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As a precious soil resource, black soils in Northeast China are currently facing severe land degradation. Visible and near-infrared spectroscopy (vis-NIR, 350–2500 nm) and mid-infrared spectroscopy (MIR, 2500–25,000 nm) have shown great potential to predict soil properties. However, there is still limited research on using MIR in situ. The aim of this study was to explore the feasibility of in situ MIR for the prediction of soil total nitrogen (TN) and total phosphorus (TP) and to compare its performance with the use of laboratory MIR, as well as the use of in situ and laboratory vis-NIR. A total of 450 samples from 90 soil profiles, along with their in situ and laboratory spectra of MIR and vis-NIR, were collected in a field with ten different tillage and management practices in a typical black soil area of Northeast China. Partial least square regression (PLSR), random forest (RF) and multivariate adaptive regression splines (MARS) were used to generate the calibrations between the spectra and the two properties. The results showed that both MIR and vis-NIR were able to predict the TN whether in laboratory or in situ conditions, but neither of them could predict the TP quantitatively since there was no sensitive band on both spectra regarding the TP. The prediction accuracy of the TN with laboratory spectra was higher than that with in situ spectra, for both vis-NIR and MIR. The optimal prediction accuracy of the TN with laboratory MIR (RMSE = 0.11 g/kg, RPD = 3.12) was higher than that of laboratory vis-NIR (RMSE = 0.14 g/kg, RPD = 2.45). The optimal prediction accuracy of in situ MIR (RMSE = 0.20 g/kg, RPD = 1.80) was lower than that of in situ vis-NIR (RMSE = 0.16 g/kg, RPD = 2.14). The prediction performance of the spectra followed laboratory MIR > laboratory vis-NIR > in situ vis-NIR > in situ MIR. The performance of in situ MIR was relatively poor, mainly due to the fact that MIR was more influenced by soil moisture. This study verified the feasibility of in situ MIR for soil property prediction and provided an approach for obtaining rapid soil information and a reference for soil research and management in black soil areas.

Background Inductively coupled plasma mass spectrometry (ICP‐MS) is widely used for the accurate measurement of minerals. However, its application to serum essential mineral measurement has not been fully evaluated. The present study aimed to assess the performance of ICP‐MS for serum minerals by comparing its measurements to those obtained using standard quantification methods. Methods Cross‐sectional data were collected from 282 participants from a single facility in Japan. Serum concentrations of eight key minerals, namely sodium, potassium, calcium, phosphorus, magnesium, iron, zinc, and copper, measured via ICP‐MS and standard methods were compared using Passing–Bablok regression and Bland–Altman plots. Results All minerals, except phosphorus, exhibited good agreement with standard methods, with more stable regression coefficients observed for minerals with greater interindividual variability. After systematically filtering outliers, the mean relative errors were approximately −3% for sodium, potassium, calcium, and magnesium; +5% for iron; 0% for zinc; and −19% for copper. The outliers for iron were primarily due to mild hemolysis, whereas those for zinc were largely attributed to nonhemolysis factors. For phosphorus, the serum total phosphorus concentration measured using ICP‐MS was approximately 3.5 times higher than the serum inorganic phosphorus concentration measured using standard methods, with a weak correlation observed between the two methods. Conclusion This study provides a practical foundation for future research. Understanding ICP‐MS characteristics will facilitate the development of new approaches in clinical diagnostics. Analysis of real‐world cross‐sectional study data revealed relative errors between the standard method and ICP‐MS for the different minerals tested. Additionally, several outliers were observed exclusively in the ICP‐MS results, likely due to hemolysis or other unidentified factors. Although these limitations in ICP‐MS performance cannot be entirely dismissed, comparative results of the standard method and the ICP‐MS approach exhibited good agreement. The unique characteristics of ICP‐MS identified in this study lay a strong foundation for future research, not only for routine clinical measurement of specific minerals but also for disease‐specific analyses leveraging the ability of ICP‐MS to simultaneously measure a wide range of parameters.

A novel approach to “treating waste with waste” was proposed to prepare porous ceramsite for the treatment of sewage containing Ag+ with total phosphorus tailings as raw material. The porous ceramsite with bulk density of 1.11 g/cm3 was fabricated via rotary granulation technique, followed by sintering at 1085 °C, whose compressive strength, water absorption, and hydrochloric acid solubility were 1.23MPa, 43.8%, and 15%, respectively. Effects of NaCl concentration, solid-liquid ratio, modification temperature, and modification time were studied in this study. After the porous ceramsite was modified by 1 mol/L NaCl solution at 70 °C for 4 h, the removal rate of Ag+ for modified ceramsite reached 99.9%. The high Ag+ removal rate was related to the surface microstructure of porous ceramsite. Numerous micro pores appeared on the surface of ceramsite after NaCl modification, which provided more contact sites for the adsorption of Ag+ by the adhered Cl− on ceramsite surface. The high removal rate of modified ceramsite for Ag+ makes it a highly promising for application in sewage disposal.

