Oxide- and Silicate-Water Interfaces and Their Roles in Technology and the Environment.

Interfacial reactions drive all elemental cycling on Earth and play pivotal roles in human activities such as agriculture, water purification, energy production and storage, environmental contaminant remediation, and nuclear waste repository management. The onset of the 21st century marked the beginning of a more detailed understanding of mineral aqueous interfaces enabled by advances in techniques that use tunable high-flux focused ultrafast laser and X-ray sources to provide near-atomic measurement resolution, as well as by nanofabrication approaches that enable transmission electron microscopy in a liquid cell. This leap into atomic- and nanometer-scale measurements has uncovered scale-dependent phenomena whose reaction thermodynamics, kinetics, and pathways deviate from previous observations made on larger systems. A second key advance is new experimental evidence for what scientists hypothesized but could not test previously, namely, interfacial chemical reactions are frequently driven by "anomalies" or "non-idealities" such as defects, nanoconfinement, and other nontypical chemical structures. Third, progress in computational chemistry has yielded new insights that allow a move beyond simple schematics, leading to a molecular model of these complex interfaces. In combination with surface-sensitive measurements, we have gained knowledge of the interfacial structure and dynamics, including the underlying solid surface and the immediately adjacent water and aqueous ions, enabling a better definition of what constitutes the oxide- and silicate-water interfaces. This critical review discusses how science progresses from understanding ideal solid-water interfaces to more realistic systems, focusing on accomplishments in the last 20 years and identifying challenges and future opportunities for the community to address. We anticipate that the next 20 years will focus on understanding and predicting dynamic transient and reactive structures over greater spatial and temporal ranges as well as systems of greater structural and chemical complexity. Closer collaborations of theoretical and experimental experts across disciplines will continue to be critical to achieving this great aspiration.

Meta-Analysis and Machine Learning Models for Anaerobic Biodegradation Rates of Organic Contaminants in Sediments and Sludge.

Anaerobic biodegradation rates (half-lives) of organic chemicals are pivotal for environmental risk assessment and remediation. Traditional experimental evaluation, constrained by prolonged, oxygen-free conditions, struggles to keep pace with emerging contaminants. Data-driven machine learning (ML) models serve as promising complements. However, reported quantitative structure-biodegradation relationships or ML models on anaerobic biodegradation are mostly based on small data sets (<100 records) and neglect experimental conditions, usually achieving compromised predictions. This work aimed to develop ML models for predicting the biodegradation half-lives of organic pollutants in anaerobic environments (i.e., sediment/soil and sludge). Focusing on important features of both chemicals and experimental conditions, we first curated two data sets, one for sediment/soil (SED) and the other for sludge (SLD), covering 978 records for 206 chemicals from the literature, and then conducted a meta-analysis. Next, we built a binary classification (half-life of 30 days as the cutoff) model with an accuracy of 81% and a regression model with R2 of 0.56 for SED based on LightGBM (80% and 0.31 for SLD based on Extra tree, respectively). The model interpretations underscored the significance of experimental conditions (e.g., temperature and inoculum dosage), as evidenced by their high feature importance, and the models were found to correctly capture the effects of chemical substructures, for example, branched structures and aromatic rings prolonged half-lives while methyl group and ortho-substitution on rings shortened half-lives. The applicability domains of the models were also defined, resulting in reasonable prediction for the half-lives of 41% (SED) or 67% (SLD) of over 4000 persistent, bioaccumulative, and toxic chemicals. Overall, this study pioneers ML models for predicting the anaerobic degradation half-lives, offering valuable support for future evaluation and implementation of chemical anaerobic biodegradation.

Predicting Odor Sensory Attributes of Unidentified Chemicals in Water Using Fragmentation Mass Spectra with Machine Learning Models.

