Background & Summary  背景与总结

There has recently been a resurgence of interest in the marine iodine cycle, reflecting its involvement in a diverse range of processes, from influencing air quality (e.g.1) to recording ocean deoxygenation in sediments (e.g.2). Iodine is a redox active element that is present in seawater in two main forms, iodide (I−) and iodate (IO3−). Sea-to-air transfer is the dominant source of iodine to the atmosphere, where it is subject to atmospheric processing prior to deposition back to the sea or onto land. It is an essential nutrient for many organisms including humans, and deficiency in humans leads to goitre, cretinism and is the leading cause of preventable mental retardation globally3. Iodine radionuclides are also released to the oceans by anthropogenic activities, where they will be subject to the same processes of biogeochemical cycling and volatilisation as the naturally occurring stable isotope4. Despite the wide ranging impacts of iodine biogeochemistry, the distribution of iodine species in the oceans remains relatively poorly understood. Here we present an updated compilation of all currently available sea surface iodide concentrations. The data set is specifically intended to inform studies of the sea-air exchange of iodine species, but may also be of use in improving understanding of the marine iodine cycle more generally.
最近人们对海洋碘循环的兴趣重新兴起，反映出它参与了各种过程，从影响空气质量（例如1 ）到记录沉积物中的海洋脱氧（例如2 ）。碘是一种氧化还原活性元素，以两种主要形式存在于海水中：碘化物 (I − ) 和碘酸盐 (IO 3 − )。海空转移是碘进入大气的主要来源，碘在沉积回海洋或陆地之前要经过大气处理。它是包括人类在内的许多生物体的必需营养素，人类缺乏它会导致甲状腺肿、克汀病，并且是全球可预防的智力低下的主要原因3 。碘放射性核素也通过人类活动释放到海洋，它们将经历与天然存在的稳定同位素相同的生物地球化学循环和挥发过程4 。尽管碘生物地球化学具有广泛的影响，但人们对海洋中碘物种的分布仍然知之甚少。在这里，我们提供了所有当前可用的海面碘化物浓度的更新汇编。该数据集专门用于为碘物质的海-气交换研究提供信息，但也可用于更广泛地增进对海洋碘循环的了解。

The reaction of iodide with ozone at the surface of the ocean has been established as an important sink for ozone, thought to be responsible for around one third of the total ozone loss by dry deposition5. The reaction liberates reactive iodine compounds to the atmosphere, which in turn contribute to further ozone removal processes. Gas phase reactions involving iodine are estimated to account for up to 15% of tropospheric ozone losses6. To incorporate this chemistry, global and regional air quality and atmospheric chemistry models have begun to include predicted sea-surface iodide fields derived from parameterisations (e.g.5,7,8,9). However, current sea surface iodide parameterisations are known to have biases10, are subject to substantial uncertainty6, and do not take advantage of recent and substantial increases in the number of available observations (e.g.11).
碘化物与海洋表面臭氧的反应已被确定为重要的臭氧汇，据认为约三分之一的干沉积臭氧损失是由臭氧汇造成的5 。该反应将活性碘化合物释放到大气中，这反过来又有助于进一步去除臭氧的过程。据估计，涉及碘的气相反应占对流层臭氧损失的 15% 6 。为了纳入这种化学作用，全球和区域空气质量和大气化学模型已开始包括从参数化得出的预测海面碘化物场（例如5 、 7 、 8 、 9 ）。然而，目前的海面碘化物参数化已知存在偏差10 ，存在很大的不确定性6 ，并且没有利用最近可用观测数量的大幅增加（例如11 ）。

