Nianzhi Jiao
email: jiao@xmu.edu.cn

Jihua Liu

Bethanie Edwards

Zongqing Lv

Ruanhong Cai

Yongqin Liu

Xilin Xiao

Jianning Wang

Fanglue Jiao

Rui Wang

Xingyu Huang

Bixi Guo

Jia Sun

Rui Zhang

Yao Zhang

Kai Tang

Qiang Zheng

Farooq Azam

John Batt

Wei-Jun Cai

Chen He

Gerhard J Herndl

Paul Hill

David Hutchins

Julie Laroche

Marlon Lewis

Hugh Macintyre

Luca Polimene

Carol Robinson

Quan Shi

Curtis A Suttle

Helmuth Thomas

Douglas Wallace

Louis Legendre
email: legendre@obs-vlfr.fr

Embargo until 14h00 US Eastern Time on the day of publication (planned for

Microbial degradation of dissolved organic carbon (DOC) in aquatic environments can cause oxygen depletion, water acidification and CO2 emissions. These problems are caused by labile DOC (LDOC), and not refractory DOC (RDOC) that resists degradation and is thus a carbon sink. For nearly a century, chemical oxygen demand (COD) has been widely used for assessment of organic pollution in aquatic systems. Here we show, through a multi-country survey and experimental studies, that COD is not an appropriate proxy of microbial degradability of organic matter because it oxidizes both LDOC and RDOC, and the latter contributes up to 90% of DOC in high-latitude forested areas. Hence COD measurements do not provide appropriate scientific information on organic pollution in natural waters, and can mislead environmental policies. We propose the replacement of the COD method with an optode-based biological oxygen demand method to accurately and efficiently assess organic pollution in natural aquatic environments.

INTRODUCTION

About one quarter of the net carbon fixation on land enters the dissolved organic carbon (DOC) pool of natural waters [START_REF] Regnier | Anthropogenic perturbation of the carbon fluxes from land to ocean[END_REF][START_REF] Bauer | The changing carbon cycle of the coastal ocean[END_REF][START_REF] Maavara | Global perturbation of organic carbon cycling by river damming[END_REF]. The nature and behavior of DOC in aquatic systems determine its fate and environmental effects. DOC is often classified based on how rapidly it turns over in the natural environment [START_REF] Hansell | Recalcitrant dissolved organic carbon fractions[END_REF]. Labile DOC (LDOC) is rapidly degraded and taken up by microbes, and when high amounts of LDOC are released into the environment, the resulting consumption of oxygen can cause oxygen depletion [START_REF] Doney | The growing human footprint on coastal and open-ocean biogeochemistry[END_REF][START_REF] Cai | Acidification of subsurface coastal waters enhanced by eutrophication[END_REF], with detrimental effects on aquatic ecosystems. Also, respiration of LDOC to CO2 can result in water acidification [START_REF] Cai | Acidification of subsurface coastal waters enhanced by eutrophication[END_REF][START_REF] Sunda | Eutrophication induced CO2-acidification of subsurface coastal waters: interactive effects of temperature, salinity, and atmospheric PCO2[END_REF] and release of excess CO2 (supersaturation) to the atmosphere where it contributes to the greenhouse effect. In this way, a productive coastal region could become a source rather than a sink of atmospheric CO2 [START_REF] Sunda | Eutrophication induced CO2-acidification of subsurface coastal waters: interactive effects of temperature, salinity, and atmospheric PCO2[END_REF][START_REF] Jiao | Increasing the microbial carbon sink in the sea by reducing chemical fertilization on the land[END_REF]. It follows that the microbial degradation of LDOC can lead to a series of environmental problems.

In contrast, the refractory components of DOC (RDOC) in the aquatic environment are not readily or rapidly metabolized by microbes and so can be preserved in natural waters where they act as a carbon sink [START_REF] Hansell | Recalcitrant dissolved organic carbon fractions[END_REF][START_REF] Jiao | Microbial production of recalcitrant dissolved organic matter: long-term carbon storage in the global ocean[END_REF]. While RDOC includes chelating agents [START_REF] Hertkorn | Characterization of a major refractory component of marine dissolved organic matter[END_REF] and therefore may act as an important transport pathway or sink for heavy metals, RDOC is not a pollutant but contributes instead to carbon sequestration. In fact, RDOC accounts for more than 95% of the total DOC in the deep ocean [START_REF] Hansell | Recalcitrant dissolved organic carbon fractions[END_REF][START_REF] Legendre | The microbial carbon pump concept: Potential biogeochemical significance in the globally changing ocean[END_REF] (man-made persistent organic pollutants are not discussed in this paper because they represent very small amounts of the DOC, Supplementary Materials 1.1). A purely chemical view of organic pollution could identify both LDOC and RDOC as organic pollutants, whereas the ecological view of organic pollution clearly restricts pollutants to LDOC. The negative environmental consequences of the microbial degradation of organic compounds mentioned above (i.e., oxygen depletion, acidification, and release of CO2 to the atmosphere) depend only on the magnitude of the LDOC fraction.

The concern generated by organic pollution led governments to monitor microbial degradability of organic matter in water bodies, which became a fundamental tool in environmental policy and management. The biodegradability of organic matter can be evaluated via its oxygen demand by microorganisms, i.e., the amount of oxygen consumed by bacteria and other microorganisms while they decompose and metabolize the organic matter under aerobic conditions.

The oxygen demand of LDOC in aquatic environments is best measured as the biological (or biochemical) oxygen demand (BOD) [START_REF] Jouanneau | Methods for assessing biochemical oxygen demand (BOD): a review[END_REF]. However, the existing standard BOD method is time consuming as it requires 5-day incubations, and has low reproducibility as it involves initial and final measurements on different subsamples (bottles) of the water samples [START_REF] Jouanneau | Methods for assessing biochemical oxygen demand (BOD): a review[END_REF]. Consequently, BOD estimates have largely been replaced by measurements of chemical oxygen demand (COD), i.e. the determination of the amount of oxygen consumed by chemical reactions in the solution. In COD, potassium dichromate or potassium permanganate is used to rapidly oxidize the organic matter (and also possibly existing reductive inorganic ions) by chemical reactions, thus shortening the measurement time to only a few hours. Given its convenience, relative simplicity, and repeatability, the COD method has been widely used by agencies and scientists for nearly a century for monitoring and managing waste effluents and natural waters [START_REF] Dubber | Replacement of chemical oxygen demand (COD) with total organic carbon (TOC) for monitoring wastewater treatment performance to minimize disposal of toxic analytical waste[END_REF] (Supplementary Materials 1.2 and 1.3). However, the COD method requires the addition of high amounts of strong chemical oxidants (i.e., potassium dichromate and permanganate), which do not exist in natural waters. Hence the COD method artificially oxidizes both LDOC and much or all of the RDOC, whereas oxygen consumption in natural waters is only caused by LDOC oxidation. Although the use of the COD method is justified when rapid measurements are required, such as in the management of wastewater treatment plants, we question its scientific basis and effectiveness for the assessment of organic pollution in natural water bodies.

Here, we hypothesize that the COD method substantially overestimates the DOC that can be degraded by microbes in natural waters, with the consequence that using this method could mislead the assessment of organic pollution in aquatic environments (Fig. 1).

To test our hypothesis, we analyzed COD and BOD data from experiments conducted for this study and measurements collated from the literature (see Materials and Methods). These data cover a wide variety of natural aquatic environments ranging from tropical to subarctic zones, farmlands to forest watersheds, high plateaus to coastal waters, and freshwaters to saline lakes and marine waters (Fig. S1 and Table S1). We thus examined how COD and BOD vary across gradients of salinity and DOC, with latitude and between ecosystems.

Two methods are currently used to determine COD, based on potassium dichromate (CODCr) and potassium permanganate oxidation (CODMn) [START_REF] Saral | Determination of real COD in highly chlorinated wastewaters[END_REF] (see Materials and Methods). Comparison of CODCr and CODMn values determined on natural waters of different salinities and DOC concentrations ([DOC]), indicated that CODCr is not a valid measure of oxidized organic matter because salinity interferes with the determination of [DOC] (salinity effect already reported in previous publications including [START_REF] Saral | Determination of real COD in highly chlorinated wastewaters[END_REF], whereas CODMn may provide appropriate measurements (Figs. S2 andS3, Tables S2 andS3). Hence, we only use CODMn values in the following analysis. To simplify terminology, we use "COD" instead of "CODMn" hereinafter, and "DOC" instead of total organic carbon (TOC) for the following discussion (Supplementary Materials 1.4).

We also used a water-column macrocosm for a long-term incubation of natural water to better understand the problems involved in the current COD method, and the chemical nature of the organic matter oxidized by this method.

We incubated in the Aquatron Tower Tank of Dalhousie University 100 m 3 of natural river water rich in humic material in darkness for 510 days (see Materials and Methods). The DOC in the incubated water was therefore subjected to microbial degradation for a much longer time than the 5 days required by the BOD method. Operationally speaking, any organic matter remaining after the 510-day incubation would have been refractory. We used, for the first time in the literature, ultra-high resolution Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) to analyze the water samples subjected to different treatments regarding the COD methodology (see Materials and Methods). A water sample from 1000-m depth in the South China Sea was used as a reference for natural-seawater RDOC.

RESULTS AND DISCUSSION

Investigating the COD Problems with Field Data

Data from our samples and the literature (Fig. S1 and Table S1) show that the relationships of COD and BOD with [DOC] vary in different natural environments (Fig. 2). The COD values increase linearly with increasing [DOC] in all the sampled natural environments, indicating that COD oxidizes both LDOC and RDOC, as expected. In contrast, the BOD values are much lower than their COD counterparts, as they reflect only LDOC. The relationships observed between BOD and [DOC] across different environments, and the variation therein can be explained by our current understanding of organic matter cycling as follows:

First, we explored latitudinal differences in COD and BOD as a function of [DOC] by grouping the data into high latitudes (subpolar areas), mid latitudes (temperate zone), and low latitudes (subtropical zone) (Fig. 2A-C). While the relationships of COD and BOD with [DOC] are totally decoupled at high latitudes (Fig. 2A), they are better aligned at lower latitudes (Fig. 2B andC). In the subpolar environment, the low BOD values and their independence from [DOC] (low correlation and regression coefficients, i.e. 0.16 and 0.39, respectively) indicate generally low LDOC concentration while the increase of (COD-BOD) with increasing [DOC] indicates accumulation of RDOC with increasing [DOC]. In the temperate zone, the values of COD and BOD both increase with [DOC], and the (COD-BOD) values are generally similar to the BOD values. This indicates similar concentrations of LDOC and RDOC at mid-latitudes. In the subtropical zone, the (COD-BOD) values are lower than BOD, indicating that COD is dominated by LDOC with less RDOC accumulating in the environment than at mid-latitudes (Fig. 2B andC). In terms of the absolute value of COD, the subpolar zone ranks the highest, followed by the temperate and subtropical zones, the latter being the lowest, whereas the BOD values remain at the same general level (Fig. 2). If these COD values were assessed according to conventions used in environmental management, the high COD values at high latitude would be interpreted as highly organic-polluted compared with low latitudes. Instead, the high COD values are due to high concentrations of non-polluting RDOC (see above).

Second, we grouped the same samples as in Fig. 2A-C by ecosystems (Fig. 2D-F). This led to the emergence of distinct patterns in LDOC and RDOC that reflect changes in organic matter as it moves through the hydrological cycle towards the sea. In waters of forested regions, BOD is almost independent of [DOC] and (COD-BOD) increased proportionally with [DOC]. The latter is consistent with DOC in forested watersheds consisting mostly of humic-like compounds, which are common components of RDOC [START_REF] Pettersson | River discharge of humic substances and humic-bound metals to the Gulf of Bothnia[END_REF][START_REF] Muscolo | Biological activity of humic substances is related to their chemical structure[END_REF]. In the other freshwater samples (Fig. 2E), the linear relations of (COD-BOD) and BOD with [DOC] were similar, indicating similar fractions of LDOC and RDOC in DOC.

