Livestock farms are recognized sources of ammonia emissions, impacting nearby regions’ fine dust particle concentrations, though the full extent of this impact remains uncertain. Air dispersion models, commonly employed to estimate particulate matter (PM) levels, are heavily reliant on data quality, resulting in varying levels of accuracy. This study compares the performance of both air dispersion models and spatiotemporal deep learning models in estimating PM concentrations in Republic of Korea’s livestock-farming areas. Hourly PM concentration data, alongside temperature, humidity, and air pressure, were collected from seven monitoring stations across the study area. Using a 200 m × 200 m prediction grid, forecasts were generated for both 1 h and 24 h intervals using the Graz Lagrangian model (GRAL) and a one-dimensional convolutional neural network combined with the long short-term memory algorithm (1DCNN-LSTM). Results highlight the potential of the deep learning model to enhance PM prediction, indicating its promise as an effective alternative or supplement to conventional air dispersion models, particularly in data-scarce areas such as those surrounding livestock farms. Gaining a comprehensive understanding and evaluating the advantages and disadvantages of each approach would offer valuable scientific insights for monitoring atmospheric pollution levels within a specific area.

To assess the health impacts of radon exposure over a lifetime, in the present study, the annual effective dose (AED) and cumulative excess lifetime cancer risk (ELCR-C) were evaluated by considering various indoor microenvironmental exposures based on age-specific time–activity patterns using Monte Carlo simulations. Significant regional variations in indoor radon concentrations across the Republic of Korea were observed, with the highest levels found in schools and single detached houses. Based on the standard annual total of 8760 h spent indoors and outdoors, the AED varied by age group and dwelling type, with the ELCR-C for single detached houses being approximately 1.36 times higher than that for apartments on average. The present study highlights the importance of comprehensive health risk assessments that consider differences across indoor environments and age groups, indicating that limited evaluations of specific sites or areas may distort actual exposure levels.