Environ. Sci. Technol. 2007, 41, 7706-7713
Long-Term Increase in Dissolved Organic Carbon in Streamwaters in Norway Is Response to Reduced Acid Deposition H E L E E N A . D E W I T , * ,† J A N M U L D E R , ‡ ATLE HINDAR,† AND LARS HOLE§ Norwegian Institute for Water Research (NIVA), Gaustadalle´en 21, N-0349 Oslo, Norway, Department of Plant and Environmental Sciences, Norwegian University of Life Sciences, P.O. Box 5003 Aas, N-1432 Norway, and Polar Environment Center, Norwegian Institute for Air Research (NILU), 9296 Tromsø, Norway
Concentrations of dissolved organic carbon (DOC) in freshwaters have increased significantly in Europe and North America, but the driving mechanisms are poorly understood. Here, we test if the significant increase in TOC (total organic carbon, 90-95% DOC) in three acidsensitive catchments in Norway of 14 to 36% between 1985 and 2003 is related to climate, hydrology, and/or acid deposition. Catchment TOC export increased between 10 and 53%, which was significant at one site only. The seasonal variation in TOC was primarily climatically controlled, while the deposition of SO4 and NO3snegatively related to TOCsexplained the long-term increase in TOC. We propose increased humic charge and reduced ionic strengths both of which increase organic matter solubilitysas mechanistic explanations for the statistical relation between reduced acid deposition and increased TOC. Between 1985 and 2003, ionic strength decreased significantly at all sites, while the charge density of TOC increased at two of the sites from 1-2 meq g-1 C to about 5 meq g-1 C and remained constant at the third site at 5 meq g-1 C. The solubility of organic matter is discussed in terms of the pHdependent deprotonation of carboxylic groups and the ionic strength-dependent repulsion of organic molecules.
Introduction Concentrations of dissolved organic carbon (DOC) in freshwaters have increased significantly in the past 20 years in the Nordic countries (1-3), the U.K., (4-6) and North America (3). In some regions where the main source of drinking water is surface water, the DOC increase has crossed water-quality limits, resulting in increased costs for water cleaning (2). In the absence of a reduced water flux, higher concentrations of DOC may lead to increased transport of associated pollutants and nutrients to freshwaters and coastal zones. This may impact bacterial production, frequency, and magnitude of algal blooms and reduce oxygen availability (7). Additionally, the increased export of dissolved C from * Corresponding author phone: +47 2218 5100; fax: +47 2218 5200; e-mail:
[email protected]. † Norwegian Institute for Water Research. ‡ Norwegian University of Life Sciences. § Norwegian Institute for Air Research. 7706
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terrestrial ecosystems could decrease the C-sink strength of terrestrial ecosystems (8). Causes for the rise in surface water DOC are poorly understood (9), but its widespread occurrence seems to suggest one regional rather than several local mechanisms. Global warming (4, 6), changes in hydrological pathways (10), increase of summer drought frequency (5), increased atmospheric CO2 levels (11), N deposition (12), and reduced atmospheric acid deposition (13, 1) have all been suggested as potential drivers for the increase in DOC. The hypothesized link between reduced acid deposition and increasing levels of DOC implies a strong relation between soil acid-base chemistry and organic matter sorption behavior, as for example, incorporated in mechanistic soil chemistry models like WHAM (14) and NICADonnan (15). Empirical studies show evidence of DOC responses to acid inputs, i.e., soil solution DOC declined in forest plots to which dissolved AlCl3 was added (16). The lack of understanding of the effects of reduced acidity and climate change on surface water DOC severely limits our ability to predict DOC in a changing environment. Statistical analyses of long data series of total organic carbon (TOC) have so far used water chemistry as the proxy for acid and sea-salt deposition, while N deposition was not included as an explanatory variable (1, 6). Additionally, the datasets were dominated by lakes where TOC is more likely to be affected by mineralization and photo-oxidation than in streams. In this study, we aim to investigate the role of climate and deposition on seasonal and long-term DOC variation in three acid-sensitive catchments in Norway, where streamwater chemistry is monitored on a weekly basis. DOC is measured as TOC, of which 90-95% consists of DOC. Statistical methods are applied to study the relations between DOC and climate (temperature, precipitation, snow), discharge, and deposition of Cl, SO4, and NO3. Additionally, relations between streamwater acid-base chemistry and DOC are used to explore mechanistic explanations of the DOC increase.
