Global geographical variation in elemental and arsenic
species concentration in paddy rice grain identifies a
close association of essential elements copper, selenium
and molybdenum with cadmium

Andrew A. Meharg, Caroline Meharg, Manus Carey, Paul N. Williams, Zhengyu Shi, Katrina Campbell, Christopher T. Elliott, Enerst M. Marwa, Xiao Jiujin, Júlia Gomes Farias, et al.

► To cite this version:

Andrew A. Meharg, Caroline Meharg, Manus Carey, Paul N. Williams, Zhengyu Shi, et al.. Global geographical variation in elemental and arsenic species concentration in paddy rice grain identifies a close association of essential elements copper, selenium and molybdenum with cadmium. Exposure and Health, 2023, 15 (3), pp.505-518. 10.1007/s12403-022-00504-1 . hal-05181270

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Distributed under a Creative Commons Attribution 4.0 International License
# Global Geographical Variation in Elemental and Arsenic Species Concentration in Paddy Rice Grain Identifies a Close Association of Essential Elements Copper, Selenium and Molybdenum with Cadmium

Andrew A. Meharg¹ Ⓓ D. Caroline Meharg¹ · Manus Carey¹ · Paul Williams¹ · Zhengyu Shi¹ ·

Katrina Campbell¹ · Christopher Elliott¹ · Ernest Marwa² · Xiao Jiujin³ · Júlia Gomes Farias⁴ ·

Fernando Teixeira Nicoloso⁴ · P. Mangala C. S. De Silva⁵ · Ying Lu⁶ · Andy J. Green⁷ · Eduardo Moreno-Jiménez⁸ ·

Ángel Antonio Carbonell-Barrachina⁹ · Alessia Sommella¹⁰ · Massimo Pigna¹⁰ · Catherine Brabet¹¹ ·

Didier Montet¹¹ · Mahmud Hossain¹² · M. Rafiqul Islam¹²

Received: 18 February 2022 / Revised: 9 August 2022 / Accepted: 12 September 2022 / Published online: 29 September 2022
© The Author(s) 2022

## Abstract

Despite the centrality of staple grains for human well-being, both as a source of nutrients and of toxic ions, there is little understanding of where and how elements vary, and if there are particular elements that correlate. Here, for shop bought polished (white) rice, we comprehensively characterized trace (arsenic species, cadmium, copper, iron, manganese, molybdenum, rubidium and zinc) and macro-nutrients (calcium, chlorine, potassium, phosphorus and sulphur) for grain purchased in 18 countries, across four continents, a total of 1045 samples. This was to investigate if there were any major differences between geographic location and elemental content, and to observe if there were any patterns in elemental distribution. Greatest variation in the median was observed for the non-essential rubidium (15-fold) and arsenic species (fivefold). Rubidium was the highest in the Americas, lowest in Europe, while inorganic arsenic (iAs) and dimethylarsonic acid (DMA) were low for Africa and high in the South American and European continents. The highest concentrations of cadmium were found in Asian samples, and lowest in South America, with variation within these regions. At the extremes of individual countries, China had fivefold higher concentrations than the global median, while Tanzania was fourfold lower than this value. Calcium, potassium, molybdenum and phosphorus were the highest in European and lowest in African grain, though the fold-differences were relatively low, ~0.2, while iron was the highest in African grain and lowest in European, Asian and South American grain, with a ~twofold difference. Selenium was also higher in Africa versus other regions, and copper, manganese and zinc were the highest in American grain. Factor analysis showed that copper, cadmium, molybdenum, rubidium and selenium were strongly associated together, and these element's factor loadings were diametrically opposed to less tightly associated calcium, chlorine, manganese, potassium, phosphorus and sulphur. Stepwise additions linear region analysis was performed on log-transformed concentrations to investigate cadmium associations in more detail. Selenium was the greatest predictor of cadmium concentration, followed by molybdenum, accounting for over 50% of the contribution to the adjusted R². Arsenic species were only weakly correlated with other elements. The implications for these findings with respect to dietary nutrition are discussed. Vietnamese rice was notable in being deficient in macro- and micro-nutrients while also being elevated in cadmium at a median of 0.02 mg/kg, with China though still having a median that is ~2.5-fold this concentration. These Chinese concentrations are of particular concern as the 75th percentile for China is 0.1 mg/kg, a value that triggers regulatory action for rice products.

**Keywords** Arsenic · Cadmium · Mineral nutrition · Rice

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<footnote>Andrew A. Meharg
aa.meharg@qub.ac.uk</footnote>
Introduction

Hidden hunger is one of the greatest issues facing society globally, defined as where food delivers adequate calories but is deficient in essential nutrients (Gashu et al. 2021). This is nowhere so prominent than in subsistence diets that rely on polished grains, where milling grains, unfortunately, removes a substantial portion of nutrients present in wholegrain (Rahman et al. 2019). Grain mineral nutrition varies due to soil conditions and cultivation and fertilization practice (Nyachoti et al. 2021; Gashu et al. 2021; Welsh and Graham 2004; White and Broadley 2005), and to crop genetics (Cobb et al. 2021; Huang et al. 2015; Norton et al. 2014). Maximizing grain nutrient density, to deliver as great a quantity of each essential element as feasible, given that bioavailability in the gut is also an issue for elements such as iron (Hoekenga et al. 2011), is a major goal for those wishing to improve human health (Gashu et al. 2021; Rahman et al. 2019).

Food security, supplying a population sufficient quantities of nutritionally balanced foods, has to be combined with food safety, minimizing exposure to toxic compounds. White rice is particularly problematic in both regards as it is not overly nutrient dense, while at the same time being widely elevated, ~tenfold compared to other grains, in cadmium and arsenic (Rahman et al. 2019). There is evidence that for certain elements such as arsenic (Williams et al. 2007), cadmium (Nan et al. 2002) and selenium (Zhu et al. 2009) that rice grain concentration is linked to the concentration in the soils in which they are grown. These elements tend to act as analogues of essential nutrients, phosphate and silicic acid for arsenic, dependant on whether arsenic is speciated as arsenate or arsenite, respectively (Meharg and Meharg 2015), nutrient divalent metal cations for cadmium (Schaaf et al. 2004), and sulphur for selenium (Zhu et al. 2009). The same is the case for non-essential rubidium, an analogue of essential potassium (Oliferuk et al. 2017). Most nutrient elements are thought to be in homeostasis with plant tissues, given that plant cells have quite stringent requirements for optimum functioning, and all elements/ions can be considered as stressors if outside those optimum activities in cells (Clemens 2001; Ernst et al. 2008; Meharg 1993). Some nutrients can be stored in excess through cellular sub-localization, or the formation of compounds/complexes suitable for storage. Phosphorus, an essential but generally poorly available in soils, undergoes both these storage strategies under luxury uptake, though plants rapidly downregulate phosphate assimilation if leaf tissue becomes too high (Chiou et al. 2006). Quantitative trait loci analysis, also indicates for some elements, notably cadmium, lead, selenium, zinc, that grain accumulation is under genetic control in rice

(Cobb et al. 2021; Huang et al. 2015; Norton et al. 2014). Given these complexities, for food security and safety mitigation strategies, it is important to know how elemental concentrations vary regionally for foods, Deficiency or toxicity mitigation for a specific region may include soil fertilization, water management, food supplementation or high grain nutrient directed breed strategies, i.e. food fortification of biofortification (Welsh and Graham 2004; White and Broadley 2005).

