Identification Of Hotspots Research Articles

Putative past, present, and future spatial distributions of deep-sea coral and sponge microbiomes revealed by predictive models

Abstract Knowledge of spatial distribution patterns of biodiversity is key to evaluate and ensure ocean integrity and resilience. Especially for the deep ocean, where in situ monitoring requires sophisticated instruments and considerable financial investments, modelling approaches are crucial to move from scattered data points to predictive continuous maps. Those modelling approaches are commonly run on the macrobial level, but spatio-temporal predictions of host-associated microbiomes are not being targeted. This is especially problematic as previous research has highlighted that host-associated microbes may display distribution patterns that are not perfectly correlated with host biogeographies, but also with other factors such as prevailing environmental conditions. We here establish a new simulation approach and present predicted spatio-temporal distribution patterns of deep-sea sponge and coral microbiomes, making use of a combination of environmental data, host data, and microbiome data. This approach allows predictions of microbiome spatio-temporal distribution patterns on scales that are currently not covered by classical sampling approaches at sea. In summary, our presented predictions allow (i) identification of microbial biodiversity hotspots in the past, present, and future, (ii) trait-based predictions to link microbial with macrobial biodiversity, and (iii) identification of shifts in microbial community composition (key taxa) across environmental gradients and shifting environmental conditions.

Wildlife mortality on the Slovenian highways: monthly patterns, identification of hotspots and effectiveness of acoustic deterrents

Wildlife mortality on the Slovenian highways: monthly patterns, identification of hotspots and effectiveness of acoustic deterrents

A Localized Evaluation of Surface Water Quality Using GIS-Based Water Quality Index along Satpara Watershed Skardu Baltistan, Pakistan

Surface water quality in Skardu, Gilgit-Baltistan, Pakistan, is of immense importance because of the city’s dependence on these resources for domestic uses, agriculture, and drinking water. The water quality index (WQI) was integrated with the Geographic Information System (GIS) to spatially envision and examine water quality data to facilitate the identification of pollution hotspots, trend analysis, and knowledge-based decision-making for effective water resource management. This study aims to evaluate the physiochemical and bacteriological parameters of the Satpara watershed and to provide the spatial distribution of these parameters. This study endeavors to achieve Sustainable Development Goal 6 (SDG 6) by identifying localities with excellent and unfit water for drinking, sanitation, and hygiene. A total of fifty-one surface water samples were collected from various parts of the Satpara watershed during the fall season of 2023. Well-established laboratory techniques were used to investigate water for parameters like Electrical Conductivity (EC), pH, turbidity, total dissolved solids (TDSs), major cations (K+, Na+, Mg2+, Ca2+), major anions (Cl−, SO42−, NO3−, HCO3−), and bacteriological contaminants (E. coli). Spatial distribution maps of all these parameters were created using the Inverse Distance Weighted (IDW) technique in a GIS environment. A significant variation in the quality of water was observed along the study area. The level of Escherichia coli (E. coli) contamination is above the permissible limit at various locations along the watershed, making water unsafe for direct human consumption in these areas. Some regions showed low TDS values, which could adversely affect human health and agricultural yield. From the WQI valuation, 58.82% of the collected samples were “Poor”, 31.8% were “Very poor” and 9.8% were found to be “Unfit for drinking”. The research findings emphasize the pressing need for consistent monitoring and adoption of water management strategies in Skardu City to warrant sustainable soil and water use. The spatial maps generated for various parameters and the water quality index WQI offer critical insights for targeted intercessions.

Investigating illicit drug hotspots and daily variations using sewer-network wastewater analysis

