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The spiraling growth of the world's population and unregulated urbanization have resulted in many environmental problems, including poor quality of air, which is associated with a wide range of health issues. Machine learning approaches have been extensively employed to predict air quality, attracting the attention of the scientific community worldwide. Bibliometric studies provide a useful means by which to visualize and analyze published works, helping researchers to make novel scientific contributions by filling existing knowledge gaps in the research. To acquire an in-depth understanding of the topic, this paper presents a bibliometric analysis of all published articles on the use of machine learning networks to predict air quality found in the Web of Science (WoS) search engine from 1992 to 2021. S-curve analysis and social network analysis were used to identify the temporal distribution of articles, productivity by countries/continents, research institutions, and scientific metrics of journal productivity. This study indicated that maximum expansion of the literature witnessed during 2017–2021 (second phase) which represents an expansion or growth stage of machine learning and air quality prediction research. The number of published works increased significantly with 1432 articles accounting for 68.51% of all publications. As a result of the increased interest in machine learning-based prediction tools, the number of articles grew 2.17-fold compared to the 1992–2016 (first phase). In terms of international collaboration impact, Italy emerged as the most successful country (43.44), followed by Greece (31.22) and Spain (23.29). Author keywords analysis was employed to explore and evaluate the emerging research trends on the subject of air quality using machine learning models. Keywords that appear most frequently in this study are 'air pollution', 'air quality', 'machine learning', and 'forecasting'. Citation burst analysis, research productivity analysis, highly influential and highly cited works were also employed to examine various research themes and questions. In this study we also discussed how conventional methods were transformed into machine learning approaches. It is expected that this paper will provide technical guidelines, research priorities, and future opportunities for the precise prediction of air quality and emergency management of air pollution globally. • Interest in air quality prediction through machine learning model is expanding. • China and US were found productive countries in terms of countries collaboration. • Big data, Internet of Things, and Smart city are emerging topic for future research. • Significant difference between citations, funded and non-funded research. • Ensemble modeling is popular in air quality prediction research. [ABSTRACT FROM AUTHOR]

The present study intends to explore the relationship between tourism growth and air pollution at a regional level for five important tourism European destinations: France, Spain, Greece, Portugal, and Italy. Most of the studies found in the literature examine this relationship on a national scale and focus only on the CO2 pollutant, which is a greenhouse gas but not a critical pollutant in terms of air quality and human exposure. This research focuses on a regional basis (NUTS 2 classification) and takes into account the main critical pollutants in terms of urban air pollution (namely: NOx, PM10, and PM2.5), and considers 10 years, from 2009 until 2018. This work aims to investigate evidence of a tourism-induced Environmental Kuznets Curve (EKC) for the countries through the construction of five panels, one for each country, including different variables: the Gross Domestic Product, the energy consumption, and the number of nights spent at tourist accommodation establishments from both domestic and foreign tourists. The Levin-Lin-Chu unit root test proves the variables to be stationary, while the Pedroni cointegration test shows that they are integrated. The pooled OLS estimator is employed throughout the countries to check the relationship among the variables. Results reveal that the tourism-induced EKC hypothesis is not validated for any of the countries. The findings also show that in Portugal, Italy, and Greece, there is a negative relationship between economic growth and environmental pollution, while mixed evidence is found for France and Spain. Moreover, differences in the impacts of international and domestic tourists on air pollution are found: foreign tourists negatively impact emissions, while domestic ones increase them. This result is clear for Spain, Greece, and Italy. The Granger panel causality test is then conducted to see the causality among the variables. [ABSTRACT FROM AUTHOR]

This review study discusses total average concentrations of arsenic (As) in PM10 and PM2.5 in different countries, which included Serbia, Taiwan, Canada, Spain, China, Portugal-Sines, Greece, Korea, France, Hong Kong, Shanghai, Finland, Scotland, Switzerland, United States (Southern California), Italy (Venice) during 2000–2011. Generally, the main sources for As in ambient air in different countries were copper smelters, traffic exhaust, coal use, industrial activities, petrochemical plant, incinerator plants, domestic heating, ship traffic, burning biomass, incinerator emissions, agriculture, mining industry, and foundries. The data show that ambient air As concentrations in PM10 in Serbia in 2000 were the highest while those in PM10 in the United States (Southern California) in 2009 were ranked the lowest. The data also indicate that ambient air concentrations in PM2.5 in Shanghai in 2008 were the highest while those in Greece in 2003 were the lowest. Average ambient air concentrations in PM10 decreased significantly during 2000–2010 in Serbia and Spain. Finally, average ambient air concentrations in PM10 decreased while ambient air particle bound As (As(p)) concentrations in PM2.5 increased during period 2000–2010 and then decreased during 2000–2010. [ABSTRACT FROM AUTHOR]