Global and regional assessments of grain mineral nutrition tend to focus on food fraud, using elemental analysis to try and identify geographical origin (Ariyama et al. 2012; Chung et al. 2018); or geo-nutrition, relating crop grain element concentrations to region of origin (Adomako et al. 2011; Gashu et al. 2021), with a number of studies also focusing on dietary consequences of grain elemental content (Gashu et al. 2021; Joy et al. 2017; Nyachoti et al. 2021). These studies generally use inductively coupled plasma-mass spectrometry (ICP-MS), and this has led to a better characterization of micro-elemental quantification, to which ICP-MS is suited, as compared to macro-elements that are generally too elevated in grains for adequate ICP-MS quantification, with the exception of phosphorus. Of particular interest with respect to ICP-MS quantification are arsenic and cadmium which are of concern in rice but too low to be measured by other techniques such as X-ray fluorescence (XRF) (Rahman et al. 2019). Furthermore, arsenic in rice grain is speciated as iAs (the sum of arsenate and arsenite) and DMA, and it is iAs that is regulated not DMA, yet DMA can constitute up to ~50% of grain speciation in some regions, and chromatography interfaced to ICP-MS is required to speciate grain arsenic (Rahman et al. 2019). XRF is better suited to macro-element analysis and is becoming more established in characterizing foods, given that issues with external calibration of XRF need to be overcome by using an appropriate range of Certified Reference Materials (CRMs) (Oliveira et al. 2020; Rahman et al. 2019). Also, for arsenic in particular, there is a need to speciate as toxicity and regulation are driven by iAs content, yet rice grain can have highly variable concentrations of dimethylarsonic acid (DMA) (Adomako et al. 2011; Carey et al. 2020). Here, we report macro- and micro-elemental, along with arsenic species, concentrations of market white rice, chosen to reflect direct dietary consumption of 1045 samples, collected across 18 countries from four major rice growing continents; Africa, South America, Asia and Europe. Rice was analysed by ICP-MS, ion chromatography (IC)-ICP-MS (to ascertain arsenic species concentrations) and XRF. Data were interpreted to examine commonalities in elemental and species patterns, and to identify specific deficiency/toxicity issues for given regions/countries.
Materials and Methods

The sampling strategies are outlined in the publications Carey et al. (2020) and Shi et al. (2020). In brief, pol- ished (white) rice was obtained from 18 countries, and 4 continents, from retail outlets so as to best characterize consumer exposure. In total 1045 samples are reported here, a subset from the 1180 reported in Carey et al. (2020) on arsenic speciation, and 2270 from Shi et al. (2020) on cadmium. The current dataset only reports samples where additional XRF characterization has been conducted, lead- ing to reduced sample coverage, in both number and in countries assessed.

As noted in Carey et al. (2020), the type of market
basket survey conducted here has its limitations due to
the fact that multiple people conducted the survey, with
multiple sampling objectives, with each country sampled
having differing geographical size, and the surveyors hav-
ing differing geographical reaches within a given country.
Furthermore, some countries only have one rice season
per annum, others have multiple, and there is often within
country variation on number of seasons based on geo-
graphical setting. Also, genetic variation in rice occurs
at the subspecies level (Aus, Japonica, Indica and hybrid
etc.), and there is considerable cultivar variation within
these subspecies. Markets may directly source rice from
neighbouring farmers, or at the other extreme, source rice
from larger mills that can operate on a regional scale. The
nature of variation in rice sourcing will lead to heteroge-
neity in retail supply. The data presented here does not
include retail outlet type, genetic information, seasonal
information, etc., and this must be born in mind in inter-
preting this study. For a global sampling campaign to
include such detail, and the subsequent increase in sam-
ple size to account for these multiple variables, were well
beyond the resources of this current investigation. How-
ever, the utility of the sampling approach used here can be
validated by comparison with local studies where details
of interest are recorded. Examples of more detailed studies
cross-referenced to the current investigation in the discus-
sion are country-specific surveys of grain (Adomako et al.
2011; Joy et al. 2017; Williams et al. 2009); and the role
of genetic variation (Huang et al. 2015; Norton et al. 2010,
2012, 2014), with respect to rice grain mineral nutrition.

Methodologies for the well-established rice inorganic
analysis that we use here are have been published already.
A concise overview of the analysis is now presented. Rice
samples were freeze dried (Christ Alpha1-4LD) and milled
(PM100 Ball Mill, Retsch, Germany) and subsampled for
ICP-MS (Thermo Scientific iCap Q), both for arsenic
speciation (interfaced with in chromatography, IC), and
elemental totals, and for XRF (Nex CG, Rigaku, Japan).

ICP-MS and IC-ICP-MS protocols are described in detail
in Carey et al. (2020) and Shi et al. (2020), and XRF proto-
cols in Rahman et al. (2019). Certified Reference Material
(CRM) rice flour, NIST 1568b, was included in all batches
of samples for both elemental totals (ICP-MS and XRF)
and for arsenic speciation, and it is certified for both a
range of elemental totals and for arsenic speciation. Total
elemental analysis was conducted by weighing ~ 100 mg of
sample, accurately, into 50 ml polypropylene (PP) tubes,
and then adding 2 ml of conc. Aristar nitric acid. The PP
tubes were then left to stand for 24 h. Before initiating
digestion, 2 ml of undiluted hydrogen peroxide (reagent
grade) was pipetted in, then microwaved digested (Mars
CEM), using programmes detailed in Carey et al. (2020).
Samples were cooled to room temperature on completion
of microwaving, and diluted to 30 ml with double distilled
deionized water. The protocol for arsenic speciation was
identical except that except that 10 ml of one percent nitric
acid was used for as the extractant instead of concentrated
nitric acid. Prior to IC-ICP-MS speciation, 7 µl of hydro-
gen peroxide added to 700 µl of the extraction, to convert
arsenite to arsenate for subsequent IC chromatography, as
arsenite elutes at the solvent front.

ICP-MS analysis was calibrated using eight standards,
including one blank. Multi-Element 2 (SPEX CLMS-2
Multi-Element Solution 2, matrix: five percent nitric acid)
and Multi-Element 4 (SPEX CLMS-4 Multi-Element Solu-
tion 4, matrix: water/Tr-HF) were used to make up all stand-
ards in a range of 0–100 µg/l in one percent nitric acid. Rho-
dium internal standard (Fluka Analytical rhodium internal
ICP-MS standard) was added to all standards and all samples
also at ten µg/l. For IC-ICP-MS, authentic samples of arse-
nate, monomethylarsonic acid (MMA) and DMA were used
to determine retention times, and the calibration was con-
ducted on DMA. XRF (Rigaku NEX CG) was performed on
the ball-milled samples to determine elements that are either
problematic on non-determinable by ICP-MS (see Table 1).

Only elements with certified quantification for rice flour CRM NIST 1568b were reported. Reported elements were within the range of 67–109% recovery, with CRM recoveries and Limits of Detection (LoD) for ICP-MS techniques, and Detection Limits (DL), for XRF as this method has an internal calibration, are given in Table 1. As a range of elements can be quantified by both ICP-MS and XRF, and for these elements, the technique with the best recovery of NIST rice flour CRM NIST 1568b was selected for reporting, with the technique used for reported analysis also listed in Table 1.

