Search Results (204)

This study aimed to evaluate the impact of vineyard cultivation time and the use of metal-based fungicides on the chemical fractions of soil organic matter (SOM) as well as their interactions with Cu, Zn, and Mn in vineyard soils from Southern Brazil with varying histories of fungicide application. Soil samples were collected in 2017 from vineyards aged 35, 37, and 39 years in the Serra Gaúcha region and 13, 19, and 36 years in the Campanha Gaúcha. In each region, samples were also collected from a non-anthropized reference area. In the oldest vineyards, sampling was conducted both within and between the rows of planting. Chemical fractionation of SOM was performed: non-humic substances (nHSs), particulate organic matter (POM), fulvic acid (FA), humic acid (HA), and humin (Hu). Fourier-transform infrared (FTIR) spectra were obtained for the HA, from which the aromaticity index (AI) and relative intensities (RIs) were calculated. In each SOM fraction, total organic carbon and the concentrations of Cu, Zn, and Mn were determined. Changes in land use alter the forms and distribution of soil organic carbon (SOC) and, consequently, of metals. Elemental and spectroscopic analyses of HS revealed that HA in the reference areas (forest and native grassland) was more aliphatic and had higher concentrations of polysaccharides, indicating fractions with a lower degree of stabilization. However, in vineyard areas, HA exhibited greater humification and aromaticity. Increasing cultivation time gradually increased soil carbon content, indicating that viticultural agroecosystems can sequester carbon in the soil over time, reaching levels similar to those observed in the reference areas. When comparing vineyard areas alone, with row collections and inter-row collections, we observed an increase in SOC levels in areas managed with cover crops, demonstrating the importance of conservation management in these areas. When evaluating the distribution of metals in these soils, we could observe the high affinity of Cu for the functional groups of SOM, with FA and HA responsible for the complexation of these elements in the soil. For Zn and Mn, the greatest accumulations were observed in the Hu fraction due to their greater affinity for soil clay minerals. This shows that soil organic matter is a key component in the complexation of metals in soils, reducing their availability and potential toxicity to cultivated plants. Full article

Surfactants can be utilized to improve oil recovery by changing the performance of reservoirs in rock pores. Kerogen is the primary organic matter in shale; however, high temperatures will affect the overall performance of this surfactant, resulting in a decrease in its activity or even failure. The effect of surfactants on kerogen pyrolysis has rarely been researched. Therefore, this study synthesized a polymeric surfactant (PS) with high temperature resistance and investigated its effect on kerogen pyrolysis under the friction of drill bits or pipes via molecular dynamics. The infrared spectra and thermogravimetric and molecular weight curves of the PS were researched, along with its surface tension, contact angle, and oil saturation measurements. The results showed that PS had a low molecular weight, with an MW value of 124,634, and good thermal stability, with a main degradation temperature of more than 300 °C. It could drop the surface tension of water to less than 25 mN·m⁻¹ at 25–150 °C, and the use of slats enhanced its surface activity. The PS also changed the contact angles from 127.96° to 57.59° on the surface of shale cores and reversed to a water-wet state. Additionally, PS reduced the saturated oil content of the shale core by half and promoted oil desorption, indicating a good cleaning effect on the shale oil reservoir. The kerogen molecules gradually broke down into smaller molecules and produced the final products, including methane and shale oil. The main reaction area in the system was the interface between kerogen and the surfactant, and the small molecules produced on the interface diffused to both ends. The kinetics of the reaction were controlled by two processes, namely, the step-by-step cleavage process of macromolecules and the side chain cleavage to produce smaller molecules in advance. PS could not only desorb oil in the core but also promote the pyrolysis of kerogen, suggesting that it has good potential for application in shale oil exploration and development. Full article

