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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.

There has been a growing concern about air quality because in recent years, industrial and vehicle emissions have resulted in unsatisfactory human health conditions. There is an urgent need for the measurements and estimations of particulate pollutants levels, especially in urban areas. As a contribution to this issue, the purpose of this paper is to use data from measured concentrations of particulate matter and meteorological conditions for the predictions of PM₁₀.

The procedure included daily data collection of current PM₁₀ concentrations for the city of São Carlos-SP, Brazil. These data series enabled to use an estimator based on artificial neural networks. Data sets were collected using the high-volume sampler equipment (VFA-MP10) in the period ranging from 1997 to 2006 and from 2014 to 2015. The predictive models were created using statistics from meteorological data. The models were developed using two neural network architectures, namely, perceptron multilayer (MLP) and non-linear autoregressive exogenous (NARX) inputs network.

It was observed that, over time, there was a decrease in the PM₁₀ concentration rates. This is due to the implementation of more strict environmental laws and the development of less polluting technologies. The model NARX that used as input layer the climatic variables and the PM₁₀ of the previous day presented the highest average absolute error. However, the NARX model presented the fastest convergence compared with the MLP network.

The presentation of a given PM₁₀ concentration of the previous day improved the performance of the predictive models. This paper brings contributions with the NARX model applications.

This paper aims to present the most influential factors on classroom indoor PM_2.5 (Particulate Matter < 2.5 µ), determining the level of PM_2.5 concentration in five pre-schools located in the most densely populated district of the Tehran metropolitan area (district 6) as a case study to consider the children's exposure to air pollutants and introducing a suitable model, for the first time, to predict PM_2.5 concentration changes, inside pre-schools.

Indoor and outdoor classes PM_2.5 concentrations were measured using two DUSTTRAK direct-reading instruments. Additional class status information was also recorded; concurrently, urban PM_2.5 concentrations and meteorological data were obtained from the fixed monitoring stations and Meteorological Organization. Then, the predicted concentrations of the indoor PM_2.5, from introduced multiple linear regression model via SPSS, compared with the nearest urban air pollution monitoring stations data.

The average outdoor PM_2.5 concentration (43 ± 0.32 µg m⁻³) was higher than the mean indoor (32 ± 0. 21 µg m⁻³), and both were significantly (p < 0.001) surpassing the 24-h EPA standard level. The indoor PM_2.5 concentrations had the highest level in the autumn (48.7 µg m⁻³) and significantly correlated with the outdoor PM_2.5 (r = 0.94, p < 0.001), the number of pupils, ambient temperature, wind speed, wind direction and open area of the doors and windows (p < 0.001). These parameters, as the main determinants, have led to present a 7-variable regression model, with R² = 0.705, which can predict PM_2.5 concentrations in the pre-school classes with more than 80% accuracy. It can be presumed that the penetration of outdoor PM_2.5 was the main source of indoor PM_2.5 concentrations.

This study faced several limitations, such as accessibility to classrooms, and limitations in technicians' numbers, leading to researchers monitoring indoor and outdoor PM concentrations in schools once a week. Additionally, regarding logistical limitations to using monitoring instruments in pre-schools simultaneously, correction factors by running the instruments were applied to obtain comparable measurements.

The author hereby declares that this submission is his own work and to the best of its knowledge it contains no materials previously published or written by another person.

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 monitoring of respirable particulate matter (PM₁₀) and of total carbon percentage (mass basis) in the atmosphere of São Carlos (SP) was performed in the period between September 1997 and January 2000. São Carlos, located in the central region of the state of São Paulo, has a population of close to 180,000 inhabitants and about 500 industrial establishments of medium to small size, mainly dealing with metallurgy, textiles, food and ceramics. The equipment used for air monitoring was a high volume sampler (GVS‐GRASEBY/GMW) equipped with a one‐stage inertial separator for a 10μm particle cut diameter. The PM₁₀ concentration was determined by gravimetry and the total carbon concentration by the Ströheim method. The results show a well defined seasonal dependence of both the PM₁₀ and of the total carbon concentration. Higher concentrations of PM₁₀ and carbon were observed in autumn and winter, which also coincided with low relative humidity and precipitation. The measured trends were compared with the PM₁₀ data from the city of São Paulo in the same period and showed similar seasonal dependence. However, in relative terms, the PM₁₀ concentration in São Carlos showed stronger seasonal dependence than in São Paulo.

In order to reduce the health impacts of air pollution effectively, developing strategies that involves individual or community level is crucial. The purpose of this paper is to assess people’s protective practices for inhalable particulate matter and its significant determinants such as general characteristics, knowledge and attitude among residents of an urban residential area, Dhaka, Bangladesh.

This cross-sectional study was conducted by systematic random sampling. A total of 424 people, who lived in that area for not less than two years before the survey, were interviewed using a structured questionnaire. χ² and Fisher’s exact test were used to analyze the data.

Only a small proportion of respondents had high practice level. In addition, a little more than half has high level of knowledge about inhalable particulate matter, its adverse health effects and protective practices and almost 70 percent had high level of attitude toward air pollution. The protective practices for small inhalable particulate matter was significantly associated with age, educational level, occupation, knowledge and attitude toward small inhalable particulate matter, its adverse health effects and protective measures.

A good level of knowledge about the prevailing air pollution and related health risks can be crucial to develop more focused attempt at changing the current situation with public participation. The environmental experts and health volunteer should disseminate precise and adequate information about long-term health hazards of particulate matter and measures of exposure prevention to improve the protective practices.

Severe airborne particulate pollution frequently occurs over the North China Plain (NCP) region in recent years. To better understand the characteristics of carbonaceous components in particulate matter (PM) over the NCP region.

PM samples were collected at a typical area affected by industrial emissions in Handan, in January 2016. The concentrations of organic carbon (OC) and elemental carbon (EC) in PM of different size ranges (i.e. PM_2.5, PM₁₀ and TSP) were measured. The concentrations of secondary organic carbon (SOC) were estimated by the EC tracer method.

The results show that the concentration of OC ranged from 14.9 μg m⁻³ to 108.4 μg m⁻³, and that of EC ranged from 4.0 μg m⁻³ to 19.4μg m⁻³, when PM_2.5 changed from 58.0μg m⁻³ to 251.1μg m⁻³ during haze days, and the carbonaceous aerosols most distributed in PM_2.5 rather than large fraction. The concentrations of OC and EC PM_2.5 correlated better (r = 0.7) than in PM_2.5−10 and PM_>10, implying that primary emissions were dominant sources of OC and EC in PM_2.5. The mean ratios of OC/EC in PM_2.5, PM_2.5–10 and PM_>10 were 4.4 ± 2.1, 3.6 ± 0.9 and 1.9 ± 0.7, respectively. Based on estimation, SOC accounted for 16.3%, 22.0% and 9.1% in PM_2.5, PM_2.5–10 and PM_>10 respectively.

The ratio of SOC/OC (48.2%) in PM_2.5 was higher in Handan than those (28%–32%) in other megacities, e.g. Beijing, Tianjin and Shijiazhuang in the NCP, suggesting that the formation of SOC contributed significantly to OC. The mean mass absorption efficiencies of EC (MACEC) in PM₁₀ and TSP were 3.4 m² g⁻¹ (1.9–6.6 m² g⁻¹) and 2.9 m² g⁻¹ (1.6–5.6 m² g⁻¹), respectively, both of which had similar variation patterns to those of OC/EC and SOC/OC.

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.

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.