Science of the Total Environment 663 (2019) 537–547

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Science of the Total Environment

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Can alkaline residuals from the pulp and paper industry neutralize acidityin forest soils without increasing greenhouse gas emissions?

Samuel Royer-Tardif a,⁎,1, Joann Whalen b, David Rivest a,c

a Département des Sciences Naturelles, Institut des Sciences de la Forêt Tempérée (ISFORT), Université du Québec en Outaouais, 58 rue Principale, Ripon, QC J0V 1V0, Canadab Department of Natural Resource Sciences, Macdonald Campus of McGill University, 21111 Lakeshore Road, Ste-Anne-de-Bellevue, QC H9X 3V9, Canadac Centre d'Étude de la Forêt, CP 8888, Succursale Centre-Ville, Montréal, QC H3C 3P8, Canada

H I G H L I G H T S G R A P H I C A L A B S T R A C T

• Alkaline residuals are potential limingagents for acidified sugar maple forests.

• Soil pH after liming is explained by theneutralizing power of alkaline residuals

• More neutralization occurred in the for-est floor layer than underlying mineralsoil.

• Greenhouse gas fluxes were lower afterapplication of alkaline residuals.

• Reduction in greenhouse gas fluxes wasrelated to the increase in soil pH.

⁎ Corresponding author.

https://doi.org/10.1016/j.scitotenv.2019.01.3370048-9697/© 2019 Elsevier B.V. All rights reserved.

a b s t r a c t

a r t i c l e i n f o

Article history:Received 1 December 2018Received in revised form 24 January 2019Accepted 25 January 2019Available online 26 January 2019

Editor: Elena Paoletti

Alkaline residuals, such aswood ash and limemud generated frompulp and papermills, could be recycled as lim-ing agents in sugar maple (Acer saccharum Marsh.) forests affected by soil acidification. The objectives of thisstudywere (1) to evaluate soil chemistry, in particular soil acidity, after the application of three alkaline residualsfrom the pulp and paper industry, and (2) to determine if these alkaline residuals altered soil greenhouse gas(GHG) emissions as a result of the change in soil pH or due to their chemical composition. Soil properties andGHG fluxes were monitored for two years after alkaline residuals were applied to six forest sites dominated bysugar maple in southeastern Quebec, Canada. Each site received six treatments: wood ash applied at 5, 10 and20 t ha−1, limemud (7.5 t ha−1), a mixture of slaker grits and green liquor sludge (7 t ha−1) and an unamendedcontrol. These treatments had acid-neutralizing power from 0 to 9 t ha−1. All alkaline residuals buffered soil acid-ity as a function of their neutralizing power, andmore neutralization occurred in the forest floor layer than in theunderlying mineral soil. In the forest floor, the alkaline residual treatments significantly increased pH by morethan one unit, nearly doubled the base saturation, and reduced exchangeable acidity, Al and Fe concentrationscompared to control plots. The CO2 and N2O fluxes were lower after application of alkaline residuals, and thiswas related to the soil pH increase and the type of alkaline residual applied. Limemud was more effective at re-ducing GHG fluxes than other alkaline residuals. We conclude that these alkaline residuals can effectively coun-teract soil acidity in sugar maple forests without increasing soil GHG emissions, at least in the short term.

© 2019 Elsevier B.V. All rights reserved.

Keywords:Wood ashLime mudNeutralization potentialSoil respirationNitrous oxideMethane

halen), david.rivest@uqo.ca (D. Rivest).ry Centre, 1055 du P.E.P.S., P.O. Box 10380, Stn. Sainte-Foy, Québec, QC G1V 4C7, Canada.

E-mail addresses: samrt05@gmail.com (S. Royer-Tardif), joann.whalen@mcgill.ca (J. W1 Present address: Natural Resources Canada, Canadian Forest Service, Laurentian Forest

538 S. Royer-Tardif et al. / Science of the Total Environment 663 (2019) 537–547

1. Introduction

Temperate forests dominated by sugar maple (Acer saccharumMarsh.) are abundant in northeastern America and are of economic im-portance in this region for timber andmaple syrup production (Horsleyet al., 2002). In the past 50 years, sporadic decline and dieback ofmaturesugar maple trees occurred throughout its native range (Bishop et al.,2015; Horsley et al., 2002), in part due to soil acidification induced byacid rain and the leaching of base cations to groundwater and surfacewater (Horsley et al., 2000; Houle et al., 2007; Long et al., 2009). More-over, forest harvesting has the potential to deplete forest Ca pools,which also contributes to soil acidification (Federer et al., 1989;Phillips and Watmough, 2012). Applying calcitic limestone (CaCO3) ordolomitic limestone (CaMg(CO3)2) to the forest floor may successfullycounteract soil acidity and promote sugar maple vigor and growth(Long et al., 2011; Moore et al., 2012; Ouimet et al., 2017). However,limestone applications are expensive and are only considered to becost-effective in forests managed for maple syrup production. Also, theextraction, crushing, handling, transport and application of limestonegenerates up to 0.75 t of CO2/t lime produced, which is a significantsource of GHG emissions (European Commission, 2001).

Pulp and paper mills generate large quantities of residuals such aswood ash, lime mud and a mixture of green liquor sludge and slakergrits, hereafter referred to as “grits and grids” (Martins et al., 2007;Monte et al., 2009). These residues are highly alkaline (pH N 10) andrich in Ca (Jia et al., 2014; Morris et al., 2012). Wood ash also containsmacronutrients required for plant growth such as K, Mg, and P(Pitman, 2006). The neutralizing power (NP) of alkaline residuals is asuitable way to consider multiple residues on an equivalent basis. It isa measure of the quantity of acidity that can be buffered relative topure calcium carbonate and ranges from about 50% for wood ash(Hébert and Breton, 2008) to N85% for lime mud (Gagnon and Ziadi,2012). There is interest to valorize these residuals as liming agents inmanaged forests (Hannam et al., 2017; Huotari et al., 2015; Vestergardet al., 2018) as an alternative to disposing them in landfills, since theymay represent a cost-effective substitute for calcitic or dolomitic lime-stone as long as they respect the legislation concerning heavy metalconcentrations (Hébert and Breton, 2008).

