This paper detailed introduces the related work for building Chinese National Database, Sinocenter, of materials life cycle assessment (MLCA) and developing the environmental burden dataset of materials. The MLCA data...This paper detailed introduces the related work for building Chinese National Database, Sinocenter, of materials life cycle assessment (MLCA) and developing the environmental burden dataset of materials. The MLCA database was built in 2004, and the basic framework mainly includes LCA methodology, materials environmental dataset about energy consumption, resource input and environmental emissions. Nowadays, the database contains about fifty-thousand records of the main materials industries, such as cement, iron and steel, nonferrous metal, etc., and also includes the primary LCI data of fossil fuels and electricity grid in China. At the same time, the LCA method localization work is going on, for instance, calculated the resource characterization factors of 42 kinds of metal and 58 sorts of nonmetal, and also obtained some heavy metal impact factors in water. Based on the database, the iron and steel dataset has been developed with the data quality analysis, and some environmental burden data could be queried in our website, www.cnmlca.com, in the future. Lastly, according to the framework of the ISO14040 series standards, the antitype of Chinese LCA evaluation system was developed to support materials and products LCA evaluation in China.展开更多
The North American Soil Moisture Database (NASMD) was initiated in 2011 to assemble and homogenize in situ soil moisture measurements from 32 observational networks in the United States and Canada encompassing more th...The North American Soil Moisture Database (NASMD) was initiated in 2011 to assemble and homogenize in situ soil moisture measurements from 32 observational networks in the United States and Canada encompassing more than 1800 stations. Although statistical quality control (QC) procedures have been applied in the NASMD, the soil moisture content tends to be systematically underestimated by in situ sensors in frozen soils, and using a single maximum threshold (i.e., 0.6 m3 m-3) may not be sufficient for robust QC because of the diverse soil textures in North America. In this study, based on the in situ soil porosity and North American Land Data Assimilation System phase 2 (NLDAS-2) Noah soil temperature, the simple automated QC method is revised to supplement the existing QC approach. This revised QC method is first validated based on the assessment at 78 of the Soil Climate Analysis Network (SCAN) stations where the manually checked data are available, and is then applied to all stations in the NASMD to produce a more strict quality-controlled dataset. The results show that the revised automated QC procedure can flag the spurious and erroneous soil moisture measurements for the SCAN stations, especially for those located in high altitudes and latitudes. Relative to station measurements in the original NASMD, the quality-controlled data show a slightly better agreement with the manually checked soil moisture content. It should be noted that this quality-controlled dataset may be over-flagged for some valid soil moisture measurements due to potential errors of the soil temperature and soil porosity data, and validation in this study is limited by the availability of benchmark soil moisture data. The updated QC and additional validation will be desirable to boost confidence in the product when high-quality data become available in the future.展开更多
文摘This paper detailed introduces the related work for building Chinese National Database, Sinocenter, of materials life cycle assessment (MLCA) and developing the environmental burden dataset of materials. The MLCA database was built in 2004, and the basic framework mainly includes LCA methodology, materials environmental dataset about energy consumption, resource input and environmental emissions. Nowadays, the database contains about fifty-thousand records of the main materials industries, such as cement, iron and steel, nonferrous metal, etc., and also includes the primary LCI data of fossil fuels and electricity grid in China. At the same time, the LCA method localization work is going on, for instance, calculated the resource characterization factors of 42 kinds of metal and 58 sorts of nonmetal, and also obtained some heavy metal impact factors in water. Based on the database, the iron and steel dataset has been developed with the data quality analysis, and some environmental burden data could be queried in our website, www.cnmlca.com, in the future. Lastly, according to the framework of the ISO14040 series standards, the antitype of Chinese LCA evaluation system was developed to support materials and products LCA evaluation in China.
基金Supported by the National Key Research and Development Program of China(2017YFA0604300)National Natural Science Foundation of China(51779278,51379224,and 41671398)NOAA/CPO Modeling,Analyses,Predictions,and Projections(MAP) Program
文摘The North American Soil Moisture Database (NASMD) was initiated in 2011 to assemble and homogenize in situ soil moisture measurements from 32 observational networks in the United States and Canada encompassing more than 1800 stations. Although statistical quality control (QC) procedures have been applied in the NASMD, the soil moisture content tends to be systematically underestimated by in situ sensors in frozen soils, and using a single maximum threshold (i.e., 0.6 m3 m-3) may not be sufficient for robust QC because of the diverse soil textures in North America. In this study, based on the in situ soil porosity and North American Land Data Assimilation System phase 2 (NLDAS-2) Noah soil temperature, the simple automated QC method is revised to supplement the existing QC approach. This revised QC method is first validated based on the assessment at 78 of the Soil Climate Analysis Network (SCAN) stations where the manually checked data are available, and is then applied to all stations in the NASMD to produce a more strict quality-controlled dataset. The results show that the revised automated QC procedure can flag the spurious and erroneous soil moisture measurements for the SCAN stations, especially for those located in high altitudes and latitudes. Relative to station measurements in the original NASMD, the quality-controlled data show a slightly better agreement with the manually checked soil moisture content. It should be noted that this quality-controlled dataset may be over-flagged for some valid soil moisture measurements due to potential errors of the soil temperature and soil porosity data, and validation in this study is limited by the availability of benchmark soil moisture data. The updated QC and additional validation will be desirable to boost confidence in the product when high-quality data become available in the future.
