Heavy metal(loid) pollution in commercial rice
The average Cd, As, Hg, Pb, and chromium (Cr) concentrations in rice were 0.068, 0.021, 0.007, 0.065, and 0.121 mg kg−1, respectively, which were far lower than the NFS standards (Cd = 0.2 mg kg−1, As = 0.2 mg kg−1, Hg = 0.02 mg kg−1, Pb = 0.2 mg kg−1, and Cr = 1.0 mg kg−1, respectively). The maximum Pb, As, Hg, and Cr concentrations were 1.455, 1.1, 2.8, and 1.05 times of their risk thresholds, respectively. The average Cu and Zn concentrations in commercial rice were 2.31 and 15.429 mg kg−1, respectively, indicating a good nutritional status.
The average value of the SFPI index followed the order of Pb (0.639) > Hg (0.595) > Zn (0.441) > Cu (0.437) > Cd (0.328) > As (0.205) > Cr (0.181). The results showed that the risk of heavy metal(loid) contamination in commercial rice was lower than the risk threshold (1.0). The SFPI values of Cd, As, Hg, Pb, Cr, Cu, and Zn in commercial rice ranged from 0.037 to 0.655, 0.058 to 1.1, 0.073 to 2.802, 0.441 to 1.5, 0.008 to 1.05, 0.187 to 1.35, and 0.199 to 0.882, respectively (Supplementary Fig. 5). The Hg concentrations in rice from Shaanxi, Guizhou, Jilin, Guangdong, and Hunan were 2.80, 2.20, 1.26, 1.07, and 1.01 times of those in the NFS standards, respectively, indicating that the food safety level of rice was not adequate. In addition, Pb concentrations in rice from Jiangsu, Anhui, Tianjin, Jilin, and Liaoning were 1.26–1.5 folds of those in the NFS standards. The As concentration in Taiwan and the Cr concentration in Sichuan were higher than their respective MACs. These results indicated that commercial rice in these provinces was contaminated by various heavy metal(loid)s. The relatively high Cu concentration in Guizhou rice indicated that the nutritional value of rice may be affected.
Risk status evaluated based on probability analysis
The average HQ of critical receptors followed the order of As (0.67) > Cd (0.65) > Cr (0.38) > Pb (0.16) > Hg (0.15) (Supplementary Table 5). For most provinces, the HQs of As and Cd were much higher than those of other three heavy metal(loid)s. The HQs of As in the three northeastern provinces were 10.20, 4.04, and 3.25 folds those of Hg, Pb, and Cr, respectively. The HQ of Cd was 2.50, 2.29, and 2.15 folds that of Hg, Pb, and Cr in the main rice-producing and rice-consuming areas (Hunan, Hubei, Jiangxi, and Guangxi) of southern China, respectively. In central and southern China, the NCR indicators (HQs) for As and Cd exceeded 1.0. The average CR for critical receptors was higher for As than for Pb. Overall, the mean CR values were all below 1 × 10−4, which were within the acceptable risk range. In addition, regardless of the type of risk, the risk values were higher for children and toddlers than for adults.
When exposed to a toxic substance, the critical receptors with the most obvious response were the representative group in the exposure assessment (Supplementary Fig. 6). In general, young people (<18 years) were the critical receptors in all provinces. However, the specific age groups of the critical receptors were different in different provinces. The critical receptors were children (5–12 years) in about two-thirds of the provinces and toddlers (2–5 years) in the remaining provinces. The critical receptors in the central rice-producing provinces tended to be toddlers.
