ABSTRACT
Inadequate air quality has adverse impacts on human well-being and contributes to the progression of climate change, leading to fluctuations in temperature. Therefore, gaining a localized comprehension of the interplay between climate variations and air pollution holds great significance in alleviating the health repercussions of air pollution. This study uses a holistic approach to make air quality predictions and multivariate modelling. It investigates the associations between meteorological factors, encompassing temperature, relative humidity, air pressure, and three particulate matter concentrations (PM10, PM2.5, and PM1), and the correlation between PM concentrations and noise levels, volatile organic compounds, and carbon dioxide emissions. Five hybrid machine learning models were employed to predict PM concentrations and then the Air Quality Index (AQI). Twelve PM sensors evenly distributed in Craiova City, Romania, provided the dataset for five months (22 September 2021-17 February 2022). The sensors transmitted data each minute. The prediction accuracy of the models was evaluated and the results revealed that, in general, the coefficient of determination (R2) values exceeded 0.96 (interval of confidence is 0.95) and, in most instances, approached 0.99. Relative humidity emerged as the least influential variable on PM concentrations, while the most accurate predictions were achieved by combining pressure with temperature. PM10 (less than 10 µm in diameter) concentrations exhibited a notable correlation with PM2.5 (less than 2.5 µm in diameter) concentrations and a moderate correlation with PM1 (less than 1 µm in diameter). Nevertheless, other findings indicated that PM concentrations were not strongly related to NOISE, CO2, and VOC, and these last variables should be combined with another meteorological variable to enhance the prediction accuracy. Ultimately, this study established novel relationships for predicting PM concentrations and AQI based on the most effective combinations of predictor variables identified.
ABSTRACT
The main objective of our research is to identify the impact of recycling and waste reduction behavior on the sustainable tourism decisions of Romanian youngsters (18-25 years old). We used the PLS-SEM method and introduced four variables in the model: sustainable tourism decisions, the interest in recycling, the interest in waste reduction, and the interest in natural and less polluted touristic destinations. The main results emphasize the direct influence of recycling and waste reduction behaviors on the decisions made by Generation Z regarding sustainable tourism and on their preference for destinations that are better preserved and less touched by human intervention. The novelty of our research consists of the fact that we introduced variables such as waste reduction from the perspective of tourists because most studies address it as a management approach of the companies in the tourism sector. The findings are useful for managers in the tourism sector to create better strategies for attracting the younger generation who are preoccupied by environmental issues and sustainability in general.
