Digital mapping of soil pH and carbonates at the European scale using environmental variables and machine learning

Ionized copolyesters with pH-responsive degradability: Accelerated degradation in specific environments

The development of environmentally responsive biodegradable polymers is a promising solution for balancing the stability and degradability of biodegradable plastics. In this study, a commercial biodegradable polyester, poly(butylene adipate-co-butylene terephthalate) (PBAT), was used as the substrate and was synthetically modified with a small amount of anionic sodium 1-3-isophthalate-5-sulfonate (SIPA) to obtain the ionized random poly(butylene adipate-co-butylene terephthalate-co-butylene 5-sodiosulfoisophthalate) (PBATS). The introduction of the sodium sulfonate ionic group enhanced the mechanical and heat-resistant properties of the material, while significantly improving the hydrophilicity and water absorption of the copolyesters of PBATSs and endowing them with special pH-responsive degradation properties. Compared with PBAT, PBATS copolyesters could accelerate degradation in acidic or alkaline buffer solutions and natural seawater, while degradation was inhibited in neutral buffer solutions at pH 7.2. Degradation experiments in simulated gastric, intestinal, and body fluids revealed that the copolyester showed specific and rapid degradation in acidic gastric fluids. This environmentally-responsive degradable material greatly expands the special applications of biodegradable polyesters in the fields of environmental remediation and medical applications.

Spatial patterns and mechanism of the impact of soil salinity on potentially toxic elements in coastal areas

Soil salinization and heavy metal pollution in the Yellow River Delta region have elicited increasing concern. Therefore, revealing the underlying mechanism of the impact of soil salinity on potential toxic elements (PTEs) is crucial for environmental protection and the rational utilization of resources in this area. In this study, we employed CatBoost-SHAP and multiscale geographically weighted regression (MGWR) models to comprehensively investigate the spatial effects of soil electrical conductivity (EC1:5) on PTEs. Additionally, we employed a space-for-time substitution strategy with the aim of investigating how increasing soil salinity, represented by EC1:5, K+, Na+, Ca2+, and Mg2+, affects the bioavailability of PTEs over time. The primary findings are as follows: (1) for most PTEs, the influence of soil EC1:5 on the bioavailable forms of these elements surpassed its impact on their total concentrations. (2) The results of the MGWR model indicated that exchangeable Ca (aCa) in the soils of the eastern coastal areas markedly increased the bioavailable Cd (aCd), bioavailable Cu (aCu), and bioavailable Zn (aZn). (3) When the soil EC1:5 ranges between 2 and 6 dS/m, exchangeable Na (aNa) primarily competed for the adsorption sites of bioavailable Pb (aPb). However, as the soil EC1:5 increases to 6-10 dS/m, exchangeable Mg (aMg) and aCa became the primary competing ions, with aMg playing a more significant role than aCa. These findings provide valuable theoretical insights and practical guidance for saline-alkali soil improvement and PTEs pollution control in the Yellow River Delta region, thereby providing a foundation for sustainable environmental management and resource utilization.

Integrating proximal soil sensing data and environmental variables to enhance the prediction accuracy for soil salinity and sodicity in a region of Xinjiang Province, China

Soil salinization and sodification, the primary causes of land degradation and desertification in arid and semi-arid regions, demand effective monitoring for sustainable land management. This study explores the utility of partial least square (PLS) latent variables (LVs) derived from visible and near-infrared (Vis-NIR) spectroscopy, combined with remote sensing (RS) and auxiliary variables, to predict electrical conductivity (EC) and sodium absorption ratio (SAR) in northern Xinjiang, China. Using 90 soil samples from the Karamay district, machine learning models (Random Forest, Support Vector Regression, Cubist) were tested in four scenarios. Modeling results showed that RS and Land use alone were unreliable predictors, but the addition of topographic attributes significantly improved the prediction accuracy for both EC and SAR. The incorporation of PLS LVs derived from Vis-NIR spectroscopy led to the highest performance by the Random Forest model for EC (CCC = 0.83, R2 = 0.80, nRMSE = 0.48, RPD = 2.12) and SAR (CCC = 0.78, R2 = 0.74, nRMSE = 0.58, RPD = 2.25). The variable importance analysis identified PLS LVs, certain topographic attributes (e.g., valley depth, elevation, channel network base level, diffuse insolation), and specific RS data (i.e., polarization index of VV + VH) as the most influential predictors in the study area. This study affirms the efficiency of Vis-NIR data for digital soil mapping, offering a cost-effective solution. In conclusion, the integration of proximal soil sensing techniques and highly relevant topographic attributes with the RF model has the potential to yield a reliable spatial model for mapping soil EC and SAR. This integrated approach allows for the delineation of hazardous zones, which in turn enables the consideration of best management practices and contributes to the reduction of the risk of degradation in salt-affected and sodicity-affected soils.

