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The carbon emissions released from buildings are correlated with various factors in social and economic systems. Thus, quantifying and then controlling those factors can decrease the release of carbon emissions further. To quantify the influencing factors of the carbon emissions of residential buildings in Yunnan Province in China, separately for urban and rural areas, this study adopted the methods of utilizing the carbon emission factor and the LMDI model and combined them with the carbon emissions data obtained from 2010 to 2019. Subsequently, with this model, the contribution of each factor to the overall carbon emissions was quantified. The results demonstrate the following: (1) the main factors influencing carbon emissions from residential buildings include the per capita floor area, energy consumption per unit area, energy intensity effect, energy structure effect, urbanization rate, and population size. (2) For urban buildings, carbon emissions are negatively correlated with the energy consumption per unit area, energy intensity effect, and energy structure effect, with contribution values of 0.34, 0.27, and 0.05, respectively. Conversely, there is a positive correlation with the per capita floor area, urbanization rate, and population size, with contribution values of 0.23, 0.11, and 0.01, respectively. (3) For rural buildings, carbon emissions are negatively correlated with urbanization rate, energy intensity effect, and energy structure effect, with contribution values of 0.16, 0.15, and 0.14, respectively. Conversely, there is a positive correlation with the per capita floor area, energy consumption per unit area, and population size, with contribution values of 0.29, 0.24, and 0.02, respectively. [ABSTRACT FROM AUTHOR]

Mountains and plateaus in Southwest China contain many subalpine and alpine wetlands, with significant hydroecological functions. But ungauged or poorly gauged conditions limit the study and understanding of hydrological regimes of these wetland types. This study selects an ungauged subalpine wetland - Napahai in Northwest Yunnan, China - as a case for developing a practical approach to revealing its storage-area relationship of open water. A Trimble R8 GNSS (Global Navigation Satellites Systems) RTK (Real-time Kinematic system) and sonar fathometer were used to survey fine-resolution elevation data and generate a digital elevation model of the Napahai Wetland. Forty-four Landsat images from 1987 to 2011 were collected, and the Normalized Difference Water Index was used to classify open water features in the area. The area of open water in Napahai was calculated for each phase. With these data and a developed conceptual model, the storage of open water for each phase was estimated using ArcGIS tools. Both storage and area of open water showed significant intra-annual and inter-annual variations. In the rainy season, the monthly change of average storage of open water in Napahai showed about 1-2 months lag behind mean monthly rainfall. The storage-area relationship of open water was well fit by a power function equation ( R≈0.91, n=44). This study indicates that if detailed elevations are available for similarly ungauged subalpine wetlands in Southwest China, researchers can use this practical approach to estimate multi-temporal areas and storages and reveal the storage-area relationship of open water in the wetlands. The study provided valuable information of this case wetland for optimizing its hydro-ecological managements and a new method to wetland researchers and managers for the hydrological study of similarly ungauged wetland complex. [ABSTRACT FROM AUTHOR]

Safflower has both medicinal and edible values but research on its nutrient composition is still lacking. This study was established for the quantitative determination of 28 nucleosides, nucleobases, and amino acids based on the ultra‐performance liquid chromatography coupled with triple‐quadrupole linear ion‐trap tandem mass spectrometry. Analysis of 30 batches of safflower from different producing areas indicated that the contents of l‐proline, l‐asparagine, l(+)‐arginine, l‐serine, l‐histidine, uracil, guanosine, and uridine was high in safflower. Principle component analysis and cluster analysis found that samples from different regions could be distinguished well, and samples from the same area could be clustered into one class, different geographical environments may cause the differences of nucleosides, nucleobases, and amino acids in safflower. The analysis of principal component analysis, cluster analysis, and counter propagation artificial neural network show similar results. Then the content of nucleosides, nucleobases, and essential amino acids were compared, and found that the content in safflower from Gansu was higher than those from other regions, and there was a little difference between the samples from Xinjiang, Sichuan, and Yunnan. This research revealed the composition of nucleosides, nucleobases, and amino acids in safflower, and provided a theoretical basis for utilization of safflower. [ABSTRACT FROM AUTHOR]