Evaluating the Soil Quality Index Using Three Methods to Assess Soil Fertility.

Soil health plays a crucial role in crop production, both in terms of quality and quantity, highlighting the importance of effective methods for preserving soil quality to ensure global food security. Soil quality indices (SQIs) have been widely utilized as comprehensive measures of soil function by integrating multiple physical, chemical, and biological soil properties. Traditional SQI analysis involves laborious and costly laboratory analyses, which limits its practicality. To overcome this limitation, our study explores the use of visible near-infrared (vis-NIR) spectroscopy as a rapid and non-destructive alternative for predicting soil properties and SQIs. This study specifically focused on seven soil indicators that contribute to soil fertility, including pH, organic matter (OM), potassium (K), calcium (Ca), magnesium (Mg), available phosphorous (P), and total nitrogen (TN). These properties play key roles in nutrient availability, pH regulation, and soil structure, influencing soil fertility and overall soil health. By utilizing vis-NIR spectroscopy, we were able to accurately predict the soil indicators with good accuracy using the Cubist model (R2 = 0.35-0.93), offering a cost-effective and environmentally friendly alternative to traditional laboratory analyses. Using the seven soil indicators, we looked at three different approaches for calculating and predicting the SQI, including: (1) measured SQI (SQI_m), which is derived from laboratory-measured soil properties; (2) predicted SQI (SQI_p), which is calculated using predicted soil properties from spectral data; and (3) direct prediction of SQI (SQI_dp), The findings demonstrated that SQI_dp exhibited a higher accuracy (R2 = 0.90) in predicting soil quality compared to SQI_p (R2 = 0.23).

Comparison of Depth-Specific Prediction of Soil Properties: MIR vs. Vis-NIR Spectroscopy.

The prediction of soil properties at different depths is an important research topic for promoting the conservation of black soils and the development of precision agriculture. Mid-infrared spectroscopy (MIR, 2500-25000 nm) has shown great potential in predicting soil properties. This study aimed to explore the ability of MIR to predict soil organic matter (OM) and total nitrogen (TN) at five different depths with the calibration from the whole depth (0-100 cm) or the shallow layers (0-40 cm) and compare its performance with visible and near-infrared spectroscopy (vis-NIR, 350-2500 nm). A total of 90 soil samples containing 450 subsamples (0-10 cm, 10-20 cm, 20-40 cm, 40-70 cm, and 70-100 cm depths) and their corresponding MIR and vis-NIR spectra were collected from a field of black soil in Northeast China. Multivariate adaptive regression splines (MARS) were used to build prediction models. The results showed that prediction models based on MIR (OM: RMSEp = 1.07-3.82 g/kg, RPD = 1.10-5.80; TN: RMSEp = 0.11-0.15 g/kg, RPD = 1.70-4.39) outperformed those based on vis-NIR (OM: RMSEp = 1.75-8.95 g/kg, RPD = 0.50-3.61; TN: RMSEp = 0.12-0.27 g/kg; RPD = 1.00-3.11) because of the higher number of characteristic bands. Prediction models based on the whole depth calibration (OM: RMSEp = 1.09-2.97 g/kg, RPD = 2.13-5.80; TN: RMSEp = 0.08-0.19 g/kg, RPD = 1.86-4.39) outperformed those based on the shallow layers (OM: RMSEp = 1.07-8.95 g/kg, RPD = 0.50-3.93; TN: RMSEp = 0.11-0.27 g/kg, RPD = 1.00-2.24) because the soil sample data of the whole depth had a larger and more representative sample size and a wider distribution. However, prediction models based on the whole depth calibration might provide lower accuracy in some shallow layers. Accordingly, it is suggested that the methods pertaining to soil property prediction based on the spectral library should be considered in future studies for an optimal approach to predicting soil properties at specific depths. This study verified the superiority of MIR for soil property prediction at specific depths and confirmed the advantage of modeling with the whole depth calibration, pointing out a possible optimal approach and providing a reference for predicting soil properties at specific depths.

Pollution and risk assessment of potentially toxic elements in soils from industrial and mining sites across China.

