Biochar enhances methane uptake in engineered green roof substrate

Biochar enhances methane uptake in engineered green roof substrate | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Biochar enhances methane uptake in engineered green roof substrate Imrul Kayes, Md Abdul Halim, Wenxi Liao, Md Rezaul Karim, Melanie A. Sifton, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8971619/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 9 You are reading this latest preprint version Abstract Biochar is increasingly applied to soils to enhance carbon sequestration and mitigate greenhouse gas emissions, yet its role in regulating methane (CH₄), a potent greenhouse gas, in engineered substrates remains poorly understood. In particular, there is a lack of field data on how biochar amendments influence CH₄ fluxes from engineered substrates in urban environments, where substrate composition, shallow profiles, and intensive management fundamentally differ from natural soils. Here, we present a long-term (2020–2024) field study examining the effects of biochar amendment on CH₄ fluxes from engineered substrates used in extensive green roofs dominated by stonecrop species ( Phedimus kamschaticus and Sedum spp.). Biochar-amended modules (~ 5% v/v; 20 t ha⁻¹) consistently exhibited greater CH 4 uptake than unamended controls across seasons, with peak uptake rates in spring 2023 nearly fivefold higher (− 1.91 ± 0.25 vs. −0.40 ± 0.10 nmol⋅m⁻²⋅s⁻¹). Importantly, biochar addition did not increase carbon dioxide (CO₂) emissions, indicating enhanced CH 4 uptake without increased carbon losses. Variability in CH 4 uptake was strongly associated with substrate moisture and water vapour flux, suggesting that biochar modifies gas diffusivity and oxygen availability through moisture-mediated physical controls that favour microbial CH 4 oxidation. These results demonstrate that biochar can substantially enhance CH 4 uptake in engineered green roof substrates and extend the application of biochar-based mitigation strategies to engineered systems beyond conventional soils. The mechanistic insights provided here are broadly relevant to understanding CH 4 cycling in biochar-amended engineered substrates. Biochar methane flux engineered substrates green roof substrates greenhouse gas fluxes urban soils Figures Figure 1 Figure 2 Figure 3 Figure 4 Highlights Biochar turns green roof substrates into consistent methane-absorbing systems Methane uptake increased without increasing carbon dioxide emissions Green roofs can be engineered as passive systems for greenhouse gas mitigation 1 Introduction Methane (CH₄) is a potent greenhouse gas with a high near-term warming potential, and small changes in its surface–atmosphere exchange can exert disproportionate impacts on climate forcing (Saunois et al. 2020 ; Mar et al. 2022 ). Biochar, a carbon-rich material produced from biomass pyrolysis, has been widely studied for its ability to enhance carbon sequestration and modulate greenhouse gas fluxes through persistent alterations to substrate physical structure, chemical properties, and microbial habitat (Lehmann and Joseph 2009 ; Spokas 2010 ). However, biochar effects on CH₄ cycling remain variable and mechanistically unresolved, with responses depending strongly on moisture status, gas diffusivity, redox conditions, and microbial activity (Le Mer and Roger 2001 ; Conrad 2007 ; Wu et al. 2019 ). Additionally, most existing evidence derives from short-term experiments in agricultural soils, while long-term field data on biochar-mediated regulation of CH₄ fluxes in engineered substrates are largely absent. Engineered substrates, characterized by shallow profiles, high organic matter content, and strong constraints on aeration and water dynamics, differ fundamentally from mineral soils and may therefore exhibit distinct biochar–CH₄ interactions. Resolving how biochar influences CH 4 flux in these systems is essential for extending biochar-based climate mitigation strategies beyond conventional soil environments widely available across urban green infrastructure. Green roofs represent a rapidly expanding class of engineered substrates within urban green infrastructure and are designed primarily to retain stormwater, moderate building energy use, and support vegetation under strict structural load limits (Oberndorfer et al. 2007 ; Wooster et al. 2022 ; Mihalakakou et al. 2023 ). The potential climate benefits of green roofs through carbon sequestration are also significant, as both vegetation and the organic carbon embedded in green roof substrates are increasingly recognized as important carbon sinks within urban green infrastructure systems (Whittinghill et al. 2014 ; Shafique et al. 2020 ). From a biogeochemical perspective, green roofs function as shallow, organic-rich engineered systems in which moisture dynamics, aeration, and microbial activity are tightly coupled, making them especially sensitive to substrate amendments that modify physical structure and gas transport. The capacity of green roofs to exchange non-CO₂ GHGs, particularly CH₄, has only recently received attention (Halim et al. 2022 ). While several studies have assessed carbon sequestration and CO₂ fluxes, substrate-driven GHG exchange remains a critical and underexplored component of green roof climate performance. Evidence is especially limited for extensive green roofs dominated by Sedum or Phedimus mats, where only a handful of studies have quantified CH₄ fluxes directly. High organic matter content—often incorporated to enhance water retention and plant performance (Hill et al. 2017 ; Xue and Farrell 2020 )—may also promote CH₄ production under localized anaerobic conditions, with reported fluxes comparable to those from organic mulches (Kayes et al. 2025 ). This highlights the need to quantify CH₄ exchange and identify mechanisms regulating methane cycling in these engineered systems. Biogenic CH₄ flux reflects the balance between methanogenesis in anoxic or hypoxic microsites and methanotrophy in oxic zones, with both processes strongly influenced by substrate physical and chemical properties (Le Mer and Roger 2001 ; Conrad 2007 ). Moisture and temperature are dominant controls, with CH₄ oxidation typically peaking at intermediate substrate moisture content and declining under excessively wet or dry conditions due to oxygen limitation or microbial water stress (Castro et al. 1995 ; Borken et al. 2006 ). In green roofs, moisture and temperature have likewise been identified as the dominant physical drivers regulating CH₄ uptake (Teemusk et al. 2019 ; Halim et al. 2022 ). Soil pH is also an important chemical control; while earlier studies suggested optimal methanotrophic activity near neutrality ranging between 6.6–7.5. (Krulwich et al. 2007 ), subsequent work has shown active CH₄ oxidation under both acidic and alkaline conditions (Trotsenko and Khmelenina 2002 ; Yao et al. 2023 ). Soil texture and structure further modulate CH₄ exchange by controlling gas diffusivity through air-filled porosity, while surface evaporation can indirectly enhance oxidation by promoting aeration in near-surface layers (Lehmann et al. 2018 ; Smith et al. 2018 ; Abeysinghe et al. 2022 ). Because methanotrophs convert CH₄ to CO₂, CH₄ and CO₂ fluxes may covary through both stoichiometric coupling and shared environmental drivers (Zheng et al. 2018 ; Ding et al. 2024 ). These controls are inherently seasonal, emphasizing the importance of long-term field observations to resolve treatment effects under variable climatic conditions (Wagner-Riddle et al. 2017 ). Green roof substrates are engineered mixtures of lightweight organic matter and porous mineral aggregates designed to balance drainage, water retention, and load constraints (A’saf et al. 2020 ). While these materials support drainage and plant survival, over time, these substrates are prone to compaction and structural degradation, reducing porosity and altering moisture regimes (De-Ville et al. 2018 ; Yang and Davidson 2021 ). Rooftop exposure further enhances evaporative demand, often necessitating irrigation, which has been shown to increase CH₄ uptake by alleviating moisture limitation (Halim et al. 2022 ). While green roof substrates generally emit CO₂, they typically function as weak CH₄ sinks, and practical strategies to strengthen this sink capacity remain largely unexplored. Biochar has been shown to enhance CH₄ uptake across a range of agricultural, forest, and urban soils (Wu et al. 2019 ; Nan et al. 2020 ; Li et al. 2024 ; Kayes et al. 2025 ), largely through improvements in porosity, aeration, and microbial habitat (Lehmann and Joseph 2009 ). In green roof systems, biochar has been reported to improve plant performance, stormwater retention, and nutrient availability (Chen et al. 2018 ; Liao et al. 2023 ; Lee and Kwon 2024 ), while reducing substrate erosion (Liao et al. 2022 ). Additionally, biochar can create a stable and favorable environment for methane-oxidizing microbes by altering substrate bulk density, electrical conductivity, and pH. Despite these benefits, its influence on CH₄ fluxes from green roof substrates has not been evaluated. Although biochar is highly recalcitrant, with carbon residence times on the order of centuries (Spokas 2010 ), transient increases in CO₂ efflux from labile carbon fractions have been reported (Wang et al. 2016 ), underscoring the need for long-term field assessment in engineered systems. Urban areas now host more than half of the global population (UN 2025), yet their contribution to global biogeochemical cycles remains poorly constrained. While natural soils are recognized as CH₄ sinks, urban ecosystems are often overlooked. Green roofs are widely promoted for stormwater retention, energy savings, and biodiversity, but their role in regulating GHG fluxes is rarely examined. Biochar is known to enhance soil carbon sequestration and stimulate CH₄ uptake in terrestrial systems, but evidence for its function in green infrastructure remains scarce. A recent meta-analysis found that biochar generally improves ecosystem functions across green infrastructure types, including reduced CO₂ and N₂O emissions, though CH₄ responses were variable (Liao et al. 2023 ). However, prior studies directly testing biochar–GHG interactions have been limited to constructed wetlands, where elevated CH₄ emissions are common (Guo et al. 2020 ; Chen et al. 2020 ). Whether biochar can similarly regulate CH₄ dynamics in aerobic, organic-rich green roof substrates remains unknown. In this study, we quantified CH₄, CO₂, and H₂O fluxes from biochar-amended and control extensive green roof systems over a five-year period. We hypothesized that biochar amendment would enhance CH₄ uptake by modifying substrate physicochemical properties, with effects mediated by changes in moisture dynamics, evaporation, and CO₂ flux. We further expected that while the direction of biochar effects would remain consistent over time, their magnitude would vary seasonally. 