Knowing odor sensory attributes of odorants lies at the core of odor tracking when addressing waterborne odor issues. However, experimental determination covering tens of thousands of odorants in authentic water is not pragmatic due to the complexity of odorant identification and odor evaluation. In this study, we propose the first machine learning (ML) model to predict odor perception/threshold aiming at odorants in water, which can use either molecular structure or MS2 spectra as input features. We demonstrate that model performance using MS2 spectra is nearly as good as that using unequivocal structures, both with outstanding accuracy. We particularly show the model's robustness in predicting odor sensory attributes of unidentified chemicals by using the experimentally obtained MS2 spectra from nontarget analysis on authentic water samples. Interpreting the developed models, we identify the intricate interaction of functional groups as the predominant influence factor on odor sensory attributes. We also highlight the important roles of carbon chain length, molecular weight, etc., in the inherent olfactory mechanisms. These findings streamline the odor sensory attribute prediction and are crucial advancements toward credible tracking and efficient control of off-odors in water.

A de novo low-frequency mosaic variant of KIF1A causes hereditary spastic paraplegia: A literature review.

OBJECTIVE: The objective of this study was to investigate the pathogenesis and inheritance pattern of a Chinese Han family with hereditary spastic paraplegia and to retrospectively analyze the characteristics of KIF1A gene variants and related clinical manifestations. METHODS: High-throughput whole-exome sequencing was performed on members of a Chinese Han family with a clinical diagnosis of hereditary spastic paraplegia, and the sequencing results were validated by Sanger sequencing. Deep high-throughput sequencing was performed on subjects with suspected mosaic variants. The previously reported pathogenic variant loci of the KIF1A gene with complete data were collected, and the clinical manifestations and characteristics of the pathogenic KIF1A gene variant were analyzed. RESULTS: A pathogenic heterozygous variant located in the neck coil of the KIF1A gene (c.1139G>C, p.Arg380Pro) was identified in the proband and four additional members of the family. It was derived from the de novo low-frequency somatic-gonadal mosaicism of the proband's grandmother and had a rate of 10.95%. INTERPRETATION: This study helps us to better understand the pathogenic mode and characteristics of mosaic variants, and to understand the location and clinical characteristics of pathogenic variants in KIF1A.

A leaf-level spectral library to support high-throughput plant phenotyping: predictive accuracy and model transfer.

Leaf-level hyperspectral reflectance has become an effective tool for high-throughput phenotyping of plant leaf traits due to its rapid, low-cost, multi-sensing, and non-destructive nature. However, collecting samples for model calibration can still be expensive, and models show poor transferability among different datasets. This study had three specific objectives: first, to assemble a large library of leaf hyperspectral data (n=2460) from maize and sorghum; second, to evaluate two machine-learning approaches to estimate nine leaf properties (chlorophyll, thickness, water content, nitrogen, phosphorus, potassium, calcium, magnesium, and sulfur); and third, to investigate the usefulness of this spectral library for predicting external datasets (n=445) including soybean and camelina using extra-weighted spiking. Internal cross-validation showed satisfactory performance of the spectral library to estimate all nine traits (mean R2=0.688), with partial least-squares regression outperforming deep neural network models. Models calibrated solely using the spectral library showed degraded performance on external datasets (mean R2=0.159 for camelina, 0.337 for soybean). Models improved significantly when a small portion of external samples (n=20) was added to the library via extra-weighted spiking (mean R2=0.574 for camelina, 0.536 for soybean). The leaf-level spectral library greatly benefits plant physiological and biochemical phenotyping, whilst extra-weight spiking improves model transferability and extends its utility.

Fe(II) Redox Chemistry in the Environment.