Evaluation of the prediction effectiveness for geochemical mapping using machine learning methods: A case study from northern Guangdong Province in China

This study compares seven machine learning models to investigate whether they improve the accuracy of geochemical mapping compared to ordinary kriging (OK). Arsenic is widely present in soil due to human activities and soil parent material, posing significant toxicity. Predicting the spatial distribution of elements in soil has become a current research hotspot. Lianzhou City in northern Guangdong Province, China, was chosen as the study area, collecting a total of 2908 surface soil samples from 0 to 20 cm depth. Seven machine learning models were chosen: Random Forest (RF), Support Vector Machine (SVM), Ridge Regression (Ridge), Gradient Boosting Decision Tree (GBDT), Artificial Neural Network (ANN), K-Nearest Neighbors (KNN), and Gaussian Process Regression (GPR). Exploring the advantages and disadvantages of machine learning and traditional geological statistical models in predicting the spatial distribution of heavy metal elements, this study also analyzes factors affecting the accuracy of element prediction. The two best-performing models in the original model, RF (R2 = 0.445) and GBDT (R2 = 0.414), did not outperform OK (R2 = 0.459) in terms of prediction accuracy. Ridge and GPR, the worst-performing methods, have R2 values of only 0.201 and 0.248, respectively. To improve the models' prediction accuracy, a spatial regionalized (SR) covariate index was added. Improvements varied among different methods, with RF and GBDT increasing their R2 values from 0.4 to 0.78 after enhancement. In contrast, the GPR model showed the least significant improvement, with its R2 value only reaching 0.25 in the improved method. This study concluded that choosing the right machine learning model and considering factors that influence prediction accuracy, such as regional variations, the number of sampling points, and their distribution, are crucial for ensuring the accuracy of predictions. This provides valuable insights for future research in this area.

Preliminary construction of a microecological evaluation model for uranium-contaminated soil

With the extensive development of nuclear energy, soil uranium contamination has become an increasingly prominent problem. The development of evaluation systems for various uranium contamination levels and soil microhabitats is critical. In this study, the effects of uranium contamination on the carbon source metabolic capacity and microbial community structure of soil microbial communities were investigated using Biolog microplate technology and high-throughput sequencing, and the responses of soil biochemical properties to uranium were also analyzed. Then, ten key biological indicators as reliable input variables, including arylsulfatase, biomass nitrogen, metabolic entropy, microbial entropy, Simpson, Shannon, McIntosh, Nocardioides, Lysobacter, and Mycoleptodisus, were screened by random forest (RF), Boruta, and grey relational analysis (GRA). The optimal uranium-contaminated soil microbiological evaluation model was obtained by comparing the performance of three evaluation methods: partial least squares regression (PLS), support vector regression (SVR), and improved particle algorithm (IPSO-SVR). Consequently, partial least squares regression (PLS) has a higher R2 (0.932) and a lower RMSE value (0.214) compared to the other. This research provides a new evaluation method to describe the relationship between soil ecological effects and biological indicators under nuclear contamination.