2 Methods 2.1 Green roof design and substrates The experiment was installed in Fall of 2019 at the Green Roof Innovation Testing Laboratory (GRIT Lab II) at 1, Spadina Crescent at University of Toronto’s downtown St. George Campus, in Toronto, Ontario, Canada (Fig. S1 a). GRIT Lab II’s green roof modules were sized 1.83 m ×1.83 m, with 15 cm substrate depth and slope of 2°. While GRIT Lab II consists of a total of 50 roof modules, the focus of this study was only on 24 green roof modules cultivated over the course of the trial using pre-grown stonecrop mats. The stonecrop mats were dominated (> 95%) by orange stonecrop [ Phedimus kamtschaticus (Fisch. & C.A.Mey.) 't Hart], with small amounts of white stonecrop [ Sedum album L.), and tasteless stonecrop [ Sedum sexangulare L.]. The experimental green roof media was a lightweight high-organic green roof mix (“Eco-blend” supplied by Bioroof Systems Inc., Burlington, Canada) with an organic matter content of 75 ± 2% (mean ± SE) (Liao et al. 2024 ). The biochar amendment was created using sugar maple ( Acer saccharum L.) sawdust pyrolyzed at ~ 700 ◦C, with 10–15 minutes of residence time (Haliburton Forest and Wildlife Reserve, Canada). Biochar and substrate properties are presented in supplementary Table S1 . 2.2 Experimental design Green roof modules were arranged in a completely randomized design with one factor (control vs. biochar) and 12 replicates per level (n = 24). Two non-vegetated bare-substrate modules of identical dimensions were also included for reference. CH 4 , CO 2 and H 2 O flux measurements were conducted from summer (June–August) 2020 through fall (September–November) 2024, spanning five growing seasons. Measurements for spring (March–May) 2022 and summer 2023 were not collected due to logistical constraints and were therefore excluded from the formal analysis. Biochar was incorporated into each treated green roof module at a rate of 20 t ha⁻¹ (equivalent to 5.4% v/v), an application level previously identified as optimal for enhancing plant performance (Gale and Thomas 2019 ). Each green roof module received uniform irrigation of 20–30 L of municipal water twice weekly during drought periods. This irrigation regime was designed primarily to sustain plant survival during dry periods and generally did not generate any surface runoff from the modules. Additional details on the experimental modules and their hydrological performance are given by (Saade et al. 2023 ). 2.3 Data collection In each replicate green roof module, one PVC collar (10 cm diameter, 10 cm height) was inserted into the substrate in early May 2020, prior to initiating seasonal gas flux measurements conducted from Summer 2020 through Fall 2024. For non-vegetated bare substrate references, three collars were installed per module to increase the statistical power for subsequent analyses. The collars were inserted 3–5 cm deep into the substrate to prevent leakage, following the method of Jovani-Sancho et al. ( 2018 ), and placed at least 30 cm away from the module's edge to avoid interference from plant roots. Any aboveground plant material rooted within the collar was trimmed off at the time of installation and subsequent gas flux measurements to eliminate aboveground plant mediated gas fluxes. For gas-exchange measurements, we used a Los Gatos Ultraportable Greenhouse Gas Analyzer (UGGA: Los Gatos Research, San Jose, CA, USA), with measurements based on off-axis integrated cavity output spectroscopy. This was connected to a pressure-equilibrated chamber (LI-8100-102 opaque survey chamber, volume 854.2 cm³; LI-COR Environmental, Lincoln, NE, USA) in a closed dynamic system with a nominal flow rate of 0.5 L/min. This setup provided real-time measurements of CH 4 , CO 2 , and water vapor (H 2 O), with respective accuracies of 2, 300, and 100 ppb. Substrate temperature was recorded adjacent to each sampling location at a depth of ~ 5 cm using a Flip Thermometer (PC, Toronto, Canada; Model 21010083) with a precision of 0.5°C. The substrate moisture content was measured at a depth of ~ 5 cm using a CS-SM2 soil moisture sensor (accuracy: ±2.5%) (CredoSense Inc., Toronto, Canada). Measurements were scheduled to avoid precipitation and irrigation in the preceding 48 hours, mitigating transient gas flux effects related to the "Birch effect" (Birch 1958 ; Schurman and Thomas 2021 ). Each measurement session lasted approximately three minutes, with data logged at one-second intervals. A subset of substrates was sampled during every gas measurement event to determine pH and EC (electrical conductivity) in laboratory. The pH and EC were determined in a 1:2 suspension of substrates in deionized water at room temperature using a pH/mV Meter (IQ Scientific Instruments, USA) and an Orion Star A112 benchtop conductivity meter (Thermo Scientific, USA), respectively. In each case, triplicate measures were conducted. 2.4 Flux Calculations Output files from the LGR gas analyzer were post-processed using R version 4.3.2 (R Core Team 2024) using an algorithm to estimate flux rates ( dc/dt ) of concentrations of CH 4 , CO 2 and H 2 O (Halim et al. 2022 , 2024 ). First, a “dead band” of initial 30s was eliminated from each measurement, which is affected by artifacts triggered by the closing of the chamber (Hoffmann et al. 2017 ; Halim et al. 2024 ). After this exclusion, we applied a straightforward algorithm using the Pearson correlation coefficient ( r ) between concentration and time, focusing specifically on CO 2 concentration data to identify the optimal time window (50–60 seconds) for flux calculations. The time window yielding the highest r value for CO 2 was selected for both CO 2 and CH 4 gases in subsequent flux calculations. We selected CO 2 -based time window for CH 4 because CO 2 fluxes tend to exhibit less noise than CH 4 , allowing potential leakage or pressure artifacts to be more easily detected from CO 2 data than from CH 4 . Figure S1 shows an example slope calculation from the raw data stream following the algorithm described above. To determine dc/dt for calculating CO 2 and CH 4 fluxes, we used either linear or non-linear regression (Hüppi et al. 2018 ) depending on the statistical properties of the concentration data. If the quadratic coefficient of the concentration data was not statistically significant (p > 0.05), we used linear regression to determine dc/dt and calculated the flux as LI-COR, ( 2015 ) (LI-COR 2015 ): $$F=\frac{10V{P}_{0}}{RS\left({T}_{0}+273.15\right)}\frac{dC}{dt}\left(1\right)$$ where F is the Flux of water-corrected CH 4 , CO 2 or H 2 O, V is total volume (sum of chamber head-space volume and the volume inside the collar aboveground) (cm 3 ), P 0 is the initial pressure ( kPa ), R is the Universal Gas Constant (0.83144598 m 3 .kPa.k − 1 .mol − 1 ), S is the soil surface area (cm 2 ), T 0 is the initial air temperature ( ◦ C), and dc/dt is the initial rate of change in the water-corrected CH 4 , CO 2 or H 2 O mole fraction. If the quadratic coefficient was significant (p < 0.05), we chose the non-linear regression approach. Throughout this paper, we expressed CH 4 fluxes as nmol.m − 2 .s − 1 and, CO 2 and H 2 O fluxes as µmol.m − 2 .s − 1 . We fitted the following empirical equation (Welles et al., 2001) to the data points of the selected time window: $${C}_{\left(t\right)}^{{\prime}}={C}_{x}^{{\prime}}+\left({C}_{0}^{{\prime}}-{C}_{x}^{{\prime}}\right){e}^{-a\left(t-{t}_{0}\right)}\left(2\right)$$ where, \({C}_{\left(t\right)}^{{\prime}}\) is the instantaneous water-corrected mole fractions for the gases, \({C}_{0}^{{\prime}}\) is the value of \({C}_{\left(t\right)}^{{\prime}}\) when the chamber just closed, \({C}_{x}^{{\prime}}\) is the asymptote parameter, a specifies the curvature of the fit, and \({t}_{0}\) is time (s) when the chamber closed. The parameters a , \({t}_{0}\) , \({C}_{x}^{{\prime}}\) , and \({C}_{0}^{{\prime}}\) were estimated from the fitted nonlinear regression. Subsequently, using the following equation (Eq. (3)), which is derived from Eq. (2) (at t = \({t}_{0}\) ) was used to calculate the initial rate of change of the water-corrected CH 4 /CO 2 /H 2 O mole fraction (LI-COR, 2015 ): $$\frac{dC}{dt}=a\left({C}_{x}^{{\prime}}-{C}_{0}^{{\prime}}\right)\left(3\right)$$ The value of dc/dt obtained from Eq. (3) was used in Eq. (1) to calculate the CH 4 , CO 2 and H 2 O flux. Overall, using the above algorithm, 18% of the total CH 4 flux, 16% of the total CO 2 flux, and 9% of H 2 O flux were calculated using the nonlinear regression approach (primarily for high flux rates). The average of the three replicated flux measurements of each gas, taken at each collar per campaign, was used for further analyses. 2.5 Statistical analysis Statistical analyses were conducted to evaluate the effects of biochar treatment and measurement period (season × year) on CH₄, CO₂, and H₂O fluxes across the study years. To evaluate these effects, a linear mixed-effects model (LMMs) was applied using the “ lmer ” function from the lme4 package in R (Bates et al. 2015 ). Fixed effects included biochar treatment, measurement period (season-year) and their interaction (treatment × measurement period (season-year)). To account for repeated measurements, Collar was included as a random intercept, while measurement period nested within year was included to capture temporal dependence across measurement years. Residual diagnostics were inspected to verify normality and homoscedasticity assumptions. The significance of fixed effects was evaluated using Type III Analysis of Variance (ANOVA) with Satterthwaite’s method for estimating denominator degrees of freedom, implemented through the “ Anova ” function in the “ lmerTest ” package (Fox et al. 2001 ). To further explore significant fixed effects and interactions, pairwise post hoc comparisons were performed using estimated marginal means (EMMs) obtained from the fitted linear mixed-effects models with the “ emmeans ” package in R. Treatment contrasts were evaluated within each season, and Sidak adjustment was applied to control for multiple comparisons (α = 0.05). This model-based approach retains the random-effects structure, providing statistically robust and interpretable pairwise inferences. Non-vegetated bare-substrate modules were excluded from the formal statistical analysis due to the limited number of replicates, which precluded adequate statistical power; their results are therefore presented descriptively only. 2.6 Structural equation modeling We used structural equation modeling (SEM) to evaluate direct and indirect associations between biochar addition, substrate properties, and gas and water-vapour fluxes. Because measurements were repeated within collars and across seasons, we implemented a piecewiseSEM in R (v4.4.0) using component regressions fit with linear mixed-effects models ( lme4 ), with random intercepts for collar and season to account for non-independence (Gunzler et al. 2013 ; Danner et al. 2015 ; Lefcheck 2016 ). Treatment was coded as an exogenous binary predictor (0 = Control, 1 = Biochar). Substrate moisture, temperature, EC, and pH were specified as mediators; H₂O, CO₂, and CH₄ fluxes were specified as endogenous responses. The a priori model specified that biochar addition could influence substrate moisture, temperature, EC, and pH, and that these substrate properties could in turn influence H₂O, CO₂, and CH₄ fluxes. We assumed no a priori causal direction among fluxes of CH₄, CO₂; and H₂O; therefore, we did not specify directed paths among the fluxes. Instead, we modeled their shared, potentially unmeasured drivers by allowing residual covariances among fluxes within the piecewise SEM framework. All component models were fit as linear mixed-effects regressions with random intercepts for collar and season; when this crossed random-effects structure resulted in singular fits, we simplified the random structure to obtain stable model fits. Overall model adequacy was evaluated using Fisher’s C statistic, where non-significant values ( p > 0.05) indicate consistency between the hypothesized network and the observed covariance structure. Standardized path coefficients, marginal R² values, and the SEM diagram were used to interpret the final model. 