Probabilistic risk assessment of heavy metal exposure via diet in Southwest China: Temporal changes and driving factors in Chongqing (2012–2022)

Probabilistic risk assessment of heavy metal exposure via diet in Southwest China: Temporal changes and driving factors in Chongqing (2012–2022) | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Probabilistic risk assessment of heavy metal exposure via diet in Southwest China: Temporal changes and driving factors in Chongqing (2012–2022) Jiahui Chen, Jingrong Chen, Mei Qin, Ping Feng, Mintao Li, Xinghui Dai, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7690165/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 14 Jan, 2026 Read the published version in Scientific Reports → Version 1 posted 11 You are reading this latest preprint version Abstract Chongqing, a traditional industrial base and agricultural hub in inland Southwest China, exhibits dietary patterns representative of this rapidly developing region. Following early 21st-century environmental policies targeting soil heavy metal remediation, this study employed probabilistic risk assessment (2012–2022) to evaluate policy impacts on population health. Results revealed significant contaminant reductions in commercially available foods during 2018–2022 versus 2012–2014, with lead (Pb), cadmium (Cd), arsenic (As), and mercury (Hg) concentrations declining to 0.016–0.061, 0.002–0.092, 0.006–0.075, and 0.002–0.006 mg/kg respectively. These declines align with Chongqing's regulatory evolution and industrial transformation, driving progressive health risk reductions: hazard quotients (HQ) for Pb and Hg remained below 1.0 (indicating negligible risks), while Cd and As posed sustained yet decreasing concerns with 6.9% and 49.5% of residents exceeding HQ = 1. Additionally, cumulative exposure to the four heavy metals yielded hazard index values of 1.57–7.60, highlighting potential health implications from combined heavy metal exposure. Notably, significant reductions observed at the 95th exposure percentile demonstrate a persistent downward trend in health risks for highly exposed subpopulations. Rice products and leafy vegetables emerged as primary exposure vectors requiring prioritized surveillance, establishing this probabilistic assessment as an evidence base for food safety policy optimization in Southwest China through comprehensive exposure characterization across core food categories. Earth and environmental sciences/Environmental sciences Health sciences/Risk factors heavy metal dietary exposure risk assessment temporal changes Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Metal contaminants represent a significant global public health concern 1 , 2 . Foodborne heavy metal contaminants primarily originate from environmental sources—such as air, soil, and surface water—through pathways including atmospheric deposition, uptake by crops, and bioaccumulation in aquatic food chains 3 . Additionally, contamination may occur during food processing, storage, and transportation due to factors like contact with contaminated equipment, leaching from packaging materials, or inadequate hygiene practices 4 . This pervasiveness across the food production continuum renders comprehensive avoidance challenging for consumers 5 , 6 . Heavy metal contaminants exhibit prolonged biological half-lives and demonstrate toxic effects at low exposure levels. Substantial evidence associates these pollutants with multisystem toxicity, including cardiovascular, neurological, endocrine, developmental, and immunological disorders 7 – 14 . Several metallic elements have been confirmed as genotoxic and carcinogenic hazards 15 – 17 . Specifically, arsenic/inorganic arsenic compounds and cadmium/cadmium compounds are classified as Group 1 human carcinogens by the International Agency for Research on Cancer (IARC) 18 . Consequently, the World Health Organization (WHO) designates lead (Pb), cadmium (Cd), mercury (Hg), and arsenic (As) among its ten chemicals of major public health concern 19 . These priority contaminants are jointly recognized by the Food and Agriculture Organization (FAO) and WHO as highly toxic heavy metals warranting monitoring in the Global Environment Monitoring System - Food Contamination Monitoring and Assessment Programme (GEMS/Food) due to their significant health risks 19 . The advancement of industrialization in China, including mining, metal processing, smelting, industrial waste disposal, automobile exhaust emissions, sewage irrigation, and the utilization of pesticides and fertilizers, has resulted in the introduction of heavy metals such as Pb, Hg and Cd into the atmosphere, water bodies, and soil, leading to serious environmental pollution 5 . According to a survey by the Chinese Ministry of Environmental Protection in 2014 20 , the total over-standard rate of heavy metals in Chinese soils was 16.1%, with Pb, Cd, Hg and As at 1.5%, 7.0%, 1.6% and 2.7%, respectively. As one of China's six traditional industrial bases, Chongqing's agricultural soils have been substantially impacted by industrial activities. Early monitoring data identified As, Cd, Hg, and Pb as primary soil contaminants, with individual pollution indices ranging from 1.589 to 5.589, corresponding to light to severe pollution levels 21 . Furthermore, Chongqing's predominantly acidic soils—representative of southwestern China's pedological characteristics—may enhance metal bioavailability in crops due to increased solubility of metal cations under acidic conditions 22 . Situated in inland southwestern China, Chongqing remains the sole municipality in central-western China and serves as a crucial agricultural production base supplying the broader southwestern region. Nevertheless, current research on regional metal contamination remains limited, with associated health risks poorly characterized. Chen et al. 23 employed a hazard-driven approach indicating that combined metal exposure among Chongqing residents may induce nephrotoxicity and neurotoxicity. These findings suggest persistent health risks from recent metal exposure in the local population. Therefore, this study conducted a comprehensive risk assessment using monitoring data for Pb, Cd, As, and Hg in 11 food categories from the Chongqing Food Safety Risk Monitoring Program (2012–2021), combined with resident food consumption data (2011, 2015, 2018). We employed probabilistic assessment methods to quantify uncertainties in health risk evaluation, thus generating more precise population exposure estimates 5 , 24 . The study had two primary objectives: (1) to evaluate the health risks posed by both individual and cumulative exposure to heavy metals through dietary intake in Chongqing residents, and (2) to identify temporal exposure trends while analyzing contributing factors. Given Chongqing's dietary patterns may represent Western China and its role as a key agricultural supplier to Southwest China, these findings provide valuable insights for regional health risk assessments of heavy metal contamination. The results will provide references on formulating relevant policies. 2. Materials and methods 2.1 Sample collection, transportation and preservation The data on heavy metal concentrations were obtained from the food safety risk monitoring system in Chongqing from 2012 to 2022. Samples were collected across all 38 districts and counties in Chongqing using multi-stage stratified random sampling, consistent with protocols in the National Food Contaminant and Harmful Factor Surveillance Manual. Sampling sites encompassed retail stores, supermarkets, grocery stores, farmers' markets, and convenience stores throughout urban and rural municipalities. Food samples encompassed various categories, including rice and rice products, wheat flour and its products, other cereals and cereals products, leafy vegetables (including stem vegetables and Brassica vegetables), root and tuber vegetables, legume vegetables, other fresh vegetables (including melons and fruits, bulbs and aquatic plants, sprouts, perennial vegetables such as bamboo shoots and cauliflower), livestock meat, poultry, meat products, edible livestock and poultry offal, eggs and egg products and fruits, totaling 12 categories. Sample mass (minimum 500 g) was determined by the edible portion of the food samples to ensure representativeness. For bulk commodities (e.g., grains), composite samples were created through multi-point collection. For vegetable samples, different parts with varying sizes, forms, and colors were collected to compose a representative sample. All samples underwent individual airtight packaging during transport to prevent cross-contamination, with immediate transfer to designated monitoring laboratories for protocol-compliant storage. 2.2 Analytical determination The food samples, ranging from 0.2 g to 0.5 g (accurate to 0.001 g), were ground and homogenized, and treated with 5 mL of concentrated nitric acid (65%, Merck) in a poly tetra fluoroethylene digestion container overnight. The container was covered with a lid and placed in the microwave digestion instrument, following the standard digestion procedure. After the digestion was completed, the acid was rinsed on a hot plate until the liquid in the digestion tube is colorless and transparent or slightly light yellow liquid, with 1 ~ 2 drops of liquid remaining. Subsequently, the inner wall of the microwave digestion tube was rinsed three times with a small amount of ultrapure water and the final volume was fixed in a 10.0 mL volumetric flask. According to the standard test method, a standard series of 1µg/L Pb, Cd, Hg and As standard solutions were prepared with 5% nitric acid in volumetric flasks and quantified using the external standard method. The standard solutions, provided by the China Academy of Metrology, included lead (GBW08619), cadmium (GBW08612), mercury (GBW08617), arsenic (GBW08611). Graphite furnace atomic absorption photometry was used to determine Pb and Cd in the samples, and atomic fluorescence photometry was used to determine Hg and As in the samples. The detection limits (LOD) of Pb, Cd, Hg and As are 0.005 mg/kg, 1.0 µg/kg, 0.5 µg/kg and 0.01 mg/kg, respectively. All samples were analyzed in parallel and repeated, and reagent blanks were also performed. The recovery for all measured metals was 85%-115%. 