Priority sources identification and risks assessment of heavy metal(loid)s in agricultural soils of a typical antimony mining watershed.

Heavy metal(loid) (HM) pollution in agricultural soils has become an environmental concern in antimony (Sb) mining areas. However, priority pollution sources identification and deep understanding of environmental risks of HMs face great challenges due to multiple and complex pollution sources coexist. Herein, an integrated approach was conducted to distinguish pollution sources and assess human health risk (HHR) and ecological risk (ER) in a typical Sb mining watershed in Southern China. This approach combines absolute principal component score-multiple linear regression (APCS-MLR) and positive matrix factorization (PMF) models with ER and HHR assessments. Four pollution sources were distinguished for both models, and APCS-MLR model was more accurate and plausible. Predominant HM concentration source was natural source (39.1%), followed by industrial and agricultural activities (23.0%), unknown sources (21.5%) and Sb mining and smelting activities (16.4%). Although natural source contributed the most to HM concentrations, it did not pose a significant ER. Industrial and agricultural activities predominantly contributed to ER, and attention should be paid to Cd and Sb. Sb mining and smelting activities were primary anthropogenic sources of HHR, particularly Sb and As contaminations. Considering ER and HHR assessments, Sb mining and smelting, and industrial and agricultural activities are critical sources, causing serious ecological and health threats. This study showed the advantages of multiple receptor model application in obtaining reliable source identification and providing better source-oriented risk assessments. HM pollution management, such as regulating mining and smelting and implementing soil remediation in polluted agricultural soils, is strongly recommended for protecting ecosystems and humans.

Human health risks associated to trace elements and metals in commercial fish from the Brazilian Amazon.

Fish constitutes the main protein source for the Amazonian population. However, the impact of different anthropogenic activities on trace element and metal accumulation in fish and their risks for human health at a regional scale remain largely unexplored. Here we assessed exposure levels of 10 trace elements and metals (Cr, Mn, Fe, Ni, Cu, Zn, As, Cd, Pb, and Hg) in 56 samples belonging to 11 different species of fish from the Brazilian Amazon. We studied the relationship between exposure levels, fish origin, and fish feeding habits, and assessed toxicological and carcinogenic risks for the Amazonian population. No significant correlation was found between sampling site and exposure levels to the studied elements, but a significant difference was found between the accumulation of some metals and the position of the fish species in the food chain. The concentrations of Cr and Hg in fish flesh were found to exceed the Brazilian limits for human consumption. This study shows that current fish consumption patterns can lead to estimated daily intakes of Hg, As and Cr that exceed the oral reference dose, thus posing a toxicological concern. Furthermore, carcinogenic risks may be expected due to the continued exposure to Cr and As. The results of this study show that the consumption of wild caught fish in the Amazon region should be controlled. Moreover, continued monitoring of trace element and metal contamination in fish and on the health of the Amazonian population is recommended, particularly for riverine and indigenous communities.

The genome sequence of the big-headed mining bee, Andrena bucephala (Stephens, 1846).

We present a genome assembly from an individual female Andrena bucephala (the Big-headed Mining Bee; Arthropoda; Insecta; Hymenoptera; Andrenidae). The genome sequence is 379.8 megabases in span. Most of the assembly is scaffolded into 5 chromosomal pseudomolecules. The mitochondrial genome has also been assembled and is 19.57 kilobases in length. Gene annotation of this assembly on Ensembl identified 12,022 protein coding genes.

Human reliability analysis in maintenance and repair operations of mining trucks: A Bayesian network approach.

Failures in mining machinery can abruptly halt mineral production and operations, emphasizing the indispensable role of humans in maintenance and repair operations. Addressing human errors is crucial for ensuring a safe and reliable system, particularly during maintenance activities where accidents frequently occur. This paper focuses on evaluating Human Reliability (HR) to enhance activity implementation effectiveness. Given the challenge of limited and uncertain data on human errors, this study aims to estimate the probability of human errors using Bayesian networks (BN) under uncertain parameters. Applying this approach to assess HR in the maintenance and repair operations of mining trucks at Golgohar Iron Ore Mine in Iran, the study identifies critical factors influencing error occurrence in a fuzzy environment. The results highlight key factors impacting human error and offer insights into estimating HR with minimal human intervention.

Mining and multifaceted applications of phage lysin for combatting Vibrio parahaemolyticus.

