Tracking the Dynamics of Air Pollution in Northern Thailand: A Spatio-Temporal Analysis Using Ground Sensor Data and NASA’s DC-8 Aircraft Observations

Abstract

The vertical structure and transboundary transport mechanisms of severe seasonal air pollution in Northern Thailand remain poorly understood, despite posing significant health and environmental challenges. This research examines the spatio-temporal dynamics of air pollution in Northern Thailand, with a particular focus on the Chiang Mai–Lamphun basin. This study integrates data from ground-based IoT sensors, airborne measurements by NASA’s Douglas DC-8 research aircraft, satellite-detected fire hotspots, radiosonde meteorological soundings, and the HYSPLIT trajectory model. Results indicate that PM_2.5 concentrations typically peak in the morning and decline in the afternoon, in association with atmospheric stagnation and temperature inversions. Spatial analysis reveals distinct patterns of pollutant distribution correlated with hotspot locations and wind trajectories transporting emissions from transboundary biomass burning sources. Vertical profile data from the DC-8 aircraft indicate substantial pollutant accumulation in the lower troposphere (1,500 – 3,000 meters above ground level), situated just above the Planetary Boundary Layer (PBL), beneath multiple inversion layers and weak upper-level winds. These conditions facilitate the formation of recirculation zones and enhance pollutant trapping. Skew-T Log-P diagrams further confirm pronounced atmospheric stratification and low CAPE values, indicating limited vertical air exchange. This study emphasizes the critical role of incorporating vertical atmospheric profiles into air quality forecasting systems, especially in basin topographies prone to pollution accumulation. The findings inform the development of proactive early warning systems, designated pollution control zones, and more effective emission control strategies particularly along prevailing wind corridors. Accordingly, continuous monitoring of both horizontal and vertical pollution patterns, along with integration of transboundary pollution data, is thus recommended to enhance sustainable air pollution quality management.