Suspended solids (SS) composed of micrometer-to-nanometer-scale particles, including silt and organic matter, significantly impact aquatic ecosystems through physicochemical interactions. Accurate monitoring of SS concentration and particle size is critical for environmental protection and pollution prevention. We constructed multiple datasets using received signals after propagation through different aqueous environments. Analysis of the performance of neural networks across different datasets revealed that high-frequency signals with rich spectra have high potential for detecting suspended solid information in complex aqueous environments. Our study explores the performance of two neural networks (Conv1dBGRU and TCN) in combination with channel attention mechanisms in classification tasks focused on the concentration of suspended solids and particle size. We also constructed neural networks for multi-task learning using both hard and soft parameter-sharing methods to simultaneously complete the classification tasks for concentration and particle size. The results show that multi-frequency acoustic signals in combination with neural networks can achieve simultaneous and accurate estimation of the concentration of suspended solids and particle size. Full article

It has been demonstrated that organic fertilisers could be a source of microplastics (MPs) in agricultural soils. These organic fertilisers comprise a diverse array of matrices including organic waste and by-products. Currently, there is no established methodology for the extraction of MP from these matrices. The present article aims to validate a standardised protocol for the extraction of MPs from a diverse range of complex, organic-rich samples. The protocol has been developed to ensure a high recovery of MPs, to preserve their integrity, and to eliminate organic particles that interfere with FTIR analyses. Spiked MPs sized 315–5000 µm were subjected to a two-step process involving chemical digestion (H₂O₂, 30% (w/v), 53 °C) and density separation (NaI, >1.60 g·cm⁻³). This resulted in a mean extraction rate exceeding 95%, with undigested matter remaining below 5%. No evidence of fragmentation was observed. Furthermore, the chemical nature of spiked microplastics is still perfectly interpretable from the FTIR spectra despite the different chemical treatments undergone. These findings thus validate the method for the microplastic range 315–5000 µm. However, a new method for reanalysing the project’s data produced contrasting results, suggesting a significant drop in recovery rates for size ranges below 250 µm. This reanalysis approach constitutes the second innovation of this protocol, and enables a more critical analysis of the results obtained in publications on microplastics. Full article

Composting is an environmentally friendly treatment technology that recycles and sanitizes organic solid waste. This study aimed to assess the evolution of nutrients, maturity, and microbial communities during the composting of different plant-derived wastes. The composting process was conducted over 49 days using three types of plant-derived waste: wheat bran (WB), peanut straw (PS), and poplar leaf litter (PL). This process was examined through physical, chemical, and biological parameters. The results revealed that after 49 days of composting, the three groups experienced significant changes. They were odorless, were insect-free, exhibited a dark brown color, had an alkaline pH value, and had an electrical conductivity (EC) value of less than 4 mS/cm. These characteristics indicated that they had reached maturity. Nutrient content was the most significant factor influencing the degree of humification of the different composting materials, while changes in microbial community diversity were the key driving factors. Significantly, the compost PS, derived from peanut straw, entered the thermophilic phase first, and by the end of composting, it had the lowest organic matter (OM) loss rate (17.4%), with increases in total nitrogen (TN), total phosphorus (TP), and total potassium (TK) in the order of PS > PL > WB. The increase in humus carbon (HSC) content and the humic acid/fulvic acid (HA/FA) ratio followed the order PS > WB > PL. FTIR spectra indicated that PS had greater aromatic characteristics compared to the other samples. The abundance and diversity of bacterial and fungal communities in the compost increased significantly, accompanied by more complex community structures. Crucially, there were no phytotoxic effects in any of the three composting treatments, and the compost PS boasted a high germination index (GI) of 94.79%, with the lowest heavy metal contents. The findings indicate that the compost PS has the highest potential for resource utilization and is suitable for agricultural applications. Our results demonstrate that composting technology for plant-derived waste has the potential to enhance soil fertility and provide a reference for the composting treatment and resource utilization of other plant-derived waste. Full article

