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To provide long-term air pollutant exposure estimates for epidemiological studies, it is essential to test the feasibility of developing land-use regression (LUR) models using only routine air quality measurement data and to evaluate the transferability of LUR models between nearby cities. In this study, we developed and evaluated the intercity transferability of annual-average LUR models for ambient respirable suspended particulates (PM 10), fine suspended particulates (PM 2.5), nitrogen dioxide (NO 2) and ozone (O 3) in the Taipei–Keelung metropolitan area of northern Taiwan in 2019. Ambient PM 10 , PM 2.5 , NO 2 and O 3 measurements at 30 fixed-site stations were used as the dependent variables, and a total of 156 potential predictor variables in six categories (i.e., population density, road network, land-use type, normalized difference vegetation index, meteorology and elevation) were extracted using buffer spatial analysis. The LUR models were developed using the supervised forward linear regression approach. The LUR models for ambient PM 10 , PM 2.5 , NO 2 and O 3 achieved relatively high prediction performance, with R2 values of > 0.72 and leave-one-out cross-validation (LOOCV) R2 values of > 0.53. The intercity transferability of LUR models varied among the air pollutants, with transfer-predictive R2 values of > 0.62 for NO 2 and < 0.56 for the other three pollutants. The LUR-model-based 500 m × 500 m spatial-distribution maps of these air pollutants illustrated pollution hot spots and the heterogeneity of population exposure, which provide valuable information for policymakers in designing effective air pollution control strategies. The LUR-model-based air pollution exposure estimates captured the spatial variability in exposure for participants in a cohort study. This study highlights that LUR models can be reasonably established upon a routine monitoring network, but there exist uncertainties when transferring LUR models between nearby cities. To the best of our knowledge, this study is the first to evaluate the intercity transferability of LUR models in Asia. [ABSTRACT FROM AUTHOR]

Human exposure to PM 2.5 , represented by population-weighted mean PM 2.5 concentration (c ρ), declines under three conditions: (1) mean PM 2.5 concentration declines, (2) PM 2.5 concentration within urban areas goes through more of a decrease than within rural areas, or (3) city planning relocates people into cleaner areas. Decomposing these effects on human exposure is essential to guide future environmental policies. The lack of ground PM 2.5 observations limits the assessment of human exposure to PM 2.5 over China. This study proposed a novel diagnostic framework using satellite observations to decompose the variation in c ρ resulting from change in the mean PM 2.5 concentration, spatial difference in PM 2.5 change, and demographic change. In this framework, we decomposed c ρ into mean PM 2.5 concentration (c 0) and pollution-population-coincidence induced PM 2.5 exposure (PPCE). We then used this framework to decompose the variation in c ρ over China within three recent Five-Year Plans (FYPs) (2001–2015). The results showed that the decline in c 0 reduced c ρ in most provinces within the eleventh and twelfth FYPs. The spatial difference in PM 2.5 change reduced the PPCE and c ρ in most provinces within the tenth and twelfth FYPs, with the most substantial reduction rate of −3.64 μg m−3·yr−1 in Tianjin within the twelfth FYP. Rural-to-urban migration resulting from rapid urbanization, however, increased the PPCE and c ρ (by as much as 0.22 μg m−3·yr−1) in all provinces except Taiwan within all three FYPs. The demographic change reduced c ρ in Taiwan because of the migration of population into less polluted areas. To better reduce human exposure, it is recommended that control efforts further target populous residential areas and urbanization planning relocates people into less polluted areas. Our decomposition framework paves a new way to decompose the human exposure to other air pollutants in China and other regions. • We proposed a novel framework to decompose the variation in human exposure. • We used satellite observations to delineate human exposure to PM 2.5 in China. • Spatial difference in PM 2.5 change extensively reduced population exposure. • Population migration increased human exposure in all provinces except Taiwan. • The framework paves a new way to apply for other air pollutants in other regions. [ABSTRACT FROM AUTHOR]

Background: Few studies have explored the role of body composition linking air pollution to obstructive sleep apnea (OSA)., Objective: To estimate the effects of air pollution on body composition and OSA, and that of body composition on OSA., Methods: This study included 3550 individuals. A spatiotemporal model estimated personal exposure. Nocturnal changes in body composition were assessed through bioelectric impedance analysis. OSA was diagnosed using polysomnography. A generalized linear model was used to evaluate the absolute nocturnal changes in body composition associated with an interquartile range (IQR) increase in pollutants. A generalized logistic model was used to estimate odds ratios (ORs) of mild-OSA compared to non-OSA. Association between body composition and apnea-hypopnea index (AHI) was investigated through partial least squares (PLS) regression., Results: Nocturnal changes in lower-limb body composition were associated with NO 2 and PM 2.5 in all patients. In participants with AHI <15, both short- and long-term NO 2 exposures affected body composition and mild-OSA, while PM 2.5 was not associated with either outcome. In a PLS model incorporating eight NO 2 -associated lower-limb parameters, the variable importance projection scores (VIP) of left leg impedance (LLIMP), predicted muscle mass (LLPMM), fat-free mass (LLFFM), and right leg impedance (RLIMP) exceeded 1; the corresponding coefficients ranked in the top four for AHI prediction. The adjusted OR (mild vs. non-OSA) was 1.67 (95 % CI: 1.36-2.03) associated with an IQR increase in prediction value estimated from body compositions. Notably, the two-pollutant model investigating the effects of pollutants on body compositions revealed associations of four parameters (LLIMP, LLPMM, LLFFM, and RLIMP) with NO 2 in all lags, which indicates their indispensability in the association between NO 2 and AHI., Conclusions: NO 2 exacerbates mild-OSA by disrupting nocturnal changes in lower-limb body composition of patients with AHI <15. PM 2.5 was associated with nocturnal changes in lower-limb body composition but not with mild-OSA., 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 © 2023 Elsevier B.V. All rights reserved.)

Background: Although recent studies have indicated an association between obstructive sleep apnea (OSA) and air pollution, they have reported inconsistent results. Moreover, few studies investigated the effects of short-term air pollution exposure., Objective: To estimate the health effects of short- and long-term exposure to traffic air pollution on mild OSA in Taipei., Methods: We collected participants' data from Taipei Sleep Center and air pollution data from Taiwan Environmental Protection Administration. A spatiotemporal model was used to estimate the individual exposure level. Generalized linear models were used to assess the percent change of overall apnea-hypopnea index (AHI), AHI in rapid eye movement period (AHI-REM), AHI in non-REM (AHI-NREM), and oxygen desaturation index (ODI) associated with an interquartile (IQR) increase in personal pollution exposure. A generalized logistic model was used to estimate the ORs of different severities of OSA compared with the reference group., Results: In the patients with AHI of <15, both short- and long-term exposure to NO 2 were significantly associated with AHI and ODI increases: an IQR increase in 2-year mean NO 2 increased 7.3% of AHI and 8.4% of ODI; these values were the highest among all exposure windows. The effects of NO 2 on AHI increase were stronger in the men and younger patients. Moreover, the association between AHI and NO 2 in the patients with AHI of <15 was mediated by the REM stage. NO 2 exposure was associated with an increased risk of mild OSA that reached up to 24.8% per IQR increase in NO 2 averaged over 2 years. PM 2.5 exerted no effects on AHI, but an IQR increase in 1-year and 2-year mean PM 2.5 was associated with 6.8% and 8.8% increases in ODI, respectively., Conclusions: Both short- and long-term exposure to traffic air pollution were associated with the risk of mild OSA, which was modified by REM stage., 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 B.V. All rights reserved.)