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Air pollution is the combination of fine particles and gases in the atmosphere that harm humans and animals. Our objective is to find the various air pollution contaminants and its repercussion on Homo sapiens’ health. We discuss how air quality is measured with the air quality index and measures that help us cope with the consequences of air pollution. To carry out this study, we carried out a systematic literature review to uncover the different dimensions of air pollution and mitigation strategies. From the existing literature and observations of the different data sets, we can conclude that air pollutants have a severe impact on the life of Homo sapiens, causing various diseases such as respiratory issues, skin diseases, etc. Today, we have ample laws, but the need of the hour is to initiate new policies to change the behavioural of Homo sapiens. Our findings will help in decision-making by stakeholders such as policy-makers, manufacturing industries, households, etc. This article will also help in highlighting the need for Homo sapiens behavioural change.

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.

The purpose of this paper is to investigate whether air pollution has significant impact on corporate cash holdings and financial leverage.

The data of 199 firms listed on Istanbul Stock Exchange during the period 2009–2020 is analyzed by using pooled ordinary least squares and two-step system generalized method of moments models.

The results indicate that firms in regions with high air pollution tend to increase cash level. In addition, the positive effect of air pollution on cash level is stronger and more significant for environmentally sensitive firms and firms with low operational and distress risk. The results also show insignificant effect of air pollution on financial leverage.

Firms in regions with high air pollution should conduct proactive environmental protection procedures and enhance their eco-efficiency instead of holding excess cash that could negatively affect financial performance. In this context, policymakers should provide financial facilities to firms located in regions with high air pollution and that have low ability to finance environmental investments. On the other hand, the environmental laws and regulations introduced by regulatory authorities can enhance the economic development and firm performance by decreasing the adverse influences of air pollution on corporate financial policies.

To the best of the author’s knowledge, this research is one of few that examines the impact of air pollution on corporate cash holdings and financial leverage in emerging markets.

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.

This research aims to explore how consumers' local brand choices differ between air-polluted days and clean days, and why the difference occurs.

Two studies were conducted. Study 1 used the longitudinal consumption data of various yogurt brands and daily air quality indexes in 2014 and 2015. Study 2 conducted three rounds of surveys on a clean day, a general air-polluted day and a seriously air-polluted day.

The findings indicate that consumers show less tendency of attribution and compensatory consumption during air-polluted days, which in turn decrease their willingness to choose local brands.

Implications are provided for future research and marketing practice, especially for local companies that rely heavily on local consumers, and retailers in heavy air-polluted areas.

This paper is the first to illustrate the influence of air pollution on consumers' local brand choices, and it extends current understanding on air pollution and consumer choices by discovering psychological process underneath to explain the effect.

This study aims to examine the relationship between pollution and life expectancy in oil producing communities, where there is a dearth of empirical evidence on how knowledge and coping strategy, agriculture and foreign capital inflows mediate the relationship between pollution and life expectancy.

The study employed a cross sectional survey design to analyze the roles of knowledge and coping strategy, agriculture and foreign capital inflows in the relationship between pollution and life expectancy in Benekuku and Okpai oil producing communities in the Niger Delta. The study employed the modern structural equation modeling (SEM) estimator.

Estimates show the mediating effect of agriculture on air pollution-longevity (coeff. = 0.398; t-value = 4.425; p < 0.05) and (coeff. = −0.120; t-value = −3.862; p < 0.05) mediating effect of foreign capital. The result revealed that agriculture and foreign capital inflows are significant mediators in pollution-life expectancy relations, affirming the Niger Delta as a pollution haven. However, knowledge and coping strategy with estimate of (coeff. = 0.233; t-value = 6.150; p < 0.05) spurs life expectancy.

The study suggests knowledge of hazard identification and reporting and awareness of coping strategy as the panacea to poor life expectancy rate in these local oil producing communities.

The study departs from existing works by estimating the mediating roles of agriculture and foreign capital inflow in air pollution-Life expectancy by controlling for knowledge and coping strategy using the structural equation model with ethical approval from Health Ethics Research Committee.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-11-2022-0734

The purpose of this paper is to use the case of York University in Canada to analyze the connection between University Social Responsibility and voluntary disclosure. The authors examine whether the university’s voluntary air emissions disclosure is performative by exploring whether York University’s espoused commitment to its community stakeholders truly guides its incentive to disclose carbon emissions in the absence of a legal mandate.

This qualitative exploratory study uses a post-humanistic approach to build on publicly available data on key measures and metrics of air quality and carbon emissions to facilitate our understanding of representational and interventionist uses of measurement models by social actors and their basis for making voluntary disclosures.

York University linked the logic of capital markets with sustainability disclosures as an incentive for managing the cost of long-term debt. This paper contributes to measurement practice of sustainability disclosure by reinforcing the practice-theoretic conception of measurement that questions the independent nature of objects measured from the measurement methods and reporting tools.

The findings of this study are important to higher education administrators, regulators and policymakers, as they offer a strategic guide for the assessment of reports on an organization’s commitment to sustainability and in determining the efficacy of voluntary reporting to community stakeholders in general although they are intended for specific groups.

Using York University as an illustrative case, the authors argue that air emissions per se are not a reality that shapes decisions at the organizations; instead, the intra-action of air emissions measurement, communications and operational investments define the reality where sustainability is advanced. Specifically, the authors find that the performative effects of emissions disclosure may be associated with socially desirable outcomes in terms of social responsibility and concrete financial rewards.

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.

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 PM_2.5, PM₁₀, NO₂ and O₃ in the atmosphere at various times of the day.

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%.

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.

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.

The result of this study will enable researchers to focus more on algorithms of benefit when using traffic data sets in AQ prediction.

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.