Widespread partisan gerrymandering mostly cancels nationally, but reduces electoral competition.

Congressional district lines in many US states are drawn by partisan actors, raising concerns about gerrymandering. To separate the partisan effects of redistricting from the effects of other factors including geography and redistricting rules, we compare possible party compositions of the US House under the enacted plan to those under a set of alternative simulated plans that serve as a nonpartisan baseline. We find that partisan gerrymandering is widespread in the 2020 redistricting cycle, but most of the electoral bias it creates cancels at the national level, giving Republicans two additional seats on average. Geography and redistricting rules separately contribute a moderate pro-Republican bias. Finally, we find that partisan gerrymandering reduces electoral competition and makes the partisan composition of the US House less responsive to shifts in the national vote.

De novo protein fold design through sequence-independent fragment assembly simulations.

De novo protein design generally consists of two steps, including structure and sequence design. Many protein design studies have focused on sequence design with scaffolds adapted from native structures in the PDB, which renders novel areas of protein structure and function space unexplored. We developed FoldDesign to create novel protein folds from specific secondary structure (SS) assignments through sequence-independent replica-exchange Monte Carlo (REMC) simulations. The method was tested on 354 non-redundant topologies, where FoldDesign consistently created stable structural folds, while recapitulating on average 87.7% of the SS elements. Meanwhile, the FoldDesign scaffolds had well-formed structures with buried residues and solvent-exposed areas closely matching their native counterparts. Despite the high fidelity to the input SS restraints and local structural characteristics of native proteins, a large portion of the designed scaffolds possessed global folds completely different from natural proteins in the PDB, highlighting the ability of FoldDesign to explore novel areas of protein fold space. Detailed data analyses revealed that the major contributions to the successful structure design lay in the optimal energy force field, which contains a balanced set of SS packing terms, and REMC simulations, which were coupled with multiple auxiliary movements to efficiently search the conformational space. Additionally, the ability to recognize and assemble uncommon super-SS geometries, rather than the unique arrangement of common SS motifs, was the key to generating novel folds. These results demonstrate a strong potential to explore both structural and functional spaces through computational design simulations that natural proteins have not reached through evolution.

Opinion dynamics in financial markets via random networks.

We investigate financial market dynamics by introducing a heterogeneous agent-based opinion formation model. In this work, we organize individuals in a financial market according to their trading strategy, namely, whether they are noise traders or fundamentalists. The opinion of a local majority compels the market exchanging behavior of noise traders, whereas the global behavior of the market influences the decisions of fundamentalist agents. We introduce a noise parameter, q, to represent the level of anxiety and perceived uncertainty regarding market behavior, enabling the possibility of adrift financial action. We place individuals as nodes in an Erdös-Rényi random graph, where the links represent their social interactions. At any given time, individuals assume one of two possible opinion states ±1 regarding buying or selling an asset. The model exhibits fundamental qualitative and quantitative real-world market features such as the distribution of logarithmic returns with fat tails, clustered volatility, and the long-term correlation of returns. We use Student's t distributions to fit the histograms of logarithmic returns, showing a gradual shift from a leptokurtic to a mesokurtic regime depending on the fraction of fundamentalist agents. Furthermore, we compare our results with those concerning the distribution of the logarithmic returns of several real-world financial indices.

Prediction of Optical Properties in Particulate Media Using Double Optimization of Dependent Scattering and Particle Distribution.

The prediction of optical properties dominated by light scattering in particulate media composed of high-concentration and polydisperse particles is greatly important in various optical applications. However, the accuracy and efficiency of light propagation simulations are still limited by the huge computational burden and complex interactions between dense and polydisperse particles. Here, we proposed a new optimization strategy that can effectively and accurately predict optical properties based on Monte Carlo simulation with particle size and dependent scattering corrections. Both the scattering parameters of particles and the experimental reflectance spectrum are fully examined for verification. Furthermore, using the weighted solar reflectance of particulate media as a representative optical property, both numerical simulations and experiments confirm the superiority and universality of the proposed optimization approach in a variety of materials systems. Moreover, our work can guide the design of particulate media with specific optical features insightfully and will be applicable in many fields involving multiparticle scattering.

