Search

A novel magnetic core–shell Fe3O4@CuS have been successfully synthesized by chemical etching and cation exchange method using Zeolitic imidazolate frameworks (ZIF) as the template. The morphology and microstructural properties characterization indicated that Fe3O4@CuS nanoparticles were rhombic dodecahedral shape, highly stable, and magnetic with a large specific surface area (772.20 m2/g). The catalytic activity of Fe3O4@CuS was assessed on sulfadiazine (SDZ) degradation by H2O2 activation. Multi-factors affecting the SDZ removal was adequately investigated. Approximately 93.2% SDZ (50 μM) was removed with 0.2 g/L Fe3O4@CuS and 5 mM H2O2 in 90 min. In particular, Fe3O4@CuS exhibited a quality catalytic performance within a wide pH range of 3.0–11.0. Radical scavenger tests and electron paramagnetic resonance (EPR) analysis confirmed that •O2−, •OH, and 1O2 all contributed to the SDZ degradation, and •OH played the dominant role. Meanwhile, mechanism investigation suggested that the effective catalytic activity of Fe3O4@CuS could be ascribed to the sulphur-enhanced copper-based Fenton reaction on the CuS shell, sulphur-enhanced iron-based Fenton reaction on the Fe3O4 core, and the effective electron transfer between the shell and core. Finally, the possible SDZ degradation pathways were further proposed on the basis of the intermediates identification. This work put forward a new strategy to synthesize magnetic core–shell Fe3O4@CuS using ZIF-8 as the template with outstanding performance for H2O2 activation to degrade SDZ.

PbZr x Ti 1-x O3 (PZT) films were fabricated on FTO substrates by using precursor solutions containing polyvinylpyrrolidone additive and spinning-coating/annealing process. The correlation of the f...

The Tibetan Plateau of China is uniquely vulnerable to the global climate change and anthropogenic disturbances. As soil bacteria exert a considerable influence on the ecosystem function, understanding their response to different climates and land-use types is important. Here, we characterized the bacterial community composition and diversity across three major ecosystems (cropland, forest, and grassland) in the Sygera Mountains of Tibet, along a typical elevational gradient (3 300–4 600 m). The abundance of taxa that preferentially inhabit neutral or weak alkaline soil environments (such as Actinobacteria, Thermoleophilia, and some non-acidophilus Acidobacteria) was significantly greater in the cropland than in the forest and grassland. Furthermore, the diversity of soil bacterial communities was also significantly greater in the cropland than in the forest and grassland. We observed a unimodal distribution of bacterial species diversity along the elevation gradient. The dominant phyla Acidobacteria and Proteobacteria exhibited consistent elevational distribution patterns that mirrored the abundance of their most abundant classes, while different patterns were observed for Acidobacteria and Proteobacteria at the class level. Soil pH was the primary edaphic property that regulated bacterial community composition across the different land-use types. Additionally, soil pH was the main factor distinguishing bacterial communities in managed soils (i.e., cropland) from the communities in the natural environments (i.e., forest and grassland). In conclusion, land use (particularly anthropogenic disturbances such as cropping) largely controlled soil environment, played a major role in driving bacterial community composition and distribution, and also surpassed climate in affecting bacterial community distribution.

Soil microbial respiration is an important source of uncertainty in projecting future climate and carbon (C) cycle feedbacks. However, its feedbacks to climate warming and underlying microbial mechanisms are still poorly understood. Here we show that the temperature sensitivity of soil microbial respiration (Q10) in a temperate grassland ecosystem persistently decreases by 12.0 ± 3.7% across 7 years of warming. Also, the shifts of microbial communities play critical roles in regulating thermal adaptation of soil respiration. Incorporating microbial functional gene abundance data into a microbially-enabled ecosystem model significantly improves the modeling performance of soil microbial respiration by 5–19%, and reduces model parametric uncertainty by 55–71%. In addition, modeling analyses show that the microbial thermal adaptation can lead to considerably less heterotrophic respiration (11.6 ± 7.5%), and hence less soil C loss. If such microbially mediated dampening effects occur generally across different spatial and temporal scales, the potential positive feedback of soil microbial respiration in response to climate warming may be less than previously predicted., Mechanisms and consequences of the acclimation of soil respiration to warming are unclear. Here, the authors combine soil respiration, metagenomics, and functional gene results from a 7-year grassland warming experiment to a microbial-enzyme decomposition model, showing functional gene information to lower uncertainty and improve fit.

Environmental pollution problems and energy-saving issues of urban tunnels have increasingly been attracting people’s attention. The paper aims at investigating a reasonable time factor for CO-based emission factors in the ventilation design of urban road tunnels. The study results show that the prediction curve of CO-based emission factor for passenger cars consists of two stages. The average reduction rates of Stage I (2004–2010) and Stage II (2010–2030) are 38% and 14% per year, respectively. The CO emission factor series of passenger cars conforms to exponential function distribution. The time factors of CO-based emission backcalculated from the measured data range from 4.9% to 12.6%, which are 2.4–6.3 times the specified value in the Chinese ventilation specification but slightly smaller than predicted ones. Based on four aspects of predicted results, back analysis results, related references, and engineering applications, it is indicated that the time factor of CO-based emission factor (2%) in the current ventilation specification is conservative. And when defining 2000 and 2010 as base years, respectively, 4% and 3% can be used as the time factors of CO-based emission factors respectively for the ventilation design of urban tunnels. This study can provide a reference for ventilation design of urban tunnels in other regions.

The synthesis of environmental-friendly metal-free photocatalysts has great significance in photocatalytic technology. In this work, we firstly report the successful synthesis of in situ epitaxial growth of g-C3N4 on carbon dots through a facile thermal polymerization technique. Characterization and density functional theory (DFT) calculations were conducted to clarify the structure engineering and the electronic/chemical properties of the in-plane interconnected carbon dots/g-C3N4 (C-CN) heterostructures. With the optimal carbon dots content, the C-CN exhibited 3.2 times higher degradation rate for sulfadiazine (SDZ) than that of g-C3N4. Besides, the C-CN heterostructures displayed excellent stability and reusability in five consecutive cycles. The enhanced photocatalytic activity was related to the narrowed band gap and the local electronic density of valance band and conduction band orbitals of the unique plane heterostructures, corroborated by the spectroscopic characterizations and theoretical calculations. Photogenerated holes dominated the degradation of SDZ, while OH showed a negligible contribution. Moreover, DFT calculation succeeded to predict that the atoms with high Fukin index (f0) on SDZ molecule were more vulnerable to radicals attack. SDZ degradation pathway mainly included smiles-type rearrangement, SO2 extrusion, ring hydroxylation and SN bond cleavage processes. The eco-toxicity assessment revealed the generation of less toxic intermediates after photocatalysis. Our findings not only afford a new technique for constructing g-C3N4-based in-plane heterostructures with high and stable photocatalytic efficiency, but also highlight the feasible application of metal-free photocatalysts in environmental remediation.

© 1965-2012 IEEE. Permanent-magnet synchronous hub motors (PMSHMs) have been investigated for electric vehicles. However, there are some challenges such as effective cooling and radial force. As PMSHMs are installed on the wheels, the radial force will directly affect the ride comfort of the vehicle. This article presents the analysis and optimization of the radial force for PMSHMs. The radial force densities of the symmetry and asymmetry PMSHMs are analyzed first. It is found that the symmetry PMSHMs have balanced force, while the asymmetry PMSHMs normally have big unbalanced radial force and vibration. To reduce the radial force of the asymmetry PMSHMs, an improved sequential Taguchi optimization method (ISTOM) with mixed orthogonal array is presented for an asymmetry PMSHM in this article. It can be found that the proposed method is efficient and can significantly reduce the radial force of the hub motor.