We plotted the cumulative distribution function of heavy metal(loid) exposure in rice (Supplementary Fig. 7). There was substantial diversity in the health risks for specific critical receptors in various provinces. In Gansu, Guizhou, Ningxia, Sichuan, Chongqing, and Taiwan, there were much higher health risks than in other provinces. The main risk in these six provinces was generated by As. The risk caused by Cd could not be ignored because its probability of exceeding the risk threshold for 20 provinces ranged from 0.003 to 0.992. Hg and Pb contamination affected three and six provinces, respectively. In contrast, the risk in Henan province was zero and there was no health effect on critical receptors due to heavy metal(loid)s. The cumulative effect of heavy metal(loid) exposure also showed significant differences among different provinces. The growth rate and distribution of the cumulative distribution function of Cd, As, Hg, Pb, and Cr varied significantly among different provinces. For example, the critical receptors in Hubei and Hainan were toddlers, and there was no significant difference in their BW and IR. However, the risk accumulation rate of Cd in Hubei was greater than that in Hainan due to the 2.5-fold difference in rice Cd concentration (yellow solid lines of HB1 and HN2 in Supplementary Fig. 7). In addition, the As concentrations in Guangdong and Jilin rice were similar (0.031 mg kg−1), and the critical receptors were the same. Because the Guangdong population had 1.75 times rice intake of the Jilin population, Guangdong had a faster risk accumulation rate of As than Jilin (purple dotted-dashed lines of GD and JL in Supplementary Fig. 7).
The P NCR values across provinces were calculated based on the HQ of the critical receptors in each province. The excess probability of all NCR indicators is shown in Fig. 1. The P NCR varied significantly among different provinces (ranging from 0.005 to 0.997). The risk was significantly higher in central China than in other regions. The NCR increased gradually from the west to the east and from the north to the south. There were also provincial differences in the probability of NCR exceeding the risk threshold. The P NCR values in 23 provinces were related to As. The P NCR values in Guangdong, Guangxi, Hunan, and Jiangxi were related to Cd. Cr dominated the P NCR in Yunnan and Guizhou. Although the average heavy metal(loid) concentrations in most provinces were lower than their respective MACs, the health risks caused by long-term exposure to relatively high levels of heavy metal(loid)s, particularly for sensitive groups (such as toddlers and children), may still be significant.
Fig. 1: Probability distribution of the non-carcinogenic health risk (NCR) exceeding the risk threshold (P NCR ). [Note: Regardless of the type of heavy metal(loid), the highest P NCR in each province was taken to indicate the highest possible NCR. The shade of blue indicated the probability of non-carcinogenic risk exceeding the standard. The darker the blue, the higher the probability]. Full size image
Arsenic and Pb were the CR assessment targets of ingested rice. The distribution of P ILCR in each province is shown in Fig. 2. In 24 provinces, there was no unacceptable CR, and the P ILCR was zero. In contrast, the CR in central and western China was slightly higher, with a mean P ILCR of 0.413. The unacceptable CR in Taiwan was due to the excessive As concentration in rice. The As concentration was critical for determining the CR in all provinces due to its high carcinogenic toxicity (Supplementary Fig. 8). Long-term exposure to As, even at levels lower than the MAC, presented a significant CR. In contrast, Pb was not linked to any significant CR in the 32 provinces.
Fig. 2: Probability distribution of the carcinogenic health risk (CR) exceeding the risk threshold (P CR ). [Note: regardless of the type of heavy metal(loid), the highest P CR in each province was taken to indicate the highest possible CR. The shade of blue indicated the probability of carcinogenic risk exceeding the standard. The darker the blue, the higher the probability]. Full size image
The total health risk was determined from the combined effect of various heavy metal(loid)s. Figure 3 shows the prominent contribution of As in northern China, with contribution rates ranging from 52.55 to 100%. There were obvious differences among provinces in southern China. The Cd concentration had the greatest contribution in Hunan, Jiangxi, Guangdong, and Guangxi, accounting for 51.60%, 97.48%, 44.31%, and 49.88% of the overall health risk, respectively. The contribution of Cr was the greatest in Guizhou and Yunnan (39.59% and 85.06%, respectively). Arsenic was the most significant contributor to human health risks, with an average contribution of 64.57%, followed by Cd with an average contribution of 22.38%. The Hg concentration only had a minuscule contribution to human health risks, with a contribution of merely 1.53%.
