From farm to table: Assessing the status and health risk assessment of heavy metal pollution in rice in Henan province

From farm to table: Assessing the status and health risk assessment of heavy metal pollution in rice in Henan province | 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 Research Article From farm to table: Assessing the status and health risk assessment of heavy metal pollution in rice in Henan province Yumeng Yan, Zhenxing Mao, Xinlu Wang, Zhiwei Chen, Cuicui Ma, and 13 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4761025/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract In recent years, industrial and agricultural advancements in Henan Province have increased heavy metal contamination in rice, raising public concerns. This study investigated heavy metal levels in rice from Henan Province and evaluated potential health risks. A total of 6,632 rice samples were collected from 18 regions between 2020 and 2022. Using inductively coupled plasma mass spectrometry (ICP-MS), we analyzed samples for cadmium (Cd), chromium (Cr), lead (Pb), mercury (Hg), and inorganic arsenic (As). Detection rates were compared using the chi-square test, and health risks were assessed per USEPA guidelines. Detection rates for Cd, Cr, Pb, Hg, and As were 27.69%, 22.57%, 2.25%, 1.95%, and 99.59%, respectively. Cd levels were significantly higher in urban areas (30.42%) than rural areas (23.13%) (P < 0.001), with regional variations for Cd, Cr, and Pb (P < 0.001). The Hazard Quotient (HQ) for inorganic As exceeded 1. Heavy metal contamination was more prevalent in urban areas, especially in the central region, posing health risks due to elevated inorganic arsenic levels. Rice Safety Health Risk Assessment Environmental Pollution Agricultural Impact Figures Figure 1 Figure 2 Figure 3 INTRODUCTION Rapid economic and social development in China, along with population growth, urbanization, industrialization, and the expansion of heavy metal-related industries, has led to significant heavy metal accumulation in agricultural soils, posing a serious threat to crop quality, safety, and human health [ 1 , 2 ] . Heavy metals such as cadmium (Cd), chromium (Cr), lead (Pb), total mercury (Hg) and inorganic arsenic (As), are often collectively referred to as the "five poisons" because of their toxicity and potential health hazards. These heavy metals primarily enter the human body through the diet and accumulate via the food chain, causing health issues [ 3 ] . Cadmium mainly accumulates in the kidneys, which may lead to renal insufficiency and osteoporosis [ 4 ] .Chromium is a heavy metal needed by the human body, but excessive intake can cause poisoning, damage the liver and kidneys, and increase the risk of cancer [ 5 ] . Lead can damage the digestive system, liver, kidneys, and nervous system, causing high blood pressure, infertility, and affecting the intellectual development of children [ 6 ] . Long-term exposure to mercury is associated with Minamata disease and neonatal neurological problems in newborns [ 7 ] .Arsenic, though a metalloid, is classified as a heavy metal due to its toxicity. Excessive exposure can cause cardiovascular disorders and cancer [ 8 ] . The United Nations Food and Agriculture Organization (FAO 2021) reports that the worldwide rise in population has increased the demand for rice, with a particularly notable surge in China [ 9 ] . As a key food crop, rice can absorb and accumulate significant amounts of heavy metals, potentially posing health risks through rice-based products [ 10 ] . For example, exposure of Bangladeshis to As through rice consumption may result in non-carcinogenic and carcinogenic health risks [ 11 , 12 ] . China, as the country with the largest population in the world, is gradually increasing the proportion of refined grains, especially rice, in its staple food [ 13 ] . However, alongside its rapid economic development, China has experienced serious soil heavy metal pollution [ 14 ] .By 2022, the area under rice cultivation in China covered 29,921.2 thousand hectares. It has been reported that the exceeding rate of metal elements in Chinese arable soils is as high as 1.71%, and the proportion of heavy metal contamination in soils that need to be fallowed is 15.58%, and this situation is particularly serious in Henan and Hunan [ 15 ] . Some studies have examined the heavy metal content in rice. A study conducted a detailed analysis of rice from Wan County in China’s Xinjiang Uygur Autonomous Region, measuring the concentrations of 10 heavy metals, and found that As posed the highest risk of causing cancer during consumption [ 16 ] . Furthermore, another research team tested rice samples from 17 provinces (cities) in China and measured the concentrations of nine heavy metals. The findings revealed that all rice samples had a heavy metal hazard index of more than 1, with As having the most significant impact, indicating a potential non-carcinogenic health risk. [ 17 ] . As an important grain province and resource reserve base in China, the safety and quality of grain production in Henan Province is directly related to the dietary health of millions of people. As a major grain crop in Henan Province, the quality and security of rice is of great concern. Therefore, investigating the content and spatial distribution of heavy metals in rice and evaluating the health hazards to human beings through dietary intake will not only help us to understand the status of heavy metal pollution in the main grain producing areas and its health impacts, but also provide a reference for the management of heavy metal pollution in other areas. MATERIALS AND METHODS 2.1 Sampling The location and number of rice samples collected were shown in Fig. 1 . In this study, 6,632 rice samples from 2020–2022 were selected for heavy metals testing.These samples were randomly selected from 18 prefecture-level cities in Henan Province. Among them, 3912 samples originated from urban areas and the other 2720 samples were from rural areas. These rice samples were collected from different sales locations such as supermarkets, wholesale markets, shopping centers, and farmers' markets. 2.2 Sample preparation and analysis The elemental contents of Cr, As, Hg, Pb and Cd in rice samples were determined by inductively coupled plasma mass spectrometry (ICP-MS). The collected rice samples were crushed into powder, and 0.3g (accurate to 0.001g) of this powder was weighed into a microwave dissolution cup. Then, 7ml of nitric acid was added and left for 1 hour with the lid on. Afterward, the lid was tightened and the sample was dissolved according to the standard procedure of microwave dissolution (Table S1 ). After cooling, the lid was slowly opened to exhaust, the inner lid was rinsed with a small amount of water, the tank was placed on a temperature-controlled hot plate or an ultrasonic water bath and heated at 100°C for 30 minutes, and then, water was added to bring the volume up to 25 ml. The mixture was stirred well and reserved.For quality control, double-parallel samples, blank samples spiked with recovery and quality control samples were used for quality control to ensure the accuracy of the experimental data, and retesting tests were carried out for the exceeding samples. 2.3 Daily intake estimation and risk assessment The cumulative risk to humans from heavy metals in rice was assessed using the health risk assessment methodology for human exposure to contaminants developed by the United States Environmental Protection Agency (USEPA) [ 18 ] . The Estimated daily intake (EDI) of heavy metals was calculated according to Eq. (1), and the hazard quotient (HQ) was used as the model for health risk assessment according to Eq. (2): EDI \(\:=\frac{\mathbf{C}\:\times\:\:\mathbf{I}\mathbf{R}}{\mathbf{B}\mathbf{W}}\) (1) where EDI (µg/kg bw/day) represents the daily intake of heavy metals through rice. C (µg/kg) is its median concentrations determined in rice, IR (g/day) is the daily ingestion rate of rice, and BW (kg) is body weight. Average IR and BW values for toddlers, children and adults used in these calculations are shown in Table S2. HQ \(\:=\frac{\varvec{E}\varvec{D}\varvec{I}}{\mathbf{R}\mathbf{f}\mathbf{D}}\) (2) where HQ measures the health risk of heavy metals intake through rice consumption. It assesses the risk by comparing the ratio of the consumption exposure to the safe intake reference dose (RfD) of these elements. When the HQ value exceeds 1, it suggests that the intake of heavy metals may have exceeded the recommended safe levels, which may pose a health risk. Taking the long-term health reference doses of common heavy metals in rice as an example, the RfDs are 0.001 mg/kg/day for Cd, 0.003 mg/kg/day for Cr, 0.004 mg/kg/day for Pb, 0.0001 mg/kg/day for Hg, and 0.0003 mg/kg/day for As. 2.4 Statistical analysis Statistical analysis was performed using IBM SPSS Version 26.0.When the trace element concentration of a sample was less than its LOD, LOD/2 was used instead of the recorded concentration for analysis. P < 0.05 was considered statistically significant. The chi-square test was used to compare the distribution of heavy metals in rice from different regions. RESULTS 3.1 Detection rates and concentrations of heavy metals in rice The results and detection rates of heavy metals As, Hg, Cr, Pb and Cd in 6,632 test rice samples collected from Henan Province were presented in Table 1 . The detection rates of Cd, Cr, Pb, Hg, and As were 27.69%, 22.57%, 2.25%, 1.95%, and 99.59%, with median concentrations of 0.002 mg/kg, 0.015 mg/kg, 0.025 mg/kg, 0.002 mg/kg, and 0.110 mg/kg, respectively. The data showed the distribution characteristics of different heavy metals in rice, with As levels particularly elevated. This high prevalence indicated that As element was widely distributed in the soil environment of Henan Province, and there was a risk of rice contamination in Henan Province, while the detection rates of Cd, Cr, Pb and Hg were relatively low. According to GB 2762 − 2017 National Standard for Food Safety Limits of Contaminants in Food, the limits of Pb, Hg, As, Cd and Cr are 0.2 mg/kg, 0.02 mg/kg, 0.2 mg/kg, 0.2 mg/kg, 1.0 mg/kg, respectively. The 6,632 rice samples examined adhered to these limits, except for Cd. Table 1 Detection rates and concentrations of heavy metals in rice samples (mg/kg). Heavy metals (n = 6632) Cd Cr Pb Hg As Mean 0.013 0.032 0.026 0.003 0.112 Median 0.002 0.015 0.025 0.002 0.110 SD 0.031 0.049 0.010 0.002 0.030 25th 0.002 0.015 0.025 0.002 0.092 50th 0.002 0.015 0.025 0.002 0.110 75th 0.005 0.015 0.025 0.005 0.130 95th 0.096 0.105 0.025 0.005 0.170 Min 0.002 0.015 0.020 0.002 0.005 Max 0.260 0.560 0.188 0.011 0.190 DR (%) 27.69 22.57 2.25 1.95 99.59 Number of exceeding limit 1 0 0 0 0 ER (%) 0.04 0 0 0 0 DR:detection rate ER:exceeding rate 3.2 Comparison with the concentrations of heavy metals in rice samples from different regions Table 2 compared the heavy metal contents in samples from Henan Province and other provinces in China. Through comparative analysis, it was found that the average content of Cd and Cr in the samples of Henan Province was significantly lower than that of other provinces. Regarding Pb, Henan samples had higher average content than Guizhou, but were lower compared to other provinces. For Hg, the average content in Henan’s samples was found to be higher than that of Heilongjiang, albeit it was still less than the levels recorded in Hunan and Fujian. In terms of As, Henan samples had lower average content than Hunan, but higher than Jiangsu, Heilongjiang, and Fujian. Taken together, the heavy metal contents in Henan Province are characterized by significant geographical features, especially