Experimental Section Site Description. Birkenes, Storgama, and Langtjern are small catchments that are part of the Norwegian program for monitoring long-range transported air pollutants (17) and are representative of dominating terrestrial ecosystems in acid-sensitive areas in southern Norway, i.e., forest, wetlands, and barren heathlands (Table 1). All sites were severely acidified when monitoring started in the mid-1970s. There has been virtually no direct disturbance in the catchments by human activities, except in Birkenes, where 7% of the catchment was logged in the mid-1980s. Birkenes is a lowelevation near-coast catchment, substantially influenced by sea-salt deposition and receiving the highest loads of acid deposition of all sites. Storgama and Langtjern are located further inland at higher elevations and have longer winters and usually a more stable snowpack than Birkenes. At all sites, summer discharge can be reduced to zero during prolonged droughts. Birkenes (58°28′ N; 8°24′ E) is covered by old-growth (>80 year old) productive Norway spruce forest. Mineral soil types (acid brown earth and podzols) are found in a shallow layer of glacial till on granite bedrock while peaty deposits have developed in poorly drained areas. On the slopes, welldrained thin organic layers on gravel or bedrock are common. Overland flow is common during high-intensity rain storms. Storgama (59°05′ N; 8°65′ E) is characterized by bare rock and shrubs/heathland (sparse Scots pine, birch, and heather). 10.1021/es070557f CCC: $37.00
2007 American Chemical Society Published on Web 10/20/2007
TABLE 1. Catchment Characteristics of Birkenes, Storgama, and Langtjern deposition (in meq m-2 yr-1)d
land cover (in %) site
area (km2)
elevation (m)
bare rocka
woodlandsb
peat
prod forestc
lake
N
S
Cl
Birkenes Storgama Langtjern
0.41 0.6 0.8
200-300 580-690 510-750
3 59 3
7 22 30
90
11 60
0 8 0
100 59 35
48 26 19
98 21 9
a
Bare rock, shrubs, thin soils.
b
7
c
Unproductive forest on thin soils. Productive forest on deeper soils.
Mineral soil types (mostly shallow podzols) are found in pockets of glacial till on granite bedrock and shallow peaty soils in poorly drained areas. There is a small lake near the outlet of the catchment. Langtjern (60°37′ N; 9°73′ E) is dominated by unproductive forest (mostly Scots pine) on organic and thin mineral soils. The mineral soils have developed on till of felsic gneisses and granites, while deeper peaty soils are common close to streams and the lake and in poorly drained topographic depressions. Water chemistry was monitored in an inlet and the outlet of the lake. Only data from the inlet are used here. Monitoring Program. The deposition at Birkenes is monitored 500 m north of the catchment (58°23′ N; 8°15′ E), while the deposition for Storgama is measured 6 km from the catchment at Treungen (59°10′ N; 8°31′ E). Until 1997, the deposition for Langtjern was monitored 6 km east of the catchment (Gulsvik; 60°22′ N; 9°39′ E), but since 1997, it is monitored at Brekkebygda (60°10′ N; 9°44′ E). The sample frequency at Langtjern and Storgama has been weekly since 1992, daily before that. Sample frequency at Birkenes is daily. Samples are analyzed for base cations, sulfate, ammonium, nitrate and chloride, and pH at the accredited laboratory at the Norwegian Institute for Air Research (NILU). Methods for analyses and quality control are described elsewhere (18). Fluxes are calculated from bulk deposition by weighting concentrations with the corresponding precipitation volume relative to the total annual precipitation volume. Since the start of TOC measurements in 1985 (1986 at Langtjern), streamwater samples have been collected weekly. Analyses of pH, conductivity, major anions and major cations, total N, TOC, and Al species were performed at accredited laboratories at the Norwegian Institute for Water Research (NIVA); methods for analysis and quality control are described elsewhere (17). The TOC consists of ca. 90-95% DOC in these catchments. Monitoring in the inlet stream at Langtjern stopped in 