Absorption mechanism of SO3 on various alkaline absorbents in the presence of SO2.

Sulfur trioxide (SO3) as a condensable particle matter has a significant influence on atmospheric visibility, which easily arouses formation of haze. It is imperative to control the SO3 emission from the industrial flue gas. Three commonly used basic absorbents, including Ca(OH)2, MgO and NaHCO3 were selected to explore the effects of temperature, SO2 concentration on the SO3 absorption, and the reaction mechanism of SO3 absorption was further illustrated. The suitable reaction temperature for various absorbents were proposed, Ca(OH)2 at the high temperatures above 500°C, MgO at the low temperatures below 320°C, and NaHCO3 at the temperature range of 320-500°C. The competitive absorption between SO2 and SO3 was found that the addition of SO2 reduced the SO3 absorption on Ca(OH)2 and NaHCO3, while had no effect on MgO. The order of the absorption selectivity of SO3 follows MgO, NaHCO3 and Ca(OH)2 under the given conditions in this work. The absorption process of SO3 on NaHCO3 follows the shrinking core model, thus the absorption reaction continues until NaHCO3 was exhausted with the utilization rate of nearly 100%. The absorption process of SO3 on Ca(OH)2 and MgO follows the grain model, and the dense product layer hinders the further absorption reaction, resulting in low utilization of about 50% for Ca(OH)2 and MgO. The research provides a favorable support for the selection of alkaline absorbent for SO3 removal in application.

Machine learning-assisted fluorescence visualization for sequential quantitative detection of aluminum and fluoride ions.

The presence of aluminum (Al3+) and fluoride (F-) ions in the environment can be harmful to ecosystems and human health, highlighting the need for accurate and efficient monitoring. In this paper, an innovative approach is presented that leverages the power of machine learning to enhance the accuracy and efficiency of fluorescence-based detection for sequential quantitative analysis of aluminum (Al3+) and fluoride (F-) ions in aqueous solutions. The proposed method involves the synthesis of sulfur-functionalized carbon dots (C-dots) as fluorescence probes, with fluorescence enhancement upon interaction with Al3+ ions, achieving a detection limit of 4.2 nmol/L. Subsequently, in the presence of F- ions, fluorescence is quenched, with a detection limit of 47.6 nmol/L. The fingerprints of fluorescence images are extracted using a cross-platform computer vision library in Python, followed by data preprocessing. Subsequently, the fingerprint data is subjected to cluster analysis using the K-means model from machine learning, and the average Silhouette Coefficient indicates excellent model performance. Finally, a regression analysis based on the principal component analysis method is employed to achieve more precise quantitative analysis of aluminum and fluoride ions. The results demonstrate that the developed model excels in terms of accuracy and sensitivity. This groundbreaking model not only showcases exceptional performance but also addresses the urgent need for effective environmental monitoring and risk assessment, making it a valuable tool for safeguarding our ecosystems and public health.

CRISPR-Mediated Construction of Gene-Knockout Mice for Investigating Antiviral Innate Immunity.

With the rapid development of CRISPR-Cas9 technology, gene editing has become a powerful tool for studying gene function. Specifically, in the study of the mechanisms by which natural immune responses combat viral infections, gene knockout mouse models have provided an indispensable platform. This article describes a detailed protocol for constructing gene knockout mice using the CRISPR-Cas9 system. This field focuses on the design of single-guide RNAs (sgRNAs) targeting the antiviral immune gene cGAS, embryo microinjection, and screening and verification of gene editing outcomes. Furthermore, this study provides methods for using cGAS gene knockout mice to analyze the role of specific genes in natural immune responses. Through this protocol, researchers can efficiently generate specific gene knockout mouse models, which not only helps in understanding the functions of the immune system but also offers a powerful experimental tool for exploring the mechanisms of antiviral innate immunity.

How sediment dredging alters phosphorus dynamics in a lowland rural river?

