Incorporating Bioaccessibility into Human Health ... - ACS Publications

Subscriber access provided by Kaohsiung Medical University

Food Safety and Toxicology

Incorporating bioaccessibility into human health risk assessment of heavy metals in rice (Oryza sativa L.): A probabilistic-based analysis Tianyuan Li, Yinxian Song, Xuyin Yuan, Jizhou Li, Junfeng Ji, Xiaowen Fu, Qiang Zhang, and Shuhai Guo J. Agric. Food Chem., Just Accepted Manuscript • DOI: 10.1021/acs.jafc.8b01525 • Publication Date (Web): 11 May 2018 Downloaded from http://pubs.acs.org on May 11, 2018

Just Accepted “Just Accepted” manuscripts have been peer-reviewed and accepted for publication. They are posted online prior to technical editing, formatting for publication and author proofing. The American Chemical Society provides “Just Accepted” as a service to the research community to expedite the dissemination of scientific material as soon as possible after acceptance. “Just Accepted” manuscripts appear in full in PDF format accompanied by an HTML abstract. “Just Accepted” manuscripts have been fully peer reviewed, but should not be considered the official version of record. They are citable by the Digital Object Identifier (DOI®). “Just Accepted” is an optional service offered to authors. Therefore, the “Just Accepted” Web site may not include all articles that will be published in the journal. After a manuscript is technically edited and formatted, it will be removed from the “Just Accepted” Web site and published as an ASAP article. Note that technical editing may introduce minor changes to the manuscript text and/or graphics which could affect content, and all legal disclaimers and ethical guidelines that apply to the journal pertain. ACS cannot be held responsible for errors or consequences arising from the use of information contained in these “Just Accepted” manuscripts.

is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Published by American Chemical Society. Copyright © American Chemical Society. However, no copyright claim is made to original U.S. Government works, or works produced by employees of any Commonwealth realm Crown government in the course of their duties.

Page 1 of 33

Journal of Agricultural and Food Chemistry

Incorporating bioaccessibility into human health risk assessment of heavy metals in rice (Oryza sativa L.): A probabilistic-based analysis

Tianyuan Li †, ‡, Yinxian Song

‡, *

, Xuyin Yuan ‡, Jizhou Li ‡, Junfeng Ji ⸹, Xiaowen

Fu †, Qiang Zhang †, *, Shuhai Guo†

†

Shandong Provincial Key Laboratory of Applied Microbiology, Ecology Institute,

Qilu University of Technology (Shandong Academy of Science), Jinan 250014, Shandong Province, P.R. China. ‡

Key Laboratory of Integrated Regulation and Resource Development on Shallow

Lake of Ministry of Education, College of Environment, Hohai University, Nanjing 210098, Jiangsu Province, P.R. China. ⸹

Key Laboratory of Surficial Geochemistry, Ministry of Education, School of Earth

Sciences and Engineering, Nanjing University, Nanjing 210046, Jiangsu Province, P.R. China.

*Corresponding author. E-mail address: [email protected] (Y. X. Song), during review procedure; [email protected] (Q. Zhang), after acceptance.

ACS Paragon Plus Environment


1

ABSTRACT: A systematic investigation into total and bioaccessible heavy metal

2

concentrations in rice grains harvested from heavy metal-contaminated regions was

3

carried out to assess the potential health risk to local residents. Arsenic, Cr, Cu, Pb

4

and Zn concentrations were within acceptable levels while Cd and Ni concentrations

5

appeared to be much higher than other studies. The bioaccessibity of As, Cd and Ni

6

was high (> 25%) and could be well predicted from their total concentrations. The

7

non-carcinogenic risk posed by As and Cd was significant. The carcinogenic risk

8

posed by all bioaccessible heavy metals at the 5th percentile was 10-fold higher than

9

the acceptable level, and Cd and Ni were the major contributors. The contribution of

10

each metal to the combined carcinogenic risk indicates that taking pertinent

11

precautions for different types of cancer, aimed at individuals with different levels of

12

exposure to heavy metals, will greatly reduce morbidity and mortality rates.

13

KEYWORDS: rice, heavy metals, bioaccessibility, health risk assessment, Monte

14

Carlo simulation


Page 2 of 33

Page 3 of 33


15

INTRODUCTION

16

Due to rapid industrialization and urbanization, heavy metal contamination in

17

soil and crops has received great attention worldwide, especially in developing

18

countries such as China and India. Rice, the staple food for half of the world’s

19

population, readily accumulates heavy metals such as As and Cd. Therefore,

20

consumption of rice can be a significant pathway by which these toxic metals enter

21

the human body, causing related health disorders, including various forms of cancer

22

1-3

23

risks from ingesting heavy metal-contaminated rice, especially in developing

24

countries where intensive farming and industrial activities co-exist.

. Hence, there is a great need to more accurately and realistically assess the health

25

In previous studies, researchers have made a great effort to ensure that health risk

26

assessments (HRA) reflect the actual conditions, for example by using probabilistic

27

risk assessment (PRA) instead of the traditional deterministic assessment 4. PRA

28

characterizes uncertainty by calculating the risks based on the statistical distribution

29

of the site-specific exposure parameters. It is also important to include the appropriate

30

parameters in risk assessment. For example, when performing risk assessment of

31

foodstuffs, bioaccessible metal concentrations are more important than total metal

32

content. Therefore, in vitro studies have been performed to estimate the amount of

33

heavy metal absorption into human systemic circulation through ingestion

34

previous study demonstrated that in vitro assays could provide a good prediction of in

35

vivo

36

Volksgezondheid en Milieu) in vitro digestion model has been created to aid in

arsenic

bioavailability7.

