RESEARCH ARTICLE | SHORT

Temporal variation of iodine in Danish groundwater

Denitza D. Voutchkova^*

Geological Survey of Denmark and Greenland (GEUS), Aarhus, Denmark

 

 

Abstract

Iodine is an essential element for human health, and both high and low iodine intake could have negative health outcomes. The spatial variation of iodine in Danish groundwater has been studied before, but to the author’s knowledge, this is the first time that the temporal variation is characterised. Nationwide data from the Danish groundwater monitoring programme (GRUMO) were analysed between 2011 and 2021, including 2924 samples from 1242 well screens at 893 wells. The sampling frequency varied and so the robust coefficient of variation (rCV) was calculated for 930 (75%) of well screens, and time-series analysis was performed for 23 (2%). Key findings are (1) iodine in Danish groundwater varies over time (0–124%, median = 10%), (2) in one quarter of the well screens rCV exceeds 20% and (3) this variation cannot be attributed solely to analytical uncertainty at 14% of the well screens. The impact of temporal variation of iodine in Danish drinking water of groundwater origin should be evaluated in future exposure or epidemiological studies with respect to the study goal, location and time period. Since the temporal variation could not be quantified over the entire concentration range, monitoring of iodine in Danish groundwater should continue.

 

Introduction

Iodine is an essential element for the proper functioning of the thyroid, and either high or low iodine intake could result in adverse human health outcomes, given that there is a U-shaped relationship between iodine intake and thyroid disorders (Laurberg et al. 2009) or autoimmunity (Wang et al. 2019). Iodine deficiency disorders (IDD) are a major public health problem globally (De Benoist et al. 2004), and even though levels of global iodine nutrition have improved since the 20^th century, Andersson et al. (2012) estimated that 1.88 billion people, including 241 million school children, still had insufficient dietary iodine intakes. Iodine deficiency has been described as “the greatest cause of preventable brain damage in childhood” (De Benoist et al. 2004). Iodine deficiency could also cause miscarriages or stillbirths (at the foetal stage); neonatal goitre or hypothyroidism, endemic mental retardation (at the neonatal stage); goitre, hypo- or hyperthyroidism, impaired mental function or retarded physical development (in children, adolescents and adults; De Benoist et al. 2004). Excessive iodine intakes, on the other hand, may result in hypo- or hyperthyroidism, goitre and/or thyroid autoimmunity for some individuals (Farebrother et al. 2019).

The recommended daily intake and the tolerable upper iodine intake vary with age (Table 1; Institute of Medicine 2001; WHO & FAOUN 2004; EFSA 2006; WHO 2007).

Table 1 Recommended daily intake (RDI) and tolerable upper intake (TUI) for iodine.

Age	RDIa (μg/day)	TUI (USA)b (μg/day)	TUI (EU)c (μg/day)
Children	1201	300–9002	200–5003
Adults	150	1100	600
Pregnant or lactating women	250	1100	600
For children, the provided values and ranges are for: 1school children; 2children in the age 4–18 years; 3children in the age 1–17 years. References are as follows: aWorld Health Organization 2007; World Health Organization & Food and Agriculture Organization of the United Nations 2004. bInstitute of Medicine, Academy of Sciences & USA 2001. cEFSA 2006.

Drinking water could be a significant contributor to the daily iodine intake for some populations (Voutchkova et al. 2014; Farebrother et al. 2019; Ma et al. 2022). Recently, a meta-analysis by Azevedo et al. (2023) showed that iodine status is directly correlated to iodine content in drinking water and concluded that iodine concentration in drinking water can be used as an indicator of dietary intake. Therefore, the spatial variation of iodine in both drinking water and groundwater and its importance for human health have been discussed in depth in the scientific literature: either with respect to the need for optimising dietary iodine intake of a given population subjected to a universal salt iodisation programme (Voutchkova et al. 2014; Ma et al. 2022) or because of the potential for the formation of unwanted iodinated by-products when implementing certain advanced methods of drinking water treatment (Sharma et al. 2019). However, less attention has been given to temporal aspects of iodine variation, which may be important regarding iodine exposure from drinking water.