Global terrestrial vegetation is greening, particularly in mountain areas, providing strong feedbacks to a series of ecosystem processes. This greening has been primarily attributed to climate change. However, the spatial variability and magnitude of such greening do not synchronize with those of climate change in mountain areas. By integrating two data sets of satellite-derived normalized difference vegetation index (NDVI) values, which are indicators of vegetation greenness, in the period 1982–2015 across the Tibetan Plateau (TP), we test the hypothesis that climate-change-induced greening is regulated by terrain, baseline climate and soil properties. We find a widespread greening trend over 91% of the TP vegetated areas, with an average greening rate (i.e. increase in NDVI) of 0.011 per decade. The linear mixed-effects model suggests that climate change alone can explain only 26% of the variation in the observed greening. Additionally, 58% of the variability can be explained by the combination of the mountainous characteristics of terrain, baseline climate and soil properties, and 32% of this variability was explained by terrain. Path analysis identified the interconnections of climate change, terrain, baseline climate and soil in determining greening. Our results demonstrate the important role of mountainous effects in greening in response to climate change.

The electro-activation of persulfate (PS) by graphite felt (GF) electrode is a novel advanced oxidation process applied for the degradation of contaminants of emerging concern (CECs). Both GF anode and cathode can electro-activate PS to degrade CECs, including atrazine (ATZ), bisphenol S, ethinylestradiol, and ibuprofen. The study examines the removal performance of ATZ in sulfate, nitrate, and perchlorate electrolyte. The degradation of ATZ followed the order of sulfate (36.3 L (Ah)−1) > perchlorate (22.2 L (Ah)−1) > nitrate (5.8 L (Ah)−1) in GF anode/PS system, while electrolytes had little influence on the degradation of ATZ (15.8–21.3 L (Ah)−1) in GF cathode/PS system. The mechanisms of PS electro-activation by GF anode and GF cathode were compared and investigated for the first time, through kinetic experiments, radical identification, and products analysis. Quenching experiments demonstrated that O2 − and 1O2 were generated in GF cathode/PS and GF anode/PS systems respectively, besides HO and SO4 −. Degradation pathways of ATZ in these two systems showed slight difference, and a total of eight transformation products of ATZ were identified. Acute toxicity of the treated ATZ solution decreased at the beginning and then increased in both systems at electric charge of 0.11 Ah L−1. Furthermore, both GF electrodes/PS systems exhibited satisfactory performance for actual effluent from wastewater treatment plants in the presence of 30 mM perchlorate (with ATZ removal of 78% at electric charge of 0.11 Ah L−1), leaving a potential possibility for its practical application.

Rapid urbanization with land use and cover change (LUCC) is making a substantially increasing contribution to global carbon emissions. Understanding the spatial processes and transitions mechanism of urban carbon metabolism system by LUCC could help local governments in regional spatial planning. Taking 13 cities in the Yangtze River Delta of China as examples, we quantitatively analyzed and mapped the spatial processes of urban carbon metabolism by LUCC from 1995 to 2015 in the region and investigated the relationships between urban size growth and urban carbon metabolism rate by LUCC (MLUCC) using panel data regression analysis. A higher MLUCC showed a larger negative impact on the urban carbon metabolism system by per unit area of land use change. We found that the highest negative carbon transitions were shifted from Shanghai to cities in the South Jiangsu Province. And the dominant negative carbon transitions and positive ones came from land use transfer into and out the industrial land and transportation land. The results of the panel model regression analysis showed the growths of urban population and land both correlated positively with MLUCC. Further, we controlled the economic growth and urban form changes on the relationship between urban size growth and MLUCC, and the results suggested both the benefits from compromising economic growth and optimizing urban form were overshadowed by the negative impact of urban size growth. The study provided a robust methodology for assessing urban carbon metabolism and provided new insights into land use controls to develop low carbon cities.

Due to frequent salt migration and large spatial variability within soil profiles, salinity characterisation by traditional drilling sampling methods is time-consuming and labour-intensive. Thus, it is necessary to develop monitoring technology and three-dimensional (3D) characterisation methods for rapid, non-invasive, and accurate soil salinity measurement. This study presents a new framework combining sensor technology and an inversion algorithm to characterise 3D soil salinity. Four typical land-use types (natural desert, natural vegetation, apple orchard, and winter wheat farmland) in the Aksu region of southern Xinjiang were surveyed and apparent conductivity (ECa) data were recorded at depths of 0.75 m and 1.50 m. ECa data were converted to electrical conductivity and salinity characterisation was conducted following U.S. Salinity Laboratory recommendations. Ordinary Kriging interpolation was used to map the spatial distribution and an iterative inversion model was used to map the vertical distribution of soil salinity. Model parameters were adjusted several times and the accuracy of different inversion algorithms was compared to obtain the best inversion effect. As a result, the Multilevel Orthogonal Inversion model was developed to characterise 3D soil salinity for different land-use types. Due to crop activities including irrigation, managed land use types (apple orchard and winter wheat plots) typically exhibited weaker salinity than natural systems (desert and vegetation plots) but greater spatial variability overall. The proposed framework combining EM (electromagnetic) sensing and the 3D inversion algorithm can effectively characterise and visualise soil salinity for the entire soil profile, which is important for land evaluation and improvement.

FeGa thin films have been deposited onto (100)-oriented GaAs and (001)-oriented Si substrates at different temperatures by pulsed laser deposition. It has been shown that at different substrate temperatures, magnetic properties of FeGa films greatly change, which is mainly dependent on different crystal textures. The presence of the structure consisting of D03-ordered grain phase and bcc α-Fe crystal structure (A2) has been revealed using X-ray diffraction analysis. The diffraction peak of FeGa (001) direction, which ‎corresponds to the D03-ordered granular phase, was observed when the temperatures of the GaAs substrate were 400 and 600°C. The diffraction peak that corresponds to (110) direction of the bcc A2 structure has appeared at 800°C. However, the FeGa film on the Si substrate did not demonstrate any obvious structural change. The changes in the magnetic properties can be mainly attributed to changes in the roughness and the lattice mismatch between FeGa and Si.‎

International audience; It is a great challenge to identify the many and varied sources of soil heavy metal pollution. Often little information is available regarding the anthropogenic factors and enterprises that could potentially pollute soils. In this study we use freely available geographical data from a search engine in conjunction with machine learning methodologies to identify and classify potentially polluting enterprises in the Yangtze Delta, China. The data were classified into 31 separate and four integrated industry types by five different machine learning approaches. Multinomial naive Bayesian (NB) methods achieved an accuracy of 87% and Kappa coefficient of 0.82 and were used to classify the geographic data from more than 260,000 enterprises. The relationship between the different industry classes and measurements of soil cadmium (Cd) and mercury (Hg) concentrations was explored using bivariate local Moran's I analysis. The analysis revealed areas where different industry classes had led to soil pollution. In the case of Cd, elevated concentrations also occurred in some areas because of excessive fertilization and coal mining. This study provides a new approach to investigate the interaction between anthropogenic pollution and natural sources of soil heavy metals to inform pollution control and planning decisions regarding the location of industrial sites. (C) 2019 Elsevier Ltd. All rights reserved.

Precipitation plays vital roles in the worldwide hydrological cycles. Gridded precipitation estimates with finer spatio-temporal resolutions are critical in various application fields. In this stud...