the low contents of Cd and Cr and the relatively high contents of As, which reflect the unique environmental conditions and pollution characteristics of Henan Province. Table 2 Comparison with the concentrations of heavy metals in rice samples from different regions. (mg/kg). Heavy metals Country or city Mean Median Range* Reference Cd Henan 0.013 0.002 0.002–0.260 This study Heilongjiang 0.0071 0.0034 <LOQ-0.0615 [ 17 ] Jiangsu 0.0226 0.0152 0.0020–0.141 [ 17 ] Hunan 0.28 0.17 0.04–1.61 [ 30 ] Fujian 0.076 - 0.000-0.684 [ 31 ] Cr Henan 0.032 0.015 0.015–0.560 This study Heilongjiang 0.0755 0.0555 <LOQ-0.322 [ 17 ] Jiangsu 0.0647 0.0488 0.0233–0.183 [ 17 ] Hunan 1.28 1.21 0.64–3.04 [ 30 ] Guizhou 0.215 - 0.0003–1.169 [ 32 ] Fujian 0.318 - 0.013–0.884 [ 31 ] Pb Henan 0.026 0.025 0.020–0.188 This study Heilongjiang 0.0426 0.0169 <LOQ-0.363 [ 17 ] Jiangsu 0.158 0.0275 <LOQ-2.1 [ 17 ] Hunan 0.660 0.440 0.21–1.80 [ 30 ] Guizhou 0.013 - 0.001–0.193 [ 32 ] Fujian 0.057 - 0.001–0.897 [ 31 ] Hg Henan 0.003 0.002 0.002–0.011 This study Heilongjiang N.D. - N.D. [ 33 ] Hunan 0.069 - 0.013–0.226 [ 34 ] Fujian 0.01 - 0.002–0.063 [ 31 ] As Henan 0.112 0.110 0.005–0.190 This study Heilongjiang 0.0990 0.0963 0.0288–0.294 [ 17 ] Jiangsu 0.0802 0.0801 0.0358–0.156 [ 17 ] Hunan 0.48 0.46 0.23–0.93 [ 30 ] Fujian 0.107 - 0.008–0.302 [ 31 ] LOQ:limits of quantitation N.D.:Not detected -:The article did not provide relevant data *:the minimum ~ maximum values 3.3 Description of the detected concentrations and comparison of detection Rates of heavy metals in urban and rural After stratifying the rice samples by place of origin (urban and rural), Table 3 revealed the distribution of heavy metal elements in rice from different regions. The analyzed data showed that the average level of Cd in rice in urban areas was 0.015 mg/kg, which was higher than rural areas. On the contrary, the average levels of As and Cr contents in rice in rural areas were higher at 0.112 mg/kg and 0.035 mg/kg, respectively. Figure 2 (a) further compared the detection rate of heavy metals in urban and rural rice, and the specific values were presented in Table S3. The Cd detection rate in urban rice was 30.42%, significantly higher than 23.13% in rural rice, and the difference was statistically significant ( P < 0.001). However, there was no significant difference in the detection rates of Cr, Pb, Hg, and As. Table 3 The levels of heavy metals in rice samples in urban and rural areas of Henan Province. (mg/kg) Site Cd Cr Pb Hg As Urban Mean 0.015 0.030 0.026 0.003 0.111 Median 0.002 0.160 0.160 0.002 0.110 95th 0.110 0.100 0.025 0.005 0.170 SD 0.034 0.038 0.010 0.002 0.031 Min 0.002 0.015 0.025 0.002 0.005 Max 0.026 0.300 0.188 0.011 0.190 Rural Mean 0.010 0.035 0.026 0.003 0.112 Median 0.002 0.015 0.025 0.002 0.110 95th 0.059 0.120 0.025 0.005 0.160 SD 0.025 0.058 0.009 0.002 0.028 Min 0.002 0.015 0.020 0.002 0.020 Max 0.170 0.056 0.157 0.011 0.190 3.4 Comparison of the detection rates of various heavy metals in rice samples from different regions of Henan Province Henan Province was strategically segmented into five distinct regions, namely east, west, south, north, and central, in accordance with its unique topographical features and river networks. The Table 4 presented the heavy metal concentrations measured within each of these regions, offering a distribution of heavy metasl detections across the five regions. After analyzing the data in Fig. 2 (b), it was found that the detection rates of Cd, Cr, and Pb in rice samples were statistically significant ( P < 0.001) among different regions(the specific values were presented in Table S4). Specifically, the highest Cd detection rate was 40.48% in central Henan, significantly higher than the 6.63% in the western region. For Cr, the detection rate was 62.5% in central Henan, while no Cr was detected in western Henan, and for Pb, the detection rate was 5.53% in central Henan, while no Pb was detected in southern Henan and western Henan. In contrast, the detection rates of As and Hg did not differ significantly between regions, which may indicate that the distribution of these two elements was more uniform or less influenced by regional environmental factors. Table 4 The levels of heavy metals in rice samples from different areas in Henan province. (mg/kg). Site Cd Cr Pb Hg As Eastern Henan (n = 1239) Mean 0.011 0.041 0.026 0.002 0.104 Median 0.002 0.015 0.025 0.002 0.100 95th 0.062 0.170 0.025 0.002 0.160 SD 0.028 0.061 0.007 0.000 0.028 Min 0.002 0.015 0.025 0.002 0.048 Max 0.170 0.400 0.085 0.002 0.180 Western Henan (n = 537) Mean 0.008 0.025 0.026 0.005 0.113 Median 0.002 0.015 0.025 0.005 0.110 95th 0.023 0.092 0.025 0.005 0.180 SD 0.024 0.023 0.005 0.001 0.032 Min 0.002 0.015 0.025 0.005 0.005 Max 0.180 0.100 0.075 0.011 0.190 Southern Henan (n = 1702) Mean 0.020 0.030 0.026 0.003 0.116 Median 0.002 0.015 0.025 0.002 0.110 95th 0.122 0.111 0.025 0.005 0.170 SD 0.039 0.043 0.008 0.002 0.028 Min 0.002 0.015 0.025 0.002 0.045 Max 0.180 0.400 0.128 0.011 0.190 Northern Henan (n = 1279) Mean 0.010 0.034 0.027 0.004 0.114 Median 0.002 0.015 0.025 0.005 0.110 95th 0.058 0.137 0.025 0.005 0.160 SD 0.025 0.059 0.013 0.002 0.030 Min 0.002 0.015 0.025 0.002 0.005 Max 0.180 0.560 0.157 0.011 0.180 Central region of Henan (n = 1875) Mean 0.011 0.023 0.026 0.002 0.120 Median 0.002 0.015 0.025 0.002 0.110 95th 0.077 0.078 0.025 0.002 0.170 SD 0.030 0.023 0.011 0.000 0.029 Min 0.002 0.015 0.020 0.002 0.050 Max 0.260 0.140 0.188 0.002 0.190 3.5 Human health risk assessment 3.5.1 EDIs of heavy metals via rice Figure 3 (a) illustrated the median EDIs of heavy metals through rice consumption among different age groups, and the specific values were presented in Table S5. The figure revealed that the daily intake of heavy metals, in descending order, were As, Pb, Cr, Cd, and Hg. The analysis indicated that adults exhibited the lowest EDIs of heavy metals from rice, with children showing moderate levels, and toddlers encountering the highest intake. This trend was consistent with the general observation that consumption of staple foods per unit of body weight tends to decline with the progression of age. 3.5.2 Potential health risk assessment Figure 3 (b) and Table S6 showed the Health Risk Index (HQ) values for five heavy metal elements (Cd, Cr, Pb, Hg, and As) ingested through rice consumption. Similar to the EDI, the HQ values for heavy metals were lowest in adults, intermediate in children and highest in toddlers. The data showed that for all age groups, the HQ values for Cd, Cr, Pb and Hg in rice were less than 1, suggesting that the potential health risk of these elements ingested through rice consumption is low. However, the HQ values for As were more than 1 for all age groups, indicating a potential health risk, especially in children and toddlers, where the HQ values for As were much greater than 1, suggesting that they have a relatively high of exposure to As and need special attention. In conclusion, with the exception of As, heavy metal elements ingested through rice consumption pose a low risk to health. Therefore, there is a need to further study the sources of As and to take effective measures to reduce the exposure risk of the rice consumption, especially young children and children. DISCUSSION The present investigation detected the presence of Cr, As, Hg, Pb and Cd in a total of 6,632 rice samples gathered from Henan Province between 2020 and 2021. It was found that As had the highest detection rate, which was similar to the results of As detection rate (100%) in rice sampled from Jiangsu Province [ 19 ] .We also found that the content of heavy metals in rice in Henan Province was lower than in southern provinces, a phenomenon largely influenced by its geographic location, industrial structure and geological background. As an agricultural province in central China, Henan Province had relatively few industrial activities, especially compared to heavy industrial areas, which reduced the possibility of heavy metal pollution. Several studies had been conducted to show that heavy metal pollution was mainly concentrated in the south-central, southwestern and eastern coastal areas where industrial activities were frequent [ 20 – 22 ] . Rice had a certain capacity to absorb heavy metals, especially in heavy metal polluted areas, where the heavy metal content in rice might be higher. However, in Henan Province, where industrial activities were not as active as other provinces, the detection of heavy metals in rice was relatively low. Through stratified analysis, we found that the detection rate of Cd in rice samples from rural areas was lower than that from urban areas.This was mainly affected by urban industrial emissions and transportation logistics. Industrial wastewater, exhaust gases and solid wastes in cities were the main sources of Cd pollution, which entered farmland through a variety of pathways resulting in the enrichment of heavy metals in rice and ultimately affecting the food chain [ 23 – 25 ] . Cd emitted from automobile exhausts due to dense urban traffic also entered farmland through atmospheric deposition [ 26 ] . In contrast, rural areas had less industrial activity, lower traffic flow and lower population density, which limited the sources and transmission pathways of Cd pollution. Scientific studies had also shown that urban soils contained higher levels of heavy metals than nearby suburban and rural soils, which, to some extent, contributed to the accumulation of heavy metals in rice [ 27 ] . A comparative analysis of heavy metal detection rates in rice across various regions of Henan Province revealed that the central region boasts higher detection rates for Cd, Cr, and Pb contrasted with other areas. This difference was attributed to the special geographical location and level of economic development of the central region [ 28 ] . As a transportation hub, the central region may have experienced increased levels of heavy metals in the environment due to leakage or emissions during the transportation of goods. These heavy metals could then enter the growing environment of rice through atmospheric deposition, water contamination, or soil contamination, thereby affecting the levels of heavy metals in the rice. The results of the health risk assessment showed that for three different age groups, namely adults, children and young children, the HQs of heavy metal elements other than As in rice were below 1. However, there were still some potential health risks associated with inorganic As exposure due to the consumption of rice in different age groups. In particular, children were found to be at a higher health risk from rice consumption than adults. The emergence of this phenomenon may be closely related to the growth and development stage of children. At this stage, the functions of children's tissues and organs are not yet fully mature, especially the relatively weak detoxification and excretion functions of metabolic organs such as the liver and kidneys. As a result, they are more sensitive to toxic and harmful substances such as heavy metals [ 29 ] .Therefore, it is necessary to strengthen the monitoring of As to ensure that no rice with excessive As content enters the market and to protect the health and life safety of consumers. Compared with other provinces or regions, relatively few researches have been conducted on heavy metal content in rice in Henan Province. The previous studies were primarily limited to some specific rice-growing areas and more polluted regions within Henan Province. To fill this gap, our study collected rice samples extensively from different regions and urban and rural areas in Henan Province, and accumulated a large