Tree-structured parzen estimator optimized-automated machine learning assisted by meta-analysis for predicting biochar-driven N2O mitigation effect in constructed wetlands

Biochar is a carbon-neutral tool for combating climate change. Artificial intelligence applications to estimate the biochar mitigation effect on greenhouse gases (GHGs) can assist scientists in making more informed solutions. However, there is also evidence indicating that biochar promotes, rather than reduces, N2O emissions. Thus, the effect of biochar on N2O remains uncertain in constructed wetlands (CWs), and there is not a characterization metric for this effect, which increases the difficulty and inaccuracy of biochar-driven alleviation effect projections. Here, we provide new insight by utilizing machine learning-based, tree-structured Parzen Estimator (TPE) optimization assisted by a meta-analysis to estimate the potency of biochar-driven N2O mitigation. We first synthesized datasets that contained 80 studies on global biochar-amended CWs. The mitigation effect size was then calculated and further introduced as a new metric. TPE optimization was then applied to automatically tune the hyperparameters of the built extreme gradient boosting (XGBoost) and random forest (RF), and the optimum TPE-XGBoost obtained adequately achieved a satisfactory prediction accuracy for N2O flux (R2 = 91.90%, RPD = 3.57) and the effect size (R2 = 92.61%, RPD = 3.59). Results indicated that a high influent chemical oxygen demand/total nitrogen (COD/TN) ratio and the COD removal efficiency interpreted by the Shapley value significantly enhanced the effect size contribution. COD/TN ratio made the most and the second greatest positive contributions among 22 input variables to N2O flux and to the effect size that were up to 18% and 14%, respectively. By combining with a structural equation model analysis, NH4+-N removal rate had significant negative direct effects on the N2O flux. This study implied that the application of granulated biochar derived from C-rich feedstocks would maximize the net climate benefit of N2O mitigation driven by biochar for future biochar-based CWs.

Global prediction of gross primary productivity under future climate change

The ecosystem gross primary productivity (GPP) is crucial to land-atmosphere carbon exchanges, and changes in global GPP as well as its influencing factors have been well studied in recent years. However, identifying the spatio-temporal variations of global GPP under future climate changes is still a challenging issue. This study aims to develop data-driven approach for predicting the global GPP as well as its monthly and annual variations up to the year 2100 under changing climate. Specifically, Catboost was employed to examine the potential relationship between the GPP and environmental factors, with climate variables, CO2 concentration and terrain attributes being selected as environmental factors. The predicted monthly and annual GPP from Coupled Model Intercomparison Project phase 6 (CMIP6) under future SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5 scenarios were analyzed. The results indicate that the global GPP is predicted to increase under the future climate change in the 21st century. The annual GPP is expected to be 115.122 Pg C, 116.537 Pg C, 117.626 Pg C, and 120.097 Pg C in 2100 under four future scenarios, and the predicted monthly GPP shows seasonal difference. Meanwhile, GPP tends to increase in the northern mid-high latitude regions and decrease in the equatorial regions. For the climate zones form Köppen-Geiger classification, the arid, cold, and polar zones present increased GPP, while GPP in the tropical zone will decrease in the future. Moreover, the high importance of climate variables in GPP prediction illustrates that the future climate change is the main driver of the global GPP dynamics. This study provides a basis for predicting how global GPP responds to future climate change in the coming decades, which contribute to understanding the interactions between vegetation and climate.

Global predictions of topsoil organic carbon stocks under changing climate in the 21st century

The organic carbon (OC) stored in global topsoil (0-30 cm) will be the most active participant in the carbon cycle under future climate change. Due to differences in focus regions or research methods, the spatio-temporal changes of future global topsoil OC stocks and how they will be affected by climate change are not systematically understood, which needs to be further explored. In this study, we developed data-driven models to predict the spatio-temporal dynamics of global topsoil OC stocks by combining 32,579 soil profiles with environmental variables and comprehensively explored the impact of future climate change on topsoil OC. It was found that the topsoil OC stocks were 1249.29 Pg in the baseline period (1971-1990). By 2100, under the normal and high representative concentration paths, it is predicted that the global topsoil OC stocks will decrease by 113.67 ± 25.93 Pg and 193.71 ± 39.76 Pg, respectively. In the future, the largest global topsoil carbon loss will occur in boreal forest areas, which are expected to lose 17.03-27.90 % (66.01-108.13Pg) of their carbon stocks. The influence of climate on topsoil OC stocks is mainly manifested in temperature, which has a negative influence on the global topsoil OC stock, and the contribution rate of temperature to the effect on the global topsoil OC stock is about 26.96 %. Overall, our results provide a high spatio-temporal resolution assessment of global topsoil OC stocks and their relationship to environmental factors, and highlight the spatial heterogeneity, which has been generally ignored in many experimental frameworks and prediction models. These results will help governments to make appropriate management decisions to mitigate climate change.