The change in soil pH resulting from liming of forest soils has beenhypothesized to increase emissions of several GHG, namely CO2, N2Oand CH4 (Huotari et al., 2015; Maljanen et al., 2014). Raising soil pHwith alkaline residuals stimulates soil microbial activity (Jokinen et al.,2006), thereby increasing heterotrophic soil respiration (Baath andArnebrant, 1994; Zimmermann and Frey, 2002), and soil organicmatterdecomposition (Perkiomaki et al., 2004). Such conditions favour Nmin-eralization generating a pool of mineral N that can be transformed intoN2O through themicrobially-mediated reactions of ammonia oxidation,nitrifier-denitrification and denitrification (Kool et al., 2011). Welldrained forest soils are considered a sink for CH4 because CH4 oxidationis generally greater than CH4 production (Fahey et al., 2005). However,CH4 oxidation can be inhibited by high concentrations of ammonium(Bodelier and Steenbergh, 2014; Steudler et al., 1989), and an increasedrate of N mineralization following liming is expected to reduce CH4 ox-idation (Maljanen et al., 2006).

Apart from their effect on soil pH, different types of alkaline residualsmay also influence GHG emissions due to their chemical properties. Forexample, Ca ions, theprincipal constituent of limemaydecrease the bio-availability of soil organic carbon by binding to dissolved organic com-pounds and thus reduce soil respiration (Balaria et al., 2015; KunhiMouvenchery et al., 2012). In addition, wood ash contains readily solu-ble salt ions (K+ and Na+) that may interfere with the microbial pro-cesses generating N2O (Liimatainen et al., 2014). Depending on itschemical composition and the tree species, wood ash may also reducetree growth (Brais et al., 2015) and fine root production (Clemensson-Lindell and Persson, 1993), thereby decreasing the autotrophic contri-bution to soil respiration. Such differences between alkaline residuals

applied may explain the discrepancy in GHG emissions among field ex-periments. For example, the application of wood ash in boreal forestswas reported to increase (Rosenberg et al., 2010), have no effect(Ernfors et al., 2010) or reduce soil respiration (Klemedtsson et al.,2010). Similarly, N2O emissions from temperate forest soils may in-crease (Butterbach-Bahl et al., 1997; Papen and Butterbach-Bahl,1999) or decrease (Borken and Brumme, 1997) following lime applica-tion. To date, however, no study has separated the effect of soil pH fromother changes in soil chemistry caused by alkaline residuals to under-stand the GHG fluxes from lime-amended forest soils.

To better understand the influence of liming on acidified soils fromsugar maple dominated forests, we designed a study were forest plotswere treated with different alkaline residuals from the pulp and paperindustry. The objectives of this study were to (1) evaluate to what ex-tent different alkaline residuals, namely wood ash, grits and grids, andlimemud, can neutralize acidity in forest soils, and (2) compare the con-tribution of soil pH, versus the type of alkaline residual, to the soil GHGfluxes from limed forest soils. We hypothesize that the capacity of alka-line residuals to neutralize acidity in forest soils is a function of theirneutralizingpower. Furthermore,we hypothesize that soil pH is respon-sible for the change in GHG fluxes from soils in acidified sugar mapleforests, and that an increase in soil pH will result in greater CO2 andN2O fluxes while reducing CH4 oxidation.

2. Method

2.1. Study area

The study was conducted in the Eastern Townships of Quebec,Canada (45°33′–45°39′N, 71°43′–71°55′W). This region is located inthe sugar maple-basswood (Tilia Americana L.) bioclimatic domain,which also contains other deciduous tree species such as yellow birch(Betula alleghaniensis Britt.), white ash (Fraxinus americana L.), easternhop-hornbeam (Ostrya virginiana (Miller) K. Koch) and black cherry(Prunus serotina Ehrhart var. serotina) (Saucier et al., 2009). This region,located at the base of the AppalachianMountains, is characterized by anundulating topography with low elevation summits and gentle slopes(Cann and Lajoie, 1943). Mean annual temperature is 5.6 °C with dailyaverages of−10.6 °C in January to 19.6 °C in July, and total annual pre-cipitation is 1146 mm (Environment Canada, 2016). Soils in the studyarea are ferro-humic Podzols and dystric Brunisols developed on glacialtill deposits composed of non-calcareous Ordovician slate and pre-Cambrian shists (Cann and Lajoie, 1943).

Soils in this region experienced acidification beginning in the mid-20th century. Between 1994 and 1998, sulphur and nitrogen depositionexceeded the soil critical loads by 1 and 600 eq ha−1 yr−1, respectively,but by 2002, the combined acidic deposition was only −199 to400 eq ha−1 yr−1 (Carou et al., 2008). From 1999 to 2002, total dryandwet acid deposition deposited about 21 kg SO4-S ha−1 yr−1 and be-tween 8 and 10 kg N ha−1 yr−1 in this area, but these depositions weredecreasing at a rate of 38 and 11% per decade, respectively, due to morestringent air quality regulations (Ouimet and Duchesne, 2009).

2.2. Site selection and alkaline residual characteristics

Six sugar maple-dominated stands were selected at random fromstands that experienced a partial harvest (ca. 30% basal area) duringwinter 2013. Partial harvest was necessary to make a roadway for mo-torized access to the sites. All selected stands were mature, coveringan area of more than 4 ha, uniform in their topography, tree composi-tion and loamy soil texture. Table 1 summarizes the principal character-istics of each site.

Alkaline residuals used in this study were obtained from the DomtarWindsor pulp and paper mill (Windsor, QC, CAN) and are deemed suit-able for land application according to the BNQ 0419-090 standard(Bureau de normalisation du Québec (BNQ), 2015). The chemical

Table 2Physical and chemical characteristics of alkaline residuals applied to sugarmaple forests inQuebec, Canada. CCE = calcium carbonate equivalent.

Wood ash Grits and grids Lime mud

Moisture content (% dry weight) 47.1 78.6 41.1pH 13.0 – 11.2CCE (%) 51 82 95.3

Macronutrients (g/kg)Ca 200 270 394Mg 14 17 3.3K 15 10 0.8P 3 0.8 2.9S 3.4 NA 0.3

Micronutrients (mg/kg)Mn 3800 7000 234As 110.0 5.0 2.7B 170 19 8.3Cd 8.9 11 0.3Co 7 3 0.8Cr 75 18 9.1Cu 140 88 3Hg 0.38 0.02 b0.01Mo 5.0 1 0.1Ni 34 25 5.6Pb 380 19 0.5Na 3900 66,000 6570Zn 1100 1000 28

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composition of each alkaline residual is given in Table 2. The wood ash(WA) was stabilized with water, allowed to harden and dry by a self-hardening process (Pitman, 2006) before it was crushed and sieved to5–15 mm pellets, prior to application. The lime mud (LM) did not re-quire additional treatment and was composed of 1–10 mm pellets.The grits and grids (GG) formed a compact slurry that was mixed withwood ash (1:2 ratio) to achieve homogenous application on plots.