Iron (Fe) is the fourth most abundant element in the earth's crust and plays important roles in both biological and chemical processes. The redox reactivity of various Fe(II) forms has gained increasing attention over recent decades in the areas of (bio) geochemistry, environmental chemistry and engineering, and material sciences. The goal of this paper is to review these recent advances and the current state of knowledge of Fe(II) redox chemistry in the environment. Specifically, this comprehensive review focuses on the redox reactivity of four types of Fe(II) species including aqueous Fe(II), Fe(II) complexed with ligands, minerals bearing structural Fe(II), and sorbed Fe(II) on mineral oxide surfaces. The formation pathways, factors governing the reactivity, insights into potential mechanisms, reactivity comparison, and characterization techniques are discussed with reference to the most recent breakthroughs in this field where possible. We also cover the roles of these Fe(II) species in environmental applications of zerovalent iron, microbial processes, biogeochemical cycling of carbon and nutrients, and their abiotic oxidation related processes in natural and engineered systems.

Abiotic Reduction of Organic and Inorganic Compounds by Fe(II)-Associated Reductants: Comprehensive Data Sets and Machine Learning Modeling.

Iron-associated reductants play a crucial role in providing electrons for various reductive transformations. However, developing reliable predictive tools for estimating abiotic reduction rate constants (logk) in such systems has been impeded by the intricate nature of these systems. Our recent study developed a machine learning (ML) model based on 60 organic compounds toward one soluble Fe(II)-reductant. In this study, we built a comprehensive kinetic data set covering the reactivity of 117 organic and 10 inorganic compounds toward four major types of Fe(II)-associated reductants. Separate ML models were developed for organic and inorganic compounds, and the feature importance analysis demonstrated the significance of resonance structures, reducible functional groups, reductant descriptors, and pH in logk prediction. Mechanistic interpretation validated that the models accurately learned the impact of various factors such as aromatic substituents, complexation, bond dissociation energy, reduction potential, LUMO energy, and dominant reductant species. Finally, we found that 38% of the 850,000 compounds in the Distributed Structure-Searchable Toxicity (DSSTox) database contain at least one reducible functional group, and the logk of 285,184 compounds could be reasonably predicted using our model. Overall, the study is a significant step toward reliable predictive tools for anticipating abiotic reduction rate constants in iron-associated reductant systems.

[Clinical characteristics and genetic variant analysis of a child with Snijders Blok-Campeau syndrome].

OBJECTIVE: To analyze the clinical phenotype and genetic variant of a child with Snijders Blok-Campeau syndrome (SBCS). METHODS: A child who was diagnosed with SBCS in June 2017 at Henan Children's Hospital was selected as the study subject. Clinical data of the child was collected. Peripheral blood samples of the child and his parents were collected and the extraction of genomic DNA, which was subjected to trio-whole exome sequencing (trio-WES) and genome copy number variation (CNV) analysis. Candidate variant was verified by Sanger sequencing of his pedigree members. RESULTS: The main clinical manifestations of the child have included language delay, intellectual impairment and motor development delay, which were accompanied with facial dysmorphisms (broad forehead, inverted triangular face, sparse eyebrows, widely spaced eyes, narrow palpebral fissures, broad nose bridge, midface hypoplasia, thin upper lip, pointed jaw, low-set ears and posteriorly rotated ears). Trio-WES and Sanger sequencing revealed that the child has harbored a heterozygous splicing variant of the CHD3 gene, namely c.4073-2A>G, for which both of his parents were of wild-type. No pathogenic variant was identified by CNV testing. CONCLUSION: The c.4073-2A>G splicing variant of the CHD3 gene probably underlay the SBCS in this patient.

[Phenotypic and genetic analysis of a child with partial trisomy 7q].

OBJECTIVE: To define the nature and origin of a chromosomal aberration in a child with unexplained growth and development retardation, and to analyze its genotype-phenotype correlation. METHODS: A child who had presented at the Affiliated Children's Hospital of Zhengzhou University on July 9, 2019 was selected as the study subject. Chromosomal karyotypes of the child and her parents were determined with routine G-banding analysis. Their genomic DNA was also analyzed with single nucleotide polymorphism array (SNP array). RESULTS: Karyotyping analysis combined with SNP array suggested that the chromosomal karyotype of the child was 46,XX,dup(7)(q34q36.3), whilst no karyotypic abnormality was found in either of her parents. SNP array has identified a de novo 20.6 Mb duplication at 7q34q36.3 [arr[hg19] 7q34q36.3(138335828_158923941)×3] in the child. CONCLUSION: The partial trisomy 7q carried by the child was rated as a de novo pathogenic variant. SNP array can clarify the nature and origin of chromosomal aberrations. Analysis of the correlation between genotype and phenotype can facilitate the clinical diagnosis and genetic counseling.