Additionally,

the

RIVM

1


(Rijksinstituut

5, 6

. A

voor


37

determining bioavailability and estimating health risks associated with heavy metal

38

consumption from rice 8. Several HRA studies have been conducted using either PRA

39

or bioaccessible metal concentrations9-11, and the combination of these two methods

40

may produce a more accurate assessment result. However, studies in this area are

41

lacking.

42

The main objective of this study was to gives an accurate and realistic estimation

43

of the risk of heavy metals in rice grains to human health in southern Jiangsu Province,

44

one of the most developed regions in China. To achieve this, total and bioaccessible

45

fraction (BAF) of heavy metals in rice grain were determined. Both the carcinogenic

46

risks (CR) and non-carcinogenic risks (NCR) to the population in the study region

47

(adults and children) from ingestion of heavy metal-contaminated rice were

48

determined using an in vitro digestion model combined with Monte Carlo simulations.

49

This risk assessment will deepen our understanding of the level of heavy metal

50

contamination in rice and the exposure risks to populations living in polluted areas in

51

other parts of the world.

52

MATERIALS AND METHODS

53

Chemicals and reagents. The inorganic reagents were purchased from

54

Sinopharm Chemical Reagent Co Ltd. (Shanghai, China). Urea, α-amylase, mucin,

55

glucose, hydrochloride, pepsin, pancreatin, bile salts, albumin from bovine serum and

56

lipase used for in vitro analysis were purchased from Sigma-Aldrich (St. Louis, MO,

57

USA). All reagents were prepared with deionized water obtained from a Milli-Q

58

system (Millipore, Billerica, MA, USA). 2


Page 4 of 33

Page 5 of 33


59

Study areas and sampling. Rice samples were collected from Yifeng, Dingshu,

60

Ehu, Wangting and Taicang villages, which are located in southern Jiangsu Province,

61

China (Figure S1). The longitude and latitude of the study regions ranged from

62

119.81°E to 121.13°E and from 31.27°N to 31.54°N. The study areas have a

63

subtropical monsoon humid climate. There are numerous heavy metal-associated

64

industries such as chemical, battery, ceramic making and foundry industries in these

65

areas

66

samples (14 samples from each area) were randomly collected. At least five

67

sub-samples per sampling site were mixed together. All rice samples collected were of

68

the Indica variety. More detailed information about the study areas and sampling

69

methods was reported in our previous study 12.

12

. In late July 2015, when rice plants reached maturity, a set of 70 rice grain

70

Sample pretreatment. Raw rice samples were used to study metal

71

concentrations and bioaccessibility. Rice grains were oven dried at 105 ℃ for 1 h,

72

and then samples were dried at 70 ⸹ to constant weight. In order to avoid the

73

influence of particle size on metal bioaccessibility and to avoid cross-contamination,

74

raw rice samples were ground manually with a pestle and mortar. Then, the rice

75

samples were passed through a 0.25 mm mesh sieve and kept in sealed plastic bags at

76

4 ⸹ prior to further analysis.

77

Determination of total heavy metal concentrations in rice. Total

78

concentrations of heavy metals were determined in a 1.000 g rice sample that was

79

digested with a mixture of HClO4 and HNO3 in beaker placed on the electric heating

80

plate for 6 hours. The heavy metal concentrations in these digests were determined by 3



Page 6 of 33

81

ICP-MS (Inductively Coupled Plasma Mass Spectrometry, XSERIES, Thermo

82

Electron, USA). Quality assurance and quality control (QA/QC) for metals in rice

83

samples were estimated by determining the metal contents of blank and duplicate

84

samples as well as of certified reference materials (GSB-1). The recovery of GSB-1

85

was 93-105%. The relative difference values for all replicates were less than 5%.

86

Bioaccessibility of heavy metals in rice grain. The BAF of heavy metals in rice

87

grain was determined using the RIVM in-vitro digestion model, which includes

88

models for three compartments: mouth, stomach and small intestine. Digestive juices

89

were prepared artificially as described in a previous in vitro study

90

constituents of the digestive juices are presented in Table S1. Briefly, the simulated

91

digestive process was initiated by adding 6 mL of artificial saliva to 4.5 g of rice

92

sample to represent digestion in the mouth. Next, 12 mL of artificial gastric juice was

93

added, and the mixture was shaken to represent digestion in the stomach. Finally, 12

94

mL of duodenal juice, 6 mL of bile juice and 2 mL of NaHCO3 solution was added to

95

the mixture to represent digestion in the small intestine. The mixture was incubated at

96

37±2 ⸹ in a shaker (55 rpm) during in vitro digestion. The incubation time was 5 min

97

for mouth and 2 hours each for stomach and small intestine. The pH of the mixture

98

was adjusted to 6.8, 2.0 and 7.0 using HCl (37%) or NaOH (0.1 M) when simulating

99

digestion in the mouth, stomach and small intestine, respectively. When in vitro

100

digestion was complete, the mixture was centrifuged at 2750 g for 5 min. The

101

supernatant was filtered through 0.45 µm filter paper. Heavy metal concentrations

102

were measured using ICP-MS. The kinetic energy discrimination (KED) mode (He as 4


13, 14

. The

Page 7 of 33


103

the reaction gas) was used to eliminate the mass spectrometric determination of

104

multi-atom spectra interference. The results from analysis of standard reference

105

material were satisfactory. QC for the in vitro experiment was assessed by using

106

duplicate and blank samples. In addition, heavy metal contents in both the soluble

107

fraction and pellet were calculated from liquid volumes, pellet weights and measured

108

metal concentrations. The sum of the heavy metal contents in the supernatant and in

109

the pellet was compared with total amount of metal introduced into the in vitro system.

110

The recovery of the As, Cd, Pb, Cr, Cu, Zn and Ni was 88.3%, 96.2%, 95.9%, 99.1%,

111

101.2%, 92.5% and 102.6%, respectively. The bioaccessibility of each metal was

112

calculated as the percentage of the metal contents in the soluble fraction relative to the

113

total concentration in rice grains.

