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Analysis and comparison of three existing indicators of the air quality: the American air quality index, the French Atmo, and the Italian Indice di Qualità dell'Aria.

International general and organic regulations to control air quality do not exist yet. Consequently many countries have independently implemented specific indicators to monitor the air pollution and then alert people of resulting health risks. The paper focuses on three of them. Each one is independently presented showing the peculiarities. Therefore, these indicators are compared to identify the features they have in common, as well as those that set them apart, and to figure out which are either restrictive or permissive, and what are their qualities and drawbacks.

The three mentioned indicators convert the real health risk due to air pollution into numerical information, in different ways. Doing this, they carry out some simplifications or assumptions, which can be questionable. The main difficulty is to understand if the indicators aggregate the different pollutant concentrations consistently with the real effects on human health.

This paper analyses only three specific indicators of the air quality, selected among the existing ones.

Indicators should carefully be analysed to understand if they properly represents the real effects of pollutants on human health. The most critical aspect to consider is the aggregation of the different pollutant concentrations in one information.

This paper analyses the efficacy of representation of some air quality indicators. It discusses if indicators aggregation is consistent with the real effects on human health.

Aiming to address the forecasting dilemma of seasonal air quality, the authors design the novel self-adaptive seasonal adjustment factor to extract the seasonal fluctuation information about the air quality index. Based on the novel self-adaptive seasonal adjustment factor, the novel seasonal grey forecasting models are established to predict the air quality in China.

This paper constructs a novel self-adaptive seasonal adjustment factor for quantifying the seasonal difference information of air quality. The novel self-adaptive seasonal adjustment factor reflects the periodic fluctuations of air quality. Therefore, it is employed to optimize the data generation of three conventional grey models, consisting of the GM(1,1) model, the discrete grey model and the fractional-order grey model. Then three novel self-adaptive seasonal grey forecasting models, including the self-adaptive seasonal GM(1,1) model (SAGM(1,1)), the self-adaptive seasonal discrete grey model (SADGM(1,1)) and the self-adaptive seasonal fractional-order grey model (SAFGM(1,1)), are put forward for prognosticating the air quality of all provinces in China .

The experiment results confirm that the novel self-adaptive seasonal adjustment factors promote the precision of the conventional grey models remarkably. Simultaneously, compared with three non-seasonal grey forecasting models and the SARIMA model, the performance of self-adaptive seasonal grey forecasting models is outstanding, which indicates that they capture the seasonal changes of air quality more efficiently.

Since air quality is affected by various factors, subsequent research may consider including meteorological conditions, pollutant emissions and other factors to perfect the self-adaptive seasonal grey models.

Given the problematic air pollution situation in China, timely and accurate air quality forecasting technology is exceptionally crucial for mitigating their adverse effects on the environment and human health. The paper proposes three self-adaptive seasonal grey forecasting models to forecast the air quality index of all provinces in China, which improves the adaptability of conventional grey models and provides more efficient prediction tools for air quality.

The self-adaptive seasonal adjustment factors are constructed to characterize the seasonal fluctuations of air quality index. Three novel self-adaptive seasonal grey forecasting models are established for prognosticating the air quality of all provinces in China. The robustness of the proposed grey models is reinforced by integrating the seasonal irregularity. The proposed methods acquire better forecasting precisions compared with the non-seasonal grey models and the SARIMA model.

In this paper, the authors assume that the matter of a good quality of air will grow in importance in the future, and that it could be a noticeable part of a quality system to be used for communication purposes. The authors propose some synthetic indicators for air quality and discuss them in-depth to provide robust indexes suitable for ranking a set of alpine destinations.

The authors use locally based data on three pollutants with reference to 25 alpine touristic destinations. Starting from hourly data for 62 days of the 2014 summer season for each pollutant, the authors end with a single synthetic air quality index for any locality. The aggregation methodologies are at the core of the paper; in particular, the authors propose a constant elasticity of substitution (CES) function – a well-known tool in Economics – to aggregate the pollutants the authors deal with. Because the degree of substitution among them is unknown, the authors simulate two extreme cases and an intermediate one to rank the localities on the bases of the synthetic air quality index.

