RESEARCH ARTICLE | SHORT Voutchkova 2023: GEUS Bulletin 53. 8352. https://doi.org/10.34194/geusb.v53.8352 1 of 8 Temporal variation of iodine in Danish groundwater Denitza D. Voutchkova* Geological Survey of Denmark and Greenland (GEUS), Aarhus, Denmark *Correspondence: dv@geus.dk Received: 15 May 2023 Revised: 18 July 2023 Accepted: 20 July 2023 Published: 18 Aug 2023 Keywords: iodine, temporal variation, groundwater, Denmark Abbreviations b.g.l.: below ground level GAM: generalised additive model GEUS: Geological Survey of Denmark and Greenland GRUMO: Danish national groundwater monitoring programme ICP-MS: inductively coupled plasma mass spectrometry IDD: Iodine deficiency disorders IQR: interquartile range LOD: limit of detection LOESS: local polynomial regression fit MAD: median absolute deviation rCV: robust coefficient of variation U: analytical uncertainty GEUS Bulletin (eISSN: 2597-2154) is an open access, peer-reviewed journal published by the Geological Survey of Denmark and Greenland (GEUS). This article is distributed under a CC-BY 4.0 licence, permitting free redistribution, and reproduction for any purpose, even commercial, provided proper citation of the original work. Author(s) retain copyright. Edited by: Adam Hambly (Technical University of Denmark) Reviewed by: Andre Banning (University of Greifswald, Germany) and one anonymous reviewer. Funding: See page 7 Competing interests: See page 7 Additional files: See page 7 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 char- acterised. 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 uncer- tainty 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 out- comes, 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 20th 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 func- tion 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). 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 con- cluded that iodine concentration in drinking water can be used as an indica- tor of dietary intake. Therefore, the spatial variation of iodine in both drinking https://doi.org/10.34194/geusb.v53.8352 https://orcid.org/0000-0003-2840-072X mailto:dv@geus.dk https://creativecommons.org/licenses/by/4.0/deed.ast Voutchkova 2023: GEUS Bulletin 53. 8352. https://doi.org/10.34194/geusb.v53.8352 2 of 8 www.geusbul let in.org water and groundwater and its importance for human health have been discussed in depth in the scientific lit- erature: 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 drink- ing 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 hypoth- esis 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 col- lected 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). Following the sampling protocols of the Geological Survey of Denmark and Greenland (GEUS), well purg- ing was carried out until pH, conductivity, O2 and tem- perature 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 accred- ited laboratories. The national requirements for ana- lytical uncertainty of all environmental measurements for iodine are 1.5 µg/l absolute expanded uncertainty (Uabs) for low concentrations and 20% relative expanded uncertainty (Urel) for high concentrations (Ministry of Environment of Denmark 2021). As Ministry of Envi- ronment of Denmark (2021) has not defined what is considered a low concentration, the highest of the two (max (Urel, Uabs)) 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), Uabs ≥ Urel, so U = Uabs. For the remain- ing 29% (n = 842), Urel was higher, so U = Urel. 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). 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. https://doi.org/10.34194/geusb.v53.8352 http://www.geusbulletin.org Voutchkova 2023: GEUS Bulletin 53. 8352. https://doi.org/10.34194/geusb.v53.8352 3 of 8 www.geusbul let in.org 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: rCV = 1.4826 × MAD/m (1) 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: MAD = median(|Xi – m|) (2) where Xi 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: > ×       → ≤ ×       →         rCV U X rCV U X if median 100% environmental median 100% analytical j i i j i i (3) 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 uncer- tainty 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 fur- ther details. 0 50 100 km Number of iodine samples in GRUMO well screens (n) 1 2–9 10–11 (labelled well sites) Germany 65 Sweden 94 96 Period: 2011–2021 Data extraction: 22 June 2021 105 121 131 141 159 216 N Fig. 1 Spatial distribution of iodine sampling sites in the study period at well screens of the Danish national groundwater monitoring programme (GRUMO). https://doi.org/10.34194/geusb.v53.8352 http://www.geusbulletin.org Voutchkova 2023: GEUS Bulletin 53. 8352. https://doi.org/10.34194/geusb.v53.8352 4 of 8 www.geusbul let in.org In addition, a trend analysis was performed solely for well screens with ≥10 years of data (n = 23; 2%). Linear regression and local polynomial regres- sion (LOESS with a generalised additive model, GAM, with  integrated smoothness estimation as implemented in Wood (2023)) and their 95% confi- dence intervals  were used to interpret the trends (Wickham 2016). All statistical analyses and summa- ries 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 Fig. 2 Iodine in groundwater wells from the Danish national groundwater monitoring programme (GRUMO). a: Histogram of number of iodine sam- ples 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). 