The observational data underpinning iodide parameterisations is sparse, and has hitherto not been publicly available in a collated form. In many cases, iodide observations are not readily accessible in a digital form (i.e. are only presented in graphical format). To facilitate the development and validation of improved sea surface iodide parameterisations, we have compiled all available sea surface iodide observations. The dataset is an extended version of that used in our earlier publication12, in which we described the large scale sea surface iodide distribution and presented correlations between iodide and other oceanographic variables, but did not publish the observations themselves. The dataset we now present incorporates more than 400 new observations (see Fig. 1), including new, basin scale transects from the Indian Ocean (currently unpublished) and the tropical eastern Pacific11, both of which were previously undersampled12. This new extended dataset is freely available via the British Oceanographic Data Centre (BODC; http://doi.org/czhx)13.
支持碘化物参数化的观测数据很少，并且迄今为止尚未以整理的形式公开提供。在许多情况下，碘化物观测结果不容易以数字形式获得（即仅以图形格式呈现）。为了促进改进的海面碘化物参数化的开发和验证，我们汇编了所有可用的海面碘化物观测结果。该数据集是我们早期出版物12中使用的数据集的扩展版本，其中我们描述了大规模海面碘化物分布，并提出了碘化物与其他海洋学变量之间的相关性，但并未发布观测结果本身。我们现在提供的数据集包含 400 多个新观测数据（见图1 ），包括来自印度洋（目前未发布）和热带东太平洋11 的新盆地规模横断面，这两个区域之前都采样不足12 。这个新的扩展数据集可通过英国海洋学数据中心（BODC；http://doi.org/czhx）免费获取13 。

Fig. 1  图1
figure 1

Locations of iodide observations included in our dataset. New data reported here is in red and existing data from Chance et al.12 is blue. Figure produced with Python Matplotlib79.
我们的数据集中包含碘化物观测位置。此处报告的新数据以红色显示，现有数据来自 Chance等人。 12是蓝色的。使用 Python Matplotlib 79生成的图。

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We anticipate that the primary use of the dataset will be modelling of ozone deposition to the sea surface and/or associated trace gas emissions to the atmosphere. It has been used to generate new monthly parameterised sea-surface iodide fields (12 × 12 km resolution) using a machine learning approach, described in our accompanying partner publication10. The dataset may also be of interest in other areas of iodine research. In particular, improved understanding of the marine iodine cycle is needed to refine the use of iodine speciation as a paleo-oceanographic tracer of past ocean oxygenation (e.g.2), and to better predict the impacts of iodine radionuclides released to the environment by anthropogenic activities (e.g.4).
我们预计该数据集的主要用途将是对海面臭氧沉积和/或相关的大气痕量气体排放进行建模。它已被用于使用机器学习方法生成新的每月参数化海面碘化物场（12 × 12 km 分辨率），如我们随附的合作伙伴出版物10中所述。该数据集也可能对碘研究的其他领域感兴趣。特别是，需要加深对海洋碘循环的了解，以完善碘形态作为过去海洋氧化作用的古海洋示踪剂的使用（例如2 ），并更好地预测人类活动释放到环境中的碘放射性核素的影响（例如4 ）。

Methods  方法

Data compilation  资料整理

The data set includes iodide measurements made by a number of different research groups (Online-only Table 1). These were collated from the following sources:
该数据集包括由多个不同研究小组进行的碘化物测量（仅在线表1 ）。这些内容是从以下来源整理的：

  1. A.  一个。

    Published manuscripts. Data was digitised from tables and graphics, either by hand or using the free online tool WebPlotDigitizer (https://automeris.io/WebPlotDigitizer).
    发表手稿。数据通过手工或使用免费在线工具 WebPlotDigitizer ( https://automeris.io/WebPlotDigitizer ) 从表格和图形中数字化。

  2. B.

    Data originators. Data (both published and unpublished) was provided directly by the owners.
    数据发起者。数据（已发布和未发布）由所有者直接提供。

  3. C.

    Data repositories. Data was obtained by request or on-demand download from hosting repositories (e.g. BODC, PANGAEA, the US JGOFS Data System).
    数据存储库。数据是通过请求或从托管存储库（例如 BODC、PANGAEA、美国 JGOFS 数据系统）按需下载获得的。