3 Results 3.1 Biochar amendment strengthens CH₄ uptake across seasons Linear mixed-effects models (LMMs) showed that biochar treatment had a highly significant effect on CH₄ flux (F₁,₂₅₅ = 136.52, p < 0.001) in green roof substrates. The main effect of season was not significant (F₁₁,₂₅₅ = 0.59, p = 0.84); however, the interaction between treatment and season was significant (F₁₁,₂₅₅ = 3.67, p < 0.001), indicating that the effect of treatment on CH₄ flux varied across seasons (Table 1 ). Table 1 Gas fluxes (CH₄, CO₂ and H₂O) from substrates under pre-grown stonecrop mat extensive green roof modules in response to biochar treatment and season. Significant p-values are shown in bold. Greenhouse gas Effects Mean sq. Df F-value p-value CH 4 Treatment 57.3 1 136.52 < 0.001 Season 0.246 11 0.587 0.838 Treatment ⋅ Season 1.54 11 3.671 < 0.001 CO 2 Treatment 2.648 1 0.316 0.579 Season 315.48 11 37.66 < 0.001 Treatment ⋅ Season 16.694 11 3.671 < 0.029 H 2 O Treatment 2483904 1 28.99 < 0.001 Season 1080987 11 12.620 < 0.001 Treatment ⋅ Season 310866 11 3.629 < 0.001 CH₄ fluxes from green roof substrates showed clear treatment variations across measurement campaigns over the years (Fig. 1 ; Table S3). During the initial measurement years (2020–2021), CH₄ uptake was modest in both treatments, but by summer 2021 biochar-amended modules exhibited significantly stronger uptake than those without biochar ( p < 0.001). From 2022 onward, biochar consistently enhanced CH₄ uptake relative to the no-biochar treatment across all seasons (all p < 0.05). The strongest treatment differences occurred in spring 2023, when CH₄ uptake in biochar-amended green roof modules reached − 1.91 ± 0.25 nmol·m⁻²·s⁻¹ compared with − 0.40 ± 0.10 nmol·m⁻²·s⁻¹ in modules without biochar ( t = − 5.84, p < 0.001). Significant treatment contrasts also occurred in fall 2021 ( p = 0.02), summer 2022 ( p = 0.001), and spring 2024 ( p = 0.007), confirming the persistence of enhanced CH₄ uptake in biochar-treated substrates. Across the five-year period, CH₄ fluxes exhibited strong temporal structure, with uptake peaks in spring and fall and weaker uptake in mid-summer, reflecting concurrent variation in substrate moisture and temperature (supplementary Figure S2). By 2024, although CH₄ uptake had slightly weakened relative to 2023, biochar-treated modules still maintained higher uptake rates (–1.26 ± 0.17 nmol·m⁻²·s⁻¹) than the no-biochar controls (– 0.03 ± 0.19 nmol·m⁻²·s⁻¹; p < 0.001) across all seasons (Fig. 1 ; Table S3). Bare substrate fluxes were also measured but excluded from formal statistical analyses due to lower replication; their descriptive trends, showing minimal CH₄, CO₂, and H₂O exchange relative to vegetated modules, are summarized in supplementary Tables S3–S5. 3.2 CO₂ flux varies seasonally with limited biochar effect Results from LMMs showed that biochar treatment had no significant effect on CO₂ flux (F₁,₂₂ = 0.32, p = 0.58). In contrast, the main effect of season was highly significant (F₁₁,₂₃₃ = 37.66, p < 0.001), indicating substantial temporal variation in CO₂ flux. The interaction between treatment and season was also significant (F₁₁,₂₃₃ = 1.99, p = 0.03), suggesting that treatment effects on CO₂ flux varied modestly across seasons (Table 1 ). CO₂ flux showed significant temporal variation, with moderate emissions during early establishment (2020–2021) and pronounced temporal fluctuations thereafter (Fig. 2 a; Table S4). Biochar treatment did not exert a consistent effect on CO₂ flux ( p > 0.05 for most comparisons), though significant treatment x season were evident (Figure S3). In fall 2022, modules without biochar exhibited the highest CO₂ emissions (15.7 ± 1.2 µmol·m⁻²·s⁻¹), significantly exceeding those from biochar-amended modules (11.4 ± 1.6 µmol·m⁻²·s⁻¹; p = 0.01). By spring 2023, both treatments showed comparable fluxes (~ 9–10 µmol·m⁻²·s⁻¹; p > 0.3), and this pattern persisted through 2024, with modest seasonal peaks in spring and fall (Fig. 2 a; Table S4). Overall, CO₂ fluxes followed a strong seasonal pattern, peaking in spring and fall and declining in summer, with interannual variability. 3.3 Biochar increases evapotranspiration through enhanced surface flux Results from LMMs showed that biochar amendment had a highly significant effect on H₂O flux in green roof substrates (F₁,₂₂ = 29.00, p < 0.001). The main effect of season was also highly significant (F₁₁,₂₃₄ = 12.62, p < 0.001), indicating strong seasonal variation in evapotranspiration. In addition, the interaction between treatment and season was significant (F₁₁,₂₃₄ = 3.63, p < 0.001), indicating temporal variation in the influence of biochar on H₂O flux (Table 1 ). H₂O fluxes exhibited strong seasonal and interannual variation, with consistently higher fluxes in biochar-amended modules than in those without biochar (Fig. 2 b; Table S4). During the establishment years (2020–2021), H₂O fluxes were already elevated in biochar-treated modules (e.g., summer 2020: 564 ± 25 µmol·m⁻²·s⁻¹) compared with controls (306 ± 3 µmol·m⁻²·s⁻¹; p = 0.05). The largest treatment effect occurred in spring 2023, when mean fluxes in biochar modules (1211 ± 192 µmol·m⁻²·s⁻¹) exceeded those without biochar (528 ± 63 µmol·m⁻²·s⁻¹; p < 0.001) by more than 2-fold. Fluxes generally peaked in spring and summer and declined in fall (Figure S4). By 2024, both treatments showed reduced fluxes, though biochar-amended modules maintained higher soil evaporation (390 ± 178 µmol·m⁻²·s⁻¹) than controls (170 ± 27 µmol·m⁻²·s⁻¹; p = 0.03). Overall, biochar addition enhanced H₂O fluxes throughout the study period, especially during warm and moist seasons when evaporative potential was highest. 3.4 CH₄ uptake covaries with CO₂ and H₂O fluxes CH₄ uptake increased significantly with both CO₂ flux ( R² = 0.05, p < 0.01) and H₂O flux ( R² = 0.16, p < 0.001) (Fig. 3 ). These positive relationships were stronger in biochar-amended modules, which consistently exhibited greater CH₄ uptake than those without biochar. Linear regressions between substrate moisture, temperature, EC, and pH with CH₄ flux are shown in Supplementary Figure S5. 3.5 Integrated pathway analysis reveals controls on GHGs fluxes The piecewise structural equation model showed an acceptable fit to the data (Fisher’s C = 11.17, df = 6, p = 0.083) and explained a substantial proportion of the variance in CH₄ flux (R² = 0.47) and CO₂ flux (R² = 0.17), with more limited explanatory power for intermediate variables (moisture = 0.11; temperature = 0.03; EC = 0.12; pH = 0.01; H₂O flux = 0.11; Fig. 4 ). Biochar addition exerted a significant direct effect on CH₄ flux (β = −0.94), indicating enhanced CH₄ uptake under biochar-amended conditions. Biochar also significantly increased substrate moisture (β = 0.65) and reduced electrical conductivity (EC; β = −0.61), and marginal effects on substrate pH ( β = −0.14; p < 0.10; Supplementary Fig. S6 ) . Higher substrate moisture significantly promoted H₂O flux (β = 0.13) and increased EC (β = 0.20), while simultaneously exerting significant suppressive effects on temperature (β = −0.18). Temperature positively influenced both H₂O flux (β = 0.17) and CO₂ flux (β = 0.32) but was negatively associated with CH₄ flux (β = −0.18), indicating uptake. EC had a modest positive effect on CO₂ flux (β = 0.20) but was not directly linked to CH₄ flux. Substrate pH was weakly but negatively associated with both CO₂ flux (β = −0.13) and CH₄ flux (β = −0.13). However, given the marginal biochar–pH pathway and the low variance explained, pH appears to act as a significant secondary chemical correlate rather than a primary biochar-mediated control on gas exchange. Residual correlations among H₂O, CO₂, and CH₄ fluxes (grey two-headed arrows) indicate shared environmental or biological drivers not explicitly resolved as causal pathways in the model, rather than direct flux–flux regulation. 