Vibrio parahaemolyticus, a prevalent foodborne pathogen found in both water and seafood, poses substantial risks to public health. The conventional countermeasure, antibiotics, has exacerbated the issue of antibiotic resistance, increasing the difficulty of controlling this bacterium. Phage lysins, as naturally occurring active proteins, offer a safe and reliable strategy to mitigate the impact of V. parahaemolyticus on public health. However, there is currently a research gap concerning bacteriophage lysins specific to Vibrio species. To address this, our study innovatively and systematically evaluates 37 phage lysins sourced from the NCBI database, revealing a diverse array of conserved domains and notable variations in similarity among Vibrio phage lysins. Three lysins, including Lyz_V_pgrp, Lyz_V_prgp60, and Lyz_V_zlis, were successfully expressed and purified. Optimal enzymatic activity was observed at 45℃, 800 mM NaCl, and pH 8-10, with significant enhancements noted in the presence of 1 mM membrane permeabilizers such as EDTA or organic acids. These lysins demonstrated effective inhibition against 63 V. parahaemolyticus isolates from clinical, food, and environmental sources, including the reversal of partial resistance, synergistic interactions with antibiotics, and disruption of biofilms. Flow cytometry analyses revealed that the combination of Lyz_V_pgp60 and gentamicin markedly increased bacterial killing rates. Notably, Lyz_V_pgrp, Lyz_V_pgp60, and Lyz_V_zlis exhibited highly efficient biofilm hydrolysis, clearing over 90 % of preformed V. parahaemolyticus biofilms within 48 h. Moreover, these lysins significantly reduced bacterial loads in various food samples and environmental sources, with reductions averaging between 1.06 and 1.29 Log CFU/cm2 on surfaces such as stainless-steel and bamboo cutting boards and approximately 0.87 CFU/mL in lake water and sediment samples. These findings underscore the exceptional efficacy and versatile application potential of phage lysins, offering a promising avenue for controlling V. parahaemolyticus contamination in both food and environmental contexts.

Cancer Prevention and Treatment on Chinese Social Media: Machine Learning-Based Content Analysis Study.

BACKGROUND: Nowadays, social media plays a crucial role in disseminating information about cancer prevention and treatment. A growing body of research has focused on assessing access and communication effects of cancer information on social media. However, there remains a limited understanding of the comprehensive presentation of cancer prevention and treatment methods across social media platforms. Furthermore, research comparing the differences between medical social media (MSM) and common social media (CSM) is also lacking. OBJECTIVE: Using big data analytics, this study aims to comprehensively map the characteristics of cancer treatment and prevention information on MSM and CSM. This approach promises to enhance cancer coverage and assist patients in making informed treatment decisions. METHODS: We collected all posts (N=60,843) from 4 medical WeChat official accounts (accounts with professional medical backgrounds, classified as MSM in this paper) and 5 health and lifestyle WeChat official accounts (accounts with nonprofessional medical backgrounds, classified as CSM in this paper). We applied latent Dirichlet allocation topic modeling to extract cancer-related posts (N=8427) and identified 6 cancer themes separately in CSM and MSM. After manually labeling posts according to our codebook, we used a neural-based method for automated labeling. Specifically, we framed our task as a multilabel task and utilized different pretrained models, such as Bidirectional Encoder Representations from Transformers (BERT) and Global Vectors for Word Representation (GloVe), to learn document-level semantic representations for labeling. RESULTS: We analyzed a total of 4479 articles from MSM and 3948 articles from CSM related to cancer. Among these, 35.52% (2993/8427) contained prevention information and 44.43% (3744/8427) contained treatment information. Themes in CSM were predominantly related to lifestyle, whereas MSM focused more on medical aspects. The most frequently mentioned prevention measures were early screening and testing, healthy diet, and physical exercise. MSM mentioned vaccinations for cancer prevention more frequently compared with CSM. Both types of media provided limited coverage of radiation prevention (including sun protection) and breastfeeding. The most mentioned treatment measures were surgery, chemotherapy, and radiotherapy. Compared with MSM (1137/8427, 13.49%), CSM (2993/8427, 35.52%) focused more on prevention. CONCLUSIONS: The information about cancer prevention and treatment on social media revealed a lack of balance. The focus was primarily limited to a few aspects, indicating a need for broader coverage of prevention measures and treatments in social media. Additionally, the study's findings underscored the potential of applying machine learning to content analysis as a promising research approach for mapping key dimensions of cancer information on social media. These findings hold methodological and practical significance for future studies and health promotion.

