We investigate the dynamic interplay between air pollution (PM₁₀) and income and their joint association with quarterly sales in commercial alleys, focusing on the pre-COVID-19 (2018–2019) and COVID-19 (2020–2021) periods in Seoul, South Korea. The objective of this study is to identify how air pollution and income collectively influence consumer spending patterns by looking at the increase and decrease in sales in commercial alleys, with a focus on contrasting these effects before and during the COVID-19 pandemic, utilizing advanced machine learning techniques for deeper insights. Using machine learning techniques, including random forest, extreme gradient boosting, catboost, and lightGBM, and employing explainable artificial intelligence (XAI), this study identifies shifts in the significance of predictor variables, particularly PM₁₀, before and during the pandemic. The results show that before the pandemic, PM₁₀ played a notable role in shaping sales predictions, highlighting the sensitivity of sales to air quality. However, during the pandemic, the importance of PM₁₀ decreased significantly, highlighting the transformative indirect impact of external events on consumer behavior. This study also examines the joint association of PM₁₀ and income with sales, revealing distinctive patterns in consumer responses to air quality changes during the pandemic. These findings highlight the need for dynamic modeling to capture evolving consumer behavior and provide valuable insights for businesses and policymakers navigating changing economic and environmental conditions. While this study’s focus is on a specific region and time frame, the findings emphasize the importance of adaptability in predictive models and contribute to understanding the complex interplay between environmental and economic factors in shaping consumer spending behavior.

Objectives: This study aimed to examine the association between regional accessibility to dental services and the utilization of preventive dental care in Korea. Methods: We combined data from the 2024 Korean Community Health Survey with data from public databases, including KOSTAT and Health Insurance and Review Assessment(HIRA). The final analysis included 231,554 participants. Preventive dental-care utilization was defined as having undergone dental scaling within the previous year. Access to dental services was calculated as the number of dental-care personnel per 10,000 people in the 252 administrative districts. After adjusting for age and education level, we performed a composite sample logistic regression analysis using SAS 9.4. The spatial distribution was visualized using QGIS 3.40.11. Results: Among participants, 50.6% reported having undergone dental scaling in the past year. In the adjusted model, individuals residing in areas with high accessibility to dental services had significantly higher odds of receiving annual scaling (odds ratio = 1.310; confidence interval: 1.27–1.35) compared with those residing in areas with low accessibility(p<0.001). Conclusions: Greater access to dental services was associated with a higher likelihood of utilizing preventive dental care. Strengthening local dental infrastructure is essential for reducing the regional oral health gap.