In this research, proximal soil sensor data fusion was defined as a multifaceted process which integrates geospatially correlated data, or information, from multiple proximal soil sensors to accurately characterize the spatial complexity of soils. This has capability of providing improved understanding of soil heterogeneity for potential applications associated with crop production and natural resource management. To assess the potential of data fusion for the purpose of improving thematic soil mapping over the single sensor approach, data from multiple proximal soil sensors were combined to develop and validate predictive relationships with laboratory-measured soil physical and chemical properties. The work was conducted in an agricultural field with both mineral and organic soils. The integrated data included: topography records obtained using a real-time kinetic (RTK) global navigation satellite system (GNSS) receiver, apparent soil electrical conductivity (ECa) obtained using an electromagnetic induction sensor, and content of several naturally occurring radioisotopes detected using a mobile gamma-ray spectrometer. In addition, the soil profile data were collected using a commercial ruggedized multi-sensor platform carrying a visible and near-infrared (vis-NIR) optical sensor and a galvanic contact soil ECa sensor. The measurements were carried out at predefined field locations covering the entire study area identified from sensor measured a priori information on field elevation, ECa and gamma-ray count. The information was used to predict: soil organic matter (SOM), pH, lime buffer capacity (LBC), as well as concentration of phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), and aluminum (Al). Partial least squares regressions (PLSRs) were used to predict soil properties from individual sensors and different sensor combinations (sensor data fusion). By integrating the data from all of the proximal soil sensors, SOM, pH, LBC, Ca, Mg, and Al were predicted simultaneously with R2 > 0.5 (RPD > 1.50). Improved predictions were observed for most soil properties based on sensor data fusion than those based on individual sensors. After choosing the optimal sensor combination for each soil property, the predictive capability was compared using different data mining algorithms, including support vector machines (SVM), random forest (RF), multivariate adaptive regression splines (MARS), and regression trees (CART). Improved predictions for SOM, Ca, Mg, and Al were observed using SVM over PLSR. The predictive capability was followed by RF and MARS, with CART. Predictions of pH and LBC were only feasible using MARS and PLSR, respectively. In this field, it was not possible to predict extractable P and K using all tested sensor combinations or algorithms. With large variability in SOM, the field presents a special situation and thus, the result could be specific to the study site. Further research includes an extended number of experimental sites covering different geographic areas around Eastern Canada.

A novel cysteine intercalated copper aluminum layered double hydroxide (CuAl-Cys-LDH) was synthesized and applied as heterogeneous catalyst for activating hydrogen peroxide (H2O2) to degrade rhodamine B (RhB) and 4-Nitrophenol (4-NP). The effects of initial pH, CuAl-Cys-LDH dosage, and H2O2 concentration on RhB and 4-NP removal were comprehensively investigated. The results indicated the intercalation of cysteine into the interlayer of LDH greatly enhanced its catalytic activity and stability. With 0.2 g/L CuAl-LDH and 50 mM H2O2, 93.7% of RhB and 80.2% of 4-NP could be removed in the CuAl-Cys-LDH activated H2O2 system. While the CuAl- LDH activated H2O2 system could only degrade 51.2% of RhB and 46.8% of 4-NP under the identical experimental conditions. Significantly, the CuAl-Cys-LDH catalyzed H2O2 system exhibited high degradation efficiency within a wide pH range from 4.0 to 10.0. Based on the electron paramagnetic resonance (EPR) tests and radical quenching experiments, it was inferred that •OH radical was the dominant species responsible for organic contaminants degradation. Mechanism study revealed that the intercalated cysteine in the interlayer of LDH strongly accelerated the rate-determining conversion of Cu(II) to Cu(I) by oxidation itself to cystine, thus enhanced the catalytic efficiency for H2O2 activation to produce •OH radicals. The findings of this work indicated that CuAl-Cys-LDH is a conveniently prepared and highly efficient and stable catalyst for the degradation of organic contaminants in environmental remediation.

Recently, the UV/free chlorine process has gained attention as a promising technology for destroying refractory organic contaminants in the aqueous phase. We have developed a kinetic model based on first-principles to describe the kinetics and mechanisms of the oxidation of organic contaminants in the UV/free chlorine process. Substituted benzoic acid compounds (SBACs) were chosen as the target parent contaminants. We determined the second-order rate constants between SBACs and reactive chlorine species (RCS; including [Formula: see text], [Formula: see text] and [Formula: see text]) by fitting our model to the experimental results. We then predicted the concentration profiles of SBACs under various operational conditions. We analyzed the kinetic data and predicted concentration profiles of reactive radicals ([Formula: see text] and RCS), we found that [Formula: see text] was the dominant radicals for SBACs destruction. In addition, we established quantitative structure activity relationships (QSARs) that can help predict the second-order rate constants for SBACs destruction by each type of reactive radicals using SBACs Hammett constants. Our first-principles-based kinetic model has been verified using experimental data. Our model can facilitate a design for the most cost-effective application of the UV/free chlorine process. For example, our model can determine the optimum chlorine dosage and UV light intensity that result in the lowest energy consumption.

A highly efficient visible-light-driven photocatalyst Ag3PO4/AgI-Graphene (Ag3PO4/AgI-G) was synthesized through a chemical coprecipitation procedure. Scanning electron microscopy (SEM), transmission electron microscopy (TEM), X-ray diffraction (XRD), and X-ray photoelectron spectroscopy (XPS) were performed to study the physicochemical structural of the photocatalysts. The photocatalytic activity of the samples was examined by the carbamazepine (CBZ) degradation under artificial visible light and natural sunlight irradiation. Experimental results indicated that the introduction of low mass content of graphene enhanced the photocatalytic performance of Ag3PO4/AgI, and the photocatalytic degradation efficiency of CBZ over Ag3PO4/AgI-3%G (mass ratio of graphene:Ag3PO4/AgI = 3:100) reached 93.06% within 21 min, which was much higher than that over pure Ag3PO4 (26.92%) and Ag3PO4/AgI (74.38%). UV–vis diffuse reflectance spectra, photoluminescence (PL) spectra, transient photocurrent responses and electrochemical impedance spectra (EIS) of the samples were conducted to verify the high photocatalytic performance of the Ag3PO4/AgI-3%G. In addition, possible photocatalytic degradation pathways of CBZ were proposed based on the analysis of transformation products during the reaction. The reactive species trapping experiments and Electron spin resonance (ESR) analysis demonstrated that h+ and O2− were the main active oxidant species responsible for CBZ photodegradation. The photocatalytic degradation mechanism of CBZ over Ag3PO4/AgI-3%G under visible light irradiation was schematically proposed. This study not only provides a new technique for the synthesis of Ag3PO4-based photocatalysts with high photocatalytic activity, but also demonstrates that the Ag3PO4/AgI-3%G composite could be a promising photocatalyst for the treatment of waters containing CBZ.

