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

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

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Science of the Total Environment journal homepage: www.elsevier.com/locate/scitotenv

Can alkaline residuals from the pulp and paper industry neutralize acidity in forest soils without increasing greenhouse gas emissions? Samuel Royer-Tardif a,⁎,1, Joann Whalen b, David Rivest a,c a b c

Département des Sciences Naturelles, Institut des Sciences de la Forêt Tempérée (ISFORT), Université du Québec en Outaouais, 58 rue Principale, Ripon, QC J0V 1V0, Canada Department of Natural Resource Sciences, Macdonald Campus of McGill University, 21111 Lakeshore Road, Ste-Anne-de-Bellevue, QC H9X 3V9, Canada 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 liming agents for acidified sugar maple forests. • Soil pH after liming is explained by the neutralizing power of alkaline residuals • More neutralization occurred in the forest floor layer than underlying mineral soil. • Greenhouse gas fluxes were lower after application of alkaline residuals. • Reduction in greenhouse gas fluxes was related to the increase in soil pH.

a r t i c l e

i n f o

Article history: Received 1 December 2018 Received in revised form 24 January 2019 Accepted 25 January 2019 Available online 26 January 2019 Editor: Elena Paoletti Keywords: Wood ash Lime mud Neutralization potential Soil respiration Nitrous oxide Methane

a b s t r a c t Alkaline residuals, such as wood ash and lime mud generated from pulp and paper mills, could be recycled as liming agents in sugar maple (Acer saccharum Marsh.) forests affected by soil acidification. The objectives of this study were (1) to evaluate soil chemistry, in particular soil acidity, after the application of three alkaline residuals from 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 and GHG fluxes were monitored for two years after alkaline residuals were applied to six forest sites dominated by sugar maple in southeastern Quebec, Canada. Each site received six treatments: wood ash applied at 5, 10 and 20 t ha−1, lime mud (7.5 t ha−1), a mixture of slaker grits and green liquor sludge (7 t ha−1) and an unamended control. These treatments had acid-neutralizing power from 0 to 9 t ha−1. All alkaline residuals buffered soil acidity as a function of their neutralizing power, and more neutralization occurred in the forest floor layer than in the underlying mineral soil. In the forest floor, the alkaline residual treatments significantly increased pH by more than one unit, nearly doubled the base saturation, and reduced exchangeable acidity, Al and Fe concentrations compared to control plots. The CO2 and N2O fluxes were lower after application of alkaline residuals, and this was related to the soil pH increase and the type of alkaline residual applied. Lime mud was more effective at reducing GHG fluxes than other alkaline residuals. We conclude that these alkaline residuals can effectively counteract soil acidity in sugar maple forests without increasing soil GHG emissions, at least in the short term. © 2019 Elsevier B.V. All rights reserved.

⁎ Corresponding author. E-mail addresses: [email protected] (S. Royer-Tardif), [email protected] (J. Whalen), [email protected] (D. Rivest). 1 Present address: Natural Resources Canada, Canadian Forest Service, Laurentian Forestry Centre, 1055 du P.E.P.S., P.O. Box 10380, Stn. Sainte-Foy, Québec, QC G1V 4C7, Canada.

https://doi.org/10.1016/j.scitotenv.2019.01.337 0048-9697/© 2019 Elsevier B.V. All rights reserved.

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1. Introduction Temperate forests dominated by sugar maple (Acer saccharum Marsh.) are abundant in northeastern America and are of economic importance in this region for timber and maple syrup production (Horsley et al., 2002). In the past 50 years, sporadic decline and dieback of mature sugar maple trees occurred throughout its native range (Bishop et al., 2015; Horsley et al., 2002), in part due to soil acidification induced by acid rain and the leaching of base cations to groundwater and surface water (Horsley et al., 2000; Houle et al., 2007; Long et al., 2009). Moreover, 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) or dolomitic limestone (CaMg(CO3)2) to the forest floor may successfully counteract 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 be cost-effective in forests managed for maple syrup production. Also, the extraction, crushing, handling, transport and application of limestone generates up to 0.75 t of CO2/t lime produced, which is a significant source of GHG emissions (European Commission, 2001). Pulp and paper mills generate large quantities of residuals such as wood ash, lime mud and a mixture of green liquor sludge and slaker grits, hereafter referred to as “grits and grids” (Martins et al., 2007; Monte et al., 2009). These residues are highly alkaline (pH N 10) and rich in Ca (Jia et al., 2014; Morris et al., 2012). Wood ash also contains macronutrients required for plant growth such as K, Mg, and P (Pitman, 2006). The neutralizing power (NP) of alkaline residuals is a suitable way to consider multiple residues on an equivalent basis. It is a measure of the quantity of acidity that can be buffered relative to pure 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 in managed forests (Hannam et al., 2017; Huotari et al., 2015; Vestergard et al., 2018) as an alternative to disposing them in landfills, since they may represent a cost-effective substitute for calcitic or dolomitic limestone as long as they respect the legislation concerning heavy metal concentrations (Hébert and Breton, 2008). The change in soil pH resulting from liming of forest soils has been hypothesized to increase emissions of several GHG, namely CO2, N2O and CH4 (Huotari et al., 2015; Maljanen et al., 2014). Raising soil pH with alkaline residuals stimulates soil microbial activity (Jokinen et al., 2006), thereby increasing heterotrophic soil respiration (Baath and Arnebrant, 1994; Zimmermann and Frey, 2002), and soil organic matter decomposition (Perkiomaki et al., 2004). Such conditions favour N mineralization generating a pool of mineral N that can be transformed into N2O through the microbially-mediated reactions of ammonia oxidation, nitrifier-denitrification and denitrification (Kool et al., 2011). Well drained forest soils are considered a sink for CH4 because CH4 oxidation is 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 increased rate of N mineralization following liming is expected to reduce CH4 oxidation (Maljanen et al., 2006). Apart from their effect on soil pH, different types of alkaline residuals may also influence GHG emissions due to their chemical properties. For example, Ca ions, the principal constituent of lime may decrease the bioavailability of soil organic carbon by binding to dissolved organic compounds and thus reduce soil respiration (Balaria et al., 2015; Kunhi Mouvenchery et al., 2012). In addition, wood ash contains readily soluble salt ions (K+ and Na+) that may interfere with the microbial processes generating N2O (Liimatainen et al., 2014). Depending on its chemical composition and the tree species, wood ash may also reduce tree growth (Brais et al., 2015) and fine root production (ClemenssonLindell and Persson, 1993), thereby decreasing the autotrophic contribution to soil respiration. Such differences between alkaline residuals