Remote sensing technology applications for water quality inversion in large rivers are common. However, their application to medium/small-sized water bodies within rural areas is limited due to the low spatial resolution of remote sensing images. In this work, a typical small rural river was selected, and high-resolution unmanned aerial vehicle (UAV) multispectral images and ground monitoring data of the river were obtained. Then, a comparative analysis of three univariate regression models and nine machine learning models (Ridge Regression (RR), Support Vector Regression (SVR), Grid Search Support Vector Regression (GS-SVR), Random Forest (RF), Grid Search Random Forest (GS-RF), eXtreme Gradient Boosting (XGBoost), Deep Neural Networks (DNN), Convolutional Neural Networks (CNN), and Catboost Regression (CBR)) for their accuracy in the prediction of turbidity (TUB), total nitrogen (TN), and total phosphorus (TP) was performed. TUB can be achieved by simple statistical regression models. The CBR model exhibited the best performance for the three index inversions on the test set evaluation metrics: R2 (0.90~0.92), RMSE (7.57 × 10−3~1.59 mg/L), MAE (0.01~1.30 mg/L), RPD (3.21~3.56), and NSE (0.84~0.92). The water pollution of the study area was closely related to its land-use pattern, excessive and irrational fertilizer application, and distribution of pollutant outlets.

Some essential water conservation areas in China have continuously suffered from various serious problems such as water pollution and water quality deterioration in recent decades and thus called for real-time water pollution monitoring system underwater resources management. On the basis of the remote sensing data and ground monitoring data, this study firstly constructed a more accurate retrieval model for total phosphorus (TP) concentration by comparing 12 machine learning algorithms, including support vector machine (SVM), artificial neural network (ANN), Bayesian ridge regression (BRR), lasso regression (Lasso), elastic net (EN), linear regression (LR), decision tree regressor (DTR), K neighbor regressor (KNR), random forest regressor (RFR), extra trees regressor (ETR), AdaBoost regressor (ABR) and gradient boosting regressor (GBR). Then, this study applied the constructed retrieval model to explore the spatial-temporal evolution of the Miyun Reservoir and finally assessed the water quality. The results showed that the model of TP concentration built by the ETR algorithm had the best accuracy, with the coefficient R2 reaching over 85% and the mean absolute error lower than 0.000433. The TP concentration in Miyun Reservoir was between 0.0380 and 0.1298 mg/L, and there was relatively significant spatial and temporal heterogeneity. It changed remarkably during the periods of the flood season, winter tillage, planting, and regreening, and it was lower in summer than in other seasons. Moreover, the TP in the southwest part of the reservoir was generally lower than in the northeast, as there was less human activities interference. According to the Environmental Quality Standard for the surface water environment, the water quality of Miyun Reservoir was overall safe, except only for an over-standard case occurrence in the spring and September. These conclusions can provide a significant scientific reference for water quality monitoring and management in Miyun Reservoir.

Remote sensing is widely used for lake-water-quality monitoring, but the inversion of the total nitrogen (TN) and total phosphorus (TP) of rivers and non-optical parameters is still a difficult problem. The use of high spatial and temporal resolution multispectral imagery combined with machine learning techniques is an effective solution for this difficulty. Three machine learning methods based on support vector regression (SVR), neural network (NN) and random forest (RF) were used to invert TN and TP using actual water-quality measurement data and Sentine-2 remote-sensing images, and analyzed the factors influencing water quality in terms of pollutant emissions and land use. The results show that RF performs the best in both TN (R2 = 0.800, RMSE = 0.640, MSE = 0.400, MAE = 0.480) and TP (R2 = 0.830, RMSE = 0.033, MSE = 0.001, MAE = 0.022) inversion models, and that the optimal selection of feature variables improves model performance. The TN and TP concentrations in the Minjiang River Meishan Water Function Development Zone were the highest in the downstream section and in 2018. Analysis of the factors influencing water quality shows that pollution sources and amounts were closely related to land-use types, and land use in riparian zones at different spatial scales had different degrees of impact on water quality.

Phosphorus, as a key nutrient, plays an essential role in both algal growth in surface waters and crop development on land. Its presence in inorganic fertilizers is crucial for maximizing crop yields. However, an excessive accumulation of phosphorus in soils can lead to its loss and exacerbate eutrophication in water bodies. This study highlights the complex interplay among phosphorus management, agricultural productivity, and environmental health, particularly in the context of climate change’s influence on sediment transport and water pollution. We focus on the Poyang Lake Basin (PLB) and use a sophisticated process-based phosphorus model to forecast phosphorus load trends from 2020 to 2049. Our predictions indicate a significant increase in the total phosphorus load of the PLB due to the impact of climate change. To address these challenges, we explore a novel strategy combining organic and inorganic phosphorus fertilizers. This approach aims to improve crop yields while reducing non-point source phosphorus pollution through adjusted anthropogenic inputs. Our findings reveal that a synergistic application of these fertilizers, coupled with a controlled use of inorganic phosphate, can reduce its usage by more than 2.5% annually. This method not only contributes to a 2.2% average annual increase in livestock and poultry production but also promotes a 0.6% yearly growth in grain output. Consequently, it effectively diminishes non-point source phosphorus pollution, offering a sustainable solution to the dual challenge of enhancing agricultural productivity and protecting environmental health.