Soil salinization is one of the main reasons for soil health and ecosystem deterioration in most degraded arid and semiarid areas. To monitor its spatial variation as precise as possible over a large area, we collected 225 samples using traditional field experiment and laboratory analysis method from the southern part of the Xinjiang Province, China, affected by soil salinity under strong arid climate. Then, we constructed both Cubist and partial least square regression (PLSR) models on electrical conductivity (EC) (150 ground-based measurements as calibration set) using various related covariates (e.g. terrain attributes, remotely sensed spectral indices of vegetation and salinity from landsat8 OLI satellite) that are at the same time period corresponding to soil sampling. Two models were validated using remaining 75 independent ground based measurements and were then used to map the soil salinity over the study area. Finally, the validation results of two models were compared under different intervals of EC, soil moisture content and vegetation coverage. The results indicated that Cubist model could predict EC value with better accuracy and stability under variable environment than PLSR. The R2, RMSE, MAE and RPD of the Cubist model were 0.91, 5.18 dS m−1, 3.76 dS m−1 and 3.15 while corresponding values of the PLSR model were 0.66, 10.46 dS m−1, 8.21 dS m−1 and 1.56 in validation dataset, respectively. Additionally, the map derived from Cubist model revealed more detailed variation information of the spatial distribution of EC than that from PLSR model across the study area. Thus, Cubist model was recommended for mapping soil salinity using indices derived from satellite and terrain in other arid areas.

The soil's pH is the single most important indicator of the soil's quality, whether for agriculture, pollution control or environmental health and ecosystem functioning. Well documented data on soil pH are sparse for the whole of China — data for only 4700 soil profiles were available from China's Second National Soil Inventory. By combining those data, standardized for the topsoil (0–20 cm), with 17 environmental covariates at a fine resolution (3 arc-second or 90 m) we have predicted the soil's pH at that resolution, that is at more than 109 points. We did so by parallel computing over tiles, each 100 km × 100 km, with two machine learning techniques, namely Random Forest and XGBoost. The predictions for the tiles were then merged into a single map of soil pH for the whole of China. The quality of the predictions were assessed by cross-validation. The root mean squared error (RMSE) was an acceptable 0.71 pH units per point, and Lin's Concordance Correlation Coefficient was 0.84. The hybrid model revealed that climate (mean annual precipitation and mean annual temperature) and soil type were the main factors determining the soil's pH. The pH map showed acid soil mainly in southern and north-eastern China, and alkaline soil dominant in northern and western China. This map can provide a benchmark against which to evaluate the impacts of changes in land use and climate on the soil's pH, and it can guide advisors and agencies who make decisions on remediation and prevention of soil acidification, salinization and pollution by heavy metals, for which we provide examples for cadmium and mercury.

Soil erosion is an important environmental threat in China. However, quantitative estimates of soil erosion in China have rarely been reported in the literature. In this study, soil loss potential in China was estimated by integrating satellite images, field samples, and ground observations based on the Revised Universal Soil Loss Equation (RUSLE). The rainfall erosivity factor was estimated from merged rainfall data using Collocated CoKriging (ColCOK) and downscaled by geographically weighted regression (GWR). The Random Forest (RF) regression approach was used as a tool for understanding and predicting the relationship between the soil erodibility factor and a set of environment factors. Our results show that the average erosion rate in China is 1.44 t ha−1 yr−1. More than 60% of the territory in China is influenced by soil erosion limitedly, with an average potential erosion rate less than 0.1 t ha−1 yr−1. Other unused land and other forested woodlands showed the highest erosion risk. Our estimates are comparable to those of runoff plot studies. Our results provide a useful tool for soil loss assessments and ecological environment protections.

International audience; Accurate digital soil maps of soil organic matter (SOM) are needed to evaluate soil fertility, to estimate stocks, and for ecological and environment modeling. We used 5982 soil profiles collected during the second national soil survey of China, along with 19 environment predictors, to derive a spatial model of SOM concentration in the topsoil (0-20 cm layer). The environmental predictors relate to the soil forming factors, climate, vegetation, relief and parent material. We developed the model using the Cubist machine-learning algorithm combined with a non-parametric bootstrap to derive estimates of model uncertainty. We optimized the Cubist model using a 10-fold cross-validation and the best model used 17 rules. The correlation coefficient between the observed and predicted values was 0.65, and the root mean squared error was 0.28 g/kg. We then applied the model over China and mapped the SOM distribution at a resolution of 90 x 90 m. Our predictions show that there is more SOM in the eastern Tibetan Plateau, northern Heilongjiang province, northeast Mongolia, and a small area of Tianshan Mountain in Xinjiang. There is less SOM in the Loess Plateau and most of the desert areas in northwest China. The average topsoil SOM content is 24.82 g/kg. The study provides a map that can be used for decision-making and contribute towards a baseline assessment for inventory and monitoring. The map could also aid the design of future soil surveys and help with the development of a SOM monitoring network in China.

International audience; Much of the carbon (C) stored in the soil of the high Qinghai Tibet Plateau could be lost as a result of global warming. To provide a baseline against which to assess the loss we have made a new map at 90-m resolution from sample data of 1148 soil profiles augmented by information on climate, vegetation, physiography and digital elevation. We used the program Cubist, which works as a form of regression tree, to predict the concentration at the nodes of the 90-m grid. The uncertainty of the predictions was computed by bootstrapping 50 times at each node. Soil type, evapotranspiration (ET), precipitation, radiation and vegetation type contributed most to the variation in C at the coarse scale; temperature, net primary productivity, normalized difference vegetation index (NDVI), ET and elevation contributed most at finer scales. We mapped the predicted concentration of C and converted the predictions to stocks of C for the main kinds of land: 1.93 Pg for the alpine steppe, 1.57 Pg for the meadow, 0.66 Pg in the coniferous forest, 0.63 Pg in the broadleaf forest, 1.06 Pg under shrub, < 0.4 Pg for each of the alpine desert and cropland. We estimate the uppermost 30 cm of soil to contain 6.81 Pg of C with 95% (3.80 to 10.27 Pg). This estimate differs substantially from the two previous coarser estimates based on global modelling which far exceed our 95% upper confidence limit. Our new estimate can now serve as a base against which to judge any change of soil C as a response to global warming.

The occurrence of carbamazepine (CBZ) in environments poses a potential risk to aquatic life and exhibits growth inhibition of human embryonic cells. In this work, for the first time a series of Ag-SnO2 quantum dots (QDs)/silver phosphate (AgSn/AgP) composites were synthesized and used as photocatalysts for CBZ degradation. The obtained AgSn/AgP composites showed superior photodegradation efficiency for CBZ removal. The degradation rate constant of 10AgSn/AgP (with 10 wt% of Ag-SnO2 QDs) was almost 8.5, 5.7, 5.7, and 1.9 times as that of Ag-SnO2 QDs, Ag3PO4, Ag/Ag3PO4, and SnO2 QDs/Ag3PO4, respectively. The improved photocatalytic activity could be primarily ascribed to the improved charge separation through a collaborative effect of Ag-SnO2 QDs and Ag3PO4, and in situ photoreduced metallic silver. Electron spin resonance (ESR) measurement and radical trapping experiments suggested that holes (h+), (superoxide radical) ·O2− and (hydroxyl radical) ·OH corporately participated in the decomposition of CBZ. Moreover, a reasonable mechanism for photocatalytic degradation of CBZ over 10AgSn/AgP composites was tentatively proposed. Additionally, eight degradation intermediates were determined by liquid chromatography-mass spectrometry (LC-MS). Toxicity evaluation using the Ecological Structure Activity Relationships (ECOSAR) program revealed that the toxicity of most photodegradation intermediates were much lower than that of the parent compound CBZ. This work not only provides a new technique for preparing Ag3PO4-based photocatalysts with high activity, but also demonstrates that 10AgSn/AgP could be a promising photocatalyst for treating water and wastewaters containing CBZ.