The alpine peatlands in western Sichuan Province are currently experiencing aridification. To understand the effects of aridification on the characteristics of organic carbon release from alpine soils, the soil in the northwest Sichuan Plateau was investigated. Soil columns were incubated under different moisture conditions in situ and in the laboratory, and ultraviolet-visible absorption spectroscopy and three-dimensional fluorescence spectroscopy were used to assess the soil dissolved organic carbon (DOC) levels. The results revealed that (1) the cumulative release of DOC from alpine soil in the northwest Sichuan Plateau decreased with decreasing moisture content. The cumulative release of soil DOC in the laboratory (0–5 cm soil reached 1.93 ± 0.43 g/kg) was greater than that from soil incubated in situ (0–5 cm soil reached 1.40 ± 0.13 g/kg); (2) the cumulative release of DOC in 0–5 cm soil exhibited the greatest response to changes in water content, and the cumulative release of DOC from the 0–5 cm soil layer (1.40 ± 0.13 g/kg) was greater than that from the 5–15 cm soil layer (1.25 ± 0.03 g/kg); and (3) UV-visible absorption spectra and 3D fluorescence spectral characteristics indicated that aridification increases the content of chromophoric dissolved organic matter (CDOM) components with strong hydrophobicity, especially tyrosine components (surface soil increased 39.59~63.31%), in alpine soil DOC. This increase in hydrophobic CDOM components enhances the aromaticity and degree of humification of DOC. Our results revealed that drought inhibits the release of soil DOC, which is unfavorable for the sequestration of organic carbon in alpine soils, potentially resulting in the loss of soil carbon pools and further degradation of alpine ecosystem functions. Full article

Accurate and timely acquisition of soil information is crucial for precision agriculture, food security, and environmental protection. Proximal visible near-infrared reflectance (vis–NIR) spectroscopy has been widely employed for rapid and accurate soil measurement, but its point measurement nature limits its direct applicability for large-scale soil surveys. On the other hand, remote sensing techniques can provide soil information at a larger scale, but their resolution is relatively coarse. While both techniques have been used independently for soil analyses, integrating vis–NIR spectroscopy with remote sensing remains a challenge and is underexplored, especially at the field scale. This study addresses this gap by combining field vis–NIR spectra with Gaofen-1 remote sensing data to spatially analyze soil organic matter and total nitrogen at the field scale. Unlike previous work, we first applied Gaofen-1 data and 10 derived spectral indices to estimate soil organic matter and total nitrogen using partial least squares regression and random forest, identifying the optimal combination of spectral indices. Then, we integrated the proximal vis–NIR spectra with this optimal spectral index combination for improved soil property estimation. This integration advanced existing methodologies by leveraging the high spatial resolution of Gaofen-1 data and the detailed spectral information from vis–NIR spectroscopy. The results showed the following: (1) the coefficient of variation across different crop growth stages of Gaofen-1 data was more crucial for modeling these two properties compared to bare soil Gaofen-1 data; (2) integrating proximal vis–NIR spectra with Gaofen-1 data improved model performance, yielding Lin’s concordance correlation coefficient (${\rho }_c$) values of 0.63 and 0.72 and ratios of performance to interquartile distance (RPIQ) of 1.99 and 1.59 for soil organic matter and total nitrogen, respectively; and (3) the combined use of vis–NIR spectra and Gaofen-1 data provided higher spatial estimation accuracy (R² of 0.68 and 0.57 for soil organic matter and total nitrogen) compared to ordinary kriging (R² of 0.63 and 0.31 for soil organic matter and total nitrogen). These results demonstrate that the synergistic use of remote sensing and proximal soil sensing is a practical approach for spatially estimating soil organic matter and total nitrogen at the field scale. Full article

