Retrieval of subsurface dissolved oxygen from surface oceanic parameters based on machine learning.

Oceanic dissolved oxygen (DO) is crucial for oceanic material cycles and marine biological activities. However, obtaining subsurface DO values directly from satellite observations is limited due to the restricted observed depth. Therefore, it is essential to develop a connection between surface oceanic parameters and subsurface DO values. Machine learning (ML) methods can effectively grasp the complex relationship between input attributes and target variables, making them a valuable approach for estimating subsurface DO values based on surface oceanic parameters. In this study, the potential of ML methods for subsurface DO retrieval is analyzed. Among the selected ML methods, namely support vector regression (SVR), random forest (RF) regression, and extreme gradient boosting (XGBoosting) regression, the RF method generally demonstrates superior performance. As the depth increases, the accuracy of DO estimates tends to initially decrease, then gradually improve, with the poorest performance occurring at the depth of 600 dbar. The range of determination coefficients (R2) and root mean square error (RMSE) values based on the test dataset at different depths lies between 0.53 and 47.59 µmol/kg to 0.99 and 4.01 µmol/kg. In addition, compared to sea surface salinity (SSS) and sea surface chlorophyll-a (SCHL), sea surface temperature (SST) plays a more significant role in DO retrieval. Finally, compared to the pelagic interactions scheme for carbon and ecosystem studies (PISCES) model, the RF method achieves higher retrieval accuracies at depths above 700 dbar. In the deep ocean, the primary differences in DO values obtained from the RF method and the PISCES model-based method are noticeable in the vicinity of the equatorial region.

Converted paddy to upland in saline-sodic land could improve soil ecosystem multifunctionality by enhancing soil quality and alleviating microbial metabolism limitation.

Soil salinization is one of the major soil degradation threats worldwide, and parameters related to soil quality and ecosystem multifunctionality (EMF) are crucial for evaluating the success of reclamation efforts in saline-sodic wasteland (WL). Microbial metabolic limitation is also one of the main factors that influences EMF in agricultural cropping systems. A ten-year localization experiment was conducted to reveal the key predictors of soil quality index (SQI) values, microbial metabolic characteristics, and EMF in different farmland cropping systems. A random forest model showed that the ß-glucosidase (BG), cellobiosidase (CBH) and saturated hydraulic conductivity (SHC) of the SQI factors were the main driving forces of soil EMF. Compared to monoculture models, such as paddy field (PF) or upland field (UF), the converted paddy field to upland field (CF) cropping system was most effective at improving EMF in reclaimed saline-sodic WL, increasing this metric by 275.35 %. CF integrates practices from both PF and UF planting systems, improved soil quality and relieves microbial metabolic limitation. Specifically, both CF and PF significantly reduced soil pH (by 16-23 %) and sodium adsorption ration (SAR) (by 65-83 %) and significantly reduced the abundance of large macroaggregates. Moreover, CF significantly improved soil saturated hydraulic conductivity relative to PF and UF (p < 0.05), indicating an improvement in soil physical properties. Overall, although reclamation improved SQI compared to WL (0.25), the EMF of CF (0.56) was significantly higher than that of other treatments (p < 0.05). Thus, while increasing SQI can improve soil EMF, it was not as effective alone as it was when combined with more comprehensive efforts that focus on improving various soil properties and alleviating microbial metabolic limitations. Therefore, our results suggested that future saline-sodic wasteland reclamation efforts should avoid monoculture systems to enhance soil EMF.

Reactive oxygen species-responsive hydrophobic crosslinked chitosan films based on triple-function crosslinkers.

Chitosan is widely used in reactive oxygen species (ROS)-responsive films but remains great challenges owing to its weak mechanical strength and strong hydrophilicity. Herein, we synthesized novel hydrophobic crosslinked CS films with ROS-responsive properties and excellent physicochemical properties. A novel crosslinker, 2-((10-carboxydecyl)thio)succinic acid, with long-chain alkanes, three carboxyl groups, and sulfhydryl groups was synthesized and then used to produce thioether-containing crosslinked CS membranes. The results suggested that crosslinking could significantly increase the tensile strength of the film from 15.67 MPa to 24.32 MPa. The compact structure of crosslinked chitosan film improved the hydrophobicity and degradability, reduced the thermal stability and swelling rates, exhibited excellent non- cytotoxicity. The in vitro release studies revealed that crosslinked chitosan films could displayed the highest flux about 1.40 mg/ (cm2 h) and significant NR fluorescence change over 80 %. Collectively, our results demonstrate the applicability of these films as ROS-responsive drug delivery systems.

Calcium-based polymers for suppression of soil acidification by improving acid-buffering capacity and inhibiting nitrification.

