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.
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*
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 Pollutants/analysis; Air Pollutants/economics; Air Pollution/analysis; Air Pollution/economics*
Pumping air through a soft tissue which acts as a membrane is a relatively easy and quick method to collect and measure radon/thoron and its daughter nuclides in air. Analysis of the activity of the radionuclides can be calculated using an alpha counter which has been calibrated. In this method the activity of radon/thoron cannot be separated from the activity of radionuclides already present in the aerosol or dust particles.
Matched MeSH terms: Air Pollutants, Radioactive/analysis*; Air Pollution, Indoor/analysis*
Due to the increase of the human population and the rapid industrial growth in the past few decades, air quality monitoring is essential to assess the pollutant levels of an area. However, monitoring air quality in a high-density area like Sunway City, Selangor, Malaysia is challenging due to the limitation of the local monitoring network. To establish a comprehensive data for air pollution in Sunway City, a mobile monitoring campaign was employed around the city area with a duration of approximately 6 months, from September 2018 to March 2019. Measurements of air pollutants such as carbon dioxide (CO2) and nitrogen dioxide (NO2) were performed by using mobile air pollution sensors facilitated with a GPS device. In order to acquire a more in-depth understanding on traffic-related air pollution, the measurement period was divided into two different time blocks, which were morning hours (8 a.m.-12 p.m.) and afternoon hours (3 p.m.-7 p.m.). The data set was analysed by splitting Sunway City into different zones and routes to differentiate the conditions of each region. Meteorological variables such as ambient temperature, relative humidity, and wind speed were studied in line with the pollutant concentrations. The air quality in Sunway City was then compared with various air quality standards such as Malaysian Air Quality Standards and World Health Organisation (WHO) guidelines to understand the risk of exposure to air pollution by the residence in Sunway City.
Matched MeSH terms: Air Pollutants/analysis*; Air Pollution/analysis*
Membrane fouling is a major challenge in membrane bioreactors (MBRs) and its effective handling is the key to improve their competitiveness. Tilting panel system offers significant improvements for fouling control but is strictly limited to one-sided panel. In this study, we assess a two-way switch tilting panel system that enables two-sided membranes and project its implications on performance and energy footprint. Results show that tilting a panel improves permeance by up to 20% to reach a plateau flux thanks to better contacts between air bubbles and the membrane surface to scour-off the foulant. A plateau permeance could be achieved at aeration rate of as low as 0.90 l min-1, a condition untenable by vertical panel even at twice of the aeration rate. Switching at short periods (<5min) can maintain the hydraulic performance as in no-switch (static system), enables application of a two-sided switching panel. A comparison of vertical panel under 1.80 l min-1 aeration rate with a switching panel at a half of the rate, switched at 1 min period shows ≈10% higher permeance of the later. Since periodic switching consumes a very low energy (0.55% of the total of 0.276 kWh m-3), with reduction of aeration by 50%, the switching tilted panel offers 41% more energy efficient than a referenced full-scale MBR (0.390 kWh m-3). Overall results are very compelling and highly attractive for significant improvements of MBR technologies.
Intensity-duration-frequency (IDF) curves can serve as useful tools in risk assessment of extreme environmental events. Thus, this study proposes an IDF approach for evaluating the risk of expected occurrences of extreme air pollution as measured by an air pollution index (API). Hourly data of Klang city in Malaysia from 1997 to 2016 are analyzed. For each year, a block maxima size is determined based on four different monsoon seasons. Generalized extreme value (GEV) distribution is used as a model to represent the probabilistic behavior of maximum intensity of the API, which is derived from each block. Based on the GEV model, the IDF curves are developed to estimate the extreme pollution intensities that correspond to various duration hours and return periods. Considering the IDF curves, we found that for any duration hour, the magnitude of pollution intensity tends to be high in parallel with increasing return periods. In fact, a high-intensity pollution event that poses a high risk of affecting the environment is less frequent than low-intensity pollution. In conclusion, the IDF curves provide a good basis for decision makers to assess the expected risk of extreme pollution events in the future.