In Denmark, drinking water is of entirely groundwater origin and undergoes mostly simple treatment (aeration and sand filtration) without chlorination or ozonation. It has been hypothesised that iodine concentrations in Danish groundwater are stable over time. This hypothesis stems from limited evidence for iodine in treated drinking water (tap water) and was mostly based on qualitative assessments, which concluded the following:

1. Day-to-day variation within a period of 10 days at two locations in Denmark was “small” (Pedersen et al. 1999)
2. “No significant” difference between samples collected in January and June (Rasmussen et al. 2000)
3. Concentration at a waterworks in the northernmost part of Denmark (Skagen) was “unaltered” in the period 1997–2000, based on one sample taken every 2 months for a duration of 6 months and one sample taken every year for a period of 4 years (Andersen et al. 2002)
4. No conclusive results concerning short-term variation were observed in eight samples taken in a 2-week period at a waterworks supplying Copenhagen (Voutchkova et al. 2014).

The purpose of this article is therefore to quantify the temporal variation of total dissolved iodine in Danish groundwater. This assessment is based on nationwide groundwater-quality monitoring data covering a 10-year period, which has not been reported and systematically analysed previously.

Data

Total dissolved iodine (‘iodine’ for brevity) was analysed in 2924 samples obtained during the period 2011–2021 (31 January 2011 – 11 May 2021) as part of Denmark’s national groundwater monitoring programme GRUMO (Thorling et al. 2023). These samples were taken at 1242 sampling points (well screens) belonging to 893 GRUMO wells located throughout the country (Fig. 1). The well screens were relatively short: 1 or 2 m long (Q25 = 1 m, median = 1 m, Q75 = 2 m, min. to max.: 0.05–69 m).

Fig. 1 Spatial distribution of iodine sampling sites in the study period at well screens of the Danish national groundwater monitoring programme (GRUMO).

Following the sampling protocols of the Geological Survey of Denmark and Greenland (GEUS), well purging was carried out until pH, conductivity, O₂ and temperature were stable (Thorling 2012). Samples were filtered in the field through 0.45-μm filters and placed in glass bottles without conservation or other sample pre-handling, after which the samples were stored in cold (0–4°C) and dark conditions for a maximum of 36 h before analysis (Ministry of Environment of Denmark 2011).

Iodine was analysed with ICP-MS (Ministry of Environment of Denmark 2011) at nationally accredited laboratories. The national requirements for analytical uncertainty of all environmental measurements for iodine are 1.5 μg/l absolute expanded uncertainty (U_abs) for low concentrations and 20% relative expanded uncertainty (U_rel) for high concentrations (Ministry of Environment of Denmark 2021). As Ministry of Environment of Denmark (2021) has not defined what is considered a low concentration, the highest of the two (max (U_rel, U_abs)) is used here as the analytical uncertainty for individual samples (U). The actual values of U are unknown for this monitoring data set. For 71% of the samples (n = 2082), U_abs ≥ U_rel, so U = U_abs. For the remaining 29% (n = 842), U_rel was higher, so U = U_rel.

Fewer than 1% of the samples (n = 26) were below the LOD. Two different LODs were used: 2 μg/l (10 samples) and 0.3 μg/l (16 samples). The values below LOD were handled by substitution with 0.5 × LOD, which is equal to 1 μg/l or 0.15 μg/l, respectively.

Statistical methods

The sampling frequency varied at the different well screens from 1 to 11 times during the study period (Figs 1 and 2a). The temporal variability could only be assessed for sampling locations with at least two samples (n = 930, 75%) using the robust coefficient of variation (rCV, equation 1; Arachchige et al. 2022). The rCV is a measure of relative dispersion similar to the coefficient of variation but is based on the median and median absolute deviation (MAD) as follows:

Fig. 2 Iodine in groundwater wells from the Danish national groundwater monitoring programme (GRUMO). a: Histogram of number of iodine samples per well screen. b: Scatter plot of robust coefficient of variation (rCV) against median iodine concentration, where each symbol refers to a well screen. c: iodine concentration and its variation (expressed by the median and median absolute deviation, MAD). Grouped according to the number of samples per sampling location, where horizontal jitter is added to minimise overlapping points. c: Grey colour is used when the MAD was not calculated (only one sample available) and if MAD = 0 (2–11 samples).

where m is the median and MAD is a robust measure of variability, similar to the standard deviation but using the median instead. MAD (equation 2) is defined as the median of the absolute deviations from the median of the data (m), such that:

where X_i are the individual iodine measurements at a given sampling location.