2003. Discharge was recorded continuously at V-notch weirs at the outlets of each watershed. Daily discharge at the Langtjern inlet stream was calculated based on the outlet and precipitation data using the hydrological model HBV (19), which is a semidistributed model with subdivision in altitude zones and a distributed snow and soil-moisture description. Climate Data. Climate variables, collected on a daily basis, include temperature, snow depth, snow cover, and precipitation. Snow cover is a categorical parameter with values ranging from 0 (no snow cover), 1, 2 (50% snow cover), 3, to 4 (complete snow cover). Climate data for Birkenes, Storgama, and Langtjern come from Herefoss (85 m.a.s.l; 58°51′ N; 8°36′ E), Tveitsund (252 m.a.s.l; 59°27′ N; 8°52′ E), and Gulsvik (147 m.a.s.l; 60°39′ N; 9°57′ E), respectively. At Herefoss and Tveitsund, only precipitation and snow data are recorded. The daily temperature was therefore interpolated from neighboring climate stations according to Tveito et al. (20). The average temperature and precipitation (1998-2003) at Herefoss, Tveitsund, and Gulsvik is 6.5, 5.5, and 3.5 °C and 1284, 979, and 716 mm, respectively. Data Treatment and Statistical Methods. At Birkenes, samples with exceptionally high TOC concentrations (>18
d
Average 1998-2003.
mg C L-1, ca. 10 measurements) were excluded from the dataset. These were sampled during periods of low discharge in the summer, when runoff has a relatively long residence time in a small pond before the v-weir. We assumed that the TOC in these samples had been modified by in-stream processes rather than being products of soil processes. The annual mean values of variables were always weighted by month. Trend analysis was done by a Mann-Kendall test (MK test) (21) and a seasonal Mann-Kendall test (SMK test) (22). The MK and SMK tests are nonparametric, rank-based tests for the detection of monotonic trends in time series and are widely used because of their robustness toward nonnormally distributed data, missing values, and values below the detection limit. The SMK test was developed to account for seasonal variation in the annual trend. Annual trends were determined using the MK test instead of the SMK test to avoid overestimation of the significance level due to serial correlation between seasons. The trend slope was estimated using Sen’s slope estimator (23), which is the median of the slopes calculated from all pairs of values in the data series. This slope estimate is little affected by data outliers and missing data. Empirical models describing weekly concentrations of TOC were built using stepwise multiple regression analysis with forward and backward selection (0.05 significance threshold). Eligible explanatory variables were discharge, climate (temperature, precipitation, snow depth, and snow cover), and deposition (Cl, NO3, and SO4), which were averaged (temperature, snow depth, snow cover) or summed (precipitation, deposition, discharge) over intervals from days (1, 3, 7, 14, 30, 60, 182) to years (1, 2, 3) prior to each observation. Variable selection was constrained as little as possible, except that (i) a given variable was not allowed in the model twice for different intervals, except if the intervals were separated by at least two time periods; and (ii) the increase in explained variation upon each entered variable must be at least 1%. The first constraint was included to reduce the internal correlation of model variables and the second was included to minimize the number of model variables. The output from the regression models was compared with the output from a “dumb” or “noncausal” model including “year” (the variable representing a linear trend) and “season” (a seasonally oscillating variable derived from mean monthly temperatures) to evaluate if a “causal” model was better at explaining the observations than a noncausal model. The speciation of labile Al (sum of Al3+, Al(OH) 2+, and Al(OH)2+) and other major cations and anions, ionic strength, and the charge balance was calculated using WHAM-W (14).