2.3 Consumption Data Sources The dietary consumption data for Chongqing was obtained from the China Health and Nutrition Survey in 2011, 2015 and 2018. The survey was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional Review Board of Chinese Center for Disease Control and Prevention, and the Institutional Review Board of Chongqing Center for Disease Control and Prevention (protocol code: KY-2023-024-2). Through multi-stage stratified random sampling, six districts and counties in Chongqing were selected as survey sites, including Nanan, Shapingba, Dazu, Feng jie, Jiangjin and Qijiang. A total of 1254 residents (585 males and 669 females) were surveyed about their food consumption in 2011, including 94 preschoolers aged 3–6 years, 165 children and adolescents aged 7–17 years, 733 adults aged 18–59 years, and 262 elderly people aged 60 years or older. In 2015, 1091 residents (502 males and 589 females) were surveyed, including 55 preschoolers aged 3–6 years, 139 children and adolescents aged 7–17 years, 514 adults aged 18–59 years, and 383 elderly people aged 60 years or older. In 2018, 979 residents (432 males and 537 females) were surveyed, including 31 preschoolers aged 3–6 years, 113 children and adolescents aged 7–17 years, 399 adults aged 18–59 years, and 426 elderly people aged 60 years or older. In this survey, after the selected households provided informed consent, consumption data (including all foods eaten at home and outside) were collected using three consecutive 24-h dietary recalls by well-trained dietary staff. The dietary survey was conducted by face-to-face questioning to collect dietary consumption information and general demographic information (gender, age, and place of residence). For study subjects under 7 years old or over 75 years old, dietary information was provided from adult family members. The food consumption within each food category was summed up for each individual to match with the occurrence data of each food category. 2.4 Health risk assessment The estimated daily intake (EDI) of heavy metals from total diet was calculated by using the following equation: $$\:EDI={\sum\:}_{i=1}^{n}\frac{{F}_{i}\times\:{C}_{i}}{BW}$$ where EDI is the estimated daily intake of a single or the total of four heavy metals (µg/kg BW/d), Fi is the consumption of each food (g/d), Ci is the level of heavy metal contamination in each food category (µg/kg), BW is the body weight of each respondent (kg). According to the United States Environmental Protection Agency (US EPA) of health risk assessment model 25 , the hazard quotient (HQ) and hazard index (HI) methods were used to measure the health risk of heavy metals. $$\:HQ=\frac{EDI}{RfD}$$ HI = HQ Pb +HQ Cd +HQ Hg + HQ As RfD represents the oral reference dose of Pb, Cd, As and Hg for non-carcinogenic effects. In this study, the RfD values for Pb, Cd, As and Hg were set as 0.004, 0.001, 0.0003, and 0.0003 mg/kg/day, respectively. Those value were obtained from the Integrated Risk Information System (IRIS) of the US EPA ( http://www.epa.gov/iris/ ). The HQ value was used to assess the non-carcinogenic risk from a single metal of all foods, while the HI value was used to assess the non-carcinogenic risk of combined exposure to heavy metals in all foods. HQ (HI) < 1 indicates that there is no obvious harm to the human body from heavy metals via dietary exposure, HQ (HI) ≥ 1 indicates that heavy metal intake has potential non-carcinogenic risk. 2.5 Monte Carlo simulation method Monte Carlo Simulation (MCS) is the most commonly used probabilistic risk analysis method and is recommended by the National Academy of Sciences and the U.S. Environmental Protection Agency for quantifying variability and uncertainty. In this study, MCS was used to estimate the risk of heavy metal exposure in the population and to characterize the uncertainty of the parameters. To ensure parameters stability, 100,000 MCS was performed using the super-Latin cubic sampling method (stability condition) to obtain robust uncertainty analysis results. The mean and 95th percentile value of the HQ (HI) according to the simulation was used to determine the health risk assessment. All input parameters included the concentrations of Pb, Cd, As and Hg in food (C), the consumption of each food (F) , and body weight (BW) . Distribution fitting for both contaminant concentrations and food consumption data was performed using @Risk's built-in probability models, with optimal distribution functions selected through Akaike Information Criterion (AIC). Parameter estimation was subsequently conducted via maximum likelihood estimation (MLE) and method of moments. 2.6 Statistical analysis and data processing For non-detected data, data below the LOD were set to 1/2 LOD, following the EFSA recommended treatment for left-censored data 26 . Microsoft Excel 2016 software was used for statistical analysis of the data. Monte Carlo simulations were conducted with @RISK software 8.2 (Palisade Coporation,8.2. Ithaca, NY, USA). 3. Results and discussion 3.1 Levels of heavy metals in food categories To analyze the changes in the risk of dietary exposure to four heavy metals (Pb, Cd, As and Hg), the monitoring data on the levels of four heavy metal contaminants in Chongqing's food safety risk monitoring system from 2012 to 2022 were divided into three periods: 2012–2014, 2015–2018 and 2019–2022. Mean concentrations and detection frequencies across food categories are presented in Figs. 1 – 2 and Supplementary Figures S1 -S2. Following Chongqing's 2012 expansion of heavy metal monitoring, this enhanced surveillance established robust baseline data reflecting actual contamination levels in market-sold foods. Analysis revealed a stepwise decrease in mean concentrations and detection rates across periods. A decreasing trend was observed in mean Pb concentrations for rice products, legume vegetables, fruits, and egg products. With the exception of leafy vegetables, Pb levels across foodstuffs were significantly higher during 2012–2014 than in subsequent periods. Similarly, Hg concentrations in wheat products, other cereals, leafy vegetables, root/tuber vegetables, other vegetables, livestock/poultry meats, offal, and egg products were highest during 2012–2014 relative to subsequent periods, while remaining food categories showed minimal temporal variation. These trends align with Chongqing's industrial legacy and regulatory evolution. As a traditional industrial base, early studies (pre-2010) documented soil contamination by cadmium and lead exceeding national and regional background values 27 . Chongqing implemented industrial relocation (2002–2011), transferring chemical, pharmaceutical, metallurgical, and paper manufacturing enterprises to designated suburban zones with centralized wastewater and waste treatment. Post-relocation soil remediation commenced in 2011. Subsequent crucial policy developments include: Work Plan for Soil Environmental Protection and Comprehensive Treatment (2013); Soil Pollution Prevention and Control Law of China (2018). The temporal concordance between policy implementation windows and contamination decline substantiates the efficacy of this integrated regulatory approach in reducing dietary metal exposure. Our findings further demonstrate consistency with established soil pollutant trends 27 – 30 . In this study, the mean concentrations of Pb, Cd, Hg, and As in Chongqing foods during 2018–2022 were 0.016 ~ 0.061, 0.002 ~ 0.092, 0.006 ~ 0.075 and 0.002 ~ 0.006 mg/kg, respectively. These values are lower than contemporaneous contamination levels reported for corresponding food products in major northern Chinese cities like Beijing and the southern industrialized zones such as Pearl River Delta region 31 , 32 . The transfer and bioavailability of heavy metals within soil-crop systems are governed by soil properties including pH, organic matter content, and cation exchange capacity. Reduced soil pH enhances metal bioaccumulation in crops such as wheat 33 , 34 . Although Chongqing exhibits more pronounced soil acidification than parts of southeastern and northeastern China 34 , 35 , 36 , the comparatively lower baseline concentrations of Pb, Cd, Hg, and As in agricultural soils may explain the relatively low concentrations of heavy metals detected in local foods when compared to those from the Pearl River Delta and northeastern China 31 , 32 . This study further confirms that rice products and leafy vegetables represent primary dietary vectors for heavy metal contamination. Compared to legume vegetables, fruits, and other food categories, rice products and leafy vegetables consistently exhibited higher concentrations and detection frequencies of Pb, Cd, As, and Hg. Evidence indicates that heavy metal levels in vegetables are influenced by species-specific factors, soil pH, and background contamination, with vegetable