China's lowland rural rivers are facing severe eutrophication problems due to excessive phosphorus (P) from anthropogenic activities. However, quantifying P dynamics in a lowland rural river is challenging due to its complex interaction with surrounding areas. A P dynamic model (River-P) was specifically designed for lowland rural rivers to address this challenge. This model was coupled with the Environmental Fluid Dynamics Code (EFDC) and the Phosphorus Dynamic Model for lowland Polder systems (PDP) to characterize P dynamics under the impact of dredging in a lowland rural river. Based on a two-year (2020-2021) dataset from a representative lowland rural river in the Lake Taihu Basin, China, the coupled model was calibrated and achieved a model performance (R2>0.59, RMSE<0.04 mg/L) for total P (TP) concentrations. Our research in the study river revealed that (1) the time scale for the effectiveness of sediment dredging for P control was ∼300 days, with an increase in P retention capacity by 74.8 kg/year and a decrease in TP concentrations of 23% after dredging. (2) Dredging significantly reduced P release from sediment by 98%, while increased P resuspension and settling capacities by 16% and 46%, respectively. (3) The sediment-water interface (SWI) plays a critical role in P transfer within the river, as resuspension accounts for 16% of TP imports, and settling accounts for 47% of TP exports. Given the large P retention capacity of lowland rural rivers, drainage ditches and ponds with macrophytes are promising approaches to enhance P retention capacity. Our study provides valuable insights for local environmental departments, allowing a comprehensive understanding of P dynamics in lowland rural rivers. This enable the evaluation of the efficacy of sediment dredging in P control and the implementation of corresponding P control measures.

The effect of fine grits and fine debris concentrations on the MLVSS/MLSS ratio of an activated sludge system.

This study embarks on an explorative investigation into the effects of typical concentrations and varying particle sizes of fine grits (FG, the involatile portion of suspended solids) and fine debris (FD, the volatile yet unbiodegradable fraction of suspended solids) within the influent on the mixed liquor volatile suspended solids (MLVSS)/mixed liquor suspended solids (MLSS) ratio of an activated sludge system. Through meticulous experimentation, it was discerned that the addition of FG or FD, the particle size of FG, and the concentration of FD bore no substantial impact on the pollutant removal efficiency (denoted by the removal rate of COD and ammonia nitrogen) under constant operational conditions. However, a notable decrease in the MLVSS/MLSS ratio was observed with a typical FG concentration of 20 mg/L, with smaller FG particle sizes exacerbating this reduction. Additionally, variations in FD concentrations influenced both MLSS and MLVSS/MLSS ratios; a higher FD concentration led to an increased MLSS and a reduced MLVSS/MLSS ratio, indicating FD accumulation in the system. A predictive model for MLVSS/MLSS was constructed based on quality balance calculations, offering a tool for foreseeing the MLVSS/MLSS ratio under stable long-term influent conditions of FG and FD. This model, validated using data from the BXH wastewater treatment plant (WWTP), showcased remarkable accuracy.

Establishment of a swine model of delayed bleeding after endoscopic procedure.

Objectives: Although delayed bleeding after endoscopic procedures has become a problem, currently, there are no appropriate animal models to validate methods for preventing it. This study aimed to establish an animal model of delayed bleeding after endoscopic procedures of the gastrointestinal tract. Methods: Activated coagulation time (ACT) was measured using blood samples drawn from a catheter inserted into the external jugular vein of swine (n = 7; age, 6 months; mean weight, 13.8 kg) under general anesthesia using the cut-down method. An upper gastrointestinal endoscope was inserted orally, and 12 mucosal defects were created in the stomach by endoscopic mucosal resection using a ligating device. Hemostasis was confirmed at this time point. The heparin group (n = 4) received 50 units/kg of unfractionated heparin via a catheter; after confirming that the ACT was ≥200 s 10 min later, continuous heparin administration (50 units/kg/h) was started. After 24 h, an endoscope was inserted under general anesthesia to evaluate the blood volume in the stomach and the degree of blood adherence at the site of the mucosal defect. Results: Delayed bleeding was observed in three swine (75%) in the heparin-treated group, who had a maximum ACT of >220 s before the start of continuous heparin administration. In the non-treated group (n = 3), no prolonged ACT or delayed bleeding was observed at 24 h. Conclusion: An animal model of delayed bleeding after an endoscopic procedure in the gastrointestinal tract was established using a single dose of heparin and continuous heparin administration after confirming an ACT of 220 s.

