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The universality of such analysis still is not being demonstrated on a worldwide scale. In this paper, we investigate physical and dynamic changes of seismic data and thereby develop a novel machine learning method, namely Inverse Boosting Pruning Trees (IBPT), to issue short-term forecast based on the satellite data of 1371 earthquakes of magnitude six or above due to their impact on the environment. We have analyzed and compared our proposed framework against several states of the art machine learning methods using ten different infrared and hyperspectral measurements collected between 2006 and 2013. Our proposed method outperforms all the six selected baselines and shows a strong capability in improving the likelihood of earthquake forecasting across different earthquake databases.The variations of non-refractory submicron aerosol (NR-PM1) were characterized using an high-resolution time-of-flight aerosol mass spectrometer (HR-ToF-AMS) and other online instruments measurements sampled at an urban site in Shanghai from 2016 to 2017. Spring (from 18 May to 4 June 2017), summer (from 23 August to 10 September 2017) and winter (from 28 November 2016 to 23 January 2017) seasons were chosen for detail investigating the seasonal variations in the aerosol chemical characteristics. The average PM1 (NR-PM1 + BC) mass concentration showed little difference in the three seasons in Shanghai. The average mass concentrations of total PM1 during spring, summer, and winter observations in Shanghai were 23.9 ± 20.7 μg/m3, 28.5 ± 17.6 μg/m3, and 31.9 ± 22.7 μg/m3, respectively. The seasonal difference on chemical compositions was more significant between them. Organic aerosol (OA) and sulfate were dominant contributor of PM1 in summer, whereas OA and nitrate primarily contribution to the increase of PM1 mass loading in spring and winter. As an abundant component in PM1 (accounting for 39%-49%), OA were resolved into two primary organic aerosol (POA) factors and two secondary aerosol (SOA) factors by using positive matrix factorization (PMF), of which OA was overwhelmingly dominated by the SOA (50-60%) across the three seasons in Shanghai. Correlation analysis with relative humidity and odd oxygen indicated that aqueous-phase processing and played an important role in more aged SOA formation in summer and winter. In spring, both aqueous-phase and photochemical processing contributed significantly to fresh SOA formation. Our results suggest the significant role of secondary particles in PM pollution in Shanghai and highlight the importance of control measures for reducing emissions of gaseous precursors, especially need to consider seasonal characteristics.Ocean acidification and warming are recognized as two major anthropogenic perturbations of the modern ocean. However, little is known about the adaptive response of phytoplankton to them. Here we examine the adaptation of a marine diatom Thalassiosira weissflogii to ocean acidification in combination with ocean warming. Our results show that ocean warming have a greater effect than acidification on the growth of T. weissflogii over the long-term selection experiment (~380 generations), as well as many temperature response traits (e.g., optimum temperatures for photosynthesis, maximal net photosynthetic oxygen evolution rates, activation energy) in thermal reaction norm. These results suggest that ocean warming is the main driver for the evolution of the marine diatom T. weissflogii, rather than oceanacidification. However, the evolution resulting from warming can be constrained by ocean acidification, where ocean warming did not impose any effects at high CO2 level. Furthermore, adaptations to ocean warming alone or to the combination of ocean acidification and warming come with trade-offs by inhibiting photochemical performances. The constrains and trade-offs associated with the adaptation to ocean acidification and warming demonstrated in this study, should be considered for parameterizing evolutionary responses in eco-evolutionary models of phytoplankton dynamics in a future ocean.The spatio-temporal variations of stream water stable isotopes are often assumed to follow atmospheric moisture transport over the Tibetan Plateau (TP). [https://www.selleckchem.com/products/ml198.html read more] However, the isotopic composition of streamflow can be modified by the extensive variation in landscape properties in large glacierized mountain basins. In this study, the isotopic composition of stream water and its dominant controls in terms of spatial variation and potential water sources of rainfall, snow and glacier melt, and groundwater are analyzed based on synoptic water sampling from September 2018 to August 2019 over the Lhasa River basin (LRB) in the Southern TP. Results showed that (1) δ18O variation in stream water is linearly proportional to longitude and latitude in the north. This spatial pattern is primarily controlled by cold mountainous environments, where stream water δ18O is more depleted and d-excess is higher towards the northwest and higher elevation in glacier-fed streams. Glacial melt could contribute considerably to streamflow generation, especially in the late monsoon season. (2) In the south, stream water δ18O does not simply follow depleted δ18O in precipitation along the strengthened Indian monsoon moisture gradient, but is enriched by strengthened local moisture recycling and increased groundwater contributions. The rainfall recharge is highly regulated and mixes with storage before it reaches the mainstem of the river. (3) The seasonal variations of stream water δ18O and d-excess are distinct, resulting from different contribution sources and catchment controls. In the pre-monsoon season, the strongest local moisture recycling obscures any simple stream water isotope lapse with elevation. These identified source areas and seasonal variations in the isotopic composition in stream water of LRB help us understand diverse water sources and flow paths to streams in this complex environment, which is a prerequisite for projecting potential future change.As a key component of the global water cycle, river flow transports both freshwater and biotic/abiotic matters from land to sea, while in recent decades its rhythm has been strongly disturbed by human activities, especially damming. Yet little is known about the long-distance transport processes along the world's major fluvial systems and the impact of large dams on their timescales. Here, taking the Changjiang River (Yangtze River) as an example, we built a hydrodynamics-based model to investigate the water age and residence time in the mainstream from the upper reach ~700 km upstream of the Three Gorges Dam (TGD) to the estuary ~1900 km downstream of the TGD. We find that since the mainstream was dammed by the TGD, the water age increases significantly by approximately 2 to 5 times from the estuary to the dam. Downstream of the dam the longitudinal ageing rate of water becomes discordant in an annual cycle, and the replenished discharge in dry season accelerates the water transport. Due to the stationary assumption, the widely applied hydraulic residence time of water is substantially larger and smaller than the age-based dynamic residence time in the large reservoir during the impounding and releasing periods, respectively.