Synergistic Effect of Ferroptosis-Inducing Nanoparticles and X-Ray Irradiation Combination Therapy.

Ferroptosis, characterized by the induction of cell death via lipid peroxidation, has been actively studied over the last few years and has shown the potential to improve the efficacy of cancer nanomedicine in an iron-dependent manner. Radiation therapy, a common treatment method, has limitations as a stand-alone treatment due to radiation resistance and safety as it affects even normal tissues. Although ferroptosis-inducing drugs help alleviate radiation resistance, there are no safe ferroptosis-inducing drugs that can be considered for clinical application and are still in the research stage. Here, the effectiveness of combined treatment with radiotherapy with Fe and hyaluronic acid-based nanoparticles (FHA-NPs) to directly induce ferroptosis, considering the clinical applications is reported. Through the induction of ferroptosis by FHA-NPs and apoptosis by X-ray irradiation, the therapeutic efficiency of cancer is greatly improved both in vitro and in vivo. In addition, Monte Carlo simulations are performed to assess the physical interactions of the X-rays with the iron-oxide nanoparticle. The study provides a deeper understanding of the synergistic effect of ferroptosis and X-ray irradiation combination therapy. Furthermore, the study can serve as a valuable reference for elucidating the role and mechanisms of ferroptosis in radiation therapy.

Relevance of the calculation of the diffusion coefficient in a capillary electrophoresis experiment.

This paper analyzes the role of the diffusion coefficient in the movement of analytes that can reversibly react with a selector given a product in the presence of drift. The problem mimics the movement of enantiomers in a capillary electrophoresis experiment. As is well known, the signal in the capillary must be sharp enough to make a good determination of the effective mobility of the analytes being analyzed. The essence of the technique is based on fast interconversion rates. Therefore, the effective diffusion coefficient must be negligible during the experiment. In the present work, an exact expression for both the apparent mobility and the diffusion coefficient is obtained. This is done by writing the rate equations governing the process and solving them using the generating function technique. The effective mobility coincides with the Wren and Rowe equation, whereas the diffusion coefficient allows us to determine the values of the parameters to be taken into account so that this quantity is minimal or close to zero. On the other hand, the numerical solution of the kinetic equations and Monte Carlo simulations allow us to follow the signal in the capillary and to determine its space-time evolution.

The effects of axonal beading and undulation on axonal diameter estimation from diffusion MRI: Insights from simulations in human axons segmented from three-dimensional electron microscopy.

The increasing availability of high-performance gradient systems in human MRI scanners has generated great interest in diffusion microstructural imaging applications such as axonal diameter mapping. Practically, sensitivity to axon diameter in diffusion MRI is attained at strong diffusion weightings b , where the deviation from the expected 1 / b scaling in white matter yields a finite transverse diffusivity, which is then translated into an axon diameter estimate. While axons are usually modeled as perfectly straight, impermeable cylinders, local variations in diameter (caliber variation or beading) and direction (undulation) are known to influence axonal diameter estimates and have been observed in microscopy data of human axons. In this study, we performed Monte Carlo simulations of diffusion in axons reconstructed from three-dimensional electron microscopy of a human temporal lobe specimen using simulated sequence parameters matched to the maximal gradient strength of the next-generation Connectome 2.0 human MRI scanner ( ≲ 500 mT/m). We show that axon diameter estimation is accurate for nonbeaded, nonundulating fibers; however, in fibers with caliber variations and undulations, the axon diameter is heavily underestimated due to caliber variations, and this effect overshadows the known overestimation of the axon diameter due to undulations. This unexpected underestimation may originate from variations in the coarse-grained axial diffusivity due to caliber variations. Given that increased axonal beading and undulations have been observed in pathological tissues, such as traumatic brain injury and ischemia, the interpretation of axon diameter alterations in pathology may be significantly confounded.

Comparison of patient setup accuracy for optical surface-guided and X-ray-guided imaging with respect to the impact on intracranial stereotactic radiotherapy.