Potentially toxic elements in soils (SPTEs) from industrial and mining sites (IMSs) often cause public health issues. However, previous studies have either focused on SPTEs in agricultural or urban areas, or in a single or few IMSs. A systematic assessment of the pollution and risk levels of SPTEs from IMS at the national scale is lacking. Here, we obtained SPTE (As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn) concentrations from IMSs across China based on 188 peer-reviewed articles published between 2004 and 2022 and quantified their pollution and risk levels using the pollution index and risk assessment model, respectively. The results indicated that the average concentrations of the eight SPTEs were 4.42-270.50 times the corresponding background values, and 19.58% of As, 14.39% of Zn, 12.79% of Pb, and 8.03% of Cd exceeded the corresponding soil risk screening values in these IMSs. In addition, 27.13% of the examined IMS had one or more SPTE pollution, mainly distributed in the southwest and south central China. On the examined IMSs, 81.91% had moderate or severe ecological risks, which were mainly caused by Cd, Hg, As, and Pb; 23.40% showed non-carcinogenic risk and 11.70% demonstrated carcinogenic risk. The primary exposure pathways of the former were ingestion and inhalation, while that for the latter was ingestion. A Monte Carlo simulation also confirmed the health risk assessment results. As, Cd, Hg, and Pb were identified as priority control SPTEs, and Hunan, Guangxi, Guangdong, Yunnan, and Guizhou were selected as the key control provinces. Our results provide valuable information for public health and soil environment management in China.

Decoupling environmental impact from economic growth to achieve Sustainable Development Goals in China.

Developing countries, such as China, have achieved unprecedented success in a single Sustainable Development Goal (SDG), which usually leads to trade-offs between the three pillars of sustainability, and even destroys sustainability. Quantifying the degrees of coupling among the pillars is essential to support policymakers' systematic actions to minimize trade-offs and maximize co-benefits between the pillars, and simultaneously achieve all SDGs. However, assessing the degrees of coupling among the pillars for the full SDGs is lacking. Here, we evaluate the progress of the pillars towards the SDGs and quantify the degrees of coupling among them at both national and sub-national levels in China from 2000 to 2015. The results indicate that the degrees of coupling among the pillars were almost constant while the degrees of coupling between the pillars and economic growth declined over time. The degrees of coupling between environmental impact and economic growth accounted for 52%-83% of the SDGs' progress. Reducing the degrees of coupling helps achieve simultaneously economic growth and environmental protection. The higher the degrees of coupling, the lower progress. This trend was universal among all provinces (sub-national level) regardless of their development levels. Our study highlights not only the necessity to track the degrees of coupling among the pillars, but also decoupling environmental impact from economic growth to achieve the SDGs.

Can N2 O emissions offset the benefits from soil organic carbon storage?

To respect the Paris agreement targeting a limitation of global warming below 2°C by 2100, and possibly below 1.5°C, drastic reductions of greenhouse gas emissions are mandatory but not sufficient. Large-scale deployment of other climate mitigation strategies is also necessary. Among these, increasing soil organic carbon (SOC) stocks is an important lever because carbon in soils can be stored for long periods and land management options to achieve this already exist and have been widely tested. However, agricultural soils are also an important source of nitrous oxide (N2 O), a powerful greenhouse gas, and increasing SOC may influence N2 O emissions, likely causing an increase in many cases, thus tending to offset the climate change benefit from increased SOC storage. Here we review the main agricultural management options for increasing SOC stocks. We evaluate the amount of SOC that can be stored as well as resulting changes in N2 O emissions to better estimate the climate benefits of these management options. Based on quantitative data obtained from published meta-analyses and from our current level of understanding, we conclude that the climate mitigation induced by increased SOC storage is generally overestimated if associated N2 O emissions are not considered but, with the exception of reduced tillage, is never fully offset. Some options (e.g. biochar or non-pyrogenic C amendment application) may even decrease N2 O emissions.

Soil erosion modelling: A bibliometric analysis.