amount of strong representative data. These data not only provide an important reference for quality and safety supervision of rice in Henan Province, but also enhance public awareness of the problem of heavy metal content in rice, and provide support for scientific research, policy making and agricultural environmental protection. However, there was limitation in this study in assessing the risk of heavy metals to human health. The study only focused on heavy metals ingested through rice and did not take into account heavy metals ingested through other foods such as meat, vegetables, and fruits in the daily diet. This could have led to an underestimation of the potential risk of heavy metals to human health. Therefore, future studies should provide a more comprehensive assessment of the contamination status of heavy metals and their risks to human health through multiple dietary and exposure routes in order to more accurately assess and protect public health. CONCLUSIONS In this study, five heavy metals, As, Hg, Pb, Cr, and Cd were determined in rice from Henan Province by ICP-MS and health risk assessment was performed. Differences between regions showed that Henan’s central region had the highest heavy metal detection in rice, with urban areas overall showing more contamination than rural ones. The HQ value of As in rice exceeding recommended threshold, suggesting that exposure to As through rice consumption poses a potential risk to human health and should be emphasized by the relevant departments. In the future, relevant special investigations and studies should be carried out to provide a scientific basis for the prevention of heavy metal contamination in crops and the formulation of relevant measures. Declarations DATA AVAILABILITY STATEMENT The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation. FUNDING This research was supported by the National Natural Science Foundation of China (Grant No.: 42177415, 21806146), the National Key Research and Development Program of China (Grant No.: 2023YFC2506505), the Postdoctoral Science Foundation of China (Grant No.: 2020T130604, 2021M702934), the Science and Technique Foundation of Henan Province (Grant No. 232102411006, 232102310213, 212102310074), the Scientific and Technological Innovation of Colleges and Universities in Henan Province Talent Support Program (Grant No. 22HASTIT044), the Young Backbone Teachers Program of Colleges and Universities in Henan Province (Grant No. 2021GGJS015), and the Excellent Youth Development Foundation of Zhengzhou University (Grant No. 2021ZDGGJS057). AUTHOR CONTRIBUTIONS STATEMENT Yumeng Yan : Writing – original draft, Visualization, Project administration, Methodology, Data curation. Zhenxing Mao : Writing-review & editing, Revision, Validation. Xinlu Wang : Reviewing, Editing, Revising. Zhiwei Chen : Reviewing, Editing, Revising. Cuicui Ma : Methodology, Data curation. Dandan Wei : Software, Data curation. Wenjing Yan : Data curation. Xueyan Wu : Software, Data curation. Yao Guo : Investigation. Haoran Xu : Investigation. Guozhen Han : Visualization. Erbao Han : Visualization. Huilin Lou : Visualization. Taimeng Chen : Visualization. Wenqian Huo : Supervision. Chongjian Wang : Supervision. Shan Huang : Supervision. Xin Zeng : Writing – review & editing, Supervision, Funding acquisition. ACKNOWLEDGMENTS The authors would like to express their sincere gratitude to all participants, coordinators, and administrators for their great support, as well as their deep appreciation to the laboratory of the School of Public Health, Zhengzhou University, for securing the facilities during the study. All authors gave valuable comments and suggestions on previous versions of the manuscript and have unanimously agreed to approve the current version of the manuscript as submitted. 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Research progress on distribution,harm and control technology of heavy metals in soil [J]. Geology of China, 2024, 51(1): 118–42. REZAPOUR S, MOGHADDAM S S, NOURI A, et al. Urbanization influences the distribution, enrichment, and ecological health risk of heavy metals in croplands [J]. Scientific Reports, 2022, 12(1). WANG H, LI W, ZHU C, et al. Analysis of Heavy Metal Pollution in Cultivated Land of Different Quality Grades in Yangtze River Delta of China [J]. International Journal of Environmental Research and Public Health, 2021, 18(18). FANG Y, NIE Z, LIU F, et al. Concentration and health risk evaluation of heavy metals in market-sold vegetables and fishes based on questionnaires in Beijing, China [J]. Environmental Science and Pollution Research, 2014, 21(19): 11401–8. CUI H, WEN J, YANG L, et al. Spatial distribution of heavy metals in rice grains and human health risk assessment in Hunan Province, China [J]. Environmental Science and Pollution Research, 2022, 29(55): 83126–37. GUO Y, HUANG M, YOU W, et al. Spatial analysis and risk assessment of heavy metal pollution in rice in Fujian Province, China [J]. Frontiers in Environmental Science, 2022, 10. ZHAO Y, LI D, XIAO D, et al. Co-exposure of heavy metals in rice and corn reveals a probabilistic health risk in Guizhou Province, China [J]. Food Chemistry-X, 2023, 20. LUO J, MENG J, YE Y, et al. Population health risk via dietary exposure to trace elements (Cu, Zn, Pb, Cd, Hg, and As) in Qiqihar, Northeastern China [J]. Environmental Geochemistry and Health, 2018, 40(1): 217–27. ZENG F, WEI W, LI M, et al. Heavy Metal Contamination in Rice-Producing Soils of Hunan Province, China and Potential Health Risks [J]. International Journal of Environmental Research and Public Health, 2015, 12(12): 15584–93. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4761025","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":339424285,"identity":"875817f7-df60-4956-bc49-35d74698fb7b","order_by":0,"name":"Yumeng Yan","email":"","orcid":"","institution":"Zhengzhou University","correspondingAuthor":false,"prefix":"","firstName":"Yumeng","middleName":"","lastName":"Yan","suffix":""},{"id":339424286,"identity":"66dfa30a-7d57-4407-ae82-836dc9858398","order_by":1,"name":"Zhenxing Mao","email":"","orcid":"","institution":"Zhengzhou University","correspondingAuthor":false,"prefix":"","firstName":"Zhenxing","middleName":"","lastName":"Mao","suffix":""},{"id":339424287,"identity":"3a2ed5c6-332a-411c-8c47-5f66938e22cf","order_by":2,"name":"Xinlu Wang","email":"","orcid":"","institution":"Collaborative Innovation Center of Prevention and Treatment of Major Diseases by Chinese and Western Medicine，Henan Province","correspondingAuthor":false,"prefix":"","firstName":"Xinlu","middleName":"","lastName":"Wang","suffix":""},{"id":339424288,"identity":"58e27e05-893b-4545-aebf-e6c1cf927518","order_by":3,"name":"Zhiwei Chen","email":"","orcid":"","institution":"Zhengzhou University","correspondingAuthor":false,"prefix":"","firstName":"Zhiwei","middleName":"","lastName":"Chen","suffix":""},{"id":339424289,"identity":"eaf0c250-517e-4dbc-befa-2ec379876653","order_by":4,"name":"Cuicui Ma","email":"","orcid":"","institution":"Zhengzhou 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University","correspondingAuthor":false,"prefix":"","firstName":"Xueyan","middleName":"","lastName":"Wu","suffix":""},{"id":339424293,"identity":"5907d546-3c7c-4265-bc9f-7b48c45df8c8","order_by":8,"name":"Yao Guo","email":"","orcid":"","institution":"Zhengzhou University","correspondingAuthor":false,"prefix":"","firstName":"Yao","middleName":"","lastName":"Guo","suffix":""},{"id":339424294,"identity":"3540d963-963d-49bd-86f1-0eb9292802cd","order_by":9,"name":"Haoran Xu","email":"","orcid":"","institution":"Zhengzhou University","correspondingAuthor":false,"prefix":"","firstName":"Haoran","middleName":"","lastName":"Xu","suffix":""},{"id":339424295,"identity":"fa22962d-3c1a-4407-ae43-0cc5f3e08b79","order_by":10,"name":"Guozhen Han","email":"","orcid":"","institution":"Zhengzhou University","correspondingAuthor":false,"prefix":"","firstName":"Guozhen","middleName":"","lastName":"Han","suffix":""},{"id":339424296,"identity":"f3d40870-fead-40fe-8668-b3b1ce06f6de","order_by":11,"name":"Erbao Han","email":"","orcid":"","institution":"Zhengzhou University","correspondingAuthor":false,"prefix":"","firstName":"Erbao","middleName":"","lastName":"Han","suffix":""},{"id":339424297,"identity":"9988be37-8261-454b-a879-c9821f5908c3","order_by":12,"name":"Huilin Lou","email":"","orcid":"","institution":"Zhengzhou University","correspondingAuthor":false,"prefix":"","firstName":"Huilin","middleName":"","lastName":"Lou","suffix":""},{"id":339424298,"identity":"2cdc87ba-26fc-4846-83c5-2a9d24ced99a","order_by":13,"name":"Taimeng Chen","email":"","orcid":"","institution":"Zhengzhou University","correspondingAuthor":false,"prefix":"","firstName":"Taimeng","middleName":"","lastName":"Chen","suffix":""},{"id":339424299,"identity":"c60eb0a0-a4bf-47a2-982f-00bcb5d1a3b8","order_by":14,"name":"Wenqian Huo","email":"","orcid":"","institution":"Zhengzhou University","correspondingAuthor":false,"prefix":"","firstName":"Wenqian","middleName":"","lastName":"Huo","suffix":""},{"id":339424300,"identity":"5004a9b2-9067-4567-b2d5-8c9b84728117","order_by":15,"name":"Chongjian Wang","email":"","orcid":"","institution":"Zhengzhou University","correspondingAuthor":false,"prefix":"","firstName":"Chongjian","middleName":"","lastName":"Wang","suffix":""},{"id":339424302,"identity":"edb91c7e-5d45-4580-94f7-2a08b333f63a","order_by":16,"name":"Shan Huang","email":"","orcid":"","institution":"Henan Province Food Inspection Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Shan","middleName":"","lastName":"Huang","suffix":""},{"id":339424303,"identity":"cb2fe090-e577-4748-a0b5-4f120032397a","order_by":17,"name":"Xin Zeng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7ElEQVRIie3PsWrDMBCA4RMCeTniVcEBv8K5BtNS47xKwKDJQx7BkMFLuvsx8ghORT2p6RpoIO6SpRkMWTJkSD12sT0Wqn87uI/jAGy2PxsBCvj4alqKk9Fk5jLDg3Kp0tF34unKCA/bV5YPrfrFi/7G5QGpfq+9mCoOjn7b9BG23qmnkk5IZpc+Z3SYACq17yNcZhG1pJH2VfCZ0YmDxKiXCP8c0aIjx4a8R9IsHyIoMWy6K9PcPHgwhkjMIlb+EBfqNFiTSsXQL35hwgve9FyA3jbXW5y4jq57SfeO/D0OrHfxdsSSzWaz/efuP05LMHrVrZEAAAAASUVORK5CYII=","orcid":"","institution":"Zhengzhou University","correspondingAuthor":true,"prefix":"","firstName":"Xin","middleName":"","lastName":"Zeng","suffix":""}],"badges":[],"createdAt":"2024-07-18 08:24:42","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4761025/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4761025/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":63470332,"identity":"edc4181a-5afe-4f37-9d79-68cd2b5cd784","added_by":"auto","created_at":"2024-08-28 13:06:59","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":133843,"visible":true,"origin":"","legend":"\u003cp\u003eThe spatial distribution and the concentrations of heavy metals in the collected samples from various regions in Henan Province.\u003c/p\u003e","description":"","filename":"figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4761025/v1/160cbab2c443d8b90e758b48.jpg"},{"id":63469609,"identity":"5916f698-cd82-4fec-91e7-d4ab41fe8ad6","added_by":"auto","created_at":"2024-08-28 12:58:59","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":105172,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Comparison of detection rates of various heavy metals between urban and rural.(b)Comparison of the detection rates of various heavy metals in rice samples in different regions of Henan Province.C:Central region of Henan;N:Northern Henan; S:Southern Henan;W:Western Henan;E:Eastern Henan.