2.3. Treatment application

The experiment was established in early September 2014. In eachselected stand (n = 6 stands, corresponding to 6 replicates), wedelimited six 5 × 5mplots, each separated by a 10m buffer zone. Treat-ments were assigned randomly to plots as follows (all masses are in dryweight equivalent and treatment abbreviations are indicated in paren-thesis): an unamended control, 5 t wood ash ha−1 (WA5), 10 t woodash ha−1 (WA10), 20 t wood ash ha−1 (WA20), 7.5 t lime mud ha−1

(LM), and 7 t ha−1 of grits and grids mixed with wood ash (WA+ GG). All treatments were applied manually to ensure a uniform cov-erage on each plot.

2.4. Soil sampling and analysis

The forest floor (F and H horizons) and the 0–15 cm of underlyingmineral soil (A and part of the B horizon), were sampled before the ex-perimentwas established (September 2014) and at the end of the studyperiod (September 2016). Five soil cores per plot were collected with ahand trowel, pooled by layer (forest floor and mineral) and placed inseparate plastic bags. In each sampling location, the forest floor thick-ness was measured with a caliper (±0.05 mm). Soil samples were airdried before analysis for physical and chemical variables.

A subsample of fresh soil was dried at 105 °C for 24 h to determinethe gravimetric water content. A second subsample was air-dried andused for physical and chemical analyses. Soil texture was assessedusing the hydrometer method (Kroetsch and Wang, 2007). Soil pHwasmeasured in distilledwater (1:10 and 1:2 soil-to-water ratio for or-ganic andmineral soils, respectively). Soil exchangeable acidity was de-termined by titration of BaCl2 (0.1 M) soil extracts (Hendershot et al.,2007b). Soil cationic exchange capacity (CEC) was determined as the

Table 1Principal characteristics of the six sugar-maple dominated stands in the Eastern Townships of

Site 1 Site 2 Site 3

Localisation N 45° 34′ 23.0″ N 45° 37′ 05.9″ N 45° 37′ 5W 71° 51′ 27.1″ W 71° 43′ 37.3″ W 71° 15′

Stand age (years) b80 b80 N80Basal area (m2/ha) 16.7 14.7 18.7Sugar maple (% BA) 38 72 66Other tree species Fagus grandifolia Fraxinus americana Fagus gran

Betula alleghaniensis Betula allegForest floor thickness (mm) 20 21 35B horizon (0–15 cm depth)pH 3.98 4.44 4.02Exchangeable acidity(cmol+ kg−1)

11.7 9.2 12.4

CEC (cmol+ kg−1) 14.8 21.2 12.6BS (%) 38.1 69.3 41.2Organic matter (g/kg) 133 99 93Clay (g/kg) 248 255 292Silt (g/kg) 387 467 536Sand (g/kg) 365 278 172Dominant species in theunderstory

Dennstaedtiapunctilobula

Dryopteriscarthusiana

Acer saccha

Fagus grandifolia Acer pensylvanicum Dennstaedpunctilobu

Dryopteris carthusiana Fraxinus americana Thelypterisnoveborace

BA: basal area, CEC: cation exchange capacity, BS: base saturation.

sum of Ca, Mg, K, Na, Fe, Al, Mn and Zn concentrations in unbufferedBaCl2 (0.1 M) soil extracts (Hendershot et al., 2007a). The concentra-tions of thesemetalsweremeasured on aflame atomic absorption spec-trophotometer (Varian 220FS, Palo Alto, CA). Base saturation (BS) wasdetermined as the percentage of CEC occupied by Ca, Mg, K and Na. Athird soil subsamplewas oven dried at 60 °C, ball-milled and used to de-termine total C and N content following high-temperature combustionon a TruMac CNS analyzer (LECO, St. Joseph, MI).

In 2016, fresh soil samples were also analyzed for mineral N (NH4,NO3) concentrations in KCl (1 N) soil extracts, by colorimetry on a mul-tichannel auto-analyzer (Lachat Instruments, Loveland, CO) (Maynardet al., 2007). In addition, the available P concentration in Mehlich IIIsoil extracts was analyzed colorimetrically (Tran and Ziadi, 2007).

Quebec, Canada.

Site 4 Site 5 Site 6

6.9″ N 45° 41′ 14.1″ N 45° 36′ 41.9″ N 45° 34′ 19.9″02.5″ W 71° 17′ 32.2″ W 71° 44′ 54.6″ W 71° 48′ 59.7″

b80 N80 b8018.0 15.2 20.794 81 40

difolia Fagus grandifolia Fagus grandifolia Acer rubrumhaniensis Betula alleghaniensis