In coastal water samples, the values of BOD are higher than those of (COD-BOD), indicating that COD is dominated by LDOC. If the conventional COD standards were applied to the assessment of water quality in these different ecosystems, waters of forested regions would be considered as loaded with organic pollutants. Instead, the high COD values are due to high concentrations of non-polluting humic-like RDOC compounds (see above). This shows again the drawback of relying on COD as an indicator of organic pollution in natural aquatic systems.

Taken together, the above field observations are consistent with our hypothesis that COD reflects the oxidation of both LDOC, which can be naturally degraded by microbes, and RDOC, which resists biodegradation. In contrast, BOD reflects the oxidation of LDOC only. Hence, COD is not a valid indicator of the microbial degradability of organic compounds, and thus organic pollution in aquatic environments, whereas BOD provides realistic estimates of biodegradability.

Investigating the COD Problems Experimentally

To further investigate which fractions of naturally occurring organic matter are oxidized by the COD method, samples from the 510-day incubation in the Aquatron Tower Tank were treated with/out the COD method, and FT-ICR MS analyses were used to identify differences between treatments. Results are summarized in Fig. 3, where a water sample from 1000-m depth in the South China Sea provides a reference for natural-seawater RDOC (Fig. 3G) [START_REF] Jiao | Unveiling the enigma of refractory carbon in the ocean[END_REF]. The FT-ICR MS raw peak distributions (m/z 200~600) showed no major changes in components of the samples before and after the 510-day incubation (Figs. 3A and3B, respectively), reflecting the natural richness of the river water in RDOC. In contrast, there was a large change in the FT-ICR MS peak distributions after the COD treatment of both the initial and the 510-day incubated samples (Figs. 3C and3D). This shows that most DOC molecules in the m/z range of 200-600 persisted during the 510-day incubation (Figs. 3A andB), but were oxidized by the COD treatment of both the initial and incubated water (Figs. 3A vs. 3C, and 3B vs. 3D).

The FT-ICR MS analysis provides a proxy for the naturally occurring RDOC, i.e., the polygons in Figs. 3E to 3G delineate carboxyl-rich alicyclic-like (CRAM-like) molecules based on the literature [START_REF] Hertkorn | Characterization of a major refractory component of marine dissolved organic matter[END_REF][START_REF] Hertkorn | High-field NMR spectroscopy and FTICR mass spectrometry: powerful discovery tools for the molecular level characterization of marine dissolved organic matter[END_REF]. These molecules dominate the DOC of the deep ocean, where they are identified as RDOC [START_REF] Hertkorn | Characterization of a major refractory component of marine dissolved organic matter[END_REF][START_REF] Jiao | Unveiling the enigma of refractory carbon in the ocean[END_REF][START_REF] Lechtenfeld | Molecular transformation and degradation of refractory dissolved organic matter in the Atlantic and Southern Ocean[END_REF]. Figures 3E and3F show the molecules that were oxidized by the COD method (i.e., samples in Figs. 3A minus 3C, and 3B minus 3D, respectively). A large part of the oxidized molecules corresponds to the naturally occurring CRAM-like molecules in the deep seawater (Fig. 3G).

These results demonstrate that the COD treatment rapidly oxidizes RDOC molecules that would otherwise persist for long periods as part of the carbon sink in natural waters.

Resolving Paradoxical Results Arising From Monitoring Pollution as COD

In the practical application of COD methods in natural waters, there is often a paradoxical decoupling between COD and BOD, including in situations where environmental protection measures have been carried out for decades. Representative examples are Lake Biwa in Japan [START_REF] Aoki | Determinations of humic substances and other dissolved organic matter and their effects on the increase of COD in Lake Biwa[END_REF], the Han River in Korea [START_REF] Lee | Temporal analysis of trends in dissolved organic matter in Han River water[END_REF], and Finnish rivers [START_REF] Räike | 36 year trends in dissolved organic carbon export from Finnish rivers to the Baltic Sea[END_REF] (Fig. 4). Lake Biwa was notorious for its organic pollution in the 1970s, but this pollution was reduced over the past three decades. It might therefore be expected that COD, as an indicator of organic pollution, would have decreased in response to the abatement of organic pollution. However, COD has increased since the 1980s, whereas BOD has decreased (Fig. 4A). This apparent paradox can be explained by a long-term accumulation of RDOC in the lake, which can be oxidized by the COD but not the BOD method. In cases such as Lake Biwa, environmental protection agencies should definitely use BOD instead of COD for assessing water quality. In the Han River (Fig. 4B), the COD values remained fairly constant over 20 years, whereas the BOD values continuously decreased. This means that the relative fraction of RDOC increased, while the LDOC fraction decreased over the past 20 years along with less eutrophication that would remobilize RDOC for decomposition [START_REF] Liu | An experimental study on the effects of nutrient enrichment on organic carbon persistence in the western Pacific oligotrophic gyre[END_REF]. In Finnish rivers (Fig. 4C), COD and BOD both decreased over the past decades, and since BOD decreased faster than COD, the (COD-BOD)/COD ratio increased. This indicates that the relative fraction of RDOC increased and that of LDOC decreased.

Again, these long-term observations show that COD measurements on their own do not provide consistent estimates of organic pollution, whereas the BOD values yield such estimates in all circumstances. Furthermore, COD data used alone may fail to detect, or mask, the success of environmental protection countermeasures at curbing organic pollution.

Replacing COD by a Modern BOD Method

The above analyses clearly show that COD is not a valid indicator of the microbial degradability of organic compounds, and thus organic pollution in natural waters. This is because COD reflects the oxygen demand of both LDOC and RDOC, and the latter is not, or at least is very inefficiently, oxidizable by microbes and is thus part of the carbon sink. The fact that COD confounds RDOC with organic pollution implies that it can mislead environmental assessments or the application of environmental policies. However, COD is currently used widely for the monitoring and management of natural waters [START_REF] Dubber | Replacement of chemical oxygen demand (COD) with total organic carbon (TOC) for monitoring wastewater treatment performance to minimize disposal of toxic analytical waste[END_REF][START_REF] Rawi | Biological and ecological diversity of aquatic macroinvertebrates in response to hydrological and physicochemical parameters in tropical forest streams of Gunung Tebu, Malaysia: implications for ecohydrological assessment[END_REF][START_REF] Lee | Characterization of river natural organic matter by high-performance size exclusion chromatography[END_REF][START_REF] Martínez | Chemical exergy assessment of organic matter in a water flow[END_REF]. As explained above, the present study has no bearing on the use of COD in wastewater treatment plants, but addresses its use with natural waters. Many governmental and international organizations presently rely on COD data to establish environmental standards and policies (Supplementary materials 1.2 and Table S4 andS5).

Hence, there is a pressing need to prevent the misinterpretation created by the use of COD, especially considering that the International Organization for Standardization recommended COD as one of the parameters for assessing the quality of natural waters [START_REF]International Organization for Standardization, Water quality -Determination of the chemical oxygen demand[END_REF]. Many countries use COD as the primary standard for water quality control and environmental assessment. For example, in China and Japan, the performance of environmental policies has been assessed based on the reduction of COD (Tables S4 andS5).

A substitute for the COD method is therefore required. TOC has been proposed for the monitoring of wastewater treatment and aquatic environments [START_REF] Dubber | Replacement of chemical oxygen demand (COD) with total organic carbon (TOC) for monitoring wastewater treatment performance to minimize disposal of toxic analytical waste[END_REF][START_REF] Lee | Relationships between water quality parameters in rivers and lakes: BOD5 , COD, NBOPs, and TOC[END_REF]. However, TOC measurements include RDOC, which disqualifies it as an appropriate measurement to determine the microbial degradability of organic compounds. Similarly, total oxygen demand (TOD) [START_REF] Genthe | Total Oxygen Demand (TOD) -An alternative parameter for real-time monitoring of wastewater organics[END_REF] is not an appropriate alternative to COD as it incorporates the oxidation of RDOC. In contrast, BOD has clear scientific basis, and remains the first choice as an alternative to COD. However, the traditional BOD method has been abandoned in many instances because of its labor-intensive nature, and also the variability introduced by the heterogeneity among the bottles used for oxygen titrations before and after incubation. Here we recommend a modern BOD method that uses oxygen optodes [START_REF] Edwards | Dose-dependent regulation of microbial activity on sinking particles by polyunsaturated aldehydes: Implications for the carbon cycle[END_REF][START_REF] Gregg | Biological oxygen demand optode analysis of coral reef-associated microbial communities exposed to algal exudates[END_REF] instead of oxygen titration for the measurement of oxygen concentration, where the latter can be conducted in the same incubated bottles before and after incubation (or even continuously). This reduces the number of incubation bottles, reduces procedural errors, and eliminates the use of hazardous chemicals (see Materials and Methods). The optode-based BOD measurements provide repeatable values that are more indicative of water quality than COD (Figs. S4 andS5).

From Field Measurements to Environmental Policy

The above results of our multi-country field survey and experimental studies are consistent with our hypothesis that the COD method substantially overestimates the DOC that can be degraded by microbes in natural waters, showing that the application of the COD method often misleads the assessment of organic pollution in aquatic environments. We recommend the modern optode-based BOD method to replace the COD method for application in natural aquatic environments. Such measurements are required to inform policy makers of the risks of adverse environmental conditions that can be triggered by high concentrations of LDOC, and to design and implement measures to improve water quality.

With the increasing need to monitor and manage water quality, it is imperative to refine and improve the scientific measurements on which policy depends.

MATERIALS AND METHODS

Sampling sites and data from the literature.

The field data used in this paper include new measurements made for this study and data collated from the literature. The field measurements were conducted in China (coastal water samples from the Xiamen Bay, Sanya Bay, Bohai Sea and Yellow Sea, and freshwater samples from Fujian, Sichuan and the Tibetan Plateau), Canada (Nova Scotia, New Brunswick, Quebec and British Columbia), and the USA (Florida). The sampling locations are shown in Fig. S1, and detailed information is provided in Table S1. In order to keep the data comparable, we only took from the literature COD values measured using the titration CODMn method and BOD data using the BOD5 method (described below) [START_REF] Aoki | Determinations of humic substances and other dissolved organic matter and their effects on the increase of COD in Lake Biwa[END_REF][START_REF] Lee | Temporal analysis of trends in dissolved organic matter in Han River water[END_REF][START_REF] Räike | 36 year trends in dissolved organic carbon export from Finnish rivers to the Baltic Sea[END_REF][START_REF] Hudson | Can fluorescence spectrometry be used as a surrogate for the Biochemical Oxygen Demand (BOD) test in water quality assessment? An example from South West England[END_REF][START_REF] Lee | Comparison of BOD, COD, TOC and DOC as the Indicator of Organic Matter Pollution of Agricultural Surface Water in Gyeongnam Province[END_REF][START_REF] Jin | A study on the relationship between BOD(5) and COD in a coastal seawater environment with a rapid BOD measurement system[END_REF][START_REF] Li | Influence of salinity on COD measurements in coastal water management[END_REF].

Potassium dichromate oxidation (CODCr) method

There are two methods for COD measurements: one with potassium dichromate as oxidant (CODCr) and the other with potassium permanganate (CODMn, which has two variants or protocols described next section). The measurement of CODCr was based on the International Standard ISO6060 "Determination of Chemical Oxygen Demand in Water" (https://www.iso.org/standard/12260.html), which was officially reviewed and confirmed in 2017. Briefly, 10 mL of a water sample was added into a 250 mL conical flask. Five mL K2Cr2O7 solution was added into the water and thoroughly mixed. After connecting the conical flask into a condensing system, 15 mL AgSO4-H2SO4 solution was slowly added into the mixture, which was then heated to boiling point for 2h. After that, about 45 mL of ultra-pure water was added. Cooling to room temperature, one or two drops of ferroin were added into the mixture to serve as a titration indicator, and titration was conducted with (NH4)2Fe(SO4)2•6H2O solution and ended after the color of the mixture turned brown. The consumed volume of (NH4)2Fe(SO4)2•6H2O solution was recorded as V1 (mL). As a control, 10 mL ultra-pure water was used for the titration described above, and the consumed volume of the (NH4)2Fe(SO4)2•6H2O solution was recorded as V0 (mL).

