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
Ferroptosis is a form of cell death that is triggered by iron-dependent lipid peroxidation and is closely associated with osteoarthritis. The primary interventions for inhibiting ferroptosis in osteoarthritis are anti-lipid peroxidation and iron chelation. The objective of our study is to investigate the characteristics of ferroptosis in osteoarthritis and identify the optimal time points for inhibiting ferroptosis to alleviate disease progression. Ferroptosis-related alterations and markers of OA were analyzed in paired intact and damaged cartilages from OA patients by immunofluorescence, qRT-PCR, mitochondrial membrane potential and immunohistochemistry. We also compared Ferroptosis-related alterations in cartilage of mild, moderate, and severe OA (according to the modified Mankin score). In addition, we compared the effect of Fer-1 on ferroptosis and the protection of chondrocytes by detecting markers of both ferroptosis and OA by immunofluorescence, CCK8 and qRT-PCR. Ferroptosis-related alterations (GPX4 downregulation, ACSL4 upregulation, MDA, LPO accumulation, Mitochondrial membrane potential decreased) in the damaged area cartilage were more severe than those in the intact area and increased with the progression of OA. Compared with mild OA group, the activity of chondrocytes treated with Fer-1 (a ferroptosis inhibitor) was increased, mitochondrial function was improved, and ferroptosis was reduced (GPX4 upregulation, SLC7A11 upregulation, ACSL4 downregulation,), and promoted the expression of COL2A1 and inhibited the expression of MMP13. However, these changes were not observed in moderate and severe OA chondrocytes. Ferroptosis occurs in a region-specific manner and is exacerbated with the progression of human OA cartilage degeneration. Inhibition of ferroptosis might had a therapeutic effect on chondrocytes with mild OA but had no significant therapeutic effect on chondrocytes with moderate to severe OA.