species constituting the predominant determinant 37 . Leafy vegetables demonstrate significantly greater bioaccumulation capacity than root/tuber or fruit-type vegetables. This phenomenon is likely attributable to their heightened transpiration rates combined with physical proximity to topsoil, which facilitates direct contaminant contact through low-growing foliage 38 , 39 . In this study, mean concentrations in rice and rice products ranged from 0.044–0.062 mg/kg for Cd, 0.060–0.105 mg/kg for As, 0.034–0.048 mg/kg for Pb, and 0.002–0.004 mg/kg for Hg. Detection frequencies for As (79.9–91.5%) and Cd (72.2–94.7%) substantially exceeded those of Pb (34.6–67.9%) and Hg (27.0–39.2%). These findings align with published literature indicating significant Cd and As contamination in rice 40 – 43 . The elevated Cd and As levels in Chongqing rice may be attributed to: (1) Relatively higher background concentrations of Cd and As in local soils 21 ; (2) Enhanced bioaccumulation factors for Cd and As in rice compared to other metals like Pb and Cr 40,44 . Comparative analysis with contemporaneous data reveals Chongqing rice Cd/As levels exceeded those documented in Nepal 45 , Japan 46 , and China's Jilin Province 47 , but remained below concentrations reported in Chengdu, China 48 and Vietnam 49 . The comparative values are shown in Table 1 . This suggests Chongqing's rice contamination levels may rank among China's lower tiers. Nevertheless, given rice's dietary significance in China and the global food safety implications of Cd/As contamination, sustained monitoring remains imperative. Table 1 Levels of Cd and As in Rice and Rice products from Chongqing compared with the literature data from similar foodstuffs (mg/kg) Food Category Present study Jilin Province, China a Chengdu, China b Japanese c Vietnam d Nepal e Cd As Cd As Cd As Cd As Cd As Cd As Rice and Rice products 0.044–0.062 0.060–0.105 0.030 0.040 0.096 0.188 0.014 0.035 0.111 0.115 0.0155 0.0434 a Wang B et al., 2020 b Wang R et al., 2018 c Watanabe T et al., 2022 d Chu DB et al., 2021 e Shao Y et al., 2023 3.2 Food consumption of Chongqing residents Food consumption patterns reflect not only regional dietary habits and living standards but also serve as critical parameters for dietary risk assessment. Given China's well-documented regional heterogeneity in dietary practices, our data reveal divergences between Chongqing's observed consumption patterns and those reported in national-level studies 50 . Figure 3 and Supplementary Tables S1-S3 present Chongqing's per capita food consumption across categories (2012–2022). Rice products and leafy vegetables constituted the most consumed food groups locally. Mean consumption of cereals and derivatives (309.10–328.36g/d) exceeded the 200–300g/day recommendation in the Chinese Dietary Guidelines 51 . Conversely, vegetable intake (270.94–307.95g/d) fell below recommended levels (300–400g/d), while animal-source foods (117.73–134.66g/d) were marginally below guidelines (120–200g/d). Fruit consumption remained substantially lower than recommended values (200–400g/d). These findings demonstrate a rice- and wheat-centric dietary pattern in Chongqing, explaining the cereal surplus and vegetable/meat deficits. Three major temporal trends emerged in residents' dietary patterns: (1) Structural modification of grain consumption characterized by a notable increase in wheat-based products (10.16% → 12.86% of total intake) with a corresponding decrease in rice consumption; (2) Vegetable preference transition toward root varieties; (3) Persistent red meat growth (10.37% →13.12%), declining poultry/fruit intake (2.59%/5.10% →2.04%/3.35%), and rising egg demand (2.36% →2.72%). This evolution signifies a transition from traditional "grain-vegetable" patterns toward diversified "grain-meat-vegetable-fruit" consumption, coupled with a shift from home-cooked meals to energy-dense, processed convenience foods 52 . 3.3 Potential health risks of individual and cumulative exposure Figure 4 presents probability density functions and temporal trends of HQ for dietary exposure to Pb, Cd, As, and Hg across three periods. Mean and 95th percentile HQ values indicate negligible health risks from Pb (0.1113-0.210; 0.293–0.557) and Hg (0.138–0.209; 0.346–0.573). Conversely, elevated Cd (mean HQ: 0.377–0.456) and As exposures (mean HQ: 1.62–2.27) demonstrated concerning profiles, with 95th percentiles exceeding the safety threshold (HQ > 1) in all periods. Based on mean HQ values, heavy metals were ranked in descending order of risk as follows: As (2.13) > Cd (0.435) > Pb (0.210) > Hg (0.209) in 2012–2014, As (2.27) > Cd (0.456) > Hg (0.138) > Pb (0.1113) in 2015–2018 and As (1.62) > Cd (0.377) > Hg (0.144) > Pb (0.116) in 2019–2022. Probabilistic assessment quantified population-level risk distributions, showing fewer than 3% and 2.3% of residents exceeded HQ = 1 for Pb and Hg respectively. However, significant proportions faced risks from Cd and As exposure: Cd exceedances affected 9.1%, 9.3%, and 6.9% of the population across the three periods, while As impacted 52.9%, 67.2%, and 49.5%. Regional comparisons revealed Chongqing's mean HQ values exceeded those reported for lower Yangtze River cities 53 and Xiamen 54 , though remaining below Beijing levels for Pb/Cd (Pb: 0.54, Cd: 0.50) 31 and national Cd/As averages (Cd: 0.54, As: 6.49) 55 . Temporal analysis demonstrated progressive reduction in Pb and Hg exposure risk post-2014. Cd and As exhibited initial increases followed by significant declines in 2019–2022, though elevated risk persisted at higher exposure percentiles. Although risk levels for all heavy metals at the 50th percentile exhibited fluctuation, the HQs at the 95th percentile demonstrated a consistent downward trend across the study periods, indicating reduced health risks for high-exposure subpopulations. These exposure dynamics correlate with dietary transitions documented in Section 3.2 and contaminant reductions revealed in Section 3.1 , confirming consumption patterns and heavy metal concentrations as primary risk determinants. Human populations are concurrently exposed to multiple heavy metals through dietary intake. Substantial evidence from food monitoring studies confirms the co-occurrence of various heavy metals in food matrices, with biomarker analyses further verifying chronic multi-metal exposure in humans 5 , 17 . Given increasing scientific and regulatory concerns about combined chemical exposures, we conducted a cumulative risk assessment for Pb, Cd, As, and Hg. Figure 5 presents the HQ profiles and temporal trends of the HI across three study periods. The HI values at the 50th and 95th percentiles for combined exposure were 1.81 and 7.60 (2012–2014), 2.13 and 7.32 (2015–2018), and 1.57 and 5.49 (2019–2022), respectively, exceeding recognized safety thresholds (HI > 1), which necessitates consideration of potential cumulative health risks. Cumulative exposure to these metals posed substantially greater risks than individual metal exposures. The HI values exhibited an initial increase followed by subsequent decline across study periods, with notable reductions observed at the 95th percentile exposure level. Specifically, 2019–2022 HI values decreased below both earlier measurement periods. 3.4 Sensitivity analysis of each heavy metal exposure risk Sensitivity analysis identified key determinants significantly influencing HQ for dietary exposure to Pb, Cd, As, and Hg, with absolute correlation coefficients quantifying each variable's impact magnitude (Fig. 6 ; top 16 determinants shown). For Pb, Cd and As, concentrations in rice products and leafy vegetables, consumption levels of these two food groups, and body weight emerged as predominant risk drivers. Additionally, Pb exposure demonstrated sensitivity to contamination levels in wheat products, root/tuber vegetables, and livestock meat, while cadmium risk was governed by concentrations and intake of wheat products and root/tuber vegetables. These findings indicate that reducing lead and cadmium concentrations in these priority foods would yield significant risk mitigation. For Hg, primary risk determinants included consumption and contamination levels in rice products and leafy vegetables, with secondary contributions from Hg concentrations in livestock meat. 3.5 Uncertainty analysis While this study employed probabilistic assessment methodology (superior to deterministic approaches in accuracy) incorporating food surveillance-derived heavy metal concentrations and 3-day 24-hour dietary recall data to mitigate uncertainty, several limitations persist. First, excluded food categories (e.g., dairy and legume products) and non-dietary exposure pathways (inhalation, dermal) may result in underestimated health risks. Second, variations in heavy metal bioaccessibility across food matrices remain unaddressed. Third, our cumulative risk assessment adopted EFSA's effect addition approach without considering: (1) toxicokinetic interactions between metals (synergistic/antagonistic effects), (2) target organ specificity, or (3) mode of action diversity. These mechanistic complexities necessitate refined risk models as toxicological evidence evolves. 