63 22 44

4.74 4.08 4.30NA 10.9 13.8

31.4 11.9 14.985.9 41.5 39.291 98 86236 199 195537 466 402227 335 403

rum Acer saccharum Dennstaedtiapunctilobula

Thelypterisnoveboracensis

tiala

Dennstaedtiapunctilobula

Dryopteris carthusiana Dryopteris carthusiana

nsisViola sp. Acer saccharum Betula alleghaniensis

540 S. Royer-Tardif et al. / Science of the Total Environment 663 (2019) 537–547

2.5. Gas sampling and flux calculation

Gas sampling occurred on nine sampling dates fromOctober 2014 toSeptember 2016 corresponding to three periods during the growingseason: spring (May), summer (June, August–September), and fall (Oc-tober). Gas samples were collected from four static chambers (14 cmhigh, 7.5 cm radius) that were randomly deployed in each plot (n =144 chambers). Chambers were made from standard PVC tubes, closedon one end with a teflon sheet, tightly sealed with polyurethane adhe-sive, and insulated with reflective bubble thermofoil. On top of eachchamber, we fixed a sampling port equipped with an injection mem-brane (Surgi-Pharm Avancée, Dorval, CA) and a 2 mm hole was drilledthrough the top to avoid pressurization of the chamber (Rochette,2011). During deployment, these chambers were fixed, using electricaltape, to PVC bases of the same dimensions as the chambers. Thesebases were inserted at a depth of 10 cm in the ground, 2 weeks priorto the first measurement, and left on site for the duration of the exper-iment. At 0, 8, 16 and 24 min after chamber closure, a 5 ml headspacegas sample was collected from each chamber with a syringe and pooledto yield one sample per plot at each sampling time, as described byArias-Navarro et al. (2013). This involved injecting four headspace gassamples per plot into pre-vacuumed (30 psi) 12 ml exetainers (Labco,High Wycombe, UK) containing 15 mg of magnesium perchlorate (Mg(ClO4)2) to absorb water vapor, and with caps packed with one extraPTFE/Silicone 13-mm septa (Superlco, Bellefonte, USA). Gas sampleswere analyzed within 24 to 48 h after sampling on a GC 450 (Bruker,Karlsruhe, DE) equipped with a thermal conductivity detector (TCD)for CO2, an electron capture detector (ECD) for N2O, and a flameionisation detector (FID) for CH4 determination. The carrier gas washelium for both the FID and TCD, and argon for the ECD.

On each sampling date, the raw gas flux rate (FHMR) was determinedusing the HMR package in R (Pedersen et al., 2010). Briefly this proce-dure uses maximum likelihoods to predict the best fit of gas concentra-tions at 0, 8, 16 and 24 min to a linear relationship or the Hutchinsonand Mosier saturation relationship. Raw gas fluxes were adjusted forair temperature and pressure using the equation of (Rochette andBertrand, 2007):

Fg ¼ dG.

dt� V�

A � Mm;g�Vm

� 1−ep=P� � ð2:1Þ

where dG�dt is the raw variation in gas concentration (mol mol−1) per

unit of time, V is the chamber volume (L), A is the soil surface coveredby the chamber (m2), ep is the partial pressure of water vapor of cham-ber air (kPa), P is the barometric pressure recorded during chamber de-ployment (kPa),Mm, g is the molecular mass of the gas considered, andVm is the molecular volume of that gas at the temperature and pressurerecorded. The HMR package already considers chamber volume and soilarea in its computation and Eq. (2.1) can be simplified to determinestandardized gas fluxes (Fg) as follows:

Fg ¼ FHMR � Mm;g�Vm

� 1−ep=P� � ð2:2Þ

During chamber deployment, air pressure was recorded on aSamsung Galaxy S3 pressure chip, and air and soil temperature weremeasured using a regular thermometer inserted into the center ofeach plot. Soil moisture (v/v) was measured close to each gas chamberusing a FieldScout TDR 100 probe (Spectrum Technologies, Inc., Aurora,CO).

2.6. Statistical analyses

Significant differences in soil chemical variables between treatmentswere tested separately for each soil layer with linear mixed-models, in-cluding a random effect of study sites (intercept) and with the treat-ment (type and dosage of fertilizer applied) as a fixed factor with the

function lme from the R package nlme (Pinheiro et al., 2016). Ashdoses were analyzed as factors to enable the comparison with theother treatments (i.e. LM and WA + GG) and because their influenceon soil variables was generally non-linear. Significant differences be-tween treatments were tested by Tukey HSD pairwise comparisonsperformed with the function glht from the multcomp R package(Hothorn et al., 2008). Residual plots were inspected for normalityand homoscedasticity.

Soil pH at the end of the experiment was modeled as a function ofthe NP of alkaline residuals added two years earlier. The NP was theproduct of the calcium carbonate equivalent (Table 2) × dry mass perha of each alkaline residual. Linear, quadratic, and non-linear relation-ships between pH andNPwere compared using the corrected Akaike in-formation criterion (AICc) and the best fit curve was retained. Non-linear relationships were modeled using generalized additive models(GAM) from the R package mgcv (Wood, 2011). Models that includedor excluded a random intercept of the site and additional covariates(soil pH, hydrogen ions activity (10−pH), forest floor thickness) werealso compared by AICc to determine the best fit curve.

Two answer the second research objective, we opted for a statisticalapproach that directly models GHG fluxes because nine sampling datesover two years were insufficient to evaluate total annual or seasonalGHG emissions. We explored the role of soil pH and the type of residualin predicting GHG fluxes (Fg) by comparing four different linear mixed-models: a base model containing ancillary variables only, two modelsadding the effect of soil pH (at the end of the experiment) or the typeof residual applied (six types were evaluated), and a full model contain-ing all variables. The ancillary variables for the base model were soiltemperature, soil moisture, their interaction and the time (d) sinceresidual application. The non-linear influence of soil temperature onGHG fluxes was described with a quadratic term in the model(Fg~temp2). Because the influence of alkaline residuals may changethrough time with their dissolution, we also tested the interactions be-tween the time since application, the type of residual and the soil pH.These interactions, included as fixed variables, did not explain a signifi-cant portion of CO2 and N2O fluxes and were excluded from the finalmodel. For CH4, however, there was a significant interaction betweentime since residuals application and soil pH, so these terms wereretained in the final model. All four models accounted for the nested ef-fect of sampling date within sites included as a random intercept. TheVarIdent option from the R package nlme (Pinheiro et al., 2016) wasused to address heteroscedasticity in gas fluxes between samplingdates. A logarithmic transformation was applied to N2O concentrationsto ensure normality of the residuals and facilitate model convergence.Three extreme outliers were removed from N2O values. Residual plotswere inspected for normality, homoscedasticity and the absence oftrends with each of the explanatory variables.

The four models described above were compared using thecorrected Akaike information criterion (AICc) and likelihood ratios.Total variance explained by eachmodel was expressed as the coefficientof determination for generalized mixed-effect models as obtained withthe MuMIn R package (Barton, 2015). Significant differences betweentypes of residuals were tested by Tukey HSD pairwise comparisons. Allstatistical analyses were performed in the R environment (R CoreTeam, 2016).

3. Results

3.1. Alkaline residuals alter soil chemistry in sugar maple forests

In the forest floor, the alkaline residuals applied in this study in-creased pH by more than one unit, nearly doubled base saturation,and reduced exchangeable acidity, Al and Fe concentrations comparedto control plots (Table 3). The highest wood ash dose (WA20) also re-sulted in significantly (P b 0.05) greater CEC. The WA10, WA20 andWA + GG treatments increased the Ca concentration significantly (P b

541S. Royer-Tardif et al. / Science of the Total Environment 663 (2019) 537–547

0.05). Compared to control plots, the ammonium concentration was re-duced by two thirds following application of alkaline residuals.