Predicting Solute Descriptors for Organic Chemicals by a Deep Neural Network (DNN) Using Basic Chemical Structures and a Surrogate Metric.

Solute descriptors have been widely used to model chemical transfer processes through poly-parameter linear free energy relationships (pp-LFERs); however, there are still substantial difficulties in obtaining these descriptors accurately and quickly for new organic chemicals. In this research, models (PaDEL-DNN) that require only SMILES of chemicals were built to satisfactorily estimate pp-LFER descriptors using deep neural networks (DNN) and the PaDEL chemical representation. The PaDEL-DNN-estimated pp-LFER descriptors demonstrated good performance in modeling storage-lipid/water partitioning coefficient (log Kstorage-lipid/water), bioconcentration factor (BCF), aqueous solubility (ESOL), and hydration free energy (freesolve). Then, assuming that the accuracy in the estimated values of widely available properties, e.g., logP (octanol-water partition coefficient), can calibrate estimates for less available but related properties, we proposed logP as a surrogate metric for evaluating the overall accuracy of the estimated pp-LFER descriptors. When using the pp-LFER descriptors to model log Kstorage-lipid/water, BCF, ESOL, and freesolve, we achieved around 0.1 log unit lower errors for chemicals whose estimated pp-LFER descriptors were deemed "accurate" by the surrogate metric. The interpretation of the PaDEL-DNN models revealed that, for a given test chemical, having several (around 5) "similar" chemicals in the training data set was crucial for accurate estimation while the remaining less similar training chemicals provided reasonable baseline estimates. Lastly, pp-LFER descriptors for over 2800 persistent, bioaccumulative, and toxic chemicals were reasonably estimated by combining PaDEL-DNN with the surrogate metric. Overall, the PaDEL-DNN/surrogate metric and newly estimated descriptors will greatly benefit chemical transfer modeling.

Classification and Regression Machine Learning Models for Predicting Aerobic Ready and Inherent Biodegradation of Organic Chemicals in Water.

Machine learning (ML) is viewed as a promising tool for the prediction of aerobic biodegradation, one of the most important elimination pathways of organic chemicals from the environment. However, available models only have small datasets (<3200 records), make binary classification predictions, evaluate ready biodegradability, and do not incorporate experimental conditions (e.g., system setup and reaction time). This study addressed all these limitations by first compiling a large database of 12,750 records, considering both ready and inherent biodegradation under different conditions, and then developing regression and classification models using different chemical representations and ML algorithms. The best regression model (R2 = 0.54 and root mean square error of 0.25) and classification model (the prediction accuracy from 85.1%) achieved very good performance. The model interpretation indicated that the models correctly captured the effects of chemical substructures, following the order of C═O > O═C-O > OH > CH3 > halogen > branching > N > 6-member ring. The consideration of chemical speciation based on pKa and α notations did not affect the regression model performance but significantly improved the classification model performance (the accuracy increased to 87.6%). The models also showed large applicability domains and provided reasonable predictions for more than 98% of over 850,000 environmentally relevant chemicals in the Distributed Structure-Searchable Toxicity database. These robust, trustable models were finally made widely accessible through two free online predictors with graphical user interface.

Machine Learning-Assisted QSAR Models on Contaminant Reactivity Toward Four Oxidants: Combining Small Data Sets and Knowledge Transfer.

