Geospatial artificial intelligence for estimating daytime and nighttime nitrogen dioxide concentration variations in Taiwan: A spatial prediction model
Journal
Journal of Environmental Management
Series/Report No.
Journal of Environmental Management
Journal Volume
360
Start Page
121198
ISSN
0301-4797
Date Issued
2024-06
Author(s)
Jennieveive Babaan
Pei-Yi Wong
Hsiu-Ling Chen
Shih-Chun Candice Lung
Yu-Cheng Chen
Chih-Da Wu
DOI
10.1016/j.jenvman.2024.121198
Abstract
Nitrogen dioxide (NO2) is a major air pollutant primarily emitted from traffic and industrial activities, posing health risks. However, current air pollution models often underestimate exposure risks by neglecting the bimodal pattern of NO2 levels throughout the day. This study aimed to address this gap by developing ensemble mixed spatial models (EMSM) using geo-artificial intelligence (Geo-AI) to examine the spatial and temporal variations of NO2 concentrations at a high resolution of 50m. These EMSMs integrated spatial modelling methods, including kriging, land use regression, machine learning, and ensemble learning. The models utilized 26 years of observed NO2 measurements, meteorological parameters, geospatial layers, and social and season-dependent variables as representative of emission sources. Separate models were developed for daytime and nighttime periods, which achieved high reliability with adjusted R2 values of 0.92 and 0.93, respectively. The study revealed that mean NO2 concentrations were significantly higher at nighttime (9.60 ppb) compared to daytime (5.61 ppb). Additionally, winter exhibited the highest NO2 levels regardless of time period. The developed EMSMs were utilized to generate maps illustrating NO2 levels pre and during COVID restrictions in Taiwan. These findings could aid epidemiological research on exposure risks and support policy-making and environmental planning initiatives.
Subjects
Air quality
Ensemble learning
Geo-AI
Land use regression
Machine learning
NO2
Publisher
Elsevier BV
Type
journal article