4. Discussion 4.1 Biochar promotes sustained methane uptake in engineered green roof substrates Over five years of field measurements on extensive green roofs, biochar amendment substantially enhanced CH₄ uptake relative to non-amended control substrates. Although the magnitude of uptake varied with season and substrate age, the direction of the biochar effect remained consistent, supporting the hypothesis that biochar strongly promotes conditions favorable for methanotrophy in organic-rich engineered media. Biochar addition also increased surface evaporation but did not elevate CO₂ emissions, indicating that enhanced CH₄ uptake was not accompanied by increased carbon losses. The sustained enhancement of CH₄ uptake across years was closely associated with higher substrate moisture and greater H₂O flux, suggesting that biochar improved gas diffusivity and maintained aerobic microsites conducive to microbial CH₄ oxidation. The CH₄ uptake rates observed in biochar-amended substrates (up to − 1.9 nmol⋅m⁻²⋅s⁻¹) exceeded those typically reported for agricultural soils (− 0.1 to − 1.0 nmol⋅m⁻²⋅s⁻¹) (Le Mer and Roger 2001 ) and urban soils, often negligible to − 1.0 nmol⋅m⁻²⋅s⁻¹; (Groffman and Pouyat 2009 ), though they remained lower than growing season uptake rates reported for intact upland temperate forest soils in the region (–2.4 to − 4.2 nmol⋅m⁻²⋅s⁻¹) (Priemé and Christensen 1997 ; Steinkamp et al. 2001 ). These comparisons highlight the potential for biochar-amended green roofs to function as meaningful CH₄ sinks within urban environments, despite their shallow profiles and engineered constraints. Only a limited number of studies have quantified GHG fluxes from green roof substrates, with most reporting minimal CH₄ uptake (Halim et al. 2022 ). While biochar application in urban green infrastructure has gained attention, its effect on CH₄ flux in extensive green roof systems had not been previously tested. Prior research in agricultural and urban soils largely focused on short-term experiments, where biochar generally reduced CH₄ emissions (Wu et al. 2019 ; Kayes et al. 2025 ), but evidence for long-term persistence has remained scarce. Our results provide the first long-term field demonstration that biochar can substantially strengthen the CH₄ sink capacity of green roof substrates, adding a previously unquantified climate co-benefit to urban green infrastructure. Together, these results indicate that biochar promotes sustained methane uptake in engineered green roof substrates by stabilizing moisture availability and enhancing gas diffusivity, thereby maintaining aerobic conditions favorable for methanotrophy over time. 4.2 Biochar-mediated methane uptake pathways Our results showed that biochar enhanced CH₄ uptake through both direct effects and significant indirect effects mediated by higher substrate moisture and H₂O flux, together creating conditions favorable for methanotrophy. Similar enhancements of CH₄ uptake following biochar application have been reported in paddy fields (Wu et al. 2019 ), urban soils (Kayes et al. 2025 ) and landfill cover materials (Chetri and Reddy 2022 ), and are commonly attributed to biochar’s porous structure and large surface area, which can support methane-oxidizing microbial habitat and improve aeration. Our findings are consistent with these mechanisms, as biochar’s high porosity likely facilitated gas exchange and supported microbial activity conducive to CH₄ oxidation in engineered substrates. The structural equation model revealed a strong relationship between substrate moisture and CH₄ uptake, and between biochar and substrate moisture, indicating that biochar improved water retention and thereby enhanced CH₄ uptake. Soil moisture strongly regulates biogeochemical processes: excessive moisture limits air-filled pore space and creates anaerobic microsites, while low moisture imposes water stress on methanotrophs (Castro et al. 1995 ; Borken et al. 2006 ). Maximum CH₄ uptake rates have been recorded in forest and grassland soils at 20–60% saturation (Bowden et al. 1998 ; Feng et al. 2021 , 2023 ). While green roof substrates are unique, we found moisture generally remained within this optimal range in both treatments but was consistently higher in biochar-amended modules, indicating that biochar maintains conditions favorable for CH₄ uptake under dry conditions. We observed a positive relationship between substrate temperature and CH₄ uptake (Fig. 4 ), suggesting that higher temperatures stimulated methane oxidation. Structural equation modeling indicated that biochar exerted a positive but non-significant effect on temperature, possibly due to its low albedo increasing heat absorption, offset by greater evaporative cooling. This thermal balance resembles prior observations of biochar effects on green roof microclimates (Chen et al. 2018 ). We also observed non-significant relationships between CH₄ uptake and substrate EC but a significant effect of pH (Fig. 4 ). The average pH in biochar-treated modules (6.57 ± 0.04 to 7.53 ± 0.05; Table S5) was near-neutral, within the optimal range (6–8) for CH₄ oxidation (Hanson and Hanson 1996 ). Although biochar commonly has a liming effect on acid soils (Zhang et al. 2025), we did not detect any effect in the present study; instead, biochar-induced pH buffering appears to have contributed to favorable conditions for methanotrophic activity. 4.3 Coupling of CH₄ uptake, evaporation, and CO₂ efflux We found a significant relationship between CH₄ and H₂O flux (Fig. 4 ), with biochar also consistently enhancing H₂O flux across seasons (Fig. 2 ). In rooftop environments characterized by high atmospheric demand of water flux compared to ground environment, biochar likely enhances capillary continuity and pore connectivity (Lehmann and Joseph 2009 ) facilitating vertical water transport in substrate, thereby improving gas diffusivity in near-surface oxic layers and facilitating microbial CH₄ oxidation (Or et al. 2013 ; Smith et al. 2018 ). This dual effect of greater water storage coupled with improved gaseous exchange appears to sustain moisture while simultaneously enhancing oxygen diffusion, promoting long-term CH₄ uptake in biochar-amended substrates. The observed relationships among CH₄, CO₂, and H₂O fluxes further indicate strong coupling between CH 4 uptake, evaporation, and carbon dioxide efflux in engineered green roof substrates. During Stage-I evaporation, removal of surface water increases air-filled porosity while capillary continuity maintains moisture supply to the surface (Or et al. 2013 ), creating oxic microsites immediately above wetter layers where CH₄ oxidation can proceed efficiently. Because methanotrophic activity is highly sensitive to oxygen availability, even modest increases in air-filled pore space can substantially enhance CH₄ uptake (Smith et al. 2018 ). In biochar-amended substrates, improved pore connectivity and sustained moisture likely amplified these effects, strengthening the coupling between evaporation and CH 4 uptake. The negative relationship between CH₄ uptake and CO₂ efflux further supports this interpretation. Because methanotrophs oxidize CH₄ to CO₂, a fraction of the measured CO₂ efflux likely originated from subsurface CH₄ oxidation (Hanson and Hanson 1996 ; Conrad 2007 ), in addition to microbial and root respiration. Collectively, these findings demonstrate that biochar modifies CH₄ flux not through a single control variable, but through interacting physical and biological processes that link water movement, gas diffusion, and microbial metabolism. 4.4 Biochar effects on CO₂ emissions and carbon balance Despite enhancing CH₄ uptake, in this study, biochar application did not significantly increase CO₂ emissions (Fig. 2 a). The tested green roof substrate was already rich in organic matter, and any transient CO₂ pulse from labile carbon likely occurred before monitoring (modules installed in 2019; measurements began 2020). CO₂ efflux showed strong seasonal patterns, higher in spring and summer, lower in fall, with temperature exerting a significant positive effect (Osanai et al. 2015 ; Chen et al. 2021 ). Seasonal variation of CO 2 flux also reflects plant root respiration during active growth (Ben-Noah and Friedman 2018 ). The absence of a sustained biochar-induced increase in CO₂ emissions suggests that enhanced CH₄ uptake did not come at the expense of greater carbon loss from the system. Instead, biochar appears to shift gas exchange dynamics toward increased methane consumption while maintaining overall carbon balance. When considered alongside the positive coupling between CH₄ uptake, evaporation, and CO₂ efflux, these results highlight the importance of integrated physical–biological controls on GHG fluxes in engineered substrates. Understanding and leveraging these interactions offers a promising pathway for designing green roof substrates that simultaneously support hydrological function, plant performance, and greenhouse gas mitigation. 4.5 Limitations and Future Perspectives This study evaluated a single biochar type (sugar maple) applied at a fixed dosage, whereas biochar properties vary widely with feedstock and pyrolysis conditions. Establishing dose–response relationships across biochar types will be essential to identify thresholds at which benefits to methane uptake plateau or unintended effects emerge. Future work should also link observed flux responses to microbial population dynamics using molecular approaches, such as functional gene assays targeting methanotroph abundance and activity, to directly resolve biological mechanisms underlying biochar-induced CH₄ uptake. Beyond these methodological gaps, our findings have clear implications for engineered substrate design. The strong coupling among biochar addition, substrate moisture, evaporation, and methane uptake suggests that biochar should be considered as a multifunctional design component, rather than a passive carbon amendment. Incorporating biochar into green roof substrates may allow designers to enhance gas diffusivity and moisture retention simultaneously, supporting methane uptake while maintaining hydrological and plant-performance objectives. Because extensive green roofs are often managed to balance drainage and aeration under shallow substrate depths, biochar offers a practical means of stabilizing these properties over time. While this study focused on extensive stonecrop-dominated systems, future research should evaluate biochar amendments in native plant green roofs, where greater rooting depth, rhizosphere complexity, and plant–microbe interactions may further influence CH₄ sink strength and broader ecosystem functions. Finally, our results connect biochar use in green roofs to urban wood-waste management strategies. Converting forestry and urban tree residues into biochar for engineered substrates could provide durable carbon storage while simultaneously enhancing greenhouse gas mitigation, stormwater regulation, long-term substrate performance relative to conventional organic amendments, and facilitates circular economy approaches to urban biomass management. 