Implementation of Circular Economy in a Developing Economy's Mining Industry Using Institutional Theory: The Case of Namibia.

Despite the global focus on sustainability, transitioning from linear to circular production systems is slow in the mining sector of most developing economies like Namibia. However, mining plays a crucial role in supporting the livelihoods of local communities. Furthermore, existing literature indicates that the potential for regenerative production systems using the remanufacture and recycle approach remains low and limited within the mining and developing economies. Institutional theory can help reveal the reasons for the slow take-up of the regenerative circular economy models in mining. This study uses a unique dataset of 40 semi-structured interviews with key players in the mining sector of Namibia to understand the current phase of circular economy adoption and the role played by institutional pressures in the process of institutional isomorphism, when companies would display a similar level of practices within a shared institutional environment. The findings reveal: (1) 72.5% of participants believe that Namibian mines are adoption-decision phase-a beginning stage of circular economy adoption; (2) companies are reliant on heavy government participation through policy/legislation and tax incentives is recommended; (3) the ranked order-coercive, normative, and mimetic pressures-describes their significance among key actors, for the successful adoption; and (4) proactive implementation and a mindset shift towards circularity is needed to meet emerging expectations on social and environmental concerns in mining.

A review of radiation doses and associated parameters in Western Australian mining operations (2020-23).

The Western Australian mining industry is a global supplier of critical minerals, including lithium and rare earths. The lithology of these minerals is associated with elevated concentrations of naturally occurring radionuclides (NORs). An increase in the number of mines producing the minerals has witnessed a commensurate increase in the number of workers potentially exposed to the radiation from NORs. The regulatory framework in Western Australia underwent significant change in March 2022. Mining operations whose workers are likely to receive doses greater than one mSvy-1 are referenced as relevant mines and are required to submit an annual report of worker doses to the mining regulator. This research provides an overview of the new legislative framework and updates the information in Ralph and Cattani (2022) to include data derived from annual radiation doses reported by relevant mines in the period spanning 2020-21 to 2022-23. In 2022-23, 38 mining operations were identified as relevant mines, an increase of sixteen from 2020-21. The mean effective dose (ED) reported in the three-year period was 1.0 mSv, and the maximum ED was 4.9 mSv. The collective effective dose of the mine worker population reached an historical maximum of 2,339 man.mSv in 2022-23. Inhalation of long-lived alpha emitting radionuclides in dust remains the most significant contributor to worker doses. Inhalation of radon-22, radon-220 and their short-lived progeny, once considered as a negligible contributor to worker doses, is the second most significant exposure pathway. A declining trend in the number of samples collected per worker is highlighted as requiring remediation to provide confidence in the reported dose estimates. The transition to the new legislative framework for radiation protection in mines has been supported by the publication of guidance materials which have been widely endorsed by the industry. .

A knowledge graph algorithm enabled deep recommendation system.

Personalized learning resource recommendations may help resolve the difficulties of online education that include learning mazes and information overload. However, existing personalized learning resource recommendation algorithms have shortcomings such as low accuracy and low efficiency. This study proposes a deep recommendation system algorithm based on a knowledge graph (D-KGR) that includes four data processing units. These units are the recommendation unit (RS unit), the knowledge graph feature representation unit (KGE unit), the cross compression unit (CC unit), and the feature extraction unit (FE unit). This model integrates technologies including the knowledge graph, deep learning, neural network, and data mining. It introduces cross compression in the feature learning process of the knowledge graph and predicts user attributes. Multimodal technology is used to optimize the process of project attribute processing; text type attributes, multivalued type attributes, and other type attributes are processed separately to reconstruct the knowledge graph. A convolutional neural network algorithm is introduced in the reconstruction process to optimize the data feature qualities. Experimental analysis was conducted from two aspects of algorithm efficiency and accuracy, and the particle swarm optimization, neural network, and knowledge graph algorithms were compared. Several tests showed that the deep recommendation system algorithm had obvious advantages when the number of learning resources and users exceeded 1,000. It has the ability to integrate systems such as the particle swarm optimization iterative classification, neural network intelligent simulation, and low resource consumption. It can quickly process massive amounts of information data, reduce algorithm complexity and requires less time and had lower costs. Our algorithm also has better efficiency and accuracy.

