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Type 2 diabetes (T2DM) as a non-communicable disease imposes heavy disease burdens on society. Limited studies have been conducted to assess the effects of short-term air pollution exposure on T2DM, especially in Asian regions. Our research aimed to determine the association between short-term exposure to ambient nitrogen dioxide (NO 2 ) and outpatient visits for T2DM in Chongqing, the largest city in western China, based on the data collected from November 28, 2013 to December 31, 2019. A generalized additive model (GAM) was applied, and stratified analyses were performed to investigate the potential modifying effects by age, gender, and season. Meanwhile, the disease burden was revealed from attributable risk. Positive associations between short-term NO 2 and daily T2DM outpatient visits were observed. The strongest association was observed at lag 04, with per 10 μg/m 3 increase of NO 2 corresponded to increased T2DM outpatient visits at 1.57% [95% confidence interval (CI): 0.48%, 2.65%]. Stronger associations were presented in middle-aged group (35-64 years old), male group, and cool seasons (October to March). Moreover, there were 1.553% (8664.535 cases) of T2DM outpatient visits attributable to NO 2 . Middle-aged adults, males, and patients who visited in cool seasons suffered heavier burdens. Conclusively, short-term exposure to NO 2 was associated with increased outpatient visits for T2DM. Attention should be paid to the impact of NO 2 on the burden of T2DM, especially for those vulnerable groups., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier Inc. All rights reserved.)

Nitrogen oxides (NO x) and ozone (O 3) are important air pollutants that are associated with adverse health effects. Land-use regression (LUR) models have been widely developed to estimate air pollution concentrations. Due to data availability, however, such models are usually not applied in developing countries. We aimed to characterize NO x and O 3 concentrations and develop LUR models to predict their spatial and temporal distributions using publicly-available data in Tianjin, a heavily polluted city in China. Seasonal samples were collected across Tianjin at 29 locations for O 3 and 49 locations for NO x. Heavy-duty vehicle counts estimated from 0.5 m × 0.5 m satellite images correlated well with field-measured counts, thus supporting the use of high-resolution satellite images to assess vehicle traffic. Concentrations of NO x were highest in winter, while the opposite pattern was observed for O 3. The majority of the variance in NO x was explained by season (36.2%) and heavy vehicle traffic (19.8%). For O 3 , the variance was explained by season (80.7%) in a pooled model, and by distance to roads (43.4%) and distance to coal plants (26.2%) in a summer model. Cross-validation showed reasonable practicability for NO x (R2 = 0.53 with field-measured heavy-duty vehicle count; R2 = 0.46 with satellite-based heavy-duty vehicle count) and O 3 (R2 = 0.90 for pooled model; R2 = 0.70 for summer model) models. This study provides utility for researchers investigating air pollution in regions where field-measured vehicle traffic data are not available, as well as for policy makers and public health officials seeking to understand the sources and spatial distribution of air pollution in Tianjin. • Ambient NO x mostly explained by season and heavy-duty vehicle traffic. • Ambient O 3 mostly explained by season and by distance to roads. • Concentration of NO x was highest in winter while O 3 was highest in summer. • Satellite images can be used to count vehicles for air pollution modeling. [ABSTRACT FROM AUTHOR]