Soil acidification is a major threat to agricultural sustainability in tropical and subtropical regions. Biodegradable and environmentally friendly materials, such as calcium lignosulfonate (CaLS), calcium poly(aspartic acid) (PASP-Ca), and calcium poly γ-glutamic acid (γ-PGA-Ca), are known to effectively ameliorate soil acidity. However, their effectiveness in inhibiting soil acidification has not been studied. This study aimed to evaluate the effect of CaLS, PASP-Ca, and γ-PGA-Ca on the resistance of soil toward acidification as directly and indirectly (i.e., via nitrification) caused by the application of HNO3 and urea, respectively. For comparison, Ca(OH)2 and lignin were used as the inorganic and organic controls, respectively. Among the materials, γ-PGA-Ca drove the substantial improvements in the pH buffering capacity (pHBC) of the soil and exhibited the greatest potential in inhibiting HNO3-induced soil acidification via protonation of carboxyl, complexing with Al3+, and cation exchange processes. Under acidification induced by urea, CaLS was the optimal one in inhibiting acidification and increasing exchangeable acidity during incubation. Furthermore, the sharp reduction in the population sizes of ammonia-oxidizing bacteria (AOB) and ammonia-oxidizing archaea (AOA) confirmed the inhibition of nitrification via CaLS application. Therefore, compared to improving soil pHBC, CaLS may play a more important role in suppressing indirect acidification. Overall, γ-PGA-Ca was superior to PASP-Ca and CaLS in enhancing the soil pHBC and the its resistance to acidification induced by HNO3 addition, whereas CaLS was the best at suppressing urea-driven soil acidification by inhibiting nitrification. In conclusion, these results provide a reference for inhibiting soil re-acidification in intensive agricultural systems.

Organic-inorganic calcium lignosulfonate compounds for soil acidity amelioration.

Soil acidification is a problem widely occurring worldwide, which severely threaten food security and agricultural sustainability. Calcium lignosulfonate (CaLS), a cheap and ecofriendly compound, is used for the first time to amend acid soil by utilizing its unique organic and inorganic functional moieties simultaneously. Both column leaching and incubation experiments were conducted to investigate the comparative effects of CaLS (four rates at 5, 10, 15, 20 g kg-1) and compared with conventional amendments, including gypsum (5 g kg-1), lignin (5 g kg-1), L + G (each at 5 g kg-1), and control. The soil pH, exchangeable acidity and base cations, organic carbon, and different Al fractions were determined to unravel the ameliorative performance and mechanism of the treatments. Regardless of application modes and dosages, the results demonstrated that CaLS incorporation significantly increased soil pH, exchangeable Ca2+, cation exchange capacity, and organic carbon and decreased the contents of exchangeable acidity, especially exchangeable Al3+. The ameliorative mechanism was that amendment material led to the displacement of H+ and Al3+ off soil colloids by Ca2+. These released H+ and Al3+ which complexed with lignosulfonate anions into soluble organo-Al were all quickly leached from the soil column. The CaLS addition enhanced the transformation of exchangeable Al3+ and low-to-medium organo-Al complexes into highly stable organically bound fractions and immobilized into the soil. The complexing of CaLS functional groups with Al3+ impeded Al3+ from undergoing hydrolysis to produce more H+. As an environmental-friendly material, CaLS can be a promising amendment for soil acidity and Al toxicity amelioration.

An Improved DINEOF Algorithm for Filling Missing Values in Spatio-Temporal Sea Surface Temperature Data.

In this study, an improved Data INterpolating Empirical Orthogonal Functions (DINEOF) algorithm for determination of missing values in a spatio-temporal dataset is presented. Compared with the ordinary DINEOF algorithm, the iterative reconstruction procedure until convergence based on every fixed EOF to determine the optimal EOF mode is not necessary and the convergence criterion is only reached once in the improved DINEOF algorithm. Moreover, in the ordinary DINEOF algorithm, after optimal EOF mode determination, the initial matrix with missing data will be iteratively reconstructed based on the optimal EOF mode until the reconstruction is convergent. However, the optimal EOF mode may be not the best EOF for some reconstructed matrices generated in the intermediate steps. Hence, instead of using asingle EOF to fill in the missing data, in the improved algorithm, the optimal EOFs for reconstruction are variable (because the optimal EOFs are variable, the improved algorithm is called VE-DINEOF algorithm in this study). To validate the accuracy of the VE-DINEOF algorithm, a sea surface temperature (SST) data set is reconstructed by using the DINEOF, I-DINEOF (proposed in 2015) and VE-DINEOF algorithms. Four parameters (Pearson correlation coefficient, signal-to-noise ratio, root-mean-square error, and mean absolute difference) are used as a measure of reconstructed accuracy. Compared with the DINEOF and I-DINEOF algorithms, the VE-DINEOF algorithm can significantly enhance the accuracy of reconstruction and shorten the computational time.