4. Conclusion This study employed probabilistic risk assessment to evaluate individual and cumulative dietary exposure to Pb, Cd, Hg, and As in Chongqing from 2012 to 2022. Key findings demonstrate that national and municipal soil remediation policies have effectively reduced heavy metal levels in market-sold foods, corresponding to decreasing exposure risks over time. Notwithstanding these improvements, significant health concerns persist: at least 6.9% and 49.5% of residents remain at risk of health impairment from Cd and As exposure, respectively. Cumulative exposure assessment revealed concerning HI ranging from 1.57 to 7.60 for the four heavy metals, underscoring the critical need for combined risk management. Rice and leafy vegetables were identified as primary exposure vectors, warranting prioritized surveillance and control measures. This study establishes a critical evidence base for food safety policy formulation in Southwest China, providing comprehensive exposure assessment data on priority heavy metal contaminants (Pb, Cd, As, Hg) across core food categories. By quantifying temporal trends and population risk distributions through probabilistic modeling, our findings enable the development of evidence-informed management frameworks for targeted food chain interventions. These scientifically-grounded approaches directly support regional initiatives to enhance food safety governance and safeguard population health, particularly through optimized surveillance of high-risk food vectors like rice and leafy vegetables. Declarations Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Funding This study was supported by the Chongqing Joint Sci-Tech and Health Medical Research Project (2025MSXM146), the China Postdoctoral Science Foundation (2022M710549), the Natural Science Foundation of Chongqing (CSTB2025NSCQ-GPX1059), the National Key Research and Development Program of China (2017YFC1602004) and the Chongqing Disease Prevention and Public Health Research Center Construction Program. Author Contribution JG Chen and JH Chen conducted data analysis and drafted the manuscript. P Feng, MT Li, and XH Dai performed the investigations and collected food samples. M Qin, L Chen, XQ Tang and J Zhao conducted sample testing. SQ Luo and XM Lian provided supervision and technical guidance. HD Zhang and J Huo conceived and supervised the project, oversaw its progression, revised the manuscript, and serve as corresponding authors. Acknowledgement The authors would like to acknowledge the China National Center for Food Safety Risk Assessment (CFSA) for their guidance on the study. We also extend our appreciation to the district and county-level Centers for Disease Control and Prevention for their diligent efforts in sample collection and laboratory analyses. Data Availability The data are available from the corresponding author on reasonable request. References Song, J. et al. A systematic analysis of chronic kidney disease burden attributable to lead exposure based on the global burden of disease study 2019. Sci. Total Environ. (2023). 908.168189. Gibb, H. J. et al. Estimates of the 2015 global and regional disease burden from four foodborne metals - arsenic, cadmium, lead and methylmercury. Environ. Res. 174 , 188–194 (2019). Oros, A. 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20:49:13","extension":"html","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":120980,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7690165/v1/3cc86942ddb01e14df5de244.html"},{"id":94144279,"identity":"0d6106f2-202a-4167-b3dd-4fb0f4b1788e","added_by":"auto","created_at":"2025-10-22 20:49:13","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":78365,"visible":true,"origin":"","legend":"\u003cp\u003ePb, Cd, As and Hg mean concentrations (mg/kg) in total food categories in 3 periods: 2012–2014, 2015–2018 and 2019–2022\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7690165/v1/a2f2692a6bdcf5461606fa6c.png"},{"id":94144280,"identity":"cb923d51-b646-4732-9631-9214dbf99a4a","added_by":"auto","created_at":"2025-10-22 20:49:13","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":184998,"visible":true,"origin":"","legend":"\u003cp\u003eThe positive detection rates of Pb, Cd , As and Hg in total food in 3 periods: 2012–2014, 2015–2018 and 2019–2022\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7690165/v1/a8317b157daa7b49f3d75562.png"},{"id":94144292,"identity":"7accccbd-12ef-4cf3-b3d6-008a0d3df7e3","added_by":"auto","created_at":"2025-10-22 20:49:13","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":155511,"visible":true,"origin":"","legend":"\u003cp\u003eFood consumption (g/3d) of Chongqing residents in 2011, 2015, 2018\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7690165/v1/2c47cff4966101ac80a47830.png"},{"id":94144775,"identity":"9a12371b-c1a0-487a-af42-21fd05195414","added_by":"auto","created_at":"2025-10-22 20:57:13","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":952829,"visible":true,"origin":"","legend":"\u003cp\u003eThe cumulative probability density functions of the HQ values of Pb , Cd, As, Hg and trend in 3 stages:2012-2014, 2015-2018 and 2019-2022\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7690165/v1/b770934f5bacfb80fd169230.png"},{"id":94145155,"identity":"0e6b93a7-04c5-4f97-95f4-2c26e5fd2047","added_by":"auto","created_at":"2025-10-22 21:05:13","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":136555,"visible":true,"origin":"","legend":"\u003cp\u003eThe cumulative probability density Functions and trends in HI values for cumulative exposure to four heavy metals in 3 stages:2012-2014, 2015-2018 and 2019-2022\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7690165/v1/8b5873a428536c852fdd10c0.png"},{"id":94145469,"identity":"f6170602-b191-403e-9558-e03ed491e4e0","added_by":"auto","created_at":"2025-10-22 21:13:13","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":3600179,"visible":true,"origin":"","legend":"\u003cp\u003eSensitivity analysis of dietary exposure to Pb, Cd, Hg and As for Chongqing residents in 3 stages:2012-2014, 2015-2018 and 2019-2022\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7690165/v1/5d617ef60e64a33c2f13cf4e.png"},{"id":100614568,"identity":"1f2c3d02-59c4-4a74-9845-a5c0139b4a9b","added_by":"auto","created_at":"2026-01-19 17:21:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5185819,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7690165/v1/e6808128-1856-4093-a8f1-500301f6cfd4.pdf"},{"id":94144774,"identity":"80b0bfab-506c-4e6e-b78b-fb67940a55a9","added_by":"auto","created_at":"2025-10-22 20:57:13","extension":"doc","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":335872,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryinformation.doc","url":"https://assets-eu.researchsquare.com/files/rs-7690165/v1/f5289ec5183063761496f35e.doc"}],"financialInterests":"No competing interests reported.","formattedTitle":"Probabilistic risk assessment of heavy metal exposure via diet in Southwest China: Temporal changes and driving factors in Chongqing (2012–2022)","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eMetal contaminants represent a significant global public health concern\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Foodborne heavy metal contaminants primarily originate from environmental sources\u0026mdash;such as air, soil, and surface water\u0026mdash;through pathways including atmospheric deposition, uptake by crops, and bioaccumulation in aquatic food chains\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Additionally, contamination may occur during food processing, storage, and transportation due to factors like contact with contaminated equipment, leaching from packaging materials, or inadequate hygiene practices\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. This pervasiveness across the food production continuum renders comprehensive avoidance challenging for consumers\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eHeavy metal contaminants exhibit prolonged biological half-lives and demonstrate toxic effects at low exposure levels. Substantial evidence associates these pollutants with multisystem toxicity, including cardiovascular, neurological, endocrine, developmental, and immunological disorders\u003csup\u003e\u003cspan additionalcitationids=\"CR8 CR9 CR10 CR11 CR12 CR13\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Several metallic elements have been confirmed as genotoxic and carcinogenic hazards\u003csup\u003e\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Specifically, arsenic/inorganic arsenic compounds and cadmium/cadmium compounds are classified as Group 1 human carcinogens by the International Agency for Research on Cancer (IARC)\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Consequently, the World Health Organization (WHO) designates lead (Pb), cadmium (Cd), mercury (Hg), and arsenic (As) among its ten chemicals of major public health concern\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. These priority contaminants are jointly recognized by the Food and Agriculture Organization (FAO) and WHO as highly toxic heavy metals warranting monitoring in the Global Environment Monitoring System - Food Contamination Monitoring and Assessment Programme (GEMS/Food) due to their significant health risks\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe advancement of industrialization in China, including mining, metal processing, smelting, industrial waste disposal, automobile exhaust emissions, sewage irrigation, and the utilization of pesticides and fertilizers, has resulted in the introduction of heavy metals such as Pb, Hg and Cd into the atmosphere, water bodies, and soil, leading to serious environmental pollution\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. According to a survey by the Chinese Ministry of Environmental Protection in 2014\u003csup\u003e20\u003c/sup\u003e, the total over-standard rate of heavy metals in Chinese soils was 16.1%, with Pb, Cd, Hg and As at 1.5%, 7.0%, 1.6% and 2.7%, respectively. As one of China's six traditional industrial bases, Chongqing's agricultural soils have been substantially impacted by industrial activities. Early monitoring data identified As, Cd, Hg, and Pb as primary soil contaminants, with individual pollution indices ranging from 1.589 to 5.589, corresponding to light to severe pollution levels\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Furthermore, Chongqing's predominantly acidic soils\u0026mdash;representative of southwestern China's pedological characteristics\u0026mdash;may enhance metal bioavailability in crops due to increased solubility of metal cations under acidic conditions\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eSituated in inland southwestern China, Chongqing remains the sole municipality in central-western China and serves as a crucial agricultural production base supplying the broader southwestern region. Nevertheless, current research on regional metal contamination remains limited, with associated health risks poorly characterized. Chen et al.