Atmospheric correction, the removal of the atmospheric signal from a satellite image, still poses a challenge over optically complex coastal water. Here, we present the first atmospheric correction validation study performed in optically complex Norwegian fjords. We compare in situ reflectance measurements and chlorophyll-a concentrations from Western Norwegian fjords with atmospherically corrected Sentinel-3 Ocean and Land Colour Instrument observations and chlorophyll-a retrievals. Measurements were taken in Hardangerfjord, Bjørnafjord and Møkstrafjord during a bright green coccolithophore bloom in May 2022, and during a period of no apparent discoloration in April 2023. Coccolithophore blooms generally peak in the blue region (490 nm), but spectra measured in this bloom peaked in the green region (559 nm), possibly due to absorption by colored dissolved organic matter (a_CDOM(440) = 0.18 ± 0.01 m⁻¹) or due to high cell counts (up to 15 million cells/L). We tested a wide range of atmospheric correction algorithms, including ACOLITE, BAC, C2RCC, iCOR, L2gen, POLYMER and the SNAP Rayleigh correction. Surprisingly, atmospheric correction algorithms generally performed better during the bloom (average MAE = 1.25) rather than in the less scattering water in the following year (average MAE = 4.67), possibly because the high water-leaving radiances due to the high backscattering by coccolithophores outweighed the adjacency effect. However, atmospheric correction algorithms consistently underestimated water-leaving reflectance in the bloom. In non-bloom matchups, most atmospheric correction algorithms overestimated the water-leaving reflectance. POLYMER appears unsuitable for use over coccolithophore blooms but performed well in non-bloom matchups. Neither BAC, used in the official Level-2 OLCI products, nor C2RCC performed well in the bloom. Nine chlorophyll-a retrieval algorithms, including two algorithms based on neural nets, four based on red and near-infrared bands and three maximum band-ratio algorithms, were also tested. Most chlorophyll-a retrieval algorithms did not perform well in either year, although several did perform within the 70% accuracy threshold for case-2 waters. A red-edge algorithm performed best in the coccolithophore blooms, while a maximum band-ratio algorithm performed best in the following year. Full article

Biosolids from stabilized sludge present a high fertilization potential, due to their rich content of nutrients and organic matter. The intrinsic and subtle properties of such fertilizers may greatly influence the fertilization efficiency. In this sense, the utility, advantages and limitations of advanced characterization methods, for the investigation of structural and dynamic properties at the microscopic scale of slightly different formulations of fertilizers were assessed. For that, three formulas of organo-mineral fertilizers based on biosolids (V1, V2 and V3), having at least 2% N, 2% P₂O₅, and 2% K₂O, were characterized by advanced methods, such as ¹H NMR relaxometry, ¹H MAS and ¹³C CP-MAS NMR spectroscopy, ¹H double-quantum NMR and FT-IR spectroscopy. Advanced structural characterization was performed using SEM, EDX and X-ray diffraction. Four dynamical components were identified in the NMR T₂ distribution showing that the rigid component has a percentage larger than 90%, which explains the broad band of NMR spectra confirmed by the distributions of many components in residual dipolar coupling as were revealed by ¹H DQ-NMR measurements. SEM and EDX measurements helped the identification of components from crystalline-like X-ray diffraction patterns. To evaluate the release properties of organo-mineral fertilizers, dynamic measurements of classical electric conductivity and pH were performed by placing 0.25 g of the formulas (V1, V2 and V3) in 200 mL of distilled water. The content of N and P were quantified using specific reactants, combined with VIS-nearIR spectroscopy. Two release mechanisms were observed and characterized. It was found that V3 presents the smallest release velocity but releases the largest number of fertilizers. Full article

Water treatment necessitates the sustainable use of natural resources. This paper focuses on the characterization of three oxidic lithological materials (OLMs) with the aim of utilizing them to prepare calcined adsorbent substrates for ionic adsorption. The three materials have pH levels of 7.66, 4.63, and 6.57, respectively, and organic matter contents less than 0.5%. All of the materials are sandy loam or loamy sand. Their electric conductivities (0.18, 0.07, and 0.23 dS/m) show low levels of salinity and solubility. Their CEC (13.40, 13.77, and 6.76 cmol(+)kg) values are low, similar to those of amphoteric oxides and kaolin clays. Their aluminum contents range from 7% up to 12%, their iron contents range from 3% up to 7%, their titanium contents range from 0.3% to 0.63%, and their manganese contents range from 0.007% up to 0.033%. The amphoteric oxides of these metals are responsible for their ionic adsorption reactions due to their variable charge surfaces. Their zirconium concentrations range from 100 to 600 mg/g, giving these materials the refractory properties necessary for the preparation of calcined adsorbent substrates. Our XRD analysis shows they share a common mineralogical composition, with quartz as the principal component, as well as albite, which leads to their thermal properties and mechanical resistance against abrasion. The TDA and IR spectra show the presence of kaolinite, which is lost during thermal treatments. The results show that the OLMs might have potential as raw materials to prepare calcined adsorbent substrates for further applications and as granular media in the sustainable treatment of both natural water and wastewater. Full article