The effect of adsorbent textural and functional properties on model naphthenic acid adsorption.

Naphthenic acids, NAs, are a major contaminant of concern and a focus of much research around remediation of oil sand process affected waters, OSPW. Using activated carbon adsorbents are an attractive option given their low cost of fabrication and implementation. A deeper evaluation of the effect NA structural differences have on uptake affinity is warranted. Here we provide an in-depth exploration of NA adsorption including many more model NA species than have been assessed previously with evaluation of adsorption kinetics and isotherms at the relevant alkaline pH of OSPW using several different carbon adsorbents with pH buffering to simulate the behaviour of real OSPW. Uptake for the NA varied considerably regardless of the activated carbon used, ranging from 350 mg/g to near zero highlighting recalcitrant NAs. The equilibrium data was explored to identify structural features of these species and key physiochemical properties that influence adsorption. We found that certain NA will be resistant to adsorption when hydrophobic adsorbents are used. Adsorption isotherm modelling helped explore interactions occurring at the interface between NA and adsorbent surfaces. We identified the importance of NA hydrophobicity for activated carbon uptake. Evidence is also presented that indicates favorable hydrogen bonding between certain NA and surface site hydroxyl groups, demonstrating the importance of adsorbent surface functionality for NA uptake. This research highlights the challenges associated with removing NAs from OSPW through adsorption and also identifies how adsorbent surface chemistry modification can be used to increase the removal efficiency of recalcitrant NA species.

Insight into the sorption and desorption pattern of pyrrolizidine alkaloids and their N-oxides in acidic tea (Camellia sinensis) plantation soils.

Pyrrolizidine alkaloids (PAs) and their N-oxides (PANOs) are phytotoxins produced by various plant species and have been emerged as environmental pollutants. The sorption/desorption behaviors of PAs/PANOs in soil are crucial due to the horizontal transfer of these natural products from PA-producing plants to soil and subsequently absorbed by plant roots. This study firstly investigated the sorption/desorption behaviors of PAs/PANOs in tea plantation soils with distinct characteristics. Sorption amounts for seneciphylline (Sp) and seneciphylline-N-oxide (SpNO) in three acidic soils ranged from 2.9 to 5.9 µg/g and 1.7 to 2.8 µg/g, respectively. Desorption percentages for Sp and SpNO were from 22.2% to 30.5% and 36.1% to 43.9%. In the mixed PAs/PANOs systems, stronger sorption of PAs over PANOs was occurred in tested soils. Additionally, the Freundlich models more precisely described the sorption/desorption isotherms. Cation exchange capacity, sand content and total nitrogen were identified as major influencing factors by linear regression models. Overall, the soils exhibiting higher sorption capacities for compounds with greater hydrophobicity. PANOs were more likely to migrate within soils and be absorbed by tea plants. It contributes to the understanding of environmental fate of PAs/PANOs in tea plantations and provides basic data and clues for the development of PAs/PANOs reduction technology.

On using an aerosol thermodynamic model to calculate aerosol acidity of coarse particles.