114

Risk assessment

115

Exposure analysis. The exposure to heavy metals in rice can be characterized by

116

the average daily dose (ADD, mg/kg/day), which is calculated by multiplying the rate

117

of consumption (ingestion rate, IR) and the heavy metal concentration (Cm) and then

118

dividing by body weight (BW). BW used in this study was based on questionnaire

119

answers from a survey performed in the study area. The BW values for adults (18

120

years old or over) and children (7-18 years old) were 59.80±7.19 and 38.9±4.81 kg,

121

respectively. BW follows a normal distribution. IR for survey participants (adults and

122

children) was calculated based on the amount of rice consumed per meal (g/meal) and

123

the frequency of rice consumption (meals/day). The daily IR averaged over one year

124

was reported as the daily intake. Survey answers were validated by asking some 5



125

logical questions with different wording and cross-checking for inconsistencies

126

among the answers

127

362.80±30.81 and 236.23±12.53 g/day per person, respectively. IR for both adults and

128

children followed the normal distribution. The 5th percentile, median (50th percentile)

129

and 95th percentile of the daily exposure levels were used to represent ADD for low,

130

average and high consumers, respectively 16.

. The estimated IR values for adults and children were

15

131

Non-carcinogenic risks. The potential human health risk from consumption of

132

rice was assessed based on the hazard quotient (HQ), which was provided by the US

133

EPA 17. An HQ value Cu > Ni > Cr > Cd > As > Pb.

172

Total concentrations of Cr, Cu, Pb and Zn (average values) were lower than the

173

recommended maximum permissible level (MPL) suggested by China, while the Cd

174

concentration was about 1.5 times greater than the MPL

175

concentrations of 1.43% and 4.29% of the samples, respectively, exceeded the MPL

176

while the Cu and Zn concentrations of all samples were lower than the MPL. The As

177

concentration in rice samples ranged from 0.01 to 0.23 mg/kg, with an average of 0.13

178

mg/kg. Approximately 92% of the samples had As concentrations < 0.2 mg/kg, the

179

Chinese limit for inorganic As in rice 21. Our finding is comparable to that of Li et al.

180

who reported a mean As concentration of 0.13 mg/kg in Chinese rice

181

concentration in 31.43% of the rice samples surpassed the MPL. The highest Cd

182

concentration was 2.77 mg/kg, nearly 14-fold higher than the MPL, and the 75%

183

percentile concentration (0.34 mg/kg) was 1.7 times higher than the MPL. Although

184

the MPL for Ni in rice has not been established by the Chinese government, the mean

185

Ni concentration in this study was higher than that previously observed for rice grown

186

in the Yangtze River Basin (0.54 mg/kg, n=681) and Jiangsu Province (0.31 mg/kg, 8


21

. The Pb and Cr

22

. The Cd

Page 11 of 33


23, 24

187

n=50)

. The rice Ni concentration in this study was even higher than that of rice

188

grown in Hunan Province (0.70 mg/kg, n=309), which is well known as “the

189

hometown of nonferrous metals” because mining activities have caused serious heavy

190

metal contamination of soil 23. Based on our analysis, we conclude that rice grains in

191

the study areas are contaminated the most by Cd and Ni and the least by Cu and Zn.

192

Soil heavy metal contamination has become increasingly severe in some areas of

193

southern Jiangsu Province, where the sampling sites in this study are located25.

194

However, the background value of some metals in these areas are low. For example,

195

the background values of As, Cr and Cu in soil were previously found to be 9.4, 75.6

196

and 23.4 mg/kg

197

activities, the concentrations of As, Cr and Cu are still below the safety threshold 27.

198

Thus these concentrations in rice grains were within acceptable levels.

26

. In other words, despite the pollution of soil by anthropogenic

199

The situation is different for Cd. The factors responsible for excessive Cd

200

accumulation in rice grains are complicated and include water management, farming

201

method and the degree of Cd transfer in the soil-rice system

202

and Cd accumulation in the soil also contribute to Cd uptake and are likely the main

203

factors contributing to the high Cd concentration in rice grains in this study 27. Human

204

activities in this region have resulted in severe soil Cd pollution. For example, in

205

Dingshu the ceramic industry has flourished over the past several decades, and Cd, 8%

206

of which is used for pigments, is an important material in ceramic processing 30.

207

According to our previous investigation, the average soil Cd concentration in this

208

region was 1.56 mg/kg, which is about 3-fold higher than the safety value 9


28, 29

. Soil acidification

12

.


Page 12 of 33

209

Furthermore, the pH value of topsoil in this region has been declining over time,

210

indicating increased soil acidification 26. Because Cd transfer from the soil to rice is

211

highly influenced by soil pH, with the highest Cd transfer occurring at approximately

212

pH 5.5,

213

rice.

31

increased soil acidification has likely led to increased Cd accumulation in

214

Bioaccessibility of heavy metals in rice. The BAF of heavy metals in rice

215

grains ranked as follows: Zn (4.02 mg/kg) > Cu (0.86 mg/kg) > Ni (0.24 mg/kg) > Cd

216

(0.06 mg/kg) > As (0.05 mg/kg) > Cr (0.03 mg/kg) > Pb (0.02 mg/kg). Thus, the

217

concentrations of Zn and Pb were the highest and the lowest, respectively, based on

218

analysis of both total and bioaccessible concentrations. Bioaccessibility (the

219

percentage of the metal that is bioaccessible) of heavy metals in rice grains ranked as

220

follows: As > Cd > Ni > Pb > Zn > Cu > Cr (Table 1). Previous research showed that 23.16-32.34% of As in rice grains was accessible

221 222

32

223

chemical form of As largely determines its bioaccessibility

224

of inorganic As was reported to be almost 100%, which is much higher than that of

225

dimethyl arsenic acid (DMAⅤ, 33%)

226

inorganic As was 93.60% of the total As in raw rice, also indicating that inorganic As

227

is more bioaccessible than organic As

228

rice observed in this study is likely due to the presence of inorganic As 32.