5 Conclusion Pre-grown stonecrop green roofs showed low but detectable rates of CH 4 uptake; biochar amendment greatly enhanced CH₄ uptake, with effects persisting over the five years of the study. Although uptake declined slightly in the fifth year, it remained consistently higher than in unamended substrates. Biochar-mediated enhancement of CH₄ uptake was primarily associated with improved substrate moisture retention and gas diffusivity, highlighting the importance of coupled aeration–evaporation processes in sustaining methanotrophic activity in engineered substrates. These findings demonstrate that a single biochar amendment can maintain elevated CH 4 uptake over multiple years while simultaneously improving hydrological function. Collectively, our results show that biochar-enhanced CH₄ oxidation can be integrated with the pronounced stormwater-regulation and plant-performance benefits of green roofs, supporting their role as multifunctional and climate-resilient components of urban green infrastructure. Declarations Acknowledgements The authors gratefully acknowledge Professors Jennifer Drake and Liat Margolis , co-principal investigators on the broader research project, for their valuable leadership and logistical support. We are grateful to Malaika Mitra , Katie Monat , Jennifer Barrett , Liam Douglas , and Jovana Shrestha for their assistance in fieldwork. We thank Tony Ung for his support in maintaining the green-roof experiment. We also acknowledge Haliburton Forest and Wildlife Reserve Ltd. , Gro-Bark Inc. , and Bioroof Systems Inc. for providing materials and product information. Author’s contribution Imrul Kayes and Sean C. Thomas contributed to the conceptualization of the research. Imrul Kayes, Md Abdul Halim, Wenxi Liao, Md Rezaul Karim, and Melanie A. Sifton participated in field data collection and data curation. Imrul Kayes conducted the formal data analysis and prepared the original manuscript draft. Sean C. Thomas provided validation and supervision and contributed to project administration and funding acquisition. All authors reviewed and approved the final manuscript. Funding This research was supported by the Natural Sciences and Engineering Research Council of Canada ( NSERC)-CREATE Discovery Grant awarded to Sean C. Thomas . Data availability The source data used to generate all graphs and charts presented in this study are publicly available via the Scholars Portal Dataverse repository at: https://doi.org/10.5683/SP3/CANT7Q Declarations Competing interests The authors have no relevant financial or non-financial interests to disclose. Author details Imrul Kayes : Institute of Forestry and Conservation, John H. Daniels Faculty of Architecture, Landscape and Design, University of Toronto, 33 Willcocks Street, Toronto, ON M5S 3B3 Canada School of the Environment, University of Toronto, 5 Bancroft Ave, Toronto, ON M5S 3J1, Canada Md Abdul Halim : Institute of Forestry and Conservation, John H. Daniels Faculty of Architecture, Landscape and Design, University of Toronto, 33 Willcocks Street, Toronto, ON M5S 3B3 Canada Wenxi Liao : School of Environmental Sciences, University of Guelph, 50 Stone Road East, Guelph, ON, N1G 2W1, Canada Md Rezaul Karim : Institute of Forestry and Conservation, John H. Daniels Faculty of Architecture, Landscape and Design, University of Toronto, 33 Willcocks Street, Toronto, ON M5S 3B3 Canada Melanie A. Sifton : Institute of Forestry and Conservation, John H. Daniels Faculty of Architecture, Landscape and Design, University of Toronto, 33 Willcocks Street, Toronto, ON M5S 3B3 Canada Sean C. Thomas : Institute of Forestry and Conservation, John H. Daniels Faculty of Architecture, Landscape and Design, University of Toronto, 33 Willcocks Street, Toronto, ON M5S 3B3 Canada References Abeysinghe AMSN, Lakshani MMT, Amarasinghe UDHN, et al (2022) Soil-Gas Diffusivity-Based Characterization of Variably Saturated Agricultural Topsoils. Water 14:2900. https://doi.org/10.3390/w14182900 A’saf TS, Al-Ajlouni MG, Ayad JY, et al (2020) Performance of six different soilless green roof substrates for the Mediterranean region. 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Biogeosciences 15:6621–6635. https://doi.org/10.5194/bg-15-6621-2018 Additional Declarations No competing interests reported. Supplementary Files Kayesetal.2026supp.docx GA.jpg Graphical abstract Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 14 May, 2026 Reviews received at journal 13 May, 2026 Reviewers agreed at journal 29 Apr, 2026 Reviews received at journal 29 Apr, 2026 Reviewers agreed at journal 21 Apr, 2026 Reviewers invited by journal 19 Mar, 2026 Editor assigned by journal 25 Feb, 2026 Submission checks completed at journal 25 Feb, 2026 First submitted to journal 25 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8971619","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":609839349,"identity":"3f52af54-b6bd-4542-b231-f94c7c782bc4","order_by":0,"name":"Imrul Kayes","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABFUlEQVRIiWNgGAWjYBACNhDxgCEBRDE+YGCQkCFOSwJEC7MBUAsPkAbLSODVB9XCBlJFWAuf2NlnEgkMafIGx9ufVd2oseBhkMg/+Lii4k4dPwPzww/YHCadbgbUkmO44cwZs9s5x4AOk0hmNjxz5pmEZAObMTar2KTT2IBaKhg33Mhhu53DBtbCJtnYdljC4AAPVtfBtNhvuP/8WXHOP1QtzD9wa8lJ3HCDwYw5tw1VCxsOW5gtEgzSkmeeyTGWzu2T4GHjeWxs2HDmsOTMZjYzCyxa5GenMd74UJFs23f8+MPPOd/q5PjZEx8+bKg4zM/P3vz4Bq6QZgDGocIBmL1wUWac6qHWNRBQMApGwSgYBSMXAADTMVWZO8E4zAAAAABJRU5ErkJggg==","orcid":"","institution":"University of Toronto","correspondingAuthor":true,"prefix":"","firstName":"Imrul","middleName":"","lastName":"Kayes","suffix":""},{"id":609839350,"identity":"55af8acb-9a46-4052-a680-6d604901dbd7","order_by":1,"name":"Md Abdul Halim","email":"","orcid":"","institution":"University of Toronto","correspondingAuthor":false,"prefix":"","firstName":"Md","middleName":"Abdul","lastName":"Halim","suffix":""},{"id":609839356,"identity":"2f4be32a-fd8e-425e-8bd6-6fcd7ff50486","order_by":2,"name":"Wenxi Liao","email":"","orcid":"","institution":"University of Guelph","correspondingAuthor":false,"prefix":"","firstName":"Wenxi","middleName":"","lastName":"Liao","suffix":""},{"id":609839360,"identity":"86bdb258-846e-46f1-afe9-5a975e55b429","order_by":3,"name":"Md Rezaul Karim","email":"","orcid":"","institution":"University of Toronto","correspondingAuthor":false,"prefix":"","firstName":"Md","middleName":"Rezaul","lastName":"Karim","suffix":""},{"id":609839364,"identity":"062865ae-a2b7-48f0-8690-1d97e932af2f","order_by":4,"name":"Melanie A. Sifton","email":"","orcid":"","institution":"University of Toronto","correspondingAuthor":false,"prefix":"","firstName":"Melanie","middleName":"A.","lastName":"Sifton","suffix":""},{"id":609839367,"identity":"17c19db2-df75-4f9e-84e8-5d4261c8131f","order_by":5,"name":"Sean C. Thomas","email":"","orcid":"","institution":"University of Toronto","correspondingAuthor":false,"prefix":"","firstName":"Sean","middleName":"C.","lastName":"Thomas","suffix":""}],"badges":[],"createdAt":"2026-02-25 22:53:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8971619/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8971619/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105319739,"identity":"fb6a5e48-e55b-44a4-8618-e59a14837a5d","added_by":"auto","created_at":"2026-03-24 17:06:44","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":113942,"visible":true,"origin":"","legend":"\u003cp\u003eSeasonal variation in mean (±SE) CH₄ flux from \u003cem\u003eextensive\u003c/em\u003e green roof modules with and without biochar amendment from summer 2020 to fall 2024. Bars represent estimated marginal means derived from a linear mixed-effects model using Sidak-adjusted pairwise comparisons. Within each measurement period (season–year), different letters indicate significant differences (\u003cem\u003ep\u003c/em\u003e\u0026lt; 0.05) between biochar and control treatments. Negative flux values indicate net CH₄ uptake by the substrate.\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8971619/v1/aa674489363f1a5666312871.jpg"},{"id":105319740,"identity":"6eebd574-1636-4c49-9c2b-60816a9aab0d","added_by":"auto","created_at":"2026-03-24 17:06:44","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":137715,"visible":true,"origin":"","legend":"\u003cp\u003eSeasonal variation in mean (± SE) \u003cstrong\u003e(a)\u003c/strong\u003e CO₂ and \u003cstrong\u003e(b)\u003c/strong\u003e H₂O fluxes from \u003cem\u003eextensive\u003c/em\u003e green roof modules with and without biochar amendment between summer 2020 and fall 2024. Bars represent estimated marginal means from linear mixed-effects models including treatment and season as fixed factors and collar as a random effect. Different letters within a season denote significant differences between treatments (Sidak-adjusted \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8971619/v1/f99ddeb154208d428081aa89.jpg"},{"id":105319743,"identity":"3965761d-8e54-48da-8c27-ad2c9d64a5d0","added_by":"auto","created_at":"2026-03-24 17:06:44","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":114408,"visible":true,"origin":"","legend":"\u003cp\u003eRelationships between CH₄ flux and (a) CO₂ flux and (b) H₂O flux from \u003cem\u003eextensive\u003c/em\u003e green roof substrates, based on pooled data measured across all seasons between 2020 and 2024. Regression equations, coefficients of determination (R²), and significance levels are shown. The shaded area around the fitted regression line represents the 95% confidence interval. Solid line indicates significant model fit while dashed line indicates non-significant fit.\u003c/p\u003e","description":"","filename":"Picture4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8971619/v1/57bcfc10524fe8482966d52d.jpg"},{"id":105319741,"identity":"27858104-0c32-47dc-9b22-57e8fcf9e81c","added_by":"auto","created_at":"2026-03-24 17:06:44","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":110533,"visible":true,"origin":"","legend":"\u003cp\u003ePiecewise structural equation model (SEM) showing the significant pathways (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05) linking biochar addition, environmental drivers, and CH₄ flux from green roof substrates. Green arrows represent positive standardized path coefficients and red arrows represent negative standardized path coefficients, with arrow thickness proportional to effect size. Numbers adjacent to paths are standardized coefficients. Endogenous variables are shown with their conditional R² values, representing variance explained by predictors. Two-headed grey arrows denote residual correlations among gas fluxes. The non-significant \u003cem\u003ep\u003c/em\u003e-value indicates adequate model fit (Fisher’s C = 11.17, df = 6, \u003cem\u003ep\u003c/em\u003e = 0.083). Non-significant causal paths are excluded here but are presented in supplementary Fig. S6. Negative standardized path coefficient for CH\u003csub\u003e4\u003c/sub\u003e flux values indicate CH\u003csub\u003e4 \u003c/sub\u003euptake.\u003c/p\u003e","description":"","filename":"Picture5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8971619/v1/e49e2b05f88b2de9a1c04d42.jpg"},{"id":105564580,"identity":"75659702-7f2a-4e00-acf8-5eb7da665723","added_by":"auto","created_at":"2026-03-27 12:50:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1663910,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8971619/v1/5c33d2b4-8ec3-444e-85c5-3d2ef0665926.pdf"},{"id":105319744,"identity":"c9b4775a-74cc-40b1-b00d-5e239053307d","added_by":"auto","created_at":"2026-03-24 17:06:44","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":7170889,"visible":true,"origin":"","legend":"","description":"","filename":"Kayesetal.2026supp.docx","url":"https://assets-eu.researchsquare.com/files/rs-8971619/v1/1ced7d05db7cddedb973a342.docx"},{"id":105319742,"identity":"079f7d5e-7496-47d5-8978-0fc0c8166508","added_by":"auto","created_at":"2026-03-24 17:06:44","extension":"jpg","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":191307,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGraphical abstract\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"GA.