312 678 110 33 36 16 13 5 12 18 9 Iodine samples (n) Sa m pl in g po in ts (n ) 0 3 6 9 12 0 200 400 600 Study period: 2011–2021 (a) 2 4 6 8 10 Samples (n) Median iodine concentration (μg/l) 0 1 10 100 500 R ob us t C V (% ) 40 80 120 (b) 1 2 3 4 5 6 7 8 9 10 11 Io di ne (m ed ia n ± M A D , μ g/ l) (c) Iodine samples per sampling point (n) 0.1 10 100 1000 1 MAD (μg/l) 10 1 0.1 0.01 https://doi.org/10.34194/geusb.v53.8352 http://www.geusbulletin.org Voutchkova 2023: GEUS Bulletin 53. 8352. https://doi.org/10.34194/geusb.v53.8352 5 of 8 www.geusbul let in.org 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 sam- pling points (n = 127, Fig. 3), iodine variability could not be attributed solely to analytical uncertainty. It could be inferred that at those locations, the variabil- ity was, at least partially, caused by environmental factors such as local hydrogeological conditions and/ or iodine variability in precipitation. No concentra- tion 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 eluci- date the governing environmental factors at specific locations. This assessment showed that there is a temporal vari- ability 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 concen- trations (>50 µg/l) and absolute variations occur at loca- tions 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 sam- ple 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 Fig. 3 Iodine variability attribution at sampling sites throughout Denmark. N 0 50 100 km Iodine variability attribution analytical uncertainty (n = 803) environmental factors (n = 127) not enough data (n = 312) Germany Sweden Period: 2011–2021 Data extraction: 22 June 2021 https://doi.org/10.34194/geusb.v53.8352 http://www.geusbulletin.org Voutchkova 2023: GEUS Bulletin 53. 8352. https://doi.org/10.34194/geusb.v53.8352 6 of 8 www.geusbul let in.org 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 anal- ysis 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 compar- ison,  the variability in iodine concentration could 141. 883_1 159. 979_2 216. 748_1 131. 1974_1 131. 1976_1 131. 1994_1 131. 1995_1 131. 1051_3 131. 1055_1 131. 1056_1 131. 1060_2 96. 1976_2 105. 1827_1 121. 958_1 131. 831_1 94. 2515_2 94. 2515_3 94. 2516_2 94. 2516_3 65. 1068_3 65. 1517_1 65. 1523_1 94. 2515_1 20 12 20 14 20 16 20 18 20 20 20 12 20 14 20 16 20 18 20 20 20 12 20 14 20 16 20 18 20 20 20 12 20 14 20 16 20 18 20 20 0 2 4 6 0 2 4 6 0 10 20 30 40 50 0 2 4 0.0 2.5 5.0 7.5 0 10 20 30 0 2 4 0 2 4 6 0 2 4 6 0.0 2.5 5.0 7.5 0 5 10 15 0 1 2 3 4 0 2 4 6 8 0 5 10 15 20 0 1 2 3 4 5 0.0 2.5 5.0 7.5 10.0 0 10 20 30 40 0.0 2.5 5.0 7.5 10.0 0 2 4 6 0 1 2 3 0.0 2.5 5.0 7.5 10.0 12.5 0.0 2.5 5.0 7.5 10.0 0 1 2 3 4 5 Sampling date Io di ne (μ g/ l) Linear model (LM) GAM model 1.7 ± 0.8 * 4.8 ± 1.3 2.9 ± 0.2 3.1 ± 0.3 5.0 ± 0.2 3.7 ± 0.2 3.9 ± 0.5 1.8 ± 0.2 10.5 ± 1.5 5.4 ± 0.7 9.7 ± 5.8 8.7 ± 1.0 3.0 ± 0.1 3.7 ± 0.2 3.4 ± 0.1 6.9 ± 0.6 * * 6.1 ± 0.5 4.8 ± 0.6 5.4 ± 0.2 2.9 ± 0.2 26.0 ± 3.0 4.7 ± 1.0 * * p < 0.05 (LM slope) median ± MAD = 18.0 ± 2.0 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. https://doi.org/10.34194/geusb.v53.8352 http://www.geusbulletin.org Voutchkova 2023: GEUS Bulletin 53. 8352. https://doi.org/10.34194/geusb.v53.8352 7 of 8 www.geusbul let in.org 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 sig- nificant 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 aqui- fer. The well screen ‘105.1827_1’ is also shallow (3–4 m b.g.l.), without a significant trend, with a possible out- lier, 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 monitor- ing 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 vari- ability 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 lin- ear/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 suffi- ciently long, and so it is presently impossible to quantify and explain the variability of the full range of iodine con- centrations found in Danish groundwater. Conclusions This is the first time that the temporal variation of iodine in Danish groundwater has been characterised system- atically. 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 vari- ation 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 concentra- tions. 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 there- fore recommended that the temporal variation anal- ysis 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. Competing interests No competing interests. 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 Endo- crinology 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 ana- logs to the coefficient of variation. 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Figures Fig. 1 Spatial distribution of iodine sampling sites in the study period at well screens of the Danish national groundwater monitoring programme (GRUMO). 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). Fig. 3 Iodine variability attribution at sampling sites throughout Denmark. 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.