All the Alpine destinations the authors considered have – or had in summer 2014 – an excellent open-air quality, and this was a permanent trait of that period. Ranks look robust (stable), as they do not depend significantly on the available options of the techniques the authors used.

The originality of the paper is inherent first in the idea that high quality air can be an issue of interest for touristic goals, especially in the case of mountain destinations, which have all proven to offer an excellent open-air quality. Second, from a methodological perspective, the paper frames dispersed and sectorial approaches into a single flexible one which has the property of being theoretically grounded into the economics mainstream and, at meantime, suitable to deal with some lack of information and research.

The purpose of this paper is to build a support vector machine (SVM) model to evaluate the city air quality level, using the three main air pollutants selected as evaluation index.

PM₁₀, SO₂, NO₂ are the most important three air pollutants and their concentration data are selected as the influencing factor. And the SVM model is build and used to evaluate the air quality level of 29 major cities in China 2011. The cross-validation is adopted to select optimal penalty parameters and optimal kernel function, and the classification accuracies achieved under different normalization methods and kernel functions are compared in the end.

The study found, the parameters and kernel functions chosen by the SVM model have influence on the model's prediction accuracy. Through continuous optimization of model parameters, finally it is found that the model performs better with [0, 1] normalization method and RBF kernel function. It proves that SVM classification model is effective in dealing with the problem of city air quality evaluation.

The result of this study shows that the SVM classification model can be well applied to predict the city air quality level by using air pollutants concentration data as evaluation index. It can help the government and relevant department issue corresponding environmental policy and environmental protection measures.

The qualitative and quantitative study method are combined in this paper, on the basis of predecessors’ research results, as well as careful analysis to select evaluation index. The SVM classification model build is simulated by using Matlab technique, beyond comparing the accuracy, its outcomes and its efficiency in classification are demonstrated.

A comprehensive investigation on the outlet air position effects on the thermal comfort and air quality has been achieved. In addition, airflow and temperature distributions in ventilated cavities filled with an air-CO₂ mixture with mixed convection are predicted. The airflow enters from the cavity through an opening in the lower side of the left vertical wall and exits through the opening in one wall of the cavity. This paper aims to investigate the outlet location effect, four different placement configurations of output ports are considered. Three of them are placed on the upper side and the fourth on top of the opposite side of the inlet opening. A uniform heat and CO₂ contaminant source are applied on the left vertical wall, while the remaining walls are impermeable and adiabatic to heat and solute. The cooling efficiency inside the enclosure and the average fluid temperature are computed for different Reynolds and Rayleigh numbers to find the most suitable fluid outlet position that ensures indoor comfortable conditions while effectively removing heat and the contaminant. This is demonstrated by three relevant indices, namely, the effectiveness for heat removal, the contaminant removal and the index of indoor air quality.

The simulations were performed via the finite-volume scSTREAM CFD solver V11. Three different values of CO₂ amount are considered, namely, 10³, 2 × 10³ and 3 × 10³ ppm, the Reynolds number being in the range 100 ≤ Re ≤ 800.

Based on the findings obtained, it is the configuration whose air outlet is placed near the heat source and the contaminant, which provides a better air distribution and a ventilation efficiency compared to the others ventilation strategies.

The studies on heat and mass transfers by natural and forced convection in ventilated cavities remain a fruitful research topic. Thereby, such a study deals with different ventilation strategies through cavities containing an air-CO₂ mixture subjected to a mixed regime. In particular, the air inlet velocity and contaminant sources’ effects on thermal comfort and air quality have been investigated.

The aim of this paper is to better understand the impact of petroleum production facilities on ambient air quality of host airshed.

Field measurements were taken daily for four consecutive months around petroleum production facilities in the Niger Delta area, of Nigeria, one of the world's important petroleum producing areas. Statistical analysis tool and air quality analytical tool known as the air quality index (AQI) were applied on the field data obtained.

