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1 – 10 of 46Nehal Elshaboury, Eslam Mohammed Abdelkader and Abobakr Al-Sakkaf
Modern human society has continuous advancements that have a negative impact on the quality of the air. Daily transportation, industrial and residential operations churn up…
Abstract
Purpose
Modern human society has continuous advancements that have a negative impact on the quality of the air. Daily transportation, industrial and residential operations churn up dangerous contaminants in our surroundings. Addressing air pollution issues is critical for human health and ecosystems, particularly in developing countries such as Egypt. Excessive levels of pollutants have been linked to a variety of circulatory, respiratory and nervous illnesses. To this end, the purpose of this research paper is to forecast air pollution concentrations in Egypt based on time series analysis.
Design/methodology/approach
Deep learning models are leveraged to analyze air quality time series in the 6th of October City, Egypt. In this regard, convolutional neural network (CNN), long short-term memory network and multilayer perceptron neural network models are used to forecast the overall concentrations of sulfur dioxide (SO2) and particulate matter 10 µm in diameter (PM10). The models are trained and validated by using monthly data available from the Egyptian Environmental Affairs Agency between December 2014 and July 2020. The performance measures such as determination coefficient, root mean square error and mean absolute error are used to evaluate the outcomes of models.
Findings
The CNN model exhibits the best performance in terms of forecasting pollutant concentrations 3, 6, 9 and 12 months ahead. Finally, using data from December 2014 to July 2021, the CNN model is used to anticipate the pollutant concentrations 12 months ahead. In July 2022, the overall concentrations of SO2 and PM10 are expected to reach 10 and 127 µg/m3, respectively. The developed model could aid decision-makers, practitioners and local authorities in planning and implementing various interventions to mitigate their negative influences on the population and environment.
Originality/value
This research introduces the development of an efficient time-series model that can project the future concentrations of particulate and gaseous air pollutants in Egypt. This research study offers the first time application of deep learning models to forecast the air quality in Egypt. This research study examines the performance of machine learning approaches and deep learning techniques to forecast sulfur dioxide and particular matter concentrations using standard performance metrics.
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Christian Nnaemeka Egwim, Hafiz Alaka, Youlu Pan, Habeeb Balogun, Saheed Ajayi, Abdul Hye and Oluwapelumi Oluwaseun Egunjobi
The study aims to develop a multilayer high-effective ensemble of ensembles predictive model (stacking ensemble) using several hyperparameter optimized ensemble machine learning…
Abstract
Purpose
The study aims to develop a multilayer high-effective ensemble of ensembles predictive model (stacking ensemble) using several hyperparameter optimized ensemble machine learning (ML) methods (bagging and boosting ensembles) trained with high-volume data points retrieved from Internet of Things (IoT) emission sensors, time-corresponding meteorology and traffic data.
Design/methodology/approach
For a start, the study experimented big data hypothesis theory by developing sample ensemble predictive models on different data sample sizes and compared their results. Second, it developed a standalone model and several bagging and boosting ensemble models and compared their results. Finally, it used the best performing bagging and boosting predictive models as input estimators to develop a novel multilayer high-effective stacking ensemble predictive model.
Findings
Results proved data size to be one of the main determinants to ensemble ML predictive power. Second, it proved that, as compared to using a single algorithm, the cumulative result from ensemble ML algorithms is usually always better in terms of predicted accuracy. Finally, it proved stacking ensemble to be a better model for predicting PM2.5 concentration level than bagging and boosting ensemble models.
Research limitations/implications
A limitation of this study is the trade-off between performance of this novel model and the computational time required to train it. Whether this gap can be closed remains an open research question. As a result, future research should attempt to close this gap. Also, future studies can integrate this novel model to a personal air quality messaging system to inform public of pollution levels and improve public access to air quality forecast.