applied may explain the discrepancy in GHG emissions among field experiments. For example, the application of wood ash in boreal forests was 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 increase (Butterbach-Bahl et al., 1997; Papen and Butterbach-Bahl, 1999) or decrease (Borken and Brumme, 1997) following lime application. To date, however, no study has separated the effect of soil pH from other changes in soil chemistry caused by alkaline residuals to understand the GHG fluxes from lime-amended forest soils. To better understand the influence of liming on acidified soils from sugar maple dominated forests, we designed a study were forest plots were treated with different alkaline residuals from the pulp and paper industry. The objectives of this study were to (1) evaluate to what extent different alkaline residuals, namely wood ash, grits and grids, and lime mud, can neutralize acidity in forest soils, and (2) compare the contribution of soil pH, versus the type of alkaline residual, to the soil GHG fluxes from limed forest soils. We hypothesize that the capacity of alkaline residuals to neutralize acidity in forest soils is a function of their neutralizing power. Furthermore, we hypothesize that soil pH is responsible for the change in GHG fluxes from soils in acidified sugar maple forests, and that an increase in soil pH will result in greater CO2 and N2O 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 in the 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.), eastern hop-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 Appalachian Mountains, is characterized by an undulating topography with low elevation summits and gentle slopes (Cann and Lajoie, 1943). Mean annual temperature is 5.6 °C with daily averages of −10.6 °C in January to 19.6 °C in July, and total annual precipitation is 1146 mm (Environment Canada, 2016). Soils in the study area are ferro-humic Podzols and dystric Brunisols developed on glacial till deposits composed of non-calcareous Ordovician slate and preCambrian shists (Cann and Lajoie, 1943). Soils in this region experienced acidification beginning in the mid20th century. Between 1994 and 1998, sulphur and nitrogen deposition exceeded the soil critical loads by 1 and 600 eq ha−1 yr−1, respectively, but by 2002, the combined acidic deposition was only −199 to 400 eq ha−1 yr−1 (Carou et al., 2008). From 1999 to 2002, total dry and wet acid deposition deposited about 21 kg SO4-S ha−1 yr−1 and between 8 and 10 kg N ha−1 yr−1 in this area, but these depositions were decreasing at a rate of 38 and 11% per decade, respectively, due to more stringent air quality regulations (Ouimet and Duchesne, 2009). 2.2. Site selection and alkaline residual characteristics Six sugar maple-dominated stands were selected at random from stands that experienced a partial harvest (ca. 30% basal area) during winter 2013. Partial harvest was necessary to make a roadway for motorized access to the sites. All selected stands were mature, covering an area of more than 4 ha, uniform in their topography, tree composition and loamy soil texture. Table 1 summarizes the principal characteristics of each site. Alkaline residuals used in this study were obtained from the Domtar Windsor pulp and paper mill (Windsor, QC, CAN) and are deemed suitable for land application according to the BNQ 0419-090 standard (Bureau de normalisation du Québec (BNQ), 2015). The chemical

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

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 selfhardening process (Pitman, 2006) before it was crushed and sieved to 5–15 mm pellets, prior to application. The lime mud (LM) did not require additional treatment and was composed of 1–10 mm pellets. The grits and grids (GG) formed a compact slurry that was mixed with wood ash (1:2 ratio) to achieve homogenous application on plots. 2.3. Treatment application The experiment was established in early September 2014. In each selected stand (n = 6 stands, corresponding to 6 replicates), we delimited six 5 × 5 m plots, each separated by a 10 m buffer zone. Treatments were assigned randomly to plots as follows (all masses are in dry weight equivalent and treatment abbreviations are indicated in parenthesis): an unamended control, 5 t wood ash ha−1 (WA5), 10 t wood ash 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 coverage on each plot. 2.4. Soil sampling and analysis The forest floor (F and H horizons) and the 0–15 cm of underlying mineral soil (A and part of the B horizon), were sampled before the experiment was established (September 2014) and at the end of the study period (September 2016). Five soil cores per plot were collected with a hand trowel, pooled by layer (forest floor and mineral) and placed in separate plastic bags. In each sampling location, the forest floor thickness was measured with a caliper (±0.05 mm). Soil samples were air dried before analysis for physical and chemical variables. A subsample of fresh soil was dried at 105 °C for 24 h to determine the gravimetric water content. A second subsample was air-dried and used for physical and chemical analyses. Soil texture was assessed using the hydrometer method (Kroetsch and Wang, 2007). Soil pH was measured in distilled water (1:10 and 1:2 soil-to-water ratio for organic and mineral soils, respectively). Soil exchangeable acidity was determined by titration of BaCl2 (0.1 M) soil extracts (Hendershot et al., 2007b). Soil cationic exchange capacity (CEC) was determined as the