Statistical analysis considered the data both as grouped by
continent or as grouped by country. As the datasets are not
normally distributed, Kruskal–Wallis test used to compare
medians and stepwise linear regression to look at relation-
ships between elements was conducted in SPSS v.27, and
<table><caption>**Table 1** Certified reference materials (CRM) recoveries by ICP-MS and XRF for rice flour NIST 1568b</caption><thead><tr><th>Element/species</th><th>Certified value (mg/kg)</th><th>Recorded value (mg/kg)</th><th>SEM</th><th>Recovery (%)</th><th>LoD (mg/kg)</th><th>DL (mg/kg)</th><th>Instrumentation</th><th>N</th></tr></thead><tbody><tr><td>DMA</td><td>0.180</td><td>0.181</td><td>0.005</td><td>100</td><td>0.002</td><td>–</td><td>IC-ICP-MS</td><td>78</td></tr><tr><td>MMA</td><td>0.012</td><td>0.010</td><td>0.000</td><td>87</td><td>0.002</td><td>–</td><td>IC-ICP-MS</td><td>78</td></tr><tr><td>iAs</td><td>0.092</td><td>0.091</td><td>0.001</td><td>99</td><td>0.002</td><td>–</td><td>IC-ICP-MS</td><td>78</td></tr><tr><td>Sum As sp.</td><td>0.284</td><td>0.282</td><td>0.005</td><td>99</td><td>0.002</td><td>–</td><td>IC-ICP-MS</td><td>78</td></tr><tr><td>As</td><td>0.285</td><td>0.228</td><td>0.004</td><td>80</td><td>0.001</td><td>–</td><td>ICP-MS</td><td>56</td></tr><tr><td>Ca</td><td>118</td><td>116</td><td>2</td><td>98</td><td>–</td><td>7.22</td><td>XRF</td><td>143</td></tr><tr><td>Cd</td><td>0.022</td><td>0.023</td><td>0.001</td><td>104</td><td>0.004</td><td>–</td><td>ICP-MS</td><td>56</td></tr><tr><td>Cl</td><td>301</td><td>232</td><td>3</td><td>77</td><td>–</td><td>0.323</td><td>XRF</td><td>143</td></tr><tr><td>Cu</td><td>2.35</td><td>1.84</td><td>0.05</td><td>78</td><td>0.043</td><td>–</td><td>ICP-MS</td><td>56</td></tr><tr><td>Fe</td><td>7.42</td><td>5.79</td><td>0.18</td><td>78</td><td>0.938</td><td>–</td><td>ICP-MS</td><td>56</td></tr><tr><td>K</td><td>1282</td><td>1061</td><td>12</td><td>83</td><td>–</td><td>4.67</td><td>XRF</td><td>143</td></tr><tr><td>Mn</td><td>19.2</td><td>21.0</td><td>0.3</td><td>109</td><td>–</td><td>3.35</td><td>XRF</td><td>143</td></tr><tr><td>Mo</td><td>1.45</td><td>1.42</td><td>0.01</td><td>98</td><td>0.149</td><td>–</td><td>ICP-MS</td><td>56</td></tr><tr><td>P</td><td>1530</td><td>1325</td><td>11</td><td>87</td><td>–</td><td>6.7</td><td>XRF</td><td>143</td></tr><tr><td>Rb</td><td>6.20</td><td>5.82</td><td>0.03</td><td>94</td><td>–</td><td>0.294</td><td>XRF</td><td>143</td></tr><tr><td>S</td><td>1200</td><td>984</td><td>8</td><td>82</td><td>–</td><td>2.14</td><td>XRF</td><td>143</td></tr><tr><td>Se</td><td>0.365</td><td>0.245</td><td>0.006</td><td>67</td><td>0.087</td><td>–</td><td>ICP-MS</td><td>56</td></tr><tr><td>Zn</td><td>19.4</td><td>17.4</td><td>0.2</td><td>89</td><td>–</td><td>0.595</td><td>XRF</td><td>143</td></tr></tbody></table>

Limits of detection (LoD) are given for IC-ICP-MS and ICP-MS as they are externally calibrated, and as detection limits (DL) for XRF as this method has an internal calibration

principal component analysis (PCA) and Spearman's rank correlation were conducted using GraphPad Prism v.9.

## Results

Medians, 25th and 75th percentiles, are presented for elements and species by continent in Fig. 1. Kruskal-Wallis analysis was used to investigate differences in medians between the four regions. For all elements and species presented here, all were significantly different between continents, all at *P* < 0.001, with the exception of Mn (*P* = 0.002) and Zn (*P* = 0.032). Greatest variation in the median was observed for the non-essential rubidium, being the highest in the Americas, lowest in Europe, with a 15-fold difference. Arsenic species, iAs and DMA, were the next most variable in their medians with both species being low in Africa and high in the South American and European continents, with a fivefold difference, while iAs was relatively high in Asia, but DMA low. Cadmium had a different profile, being the highest in Asia and lowest in South America. A range of macro-nutrient elements, calcium, potassium, molybdenum and phosphorus, were the highest in European, and lowest in African grain, though the fold-differences were relatively low, ~ 0.2. Conversely, iron was high in African grain and lowest in European, Asian and South American grain, ~ two-fold difference. Chloride was also high in African and Asian

grain, as compared to American and European. Selenium was also higher in Africa versus other regions. Copper, manganese and zinc were the highest in American grain, though there was relatively good homeostasis in concentrations across the continents. Sulphur was also found to have a similar homeostasis.

Presentation of medians for individual countries are given in Table 2, and country medians as a percentage of overall mean of country medians in Fig. 2. Kruskal-Wallis analysis found that all counties differed in their median, all at *P* < 0.0001 with the exception of zinc where *P* = 0.046. Tanzania is particularly low in iAs DMA, cadmium and rubidium, but high in chloride. Mali is low in selenium, along with Argentina, Paraguay, Uruguay South Korea, Indonesia and Spain. There is a fourfold difference in selenium concentrations for countries across the globe. For some countries there are notable high or low concentrations for particular elements. South Korea is high in calcium compared to all other countries, while Vietnam is particularly low in this element. Cadmium is specifically highly elevated in China, while other Asian countries also tend to be high in this element, ~ 500% higher than the global mean. Chloride is elevated in Chile, phosphorus in Sri Lanka, and sulphur in Bangladesh. Bangladesh is specifically low in zinc, with a median 70% of that of the overall country medians, but high in iron at 200% of the median (Fig. 2). Countries with notably low macro- and
<figure><img src="image_3.png" /><figcaption>**Fig. 1** Boxplots of Median (central line), 25 and 75th (outer box), 5 and 95th (whiskers) and outliers for each element by continental region: green, Africa; light blue, South America; yellow, S.E. Asia; dark blue, Europe</figcaption></figure>

micro-nutrient elements are South Korea for copper, Chile and Sri Lanka for sulphur, zinc in Uruguay and Argentina, and particularly low zinc for Bangladesh, with almost half the concentration of this element compared to other countries. Vietnam is notable as it is deficient in calcium, iron, potassium, manganese and molybdenum, and no other country matches this broad pattern of macro- and micro-nutrient deficiencies (Fig. 2). For Africa, a depletion in arsenic species is apparent, as is elevation in selenium

