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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 aim of this paper is to quantify the impact of transboundary air pollutants, particularly those related to urban traffic, on health outcomes. The importance of focusing on the health implications of transboundary pollution is due to the fact that these emissions originate from another jurisdiction, thus constituting international negative externalities. Thus, by isolating and quantifying the impact of these transboundary air pollutants on domestic health outcomes, the authors can understand more clearly the extent of these externalities, identify their ramifications for health and emphasise the importance of cross-country cooperation in the fight against air pollution.

The authors employ panel data regression analysis to look at the relationship between emissions of transboundary air pollution and mortality rates from various respiratory diseases among a sample of 40 European countries, over the period 2003–2014. In turn, the authors use annual data on transboundary emissions of sulphur oxides (SOx), nitrogen oxides (NOx) and fine particulate matter (PM_2.5), together with detailed data on the per capita incidence of various respiratory diseases, including lung cancer, asthma and chronic obstructive pulmonary disease (COPD). The authors consider a number of different regression equation specifications and control for potential confounders like the quality of healthcare and economic prosperity within each country.

The results show that transboundary emissions of PM_2.5 are positively and significantly related to mortality rates from asthma in our sample of countries. Quantitatively, a 10% increase in PM_2.5 transboundary emissions per capita from neighbouring countries is associated with a 1.4% increase in the asthma mortality rate within the recipient country or roughly 200 deaths by asthma per year across our sample.

These findings have important policy implications for cross-country cooperation and regulation in the field of pollution abatement and control, particularly since all the countries under consideration form a part of the UN's Convention on Long-Range Transboundary Air Pollution (CLRTAP), a transnational cooperative agreement aimed at curtailing such pollutants on an international level.

Bangladesh is one of the leading countries that has been facing serious air pollution issues, with an exponentially higher death rate attributed to it than other environmental pollution. This study aims to identify the sources and dynamics of particulate matter (PM) pollution across different micro-environments in Rajshahi City.

PMs’ concentration data were collected from 60 sampling stations, located across the six micro-environments of the study area, throughout the year using “HT 9600 Particle Counter.” To assess the level of pollution, the air quality index (AQI) was calculated, and different methods, including observation, group discussion, interview and questionnaire survey, were used to identify the pollution sources.

Both PM_2.5 and PM₁₀ exhibit varied concentrations in different micro-environments, and the area covered by different AQI classes differs considerably throughout the year. The monthly average concentration of PM_2.5 and PM₁₀ was highest in January, 200 and 400 µg/m³ and was lowest in September, 46 and 99 µg/m³, respectively. Among the total 1,440 observations, 853 observations (59.24%) exceeded the national standard. Based on the pollution level, different months and micro-environments in the city have been ranked in descending order as January > December > February > March > April > November > October > May > June > July > August > September and traffic > commercial > industrial > residential > green cover > riverine environment.

Although numerous research has been conducted on air pollution in Bangladesh, the authors are certain that no attempt has been made to address the issue from a multi- micro-environmental perspective. This makes the methodology and findings truly unique and significant in the context of air pollution research in Bangladesh.

The current study investigates the impact of the coronavirus disease 2019 (COVID-19) lockdown restrictions on air quality in an industrial town in Himachal Pradesh (HP) (India) and recommends policies and strategies for mitigating air pollution.

The air quality parameters under study are particulate matter₁₀ (PM₁₀), PM_2.5, SO₂ and NO₂. One-way ANOVA with post-hoc analysis and non-parametric Kruskal–Wallis test, and multiple linear regression analysis are used to validate the data analysis results.

The findings indicate that the lockdown and post-lockdown periods affected pollutant levels even after considering the meteorological conditions. Except for SO₂, all other air quality parameters dropped significantly throughout the lockdown period. Further, the industrial and transportation sectors are the primary sources of air pollution in Paonta Sahib.