CODCr (mg O2 L -1 ) was calculated from the following equation: CODCr=(c((NH4)2Fe(SO4)2•6H2O)×(V0-V1) × 8000)/10. Triplicate water samples were analyzed at each station.

Potassium permanganate oxidation (CODMn) protocols

There are two CODMn protocols in the literature: alkaline CODMn and acidic CODMn protocols. The alkaline CODMn protocol is described in the Chinese national standard method of COD for seawater (GB1737. and uses KMnO4 as oxidant in alkaline conditions. Briefly, a 100 mL water sample was added into a 250 mL conical flask. One mL NaOH (250 g L -1 ) and 10 mL KMnO4 solution (0.01 mol L -1 ) were added to the water and thoroughly mixed. The mixture was heated to boil for 10 min, after which it was quickly cooled to room temperature. After this, 5 mL H2SO4 solution (5 mol L -1 ) and 0.5 g KI were added to the cooled mixture, which was then placed in the dark for 5 min. With the solution continuously shaken, titration was conducted with Na2S2O3•5H2O solution (c≈0.01 mol L -1 , calibrated by KIO3 standard solution) until the color of the mixture turned light yellow. Then, 1 mL of starch solution (5 g L -1 ) was added to the mixture and the titration was continued until the mixture turned transparent. The consumed volume of the Na2S2O3•5H2O solution was recorded as V1 (mL). As a control, 100 mL ultra-pure water was used for the titration described above, and the consumed volume of the Na2S2O3•5H2O solution was recorded as V0 (mL). Triplicate water samples were analyzed at each station. CODMn (mg O2 L -1 ) was calculated using the following equation:

Alkaline CODMn=(c(Na2S2O3•5H2O)×(V0-V1)×8000)/100
The acidic CODMn protocol is described in the Chinese national standard method of COD for surface water (GB11892-89) and uses KMnO4 as oxidant in acidic conditions. Briefly, a 100 mL water sample was added into a 250 mL conical flask. Five mL H2SO4 solution (5 mol L -1 ) and 10 mL KMnO4 solution (0.01 mol L -1 ) were added to the water and evenly mixed. The mixture was heated for 30 min in a boiling water bath. Ten mL Na2C2O4 solution (0.01 mol L -1 ) was then added to the mixture, which turned transparent. With the solution continuously shaken, the titration was conducted with KMnO4 solution (0.01 mol L -1 ) until the color of the mixture turned pink, and remained pink for at least 30 sec. The consumed volume of the KMnO4 solution was recorded as V1 (mL). As a control, 100 mL ultra-pure water was used for the titration described above,10 mL Na2C2O4 solution (0.01 mol L -1 ) was added to the mixture, KMnO4 solution was used as the titrant, and the consumed volume recorded as V0 (mL). Triplicate water samples were analyzed at each station. Acidic CODMn (mg O2 L -1 ) was calculated using the following equation:

Acidic CODMn=((10+V1)×10/V0-10)×c(KMnO4)×8000)/100
The acidic CODMn protocol is recognized to be applicable for freshwater but not saline water, because of a salinity effect, i.e. an over-estimation due to interference of inorganic ions [START_REF] Rho | Optimization of chemical oxygen demand determination in seawater samples using the alkaline potassium permanganate method[END_REF][START_REF] Goh | Potassium permanganate as oixdant in the COD test for saline water samples[END_REF][START_REF] Di | Effect of chlorine concentration on determining COD by acid and alkaline potassium permanganate[END_REF]. We investigated this by comparing results from the acidic and alkaline protocols for samples with salinities ranging from 0 to 30 (Table S3). The data from the acidic CODMn protocol were systematically larger than those obtained with the alkaline CODMn protocol for salinities >0.3, and the difference increased with salinity (Fig. S3). In fresh waters (salinities ≤0.3), some data from the acidic CODMn protocol were smaller than those from the alkaline CODMn protocol. Since both methods use excessive amounts of oxidants, they are unlikely to produce underestimated values. Given that the data in this study came from freshwater, estuarine and marine environments, we decided to use only the values obtained with the alkaline CODMn protocol, in order to avoid overestimation of COD values in estuarine and saline waters.

Comparison of CODCr and alkaline CODMn measurements

The CODCr and alkaline CODMn measurements of natural water samples with different salinities showed that the COD values from the two methods were not influenced in the same way by the presence of inorganic reductants (Table S2).

The values of CODCr were significantly higher than those of CODMn in all samples, the former being up to 80 times the latter in saline waters (Fig. S2A). In addition, the coefficients of correlation of CODCr and CODMn with salinity (r = 0.94, prob < 0.001, and r = 0.54, prob = 0.075, respectively) indicate a significant positive relationship between CODCr and salinity, and no relationship between CODMn, and salinity. As a consequence, the ratio CODCr/CODMn significantly increased with salinity (Fig. S2B). These results show that salinity had a systematic effect on CODCr measurements.

It follows that the CODCr does not provide a proper measure of oxidized organic matter especially in saline waters, whereas alkaline CODMn may provide realistic estimates of oxidized DOC. Hence in order to make all our data comparable, we only used CODMn measurements, either made for this study or collated from the literature (as listed in Table S1). To simplify terminology, we use "COD" instead of "alkaline CODMn" in this paper.

Protocol of Biochemical Oxygen Demand (BOD) by the BOD5 Winkler method

Five-day BOD (BOD5) measurement of oxygen consumption by Winkler titration is a standard method by the International Organization for Standardization. Water samples for BOD measurements were incubated in the dark at 20°C for 5 days. The measurement of dissolved oxygen ([O2]) was based on the ISO 5813:1983 Determination of dissolved oxygen-Iodometric (Winkler) method (https://www.iso.org/obp/ui/#iso:std:iso:5813:ed-1:v1:en). Briefly, to determine the initial oxygen concentration, 1 mL of MnCl2 solution (420 g L -1 ) and 1 mL of alkaline KI solution (150 g L -1 ) were sequentially added into triplicate water samples in 140 mL opaque BOD bottles, then mixed thoroughly and the samples allowed to sit in a water bath held at 20°C for at least 4 h. Thereafter, the precipitate was dissolved with, 2 mL of H2SO4 solution (5 mol L -1 ). After transferring 100 mL of the solution into a 250 mL conical flask, the titration was conducted as described for the CODMn method, and the titrated volume of Na2S2O3•5H2O solution (c, mol L -1 ) recorded as V0 (mL).

After the 5-day incubation, triplicate water samples were collected and treated by the same procedure as described above, and the titrated volume of Na2S2O3•5H2O solution was recorded as V5 (mL). BOD5 (mg O2 L -1 ) was calculated using the following equation:

BOD5=c(Na2S2O3•5H2O) × (V0-V5) ×1000×8/100
Calibration for concentration of Na2S2O3•5H2O standard solution: 10 mL KIO3 standard solution (0.0100 mol L -1 ) were poured into a 250 mL conical flask, and 0.5 g KI and 1 mL H2SO4 solution (5 mol L -1 ) were sequentially added to the flask, which was then sealed and gently shaken. After keeping the mixture in the dark for 2 min, 50 mL of ultra-pure water were added and gently mixed, in preparation for titration. The titration was conducted with a Na2S2O3•5H2O solution until the color of the mixture turned light yellow, after which 1 mL of starch solution (5 g L -1 ) was added, and the titration was continued until the mixture became transparent. The consumed volume of the Na2S2O3•5H2O solution was recorded as V (mL). The concentration of the Na2S2O3•5H2O solution (mol L -1 ) was calculated using the following equation:

c(Na2S2O3•5H2O)=10×0.0100/V

Determination of dissolved organic carbon (DOC)

Samples for DOC measurements were filtered through pre-combusted (450°C, 4 h) Whatman GF/F glass fiber filters. All the samples were then acidified to pH=2 with H3PO4 and stored at -20°C until analyzed. The carbon content of samples was measured using the high-temperature combustion method with a Shimadzu TOC-LCPH TOC analyzer and ultrapure water for system blank subtraction [START_REF] Callahan | Distribution of dissolved organic matter in the Pearl River Estuary, China[END_REF]. Reference deep seawater (provided by the Hansell Organic Biogeochemistry Laboratory at the University of Miami, USA) served as an additional control [START_REF] Cai | Microbial processing of sediment-derived dissolved organic matter: implications for Its subsequent biogeochemical cycling in overlying seawater[END_REF]. The analytical methodology for DOC was the same for all studies reported in our work.

Aquatron Tower Tank and long-term incubation experiments

Located at Dalhousie University (Halifax, Canada), the Aquatron is the site of the Tower Tank, which is 10. [START_REF] Lapierre | Increases in terrestrially derived carbon stimulate organic carbon processing and CO2 emissions in boreal aquatic ecosystems[END_REF]

Solid-phase extraction of natural DOM and COD treated DOM, and Fourier Transformation Cyclotron

Resonance Mass Spectrometry (FT-ICR MS) analysis DOM was solid-phase extracted following a standard procedure [START_REF] Dittmar | A simple and efficient method for the solid-phase extraction of dissolved organic matter (SPE-DOM) from seawater[END_REF]. For the initial (DOC concentration = 885.96±6.60 μmol C L -1 ) and long-term incubation (510-day) (DOC concentration = 744.06±6.80 μmol C L -1 ), DOM was extracted from water samples using 500 mg Bond Elut-PPL cartridges (Agilent) activated with HPLC grade methanol (Merck) and rinsed with acidified Milli-Q water (pH=2). An aliquot of 200 mL water sample was filtered through a pre-combusted (450°C) GF/F glass fiber filter and then passed by gravity through the cartridge, which was subsequently extensively rinsed with acidified Milli-Q water (pH=2) and completely dried before elution with HPLC grade methanol. Each cartridge was eluted with 3 mL HPLC grade methanol, and this DOM extract was adjusted to yield almost the same DOC concentration for each sample before injecting to the FT-ICR MS for analysis.

To investigate which fractions of naturally occurring organic matter were oxidized by the COD method, the initial and long-term incubation of river water samples were subjected to CODMn prior to DOM extraction. Two mL NaOH solution (250 g L -1 ) and 20 mL KMnO4 solution (0.01 mol L -1 ) were added to a 200 mL aliquot of the water sample in a 500 mL conical flask. The mixture was then heated to boil for 10 min. After cooling down to room temperature and being neutralized by HCl (HPLC grade) to pH=7, 0.02 g of NaHSO3 (Sigma-Aldrich) was added to the mixture to reduce the residue KMnO4. Then DOM was extracted using the standard solid phase extraction procedure described above. As a control, 200 mL Milli-Q water was used for the COD treatment described above, and DOM molecules commonly existing in the control and the CODMn treated water samples were excluded from further analyses. All the glassware used for COD treatments was acid cleaned and combusted (480°C for 4 h).

The DOM extracts were adjusted to yield approximately 25 mM DOC and analyzed using a Bruker Apex Ultra FT-ICR mass spectrometer equipped with a 9.4 T superconducting magnet. Sample solutions were infused via an Apollo II electrospray ion source (ESI) at 180 μL h -1 with a syringe pump. Typical operating conditions for negative ESI were as follows: spray shield voltage 3.5 kV, capillary column initial voltage 4 kV, and capillary column end voltage -320 V.

