RESUMO
The concentration of Greenhouse Gas (GHG) in the atmosphere has sharply increased since the Industrial Revolution, leading to climate warming and severe environmental problems. It has become a consensus that GHG emissions of large reservoirs essentially constitute inland aquatic GHG emissions. However, questions remain regarding whether small karst reservoir (SKR) is only a substantial source of GHG emissions like large reservoirs, and how much GHG emission it can offset by affecting the terrestrial carbon sink (TCS) of its controlled basin. We selected two basins in the karst area of southwestern China, with built and planned SKRs, and quantitatively analysed the impact of the SKR on basin-scale water and carbon cycles during 2000-2020 using multi-source remote sensing data and the Google Earth Engine. Results showed that the associated increase in the TCS in the SKR-controlled basin can completely offset the GHG emissions and TCS losses caused by submerged land, resulting in a 21.48 % faster increase rate of TCS and a 12.20 % greater increase in TCS caused by human activities than in non-karst reservoir basin. Meanwhile, by intercepting both surface and groundwater runoff, the SKR-controlled basin showed a 329.55 % faster increase rate of available surface water resources than the non-karst reservoir basin, alleviating the problem of engineering water shortages and enhancing the drought resistance capacity. Moreover, in the three major karst areas worldwide, and especially in southwestern China, faster vegetation restoration and TCS increase exist in most SKR-controlled basins, and this increase is enhanced with increasing proximity to the water surface. This study revealed that SKR is more than a substantial source of GHG emissions; it can also effectively enhance the TCS and available surface water resources in controlled basin, which is of great significance for achieving carbon neutrality goals while maintaining the sustainability of water and carbon cycle in karst areas.
RESUMO
The coupling of multisource remote sensing data and the lack of measured runoff introduce input data and model parameters uncertainties to the remote sensing-driven distributed hydrological model (RS-DHM). The PB satellite remote sensing datasets of the Google Earth Engine (GEE) are widely used in RS-DHM and remote sensing runoff inversion research, but whether GEE can reduce the two abovementioned uncertainties is still unknown. To answer this question, twelve remote sensing data sources provided by GEE were used in this study to drive a typical RS-DHM called the remote sensing-driven distributed time-variant gain model (RS-DTVGM) and the remote sensing runoff inversion technology called remote sensing hydrological station (RSHS), and the contribution of GEE to the improving hydrological model uncertainties was quantitatively analyzed from 2001 to 2020. The results showed that (1) the GEE-based improved data preparation not only effectively reduced the uncertainty in the input data with better spatial-temporal continuity and a 6.20 % reduction in the total area occupied by invalid grids, but also enhanced the operational efficiency by reducing the image number, memory size and data processing time of the satellite remote sensing data by 83.63 %, 99.53 %, and 98.73 %, respectively; (2) the GEE-based RSHS technology provided sufficient data support for parameter adjustment and accuracy validation of the RS-DTVGM, which effectively reduced the uncertainty in the model parameters and increased the Nash efficiency coefficient (NSE) in the calibration and validation period from 0.67 to 0.87 and 0.75, respectively; and (3) the calibrated RS-DTVGM was more reliable and robust, and its runoff and evapotranspiration were consistent with the actual statistical data. In the future, GEE and RSHS technology should be widely adopted to drive the RS-DHM to more quickly and easily provide reliable hydrological processes simulation results for integrated water resource management, therefore achieving win-win results in terms of efficiency and accuracy.
RESUMO
A core-shell surface imprinted uranyl magnetic chelating adsorbent (UMCA) was synthesized by combining the sol-gel process with the surface molecular imprinting technique (SMIT). A specific salophen and uranyl-salophen were designed and synthesized. Then, the synthesized uranyl-salophen complex was used as a template (in which uranyl is the target analyte), 3-aminopropyltriethoxysilane as a functional monomer and tetraethylorthosilicate as a cross-linker. The obtained UMCA was characterized by a variety of modern analytical and detection techniques. The adsorbent can be used for the solid-phase extraction of uranyl with good selectivity, high adsorption capacity, magnetic separation characteristics and good reusability. The chelating sorbent was successfully applied for the separation of uranyl, followed by multiphase photocatalytic resonance fluorescence method determination in several environmental water samples with a relative standard deviation of <5.48% and spiked recoveries of 92.5% to 103.0%. The adsorption mechanism was preliminarily discussed.
RESUMO
In this paper, two kinds of salophens (Sal) with different solubilities, Sal1 and Sal2, have been respectively synthesized, and they all can combine with uranyl to form stable complexes: [UO2(2+)-Sal1] and [UO2(2+)-Sal2]. Among them, [UO2(2+)-Sal1] was used as ligand to extract uranium in complex samples by dual cloud point extraction (dCPE), and [UO2(2+)-Sal2] was used as catalyst for the determination of uranium by photocatalytic resonance fluorescence (RF) method. The photocatalytic characteristic of [UO2(2+)-Sal2] on the oxidized pyronine Y (PRY) by potassium bromate which leads to the decrease of RF intensity of PRY were studied. The reduced value of RF intensity of reaction system (ΔF) is in proportional to the concentration of uranium (c), and a novel photo-catalytic RF method was developed for the determination of trace uranium (VI) after dCPE. The combination of photo-catalytic RF techniques and dCPE procedure endows the presented methods with enhanced sensitivity and selectivity. Under optimal conditions, the linear calibration curves range for 0.067 to 6.57ngmL(-1), the linear regression equation was ΔF=438.0 c (ngmL(-1))+175.6 with the correlation coefficient r=0.9981. The limit of detection was 0.066ngmL(-1). The proposed method was successfully applied for the separation and determination of uranium in real samples with the recoveries of 95.0-103.5%. The mechanisms of the indicator reaction and dCPE are discussed.