. The mode of transmission is identified through airdrops from patients' body fluids such as during sneezing, coughing and talking. However, the relative importance of environmental effects in the transmission of the virus has not been vastly studied. In addition, the role of temperature and humidity in air-borne transmission of infection is presently still unclear. This study aims to identify the effect of temperature, humidity and air quality in the transmission of SARS-CoV-2.
METHODS AND ANALYSIS: We will systematically conduct a comprehensive literature search using various databases including PubMed, EMBASE, Scopus, CENTRAL and Google Scholar to identify potential studies. The search will be performed for any eligible articles from the earliest published articles up to latest available studies in 2020. We will include all the observational studies such as cohort case-control and cross-sectional studies that explains or measures the effects of temperature and/or humidity and/or air quality and/or anthropic activities that is associated with SARS-CoV-2. Study selection and reporting will follow the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and Meta-Analysis of Observational Studies in Epidemiology guideline. All data will be extracted using a standardised data extraction form and quality of the studies will be assessed using the Newcastle-Ottawa Scale guideline. Descriptive and meta-analysis will be performed using a random effect model in Review Manager File.
ETHICS AND DISSEMINATION: No primary data will be collected, and thus no formal ethical approval is required. The results will be disseminated through a peer-reviewed publication and conference presentation.
PROSPERO REGISTRATION NUMBER: CRD42020176756.

Matched MeSH terms: Air Pollution*; Air Pollution, Indoor
A systematic review of financial implications of air pollution on health in Asia

Jaafar H, Razi NA, Azzeri A, Isahak M, Dahlui M

Environ Sci Pollut Res Int, 2018 Oct;25(30):30009-30020.
PMID: 30187406 DOI: 10.1007/s11356-018-3049-0

Economic losses due to health-related implications of air pollution were huge and incurred significant burdens towards healthcare providers. The objective of this study is to systematically review published literature on the financial implications of air pollution on health in Asia. Four databases: PubMed, Scopus, NHS Economic Evaluation Database (NHS EED), and Web of Science (WoS) were used to identify all the relevant articles. It was limited to all articles that had been published in the respected databases from January 2007 until March 2017. Twenty-four articles were included in this review. Five of the 24 studies (20.8%) reported financial implications of air pollution-related disease through value of statistical life (VOSL) which ranged from USD180 million to USD2.2 billion, six (25%) studies used cost of illness (COI) to evaluate air pollution-related morbidity and found that the cost ranged from USD5.4 million to USD9.1 billion. Another six studies (25%) used a combination of VOSL and COI for both mortality and morbidity valuation and found that the financial implications ranging from USD253 million to USD2.9 billion. Thirteen (54.2%) studies reported healthcare cost associated with both hospital admission and outpatient visit, five (20.1%) on hospital admission only, and one (4.2%) on outpatient visit only. Economic impacts of air pollution can be huge with significant deterioration of health among the Asians.

Matched MeSH terms: Air Pollution/analysis; Air Pollution/economics*

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