These metrics for central tendency and variability are more robust against outliers or skewed distributions and so were preferred for this study.

Next, rCV was compared against the analytical uncertainty across Denmark to provide an indication as to whether the observed iodine variability might be associated with environmental factors or else most probably because of the analytical uncertainty. This was done as follows:

where i is the index of an individual sample, j is the index of the well screen, X is iodine concentration and U is analytical uncertainty. Here the analytical uncertainty is converted to a percentage, so that it could be compared to rCVj. This comparison is only used as a screening tool to provide a preliminary estimate of the potential cause of the observed variation. A more comprehensive assessment at a local scale involving an increased number of samples, as well as additional hydrogeochemical, hydrogeological and environmental data would be needed to provide further details.

In addition, a trend analysis was performed solely for well screens with ≥10 years of data (n = 23; 2%). Linear regression and local polynomial regression (LOESS with a generalised additive model, GAM, with integrated smoothness estimation as implemented in Wood (2023)) and their 95% confidence intervals were used to interpret the trends (Wickham 2016). All statistical analyses and summaries were implemented in R v. 4.2.1 (R Core Team 2022).

Results and discussion

Nationwide temporal variability

The results of this nationwide assessment showed that the robust coefficient of variation (rCV) for iodine at the 930 sampling points in Denmark had a wide range (0–124%) with a median of 10% (IQR 4–21%; Fig. 2b). This means that at half (quarter) of the sampling locations, iodine variation in the period exceeded 10% (20%). Part of the observed variation could be due to analytical uncertainty, and so comparison between rCV and U was used as a screening tool at the national scale. This comparison showed that at 14% of the sampling points (n = 127, Fig. 3), iodine variability could not be attributed solely to analytical uncertainty. It could be inferred that at those locations, the variability was, at least partially, caused by environmental factors such as local hydrogeological conditions and/or iodine variability in precipitation. No concentration dependency or depth dependency (not shown here) or spatial pattern (Fig. 3) was associated with the observed iodine variability at the national scale. Further, more focused investigations could elucidate the governing environmental factors at specific locations.

Fig. 3 Iodine variability attribution at sampling sites throughout Denmark.

This assessment showed that there is a temporal variability of iodine in groundwater, but its importance in exposure and epidemiologic studies cannot be inferred purely from these data alone. As all drinking water in Denmark originates from groundwater, variability in groundwater iodine may have a significant effect on the concentration of iodine in finished (i.e. treated) drinking water, especially in parts of the country where high levels of iodine are observed. Other factors could also contribute to iodine variability in drinking water. For example, the well sites, wells and/or the pumping strategies could change in time, resulting in the use of other aquifers or parts of the aquifer where the iodine concentration is different. It is also possible that the water treatment and its performance over time affect the iodine content of the finished product. Therefore, the recommendation is to consider temporal aspects when designing future studies, so that the significance of iodine variability can be assessed with respect to the specific location of interest.

Even though the GRUMO programme has yielded plenty of new iodine data over the past decade, it is still challenging to assess variability for the full range of observed concentrations. The highest iodine concentrations (>50 μg/l) and absolute variations occur at locations with only two or very few samples (Fig. 2c), which limits analysis solely to a rCV to U comparison. At 25% of the sampling locations (n = 312), only a single sample was acquired, and the variability could therefore not be assessed at those sites. It is therefore recommended that iodine continues to be monitored as part of the GRUMO programme. It should be noted that iodine is not analysed in drinking water in Denmark because there is no legal requirement to do so as part of quality control at waterworks.