Previous wastewater-based epidemiology (WBE) research on illicit drug use has predominantly focused on wastewater treatment plant (WWTP) influents, but information on sewer-network wastewater is very limited. This study represents a pioneering small-scale WBE investigation based on the analysis of sewer-network wastewater samples from different sewer manholes in suburban (Tamsui region) and urban areas (Zhongshan and Wanhua regions) and a comparison of the results with those obtained from corresponding WWTP influents. Among sixteen illicit drugs, methamphetamine exhibited the highest concentration in sewer-network wastewater across both areas. Suburban–urban variations were observed, with more types of illicit drugs detected in the suburban area. Back-calculation indicated that methamphetamine and ketamine were the most-consumed illicit drugs in both sewer-network wastewaters and WWTP influents. Similar types of illicit drugs were detected in the sewer-network wastewaters and WWTP influents, indicating the representativeness of WWTP influents in assessing regional illicit drug abuse. Nevertheless, the sewer-network wastewater results offered additional information making it possible to pinpoint potential hotspots of illicit drug and identify peak usage periods throughout the day, in contrast to the WWTP influent results. In the non-suspected suburban area of Tamsui, high potential hotspots of methamphetamine (sampling points 3 and 6) and ketamine (sampling points 1 and 8) were identified. Although the Zhongshan and Wanhua regions were chosen as suspected hotspots of illicit drug abuse, more severe illicit drug use was observed in Wanhua. Moreover, a trend toward higher illicit drug use from early morning to morning was observed. Despite sampling challenges and higher costs, small-scale WBE via sewer-network wastewater analysis provides superior identification of drug abuse hotspots and peak usage periods. Therefore, this study provides valuable insights for law enforcement and can help prevent and combat illicit drug abuse by targeting potential hotspots and understanding daily illicit drug use dynamics.

Evaluating the Effectiveness of Predictive Policing in Nigeria: A Qualitative study of law Enforcement Practices in Lagos State

This qualitative study evaluates the effectiveness of predictive policing technologies by examining the implementation within law enforcement operations in Lagos State. The research aims to explore how predictive systems influence crime prevention, and decision-making processes in policing. Drawing on semi-structured interviews with NDLEA officers, the study reveals the perceived benefits and challenges of integrating predictive policing tools into traditional methods. Through thematic analysis of interviews and review of secondary sources, the research identifies key themes such as the tension between technology and human judgement, and the potential for over-reliance on system predictions. The findings indicate that while predictive policing can improve efficiency in crime hotspot identification, its efficacy is undermined by concerns over privacy, and fairness. The study concludes that for predictive policing to be effective, it must come with strong regulatory frameworks and ethical guidelines to ensure support from technologies. This research contributes to the growing discourse on the intersection of technology and law enforcement, offering insights into how predictive policing can evolve to better serve the people.

MaxEnt modeling of Klebsiella pneumoniae: predicting future distribution and evaluating the risk for public health

Klebsiella pneumoniae is a Gram-negative bacterium known for causing infections such as pneumonia, urinary tract infections, and bloodstream infections in humans. Its increasing drug resistance poses a significant threat. Understanding the effects of climate change on this bacterium is crucial to managing its impacts effectively. In this study, we employed MaxEnt modeling to explore the current and future distribution of K. pneumoniae under different climate change scenarios for 2050 and 2070. Our models utilized 280 valid occurrence points and 19 bioclimatic variables, with Mean Annual Temperature and Mean Annual Precipitation being the most influential variables. The models demonstrated high accuracy, with an AUC of 0.889. Our findings revealed that K. pneumoniae has a cosmopolitan distribution, with the highest suitability observed in Southeastern Asia, Central Africa, and Southern Europe. Future predictions indicated an apparent expansion of its range across almost all continents, particularly in the RCP 8.5 2070 scenario. Results have crucial implications for epidemiology, as they enable the identification of current and potential future disease hotspots and geographical patterns. This knowledge can inform the development of effective infection control strategies. Furthermore, it provides valuable insights for decision-makers and policymakers in allocating resources efficiently for medical control and prevention strategies.

Seafloor sediment characterization improves estimates of organic carbon standing stocks: an example from the Eastern Shore Islands, Nova Scotia, Canada

Abstract. Continental shelf sediments contain some of the largest stocks of organic carbon (OC) on Earth and play a vital role in influencing the global carbon cycle. Quantifying how much OC is stored in shelf sediments and determining its residence time is key to assessing how the ocean carbon cycle will be altered by climate change and possibly human activities. Spatial variations in terrestrial carbon stocks are well studied and mapped at high resolutions, but our knowledge of the distribution of marine OC in different seafloor settings is still very limited, particularly in dynamic and spatially variable shelf environments. This lack of knowledge reduces our ability to understand and predict how much and for how long the ocean sequesters CO2. In this study, we use high-resolution multibeam echosounder (MBES) data from the Eastern Shore Islands offshore Nova Scotia (Canada), combined with OC measurements from discrete samples, to assess the distribution of OC content in seafloor sediments. We derive four different spatial estimates of organic carbon stock: (i) OC density estimates scaled to the entire study region assuming a homogenous seafloor, (ii) interpolation of OC density estimates using empirical Bayesian kriging, (iii) OC density estimates scaled to areas of soft substrate estimated using a high-resolution classified substrate map, and (iv) empirical Bayesian regression kriging of OC density within areas of estimated soft sediment only. These four distinct spatial models yielded dramatically different estimates of standing stock of OC in our study area of 223 km2: 80 901, 58 406, 16 437 and 6475 t of OC, respectively. Our study demonstrates that high-resolution mapping is critically important for improved estimates of OC stocks on continental shelves and for the identification of carbon hotspots that need to be considered in seabed management and climate mitigation strategies.

