The water quality of the Mun River, one of the largest tributaries of the Mekong River and an important agricultural area in Thailand, is investigated to determine its status, identify spatiotemporal variations and distinguish the potential causes. Water quality dataset based on monitoring in the last two decades (1997–2017) from 21 monitoring sites distributed across the basin were analyzed using seasonal Kendall test and water quality index (WQI) method. The Kendall test shows significant declines in fecal coliform bacteria (FCB) and ammonia (NH₃) in the upper reaches and increases in nitrate (NO₃) and NH₃ in the lower reaches. Strong temporal and spatial fluctuations were observed in both the concentrations of individual parameters and the WQI values. Seasonal variation of water quality was observed at each monitoring site. WQI values in August (flood season) were generally among the lowest, compared to other seasons. Spatially, sites in the upper reaches generally having lower WQI values than those in the lower reaches. Excessive phosphorus is the primary cause of water quality degradation in the upper reaches, while nitrogen is the primary parameter for water quality degradation in the lower reaches. Urban built-up land is an important “source” of water pollutants in the lower basin, while agricultural land plays a dual role, affecting across the basin.

Aerosols in Southeast Asia (SEA) are entangled with complex land–sea–atmosphere–human interactions, and it is difficult for scientists to understand their dynamic behaviors. This study aims to provide an insightful understanding of aerosols across SEA with respect to their radiative properties using several lines of evidence obtained from remote sensing instruments, including those from onboard Earth observation satellites (MODIS/Terra and MODIS/Aqua, CALIOP/CALIPSO) and from ground-based observation (AERONET). The findings, obtained from cluster analysis of aerosol optical properties, showed seven aerosol types which were dominant across the country, exhibiting diverse radiative forcing potentials. The light-absorbing (prone to warm the atmosphere) aerosols were likely found in mainland SEA, both for background and high-aerosol events. The light-scattering aerosols were associated with aging processes and hygroscopic growth. The neutral potential, which comprised a mixture of oceanic and local anthropogenic aerosols, was predominant in background aerosols in insular SEA. Further studies should focus on carbonaceous aerosols (organic carbons, black carbon, and brown carbon), the aging processes, and the hygroscopic growth of these aerosols, since they play significant roles in the regional aerosol optical properties.

Thailand continues to grapple with persistent air pollution, particularly in urban and industrial areas, despite overall improvements in the past decade. The ongoing challenge of elevated \(\textrm{PM}_{2.5}\) levels in the northern and northeastern regions is a cause for concern. Key contributors to this issue include transportation, industrial activities, and power generation, with a heightened focus due to the exacerbating effects of climate change. \(\textrm{PM}_{2.5}\) pollution emerges as a significant environmental and health issue, especially during dry periods. In our research, we employ a combination of non-stationary analysis using the Peak–Over–Threshold method, the Generalized Pareto distribution, and an Artificial Neural Network (ANN) to examine extreme \(\textrm{PM}_{2.5}\) pollution events in Khon Kaen Province. We utilize data spanning from 2012 to 2022, sourced from Thailand’s Pollution Control Department, encompassing various air quality parameters such as \(\textrm{PM}_{2.5}\), PM\({}_{10}\), CO, NO, NO\({}_{X}\), NO\({}_{2}\), SO\({}_{2}\), O\({}_{3}\), wind characteristics, temperature, and precipitation. We employ an ANN based on Maximum Likelihood Estimation to determine model parameters, with model performance assessed using Nash–Sutcliffe Efficiency and Root Mean Squared Error. Our research aims to forecast \(\textrm{PM}_{2.5}\) concentrations for different return intervals (2, 5, 20, 50, and 100 years), offering insights into potential \(\textrm{PM}_{2.5}\) levels in Khon Kaen. Additionally, it sheds light on the intricate relationship between air quality and climatic patterns, providing valuable information for addressing this persistent environmental issue.