Following the approach adopted previously12, ‘surface’ concentrations are considered to be those from depths of less than 20 m. As discussed in Chance et al.12, the ocean is usually considered well mixed to this depth, and to restrict ‘surface data’ to shallower depths would substantially reduce the number of observations included. We examined a sub-set of data (n = 93) where observations were available from multiple depths within the upper 20 m of the water column. While significant differences were occasionally found between individual pairs of samples collected from depths of ~1-2 m and ~10 m at a given station, concentrations were within 10 nM in almost 50% of pairs (49.5%), and 80% were within 26 nM. Statistical analysis (using a paired students t-test) found no significant difference between samples from different depths within the upper 20 m. The exact depth of near surface samples can itself have high relative uncertainty, as factors such as sea swell can lead to metre scale fluctuations to the exact depth of e.g. a ship seawater inlet. Furthermore, the exact depths of such inlets, or the ‘surface’ sample bottle, was not always stated in the original data sources. Therefore, we have not included depth as a parameter in our compiled data set and no distinction has been made between samples obtained using a CTD rosette fitted with Niskin bottles (or similar), a pumped underway seawater supply or a manual method (such as bucket sampling).
按照之前采用的方法12 ，“表面”浓度被认为是来自深度小于 20 m 的浓度。正如 Chance等人所讨论的。如图12所示，海洋通常被认为在这个深度上混合得很好，并且将“表面数据”限制在较浅的深度将大大减少所包括的观测数量。我们检查了数据子集 (n = 93)，其中可从水体上部 20 m 内的多个深度进行观测。虽然在给定站点从 ~1-2 m 和 ~10 m 深度采集的各个样本对之间偶尔会发现显着差异，但几乎 50% 的样本对 (49.5%) 的浓度在 10 nM 以内，80% 在 10 nM 以内。 26纳米。统计分析（使用配对学生 t 检验）发现上部 20 m 内不同深度的样本之间没有显着差异。近地表样本的精确深度本身可能具有较高的相对不确定性，因为海浪等因素可能导致船舶海水入口的精确深度出现米级波动。此外，原始数据源中并不总是说明此类入口或“表面”样品瓶的确切深度。因此，我们没有将深度作为参数纳入我们编译的数据集中，并且使用装有 Niskin 瓶（或类似装置）的 CTD 玫瑰花结、泵送的正在进行的海水供应或手动方法（例如桶）获得的样本之间没有区别。采样）。

Each data set was entered onto an individual Excel spreadsheet in a standard format. Rarely, source values were below the limit of detection (LoD) for the method used. Where this was the case, we have used a substitute value of 0.75 x the estimated LoD and the data point was flagged (column ‘ErrorMethod’). No further processing has been applied to any of the data. It has not been normalised e.g. to salinity. Required fields from individual ‘input’ files were then collated into a single comma-separated value (.csv) file using open-source Python code, including the Pandas package14.
每个数据集都以标准格式输入到单独的 Excel 电子表格中。极少数情况下，源值低于所用方法的检测限 (LoD)。在这种情况下，我们使用了 0.75 x 估计 LoD 的替代值，并标记了数据点（“ErrorMethod”列）。没有对任何数据进行进一步处理。它尚未标准化，例如盐度。然后使用开源 Python 代码（包括 Pandas 包14 ）将各个“输入”文件中的必填字段整理成单个逗号分隔值 (.csv) 文件。

A total of 1342 observations, from 57 individual data sets has been collated (Online-only Table 1). This is an increase of 417 observations (45%) on that included in our earlier compilation12. Locations of individual data points are shown in Fig. 1, which highlights how the expanded dataset increases spatial coverage. The earliest observations were made in 1967 and the most recent in 2018. For some data points (n = 32) the date of sampling is not specified as this was not given in the original publication. Ten of the input data sets are currently unpublished.
已对来自 57 个单独数据集的总共 1342 个观察结果进行了整理（仅在线表1 ）。与我们之前的汇编中包含的观测值相比，增加了 417 个观测值 (45%) 12 。各个数据点的位置如图1所示，它突出显示了扩展数据集如何增加空间覆盖范围。最早的观测是在 1967 年进行的，最近一次是在 2018 年进行的。对于某些数据点 (n = 32)，未指定采样日期，因为原始出版物中没有给出。目前有 10 个输入数据集尚未发布。

Additional fields  附加字段

Each iodide observation is associated with the record fields listed in Table 1. In addition to spatial and temporal co-ordinates, the estimated uncertainty and analytical method used to generate the observations are provided.
每个碘化物观测值都与表1中列出的记录字段相关联。除了空间和时间坐标之外，还提供了用于生成观测值的估计不确定性和分析方法。

Table 1 Data record fields or Column names, column description and units for each field included in the sea surface iodide database.
表1 海面碘化物数据库中包含的数据记录字段或每个字段的列名称、列描述和单位。
Full size table