The liming effect of alkaline residuals was less pronounced in themineral soil than in the forest floor, since only LM increased pH in themineral soil significantly, by 0.5 unit (P b 0.05; Table 3). Most alkalineresidual treatments (except WA5) increased BS by N2 times the BSvalue of the control plot. The WA + GG and LM treatments also in-creased the Ca concentration (P b 0.05), and the WA + GG residual in-creased the Mg concentration (P b 0.05), relative to the unamendedmineral soil.

Two years after alkaline residuals were applied, the final soil pH inforest floor and mineral soil was predicted from the NP of the alkalineresidual and the initial soil pH (Fig. 1). The best-fit lines describingthis relationship were a quadratic relationship (R2 = 0.73) in the forestfloor and a generalized additive model (R2 = 0.56) in the mineral soil(Table S1).

3.2. GHG fluxes affected by alkaline residuals

The CO2 fluxes varied between 84.5 and 898.2 mgm−2 h−1 with anaverage of 321.9mgm−2 h−1 (Fig. 2). Thesefluxeswere correlatedwithsoil and air temperature, with the highest fluxes recorded in late August2016 and early September 2015 (Fig. 2). N2O fluxes varied between

Table 3Soil chemistry properties in organic andmineral soil layers of sugarmaple forests, two years afteinparenthesis.Within a row, valueswith different lowercase letters differ significantly at theP bsaturation, WA5= 5 t wood ash ha−1, WA10 = 10 t wood ash ha−1, WA20 = 20 t wood ashmud ha−1.

Forest floor (FH horizons)

Control WA5

C (%) 33.5 (5.47) 29.06 (4.32)N (%) 2.03 (0.36) 1.65 (0.23)CN (ratio) 16.55 (1.17) 17.6 (0.8)Exchangeable cations (cmol+ kg−1) Ca 9.42 (6.38)b 27.47 (9.48)ab

K 0.73 (0.16) 0.76 (0.17)Mg 1.91 (1.27) 3 (0.51)Na 0.1 (0.09)b 0.08 (0.02)bAl 8.26 (6.46)b 0.92 (0.73)aFe 0.24 (0.17)b 0.06 (0.05)aMn 1.92 (1.49)b 1.4 (0.58)ab

CEC (cmol+ kg−1) 22.57 (8.55)b 33.69 (9.42)abBS (% of CEC) 54.14 (20.06)b 92.38 (4.51)aExchangeable acidity (cmol+ kg−1) 14.46 (7.17)b 4.62 (1.31)apH (H2O) 4.43 (0.21)c 4.93 (0.27)bNH4

+ (mg/kg) 516.0 (351.6)a 169.7 (170.8)bNO3

− (mg/kg) 117.0 (45.8) 193.9 (175.0)Available P (mg/kg) 92.45 (44.89) 76.44 (32.41)

Mineral soil (AB horizons)

Control WA5

C (%) 5.72 (2.26) 5.37 (2.73)N (%) 0.37 (0.1) 0.36 (0.13)CN (ratio) 15.12 (2.23) 14.57 (2.4)Exchangeable cations (cmol+ kg−1) Ca 0.94 (0.5)b 1.92 (0.74)ab

K 0.13 (0.02) 0.14 (0.05)Mg 0.18 (0.12)b 0.42 (0.23)abNa 0.01 (0.01)ab 0 (0)bAl 6.59 (1.69) 6.12 (2.82)Fe 0.15 (0.08) 0.16 (0.17)Mn 0.12 (0.06) 0.17 (0.24)

CEC (cmol+ kg−1) 8.11 (1.83) 8.93 (3.55)BS (% of CEC) 15.94 (6.93)c 28.1 (7.18)bcExchangeable acidity (cmol+ kg−1) 8.85 (2.13) 8.84 (4.53)pH (H2O) 4.29 (0.3)b 4.26 (0.37)abNH4

+ (mg/kg) 9.5 (4.7) 18.6 (16.9)NO3

− (mg/kg) 23.2 (13) 24.9 (14.9)Available P (mg/kg) 4.72 (3.04) 5.32 (4.07)

−22.1 and 291.5 μgm−2 h−1 butwere positively skewedwith amedianof 8.1 μg m−2 h−1. The highest N2O fluxes were observed in May andJune during 2015 and 2016. CH4 fluxes were mostly negative, varyingfrom −756.1 to 204.0 μg m−2 h−1, and were negatively skewed witha median of −88.9 μg m−2 h−1.

The best fit model describing CO2 flux included final soil pH and thetype of alkaline residual as explanatory variables (model 4; Table 4). TheCO2 flux was negatively related to final soil pH (Fig. 3a) and most treat-ments produced CO2 fluxes that were similar to the control (Fig. 3b), al-though the CO2fluxwasmarginally higher in the LM treatment than thecontrol (t1,187= 1.94, P= 0.0536). Final soil pH did not explain a signif-icant portion of N2O flux, and the best fit model included the type of al-kaline residual plus ancillary variables (model 3; Table 4). The WA20and LM treatments reduced the N2O flux significantly, compared tothe control (Fig. 3e). The best fit model for CH4 flux included final soilpH and the type of alkaline residual (model 4; Table 4), and there wasa significant interaction between the time since residual applicationand final soil pH (Fig. 3e). A smaller CH4 sink capacity was measuredas soil pH increased soon after residual application, but the trendchanged during the experimental period and a slightly positive relation-ship between CH4 oxidation and soil pHwas observed by the end of theexperiment. There was a significant increase in CH4 oxidation in the LMtreatment, compared to the control.

r the application of alkaline residuals. Values are themean (n=6)with standard deviation0.05 level (TukeyHSDpairwise comparisons). CEC=Cationic exchange capacity, BS=Baseha−1, WA + GG= wood ash mixed with grits and grids at 7 t ha−1, and LM = 7.5 t lime