Soil erosion can present a major threat to agriculture due to loss of soil, nutrients, and organic carbon. Therefore, soil erosion modelling is one of the steps used to plan suitable soil protection measures and detect erosion hotspots. A bibliometric analysis of this topic can reveal research patterns and soil erosion modelling characteristics that can help identify steps needed to enhance the research conducted in this field. Therefore, a detailed bibliometric analysis, including investigation of collaboration networks and citation patterns, should be conducted. The updated version of the Global Applications of Soil Erosion Modelling Tracker (GASEMT) database contains information about citation characteristics and publication type. Here, we investigated the impact of the number of authors, the publication type and the selected journal on the number of citations. Generalized boosted regression tree (BRT) modelling was used to evaluate the most relevant variables related to soil erosion modelling. Additionally, bibliometric networks were analysed and visualized. This study revealed that the selection of the soil erosion model has the largest impact on the number of publication citations, followed by the modelling scale and the publication's CiteScore. Some of the other GASEMT database attributes such as model calibration and validation have negligible influence on the number of citations according to the BRT model. Although it is true that studies that conduct calibration, on average, received around 30% more citations, than studies where calibration was not performed. Moreover, the bibliographic coupling and citation networks show a clear continental pattern, although the co-authorship network does not show the same characteristics. Therefore, soil erosion modellers should conduct even more comprehensive review of past studies and focus not just on the research conducted in the same country or continent. Moreover, when evaluating soil erosion models, an additional focus should be given to field measurements, model calibration, performance assessment and uncertainty of modelling results. The results of this study indicate that these GASEMT database attributes had smaller impact on the number of citations, according to the BRT model, than anticipated, which could suggest that these attributes should be given additional attention by the soil erosion modelling community. This study provides a kind of bibliographic benchmark for soil erosion modelling research papers as modellers can estimate the influence of their paper.

Evaluation of Optimized Preprocessing and Modeling Algorithms for Prediction of Soil Properties Using VIS-NIR Spectroscopy.

The absorbance spectra for air-dried and ground soil samples from Ontario, Canada were collected in the visible and near-infrared (VIS-NIR) region from 343 to 2200 nm. The study examined thirteen combination of six preprocessing (1st derivative, 2nd derivative, Savitzky-Golay, Gap, SNV and Detrend) method included in 'prospectr' R package along with four modeling approaches: partial least square regression (PLSR), cubist, random forest (RF), and extreme learning machine (ELM) for prediction of the soil organic matter (SOM). The 1st derivative + gap, 2nd derivative + gap and standard normal variance (SNV) were the best preprocessing algorithms. Thus, only these three preprocessing algorithms along with four modeling approaches were used for prediction of soil pH, electrical conductively (EC), %sand, %silt, %clay, %very coarse sand (VCS), %coarse sand (CS), %medium sand (ms) and %fine sand (fs). The results showed that OM, pH, %sand, %silt and %CS were all predicted with confidence (R2 > 0.60) and the combination of 1st derivative + gap and RF gained the best performance. A detailed comparison of the preprocessing and modeling algorithms for various soil properties in this study demonstrate that for better prediction of soil properties using VIS-NIR spectroscopy requires different preprocessing and modeling algorithms. However, in general RF and 1st derivative + gap can be labeled at the best combination of preprocessing and modelling algorithms.

Evaluation of Machine Learning Approaches to Predict Soil Organic Matter and pH Using vis-NIR Spectra.