Hydrochemical Assessment of Groundwater in Ludhiana and Amritsar Districts of Punjab and Identification of Fluoride Hotspots using GIS

High fluoride concentrations in soil, water, or air can pose serious environmental and health risks to plants, and animals. Along with other hydrochemical parameters, this study investigates fluoride concentrations in the groundwater in the Ludhiana and Amritsar districts of Punjab, India. A total of 222 water samples were uniformly collected at approximately five-kilometer intervals for hydrochemical analyses. Statistical methods such as inverse distance weighting (IDW) and correlation matrices were used to assess the fluoride distribution and its relationships with other parameters. According to WHO guidelines, most fluoride concentrations were below 0.6 ppm in Ludhiana (84.30%) and Amritsar (77.23%). Fluoride levels that were within the permissible range (0.6–1.5 ppm) were found in 15.70% of Ludhiana’s samples and 21.78% of Amritsar’s samples; only 1% of Amritsar’s samples exceeded the permissible limit (>1.5 ppm). The water quality index (WQI) analysis indicated that0.83% of the groundwater samples from the Ludhiana district and 4.95% from the Amritsar district were unfit for consumption. This study demonstrates the importance of standardized sample collection and the use of GIS technology for comprehensive hydrochemical assessments, raising awareness and reducing health risks.

Experimental testing of additively manufactured embedded fiber optic smart devices for clean energy applications

Abstract An additively manufactured prototype smart device was created to investigate in-flow temperature distributions using embedded high-definition fiber optic sensors within a component for clean energy systems. The devices were created using Ultrasonic Additive Manufacturing to create a unique embedded pathway within a flow conditioner for the high-definition fiber optic sensors to be placed within. The fibers used allowed for temperature measurements to be taken every 0.65 mm along the fiber. The high-resolution fibers were thermally calibrated enable the 2D reconstruction of the temperature profile in the flow path of the structure. This is due to the temperature-related strain response of the material and strain measurements of the fibers. Hot airflow testing of these devices showed the ability to identify localized temperature differences in the flow. The observed strain response within the smart device had higher strain concentrations in the thicker web regions than in the thinner web regions. These higher strain regions resulted in higher uncertainties for the temperature responses. Further calibration is needed to improve the accuracy of the smart devices, specifically within the inner web structures of a flow straightening component. Further investigations of the devices within flow showed the temperature sensing to be independent of the effects of flow velocity. The devices were able to distinguish temperature differences within single and two-phase flow and showed local sensitivity to the temperature changes with the identification of hot and cold spots. The presented results showed the viability of the smart device for obtaining detailed temperature distributions using common industrial components. Eventually, the goal for these smart devices will be to withstand higher temperature and pressure environments such as those experienced in nuclear, fusion, and concentrated solar energy systems.

What is the carbon footprint of academic clinical trials? A study of hotspots in 10 trials

BackgroundClinical trials are fundamental to healthcare, however, they also contribute to anthropogenic climate change. Following previous work to develop and test a method and guidance to calculate the carbon footprint...

Mapping coastal resilience: a Gis-based Bayesian network approach to coastal hazard identification for Queensland’s dynamic shorelines