Total phosphorus (TP) concentration impacts river ecosystems, yet its dynamics remain poorly understood in the contiguous United States (CONUS). We used an XGBLinear model to estimate riverine TP solely from Landsat reflectance data. Using this model, we developed a remotely estimated riverine TP database (CONUS’s Landsat-based Estimation and Assessment of Riverine TP, CLEAR-TP), covering 33,497 river reaches (107,000 km in river length) from 1984 to 2018. Using CLEAR-TP, we estimated long-term (≥15 years) annual TP concentrations for 16,258 reaches, and long-term TP trend analysis indicates that most reaches (13,092; 80.53%) exhibited stationary TP trends, while 3,166 reaches (19.47%) showed significant long-term trends, including 2,637 reaches (16.22%) with declining TP (−0.995%/year on median) and 529 reaches (3.25%) with increasing TP (1.216%/year on median). CLEAR-TP reliably captured longitudinal variations in TP concentrations along river profiles. Our results suggest that, at the reach scale, TP trends in reaches with significant trends, and, at the river-course scale, longitudinal spatial variations in most CONUS rivers, were primarily associated with climate, hydrology, and land cover.

Pen aquaculture is the main form of aquaculture in some shallow lakes in eastern China. It is valuable to map the spatiotemporal changes of pen aquaculture in eutrophic lakes to assess its effect on water quality, thereby helping the relevant decision-making agencies to manage the water quality (WQ) of lakes. In this study, an automatic approach for extracting the pen aquaculture area was developed based on Landsat data. The approach integrates five algorithms, including grey transformation, discrete wavelet transform, fast Fourier transform, singular value decomposition and k-nearest neighbor classification. It was successfully applied in the automatic mapping of the pen aquaculture areas in Lake Yangcheng from 1990 to 2016. The overall accuracies were greater than 92%. The result indicted that the practice of pen aquaculture experienced five stages, with the general area increasing in the beginning and decreasing by the end of the last stage. Meanwhile, the changes of nine WQ parameters observed from 2000 to 2016, such as ammonia (NH3-N), pH, total nitrogen (TN), total phosphorus (TP), chlorophyll a, biochemical oxygen demand (BOD), chemiluminescence detection of permanganate index (CODMn), Secchi disk depth (SDD) and dissolved oxygen (DO), were analyzed in the lake sectors of Lake Yangcheng, and then their relationships were explored with the percentage of pen aquaculture area. The result suggested that the percentage of pen aquaculture area exhibits significantly positive correlations with NH3-N, TN, TP, chlorophyll a, BOD and CODMn, but significantly negative correlations with SDD and DO. The experimental results may offer an important implication for managing similar shallow lakes with pen aquaculture expansion and water pollution problems.

The influence of wood biochar on the contents of various forms of phosphates was studied in laboratory experiments on soils with different phosphorus availability. Soils of the middle taiga subzone of Karelia were used in this work: a sandy Umbric Podzol and a heavy loamy Umbric Retisol. The tests studied the effect of two fractions of biochar (3–5 and ≤2 mm) applied at the rates of 2 and 5% of the soil mass on pH KCl , the content of available and total phosphorus, the inorganic phosphorus fractions (Chang–Jackson method), and the total phosphatase activity of soils, as well as the effect of separate and combined application of biochar and fertilizer (NPK) on the content of available phosphorus in a pot experiment with spring barley. The research revealed that biochar significantly increased the content of available phosphorus (by 20–40%); increased the contents of the fractions of Ca-bounded P, Al-bounded P, and loosely bounded P; and also increased the phosphatase activity in the Umbric Podzol. In pot experiments, a higher content of P 2 O 5 was noted in variants with biochar ≤2 mm, in variants with fertilizer, and with combined application of biochar and fertilizer. Biochar increased the content of available phosphorus by 2–6%, increased the content of Ca-bounded P and loosely bounded P (with biochar ≤2 mm at 5% dosage), and had no significant effect on the phosphatase activity in the Umbric Retisol. Only combination of biochar ≤2 mm and fertilizer had a significant effect on the content of P 2 O 5 in pot experiment with Umbric Retisol. In general, the most noticeable effect on almost all studied indicators was provided by ≤2 mm fraction of biochar in a 5% dosage. The use of biochar led to a statistically significant increase in pH KCl values and did not affect the content of total phosphorus in both soils. Biochar had a greater effect on the phosphate regime of coarse-textured soil with an initially lower pH and a lower content of available and total phosphorus.