\u003c/p\u003e","description":"","filename":"figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4761025/v1/682af890ebd60777a38ed238.jpg"},{"id":63469607,"identity":"ceea5d36-a14a-4182-9cbb-ce758003dfc0","added_by":"auto","created_at":"2024-08-28 12:58:59","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":110873,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Estimated daily intakes (median) of trace elements via rice consumption. (μg/kg bw/day); (b) Hazard quotients of trace element exposure through ingestion of rice. A: adults; C: children; T: toddlers.\u003c/p\u003e","description":"","filename":"figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4761025/v1/06f6e2e70f71541951eb8934.jpg"},{"id":64157624,"identity":"a3b72250-d7dc-4f77-8fa1-1027c16eb0d2","added_by":"auto","created_at":"2024-09-09 06:02:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1260660,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4761025/v1/4f898db4-1814-48b0-869a-4bfa10ec4793.pdf"},{"id":63470910,"identity":"2a1773aa-bbba-44ec-9f71-6e34001fee49","added_by":"auto","created_at":"2024-08-28 13:14:59","extension":"jpg","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":174905,"visible":true,"origin":"","legend":"","description":"","filename":"graphicalabstract.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4761025/v1/607db32eac349774cdbde46a.jpg"},{"id":63469608,"identity":"9c8ec78e-ffe3-4e66-a951-a8a123d1d681","added_by":"auto","created_at":"2024-08-28 12:58:59","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":23550,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-4761025/v1/f94712af6543393f8eb692c0.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"From farm to table: Assessing the status and health risk assessment of heavy metal pollution in rice in Henan province","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eRapid economic and social development in China, along with population growth, urbanization, industrialization, and the expansion of heavy metal-related industries, has led to significant heavy metal accumulation in agricultural soils, posing a serious threat to crop quality, safety, and human health\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. Heavy metals such as cadmium (Cd), chromium (Cr), lead (Pb), total mercury (Hg) and inorganic arsenic (As), are often collectively referred to as the \"five poisons\" because of their toxicity and potential health hazards. These heavy metals primarily enter the human body through the diet and accumulate via the food chain, causing health issues\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Cadmium mainly accumulates in the kidneys, which may lead to renal insufficiency and osteoporosis\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e.Chromium is a heavy metal needed by the human body, but excessive intake can cause poisoning, damage the liver and kidneys, and increase the risk of cancer\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. Lead can damage the digestive system, liver, kidneys, and nervous system, causing high blood pressure, infertility, and affecting the intellectual development of children\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. Long-term exposure to mercury is associated with Minamata disease and neonatal neurological problems in newborns\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e.Arsenic, though a metalloid, is classified as a heavy metal due to its toxicity. Excessive exposure can cause cardiovascular disorders and cancer\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe United Nations Food and Agriculture Organization (FAO 2021) reports that the worldwide rise in population has increased the demand for rice, with a particularly notable surge in China\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. As a key food crop, rice can absorb and accumulate significant amounts of heavy metals, potentially posing health risks through rice-based products\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. For example, exposure of Bangladeshis to As through rice consumption may result in non-carcinogenic and carcinogenic health risks\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eChina, as the country with the largest population in the world, is gradually increasing the proportion of refined grains, especially rice, in its staple food\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. However, alongside its rapid economic development, China has experienced serious soil heavy metal pollution\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e.By 2022, the area under rice cultivation in China covered 29,921.2 thousand hectares. It has been reported that the exceeding rate of metal elements in Chinese arable soils is as high as 1.71%, and the proportion of heavy metal contamination in soils that need to be fallowed is 15.58%, and this situation is particularly serious in Henan and Hunan\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. Some studies have examined the heavy metal content in rice. A study conducted a detailed analysis of rice from Wan County in China\u0026rsquo;s Xinjiang Uygur Autonomous Region, measuring the concentrations of 10 heavy metals, and found that As posed the highest risk of causing cancer during consumption\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. Furthermore, another research team tested rice samples from 17 provinces (cities) in China and measured the concentrations of nine heavy metals. The findings revealed that all rice samples had a heavy metal hazard index of more than 1, with As having the most significant impact, indicating a potential non-carcinogenic health risk.\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eAs an important grain province and resource reserve base in China, the safety and quality of grain production in Henan Province is directly related to the dietary health of millions of people. As a major grain crop in Henan Province, the quality and security of rice is of great concern. Therefore, investigating the content and spatial distribution of heavy metals in rice and evaluating the health hazards to human beings through dietary intake will not only help us to understand the status of heavy metal pollution in the main grain producing areas and its health impacts, but also provide a reference for the management of heavy metal pollution in other areas.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n\u003ch2\u003e2.1 Sampling\u003c/h2\u003e\n\u003cp\u003eThe location and number of rice samples collected were shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. In this study, 6,632 rice samples from 2020\u0026ndash;2022 were selected for heavy metals testing.These samples were randomly selected from 18 prefecture-level cities in Henan Province. Among them, 3912 samples originated from urban areas and the other 2720 samples were from rural areas. These rice samples were collected from different sales locations such as supermarkets, wholesale markets, shopping centers, and farmers' markets.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n\u003ch2\u003e2.2 Sample preparation and analysis\u003c/h2\u003e\n\u003cp\u003eThe elemental contents of Cr, As, Hg, Pb and Cd in rice samples were determined by inductively coupled plasma mass spectrometry (ICP-MS).\u003c/p\u003e\n\u003cp\u003eThe collected rice samples were crushed into powder, and 0.3g (accurate to 0.001g) of this powder was weighed into a microwave dissolution cup. Then, 7ml of nitric acid was added and left for 1 hour with the lid on. Afterward, the lid was tightened and the sample was dissolved according to the standard procedure of microwave dissolution (Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e). After cooling, the lid was slowly opened to exhaust, the inner lid was rinsed with a small amount of water, the tank was placed on a temperature-controlled hot plate or an ultrasonic water bath and heated at 100\u0026deg;C for 30 minutes, and then, water was added to bring the volume up to 25 ml. The mixture was stirred well and reserved.For quality control, double-parallel samples, blank samples spiked with recovery and quality control samples were used for quality control to ensure the accuracy of the experimental data, and retesting tests were carried out for the exceeding samples.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n\u003ch2\u003e2.3 Daily intake estimation and risk assessment\u003c/h2\u003e\n\u003cp\u003eThe cumulative risk to humans from heavy metals in rice was assessed using the health risk assessment methodology for human exposure to contaminants developed by the United States Environmental Protection Agency (USEPA)\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. The Estimated daily intake (EDI) of heavy metals was calculated according to Eq.\u0026nbsp;(1), and the hazard quotient (HQ) was used as the model for health risk assessment according to Eq.\u0026nbsp;(2):\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEDI\u003c/strong\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:=\\frac{\\mathbf{C}\\:\\times\\:\\:\\mathbf{I}\\mathbf{R}}{\\mathbf{B}\\mathbf{W}}\\)\u003c/span\u003e \u003c/span\u003e (1)\u003c/p\u003e\n\u003cp\u003ewhere EDI (\u0026micro;g/kg bw/day) represents the daily intake of heavy metals through rice. C (\u0026micro;g/kg) is its median concentrations determined in rice, IR (g/day) is the daily ingestion rate of rice, and BW (kg) is body weight. Average IR and BW values for toddlers, children and adults used in these calculations are shown in Table S2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHQ\u003c/strong\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:=\\frac{\\varvec{E}\\varvec{D}\\varvec{I}}{\\mathbf{R}\\mathbf{f}\\mathbf{D}}\\)\u003c/span\u003e \u003c/span\u003e (2)\u003c/p\u003e\n\u003cp\u003ewhere HQ measures the health risk of heavy metals intake through rice consumption. It assesses the risk by comparing the ratio of the consumption exposure to the safe intake reference dose (RfD) of these elements. When the HQ value exceeds 1, it suggests that the intake of heavy metals may have exceeded the recommended safe levels, which may pose a health risk. Taking the long-term health reference doses of common heavy metals in rice as an example, the RfDs are 0.001 mg/kg/day for Cd, 0.003 mg/kg/day for Cr, 0.004 mg/kg/day for Pb, 0.0001 mg/kg/day for Hg, and 0.0003 mg/kg/day for As.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n\u003ch2\u003e2.4 Statistical analysis\u003c/h2\u003e\n\u003cp\u003eStatistical analysis was performed using IBM SPSS Version 26.0.When the trace element concentration of a sample was less than its LOD, LOD/2 was used instead of the recorded concentration for analysis. \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant. The chi-square test was used to compare the distribution of heavy metals in rice from different regions.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n\u003ch2\u003e3.1 Detection rates and concentrations of heavy metals in rice\u003c/h2\u003e\nThe results and detection rates of heavy metals As, Hg, Cr, Pb and Cd in 6,632 test rice samples collected from Henan Province were presented in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. The detection rates of Cd, Cr, Pb, Hg, and As were 27.69%, 22.57%, 2.25%, 1.95%, and 99.59%, with median concentrations of 0.002 mg/kg, 0.015 mg/kg, 0.025 mg/kg, 0.002 mg/kg, and 0.110 mg/kg, respectively. The data showed the distribution characteristics of different heavy metals in rice, with As levels particularly elevated. This high prevalence indicated that As element was widely distributed in the soil environment of Henan Province, and there was a risk of rice contamination in Henan Province, while the detection rates of Cd, Cr, Pb and Hg were relatively low. According to GB 2762\u0026thinsp;\u0026minus;\u0026thinsp;2017 National Standard for Food Safety Limits of Contaminants in Food, the limits of Pb, Hg, As, Cd and Cr are 0.2 mg/kg, 0.02 mg/kg, 0.2 mg/kg, 0.2 mg/kg, 1.0 mg/kg, respectively. The 6,632 rice samples examined adhered to these limits, except for Cd.