Coastal regions worldwide face increasing threats from climate change-induced hazards, necessitating more accurate and comprehensive vulnerability assessment tools. This study introduces an innovative approach to coastal vulnerability assessment by integrating Bayesian Networks (BN) with the modern coastal vulnerability (CV) framework. The resulting BN-CV model was applied to Queensland's coastal regions, with a particular focus on tide-modified and tide-dominated beaches, which constitute over 85% of the studied area. The research methodology involved beach classification based on morphodynamic characteristics, spatial subdivision of Queensland's coast into 78 sections, and the application of the BN-CV model to analyze interactions between geomorphological features and oceanic dynamics. This approach achieved over 90% accuracy in correlating beach types with vulnerability factors, significantly outperforming traditional CVI applications. Key findings include the identification of vulnerability hotspots and the creation of detailed exposure and sensitivity maps for Gold Coast City, Redland City, Brisbane City, and the Sunshine Coast Regional area. The study revealed spatial variability in coastal vulnerability, providing crucial insights for targeted management strategies. The BN-CV model demonstrates superior precision and customization capabilities, offering a more nuanced understanding of coastal vulnerability in regions with diverse beach typologies. This research advocates for the adoption of the BN-CV approach to inform tailored coastal planning and management strategies, emphasizing the need for regular reassessments and sustained stakeholder engagement to build resilience against climate change impacts.Recommendations include prioritizing adaptive infrastructure in high-exposure areas like the Gold Coast, enhancing flood management in Brisbane, improving socio-economic adaptive capacity in Redland, and maintaining natural defences in Moreton Bay. This study contributes significantly to the field of coastal risk management, providing a robust tool for policymakers and coastal managers to develop more effective strategies for building coastal resilience in the face of climate change.

Identifying power outage hotspots to support risk management planning.

Secure and reliable power systems are vital for modern societies and economies. While there is a focus in the literature on predicting power outages caused by severe weather events, relatively little literature exists on identifying hot spots, locations where outages occur repeatedly and at a higher rate than expected. Reliably identifying hotspots can provide critical input for risk management efforts by power utilities, helping them to focus scarce resources on the most problematic portions of their system. In this article, we show how existing work on Moran's I spatial statistic can be adapted to identify power outage hotspots based on the types and quantities of data available to utilities in practice. The local Moran's I statistic was calculated on a grid cell level and a set of criteria were used to filter out which grid cells are considered hotspots. The hotspot identification approach utilized in this article is an easy method for utilities to use in practice, and it provides the type of information needed to directly support utility decisions about prioritizing areas of a power system to inspect and potentially reinforce.

GIS-Enhanced Survey of Potential Aedes aegypti and Aedes albopictus Artificial Oviposition Containers Distributed across Communities in Trinidad, West Indies.

Dengue and other arboviruses remain a global threat, and enhanced efforts to control the mosquitoes that transmit them are urgently needed. A survey of potential manmade Aedes aegypti (L.) and Aedes albopictus (Skuse) oviposition containers was performed in four communities near the end of the typical dry season in 2018 in Trinidad, West Indies. The purpose was to conduct individual premise surveys and use GIS mapping to visualize premises within communities that had Aedes-positive containers, as this information could be used for the prioritization of mosquito control efforts in potential high risk areas as the wet season progressed. Accessible premises were surveyed following standard inspection protocols used by the Insect Vector Control Division (IVCD), Ministry of Health (MOH). The results indicated that two of the four locations would be at high risk for arbovirus transmission going into the wet season. The GIS mapping of premises with Aedes-positive containers facilitated the identification of potential hot spots for arbovirus transmission risk within communities that should be prioritized for enhanced monitoring and vector control efforts, emphasizing the need to increase community participation in standard surveys by IVCD.

An Integrated Field Study of Turbulence and Dispersion Variations in Road Microenvironments.

Traffic-related air pollutants (TRAPs) emitted from vehicle tailpipes disperse into nearby microenvironments, posing potential exposure risks. Thus, accurately identifying the emission hotspots of TRAPs is essential for assessing potential exposure risks. We investigated the relationship between turbulent kinetic energy (TKE) and pollutant dispersion (D) through an integrated field measurement. A five-year near-road sampling campaign (5 min based) near a light-duty vehicle-restricted roadway and an on-road sampling campaign (5 s based) on isolated proving grounds were conducted. The D was first calculated based on vehicle emission and pollutant concentrations and then paired with TKE measurements. Here, 198 near-road and 377 on-road measurement pairs were collected. In the near-road measurements, TKE and D showed a positive relationship (R2 ≥ 0.69) with the vehicle flow rate, while they showed similar decay patterns and sensitivity to vehicle types in the on-road measurements. A relationship between TKE and D (TKE-D) was developed through these measurements, demonstrating a robust correlation (R2 ≥ 0.61) and consistent slope values (1.1-1.3). These findings provide field evidence for the positive association between TKE and D, irrespective of the measurement techniques or locations. The TKE-D relationship enables vehicle emission estimation with TKE as the sole input, facilitating the identification of emission hotspots with high spatiotemporal resolution.