, which is lower than that observed in the present study (37.90±7.89%). The 32-34

. The bioaccessibility

35

. In intestinal extracts, the percentage of

34

. Therefore, the high As bioaccessibility in

229

The bioaccessibility of Cd (35.25%) in rice grains in this study was higher than

230

previously observed in rice grains collected from mining areas (16.94%) 36 and lower 10


Page 13 of 33


37

231

than in rice grains from the market

. The relatively high Cd bioaccessibility

232

observed in this study may due to the high concentration of total Cd in rice. For

233

example, a significant relationship was found between Cd bioaccessibility and total

234

concentration in oysters

235

controlling Cd bioaccessibility in rice remains unknown. Further research should

236

focus on the factors (such as subcellular distribution and the chemical form of Cd) and

237

the mechanisms affecting Cd bioaccessibility in crops.

38

. But whether total Cd concentration is the main factor

238

Mean Ni bioaccessibility was 27.77%, which is much higher than that observed

239

in convenience and fast food (4.50-7.75%) 39. Little information is available about Ni

240

bioaccessibility in rice grain, and our finding suggests that further studies on Ni

241

bioaccessibility and the factors influencing it are needed.

242

We found a positive linear correlation between the total concentrations and BAF 36, 39

243

of As, Cd and Ni; thus, consistent with previous studies

, BAF for these metals

244

could be well predicted by total concentrations in rice (Figure S2).

245

Health risk assessment of heavy metals in rice

246

Non-carcinogenic risk. It is crucial to conduct HRA to assess human health

247

risks due to heavy metal contamination of food 40. BAF concentrations were used to

248

conduct ADD, and the results were compared to those based on total heavy metal

249

concentrations (Figure S3). In order to assess the risks more precisely, PRA, which

250

provides a detailed risk distribution, was used (Figure 1 and Table 2). The highest HQ

251

for the local residents was found for As at P95 (HQ=2.20 and 1.83 for children and

252

adults, respectively), followed by Cd (HQ=1.97 and 1.53 for children and adults, 11



253

respectively). The HQ value of As for children (HQ =1.18) at P50 was still greater

254

than 1, indicating that exposure to As and Cd in particular via rice ingestion poses

255

great NCR to local residents. The HQ values for the remaining heavy metals were less

256

than 1, indicating they pose no potential NCR to the population. P50 for combined

257

HQ (HI) was 1.74 and 1.36 for children and adults, respectively. HI for children at P5

258

was 0.71, which is very close to the maximum admissible, indicating that most

259

residents may be exposed to metals at hazard levels from consumption of locally

260

produced rice. The situation was even worse for the high rice consumption population

261

(HI=5.06 and 4.08 for children and adults, respectively, at P95). In addition, result

262

showed that HI value based on total metal contents in rice were nearly 4 folds higher

263

than that based on bioaccessible metal contents at different percentiles. Our results

264

suggest that NCR based on PRA combined with bioaccessible heavy metal

265

concentrations in rice could give a more accurate and precise risk assessment. The

266

health risk posed by Pb, Cr, Cu, Zn and Ni is negligible, while exposure to As and Cd

267

via rice ingestion poses a great threat to the heath of local residents.

268

Carcinogenic risk. For CR assessment based on total metal concentrations in

269

rice, all Riski values for individual metals, were greater than the acceptable range

270

(1E-06 to 1E-04) specified by the US EPA except for Pb (Table S2). When assessing

271

CR with BAF, the CR posed by Cr was also considered acceptable at P5 for both

272

adults and children, and at P50 for adults only. The highest Riski for both adults and

273

children was found for Cd (P95=1.71E-01 for children and 1.36E-01 for adults). Even

274

though the Riski was assessed based on bioaccessible Cd concentration in rice, the 12


Page 14 of 33

Page 15 of 33


275

Riski value (P95) was still much higher than the acceptable value (Riski for Cd > 0.02

276

for both adults and children). The results for Ni were also striking: when CR was

277

assessed based on total and bioaccessible concentrations, CR for Ni was about 100

278

and 27 times higher than the acceptable level (Median) for children and adults,

279

respectively. The RISK value for all heavy metals was much higher than the

280

acceptable range (> 1.00E-03), even at P5, irrespective of whether CR was assessed

281

based on total or bioaccessible metal concentrations. This result indicates that heavy

282

metal exposure from rice ingestion poses a great CR to the local residents (Table S2

283

and Figure S4).

284

The contribution of each metal to the RISK showed great variation between

285

different Monte-Carlo-derived percentiles. The contribution of Ni, As and Cr to RISK

286

at P5, Median and P95 decreased, while Cd showed the opposite trend. In addition,

287

the contribution of each metal to RISK is different when CR was assessed based on

288

total concentrations and BAF. For example, the contribution of As to RISK was

289

higher when CR was assessed based on BAF than when assessed based on total As

290

(Figure 2). This observation illustrates that different types of cancer may occur in

291

populations with different levels of rice consumption. For example, chronic exposure

292

to cadmium is related to lung cancer and breast cancer 41 while As can cause ovarian

293

and kidney cancer

294

more likely to suffer from lung and breast cancer while those with low rice

295

consumption have a high risk of getting ovarian and kidney cancer. Hence, taking

296

pertinent precautions for different types of cancer, aimed at individuals with different

42, 43

, indicating that individuals with high rice consumption are

13



297

heavy metal exposure, will greatly reduce the morbidity and mortality rate.