To develop predictive models for the reactivity of organic contaminants toward four oxidants─SO4•-, HClO, O3, and ClO2─all with small sample sizes, we proposed two approaches: combining small data sets and transferring knowledge between them. We first merged these data sets and developed a unified model using machine learning (ML), which showed better predictive performance than the individual models for HClO (RMSEtest: 2.1 to 2.04), O3 (2.06 to 1.94), ClO2 (1.77 to 1.49), and SO4•- (0.75 to 0.70) because the model "corrected" the wrongly learned effects of several atom groups. We further developed knowledge transfer models for three pairs of the data sets and observed different predictive performances: improved for O3 (RMSEtest: 2.06 to 2.01)/HClO (2.10 to 1.98), mixed for O3 (2.06 to 2.01)/ClO2 (1.77 to 1.95), and unchanged for ClO2 (1.77 to 1.77)/HClO (2.1 to 2.1). The effectiveness of the latter approach depended on whether there was consistent knowledge shared between the data sets and on the performance of the individual models. We also compared our approaches with multitask learning and image-based transfer learning and found that our approaches consistently improved the predictive performance for all data sets while the other two did not. This study demonstrated the effectiveness of combining small, similar data sets and transferring knowledge between them to improve ML model performance.

Investigation of water quality and its spatial distribution in the Kor River basin, Fars province, Iran.

As one of the largest rivers in the southwest of Iran, the Kor River plays an important role in local economy and ecosystem. However, the rapid development of industry has caused significant pollution in this river in recent years. Despite of a number of studies reported on this river regarding water pollution, few have conducted a comprehensive investigation of a wide range of water quality parameters to map the current pollution status. This study focuses on 21 water quality parameters around the industrial centers of the Kor River basin with samples taken from 25 stations. With the measured parameters, the interpolation maps of each parameter were determined using the Kriging method, and the water quality was quantified using the Water Quality Index (WQI) method. The results showed that the WQI values were between 28 and 73, showing more pollution around the factories than in the upstream areas. The results of the principal component analysis (PCA) indicated that BOD, COD, NO3-, and coliforms were the most important parameters among the 21 parameters affecting the water quality. Linear regression results suggested that the best parameters for determining coliforms and WQI values were BOD, and Cr, PO43-, Hg and Zn levels, respectively, with R2 greater than 0.87. These results can also simplify the prediction of coliforms and WQI using only a few parameters. We further found that flatter regions generally had more pollution, primarily due to pollutant accumulation as a result of water stagnation.

Enhanced dewaterability of waste activated sludge by UV assisted ZVI-PDS oxidation.

Ultraviolet (UV) assisted zero-valent iron (ZVI)-activated sodium persulfate (PDS) oxidation (UV-ZVI-PDS) was used to treat waste activated sludge (WAS) in this study. The dewaterability performance and mechanism of WAS dewatering were analyzed. The results showed that UV-ZVI-PDS can obtain better sludge dewatering performance in a wide pH range (2.0-8.0). When the molar ratio of ZVI/PDS was 0.6, UV was 254nm, PDS dosage was 200 mg/g TS (total solid), pH was 6.54, reaction time was 20 min, the CST (capillary suction time) and SRF (specific resistance to filtration) were decreased by 64.0% and 78.2%, respectively. The molar ratio of ZVI/PDS used in this paper is much lower than that of literatures, and the contents of total Fe and Fe2+ in sludge supernatant remained at a low level, as 3.7 mg/L and 0.0 mg/L. The analysis of extracellular polymeric substances (EPS), scanning electron microscope (SEM) and particle size distribution showed that the EPS could be effectively destroyed by UV-ZVI-PDS, the sludge flocs broken down into smaller particles, cracks and holes appeared, and then the bound water was released. At the same time, the highly hydrophilic tightly bound-EPS (TB-EPS) were converted into loosely bound EPS (LB-EPS) and soluble EPS (S-EPS). During sludge pretreated by UV-ZVI-PDS, positively charged ions, such as Fe2+, Fe3+ and H+, produced in the reaction system could reduce the electronegativity of sludge surface, promote sludge particles aggregation, and then enhanced the sludge dewaterability.