\u003csup\u003e23\u003c/sup\u003e employed a hazard-driven approach indicating that combined metal exposure among Chongqing residents may induce nephrotoxicity and neurotoxicity. These findings suggest persistent health risks from recent metal exposure in the local population.\u003c/p\u003e\u003cp\u003eTherefore, this study conducted a comprehensive risk assessment using monitoring data for Pb, Cd, As, and Hg in 11 food categories from the Chongqing Food Safety Risk Monitoring Program (2012\u0026ndash;2021), combined with resident food consumption data (2011, 2015, 2018). We employed probabilistic assessment methods to quantify uncertainties in health risk evaluation, thus generating more precise population exposure estimates\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. The study had two primary objectives: (1) to evaluate the health risks posed by both individual and cumulative exposure to heavy metals through dietary intake in Chongqing residents, and (2) to identify temporal exposure trends while analyzing contributing factors. Given Chongqing's dietary patterns may represent Western China and its role as a key agricultural supplier to Southwest China, these findings provide valuable insights for regional health risk assessments of heavy metal contamination. The results will provide references on formulating relevant policies.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Sample collection, transportation and preservation\u003c/h2\u003e\u003cp\u003eThe data on heavy metal concentrations were obtained from the food safety risk monitoring system in Chongqing from 2012 to 2022. Samples were collected across all 38 districts and counties in Chongqing using multi-stage stratified random sampling, consistent with protocols in the National Food Contaminant and Harmful Factor Surveillance Manual. Sampling sites encompassed retail stores, supermarkets, grocery stores, farmers' markets, and convenience stores throughout urban and rural municipalities. Food samples encompassed various categories, including rice and rice products, wheat flour and its products, other cereals and cereals products, leafy vegetables (including stem vegetables and Brassica vegetables), root and tuber vegetables, legume vegetables, other fresh vegetables (including melons and fruits, bulbs and aquatic plants, sprouts, perennial vegetables such as bamboo shoots and cauliflower), livestock meat, poultry, meat products, edible livestock and poultry offal, eggs and egg products and fruits, totaling 12 categories. Sample mass (minimum 500 g) was determined by the edible portion of the food samples to ensure representativeness. For bulk commodities (e.g., grains), composite samples were created through multi-point collection. For vegetable samples, different parts with varying sizes, forms, and colors were collected to compose a representative sample. All samples underwent individual airtight packaging during transport to prevent cross-contamination, with immediate transfer to designated monitoring laboratories for protocol-compliant storage.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Analytical determination\u003c/h2\u003e\u003cp\u003eThe food samples, ranging from 0.2 g to 0.5 g (accurate to 0.001 g), were ground and homogenized, and treated with 5 mL of concentrated nitric acid (65%, Merck) in a poly tetra fluoroethylene digestion container overnight. The container was covered with a lid and placed in the microwave digestion instrument, following the standard digestion procedure. After the digestion was completed, the acid was rinsed on a hot plate until the liquid in the digestion tube is colorless and transparent or slightly light yellow liquid, with 1\u0026thinsp;~\u0026thinsp;2 drops of liquid remaining. Subsequently, the inner wall of the microwave digestion tube was rinsed three times with a small amount of ultrapure water and the final volume was fixed in a 10.0 mL volumetric flask. According to the standard test method, a standard series of 1\u0026micro;g/L Pb, Cd, Hg and As standard solutions were prepared with 5% nitric acid in volumetric flasks and quantified using the external standard method. The standard solutions, provided by the China Academy of Metrology, included lead (GBW08619), cadmium (GBW08612), mercury (GBW08617), arsenic (GBW08611). Graphite furnace atomic absorption photometry was used to determine Pb and Cd in the samples, and atomic fluorescence photometry was used to determine Hg and As in the samples. The detection limits (LOD) of Pb, Cd, Hg and As are 0.005 mg/kg, 1.0 \u0026micro;g/kg, 0.5 \u0026micro;g/kg and 0.01 mg/kg, respectively. All samples were analyzed in parallel and repeated, and reagent blanks were also performed. The recovery for all measured metals was 85%-115%.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Consumption Data Sources\u003c/h2\u003e\u003cp\u003eThe dietary consumption data for Chongqing was obtained from the China Health and Nutrition Survey in 2011, 2015 and 2018. The survey was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional Review Board of Chinese Center for Disease Control and Prevention, and the Institutional Review Board of Chongqing Center for Disease Control and Prevention (protocol code: KY-2023-024-2). Through multi-stage stratified random sampling, six districts and counties in Chongqing were selected as survey sites, including Nanan, Shapingba, Dazu, Feng jie, Jiangjin and Qijiang. A total of 1254 residents (585 males and 669 females) were surveyed about their food consumption in 2011, including 94 preschoolers aged 3\u0026ndash;6 years, 165 children and adolescents aged 7\u0026ndash;17 years, 733 adults aged 18\u0026ndash;59 years, and 262 elderly people aged 60 years or older. In 2015, 1091 residents (502 males and 589 females) were surveyed, including 55 preschoolers aged 3\u0026ndash;6 years, 139 children and adolescents aged 7\u0026ndash;17 years, 514 adults aged 18\u0026ndash;59 years, and 383 elderly people aged 60 years or older. In 2018, 979 residents (432 males and 537 females) were surveyed, including 31 preschoolers aged 3\u0026ndash;6 years, 113 children and adolescents aged 7\u0026ndash;17 years, 399 adults aged 18\u0026ndash;59 years, and 426 elderly people aged 60 years or older. In this survey, after the selected households provided informed consent, consumption data (including all foods eaten at home and outside) were collected using three consecutive 24-h dietary recalls by well-trained dietary staff. The dietary survey was conducted by face-to-face questioning to collect dietary consumption information and general demographic information (gender, age, and place of residence). For study subjects under 7 years old or over 75 years old, dietary information was provided from adult family members. The food consumption within each food category was summed up for each individual to match with the occurrence data of each food category.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Health risk assessment\u003c/h2\u003e\u003cp\u003eThe estimated daily intake (EDI) of heavy metals from total diet was calculated by using the following equation:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:EDI={\\sum\\:}_{i=1}^{n}\\frac{{F}_{i}\\times\\:{C}_{i}}{BW}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewhere \u003cem\u003eEDI\u003c/em\u003e is the estimated daily intake of a single or the total of four heavy metals (\u0026micro;g/kg BW/d), \u003cem\u003eFi\u003c/em\u003e is the consumption of each food (g/d), \u003cem\u003eCi\u003c/em\u003e is the level of heavy metal contamination in each food category (\u0026micro;g/kg), \u003cem\u003eBW\u003c/em\u003e is the body weight of each respondent (kg).\u003c/p\u003e\u003cp\u003eAccording to the United States Environmental Protection Agency (US EPA) of health risk assessment model\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e, the hazard quotient (HQ) and hazard index (HI) methods were used to measure the health risk of heavy metals.