Practical implications
The outcome of this study will aid the public to proactively identify highly polluted areas thus potentially reducing pollution-associated/ triggered COVID-19 (and other lung diseases) deaths/ complications/ transmission by encouraging avoidance behavior and support informed decision to lock down by government bodies when integrated into an air pollution monitoring system
Originality/value
This study fills a gap in literature by providing a justification for selecting appropriate ensemble ML algorithms for PM2.5 concentration level predictive modeling. Second, it contributes to the big data hypothesis theory, which suggests that data size is one of the most important factors of ML predictive capability. Third, it supports the premise that when using ensemble ML algorithms, the cumulative output is usually always better in terms of predicted accuracy than using a single algorithm. Finally developing a novel multilayer high-performant hyperparameter optimized ensemble of ensembles predictive model that can accurately predict PM2.5 concentration levels with improved model interpretability and enhanced generalizability, as well as the provision of a novel databank of historic pollution data from IoT emission sensors that can be purchased for research, consultancy and policymaking.
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Gomaa Abdel-Maksoud, Hanaa Nasr, Sayed Hussein Samaha and Mahmoud Saad ELdeen Kassem
This study aims to evaluate the state of preservation of one of the most famous manuscripts dated back to the 15th century using some analytical techniques to identify the…
Abstract
Purpose
This study aims to evaluate the state of preservation of one of the most famous manuscripts dated back to the 15th century using some analytical techniques to identify the manuscript components, explain its deterioration mechanisms and produce some solutions for conservation processes in future studies.
Design/methodology/approach
The analytical techniques used were visual assessment, digital microscope, scanning electron microscope (SEM) with EDX, pH measurement, attenuated total reflection – Fourier transform infrared spectroscopy (ATR/FTIR) and cellulose crystallinity.
Findings
Stains, missed parts and scratching were the most common aspects of deterioration. Some insects were observed by digital microscope. The SEM showed that linen fibers and goat skin were used to manufacture paper sheets and leather binding. Energy dispersive X-ray analysis proved that niobium and tantalum were added during the manufacture of paper sheets. Carbon black ink was the main writing material. The other pigments used were cinnabar in red ink, gold color from brass and blue color from lapis lazuli. FTIR analysis proved that some chemical changes were noticed. Low crystallinity of the historical paper was obtained. There was a reduction in the pH value of the historical bookbinding.
Originality/value
The importance of the analytical techniques used to detect the main components, forms and mechanism of deterioration of the studied manuscript. The elements of niobium and tantalum were added to paper sheets, which protected them from deterioration. The insects such as house flies and Sitophilus granarius were found in the manuscripts.
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Dinesh R., Stanly Jones Retnam, Dev Anand M. and Edwin Raja Dhas J.
The demand for energy is increasing massively due to urbanization and industrialization. Due to the massive usage of diesel engines in the transportation sector, global warming is…
Abstract
Purpose
The demand for energy is increasing massively due to urbanization and industrialization. Due to the massive usage of diesel engines in the transportation sector, global warming is increasing rapidly. The purpose of this paper is to use hydrogen as the potential alternative for diesel engine.
Design/methodology/approach
A series of tests conducted in the twin cylinder four stroke diesel engine at various engine speeds. In addition to the hydrogen, the ultrasonication is applied to add the nanoparticles to the neat diesel. The role of nanoparticles on engine performance is effective owing to its physicochemical properties. Here, neat diesel mixed 30% of biodiesel along with the hydrogen at the concentration of 10%, 20% and 30% and 50 ppm of graphite oxide to form the blends DNH10, DNH20 and DNH30.
Findings
Inclusion of both hydrogen and nanoparticles increases the brake power and brake thermal efficiency (BTE) of the engine with relatively less fuel consumption. Compared to all blends, the maximum BTE of 33.3% has been reported by 30% hydrogen-based fuel. On the contrary, the production of the pollutants also reduces as the hydrogen concentration increases.
Originality/value
Majority of the pollutants such as carbon monoxide, carbon dioxide and hydrocarbon were dropped massively compared to diesel. On the contrary, there is no reduction in nitrogen of oxides (NOx). Highest production of NOx was witnessed for 30% hydrogen fuel due to the premixed combustion phase and cylinder temperatures.
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Li Wang, Yanhong Lv, Tao Wang, Shuting Wan and Yanling Ye
The purpose of this research is to address the existing gap in the study of construction and demolition waste (C&DW) by focusing on its impact on human health throughout the…
Abstract
Purpose
The purpose of this research is to address the existing gap in the study of construction and demolition waste (C&DW) by focusing on its impact on human health throughout the entire life cycle. And this research provides a comprehensive assessment model that incorporates the release of gaseous pollutants and particulate matter during the whole life cycle of C&DW, thereby contributing to a more holistic understanding of its impact on human health.