Results and Discussion Trends in TOC. Streamwater TOC showed a distinct seasonal variation with maximum concentrations in the late summer and early fall, while the lowest values were found in the spring during snowmelt periods (Figure 1). Mean annual concentrations of TOC at Langtjern (11.6 mg C L-1) were about twice as high as those at Birkenes (5.3 mg C L-1) and Storgama (4.9 VOL. 41, NO. 22, 2007 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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FIGURE 1. Weekly measurements of TOC concentrations (dots) from 1985 to 2003 at study sites. Lines show predicted TOC concentrations based on the regression models in Table 2. mg C L-1), probably due to its high proportion of wetlands and organic soils (Table 1) and relatively low annual precipitation. The difference between the seasonal minimum and maximum concentrations of TOC was considerably larger than the magnitude of the long-term trend. Mean annual TOC increased significantly in all sites from 1.0% at Langtjern (0.13 mg C L-1 yr-1; p < 0.008) and Birkenes (0.06 mg C L-1 yr-1; p < 0.002) to 1.8% at Storgama (0.09 mg C L-1 yr-1; p < 0.001), which is within the range reported for Norwegian lakes (3). For the entire period of 1985-2003, the increase in TOC amounted to 14, 36, and 22% (calculated as trend divided by mean TOC) at Langtjern, Storgama, and Birkenes, respectively, which is substantially lower than the 95% increase of DOC in 15 years in lakes in the U.K. (6) but similar to TOC trends in Finnish lakes (1). The catchment export of TOC increased with respective values of 16, 53, and 11% at Langtjern, Storgama, and Birkenes between 1985 and 2003, but this was significant only at Storgama (p < 0.04). Changes in annual discharge were less than 1% of the mean annual discharge and not significant. The seasonal pattern in TOC increase was characterized by a lack of months with declines in TOC while the TOC increased significantly from August to October in all sites (Figure 2). At Birkenes, the relative changes in mean monthly TOC were largest and most significant from August to December, whereas the TOC increase at Storgama was more uniformly spread through the year. TOC increase at Langtjern 7708
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was most distinct and significant during summer and autumn. In Finland, most significant increases in lake TOC were found in May and from September to November (1). Regression Analysis: Causal Models versus Dumb Models. It could be argued that any model that includes a variable with long-term trend and a seasonally oscillating variable can successfully reproduce the TOC time series presented here. Additionally, the large number of eligible explanatory variables might lead to “overfitting” of the observations. To test if the causal regression models described TOC better than a noncausal model, we compared the output of a model containing dumb variables with the output of models obtained through a stepwise regression of causal variables (i.e., climate, deposition, discharge). The “best” causal models (Table 2; “models A” in Table 3), i.e., those that explained most of the total variation, all described the total variation and the variation in annual mean TOC better than the dumb models but contained up to seven variables in contrast with the dumb models that contained only two variables. The causal models with the lowest number of variables that were still superior to the dumb models in describing both weekly and annual mean TOC while correctly describing the long-term trend (“models B” in Table 3) contained two (Langtjern) or three (Birkenes, Storgama) variables. This indicated that a good fit of TOC could be obtained with a limited number of variables. There were various sets of variables that explained the long-term trend equally well as models A and B but that
FIGURE 2. Relative monthly change in TOC in each site calculated as monthly TOC trend (estimated by Sen-slope estimator) divided by mean monthly TOC concentration (%). Significant changes are marked with a circle (seasonal Mann-Kendall test, p < 0.05). L means that a given circle refers to Langtjern only.
