Clinical experience suggests increased incidences of neonatal jaundice when air quality worsens, yet no studies have quantified this relationship. Here we reports investigations in 25,782 newborns showing an increase in newborn’s bilirubin levels, the indicator of neonatal jaundice risk, by 0.076 (95% CI: 0.027–0.125), 0.029 (0.014–0.044) and 0.009 (95% CI: 0.002–0.016) mg/dL per μg/m³ for PM_2.5 exposure in the concentration ranges of 10–35, 35–75 and 75–200 μg/m³, respectively. The response is 0.094 (0.077–0.111) and 0.161 (0.07–0.252) mg/dL per μg/m³ for SO₂ exposure at 10–15 and above 15 μg/m³, respectively, and 0.351 (0.314–0.388) mg/dL per mg/m³ for CO exposure. Bilirubin levels increase linearly with exposure time between 0 and 48 h. Positive relationship between maternal exposure and newborn bilirubin level is also quantitated. The jaundice−pollution relationship is not affected by top-of-atmosphere incident solar irradiance and atmospheric visibility. Improving air quality may therefore be key to lowering the neonatal jaundice risk.

Spatiotemporal fusion (STF) is considered a feasible and cost-effective way to deal with the trade-off between the spatial and temporal resolution of satellite sensors, and to generate satellite images with high spatial and high temporal resolutions. This is achieved by fusing two types of satellite images, i.e., images with fine temporal but rough spatial resolution, and images with fine spatial but rough temporal resolution. Numerous STF methods have been proposed, however, it is still a challenge to predict both abrupt landcover change, and phenological change, accurately. Meanwhile, robustness to radiation differences between multi-source satellite images is crucial for the effective application of STF methods. Aiming to solve the abovementioned problems, in this paper we propose a hybrid deep learning-based STF method (HDLSFM). The method formulates a hybrid framework for robust fusion with phenological and landcover change information with minimal input requirements, and in which a nonlinear deep learning-based relative radiometric normalization, a deep learning-based superresolution, and a linear-based fusion are combined to address radiation differences between different types of satellite images, landcover, and phenological change prediction. Four comparative experiments using three popular STF methods, i.e., spatial and temporal adaptive reflectance fusion model (STARFM), flexible spatiotemporal data fusion (FSDAF), and Fit-FC, as benchmarks demonstrated the effectiveness of the HDLSFM in predicting phenological and landcover change. Meanwhile, HDLSFM is robust for radiation differences between different types of satellite images and the time interval between the prediction and base dates, which ensures its effectiveness in the generation of fused time-series data.