Time series analysis

Time series analyses could provide further insight into the way iodine concentrations vary over time. However, there is presently insufficient data coverage to conduct time series analyses across Denmark; a time series analysis could be performed at only 23 of the well screens, which have at least 10 years of iodine data. All these well screens are relatively shallow (3–17.6 m b.g.l.) and located at nine GRUMO well sites (Fig. 1), one in East Denmark, the rest in West Denmark. These screens are in Quaternary sand aquifers except for one in a pre-Quaternary sand aquifer (named ‘96.1976_2’). They are associated with 10 different groundwater bodies, as defined by Troldborg (2020). The parent sediment (at 1 m depth), where known, is either proglacial (n = 4), glacial (n = 14) or postglacial (n = 1). There are mostly low iodine concentrations at these sampling points: at 18 of the well screens (78%), the median iodine concentration is ≤10 μg/l and only at three it is >20 μg/l. See Table S1 for more details.

Figure 4 presents the time series analysis for iodine at these well screens. Based on the rCV to U comparison, the variability in iodine concentration could be attributed to environmental factors at three of these wells (’65.1068_3’, ‘105.1827_1’, ‘131.831_1’). At ’65.1068_3’, there is a positive, but not statistically significant linear trend, no non-linear trend and potentially an outlier. However, the concentrations at this well are low overall at 4.8 ± 1.3 μg/l. The well screen is shallow (4.5–5.5 m b.g.l.), located in a Quaternary sand aquifer. The well screen ‘105.1827_1’ is also shallow (3–4 m b.g.l.), without a significant trend, with a possible outlier, but the iodine concentrations over the period are a bit higher at 10.5 ± 1.5 μg/l. However, at ‘131.831_1’, there is a significant decreasing trend, the screen is a bit deeper (14.5–20.5 μg/l), and the concentrations are higher, especially at the beginning of the monitoring period (9.7 ± 5.8 μg/l). Two other well screens have significant linear trends (‘94.2516_3’ and ‘131.1060_2’), but both have low concentrations (3.9 ± 0.5 μg/l and 3.4 ± 0.1 μg/l, respectively) and are relatively shallow (6.5–7.5 m b.g.l. and 10.5–11.5 m b.g.l.). Their variability can potentially also be attributed to analytical uncertainty. Similarly, four other well screens exhibit non-linear trends (‘65.1517_1’, ‘94.2515_2’, ‘131.1051_3’, ‘216.748_1’), and their variability can also be attributed to analytical uncertainty. In addition, Fig. 4 shows how the analytical uncertainty of each sample (error bars) compares to the variability, concentration level and linear/non-linear trends. The observed variability, when attributed to environmental factors, was relatively low at these well screens and so could most probably be explained by variation in the precipitation concentration and potentially by varying amounts of leaching from the soil. Unfortunately, none of the locations with higher iodine concentrations had time series that were sufficiently long, and so it is presently impossible to quantify and explain the variability of the full range of iodine concentrations found in Danish groundwater.

Fig. 4 Iodine time series for well screens with data of at least a 10-year duration (n = 23; Fig. 1; Table S1 in supplementary information). The error bars show the analytical uncertainty. 95% confidence intervals are displayed for both the linear and generalised additive (GAM) models.

Conclusions

This is the first time that the temporal variation of iodine in Danish groundwater has been characterised systematically. The data spanned a decade (2011–2021), but the sampling frequency varied, according to the GRUMO programme. This nationwide assessment showed that temporal variability of groundwater iodine in Denmark exceeded 20% during 2011 to 2021 at 25% of the well screens that had at least two samples (n = 930). Based on comparison between the robust coefficient of variation and the analytical uncertainty, it was found that the observed variability cannot be attributed solely to analytical uncertainty for 14% of the well screens. Although there were insufficient data to undertake a trend analysis at a national scale, the time series (n = 23) indicated that there could also be statistically significant linear and non-linear trends in iodine concentration at some locations. Unfortunately, the time series did not include sampling locations with high iodine concentrations. Moreover, the calculation of robust coefficient of variation was also limited for well screens with iodine >50 μg/l. For a quarter of the GRUMO well screens, there was only one sample available in the 2011–2021 period, so the variability could not be assessed at all. It is therefore recommended that the temporal variation analysis be repeated when more data have been collected as part of the GRUMO programme. In short, iodine in Danish groundwater varies temporally; thus it may be important to evaluate this aspect in future exposure or epidemiological studies focusing on iodine in drinking water.