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8971619/v1/4779c143d08d562aeb53f757.jpg"}],"financialInterests":"No competing interests reported.","formattedTitle":"Biochar enhances methane uptake in engineered green roof substrate","fulltext":[{"header":"Highlights","content":"\u003cul\u003e\n \u003cli\u003eBiochar turns green roof substrates into consistent methane-absorbing systems\u003c/li\u003e\n \u003cli\u003eMethane uptake increased without increasing carbon dioxide emissions\u003c/li\u003e\n \u003cli\u003eGreen roofs can be engineered as passive systems for greenhouse gas mitigation\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"1 Introduction","content":"\u003cp\u003eMethane (CH₄) is a potent greenhouse gas with a high near-term warming potential, and small changes in its surface\u0026ndash;atmosphere exchange can exert disproportionate impacts on climate forcing (Saunois et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Mar et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Biochar, a carbon-rich material produced from biomass pyrolysis, has been widely studied for its ability to enhance carbon sequestration and modulate greenhouse gas fluxes through persistent alterations to substrate physical structure, chemical properties, and microbial habitat (Lehmann and Joseph \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Spokas \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). However, biochar effects on CH₄ cycling remain variable and mechanistically unresolved, with responses depending strongly on moisture status, gas diffusivity, redox conditions, and microbial activity (Le Mer and Roger \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Conrad \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Wu et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Additionally, most existing evidence derives from short-term experiments in agricultural soils, while long-term field data on biochar-mediated regulation of CH₄ fluxes in engineered substrates are largely absent. Engineered substrates, characterized by shallow profiles, high organic matter content, and strong constraints on aeration and water dynamics, differ fundamentally from mineral soils and may therefore exhibit distinct biochar\u0026ndash;CH₄ interactions. Resolving how biochar influences CH\u003csub\u003e4\u003c/sub\u003e flux in these systems is essential for extending biochar-based climate mitigation strategies beyond conventional soil environments widely available across urban green infrastructure.\u003c/p\u003e \u003cp\u003eGreen roofs represent a rapidly expanding class of engineered substrates within urban green infrastructure and are designed primarily to retain stormwater, moderate building energy use, and support vegetation under strict structural load limits (Oberndorfer et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Wooster et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Mihalakakou et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The potential climate benefits of green roofs through carbon sequestration are also significant, as both vegetation and the organic carbon embedded in green roof substrates are increasingly recognized as important carbon sinks within urban green infrastructure systems (Whittinghill et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Shafique et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). From a biogeochemical perspective, green roofs function as shallow, organic-rich engineered systems in which moisture dynamics, aeration, and microbial activity are tightly coupled, making them especially sensitive to substrate amendments that modify physical structure and gas transport.\u003c/p\u003e \u003cp\u003eThe capacity of green roofs to exchange non-CO₂ GHGs, particularly CH₄, has only recently received attention (Halim et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). While several studies have assessed carbon sequestration and CO₂ fluxes, substrate-driven GHG exchange remains a critical and underexplored component of green roof climate performance. Evidence is especially limited for extensive green roofs dominated by \u003cem\u003eSedum\u003c/em\u003e or \u003cem\u003ePhedimus\u003c/em\u003e mats, where only a handful of studies have quantified CH₄ fluxes directly. High organic matter content\u0026mdash;often incorporated to enhance water retention and plant performance (Hill et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Xue and Farrell \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u0026mdash;may also promote CH₄ production under localized anaerobic conditions, with reported fluxes comparable to those from organic mulches (Kayes et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). This highlights the need to quantify CH₄ exchange and identify mechanisms regulating methane cycling in these engineered systems.\u003c/p\u003e \u003cp\u003eBiogenic CH₄ flux reflects the balance between methanogenesis in anoxic or hypoxic microsites and methanotrophy in oxic zones, with both processes strongly influenced by substrate physical and chemical properties (Le Mer and Roger \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Conrad \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Moisture and temperature are dominant controls, with CH₄ oxidation typically peaking at intermediate substrate moisture content and declining under excessively wet or dry conditions due to oxygen limitation or microbial water stress (Castro et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Borken et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). In green roofs, moisture and temperature have likewise been identified as the dominant physical drivers regulating CH₄ uptake (Teemusk et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Halim et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Soil pH is also an important chemical control; while earlier studies suggested optimal methanotrophic activity near neutrality ranging between 6.6\u0026ndash;7.5. (Krulwich et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), subsequent work has shown active CH₄ oxidation under both acidic and alkaline conditions (Trotsenko and Khmelenina \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Yao et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Soil texture and structure further modulate CH₄ exchange by controlling gas diffusivity through air-filled porosity, while surface evaporation can indirectly enhance oxidation by promoting aeration in near-surface layers (Lehmann et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Smith et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Abeysinghe et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Because methanotrophs convert CH₄ to CO₂, CH₄ and CO₂ fluxes may covary through both stoichiometric coupling and shared environmental drivers (Zheng et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Ding et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These controls are inherently seasonal, emphasizing the importance of long-term field observations to resolve treatment effects under variable climatic conditions (Wagner-Riddle et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGreen roof substrates are engineered mixtures of lightweight organic matter and porous mineral aggregates designed to balance drainage, water retention, and load constraints (A\u0026rsquo;saf et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). While these materials support drainage and plant survival, over time, these substrates are prone to compaction and structural degradation, reducing porosity and altering moisture regimes (De-Ville et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Yang and Davidson \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Rooftop exposure further enhances evaporative demand, often necessitating irrigation, which has been shown to increase CH₄ uptake by alleviating moisture limitation (Halim et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). While green roof substrates generally emit CO₂, they typically function as weak CH₄ sinks, and practical strategies to strengthen this sink capacity remain largely unexplored.\u003c/p\u003e \u003cp\u003eBiochar has been shown to enhance CH₄ uptake across a range of agricultural, forest, and urban soils (Wu et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Nan et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Li et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Kayes et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), largely through improvements in porosity, aeration, and microbial habitat (Lehmann and Joseph \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). In green roof systems, biochar has been reported to improve plant performance, stormwater retention, and nutrient availability (Chen et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Liao et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Lee and Kwon \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), while reducing substrate erosion (Liao et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Additionally, biochar can create a stable and favorable environment for methane-oxidizing microbes by altering substrate bulk density, electrical conductivity, and pH. Despite these benefits, its influence on CH₄ fluxes from green roof substrates has not been evaluated. Although biochar is highly recalcitrant, with carbon residence times on the order of centuries (Spokas \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), transient increases in CO₂ efflux from labile carbon fractions have been reported (Wang et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), underscoring the need for long-term field assessment in engineered systems.\u003c/p\u003e \u003cp\u003eUrban areas now host more than half of the global population (UN 2025), yet their contribution to global biogeochemical cycles remains poorly constrained. While natural soils are recognized as CH₄ sinks, urban ecosystems are often overlooked. Green roofs are widely promoted for stormwater retention, energy savings, and biodiversity, but their role in regulating GHG fluxes is rarely examined. Biochar is known to enhance soil carbon sequestration and stimulate CH₄ uptake in terrestrial systems, but evidence for its function in green infrastructure remains scarce. A recent meta-analysis found that biochar generally improves ecosystem functions across green infrastructure types, including reduced CO₂ and N₂O emissions, though CH₄ responses were variable (Liao et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, prior studies directly testing biochar\u0026ndash;GHG interactions have been limited to constructed wetlands, where elevated CH₄ emissions are common (Guo et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Chen et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Whether biochar can similarly regulate CH₄ dynamics in aerobic, organic-rich green roof substrates remains unknown.