TABLE 2. Stepwise Regression Models for Weekly TOC in Each Catchment (1985-2003) (Best Model A in Table 3)a Langtjern variable
r2 ) 0.75 interval
t ratio
temperature discharge dep NO3 snowcover precipitation dep SO4
60 30 1095 60 1 60
9.2 -17 -12 31 6.7 -5.3
Storgama variable
r2 ) 0.61 interval
t ratio
dep SO4 temp snowdepth dep Cl precipitation dep NO3 precipitation discharge
730 182 60 182 182 182 14 60
-13 5.7 7.8 -16 13 -7.0 7.3 -6.3
Birkenes variable
r2 ) 0.54 interval
t ratio
temperature snowcover precipitation dep SO4 dep Cl
30 14 1 1095 182
22 9.2 8.3 -6.0 -4.7
a Variables shown in the order of selection, from above: r2, amount of variation in weekly TOC explained; t ratio, relative amount of variation explained by each variable in model and relation to dependent variable. Explanatory variables: dep NO3, wet nitrate deposition; dep SO4, wet deposition of SO4; dep Cl, wet deposition of Cl. Interval is the interval for which explanatory variables were averaged, in days.
were less successful in describing weekly TOC and annual mean TOC. A comparison of various subsets of models A suggests that the variation in weekly TOC at all sites was primarily controlled by climate, especially temperature, while the long-term trend at all sites was controlled by acid deposition. In Birkenes and to a lesser extent in Storgama, sea-salt deposition affected the variation in annual mean TOC, but not the long-term trend. Both sites are strongly to moderately influenced by sea-salt deposition (Table 1). Stepwise Regression Models. The best causal regression models described 75, 61, and 54% of the total variation in TOC at Langtjern, Storgama, and Birkenes, respectively (Table 2). The seasonal variability was captured well, but maximum TOC concentrations were underestimated (Figure 1). Maximum TOC concentrations occurred at Langtjern and Birkenes during the summer in low-flow periods and might be related to the diffusion of TOC from the riparian zone to stagnant water near the outlet, a local factor that is difficult to capture in this analysis. At Storgama, TOC maxima were measured during the summer and autumn, both at low and intermediate flow periods. Minimum TOC concentrations at Storgama, occurring in the spring snowmelt, were overestimated. Temperature was among the first variables to be selected and was always positively related to TOC (Table 2). Snow cover (or snow depth) was selected after temperature and allowed an upward adjustment of TOC at low air temperatures in winter. Previously, positive relations have been found between within-year temperature variations and soil solution DOC (i.e., 24-26). Precipitation at short time intervals (1-14 days) related positively to TOC, which could indicate that
short spells of rain transport TOC from soils to the stream, consistent with a superficial hydrological pathway through the humus-rich topsoil. Increased DOC in upland catchments under high-flow events of short duration has been shown in numerous watersheds and is commonly attributed to changes in hydrological pathways (27, 28). The discharge data, summed over intervals of 30-60 days, was negatively related to TOC, suggesting that prolonged periods of low flow tended to concentrate TOC whereas high-flow periods led to dilution of TOC. The deposition of NO3 and SO4sintegrated over 2-3 yearsswas the key variable explaining the long-term TOC trend at all sites (Tables 2 and 3). The relation between TOC and the deposition of SO4, NO3, and Cl was always negative. The deposition of NO3 and SO4 was strongly and significantly correlated at all sites (correlation test; r2 ) 0.8-0.9, results not shown) which could mean that the choice for one variable by stepwise selection obstructed the choice of the other. Not allowing NO3 deposition as an explanatory variable at Langtjern and Storgama resulted in equally successful models as that of Model A but now with SO4 deposition (integrated over 3 years) in the place of NO3 deposition (results not shown). At Birkenes, however, excluding SO4 deposition as an eligible variable resulted in a regression model without NO3 deposition and without prediction of a significant TOC rise, although r2 for weekly TOC (0.53) was similar to r2 for best model A. This underlines that statistical relations must be interpreted with caution and should be substantiated by other empirical evidence. VOL. 41, NO. 22, 2007 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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TABLE 3. Ability of Regression Models (Causal Models) and Dumb Models to Describe Weekly TOC and Annual Mean TOC; Predicted TOC Trend for Each Model (Sen-slope)a model variables