Thermodynamic modeling is still the most widely used method to characterize aerosol acidity, a critical physicochemical property of atmospheric aerosols. However, it remains unclear whether gas-aerosol partitioning should be incorporated when thermodynamic models are employed to estimate the acidity of coarse particles. In this work, field measurements were conducted at a coastal city in northern China across three seasons, and covered wide ranges of temperature, relative humidity and NH3 concentrations. We examined the performance of different modes of ISORROPIA-II (a widely used aerosol thermodynamic model) in estimating aerosol acidity of coarse and fine particles. The M0 mode, which incorporates gas-phase data and runs the model in the forward mode, provided reasonable estimation of aerosol acidity for coarse and fine particles. Compared to M0, the M1 mode, which runs the model in the forward mode but does not include gas-phase data, may capture the general trend of aerosol acidity but underestimates pH for both coarse and fine particles; M2, which runs the model in the reverse mode, results in large errors in estimated aerosol pH for both coarse and fine particles and should not be used for aerosol acidity calculations. However, M1 significantly underestimates liquid water contents for both fine and coarse particles, while M2 provides reliable estimation of liquid water contents. In summary, our work highlights the importance of incorporating gas-aerosol partitioning when estimating coarse particle acidity, and thus may help improve our understanding of acidity of coarse particles.

In-silico study of the impact of system design parameters on microcalcification detection in wide-angle digital breast tomosynthesis.

Purpose: Accurate detection of microcalcifications ( µ Calcs ) is crucial for the early detection of breast cancer. Some clinical studies have indicated that digital breast tomosynthesis (DBT) systems with a wide angular range have inferior µ Calc detectability compared with those with a narrow angular range. This study aims to (1) provide guidance for optimizing wide-angle (WA) DBT for improving µ Calcs detectability and (2) prioritize key optimization factors. Approach: An in-silico DBT pipeline was constructed to evaluate µ Calc detectability of a WA DBT system under various imaging conditions: focal spot motion (FSM), angular dose distribution (ADS), detector pixel pitch, and detector electronic noise (EN). Images were simulated using a digital anthropomorphic breast phantom inserted with 120 µ m µ Calc clusters. Evaluation metrics included the signal-to-noise ratio (SNR) of the filtered channel observer and the area under the receiver operator curve (AUC) of multiple-reader multiple-case analysis. Results: Results showed that FSM degraded µ Calcs sharpness and decreased the SNR and AUC by 5.2% and 1.8%, respectively. Non-uniform ADS increased the SNR by 62.8% and the AUC by 10.2% for filtered backprojection reconstruction with a typical clinical filter setting. When EN decreased from 2000 to 200 electrons, the SNR and AUC increased by 21.6% and 5.0%, respectively. Decreasing the detector pixel pitch from 85 to 50 µ m improved the SNR and AUC by 55.6% and 7.5%, respectively. The combined improvement of a 50 µ m pixel pitch and EN200 was 89.2% in the SNR and 12.8% in the AUC. Conclusions: Based on the magnitude of impact, the priority for enhancing µ Calc detectability in WA DBT is as follows: (1) utilizing detectors with a small pixel pitch and low EN level, (2) allocating a higher dose to central projections, and (3) reducing FSM. The results from this study can potentially provide guidance for DBT system optimization in the future.

Predicting Long-Term Clinical Outcomes of Patients With Recurrent Pericarditis.

BACKGROUND: Recurrent pericarditis (RP) is a complex condition associated with significant morbidity. Prior studies have evaluated which variables are associated with clinical remission. However, there is currently no established risk-stratification model for predicting outcomes in these patients. OBJECTIVES: We developed a risk stratification model that can predict long-term outcomes in patients with RP and enable identification of patients with characteristics that portend poor outcomes. METHODS: We retrospectively studied a total of 365 consecutive patients with RP from 2012 to 2019. The primary outcome was clinical remission (CR), defined as cessation of all anti-inflammatory therapy with complete resolution of symptoms. Five machine learning survival models were used to calculate the likelihood of CR within 5 years and stratify patients into high-risk, intermediate-risk, and low-risk groups. RESULTS: Among the cohort, the mean age was 46 ± 15 years, and 205 (56%) were women. CR was achieved in 118 (32%) patients. The final model included steroid dependency, total number of recurrences, pericardial late gadolinium enhancement, age, etiology, sex, ejection fraction, and heart rate as the most important parameters. The model predicted the outcome with a C-index of 0.800 on the test set and exhibited a significant ability in stratification of patients into low-risk, intermediate-risk, and high-risk groups (log-rank test; P < 0.0001). CONCLUSIONS: We developed a novel risk-stratification model for predicting CR in RP. Our model can also aid in stratifying patients, with high discriminative ability. The use of an explainable machine learning model can aid physicians in making individualized treatment decision in RP patients.