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Table 2 Physical and chemical characteristics of alkaline residuals applied to sugar maple forests in Quebec, Canada. CCE = calcium carbonate equivalent. Wood ash

Grits and grids

Lime mud

Moisture content (% dry weight) pH CCE (%)

47.1 13.0 51

78.6 – 82

41.1 11.2 95.3

Macronutrients (g/kg) Ca Mg K P S

200 14 15 3 3.4

270 17 10 0.8 NA

394 3.3 0.8 2.9 0.3

Micronutrients (mg/kg) Mn As B Cd Co Cr Cu Hg Mo Ni Pb Na Zn

3800 110.0 170 8.9 7 75 140 0.38 5.0 34 380 3900 1100

7000 5.0 19 11 3 18 88 0.02 1 25 19 66,000 1000

234 2.7 8.3 0.3 0.8 9.1 3 b0.01 0.1 5.6 0.5 6570 28

sum of Ca, Mg, K, Na, Fe, Al, Mn and Zn concentrations in unbuffered BaCl2 (0.1 M) soil extracts (Hendershot et al., 2007a). The concentrations of these metals were measured on a flame atomic absorption spectrophotometer (Varian 220FS, Palo Alto, CA). Base saturation (BS) was determined as the percentage of CEC occupied by Ca, Mg, K and Na. A third soil subsample was oven dried at 60 °C, ball-milled and used to determine total C and N content following high-temperature combustion on 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 multichannel auto-analyzer (Lachat Instruments, Loveland, CO) (Maynard et al., 2007). In addition, the available P concentration in Mehlich III soil extracts was analyzed colorimetrically (Tran and Ziadi, 2007).

Table 1 Principal characteristics of the six sugar-maple dominated stands in the Eastern Townships of Quebec, Canada.

Localisation Stand age (years) Basal area (m2/ha) Sugar maple (% BA) Other tree species Forest floor thickness (mm) B horizon (0–15 cm depth) pH Exchangeable acidity (cmol+ kg−1) CEC (cmol+ kg−1) BS (%) Organic matter (g/kg) Clay (g/kg) Silt (g/kg) Sand (g/kg) Dominant species in the understory

Site 1

Site 2

Site 3

Site 4

Site 5

Site 6

N 45° 34′ 23.0″ W 71° 51′ 27.1″ b80 16.7 38 Fagus grandifolia Betula alleghaniensis 20

N 45° 37′ 05.9″ W 71° 43′ 37.3″ b80 14.7 72 Fraxinus americana

N 45° 41′ 14.1″ W 71° 17′ 32.2″ b80 18.0 94 Fagus grandifolia

N 45° 36′ 41.9″ W 71° 44′ 54.6″ N80 15.2 81 Fagus grandifolia

21

N 45° 37′ 56.9″ W 71° 15′ 02.5″ N80 18.7 66 Fagus grandifolia Betula alleghaniensis 35

63

22

N 45° 34′ 19.9″ W 71° 48′ 59.7″ b80 20.7 40 Acer rubrum Betula alleghaniensis 44

3.98 11.7

4.44 9.2

4.02 12.4

4.74 NA

4.08 10.9

4.30 13.8

14.8 38.1 133 248 387 365 Dennstaedtia punctilobula Fagus grandifolia

21.2 69.3 99 255 467 278 Dryopteris carthusiana Acer pensylvanicum

12.6 41.2 93 292 536 172 Acer saccharum

31.4 85.9 91 236 537 227 Acer saccharum

Dennstaedtia punctilobula Thelypteris noveboracensis

Dennstaedtia punctilobula Viola sp.

11.9 41.5 98 199 466 335 Dennstaedtia punctilobula Dryopteris carthusiana

14.9 39.2 86 195 402 403 Thelypteris noveboracensis Dryopteris carthusiana

Acer saccharum

Betula alleghaniensis

Dryopteris carthusiana Fraxinus americana BA: basal area, CEC: cation exchange capacity, BS: base saturation.