PURPOSE: The objective of this work is to estimate the patient positioning accuracy of a surface-guided radiation therapy (SGRT) system using an optical surface scanner compared to an X­ray-based imaging system (IGRT) with respect to their impact on intracranial stereotactic radiotherapy (SRT) and intracranial stereotactic radiosurgery (SRS). METHODS: Patient positioning data, both acquired with SGRT and IGRT systems at the same linacs, serve as a basis for determination of positioning accuracy. A total of 35 patients with two different open face masks (578 datasets) were positioned using X­ray stereoscopic imaging and the patient position inside the open face mask was recorded using SGRT. The measurement accuracy of the SGRT system (in a "standard" and an SRS mode with higher resolution) was evaluated using both IGRT and SGRT patient positioning datasets taking into account the measurement errors of the X­ray system. Based on these clinically measured datasets, the positioning accuracy was estimated using Monte Carlo (MC) simulations. The relevant evaluation criterion, as standard of practice in cranial SRT, was the 95th percentile. RESULTS: The interfractional measurement displacement vector of the SGRT system, σSGRT, in high resolution mode was estimated at 2.5 mm (68th percentile) and 5 mm (95th percentile). If the standard resolution was used, σSGRT increased by about 20%. The standard deviation of the axis-related σSGRT of the SGRT system ranged between 1.5 and 1.8 mm interfractionally and 0.5 and 1.0 mm intrafractionally. The magnitude of σSGRT is mainly due to the principle of patient surface scanning and not due to technical limitations or vendor-specific issues in software or hardware. Based on the resulting σSGRT, MC simulations served as a measure for the positioning accuracy for non-coplanar couch rotations. If an SGRT system is used as the only patient positioning device in non-coplanar fields, interfractional positioning errors of up to 6 mm and intrafractional errors of up to 5 mm cannot be ruled out. In contrast, MC simulations resulted in a positioning error of 1.6 mm (95th percentile) using the IGRT system. The cause of positioning errors in the SGRT system is mainly a change in the facial surface relative to a defined point in the brain. CONCLUSION: In order to achieve the necessary geometric accuracy in cranial stereotactic radiotherapy, use of an X­ray-based IGRT system, especially when treating with non-coplanar couch angles, is highly recommended.

Deep transformer-based personalized dosimetry from SPECT/CT images: a hybrid approach for [177Lu]Lu-DOTATATE radiopharmaceutical therapy.

PURPOSE: Accurate dosimetry is critical for ensuring the safety and efficacy of radiopharmaceutical therapies. In current clinical dosimetry practice, MIRD formalisms are widely employed. However, with the rapid advancement of deep learning (DL) algorithms, there has been an increasing interest in leveraging the calculation speed and automation capabilities for different tasks. We aimed to develop a hybrid transformer-based deep learning (DL) model that incorporates a multiple voxel S-value (MSV) approach for voxel-level dosimetry in [177Lu]Lu-DOTATATE therapy. The goal was to enhance the performance of the model to achieve accuracy levels closely aligned with Monte Carlo (MC) simulations, considered as the standard of reference. We extended our analysis to include MIRD formalisms (SSV and MSV), thereby conducting a comprehensive dosimetry study. METHODS: We used a dataset consisting of 22 patients undergoing up to 4 cycles of [177Lu]Lu-DOTATATE therapy. MC simulations were used to generate reference absorbed dose maps. In addition, MIRD formalism approaches, namely, single S-value (SSV) and MSV techniques, were performed. A UNEt TRansformer (UNETR) DL architecture was trained using five-fold cross-validation to generate MC-based dose maps. Co-registered CT images were fed into the network as input, whereas the difference between MC and MSV (MC-MSV) was set as output. DL results are then integrated to MSV to revive the MC dose maps. Finally, the dose maps generated by MSV, SSV, and DL were quantitatively compared to the MC reference at both voxel level and organ level (organs at risk and lesions). RESULTS: The DL approach showed slightly better performance (voxel relative absolute error (RAE) = 5.28 ± 1.32) compared to MSV (voxel RAE = 5.54 ± 1.4) and outperformed SSV (voxel RAE = 7.8 ± 3.02). Gamma analysis pass rates were 99.0 ± 1.2%, 98.8 ± 1.3%, and 98.7 ± 1.52% for DL, MSV, and SSV approaches, respectively. The computational time for MC was the highest (~2 days for a single-bed SPECT study) compared to MSV, SSV, and DL, whereas the DL-based approach outperformed the other approaches in terms of time efficiency (3 s for a single-bed SPECT). Organ-wise analysis showed absolute percent errors of 1.44 ± 3.05%, 1.18 ± 2.65%, and 1.15 ± 2.5% for SSV, MSV, and DL approaches, respectively, in lesion-absorbed doses. CONCLUSION: A hybrid transformer-based deep learning model was developed for fast and accurate dose map generation, outperforming the MIRD approaches, specifically in heterogenous regions. The model achieved accuracy close to MC gold standard and has potential for clinical implementation for use on large-scale datasets.