Protocol for establishing and evaluating a cancer cachexia mouse model.

Cancer cachexia mouse models are needed to recapitulate the clinical features of patients with cachexia. Here, we present a protocol for the establishment and evaluation of cancer cachexia mouse models. We delineate the steps in preparing tumor cells for inoculation and surgical procedures. After the establishment of these mouse models, we describe essential techniques to assess cancer cachexia, including grip strength evaluation, tissue collection, and the calculation of cross-sectional areas of muscle tissue. For complete details on the use and execution of this protocol, please refer to Liu et al.,1 Yang et al.,2 Shi et al.,3 and Zhou et al.4.

Aprepitant's roles in abating seizures, behavioral, and cognitive deficits in mice model of epilepsy.

BACKGROUND: Aprepitant (APR), a neurokinin 1 receptor antagonist, is an approved drug for treating chemotherapy-induced nausea and vomiting. OBJECTIVES: Investigate the beneficial roles of APR alone or in combination with sodium valproate (VPA) against lithium pilocarpine [li-pilo]-induced seizures, behavioral changes, and cognitive deficits. METHODS: Thirty male mice were divided into five groups, each containing 6. "Vehicle Group I," "Control Group II "li-pilo, " Valproate (VPA) group III (400 mg/kg/i.p.), "APR group IV, " and "Combination Group V." Videos of mice were recorded, and they were watched for episodes of spontaneous recurring seizures (SRS). Behavioral Tests were performed. At the end of the study, animal brains were taken for biochemical assays and gene expression studies. RESULTS: APR partially protected against SRS with partial restoration of average behavioral and standard cognitive skills associated with a significant increase in brain SOD activity and a significant decrease in MDA, IL-1ß, NF-КB, and SP-3 levels in relation to the control group. Interestingly, a combination of APR with VPA in epileptic mice showed complete protection against li-pilo-induced behavioral changes and cognitive deficits, a significant increase in brain SOD activity, and a considerable decrease in MDA, IL-1ß, NF-ΚB, and SP levels to normal. CONCLUSION: Using APR as an adjuvant to VPA is more effective in protecting against li-pilo-induced seizures, behavioral changes, and cognitive deficits due to its antioxidant, anti-inflammatory, and NK1 antagonist effects than using APR alone as drug therapy.

Ecotoxicological study of seven pharmaceutically active compounds: Mixture effects and environmental risk assessment.

Pharmaceutically active compounds (PhACs) have been detected in several aquatic compartments, which has been of environmental concern since PhACs can cause adverse effects on the aquatic ecosystem at low concentrations. Despite the variety of PhACs detected in surface water, ecotoxicological studies are non-existent for many of them, mainly regarding their mixture. In addition, water bodies can continuously receive the discharge of raw or treated wastewater with micropollutants. Thus, PhACs are subject to mixture and interactions, potentiating or reducing their toxicity. Therefore, the present study evaluated the toxicity on Aliivibrio fischeri of seven PhACs, which still needs to be explored in the literature. The effects were evaluated for the PhACs individually and for their binary and tertiary mixture. Also, the experimental effects were compared with the concentration addition (CA) and independent action (IA) models. Finally, an environmental risk assessment was carried out. Fenofibrate (FEN), loratadine (LOR), and ketoprofen (KET) were the most toxic, with EC50 of 0.32 mg L-1, 6.15 mg L-1 and 36.8 mg L-1, respectively. Synergistic effects were observed for FEN + LOR, KET + LOR, and KET + FEN + LOR, showing that the CA and IA may underestimate the toxicity. Environmental risks for KET concerning algae, and LOR e 17α-ethynylestradiol (EE2) for crustaceans and fish were high for several locations. Besides, high removals by wastewater treatment technologies are required to achieve the concentrations necessary for reducing KET and LOR risk quotients. Thus, this study contributed to a better understanding of the toxic interactions and environmental risks of PhACs.