T Birkenes
causal models
best model B best model A dumb model observed Storgama
causal models
best model B best model A dumb model observed Langtjern
causal models
best model B best model A dumb model observed
x x x x x x x x x
x x x x x x x x x x x
x x x x x x x x
snowb
prec
x x x x
x x
x
x
x
x
x x x x x x x x x
x x x
disch
dep Cl
x x x
x x x
x x
x x
x
x
x x x
x
x
dep NO3
x x x
x x x x
x x
x x
x x x x x
dep SO4
x
x x x x x x
x x x
x x x
x x x
weekly TOC r2
annual mean TOC r2
slope µg C yr-1
0.43 0.47 0.50 0.53 0.49 0.45 0.52 0.46 0.54 0.36
0.06 0.07 0.05 0.36 0.39 0.34 0.20 0.51 0.48 0.42
9 8 5 64 58 61 8 61 62 59 64
0.17 0.22 0.26 0.39 0.47 0.51 0.36 0.45 0.51 0.43 0.61 0.17
0.00 0.03 0.08 0.17 0.57 0.67 0.53 0.60 0.66 0.59 0.85 0.58
12 4 0 11 90 92 87 57 85 89 105 91 94
0.50 0.53 0.67 0.74 0.70 0.61 0.59 0.75 0.43
0.03 0.03 0.21 0.89 0.52 0.80 0.82 0.91 0.51
12 17 19 124 79 131 133 129 132 130
a Variables in the dumb model are year (the long-term trend) and mean monthly temperature (seasonal variation). Best model A is best model selected according to criteria formulated for stepwise regression analysis (see also Table 2); best model B is the subset of best model A with the fewest number of variables that is still superior to the dumb model. The trend in observed TOC is shown for comparison. Abbreviations: prec, precipitation; disch, discharge; dep, deposition. b Snow cover or snow depth; see Table 2.
The considerably higher decline in SO4 deposition compared with NO3 deposition in Norway between the 1980s and 2000 (60 and 10%, respectively (29)) combined with the lack of a relationship between N deposition and the TOC trend at Birkenes point toward the deposition of SO4 rather than NO3 as the long-term control on TOC. Negative relations between lake DOC and S deposition were also found in the U.K. (6) and Finland (1). Deposition of Cl is a proxy for sea-salt deposition, which leads to episodes of soil and water acidification (30) similar to the effects of acid deposition. The negative relations between TOC and deposition of acids and sea salts point to a chemical control of organic matter solubility where reduced strong acids and salts drive increases in DOC concentrations to preacidification levels, as suggested by Evans et al. (13). Acid-Base Chemistry and Organic Acidity. Humic substances, the major constituents of TOC, vary in molecular weight and structure, hydrophobicity, and charge density (31). Humic charge densitysgoverned by the sorption of metals and protons to binding sites like carboxylic and phenolic groupsscan overcome the inherent hydrophobicity of humic molecules and thus affect organic matter solubility (32, 33). Decreases in strong acid input (i.e., H2SO4 and HNO3) may lead to a gradual decrease in occupation of binding sites with protons and to increased charge density. Physical7710
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chemical models that incorporate these mechanisms (14, 15) have been tested successfully for batch studies of natural soil organic matter solubility (34, 35) and purified humic material (36). So far, these concepts have been used on a limited scale to explain the behavior of soil-derived DOC in freshwaters (37, 38). In all catchments, pH increased while ionic strength and concentrations of labile Al decreased significantly (Figure 3) as a consequence of reduced acid deposition (17). Additionally, negative charge deficit or organic acidity increased significantly at a rate between -0.7 and -1.0 meq L-1 yr-1 (Figure 3). The charge density of TOC increased at Storgama and Birkenes from about -1 to around -5 meq g-1 C while the charge density of Langtjern remained constant at about -5 meq g-1 C. Thus, the results for Storgama and Birkenes were consistent with the hypothesis of charge-density-driven organic matter solubility. An earlier study estimated by titration an average content of 5.5 meq g-1 C of carboxylic groups on TOC in 75 acidsensitive Norwegian lakes (39). The relatively large charge density of TOC at Langtjern, combined with the lack of change in charge density despite reduced inorganic acidity, suggest that the majority of carboxylic groups were deprotonated at the prevailing acid-base chemistry at the start of the observation period in 1986. Given the typical content of 5.5