With rapid urbanization in China, there is an urgent need to better understand spatial patterns of cities’ carbon transitions in urban metabolism processes and to explore ways to achieve low carbon cities through sustainable urban planning. In this study, we took Hangzhou City as an example, and firstly analyzed changes in carbon emission and sequestration based on land use types, using data with 5 years interval from 1995 to 2015. We then quantified both harmful and beneficial carbon transitions by land use changes spatially. Harmful carbon transitions are processes that cause carbon sequestration decreases or carbon emission increases and the beneficial ones are the opposite processes. Finally, we employed a panel data regression analysis to investigate how urban forms and road structures influence urban carbon emission at the district level. We found that carbon emissions experienced a nearly five times increase, while carbon sequestration decreased by 22.29%. And the land use change from cultivated land to industrial land accounted for 61.05% of the harmful carbon transitions. In addition, the spatial analysis of urban carbon transitions indicated harmful carbon transitions always sourced from the eastern parts of Hangzhou. Most beneficial carbon transitions were located in the urban central area and western mountainous areas. The panel data analysis demonstrated that urban expansion modes with high connectedness and a better coupling relationship between urban form and road structure could help to emission reduction, and a mononuclear urban expansion mode at the district-level exerted a positive influence in relation to carbon emissions increase. This study provided an approach to assess urban carbon metabolism and help to better understanding of low-carbon urban form.

UV/monochloramine (NH2Cl) process has attracted some attention for the elimination of contaminants of emerging concern as a novel advanced oxidation process. However, there is still much uncertainty on the performance and mechanisms of UV/NH2Cl process because of its complexity and generation of various species of radicals, including NH2•, HO•, Cl• and other reactive chlorine species (RCS). The mechanism and influence factors of degradation of carbamazepine (CBZ) in the UV/NH2Cl process were investigated, and a synergistic effect was observed. Degradation of CBZ under all investigated conditions followed pseudo-first order kinetics. The corresponding rate constant increased along with the dosage of NH2Cl, and was affected significantly by the presence of bicarbonate and natural organic matter. The process has little pH-dependency, while the specific contribution of RCS and HO• changed with solution pH, and RCS always act as a major contributor to the degradation of CBZ. Eleven byproducts of CBZ were identified and their respective evolution profiles were determined. The participation of UV in chloramination can reduce the formation of nitrogenous DBPs, but promote the formation of carbonaceous DBPs. Furthermore, when influent, sand filtered, and granular activated carbon filtered water was respectively used as background, degradation of CBZ was inhibited to different degree and more disinfection byproducts (DBPs) were generated, compared to deionized water. The electrical energy per order for degradation of CBZ in the UV/NH2Cl process was also calculated to obtain some preliminary cost information.

Visible-near infrared (vis–NIR) spectroscopy has been widely used to characterize soil information from field to global scales. Before applying a calibrated spectral predictive model to acquire soil information, either independent validation ork-fold cross validation is used to evaluate model performance. However, there is no consensus on which validation strategy is more suitable and robust when evaluating model performance for the studies in different scales. The objective of this study is to evaluate and compare the model performance of two validation strategies coupling different calibration sizes (a ratio of calibration to validation of 2:1, 4:1 and 9:1) and calibration sampling strategies (random sampling (RS), rank, Kennard-Stone (KS), rank-Kennard-Stone (RKS) and conditioned Latin hypercube sampling (cLHS)) across scales. A total of 17,272 vis–NIR spectra of mineral soils from LUCAS data (continental scale) and theirsoil organic carbon(SOC) and clay contents were used in this study, and the dataset was further split into national (2761 samples in France) and five regional datasets (110 to 248 samples from five French administrative regions). To eliminate the effect of changing validation set on the model performance, a consistent test set (20% of total samples at each scale) was split to evaluate all the combinations involved in two validation strategies. The Lin’s concordancecorrelation coefficient(CCC) of the cubist model were stable for both SOC and clay for different calibration sizes, calibration sampling and validation strategies for a large calibration size (>1400) at the national and continental scales. A larger calibration size can potentially improve model performance for a small dataset (k-fold cross validation would be appropriate; (2) for a small data set (dozens to hundreds),k-fold cross validation and/or repeated random sampling for independent validation would be more robust for spectral predictive model evaluation.

Recent advances in remote and proximal sensing technologies provide a valuable source of information for enriching our geo-datasets, which are necessary for soil management and the precision application of farming input resources. This is because of the advantages of these modern technologies in that they provide a high sampling density enabling the exploration of the within-field spatial and temporal variability of soil characteristics, and being fast and cost-effective, compared to the traditional laboratory analysis methods. However, soils are complex in nature, and measurements of key soil properties or processes in soils might not be achievable by the use of a single sensor. This necessitates innovative solutions beyond the single-sensor approach, which should be implementable not only under laboratory conditions but in situ in either stationary or on-line measurement modes. In the past few years, several studies on the implementation of multi-sensor (remote and proximal) and data fusion approaches in soil science have been reported in the literature, although this research area is still at its early stages of development. The aim of such integration is twofold: (1) improves the detection accuracy of a soil property or a list of soil properties, and (2) improves the quality of soil maps, management zone mapsand the resulting decision support for variable rate applications in precision agriculture. The integration of different data layers has greatly benefited many other applications in agriculture and environmental soil modeling that require more extensive temporal and spatial information than that contained in any individual dataset provided by a single sensor. At the same time, the major progress that has been made in different aspects of digital soil mapping (DSM) make DSM more mature and operable than ever before. The integration of multi-sensor source data fusion with the DSM technique is foreseen to provide a better understanding of soil processes and enable a more accurate estimation of soil properties at various spatial and temporal scales. It will also provide new insights into processes occurring in soils and sources of variabilities linked to soil dynamics in different scenarios of land management practices, precision agriculture, environmental pollution, and climate change.

As an important parameter to characterize physical and biogeochemical processes, sea surface salinity (SSS) has received extensive attention. Cubist is a data mining model, which can be well-suited to estimate and analyze SSS in the Gulf of Mexico (GOM) because it can reflect the SSS internal heterogeneity in the GOM—overall circular distribution, and the seasonality related to temperature and river discharge changes. Using remote sensing reflectance (Rrs) at 412, 443, 488 (490), 555, and 667 (670) nm and sea surface temperature (SST), a cubist model was developed to estimate SSS with high accuracy with the overall performance demonstrates a root mean square error (RMSE) of 0.27 psu and correlation coefficient of 0.97 of R2. The model divides the GOM area according to model rules into four sub-regions, which include estuary, nearshore, and open sea, reflecting the gradient distribution of SSS. The division of sub-regions and seasonal changes can be explained by the distribution of water bodies, river discharges, and local wind forces since it is obvious that the estuary region reaches the largest low-value area and spreads eastward with the monsoon in the spring when the river flow increases to the highest value. While the east to west wind in the non-summer monsoon period guides the plume westward, and the lowest river discharge in winter corresponds to the smallest low value area. After comparison with other statistical models, the cubist model showed satisfactory results in independent verification of cruise data, proving the estimation capability under different geographical conditions (such as estuaries and open seas) and seasons. Therefore, considering high accuracy and heterogeneity mining, the cubist-based model is an ideal method for coastal SSS estimation and spatial-temporal heterogeneity analysis, and can provide ideas for model construction for coastal areas with similar geographic environments.