Complex-valued soft-log threshold reweighting for sparsity of complex-valued convolutional neural networks.

Complex-valued convolutional neural networks (CVCNNs) have been demonstrated effectiveness in classifying complex signals and synthetic aperture radar (SAR) images. However, due to the introduction of complex-valued parameters, CVCNNs tend to become redundant with heavy floating-point operations. Model sparsity is emerged as an efficient method of removing the redundancy without much loss of performance. Currently, there are few studies on the sparsity problem of CVCNNs. Therefore, a complex-valued soft-log threshold reweighting (CV-SLTR) algorithm is proposed for the design of sparse CVCNN to reduce the number of weight parameters and simplify the structure of CVCNN. On one hand, considering the difference between complex and real numbers, we redefine and derive the complex-valued log-sum threshold method. On the other hand, by considering the distinctive characteristics of complex-valued convolutional (CConv) layers and complex-valued fully connected (CFC) layers of CVCNNs, the complex-valued soft and log-sum threshold methods are respectively developed to prune the weights of different layers during the forward propagation, and the sparsity thresholds are optimized during the backward propagation by inducing a sparsity budget. Furthermore, different optimizers can be integrated with CV-SLTR. When stochastic gradient descent (SGD) is used, the convergence of CV-SLTR is proved if Lipschitzian continuity is satisfied. Experiments on the RadioML 2016.10A and S1SLC-CVDL datasets show that the proposed algorithm is efficient for the sparsity of CVCNNs. It is worth noting that the proposed algorithm has fast sparsity speed while maintaining high classification accuracy. These demonstrate the feasibility and potential of the CV-SLTR algorithm.

Pollution area identification, receptor model-oriented sources and probabilistic health hazards to prioritize control measures for heavy metal management in soil.

Identifying the primary source of heavy metals (HMs) pollution and the key pollutants is crucial for safeguarding eco-health and managing risks in industrial vicinity. For this purpose, this investigation was carried out to investigate the pollution area identification with soil static environmental capacity (QI), receptor model-oriented critical sources, and Monte Carlo simulation (MCS) based probabilistic environmental and human health hazards associated with HMs in agricultural soils of Narayanganj, Bangladesh. The average concentration of Cr, Ni, Cu, Cd, Pb, Co, Zn, and Mn were 98.67, 63.41, 37.39, 1.28, 23.93, 14.48, 125.08, and 467.45 mg/kg, respectively. The geoaccumulation index identified Cd as the dominant metal, indicating heavy to extreme contamination in soils. The QI revealed that over 99% of the areas were polluted for Ni and Cd with less uncertain regions whereas Cr showed a significant portion of areas with uncertain pollution status. The positive matrix factorization (PMF) model identified three major sources: agricultural (29%), vehicular emissions (25%), and industrial (46%). The probabilistic assessment of health hazards indicated that both carcinogenic and non-carcinogenic risks for adult male, adult female, and children were deemed unacceptable. Moreover, children faced a higher health hazard compared to adults. For adult male, adult female, and children, industrial operations contributed 48.4%, 42.7%, and 71.2% of the carcinogenic risks, respectively and these risks were associated with Ni and Cr as the main pollutants of concern. The study emphasizes valuable scientific insights for environmental managers to tackle soil pollution from HMs by effectively managing anthropogenic sources. It could aid in devising strategies for environmental remediation engineering and refining industry standards.