FIGURE 3. Annual mean values of TOC, pH, labile Al, ionic strength, pH, charge deficit and TOC charge density in sites. Trends (Sen-slope) given with level of significance (calculated by Mann-Kendall test) (*, p < 0.5; **, p < 0.01; ***, p < 0.001; ****, p < 0.0001) for each site (B ) Birkenes; S ) Storgama; L ) Langtjern). meq g C-1 of carboxylic groups in humus in Norwegian lakes, it seems reasonable to assume that the charge density of TOC at Langtjern is similar to the site density of carboxylic groups. The observed increase in TOC at Langtjern may be related to increased repulsion of colloids related to reduced ionic strength. Surface charge is balanced by total charge in the diffuse double layer. The diffuse double layer expands when the ionic strength of the surrounding solution declines (40) leading to increased repulsion between colloids at short distances and an increased tendency to desorb from the surface to which they are attached. The combined effect of increased humic charge and reduced ionic strength on organic matter solubility provides a mechanistic explanation of the empirical relation between the long-term TOC increase and the reduction of acid deposition. Chemical Controls of Organic Matter Solubility. In the early days of acid rain research, a link was suggested between the depression of organic matter solubility and increased
acid inputs from the atmosphere (41). According to these authors, decreased organic acidity was simply replaced by mineral acidity on a 1:1 basis, which implied that the input of acid deposition did not acidify surface waters. However, this hypothesis was not in line with experimental evidence (42). Research on acid deposition effects focused on the inorganic chemistry of surface waters and soils, and thus, the postulated link between humic matter solubility and acidification received little attention. While the pH effect of dissolved organic matter in freshwaters is widely accepted and, for example, incorporated in process-based acidification models like MAGIC (43), the response of soil organic matter solubility to reduced acid deposition, relevant for predictions of surface-water acidity and also for calculation of export of catchment TOC, is as yet not considered. Several studies have shown empirical relations between acid deposition and long-term trends in DOC (1, 13, this study). Here we propose that the combined effect of increased humic charge and reduced ionic strength on organic matter VOL. 41, NO. 22, 2007 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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solubility provides one step forward to a mechanistic explanation of this statistical relation. The application of mechanistic models on humic charge interactions with acidbase chemistry could further elucidate the response of organic matter solubility to reduced acid deposition.
(17)
(18)
Acknowledgments Thanks to Torill Engen-Skaugen (met.no) and Wenche Aas (NILU) for supplying respective climate and deposition data, to Tore Høgåsen (NIVA) for assisting with linking databases, and to Dr. Yasumi Yagasaki for his macros designed for WHAM-W file preparation. This work was supported by the Norwegian Research Council (projects 55826/S30 and 165139/ S30), the EU project Eurolimpacs (the Commission of European Communities GOCE-CT-2003-505540), and the Norwegian Directorate for Nature Management. Constructive comments from colleagues and three anonymous reviewers are gratefully acknowledged. The monitoring data used in this paper were funded by the Norwegian Pollution Control Authority (SFT).
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Received for review March 6, 2007. Revised manuscript received June 27, 2007. Accepted July 5, 2007. ES070557F
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