This study theoretically and experimentally examined effects of surface roughness on detachment of colloids deposited under favorable chemical conditions on reduction of solution ionic strength. A superposition approach based on elemental geometric models was developed to estimate variation of Derjaguin–Landau–Verwey–Overbeek (DLVO) interaction energies between a colloid and a rough surface under different solution chemistries. Theoretical analysis showed that most colloids attached at rough surfaces via primary‐minimum association are irreversible on reduction of solution ionic strength because primary minima are deeper and the detachment energy barriers are greater at lower ionic strength. A fraction of colloids initially attached at tips of nanoscale protruding asperities, however, will detach from a rough surface at low ionic strength because the net force acting on the colloids can become repulsive (i.e., calculated DLVO interaction energy curves show monotonic decreases of interaction energies with separation distance at low ionic strength). Column experiments were conducted with 1156‐nm polystyrene latex particles and rough sand (300–355‐μm diameter) to examine the detachment of colloids initially deposited at primary minima. Experimental results confirmed that a fraction of colloids are released at low ionic strengths. Our theoretical and experimental results are consistent with literature observations, adding convincing evidence to challenge the usual belief that colloids attached at primary minima are irreversible on reduction of solution ionic strength. Although the importance of surface heterogeneity on colloid deposition has been widely recognized, our study implies that surface heterogeneity also plays a critical role in colloid detachment under both favorable and unfavorable conditions.
Sand column experiments were conducted to examine the effects of the concentration of attached colloids (CAC) on their subsequent detachment upon decreasing solution ionic strength (IS). Different pore volumes of latex microparticle suspensions were injected into the columns to allow different amounts of colloids to attach at ISs of 0.001, 0.01, and 0.2 M. Then, deionized water was introduced to release the attached colloids. Results show that the fraction of attachments that were reversible to reduction of IS (FRA) increased with increasing CAC at a given IS if the sand was extensively treated using acids to reduce surface charge heterogeneity. This indicates that colloids were preferentially immobilized in sites favoring irreversible attachment and then gradually occupied reversible sites. In contrast, the FRA decreased with increasing CAC at 0.001 M in sand without the acid treatment, illustrating the opposite attachment sequence. Scanning electron microscope examinations reveal that the concave regions favored irreversible colloid attachment. Reversible attachment is likely due to immobilization on flat surfaces with charge heterogeneities, retention in stagnation point regions via secondary minimum association, ripening in the acid-treated sand, and capture of colloids by protruding asperities with charge heterogeneity in the untreated sand. At ISs of 0.01 and 0.2 M, the FRA was essentially independent of CAC in the untreated sand because the colloids were randomly attached on the sand surfaces over time.
The spatial interpolation for soil texture does not necessarily satisfy the constant sum and nonnegativity constraints. Meanwhile, although numeric and categorical variables have been used as auxiliary variables to improve prediction accuracy of soil attributes such as soil organic matter, they (especially the categorical variables) are rarely used in spatial prediction of soil texture. The objective of our study was to comparing the performance of the methods for spatial prediction of soil texture with consideration of the characteristics of compositional data and auxiliary variables. These methods include the ordinary kriging with the symmetry logratio transform, regression kriging with the symmetry logratio transform, and compositional kriging (CK) approaches. The root mean squared error (RMSE), the relative improvement value of RMSE and Aitchison's distance (DA) were all utilized to assess the accuracy of prediction and the mean squared deviation ratio was used to evaluate the goodness of fit of the theoretical estimate of error. The results showed that the prediction methods utilized in this paper could enable interpolation results of soil texture to satisfy the constant sum and nonnegativity constraints. Prediction accuracy and model fitting effect of the CK approach were better, suggesting that the CK method was more appropriate for predicting soil texture. The CK method is directly interpolated on soil texture, which ensures that it is optimal unbiased estimator. If the environment variables are appropriately selected as auxiliary variables, spatial variability of soil texture can be predicted reasonably and accordingly the predicted results will be satisfied.