Espial: Electrochemical Soil pH Sensor for In Situ Real-Time Monitoring

We present a first-of-its-kind electrochemical sensor that demonstrates direct real-time continuous soil pH measurement without any soil pre-treatment. The sensor functionality, performance, and in-soil dynamics have been reported. The sensor coating is a composite matrix of alizarin and Nafion applied by drop casting onto the working electrode. Electrochemical impedance spectroscopy (EIS) and squarewave voltammetry (SWV) studies were conducted to demonstrate the functionality of each method in accurately detecting soil pH. The studies were conducted on three different soil textures (clay, sandy loam, and loamy clay) to cover the range of the soil texture triangle. Squarewave voltammetry showed pH-dependent responses regardless of soil texture (while electrochemical impedance spectroscopy's pH detection range was limited and dependent on soil texture). The linear models showed a sensitivity range from -50 mV/pH up to -66 mV/pH with R2 > 0.97 for the various soil textures in the pH range 3-9. The validation of the sensor showed less than a 10% error rate between the measured pH and reference pH for multiple different soil textures including ones that were not used in the calibration of the sensor. A 7-day in situ soil study showed the capability of the sensor to measure soil pH in a temporally dynamic manner with an error rate of less than 10%. The test was conducted using acidic and alkaline soils with pH values of 5.05 and 8.36, respectively.

Effects of long-term herbaceous plant restoration on microbial communities and metabolic profiles in coal gangue-contaminated soil

The soil microbial diversity in the gangue accumulation area is severely stressed by a variety of heavy metals, while the influence of long-term recovery of herbaceous plants on the ecological structure of gangue-contaminated soil is to be explored. Therefore, we analysed the differences in physicochemical properties, elemental changes, microbial community structure, metabolites and expression of related pathways in soils in the 10- and 20-year herbaceous remediation areas of coal gangue. Our results showed that phosphatase, soil urease, and sucrase activities of gangue soils significantly increased in the shallow layer after herbaceous remediation. However, in zone T1 (10-year remediation zone), the contents of harmful elements, such as Thorium (Th; 1.08-fold), Arsenic (As; 0.78-fold), lead (Pb; 0.99-fold), and uranium (U; 0.77-fold), increased significantly, whereas the soil microbial abundance and diversity also showed a significant decreasing trend. Conversely, in zone T2 (20-year restoration zone), the soil pH significantly increased by 1.03- to 1.06-fold and soil acidity significantly improved. Moreover, the abundance and diversity of soil microorganisms increased significantly, the expression of carbohydrates in soil was significantly downregulated, and sucrose content was significantly negatively correlated with the abundance of microorganisms, such as Streptomyces. A significant decrease in heavy metals was observed in the soil, such as U (1.01- to 1.09-fold) and Pb (1.13- to 1.25-fold). Additionally, the thiamin synthesis pathway was inhibited in the soil of the T1 zone; the expression level of sulfur (S)-containing histidine derivatives (Ergothioneine) was significantly up-regulated by 0.56-fold in the shallow soil of the T2 zone; and the S content in the soil significantly reduced. Aromatic compounds were significantly up-regulated in the soil after 20 years of herbaceous plant remediation in coal gangue soil, and microorganisms (Sphingomonas) with significant positive correlations with benzene ring-containing metabolites, such as Sulfaphenazole, were identified.