Various microbes used for the recovery of rare earth elements from mine wastewater.

Microbes used for the recovery of rare earth elements (REEs) from mining wastewater indicated traces of Escherichia coli (E. coli, 2149.6 µg/g), Bacillus sphaericus (1636.6 µg/g), Bacillus mycoides (1469.3 µg/g), and Bacillus cereus (1083.9 µg/g). Of these, E. coli showed an affinity for REEs than non-REEs (Mn and Zn). The amount of heavy REEs adsorbed (1511.1 µg/g) on E. coli was higher than light REEs (638.0 µg/g) due to the process of increasing adsorption with decreasing ionic radius. Additionally, E. coli demonstrated stability in the recovery of REEs from mining wastewater, as evidenced by 4 cycles. SEM-EDS, XPS and FTIR showed that REEs had a disruptive effect on cells, REEs absorbed and desorbed on the cell surface including ion exchange with ions such as Na+, ligand binding with functional groups like -NH2. Finally, the cost assessment confirmed the economically feasible of E. coli in recovery of REEs from mining wastewater.

Predicting metallophore structure and function through genome mining.

Metallophores are small molecule chelators that many microbes use to obtain trace metals from their environment. Through genome mining, where genomes are scanned for metallophore biosynthesis genes, one can not only identify which organisms are likely to produce a metallophore, but also predict the metallophore structure, thus preventing undesired reisolation of known compounds and accelerating characterization. Furthermore, the presence of accessory genes for the transport, utilization, and regulation can suggest the biological function and fate of a metallophore. Modern, user-friendly tools have made powerful genomic analyses accessible to scientists with no bioinformatics experience, but these tools are often not utilized to their full potential. This chapter provides an introduction to metallophore genomics and demonstrates how to use the free, publicly available antiSMASH platform to infer metallophore function and structure.

Metagenome sequence data mining for viral interaction studies: review on progress and prospects.

Metagenomics has been greatly accelerated by the development of next-generation sequencing (NGS) technologies, which allow scientists to discover and describe novel microorganisms without the need for conventional culture techniques. Examining integrative bioinformatics methods used in viral interaction research, this study highlights metagenomic data from various contexts. Accurate viral identification depends on high-purity genetic material extraction, appropriate NGS platform selection, and sophisticated bioinformatics tools like VirPipe and VirFinder. The efficiency and precision of metagenomic analysis are further improved with the advent of AI-based techniques. The diversity and dynamics of viral communities are demonstrated by case studies from a variety of environments, emphasizing the seasonal and geographical variations that influence viral populations. In addition to speeding up the discovery of new viruses, metagenomics offers thorough understanding of virus-host interactions and their ecological effects. This review provides a promising framework for comprehending the complexity of viral communities and their interactions with hosts, highlighting the transformational potential of metagenomics and bioinformatics in viral research.

Do OPEC+ policies help predict the oil price: A novel news-based predictor.

The OPEC+, composed of the Organization of the Petroleum Exporting Countries (OPEC) and non-OPEC oil-producing countries, exerts considerable influence over the global crude oil market. However, existing literature lacks a comprehensive application of this factor in oil price forecasting, primarily due to the complexity of measuring such policy evolutions. To address this research gap, this study develops a news-based OPEC+ policy index based on text mining methods for comprehensive analysis and forecasting of the oil price. First, by crawling and mining news headlines related to OPEC+ production decisions, a dynamic and high-frequency (weekly) OPEC+ policy index is established. Second, the linear and nonlinear relationship between the proposed OPEC+ policy index and the WTI crude oil futures price is thoroughly examined, assessing the potential predictive power of the index in explaining the movements of the crude oil price. Third, the forecasting efficacy of the constructed index on the oil price is rigorously evaluated across eight econometric and machine learning models. Key findings include: (1) The proposed weekly OPEC+ policy index demonstrates strong concordance with OPEC+ production change decisions, exhibiting notable peaks and troughs corresponding to OPEC+ Ministerial Meetings. (2) The relationship analysis demonstrates a strong linear and nonlinear association between the proposed OPEC+ policy index and the crude oil price. (3) For oil price prediction, models incorporating our proposed OPEC+ policy index demonstrate superior performance compared to models without this index. In particular, the index exhibits a more significant predictive effect within three-week forecasting horizons and performs exceptionally well during periods of pandemic and the Russia-Ukraine conflict. In addition, the OPEC+ policy index also exhibits a significant predictive effect on the daily crude oil price and natural gas price, further confirming the robust and powerful forecasting capability of this index within the energy system.