Soil organic matter (SOM) and pH are essential soil fertility indictors of paddy soil in the middle-lower Yangtze Plain. Rapid, non-destructive and accurate determination of SOM and pH is vital to preventing soil degradation caused by inappropriate land management practices. Visible-near infrared (vis-NIR) spectroscopy with multivariate calibration can be used to effectively estimate soil properties. In this study, 523 soil samples were collected from paddy fields in the Yangtze Plain, China. Four machine learning approaches-partial least squares regression (PLSR), least squares-support vector machines (LS-SVM), extreme learning machines (ELM) and the Cubist regression model (Cubist)-were used to compare the prediction accuracy based on vis-NIR full bands and bands reduced using the genetic algorithm (GA). The coefficient of determination (R²), root mean square error (RMSE), and ratio of performance to inter-quartile distance (RPIQ) were used to assess the prediction accuracy. The ELM with GA reduced bands was the best model for SOM (SOM: R² = 0.81, RMSE = 5.17, RPIQ = 2.87) and pH (R² = 0.76, RMSE = 0.43, RPIQ = 2.15). The performance of the LS-SVM for pH prediction did not differ significantly between the model with GA (R² = 0.75, RMSE = 0.44, RPIQ = 2.08) and without GA (R² = 0.74, RMSE = 0.45, RPIQ = 2.07). Although a slight increase was observed when ELM were used for prediction of SOM and pH using reduced bands (SOM: R² = 0.81, RMSE = 5.17, RPIQ = 2.87; pH: R² = 0.76, RMSE = 0.43, RPIQ = 2.15) compared with full bands (R² = 0.81, RMSE = 5.18, RPIQ = 2.83; pH: R² = 0.76, RMSE = 0.45, RPIQ = 2.07), the number of wavelengths was greatly reduced (SOM: 201 to 44; pH: 201 to 32). Thus, the ELM coupled with reduced bands by GA is recommended for prediction of properties of paddy soil (SOM and pH) in the middle-lower Yangtze Plain.

[Study on the Characterization of VNIR-MIR Spectra and Prediction of Soil Organic Matter in Paddy Soil].

Soil organic matter (SOM) is an essential indicator for the fertility assessment of farmland. and An efficient and stable prediction model is in need to rapidly estimate SOM in larger scale. Spectroscopic technology has been proved as a powerful tool to access SOM in the last decade. The aims of this paper were: to compare different selection method of calibration set for modeling SOM in paddy soil by using visible-near infrared (VNIR), mid-infrared (MIR) and VNIR-MIR spectra and to assess the prediction ability of the results. All spectra were transformed from reflectance to absorbance, and preprocessed by Savitzky-Golay smoothing algorithm. The prediction models of SOM were built by using partial least squares regression (PLSR) coupled with three selection methods of calibration set in VNIR, MIR and VNIR-MIR regions. The selection method of calibration Rank-KS performed better than Rank method and KS method, meanwhile the models in MIR region showed stronger prediction ability than VNIR and VNIR-MIR regions. The best prediction model was obtained with the MIR model combined with Rank-KS, and the root mean square error of prediction (RMSEP) and ratio of performance to deviation (RPD) were 3.25 g·kg-1 and 4.24. According to variable in the projection (VIP) score, important bands for SOM prediction in paddy soil were identified in VNIR and MIR region. Our results show that MIR spectroscopy could make quantitative prediction of SOM in paddy soil and Rank-KS is an effective method for selection of calibration sets, so as to provide some scientific basis for fertility assessment of farmland and rational fertilization.

In situ measurements of organic carbon in soil profiles using vis-NIR spectroscopy on the Qinghai-Tibet plateau.

We wish to estimate the amount of carbon (C) stored in the soil at high altitudes, for which there is little information. Collecting and transporting large numbers of soil samples from such terrain are difficult, and we have therefore evaluated the feasibility of scanning with visible near-infrared (vis-NIR) spectroscopy in situ for the rapid measurement of the soil in the field. We took 28 cores (≈1 m depth and 5 cm diameter) of soil at altitudes from 2900 to 4500 m in the Sygera Mountains on the Qinghai-Tibet Plateau, China. Spectra were acquired from fresh, vertical faces 5 × 5 cm in area from the centers of the cores to give 413 spectra in all. The raw spectra were pretreated by several methods to remove noise, and statistical models were built to predict of the organic C in the samples from the spectra by partial least-squares regression (PLSR) and least-squares support vector machine (LS-SVM). The bootstrap was used to assess the uncertainty of the predictions by the several combinations of pretreatment and models. The predictions by LS-SVM from the field spectra, for which R(2) = 0.81, the root-mean-square error RMSE = 8.40, and the ratio of the interquartile distance RPIQ = 2.66, were comparable to the PLSR predictions from the laboratory spectra (R(2) = 0.85, RMSE = 7.28, RPIQ = 3.09). We conclude that vis-NIR scanning in situ in the field is a sufficiently accurate rapid means of estimating the concentration of organic C in soil profiles in this high region and perhaps elsewhere.