\u003cbr /\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eDetection rates and concentrations of heavy metals in rice samples (mg/kg).\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eHeavy metals (n\u0026thinsp;=\u0026thinsp;6632)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCd\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCr\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePb\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eHg\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eAs\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMean\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.013\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.032\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.026\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.003\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.112\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMedian\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.002\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.015\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.025\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.002\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.110\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSD\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.031\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.049\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.010\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.002\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.030\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e25th\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.002\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.015\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.025\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.002\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.092\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e50th\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.002\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.015\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.025\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.002\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.110\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e75th\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.005\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.015\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.025\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.005\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.130\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e95th\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.096\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.105\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.025\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.005\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.170\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMin\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.002\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.015\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.020\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.002\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.005\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMax\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.260\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.560\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.188\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.011\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.190\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDR (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e27.69\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e22.57\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.95\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e99.59\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNumber of exceeding limit\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eER (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"6\"\u003eDR:detection rate\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"6\"\u003eER:exceeding rate\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp id=\"Sec9\" class=\"Section3\"\u003e\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e3.2 Comparison with the concentrations of heavy metals in rice samples from different regions\u003c/h2\u003e\nTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e compared the heavy metal contents in samples from Henan Province and other provinces in China. Through comparative analysis, it was found that the average content of Cd and Cr in the samples of Henan Province was significantly lower than that of other provinces. Regarding Pb, Henan samples had higher average content than Guizhou, but were lower compared to other provinces. For Hg, the average content in Henan\u0026rsquo;s samples was found to be higher than that of Heilongjiang, albeit it was still less than the levels recorded in Hunan and Fujian. In terms of As, Henan samples had lower average content than Hunan, but higher than Jiangsu, Heilongjiang, and Fujian. Taken together, the heavy metal contents in Henan Province are characterized by significant geographical features, especially the low contents of Cd and Cr and the relatively high contents of As, which reflect the unique environmental conditions and pollution characteristics of Henan Province.\u003cbr /\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eComparison with the concentrations of heavy metals in rice samples from different regions. (mg/kg).\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eHeavy metals\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCountry or city\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMean\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMedian\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eRange*\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eReference\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"5\" align=\"left\"\u003e\n\u003cp\u003eCd\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHenan\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.013\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.002\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.002\u0026ndash;0.260\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eThis study\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHeilongjiang\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0071\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0034\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;LOQ-0.0615\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eJiangsu\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0226\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0152\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0020\u0026ndash;0.141\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHunan\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.28\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.04\u0026ndash;1.61\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFujian\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.076\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.000-0.684\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"6\" align=\"left\"\u003e\n\u003cp\u003eCr\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHenan\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.032\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.015\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.015\u0026ndash;0.560\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eThis study\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHeilongjiang\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0755\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0555\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;LOQ-0.322\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eJiangsu\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0647\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0488\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0233\u0026ndash;0.183\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHunan\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.28\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.21\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.64\u0026ndash;3.04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003csup\u003e[\u003cspan 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align=\"left\"\u003e\n\u003cp\u003e0.013\u0026ndash;0.884\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"6\" align=\"left\"\u003e\n\u003cp\u003ePb\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHenan\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.026\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.025\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.020\u0026ndash;0.188\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eThis study\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHeilongjiang\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0426\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0169\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;LOQ-0.363\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eJiangsu\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.158\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0275\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;LOQ-2.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003csup\u003e[\u003cspan 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align=\"left\"\u003e\n\u003cp\u003e0.001\u0026ndash;0.193\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFujian\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.057\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.001\u0026ndash;0.897\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"4\" align=\"left\"\u003e\n\u003cp\u003eHg\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHenan\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.003\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.002\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.002\u0026ndash;0.011\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eThis study\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHeilongjiang\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eN.D.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eN.D.