Assessing urban growth and pollution through nightlight data: a case study in Thailand

Abstract

This study explores the relationship between urban development and air pollution in Thailand by analyzing remote sensing nightlight data and carbon monoxide (CO) concentrations over six years (2019-2024). Using data from VIIRS Day/Night Band (DNB) satellite imagery, CO levels, electricity consumption, and lignite production, the study finds a significant positive correlation (Pearson coefficient = 0.586) between nightlight intensity and CO concentrations. This suggests that nightlight data can be an effective tool for monitoring urban-related pollution. Seasonal and regression analyses show that urban growth contributes to pollution, but this is influenced by seasonal patterns and energy consumption. Multiple regression models highlight nightlight intensity as the strongest predictor of CO levels, with energy factors adding significant explanatory power. Regional analysis identifies the Bangkok Metropolitan Region as having the highest nightlight intensity and CO levels (correlation = 0.598). Lag correlation analysis suggests that changes in CO and nightlight intensity are most strongly correlated at zero lag, with CO changes slightly leading in some areas. These findings have implications for urban planning, environmental policy, and public health in Southeast Asia.