298

Although CR assessed based on the total and bioaccessible heavy metal

299

concentrations in rice grains showed differences in the contributions of different

300

metals to RISK at different percentiles (Figure 2), both analyses suggest that local

301

residents have likely been exposed to high levels of heavy metals from ingesting rice

302

produced in the study areas, which poses a high CR. This is consistent with previous

303

research demonstrating that southern Jiangsu Province has one of the highest cancer

304

incidence rates in China

305

been reduced by the following factors: 1. The Grain Purchase Policy issued by the

306

State Administration of Grain of the People’s Republic of China may greatly reduce

307

the cancer incidence

308

(the floor price policy for grain) by the government. In addition, southern Jiangsu

309

Province is one of the most developed regions in China, and the convenient transport

310

and logistical conditions allows more frequent movement of the population and

311

cereals (such as rice). Together these factors could effectively reduce the consumption

312

of locally produced rice. 2. Proper medical care and the high level of income and

313

education of the local residents can reduce the cancer morbidity and mortality rate.

314

Nevertheless, the situation will get worse if heavy metal pollution occurs in less

315

developed areas or if the pollution range gets wider. Hence, proper measures such as

316

soil remediation to prevent metal transfer from soil to the human body are urgently

317

needed. Soil threshold values for heavy metal concentrations should also be improved

318

based on accurate health risk assessments to ensure food security. In addition, rice

44

. To some extent, cancer incidence in the study areas has

45

. Unqualified cereals must be purchased at the original price

14


Page 16 of 33

Page 17 of 33


319

consumption is only one of the exposure routes to humans, and exposure to heavy

320

metals through vegetables and drinking water is also an important health threat to

321

populations in metal-contaminated regions. Thus further research considering

322

multiple types of exposure to bioaccessible metals should be conducted.

323

ABBREVIATIONS USED

324

Bioaccessible fraction of heavy metal concentration

325

Health Risk Assessment

326

Probabilistic Risk Assessment

327

Hazard Quotient

328

Hazard Index

329

Maximum Permissible Levels

330

Rijksinstituut voor Volksgezondheid en Milieu

331

Carcinogenic Risk

332

Non-Carcinogenic Risk

333

Carcinogenic risk value for single element

334

Combined carcinogenic risk value of each element

335

ACKNOWLEDGEMENT

BAF

HRA PRA

HQ HI MPL RIVM

CR NCR Risk RISK

336

We sincerely acknowledge Hongyan Chen, Xueqiang Zhao, Hailong Chen, Qing

337

Liu and Sun Hu, for their contribution to field sampling. We also acknowledge Qing

338

Chang from Geological Survey of Jiangsu Province, for her precious contribution to

339

analytical work.

340

SUPPORTING INFORMATION 15



341

Brief statement in non-sentence format listing the contents of the material

342

supplied as Supporting Information. Supporting information description are as

343

follows:

344 345

Table S1. Constituents of the Various Synthetic Digestive Juices Used for RIVM In Vitro Digestion.

346

Table S2. Carcinogenic Risk (Risk) and ∑Risk (RISK) From Rice Ingestion

347

Calculated Based on Total and Bioaccessible Heavy Metal Concentrations Using

348

Monte-Carlo Simulation.

349

Figure S1. The locations of the sites where rice grains were sampled. Five

350

regions with severe soil heavy metal pollution in southern Jiangsu Province, China

351

were selected.

352

Figure S2. Correlation between the bioaccessible As, Cd and Ni fractions and

353

total concentrations in rice grains. The significant positive correlations indicate that

354

bioaccessible As, Cd and Ni concentrations can be predicted from their total

355

concentrations in rice grains. The dashed lines represent the 95% confidence intervals.

356

Figure S3. Probability density functions and best fitted distributions of the

357

average daily dose (ADD) of heavy metals from rice ingestion in the study region.

358

ADD values calculated based on bioaccessible and total heavy metal concentrations

359

are compared. ADD was evaluated using a Monte Carlo in crystal ball with 10000

360

iterations.

361

Figure S4. Probability density functions (A) and cumulative probability (B) of

362

the carcinogenic risk of all heavy metals (RISK) from rice ingestion in the study

363

region. RISK values calculated based on bioaccessible and total heavy metal

364

concentrations are compared. RISK was evaluated using a Monte Carlo simulation in

365

Crystal Ball with 10000 iterations. 16


Page 18 of 33

Page 19 of 33


366

17



367

REFERENCES

368

1.

Lin, Y.; Ma, J.; Zhang, Z.; Zhu, Y.; Hou, H.; Zhao, L.; Sun, Z.; Xue, W.; Shi, H.,

369

Linkage between human population and trace elements in soils of the Pearl River

370

Delta: Implications for source identification and risk assessment. Science of the

371

Total Environment 2017, 610-611, 944-959.

372

2.

Chervona, Y.; Arita, A.; Costa, M., Carcinogenic metals and the epigenome:

373

understanding the effect of nickel, arsenic, and chromium. Metallomics 2012, 4,

374

(7), 619-627.

375

3.

Liu, C.P.; Luo, C. L.; Gao, Y.; Li, F. B.; Lin, L. W.; Wu, C. A.; Li, X. D., Arsenic

376

contamination and potential health risk implications at an abandoned tungsten

377

mine, southern China. Environmental Pollution 2010, 158, (3), 820-826.

378

4.

Kim, E.; Moon, J. K.; Choi, H.; Kim, J. H., Probabilistic exposure assessment for

379

applicators during treatment of the fungicide kresoxim-methyl on apple orchard

380

by speed sprayer. Journal of Agricultural & Food Chemistry 2015, 63, (48),

381

53-66.

382

5.

Juhasz, A. L.; Smith, E.; Weber, J.; Rees, M.; Rofe, A.; Kuchel, T.; Sansom, L.;

383

Naidu, R., Comparison of in vivo and in vitro methodologies for the assessment

384

of arsenic bioavailability in contaminated soils. Chemosphere 2007, 69, (6),

385

961-966.

