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Satellite observations of atmospheric CO are able to truly capture the variation of global and regional CO concentration. The model simulations based on atmospheric transport models can also assess variations of atmospheric CO concentrations in a continuous space and time, which is one of approaches for qualitatively and quantitatively studying the atmospheric transport mechanism and spatio-temporal variation of atmospheric CO in a global scale. Satellite observations and model simulations of CO offer us two different approaches to understand the atmospheric CO. However, the difference between them has not been comprehensively compared and assessed for revealing the global and regional features of atmospheric CO. In this study, we compared and assessed the spatio-temporal variation of atmospheric CO using two datasets of the column-averaged dry air mole fractions of atmospheric CO (XCO) in a year from June 2009 to May 2010, respectively from GOSAT retrievals (V02.xx) and from Goddard Earth Observing System-Chemistry (GEOS-Chem), which is a global 3-D chemistry transport model. In addition to the global comparison, we further compared and analyzed the difference of CO between the China land region and the United States (US) land region from two datasets, and demonstrated the reasonability and uncertainty of satellite observations and model simulations. The results show that the XCO retrieved from GOSAT is globally lower than GEOS-Chem model simulation by 2 ppm on average, which is close to the validation conclusion for GOSAT by ground measures. This difference of XCO between the two datasets, however, changes with the different regions. In China land region, the difference is large, from 0.6 to 5.6 ppm, whereas it is 1.6 to 3.7 ppm in the global land region and 1.4 to 2.7 ppm in the US land region. The goodness of fit test between the two datasets is 0.81 in the US land region, which is higher than that in the global land region (0.67) and China land region (0.68). The analysis results further indicate that the inconsistency of CO concentration between satellite observations and model simulations in China is larger than that in the US and the globe. This inconsistency is related to the GOSAT retrieval error of CO caused by the interference among input parameters of satellite retrieval algorithm, and the uncertainty of driving parameters in GEOS-Chem model. [ABSTRACT FROM AUTHOR]

In order to solve the problem of insufficient supply of aggregate for cemented filling material, the experimental research on the mix ratio of cemented filling material using construction waste as recycled coarse aggregate was carried out. A ternary quadratic regression model was created using the Box-Behnken design (BBD) based on the response surface methodology (RSM), using slump, bleeding ratio, and 28 d uniaxial compressive strength of the filling material as response variables, and the model's accuracy and reliability were confirmed. The findings of the regression model reveal that the response value is influenced by a single component as well as the interaction between the two factors. Finally, based on filling material cost optimization model, an optimal mix ratio is given, in which the wet fly ash/aggregate ratio, Talbol gradation index of recycled aggregate, and dosage of water reducing agent to cement are 0.507, 0.5, and 0.678%, respectively. The filling material which adopted the optimal mix ratio exhibited a good performance with slump of 215 mm and bleeding ratio of 4.00%, uniaxial compressive strength increased significantly within 3 d, and 28 d uniaxial compressive strength was 4.08 MPa, which meet the requirements of field construction. [ABSTRACT FROM AUTHOR]