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Electrocardiogram (ECG) outcomes serve as early manifestations of cardiovascular functional or structural changes. While temperature fluctuation has been demonstrated to be a risk factor for cardiovascular diseases, few epidemiological studies have reported its relationship with ECG outcomes. In this study, we employed temperature anomaly (TA) as an innovative indicator of temperature fluctuation to quantify its detrimental impacts on ECG outcomes. A longitudinal study design was conducted using the repeated ECG records of the China National Stroke Screening Survey from 2013 to 2019. Only individuals undergoing at least two ECG tests were included. The daily temperature was assimilated by combining three kinds of data: in situ observations, satellite remote sensing measurements and weather research forecast simulations. We used generalized estimating equations to control for autocorrelation among repeated records and to estimate the association between TA and the risk of ECG abnormalities. We found 6837 events of ECG abnormalities in 47,286 individuals with 102,030 visits. Each unit increment of TA increased the risk of ECG abnormalities [odds ratio (OR) = 1.009, 95% confidence interval (CI): 1.001–1.017] and the risk of myocardial ischemia (OR = 1.061, 95% CI: 1.012–1.111). Hierarchic analyses presented a similar association of TA with both ECG abnormalities (OR = 1.017, 95% CI: 1.008–1.026) and myocardial ischemia (OR = 1.061, 95%CI: 1.011–1.114) in Northern China, but not in Southern China. The exposure-response relationship was estimated as a U-shaped curve centered at the TA value of zero. Sudden warming tended to increase the risk of ECG abnormalities and myocardial ischemia, and sudden cooling tended to increase the risk of atrial fibrillation. All these detrimental effects of TA could be modified by specific individual characteristics. In summary, ambient temperature fluctuation increased the risk of ECG abnormalities. This result indicated that regular ECG tests could be an early-warning measure for monitoring the adverse health effects of temperature fluctuations. • Temperature anomaly (TA) is an innovative index reflecting temperature fluctuations. • Each unit increment of TA increases the risk of ECG abnormalities. • The exposure-response curve is U-shaped which centered at the TA value of zero. • ECG tests can be used to monitor the health effects of temperature fluctuations. [ABSTRACT FROM AUTHOR]

Temperature and precipitation are considered to be the most important indicators affecting the green-up date. Sensitivity of the green-up date to temperature and precipitation is considered to be one of the key indicators to characterize the response of terrestrial ecosystems to climate change. We selected the main grassland types for analysis, including temperate steppe, temperate meadow steppe, upland meadow, and lowland meadow. This study investigates the variation in key meteorological indicators (daily maximum temperature (Tmax), daily minimum temperature (Tmin), and precipitation) between 2001 and 2018. We then examined the partial correlation and sensitivity of green-up date (GUD) to Tmax, Tmin, and precipitation. Our analysis indicated that the average GUD across the whole area was DOY 113. The mean GUD trend was −3.1 days/decade and the 25% region advanced significantly. Tmax and Tmin mainly showed a decreasing trend in winter (p > 0.05). In spring, Tmax mainly showed an increasing trend (p > 0.05) and Tmin a decreasing trend (p > 0.05). Precipitation showed no significant (p > 0.05) change trend and the trend range was ±10 mm/decade. For temperate steppe, the increase in Tmin in March promotes green-up (27.3%, the proportion of significant pixels), with a sensitivity of −0.17 days/°C. In addition, precipitation in April also promotes green-up (21.7%), with a sensitivity of −0.32 days/mm. The GUDs of temperate meadow steppe (73.9%), lowland meadow (65.9%), and upland meadow (22.1%) were mainly affected by Tmin in March, with sensitivities of −0.15 days/°C, −0.13 days/°C, and −0.14 days/°C, respectively. The results of this study reveal the response of vegetation to climate warming and contribute to improving the prediction of ecological changes as temperatures increase in the future. [ABSTRACT FROM AUTHOR]

The accumulation of heat and moderate precipitation are the primary factors that are used by grasslands to trigger a green-up date. The accumulated growing degree-days (AGDD) requirement over the preseason is an important indicator of the response of grassland spring phenology to climate change. This study adopted the Normalized Difference Phenology Index (NDPI), which derived from the Moderate Resolution Imaging Spectroradiometer (MODIS), to extract annual green-up dates in the Hulun Buir grassland in China between 2001–2015. Our analysis indicated that the range (standard deviation) and trend for the green-up date were DOY (day of year) 104 to DOY 144 (10.6 days) and −2.0 days per decade. Nine point two percent of the study area had significant (p < 0.05) changes in AGDD requirements. The partial correlations between the AGDD requirements and chilling days (67.04%, pixels proportion) were negative and significant (p < 0.05). The partial correlations between the AGDD requirement and precipitation (28.87%) were positive and significant (p < 0.05). Finally, the partial correlation between the AGDD requirement and insolation (97.65%) were positive and significant (p < 0.05). The results of this study could reveal the response of vegetation to climate warming and contribute to improving the phenological mechanism model of different grassland types in future research. [ABSTRACT FROM AUTHOR]

Northern China has been long threatened by aeolian desertification. In recent years, all levels of the Chinese government have performed a series of ecological protection and sand control projects. To grasp the implementation effects of these projects and adjust policies in time, it is necessary to understand the process of aeolian desertification quickly and accurately. Remote sensing technologies play an irreplaceable role in aeolian desertification monitoring. In this study, the Zhenglan Banner, which is in the hinterland of the Hunshandake Sandy Land, was considered as the research area. Based on unmanned aerial vehicle (UAV) images, ground survey data, and Landsat images called in Google Earth Engine (GEE), the aeolian desertified land (ADL) in 2000, 2004, 2010, 2015, and 2019 was extracted using spectral mixture analysis. A desertification index (DI) was constructed to evaluate the spatial and temporal dynamics of the ADL in the Zhenglan Banner. Finally, a residual analysis explored the driving forces of aeolian desertification. The results showed that (1) the ADL area in the Zhenglan Banner has been trending downwards over the past 20 years but rebounded from 2004 to 2010; (2) over the past 20 years, the area of slightly, moderately, and severely desertified land has decreased at annual rates of 0.4%, 2.7%, and 3.4%, respectively; (3) human activities had significantly positive and negative impacts on the aeolian desertification trend for 20.0% and 21.0% of the study area, respectively, but not for the rest. This paper explored new techniques for rapid aeolian desertification monitoring and is of great significance for controlling and managing aeolian desertification in this region. [ABSTRACT FROM AUTHOR]