386

6.

Kong, F.; Oztop, M. H.; Singh, R. P.; McCarthy, M. J., Physical changes in white

387

and brown rice during simulated gastric digestion. Journal of Food Science 2011,

388

76, (6), E450-E457. 18


Page 20 of 33

Page 21 of 33


389

7.

Juhasz, A. L.; Weber, J.; Smith, E.; Naidu, R.; Rees, M.; Rofe, A.; Kuchel, T.;

390

Sansom, L., Assessment of four commonly employed in vitro arsenic

391

bioaccessibility assays for predicting in vivo relative arsenic bioavailability in

392

contaminated soils. Environmental Science & Technology 2009, 43, (24),

393

9487-9494.

394

8.

Chm, V.; De, K. E. V.; Cjm, R., Development and applicability of an in vitro

395

digestion model in assessing the bioaccessibility of contaminants from food.

396

Rijksinstituut Voor Volksgezondheid En Milieu Rivm 2007.

397

9.

Yang, Y.; Wang, M.; Chen, W.; Li, Y.; Chi, P., Cadmium accumulation risk in

398

vegetables and rice in southern china: insights from solid-solution partitioning

399

and plant uptake factor. Journal of Agricultural & Food Chemistry 2017, 65,

400

(27), 5463-5469.

401

10. Yu, Y. X.; Li, J. L.; Zhang, X. Y.; Yu, Z. Q.; Van, d. W. T.; Han, S. Y.; Wu, M. H.;

402

Sheng, G. Y.; Fu, J. M., Assessment of the bioaccessibility of polybrominated

403

diphenyl ethers in foods and the correlations of the bioaccessibility with nutrient

404

contents. Journal of Agricultural & Food Chemistry 2010, 58, (1), 301-308.

405

11. Jadán-Piedra, C.; Clemente, M. J.; Devesa, V.; Vélez, D., Influence of

406

physiological gastrointestinal parameters on the bioaccessibility of mercury and

407

selenium from swordfish. Journal of Agricultural & Food Chemistry 2016, 64,

408

(3), 690-698.

409

12. Li, T.; Chang, Q.; Yuan, X.; Li, J.; Ayoko, G. A.; Frost, R. L.; Chen, H.; Zhang,

410

X.; Song, Y.; Song, W., Cadmium transfer from contaminated soils to the human 19



411

body through rice consumption in southern Jiangsu Province, China.

412

Environmental Science Processes & Impacts 2017, 19, (6), 843-850.

413

13. Omar, N. A.; Praveena, S. M.; Aris, A. Z.; Hashim, Z., Health Risk Assessment

414

using in vitro digestion model in assessing bioavailability of heavy metal in rice:

415

A preliminary study. Food Chemistry 2015, 188, 46-50.

416

14. Versantvoort, C. H.; Oomen, A. G.; Van de Kamp, E.; Rompelberg, C. J.; Sips, A.

417

J., Applicability of an in vitro digestion model in assessing the bioaccessibility of

418

mycotoxins from food. Food and Chemical Toxicology 2005, 43, (1), 31-40.

419

15. Iarossi, B. G., The power of survey design: a user's guide for managing surveys,

420

interpreting results, and influencing respondents. World Bank Publications 2010.

421

16. Huang, Z.; Pan, X. D.; Wu, P. G.; Han, J. L.; Chen, Q., Health risk assessment of

422

heavy metals in rice to the population in Zhejiang, China. Plos One 2013, 8, (9),

423

75-87.

424

17. Luna, C. M.; Pastori, G. M.; Driscoll, S.; Groten, K.; Bernard, S.; Foyer, C. H.,

425

Drought controls on H2O2 accumulation, catalase (CAT) activity and CAT gene

426

expression in wheat. Journal of Experimental Botany 2005, 56, (411), 417-423.

427

18. US EPA, Office of Solid Waste Emergency Response, waste and cleanup risk

428

assessment, Risk Assessment Guidance for Superfund (RAGS) Part A, 1989, 804,

429

(7), 636-640.

430

19. Li, Z.; Ma, Z.; van der Kuijp, T. J.; Yuan, Z.; Huang, L., A review of soil heavy

431

metal pollution from mines in China: Pollution and health risk assessment.

432

Science of The Total Environment 2014, 468–469, 843-853. 20


Page 22 of 33

Page 23 of 33


433

20. Nakazawa, K.; Nagafuchi, O.; Kawakami, T.; Inoue, T.; Yokota, K.; Serikawa, Y.;

434

Cyio, B.; Elvince, R., Human health risk assessment of mercury vapor around

435

artisanal small-scale gold mining area, Palu city, Central Sulawesi, Indonesia.

436

Ecotoxicology and Environmental Safety 2016, 124, 155-162.

437

21. Ministry of Health of the People’s Republic of China (MHPC). National food

438

safety standard, Maximum level of contaminants in foods (GB 2762-2012).

439

Beijing: China Standard Press, 2012 (in Chinese).

440

22. Li, H. B.; Li, J.; Zhao, D.; Li, C.; Wang, X.; Sun, H. J.; Juhasz, A. L.; Ma, L. Q.,

441

Arsenic relative bioavailability in rice using a mouse arsenic urinary excretion

442

bioassay and its application to assess human health risk. Environmental Science

443

& Technology 2017, 51, (8), 4689-4696.

444

23. Cao, Z.; Mou, R.; Cao, Z.; Lin, X.; Xu, P.; Chen, Z.; Zhu, Z.; Chen, M., Nickel

445

in milled rice (Oryza sativa L.) from the three main rice-producing regions in

446

China. Food Additives & Contaminants Part B Surveillance 2016, 10, (1), 69-82.