Using process-oriented model output to enhance machine learning-based soil organic carbon prediction in space and time.

Monitoring and modelling soil organic carbon (SOC) in space and time can help us to better understand soil carbon dynamics and is of key importance to support climate change research and policy. Although machine learning (ML) has attracted a lot of attention in the digital soil mapping (DSM) community for its powerful ability to learn from data and predict soil properties, such as SOC, it is better at capturing soil spatial variation than soil temporal dynamics. By contrast, process-oriented (PO) models benefit from mechanistic knowledge to express physiochemical and biological processes that govern SOC temporal changes. Therefore, integrating PO and ML models seems a promising means to represent physically plausible SOC dynamics while retaining the spatial prediction accuracy of ML models. In this study, a hybrid modelling framework was developed and tested for predicting topsoil SOC stock in space and time for a regional cropland area located in eastern China. In essence, the hybrid model uses predictions of the PO model in unsampled years as additional training data of the ML model, with a weighting parameter assigned to balance the importance of SOC values from the PO model and real measurements. The results indicated that temporal trends of SOC stock modelled by PO and ML models were largely different, while they were notably similar between the PO and hybrid models. Cross-validation showed that the hybrid model had the best performance (RMSE = 0.29 kg m-2), with a 19 % improvement compared with the ML model. We conclude that the proposed hybrid framework not only enhances space-time soil carbon mapping in terms of prediction accuracy and physical plausibility, it also provides insights for soil management and policy decisions in the face of future climate change and intensified human activities.

Drivers distinguishing of PAHs heterogeneity in surface soil of China using deep learning coupled with geo-statistical approach.

Although numerous studies have reported the influencing factors of polycyclic aromatic hydrocarbons (PAHs) in surface soil from source, process or soil perspectives, the mechanism of PAHs heterogeneity in surface soil are still not well understood. In this study, the effects of 16 PAHs in surface soil of China sampled between 2003 and 2020 with their 17 "source-process-sink" factors at 1 km resolution (N = 660)) were explored using deep learning (eXtreme Gradient Boosting) to mine key information from complex dataset under the optimized parameters (i.e., learning rate = 0.05, maximum depth = 5, sub-sample = 0.8). It was observed that top five factors of 16 PAH had the largest cumulative contribution (i.e., from 84.8% to 98.1%) on their soil concentrations. PAH emission was the predominant driver, and its effect on soil PAH increases with increasing logKow. Soil was the second driver, in which clay can promote the partition of PAHs with low or middle logKow. However, sand can accumulate those congeners with high logKow. Moreover, the deep learning plus geo-statistical models (with low deviation for testing dataset (N = 283)) were capable of predicting soil PAH concentrations using their drivers with high accuracy. This study improved the understanding of the environmental fate and spatial variability of soil PAHs, as well as provided a novel technique (i.e., deep learning coupled with geo-statistics) for accurate prediction of soil pollutants.

Effects of Chinese "double carbon strategy" on soil polycyclic aromatic hydrocarbons pollution.

Polycyclic aromatic hydrocarbons (PAHs) and carbon dioxide primarily originate from the combustion of fossil fuels and biomass. The implementation of the Chinese "double carbon strategy" is expected to impact the distribution of PAH emissions, consequently influencing the spatial distribution trend of PAHs in surface soil. Therefore, it is crucial to quantitatively evaluate the effectiveness of the Chinese "double carbon strategy" on soil PAH pollution for the purpose of "the reduction of pollution and carbon emissions". This study utilized 15,088 individual PAH concentration data from 943 soil samples collected between 2003 and 2020 in China, in conjunction with PAH emissions at a 10 km resolution, for meta-analysis. The calculated PAH emissions in this study are in line with the global PAH emission inventory (PKU-PAH-2007), with a relative standard deviation at the provincial level of less than 25 %. Subsequently, a novel method was developed using emission density and Kow of PAHs to predict PAH concentrations in surface soil based on a least-squares regression model. Compared to other environmental models, the method established in this study significantly reduced the percent sample deviation to less than 70 %. Furthermore, energy consumption data for China were simulated based on the implementation plan of the "double carbon strategy" to project PAH emissions and soil PAH levels for the years 2030 and 2060. The predicted PAH emissions in China were estimated to decrease to 41,300 t in 2030 and 10,406.5 t in 2060 from 78,815 t in 2020. Moreover, the heavily contaminated areas of soil PAHs (i.e., total PAH concentrations in soil exceeding 1000 µg kg-1) were projected to decrease by 45 % and 82 % in 2030 and 2060, respectively, compared to levels in 2020. These findings suggest that the implementation of the "double carbon strategy" can fundamentally reduce the pollution of PAHs in surface soil of China.