The mass range was set to m/z 150-800. The 4M word size was selected for the time domain signal acquisition. A number of 128-time domain signals were co-added to enhance the signal-to-noise ratio and dynamic range. The magnitude threshold for the peak assignment was set to a signal-to-noise ratio of ≥4. The FT-ICR MS was calibrated using a known homologous series of the Suwannee River natural organic matter sample (obtained from the International Humic Substances Society, USA), which contained a relatively high abundance of oxygen-containing compounds. Molecular formulae were assigned with the calibrated mass data using in-house software [START_REF] Cai | Microbial processing of sediment-derived dissolved organic matter: implications for Its subsequent biogeochemical cycling in overlying seawater[END_REF]. The elemental compositions were assigned from the m/z peaks using a mass calculator program limited to molecular formulae consisting of 12 C0-100, 13 C0-2, 1 H0-200, 14 N0-5, 16 O0-35, 32 S0-2 and 34 S0-1 [START_REF] Cai | Microbial processing of sediment-derived dissolved organic matter: implications for Its subsequent biogeochemical cycling in overlying seawater[END_REF][START_REF] Li | Stratification of dissolved organic matter in the upper 2000 m water column at the Mariana Trench[END_REF]. All assigned formulae had to meet the following basic chemical criteria:

(1) the number of H atoms should not exceed 2C + N + 2; (2) the sum of H and N atoms should be even (the "nitrogen rule"); and (3) the number of N or O atoms should not exceed the number of C atoms [START_REF] Kujawinski | Automated analysis of electrospray ionization Fourier transform ion cyclotron resonance mass spectra of natural organic matter[END_REF][START_REF] Koch | Fundamentals of molecular formula assignment to ultrahigh resolution mass data of natural organic matter[END_REF].

Proposed new technique for BOD determination: The oxygen-optode BOD method

Oxygen optodes are optical sensors that are based on a chemical indicator (dynamic fluorescence quencher) [START_REF] Xu | Oxygen sensors based on luminescence quenching: interactions of metal complexes with the polymer supports[END_REF]. These robust and high-resolution sensors [START_REF] Wang | Optical methods for sensing and imaging oxygen: materials, spectroscopies and applications[END_REF][START_REF] Wikner | Precise continuous measurements of pelagic respiration in coastal waters with Oxygen Optodes[END_REF] have been used to study a number of oxygen-related processes in freshwater and marine systems [START_REF] Medina-Sánchez | Optode use to evaluate microbial planktonic respiration in oligotrophic ecosystems as an indicator of environmental stress[END_REF][START_REF] Garcia-Robledo | Determination of Respiration Rates in Water with Sub-Micromolar Oxygen Concentrations[END_REF][START_REF] Collins | An autonomous, in situ light-dark bottle device for determining community respiration and net community production: Autonomous observations of community metabolism[END_REF][START_REF] Edwards | Rapid microbial respiration of oil from the Deepwater Horizon spill in offshore surface waters of the Gulf of Mexico[END_REF][START_REF] Collins | The multiple fates of sinking particles in the North Atlantic Ocean[END_REF], yielding the same sensitivity as the chemical Winkler method [START_REF] Holtappels | Aquatic respiration rate measurements at low oxygen concentrations[END_REF][START_REF] Warkentin | New and fast method to quantify respiration rates of bacterial and plankton communities in freshwater ecosystems by using optical oxygen sensor spots[END_REF]. For example, oxygen optodes have already been used in oceanography for measurements of community respiration rates in the mesopelagic and surface waters of the oligotrophic open ocean [START_REF] Medina-Sánchez | Optode use to evaluate microbial planktonic respiration in oligotrophic ecosystems as an indicator of environmental stress[END_REF][START_REF] Garcia-Robledo | Determination of Respiration Rates in Water with Sub-Micromolar Oxygen Concentrations[END_REF][START_REF] Collins | An autonomous, in situ light-dark bottle device for determining community respiration and net community production: Autonomous observations of community metabolism[END_REF]. They have also been used for measuring BOD in wastewater [START_REF] Preininger | Optical fiber sensor for biological oxygen demand[END_REF][START_REF] Recoules | A MEMS approach to determine the biochemical oxygen demand (BOD) of wastewaters[END_REF]. Here, we propose a novel, modern BOD method using oxygen optodes instead of the traditional Winkler titration for the measurement of dissolved oxygen concentration [O2] in the BOD bottles. Oxygen optodes allow the recording of oxygen consumption in the incubated BOD bottles continuously in a non-invasive and non-destructive manner, so that the initial and final dissolved oxygen determinations can be conducted in the same BOD bottles (Fig. S4). This is a major advantage over the Winkler method, where the initial and final titrations must be done on different bottles. After proper calibration of the optodes (see below), the [O2] and thus BOD values meet the quality standard of those obtained with the conventional Winkler titration method.

A detailed description of the BOD optode method will be provided in a separate specialized paper. Briefly, the steps are:

(1) water sampling and filling of BOD bottles, which are incubated for 5 days; (2) oxygen optode measurements in triplicate incubated BOD bottles, and (3) calculation of BOD5 (where the index 5 stands for 5 days) from oxygen-optode measurements.

Before using the OXSP5 optodes (PyroScience, Germany) for measuring O2 in samples, we calibrated them with Winkler titrations. We used a Metrohm auto-titrator (888 Titrando with a combined platinum ring electrode) for the Winkler O2 determinations. The 5-day incubations were conducted at 20 ± 0.1°C. Winkler O2 was measured at t=0, every second hour until t=8 hours, and every day until day 5 on triplicate bottles during the course of the incubation (total of 30 bottles). The optode signal was measured every 10 min in triplicate bottles during the course of the incubation, with one measurement every 3 s during 2.5 min to acquire 50 values at each Winkler sampling point (total of 3 bottles).

We tested the validity of the optode BOD method on a variety of natural freshwater and seawater samples. One example was from the DaRen River, Qingdao, China. The results showed good agreement between the optode and Winkler measurements (Fig. S5). The coefficient of variation (CV) of the measurements by the optode method (CV = 0.0824; n=145) was an order of magnitude lower than that of the Winkler method (CV =0.4472; n=42), indicating that the novel oxygen-optode approach can improve the reliability of the oxygen measurement in a BOD determination.

In summary, the novel oxygen-optode BOD method offers several advantages over the traditional Winkler method.

Firstly, it provides a more precise measurement of BOD, i.e., replicates from a given sample measured by the optode method reveal a much smaller standard deviation than the respective Winkler replicates do. Secondly, it eliminates the chemical waste associated with the Winkler method as well as the need to take into the field chemicals that are toxic to aquatic life such as alkaline iodide azide and MnCl2. Thirdly, it removes operator error associated with labor-intensive titrations.

sampling and data mining are given in the Materials and Methods, Fig. S1 and Tables S1, S2 and S3. The mass spectrometry data are available on PANGAEA (doi.pangaea.de/10.1594/PANGAEA.910007). We are grateful to the three anonymous reviewers who provided very useful suggestions, comments and criticisms on our manuscript. Persistent organic pollutants (POPs) are organic compounds that persist in the environment for a long time, migrating in the air, water and soil and accumulating in sediments and food webs [START_REF] Jones | Persistent organic pollutants (POPs): state of the science[END_REF][START_REF] El-Shahawi | An overview on the accumulation, distribution, transformations, toxicity and analytical methods for the monitoring of persistent organic pollutants[END_REF]. The POPs are mainly man-made chemicals that can be divided into intentionally produced compounds (including industrial chemicals and organochlorine pesticides) and unintentional substances resulting from the combustion of organic compounds. The total concentration of recognized POPs in natural waters is usually very low, but can be high in some polluted areas, ranging from a few to thousands of nanograms per liter in natural waters [START_REF] Zhang | Fate and assessment of persistent organic pollutants in water and sediment from Minjiang River Estuary, Southeast China[END_REF][START_REF] Loos | EU-wide survey of polar organic persistent pollutants in European river waters[END_REF]. Although the COD method can oxidize many POPs [START_REF] Yan | Oxidative degradation and kinetics of chlorinated ethylenes by potassium permanganate[END_REF][START_REF] Bastos | A standardized method for assessment of oxidative transformations of brominated phenols in water[END_REF], the total amount of carbon in POPs is trivial compared to natural DOC concentrations [START_REF] Hansell | Dissolved organic matter in the ocean: new insights stimulated by a controversy[END_REF][START_REF] Lapierre | Increases in terrestrially derived carbon stimulate organic carbon processing and CO2 emissions in boreal aquatic ecosystems[END_REF].

Applications of COD to water quality assessment, and related standards and policy

The COD methods have been used for nearly a century around the world for water quality assessment [START_REF] Dubber | Replacement of chemical oxygen demand (COD) with total organic carbon (TOC) for monitoring wastewater treatment performance to minimize disposal of toxic analytical waste[END_REF]. The International Organization for Standardization recommends COD as one of the parameters for assessing the quality of natural waters [START_REF]International Organization for Standardization, Water quality -Determination of the chemical oxygen demand[END_REF]. In many countries, such as China and Japan, reduction of COD in aquatic environments is used as a measure of environmental policy performance in the evaluation of governmental accomplishments (Table S4). Scientists from different countries also use COD as an indicator to monitor natural waters (examples are shown in the references of [START_REF] Lee | Relationships between water quality parameters in rivers and lakes: BOD5 , COD, NBOPs, and TOC[END_REF][START_REF] Rho | Optimization of chemical oxygen demand determination in seawater samples using the alkaline potassium permanganate method[END_REF][START_REF] Vidal-Durà | Reoxidation of estuarine sediments during simulated resuspension events: Effects on nutrient and trace metal mobilisation[END_REF][START_REF] Nandini | Water quality indicators in Lake Xochimilco, Mexico: zooplankton and Vibrio cholerae[END_REF][START_REF] Lian | Analysis of spatial variability of water quality and pollution sources in Lihe River Watershed, Taihu Lake Basin[END_REF][START_REF] Ueda | Long-term variations in water quality of lakes in Rokkasho, Aomori, Japan, from 2004 to 2015 species[END_REF][START_REF] Ahmad | Improving water quality index prediction in Perak River basin Malaysia through a combination of multiple neural networks[END_REF][START_REF] Kim | Chemical oxygen demand (COD) model for the assessment of water quality in the Han River, Korea[END_REF]. In addition, various countries (for example Japan, China, and Malaysia) categorize natural waters based on their COD values (Table S5).

Chemical oxygen demand, and dissolved organic carbon: A Google Scholar search

In a Google Scholar search conducted on January 26, 2020, there were 2,020,000 returns for "chemical oxygen demand" and 210,000 for a combination of "chemical oxygen demand" and "dissolved organic carbon". This shows the great interest of the community for the chemical oxygen demand, and the close connection of this topic with dissolved organic carbon.

Terminology of organic carbon

Total organic carbon (TOC) includes particulate organic carbon (POC) and dissolved organic carbon (DOC), the latter two being operationally differentiated based on filtration of water samples. Although the proportion of POC and DOC in a given water sample varies with the pore size of the filters used by the investigators, the term DOC is generally found in the scientific literature. Given the fact that DOC accounts for the majority of the TOC and is even equivalent to TOC in many waters, we only use DOC for the discussion in this study [START_REF] Hansell | Index -biogeochemistry of marine dissolved organic matter (second edition)[END_REF][START_REF] Yahel | In situ feeding and element removal in the symbiont-bearing sponge Theonella swinhoei: Bulk DOC is the major source for carbon[END_REF].

Supplementary figures

Supplementary tables

Fig. 1 .

 1 Fig. 1. Schematic representation of LDOC and RDOC in the environment, and their determination using the BOD and COD approaches. Degradation of LDOC can cause oxygen depletion, water acidification and CO2 emission, and is best assessed by the BOD method. Only LDOC is readily degradable by microbes in natural waters; RDOC has no adverse environmental effects and is a carbon sink. The widely used COD method oxidizes both LDOC and RDOC, and so could result in a major error in the assessment of organic pollution.