447

24. Wang, W.; Zhang, Z.; Yang, G.; Wang, Q., Health risk assessment of Chinese

448

consumers to nickel via dietary intake of foodstuffs. Food Additives &

449

Contaminants Part A Chemistry Analysis Control Exposure & Risk Assessment

450

2014, 31, (11), 1861-1871.

451

25. Xing, P. G.; Min, C. J.; Qin, G. J.; Liang, L. S.; Wei, Z. J., On the change of

452

status of some heavy metal elements in soil environment under intensive

453

economical development. Resources & Enuironment in the Yangtza Basin 2000.

454

26. Liao, Q. L.; Evans, L. J.; Gu, X.; Fan, D. F.; Jin, Y.; Wang, H., A regional 21



455

geochemical survey of soils in Jiangsu Province, China: Preliminary assessment

456

of soil fertility and soil contamination. Geoderma 2007, 142, (1–2), 18-28.

457

27. Chen, L.; Zhou, S.; Shi, Y.; Wang, C.; Li, B.; Li, Y.; Wu, S., Heavy metals in

458

food crops, soil, and water in the Lihe River Watershed of the Taihu Region and

459

their potential health risks when ingested. Science of the Total Environment 2017,

460

615, 141-149.

461

28. Wang, J.; Feng, X.; Anderson, C. W.; Qiu, G.; Bao, Z.; Shang, L., Effect of

462

cropping systems on heavy metal distribution and mercury fractionation in the

463

Wanshan mining district, China: implications for environmental management.

464

Environmental Toxicology & Chemistry 2015, 33, (9), 2147-2155.

465

29. Chen, H.; Yuan, X.; Li, T.; Hu, S.; Ji, J.; Wang, C., Characteristics of heavy metal

466

transfer and their influencing factors in different soil–crop systems of the

467

industrialization region, China. Ecotoxicology and Environmental Safety 2016,

468

126, 193-201.

469

30. Obaid F.; Annette A.; Scott W.; Pam T.; Kim J. Agency for Toxic Substances and

470

Diseases Registry (ATSDR), 2012. Toxicological profile for cadmium. URL

471

(http://www.atsdr.cdc.gov/toxprofiles/tp.asp?id=48&tid=15).

472

31. Rafiq, M. T.; Aziz, R.; Yang, X.; Xiao, W.; Rafiq, M. K.; Ali, B.; Li, T.,

473

Cadmium phytoavailability to rice (Oryza sativa L.) grown in representative

474

Chinese soils. A model to improve soil environmental quality guidelines for food

475

safety. Ecotoxicology & Environmental Safety 2014, 103, (1), 101-107.

476

32. Bolan, S.; Kunhikrishnan, A.; Chowdhury, S.; Seshadri, B.; Naidu, R.; Ok, Y. S., 22


Page 24 of 33

Page 25 of 33


477

Comparative analysis of speciation and bioaccessibility of arsenic in rice grains

478

and complementary medicines. Chemosphere 2017, 182, 433-440.

479

33. Meliker, J. R.; Franzblau, A., Major contributors to inorganic arsenic intake in

480

southeastern Michigan. International Journal of Hygiene & Environmental

481

Health 2006, 209, (5), 399-411.

482

34. Sun, G. X.; Van de Wiele, T.; Alava, P.; Tack, F.; Du Laing, G., Arsenic in cooked

483

rice: Effect of chemical, enzymatic and microbial processes on bioaccessibility

484

and speciation in the human gastrointestinal tract. Environmental Pollution 2012,

485

162, 241-246.

486

35. Juhasz, A. L.; Smith, E.; Weber, J.; Rees, M.; Rofe, A.; Kuchel, T.; Sansom, L.;

487

Naidu, R., In vivo assessment of arsenic bioavailability in rice and its

488

significance for human health risk assessment. Environmental Health

489

Perspectives 2006, 114, (2), 1826-1831.

490

36. Yang, L. S.; Zhang, X. W.; Li, Y. H.; Li, H. R.; Wang, Y.; Wang, W. Y.,

491

Bioaccessibility and risk assessment of cadmium from uncooked rice using an in

492

vitro digestion model. Biological Trace Element Research 2012, 145, (1), 81-86.

493

37. Zhuang, P.; Zhang, C.; Li, Y.; Zou, B.; Mo, H.; Wu, K.; Wu, J.; Li, Z.,

494

Assessment of influences of cooking on cadmium and arsenic bioaccessibility in

495

rice, using an in vitro physiologically-based extraction test. Food Chemistry

496

2016, 213, 206-214.

497

38. He, M.; Ke, C. H.; Tian, L.; Li, H. B., Bioaccessibility and health risk assessment

498

of Cu, Cd, and Zn in “colored” oysters. Archives of Environmental 23



499

Contamination and Toxicology 2016, 70, (3), 595-606.

500

39. Cabrera-Vique, C.; Mesías, M.; Bouzas, P. R., Nickel levels in convenience and

501

fast foods: In vitro study of the dialyzable fraction. Science of The Total

502

Environment 2011, 409, (8), 1584-1588.

503

40. Lee, S. W.; Lee, B. T.; Kim, J. Y.; Kim, K. W.; Lee, J. S., Human risk assessment

504

for heavy metals and as contamination in the abandoned metal mine areas, Korea.

505

Environmental Monitoring and Assessment 2006, 119, (1), 233-244.

506

41. Nawrot, T. S.; Martens, D. S.; Hara, A.; Plusquin, M.; Vangronsveld, J.; Roels, H.

507

A.; Staessen, J. A., Association of total cancer and lung cancer with

508

environmental exposure to cadmium: the meta-analytical evidence. Cancer

509

Causes & Control 2015, 26, (9), 1281-1288.

510

42. Arsenic, R.; Braicu, E. I.; Letsch, A.; Dietel, M.; Sehouli, J.; Keilholz, U.;

511

Ochsenreither, S., Cancer-testis antigen cyclin A1 is broadly expressed in ovarian

512

cancer and is associated with prolonged time to tumor progression after

513

platinum-based therapy. Bmc Cancer 2015, 15, (1), 1-11.