A novel framework for improving soil organic matter prediction accuracy in cropland by integrating soil, vegetation and human activity information.

Remote sensing is an important tool for monitoring soil information. However, accurate spatial modeling of soil organic matter (SOM) in areas with high vegetation coverage, typically represented by agroecosystems, remains a challenge for field-scale estimation using remote sensing. To date, studies have focused on using single-period or multi-temporal vegetation information to characterize SOM. Thus, the relationship between SOM content and time-series vegetation biomass has not yet been fully explored. In addition, most studies have ignored the effects of critical soil properties and human activities (e.g., soil salinization, soil particle size fractions, history of land-use changes) on SOM. By integrating information on vegetation, soil, and human activities, we propose a novel framework for assessing SOM in cotton fields of artificial oases in northwest China, where returned straw is one of the primary sources of SOM coming from vegetation. We developed an Annual Maximum Biomass Accumulation Index (AMBAI) using time-series Landsat images from 1990 to 2019. Subsequently, we quantified the information of the planting years (PY) of cropland using spectral index threshold and incorporated proximal sensing data (soil hyperspectral and apparent conductivity data) and soil particle size fractions to establish a predictive model of SOM using partial least squares regression (PLSR), random forest (RF), and convolutional neural network (CNN). The results revealed that AMBAI had the highest correlation coefficient (r) with SOM (0.76, P < 0.01). AMBAI, soil hyperspectral data, and PY were the most relevant predictors for estimating SOM. The CNN model integrating vegetation, soil, and human activity information performed best, with coefficient of determination (R2), relative analysis error (RPD), and root mean square error (RMSE) of 0.83, 2.38 and 1.38 g kg-1, respectively. This study confirmed that AMBAI and PY had great potential for characterizing SOM in arid and semi-arid regions, providing a reference for other relevant studies.

A new approach to estimate soil organic carbon content targets in European croplands topsoils.

Adopting land management practices that increase the stock of soil organic carbon (SOC) in croplands is widely promoted as a win-win strategy to enhance soil health and mitigate climate change. In this context, the definition of reference SOC content and stock values is needed to provide reliable targets to farmers, policymakers, and stakeholders. In this study, we used the LUCAS dataset to compare different methods for evaluating reference SOC content and stock values in European croplands topsoils (0-20 cm depth). Methods gave generally similar estimates although being built on very different assumptions. In the absence of an objective criterion to establish which approach is the most suitable to determine SOC reference values, we propose an ensemble modelling approach that consists in extracting the estimates using different relevant methods and retaining the median value among them. Interestingly, this approach led us to select values from the three different approaches with similar frequencies. Using estimated bulk density values, we obtained a first rough estimate of 3.5 Gt C of SOC storage potential in the cropland topsoils that we interpret as a long-term aspirational target that would be reachable only under extreme changes in agricultural practices. The use of additional methods in the ensemble modelling approach and more valid statistical spatial estimates may further refine our approach designed for the estimation of SOC reference values for croplands.

Fruit variability impacts puree quality: Assessment on individually processed apples using the visible and near infrared spectroscopy.