\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:HQ=\\frac{EDI}{RfD}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eHI\u0026thinsp;=\u0026thinsp;HQ\u003c/em\u003e\u003csub\u003e\u003cem\u003ePb\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e+HQ\u003c/em\u003e\u003csub\u003e\u003cem\u003eCd\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e+HQ\u003c/em\u003e\u003csub\u003e\u003cem\u003eHg\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e+ HQ\u003c/em\u003e\u003csub\u003e\u003cem\u003eAs\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eRfD\u003c/em\u003e represents the oral reference dose of Pb, Cd, As and Hg for non-carcinogenic effects. In this study, the \u003cem\u003eRfD\u003c/em\u003e values for Pb, Cd, As and Hg were set as 0.004, 0.001, 0.0003, and 0.0003 mg/kg/day, respectively. Those value were obtained from the Integrated Risk Information System (IRIS) of the US EPA (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.epa.gov/iris/\u003c/span\u003e\u003cspan address=\"http://www.epa.gov/iris/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The \u003cem\u003eHQ\u003c/em\u003e value was used to assess the non-carcinogenic risk from a single metal of all foods, while the \u003cem\u003eHI\u003c/em\u003e value was used to assess the non-carcinogenic risk of combined exposure to heavy metals in all foods. \u003cem\u003eHQ (HI)\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;1 indicates that there is no obvious harm to the human body from heavy metals via dietary exposure, \u003cem\u003eHQ (HI)\u003c/em\u003e\u0026thinsp;\u0026ge;\u0026thinsp;1 indicates that heavy metal intake has potential non-carcinogenic risk.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Monte Carlo simulation method\u003c/h2\u003e\u003cp\u003eMonte Carlo Simulation (MCS) is the most commonly used probabilistic risk analysis method and is recommended by the National Academy of Sciences and the U.S. Environmental Protection Agency for quantifying variability and uncertainty. In this study, MCS was used to estimate the risk of heavy metal exposure in the population and to characterize the uncertainty of the parameters. To ensure parameters stability, 100,000 MCS was performed using the super-Latin cubic sampling method (stability condition) to obtain robust uncertainty analysis results. The mean and 95th percentile value of the HQ (HI) according to the simulation was used to determine the health risk assessment.\u003c/p\u003e\u003cp\u003eAll input parameters included the concentrations of Pb, Cd, As and Hg in food (C), the consumption of each food \u003cem\u003e(F)\u003c/em\u003e, and body weight \u003cem\u003e(BW)\u003c/em\u003e. Distribution fitting for both contaminant concentrations and food consumption data was performed using @Risk's built-in probability models, with optimal distribution functions selected through Akaike Information Criterion (AIC). Parameter estimation was subsequently conducted via maximum likelihood estimation (MLE) and method of moments.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6 Statistical analysis and data processing\u003c/h2\u003e\u003cp\u003eFor non-detected data, data below the LOD were set to 1/2 LOD, following the EFSA recommended treatment for left-censored data\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Microsoft Excel 2016 software was used for statistical analysis of the data. Monte Carlo simulations were conducted with @RISK software 8.2 (Palisade Coporation,8.2. Ithaca, NY, USA).\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results and discussion","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Levels of heavy metals in food categories\u003c/h2\u003e\u003cp\u003eTo analyze the changes in the risk of dietary exposure to four heavy metals (Pb, Cd, As and Hg), the monitoring data on the levels of four heavy metal contaminants in Chongqing's food safety risk monitoring system from 2012 to 2022 were divided into three periods: 2012\u0026ndash;2014, 2015\u0026ndash;2018 and 2019\u0026ndash;2022. Mean concentrations and detection frequencies across food categories are presented in Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Supplementary Figures \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e-S2. Following Chongqing's 2012 expansion of heavy metal monitoring, this enhanced surveillance established robust baseline data reflecting actual contamination levels in market-sold foods. Analysis revealed a stepwise decrease in mean concentrations and detection rates across periods. A decreasing trend was observed in mean Pb concentrations for rice products, legume vegetables, fruits, and egg products. With the exception of leafy vegetables, Pb levels across foodstuffs were significantly higher during 2012\u0026ndash;2014 than in subsequent periods. Similarly, Hg concentrations in wheat products, other cereals, leafy vegetables, root/tuber vegetables, other vegetables, livestock/poultry meats, offal, and egg products were highest during 2012\u0026ndash;2014 relative to subsequent periods, while remaining food categories showed minimal temporal variation. These trends align with Chongqing's industrial legacy and regulatory evolution. As a traditional industrial base, early studies (pre-2010) documented soil contamination by cadmium and lead exceeding national and regional background values\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Chongqing implemented industrial relocation (2002\u0026ndash;2011), transferring chemical, pharmaceutical, metallurgical, and paper manufacturing enterprises to designated suburban zones with centralized wastewater and waste treatment. Post-relocation soil remediation commenced in 2011. Subsequent crucial policy developments include: \u003cem\u003eWork Plan for Soil Environmental Protection and Comprehensive Treatment\u003c/em\u003e (2013); \u003cem\u003eSoil Pollution Prevention and Control Law of China\u003c/em\u003e (2018). The temporal concordance between policy implementation windows and contamination decline substantiates the efficacy of this integrated regulatory approach in reducing dietary metal exposure. Our findings further demonstrate consistency with established soil pollutant trends\u003csup\u003e\u003cspan additionalcitationids=\"CR28 CR29\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eIn this study, the mean concentrations of Pb, Cd, Hg, and As in Chongqing foods during 2018\u0026ndash;2022 were 0.016\u0026thinsp;~\u0026thinsp;0.061, 0.002\u0026thinsp;~\u0026thinsp;0.092, 0.006\u0026thinsp;~\u0026thinsp;0.075 and 0.002\u0026thinsp;~\u0026thinsp;0.006 mg/kg, respectively. These values are lower than contemporaneous contamination levels reported for corresponding food products in major northern Chinese cities like Beijing and the southern industrialized zones such as Pearl River Delta region\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. The transfer and bioavailability of heavy metals within soil-crop systems are governed by soil properties including pH, organic matter content, and cation exchange capacity. Reduced soil pH enhances metal bioaccumulation in crops such as wheat\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Although Chongqing exhibits more pronounced soil acidification than parts of southeastern and northeastern China\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e,\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e, the comparatively lower baseline concentrations of Pb, Cd, Hg, and As in agricultural soils may explain the relatively low concentrations of heavy metals detected in local foods when compared to those from the Pearl River Delta and northeastern China\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThis study further confirms that rice products and leafy vegetables represent primary dietary vectors for heavy metal contamination. Compared to legume vegetables, fruits, and other food categories, rice products and leafy vegetables consistently exhibited higher concentrations and detection frequencies of Pb, Cd, As, and Hg. Evidence indicates that heavy metal levels in vegetables are influenced by species-specific factors, soil pH, and background contamination, with vegetable species constituting the predominant determinant\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. Leafy vegetables demonstrate significantly greater bioaccumulation capacity than root/tuber or fruit-type vegetables. This phenomenon is likely attributable to their heightened transpiration rates combined with physical proximity to topsoil, which facilitates direct contaminant contact through low-growing foliage\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eIn this study, mean concentrations in rice and rice products ranged from 0.044\u0026ndash;0.062 mg/kg for Cd, 0.060\u0026ndash;0.105 mg/kg for As, 0.034\u0026ndash;0.048 mg/kg for Pb, and 0.002\u0026ndash;0.004 mg/kg for Hg. Detection frequencies for As (79.9\u0026ndash;91.5%) and Cd (72.2\u0026ndash;94.7%) substantially exceeded those of Pb (34.6\u0026ndash;67.9%) and Hg (27.0\u0026ndash;39.2%). These findings align with published literature indicating significant Cd and As contamination in rice\u003csup\u003e\u003cspan additionalcitationids=\"CR41 CR42\" citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. The elevated Cd and As levels in Chongqing rice may be attributed to: (1) Relatively higher background concentrations of Cd and As in local soils\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e; (2) Enhanced bioaccumulation factors for Cd and As in rice compared to other metals like Pb and Cr\u003csup\u003e40,44\u003c/sup\u003e. Comparative analysis with contemporaneous data reveals Chongqing rice Cd/As levels exceeded those documented in Nepal\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e, Japan\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e, and China's Jilin Province\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e, but remained below concentrations reported in Chengdu, China\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e and Vietnam\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. The comparative values are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. This suggests Chongqing's rice contamination levels may rank among China's lower tiers. Nevertheless, given rice's dietary significance in China and the global food safety implications of Cd/As contamination, sustained monitoring remains imperative.