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHunan\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.069\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.013\u0026ndash;0.226\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFujian\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.002\u0026ndash;0.063\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"5\" align=\"left\"\u003e\n\u003cp\u003eAs\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHenan\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.112\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.110\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.005\u0026ndash;0.190\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eThis study\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHeilongjiang\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0990\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0963\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0288\u0026ndash;0.294\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eJiangsu\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0802\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0801\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0358\u0026ndash;0.156\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHunan\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.48\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.46\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.23\u0026ndash;0.93\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFujian\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.107\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.008\u0026ndash;0.302\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"6\"\u003eLOQ:limits of quantitation\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"6\"\u003eN.D.:Not detected\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"6\"\u003e-:The article did not provide relevant data\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"6\"\u003e*:the minimum\u0026thinsp;~\u0026thinsp;maximum values\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Description of the detected concentrations and comparison of detection Rates of heavy metals in urban and rural\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter stratifying the rice samples by place of origin (urban and rural), Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e revealed the distribution of heavy metal elements in rice from different regions. The analyzed data showed that the average level of Cd in rice in urban areas was 0.015 mg/kg, which was higher than rural areas. On the contrary, the average levels of As and Cr contents in rice in rural areas were higher at 0.112 mg/kg and 0.035 mg/kg, respectively. Figure\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e(a) further compared the detection rate of heavy metals in urban and rural rice, and the specific values were presented in Table S3. The Cd detection rate in urban rice was 30.42%, significantly higher than 23.13% in rural rice, and the difference was statistically significant (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). However, there was no significant difference in the detection rates of Cr, Pb, Hg, and As.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eThe levels of heavy metals in rice samples in urban and rural areas of Henan Province. (mg/kg)\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSite\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCd\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCr\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePb\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eHg\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eAs\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"6\" align=\"left\"\u003e\n\u003cp\u003eUrban\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMean\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.015\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.030\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.026\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.003\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.111\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMedian\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.002\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.160\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.160\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.002\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.110\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e95th\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.110\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.100\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.025\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.005\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.170\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSD\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.034\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.038\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.010\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.002\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.031\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMin\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.002\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.015\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.025\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.002\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.005\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMax\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.026\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.300\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.188\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.011\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.190\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"6\" align=\"left\"\u003e\n\u003cp\u003eRural\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMean\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.010\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.035\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.026\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.003\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.112\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMedian\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.002\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.015\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.025\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.002\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.110\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e95th\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.059\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.120\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.025\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.005\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.160\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSD\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.025\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.058\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.009\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.002\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.028\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMin\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.002\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.015\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.020\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.002\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.020\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMax\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.170\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.056\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.157\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.011\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.190\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cstrong\u003e3.4 Comparison of the detection rates of various heavy metals in rice samples from different regions of Henan Province\u003c/strong\u003e\u003cbr /\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHenan Province was strategically segmented into five distinct regions, namely east, west, south, north, and central, in accordance with its unique topographical features and river networks. The Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e presented the heavy metal concentrations measured within each of these regions, offering a distribution of heavy metasl detections across the five regions. After analyzing the data in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e(b), it was found that the detection rates of Cd, Cr, and Pb in rice samples were statistically significant (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) among different regions(the specific values were presented in Table S4). Specifically, the highest Cd detection rate was 40.48% in central Henan, significantly higher than the 6.63% in the western region. For Cr, the detection rate was 62.5% in central Henan, while no Cr was detected in western Henan, and for Pb, the detection rate was 5.53% in central Henan, while no Pb was detected in southern Henan and western Henan. In contrast, the detection rates of As and Hg did not differ significantly between regions, which may indicate that the distribution of these two elements was more uniform or less influenced by regional environmental factors.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"char\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"char\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab4\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eThe levels of heavy metals in rice samples from different areas in Henan province. (mg/kg).\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSite\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCd\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCr\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePb\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eHg\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eAs\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"6\" align=\"left\"\u003e\n\u003cp\u003eEastern Henan\u003c/p\u003e\n\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;1239)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMean\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.011\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.041\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.026\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.002\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.104\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMedian\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.002\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.015\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.025\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.002\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.100\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e95th\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.062\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.170\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.025\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.002\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.160\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSD\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.028\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.061\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.007\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.028\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMin\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.002\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.015\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.025\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.002\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.048\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMax\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.170\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.400\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.085\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.002\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.180\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"6\" align=\"left\"\u003e\n\u003cp\u003eWestern