Reverse Mapping of Coarse Grained Polyglutamine Conformations from PRIME20 Sampling.

An inverse coarse-graining protocol is presented for generating and validating atomistic structures of large (bio-) molecules from conformations obtained via a coarse-grained sampling method. Specifically, the protocol is implemented and tested based on the (coarse-grained) PRIME20 protein model (P20/SAMC), and the resulting all-atom conformations are simulated using conventional biomolecular force fields. The phase space sampling at the coarse-grained level is performed with a stochastical approximation Monte Carlo approach. The method is applied to a series of polypeptides, specifically dimers of polyglutamine with varying chain length in aqueous solution. The majority (>70 %) of the conformations obtained from the coarse-grained peptide model can successfully be mapped back to atomistic structures that remain conformationally stable during 10 ns of molecular dynamics simulations. This work can be seen as the first step towards the overarching goal of improving our understanding of protein aggregation phenomena through simulation methods.

Assignment of a Physical Energy Scale for the Dimensionless Interaction Energies within the PRIME20 Peptide Model.

We present a calibration scheme to determine the conversion factors from a coarse-grained stochastic approximation Monte Carlo approach using the PRIME20 peptide interaction model to atomistic force-field interaction energies at full explicit aqueous solvation. The conversion from coarse-grained to atomistic structures was performed according to our previously established inverse coarse-graining protocol. We provide a physical energy scale for both the backbone hydrogen bonding interactions and the sidechain interactions by correlating the dimensionless energy descriptors of the PRIME20 model with the energies averaged over molecular dynamics simulations. The conversion factor for these interactions turns out to be around 2 kJ/mol for the backbone interactions, and zero for the sidechain interactions. We discuss these surprisingly small values in terms of their molecular interpretation.

Model-based comparisons of post-treatment free IgE and FEV1 between omalizumab asthma dosing tables in the United States and European Union.

AIMS: Omalizumab is an anti-immunoglobulin E (IgE) monoclonal antibody that was first approved by the United States (US) Food and Drug Administration (FDA) for the treatment of allergic asthma in 2003. The pivotal trials supporting the initial approval of omalizumab used dosing determined by patient's baseline IgE and body weight, with the goal of reducing the mean free IgE level to approximately 25 ng/mL or less. While the underlying parameters supporting the dosing table remained the same, subsequent studies and analyses have resulted in approved alternative versions of the dosing table, including the European Union (EU) asthma dosing table, which differs in weight bands and maximum allowable baseline IgE and omalizumab dose. In this study, we leveraged modelling and simulation approaches to predict and compare the free IgE reduction and forced expiratory volume in 1 second (FEV1) improvement with omalizumab dosing based on the US and EU asthma dosing tables. METHODS: Previously established population pharmacokinetic-IgE and IgE-FEV1 models were used to predict and compare post-treatment free IgE and FEV1 based on the US and EU dosing tables. Clinical trial simulations (with virtual asthma populations) and Monte Carlo simulations were performed to provide both breadth and depth in the comparisons. RESULTS: The US and EU asthma dosing tables were predicted to result in generally comparable free IgE suppression and FEV1 improvement. CONCLUSIONS: Despite the similar free IgE and FEV1 outcomes from simulations, this has not been clinically validated with respect to the registrational endpoint of reduction in annualized asthma exacerbations.