Method  方法

Analytical methods are summarised in Table 2. In the majority of cases (~53%), iodide was measured by cathodic stripping square wave voltammetry (CSSWV) according to the method of Campos15. However a range of other measurements techniques were also used. Iodide was sometimes measured as the difference between the total inorganic iodine (TII) concentration and the iodate concentration.
分析方法总结于表2中。在大多数情况下 (~53%)，碘化物是根据 Campos 15的方法通过阴极溶出方波伏安法 (CSSWV) 测量的。然而，还使用了一系列其他测量技术。碘化物有时被测量为总无机碘 (TII) 浓度和碘酸盐浓度之间的差值。

Table 2 Analytical methods and associated uncertainties.
表 2 分析方法和相关不确定性。
Full size table

Uncertainty  不确定

Measurements of iodide in seawater are subject to non-trivial analytical uncertainties, which should be considered when using the data set. An estimate of the uncertainty associated with each observation has been included, using either information provided by the data source where available, or comparison to other measurements using the same analytical method. The uncertainty estimates provided are typically derived from replicate analyses of the same sample, and so represent the precision of the measurements. As insufficient information was available to quantify the precision in the same way for all observations, the approach used to estimate the precision is also included (see Table 1). Relative uncertainty estimates for each analytical method, for typical ambient concentrations in a seawater matrix, are also provided in Table 2. The precision given for each data set is often 5% (Table 2), which reflects the stated repeatability of the CSSWV method15 and a number of other measurements used. However, we note that repeat analyses of samples using this method can sometimes give lower precision (e.g. ~10%)16. Considering all data points in our dataset, we find ~75% have a precision of 10% or less, and ~51% have an precision of 5% or less. Such uncertainties are modest in comparison to the global scale variation in sea surface iodide concentrations (from less than 10 to more than 200 nM; Fig. 2).
海水中碘化物的测量存在很大的分析不确定性，在使用数据集时应考虑到这一点。使用数据源（如果可用）提供的信息或使用相同分析方法与其他测量值进行比较，对与每个观测值相关的不确定性进行了估计。所提供的不确定性估计通常来自同一样品的重复分析，因此代表了测量的精度。由于没有足够的信息来以相同的方式量化所有观测的精度，因此还包括用于估计精度的方法（参见表1 ）。表2还提供了每种分析方法、海水基质中典型环境浓度的相对不确定性估计。每个数据集给出的精度通常为 5%（表2 ），这反映了 CSSWV 方法15和使用的许多其他测量的规定重复性。然而，我们注意到，使用这种方法重复分析样品有时会给出较低的精度（例如~10%） 16 。考虑到数据集中的所有数据点，我们发现约 75% 的精度为 10% 或更低，约 51% 的精度为 5% 或更低。与海面碘化物浓度的全球尺度变化（从小于 10 到大于 200 nM；图2 ）相比，这种不确定性很小。

Fig. 2  图2
figure 2

Estimated probability density function (PDF; kernel density estimate) for sea surface iodide observations. Plot shows all data (blue) combined, and open ocean (green) and coastal (red) data treated separately. Expanded inset shows values <400 nM only. Figure produced with Python Matplotlib79 and Seaborn80 packages.
海面碘化物观测的估计概率密度函数（PDF；核密度估计）。该图显示了所有数据（蓝色）的组合，以及单独处理的公海（绿色）和沿海（红色）数据。扩展插图仅显示值 <400 nM。使用 Python Matplotlib 79和 Seaborn 80软件包生成的图。