Henan\u003c/p\u003e\n\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;537)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMean\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.008\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.025\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.026\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.005\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.113\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMedian\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.002\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.015\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.025\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.005\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.110\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e95th\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.023\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.092\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.025\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.005\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.180\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSD\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.024\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.023\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.005\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.032\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMin\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.002\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.015\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.025\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.005\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.005\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMax\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.180\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.100\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.075\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.011\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.190\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"6\" align=\"left\"\u003e\n\u003cp\u003eSouthern Henan\u003c/p\u003e\n\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;1702)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMean\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.020\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.030\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.026\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.003\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.116\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMedian\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.002\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.015\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.025\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.002\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.110\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e95th\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.122\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.111\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.025\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.005\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.170\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSD\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.039\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.043\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.008\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.002\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.028\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMin\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.002\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.015\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.025\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.002\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.045\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMax\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.180\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.400\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.128\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.011\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.190\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"6\" align=\"left\"\u003e\n\u003cp\u003eNorthern Henan\u003c/p\u003e\n\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;1279)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMean\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.010\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.034\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.027\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.004\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.114\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMedian\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.002\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.015\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.025\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.005\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.110\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e95th\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.058\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.137\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.025\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.005\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.160\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSD\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.025\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.059\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.013\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.002\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.030\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMin\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.002\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.015\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.025\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.002\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.005\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMax\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.180\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.560\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.157\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.011\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.180\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"6\" align=\"left\"\u003e\n\u003cp\u003eCentral region of Henan\u003c/p\u003e\n\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;1875)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMean\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.011\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.023\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.026\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.002\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.120\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMedian\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.002\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.015\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.025\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.002\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.110\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e95th\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.077\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.078\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.025\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.002\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.170\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSD\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.030\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.023\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.011\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.029\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMin\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.002\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.015\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.020\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.002\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.050\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMax\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.260\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.140\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.188\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.002\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.190\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n\u003ch2\u003e3.5 Human health risk assessment\u003c/h2\u003e\n\u003cdiv id=\"Sec11\" class=\"Section3\"\u003e\n\u003ch2\u003e3.5.1 EDIs of heavy metals via rice\u003c/h2\u003e\nFigure \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e(a) illustrated the median EDIs of heavy metals through rice consumption among different age groups, and the specific values were presented in Table S5. The figure revealed that the daily intake of heavy metals, in descending order, were As, Pb, Cr, Cd, and Hg. The analysis indicated that adults exhibited the lowest EDIs of heavy metals from rice, with children showing moderate levels, and toddlers encountering the highest intake. This trend was consistent with the general observation that consumption of staple foods per unit of body weight tends to decline with the progression of age.\u003c/div\u003e\n\u003c/div\u003e\n\u003ch2\u003e3.5.2 Potential health risk assessment\u003c/h2\u003e\n\u003cp id=\"Sec12\" class=\"Section2\"\u003eFigure \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e(b) and Table S6 showed the Health Risk Index (HQ) values for five heavy metal elements (Cd, Cr, Pb, Hg, and As) ingested through rice consumption. Similar to the EDI, the HQ values for heavy metals were lowest in adults, intermediate in children and highest in toddlers. The data showed that for all age groups, the HQ values for Cd, Cr, Pb and Hg in rice were less than 1, suggesting that the potential health risk of these elements ingested through rice consumption is low. However, the HQ values for As were more than 1 for all age groups, indicating a potential health risk, especially in children and toddlers, where the HQ values for As were much greater than 1, suggesting that they have a relatively high of exposure to As and need special attention. In conclusion, with the exception of As, heavy metal elements ingested through rice consumption pose a low risk to health. Therefore, there is a need to further study the sources of As and to take effective measures to reduce the exposure risk of the rice consumption, especially young children and children.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThe present investigation detected the presence of Cr, As, Hg, Pb and Cd in a total of 6,632 rice samples gathered from Henan Province between 2020 and 2021. It was found that As had the highest detection rate, which was similar to the results of As detection rate (100%) in rice sampled from Jiangsu Province\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e.We also found that the content of heavy metals in rice in Henan Province was lower than in southern provinces, a phenomenon largely influenced by its geographic location, industrial structure and geological background. As an agricultural province in central China, Henan Province had relatively few industrial activities, especially compared to heavy industrial areas, which reduced the possibility of heavy metal pollution. Several studies had been conducted to show that heavy metal pollution was mainly concentrated in the south-central, southwestern and eastern coastal areas where industrial activities were frequent\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. Rice had a certain capacity to absorb heavy metals, especially in heavy metal polluted areas, where the heavy metal content in rice might be higher. However, in Henan Province, where industrial activities were not as active as other provinces, the detection of heavy metals in rice was relatively low.