Fig. 3: Probability distribution of ultimate health risks and the contributions of Cd, As, Hg, Pb, and Cr to human health risks. [Note: The pie chart showed the contribution of five heavy metals(loid) to the health risk. The large (small) sector area indicates the high (low) risk contribution. A complete pie chart indicated a contribution of 100%. The size of the pie chart represented the sum of the probability of five heavy metal(loid)s exceeding the standard. The following abbreviations are used: Arsenic (As; purple), Cadmium (Cd; aqua), Chromium (Cr; green), Mercury (Hg; sky blue), Lead (Pb, pink)]. Full size image
To ensure nutritional safety, the high P NV between Cu and Zn was selected as the critical criterion for determining the impact on the nutritional value of rice. The results showed that the nutritional value of rice was not affected in most provinces, and the corresponding P NV was zero. The final P NV values of Anhui, Guangdong, and Inner Mongolia were 0.043, 0.095, and 0.033, respectively (Supplementary Figs. 9 and 10), confirming that the concentrations of nutrient elements in rice in these provinces were at a risk of exceeding the nutrient limits.
Rice quality score evaluated based on fuzzy analysis
Social demand for food is based on not only safety but also nutrition. Therefore, a comprehensive method is needed to assess both the heavy metal(loid) pollution level and nutritional value of rice. Here, we used a fuzzy analysis to integrate the P HR and P NV obtained from a probability analysis and finally obtained a comprehensive and specific RQHM score.
The P HR and P NV values of Heilongjiang Province were 0.265 and 0, respectively. The P HR was mapped to the fuzzy membership function as shown in Supplementary Fig. 2. The critical level of health risk could then be described as partially L (μL HR = 0.175) and partially LM (μLM HR = 0.825), and the critical level of nutritional value was L (μL NV = 1). Therefore, two different combinations of the health risk and nutritional value would affect the critical level. The fuzzy AND operator connects health risk and nutritional value effects. The fuzzy rice safety quality level can be determined according to the generated fuzzy rules as shown in Supplementary Table 4. For example, when the health risk was “L” (μL HR = 0.175), and the nutritional value impact was “L” (μL NV = 1), the RQHM was identified as “excellent (E)” (μE RQHM = 0.175) (Supplementary Fig. 3). The different RQHM levels were aggregated into a shape representing the final fuzzy RQHM using the fuzzy OR operator. The RQHM in Heilongjiang Province was determined by calculating the centroid of the final shape (Fig. 4). Heilongjiang Province scored 72.72. Although the nutritional value of rice in Heilongjiang Province was good, and the heavy metal(loid) concentrations in rice also met the NFS standards, the health risks posed by Cd and As could not be accepted. The same fuzzy method was applied to various provinces in China to obtain the quality score of rice safety (Supplementary Fig. 11).
Fig. 4: Fuzzy inference process in Heilongjiang Province. [Note: a, d: health risks identified using health risk guidelines (pink); b, e: nutritional value impact (yellow); c, f, g: the rice quality–heavy metal(loid) (RQHM) score (green). The fuzzy AND operator of a and b and of d and e were used to obtain c and f; the fuzzy OR operator of c and f was used to obtain (g); μ indicated critical level, which was obtained by mapping the exceeding probability to the fuzzy membership function. The pale gray shading represented the initial fuzzy membership function, and the bright shading represented the fuzzy graph obtained by mapping the critical level into the fuzzy membership function]. Full size image
A high score indicates a high safety level for the rice and a lower risk to human health. The scores indicated good safety and quality of rice in northwest and northeast China (Fig. 5). Therefore, no measures are necessary to control heavy metal(loid)s in these areas. The scores in the central and western regions of China were not high, ranging from 46.60 to 81.07 (Supplementary Table 6). High As concentrations can cause significant health risks to sensitive populations. These provinces should make efforts to further reduce the heavy metal(loid) concentrations in rice and encourage producers and consumers to integrate heavy metal(loid) removal technologies into rice production and cooking process. The scores in southern China indicated that rice quality needs to be improved, with the lowest score being 10.83. The high Cd and Cr concentrations pose a significant NCR to sensitive people. Hainan, Guangxi, and Hunan are the main rice-producing areas of China. In addition to the approaches mentioned above, risk control measures in these provinces can be started from management of the pollution source, such as farmland rehabilitation and planting rice varieties with low accumulation of heavy metal(loid)s. In other major rice consumption areas such as Guangdong, residents should adjust their dietary structure to reduce their rice intake. At the same time, rice could be imported from places with lower heavy metal(loid) concentrations, such as northeast China. In provinces with low scores, there was a need to reduce the heavy metal(loid) concentrations in rice, thereby reducing human health risks.