\u003c/p\u003e \u003cp\u003eIn this study, we quantified CH₄, CO₂, and H₂O fluxes from biochar-amended and control extensive green roof systems over a five-year period. We hypothesized that biochar amendment would enhance CH₄ uptake by modifying substrate physicochemical properties, with effects mediated by changes in moisture dynamics, evaporation, and CO₂ flux. We further expected that while the direction of biochar effects would remain consistent over time, their magnitude would vary seasonally.\u003c/p\u003e"},{"header":"2 Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Green roof design and substrates\u003c/h2\u003e \u003cp\u003eThe experiment was installed in Fall of 2019 at the Green Roof Innovation Testing Laboratory (GRIT Lab II) at 1, Spadina Crescent at University of Toronto\u0026rsquo;s downtown St. George Campus, in Toronto, Ontario, Canada (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003ea). GRIT Lab II\u0026rsquo;s green roof modules were sized 1.83 m \u0026times;1.83 m, with 15 cm substrate depth and slope of 2\u0026deg;. While GRIT Lab II consists of a total of 50 roof modules, the focus of this study was only on 24 green roof modules cultivated over the course of the trial using pre-grown stonecrop mats. The stonecrop mats were dominated (\u0026gt;\u0026thinsp;95%) by orange stonecrop [\u003cem\u003ePhedimus kamtschaticus\u003c/em\u003e (Fisch. \u0026amp; C.A.Mey.) 't Hart], with small amounts of white stonecrop [\u003cem\u003eSedum album\u003c/em\u003e L.), and tasteless stonecrop [\u003cem\u003eSedum sexangulare\u003c/em\u003e L.].\u003c/p\u003e \u003cp\u003eThe experimental green roof media was a lightweight high-organic green roof mix (\u0026ldquo;Eco-blend\u0026rdquo; supplied by Bioroof Systems Inc., Burlington, Canada) with an organic matter content of 75\u0026thinsp;\u0026plusmn;\u0026thinsp;2% (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SE) (Liao et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The biochar amendment was created using sugar maple (\u003cem\u003eAcer saccharum\u003c/em\u003e L.) sawdust pyrolyzed at ~\u0026thinsp;700 ◦C, with 10\u0026ndash;15 minutes of residence time (Haliburton Forest and Wildlife Reserve, Canada). Biochar and substrate properties are presented in supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Experimental design\u003c/h2\u003e \u003cp\u003eGreen roof modules were arranged in a completely randomized design with one factor (control vs. biochar) and 12 replicates per level (n\u0026thinsp;=\u0026thinsp;24). Two non-vegetated bare-substrate modules of identical dimensions were also included for reference. CH\u003csub\u003e4\u003c/sub\u003e, CO\u003csub\u003e2\u003c/sub\u003e and H\u003csub\u003e2\u003c/sub\u003eO flux measurements were conducted from summer (June\u0026ndash;August) 2020 through fall (September\u0026ndash;November) 2024, spanning five growing seasons. Measurements for spring (March\u0026ndash;May) 2022 and summer 2023 were not collected due to logistical constraints and were therefore excluded from the formal analysis. Biochar was incorporated into each treated green roof module at a rate of 20 t ha⁻\u0026sup1; (equivalent to 5.4% v/v), an application level previously identified as optimal for enhancing plant performance (Gale and Thomas \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Each green roof module received uniform irrigation of 20\u0026ndash;30 L of municipal water twice weekly during drought periods. This irrigation regime was designed primarily to sustain plant survival during dry periods and generally did not generate any surface runoff from the modules. Additional details on the experimental modules and their hydrological performance are given by (Saade et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Data collection\u003c/h2\u003e \u003cp\u003eIn each replicate green roof module, one PVC collar (10 cm diameter, 10 cm height) was inserted into the substrate in early May 2020, prior to initiating seasonal gas flux measurements conducted from Summer 2020 through Fall 2024. For non-vegetated bare substrate references, three collars were installed per module to increase the statistical power for subsequent analyses. The collars were inserted 3\u0026ndash;5 cm deep into the substrate to prevent leakage, following the method of Jovani-Sancho et al. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), and placed at least 30 cm away from the module's edge to avoid interference from plant roots. Any aboveground plant material rooted within the collar was trimmed off at the time of installation and subsequent gas flux measurements to eliminate aboveground plant mediated gas fluxes.\u003c/p\u003e \u003cp\u003eFor gas-exchange measurements, we used a Los Gatos Ultraportable Greenhouse Gas Analyzer (UGGA: Los Gatos Research, San Jose, CA, USA), with measurements based on off-axis integrated cavity output spectroscopy. This was connected to a pressure-equilibrated chamber (LI-8100-102 opaque survey chamber, volume 854.2 cm\u0026sup3;; LI-COR Environmental, Lincoln, NE, USA) in a closed dynamic system with a nominal flow rate of 0.5 L/min. This setup provided real-time measurements of CH\u003csub\u003e4\u003c/sub\u003e, CO\u003csub\u003e2\u003c/sub\u003e, and water vapor (H\u003csub\u003e2\u003c/sub\u003eO), with respective accuracies of 2, 300, and 100 ppb.\u003c/p\u003e \u003cp\u003eSubstrate temperature was recorded adjacent to each sampling location at a depth of ~\u0026thinsp;5 cm using a Flip Thermometer (PC, Toronto, Canada; Model 21010083) with a precision of 0.5\u0026deg;C. The substrate moisture content was measured at a depth of ~\u0026thinsp;5 cm using a CS-SM2 soil moisture sensor (accuracy: \u0026plusmn;2.5%) (CredoSense Inc., Toronto, Canada). Measurements were scheduled to avoid precipitation and irrigation in the preceding 48 hours, mitigating transient gas flux effects related to the \"Birch effect\" (Birch \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1958\u003c/span\u003e; Schurman and Thomas \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Each measurement session lasted approximately three minutes, with data logged at one-second intervals. A subset of substrates was sampled during every gas measurement event to determine pH and EC (electrical conductivity) in laboratory. The pH and EC were determined in a 1:2 suspension of substrates in deionized water at room temperature using a pH/mV Meter (IQ Scientific Instruments, USA) and an Orion Star A112 benchtop conductivity meter (Thermo Scientific, USA), respectively. In each case, triplicate measures were conducted.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Flux Calculations\u003c/h2\u003e \u003cp\u003eOutput files from the LGR gas analyzer were post-processed using R version 4.3.2 (R Core Team 2024) using an algorithm to estimate flux rates (\u003cem\u003edc/dt\u003c/em\u003e) of concentrations of CH\u003csub\u003e4\u003c/sub\u003e, CO\u003csub\u003e2\u003c/sub\u003e and H\u003csub\u003e2\u003c/sub\u003eO (Halim et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). First, a \u0026ldquo;dead band\u0026rdquo; of initial 30s was eliminated from each measurement, which is affected by artifacts triggered by the closing of the chamber (Hoffmann et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Halim et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). After this exclusion, we applied a straightforward algorithm using the Pearson correlation coefficient (\u003cem\u003er\u003c/em\u003e) between concentration and time, focusing specifically on CO\u003csub\u003e2\u003c/sub\u003e concentration data to identify the optimal time window (50\u0026ndash;60 seconds) for flux calculations. The time window yielding the highest \u003cem\u003er\u003c/em\u003e value for CO\u003csub\u003e2\u003c/sub\u003e was selected for both CO\u003csub\u003e2\u003c/sub\u003e and CH\u003csub\u003e4\u003c/sub\u003e gases in subsequent flux calculations. We selected CO\u003csub\u003e2\u003c/sub\u003e-based time window for CH\u003csub\u003e4\u003c/sub\u003e because CO\u003csub\u003e2\u003c/sub\u003e fluxes tend to exhibit less noise than CH\u003csub\u003e4\u003c/sub\u003e, allowing potential leakage or pressure artifacts to be more easily detected from CO\u003csub\u003e2\u003c/sub\u003e data than from CH\u003csub\u003e4\u003c/sub\u003e. Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e shows an example slope calculation from the raw data stream following the algorithm described above.\u003c/p\u003e \u003cp\u003eTo determine \u003cem\u003edc/dt\u003c/em\u003e for calculating CO\u003csub\u003e2\u003c/sub\u003e and CH\u003csub\u003e4\u003c/sub\u003e fluxes, we used either linear or non-linear regression (H\u0026uuml;ppi et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) depending on the statistical properties of the concentration data. If the quadratic coefficient of the concentration data was not statistically significant (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05), we used linear regression to determine \u003cem\u003edc/dt\u003c/em\u003e and calculated the flux as LI-COR, (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) (LI-COR \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2015\u003c/span\u003e):\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$F=\\frac{10V{P}_{0}}{RS\\left({T}_{0}+273.15\\right)}\\frac{dC}{dt}\\left(1\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cem\u003eF\u003c/em\u003e is the Flux of water-corrected CH\u003csub\u003e4\u003c/sub\u003e, CO\u003csub\u003e2\u003c/sub\u003e or H\u003csub\u003e2\u003c/sub\u003eO, \u003cem\u003eV\u003c/em\u003e is total volume (sum of chamber head-space volume and the volume inside the collar aboveground) (cm\u003csup\u003e3\u003c/sup\u003e), \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003e0\u003c/em\u003e\u003c/sub\u003e is the initial pressure (\u003cem\u003ekPa\u003c/em\u003e), \u003cem\u003eR\u003c/em\u003e is the Universal Gas Constant (0.83144598 m\u003csup\u003e3\u003c/sup\u003e.kPa.k\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e.mol\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), \u003cem\u003eS\u003c/em\u003e is the soil surface area (cm\u003csup\u003e2\u003c/sup\u003e), \u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003e0\u003c/em\u003e\u003c/sub\u003e is the initial air temperature (\u003csup\u003e◦\u003c/sup\u003eC), and \u003cem\u003edc/dt\u003c/em\u003e is the initial rate of change in the water-corrected CH\u003csub\u003e4\u003c/sub\u003e, CO\u003csub\u003e2\u003c/sub\u003e or H\u003csub\u003e2\u003c/sub\u003eO mole fraction. If the quadratic coefficient was significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), we chose the non-linear regression approach. Throughout this paper, we expressed CH\u003csub\u003e4\u003c/sub\u003e fluxes as