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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.

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.

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 PM_2.5 concentration level than bagging and boosting ensemble models.

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.

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

This study fills a gap in literature by providing a justification for selecting appropriate ensemble ML algorithms for PM_2.5 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 PM_2.5 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.

This systematic review examined the emerging threat of indoor and outdoor pollutants to public health in Latin America and the Caribbean (LAC).

Pollutants and pollution levels are becoming an increasing cause for concern within the LAC region, primarily because of the rapid increase in urbanization and the use of fossil fuels. The rise in indoor and outdoor air pollutants impacts public health, and there are limited regional studies on the impact of these pollutants and how they affect public health. A comprehensive literature search was conducted using Google Scholar, PubMed, Scopus, EBSCOhost, Web of Science and ScienceDirect databases. Significant search terms included “indoor air pollution,” “outdoor air pollution,” “pollution,” “Latin America,” “Central America,” “South America” and “Caribbean was used.” The systematic review utilized the Rayyan systematic software for uploading and sorting study references.

Database searches produced 1,674 results, of which, after using the inclusion–exclusion criteria and assessing for bias, 16 studies were included and used for the systematic review. These studies covered both indoor and outdoor pollution. Various indoor and outdoor air pollutants linked to low birth weight, asthma, cancer and DNA impairment were reported in this review. Even though only some intervention programs are available within the region to mitigate the harmful effects of pollution, these programs need to be robust and appropriately implemented, causing possible threats to public health. Significant gaps in the research were identified, especially in the Caribbean.

Limitations of the study include limited available research done within LAC, with most of the research quantifying pollutants rather than addressing their impacts. Additionally, most studies focus on air pollution but neglect water and land pollution’s effects on public health. For this reason, the 16 studies included limited robustness of the review.

Although available studies quantifying pollution threats in LAC were identified in this review, research on the adverse impacts of pollution, especially concerning public health, is limited. LAC countries should explore making cities more energy-efficient, compact and green while improving the transportation sector by utilizing clean power generation. In order to properly lessen the effects of pollution on public health, more research needs to be done and implemented programs that are working need to be strengthened and expanded.

The purpose of this paper is to explore the significance impact of air quality as a contributing factor on residential property rents by applying geo-informatics to economic issues. Since air pollution poses a severe health threat, city residents should have a right to know about the (invisible) hazards they are exposed to.

Within spatial-temporal modeling of air pollutants in Berlin, Germany, three interpolation techniques are tested. The most suitable one is selected to create seasonal maps for 2018 and 2021 with pollution concentrations for particulate matter values and nitrogen dioxide for each 1,000 m² cell within the administrative boundaries. Based on the evaluated pollution particulate matter values, which are used as additional variables for semi-parametric regressions the impact of the air quality on rents is estimated.

The findings reveal a compelling association between air quality and the economic aspect of the residential real estate market, with noteworthy implications for both tenants and property investors. The relationship between air pollution variables and rents is statistically significant. However, there is only a “willingness-to- pay” for low particulate matter values, but not for nitrogen dioxide concentrations. With good air quality, residents in Berlin are willing to pay a higher rent (3%).

These results suggest that a “marginal willingness-to-pay” occurs in a German city. The research underscores the multifaceted impact of air quality on the residential rental market in Berlin. The evidence supports the notion that a cleaner environment not only benefits human health and the planet but also contributes significantly to the economic bottom line of property investors.

The paper has a unique data engineering approach. It collects spatiotemporal data from network of state-certified measuring sites to create an index of air pollution. This spatial information is merged with residential listings. Afterward non-linear regression models are estimated.

As commercial cooking is known as a source that generates great concentrations of particulate matter (PM) emissions first accumulating in kitchens before spreading to dining areas, this study aims to explore how to improve restaurants’ efforts to reduce PM emissions by the application of attribution theory.

Data were obtained from restaurant managers operating their business in South Korea, considered to be qualified to provide accurate information regarding the survey questions. A scenario-based experimental approach was used to test the hypothesized relationships. Cognitive and emotional risk judgements were assessed for its potential interaction effects on the relationships between restaurant perceptions of PM source attributions, preventions attitudes and mitigation behavioral intentions.

Results revealed that perceptions of PM main sources were attributed to internal rather than external factors, which improved mitigation behavioral intentions. Such an effect was partially mediated through PM pollution prevention attitudes. Additionally, when applying external source attributions, PM mitigation behavioral intentions were improved by cognitive risk judgements, and PM prevention attitudes were enhanced by affective risk judgements.