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2.5. Gas sampling and flux calculation Gas sampling occurred on nine sampling dates from October 2014 to September 2016 corresponding to three periods during the growing season: spring (May), summer (June, August–September), and fall (October). Gas samples were collected from four static chambers (14 cm high, 7.5 cm radius) that were randomly deployed in each plot (n = 144 chambers). Chambers were made from standard PVC tubes, closed on one end with a teflon sheet, tightly sealed with polyurethane adhesive, and insulated with reflective bubble thermofoil. On top of each chamber, we fixed a sampling port equipped with an injection membrane (Surgi-Pharm Avancée, Dorval, CA) and a 2 mm hole was drilled through the top to avoid pressurization of the chamber (Rochette, 2011). During deployment, these chambers were fixed, using electrical tape, to PVC bases of the same dimensions as the chambers. These bases were inserted at a depth of 10 cm in the ground, 2 weeks prior to the first measurement, and left on site for the duration of the experiment. At 0, 8, 16 and 24 min after chamber closure, a 5 ml headspace gas sample was collected from each chamber with a syringe and pooled to yield one sample per plot at each sampling time, as described by Arias-Navarro et al. (2013). This involved injecting four headspace gas samples 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 extra PTFE/Silicone 13-mm septa (Superlco, Bellefonte, USA). Gas samples were 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 flame ionisation detector (FID) for CH4 determination. The carrier gas was helium for both the FID and TCD, and argon for the ECD. On each sampling date, the raw gas flux rate (FHMR) was determined using the HMR package in R (Pedersen et al., 2010). Briefly this procedure uses maximum likelihoods to predict the best fit of gas concentrations at 0, 8, 16 and 24 min to a linear relationship or the Hutchinson and Mosier saturation relationship. Raw gas fluxes were adjusted for air temperature and pressure using the equation of (Rochette and Bertrand, 2007): F g 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 covered by the chamber (m2), ep is the partial pressure of water vapor of chamber air (kPa), P is the barometric pressure recorded during chamber deployment (kPa), Mm, g is the molecular mass of the gas considered, and Vm is the molecular volume of that gas at the temperature and pressure recorded. The HMR package already considers chamber volume and soil area in its computation and Eq. (2.1) can be simplified to determine standardized gas fluxes (Fg) as follows: F g ¼ F HMR  Mm;g

 Vm

   1−ep =P

ð2:2Þ

During chamber deployment, air pressure was recorded on a Samsung Galaxy S3 pressure chip, and air and soil temperature were measured using a regular thermometer inserted into the center of each plot. Soil moisture (v/v) was measured close to each gas chamber using a FieldScout TDR 100 probe (Spectrum Technologies, Inc., Aurora, CO). 2.6. Statistical analyses Significant differences in soil chemical variables between treatments were tested separately for each soil layer with linear mixed-models, including a random effect of study sites (intercept) and with the treatment (type and dosage of fertilizer applied) as a fixed factor with the

function lme from the R package nlme (Pinheiro et al., 2016). Ash doses were analyzed as factors to enable the comparison with the other treatments (i.e. LM and WA + GG) and because their influence on soil variables was generally non-linear. Significant differences between treatments were tested by Tukey HSD pairwise comparisons performed with the function glht from the multcomp R package (Hothorn et al., 2008). Residual plots were inspected for normality and homoscedasticity. Soil pH at the end of the experiment was modeled as a function of the NP of alkaline residuals added two years earlier. The NP was the product of the calcium carbonate equivalent (Table 2) × dry mass per ha of each alkaline residual. Linear, quadratic, and non-linear relationships between pH and NP were compared using the corrected Akaike information criterion (AICc) and the best fit curve was retained. Nonlinear relationships were modeled using generalized additive models (GAM) from the R package mgcv (Wood, 2011). Models that included or excluded a random intercept of the site and additional covariates (soil pH, hydrogen ions activity (10−pH), forest floor thickness) were also compared by AICc to determine the best fit curve. Two answer the second research objective, we opted for a statistical approach that directly models GHG fluxes because nine sampling dates over two years were insufficient to evaluate total annual or seasonal GHG emissions. We explored the role of soil pH and the type of residual in predicting GHG fluxes (Fg) by comparing four different linear mixedmodels: a base model containing ancillary variables only, two models adding the effect of soil pH (at the end of the experiment) or the type of residual applied (six types were evaluated), and a full model containing all variables. The ancillary variables for the base model were soil temperature, soil moisture, their interaction and the time (d) since residual application. The non-linear influence of soil temperature on GHG fluxes was described with a quadratic term in the model (Fg~temp2). Because the influence of alkaline residuals may change through time with their dissolution, we also tested the interactions between the time since application, the type of residual and the soil pH. These interactions, included as fixed variables, did not explain a significant portion of CO2 and N2O fluxes and were excluded from the final model. For CH4, however, there was a significant interaction between time since residuals application and soil pH, so these terms were retained in the final model. All four models accounted for the nested effect of sampling date within sites included as a random intercept. The VarIdent option from the R package nlme (Pinheiro et al., 2016) was used to address heteroscedasticity in gas fluxes between sampling dates. A logarithmic transformation was applied to N2O concentrations to ensure normality of the residuals and facilitate model convergence. Three extreme outliers were removed from N2O values. Residual plots were inspected for normality, homoscedasticity and the absence of trends with each of the explanatory variables. The four models described above were compared using the corrected Akaike information criterion (AICc) and likelihood ratios. Total variance explained by each model was expressed as the coefficient of determination for generalized mixed-effect models as obtained with the MuMIn R package (Barton, 2015). Significant differences between types of residuals were tested by Tukey HSD pairwise comparisons. All statistical analyses were performed in the R environment (R Core Team, 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 increased pH by more than one unit, nearly doubled base saturation, and reduced exchangeable acidity, Al and Fe concentrations compared to control plots (Table 3). The highest wood ash dose (WA20) also resulted in significantly (P b 0.05) greater CEC. The WA10, WA20 and WA + GG treatments increased the Ca concentration significantly (P b