Characterization and genome mining of Bacillus subtilis BDSA1 isolated from river water in Bangladesh: A promising bacterium with diverse biotechnological applications.

The metabolic versatility of Bacillus subtilis makes it useful for a wide range of applications in biotechnology, from bioremediation to industrially important metabolite production. Understanding the molecular attributes of the biocontrol characteristics of B. subtilis is necessary for its tailored use in the environment and industry. Therefore, the present study aimed to conduct phenotypic characterization and whole genome analysis of the B. subtilis BDSA1 isolated from polluted river water from Dhaka, Bangladesh to explore its biotechnological potential. The chromium reduction capacity at 100 ppm Cr (VI) showed that B. subtilis BDSA1 reduced 40 % of Cr (VI) within 24hrs at 37 °C. Exposure of this bacterium to 200 ppm cadmium resulted in 43 % adsorption following one week of incubation at 37 °C. Molecular detection of chrA and czcC gene confirmed chromium and cadmium resistance characteristics of BDSA1. The size of the genome of the B. subtilis BDSA1 was 4.2 Mb with 43.4 % GC content. Genome annotation detected the presence of numerous genes involved in the degradation of xenobiotics, resistance to abiotic stress, production of lytic enzymes, siderophore formation, and plant growth promotion. The assembled genome also carried chromium, cadmium, copper, and arsenic resistance-related genes, notably cadA, czcD, czrA, arsB etc. Genome mining revealed six biosynthetic gene clusters for bacillaene, bacillibacin, bacilysin, subtilosin, fengycin and surfactin. Importantly, BDSA1 was predicted to be non-pathogenic to humans and had only two acquired antimicrobial resistance genes. The pan-genome analysis showed the openness of the B. subtilis pan-genome. Our findings suggested that B. subtilis BDSA1 might be a promising candidate for diverse biotechnological uses.

Exploring the role of candidalysin in the pathogenicity of Candida albicans by gene set enrichment analysis and evolutionary dynamics.

AIMS: To explore the pathogenic mechanisms of Candida albicans (C. albicans), focusing on its impact on human health, particularly through invasive infections in the gastrointestinal and respiratory tracts. METHODS: In this study, we evaluated the demographic and clinical profiles of 7 pneumonia patients. Meanwhile, we used Gene Set Enrichment Analysis (GSEA) and Evolutionary Dynamics method to analyze the role of candidalysin in C. albicans pathogenicity. RESULTS: By analyzing genomic data and conducting biomedical text mining, we identified novel mutation sites in the candidalysin coding gene ECE1-III, shedding light into the genetic diversity within C. albicans strains and their potential implications for antifungal resistance. Our results revealed significant associations between C. albicans and respiratory as well as gastrointestinal diseases, emphasizing the fungus's role in the pathogenesis of these diseases. Additionally, we identified a new mutation site in the C. albicans strain YF2-5, isolated from patients with pneumonia. This mutation may be associated with its heightened pathogenicity. CONCLUSION: Our research advances the understanding of C. albicans pathogenicity and opens new avenues for developing targeted antifungal therapies. By focusing on the molecular basis of fungal virulence, we aim to contribute to the development of more effective treatment strategies, addressing the challenge of multidrug resistance in invasive fungal infections.

Industry 4.0 Technologies in Maternal Health Care: Bibliometric Analysis and Research Agenda.