\u003c/p\u003e\n\u003cp\u003eThrough stratified analysis, we found that the detection rate of Cd in rice samples from rural areas was lower than that from urban areas.This was mainly affected by urban industrial emissions and transportation logistics. Industrial wastewater, exhaust gases and solid wastes in cities were the main sources of Cd pollution, which entered farmland through a variety of pathways resulting in the enrichment of heavy metals in rice and ultimately affecting the food chain\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. Cd emitted from automobile exhausts due to dense urban traffic also entered farmland through atmospheric deposition\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. In contrast, rural areas had less industrial activity, lower traffic flow and lower population density, which limited the sources and transmission pathways of Cd pollution. Scientific studies had also shown that urban soils contained higher levels of heavy metals than nearby suburban and rural soils, which, to some extent, contributed to the accumulation of heavy metals in rice\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eA comparative analysis of heavy metal detection rates in rice across various regions of Henan Province revealed that the central region boasts higher detection rates for Cd, Cr, and Pb contrasted with other areas. This difference was attributed to the special geographical location and level of economic development of the central region\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. As a transportation hub, the central region may have experienced increased levels of heavy metals in the environment due to leakage or emissions during the transportation of goods. These heavy metals could then enter the growing environment of rice through atmospheric deposition, water contamination, or soil contamination, thereby affecting the levels of heavy metals in the rice.\u003c/p\u003e\n\u003cp\u003eThe results of the health risk assessment showed that for three different age groups, namely adults, children and young children, the HQs of heavy metal elements other than As in rice were below 1. However, there were still some potential health risks associated with inorganic As exposure due to the consumption of rice in different age groups. In particular, children were found to be at a higher health risk from rice consumption than adults. The emergence of this phenomenon may be closely related to the growth and development stage of children. At this stage, the functions of children's tissues and organs are not yet fully mature, especially the relatively weak detoxification and excretion functions of metabolic organs such as the liver and kidneys. As a result, they are more sensitive to toxic and harmful substances such as heavy metals\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e.Therefore, it is necessary to strengthen the monitoring of As to ensure that no rice with excessive As content enters the market and to protect the health and life safety of consumers.\u003c/p\u003e\n\u003cp\u003eCompared with other provinces or regions, relatively few researches have been conducted on heavy metal content in rice in Henan Province. The previous studies were primarily limited to some specific rice-growing areas and more polluted regions within Henan Province. To fill this gap, our study collected rice samples extensively from different regions and urban and rural areas in Henan Province, and accumulated a large amount of strong representative data. These data not only provide an important reference for quality and safety supervision of rice in Henan Province, but also enhance public awareness of the problem of heavy metal content in rice, and provide support for scientific research, policy making and agricultural environmental protection. However, there was limitation in this study in assessing the risk of heavy metals to human health. The study only focused on heavy metals ingested through rice and did not take into account heavy metals ingested through other foods such as meat, vegetables, and fruits in the daily diet. This could have led to an underestimation of the potential risk of heavy metals to human health. Therefore, future studies should provide a more comprehensive assessment of the contamination status of heavy metals and their risks to human health through multiple dietary and exposure routes in order to more accurately assess and protect public health.\u003c/p\u003e"},{"header":"CONCLUSIONS","content":"\u003cp\u003eIn this study, five heavy metals, As, Hg, Pb, Cr, and Cd were determined in rice from Henan Province by ICP-MS and health risk assessment was performed. Differences between regions showed that Henan\u0026rsquo;s central region had the highest heavy metal detection in rice, with urban areas overall showing more contamination than rural ones. The HQ value of As in rice exceeding recommended threshold, suggesting that exposure to As through rice consumption poses a potential risk to human health and should be emphasized by the relevant departments. In the future, relevant special investigations and studies should be carried out to provide a scientific basis for the prevention of heavy metal contamination in crops and the formulation of relevant measures.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eDATA AVAILABILITY STATEMENT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFUNDING\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by the National Natural Science Foundation of China (Grant No.: 42177415, 21806146), the National Key Research and Development Program of China (Grant No.: 2023YFC2506505), the Postdoctoral Science Foundation of China (Grant No.: 2020T130604, 2021M702934), the Science and Technique Foundation of Henan Province (Grant No. 232102411006, 232102310213, 212102310074), the Scientific and Technological Innovation of Colleges and Universities in Henan Province Talent Support Program (Grant No. 22HASTIT044), the Young Backbone Teachers Program of Colleges and Universities in Henan Province (Grant No. 2021GGJS015), and the Excellent Youth Development Foundation of Zhengzhou University (Grant No. 2021ZDGGJS057).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAUTHOR CONTRIBUTIONS STATEMENT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eYumeng Yan\u003c/strong\u003e: Writing \u0026ndash; original draft, Visualization, Project administration, Methodology, Data curation. \u003cstrong\u003eZhenxing Mao\u003c/strong\u003e: Writing-review \u0026amp; editing, Revision, Validation. \u003cstrong\u003eXinlu Wang\u003c/strong\u003e: Reviewing, Editing, Revising. \u003cstrong\u003eZhiwei Chen\u003c/strong\u003e: Reviewing, Editing, Revising. \u003cstrong\u003eCuicui Ma\u003c/strong\u003e: Methodology, Data curation. \u003cstrong\u003eDandan Wei\u003c/strong\u003e: Software, Data curation. \u003cstrong\u003eWenjing Yan\u003c/strong\u003e: Data curation. \u003cstrong\u003eXueyan Wu\u003c/strong\u003e: Software, Data curation. \u003cstrong\u003eYao Guo\u003c/strong\u003e: Investigation. \u003cstrong\u003eHaoran Xu\u003c/strong\u003e: Investigation. \u003cstrong\u003eGuozhen Han\u003c/strong\u003e: Visualization. \u003cstrong\u003eErbao Han\u003c/strong\u003e: Visualization. \u003cstrong\u003eHuilin Lou\u003c/strong\u003e: Visualization. \u003cstrong\u003eTaimeng Chen\u003c/strong\u003e: Visualization. \u003cstrong\u003eWenqian Huo\u003c/strong\u003e: Supervision. \u003cstrong\u003eChongjian Wang\u003c/strong\u003e: Supervision. \u003cstrong\u003eShan Huang\u003c/strong\u003e: Supervision. \u003cstrong\u003eXin Zeng\u003c/strong\u003e: Writing \u0026ndash; review \u0026amp; editing, Supervision, Funding acquisition.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eACKNOWLEDGMENTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to express their sincere gratitude to all participants, coordinators, and administrators for their great support, as well as their deep appreciation to the laboratory of the School of Public Health, Zhengzhou University, for securing the facilities during the study. All authors gave valuable comments and suggestions on previous versions of the manuscript and have unanimously agreed to approve the current version of the manuscript as submitted.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCHEN R, DE SHERBININ A, YE C, et al. China's Soil Pollution: Farms on the Frontline [J]. Science, 2014, 344(6185): 691-.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKOU M, HOU J, CHEN C, et al. Quantitative analysis of dose interval effect of Pb-Cd interaction on Oryza sativa L. root [J]. Ecotoxicology and Environmental Safety, 2023, 252.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLIANG G, GONG W, LI B, et al. Analysis of Heavy Metals in Foodstuffs and an Assessment of the Health Risks to the General Public via Consumption in Beijing, China [J]. 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International Journal of Environmental Research and Public Health, 2015, 12(12): 15584\u0026ndash;93.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Rice Safety, Health Risk Assessment, Environmental Pollution, Agricultural Impact","lastPublishedDoi":"10.21203/rs.3.rs-4761025/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4761025/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn recent years, industrial and agricultural advancements in Henan Province have increased heavy metal contamination in rice, raising public concerns. This study investigated heavy metal levels in rice from Henan Province and evaluated potential health risks. A total of 6,632 rice samples were collected from 18 regions between 2020 and 2022. Using inductively coupled plasma mass spectrometry (ICP-MS), we analyzed samples for cadmium (Cd), chromium (Cr), lead (Pb), mercury (Hg), and inorganic arsenic (As). Detection rates were compared using the chi-square test, and health risks were assessed per USEPA guidelines. Detection rates for Cd, Cr, Pb, Hg, and As were 27.69%, 22.57%, 2.25%, 1.95%, and 99.59%, respectively. Cd levels were significantly higher in urban areas (30.42%) than rural areas (23.13%) (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with regional variations for Cd, Cr, and Pb (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The Hazard Quotient (HQ) for inorganic As exceeded 1. Heavy metal contamination was more prevalent in urban areas, especially in the central region, posing health risks due to elevated inorganic arsenic levels.\u003c/p\u003e","manuscriptTitle":"From farm to table: Assessing the status and health risk assessment of heavy metal pollution in rice in Henan province","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-28 12:58:54","doi":"10.21203/rs.3.rs-4761025/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"07e83078-b0b0-4f1b-8f2f-5cd98244d391","owner":[],"postedDate":"August 28th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-09-09T05:46:26+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-28 12:58:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4761025","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4761025","identity":"rs-4761025","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}