Fig. 2 .

 2 Fig. 2. COD and BOD as a function of [DOC] in different environments: (A) high latitudes/subpolar areas, (B) midlatitudes/temperate zone, (C) low latitudes/subtropical zone, (D) forested watersheds, (E) fresh waters, (F) seawater. Note the different X and Y-axes scales of the different panels. The same samples were grouped by (A-C) latitudes and also (D-F) ecosystems. Regressions: Model 2 Standard Major Axis. n: number of samples.

Fig. 3 .

 3 Fig. 3. Results of FT-ICR MS analyses showing changes in organic matter in the natural and incubated river water samples before and after COD treatment. Panels A and B are the raw peak distributions (m/z 200~600) of organic matter in the water samples before and after long-term (510 days) incubation, respectively, showing no major differences before and after long-term microbial degradation. Panels C and D show the raw peak distributions (m/z 200~600) of remaining organic matter after the COD treatment of the same samples as in panels A and B, respectively. Panels E and F show the van Krevelen diagrams of the RDOC-like components oxidized by the COD treatment, i.e., samples in panels A minus C, and B minus D, respectively. Panel G is a deep-water (1000-m) sample from the South China Sea, used as a RDOC reference. The black polygons in panels E to G delineate the CRAM-like molecules (RDOC proxy), based on the literature.

Fig. 4 .. Supplementary statements 1 . 1 .

 411 Fig. 4. Three representative field cases of long-term trends in COD and BOD changes: (A) increase in COD and decrease in BOD, and thus accumulation of RDOC (as indicated by [COD-BOD]) in Lake Biwa, Japan; (B) sharp decrease in BOD and relatively stable COD, and thus accumulation of RDOC in the Han River, Korea; and (C) decrease in both BOD and COD, but increase in the fraction of RDOC in Finnish rivers

Fig. S1 .

 S1 Fig. S1. Locations of the sampling sites (triangles) and data from the literature (circles).

Fig. S2 .

 S2 Fig. S2. (A) CODCr and alkaline CODMn as a function of salinity, and coefficients of linear correlations, and (B) ratio CODCr/CODMn as a function of salinity, coefficient of linear correlation and equation of Model 2 Standard Major Axis regression. n: number of samples.

Fig. S3 .

 S3 Fig. S3. Acidic minus alkaline CODMn as a function of salinity. Coefficient of linear correlation and equation of Model 2 Standard Major Axis regression. n: number of samples.

Fig. S4 .

 S4 Fig. S4. System to measure dissolved oxygen concentrations with oxygen optodes in triplicate. (A) Computer with the data logging software (Pyro Oxygen logger) used for O2 measurements. (B) Optical fiber sensors attached to the outside of BOD bottles with Pyroscience optodes installed internally, and connected to an optical oxygen meter (FireStingO2) through which the signals are transmitted to the computer. (C) Diagram depicting how O2 measurements are made using red light to excite the REDFLASH dye within the optode/sensor spot in contact with the water sample inside the BOD bottles. The dye emits infrared light that is channeled by the optical fiber into the optical oxygen meter, where it is converted into O2 concentration (Modified from diagrams on the Pyroscience website: www.pyroscience.com). Photo Credit: Jihua Liu, Institute of Marine Science and Technology, Shandong University, Qingdao, China.

Fig. S5 .

 S5 Fig. S5. Comparison of the new optode-based and usual Winkler BOD methods. Oxygen concentrations measured with the two methods during the course of the 5-day incubation. The incubated water was from the DaRen River, Qingdao, China. The two inset figures provide details of measurements with the two methods from 4.7 to 8.1 h, and 47.8 to 48.6 h.

  To conduct our long-term incubation experiment, we filled the Tower Tank with about 20 tons of humic-like water from the Ingramport River near Halifax. The incubation was run at room temperature (20°C) in the dark for 510 days. Water samples were taken before and after the incubation, and filtered through Whatman GF/F filters (pre-combusted as above) for solid-phase extraction of dissolved organic matter (see below).

											m deep
	and	3.66 m	in	diameter,	with	an	approximate	water	volume	of	117 m 3 ,
	(https://www.dal.ca/faculty/science/biology/research/facilities/aquatron.html). It is well suited for biogeochemical
	research requiring depth and stratification.

Table S1 . Sampling locations and data sources.

 S1

	Locality Locality Locality Locality Locality	Latitude (°N) Longitude (°E) DOC (mg/L) COD (mg/L) BOD (mg/L) COD-BOD (mg/L) Source Latitude (°N) Longitude (°E) DOC (mg/L) COD (mg/L) BOD (mg/L) COD-BOD (mg/L) Source Latitude (°N) Longitude (°E) DOC (mg/L) COD (mg/L) BOD (mg/L) COD-BOD (mg/L) Source Latitude (°N) Longitude (°E) DOC (mg/L) COD (mg/L) BOD (mg/L) COD-BOD (mg/L) Source Latitude (°N) Longitude (°E) DOC (mg/L) COD (mg/L) BOD (mg/L) COD-BOD (mg/L) Source
	St. Austell River Downstream Of, UK R Wey At Nottington, UK North Brook At Northbrook Park, UK Congres Yeo Tutshill, UK	50.33 50.64 51.20 51.40		-4.79 -2.48 -0.85 -2.40	5.45 3.09 4.32 1.52 3.40	6.64	3.10 1.63 3.38 1.09 1.37	3.54	This study (32) (32) (32) (32)
	Locality Shubenacadie River, Canada St Austell At Pentewan Bridge, UK River Wey At Broadwey, UK Misc Lower Frome, UK Congres Yeo Iwood, UK	Latitude (°N) Longitude (°E) DOC (mg/L) COD (mg/L) BOD (mg/L) COD-BOD (mg/L) Source 44.93 -63.54 6.18 7.60 2.09 5.51 This study 50.33 -4.79 2.35 1.11 (32) 51.15 -0.80 3.13 1.33 (32) 51.23 -2.32 5.98 1.54 (32) 51.36 -2.79 5.96 1.59 (32)
	Text Fig. 2 Rodden D/S Langton Herring Stw, UK River Wey At Broadwey, UK Mill Stream D/S Horsepool, UK Carminowe Creek Inflow To Loe Pool,	50.64 NA 51.16 50.07		-2.55 NA -2.73 -5.28	6.85 1.16 0.30 5.59 5.41	7.84	1.59 1.73 1.88 1.49 3.85	6.33	This study (32) (32) (32) (32)
	Ingramport River, Canada River Wey At Radipole, UK River Tone Coalharbor Br, UK Medlyn Stream At Chy Bridge, UK UK	44.67 50.61 NA NA	(A) High latitudes / Subpolar region -63.97 11.56 12.00 -2.46 2.40 NA 5.27 NA 3.55	1.23 3.55 1.07		This study (32) (32) (32)
	Richibucto River, Canada River Wey At Nottington, UK River Tone Clatworthy, UK Lt. Dart R., D/S Chawleigh Stw, UK By Brook Middlehill, UK	46.65 50.61 51.02 50.91 51.42		-64.85 -2.46 -3.14 -3.83 -2.27	3.00 7.97 2.28 1.72 5.59 0.89	7.40 9.83	2.62 2.39 1.20 1.44 1.11 1.05	4.78 7.53	This study This study (32) (32) (32) (32)
	Miramichi Bay, Canada Mersey River, Canada River Mole D/S South Molton Stw, UK River Tone Bishops Hull, UK Lt. Dart R., D/S Chawleigh Stw, UK By Brook Fosse, UK	47.08 44.43 51.24 51.02 NA 51.50		-65.21 -65.21 -0.31 -3.14 NA -2.24	3.41 6.71 1.80 2.41 3.09 4.40	6.60 10.20	3.09 2.42 1.08 1.43 1.07 1.01	3.51 7.78	This study This study (32) (32) (32) (32)
	Chaleurs Bay, Canada River Cober D/S Helston Stw, UK River Tone Bathpool, UK Huish Trout Farm Easthill Inlet, UK By Bk Long Dean Mill, UK	47.99 50.09 51.04 50.79 51.48		-66.74 -5.28 -3.05 -2.52 -2.22	3.08 8.08 2.99 4.15 1.10 0.92	3.20 10.18	1.96 2.48 1.19 1.56 1.36 1.01	1.24 7.88	This study This study (32) (32) (32) (32)
	Kejimkujik Lake, Canada River Char At Whitchurch Canonicorum, River Brue Cow Bridge, UK Hancocks U/S St Ivel, UK Back Stream Fitzroy, UK	44.38 50.75 51.13 50.12 51.04		-65.21 -2.86 -2.71 -5.29 -3.15	3.78 6.08 3.49 1.10 5.17 0.66	5.23 9.80	1.55 2.29 1.17 1.81 3.76 3.17	3.76 7.51	This study This study (32) (32) (32) (32)
	Lac Matapedia, Canada Shubenacadie Grand Lake, Canada UK River Banwell At M5 M'way, UK Halse Water Tyler Br, UK Avon Scotland Road, UK	48.54 44.89 51.36 51.05 NA		-67.56 -63.6 -2.90 -3.25 NA	3.86 4.29 0.77 0.99 1.30	5.60 5.20	1.49 1.49 1.94 1.76 2.24	4.11 3.71	This study This study (32) (32) (32)
	Lake Major, Canada Ramsgate (Reen) Roseworthy, UK River Banwell @ M5 M'way, UK Halse W Norton Br, UK Avon Melksham Bypass, UK	44.72 50.20 51.36 52.87 51.37		-63.48 -1.25 -2.90 -2.19 -2.14	3.90 3.96 3.54 2.96 1.38 1.99	5.67 8.60	1.55 1.82 1.72 1.38 2.41 2.92	4.12 6.78	This study This study (32) (32) (32) (32)
	Saint Lawrence River, Canada Pockwock Lake, Canada River Yeo (Lapford)-Bow Bridge, UK River Banwell @ Ebdon Br, UK Halse W Halse G Stn, UK Avon Malford Church, UK	48.45 44.78 50.86 51.38 51.25 51.50		-68.52 -63.84 -3.81 -2.92 -3.13 -2.06	3.45 6.73 5.54 5.04 0.78 0.81	5.00 6.20	1.42 2.16 1.29 1.86 1.76 1.55	3.58 4.04	This study This study (32) (32) (32) (32)
	Yealm River D/S Yealmpton Stw, UK River Wey At Radipole, UK River Axe Loxton, UK Halse W Asylum Br, UK Avon Lacock, UK	50.38 50.63 51.29 51.25 51.42		-3.97 -2.47 -2.90 -3.13 -2.12	4.01 2.55 1.64 5.90 1.02 2.27	6.24	2.21 1.30 2.13 1.78 2.13 1.74	4.12	This study (32) (32) (32) (32) (32)
	Montmorency River, Canada Yealm At Yealm Bridge, UK River Teign-Preston, UK River Axe Henley Hill, UK Gwithian Towans Red River, UK At Austell At Molingey G Stat, UK	46.89 50.31 50.62 53.50 50.22 50.20		-71.15 -4.07 -3.64 -1.40 -5.39 -4.48	1.79 2.51 3.30 3.49 2.72 2.81	5.80	2.29 1.08 2.46 1.07 3.53 1.67	3.51	This study (32) (32) (32) (32) (32)
	Grand Falls Dam, Canada Yealm At Puslinch Bridge, UK Tort. Bk. Falfield, UK Tn Bwtr C N Newton, UK River Teign-Chudleigh Bridge, UK River Teign D/S Heathfield Tip, UK River Axe Clewer, UK Frome U/S Louds Mill, UK Alphin Brook At Countess Wear Bridge, UK River Char U/S Whitchurch Can., UK River Axe, UK River Avill Frackford Br, UK Exeter Canal At A38 Bridge Countess, UK	45.28 50.34 51.64 50.31 50.62 50.62 51.26 50.71 50.70 50.75 51.27 51.16 50.69	(B)	-67.48 -4.01 -2.45 -3.35 -3.64 -3.64 -2.80 -2.41 -3.50 -2.86 -2.83 -3.51 -3.49	4.18 4.60 4.70 2.77 4.21 4.57 3.50 3.50 4.17 5.76 0.76 3.76 2.40 1.74 1.85	5.80 7.80 7.66	2.30 1.49 2.13 1.08 2.04 2.52 1.59 1.85 1.56 1.11 1.12 1.53 1.05 1.19 1.33	3.54 6.31 5.58	This study This study This study (32) (32) (32) (32) (32) (32) (32) (32) (32) (32) (32) (32)
	Saint John River, Canada T R Avill Timberscom, UK River Yeo Yeovil Bridge, UK Point Above South Crofty R, UK Ebble U/S Chalkvalley Inlet Rside, UK	47.06 51.17 50.94 50.22 51.04		-67.78 -3.5 -2.61 -5.28 -1.87	3.67 3.55 2.44 3.54 0.46	2.20	2.02 1.07 1.10 1.13 1.08	0.18	This study (32) (32) (32) (32)
	T River Avill Timberscom, UK River Yeo Yeovil Bridge, UK Pill R Blue Anchor, UK Ebble D/S Chalkevalley, UK	NA 50.94 51.18 51.02		NA -2.61 -3.39 -1.99	4.82 5.90 2.44 0.74 4.14	7.44	2.66 1.30 1.10 1.44 1.34	4.83	This study (32) (32) (32) (32)
	Bay of Fundy, Canada Sydling Water, Downstream Shearplace, River Yeo Over Compton, UK Pill R Bilbrook Ford, UK Dawlish Water At Dawlish, UK	45.81 50.22 50.93 51.15 50.60		-64.57 -5.39 -2.60 -3.40 -3.51	0.74 2.55 3.31 0.64 4.14	10.20	3.36 1.21 3.91 2.71 1.34	6.84	This study (32) (32) (32) (32)
	UK River Yeo Goldings Lane, UK Penponds Roseworthy Stream, UK Congresbury Yeo A370, UK	51.00 50.21 51.37		-2.65 -5.32 -2.81	4.82 0.52 3.22 0.77	5.05	1.46 1.76 1.58 1.41	3.63	This study (32) (32) (32)
	Stewiacke River, Canada Sydling D/S Huish Fm, UK River Yeo Goathill, UK Ozle. Bk Low. Barnes, UK Congresby Yeo Beam B, UK	45.14 51.18 50.95 51.28 NA		-63.35 -2.27 -2.47 -0.13 NA	5.36 3.08 3.63 3.95 2.08		1.36 1.19 1.95 1.28 1.18		This study (32) (32) (32) (32)