Predicting Heavy Metal Adsorption on Soil with Machine Learning and Mapping Global Distribution of Soil Adsorption Capacities.

Studying heavy metal adsorption on soil is important for understanding the fate of heavy metals and properly assessing the related environmental risks. Existing experimental methods and traditional models for quantifying adsorption, however, are time-consuming and ineffective. In this study, we developed machine learning models for the soil adsorption of six heavy metals (Cd(II), Cr(VI), Cu(II), Pb(II), Ni(II), and Zn(II)) using 4420 data points (1105 soils) extracted from 150 journal articles. After a comprehensive comparison, our results showed that the gradient boosting decision tree had the best performance for a combined model based on all the data. The Shapley additive explanation method was used to identify the feature importance and the effects of these features on the adsorption, based on which six independent models were developed for the six metals to achieve better model performance than the combined model. Using these independent models, the global distribution of heavy metal adsorption capacities on soils was predicted with known soil properties. Reversed models, including one combined model for all the six metals and six independent models, were also built using the same data sets to predict the heavy metal concentration in water when the adsorbed amount is known for a soil/sediment.

Machine Learning: New Ideas and Tools in Environmental Science and Engineering.

The rapid increase in both the quantity and complexity of data that are being generated daily in the field of environmental science and engineering (ESE) demands accompanied advancement in data analytics. Advanced data analysis approaches, such as machine learning (ML), have become indispensable tools for revealing hidden patterns or deducing correlations for which conventional analytical methods face limitations or challenges. However, ML concepts and practices have not been widely utilized by researchers in ESE. This feature explores the potential of ML to revolutionize data analysis and modeling in the ESE field, and covers the essential knowledge needed for such applications. First, we use five examples to illustrate how ML addresses complex ESE problems. We then summarize four major types of applications of ML in ESE: making predictions; extracting feature importance; detecting anomalies; and discovering new materials or chemicals. Next, we introduce the essential knowledge required and current shortcomings in ML applications in ESE, with a focus on three important but often overlooked components when applying ML: correct model development, proper model interpretation, and sound applicability analysis. Finally, we discuss challenges and future opportunities in the application of ML tools in ESE to highlight the potential of ML in this field.

Spatial heterogeneity modeling of water quality based on random forest regression and model interpretation.

A systematic understanding of the spatial distribution of water quality is critical for successful watershed management; however, the limited number of physical monitoring stations has restricted the evaluation of spatial water quality distribution and the identification of features impacting the water quality. To fill this gap, we developed a modeling process that employed the random forest regression (RFR) to model the water quality distribution for the Taihu Lake basin in Zhejiang Province, China, and adopted the Shapley Additive exPlanations (SHAP) method to interpret the underlying driving forces. We first used RFR to model three water quality parameters: permanganate index (CODMn), total phosphorus (TP), and total nitrogen (TN), based on 16 watershed features. We then applied the built models to generate water quality distribution maps for the basin, with the CODMn ranging from 1.39 to 6.40 mg/L, TP from 0.02 to 0.23 mg/L, and TN from 1.43 to 4.27 mg/L. These maps showed generally consistent patterns among the CODMn, TN, and TP with minor differences in the spatial distribution. The SHAP analysis showed that the TN was mainly affected by agricultural non-point sources, while the CODMn and TP were affected by agricultural and domestic sources. Due to differences in sewage collection and treatment between urban and rural areas, the water quality in highly populated urban areas was better than that in rural areas, which led to an unexpected positive relationship between water quality and population density. Overall, with the RFR models and SHAP interpretation, we obtained a continuous distribution pattern of the water quality and identified its driving forces in the basin. These findings provided important information to assist water quality restoration projects.

Ecological risk potential assessment of heavy metal contaminated soils in Ophiolitic formations.

