Rice, a staple food for over half the world's population, is vulnerable to heavy metal contamination. Existing maximum acceptable concentrations (MACs) for heavy metals in rice, set by organizations like the WHO and FAO, may not fully reflect the health risks associated with long-term consumption, particularly at low concentrations. Furthermore, existing risk assessments often neglect spatial variability and uncertainty in exposure parameters like body weight, age, and dietary habits, which significantly impact risk levels across different populations. This study addresses this gap by investigating heavy metal concentrations in commercial rice across 32 provinces in China, incorporating probabilistic and fuzzy methods to quantify the health risks more accurately, considering variations in exposure parameters and regional differences.

Numerous studies have investigated heavy metal contamination in rice, but most are localized and lack nationwide scope, simplifying calculations using uniform body weight and rice intake assumptions. While some national surveys exist (China, Spain, Kuwait, USA, and Southeast Asian countries), they often lack the detail necessary to account for the significant influence of receptor differences and regional variations in dietary habits. Existing health risk assessment (HHRA) methods, while useful, often struggle with parameter uncertainty, leading to over- or underestimation of risks. This study aims to improve upon existing methods by integrating probabilistic and fuzzy approaches to address aleatory and epistemic uncertainties, respectively. Monte Carlo simulation is used to handle aleatory uncertainty arising from the inherent variability in pollutant concentrations, body weight, and dietary intake. Fuzzy analysis handles epistemic uncertainty stemming from data scarcity and ambiguity in risk perception among assessors. By combining these methods, this study offers a more comprehensive and accurate evaluation of the health risks associated with rice consumption.

Data on heavy metal (cadmium, arsenic, mercury, lead, chromium) and nutrient element (copper, zinc) concentrations in commercial rice were collected from 408 peer-reviewed articles (3376 data points). Data from mining or sewage irrigation areas were excluded to ensure reliability. The study used the national food safety (NFS) standards issued by the Chinese government. Human health risk assessment (HHRA) was performed, considering four age groups (toddlers, children, teenagers, adults). The average daily dose (ADD) was calculated using a formula incorporating concentration, daily rice intake, exposure duration, body weight, and average time. Non-carcinogenic risk (NCR) was assessed using the hazard quotient (HQ), while carcinogenic risk (CR) was calculated using the carcinogenic risk slope factor (SF). Both probabilistic and fuzzy methods were employed. Probability analysis, using Monte Carlo simulation, quantified the probability of HQ and CR exceeding risk thresholds. Fuzzy analysis integrated the probabilistic risk assessment with a nutritional value assessment using a fuzzy logic approach to derive a rice quality-heavy metal (RQHM) score, representing a comprehensive safety and quality assessment. The single factor pollution index (SFPI) was also calculated for comparative purposes.

Probability analysis revealed that arsenic (As) and cadmium (Cd) posed the most significant health risks, contributing 64.57% and 22.38% of the overall risk, respectively. The average hazard quotient (HQ) values for critical receptors (children and toddlers) indicated that even rice meeting NFS standards can pose non-negligible health risks. The highest HQs were observed in central and southern China. Fuzzy assessment showed that the RQHM score was significantly higher in northern China compared to southern China. The study highlighted considerable regional differences in the probability of exceeding risk thresholds for both carcinogenic and non-carcinogenic risks. Specific findings include: Average HQs: As (0.67) > Cd (0.52) > Cr (0.38) > Pb (0.16) > Hg (0.15). Children and toddlers were consistently identified as critical receptors across most provinces. Spatial variation in risks was substantial, with higher risks observed in central and western China. The contribution of heavy metals to overall health risk varied regionally, with As dominating in northern China and Cd dominating in southern China. Fuzzy analysis indicated that the safety and quality of rice were higher in the north and lower in the south.

The findings highlight a significant mismatch between current NFS standards and actual human health risks associated with rice consumption. The study's use of probabilistic and fuzzy methods provides a more nuanced understanding of risk than traditional approaches, emphasizing the importance of considering both the concentration of heavy metals and the characteristics of the exposed population. The regional disparities in risk underscore the need for locally tailored food safety standards that account for variations in rice production practices, consumption patterns, and population vulnerability. The high contribution of arsenic and cadmium highlights the need for focused efforts to reduce their levels in rice. The identification of children and toddlers as critical receptors underscores the need for preventative measures targeted at this vulnerable population. The study's insights are relevant for policymakers in developing more effective food safety regulations and risk management strategies.

This study demonstrates that existing food safety standards may not adequately protect vulnerable populations from the health risks associated with heavy metal contamination in rice. The integrated probabilistic and fuzzy approach provides a more comprehensive and accurate assessment of these risks. The significant regional disparities emphasize the need for locally specific standards and risk management strategies. Future research should focus on improving data collection on dietary intake, expanding the scope beyond rice to other food sources, and investigating the effectiveness of various mitigation strategies. Policy recommendations include refining exposure parameters, incorporating explicit indicators into limit criteria, and implementing measures to reduce heavy metal concentrations in rice, particularly in regions with higher risk levels.

The study focused on commercial rice and may not fully represent the range of heavy metal contamination in locally grown rice. The use of average intake levels for different age groups might have underestimated individual variations in exposure. The study did not consider other food sources, potentially underestimating the overall health risk from heavy metals. The study's reliance on existing literature data introduces limitations related to data quality and consistency.