WA10 WA20 WA + GG LM

28.03 (5.05) 31.22 (6.48) 34.1 (9.97) 28.02 (2.75)1.6 (0.3) 1.75 (0.5) 1.89 (0.53) 1.68 (0.2)17.6 (1.53) 18.28 (2.68) 18.03 (0.38) 16.68 (1.11)33.87 (11.15)a 45.95 (11.72)a 36.25 (24.61)a 31.14 (12.81)ab0.54 (0.12) 0.6 (0.14) 0.53 (0.22) 0.47 (0.2)2.92 (0.94) 3.59 (0.73) 3.36 (2.54) 2.35 (0.97)0.09 (0.05)b 0.1 (0.03)b 0.22 (0.07)a 0.17 (0.08)ab0.55 (0.98)a 0.13 (0.12)a 2.28 (4.11)a 1.25 (2.2)a0.03 (0.01)a 0.03 (0.01)a 0.04 (0.02)a 0.03 (0.02)a0.71 (0.2)a 0.59 (0.51)a 0.86 (0.46)ab 0.75 (0.41)a38.71 (11.46)ab 50.99 (12.84)a 43.53 (24.37)ab 36.17 (12.02)ab95.91 (4.37)a 98.57 (0.56)a 87.07 (20.26)a 92.43 (9.84)a3.36 (1.36)a 2.59 (0.73)a 5.48 (4.41)a 3.98 (2.74)a5.57 (0.24)ab 5.88 (0.39)a 5.51 (0.6)ab 5.77 (0.51)a159.4 (234.0)b 94.6 (100.9)b 64.4 (62.6)b 79.4 (68.5)b239.2 (223.9) 171.4 (158.6) 241.1 (118.5) 107.3 (60.6)66.68 (25.65) 64.86 (19.05) 60.58 (21.51) 73.78 (28.47)

WA10 WA20 WA + GG LM

5.02 (1.54) 6.13 (2.16) 6.55 (1.11) 5.79 (1.71)0.35 (0.11) 0.38 (0.09) 0.43 (0.08) 0.4 (0.11)14.45 (1.78) 15.91 (2.02) 15.43 (0.94) 14.76 (1.66)2.8 (1.55)ab 3.12 (1.65)ab 3.87 (3.48)a 3.86 (2.27)a0.15 (0.05) 0.17 (0.05) 0.16 (0.02) 0.15 (0.03)0.51 (0.42)ab 0.46 (0.16)ab 0.67 (0.58)a 0.42 (0.24)b0.01 (0.01)ab 0.01 (0.01)ab 0.03 (0.03)a 0.03 (0.01)a4.76 (1.55) 4.95 (0.97) 5.03 (0.82) 4.49 (1.49)0.1 (0.06) 0.08 (0.06) 0.06 (0.03) 0.06 (0.04)0.11 (0.06) 0.19 (0.19) 0.26 (0.17) 0.23 (0.24)8.45 (2.69) 8.97 (2.38) 10.09 (3.2) 9.23 (1.97)39.9 (12.24)ab 40.5 (11.67)ab 41.7 (22.46)ab 46.44 (18.41)a6.85 (1.7) 6.99 (1.04) 7.07 (1.24) 6.3 (2.22)4.45 (0.3)ab 4.5 (0.3)ab 4.6 (0.13)ab 4.77 (0.31)a17.4 (14.8) 9.4 (5.6) 8.4 (7.2) 12.5 (6.1)18.6 (9.1) 12.1 (6.1) 19.2 (3.7) 15.0 (8.7)8.09 (4.26) 8.24 (3.01) 6.84 (5.51) 8.05 (6.11)

Fig. 1.Neutralizing power of alkaline residuals affects the soil pH in (a) the forest floor and (b) the mineral soil of sugarmaple forests, two years after alkaline residuals were applied. Theshaded grey area corresponds to the 95% confidence interval of the model predicted (black curve) from the average initial pH (4.1 in the forest floor and 4.2 in the mineral soil). WA5=5 t wood ash ha−1, WA10 = 10 t wood ash ha−1, WA20 = 20 t wood ash ha−1, WA + GG= wood ash mixed with grits and grids at 7 t ha−1, and LM = 7.5 t lime mud ha−1.

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4. Discussion

4.1. Liming and fertilizing effect of alkaline residuals

As we hypothesized, NPwas the principal attribute of alkaline resid-uals that determined their efficiency in neutralizing soil acidity. Thefinal soil pH, two years after alkaline residuals were applied, was an as-ymptotic function determined by the NP of the alkaline residuals, ratherthan application rate. For example, doubling the amount of wood ashfrom 10 to 20 t wood ash ha−1 had little effect on the soil pH of the for-est floor and the mineral soil. These soil layers appear to resist furtherchange in soil pH beyond pH 6 in the forest floor and around pH 4.5 inthe mineral soil. In acidified sugar maple forests, Moore et al. (2012)also reported a saturation in soil pH (around pH 6.25 in the forestfloor and pH 5.75 in themineral soil) with increasing lime doses. In for-est soils, sources of acidity that lower soil pH include the deprotonationof weak organic acids (Magdoff and Bartlett, 1985) and the hydroxyl-ation of Al3+ cations (Bloom and Skyllberg, 2012).

All alkaline residuals increased BS and reduced exchangeable acidityto the same extent, which is further evidence that alkaline residualswere effectively altering the soil acid-base status in forest soils. Eventhe WA5 dose added 1 t of Ca on the forest floor, which was enoughto replace most of the exchangeable acidity from cation exchange sitesand saturate CEC with base cations. However, there was still a signifi-cant amount of exchangeable acidity present after application of alka-line residuals since only the WA20 caused a significant increase inCEC. Alkaline residuals dissolve slowly in natural forests and theirliming effect may last for N15 years (Moore et al., 2012; Saarsalmiet al., 2012), suggesting that more reaction time is needed to re-duce the exchangeable acidity. Further changes in the soil acid-base status of these treated forest soils may be expected in thefuture.

Our results also suggest that the organic layer was more responsiveto the liming effects of alkaline residuals than the underlying minerallayer, similar to other reports (Brais et al., 2015; Moore et al., 2012;Reid and Watmough, 2014). This observation is consistent with the re-action of liming agents added to the soil surface (rather thanmixedwith

the soil), since Ca and other base cations dissolve slowly and need timeto leach through the forest floor and reach the mineral horizons. Simi-larly, Callesen et al. (2007) observed that 65% of Ca, Mg and K and 81%of P were still present in wood ash at the soil surface, seven years aftertheir application to forests in Denmark.