Design/methodology/approach
The research was conducted in two stages. Firstly, the quantitative model framework of pollutants emitted by C&DW was established. Three types of pollutants were considered, namely nitrogen dioxide (NO2), sulfur dioxide (SO2) and inhalable particulate matter (PM10). Second, disability-adjusted life year (DALY) and willingness to pay (WTP) assessments were used to provide a monetary quantified health impact for pollutants released by C&DW.
Findings
The results show that the WTP value of PM10 is the highest among all pollutants and 8.68E+07 dollars/a, while the WTP value in the disposal stage accounts for the largest proportion compared to the generation and transportation stage. These findings emphasize the importance of PM10 and C&DW treatment stage for pollutant treatment.
Originality/value
The results of this study are of great significance for the management department to optimize the construction management scheme to reduce the total amount of pollutants produced by C&DW and its harm to human health. Meanwhile, this study fills the gap in existing research on the impact assessment of C&DW on human health throughout the whole life cycle, and provides reference and basis for future research and policy formulation.
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Francis Olawale Abulude, Domenico Suriano, Samuel Dare Oluwagbayide, Akinyinka Akinnusotu, Ifeoluwa Ayodeji Abulude and Emmanuel Awogbindin
This study aimed to characterize the concentrations of indoor pollutants (such as carbon dioxide (CO2), ozone (O3), nitrogen dioxide (NO2) and sulfur dioxide (SO2), as well as…
Abstract
Purpose
This study aimed to characterize the concentrations of indoor pollutants (such as carbon dioxide (CO2), ozone (O3), nitrogen dioxide (NO2) and sulfur dioxide (SO2), as well as particulate matter (PM) (PM1, PM2.5 and PM10) in Akure, Nigeria, as well as the relationship between the parameters’ concentrations.
Design/methodology/approach
The evaluation, which lasted four months, used a low-cost air sensor that was positioned two meters above the ground. All sensor procedures were correctly carried out.
Findings
CO2 (430.34 ppm), NO2 (93.31 ppb), O3 (19.94 ppb), SO2 (40.87 ppb), PM1 (29.31 µg/m3), PM2.5 (43.56 µg/m3), PM10 (50.70 µg/m3), temperature (32.4°C) and relative humidity (50.53%) were the average values obtained. The Pearson correlation depicted the relationships between the pollutants and weather factors. With the exception of April, which had significant SO2 (18%) and low PM10 (49%) contributions, NO2 and PM10 were the most common pollutants in all of the months. The mean air quality index (AQI) for NO2 indicated that the AQI was “moderate” (51–100). In contrast to SO2, whose AQI ranged from “moderate” to “very unhealthy,” O3's AQI ranged from “good” (50) to “unhealthy” (151–200). Since PM1, PM2.5 and PM10 made up the majority of PC1’s contribution, both PM2.5 and PM10 were deemed “hazardous.”
Practical implications
The practical implication of indoor air pollution is long-term health effects, including heart disease, lung cancer and respiratory diseases such as emphysema. Indoor air pollution can also cause long-term damage to people’s nerves, brain, kidneys, liver and other organs.
Originality/value
Lack of literature in terms of indoor air quality (IAQ) in Akure, Ondo State. With this work, the information obtained will assist all stakeholders in policy formulation and implementation. Again, the low-cost sensor used is new to this part of the world.
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Fatemeh Goodarzi, Kavitha Palaniappan, Manikam Pillay and Mahmoud Ershadi
Exposure to poor indoor air in refurbished buildings is a matter of health concern due to the growing concentrations of various contaminants as a result of building airtightness…
Abstract
Purpose
Exposure to poor indoor air in refurbished buildings is a matter of health concern due to the growing concentrations of various contaminants as a result of building airtightness without amendment of ventilation, or the use of building materials such as glue, paint, thinner and varnishes. Recent studies have been conducted to measure indoor air pollutants and assess the health risks affecting the quality of life, productivity and well-being of human beings. However, limited review studies have been recently conducted to provide an overview of the state of knowledge. This study aims to conduct a scoping review of indoor air quality (IAQ) in the context of refurbished or energy-retrofitted buildings.