Framework for multivariate carbon price forecasting: A novel hybrid model.

The complex characteristics of volatility and non-linearity of carbon price pose a serious challenge to accurately predict carbon price. Therefore, this study proposes a new hybrid model for multivariate carbon price forecasting, including feature selection, deep learning, intelligent optimization algorithms, model combination and evaluation indicators. First, this study collects and organizes the historical carbon price series of Hubei and Shanghai as well as the influencing factors in five dimensions including structured and unstructured data, totaling twenty variables. Second, data dimensionality reduction is performed and input variables are obtained using the least absolute shrinkage and selection operator, followed by the introduction of nine advanced deep learning models to predict carbon price and compare the prediction effects. Then, through the combination of models, three models with the best performance are combined with Pelican optimization algorithm to construct a hybrid forecasting model. Finally, the experimental results show that the developed forecasting model outperforms other comparation models in terms of prediction accuracy, stability and statistical hypothesis testing, and exhibits excellent prediction performance. Furthermore, this study also applies the developed model to European carbon market price prediction and uses the Hubei carbon market as an example for quantitative trading simulation, and the empirical results further verify its robust prediction performance and investment application value. In conclusion, the proposed hybrid prediction model can not only provide high-precision carbon market price prediction for the government and corporate decision makers, but also help investors optimize their trading strategies and improve their returns.

Fostering green technology innovation with green credit: Evidence from spatial quantile approach.

Green technology is an important path to achieve low-carbon development, and green credit provides financial support for green technology innovation. Existing literature often fails to pay attention to the important role of spatial factors and outliers in green technology innovation. Based on 2005-2022 provincial panel data in China, this paper uses a novel spatial lag quantile model to explore the impact of green credit on green technology innovation and its impact mechanism. The empirical results indicate that green credit exerts a greater positive impact on green technology in the provinces with moderate technical level. Technological innovation has the characteristic of spatial spillover. The spatial spillover of technology contributes more to green technology innovation in the provinces with low- and medium-tech level. This result has been proven even after robustness test of the changes in sample units, and the replacement of core variable values. Further mechanistic analysis demonstrates that banking market structure and enterprise R&D investment both produces the greater impact on green technology innovation in the low-tech provinces such as Qinghai, Ningxia, and Hainan. This article provides policy reference for local governments to formulate green finance policies and promote carbon neutrality strategies.

Spatial heterogeneity: Necessary and feasible for revealing soil trace elements pollution, sources, risks, and their links.

The source diversity and health risk of trace elements (TEs) in soil make it necessary to reveal the relationship between pollution, source, and risk. However, neglect of spatial heterogeneity restricts the reliability of existing identification methods. In this study, spatial heterogeneity is proposed as a necessary and feasible factor for accurately dissecting the pollution-source-risk link of soil TEs. A comprehensive framework is developed by integrating positive matrix factorization, Geodetector, and risk evaluation tools, and successfully applied in a mining-intensive city in northern China. Overall, the TEs are derived from natural background (28.5 %), atmospheric deposition (25.6 %), coal mining (21.8 %), and metal industry (24.1 %). The formation mechanism of heterogeneity for high-variance TEs (Se, Hg, Cd) is first systematically deciphered by revealing the heterogeneous source-sink relationship. Specifically, Se is dominated (76.5 %) by heterogeneous coal mining (q=0.187), Hg is determined (92.6 %) by the heterogeneity of metal mining (q=0.183) and smelting (q=0.363), and Cd is caused (50.9 %) by heterogeneous atmospheric deposition (q>0.254) co-influenced by the terrains and soil properties. Highly heterogeneous sources are also noteworthy for their potential to pose extreme risks (THI=1.122) in local areas. This study highlights the necessity of integrating spatial heterogeneity in pollution and risk assessment of soil TEs.