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0.05). Compared to control plots, the ammonium concentration was reduced by two thirds following application of alkaline residuals. The liming effect of alkaline residuals was less pronounced in the mineral soil than in the forest floor, since only LM increased pH in the mineral soil significantly, by 0.5 unit (P b 0.05; Table 3). Most alkaline residual treatments (except WA5) increased BS by N2 times the BS value of the control plot. The WA + GG and LM treatments also increased the Ca concentration (P b 0.05), and the WA + GG residual increased the Mg concentration (P b 0.05), relative to the unamended mineral soil. Two years after alkaline residuals were applied, the final soil pH in forest floor and mineral soil was predicted from the NP of the alkaline residual and the initial soil pH (Fig. 1). The best-fit lines describing this relationship were a quadratic relationship (R2 = 0.73) in the forest floor 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 mg m−2 h−1 with an average of 321.9 mg m−2 h−1 (Fig. 2). These fluxes were correlated with soil and air temperature, with the highest fluxes recorded in late August 2016 and early September 2015 (Fig. 2). N2O fluxes varied between

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−22.1 and 291.5 μg m−2 h−1 but were positively skewed with a median of 8.1 μg m−2 h−1. The highest N2O fluxes were observed in May and June during 2015 and 2016. CH4 fluxes were mostly negative, varying from −756.1 to 204.0 μg m−2 h−1, and were negatively skewed with a median of −88.9 μg m−2 h−1. The best fit model describing CO2 flux included final soil pH and the type of alkaline residual as explanatory variables (model 4; Table 4). The CO2 flux was negatively related to final soil pH (Fig. 3a) and most treatments produced CO2 fluxes that were similar to the control (Fig. 3b), although the CO2 flux was marginally higher in the LM treatment than the control (t1,187 = 1.94, P = 0.0536). Final soil pH did not explain a significant portion of N2O flux, and the best fit model included the type of alkaline residual plus ancillary variables (model 3; Table 4). The WA20 and LM treatments reduced the N2O flux significantly, compared to the control (Fig. 3e). The best fit model for CH4 flux included final soil pH and the type of alkaline residual (model 4; Table 4), and there was a significant interaction between the time since residual application and final soil pH (Fig. 3e). A smaller CH4 sink capacity was measured as soil pH increased soon after residual application, but the trend changed during the experimental period and a slightly positive relationship between CH4 oxidation and soil pH was observed by the end of the experiment. There was a significant increase in CH4 oxidation in the LM treatment, compared to the control.

Table 3 Soil chemistry properties in organic and mineral soil layers of sugar maple forests, two years after the application of alkaline residuals. Values are the mean (n = 6) with standard deviation in parenthesis. Within a row, values with different lowercase letters differ significantly at the P b 0.05 level (Tukey HSD pairwise comparisons). CEC = Cationic exchange capacity, BS = Base saturation, 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. Forest floor (FH horizons)

C (%) N (%) CN (ratio) Exchangeable cations (cmol+ kg−1)

Ca K Mg Na Al Fe Mn

CEC (cmol+ kg−1) BS (% of CEC) Exchangeable acidity (cmol+ kg−1) pH (H2O) NH+ 4 (mg/kg) NO− 3 (mg/kg) Available P (mg/kg)

Control

WA5

WA10

WA20

WA + GG

LM

33.5 (5.47) 2.03 (0.36) 16.55 (1.17) 9.42 (6.38)b 0.73 (0.16) 1.91 (1.27) 0.1 (0.09)b 8.26 (6.46)b 0.24 (0.17)b 1.92 (1.49)b 22.57 (8.55)b 54.14 (20.06)b 14.46 (7.17)b 4.43 (0.21)c 516.0 (351.6)a 117.0 (45.8) 92.45 (44.89)

29.06 (4.32) 1.65 (0.23) 17.6 (0.8) 27.47 (9.48)ab 0.76 (0.17) 3 (0.51) 0.08 (0.02)b 0.92 (0.73)a 0.06 (0.05)a 1.4 (0.58)ab 33.69 (9.42)ab 92.38 (4.51)a 4.62 (1.31)a 4.93 (0.27)b 169.7 (170.8)b 193.9 (175.0) 76.44 (32.41)

28.03 (5.05) 1.6 (0.3) 17.6 (1.53) 33.87 (11.15)a 0.54 (0.12) 2.92 (0.94) 0.09 (0.05)b 0.55 (0.98)a 0.03 (0.01)a 0.71 (0.2)a 38.71 (11.46)ab 95.91 (4.37)a 3.36 (1.36)a 5.57 (0.24)ab 159.4 (234.0)b 239.2 (223.9) 66.68 (25.65)

31.22 (6.48) 1.75 (0.5) 18.28 (2.68) 45.95 (11.72)a 0.6 (0.14) 3.59 (0.73) 0.1 (0.03)b 0.13 (0.12)a 0.03 (0.01)a 0.59 (0.51)a 50.99 (12.84)a 98.57 (0.56)a 2.59 (0.73)a 5.88 (0.39)a 94.6 (100.9)b 171.4 (158.6) 64.86 (19.05)

34.1 (9.97) 1.89 (0.53) 18.03 (0.38) 36.25 (24.61)a 0.53 (0.22) 3.36 (2.54) 0.22 (0.07)a 2.28 (4.11)a 0.04 (0.02)a 0.86 (0.46)ab 43.53 (24.37)ab 87.07 (20.26)a 5.48 (4.41)a 5.51 (0.6)ab 64.4 (62.6)b 241.1 (118.5) 60.58 (21.51)