(Tanzania and Ivory Coast) and iron (universally) (Fig. 2). There is a specific elevation in Ivory Coast grain cadmium relative to other African countries (Fig. 2). What distinguishes American countries, with the exception of Bolivia, from other regions of the world are that they vary from moderate to highly elevated DMA, ~300%, and are relatively depleted in selenium especially in Argentina, Paraguay and Uruguay. Asian countries tend to be elevated in cadmium, but interestingly not South Korea. European
<table><caption>**Table 2** Medians and 25 and 75th percentiles for each element by country with units reported in mg/kg dry wt</caption><thead><tr><th rowspan="2"></th><th colspan="3">iAs</th><th colspan="3">DMA</th><th colspan="3">Ca</th></tr><tr><th>Median</th><th>25th</th><th>75th</th><th>Median</th><th>25th</th><th>75th</th><th>Median</th><th>25th</th><th>75th</th></tr></thead><tbody><tr><td>Tanzania</td><td>0.009</td><td>0.0275</td><td>0.0055</td><td>0.003</td><td>0.008</td><td>0.002</td><td>39.8</td><td>47.3</td><td>36</td></tr><tr><td>Ivory Coast</td><td>0.0492</td><td>0.0747</td><td>0.0142</td><td>0.013</td><td>0.019</td><td>0.004</td><td>42.8</td><td>51.1</td><td>35.1</td></tr><tr><td>Mali</td><td>0.0696</td><td>0.089</td><td>0.0193</td><td>0.032</td><td>0.04</td><td>0.013</td><td>46.3</td><td>57.7</td><td>40.9</td></tr><tr><td>Argentina</td><td>0.0614</td><td>0.0867</td><td>0.0488</td><td>0.045</td><td>0.149</td><td>0.03</td><td>40.1</td><td>47.6</td><td>37.8</td></tr><tr><td>Bolivia</td><td>0.0724</td><td>0.0984</td><td>0.0521</td><td>0.032</td><td>0.047</td><td>0.009</td><td>52.2</td><td>60.7</td><td>36.3</td></tr><tr><td>Brazil</td><td>0.0688</td><td>0.0899</td><td>0.0484</td><td>0.071</td><td>0.105</td><td>0.019</td><td>41.4</td><td>58.4</td><td>34</td></tr><tr><td>Chile</td><td>0.0634</td><td>0.0716</td><td>0.0599</td><td>0.113</td><td>0.141</td><td>0.081</td><td>53.4</td><td>76.7</td><td>45.4</td></tr><tr><td>Paraguay</td><td>0.1074</td><td>0.1393</td><td>0.0954</td><td>0.086</td><td>0.12</td><td>0.05</td><td>39.6</td><td>55.6</td><td>34.8</td></tr><tr><td>Uruguay</td><td>0.0679</td><td>0.087</td><td>0.0614</td><td>0.105</td><td>0.13</td><td>0.066</td><td>43.1</td><td>63.2</td><td>39.2</td></tr><tr><td>China</td><td>0.0823</td><td>0.0946</td><td>0.072</td><td>0.025</td><td>0.033</td><td>0.019</td><td>51.1</td><td>61.3</td><td>41.2</td></tr><tr><td>South Korea</td><td>0.0639</td><td>0.0812</td><td>0.0552</td><td>0.016</td><td>0.019</td><td>0.011</td><td>79.9</td><td>99.5</td><td>65.9</td></tr><tr><td>Bangladesh</td><td>0.1116</td><td>0.1502</td><td>0.0794</td><td>0.028</td><td>0.037</td><td>0.013</td><td>55.2</td><td>67.3</td><td>37.9</td></tr><tr><td>Sri Lanka</td><td>0.0355</td><td>0.0543</td><td>0.0234</td><td>0.002</td><td>0.006</td><td>0.001</td><td>43.8</td><td>66.8</td><td>34.9</td></tr><tr><td>Indonesia</td><td>0.0325</td><td>0.0501</td><td>0.0152</td><td>0.004</td><td>0.007</td><td>0.001</td><td>43.9</td><td>58.8</td><td>36</td></tr><tr><td>Vietnam</td><td>0.0679</td><td>0.0852</td><td>0.0556</td><td>0.025</td><td>0.033</td><td>0.018</td><td>29.3</td><td>36.8</td><td>25.8</td></tr><tr><td>Italy</td><td>0.0943</td><td>0.1151</td><td>0.0706</td><td>0.059</td><td>0.092</td><td>0.036</td><td>52.2</td><td>58.9</td><td>47.8</td></tr><tr><td>Spain</td><td>0.0621</td><td>0.084</td><td>0.0469</td><td>0.054</td><td>0.092</td><td>0.023</td><td>40.3</td><td>54.8</td><td>33.7</td></tr><tr><td>Turkey</td><td>0.0757</td><td>0.0904</td><td>0.0682</td><td>0.033</td><td>0.065</td><td>0.027</td><td>50.8</td><td>58.4</td><td>42.4</td></tr><tr><th rowspan="2"></th><th colspan="3">Cl</th><th colspan="3">Cd</th><th colspan="3">Cu</th></tr><tr><th>Median</th><th>25th</th><th>75th</th><th>Median</th><th>25th</th><th>75th</th><th>Median</th><th>25th</th><th>75th</th></tr><tr><td>Tanzania</td><td>234</td><td>274</td><td>187</td><td>0.0049</td><td>0.0049</td><td>0.0049</td><td>1.74</td><td>2.07</td><td>1.47</td></tr><tr><td>Ivory Coast</td><td>183</td><td>201</td><td>159</td><td>0.0217</td><td>0.0347</td><td>0.0176</td><td>1.83</td><td>2.18</td><td>1.51</td></tr><tr><td>Mali</td><td>135</td><td>156</td><td>123</td><td>0.0172</td><td>0.0238</td><td>0.0049</td><td>1.98</td><td>2.28</td><td>1.7</td></tr><tr><td>Argentina</td><td>126</td><td>139</td><td>113</td><td>0.0049</td><td>0.012</td><td>0.0049</td><td>1.95</td><td>2.11</td><td>1.83</td></tr><tr><td>Bolivia</td><td>115</td><td>182</td><td>100</td><td>0.023</td><td>0.0697</td><td>0.0049</td><td>2.45</td><td>2.85</td><td>2.19</td></tr><tr><td>Brazil</td><td>142</td><td>164</td><td>125</td><td>0.0122</td><td>0.0175</td><td>0.0049</td><td>2.21</td><td>3.02</td><td>1.7</td></tr><tr><td>Chile</td><td>275</td><td>304</td><td>253</td><td>0.0049</td><td>0.0049</td><td>0.0049</td><td>1.94</td><td>2.23</td><td>1.9</td></tr><tr><td>Paraguay</td><td>168</td><td>187</td><td>142</td><td>0.0049</td><td>0.0049</td><td>0.0049</td><td>2.41</td><td>2.54</td><td>2.32</td></tr><tr><td>Uruguay</td><td>154</td><td>178</td><td>122</td><td>0.0049</td><td>0.0082</td><td>0.0049</td><td>1.7</td><td>1.73</td><td>1.39</td></tr><tr><td>China</td><td>218</td><td>237</td><td>190</td><td>0.0539</td><td>0.118</td><td>0.0103</td><td>1.98</td><td>2.31</td><td>1.78</td></tr><tr><td>South Korea</td><td>170</td><td>194</td><td>145</td><td>0.0154</td><td>0.0242</td><td>0.0116</td><td>1.38</td><td>1.7</td><td>1.2</td></tr><tr><td>Bangladesh</td><td>175</td><td>202</td><td>151</td><td>0.0247</td><td>0.0432</td><td>0.017</td><td>1.56</td><td>1.93</td><td>1.41</td></tr><tr><td>Sri Lanka</td><td>173</td><td>219</td><td>135</td><td>0.0285</td><td>0.0588</td><td>0.0168</td><td>2.24</td><td>2.85</td><td>1.65</td></tr><tr><td>Indonesia</td><td>223</td><td>259</td><td>194</td><td>0.0208</td><td>0.0337</td><td>0.0114</td><td>1.59</td><td>1.81</td><td>1.25</td></tr><tr><td>Vietnam</td><td>186</td><td>214</td><td>155</td><td>0.019</td><td>0.0345</td><td>0.0134</td><td>1.72</td><td>2.27</td><td>1.34</td></tr><tr><td>Italy</td><td>142</td><td>157</td><td>130</td><td>0.0261</td><td>0.0401</td><td>0.014</td><td>2.07</td><td>2.65</td><td>1.72</td></tr><tr><td>Spain</td><td>176</td><td>191</td><td>154</td><td>0.0049</td><td>0.0086</td><td>0.0049</td><td>1.72</td><td>2.01</td><td>1.31</td></tr><tr><td>Turkey</td><td>186</td><td>206</td><td>162</td><td>0.0049</td><td>0.0126</td><td>0.0049</td><td>2.03</td><td>2.44</td><td>1.65</td></tr><tr><th rowspan="2"></th><th colspan="3">Fe</th><th colspan="3">K</th><th colspan="3">Mn</th></tr><tr><th>Median</th><th>25th</th><th>75th</th><th>Median</th><th>25th</th><th>75th</th><th>Median</th><th>25th</th><th>75th</th></tr><tr><td>Tanzania</td><td>6.52</td><td>8.58</td><td>5.27</td><td>812</td><td>910</td><td>744</td><td>12.5</td><td>14.15</td><td>10.75</td></tr><tr><td>Ivory Coast</td><td>7.79</td><td>15.37</td><td>5.72</td><td>777</td><td>863</td><td>690</td><td>9.16</td><td>10.75</td><td>7.8</td></tr><tr><td>Mali</td><td>7.64</td><td>11.44</td><td>6.52</td><td>926</td><td>1443</td><td>597</td><td>11.75</td><td>14.25</td><td>7.86</td></tr><tr><td>Argentina</td><td>4.02</td><td>4.49</td><td>3.62</td><td>832</td><td>928</td><td>787</td><td>10.46</td><td>11.15</td><td>9.49</td></tr><tr><td>Bolivia</td><td>3.75</td><td>5.49</td><td>3.19</td><td>581</td><td>800</td><td>511</td><td>10.85</td><td>16.83</td><td>9.79</td></tr><tr><td>Brazil</td><td>4.28</td><td>5.78</td><td>3.78</td><td>797</td><td>1258</td><td>579</td><td>12.25</td><td>15.4</td><td>10.53</td></tr></tbody></table>