All alkaline residuals significantly reduced the ammonium concen-tration in the forestfloor. This response is unlikely to representN immo-bilization by plants or microbes, since wood ash has rarely been shownto increase plant N concentrations (Augusto et al., 2008) and more am-monium consumption by microorganisms should have promoted soilrespiration (Baath and Arnebrant, 1994). Moreover, in another trialusing the same plots, the nutrition and growth of sugar maple andbeech seedlings did not increase two years following the applicationof alkaline residuals (unpublished data). In alkaline conditions, ammo-nium can be deprotonated to ammonia, which is vulnerable to volatili-zation as gaseous ammonia. Another possibility is that increasing soilpH could stimulate the activity of ammonia oxidizing archaea and am-monia oxidizing bacteria, which convert ammonium to nitrite. Manyof the ammonia oxidizers are chemolithoautotrophs that acquire C byconsuming CO2 rather than from heterotrophic oxidation of soil organicmatter (releases CO2). The nitrite produced by ammonia oxidizers israpidly converted to nitrate by autotrophic or heterotrophic nitrifiersunder aerobic conditions. However, nitrate concentrations did not fol-low the same pattern as ammonium concentrations following applica-tion of alkaline residuals. This indicates that excess nitrate could haveeither been lost though leaching, immobilized in plant and microbialbiomass or lost through denitrification. However, soil respiration andN2O fluxes did not increase with application of alkaline residuals, there-fore ruling out the last two possibilities. In contrast, dissolved cations re-leased from alkaline residuals will leach through the soil profile,accompanied by anions such as nitrate to maintain electrical neutrality,and this is a known pathway for ecosystem-level N loss (Kahl et al.,1996;Williams et al., 1996). Alternatively, the alkaline residuals appliedin this study may have reduced the Nmineralization rate by interferingwith the activity of extracellular enzymes responsible for protein degra-dation, resulting in less ammonium in the soil solution (Bjork et al.,2010).

Fig. 2. Standardized CO2, N2O and CH4 fluxes (Fg) from forest soils, following application of alkaline residuals, fromOctober 2014 to August 2016.Within the box plot chart, the crosspiecesof each box plot represent (from top to bottom)maximum, upper-quartile, median (thick bar), lower-quartile andminimum values. Outliers are represented by round dots. Themean airand soil temperatures, and soilmoisture at each sampling date are shown, alongwith the standard deviation.WA5=5 twood ash ha−1,WA10=10 twood ash ha−1,WA20=20 twoodash ha−1, WA + GG= wood ash mixed with grits and grids at 7 t ha−1, and LM= 7.5 t lime mud ha−1.

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Table 4Test statistics in four models explaining CO2, N2O and CH4 fluxes following application ofalkaline residuals. The Base model (1) included only ancillary variables whereas pH(2) and Type (3) models included final soil pH and the type of residuals, respectively.The pH + Type model (4) included all variables. The column “Test” indicates, for eachgas, whichmodels were compared for the likelihood ratio. These comparisons weremadefrom the simplest to themore complexmodel (1 to 4). The best model selected, indicatedin bold, had the lowest AICc and explained significantly more variance (significant likeli-hood ratio) than other models.

Model R2 AICc Test Likelihoodratio

P-value

CO2

1 Base 0.7004 2817.42 – – –2 pH 0.7076 2817.43 1 vs 2 1.999 0.15743 Type 0.7095 2812.95 1 vs

3a12.472 0.0142

4 pH+ Type

0.7109 2809.95 3 vs 4 5.002 0.0253

N2O1 Base 0.1280 304.65 – – –2 pH 0.1464 304.03 1 vs 2 2.6168 0.10573 Type 0.2226 302.89 1 vs

3a11.7590 0.0382

4 pH +Type

0.2228 303.93 3 vs 4 0.9547 0.3285

CH4

1 Base 0.5864 2419.86 – – –2 pH 0.6066 2416.15 1 vs 2 7.706 0.02123 Type 0.6450 2415.37 2 vs 3 6.779 0.07934 pH

+ Type0.6808 2407.56 3 vs 4 11.814 0.0027

a In these cases, the model 2 did not explain more variation than the model 1 so themodel 3 was compared to the model 1.

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4.2. GHG fluxes affected by alkaline residuals

Based on previous observations that liming strongly stimulates mi-crobial activity in forest soils (Baath and Arnebrant, 1994; Jokinenet al., 2006; Zimmermann and Frey, 2002), we hypothesized that alka-line residuals would increase GHG fluxes from sugar maple forest soilsdue to their effect on soil pH. Contrary to this expectation, the CO2 andN2O fluxes were lower in the two-year period after application of alka-line residuals, while the CH4 flux showed a variable trend. Our resultsindicate that soil pH and the type of alkaline residual were both respon-sible for the reduction in GHG fluxes. Among alkaline residues, the LMhad the greatest impact on GHG fluxes, probably due to the faster disso-lution of fine-textured LM compared to the other alkaline residuals(Royer-Tardif, personal observation). This assumption is supported bythe fact that LM had a greater effect on pH and chemical parametersin the mineral soil than the other alkaline residuals.

In this study, the soil pH increase from alkaline residuals reducedCO2 fluxes significantly. Klemedtsson et al. (2010) also observed a re-duction in soil respiration (by 17–23%) following the application of 3.3and 6.6 t WA ha−1 in a Norway spruce (Picea abies L.) plantation.While there was no change in soil respiration during the first fiveyears after lime or wood ash application in several forests (Ernforset al., 2010; Maljanen et al., 2006; Winsborough et al., 2017), longerterm experiments (N9 years) found a significant increase in soil respira-tion after wood ash application in forests (Maljanen et al., 2014;Maljanen et al., 2006; Rosenberg et al., 2010). This time-dependent re-sponse of soil respiration to wood ash application could be related toa lag in microbial adjustment to soil chemical conditions. For exam-ple, it took four years after wood ash application to oligotrophicpeatlands before Bjork et al. (2010) observed a reduction of micro-bial phospholipid fatty acid (PLFA) biomarkers in the topsoil layer(0–5 cm) and a concomitant reduction of N mineralization and am-monification rates.