Future research should include other industrial locations in the state to understand the relationship between regional air pollution levels and climate change. The findings of this study may add to the discussion on the role of adopting clean technologies and also provide directions for future research on improving air quality in the emerging industrial towns in India.

Very few studies have examined how the pandemic-induced lockdowns impacted air pollution levels in emerging industrial towns in India while also considering the confounding meteorological factors.

The purpose of this paper is to assess the exposure of cooks in rural India (55 households) to the indoor air pollution levels emitted from burning of different fuels, i.e. cow dung, wood, liquefied petroleum gas (LPG) and propane natural gas(PNG) kerosene for cooking purposes.

Indoor air quality was monitored during cooking hours in 55 rural households to estimate the emissions of PM10, PM2.5, CO, NO₂, VOCs and polyaromatic hydrocarbons (PAHs). While, PM10 and PM2.5 were monitored using personal dust samplers on quartz filter paper, CO and VOCs were monitored using on line monitors. The PM10 and PM2.5 mass collected on filter papers was processed to analyse the presence of PAHs using GC.

Results revealed that cow dung is the most polluting fuel with maximum emissions of PM10, PM 2.5, VOCs, CO, NO₂ and Benzene followed by wood and kerosene. Interestingly kerosene combustion emits the highest amount of PAHs. Emissions for all the fuels show the presence of carcinogenic PAHs which could be a serious health concern. The composition of LPG/PNG leads to reductions of pollutants because of better combustion process. LPG which is largely propane and butane, and PNG which is 90 per cent methane prove to be healthier fuels. Based on the results, the authors suggest that technological intervention is required to replace the traditional stoves with improved fuel efficient stoves.

The prevailing weather condition and design of the kitchen in these rural houses severely affect the concentration of pollutants in the kitchen as winter season combined with inadequate ventilation leads to reduced dispersion and accumulation of air pollutants in small kitchens.

The present study provides a detailed analysis of impact of widely‐used cooking practices in India. Even today, countries such as India rely on biomass for cooking practices exposing the cooks to high level of carcinogenic pollutants. Further, women and girls are the most threatened group as they are the primary cooks in these rural Indian settings. Based on the results, the authors suggest that technological as well as policy intervention is required to replace the traditional stoves with improved fuel efficient stoves.

The purpose of this paper is to analyze research works on air pollution published in 2005-2014 and indexed in Web of Science Core Collection.

The data of research publications on “air pollution” from the Web of Science Core Collection database were collected with following search strategy: publications with terms “Air contaminat*”, “Air pollut*”, “pollut* air” or “contaminat* air” in their titles for the period of 2005-2014 were collected. A total of 4,424 articles were published on air pollution during the period of 2005-2014, and the data were used for creation of database in Microsoft Excel for the analysis purpose. Bibliometric analysis techniques were applied wherever necessary.

Out of 4,424 articles published on air pollution in different languages, 4,276 articles were in English. The years 2013 and 2014 showed rapid increase in number of articles published, 563 and 638, respectively. The increased number of articles resulted in an increase in number of pages published and references cited in the articles. The articles published in the year 2006 had received more number of citations (12,318), and the average citation per article for the period was 17.59. Environmental Science was the major Web of Science subject category under which a greater number of articles were published. Article entitled as “Health effects of fine particulate air pollution: Lines that connect”, published in Journal of The Air & Waste Management Association by Pope and Dockery (2006), was the highest cited article (1,743) for the period, and the top most active journals that published huge number of articles were Atmospheric Environment and Environmental Health Perspective, with 11.79 per cent of the total articles (4,424) published.

The findings of the study are limited to the journals covered under Web of Science Core Collection database and articles having the following keywords in their titles: “Air contaminat*”, “Air pollut*”, “pollut* air” or “contaminat* air”.

This study would be useful to researchers and policy makers to get an insight into the research trends of air pollution for effective decision-making and formulation of new research proposals.

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.