<figure><img src="image_4.png" /></figure>
<table><caption>**Table 2 (continued)**</caption><thead><tr><th rowspan="2"></th><th colspan="3">Fe</th><th colspan="3">K</th><th colspan="3">Mn</th></tr><tr><th>Median</th><th>25th</th><th>75th</th><th>Median</th><th>25th</th><th>75th</th><th>Median</th><th>25th</th><th>75th</th></tr></thead><tbody><tr><td>Chile</td><td>4.43</td><td>5.55</td><td>3.02</td><td>1010</td><td>1860</td><td>908</td><td>12.6</td><td>23.35</td><td>9.99</td></tr><tr><td>Paraguay</td><td>4.54</td><td>6.21</td><td>4.13</td><td>592</td><td>677</td><td>531</td><td>10.6</td><td>11.8</td><td>9.71</td></tr><tr><td>Uruguay</td><td>4.32</td><td>4.54</td><td>4.08</td><td>793</td><td>1190</td><td>665</td><td>12</td><td>13.15</td><td>9</td></tr><tr><td>China</td><td>4.5</td><td>5.22</td><td>3.84</td><td>670</td><td>859</td><td>599</td><td>12.65</td><td>14.53</td><td>10.63</td></tr><tr><td>South Korea</td><td>5.53</td><td>7.32</td><td>4.74</td><td>1050</td><td>1370</td><td>908</td><td>16</td><td>21.78</td><td>13.38</td></tr><tr><td>Bangladesh</td><td>7.9</td><td>11.72</td><td>6.18</td><td>1160</td><td>1488</td><td>1098</td><td>8.62</td><td>12.25</td><td>1.68</td></tr><tr><td>Sri Lanka</td><td>5.69</td><td>7.44</td><td>4.6</td><td>1125</td><td>1378</td><td>978</td><td>10.8</td><td>14.98</td><td>9.32</td></tr><tr><td>Indonesia</td><td>4.5</td><td>6.66</td><td>4.09</td><td>748</td><td>838</td><td>632</td><td>10.85</td><td>13.6</td><td>9.37</td></tr><tr><td>Vietnam</td><td>3.74</td><td>4.12</td><td>3.41</td><td>560</td><td>690</td><td>493</td><td>9.17</td><td>10</td><td>8.14</td></tr><tr><td>Italy</td><td>4.63</td><td>5.05</td><td>4.21</td><td>991</td><td>1160</td><td>870</td><td>11.8</td><td>13.93</td><td>10.08</td></tr><tr><td>Spain</td><td>4.43</td><td>4.71</td><td>3.98</td><td>816</td><td>947</td><td>748</td><td>9.58</td><td>11.93</td><td>7.75</td></tr><tr><td>Turkey</td><td>3.68</td><td>4.05</td><td>3.11</td><td>811</td><td>921</td><td>737</td><td>10.07</td><td>11.93</td><td>8.63</td></tr><tr><th rowspan="2"></th><th colspan="3">Mo</th><th colspan="3">P</th><th colspan="3">Rb</th></tr><tr><th>Median</th><th>25th</th><th>75th</th><th>Median</th><th>25th</th><th>75th</th><th>Median</th><th>25th</th><th>75th</th></tr><tr><td>Tanzania</td><td>0.536</td><td>0.621</td><td>0.379</td><td>842</td><td>932</td><td>791</td><td>2.07</td><td>5.53</td><td>1.101</td></tr><tr><td>Ivory Coast</td><td>0.463</td><td>0.669</td><td>0.385</td><td>799</td><td>929</td><td>704</td><td>4.148</td><td>5.875</td><td>2.278</td></tr><tr><td>Mali</td><td>0.61</td><td>0.784</td><td>0.398</td><td>850</td><td>1171</td><td>624</td><td>7.034</td><td>11.152</td><td>3.531</td></tr><tr><td>Argentina</td><td>0.549</td><td>0.558</td><td>0.43</td><td>1014</td><td>1106</td><td>902</td><td>2.365</td><td>6.182</td><td>1.096</td></tr><tr><td>Bolivia</td><td>0.737</td><td>0.917</td><td>0.597</td><td>681</td><td>734</td><td>614</td><td>1.085</td><td>1.707</td><td>0.743</td></tr><tr><td>Brazil</td><td>0.595</td><td>0.714</td><td>0.493</td><td>1112</td><td>1702</td><td>850</td><td>7.011</td><td>12.133</td><td>4.753</td></tr><tr><td>Chile</td><td>0.552</td><td>0.569</td><td>0.509</td><td>805</td><td>1784</td><td>744</td><td>9.52</td><td>21.794</td><td>7.886</td></tr><tr><td>Paraguay</td><td>0.699</td><td>0.739</td><td>0.607</td><td>785</td><td>837</td><td>723</td><td>6.61</td><td>8.098</td><td>4.308</td></tr><tr><td>Uruguay</td><td>0.459</td><td>0.565</td><td>0.402</td><td>703</td><td>815</td><td>679</td><td>7.677</td><td>9.767</td><td>4.853</td></tr><tr><td>China</td><td>0.529</td><td>0.634</td><td>0.446</td><td>685</td><td>762</td><td>637</td><td>3.256</td><td>4.405</td><td>2.604</td></tr><tr><td>South Korea</td><td>0.452</td><td>0.505</td><td>0.405</td><td>898</td><td>1204</td><td>800</td><td>2.881</td><td>3.991</td><td>1.966</td></tr><tr><td>Bangladesh</td><td>0.595</td><td>0.814</td><td>0.51</td><td>969</td><td>1109</td><td>848</td><td>8.982</td><td>11.948</td><td>6.598</td></tr><tr><td>Sri Lanka</td><td>0.579</td><td>0.75</td><td>0.441</td><td>1408</td><td>1880</td><td>1106</td><td>6.178</td><td>10.633</td><td>3.221</td></tr><tr><td>Indonesia</td><td>0.484</td><td>0.573</td><td>0.411</td><td>794</td><td>963</td><td>682</td><td>3.293</td><td>5.157</td><td>2.279</td></tr><tr><td>Vietnam</td><td>0.425</td><td>0.553</td><td>0.336</td><td>707</td><td>999</td><td>578</td><td>3.751</td><td>5.784</td><td>1.98</td></tr><tr><td>Italy</td><td>0.782</td><td>0.988</td><td>0.625</td><td>1321</td><td>1474</td><td>1186</td><td>0.761</td><td>1.127</td><td>0.487</td></tr><tr><td>Spain</td><td>0.351</td><td>0.4</td><td>0.315</td><td>1022</td><td>1098</td><td>915</td><td>0.723</td><td>1.527</td><td>0.461</td></tr><tr><td>Turkey</td><td>0.519</td><td>0.623</td><td>0.458</td><td>842</td><td>916</td><td>772</td><td>0.818</td><td>1.222</td><td>0.597</td></tr><tr><th rowspan="2"></th><th colspan="3">S</th><th colspan="3">Se</th><th colspan="3">Zn</th></tr><tr><th>Median</th><th>25th</th><th>75th</th><th>Median</th><th>25th</th><th>75th</th><th>Median</th><th>25th</th><th>75th</th></tr><tr><td>Tanzania</td><td>861</td><td>980</td><td>794</td><td>0.0354</td><td>0.0602</td><td>0.0235</td><td>9.88</td><td>11.10</td><td>9.01</td></tr><tr><td>Ivory Coast</td><td>837</td><td>892</td><td>774</td><td>0.0441</td><td>0.0653</td><td>0.0216</td><td>10.26</td><td>11.48</td><td>8.81</td></tr><tr><td>Mali</td><td>731</td><td>810</td><td>696</td><td>0.016</td><td>0.0277</td><td>0.0103</td><td>10.78</td><td>11.51</td><td>6.88</td></tr><tr><td>Argentina</td><td>799</td><td>834</td><td>794</td><td>0.0103</td><td>0.016</td><td>0.0103</td><td>8.22</td><td>9.06</td><td>7.53</td></tr><tr><td>Bolivia</td><td>793</td><td>945</td><td>737</td><td>0.0214</td><td>0.0353</td><td>0.0103</td><td>11.28</td><td>13.46</td><td>10.28</td></tr><tr><td>Brazil</td><td>786</td><td>842</td><td>725</td><td>0.0238</td><td>0.0359</td><td>0.0103</td><td>11.98</td><td>14.66</td><td>9.36</td></tr><tr><td>Chile</td><td>680</td><td>742</td><td>644</td><td>0.0333</td><td>0.0356</td><td>0.0276</td><td>11.37</td><td>13.89</td><td>10.96</td></tr><tr><td>Paraguay</td><td>784</td><td>816</td><td>752</td><td>0.0103</td><td>0.0103</td><td>0.0103</td><td>11.80</td><td>12.47</td><td>10.70</td></tr><tr><td>Uruguay</td><td>778</td><td>855</td><td>751</td><td>0.0103</td><td>0.0179</td><td>0.0103</td><td>7.91</td><td>9.11</td><td>6.62</td></tr><tr><td>China</td><td>880</td><td>942</td><td>829</td><td>0.0226</td><td>0.0354</td><td>0.0103</td><td>11.66</td><td>12.35</td><td>11.37</td></tr><tr><td>South Korea</td><td>809</td><td>877</td><td>740</td><td>0.0103</td><td>0.0103</td><td>0.0103</td><td>9.52</td><td>11.22</td><td>8.81</td></tr><tr><td>Bangladesh</td><td>1015</td><td>1093</td><td>944</td><td>0.0275</td><td>0.0357</td><td>0.0217</td><td>5.92</td><td>8.18</td><td>4.41</td></tr></tbody></table>
<table><caption>**Table 2** (continued)</caption><thead><tr><th rowspan="2"></th><th colspan="3">S</th><th colspan="3">Se</th><th colspan="3">Zn</th></tr><tr><th>Median</th><th>25th</th><th>75th</th><th>Median</th><th>25th</th><th>75th</th><th>Median</th><th>25th</th><th>75th</th></tr></thead><tbody><tr><td>Sri Lanka</td><td>712</td><td>788</td><td>664</td><td>0.0412</td><td>0.0651</td><td>0.0267</td><td>11.03</td><td>13.26</td><td>8.18</td></tr><tr><td>Indonesia</td><td>911</td><td>1020</td><td>856</td><td>0.0103</td><td>0.0227</td><td>0.0103</td><td>10.45</td><td>11.41</td><td>9.23</td></tr><tr><td>Vietnam</td><td>890</td><td>952</td><td>842</td><td>0.0234</td><td>0.037</td><td>0.0103</td><td>12.39</td><td>14.59</td><td>11.03</td></tr><tr><td>Italy</td><td>839</td><td>888</td><td>777</td><td>0.0366</td><td>0.0429</td><td>0.0305</td><td>10.05</td><td>11.83</td><td>8.52</td></tr><tr><td>Spain</td><td>862</td><td>905</td><td>810</td><td>0.0103</td><td>0.0103</td><td>0.0103</td><td>9.15</td><td>10.27</td><td>7.79</td></tr><tr><td>Turkey</td><td>780</td><td>837</td><td>726</td><td>0.0271</td><td>0.0354</td><td>0.0103</td><td>10.19</td><td>11.10</td><td>8.88</td></tr></tbody></table>