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eLevels of Cd and As in Rice and Rice products from Chongqing compared with the literature data from similar foodstuffs (mg/kg)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"13\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eFood Category\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003ePresent study\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eJilin Province, China \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eChengdu, China \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003eJapanese \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003eVietnam \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003eNepal \u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCd\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAs\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCd\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAs\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCd\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eAs\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eCd\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eAs\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eCd\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003eAs\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c12\"\u003e\u003cp\u003eCd\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c13\"\u003e\u003cp\u003eAs\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRice and Rice products\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.044\u0026ndash;0.062\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.060\u0026ndash;0.105\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.030\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.040\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.096\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.188\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.035\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.111\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.115\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e0.0155\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e0.0434\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"13\"\u003e\u003csup\u003ea\u003c/sup\u003e Wang B et al., 2020\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"13\"\u003e\u003csup\u003eb\u003c/sup\u003e Wang R et al., 2018\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"13\"\u003e\u003csup\u003ec\u003c/sup\u003e Watanabe T et al., 2022\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"13\"\u003e\u003csup\u003ed\u003c/sup\u003e Chu DB et al., 2021\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"13\"\u003e\u003csup\u003ee\u003c/sup\u003e Shao Y et al., 2023\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Food consumption of Chongqing residents\u003c/h2\u003e\u003cp\u003eFood consumption patterns reflect not only regional dietary habits and living standards but also serve as critical parameters for dietary risk assessment. Given China's well-documented regional heterogeneity in dietary practices, our data reveal divergences between Chongqing's observed consumption patterns and those reported in national-level studies\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Supplementary Tables S1-S3 present Chongqing's per capita food consumption across categories (2012\u0026ndash;2022). Rice products and leafy vegetables constituted the most consumed food groups locally. Mean consumption of cereals and derivatives (309.10\u0026ndash;328.36g/d) exceeded the 200\u0026ndash;300g/day recommendation in the Chinese Dietary Guidelines\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. Conversely, vegetable intake (270.94\u0026ndash;307.95g/d) fell below recommended levels (300\u0026ndash;400g/d), while animal-source foods (117.73\u0026ndash;134.66g/d) were marginally below guidelines (120\u0026ndash;200g/d). Fruit consumption remained substantially lower than recommended values (200\u0026ndash;400g/d). These findings demonstrate a rice- and wheat-centric dietary pattern in Chongqing, explaining the cereal surplus and vegetable/meat deficits. Three major temporal trends emerged in residents' dietary patterns: (1) Structural modification of grain consumption characterized by a notable increase in wheat-based products (10.16% \u0026rarr; 12.86% of total intake) with a corresponding decrease in rice consumption; (2) Vegetable preference transition toward root varieties; (3) Persistent red meat growth (10.37% \u0026rarr;13.12%), declining poultry/fruit intake (2.59%/5.10% \u0026rarr;2.04%/3.35%), and rising egg demand (2.36% \u0026rarr;2.72%). This evolution signifies a transition from traditional \"grain-vegetable\" patterns toward diversified \"grain-meat-vegetable-fruit\" consumption, coupled with a shift from home-cooked meals to energy-dense, processed convenience foods\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Potential health risks of individual and cumulative exposure\u003c/h2\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents probability density functions and temporal trends of HQ for dietary exposure to Pb, Cd, As, and Hg across three periods. Mean and 95th percentile HQ values indicate negligible health risks from Pb (0.1113-0.210; 0.293\u0026ndash;0.557) and Hg (0.138\u0026ndash;0.209; 0.346\u0026ndash;0.573). Conversely, elevated Cd (mean HQ: 0.377\u0026ndash;0.456) and As exposures (mean HQ: 1.62\u0026ndash;2.27) demonstrated concerning profiles, with 95th percentiles exceeding the safety threshold (HQ\u0026thinsp;\u0026gt;\u0026thinsp;1) in all periods. Based on mean HQ values, heavy metals were ranked in descending order of risk as follows: As (2.13)\u0026thinsp;\u0026gt;\u0026thinsp;Cd (0.435)\u0026thinsp;\u0026gt;\u0026thinsp;Pb (0.210)\u0026thinsp;\u0026gt;\u0026thinsp;Hg (0.209) in 2012\u0026ndash;2014, As (2.27)\u0026thinsp;\u0026gt;\u0026thinsp;Cd (0.456)\u0026thinsp;\u0026gt;\u0026thinsp;Hg (0.138)\u0026thinsp;\u0026gt;\u0026thinsp;Pb (0.1113) in 2015\u0026ndash;2018 and As (1.62)\u0026thinsp;\u0026gt;\u0026thinsp;Cd (0.377)\u0026thinsp;\u0026gt;\u0026thinsp;Hg (0.144)\u0026thinsp;\u0026gt;\u0026thinsp;Pb (0.116) in 2019\u0026ndash;2022.\u003c/p\u003e\u003cp\u003eProbabilistic assessment quantified population-level risk distributions, showing fewer than 3% and 2.3% of residents exceeded HQ\u0026thinsp;=\u0026thinsp;1 for Pb and Hg respectively. However, significant proportions faced risks from Cd and As exposure: Cd exceedances affected 9.1%, 9.3%, and 6.9% of the population across the three periods, while As impacted 52.9%, 67.2%, and 49.5%. Regional comparisons revealed Chongqing's mean HQ values exceeded those reported for lower Yangtze River cities\u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e and Xiamen\u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e, though remaining below Beijing levels for Pb/Cd (Pb: 0.54, Cd: 0.50)\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e and national Cd/As averages (Cd: 0.54, As: 6.49)\u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eTemporal analysis demonstrated progressive reduction in Pb and Hg exposure risk post-2014. Cd and As exhibited initial increases followed by significant declines in 2019\u0026ndash;2022, though elevated risk persisted at higher exposure percentiles. Although risk levels for all heavy metals at the 50th percentile exhibited fluctuation, the HQs at the 95th percentile demonstrated a consistent downward trend across the study periods, indicating reduced health risks for high-exposure subpopulations. These exposure dynamics correlate with dietary transitions documented in Section \u003cspan refid=\"Sec11\" class=\"InternalRef\"\u003e3.2\u003c/span\u003e and contaminant reductions revealed in Section \u003cspan refid=\"Sec10\" class=\"InternalRef\"\u003e3.1\u003c/span\u003e, confirming consumption patterns and heavy metal concentrations as primary risk determinants.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eHuman populations are concurrently exposed to multiple heavy metals through dietary intake. Substantial evidence from food monitoring studies confirms the co-occurrence of various heavy metals in food matrices, with biomarker analyses further verifying chronic multi-metal exposure in humans\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Given increasing scientific and regulatory concerns about combined chemical exposures, we conducted a cumulative risk assessment for Pb, Cd, As, and Hg. Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e presents the HQ profiles and temporal trends of the HI across three study periods. The HI values at the 50th and 95th percentiles for combined exposure were 1.81 and 7.60 (2012\u0026ndash;2014), 2.13 and 7.32 (2015\u0026ndash;2018), and 1.57 and 5.49 (2019\u0026ndash;2022), respectively, exceeding recognized safety thresholds (HI\u0026thinsp;\u0026gt;\u0026thinsp;1), which necessitates consideration of potential cumulative health risks. Cumulative exposure to these metals posed substantially greater risks than individual metal exposures. The HI values exhibited an initial increase followed by subsequent decline across study periods, with notable reductions observed at the 95th percentile exposure level. Specifically, 2019\u0026ndash;2022 HI values decreased below both earlier measurement periods.