Acknowledgements

I would like to thank Birgitte Hansen and Lærke Thorling for early discussions about temporal variability and the national groundwater monitoring programme (GRUMO).

Additional information

Funding statement

The study was funded internally by the Geological Survey of Denmark and Greenland (GEUS). The collection of the monitoring data was funded by the Danish Environmental Protection Agency (DK EPA). The conclusions and recommendations presented in this article are those of the author and do not necessarily represent the views of GEUS or DK EPA.

Author contributions

DV is the sole author.

Additional files

Three supplementary files are available with this manuscript at https://doi.org/10.22008/FK2/ELLKNC

1. Aggregated_data.xlsx – Data set, aggregated at well-screen-level, as used in this study.
2. Table_S1.docx – Characteristics of the well screens included in the time-series analysis
3. READ_ME.docx: A short description of the data set parameters in ‘Aggregated_data.xlsx’.

References

Andersen, S.B., Petersen, S. & Laurberg, P. 2002: Iodine in drinking water in Denmark is bound in humic substances. European Journal of Endocrinology 147, 663–670. https://doi.org/10.1530/eje.0.1470663

Andersson, M., Karumbunathan, V. & Zimmermann, M.B. 2012: Global iodine status in 2011 and trends over the past decade. The Journal of Nutrition 142(4), 744–750. https://doi.org/10.3945/jn.111.149393

Arachchige, C.N.P.G., Prendergast, L.A. & Staudte, R.G. 2022: Robust analogs to the coefficient of variation. Journal of Applied Statistics 49(2), 268–290. https://doi.org/10.1080/02664763.2020.1808599

Azevedo, F.M., Machamba, A.A.L., Candido, A.C., Pinto, C.A., Lopes, S.O., Macedo, M.D.S., Ribeiro, S.A.V., Priore, S.E. & Franceschini, S.D.C.C. 2023: Correlation between drinking water and iodine status: A systematic review and meta-analysis. Biological Trace Element Research 201(1), 129–138. https://doi.org/10.1007/s12011-022-03127-4

De Benoist et al. (eds) 2004: Iodine status worldwide: WHO global database on iodine deficiency. Department of Nutrition for Health and Development, World Health Organization. Published report. 48 pp. https://www.who.int/publications/i/item/9241592001 (accessed May 2023)

EFSA (European Food Safety Authority) 2006: Tolerable upper intake levels for vitamins and minerals. Published report. 480 pp. http://www.efsa.europa.eu/de/ndatopics/docs/ndatolerableuil.pdf (accessed May 2023)

Farebrother, J., Zimmermann, M.B. & Andersson, M. 2019: Excess iodine intake: sources, assessment, and effects on thyroid function. Annals of the New York Academy of Sciences 1446, 44–65. https://doi.org/10.1111/nyas.14041

Institute of Medicine 2001: Dietary reference intakes for vitamin A, vitamin K, arsenic, boron, chromium, copper, iodine, iron, manganese, molybdenum, nickel, silicon, vanadium, and zinc. Washington: The National Academies Press. https://doi.org/10.17226/10026

Laurberg, P., Pedersen, I.B., Carlé, A., Andersen, S., Knudsen, N., Ovesen, L. & Rasmussen, L.B. 2009: The U-shaped curve of iodine intake and thyroid disorders. In: Preedy, V.R., Burrow, G.N. & Watson, R. (eds): Comprehensive handbook of iodine, pp. 449–455. Elsevier. https://doi.org/10.1016/B978-0-12-374135-6.00047-9