Evaluation of adherence to abiraterone therapy in prostate cancer patients based on a population pharmacokinetic model.

AIMS: Abiraterone treatment requires regular drug intake under fasting conditions due to pronounced food effect, which may impact patient adherence. The aim of this prospective study was to evaluate adherence to abiraterone treatment in patients with prostate cancer. To achieve this aim, an abiraterone population pharmacokinetic model was developed and patients' adherence has been estimated by comparison of measured levels of abiraterone with population model-based simulations. METHODS: A total of 1469 abiraterone plasma levels from 83 healthy volunteers collected in a bioequivalence study were analysed using a nonlinear mixed-effects model. Monte Carlo simulation was used to describe the theoretical distribution of abiraterone pharmacokinetic profiles at a dose of 1000 mg once daily. Adherence of 36 prostate cancer patients treated with abiraterone was then evaluated by comparing the real abiraterone concentration measured in each patient during follow-up visit with the theoretical distribution of profiles based on simulations. Patients whose abiraterone levels were ˂5th or ˃95th percentile of the distribution of simulated profiles were considered to be non-adherent. RESULTS: Based on this evaluation, 13 patients (36%) have been classified as non-adherent. We observed significant association (P = .0361) between richness of the breakfast and rate of non-adherence. Adherent patients reported significantly better overall condition in self-assessments (P = .0384). A trend towards a higher occurrence of adverse effects in non-adherent patients was observed. CONCLUSIONS: We developed an abiraterone population pharmacokinetic model and proposed an advanced approach to medical adherence evaluation. Due to the need for administration under fasting conditions, abiraterone therapy is associated with a relatively high rate of non-adherence.

Accurate and fast small p-value estimation for permutation tests in high-throughput genomic data analysis with the cross-entropy method.

Permutation tests are widely used for statistical hypothesis testing when the sampling distribution of the test statistic under the null hypothesis is analytically intractable or unreliable due to finite sample sizes. One critical challenge in the application of permutation tests in genomic studies is that an enormous number of permutations are often needed to obtain reliable estimates of very small p-values, leading to intensive computational effort. To address this issue, we develop algorithms for the accurate and efficient estimation of small p-values in permutation tests for paired and independent two-group genomic data, and our approaches leverage a novel framework for parameterizing the permutation sample spaces of those two types of data respectively using the Bernoulli and conditional Bernoulli distributions, combined with the cross-entropy method. The performance of our proposed algorithms is demonstrated through the application to two simulated datasets and two real-world gene expression datasets generated by microarray and RNA-Seq technologies and comparisons to existing methods such as crude permutations and SAMC, and the results show that our approaches can achieve orders of magnitude of computational efficiency gains in estimating small p-values. Our approaches offer promising solutions for the improvement of computational efficiencies of existing permutation test procedures and the development of new testing methods using permutations in genomic data analysis.

Evaluating the efficacy of different antibiotics against Neisseria gonorrhoeae: a pharmacokinetic/pharmacodynamic analysis using Monte Carlo simulation.

BACKGROUND: With the widespread use of antibiotics, antimicrobial resistance in Neisseria gonorrhoeae is worsening. The objective of this study was to evaluate the efficacy changes of seven antibiotics in the treatment of N. gonorrhoeae by using Monte Carlo simulation combined with pharmacokinetics/pharmacodynamics/ (PK/PD). METHODS: The minimum inhibitory concentration (MIC) of antibiotics against clinical isolates from 2013 to 2020 in Nanjing, China, was determined by agar dilution method. The probability of target attainment (PTA) was estimated at each MIC value and the cumulative fraction of response (CFR) was calculated to evaluate the efficacy of these regimens. RESULTS: All dosage regimens of seven antibiotics achieved PTAs ≥ 90% for MIC ≤ 0.06 µg/ml. But when the MIC was increased to 1 µg/ml, PTAs at each MIC value exceeded 90% only for ceftriaxone 1,000 mg and 2,000 mg, zoliflodacin 2,000 mg and 3,000 mg. Among them, the CFR values of each dosing regimen against N. gonorrhoeae only for ceftriaxone, cefixime and zoliflodacin were ≥ 90% in Nanjing from 2013 to 2020. CONCLUSIONS: Cephalosporins are still the first-line drugs in the treatment of gonorrhea. However, the elevated MIC values of cephalosporins can lead to decline in clinical efficacy of the conventional dose regimens, and increasing the dose of ceftriaxone to 1,000 mg-2,000 mg may improve the efficacy. In addition, zoliflodacin is possible to be a potential therapeutic agent in the future.