<figure><img src="image_5.png" /><figcaption>**Fig. 2** Country medians expressed as a percentage relative to the mean of all countries for each element, plotted by continent</figcaption></figure>

rice was high in DMA and low in rubidium, and Italy was relatively high in cadmium. Turkish rice was notably low, ~50%, in selenium.

PCA and associated factor analysis for the entire data set are presented in Fig. 3. The PCA score means by country are given with the individual scores due to the high data density. The factor loadings analysis showed that copper, cadmium, molybdenum, rubidium and selenium were strongly associated together, and these element's factor loadings were diametrically opposed to less tightly associated calcium,

chlorine, manganese, potassium phosphorus and sulphur, with similar strengths of loadings. iAs and zinc were also associated, but less strongly, with this latter cluster. Iron and DMA had factor loadings close to the origin, suggesting that there were not closely associated with any other elements. Element/species pairs that might be thought to be associated with other are diametrically opposed such as copper and zinc (elevated concentrations of both derived from the same pollutant sources); iAs and cadmium (again being derived in elevated concentrations in soil through pollutant

<figure><img src="image_6.png" /></figure>
<figure><img src="image_7.png" /><figcaption>**Fig. 3** Factor loading plot for the principal component analysis (PCA) overlain with PCA scores means by country</figcaption></figure>

sources, such as atmospheric deposition of contamination caused by base and precious mining and smelting activities, fossil fuel burning etc.); and the physiological analogues sulphur and selenium. However, potassium and phosphorus factor loadings were similar in direction and strength, which may relate to the fact that these two elements are commonly fertilized together. The American countries, with the exception of Chile, were associated with the copper, cadmium, molybdenum, rubidium and selenium factor loadings, but the countries of other continents were dispersed around the plot, though the nearby countries of Mali and Ivory Coast differed considerably in cadmium content. Vietnam, in particular, has a mean PCA score to the right of the graph but near to the X-axis. This shows, again, that Vietnamese grains are generally nutrient deficient, as shown by plots of the country value of each element expressed as a percentage of the median of country medians given in Table 2 and percentages of medians in Fig. 3.

To validate/cross-reference the PCA factor loading observations for cadmium, log-transformed elemental concentrations underwent stepwise additions linear regression

