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
Journal of Agricultural and Food Chemistry
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
ACS Paragon Plus Environment
Page 2 of 33
Page 3 of 33
Journal of Agricultural and Food Chemistry
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
ACS Paragon Plus Environment
(Rijksinstituut
5, 6
. A
voor
Journal of Agricultural and Food Chemistry
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
ACS Paragon Plus Environment
Page 4 of 33
Page 5 of 33
Journal of Agricultural and Food Chemistry
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
ACS Paragon Plus Environment
Journal of Agricultural and Food Chemistry
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
ACS Paragon Plus Environment
13, 14
. The
Page 7 of 33
Journal of Agricultural and Food Chemistry
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
ACS Paragon Plus Environment
Journal of Agricultural and Food Chemistry
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
ACS Paragon Plus Environment
21
. The Pb and Cr
22
. The Cd
Page 11 of 33
Journal of Agricultural and Food Chemistry
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
ACS Paragon Plus Environment
28, 29
. Soil acidification
12
.
Journal of Agricultural and Food Chemistry
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
ACS Paragon Plus Environment
Page 13 of 33
Journal of Agricultural and Food Chemistry
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
ACS Paragon Plus Environment
Journal of Agricultural and Food Chemistry
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
ACS Paragon Plus Environment
Page 14 of 33
Page 15 of 33
Journal of Agricultural and Food Chemistry
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
ACS Paragon Plus Environment
Journal of Agricultural and Food Chemistry
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
ACS Paragon Plus Environment
Page 16 of 33
Page 17 of 33
Journal of Agricultural and Food Chemistry
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
ACS Paragon Plus Environment
Journal of Agricultural and Food Chemistry
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
ACS Paragon Plus Environment
Page 18 of 33
Page 19 of 33
Journal of Agricultural and Food Chemistry
366
17
ACS Paragon Plus Environment
Journal of Agricultural and Food Chemistry
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
ACS Paragon Plus Environment
Page 20 of 33
Page 21 of 33
Journal of Agricultural and Food Chemistry
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
ACS Paragon Plus Environment
Journal of Agricultural and Food Chemistry
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
ACS Paragon Plus Environment
Page 22 of 33
Page 23 of 33
Journal of Agricultural and Food Chemistry
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
ACS Paragon Plus Environment
Journal of Agricultural and Food Chemistry
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
ACS Paragon Plus Environment
Page 24 of 33
Page 25 of 33
Journal of Agricultural and Food Chemistry
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
ACS Paragon Plus Environment
Journal of Agricultural and Food Chemistry
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
ACS Paragon Plus Environment
Page 26 of 33
Page 27 of 33
Journal of Agricultural and Food Chemistry
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
ACS Paragon Plus Environment
Journal of Agricultural and Food Chemistry
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
ACS Paragon Plus Environment
Page 28 of 33
Page 29 of 33
Journal of Agricultural and Food Chemistry
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
ACS Paragon Plus Environment
Journal of Agricultural and Food Chemistry
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
ACS Paragon Plus Environment
Page 31 of 33
Journal of Agricultural and Food Chemistry
Figure 1
29
ACS Paragon Plus Environment
Journal of Agricultural and Food Chemistry
Figure 2
30
ACS Paragon Plus Environment
Page 32 of 33
Page 33 of 33
Journal of Agricultural and Food Chemistry
GRAPHIC FOR TABLE OF CONTENTS
31
ACS Paragon Plus Environment