Quantitative Microbial Risk Assessment of Antibiotic-Resistant E. coli, Legionella pneumophila, and Mycobacteria in Nonpotable Wastewater Reuse Applications.

Antibiotic-resistant bacteria (ARB) have become a major threat to public health and modern medicine. A simple death kinetics-based dose-response model (SD-DRM) was incorporated into a quantitative microbial risk assessment (QMRA) to assess the risks of exposure to reclaimed wastewater harboring antibiotic-resistant E. coli, Legionella pneumophila, and Mycobacterium avium for multiple exposure scenarios. The fractions of ARB and trace antibiotics present in the body were incorporated to demonstrate their impact on infection risks. Both ARB and antibiotic susceptible bacteria, ASB, are assumed to have the same dose-response in the absence of antibiotics but behave differently in the presence of residual antibiotics in the body. Annual risk of L. pneumophila infection exceeded the EPA 10-4 pppy (per person per year) benchmark at concentrations in reclaimed water greater than 103-104 CFU/L, depending on parameter variation. Enteropathogenic E. coli infection risks meet the EPA annual benchmark at concentrations around 105-106 total E. coli. The results illustrated that an increase in residual antibiotics from 0 to 40% of the minimum inhibitory concentration (MIC) reduced the risk by about 1 order of magnitude for E. coli but was more likely to result in an untreatable infection.

Indoor Emissions Contributed the Majority of Ultrafine Particles in Chinese Urban Residences.

Ultrafine particle (UFP) pollution should be controlled to reduce its effects on health. The design of control measures is limited owing to the uncertainty of source contributions in Chinese residences, where indoor UFP pollution is more severe than in Western residences. Herein, a source-specific, time-dependent UFP concentration model was developed by applying an infiltration factor model incorporating coagulation effects. A Monte Carlo framework with the UFP concentration model was employed to estimate the probabilistic distribution of source contributions in Chinese residences. The input parameter distributions were determined based on our survey and previous studies. The annually averaged indoor UFP concentration was estimated at (2.75 ± 1.71) × 104 #/cm3, ranging from 2.35 × 103 to 1.27 × 105 #/cm3 outside the kitchen, and at (5.48 ± 3.08) × 104 #/cm3, ranging from 2.90 × 103 to 1.94 × 105 #/cm3 in the kitchen. Indoor sources contributed more to indoor UFPs, accounting for 61% in the nonkitchen and 80% in the kitchen, surpassing their contribution to indoor PM2.5 in Chinese residences. Meanwhile, the indoor UFP emission contributions were higher than those in the United States, Canada, and Germany, owing to higher emissions from cooking and cigarette smoking. These results will aid in elucidating human exposure to UFPs and in designing more targeted control measures.

Thin-Film Composite Membrane Compaction: Exploring the Interplay among Support Compressive Modulus, Structural Characteristics, and Overall Transport Efficiency.