Full size image

As the uncertainty estimates provided are typically derived from replicate analyses of the same sample they only estimate the short (days) to medium term (approx. monthly) repeatability. A fuller consideration of the uncertainty should also include the longer term (months to years) reproducibility, and an estimate of any uncertainties arising from bias, and thus may result in a larger uncertainty value. These sources of uncertainty are as yet poorly documented for the determination of iodide in seawater. At least in the case of the most commonly used method (CSSWV), we believe the contribution of long term reproducibility and bias to be small relative to the short-term precision. This is because the key of sources of uncertainty (e.g. that associated with making standard additions and sample dilutions by pipette, or variation in mercury electrode drop size) operate over a short time scale. Within our own laboratory, we have been monitoring long term reproducibility of the CSSWV method using aliquots of a near shore seawater sample, and estimate it at ~12% RSD over a period of 11 months (analysis by three operators using two different instruments; individual aliquots stored at −20 °C and defrosted within a few days of analysis), compared to ~monthly repeatability of 7–12% and repeatability over a few days of 5 to 18%. Changes taking place during storage will also contribute to the overall uncertainty of reported observations; for samples stored frozen (−16 °C), average iodide recovery after one year was 95–96%, compared to an average standard deviation of 5–8%15. In the majority of data sets we include, samples were stored frozen for less than one year prior to analysis, others were either analysed immediately following collection or stored for a shorter period refrigerated. Therefore we assume that storage artefacts were minimal. This view is supported by the oceanographic consistency found between stored and freshly analysed samples.
由于提供的不确定性估计通常来自同一样本的重复分析，因此它们仅估计短期（天）到中期（大约每月）的可重复性。对不确定性的更全面考虑还应包括长期（数月至数年）的再现性，以及对偏差引起的任何不确定性的估计，因此可能会导致更大的不确定性值。对于海水中碘化物的测定，这些不确定性来源的记录还很少。至少在最常用的方法（CSSWV）的情况下，我们认为长期再现性和偏差的贡献相对于短期精度来说很小。这是因为不确定性来源的关键（例如，与通过移液器添加标准品和样品稀释相关的不确定性，或汞电极液滴尺寸的变化）在短时间内起作用。在我们自己的实验室内，我们一直使用近岸海水样品的等分试样来监测 CSSWV 方法的长期再现性，并在 11 个月的时间内估计其 RSD 约为 12%（由三名操作员使用两种不同的仪器进行分析；个人等分试样储存在 -20 °C 并在分析后几天内解冻），相比之下，每月重复性为 7-12%，几天重复性为 5-18%。储存期间发生的变化也会增加报告观测结果的总体不确定性；对于冷冻保存的样品（−16 °C），一年后平均碘化物回收率为 95–96%，而平均标准偏差为 5–8% 15 。 在我们纳入的大多数数据集中，样品在分析前冷冻保存时间不到一年，其他样品要么在收集后立即分析，要么冷藏保存较短时间。因此，我们假设存储文物是最少的。这一观点得到了存储样本和新分析样本之间海洋学一致性的支持。

Assessment of bias in iodide in seawater determinations is hindered by the lack of a suitable reference material – many similar reference materials e.g. for trace metals, are acidified, which is unsuitable for the preservation of iodine speciation. Inaccuracy in standard preparation will contribute to bias in the short-term (all samples analysed using same standard), but are likely to become a random error in the longer term (several standards used over time). In either case, this should be a small contribution, as the uncertainty associated with preparing a typical analytical standard (e.g. 10 μM standard) should be less than 1% in a competent lab with well-maintained and calibrated equipment (e.g. balance, pipette). Other contributions to bias, such as matrix effects, are minimised by the use of standard additions rather than external calibration in the CSSWV protocol. In the absence of an iodide reference material, Campos15 tested the accuracy of the CSSWV method using solutions of known iodate concentration and a reduction step, and found it to be 99 ± 5.7% for 34 analyses. Given the current interest in marine iodide concentrations2,10,11, we believe that an inter-laboratory calibration exercise leading to development of a saline iodide reference material with a consensus value would be very timely. Such an exercise could follow the model of the recent GEOTRACES inter-calibration scheme (http://www.geotraces.org/Intercalibration).
由于缺乏合适的标准物质，海水测定中碘化物偏差的评估受到阻碍——许多类似的标准物质（例如痕量金属）都被酸化，这不适合保存碘形态。标准品制备的不准确会在短期内造成偏差（所有样品都使用相同的标准品进行分析），但从长期来看可能会成为随机误差（随着时间的推移使用多个标准品）。无论哪种情况，这都应该是一个很小的贡献，因为在拥有良好维护和校准设备（例如天平、移液器）的合格实验室中，与制备典型分析标准品（例如 10 μM 标准品）相关的不确定性应小于 1% 。通过在 CSSWV 协议中使用标准添加而不是外部校准，可以最大限度地减少对偏差的其他影响，例如基质效应。在没有碘化物标准物质的情况下，Campos 15使用已知碘酸盐浓度的溶液和还原步骤测试了 CSSWV 方法的准确性，发现 34 次分析的准确性为 99 ± 5.7%。鉴于目前人们对海洋碘化物浓度2 , 10 , 11的兴趣，我们相信，通过实验室间校准工作来开发具有共识值的盐水碘化物参考材料将是非常及时的。这样的练习可以遵循最近的 GEOTRACES 相互校准方案 ( http://www.geotraces.org/Intercalibration ) 的模型。