Chromophoric dissolved organic matter (CDOM) is a mixture of various types of organic matter and a useful parameter for monitoring complex inland surface waters. Remote sensing has been widely utilized to detect CDOM in various studies; however, in many cases, the dataset is relatively imbalanced in a single region. To address these concerns, data were acquired from hyperspectral images, field reflection spectra, and field monitoring data, and the imbalance problem was solved using a synthetic minority oversampling technique (SMOTE). Using the on-site reflectance ratio of the hyperspectral images, the input variables R_rs (452/497), R_rs (497/580), R_rs (497/618), and R_rs (684/618), which had the highest correlation with the CDOM absorption coefficient a_CDOM (355), were extracted. Random forest and light gradient boosting machine algorithms were applied to create a CDOM prediction algorithm via machine learning, and to apply SMOTE, low-concentration and high-concentration datasets of CDOM were distinguished by 5 m⁻¹. The training and testing datasets were distinguished at a 75%:25% ratio at low and high concentrations, and SMOTE was applied to generate synthetic data based on the training dataset, which is a sub-dataset of the original dataset. Datasets using SMOTE resulted in an overall improvement in the algorithmic accuracy of the training and test step. The random forest model was selected as the optimal model for CDOM prediction. In the best-case scenario of the random forest model, the SMOTE algorithm showed superior performance, with testing R², absolute error (MAE), and root mean square error (RMSE) values of 0.838, 0.566, and 0.777 m⁻¹, respectively, compared to the original algorithm’s test values of 0.722, 0.493, and 0.802 m⁻¹. This study is anticipated to resolve imbalance problems using SMOTE when predicting remote sensing-based CDOM. It is expected to produce and implement a machine learning model with improved reliable performance. Full article

Groundwater, a primary source of freshwater on Earth, is rapidly declining due to natural and anthropogenic activities. This study aimed to investigate the spatial distribution of dissolved organic matter (DOM) and heavy metals (HMs) in two municipal groundwater networks (A and B) from tube wells to taps in an industrial city, Faisalabad. The results showed that parameters such as color, turbidity, pH, EC, TDS, Ca²⁺, Mg²⁺, CO₃²⁻, HCO₃⁻, Cl⁻, CaCO₃, Na+, and NO₃⁻ were within the permissible limits set by the World Health Organization (WHO) and Pakistan Environmental Quality Standards (PEQSs). However, parameters like DO and COD exceeded standard values along the routes. Odor, taste, temperature, BOD, NH₄⁺, T. coli, and F. coli surpassed acceptable levels at the tap end of both networks. Fluorescence EEM-PARAFAC spectra were analyzed at an excitation wavelength of 220–500 nm and emission wavelength of 240–550 nm, revealing UVA-humic-like (C1–C2) and UVC-humic-like (C3) components in the DOM. Based on fluorescence intensity, DOM was dominated by C2 > C1 > C3 compounds in both networks. The mean concentrations of HMs, including Cu, Zn, and Fe, fell below the prescribed limits in both networks. However, concentrations of Pb (A: 0.015–0.028 mg/L), (B: 0.013–0.027 mg/L), and Cd (A: 0.004–0.006 mg/L), (B: 0.005–0.008 mg/L) exceeded permissible limits from tube wells to taps. Moreover, C1 demonstrated a significant positive correlation with Cd and Cu in networks A and B, respectively. Furthermore, C2 displayed a significant positive correlation with Cd in network A. This study concludes that the groundwater in both networks (A and B) is contaminated by agricultural runoff, industrial and sewage water, plumbing materials, and eroded pipelines. As a result, the water is unsafe for cooking and drinking, posing risks of kidney, lung, and bladder cancers. Therefore, this study urgently recommends pipeline reconstruction and the implementation of proper groundwater remediation approaches before these sources are used for drinking. Full article