This study was designed to have the absolute definition of 'one apple to one puree', which gave a first insight into the impacts of fruit inter-variability (between varieties) and intra-variability (between individual fruits) on the quality of processed purees. Both the inter-variability of apple varieties and the intra-variability of single apples induced intensive changes of appearance, chemical and textural properties of their corresponding microwave-cooked purees. The intra-variability of cooked purees was different according to apple cultivars. Some strong correlations of visible-near infrared (VIS-NIR) spectra were observed between fresh and cooked apples, particularly in the regions 665-685 nm and 1125-1400 nm. These correlations allowed then the indirect predictions of puree color (a* and b*, RPD ≧ 2.1), viscosity (RPD ≧ 2.3), soluble solids content (SSC, RPD = 2.1), titratable acidity (RPD = 2.8), and pH (RPD = 2.5) from the non-destructive acquired VIS-NIR spectra of raw apples.

Preliminary risk assessment of regional industrial enterprise sites based on big data.

An accurate and inexpensive preliminary risk assessment of industrial enterprise sites at a regional scale is critical for environmental management. In this study, we propose a novel framework for the preliminary risk assessment of industrial enterprise sites in the Yangtze River Delta, which is one of the fastest economic development and most prominent contaminated regions in China. Based on source-pathway-receptors, this framework integrated text and spatial analyses and machine learning, and its feasibility was validated with 8848 positive and negative samples with a calibration and validation set ratio of 8:2. The results indicated that the random forest performed well for risk assessment; and its accuracy, precision, recall, and F1 scores in the calibration set were all 1.0, and the four indicators for the validation set ranged from 0.97 to 0.98, which was better than that for the other models (e.g., logistic regression, support vector machine, and convolutional neural network). The preliminary risk ranking of industrial enterprise sites by the random forest showed that high risks (probabilities) were mainly distributed in Shanghai, southern Jiangsu, and northeastern Zhejiang from 2000 to 2015. The relative importance of the site industrial, production, and geographical features in the random forest was 69%, 22%, and 9%, respectively. Our study highlights that we could quickly and effectively establish a priority (or ranking) list of industrial enterprise sites that require further investigations, using the proposed framework, and identify potentially contaminated sites.

An integrated assessment methodology for management of potentially contaminated sites based on public data.

Ranking assessment of potentially contaminated sites (PCS) provides a great quantity of information (namely the risk screening list) that is usually examined by environmental managers, and therefore reduces the cost of risk management in terms of site investigation. Here we propose an integrated assessment methodology to establish a risk screening list of PCS in China using the Choquet integral correlation coefficient (ICC), which takes the uncertainty and interaction of PCS attributes into explicit account. The proposed method globally considers the importance and ordered positions of PCS attributes while reflecting their overall ranking. The model evaluation and actual validation results demonstrate the success in PCS ranking by the proposed method, which is superior to other methods such as the intuitionistic fuzzy multiple attribute decision-making, the technique for order preference by similarity to an ideal solution, and the weighted average. The resulting spatial distribution of Choquet ICC indicates that high-attention PCS in China are mainly located in Guangdong, Jiangsu, Zhejiang, and Shandong Provinces. This study is the first attempt to conduct a ranking assessment of PCS across China. The proposed assessment method based on Choquet ICC offers a step towards establishing a risk screening list of PCS globally.

Mid-infrared technique to forecast cooked puree properties from raw apples: A potential strategy towards sustainability and precision processing.

The potential of MIRS was investigated to: i) differentiate cooked purees issued from different apples and process conditions, and ii) predict the puree quality characteristics from the spectra of homogenized raw apples. Partial least squares (PLS) regression was tested both, on the real spectra of cooked purees and their reconstructed spectra calculated from the spectra of homogenized raw apples by direct standardization. The cooked purees were well-classified according to apple thinning practices and cold storage durations, and to different heating and grinding conditions. PLS models using the spectra of homogenized raw apples can anticipate the titratable acidity (the residual predictive deviation (RPD) = 2.9), soluble solid content (RPD = 2.8), particle averaged size (RPD = 2.6) and viscosity (RPD ≥ 2.5) of cooked purees. MIR technique can provide sustainable evaluations of puree quality, and even forecast texture and taste of purees based on the prior information of raw materials.

Diagnosis of cadmium contamination in urban and suburban soils using visible-to-near-infrared spectroscopy.

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 soil Cd contamination from 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 squares discriminant analysis, random forest [RF], and support vector machine) for diagnosing soil Cd 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 in spectral reflectance magnitude. 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.