A growing number of travelers seek well-being when traveling. As concerning about outdoor air pollution in tourism destinations escalates, little is known about indoor air pollution in hotel guestrooms. The purpose of the present study is to assess particulate matter (PM) pollution in US hotel guestrooms and to provide baseline indoor PM readings in occupied and unoccupied rooms.

A series of field tests and experiments monitoring PM levels were conducted in the guestrooms overnight – with and without occupants – using the sophisticated, industrial-grade PM-monitoring equipment.

The results revealed that PM levels were very low when rooms were unoccupied or when guests were asleep. However, unhealthy PM mass concentrations were observed in occupied rooms when guests engaged in physical activity such as showering and walking around or while room attendants cleaned rooms. Among the physical activities, room cleaning caused hazardous indoor PM pollution, reaching 1,665.9 µg/m³ of PM10 and 140.4 µg/m³ of PM2.5 although they tended to be brief.

Leveraging increasing guest demand in well-being is essential for sustainable business and further growth. Indoor air quality must be recognized as an important factor to be controlled for well-being and health of guests and employees. Major hotel brands should take it into consideration as they infuse well-being DNA into their products and culture.

To the best of the authors’ knowledge, this study is the first empirical investigation of PM pollution both in occupied and unoccupied hotel guestrooms in the USA, which reveals unhealthy PM pollution associated with the routine human activities in occupied guestrooms.

The quantitative assessment of environmental effects should be carried out using the indicators that ensure the best objectivity and efficiency of the environmental impact assessment (EIA) process. The aim of this paper is to clarify the effectiveness of the methods used for prognoses of air pollution from roads. The benchmark method proposed in this paper is based on theoretical knowledge and on analysis of data collected from EIA reports.

The paper includes a literature review and developing of the benchmark method. This research was based on data dispersion analysis, determination of benchmarks, usage of regression method, as well as confrontation of different methods of determination of air emission concentration used in EIA.

The simplified model has been designed for determination of concentration of dust emission in the air from motor roads, the building or reconstruction of which is planned. The results indicate that the benchmark method for determination of air pollution with particulate matter PM10 has been elaborated, which can be used for environmental impact evaluation in motor road construction. The method has been elaborated relying on measurement data of existing motor roads and displays a high level of probability of credibility.

The method is simpler and less time‐consuming than the currently used calculation model. It produces a more precise result than the calculation model. It is outstanding and important since the further development of motor road projects can undoubtedly be judged by the results of EIA.

The results of this research provide a rational and comparative approach for finding the methods of determination of the air emission concentration used in EIA in producing the credible outcome. The results reported in the research show existing problems with calculation of emissions from roads. The proposed benchmark method is simple, easy to use and credible.

This paper aims to present a synergistic approach that combines both construction and environmental expertise to lay the groundwork for a model that can be used to estimate the productivity rate and emissions from construction equipment activities.

The proposed estimating tool is developed by combining the productivity rate model from a reliable construction estimating data sources and the calculation algorithm employed by the EPA's NONROAD model. In order to develop productivity models, simple earthwork activities involving bulldozer, excavator, and dump truck were selected.

The MLR approach proved to be a useful alternative for estimating productivity rate of three pieces of equipment. The MLR models for the productivity rate can explain high percentage of the variability in the data. The models are good to be used as a benchmark for estimating NO_X and PM emissions from some certain types of construction equipment performing earthwork activities. The productivity rate from this model (lcy/hr) is used with emission factors (g/hp‐hr) from EPA's NONROAD model to estimate the total emissions.

The estimating tool proposed in this paper will be an effective means for assessing the environmental impacts of construction activities and will allow equipment owners or fleet managers, policy makers, and project stakeholders to evaluate more sustainable alternatives. The tool will help the contractor to estimate the total expected pollutant emissions for the project, which would be valuable information for a preliminary environmental assessment of the project.

Although there are already methods and models for estimating productivity for construction equipment, there currently is not a means for doing estimates of air pollutant emissions at the same time, particularly for NO_X and PM.