Results assist restaurants to better understand their operations that may be emitting significant levels of PM, thereby encouraging them to set more ambitious and effective PM mitigation operational guidelines for their employees and diners.

This study provides a fundamental baseline of management perceptions regarding PM emissions related to restaurant mitigation behavioral intentions. Results are useful in designing appropriate communication strategies addressing restaurant PM pollution issues to improve internal restaurant practices regarding clean air quality.

This study aims to better understand the morphological characteristics of single particle and the health risk characteristics of heavy metals in PM_2.5 in different functional areas of Handan City.

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.

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 PM_2.5 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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

This study aims to examine the impact of local air pollution on the presence of central air conditioners in apartments in Lebanon.

This study applies a Probit model in a unique data set on apartments’ listings for sale in Lebanon collected by Marrouch and Sayour (2021). The data set includes information about air pollution concentrations, dwellings’ characteristics, geographic features and location characteristics.

This study finds that local air pollution positively and significantly affects the presence of central air conditioning in dwellings. The estimated increase in the probability of having central air conditioning for a one microgram per cubic meter increase in Particulate Matter 2.5 concentration is 6.4%.

The data set in this study is cross-sectional and thus does not capture variations over time for the examined variables.

The Probit regression approximates an equation that can predict the presence of central air conditioners in dwellings, which might be useful to policymakers.

The findings suggest that local pollution is a significant factor in household behavior in Lebanon.

This paper adds to the scant literature studying the effects of air pollution on the presence of central air conditioning in developing countries. To the best of the authors’ knowledge, this is the first paper to study the impact of air pollution on the presence of central air conditioning in the Middle East and North Africa Region.

This study provides real-world evidence for the relationship between strategic communication from a global/multinational perspective and the effectiveness of corporate message strategies in the context of environment risk communication. Among sustainability issues, particulate matter (PM) air pollution has threatened the health and social wellbeing of citizens in many countries. The purpose of this paper is to apply the message framing and attribution theories in the context of sustainability communication to determine the effects of risk message characteristics on publics’ risk responses.

Using a 2 (message frame: gain vs loss) × 2 (attribution type: internal vs external) × 2 (country: China vs South Korea) between-subjects experimental design, the study examines the message framing strategies' on publics' risk responses (i.e. risk perception, risk responsibility attribution held toward another country and sustainable behavioral intention for risk prevention).

Findings include (1) main effects of message characteristics on participants’ risk responses; (2) the impact of country difference on participants’ differential risk responses and (3) three-way interactions on how risk message framing, risk threats type and country difference jointly affect not only participants’ risk perception and risk responsibility attribution but also their sustainable behavioral intention to prevent PM.

Although this study used young–adult samples in China and South Korea, the study advances the theory building in strategic environmental risk communication by emphasizing a global/multinational perspective in investigating differences among at-risk publics threatened by large-scale environmental risks.

The study's findings provide evidence-based implications such as how government agencies can enhance the environmental risk message strategy so that it induces more desired risk communication outcomes among at-risk publics. Insights from our study offer practical recommendations on which message feature is relatively more impactful in increasing intention for prosocial behavioral changes.

This study on all measured risk responses reveals important differences between at-risk young publics in China and South Korea and how they respond differently to a shared environmental risk such as PM. The study's findings provide new evidence that media coverage of global environmental issues needs to be studied at the national level, and cross-cultural comparisons are imperative to understand publics’ responses to different news strategies. Thus, this study offers implications for practitioners to understand and apply appropriate strategies to publics in a social way across different countries so as to tailor risk communication messaging.

This study offers new insights to help connect message framing effects with communication management practice at the multi-national level, providing recommendations for government communication practitioners regarding which PM message features are more likely to be effective in forming proper risk perception and motivate sustainable actions among at-risk publics in different countries.

The aim of this study is to understand the environmental benefits and economic savings associated with adoption of circular economy in the construction sector. The research findings will support different stakeholders and decision makers to develop business models based on responsible consumption of resources and build sustainable business models.

The research uses mixed methodology wherein inventory for life cycle assessment and life cycle costing for environmental and economic impacts is based on primary data using on-site visits for qualitative and quantitative data.

Different types of land transportation infrastructures are compared for their environmental impacts. It is found that bridges have the highest environmental impacts as compared to tunnels, roads and railways. Further, the results affirm the environmental and economic benefits of adopting circular economy practices.

This is one of a kind research that compares the environmental and economic tradeoffs of adopting circular economy in different types of land transportation infrastructures.