514

43. Saint-Jacques, N.; Brown, P.; Nauta, L.; Boxall, J.; Parker, L.; Dummer, T. J. B.,

515

Estimating the risk of bladder and kidney cancer from exposure to low-levels of

516

arsenic in drinking water, Nova Scotia, Canada. Environment International 2017,

517

110, (32), 95-104.

518

44. Lu, Y.; Song, S.; Wang, R.; Liu, Z.; Meng, J.; Sweetman, A. J.; Jenkins, A.;

519

Ferrier, R. C.; Li, H.; Luo, W., Impacts of soil and water pollution on food safety

520

and health risks in China. Environment International 2015, 77, (1), 5-15. 24


Page 26 of 33

Page 27 of 33


521

45. URL (http://www.chinagrain.gov.cn/n787423/c938910/content.html). Important

522

works relevant to grain quality in 2016 issued by the State Administration of

523

Grain of People’s Republic of China.

524

FUNDING SOURCES

525

This study was supported by the the Natural Science Foundation of Shandong

526

Province (ZR2016YL002), Research Project (Youth Fund) of Shandong Academy of

527

Science (Grant No. 2018QN0023), the Key Research and Development Project of

528

Jiangsu Province, China (Grant No. BE2015708), the National Natural Science

529

Foundation of China (Grant No. 41601540), the Foundation Research Project of

530

Jiangsu Province (Grant No. BK20160859) and the Fundamental Research Funds for

531

the Central Universities (Grant No. 2014B18514).

25



532

FIGURE CAPTIONS

533

Figure 1. Probability density functions (A) and cumulative probability (B) of the hazard

534

index (HI) of heavy metals from rice ingestion in the study region. HI values calculated based

535

on bioaccessible and total heavy metal concentrations are compared. HI was evaluated using a

536

Monte Carlo simulation in Crystal Ball with 10000 iterations.

537

Figure 2. The relative contribution of each metal to the RISK (sum of carcinogenic

538

risk due to each metal) at Monte Carlo-derived P5 (5th percentile), Median (50th

539

percentile) and P95 (95th percentile). (A) Carcinogenic risk for children based on

540

total metal concentrations; (B) Carcinogenic risk for adults based on total metal

541

concentrations; (C) Carcinogenic risk for children based on bioaccessible metal

542

concentrations; (D) Carcinogenic risk for adults based on bioaccessible metal

543

concentrations.

26


Page 28 of 33

Page 29 of 33


Table 1 Descriptive Statistics for Total Concentrations (mg kg -1) and Bioaccessibility (%) of Heavy Metals in Rice Grains (n=70). As

Cd

Pb

Cr

Cu

Zn

Ni

Total Heavy Metal

0.13±0.05

0.30±0.52

0.09±0.04

0.44±0.23

3.94±0.88

18.87±2.94

0.84±0.40

Concentrations (mg kg-1)

[0.01,0.23]

[0.01,2.77]

[0.05,0.29]

[0.13,1.22]

[1.54,6.17]

[14.00,30.10]

[0.38,2.62]

Probabilistic Distributionsa

Logistic

Lognormal

Lognormal

Lognormal

Normal

Lognormal

Lognormal

0.2d

0.2

0.2

1.0

10

50

-

Regulated -1 b

Concentrations (mg kg )

37.90±7.89

35.25±20.06 23.94±11.00

9.87±5.66

21.19±11.18

21.67±8.22

27.77±8.05

[18.04,58.64]

[6.67,87.50]

[7.58,48.34]

[3.26,32.04]

[4.02,49.25]

[9.01,40.89]

[18.22,60.08]

Lognormal

Lognormal

Lognormal

Beta

Gamma

Gamma

Lognormal

Bioaccessibility (%) Probabilistic Distributionc

a: The Probabilistic Distributions of Total Heavy Metal Concentrations b: Staple Rice Safety Criteria Issued by the Ministry of Agriculture, P.R. China c: The Probabilistic Distributions of Bioaccessible Heavy Metal Concentrations d: The Maximum Level of Inorganic As in Rice Specified by the Ministry of Agriculture P.R. China.

27



Page 30 of 33

Table 2. Hazard Quotient (HQ) and Hazard Index (HI) for Non-Carcinogenic Risk from Rice Ingestion Calculated Based on Bioaccessible Heavy Metals Concentrations Using Monte-Carlo Simulation. P5

Metal

Median

P95

HQ

Children

Adults

Children

Adults

Children

Adults

As

5.10E-01

3.43E-01

1.18E+00

8.97E-01

2.20E+00

1.83E+00

Cd

4.82E-02

3.45E-02

2.06E-01

1.90E-01

1.97E+00

1.53E+00

Pb

1.54E-02

1.07E-02

3.73E-02

2.83E-02

9.00E-02

7.40E-02

Cr

1.10E-04

7.07E-05

1.71E-04

1.31E-04

2.83E-04

2.43E-04

Cu

4.45E-02

3.20E-02

1.36E-01

1.04E-01

4.18E-01

3.35E-01

Zn

4.67E-02

3.24E-02

9.83E-02

7.47E-02

1.96E-01

1.63E-01

Ni

4.48E-02

2.96E-02

8.05E-02

6.20E-02

1.85E-01

1.52E-01

7.10E-01

4.83E-01

1.74E+00

1.36E+00

5.06E+00

4.08E+00

HI (∑ HQ)

28


Page 31 of 33


Figure 1

29



Figure 2

30


Page 32 of 33

Page 33 of 33


GRAPHIC FOR TABLE OF CONTENTS

31


Incorporating Bioaccessibility into Human Health ... - ACS Publications

Recommend Documents