<table><caption>**Table 3** Stepwise regression analysis of log cadmium grain concentrations versus the log transformed other elements quantified</caption><thead><tr><th>Model</th><th>R</th><th>R Square</th><th>Adjusted R square</th><th>SE of the estimate</th><th>Sign</th></tr></thead><tbody><tr><td>A</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Se</td><td>.274<sup>a</sup></td><td>0.075</td><td>0.074</td><td>0.41024</td><td>&lt;0.0001</td></tr><tr><td>Se, Mo</td><td>.325<sup>b</sup></td><td>0.106</td><td>0.104</td><td>0.40364</td><td>&lt;0.0001</td></tr><tr><td>Se, Mo, DMA</td><td>.388<sup>c</sup></td><td>0.151</td><td>0.148</td><td>0.39350</td><td>&lt;0.0001</td></tr><tr><td>Se, Mo, DMA, iAs</td><td>.454<sup>d</sup></td><td>0.206</td><td>0.203</td><td>0.38062</td><td>&lt;0.0001</td></tr><tr><td>Se, Mo, DMA, iAs, Cl</td><td>.477<sup>e</sup></td><td>0.228</td><td>0.224</td><td>0.37559</td><td>&lt;0.0001</td></tr><tr><td>Se, Mo, DMA, iAs, Cl, S</td><td>.493<sup>f</sup></td><td>0.243</td><td>0.239</td><td>0.37193</td><td>&lt;0.0001</td></tr><tr><td>Se, Mo, DMA, iAs, Cl, S, Cu</td><td>.506<sup>g</sup></td><td>0.256</td><td>0.251</td><td>0.36895</td><td>&lt;0.0001</td></tr><tr><td>Se, Mo, DMA, iAs, Cl, S, Cu, P</td><td>.516<sup>h</sup></td><td>0.266</td><td>0.260</td><td>0.36675</td><td>&lt;0.0001</td></tr><tr><td>Se, Mo, DMA, iAs, Cl, S, Cu, P, Zn</td><td>.521<sup>i</sup></td><td>0.272</td><td>0.265</td><td>0.36546</td><td>&lt;0.0001</td></tr><tr><td>Se, Mo, DMA, iAs, Cl, S, Cu, P, Zn, Rb</td><td>.526<sup>j</sup></td><td>0.277</td><td>0.270</td><td>0.36437</td><td>&lt;0.0001</td></tr><tr><td>B</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Constant</td><td></td><td></td><td>-3.694</td><td></td><td>&lt;0.001</td></tr><tr><td>Se</td><td>0.163</td><td></td><td>5.507</td><td></td><td>&lt;0.001</td></tr><tr><td>Mo</td><td>0.141</td><td></td><td>4.536</td><td></td><td>&lt;0.001</td></tr><tr><td>DMA</td><td>-0.564</td><td></td><td>-13.289</td><td></td><td>&lt;0.001</td></tr><tr><td>iAs</td><td>0.453</td><td></td><td>10.724</td><td></td><td>&lt;0.001</td></tr><tr><td>Cl</td><td>-0.203</td><td></td><td>-6.876</td><td></td><td>&lt;0.001</td></tr><tr><td>S</td><td>0.122</td><td></td><td>4.138</td><td></td><td>&lt;0.001</td></tr><tr><td>Cu</td><td>0.117</td><td></td><td>3.895</td><td></td><td>&lt;0.001</td></tr><tr><td>P</td><td>-0.124</td><td></td><td>-4.289</td><td></td><td>&lt;0.001</td></tr><tr><td>Zn</td><td>0.097</td><td></td><td>3.269</td><td></td><td>0.001</td></tr><tr><td>Rb</td><td>0.074</td><td></td><td>2.680</td><td></td><td>0.007</td></tr></tbody></table>

Section A shows the R² and the significance of each model, while B shows the slopes (Beta) and significance for the model containing all elements in the final model
with log cadmium concentration as the dependent variable (Table 3). Selenium was the greatest predictor of cadmium, followed by molybdenum, accounting for over 50% of the contribution to the adjusted $R^2$. Log iAs in stepwise linear regression was highly correlated with log DMA with an adjusted $R^2$ of 0.556, but not with other elements. When log DMA was removed as a predictor, the next highest correlating parameter was log molybdenum with an adjusted $R^2$ of 0.029 (data not tabulated).

Figure 4 shows the correlation matrix for the log transformed concentration dataset to enable further comparisons of all elements/species against each other. Besides the strong positive correlation between iAs and DMA, there was also a very strong relationship between potassium and phosphorus (Spearman's R of 0.799). This strong association between potassium and phosphorus is also seen for the factor analysis (Fig. 3). Also notable from the Spearman's rank correlation is that chlorine is negatively correlated with a suite of elements/species including arsenic species, cadmium, copper, molybdenum, phosphorus and rubidium.

## Discussion

Plant nutrition is dependent on soil availability and concentration, and plant physiological regulation as determined by genetics (Cobb et al. 2021; Norton et al. 2014). It is considered that most essential elements and ions are in homeostasis for plant tissues in general, and in grain specifically, as outside an individual ion/element's concentration range, the plant will either be in deficiency or toxicity (Bohnert et al. 2006). One essential element that does not appear under

tight physiological control, with grain concentration being closely related to soil concentrations is selenium (Zhu et al. 2009). This is thought to be because selenium species are analogues of the much more abundant sulphur, and with sulphur ameliorating selenium species toxicity (Zhu et al. 2009). Non-essential elements/ions also enter grain as analogues of essential nutrients, such as arsenate, a phosphate analogue (Meharg 1993); arsenite, a silicic acid analogue (Meharg and Meharg 2015); rubidium, a potassium analogue (Oliferuk et al. 2017); and cadmium an analogue of a range of divalent cations (Nan et al. 2002). Thus, a key finding here is that the essential elements copper, molybdenum and selenium are tightly associated with non-essential cadmium and rubidium. Hypotheses regarding the relationship between cadmium with copper, molybdenum, rubidium and selenium are now considered.

Copper and selenium can be elevated in soils due to anthropogenic processes, such as mineral smelting and processing and coal burning, where selenium soil pollution actually supplements plants as it is deficient in most soils (Zhu et al. 2009). As noted, selenium can be very variable in plant tissues, while copper is under tight plant homeostasis due its essentiality in a range of cellular functions and due to its toxicity (Clemens 2001). Contrastingly to copper, a divalent metal ion that is also an environmental contaminant, divalent cadmium is also a widely dispersed pollutant that can reach concentrations in grain that cause human health concerns in a wide range of agricultural settings, yet is highly variable in grain (Hu et al. 2016; Nan et al. 2002; Shi et al. 2020). Rubidium differs yet again, being a monovalent ion, and is thought to be preferentially mobilized into plants under acidic soil conditions where soil available potassium is low (Drobner and Tyler 1998). Molybdenum chemistry in soils and plants is complex, with molybdate thought to be a sulphate analogue, and is assimilated by plants more readily at higher pHs due to reduced binding adsorption to amorphous oxides, while sulphur addition also decreased molybdenum assimilation (McGrath et al. 2010). In the context of the close assimilation of the group of elements found to cluster here (cadmium with copper, molybdenum, rubidium and selenium), cadmium is most studied (Hu et al. 2015, 2016; Shi et al. 2020). Cadmium is assimilated under more aerobic conditions of rice cultivation (Hu et al. 2015) and this may be a clue as to why other elements are associated with cadmium, i.e. it could be hypothesized that more aerobic conditions are needed to mobilize these elements in paddy soils effectively. This supposition remains to be tested.

Cadmium, in both soils and plants, is readily complexed by sulphur (Ernst et al. 2008). High grain cadmium is diametrically opposed to high grain sulphur for factor loading plots, and thus when sulphur is high in plants it can be postulated that translocation of cadmium to grain is low

<figure><img src="image_8.png" /><figcaption>**Fig. 4** Spearman's rank correlation matrix of log-transformed elements for rice grain concentrations. The scale to the right of the graph is the R-value for analysis</figcaption></figure>

<figure><img src="image_9.png" /></figure>
as cadmium complexation with sulphur groups of phyto-
chelatins (PCs) are sequestered in the vacuole, retarding
their translocation to the plant (Ernst et al. 2008). It is
sulphide immobilization under reduced redox conditions
that causes cadmium to have poor soil availability under
flooded paddy conditions (de Rivera et al. 2011; Rinklebe
et al. 2016), again emphasizing the intimate link between
cadmium and sulphur highlighted in this current study.
This sulphur association may also explain grain copper
concentrations, with copper like cadmium having a high
affinity for sulphur (Ernst et al. 2008; Hartley-Whitaker
et al. 2001). Notably, selenium is a sulphur analogue (Zhu
et al. 2009). For selenium, the ratio of selenium to sulphur
increases, maybe as the plant tries to mine plant sulphur,
inadvertently assimilating more selenium, i.e. that sele-
nium may be increased in grain under sulphur deficiency.
The same could be argued for rubidium and potassium,
i.e. that rubidium is enhanced in grain when potassium
is deficient, as rubidium is orthogonal to potassium with
respect to its grain PCA factor loading plot. Molybdenum
is assimilated through plant roots as molybdate (Baxter
et al. 2008), an anion that is thought to interact strongly
with sulphate (McGrath et al. 2010), i.e. it is postulated
that molybdate is a sulphate analogue. Molybdenum is
assimilated into grain when sulphur is low, according
to PCA analysis, suggesting that under plant sulphur
deficiency conditions that molybdenum is more readily
assimilated and translocated. Interestingly, Williams et al.
(2009) found that grain zinc and selenium was negatively
correlated with grain arsenic for field sites across Bang-
ladesh. Thus elemental toxicity, arsenic in this case, is
impacting grain nutritional composition. For the current
study, Bangladesh had the highest grain arsenic of this
survey and the lowest zinc, supporting this finding, but the
selenium grain concentration was median.