Therapeutic inhibition of isocitrate dehydrogenase mutations in glioma and cholangiocarcinoma: new insights and promises-a narrative review.

The identification of mutation hot spots in the isocitrate dehydrogenase (IDH) genes is one of the most important cancer genome-wide sequencing discoveries with relevant impact in the treatment of some orphan tumors. These genes were mostly found mutated in lower-grade gliomas (LGGs), acute myeloid leukaemia (AML), myelodysplastic syndromes (MDS) and myeloproliferative neoplasms (MPNs) and in cholangiocarcinoma. This aberrant genomic condition represents a therapeutic target of great interest in cancer research, especially in AML, given the limitations of currently approved therapies in this field. In this review, we investigate the role of IDH mutation and the mutant IDH (mIDH)- targeted therapies for cholangiocarcinoma and glioma. Here, we provide an overview of the IDH mutation role and discuss its role in tumorigenesis and progression of some solid cancers, in which the therapeutic strategy can be completely changed thanks to these brand-new therapeutic options. The encouraging early clinical data demonstrated to be a proof of concept for investigational mIDH1/2 inhibitors in tumors with a paucity of therapeutic possibilities. Moreover, we list the most important randomised clinical trials still active with their preliminary results.

Research Hotspots of Acupuncture Treatment for Myopia Over the Past Decade: A Bibliometric Analysis (2014-2023).

To explore the research hotspots in acupuncture treatment for myopia from 2014 to 2023. A comprehensive search was conducted in the Science Citation Index-Expanded (SCI-E) within the Web of Science Core Collection (WOSCC). We used CiteSpace 6.2.R6 to perform an in-depth analysis of the annual publication trends, prolific authors, institutions, keyword co-occurrences, and citation bursts. The study followed the PICO framework: the population (P) includes studies on patients with myopia, the intervention (I) is acupuncture treatment, no direct comparison (C) is applied, and the primary outcome (O) focuses on the identification of research trends and hotspots. Major outcome assessments included the analysis of publication trends over time, author productivity, influential journals, and the detection of emerging research topics through citation burst analysis. This bibliometric analysis was conducted on November 15, 2023. A total of 281 articles were included in the analysis. The trend line of annual publications over the past decade showed a modest increase, with a significant decline in 2019 and a surge in 2021. China and its affiliated academic institutions led globally in publication volume, with Ma Xiaopeng being the most prolific author and Fudan University the most influential institution. Ophthalmology emerged as the journal with the highest citation frequency. The most frequently occurring keywords were related to adolescent myopia and its complications. The developmental trajectory of the field is distinct, characterized by the integration of a single discipline. Acupuncture shows promise as a complementary and alternative therapy for treating myopia, with growing interest in its clinical efficacy and mechanisms of action. Future research is likely to focus on optimizing treatment protocols and understanding the underlying biological mechanisms of acupuncture in myopia management.

Innovative lake pollution profiling: unveiling pollutant sources through advanced multivariate clustering techniques.

In many developed and developing nations, lakes are the primary source of drinking water. In the current scenario, due to rapid mobilization in anthropogenic activities, lakes are becoming increasingly contaminated. Such practices not only destroy lake ecosystems but also jeopardize human health through water-borne diseases. This study employs advanced hierarchical clustering through multivariate analysis to establish a novel method for concurrently identifying significantly polluted lakes and critical pollutants. A systematic approach has been devised to generate rotating component matrices, dendrograms, monoplots, and biplots by combining R-mode and Q-mode analyses. This enables the identification of contaminant sources and their grouping. A case study analyzing five lakes in Bengaluru, India, has been conducted to demonstrate the effectiveness of the proposed methodology. Additionally, one pristine lake from Jammu & Kashmir, India, has been included to validate the findings from the aforementioned five lakes. The study explored correlations among various physical, chemical, and biological characteristics such as temperature, pH, dissolved oxygen, conductivity, nitrates, biological oxygen demand (BOD), fecal coliform (FC), and total coliform (TC). Critical contaminants forming clusters included conductivity, nitrates, BOD, TC, and FC. Factor analysis identified four primary components that collectively accounted for 85% of the overall variance. Following identification of pollution hotspots, the study recommends source-based pollution control and integrated watershed management, which could significantly reduce lake pollution levels. Continuous monitoring of lake water quality is essential for identifying actual contaminant sources. These findings provide practical recommendations for maximizing restoration efforts, enforcing regulations on pollutant sources, and improving water quality conditions to ensure sustainable development of lakes.