Design/methodology/approach
A systematic screening process based on the PRISMA protocol was followed to extract relevant articles. Web of Science, Scopus, Google Scholar and PubMed were searched using customised search formulas. Among 276 potentially relevant records, 38 studies were included in the final review covering a period from 2015 to 2022.
Findings
Researchers mapped out the measured compounds in the selected studies and found that carbon dioxide (CO2) (11%) and total volatile organic compounds (11%) were among the most commonly measured contaminants. Two trends of research were found including (1) the impact of ventilative properties on IAQ and (2) the impact of introducing building materials on IAQ.
Originality/value
The contribution of this study lies in summarising evidence on IAQ measurements in refurbished buildings, discussing recent advancements, revealing significant gaps and limitations, identifying the trends of research and drawing conclusions regarding future research directions on the topic.
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Chuloh Jung, Muhammad Azzam Ismail, Mohammad Arar and Nahla AlQassimi
This study aims to evaluate the efficiency of various techniques for enhancing indoor air quality (IAQ) in construction. It analyzed the alterations in the concentration of indoor…
Abstract
Purpose
This study aims to evaluate the efficiency of various techniques for enhancing indoor air quality (IAQ) in construction. It analyzed the alterations in the concentration of indoor air pollutants over time for each product employed in controlling pollution sources and removing it, which included eco-friendly substances and adsorbents. The study will provide more precise and dependable data on the effectiveness of these control methods, ultimately supporting the creation of more efficient and sustainable approaches for managing indoor air pollution in buildings.
Design/methodology/approach
The research investigates the impact of eco-friendly materials and adsorbents on improving indoor air quality (IAQ) in Dubai's tall apartment buildings. Field experiments were conducted in six units of The Gate Tower, comparing the IAQ of three units built with “excellent” grade eco-friendly materials with three built with “good” grade materials. Another experiment evaluated two adsorbent products (H and Z) in the Majestic Tower over six months. Results indicate that “excellent” grade materials significantly reduced toluene emissions. Adsorbent product Z showed promising results in pollutant reduction, but there is concern about the long-term behavior of adsorbed chemicals. The study emphasizes further research on household pollutant management.
Findings
The research studied the effects of eco-friendly materials and adsorbents on indoor air quality in Dubai's new apartments. It found that apartments using “excellent” eco-friendly materials had significantly better air quality, particularly reduced toluene concentrations, compared to those using “good” materials. However, high formaldehyde (HCHO) emissions were observed from wood products. While certain construction materials led to increased ethylbenzene and xylene levels, adsorbent product Z showed promise in reducing pollutants. Yet, there is a potential concern about the long-term rerelease of these trapped chemicals. The study emphasizes the need for ongoing research in indoor pollutant management.
Research limitations/implications
The research, while extensive, faced limitations in assessing the long-term behavior of adsorbed chemicals, particularly the potential for rereleasing trapped pollutants over time. Despite the study spanning a considerable period, indoor air pollutant concentrations in target households did not stabilize, making it challenging to determine definitive improvement effects and reduction rates among products. Comparisons were primarily relative between target units, and the rapid rise in pollutants during furniture introduction warrants further examination. Consequently, while the research provides essential insights, it underscores the need for more prolonged and comprehensive evaluations to fully understand the materials' and adsorbents' impacts on indoor air quality.
Practical implications
The research underscores the importance of choosing eco-friendly materials in new apartment constructions for better IAQ. Specifically, using “excellent” graded materials can significantly reduce harmful pollutants like toluene. However, the study also highlights that certain construction activities, such as introducing furniture, can rapidly elevate pollutant levels. Moreover, while adsorbents like product Z showed promise in reducing pollutants, there is potential for adsorbed chemicals to be rereleased over time. For practical implementation, prioritizing higher-grade eco-friendly materials and further investigation into furniture emissions and long-term behavior of adsorbents can lead to healthier indoor environments in newly built apartments.
Originality/value
The research offers a unique empirical assessment of eco-friendly materials' impact on indoor air quality within Dubai's rapidly constructed apartment buildings. Through field experiments, it directly compares different material grades, providing concrete data on pollutant levels in newly built environments. Additionally, it explores the efficacy of specific adsorbents, which is of high value to the construction and public health sectors. The findings shed light on how construction choices can influence indoor air pollution, offering valuable insights to builders, policymakers and residents aiming to promote public health and safety in urban living spaces.