Macromolecules, such as RNAs, reside in crowded cell environments, which could strongly affect the folded structures and stability of RNAs. The emergence of RNA-driven phase separation in biology further stresses the potential functional roles of molecular crowding. In this work, we employed the coarse-grained model that was previously developed by us to predict 3D structures and stability of the mouse mammary tumor virus (MMTV) pseudoknot under different spatial confinements over a wide range of salt concentrations. The results show that spatial confinements can not only enhance the compactness and stability of MMTV pseudoknot structures but also weaken the dependence of the RNA structure compactness and stability on salt concentration. Based on our microscopic analyses, we found that the effect of spatial confinement on the salt-dependent RNA pseudoknot stability mainly comes through the spatial suppression of extended conformations, which are prevalent in the partially/fully unfolded states, especially at low ion concentrations. Furthermore, our comprehensive analyses revealed that the thermally unfolding pathway of the pseudoknot can be significantly modulated by spatial confinements, since the intermediate states with more extended conformations would loss favor when spatial confinements are introduced.

Climate warming is known to impact ecosystem composition and functioning. However, it remains largely unclear how soil microbial communities respond to long-term, moderate warming. In this study, we used Illumina sequencing and microarrays (GeoChip 5.0) to analyze taxonomic and functional gene compositions of the soil microbial community after 14 years of warming (at 0.8–1.0 °C for 10 years and then 1.5–2.0 °C for 4 years) in a Californian grassland. Long-term warming had no detectable effect on the taxonomic composition of soil bacterial community, nor on any plant or abiotic soil variables. In contrast, functional gene compositions differed between warming and control for bacterial, archaeal, and fungal communities. Functional genes associated with labile carbon (C) degradation increased in relative abundance in the warming treatment, whereas those associated with recalcitrant C degradation decreased. A number of functional genes associated with nitrogen (N) cycling (e.g., denitrifying genes encoding nitrate-, nitrite-, and nitrous oxidereductases) decreased, whereas nifH gene encoding nitrogenase increased in the warming treatment. These results suggest that microbial functional potentials are more sensitive to long-term moderate warming than the taxonomic composition of microbial community.

Previous studies have mostly focused on using visible-to-near-infrared spectral technique to quantitatively estimate soil cadmium (Cd) content, whereas little attention has been paid to identifying soilCd contaminationfrom a perspective of spectral classification. Here, we developed a framework to compare the potential of two spectral transformations (i.e., raw reflectance and continuum removal [CR]), three optimization strategies (i.e., full-spectrum, Boruta feature selection, and synthetic minority over-sampling technique [SMOTE]), and three classification algorithms (i.e., partial least squaresdiscriminant analysis, random forest [RF], and support vector machine) for diagnosing soilCd contamination. A total of 536 soil samples were collected from urban and suburban areas located in Wuhan City, China. Specifically, Boruta and SMOTE strategies were aimed at selecting the most informative predictors and obtaining balanced training datasets, respectively. Results indicated that soils contaminated by Cd induced decrease inspectral reflectancemagnitude. Classification models developed after Boruta and SMOTE strategies out-performed to those from full-spectrum. A diagnose model combining CR preprocessing, SMOTE strategy, and RF algorithm achieved the highest validation accuracy for soil Cd (Kappa=0.74). This study provides a theoretical reference for rapid identification of and monitoring of soil Cd contamination in urban and suburban areas.

The authors would like to thank the comment by Diego M. Juela for contributing to helpful scientific discussions and correction suggestions, and to also express thanks to the editors.

Potentially toxic elements (PTEs) pollution in the agricultural soil of China, especially in developed regions such as the Yangtze River Delta (YRD) in eastern China, has received increasing attention. However, there are few studies on the long-term assessment of soil pollution by PTEs over large regions. Therefore, in this study, a meta-analysis was conducted to evaluate the current state and temporal trend of PTEs pollution in the agricultural land of the Yangtze River Delta. Based on a review of 118 studies published between 1993 and 2020, the average concentrations of Cd, Hg, As, Pb, Cr, Cu, Zn, and Ni were found to be 0.25 mg kg&minus, 1, 0.14 mg kg&minus, 1, 8.14 mg kg&minus, 1, 32.32 mg kg&minus, 1, 68.84 mg kg&minus, 1, 32.58 mg kg&minus, 1, 92.35 mg kg&minus, 1, and 29.30 mg kg&minus, 1, respectively. Among these elements, only Cd and Hg showed significant accumulation compared with their background values. The eastern Yangtze River Delta showed a relatively high ecological risk due to intensive industrial activities. The contents of Cd, Pb, and Zn in soil showed an increasing trend from 1993 to 2000 and then showed a decreasing trend. The results obtained from this study will provide guidance for the prevention and control of soil pollution in the Yangtze River Delta.

International audience; Soil organic carbon (SOC) leads to a significant impact on global carbon (C) cycling and soil quality. Variations in SOC are controlled by vegetation, geomorphic, geological and climatic factors, but the dominant environmental differs. In the Qinghai -Tibet Plateau, which contains large amount of low-latitude permafrost, the impact of environmental factors for the variations of SOC may be different due to the unique and complicated geographical condition. In this study, the two-dimension empirical mode decomposition (2D-EMD) is applied to examine the variations of SOC at different scales and locations, and the correlations between SOC and environmental factors are explained. The spatial distribution of SOC in Tibet was decomposed into three intrinsic mode functions (IMFs) under different scales, with spatial variation scales of approximately 7 km, 109 km and 338 km, which represented the small, medium and large scale, respectively. The remaining residual represented the variation trend of SOC across Tibet. The correlations between SOC and environmental factors (elevation, radiation, evapotranspiration and temperature) are distinguished by the physiographic zone at small and medium scales. Temperature is weekly or nonsignificantly correlated to SOC in cold-dry western Tibet at large scale. Normalized difference vegetation index (NDVI) and precipitation influenced SOC mainly at small scales, while the effects of precipitation and evapotranspiration on the distribution of SOC were due to geomorphology and type of permafrost. The combined effect of climate on SOC was larger than other factors at large scale while factors refer to DEM, evapotranspiration, water erosion and NDVI accounted for more contribution at small scale. The results indicated that the environmental factors influence SOC under a combination of scale and location effect. These findings are of great significance for future studies in SOC dynamic modelling under the influence of natural changes and human activities.

Soil pollution by potentially toxic elements (PTEs) has become a core issue around the world. Knowledge of the spatial distribution of PTEs in soil is crucial for soil remediation. Portable X-ray fluorescence spectroscopy (p-XRF) provides a cost-saving alternative to the traditional laboratory analysis of soil PTEs. In this study, we collected 293 soil samples from Fuyang County in Southeast China. Subsequently, we used several geostatistical methods, such as inverse distance weighting (IDW), ordinary kriging (OK), and empirical Bayesian kriging (EBK), to estimate the spatial variability of soil PTEs measured by the laboratory and p-XRF methods. The final maps of soil PTEs were outputted by the model averaging method, which combines multiple maps previously created by IDW, OK, and EBK, using both lab and p-XRF data. The study results revealed that the mean PTE content measured by the laboratory methods was as follows: Zn (127.43 mg kg−1) > Cu (31.34 mg kg−1) > Ni (20.79 mg kg−1) > As (10.65 mg kg−1) > Cd (0.33 mg kg−1). p-XRF measurements showed a spatial prediction accuracy of soil PTEs similar to that of laboratory analysis measurements. The spatial prediction accuracy of different PTEs outputted by the model averaging method was as follows: Zn (R2 = 0.71) > Cd (R2 = 0.68) > Ni (R2 = 0.67) > Cu (R2 = 0.62) > As (R2 = 0.50). The prediction accuracy of the model averaging method for five PTEs studied herein was improved compared with that of the laboratory and p-XRF methods, which utilized individual geostatistical methods (e.g., IDW, OK, EBK). Our results proved that p-XRF was a reliable alternative to the traditional laboratory analysis methods for mapping soil PTEs. The model averaging approach improved the prediction accuracy of the soil PTE spatial distribution and reduced the time and cost of monitoring and mapping PTE soil contamination.