Identification of hotspots and cold-spots of groundwater potential using spatial statistics

Study regionThe Guna-Tana landscape is located in Ethiopia. This landscape is seriously facing water scarcity problems, that’s why we studied this landscape and provided the hotspots of groundwater potential areas in this region. Study focusIn this study the hotspots and cold-spots of groundwater potential at, 99, 95, and 90 % confidence levels has been deciphered. Using Gi-Bin values, four classes has been identified viz., 2–3 (highly favorable), 0–1 (fairly favorable), −2 to −1 (poorly favorable) and −3 (very poorly favorable). The hotspots was subjected to ordinary least squared (OLS) regression to understand the impact of chosen parameters (viz., geology, land-use, soil, rainfall, slope, and distance to rivers) towards groundwater potential. New hydrological insights for the regionThe absence of redundancy among the selected parameters was indicated by the VIF values of the parameters, which were determined to be less than 7.5. It was discovered that the Robust Probability (Robust_Pr) was statistically significant (p < 0.01). The OLS model appears to have captured the variability of exploratory variables, as evidenced by the decreased values of Akaike's Information Criterion (AICc). The Adjusted R-squared value of 0.9119 indicates that exploratory variables has successfully explained 91.19 % of the variance of the model.

Exploring and identification of road traffic crash hotspots in Kigali-city, Rwanda

Road traffic crashes are on the rise worldwide, particularly in African countries. Rwanda, in this context, faces the challenge of an increasing frequency of crashes each year. Kigali, the capital city of Rwanda, witnesses over three-quarters of the total population as daily road users, leading to high traffic volumes and congestion during peak hours. Identifying dangerous locations and providing insights to transport policymakers is crucial to minimise the severity of crashes in the city. This study focuses on identifying crash hotspots through spatial analysis and explores the various factors contributing to crash severity in the capital city of Rwanda. The study utilises crash data recorded between 2015 and 2022 by the Rwanda National Police, conducting temporal, spatial, and statistical analyses. A multinomial regression model was employed to examine the significance of contributing factors to crash severity. Additionally, six supervised machine learning classifiers were employed to assess their predictive capabilities for accident severity in Kigali city. The findings indicate that the Gasabo district recorded the highest number of crashes among all districts. Time series analysis reveals a decrease in fatal accidents from 2020 to 2022 in Kigali city, while the number of injury and property damage crashes progressively increased from 2019 to 2022. Among the predictive models, the Random Forest model demonstrated strong predictive abilities, achieving an accuracy of 88.11% compared to other models. These results provide valuable insights for road safety policymakers to proactively make decisions and take precautions to prevent crashes before they occur.

Risk hotspots and influencing factors identification of heavy metal(loid)s in agricultural soils using spatial bivariate analysis and random forest

Heavy metal(loid)s (HMs) in agricultural soils not only affect soil function and crop security, but also pose health risks to residents. However, previous concerns have typically focused on only one aspect, neglecting the other. This lack of a comprehensive approach challenges the identification of hotspots and the prioritization of factors for effective management. To address this gap, a novel method incorporating spatial bivariate analysis with random forest was proposed to identify high-risk hotspots and the key influencing factors. A large-scale dataset containing 2995 soil samples and soil HMs (As, Cd, Cr, Cu, Mn, Ni, Pb, Sb, and Zn) was obtained from across Henan province, central China. Spatial bivariate analysis of both health risk and ecological risks revealed risk hotspots. Positive matrix factorization model was initially used to investigate potential sources. Twenty-two environmental variables were selected and input into random forest to further identify the key influencing factors impacting soil accumulation. Results of local Moran's I index indicated high-high HM clusters at the western and northern margins of the province. Hotspots of high ecological and health risk were primarily observed in Xuchang and Nanyang due to the widespread township enterprises with outdated pollution control measures. As concentration and exposure frequency dominated the non-carcinogenic and carcinogenic risks. Anthropogenic activities, particularly vehicular traffic (contributing ∼37.8 % of the total heavy metals accumulation), were the dominant sources of HMs in agricultural soils. Random forest modeling indicated that soil type and PM2.5 concentrations were the most influencing natural and anthropogenic variables, respectively. Based on the above findings, control measures on traffic source should be formulated and implemented provincially; in Xuchang and Nanyang, scattered township enterprises with outdated pollution control measures should be integrated and upgraded to avoid further pollution from these sources.