The global distribution of arsenic species in polished
grain has been previously studied for this data-set (Carey
et al. 2020). What is new here is the understanding that both
iAs and DMA are not intimately linked to other elements
with respect to co-location in grain. Arsenic species mobi-
lization is strongly driven by soil redox, linked to the dis-
solution of iron plaque and to the microbial methylation of
iAs (Meharg and Zhao 2012). It is notable that grain iron
is also not strongly linked to other elements in this current
investigation. It appears that other factors, such as poten-
tial pollution impact and weathering of soils, have a greater
influence on grain arsenic species than co-element nutrition
of plant or soils, given that arsenic species can also act as
phosphate and silicic acid analogues in both soils and plants
(Meharg and Zhao 2012). For example, it is the low pollutant
impacted and heavy weathered soils of African and Asian
southern hemispheres that are low in grain arsenic species
in this current study.

With respect to genetic variation in grain elemental con-
tent, Norton et al. (2010) investigating 17 elements in grain
through ICP-MS quantification found for an *indica-japonica*
cross that there were 41 QTLs. QTLs for iron, molybde-
num, selenium and zinc, in particular, were suggested to be
suitably strong to be targets for improving grain concentra-
tions through marker assisted selection. Huang et al. (2015)
reported genetic variation in cadmium, iron, lead, selenium,
and zinc, for wholegrain rice using Chinese association map-
ping accessions. Genome-wide association mapping found a
strong genotype*environment interaction for loci associated
with elements in whole grain rice grown at four locations
across the globe, but variation was found for arsenic, cop-
per, molybdenum and zinc (Norton et al. 2014), indicating
weak genetic links between the elements. Genetic variation
was found to be a large determining factor for grain arsenic
concentrations across four field sites globally, Bangladesh,
China, India and N. America (Norton et al. 2010). Associa-
tion mapping has identified root and shoot transporters for
cadmium and molybdenum, but these are on different loci
and are for different putative transporters (Cobb et al. 2021).
Thus, rice genetics, particularly between *japonica* and *indica*
groups (Huang et al. 2015; Norton et al. 2010), could be
an important source of variation in rice’s global grain sup-
ply chain, but this needs to be systematically investigated in
order to validate.

The data sets reported here enable rice from different
regions to be grouped as deficient in particular nutrients.
The key country with respect to globally mineral nutrient
deficiency in the grain was Vietnam as its grain was particu-
larly deficient in a suite of macro- and micro-nutrients, while
also being relatively elevated in cadmium. These deficien-
cies in grain elemental nutrients suggest that Vietnamese
soils may be generally deficient in nutrients. This may be
due to a range of factors such as over cropping as many Viet-
namese paddies have multiple harvests per year (Ba et al.
2016). Soil acidification is widespread in S.E. Asia due to
over use of mineral nitrogen fertilizers (Xie et al. 2019), and
acidification will lead to issues with nutrient leaching and
availability. Straw biomass is widely burnt in Vietnam lead-
ing to poor organic waste-stream recycling, including animal
manures produced through straw consumption (Hung et al.
2019; Watanabe et al. 2019). The health consequences for
the Vietnamese population for having rice low in nutrient
density but high in toxicants needs assessing, given that it
is a subsistence rice economy with high rates of per capi-
tal consumption (Hung et al. 2019; Watanabe et al. 2017).
Another notable health concern is Bangladesh that has high
iAs, high cadmium and low zinc, given that zinc is vital in
enzymatic roles that are key to the general health of the pop-
ulation (Williams et al. 2009). The low selenium content of
rice is of concern for a range of South American countries,
and for Spain, South Korea and Indonesia, because selenium
deficiency is implicated in a wide range of disease outcomes at a population level (Zhu et al. 2009).

The research findings of this current study provide the basis to start to redress dietary deficiencies in populations that have rice as a subsistence diet. This could be through changing agronomic practices or ensuring key nutrients reach the diet through other food groups. The underlying rice grain nutrient deficiencies reported here require further epidemiological studies to investigate human consequences, and agronomic investigations with respect to how to mitigate against deficiencies through soil management and rice breeding.

**Acknowledgements** None

**Author contribution** AAM and CM were involved with supervision and writing. MC, ZS, XJ and JGF conducted the analysis. All other authors helped in sourcing samples. All authors were involved in proofing and correcting the submitted manuscript.

**Funding** The authors have not disclosed any funding.

**Data availability** Will be issued on reasonable request.

**Declarations**

**Conflict of interest** None.

**Open Access** This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

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## Authors and Affiliations

Andrew A. Meharg¹ ID · Caroline Meharg¹ · Manus Carey¹ · Paul Williams¹ · Zhengyu Shi¹ ·

Katrina Campbell¹ · Christopher Elliott¹ · Ernest Marwa² · Xiao Jiujin³ · Júlia Gomes Farias⁴ ·

Fernando Teixeira Nicoloso⁴ · P. Mangala C. S. De Silva⁵ · Ying Lu⁶ · Andy J. Green⁷ · Eduardo Moreno-Jiménez⁸ ·

Ángel Antonio Carbonell-Barrachina⁹ · Alessia Sommella¹⁰ · Massimo Pigna¹⁰ · Catherine Brabet¹¹ ·

Didier Montet¹¹ · Mahmud Hossain¹² · M. Rafiqul Islam¹²

Caroline Meharg
caroline.meharg@qub.ac.uk

¹ Biological Sciences, Institute for Global Food Security, Queen's University Belfast, 19 Chlorine Gardens, BT9 5DL Belfast, Northern Ireland

² Department of Soil & Geological Sciences, Sokoine University of Agriculture, P. O. Box 3008, Morogoro, Tanzania

³ Sichuan Agricultural University, Chengdu 611130, Sichuan, People's Republic of China

⁴ Departamento de Biologia, Centro de Ciências Naturais e Exatas, Universidade Federal de Santa Maria, Santa Maria, RS 97105-900, Brazil

⁵ Department of Zoology, University of Ruhuna, Matara 81000, Sri Lanka

⁶ College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China

⁷ Department of Wetland Ecology, Estación Biológica de Doñana-CSIC, C/Américo Vespucio s/n, 41092 Seville, Spain
⁸ Department of Agricultural and Food Chemistry,
Faculty of Sciences, Universidad Autónoma de Madrid,
28049 Madrid, Spain

⁹ Grupo Calidad y Seguridad Alimentaria, Departamento
Tecnología Agroalimentaria, Universidad Miguel Hernández
de Elche, 03312 Orihuela, Alicante, Spain

¹⁰ Dipartimento di Agraria, Università degli Studi di Napoli
Federico II, Via Università 100, 80055 Portici, NA, Italy

¹¹ CIRAD, UMR Qualisud, TA B95/16, 73 rue Jean-François
Breton, 34 398 Montpellier Cedex 5, France

¹² Department of Soil Science, Bangladesh Agricultural
University, Mymensingh 2202, Bangladesh

<figure><img src="image_10.png" /></figure>