To improve the stability of hub drive electric vehicle, the fault-tolerant control algorithm of five-phase permanent-magnet synchronous motor is studied in this paper. A Model predicate current control (MPCC) algorithm with pre-selection and duty cycle is designed for 5-phase PMSHM in different operations. The model of 5-phase PMSHM in fault operations is discussed and coordinate transformation matrix in single-fault, adjacent two phases fault, and nonadjacent two phases fault operations are deduced, respectively. The proposed fault-tolerant model predictive current control can deal with a difference of current fault operations, effectively. Thus, the stability of the hub drive system is improved.

International audience; In this study we systematically reviewed 1203 research papers published between 2008 and 2018 in China and recorded related data on eight kinds of soil heavy metals (Cr, Pb, Cd, Hg, As, Cu, Zn, and Ni). Based on that, the pollution levels, ecological risk and health risk caused by soil heavy metals were evaluated and the pollution hot spots and potential driving factors of different heavy metals in different provinces were also identified. Results indicated accumulation of heavy metals in soils of most provinces in China compared with background values. Consistent with previous findings, the most prevalent polluted heavy metals were Cd and Hg. Polluted regions are mainly located in central, southern and southwestern China. Hunan, Guangxi, Yunnan, and Guangdong provinces were the most polluted provinces. For the potential health risk caused by heavy metals pollution, children are more likely confront with non-carcinogenic risk than adults and seniors. And children in Hunan and Guangxi province were experiencing relatively larger non-carcinogenic risk. In addition, children in part of provinces were undergoing potentially carcinogenic risks due to soil heavy metals exposure. Furthermore, in our study the 31 provinces in mainland China were divided into six subsets according to corresponding potential driving factors for heavy metal accumulation. Our study provide more comprehensive and updated information for contributing to better soil management, soil remediation, and soil contamination control in China.

© 2015 IEEE. The electrical drive system is crucial to the drive performance and safety of electric vehicles (EVs). In contrast to the traditional two-wheel-driven EVs, the hub motor four-wheel-drive system can steer the vehicle by controlling the torque and speed of each wheel independently, yielding a very simple distributed drivetrain with high efficiency and reliability. This article presents a system-level design optimization method for a permanent magnet hub motor drive system for a campus patrol EV based on a practical driving cycle. An outer rotor permanent-magnet synchronous hub motor (PMSHM) and an improved model predicate current control are proposed for the drive system. Due to the lack of reducers, the direct-drive PMSHM needs to face more complex working conditions and design constraints. In the implementation, the motor design requirements are obtained through the collection of practical EV driving cycles on the campus. Based on these requirements, two models are proposed as the preliminary designs for the PMSHM. To improve their performance, an efficient multiobjective optimization method is employed to the motor considering different operational conditions. The finite-element model and thermal network model are employed to verify the performance of the optimized PMSHM. An optimal design scheme is selected by comparing the comprehensive performance of the two optimized motors. In addition, a duty-cycle model predictive current control is adopted to drive the motor. Finally, a prototype is developed and tested, and the experimental results are presented.

Automatic road extraction from very-high-resolution remote sensing images has become a popular topic in a wide range of fields. Convolutional neural networks are often used for this purpose. However, many network models do not achieve satisfactory extraction results because of the elongated nature and varying sizes of roads in images. To improve the accuracy of road extraction, this paper proposes a deep learning model based on the structure of Deeplab v3. It incorporates squeeze-and-excitation (SE) module to apply weights to different feature channels, and performs multi-scale upsampling to preserve and fuse shallow and deep information. To solve the problems associated with unbalanced road samples in images, different loss functions and backbone network modules are tested in the model’s training process. Compared with cross entropy, dice loss can improve the performance of the model during training and prediction. The SE module is superior to ResNext and ResNet in improving the integrity of the extracted roads. Experimental results obtained using the Massachusetts Roads Dataset show that the proposed model (Nested SE-Deeplab) improves F1-Score by 2.4% and Intersection over Union by 2.0% compared with FC-DenseNet. The proposed model also achieves better segmentation accuracy in road extraction compared with other mainstream deep-learning models including Deeplab v3, SegNet, and UNet.

International audience; Agricultural pollution poses a considerable challenge to grain security and human health, especially in economically developed areas. Mineral exploitation, chemical enterprise operation, pesticide and fertilizer application, sewage discharge, and vehicle emissions are the pollution sources of agricultural land. Identifying and assessing potential agricultural pollution (PAP) is, therefore, the most urgent task to achieve grain security and the human health. Large-scale (e.g., regional or national) PAP assessment can be very expensive, which could also generate a certain amount of information that usually discourages evaluation by decision-makers. To identify areas for regional priority investigation, here we proposed an assessment framework for PAP in economically developed areas. The framework consisted of PAP assessment, vulnerability assessment, hazard assessment, and socio-economic assessment. Then, we conducted a case study by using the proposed framework in one of China’s economically developed areas, Zhejiang Province. The results showed that PAP, especially soil heavy metal pollution, soil acidification, and surface water pollution involved almost the entire study area. High-vulnerability high-hazard areas were mainly associated with high socio-economic development or high grain yield. These areas had negatively affected grain security and increased carcinogenic risk, potentially contributing to the formation of cancer villages. Based on the results, we proposed measures for environmental risk managers to alleviate the impact of PAP on grain security and human health in economically developed areas.

Widespread soil contamination threatens living standards and weakens global efforts towards the Sustainable Development Goals (SDGs). Detailed soil mapping is needed to guide effective countermeasures and sustainable remediation operations. Here, we review visible and infrared reflectance spectroscopy (VIRS) based detection methods in combination with machine learning. To date, proximal, airborne and spaceborne carrier devices have been employed for soil contamination detection, allowing large areas to be covered at low cost and with minimal secondary environmental impact. In this way, soil contaminants can be monitored remotely, either directly or through correlation with soil components (e.g. Fe-oxides, soil organic matter, clay minerals). Observed vegetation reflectance spectra has also been proven an effective indicator for mapping soil pollution. Calibration models based on machine learning are used to interpret spectral data and predict soil contamination levels. The algorithms used for this include partial least squares regression, neural networks, and random forest. The processes underlying each of these approaches are outlined in this review. Finally, current challenges and future research directions are explored and discussed.

International audience; From the perspective of the mechanism of soil pollution, it is difficult to explain the process of predicting the spatial distributions of soil heavy metal pollution using traditional geostatistical methods at a regional scale. Furthermore, few methods are available to proactively identify potential risk areas for preventing soil contamination. In this study, we selected 13 environmental factors related to the accumulation of soil heavy metals based on the source-sink theory. Then, the fuzzy k-means method in combination with the random forest (RF) method was used to classify potential risk areas. The concentrations and spatial distributions of the heavy metals were well predicted by RF, and the average values of the root mean square error of the prediction and R-2 were 4.84 mg kg(-1) and 0.57, respectively. The results indicated that the soil pH, fine particulate matter, and proximity to polluting enterprises significantly influenced the heavy metal pollution in soils, and the environmental variables varied significantly across the identified subregions. This study provides a theoretical basis for the sustainable management and control of soil pollution at the regional scale.