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Sensitivity analysis of each heavy metal exposure risk\u003c/h2\u003e\u003cp\u003eSensitivity analysis identified key determinants significantly influencing HQ for dietary exposure to Pb, Cd, As, and Hg, with absolute correlation coefficients quantifying each variable's impact magnitude (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e; top 16 determinants shown). For Pb, Cd and As, concentrations in rice products and leafy vegetables, consumption levels of these two food groups, and body weight emerged as predominant risk drivers. Additionally, Pb exposure demonstrated sensitivity to contamination levels in wheat products, root/tuber vegetables, and livestock meat, while cadmium risk was governed by concentrations and intake of wheat products and root/tuber vegetables. These findings indicate that reducing lead and cadmium concentrations in these priority foods would yield significant risk mitigation. For Hg, primary risk determinants included consumption and contamination levels in rice products and leafy vegetables, with secondary contributions from Hg concentrations in livestock meat.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e3.5 Uncertainty analysis\u003c/h2\u003e\u003cp\u003eWhile this study employed probabilistic assessment methodology (superior to deterministic approaches in accuracy) incorporating food surveillance-derived heavy metal concentrations and 3-day 24-hour dietary recall data to mitigate uncertainty, several limitations persist. First, excluded food categories (e.g., dairy and legume products) and non-dietary exposure pathways (inhalation, dermal) may result in underestimated health risks. Second, variations in heavy metal bioaccessibility across food matrices remain unaddressed. Third, our cumulative risk assessment adopted EFSA's effect addition approach without considering: (1) toxicokinetic interactions between metals (synergistic/antagonistic effects), (2) target organ specificity, or (3) mode of action diversity. These mechanistic complexities necessitate refined risk models as toxicological evidence evolves.\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Conclusion","content":"\u003cp\u003eThis study employed probabilistic risk assessment to evaluate individual and cumulative dietary exposure to Pb, Cd, Hg, and As in Chongqing from 2012 to 2022. Key findings demonstrate that national and municipal soil remediation policies have effectively reduced heavy metal levels in market-sold foods, corresponding to decreasing exposure risks over time. Notwithstanding these improvements, significant health concerns persist: at least 6.9% and 49.5% of residents remain at risk of health impairment from Cd and As exposure, respectively. Cumulative exposure assessment revealed concerning HI ranging from 1.57 to 7.60 for the four heavy metals, underscoring the critical need for combined risk management. Rice and leafy vegetables were identified as primary exposure vectors, warranting prioritized surveillance and control measures.\u003c/p\u003e\u003cp\u003eThis study establishes a critical evidence base for food safety policy formulation in Southwest China, providing comprehensive exposure assessment data on priority heavy metal contaminants (Pb, Cd, As, Hg) across core food categories. By quantifying temporal trends and population risk distributions through probabilistic modeling, our findings enable the development of evidence-informed management frameworks for targeted food chain interventions. These scientifically-grounded approaches directly support regional initiatives to enhance food safety governance and safeguard population health, particularly through optimized surveillance of high-risk food vectors like rice and leafy vegetables.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eDeclaration of competing interest\u003c/h2\u003e\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis study was supported by the Chongqing Joint Sci-Tech and Health Medical Research Project (2025MSXM146), the China Postdoctoral Science Foundation (2022M710549), the Natural Science Foundation of Chongqing (CSTB2025NSCQ-GPX1059), the National Key Research and Development Program of China (2017YFC1602004) and the Chongqing Disease Prevention and Public Health Research Center Construction Program.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eJG Chen and JH Chen conducted data analysis and drafted the manuscript. P Feng, MT Li, and XH Dai performed the investigations and collected food samples. M Qin, L Chen, XQ Tang and J Zhao conducted sample testing. SQ Luo and XM Lian provided supervision and technical guidance. HD Zhang and J Huo conceived and supervised the project, oversaw its progression, revised the manuscript, and serve as corresponding authors.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors would like to acknowledge the China National Center for Food Safety Risk Assessment (CFSA) for their guidance on the study. We also extend our appreciation to the district and county-level Centers for Disease Control and Prevention for their diligent efforts in sample collection and laboratory analyses.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSong, J. et al. 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Total Environ.\u003c/em\u003e \u003cb\u003e689\u003c/b\u003e, 1141\u0026ndash;1148 (2019).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"heavy metal, dietary exposure, risk assessment, temporal changes","lastPublishedDoi":"10.21203/rs.3.rs-7690165/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7690165/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eChongqing, a traditional industrial base and agricultural hub in inland Southwest China, exhibits dietary patterns representative of this rapidly developing region. Following early 21st-century environmental policies targeting soil heavy metal remediation, this study employed probabilistic risk assessment (2012\u0026ndash;2022) to evaluate policy impacts on population health. Results revealed significant contaminant reductions in commercially available foods during 2018\u0026ndash;2022 versus 2012\u0026ndash;2014, with lead (Pb), cadmium (Cd), arsenic (As), and mercury (Hg) concentrations declining to 0.016\u0026ndash;0.061, 0.002\u0026ndash;0.092, 0.006\u0026ndash;0.075, and 0.002\u0026ndash;0.006 mg/kg respectively. These declines align with Chongqing's regulatory evolution and industrial transformation, driving progressive health risk reductions: hazard quotients (HQ) for Pb and Hg remained below 1.0 (indicating negligible risks), while Cd and As posed sustained yet decreasing concerns with 6.9% and 49.5% of residents exceeding HQ\u0026thinsp;=\u0026thinsp;1. Additionally, cumulative exposure to the four heavy metals yielded hazard index values of 1.57\u0026ndash;7.60, highlighting potential health implications from combined heavy metal exposure. Notably, significant reductions observed at the 95th exposure percentile demonstrate a persistent downward trend in health risks for highly exposed subpopulations. Rice products and leafy vegetables emerged as primary exposure vectors requiring prioritized surveillance, establishing this probabilistic assessment as an evidence base for food safety policy optimization in Southwest China through comprehensive exposure characterization across core food categories.\u003c/p\u003e","manuscriptTitle":"Probabilistic risk assessment of heavy metal exposure via diet in Southwest China: Temporal changes and driving factors in Chongqing (2012–2022)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-22 20:49:08","doi":"10.21203/rs.3.rs-7690165/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-10T13:18:16+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-09T09:26:18+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-04T10:11:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"239251027308987639007634136754137035430","date":"2025-10-25T03:04:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"70975532203780380239970547215493365503","date":"2025-10-25T02:16:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"181099780759205356299682977237168874176","date":"2025-10-09T03:35:58+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-09T00:39:08+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-26T19:06:29+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-25T10:10:44+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-23T09:29:05+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-09-23T06:00:39+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4b3f9939-94ab-4a0e-8523-b9b07d736f0e","owner":[],"postedDate":"October 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":56530891,"name":"Earth and environmental sciences/Environmental sciences"},{"id":56530892,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2026-01-19T16:46:29+00:00","versionOfRecord":{"articleIdentity":"rs-7690165","link":"https://doi.org/10.1038/s41598-026-36387-4","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2026-01-14 16:29:06","publishedOnDateReadable":"January 14th, 2026"},"versionCreatedAt":"2025-10-22 20:49:08","video":"","vorDoi":"10.1038/s41598-026-36387-4","vorDoiUrl":"https://doi.org/10.1038/s41598-026-36387-4","workflowStages":[]},"version":"v1","identity":"rs-7690165","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7690165","identity":"rs-7690165","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}