Particulate matter (PM) is a hazardous airborne pollutant that encompasses all airborne particles with diameters ranging from 0.001 to 100 μm. It is composed of total suspended particles (TSPs), consisting of two main particle sizes: PM10 and PM2.5. PM poses various threats to human health because of its rapid mobility and its ability to spread over a wide area. In particular, it has long-term negative effects on such organs as the lungs and heart. China and South Korea, located in Northeast Asia, are representative of the countries at risk of PM, and their populations live with an awareness that the harms of PM go beyond physical risks. Therefore, based on previous studies, this study classifies the perceived PM risks into physical, psychological, financial, functional, and time risks. It has tried to verify the effect of this risk perception on the behavior intention of Chinese and Koreans and examine the moderating effect according to the difference in nationality. The study's conceptual model was constructed by applying Ajzen's proven theory of planned action. Utilizing AMOS 22.0 and SPSS 22.0, an analysis was performed. Following this analysis, it was determined that there was a significant causal relationship between perceived PM risk and behavioral attitudes, subjective norms, and perceived behavioral control. Additionally, it was discovered that perceived PM risk significantly impacted desire and behavioral intention. These findings demonstrate that when persons are exposed to high concentrations of PM, they perceive a variety of risks that go beyond the merely physical, and they can form different attitudes depending on their nationality. This study greatly contributes to the theoretical and practical implications by presenting more diverse perspectives on PM risk.

Although city air pollution levels significantly affect the hotel industry, few studies have addressed the impact of air quality management on guests’ cognitive and affective image formation and revisit intentions. Therefore, this research examined the effects of hotel air quality management on the formation of guests’ cognitive and affective images and their revisit intentions. A total of 322 valid samples were obtained by surveying hotel guests who had perceived hotel air quality management activities in the past year, with SPSS 22.0 (IBM, New York, NY, USA) and AMOS 22.0 (IBM, New York, NY, USA) employed for the empirical analysis. The cognitive and affective image constructs revealed that cognitive (perceived value and perceived quality) image influenced revisit intentions but affective image did not. These results provide insights into the need for hotel managers to develop positive cognitive and emotional images through good air quality management and the need to induce customers to revisit based on these images.

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This study proposes a fuel efficiency prediction model using a newly defined fuel efficiency index. The process is divided into three steps: (1) define a new fuel efficiency index after identifying the limitations of the existing fuel efficiency index; (2) set up a fuel efficiency prediction model; and (3) formulate possible actions for the airline to enhance fuel efficiency and reduce carbon emissions based on the model established in the previous step. The fuel efficiency prediction model was established using the actual flight data of Airbus 330-300 (engine type: PW4168A). The flight data were obtained from the fuel management and information system of an airline. The multiple regression model is used to identify the independent variables affecting the fuel efficiency and the degree of influence of each variable. The results indicate that variables such as payload, aircraft fuel mileage deterioration, center of gravity, extra fuel loaded, flight distance, and outside air temperature affect the fuel efficiency. Some variables can be controlled and managed by airlines, others are not. The proposed fuel efficiency prediction model is expected to be utilized as a measurable method for enhancing the fuel efficiency and reducing the carbon emissions.

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