A novel model named random-forest-spatiotemporal-kriging (RF-STK) was developed to estimate the daily ambient NO concentrations across China during 2013-2016 based on the satellite retrievals and geographic covariates. The RF-STK model showed good prediction performance, with cross-validation R = 0.62 (RMSE = 13.3 μg/m) for daily and R = 0.73 (RMSE = 6.5 μg/m) for spatial predictions. The nationwide population-weighted multiyear average of NO was predicted to be 30.9 ± 11.7 μg/m (mean ± standard deviation), with a slowly but significantly decreasing trend at a rate of -0.88 ± 0.38 μg/m/year. Among the main economic zones of China, the Pearl River Delta showed the fastest decreasing rate of -1.37 μg/m/year, while the Beijing-Tianjin Metro did not show a temporal trend ( P = 0.32). The population-weighted NO was predicted to be the highest in North China (40.3 ± 10.3 μg/m) and lowest in Southwest China (24.9 ± 9.4 μg/m). Approximately 25% of the population lived in nonattainment areas with annual-average NO > 40 μg/m. A piecewise linear function with an abrupt point around 100 people/km characterized the relationship between the population density and the NO, indicating a threshold of aggravated NO pollution due to urbanization. Leveraging the ground-level NO observations, this study fills the gap of statistically modeling nationwide NO in China, and provides essential data for epidemiological research and air quality management.

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Climate change affects the productivity of agricultural ecosystems. Farmers cope with climate change based on their perceptions of changing climate patterns. Using a case study from the Middle Yarlung Zangbo River Valley, we present a new research framework that uses questionnaire and interview methods to compare local farmers' perceptions of climate change with the adaptive farming strategies they adopt. Most farmers in the valley believed that temperatures had increased in the last 30 years but did not note any changes in precipitation. Most farmers also reported sowing and harvesting hulless barley 10-15 days earlier than they were 20 years ago. In addition, farmers observed that plants were flowering and river ice was melting earlier in the season, but they did not perceive changes in plant germination, herbaceous vegetation growth, or other spring seasonal events. Most farmers noticed an extended fall season signified by delays in the freezing of rivers and an extended growing season for grassland vegetation. The study results showed that agricultural practices in the study area are still traditional; that is, local farmers' perceptions of climate change and their strategies to mitigate its impacts were based on indigenous knowledge and their own experiences. Adaptive strategies included adjusting planting and harvesting dates, changing crop species, and improving irrigation infrastructure. However, the farmers' decisions could not be fully attributed to their concerns about climate change. Local farming systems exhibit high adaptability to climate variability. Additionally, off-farm income has reduced the dependence of the farmers on agriculture, and an agricultural subsidy from the Chinese Central Government has mitigated the farmers' vulnerability. Nevertheless, it remains necessary for local farmers to build a system of adaptive climate change strategies that combines traditional experience and indigenous knowledge with scientific research and government polices as key factors.

Debris flow susceptibility mapping is considered to be useful for hazard prevention and mitigation. As a frequent debris flow area, many hazardous events have occurred annually and caused a lot of damage in the Sichuan Province, China. Therefore, this study attempted to evaluate and compare the performance of four state-of-the-art machine-learning methods, namely Logistic Regression (LR), Support Vector Machines (SVM), Random Forest (RF), and Boosted Regression Trees (BRT), for debris flow susceptibility mapping in this region. Four models were constructed based on the debris flow inventory and a range of causal factors. A variety of datasets was obtained through the combined application of remote sensing (RS) and geographic information system (GIS). The mean altitude, altitude difference, aridity index, and groove gradient played the most important role in the assessment. The performance of these modes was evaluated using predictive accuracy (ACC) and the area under the receiver operating characteristic curve (AUC). The results of this study showed that all four models were capable of producing accurate and robust debris flow susceptibility maps (ACC and AUC values were well above 0.75 and 0.80 separately). With an excellent spatial prediction capability and strong robustness, the BRT model (ACC = 0.781, AUC = 0.852) outperformed other models and was the ideal choice. Our results also exhibited the importance of selecting suitable mapping units and optimal predictors. Furthermore, the debris flow susceptibility maps of the Sichuan Province were produced, which can provide helpful data for assessing and mitigating debris flow hazards.

Hypertensive disorders in pregnancy (HDPs) are leading perinatal diseases. Using a national cohort of 2,043,182 pregnant women in China, we evaluated the association between ambient temperatures and HDP subgroups, including preeclampsia or eclampsia, gestational hypertension, and superimposed preeclampsia. Under extreme temperatures, very cold exposure during preconception (12 weeks) increases odds of preeclampsia or eclampsia and gestational hypertension. Compared to preconception, in the first half of pregnancy, the impact of temperature on preeclampsia or eclampsia and gestational hypertension is opposite. Cold exposure decreases the odds, whereas hot exposure increases the odds. Under average temperatures, a temperature increase during preconception decreases the risk of preeclampsia or eclampsia and gestational hypertension. However, in the first half of pregnancy, temperature is positively associated with a higher risk. No significant association is observed between temperature and superimposed preeclampsia. Here we report a close relationship exists between ambient temperature and preeclampsia or eclampsia and gestational hypertension.