The rapid acquisition of high-resolution spatial distribution of soil organic matter (SOM) at the field scale is essential for precision agriculture. The UAV imaging hyperspectral technology, with its high spatial resolution and timeliness, can fill the research gap between ground-based monitoring and remote sensing. This study aimed to test the feasibility of using UAV hyperspectral data (400–1000 nm) with a small-sized calibration sample set for mapping SOM at a 1 m resolution in typical low-relief black soil areas of Northeast China. The experiment was conducted in an approximately 20 ha field. For calibration, 20 samples were collected using a 100 × 100 m grid sampling strategy, while 20 samples were randomly collected for independent validation. UAV captured hyperspectral images with a spatial resolution of 0.05 × 0.05 m. The extracted spectra within every 1 × 1 m were then averaged to represent the spectra of that grid; this procedure was also performed across the whole field. Upon applying various spectral pretreatments, including absorbance conversion, multiple scattering correction, Savitzky–Golay smoothing filtering, and first-order differentiation, the absolute maximum values of the correlation coefficients of the spectra for SOM increased from 0.41 to 0.58. Importance analysis from the optimal random forest (RF) model showed that the characterized bands of SOM were located in the 450–600 and 750–900 nm regions. When the RF model was used, the UAV hyperspectra data (UAV-RF) were able to successfully predict SOM, with an R2 of 0.53 and RMSE of 1.48 g kg−1. The prediction accuracy was then compared with that obtained using ordinary kriging (OK) and the RF model based on proximal sensing (PS-RF) with the same number of calibration samples. However, the OK method failed to predict the SOM accuracy (RMSE = 2.17 g kg−1; R2 = 0.02) due to a low sampling density. The semi-covariance function was unable to describe the spatial variability of SOM effectively. When the sampling density was increased to 50 × 50 m, OK successfully predicted SOM, with RMSE = 1.37 g kg−1 and R2 = 0.59, and its results were comparable to those of UAV-RF. The prediction accuracy of PS-RF was generally consistent with that of UAV-RF, with RMSE values of 1.41 g kg−1 and 1.48 g kg−1 and R2 values of 0.57 and 0.53, respectively, which indicated that SOM prediction based on UAV-RF is feasible. Additionally, compared with the PS platforms, the UAV hyperspectral technology could simultaneously provide spectral information of tens or even hundreds of continuous bands and spatial information at the same time. This study provides a reference for further research and development of UAV hyperspectral techniques for fine-scale SOM mapping using a small number of samples.
Different types of soil samples from a typical farmland in northern China were collected and evaluated for the presence of the pesticides and antibiotics. 47 pesticides were extracted with a quick, easy, cheap, effective, rugged, and safe (QuEChERS) preparation method and cleanup with 50 mg C18, while 10 antibiotics were extracted with methanol/EDTA-McIlvaine buffer solution (v/v = 1/1), then both of them were analyzed with high performance liquid chromatography-tandem mass spectrometer (HPLC-MS/MS). Total concentrations of the 47 pesticides in the soil samples ranged from not detectable (ND) to 3.8 mg kg-1. The soil exhibited relatively high ecological risk for atrazine, chlorpyrifos, tebuconazole, difenoconazole, pymetrozine, and thiamethoxam, as over 1.0% of the sample concentrations exceeded 0.1 mg kg-1. The residual levels of the 10 antibiotics were relatively low (ND-951.0 μg kg-1). Tetracyclines exhibited a high detection rate (20.9%), with 2.8% of the soil samples exhibiting tetracyclines concentrations exceeding 100 μg kg-1, implying high ecological risk. The 4 sulfonamides and 2 macrolides analyzed showed detection rates below 0.8%. Spatial changes in the distribution of pesticides and antibiotics appear to be related to land use patterns, particularly orchards and vegetable plots. The over-standard rate of pesticides and antibiotics in orchards was greater than that of vegetable plots, and grain fields had the lowest over-standard rate. These data were helpful to figure out the pollution of these pesticides and antibiotics, and provided valuable information for soil quality assessment and risk assessment.
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