The mean measured daily concentrations of both carbon monoxide (CO) and nitrogen dioxide (NO₂) between distances 50 and 500 m of petroleum flow stations were of the range 140 – 3400 μg/m3 and 23 – 1250 μg/m3 respectively. The AQI from measured CO concentrations in the study area ranged between 1 and 44, an indication of good AQI category with no known health effects but a need for cautionary statement. Similarly, over 97 percent of the measured concentrations of NO₂ were below 0.60 ppm which implies that the AQI of the host environment of the flow stations were below 200 with respect to NO₂ thus indicating a good category of air with no health alarm. However, at the 60 m distance around a flow station, the AQI was 210 thus the quality of available air at this point could be described as very unhealthy. Generally the concentrations of CO were higher than NO₂ in all the distances from the flow stations and were corroborated with their significant T‐test values. The T‐test results of the relationship between the concentrations of the air pollutants per time of the day, showed that their T‐test values were not significant, indicating that concentrations of these air pollutants were independent of the sampling time. A strong and positive correlation existed between the two air pollutants signifying common sources.

The paper highlights that at 60 m distance around petroleum production facilities, people with respiratory or heart disease, the elderly and children should be prevented from gaining access in the morning without taking necessary precautionary measures against the inhalation of air pollutants.

The purpose of this paper is to provide a better method for quality management to maintain an essential level of quality in different fields like product quality, service quality, air quality, etc.

In this paper, a hybrid adaptive time-variant fuzzy time series (FTS) model with genetic algorithm (GA) has been applied to predict the air pollution index. Fuzzification of data is optimized by GAs. Heuristic value selection algorithm is used for selecting the window size. Two algorithms are proposed for forecasting. First algorithm is used in training phase to compute forecasted values according to the heuristic value selection algorithm. Thus, obtained sequence of heuristics is used for second algorithm in which forecasted values are selected with the help of defined rules.

The proposed model is able to predict AQI more accurately when an appropriate heuristic value is chosen for the FTS model. It is tested and evaluated on real time air pollution data of two popular tourism cities of India. In the experimental results, it is observed that the proposed model performs better than the existing models.

The management and prediction of air quality have become essential in our day-to-day life because air quality affects not only the health of human beings but also the health of monuments. This research predicts the air quality index (AQI) of a place.

The proposed method is an improved version of the adaptive time-variant FTS model. Further, a nature-inspired algorithm has been integrated for the selection and optimization of fuzzy intervals.

Higher education institutions are widely known both for their promotion to education for sustainable development (ESD) and for their contribution as living labs to urban management strategies. As for strategies, smart and learning campuses have recently gained significant attention. This paper aims to report an air quality monitoring experience with focus on the smart and learning campus and discuss its implications for the university context with regard to ESD and sustainable development goal (SDG) integration.

The air quality monitoring was held at the main campus of University of Passo Fundo and focused on three pollutants directly related to vehicle emissions. The air quality index (AQI) was presented on a website, along with information regarding health problems caused by air pollution, main sources of emissions and strategies to reduce it.

The results showed how the decrease in air quality is related to the traffic emissions and the fact that exposing students to a smart and learning environment could teach them about sustainability education.

This case study demonstrated how monitoring air quality in a smart environment could highlight and communicate the impact of urban mobility on air quality and alerted to the need for more sustainable choices, including transports.

This paper contributes to the literature by showing the potential of a smart-learning campus integration and its contribution towards the ESD and the UN SDGs.

This paper analyzes how air pollution and the public attention to it influence the returns of stocks in the Indian context.

The study uses firm-level data for the stocks listed on National Stock Exchange in India. Air quality is measured using the Air Quality Index (AQI) values provided by US Embassy and Consulates’ Air Quality Monitor in India. Google Search Volume Index (GSVI) of the relevant terms acts as the measure of public attention. Appropriate regression models are used to address how AQI and attention influence stock returns.

It is observed that degrading air quality alone is unable to explain the stock returns. It is the combined effect of increasing AQI and subsequent rise in associated public attention that negatively impacts these returns. Returns of firms with poor environment score component in their environmental, social, governance (ESG) scores are more negatively affected compared to firms with higher environment scores.

Investors can make use of this knowledge to formulate effective trading strategies and ensure higher chances of profitability in the share market.

To the knowledge of the authors, no earlier study has investigated the effects of AQI and attention together to explain stock price movements. The study is conducted in the Indian context providing a unique opportunity to study the behavioral impact of these effects in one of the fastest growing global economies, which is also plagued by an alarming increase in ambient air pollution.