Soil moisture strongly interferes with the spectra of soil organic matter (SOM) in the near-infrared region, which reduces the correlation between organic matter and spectra and decreases accuracy in the prediction of SOM. In this study, we explored the feasibility of two types of spectral indices, two- and three-band mixed (SI) and three-band spectral indices (SI3), and two water removal algorithms, direct standardization (DS) and external parameter orthogonalization (EPO), to estimate SOM in wet soils using a total of 192 soil samples at six water content gradients. The estimation accuracies of spectral indices combined with water removal algorithms were better than those of full spectral data combined with water removal algorithms: the prediction accuracies of SI-EPO (R² = 0.735, RMSEp = 3.4102 g/kg) were higher than those of EPO (R² = 0.63, RMSEp = 4.1021 g/kg), and those of SI-DS (R² = 0.70, RMSEp = 3.7085 g/kg) were higher than those of DS (R² = 0.61, RMSEp = 4.2806 g/kg); SI3-EPO (R² = 0.752, RMSEp = 3.1344 g/kg) was better than SI-EPO; both EPO and DS effectively mitigated the influence of soil moisture, with EPO demonstrating superior performance in small-sample prediction scenarios. This study introduces a novel approach to counteract the impact of soil moisture on SOM estimation. Full article

Coral reefs are threatened globally by compounding stressors of accelerating climate change and deteriorating water quality. Water quality plays a central role in coral reef health. Yet, accurately quantifying water quality at large scales meaningful for monitoring impacts on coral health remains a challenge due to the complex optical conditions typical of shallow water coastal systems. Here, we report the performance of 32 remote sensing water quality models for suspended particulate matter and chlorophyll concentrations as well as colored dissolved organic matter absorption, over concentration ranges relevant for reef ecology using airborne imaging spectroscopy and field measurements across 62 stations in nearshore Hawaiian waters. Models were applied to reflectance spectra processed with a suite of approaches to compensate for glint and other above-water impacts on reflectance spectra. Results showed reliable estimation of particulate matter concentrations (RMSE = 2.74 mg L⁻¹) and accurate but imprecise estimation of chlorophyll (RMSE = 0.46 μg L⁻¹) and colored dissolved organic matter (RMSE = 0.03 m⁻¹). Accurately correcting reflectance spectra to minimize sun and sky glint effects significantly improved model performance. Results here suggest a role for both hyperspectral and multispectral platforms and rapid application of simple algorithms can be useful for nearshore water quality monitoring over coral reefs. Full article

Soil organic matter (SOM) is important for the global carbon cycle, and hyperspectral remote sensing has proven to be a promising method for fast SOM content estimation. However, because of the neglect of the spectral response of soil physical properties, the accuracy and spatiotemporal transferability of the SOM prediction model are poor. This study aims to improve the spatiotemporal transferability of the SOM prediction model by alleviating the coupling effect of soil physical properties on spectra. Based on satellite hyperspectral images and soil physical variables, including soil moisture (SM), soil surface roughness (root-mean-square height, RMSH), and soil bulk weight (SBW), a soil spectral correction model was established based on the information unmixing method. Two important grain-producing areas in Northeast China were selected as study areas to verify the performance and transferability of the spectral correction model and SOM content prediction model. The results showed that soil spectral corrections based on fourth-order polynomials and the XG-Boost algorithm had excellent accuracy and generalization ability, with residual predictive deviations (RPDs) exceeding 1.4 in almost all the bands. In addition, when the soil spectral correction strategy was adopted, the accuracy of the SOM prediction model and the generalization ability after the model migration were significantly improved. The SOM prediction accuracy based on the XG-Boost-corrected spectrum was the highest, with a coefficient of determination (R²) of 0.76, a root-mean-square error (RMSE) of 5.74 g/kg, and an RPD of 1.68. The prediction accuracy, R² value, RMSE, and RPD of the model after the migration were 0.72, 6.71 g/kg, and 1.53, respectively. Compared with the direct migration prediction of the model, adopting the soil spectral correction model based on fourth-order polynomials and XG-Boost reduced the RMSE of the SOM prediction results by 57.90% and 60.27%, respectively. This performance comparison highlighted the advantages for considering soil physical properties in regional-scale SOM predictions. Full article