Water scarcity has driven the demand for water production from unconventional sources and the reuse of industrial wastewater. Pressure-driven membranes, notably thin-film composite (TFC) membranes, stand as energy-efficient alternatives to the water scarcity challenge and various wastewater treatments. While pressure drives solvent movement, it concurrently triggers membrane compaction and flux deterioration. This necessitates a profound comprehension of the intricate interplay among compressive modulus, structural properties, and transport efficacy amid the compaction process. In this study, we present an all-encompassing compaction model for TFC membranes, applying authentic structural and mechanical variables, achieved by coupling viscoelasticity with Monte Carlo flux calculations based on the resistance-in-series model. Through validation against experimental data for multiple commercial membranes, we evaluated the influence of diverse physical parameters. We find that support polymers with a higher compressive modulus (lower compliance), supports with higher densities of "finger-like" pores, and "sponge-like" pores with optimum void fractions will be preferred to mitigate compaction. More importantly, we uncover a trade-off correlation between steady-state permeability and the modulus for identical support polymers displaying varying porosities. This model holds the potential as a valuable guide in shaping the design and optimization for further TFC applications and extending its utility to biological scaffolds and hydrogels with thin-film coatings in tissue engineering.

Heavy metal contamination assessment and source attribution in the Vicinity of an iron slag pile in Hechi, China: Integrating multi-medium analysis.

Heavy metals, such as mercury, cadmium, and nickel, may contaminate human inhabited environments, with critical consequences for human health. This study examines the health impacts of heavy metal pollution from an iron slag pile in Hechi, China, by analyzing heavy metal contamination in water, sediment, soil, and crops. Here, the Nemerow pollution index (NI) indicated severe pollution at most sampling sites, the mean NI of groundwater, and surface water had reached 594.13 and 26.79, respectively. Bioaccumulation of mercury (Hg), cadmium (Cd), and nickel (Ni) was noted in crops, cucumbers showed comparatively lower risk levels. Logarithmic surface water-sediment partition coefficient calculations indicated that heavy metals such as chromium (Cr), ferrum (Fe), zinc (Zn), copper (Cu), Ni, arsenic (As), and lead (Pb) tend to accumulate in sediments. There was a high risk in groundwater (67.48-6590.54) and surface water (13.73-2500.85). Variably influenced by rainfall, these metals can be diluted and mobilized from surface water and sediments, thereby changing the contamination levels and ecological risks. Probabilistic health risk assessments indicated that health risks were higher in children than in adults, the mean total carcinogenic risk values of soil, groundwater, and surface water, were 6.79E-04, 4.20E-06, and 1.15E-6 for children, respectively. Moderate soil pollution is the main health hazard. A Positive Matrix Factorization model attributed over 60% of the pollution to slag stacking. Biotechnologies, solidification/stabilization techniques, field management, and institutional controls, driven by principles of green, low-carbon, and economic efficiency may mitigate. These findings contribute to the management of heavy metal pollution in iron slag pile areas.

Health risk assessment of soil heavy metals in a typical mining town in north China based on Monte Carlo simulation coupled with Positive matrix factorization model.

The accumulation of heavy metals (HMs) in soil caused by mineral resource exploitation and its ancillary industrial processes poses a threat to ecology and public health. Effective risk control measures require a quantification of the impacts and contributions to health risks from individual sources of soil HMs. Based on high-density sampling, soil contamination risk indexes, positive matrix factorization (PMF) model, Monte Carlo simulation and human health risk analysis model were applied to investigate the risk of HMs in a typical mining town in North China. The results showed that As was the most dominant soil pollutant factor, Cd and Hg were the most dominant soil ecological risk factors, and Cr and Ni were the most dominant health risk factors in the study area. Overall, both pollution and ecological risks were at low levels, while there were still some higher hazard areas located in the central and south-central part of the region. According to the probabilistic health risk assessment (HRA), children suffered greater health risks than adults, with 21.63% of non-carcinogenic risks and 53.24% of carcinogenic risks exceeding the prescribed thresholds (HI > 1 and TCR>1E-4). The PMF model identified five potential sources: fuel combustion (FC), processing of building materials with limestone as raw materials (PBML), industry source (IS), iron ore mining combined with garbage (IOG), and agriculture source (AS). PBML is the primary source of soil HM contamination, as well as the major anthropogenic source of carcinogenic risk for all populations. Agricultural inputs associated with As are the major source of non-carcinogenic risk. This study offers a good example of probabilistic HRA using specific sources, which can provide a valuable reference for strategy establishment of pollution remediation and risk prevention and control.