BACKGROUND: Industry 4.0 (I4.0) technologies have improved operations in health care facilities by optimizing processes, leading to efficient systems and tools to assist health care personnel and patients. OBJECTIVE: This study investigates the current implementation and impact of I4.0 technologies within maternal health care, explicitly focusing on transforming care processes, treatment methods, and automated pregnancy monitoring. Additionally, it conducts a thematic landscape mapping, offering a nuanced understanding of this emerging field. Building on this analysis, a future research agenda is proposed, highlighting critical areas for future investigations. METHODS: A bibliometric analysis of publications retrieved from the Scopus database was conducted to examine how the research into I4.0 technologies in maternal health care evolved from 1985 to 2022. A search strategy was used to screen the eligible publications using the abstract and full-text reading. The most productive and influential journals; authors', institutions', and countries' influence on maternal health care; and current trends and thematic evolution were computed using the Bibliometrix R package (R Core Team). RESULTS: A total of 1003 unique papers in English were retrieved using the search string, and 136 papers were retained after the inclusion and exclusion criteria were implemented, covering 37 years from 1985 to 2022. The annual growth rate of publications was 9.53%, with 88.9% (n=121) of the publications observed in 2016-2022. In the thematic analysis, 4 clusters were identified-artificial neural networks, data mining, machine learning, and the Internet of Things. Artificial intelligence, deep learning, risk prediction, digital health, telemedicine, wearable devices, mobile health care, and cloud computing remained the dominant research themes in 2016-2022. CONCLUSIONS: This bibliometric analysis reviews the state of the art in the evolution and structure of I4.0 technologies in maternal health care and how they may be used to optimize the operational processes. A conceptual framework with 4 performance factors-risk prediction, hospital care, health record management, and self-care-is suggested for process improvement. a research agenda is also proposed for governance, adoption, infrastructure, privacy, and security.

Utilizing online reviews for analyzing digital healthcare consultation services: Examining perspectives of both healthcare customers and healthcare professionals.

INTRODUCTION: Digital healthcare consultation services, also known as telemedicine, have seen a surge in their usage, especially after the COVID-19 pandemic. The purpose of this study is to investigate the satisfaction determinants of healthcare customers (patients) and healthcare professionals (doctors), providing digital healthcare consultation services. METHODS: The analysis involved scraping online reviews of 11 telemedicine apps meant for patients and 7 telemedicine apps meant for doctors, yielding a total of 44,440 patient reviews and 4748 doctor reviews. A structural topic modeling analysis followed by regression, dominance, correspondence, and emotion analysis was conducted to derive insights. RESULTS: The study identified ten determinants of satisfaction from patients' and eight from doctors' perspectives. For patients, 'service variety and quality' (ß = 0.5527) was the top positive determinant, while 'payment disputes' (ß = -0.1173) and 'in-app membership' (ß = -0.031) negatively impacted satisfaction. For doctors, 'patient consultation management' (ß = 0.2009) was the leading positive determinant, with 'profile management' (ß = -0.1843), 'subscription' (ß = -0.183), and 'customer care support' (ß = -0.0908) being the negative ones. The most influential negative emotion for patients, anger, was closely associated with 'customer care service' and 'in-app memberships,' while joy was tied to 'service variety and quality' and 'offers and discounts.' For doctors, anger was associated with 'cost-effectiveness,' and joy with 'app responsiveness.' CONCLUSION: This study offers new insights by examining patient and doctor determinants at a granular level which can be used by telemedicine app developers and managers to build customer-centric services.

Effect driven prioritization of contaminants in wastewater treatment plants across China: A data mining-based toxicity screening approach.

A diversity of contaminants of emerging concern (CECs) are present in wastewater effluent, posing potential threats to receiving waters. It is urgent for a holistic assessment of the occurrence and risk of CECs related to wastewater treatment plants (WWTP) on national and regional scales. A data mining-based risk prioritization method was developed to collect the reported contaminants and their respective concentrations in municipal and industrial WWTPs and their receiving waters across China over the past 20 years. A total of 10,781 chemicals were reported in 8336 publications, of which 1037 contaminants were reported with environmental concentrations. While contaminant categories varied across WWTP types (municipal vs. industrial) and regions, pharmaceuticals and cyclic hydrocarbons were the most studied CECs. Contaminant composition in receiving water was closer to that in municipal than industrial WWTPs. Publications on legacy pesticides and polycyclic aromatic hydrocarbons in WWTP decreased recently compared to the past, while pharmaceuticals and perfluorochemicals have received increasing attention, showing a changing concern over time. Detection frequency, concentration, removal efficiency, and toxicity data were integrated for assessing potential risks and prioritizing CECs on national and regional scales using an environmental health prioritization index (EHPi) approach. Among 666 contaminants in municipal WWTP effluent, trichlorfon and perfluorooctanesulfonic acid were with the highest EHPi scores, while 17ɑ-ethinylestradiol and bisphenol A had the highest EHPi scores among 304 contaminants in industrial WWTPs. The prioritized contaminants varied across regions, suggesting a need for tailoring regional measures of wastewater treatment and control.