28.02 (2.75) 1.68 (0.2) 16.68 (1.11) 31.14 (12.81)ab 0.47 (0.2) 2.35 (0.97) 0.17 (0.08)ab 1.25 (2.2)a 0.03 (0.02)a 0.75 (0.41)a 36.17 (12.02)ab 92.43 (9.84)a 3.98 (2.74)a 5.77 (0.51)a 79.4 (68.5)b 107.3 (60.6) 73.78 (28.47)

Mineral soil (AB horizons)

C (%) N (%) CN (ratio) Exchangeable cations (cmol+ kg−1)

CEC (cmol+ kg−1) BS (% of CEC) Exchangeable acidity (cmol+ kg−1) pH (H2O) NH+ 4 (mg/kg) NO− 3 (mg/kg) Available P (mg/kg)

Ca K Mg Na Al Fe Mn

Control

WA5

WA10

WA20

WA + GG

LM

5.72 (2.26) 0.37 (0.1) 15.12 (2.23) 0.94 (0.5)b 0.13 (0.02) 0.18 (0.12)b 0.01 (0.01)ab 6.59 (1.69) 0.15 (0.08) 0.12 (0.06) 8.11 (1.83) 15.94 (6.93)c 8.85 (2.13) 4.29 (0.3)b 9.5 (4.7) 23.2 (13) 4.72 (3.04)

5.37 (2.73) 0.36 (0.13) 14.57 (2.4) 1.92 (0.74)ab 0.14 (0.05) 0.42 (0.23)ab 0 (0)b 6.12 (2.82) 0.16 (0.17) 0.17 (0.24) 8.93 (3.55) 28.1 (7.18)bc 8.84 (4.53) 4.26 (0.37)ab 18.6 (16.9) 24.9 (14.9) 5.32 (4.07)

5.02 (1.54) 0.35 (0.11) 14.45 (1.78) 2.8 (1.55)ab 0.15 (0.05) 0.51 (0.42)ab 0.01 (0.01)ab 4.76 (1.55) 0.1 (0.06) 0.11 (0.06) 8.45 (2.69) 39.9 (12.24)ab 6.85 (1.7) 4.45 (0.3)ab 17.4 (14.8) 18.6 (9.1) 8.09 (4.26)

6.13 (2.16) 0.38 (0.09) 15.91 (2.02) 3.12 (1.65)ab 0.17 (0.05) 0.46 (0.16)ab 0.01 (0.01)ab 4.95 (0.97) 0.08 (0.06) 0.19 (0.19) 8.97 (2.38) 40.5 (11.67)ab 6.99 (1.04) 4.5 (0.3)ab 9.4 (5.6) 12.1 (6.1) 8.24 (3.01)

6.55 (1.11) 0.43 (0.08) 15.43 (0.94) 3.87 (3.48)a 0.16 (0.02) 0.67 (0.58)a 0.03 (0.03)a 5.03 (0.82) 0.06 (0.03) 0.26 (0.17) 10.09 (3.2) 41.7 (22.46)ab 7.07 (1.24) 4.6 (0.13)ab 8.4 (7.2) 19.2 (3.7) 6.84 (5.51)

5.79 (1.71) 0.4 (0.11) 14.76 (1.66) 3.86 (2.27)a 0.15 (0.03) 0.42 (0.24)b 0.03 (0.01)a 4.49 (1.49) 0.06 (0.04) 0.23 (0.24) 9.23 (1.97) 46.44 (18.41)a 6.3 (2.22) 4.77 (0.31)a 12.5 (6.1) 15.0 (8.7) 8.05 (6.11)

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Fig. 1. Neutralizing power of alkaline residuals affects the soil pH in (a) the forest floor and (b) the mineral soil of sugar maple forests, two years after alkaline residuals were applied. The shaded 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.

4. Discussion 4.1. Liming and fertilizing effect of alkaline residuals As we hypothesized, NP was the principal attribute of alkaline residuals that determined their efficiency in neutralizing soil acidity. The final soil pH, two years after alkaline residuals were applied, was an asymptotic function determined by the NP of the alkaline residuals, rather than application rate. For example, doubling the amount of wood ash from 10 to 20 t wood ash ha−1 had little effect on the soil pH of the forest floor and the mineral soil. These soil layers appear to resist further change in soil pH beyond pH 6 in the forest floor and around pH 4.5 in the mineral soil. In acidified sugar maple forests, Moore et al. (2012) also reported a saturation in soil pH (around pH 6.25 in the forest floor and pH 5.75 in the mineral soil) with increasing lime doses. In forest soils, sources of acidity that lower soil pH include the deprotonation of weak organic acids (Magdoff and Bartlett, 1985) and the hydroxylation of Al3+ cations (Bloom and Skyllberg, 2012). All alkaline residuals increased BS and reduced exchangeable acidity to the same extent, which is further evidence that alkaline residuals were effectively altering the soil acid-base status in forest soils. Even the WA5 dose added 1 t of Ca on the forest floor, which was enough to replace most of the exchangeable acidity from cation exchange sites and saturate CEC with base cations. However, there was still a significant amount of exchangeable acidity present after application of alkaline residuals since only the WA20 caused a significant increase in CEC. Alkaline residuals dissolve slowly in natural forests and their liming effect may last for N15 years (Moore et al., 2012; Saarsalmi et al., 2012), suggesting that more reaction time is needed to reduce the exchangeable acidity. Further changes in the soil acidbase status of these treated forest soils may be expected in the future. Our results also suggest that the organic layer was more responsive to the liming effects of alkaline residuals than the underlying mineral layer, similar to other reports (Brais et al., 2015; Moore et al., 2012; Reid and Watmough, 2014). This observation is consistent with the reaction of liming agents added to the soil surface (rather than mixed with