Mid-latitudes/Temperate zone

  Longitude (°E) DOC (mg/L) COD (mg/L) BOD (mg/L) COD-BOD (mg/L) SourceLocalityLatitude (°N) Longitude (°E) DOC (mg/L) COD (mg/L) BOD (mg/L) COD-BOD (mg/L) Source

	Locality Jiulong River, China Locality Yellow Sea, China Locality Juksan, South Korea Locality Sanya Bay, China Locality Locality Ingramport River, Canada Locality Daehyun, South Korea Locality Wolgok, South Korea Locality River Mole D/S South Molton Stw, UK Locality River Tone Bishops Hull, UK Locality Lt. Dart R., D/S Chawleigh Stw, UK Locality By Bk Long Dean Mill, UK Locality Latitude (°N) Jiulong River, China Latitude (°N) Longitude (°E) DOC (mg/L) COD (mg/L) BOD (mg/L) COD-BOD (mg/L) Source 24.42 118.06 1.21 0.41 0.22 0.19 This study Latitude (°N) Longitude (°E) DOC (mg/L) COD (mg/L) BOD (mg/L) COD-BOD (mg/L) Source 36.37 120.88 1.06 1.15 0.76 0.39 This study Latitude (°N) Longitude (°E) DOC (mg/L) COD (mg/L) BOD (mg/L) COD-BOD (mg/L) Source 35.52 127.81 6.00 3.90 1.70 2.20 (33) Latitude (°N) Longitude (°E) DOC (mg/L) COD (mg/L) BOD (mg/L) COD-BOD (mg/L) Source 18.20 109.08 1.11 0.73 0.65 0.08 This study Latitude (°N) Longitude (°E) DOC (mg/L) COD (mg/L) BOD (mg/L) COD-BOD (mg/L) Source 1.26 3.23 2.04 1.28 This study Latitude (°N) Longitude (°E) DOC (mg/L) COD (mg/L) BOD (mg/L) COD-BOD (mg/L) Source 44.67 -63.97 11.56 12.00 This study Latitude (°N) Longitude (°E) DOC (mg/L) COD (mg/L) BOD (mg/L) COD-BOD (mg/L) Source 35.45 128.16 7.80 3.30 1.70 1.60 (33) Latitude (°N) Longitude (°E) DOC (mg/L) COD (mg/L) BOD (mg/L) COD-BOD (mg/L) Source 35.49 127.86 9.50 4.80 2.30 2.50 (33) Latitude (°N) Longitude (°E) DOC (mg/L) COD (mg/L) BOD (mg/L) COD-BOD (mg/L) Source 51.24 -0.31 1.80 1.08 (32) Latitude (°N) Longitude (°E) DOC (mg/L) COD (mg/L) BOD (mg/L) COD-BOD (mg/L) Source 51.02 -3.14 2.41 1.43 (32) Latitude (°N) Longitude (°E) DOC (mg/L) COD (mg/L) BOD (mg/L) COD-BOD (mg/L) Source NA NA 3.09 1.07 (32) Latitude (°N) Longitude (°E) DOC (mg/L) COD (mg/L) BOD (mg/L) COD-BOD (mg/L) Source 51.48 -2.22 0.92 1.01 (32) 24.46 117.80 1.58 2.90 1.61 1.28 This study Mirs Bay, China 22.53 114.46 1.29 1.52 1.45 0.07 (34)
	Jiulong River, China Gilgok, South Korea Hachon, South Korea Sanya Bay, China Miramichi Bay, Canada Jikjun, South Korea Massang, South Korea River Cober D/S Helston Stw, UK River Tone Bathpool, UK Huish Trout Farm Easthill Inlet, UK Back Stream Fitzroy, UK Xiamen Bay, China Mirs Bay, China	24.4 35.4 35.23 18.19 47.08 35.12 35.37 50.09 51.04 50.79 51.04 24.44 22.50	117.91 129.29 128.12 109.07 -65.21 127.9 128.11 -5.28 -3.05 -2.52 -3.15 118.04 114.48	1.55 7.20 10.40 1.02 3.41 7.97 2.60 8.50 2.99 4.15 1.10 0.66 0.99 1.51	1.61 4.00 6.40 0.73 6.60 9.83 2.30 3.70 1.20 1.34	1.02 2.10 3.70 0.48 3.09 2.39 1.10 1.70 1.19 1.56 1.36 3.17 0.68 1.20	0.59 1.90 2.70 0.24 3.51 7.53 1.20 2.00 0.52 0.14	This study (33) (33) This study This study This study (33) (33) (32) (32) (32) (32) This study (34)
	Jiulong River, China Shincheon, South Korea Whagye, South Korea Sanya Bay, China Mersey River, Canada Hogye, South Korea Garye, South Korea River Char At Whitchurch Canonicorum, River Brue Cow Bridge, UK Hancocks U/S St Ivel, UK Avon Scotland Road, UK Xiamen Bay, China Mirs Bay, China	24.46 35.36 35.11 18.17 44.43 35.20 35.33 50.75 51.13 50.12 NA 24.42 22.53	117.8 128.64 128.1 109.06 -65.21 127.88 127.86 -2.86 -2.71 -5.29 NA 118.16 114.48	1.58 6.40 11.00 0.85 2.09 6.71 2.00 3.40 3.49 1.10 5.17 1.30 0.97 1.74	2.90 3.80 4.90 0.44 2.21 10.20 1.60 2.90 0.80 1.68	1.61 1.80 2.80 0.32 2.08 2.42 0.40 1.30 1.17 1.81 3.76 2.24 0.62 0.97	1.28 2.00 2.10 0.12 0.27 7.78 1.20 1.60 0.18 0.72	This study (33) (33) This study This study This study (33) (33) (32) (32) (32) (32) This study (34)
	Xiamen Bay, China Deokgok, South Korea Jinae, South Korea Sanya Bay, China Chaleurs Bay, Canada Sachon, South Korea Unam, South Korea UK River Banwell At M5 M'way, UK Halse Water Tyler Br, UK Avon Melksham Bypass, UK Xiamen Bay, China Mirs Bay, China	24.44 35.11 35.23 18.15 47.99 35.32 35.34 51.36 51.05 51.37 24.51 22.53	118.04 128.48 128.14 109.18 -66.74 128.32 128.23 -2.90 -3.25 -2.14 118.24 114.47	0.99 5.80 8.70 0.90 3.08 8.08 4.20 4.60 0.77 0.99 1.99 1.04 1.67	1.20 3.70 6.00 0.48 3.20 10.18 2.10 2.80 1.12 2.80	0.68 1.60 2.40 0.40 1.96 2.48 0.90 1.00 1.94 1.76 2.92 0.97 2.06	0.52 2.10 3.60 0.08 1.24 7.88 1.20 1.80 0.15 0.74	This study (33) (33) This study This study (33) (33) (32) (32) (32) This study (34)
	Xiamen Bay, China Imgok, South Korea Unjung, South Korea Mirs Bay, China Kejimkujik Lake, Canada Ssanggye, South Korea Jukjeon, South Korea Ramsgate (Reen) Roseworthy, UK River Banwell @ M5 M'way, UK Halse W Norton Br, UK Avon Malford Church, UK Xiamen Bay, China Mirs Bay, China	24.42 35.11 35.49 22.53 44.38 35.22 35.37 50.20 51.36 52.87 51.50 24.57 22.53	118.16 128.46 128.68 114.48 -65.21 128.44 128.32 -1.25 -2.90 -2.19 -2.06 118.16 114.46	0.97 6.80 4.20 1.89 3.78 6.08 3.60 5.40 3.54 2.96 1.38 0.81 1.13 1.51	0.80 4.40 2.80 2.13 5.23 9.80 1.80 3.10 1.12 1.57	0.62 2.70 1.20 1.64 1.55 2.29 0.80 0.90 1.72 1.38 2.41 1.55 0.87 1.47	0.18 1.70 1.60 0.49 3.76 7.51 1.00 2.20 0.25 0.10	This study (33) (33) (34) This study This study (33) (33) (32) (32) (32) (32) This study (34)
	Xiamen Bay, China Jungchon, South Korea Whansa, South Korea Mirs Bay, China Lac Matapedia, Canada Shubenacadie Grand Lake, Canada Gwigok, South Korea Whajung, South Korea River Yeo (Lapford)-Bow Bridge, UK River Banwell @ Ebdon Br, UK Halse W Halse G Stn, UK Avon Lacock, UK Xiamen Bay, China Mirs Bay, China	24.51 35.13 35.14 22.53 48.54 44.89 35.64 35.34 50.86 51.38 51.25 51.42 24.53 22.50	118.24 128.43 127.99 114.46 -67.56 -63.6 127.84 128.98 -3.81 -2.92 -3.13 -2.12 118.09 114.48	1.04 3.00 5.00 1.29 3.86 4.29 5.30 2.50 5.54 5.04 0.78 2.27 1.20 1.67	1.12 1.80 3.50 1.52 5.60 5.20 3.90 1.50 1.68 2.66	0.97 0.80 2.00 1.45 1.49 1.49 2.20 0.60 1.29 1.86 1.76 1.74 1.04 1.71	0.15 1.00 1.50 0.07 4.11 3.71 1.70 0.90 0.64 0.95	This study (33) (33) (34) This study This study (33) (33) (32) (32) (32) (32) This study (34)
	Xiamen Bay, China Mari, South Korea Seongbang, South Korea Mirs Bay, China Lake Major, Canada Anui, South Korea Yealm River D/S Yealmpton Stw, UK River Wey At Radipole, UK River Axe Loxton, UK Halse W Asylum Br, UK At Austell At Molingey G Stat, UK Bohai Sea, China Mirs Bay, China	24.57 35.7 35.12 22.50 44.72 35.64 50.38 50.63 51.29 51.25 50.20 37.39 22.50	118.16 127.86 127.96 114.48 -63.48 127.81 -3.97 -2.47 -2.90 -3.13 -4.48 119.95 114.48	1.13 4.70 5.50 1.51 3.90 3.96 6.50 2.55 1.64 5.90 1.02 2.81 1.67 1.51	1.12 3.20 3.20 1.34 5.67 8.60 4.10 1.62 2.41	0.87 1.50 2.10 1.20 1.55 1.82 2.20 1.30 2.13 1.78 2.13 1.67 1.32 1.48	0.25 1.70 1.10 0.14 4.12 6.78 1.90 0.30 0.93	This study (33) (33) (34) This study This study (33) (32) (32) (32) (32) (32) This study (34)
	Xiamen Bay, China Anha, South Korea Daegok, South Korea Mirs Bay, China Saint Lawrence River, Canada Pockwock Lake, Canada Jiwoo, South Korea Yealm At Yealm Bridge, UK River Teign-Preston, UK River Axe Henley Hill, UK Gwithian Towans Red River, UK Alphin Brook At Countess Wear Bridge, Bohai Sea, China Mirs Bay, China	24.53 35.31 35.06 22.53 48.45 44.78 35.66 50.31 50.62 53.50 50.22 50.70 37.39 22.53	118.09 128.83 128.13 114.48 -68.52 -63.84 127.81 -4.07 -3.64 -1.40 -5.39 -3.50 119.95 114.48	1.20 9.20 4.50 1.74 3.45 6.73 1.60 2.51 3.30 3.49 2.72 0.76 1.59 2.42	1.68 4.80 3.50 1.68 5.00 6.20 1.50 1.62 2.12	1.04 2.30 1.60 0.97 1.42 2.16 0.70 1.08 2.46 1.07 3.53 1.12 1.30 1.36	0.64 2.50 1.90 0.72 3.58 4.04 0.80 0.32 0.76	This study (33) (33) (34) This study This study (33) (32) (32) (32) (32) (32) This study (34)
	Bohai Sea, China Juwhang, South Korea Gamgok, South Korea Mirs Bay, China Juksan, South Korea Yealm At Puslinch Bridge, UK River Teign-Chudleigh Bridge, UK River Axe Clewer, UK Frome U/S Louds Mill, UK UK Bohai Sea, China Mirs Bay, China	37.39 35.29 35.05 22.53 35.52 50.34 50.62 51.26 50.71 37.39 22.53	119.95 128.71 128.15 114.47 (E) Fresh waters 1.67 6.00 3.40 1.67 4.01 127.81 6.00 -4.01 2.77 -3.64 3.50 -2.80 4.17 -2.41 5.76 119.94 1.70 114.47 2.27	1.62 3.40 2.50 2.80 6.24 3.90 1.78 2.90	1.32 1.80 1.20 2.06 2.21 1.70 1.08 1.59 1.56 1.11 1.40 1.86	0.30 1.60 1.30 0.74 4.12 2.20 0.38 1.04	This study (33) (33) (34) This study (33) (32) (32) (32) (32) This study (34)
	Bohai Sea, China Wolpyeong, South Korea Gobong, South Korea Mirs Bay, China Montmorency River, Canada Gilgok, South Korea Hachon, South Korea Tort. Bk. Falfield, UK River Teign D/S Heathfield Tip, UK River Axe, UK Exeter Canal At A38 Bridge Countess, Bohai Sea, China Mirs Bay, China	37.39 34.95 35.04 22.53 46.89 35.4 35.23 51.64 50.62 51.27 50.69 37.39 22.53	119.95 128.35 128.17 114.46 -71.15 129.29 128.12 -2.45 -3.64 -2.83 -3.49 (F) Seawater 1.59 2.70 3.30 1.51 1.79 7.20 10.40 4.21 3.50 2.40 1.85 119.93 1.60 114.46 1.36	1.62 2.80 2.70 1.57 5.80 4.00 6.40 1.58 2.00	1.30 1.10 1.40 1.47 2.29 2.10 3.70 2.04 1.85 1.05 1.33 1.26 1.26	0.32 1.70 1.30 0.10 3.51 1.90 2.70 0.32 0.74	This study (33) (33) (34) This study (33) (33) (32) (32) (32) (32) This study (34)
