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https://www.selleckchem.com/products/nb-598.html We evaluate air pollution predictability in the point of focusing on to what degree the root mean square errors between the modeled concentration and the targeted air pollution are limited by the optimal observational network. Results show that air pollution predictability in association with the optimal observational network is limited in the time scales about 6 days. With the high efficiency and in an economic fashion, such a sensitivity-based optimal observing system holds promise for accurately predicting an air pollution event in the targeted area once the adjoint and variational procedure of a realistic atmosphere model including transport and chemical processes is performed.Vegetation is an important component of the terrestrial ecosystem, driven by climate change and human activities. Quantifying the relative contributions of climate change and anthropogenic activities to vegetation dynamics are essential to cope with global climate change. In this paper, the relative contributions of anthropogenic activities and climate change to net primary productivity (NPP) in China were analyzed by a two-step methodology based on the residual trend analysis (RESTREND). Firstly, the unaltered natural vegetation only affected by climate change (Vclimate) and the vegetation affected by climate change and human activities (Vclimate+human) were separated by the multi-temporal land use land cover (LULC) data. Secondly, RESTREND was applied to NPP of Vclimate and Vclimate+human, respectively, to calculate contributions of climatic factors and human activities to vegetation growth. Results revealed that NPP exhibited a significant increase with 3.13 g C m-2 yr-1 from 2001 to 2016 in China. Climate change and human activities both made favorable impacts on vegetation growth during the study period. Besides, with the separation of Vclimate and Vclimate+human, contributions of climatic factors to vegetation changes increased from 1.

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