Another explanation for the lower CO2 fluxes with alkaline residualsis due to an alteration of root activity or root biomass production

following application of alkaline residuals, since root-derived respira-tion may account for N40% of total CO2 emissions from temperate forestsoils (Fahey et al., 2005; Hanson et al., 2000). This conjecture is sup-ported by previous experiments, which reported less fine root biomassinwood ash-amended soils than the control soils in the first years (1–4)following wood ash application (Clemensson-Lindell and Persson,1995; Klavina et al., 2016; Persson and Ahlstrom, 1994). It is notknown if this short-term reduction in root growth is caused by atoxic effect of wood ash or by a reduced plant investment in theroot system because of better soil fertility and more plant-availablenutrients.

Soil pH influence on CH4 fluxes depended on the time since alkalineresidual application, because therewas an initial decrease in CH4 oxida-tion, followed by more oxidation of this gas at the end of the experi-ment. Few studies have found that alkaline residuals influence CH4

emissions. Maljanen et al. (2006) found a significant reduction in CH4

emissions fromdrained peatlands treatedwithwood ash,whichwas at-tributed to better tree growth that lowered the water table and madesoil conditions unfavorable for methanogenesis. However, our forestplots were onwell-drainedmesic sites wheremethanogenesis is gener-ally low (Serrano-Silva et al., 2014). Therefore, the increase in soil pHlikely altered CH4 fluxes by interfering with methanotrophy, perhapsdue to the release of salt ions (such as K+ and Na+) (Maresca et al.,2018) that may have inhibited methanotrophy in the short-term(Serrano-Silva et al., 2014). This effect would diminish with time asthis limited source of K+ and Na+ dissolved and leached into the soilprofile.

The N2O fluxes were best explained by the type of alkaline re-sidual than the final soil pH, and the WA20 and LM treatments re-duced N2O fluxes significantly. Although this leads us to reject thehypothesis that increasing soil pH would increase N2O fluxes, it isconsistent with the significant decline in ammonium concentra-tion of the forest floor, two years after the application of alkalineresiduals. The magnitude of soil N2O fluxes is mainly determinedby mineral N availability (Ambus et al., 2006), and in addition tothe lower ammonium concentration, soluble salts released fromalkaline residues could interfere with the nitrification process(Martikainen, 1985). Under laboratory conditions, Liimatainenet al. (2014) reported similar reductions in N2O production fromsoils treated with soluble salts (K+, NH4

+) as with soils amendedwith wood ash.

5. Conclusion

This study provides evidence that alkaline residuals from the pulpand paper industry are effective at neutralizing soil acidity andreplenishing soil base cations without increasing soil GHG emissionswhen applied to sugar maple-dominated forests of eastern NorthAmerica. Two years after treatment, soil pH was modeled as a func-tion of the NP of the alkaline residuals, which indicates that NP is asuitable metric to compare the buffering activity of diverse alkalineresiduals. However, alkaline residuals are not comparable regardingtheir effect on soil GHG fluxes, since LM appears to exert a strongerinfluence on the microbially-mediated production of GHG thanother alkaline residuals. This indicates that change in soil pH is notthe sole parameter influencing GHG fluxes after liming. Contrary toour original expectation, we report a reduction in CO2 and N2Ofluxes, and a variable effect on CH4 fluxes, following alkaline residualapplication.

Additional research is needed to understand why GHG fluxes fromforest soils decline after liming. In particular, it would be important toassess the long-term response of sugar maple forests to alkaline resid-uals, and to partition the soil CO2 flux between heterotrophic and auto-trophic respiration to understand themechanisms responsible for lowersoil respiration.We also require studies to explain how the type of alka-line residual affects soil N transformations, particularly the microbially-

Fig. 3. Influence of final soil pH and type of alkaline residue on soil CO2, N2O and CH4 fluxes as predicted by the final model for each gas. Panels on the left (a, b, c) present the raw fluxmeasurements for each treatment and the modeled relationships with soil pH. For CO2 fluxes, the shaded area corresponds to the 95% confidence interval of the linear relationship. ForCH4 fluxes, the interaction between time since application and soil pH is represented by four lines depicting the effect of soil pH at four different times: 30, 280, 400 and 720 days afterapplication. The relationship with soil pH was not significant for N2O fluxes. Panels on the right indicate the predicted fluxes for each treatment at a common pH of 5.4, whichcorresponds to the average pH value measured at the end of the experiment. Error bars indicate the 95% confidence intervals. Significant differences between treatments and thecontrol are represented by different symbols: * P b 0.05, ** P b 0.01, black square 0.05 b P b 0.06. WA5 = 5 t wood ash ha−1, WA10 = 10 t wood ash ha−1, WA20 = 20 t woodash ha−1, WA + GG= wood ash mixed with grits and grids at 7 t ha−1, and LM= 7.5 t lime mud ha−1.

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mediated processes that produce N2O like ammonia volatilization andoxidization, nitrifier-denitrification and denitrification. Moreover, thephysical and chemical parameters of forest soils treated with alkalineresiduals should be compared to forests that are unaffected by soil acid-ification, to determine whether alkaline residuals are restoring acidicforest soils to a state that will benefit forest ecosystem functions in thelong-term.

Supplementary data to this article can be found online at https://doi.org/10.1016/j.scitotenv.2019.01.337.

CRediT authorship contribution statement

Samuel Royer-Tardif: Conceptualization, Data curation, Formal analysis,Funding acquisition, Investigation, Methodology, Project administration,Writing - original draft,Writing - review& editing. JoannWhalen:Meth-odology, Supervision, Validation, Writing - review & editing. DavidRivest: Conceptualization, Funding acquisition, Methodology, Project ad-ministration, Resources, Supervision, Validation, Writing - review &editing.

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Acknowledgements

This studywas initiated by aMitacs Acceleration grant awarded toD.Rivest in partnership with Domtar Windsor pulp and paper mill(Windsor, Québec, Canada). Further fundingwas provided by the Natu-ral Sciences and Engineering Research Council of Canada (NSERC)through a Collaborative Research and Development grant awarded toA. Dupuch (RDCPJ: 462583-13). S. Royer-Tardif was awarded a postdoc-toral scholarship from NSERC's CREATE Forest Complexity Modellingprogram. We acknowledge the valuable contribution of Patrick Cartierand Steve Reynolds from Domtar in the design of this experiment. Weare also grateful to the many students and technicians who contributedto this study by their implication in field samplings and laboratoryanalysis.

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