Geographical categorisation
地理分类

Data points are categorised as either ‘coastal’ or ‘non-coastal’. Following the approach used in Chance et al.12, this is determined by the designation of their static Longhurst biogeochemical province17. In most cases, the Longhurst province was assigned automatically, according to the nearest whole number degree of latitude and longitude. For a small number of samples collected very close to the coast, province (and hence coastal/non-coastal) was assigned manually - these samples are flagged (see Table 1). As in Chance et al.12, a small number of samples collected near Bermuda were also categorised as ‘coastal’ despite being located in an open ocean province (North Atlantic Subtropical Gyre Province (West)), as they were collected from an inshore area18. These samples are identified as such in the ‘Locator Method’ column.
数据点分为“沿海”或“非沿海”。遵循 Chance等人使用的方法。 12 、这是由他们指定的静态朗赫斯特生物地球化学省17确定的。在大多数情况下，朗赫斯特省是根据纬度和经度最接近的整数度数自动分配的。对于在非常靠近海岸的地方收集的少量样本，手动分配省份（以及沿海/非沿海） - 这些样本被标记（参见表1 ）。正如钱斯等人所言。如图12所示，在百慕大附近采集的少量样本也被归类为“沿海”，尽管百慕大位于公海省份（北大西洋副热带环流省（西）），因为它们是从近海区域采集的18 。这些样本在“定位方法”列中被识别。

Ancillary data  辅助数据

Note that original ancillary data such as temperature and salinity is not included, as this was not reliably available for all data sets. Instead we recommend the use of climatological data (e.g. the World Ocean Database and World Ocean Atlas Series) selected according to user needs.
请注意，原始辅助数据（例如温度和盐度）不包括在内，因为这并非对所有数据集都可靠可用。相反，我们建议使用根据用户需求选择的气候数据（例如世界海洋数据库和世界海洋地图集系列）。

Data Records  数据记录

The compiled dataset is hosted by BODC (https://doi.org/10.5285/7e77d6b9-83fb-41e0-e053-6c86abc069d0)13, and is available as a single.csv file (plus a separate metadata file). It includes the fields listed in Table 1. It is anticipated that updated versions will be made available periodically, as new sea surface iodide observations become available. The current iteration is termed Version 1.0, future iterations will be named sequentially (i.e. version 2.0 etc). The lead authors would be very pleased to be contacted regarding new or omitted iodide observations for inclusion in future iterations of the dataset.
编译后的数据集由 BODC ( https://doi.org/10.5285/7e77d6b9-83fb-41e0-e053-6c86abc069d0 ) 13托管，并作为单个 .csv 文件（加上单独的元数据文件）提供。它包括表1中列出的字段。预计随着新的海面碘化物观测结果的出现，将定期提供更新版本。当前迭代被称为版本 1.0，未来迭代将按顺序命名（即版本 2.0 等）。主要作者将非常高兴就新的或遗漏的碘化物观测结果与我们联系，以便将其纳入数据集的未来迭代中。

Technical Validation  技术验证

Of the records included in our database, the majority (47/57) are described in peer-reviewed literature, and a further two are from PhD theses, and so their quality has already been subject to scientific scrutiny. Unpublished data sets made use of well-established analytical techniques, including the use of calibration standards and replicate analyses. In addition, the majority of data points were described in our earlier peer reviewed manuscript12, and were shown to have to a cohesive global distribution. The distribution of observations in the extended dataset continues to conform to this distribution (not shown), with concentrations remaining in the expected range (Fig. 2).
在我们数据库中包含的记录中，大多数（47/57）是在同行评审的文献中描述的，另外两个来自博士论文，因此它们的质量已经受到科学审查。未发表的数据集利用了完善的分析技术，包括使用校准标准和重复分析。此外，大多数数据点在我们之前的同行评审手稿12中进行了描述，并且被证明具有一致的全球分布。扩展数据集中的观测值分布继续符合该分布（未显示），浓度保持在预期范围内（图2 ）。