Ma, R. et al. 2022: Deficiency and excess of groundwater iodine and their health associations. Nature Communications 13(1), 7354. https://doi.org/10.1038/s41467-022-35042-6

Ministry of Environment of Denmark 2011: Total opløst jod. Method data sheet M050. Miljøstyrelsens Referencelaboratorium for Kemiske og Mikrobiologiske Miljømålinger. https://cdnmedia.eurofins.com/Microsites/media/1107/m050_iod_01.pdf (accessed August 2022)

Ministry of Environment of Denmark 2021: Bekendtgørelse om kvalitetskrav til miljømålinger, BEK nr 2362 af 26/11/2021 (Analysekvalitetsbekendtgørelsen). Miljøministeriet. https://www.retsinformation.dk/eli/lta/2021/2362 (accessed August 2022)

Pedersen, K.M., Laurberg, P., Nohr, S., Jorgensen, A. & Andersen, S. 1999: Iodine in drinking water varies by more than 100-fold in Denmark. Importance for iodine content of infant formulas. European Journal of Endocrinology 140, 400–403. https://doi.org/10.1530/eje.0.1400400

R Core Team 2022: R: A language and environment for statistical computing (v. 4.2.1.). R Foundation for Statistical Computing, Austria. https://www.R-project.org/

Rasmussen, L., Larsen, E. & Ovesen, L. 2000: Iodine content in drinking water and other beverages in Denmark. European Journal of Clinical Nutrition 54, 57–60. https://doi.org/10.1038/sj.ejcn.1600893

Sharma, N., Karanfil, T. & Westerhoff, P. 2019: Historical and future needs for geospatial iodide occurrence in surface and groundwaters of the United States of America. Environmental Science & Technology Letters 6(7), 379–388. https://doi.org/10.1021/acs.estlett.9b00278

Thorling, L. 2012: Groundwater sampling (v. 1). Technical guideline. Geological Survey of Denmark and Greenland. 43pp. https://www.geus.dk/media/8324/g02_proevetagning-okt12_uk.pdf (accessed August 2022)

Thorling, L., Albers, C.N., Hansen, B., Johnsen, A.R., Kazmierczak, J., Mortensen, M.H., Troldborg, L. & Møller, I. 2023: Grundvandsovervågning. Status og udvikling 1989 – 2021. Technical report. 144 pp. Geological Survey of Denmark and Greenland.

Troldborg, L. 2020: Afgrænsning af de danske grundvandsforekomster: ny afgrænsning og delkarakterisering samt fagligt grundlag for udpegning af drikkevandsforekomster. Copenhagen: Danmarks og Grønlands Geologiske Undersøgelse Rapport 2020/1. 32 pp. https://doi.org/10.22008/gpub/32641

Voutchkova, D.D., Ernstsen, V., Hansen, B., Sørensen, B.L., Zhang, C. & Kristiansen, S.M. 2014: Assessment of spatial variation in drinking water iodine and its implications for dietary intake: a new conceptual model for Denmark. Science of the Total Environment 493, 432–444. https://doi.org/10.1016/j.scitotenv.2014.06.008

Wang, B., He, W., Li, Q., Jia, X., Yao, Q., Song, R., Qin, Q. & Zhang, J. 2019: U-shaped relationship between iodine status and thyroid autoimmunity risk in adults. European Journal of Endocrinology 181(3), 255–266. https://doi.org/10.1530/EJE-19-0212

Wickham, H. 2016: ggplot2: elegant graphics for data analysis (v. 3.4.1). Springer-Verlag. https://ggplot2.tidyverse.org

Wood, S. 2023: mgcv: Mixed GAM computation vehicle with automatic smoothness estimation (R package v. 1.8-42). https://CRAN.R-project.org/package=mgcv

WHO (World Health Organization) 2007: Assessment of iodine deficiency disorders and monitoring their elimination: a guide for programme managers (3rd edition). 97 pp. Geneva: World Health Organization.

WHO & FAOUN (World Health Organization & Food and Agriculture Organization of the United Nations) 2004: vitamin and mineral requirements in human nutrition (2nd edition). 341 pp. Geneva: World Health Organization.