International audience; Wise soil management requires detailed soil information, but conventional soil class mapping in a rather coarse spatial resolution cannot meet the demand for precision agriculture. With the advantages of non-destructiveness, rapid cost-efficiency, and labor savings, the spectroscopic technique has proved its high potential for success in soil classification. Previous studies mainly focused on predicting soil classes using a single sensor. In this study, we attempted to compare the predictive ability of visible near infrared (vis-NIR) spectra, mid-infrared (MIR) spectra, and their fused spectra for soil classification. A total of 146 soil profiles were collected from Zhejiang, China, and the soil properties and spectra were measured by their genetic horizons. Along with easy-to-measure auxiliary soil information (soil organic matter, soil texture, color and pH), four spectral data, including vis-NIR, MIR, their simple combination (vis-NIR-MIR), and outer product analysis (OPA) fused spectra, were used for soil classification using a multiple objectives mixed support vector machine model. The independent validation results showed that the classification model using MIR (accuracy of 64.5%) was slightly better than that using vis-NIR (accuracy of 64.2%). The predictive model built on vis-NIR-MIR did not improve the classification accuracy, having the lowest accuracy of 61.1%, which likely resulted from an over-fitting problem. The model based on OPA fused spectra performed best with an accuracy of 68.4%. Our results prove the potential of fusing vis-NIR and MIR using OPA for improving prediction ability for soil classification.

In this paper, lignite activated coke was used as adsorbent for dynamic column adsorption experiments to remove sulfamethoxazole from aqueous solution. The effects of column height, flow rate, initial concentration, pH and humic acids concentration on the dynamic adsorption penetration curve and mass transfer zone length were investigated. Results showed penetration time would be prolonged significantly by increasing column height, while inhibited by the increasement of initial concentration and flow rate. Thomas and Yoon-Nelson model and the Adams-Bohart model were used to elucidate the adsorption mechanism, high coefficients of R2 &gt, 0.95 were obtained in Thomas model for most of the adsorption entries, which revealed that the adsorption rate could probably be dominated by mass transfer at the interface. The average change rates of mass transfer zone length to the changes of each parameters, such as initial concentration, the column height, the flow rate and pH, were 0.0003, 0.6474, 0.0076, 0.0073 and 0.0191 respectively, revealed that column height may play a vital role in dynamic column adsorption efficiency. These findings suggested that lignite activated coke can effectively remove sulfamethoxazole contaminants from wastewater in practice.

The source identification and apportionment of heavy metals (HMs) is a vital issue for restoring contaminated soil. In this study, qualitative approaches [a finite mixture distribution model (FMDM) and raster-based principal components analysis (RB-PCA)] and a quantitative approach [positive matrix factorization (PMF)] were composed to identify and apportion the sources of five HMs (Cd, Hg, As, Pb, Cr) in Wenzhou City, China, using several crucial auxiliary variables. An initial ecological risk assessment suggested that the ecological risk level in the study area was generally considered low, with the greatest contamination contributions coming from Cd and Hg. The result of the FMDM showed that Cd and Pb fit a single log-normal distribution, Hg fit a double log-normal mixed distribution, and As and Cr presented a triple log-normal distribution. Each element was identified and separated from its natural or anthropogenic sources. A map of RB-PCA combined with an analysis of corresponding auxiliary variables suggested that the three main contribution sources in the entire study area were parental materials, industrial and agricultural mixed pollution, and mining exploration activities. Each element was discussed, using the PMF model, with regard to its quantitative contributions. Parental materials contributed to all elements (Cd, Hg, As, Pb, Cr) at 89.22%, 7.31%, 35.84%, 84.81% and 27.42%, respectively. Industrial emissions and agricultural inputs mixed pollution contributed 2.94%, 80.77%, 15.93%, 4.79%, and 25.63%, respectively. Mining activities contributed 7.84%,11.92%, 48.23%, 10.40% and 46.95%, respectively, to the five HMs. Such result could be used efficiently to generate scientific decisions and strategies in terms of decision-making on regulating HM pollution in soils.

International audience; Soil contamination posed by potentially toxic elements is becoming more serious under continuously development of industrialization and the abuse of fertilizers and pesticides. The investigation of soil potentially toxic elements is therefore urgently needed to ensure human and other organisms’ health. In this study, we investigated the feasibility of the separate and combined use of portable X-ray fluorescence (pXRF) and visible near-infrared reflectance (vis-NIR) sensors for measuring eight potentially toxic elements in soil. Low-level fusion was achieved by the direct combination of the pXRF and vis-NIR spectra; middle-level fusion was achieved by the combination of selected bands of the pXRF and vis-NIR spectra using the Boruta feature selection algorithm; and high-level fusion was conducted by outer-product analysis (OPA) and Granger–Ramanathan averaging (GRA). The estimation accuracy for the eight considered elements were in the following order: Zn > Cu > Ni > Cr > As > Cd > Pb > Hg. The measurement for Cu and Zn could be achieved by pXRF spectra alone with Lin’s concordance correlation coefficient (LCCC) values of 0.96 and 0.98, and ratio of performance to interquartile distance (RPIQ) values of 2.36 and 2.69, respectively. The measurement of Ni had the highest model performance for high-level fusion GRA with LCCC of 0.89 and RPIQ of 3.42. The measurements of Cr using middle- and high-level fusion were similar, with LCCC of 0.86 and RPIQ of 2.97. The best estimation accuracy for As, Cd, and Pb were obtained by high-level fusion using OPA, with LCCC >0.72 and RPIQ >1.2. However, Hg measurement by these techniques failed, having an unacceptable performance of LCCC

Precipitation estimates with finer quality and spatio-temporal resolutions play significant roles in understanding the global and regional cycles of water, carbon and energy. Satellite-based precipitation products are capable of detecting spatial patterns and temporal variations of precipitation at finer resolutions, which is particularly useful over poorly gauged regions. However, satellite-based precipitation product are the indirect estimates of precipitation, inherently containing regional and seasonal systematic biases and random errors. In this study, focusing on the potential drawbacks in generating Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) and its recently updated retrospective IMERG in Tropical Rainfall Measuring Mission (TRMM) era (finished in July, 2019), which were only calibrated at monthly scale using ground observations, Global Precipitation Climatology Centre (GPCC, 1.0°/Monthly), we aimed to propose a new calibration algorithm for IMERG at daily scale, and to provide a new AIMERG precipitation dataset (0.1°/half-hourly, 2000–2015, Asia) with better quality, calibrated by Asian Precipitation Highly Resolved Observational Data Integration (APHRODITE, 0.25°/Daily) at daily scale for the Asian applications. And the main conclusions included but not limited to: (1) the proposed daily calibration algorithm (Daily Spatio-Temporal Disaggregation Calibration Algorithm, DSTDCA) was effective in considering the advantages from both satellite-based precipitation estimates and the ground observations; (2) AIMERG performed better than IMERG at different spatio-temporal scales, in terms of both systematic biases and random errors, over the China Main land; and (3) APHRODITE demonstrated significant advantages than GPCC in calibrating the IMERG, especially over the mountainous regions with complex terrain, e.g., the Tibetan Plateau. Additionally, Results of this study suggests that it is a promising and applicable daily calibration algorithm for GPM in generating the future IMERG in either operational scheme or retrospective manner. The AIMERG data record (0.1°/half-hourly, 2000–2015, Asia) is freely available at http://argi-basic.hihanlin.com:8000/d/d925fecf60/. Additionally, the AIMERG data is also freely accessible at https://doi.org/10.5281/zenodo.3609352 (for the period from 2000 to 2008) and https://doi.org/10.5281/zenodo.3609507 (for the period from 2009 to 2015) (Ma et al., 2020).

Monitoring soil organic carbon in alpine soils using in situ vis‐NIR spectroscopy and a multilayer perceptron