	Bohai Sea, China Sangdong, South Korea Jeonggok, South Korea Mirs Bay, China Shincheon, South Korea Whagye, South Korea Tn Bwtr C N Newton, UK River Char U/S Whitchurch Can., UK River Avill Frackford Br, UK UK Edward B. Knight Pier, USA Bohai Sea, China Mirs Bay, China	37.39 35 35.41 22.50 35.36 35.11 50.31 50.75 51.16 24.55 37.39 22.50	119.94 128.2 127.91 114.48 128.64 128.1 -3.35 -2.86 -3.51 -81.78 119.93 114.47	1.70 2.70 3.00 1.67 4.18 6.40 11.00 4.57 3.76 1.74 2.32 1.55 2.27	1.78 2.30 2.20 2.66 5.80 3.80 4.90 2.40 1.54 3.00	1.40 1.40 1.00 1.71 2.30 1.80 2.80 2.52 1.53 1.19 1.49 1.22 1.84	0.38 0.90 1.20 0.95 3.54 2.00 2.10 0.91 0.32 1.16	This study (33) (33) (34) This study (33) (33) (32) (32) (32) This study This study (34)
	Bohai Sea, China Deokam, South Korea Banggok, South Korea Mirs Bay, China Grand Falls Dam, Canada Deokgok, South Korea Jinae, South Korea T R Avill Timberscom, UK River Yeo Yeovil Bridge, UK Point Above South Crofty R, UK Ebble U/S Chalkvalley Inlet Rside, UK South Pointe Pier, USA Bohai Sea, China Mirs Bay, China	37.39 35.51 35.36 22.50 45.28 35.11 35.23 51.17 50.94 50.22 51.04 25.76 37.39 22.50	119.93 128.18 127.87 114.48 -67.48 128.48 128.14 -3.50 -2.61 -5.28 -1.87 -80.130 119.94 114.48	1.60 10.50 9.30 1.51 4.60 5.80 8.70 3.55 2.44 3.54 0.46 2.46 1.74 1.51	1.58 3.50 3.80 2.41 7.80 3.70 6.00 1.81 1.80 2.17	1.26 2.10 2.00 1.48 1.49 1.60 2.40 1.07 1.10 1.13 1.08 1.55 1.44 1.24	0.32 1.40 1.80 0.93 6.31 2.10 3.60 0.26 0.36 0.93	This study (33) (33) (34) This study (33) (33) (32) (32) (32) (32) This study This study (34)
	Edward B. Knight Pier, USA South Pointe Pier, USA Naples Pier, USA Pine Glades Lake, USA Nine Mine Pond, USA Jiulong River, China Bohai Sea, China Uicho, South Korea Migok, South Korea Mirs Bay, China Imgok, South Korea Unjung, South Korea T River Avill Timberscom, UK River Yeo Yeovil Bridge, UK Pill R Blue Anchor, UK Ebble D/S Chalkevalley, UK Naples Pier, USA Yellow Sea, China Text Fig. 3 Bohai Sea, China Daehyun, South Korea Wolgok, South Korea Mirs Bay, China Saint John River, Canada Jungchon, South Korea Whansa, South Korea Sydling Water, UK Downstream River Yeo Over Compton, UK Pill R Bilbrook Ford, UK Dawlish Water At Dawlish, UK Sanya Bay, China Yellow Sea, China Ingramport River, Canada Yellow Sea, China Jikjun, South Korea Massang, South Korea Mirs Bay, China Mari, South Korea Seongbang, South Korea Shearplace, UK River Yeo Goldings Lane, UK Penponds Roseworthy Stream, UK Congresbury Yeo A370, UK Sanya Bay, China Yellow Sea, China Text Fig. 4 Yellow Sea, China Hogye, South Korea Garye, South Korea Mirs Bay, China Bay of Fundy, Canada Anha, South Korea Daegok, South Korea Sydling D/S Huish Fm, UK River Yeo Goathill, UK Ozle. Bk Low. Barnes, UK Congresby Yeo Beam B, UK Sanya Bay, China Yellow Sea, China Biwa Lake, Japan Yellow Sea, China Sachon, South Korea Unam, South Korea Mirs Bay, China Juwhang, South Korea Gamgok, South Korea St. Austell River Downstream Of, UK R Wey At Nottington, UK North Brook At Northbrook Park, UK Congres Yeo Tutshill, UK Sanya Bay, China Yellow Sea, China Han River, South Korea Yellow Sea, China Yellow Sea, China Yellow Sea, China Yellow Sea, China Gwigok, South Korea Anui, South Korea Jiwoo, South Korea Whajung, South Korea Sanya Bay, China Pine Glades Lake, USA Nine Mine Pond, USA Richibucto River, Canada Shubenacadie River, Canada Sangdong, South Korea Deokam, South Korea Uicho, South Korea Jeonggok, South Korea Banggok, South Korea Migok, South Korea Rodden D/S Langton Herring Stw, UK River Wey At Radipole, UK River Wey At Nottington, UK River Wey At Broadwey, UK River Tone Coalharbor Br, UK River Tone Clatworthy, UK Mill Stream D/S Horsepool, UK Medlyn Stream At Chy Bridge, UK Lt. Dart R.,D/S Chawleigh Stw, UK Carminowe Creek Inflow To Loe Pool, By Brook Middlehill, UK By Brook Fosse, UK Jiulong River, China Mirs Bay, China Jiulong River, China Yellow Sea, China UK Jiulong River, China Yellow Sea, China Ssanggye, South Korea Jukjeon, South Korea Stewiacke River, Canada Wolpyeong, South Korea Gobong, South Korea St Austell At Pentewan Bridge, UK River Wey At Broadwey, UK Misc Lower Frome, UK Congres Yeo Iwood, UK Sanya Bay, China Yellow Sea, China Finnish rivers, Finland (1989)	24.55 25.76 26.13 25.43 25.25 24.36 37.39 35.42 35.36 22.53 35.11 35.49 NA 50.94 51.18 51.02 26.13 36.37 37.39 35.45 35.49 22.53 47.06 35.13 35.14 50.22 50.93 51.15 50.60 18.19 36.37 44.67 36.37 35.12 35.37 22.53 35.7 35.12 51.00 50.21 51.37 18.20 36.37 36.37 35.2 35.33 22.50 45.81 35.31 35.06 51.18 50.95 51.28 NA 18.19 36.37 35.33 36.37 35.32 35.34 22.50 35.29 35.05 50.33 50.64 51.20 51.40 18.17 36.37 37.48 36.37 36.37 36.37 36.37 35.64 35.64 35.66 35.34 18.19 25.43 25.25 46.65 44.93 35.00 35.51 35.42 35.41 35.36 35.36 50.64 50.61 50.61 NA NA 51.02 51.16 NA 50.91 51.42 51.50 24.40 22.53 24.42 36.37 50.07 24.36 36.37 35.22 35.37 45.14 34.95 35.04 50.33 51.15 51.23 51.36 18.15 36.37 64.00	-81.78 -80.13 -81.81 -80.72 -80.80 118.13 119.93 128.14 128.08 114.48 128.46 128.68 NA -2.61 -3.39 -1.99 -81.807 120.88 119.94 128.16 127.86 114.47 -67.78 128.43 127.99 -5.39 -2.60 -3.40 -3.51 109.08 120.88 -63.97 120.88 127.9 128.11 114.46 127.86 127.96 -2.65 -5.32 -2.81 109.08 120.88 120.88 127.88 127.86 114.47 -64.57 128.83 128.13 -2.27 -2.47 -0.13 NA 109.07 120.88 136.17 120.88 128.32 128.23 114.48 128.71 128.15 -4.79 -2.48 -0.85 -2.40 109.06 120.88 126.43 120.88 120.88 120.88 120.88 127.84 127.81 127.81 128.98 (C) Low latitudes/Tropical zone 2.32 2.46 6.88 13.24 2.63 1.29 1.55 6.70 7.80 2.42 4.70 6.80 4.20 5.90 2.44 0.74 4.14 2.63 1.10 1.74 7.80 9.50 2.27 3.67 3.00 5.00 2.55 3.31 0.64 4.14 0.88 1.15 1.10 2.60 8.50 1.36 4.82 4.70 5.50 0.52 3.22 0.77 1.11 1.01 1.15 2.00 3.40 2.27 0.74 9.20 4.50 3.08 3.63 3.95 2.08 1.02 1.18 1.01 4.20 4.60 1.51 4.82 6.00 3.40 3.09 4.32 1.52 3.40 0.85 0.96 1.18 0.96 1.03 1.10 5.30 6.50 1.60 2.50 109.08 0.88 -80.72 6.88 -80.796 13.24 -64.85 3.00 5.45 -63.54 6.18 6.85 128.20 2.70 128.18 10.50 128.14 6.70 127.91 3.00 127.87 9.30 128.08 7.80 -2.55 1.16 -2.46 2.40 -2.46 2.28 NA 0.30 NA 5.27 -3.14 1.72 -2.73 5.59 NA 3.55 -3.83 5.59 -2.27 0.89 -2.24 4.40 117.91 1.55 114.48 1.89 118.06 1.21 120.88 1.06 -5.28 5.41 118.13 1.29 120.88 1.10 128.44 3.60 128.32 5.40 (D) Forest watershed -63.35 5.36 128.35 2.70 128.17 3.30 -4.79 2.35 -0.80 3.13 -2.32 5.98 -2.79 5.96 109.18 0.90 120.88 1.03 26.00	2.40 1.81 5.89 10.56 2.35 0.70 1.54 3.90 3.60 2.12 7.66 4.40 2.80 2.35 1.18 1.80 3.30 4.80 2.90 2.20 1.80 3.50 0.48 1.22 1.18 2.30 3.70 2.00 7.44 3.20 3.20 0.73 1.10 1.22 1.60 2.90 3.00 10.20 4.80 3.50 0.73 1.27 1.10 2.10 2.80 2.17 5.05 3.40 2.50 0.44 1.10 1.27 1.10 1.15 1.18 3.90 4.10 1.50 1.50 0.48 5.89 10.56 7.40 6.64 7.60 7.84 2.30 3.50 3.90 2.20 3.80 3.60 1.61 2.13 0.41 1.15 0.70 1.18 1.80 3.10 2.80 2.70 0.48 1.15	1.49 1.55 1.07 3.17 1.86 0.39 1.22 1.40 1.80 1.36 2.13 2.70 1.20 1.30 1.10 1.44 1.34 1.86 0.80 1.44 1.70 2.30 1.86 2.02 0.80 2.00 1.21 3.91 2.71 1.34 0.40 0.82 0.80 1.10 1.70 1.26 2.66 1.50 2.10 1.76 1.58 1.41 0.65 0.74 0.82 0.40 1.30 1.84 3.36 2.30 1.60 1.19 1.95 1.28 1.18 0.48 0.88 0.74 0.90 1.00 1.24 1.46 1.80 1.20 1.63 3.38 1.09 1.37 0.32 0.74 0.88 0.74 0.76 0.82 2.20 2.20 0.70 0.60 0.40 1.07 3.17 2.62 3.10 2.09 1.59 1.40 2.10 1.40 1.00 2.00 1.80 1.73 1.23 1.20 1.88 3.55 1.44 1.49 1.07 1.11 1.05 1.01 1.02 1.64 0.22 0.76 3.85 0.39 0.82 0.80 0.90 1.36 1.10 1.40 1.11 1.33 1.54 1.59 0.40 0.76	0.91 0.26 4.82 7.39 0.49 0.30 0.32 2.50 1.80 0.76 5.58 1.70 1.60 0.49 0.38 0.36 1.60 2.50 1.04 0.18 1.00 1.50 0.08 0.40 0.38 1.20 2.00 0.74 4.83 1.70 1.10 0.08 0.36 0.40 1.20 1.60 1.16 6.84 2.50 1.90 0.24 0.39 0.36 1.20 1.80 0.93 3.63 1.60 1.30 0.12 0.36 0.39 0.36 0.39 0.36 1.70 1.90 0.80 0.90 0.08 4.82 7.39 4.78 3.54 5.51 6.33 0.90 1.40 2.50 1.20 1.80 1.80 0.59 0.49 0.19 0.39 0.30 0.36 1.00 2.20 1.70 1.30 0.08 0.39	This study This study This study This study This study This study (33) (33) (34) This study (33) (33) (32) (32) (32) (32) This study This study This study (33) (33) (34) This study (33) (33) (32) (32) (32) (32) This study This study This study This study (33) (33) (34) This study (33) (33) (32) (32) (32) This study This study This study (33) (33) (34) This study (33) (33) (32) (32) (32) (32) This study This study (20) This study (33) (33) (34) This study (33) (33) (32) (32) (32) (32) This study This study (21, 73) This study This study This study This study (33) (33) (33) (33) This study This study This study This study This study This study This study (33) (33) (33) (33) (33) (33) (32) (32) (32) (32) (32) (32) (32) (32) (32) (32) (34) This study (32) This study This study (32) This study This study This study (33) (33) This study (33) (33) (32) (32) (32) (32) This study This study (22)