Matched MeSH terms: Air Pollutants*; Air Pollution*
Modeling and evaluating the behavior of particulate matter (PM10) is an important step in obtaining valuable information that can serve as a basis for environmental risk management, planning, and controlling the adverse effects of air pollution. This study proposes the use of a Markov chain model as an alternative approach for deriving relevant insights and understanding of PM10 data. Using first- and higher-order Markov chains, we analyzed daily PM10 index data for the city of Klang, Malaysia and found the Markov chain model to fit the PM10 data well. Based on the fitted model, we comprehensively describe the stochastic behaviors in the PM10 index based on the properties of the Markov chain, including its states classification, ergodic properties, long-term behaviors, and mean return times. Overall, this study concludes that the Markov chain model provides a good alternative technique for obtaining valuable information from different perspectives for the analysis of PM10 data.
Matched MeSH terms: Air Pollutants/analysis*; Air Pollution/analysis*
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*
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.
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
A month hourly measurement of radon concentration was taken in the bedroom of a two story link house in Kuala Lumpur. The house is a typical urban house in Malaysia, constructed with bricks, concrete and cement plaster. These materials are natural sources of radon in the house. The hourly radon concentration was found to vary from 0 pCiL-1 to 3 pCiL-1. It was found to peak during early morning and to minimize in the evening. The daily average radon concentration varied from 0.2 pCiL-1 to 1.0 pCiL-1.
Matched MeSH terms: Air Pollutants, Radioactive; Air Pollution, Radioactive
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
This study examines the three-way linkage relationships between CO2 emission, energy consumption and economic growth in Malaysia, covering the 1975-2015 period. An autoregressive distributed lag approach was employed to achieve the objective of the study and gauged by dynamic ordinary least squares. Additionally, vector error correction model, variance decompositions and impulse response functions were employed to further examine the relationship between the interest variables. The findings show that economic growth is neither influenced by energy consumption nor by CO2 emission. Energy consumption is revealed to be an increasing function of CO2 emission. Whereas, CO2 emission positively and significantly depends on energy consumption and economic growth. This implies that CO2 emission increases with an increase in both energy consumption and economic growth. Conclusively, the main drivers of CO2 emission in Malaysia are proven to be energy consumption and economic growth. Therefore, renewable energy sources ought to be considered by policy makers to curb emission from the current non-renewable sources. Wind and biomass can be explored as they are viable sources. Energy efficiency and savings should equally be emphasised and encouraged by policy makers. Lastly, growth-related policies that target emission reduction are also recommended.
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.
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.
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.
Anterior thoracic or thoracolumbar spinal surgery by retropleural approach always carries a risk of pneumothorax as its consequence. Conventionally, the Aerospace Medicine Association and the British Thoracic Society recommend 2 weeks delay of air travel for a patient with resolved postoperative pneumothorax. They also label active pneumothorax as an absolute contraindication for commercial air travel. Such a delay always causes psychological and financial stress to patients and family who are far from home. Here, we report three patients with postoperative pneumothorax, who insisted on early air travel despite being informed of the possible consequences.
Recent findings of an unexpected slowdown in the decline of CFC-11 mixing ratios in the atmosphere have led to the conclusion that global CFC-11 emissions have increased over the past decade and have been attributed in part to eastern China. This study independently assesses these findings by evaluating enhancements of CFC-11 mixing ratios in air samples collected in Taiwan between 2014 and 2018. Using the NAME (Numerical Atmospheric Modeling Environment) particle dispersion model, we find the likely source of the enhanced CFC-11 observed in Taiwan to be East China. Other halogenated trace gases were also measured, and there were positive interspecies correlations between CFC-11 and CHCl3, CCl4, HCFC-141b, HCFC-142b, CH2Cl2, and HCFC-22, indicating co-location of the emissions of these compounds. These correlations in combination with published emission estimates of CH2Cl2 and HCFC-22 from China, and of CHCl3 and CCl4 from eastern China, are used to estimate CFC-11 emissions. Within the uncertainties, these estimates do not differ for eastern China and the whole of China, so we combine them to derive a mean estimate that we term as being from "(eastern) China". For 2014-2018, we estimate an emission of 19 ± 5 Gg year-1 (gigagrams per year) of CFC-11 from (eastern) China, approximately one-quarter of global emissions. Comparing this to previously reported CFC-11 emissions estimated for earlier years, we estimate CFC-11 emissions from (eastern) China to have increased by 7 ± 5 Gg year-1 from the 2008-2011 average to the 2014-2018 average, which is 50 ± 40% of the estimated increase in global CFC-11 emissions and is consistent with the emission increases attributed to this region in an earlier study.