A very small number of unusually high concentration points (19 with iodide levels higher than 400 nM) are present in the data set. These are not representative of the overall iodide distribution, all being above the 98th percentile and also defined as outliers under the Tukey definition19. Where present, these extreme outlier values have been subject to rigorous scrutiny and are believed to be real.
数据集中存在极少数异常高的浓度点（19 个碘化物水平高于 400 nM）。这些并不代表整体碘化物分布，全部都高于第98 个百分位数，并且根据 Tukey 定义也被定义为异常值19 。如果存在，这些极端异常值都经过严格审查并被认为是真实的。

We have not evaluated the data set to look for systematic differences between measurement techniques, as method used and location (and hence iodide concentration) are not independent variables. In most cases, only a small number of geographically limited points are available for a given method (Table 2). As noted, more than half the observations have been made using the same CSSWV technique. The remainder have been analysed using a wide range of other approaches, including, for some of the earliest datasets, labour intensive ‘wet chemical’ procedures which have since been superseded. In particular, a large proportion of the Pacific measurements were made in between 1968 and 197020,21 using a revised version of the Sugawara precipitation method22. The scarcity of more modern data from the Pacific limits comparisons, but we note that the range of this early Pacific data (3–168 nM) falls within that of the global data set, with a well-defined latitudinal distribution consistent with that observed overall. Regional concentrations (e.g. high latitudes, north Pacific23) are in agreement with those measured subsequently using different methods. Furthermore, the original data sources report vertical iodide profiles consistent in shape and magnitude with more recent measurements. Data obtained using the original, unmodified Sugawara method24 (1955) is not included, as this method is known to have poor performance22.
我们尚未评估数据集来寻找测量技术之间的系统差异，因为所使用的方法和位置（以及碘化物浓度）不是自变量。在大多数情况下，只有少数地理上有限的点可用于给定方法（表2 ）。如前所述，超过一半的观测是使用相同的 CSSWV 技术进行的。其余的数据已使用各种其他方法进行了分析，其中包括一些最早的数据集的劳动密集型“湿化学”程序，这些程序后来已被取代。特别是，太平洋地区的大部分测量是在 1968 年至 1970 年间20 , 21使用菅原降水法22的修订版进行的。来自太平洋的更现代数据的稀缺限制了比较，但我们注意到，早期太平洋数据的范围（3-168 nM）属于全球数据集的范围，其明确的纬度分布与观察到的总体数据一致。区域浓度（例如高纬度地区、北太平洋23 ）与随后使用不同方法测量的浓度一致。此外，原始数据源报告的垂直碘化物分布在形状和大小上与最近的测量一致。不包括使用原始的、未经修改的菅原方法24 (1955) 获得的数据，因为已知该方法的性能较差22 。

As described earlier, iodide observations are subject to non-negligible analytical uncertainty; we have reviewed the uncertainty estimation for each data set, and present this alongside the observations. As noted above, precision has usually been taken to represent method uncertainty. A variety of different methods have been used to estimate this, and so uncertainty magnitudes may not be directly comparable across all datasets.
如前所述，碘化物观测结果受到不可忽略的分析不确定性的影响；我们审查了每个数据集的不确定性估计，并将其与观察结果一起呈现。如上所述，精度通常用来表示方法的不确定性。已使用各种不同的方法来估计这一点，因此不确定性大小可能无法在所有数据集中直接进行比较。

Usage Notes  使用说明

For computational convenience, iodide concentrations and associated uncertainties are provided to one decimal place (units are nM for both). However, note that this does not usually reflect the precision of the data points correctly, as this is typically a few percent.
为了计算方便，碘化物浓度和相关不确定性均保留至小数点后一位（单位均为 nM）。但是，请注意，这通常不能正确反映数据点的精度，因为这通常是几个百分点。

For the purposes of investigating large-scale trends and creating regional iodide parameterisations, it may be appropriate to exclude the very high outlier values noted in the preceding section. Similarly, a number of points are from relatively low salinity estuarine areas (e.g. the Skaggerak), and so may not be representative of true marine trends in iodine speciation.
为了调查大规模趋势和创建区域碘化物参数化的目的，排除上一节中提到的非常高的异常值可能是适当的。同样，许多点来自盐度相对较低的河口地区（例如斯卡格拉克），因此可能无法代表碘形态的真实海洋趋势。

Missing fields are shown as not a number (“NaN”) in the output data file.
缺失字段在输出数据文件中显示为非数字（“NaN”）。