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Hongya Niu, Chunmiao Wu, Xinyi Ma, Xiaoteng Ji, Yuting Tian and Jinxi Wang
This study aims to better understand the morphological characteristics of single particle and the health risk characteristics of heavy metals in PM2.5 in different functional…
Abstract
Purpose
This study aims to better understand the morphological characteristics of single particle and the health risk characteristics of heavy metals in PM2.5 in different functional areas of Handan City.
Design/methodology/approach
High resolution transmission electron microscopy was used to observe the aerosol samples collected from different functional areas of Handan City. The morphology and size distribution of the particles collected on hazy and clear days were compared. The health risk evaluation model was applied to evaluate the hazardous effects of particles on human health in different functional areas on hazy days.
Findings
The results show that the particulate matter in different functional areas is dominated by spherical particles in different weather conditions. In particular, the proportion of spherical particles exceeds 70% on the haze day, and the percentage of soot aggregates increases significantly on the clear day. The percentage of each type of particle in the teaching and living areas varied less under different weather conditions. Except for the industrial area, the size distribution of each type of particle in haze samples is larger than that on the clear day. Spherical particles contribute more to the small particle size segment. Soot aggregate and other shaped particles contribute more to the large size segment. The mass concentrations of hazardous elements (HEs) in PM2.5 in different functional areas on consecutive haze pollution days were illustrated as industrial area > traffic area > living area > teaching area. Compared with the other functional areas, the teaching area had the lowest noncarcinogenic risk of HEs. The lifetime carcinogenic risk values of Cr and As elements in each functional area have exceeded residents’ threshold levels and are at high risk of carcinogenicity. Among the four functional areas, the industrial area has the highest carcinogenic and noncarcinogenic risks. But the effects of HEs on human health in the other functional areas should also be taken seriously and continuously controlled.
Originality/value
The significance of the study is to further understand the morphological characteristics of single particles and the health risks of heavy metals in different functional areas of Handan City. the authors hope to provide a reference for other coal-burning industrial cities to develop plans to improve air quality and human respiratory health.
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Ismail Abiodun Sulaimon, Hafiz Alaka, Razak Olu-Ajayi, Mubashir Ahmad, Saheed Ajayi and Abdul Hye
Road traffic emissions are generally believed to contribute immensely to air pollution, but the effect of road traffic data sets on air quality (AQ) predictions has not been fully…
Abstract
Purpose
Road traffic emissions are generally believed to contribute immensely to air pollution, but the effect of road traffic data sets on air quality (AQ) predictions has not been fully investigated. This paper aims to investigate the effects traffic data set have on the performance of machine learning (ML) predictive models in AQ prediction.
Design/methodology/approach
To achieve this, the authors have set up an experiment with the control data set having only the AQ data set and meteorological (Met) data set, while the experimental data set is made up of the AQ data set, Met data set and traffic data set. Several ML models (such as extra trees regressor, eXtreme gradient boosting regressor, random forest regressor, K-neighbors regressor and two others) were trained, tested and compared on these individual combinations of data sets to predict the volume of PM2.5, PM10, NO2 and O3 in the atmosphere at various times of the day.
Findings
The result obtained showed that various ML algorithms react differently to the traffic data set despite generally contributing to the performance improvement of all the ML algorithms considered in this study by at least 20% and an error reduction of at least 18.97%.
Research limitations/implications
This research is limited in terms of the study area, and the result cannot be generalized outside of the UK as some of the inherent conditions may not be similar elsewhere. Additionally, only the ML algorithms commonly used in literature are considered in this research, therefore, leaving out a few other ML algorithms.
Practical implications
This study reinforces the belief that the traffic data set has a significant effect on improving the performance of air pollution ML prediction models. Hence, there is an indication that ML algorithms behave differently when trained with a form of traffic data set in the development of an AQ prediction model. This implies that developers and researchers in AQ prediction need to identify the ML algorithms that behave in their best interest before implementation.
Originality/value
The result of this study will enable researchers to focus more on algorithms of benefit when using traffic data sets in AQ prediction.
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