the soil), since Ca and other base cations dissolve slowly and need time to leach through the forest floor and reach the mineral horizons. Similarly, 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 after their application to forests in Denmark. All alkaline residuals significantly reduced the ammonium concentration in the forest floor. This response is unlikely to represent N immobilization by plants or microbes, since wood ash has rarely been shown to increase plant N concentrations (Augusto et al., 2008) and more ammonium consumption by microorganisms should have promoted soil respiration (Baath and Arnebrant, 1994). Moreover, in another trial using the same plots, the nutrition and growth of sugar maple and beech seedlings did not increase two years following the application of alkaline residuals (unpublished data). In alkaline conditions, ammonium can be deprotonated to ammonia, which is vulnerable to volatilization as gaseous ammonia. Another possibility is that increasing soil pH could stimulate the activity of ammonia oxidizing archaea and ammonia oxidizing bacteria, which convert ammonium to nitrite. Many of the ammonia oxidizers are chemolithoautotrophs that acquire C by consuming CO2 rather than from heterotrophic oxidation of soil organic matter (releases CO2). The nitrite produced by ammonia oxidizers is rapidly converted to nitrate by autotrophic or heterotrophic nitrifiers under aerobic conditions. However, nitrate concentrations did not follow the same pattern as ammonium concentrations following application of alkaline residuals. This indicates that excess nitrate could have either been lost though leaching, immobilized in plant and microbial biomass or lost through denitrification. However, soil respiration and N2O fluxes did not increase with application of alkaline residuals, therefore ruling out the last two possibilities. In contrast, dissolved cations released 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 applied in this study may have reduced the N mineralization rate by interfering with the activity of extracellular enzymes responsible for protein degradation, resulting in less ammonium in the soil solution (Bjork et al., 2010).

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Fig. 2. Standardized CO2, N2O and CH4 fluxes (Fg) from forest soils, following application of alkaline residuals, from October 2014 to August 2016. Within the box plot chart, the crosspieces of each box plot represent (from top to bottom) maximum, upper-quartile, median (thick bar), lower-quartile and minimum values. Outliers are represented by round dots. The mean air and soil temperatures, and soil moisture at each sampling date are shown, along with the standard deviation. 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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Table 4 Test statistics in four models explaining CO2, N2O and CH4 fluxes following application of alkaline 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 each gas, which models were compared for the likelihood ratio. These comparisons were made from the simplest to the more complex model (1 to 4). The best model selected, indicated in bold, had the lowest AICc and explained significantly more variance (significant likelihood ratio) than other models. R2

AICc

Test

Likelihood ratio

P-value

Base pH Type

0.7004 0.7076 0.7095

2817.42 2817.43 2812.95

– 1.999 12.472

– 0.1574 0.0142

pH + Type

0.7109

2809.95

– 1 vs 2 1 vs 3a 3 vs 4

5.002

0.0253

Base pH Type

0.1280 0.1464 0.2226

304.65 304.03 302.89

– 2.6168 11.7590

– 0.1057 0.0382

pH + Type

0.2228

303.93

– 1 vs 2 1 vs 3a 3 vs 4

0.9547

0.3285

Base pH Type pH + Type

0.5864 0.6066 0.6450 0.6808

2419.86 2416.15 2415.37 2407.56

– 1 vs 2 2 vs 3 3 vs 4

– 7.706 6.779 11.814

– 0.0212 0.0793 0.0027

Model CO2 1 2 3 4

N2 O 1 2 3 4

CH4 1 2 3 4

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

4.2. GHG fluxes affected by alkaline residuals Based on previous observations that liming strongly stimulates microbial activity in forest soils (Baath and Arnebrant, 1994; Jokinen et al., 2006; Zimmermann and Frey, 2002), we hypothesized that alkaline residuals would increase GHG fluxes from sugar maple forest soils due to their effect on soil pH. Contrary to this expectation, the CO2 and N2O fluxes were lower in the two-year period after application of alkaline residuals, while the CH4 flux showed a variable trend. Our results indicate that soil pH and the type of alkaline residual were both responsible for the reduction in GHG fluxes. Among alkaline residues, the LM had the greatest impact on GHG fluxes, probably due to the faster dissolution of fine-textured LM compared to the other alkaline residuals (Royer-Tardif, personal observation). This assumption is supported by the fact that LM had a greater effect on pH and chemical parameters in the mineral soil than the other alkaline residuals. In this study, the soil pH increase from alkaline residuals reduced CO2 fluxes significantly. Klemedtsson et al. (2010) also observed a reduction in soil respiration (by 17–23%) following the application of 3.3 and 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 five years after lime or wood ash application in several forests (Ernfors et al., 2010; Maljanen et al., 2006; Winsborough et al., 2017), longer term experiments (N9 years) found a significant increase in soil respiration after wood ash application in forests (Maljanen et al., 2014; Maljanen et al., 2006; Rosenberg et al., 2010). This time-dependent response of soil respiration to wood ash application could be related to a lag in microbial adjustment to soil chemical conditions. For example, it took four years after wood ash application to oligotrophic peatlands before Bjork et al. (2010) observed a reduction of microbial phospholipid fatty acid (PLFA) biomarkers in the topsoil layer (0–5 cm) and a concomitant reduction of N mineralization and ammonification rates. Another explanation for the lower CO2 fluxes with alkaline residuals is due to an alteration of root activity or root biomass production