The purpose of this study is to generate maps of contamination risk for cadmium (Cd), copper (Cu), lead (Pb), nickel (Ni), and zinc (Zn) in soils of a large alluvial fan located in Neyriz, Iran and to investigate their possible entry into the food chain. To this aim, the concentrations of the heavy metals of the soils are measured. The Geo-accumulation index (Igeo), Muller index, and potential ecological risk index are then used to evaluate soil contamination. The spatial distribution map of elements is also prepared using the kriging method. The results show that the Cd concentration in soils (mean 23 mg/kg) is 10-40 times higher than the global standard threshold (0.30-0.70 mg/kg), the Ni concentration (mean 13 mg/kg) is lower than the threshold (34 -12 mg/kg), the Cu concentration (mean 19.39 mg/kg) is below the threshold (24-13 mg/kg), the Zn concentration (mean 14.11 mg/kg) is also below the threshold (45-100 mg/kg), and the Pb concentration (mean 93.78 mg/kg) is higher than the threshold (44-22 mg/kg). The accumulation index values for Pb and Cd are 1.61 and 5.3, respectively, which decrease from the top to bottom of the study area. The enrichment factor values for Cu, Zn, Pb, Cd, and Ni are 0.43, 0.14, 4.60, 62.57, and 0.27, respectively, which also decrease from top to bottom. The accumulation index values in the soils confirm the occurrence of contamination and further indicate that the elements in the soils originated from local materials and Ophiolitic formations masses in the area. Overall, this research for the first time investigates the effect of natural factors (geological formation) on the soil and plant pollution in the study area and shows that, in addition to pollution by human activity, natural factors such as type of formation can lead to soil and plant pollution.

An improved weighted index for the assessment of heavy metal pollution in soils in Zhejiang, China.

Soil heavy metal pollution assessment is an important procedure in soil quality and ecological risk management, for which different mathematical models have been developed. However, these models have often failed to consider the characteristics of both heavy metals and the polluted sites. In this study, we analyzed the concentrations of seven heavy metals in soils in Zhejiang Province, China, and developed an improved weighted index (IWI) model to evaluate pollution levels. In contrast to traditional models, weights were assigned to different heavy metals using statistical tools, including hierarchical cluster analysis and principal component analysis. Of the 89 sites, 61.8% were considered unpolluted with IWI values < 1; 32.58% were slighted polluted with IWI values from 1 to 2, and only 2.25% of the sites were seriously polluted with IWI values > 3. The IWI results agree well with two traditional integrated index models, but can be also applied to much wider heavy metal concentration ranges. Possible pollution sources were then proposed based on the IWI model. The IWI overcame several shortcomings of the traditional indices and could be very beneficial for assessing heavy metal pollution in soil. Overall, this study developed a new model for soil pollution assessment and soil ecological risk management and comprehensively evaluated the current pollution status of soil surrounding potable surface water sources in Zhejiang Province, China.

[Compound heterozygous NDUFS1 variants identified in a Chinese pedigree affected with mitochondrial respiratory chain complex I deficiency].

OBJECTIVE: To explore the genetic basis for a Chinese pedigree with suspected mitochondrial functional defects through combined next-generation sequencing (NGS), copy number variation sequencing (CNV-seq), and mitochondrial DNA (mtDNA) sequencing. METHODS: Clinical data of the proband and his family members were collected. The patient and his parents were subjected to family-trio whole-exome sequencing (WES), CNV-seq and mtDNA variant detection. Candidate variant was verified by Sanger sequencing. RESULTS: Trio-WES revealed that the proband has carried compound heterozygous variants of the NDUFS1 gene, including a paternally derived c.64C>T (p.R22X) nonsense variant and a maternally derived c.845A>G (p.N282S) missense variant. Both variants may cause loss of protein function. No variant that may cause the phenotype was identified by CNV-seq and mtDNA variant analysis. CONCLUSION: Children with suspected mitochondrial disorders may have no specific syndromes or laboratory findings. A comprehensive strategy including mtDNA testing may facilitate the diagnosis and early clinical interventions.