Table S2 .

 S2 Sampling information and data corresponding to Fig.S2. The samples were from various lakes on the Qinghai Tibet Plateau, China (this study), and the Jiahe River valley (35, Table2). The data cover a range of salinities from near zero to 43.LocalitySalinity CODCr (mg L -1 ) CODMn (mg L -1 ) CODCr/CODMn Data source

	Kala Kuli Lake, China	0.10	5.60	1.10	5.09	This study
	Qiangyong Lake, China	0.10	5.20	1.00	5.20	This study
	Songmuxi Co, China	0.26	8.30	0.50	16.60	This study
	Bangong Co, China	0.46	20.30	2.30	8.83	This study
	Namu Co, China	0.99	18.70	1.10	17.00	This study
	Darebu Co, China	1.27	22.00	1.80	12.22	This study
	Lang Co, China	1.51	30.20	1.90	15.89	This study
	Gongzhu Co, China	5.16	164.10	18.00	9.12	This study
	Daze Co, China	15.99	45.30	3.20	14.16	This study
	Bieruoze Co, China	27.50	630.50	12.60	50.04	This study
	Dong Co, China	43.46	906.70	12.20	74.32	This study
	Jiahe River valley, China	0.40	24.74	4.00	6.19	(35)
	Jiahe River valley, China	0.41	8.25	3.26	2.53	(35)
	Jiahe River valley, China	0.40	11.55	3.37	3.43	(35)
	Jiahe River valley, China	0.30	28.04	5.18	5.41	(35)
	Jiahe River valley, China	0.26	18.15	4.97	3.65	(35)
	Jiahe River valley, China	0.28	24.74	5.20	4.76	(35)
	Jiahe River valley, China	0.27	9.90	4.97	1.99	(35)
	Jiahe River valley, China	0.33	16.50	5.29	3.12	(35)
	Jiahe River valley, China	0.27	26.39	6.04	4.37	(35)
	Jiahe River valley, China	0.32	21.44	10.82	1.98	(35)
	Jiahe River valley, China	4.61	51.14	8.82	5.80	(35)
	Jiahe River valley, China 14.08	493.23	9.26	53.26	(35)

Table S3 . Comparison of measurements with the alkaline CODMn and acidic CODMn protocols along a salinity gradient.

 S3

	Sample Source	Salinity Alkaline CODMn	Acidic CODMn	Acid. CODMn -	Data source
			(mg L -1 )	(mg L -1 )	Alk. CODMn (mg L -1 )
	Xiamen U. Reservoir, China	0	2.73	2.64	-0.09	This study
	Furong Lake, China	0	10.19	11.62	1.43	This study
	Xiang'an Mountain Lake,	0	2.62	4.01	1.39	This study
	China
	Xiang'an Artificial Lake, China	0	6.08	6.26	0.18	This study
	Wenqing Lake, China	0	3.78	6.55	2.77	This study
	Xiamen botanical Garden Lake,	0	2.88	5.08	2.20	This study
	China
	Tibet Xironggou, China	0	1.61	1.74	0.13	This study
	Tibet Lulang, China	0	11.15	11.70	0.55	This study
	Jiulong R. Estuary, China	4.20	2.19	4.34	2.15	This study
	Xiamen Bay, China	16.50	1.55	5.86	4.31	This study
	Xiamen Bay, China	29.40	0.76	17.28	16.52	This study
	Sackville River, Canada	0.04	4.82	7.22	2.40	This study
	Sackville Estuary, Canada	3.60	4.82	5.52	0.70	This study
	Sackville Estuary, Canada	8.50	5.08	6.35	1.27	This study
	Sackville Estuary, Canada	15.80	2.78	6.19	3.41	This study
	Sackville Estuary, Canada	20.40	2.15	6.28	4.13	This study
	Northwest Arm, Canada	30.00	0.52	7.42	6.90	This study
	Jiahe River valley, China	0.40	4.00	4.40	0.40	(35)
	Jiahe River valley, China	0.41	3.26	3.38	0.12	(35)
	Jiahe River valley, China	0.40	3.37	3.40	0.03	(35)
	Jiahe River valley, China	0.30	5.18	5.36	0.18	(35)
	Jiahe River valley, China	0.26	4.97	4.57	-0.40	(35)
	Jiahe River valley, China	0.28	5.20	4.88	-0.32	(35)
	Jiahe River valley, China	0.27	4.97	4.80	-0.17	(35)
	Jiahe River valley, China	0.33	5.29	5.19	-0.10	(35)
	Jiahe River valley, China	0.27	6.04	6.08	0.04	(35)
	Jiahe River valley, China	0.32	10.82	8.00	-2.82	(35)
	Jiahe River valley, China	4.61	8.82	11.00	2.18	(35)
	Jiahe River valley, China	14.08	9.26	17.80	8.54	(35)

Supplementary Materials for

Correcting a Major Error in Assessing Organic Carbon Pollution in Natural Waters

Nianzhi Jiao 1,2 *, Jihua Liu 2,3 , Bethanie Edwards 4 , Zongqing Lv 1 , Ruanhong Cai 1,2 , Yongqin Liu 5 , Xilin Xiao 1,2 , Jianning Wang 1,2 , Fanglue Jiao 2,6 , Rui Wang 1 , Xingyu Huang 1 , Bixi Guo 5 , Jia Sun 1,2 , Rui Zhang 1,2 , Yao Zhang 1,2 , Kai Tang 1,2 , Qiang Zheng 1,2 , Farooq Azam 7 , John Batt 2,6 , Wei-Jun Cai 8 , Chen He 9 , Gerhard J. Herndl 10,11