following application of alkaline residuals, since root-derived respiration may account for N40% of total CO2 emissions from temperate forest soils (Fahey et al., 2005; Hanson et al., 2000). This conjecture is supported by previous experiments, which reported less fine root biomass in wood 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 not known if this short-term reduction in root growth is caused by a toxic effect of wood ash or by a reduced plant investment in the root system because of better soil fertility and more plant-available nutrients. Soil pH influence on CH4 fluxes depended on the time since alkaline residual application, because there was an initial decrease in CH4 oxidation, followed by more oxidation of this gas at the end of the experiment. Few studies have found that alkaline residuals influence CH4 emissions. Maljanen et al. (2006) found a significant reduction in CH4 emissions from drained peatlands treated with wood ash, which was attributed to better tree growth that lowered the water table and made soil conditions unfavorable for methanogenesis. However, our forest plots were on well-drained mesic sites where methanogenesis is generally low (Serrano-Silva et al., 2014). Therefore, the increase in soil pH likely altered CH4 fluxes by interfering with methanotrophy, perhaps due 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 as this limited source of K+ and Na+ dissolved and leached into the soil profile. The N2O fluxes were best explained by the type of alkaline residual than the final soil pH, and the WA20 and LM treatments reduced N2O fluxes significantly. Although this leads us to reject the hypothesis that increasing soil pH would increase N2O fluxes, it is consistent with the significant decline in ammonium concentration of the forest floor, two years after the application of alkaline residuals. The magnitude of soil N2O fluxes is mainly determined by mineral N availability (Ambus et al., 2006), and in addition to the lower ammonium concentration, soluble salts released from alkaline residues could interfere with the nitrification process (Martikainen, 1985). Under laboratory conditions, Liimatainen et al. (2014) reported similar reductions in N 2O production from soils treated with soluble salts (K+, NH4+) as with soils amended with wood ash. 5. Conclusion This study provides evidence that alkaline residuals from the pulp and paper industry are effective at neutralizing soil acidity and replenishing soil base cations without increasing soil GHG emissions when applied to sugar maple-dominated forests of eastern North America. Two years after treatment, soil pH was modeled as a function of the NP of the alkaline residuals, which indicates that NP is a suitable metric to compare the buffering activity of diverse alkaline residuals. However, alkaline residuals are not comparable regarding their effect on soil GHG fluxes, since LM appears to exert a stronger influence on the microbially-mediated production of GHG than other alkaline residuals. This indicates that change in soil pH is not the sole parameter influencing GHG fluxes after liming. Contrary to our original expectation, we report a reduction in CO 2 and N 2 O fluxes, and a variable effect on CH4 fluxes, following alkaline residual application. Additional research is needed to understand why GHG fluxes from forest soils decline after liming. In particular, it would be important to assess the long-term response of sugar maple forests to alkaline residuals, and to partition the soil CO2 flux between heterotrophic and autotrophic respiration to understand the mechanisms responsible for lower soil respiration. We also require studies to explain how the type of alkaline residual affects soil N transformations, particularly the microbially-

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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 flux measurements 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. For CH4 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 after application. 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, which corresponds to the average pH value measured at the end of the experiment. Error bars indicate the 95% confidence intervals. Significant differences between treatments and the control 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 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.

mediated processes that produce N2O like ammonia volatilization and oxidization, nitrifier-denitrification and denitrification. Moreover, the physical and chemical parameters of forest soils treated with alkaline residuals should be compared to forests that are unaffected by soil acidification, to determine whether alkaline residuals are restoring acidic forest soils to a state that will benefit forest ecosystem functions in the long-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. Joann Whalen: Methodology, Supervision, Validation, Writing - review & editing. David Rivest: Conceptualization, Funding acquisition, Methodology, Project administration, Resources, Supervision, Validation, Writing - review & editing.

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Acknowledgements This study was initiated by a Mitacs Acceleration grant awarded to D. Rivest in partnership with Domtar Windsor pulp and paper mill (Windsor, Québec, Canada). Further funding was provided by the Natural Sciences and Engineering Research Council of Canada (NSERC) through a Collaborative Research and Development grant awarded to A. Dupuch (RDCPJ: 462583-13). S. Royer-Tardif was awarded a postdoctoral scholarship from NSERC's CREATE Forest Complexity Modelling program. We acknowledge the valuable contribution of Patrick Cartier and Steve Reynolds from Domtar in the design of this experiment. We are also grateful to the many students and technicians who contributed to this study by their implication in field samplings and laboratory analysis. References Ambus, P., Zechmeister-Boltenstern, S., Butterbach-Bahl, K., 2006. Sources of nitrous oxide emitted from European forest soils. Biogeosciences 3, 135–145. 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