nmol.m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e.s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and, CO\u003csub\u003e2\u003c/sub\u003e and H\u003csub\u003e2\u003c/sub\u003eO fluxes as \u0026micro;mol.m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e.s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. We fitted the following empirical equation (Welles et al., 2001) to the data points of the selected time window:\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$${C}_{\\left(t\\right)}^{{\\prime}}={C}_{x}^{{\\prime}}+\\left({C}_{0}^{{\\prime}}-{C}_{x}^{{\\prime}}\\right){e}^{-a\\left(t-{t}_{0}\\right)}\\left(2\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({C}_{\\left(t\\right)}^{{\\prime}}\\)\u003c/span\u003e\u003c/span\u003e is the instantaneous water-corrected mole fractions for the gases, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({C}_{0}^{{\\prime}}\\)\u003c/span\u003e\u003c/span\u003e is the value of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({C}_{\\left(t\\right)}^{{\\prime}}\\)\u003c/span\u003e\u003c/span\u003e when the chamber just closed, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({C}_{x}^{{\\prime}}\\)\u003c/span\u003e\u003c/span\u003e is the asymptote parameter, a specifies the curvature of the fit, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({t}_{0}\\)\u003c/span\u003e\u003c/span\u003e is time (s) when the chamber closed. The parameters \u003cem\u003ea\u003c/em\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({t}_{0}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({C}_{x}^{{\\prime}}\\)\u003c/span\u003e\u003c/span\u003e, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({C}_{0}^{{\\prime}}\\)\u003c/span\u003e\u003c/span\u003e were estimated from the fitted nonlinear regression. Subsequently, using the following equation (Eq.\u0026nbsp;(3)), which is derived from Eq.\u0026nbsp;(2) (at t = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({t}_{0}\\)\u003c/span\u003e\u003c/span\u003e) was used to calculate the initial rate of change of the water-corrected CH\u003csub\u003e4\u003c/sub\u003e/CO\u003csub\u003e2\u003c/sub\u003e/H\u003csub\u003e2\u003c/sub\u003eO mole fraction (LI-COR, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2015\u003c/span\u003e):\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\frac{dC}{dt}=a\\left({C}_{x}^{{\\prime}}-{C}_{0}^{{\\prime}}\\right)\\left(3\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe value of \u003cem\u003edc/dt\u003c/em\u003e obtained from Eq.\u0026nbsp;(3) was used in Eq.\u0026nbsp;(1) to calculate the CH\u003csub\u003e4\u003c/sub\u003e, CO\u003csub\u003e2\u003c/sub\u003e and H\u003csub\u003e2\u003c/sub\u003eO flux. Overall, using the above algorithm, 18% of the total CH\u003csub\u003e4\u003c/sub\u003e flux, 16% of the total CO\u003csub\u003e2\u003c/sub\u003e flux, and 9% of H\u003csub\u003e2\u003c/sub\u003eO flux were calculated using the nonlinear regression approach (primarily for high flux rates). The average of the three replicated flux measurements of each gas, taken at each collar per campaign, was used for further analyses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Statistical analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses were conducted to evaluate the effects of biochar treatment and measurement period (season \u0026times; year) on CH₄, CO₂, and H₂O fluxes across the study years. To evaluate these effects, a linear mixed-effects model (LMMs) was applied using the \u0026ldquo;\u003cem\u003elmer\u003c/em\u003e\u0026rdquo; function from the \u003cem\u003elme4\u003c/em\u003e package in R (Bates et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Fixed effects included biochar treatment, measurement period (season-year) and their interaction (treatment \u0026times; measurement period (season-year)). To account for repeated measurements, Collar was included as a random intercept, while measurement period nested within year was included to capture temporal dependence across measurement years. Residual diagnostics were inspected to verify normality and homoscedasticity assumptions.\u003c/p\u003e \u003cp\u003eThe significance of fixed effects was evaluated using Type III Analysis of Variance (ANOVA) with Satterthwaite\u0026rsquo;s method for estimating denominator degrees of freedom, implemented through the \u0026ldquo;\u003cem\u003eAnova\u003c/em\u003e\u0026rdquo; function in the \u0026ldquo;\u003cem\u003elmerTest\u003c/em\u003e\u0026rdquo; package (Fox et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). To further explore significant fixed effects and interactions, pairwise post hoc comparisons were performed using estimated marginal means (EMMs) obtained from the fitted linear mixed-effects models with the \u0026ldquo;\u003cem\u003eemmeans\u003c/em\u003e\u0026rdquo; package in R. Treatment contrasts were evaluated within each season, and \u003cem\u003eSidak\u003c/em\u003e adjustment was applied to control for multiple comparisons (α\u0026thinsp;=\u0026thinsp;0.05). This model-based approach retains the random-effects structure, providing statistically robust and interpretable pairwise inferences. Non-vegetated bare-substrate modules were excluded from the formal statistical analysis due to the limited number of replicates, which precluded adequate statistical power; their results are therefore presented descriptively only.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Structural equation modeling\u003c/h2\u003e \u003cp\u003eWe used structural equation modeling (SEM) to evaluate direct and indirect associations between biochar addition, substrate properties, and gas and water-vapour fluxes. Because measurements were repeated within collars and across seasons, we implemented a \u003cem\u003epiecewiseSEM\u003c/em\u003e in R (v4.4.0) using component regressions fit with linear mixed-effects models (\u003cem\u003elme4\u003c/em\u003e), with random intercepts for collar and season to account for non-independence (Gunzler et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Danner et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Lefcheck \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Treatment was coded as an exogenous binary predictor (0\u0026thinsp;=\u0026thinsp;Control, 1\u0026thinsp;=\u0026thinsp;Biochar). Substrate moisture, temperature, EC, and pH were specified as mediators; H₂O, CO₂, and CH₄ fluxes were specified as endogenous responses.\u003c/p\u003e \u003cp\u003eThe a priori model specified that biochar addition could influence substrate moisture, temperature, EC, and pH, and that these substrate properties could in turn influence H₂O, CO₂, and CH₄ fluxes. We assumed no a priori causal direction among fluxes of CH₄, CO₂; and H₂O; therefore, we did not specify directed paths among the fluxes. Instead, we modeled their shared, potentially unmeasured drivers by allowing residual covariances among fluxes within the piecewise SEM framework. All component models were fit as linear mixed-effects regressions with random intercepts for collar and season; when this crossed random-effects structure resulted in singular fits, we simplified the random structure to obtain stable model fits. Overall model adequacy was evaluated using Fisher\u0026rsquo;s C statistic, where non-significant values (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) indicate consistency between the hypothesized network and the observed covariance structure. Standardized path coefficients, marginal R\u0026sup2; values, and the SEM diagram were used to interpret the final model.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Biochar amendment strengthens CH₄ uptake across seasons\u003c/h2\u003e \u003cp\u003eLinear mixed-effects models (LMMs) showed that biochar treatment had a highly significant effect on CH₄ flux (F₁,₂₅₅ = 136.52, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) in green roof substrates. The main effect of season was not significant (F₁₁,₂₅₅ = 0.59, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.84); however, the interaction between treatment and season was significant (F₁₁,₂₅₅ = 3.67, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating that the effect of treatment on CH₄ flux varied across seasons (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGas fluxes (CH₄, CO₂ and H₂O) from substrates under pre-grown stonecrop mat extensive green roof modules in response to biochar treatment and season. Significant p-values are shown in bold.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGreenhouse gas\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEffects\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean sq.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ep-value\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eCH\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTreatment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e136.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSeason\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.587\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.838\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTreatment \u0026sdot; Season\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.671\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTreatment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.648\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.316\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.579\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSeason\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e315.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e37.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTreatment \u0026sdot; Season\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.694\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.671\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.029\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eH\u003csub\u003e2\u003c/sub\u003eO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTreatment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2483904\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e28.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSeason\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1080987\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12.620\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTreatment \u0026sdot; Season\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e310866\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.629\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eCH₄ fluxes from green roof substrates showed clear treatment variations across measurement campaigns over the years (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; Table S3). During the initial measurement years (2020\u0026ndash;2021), CH₄ uptake was modest in both treatments, but by summer 2021 biochar-amended modules exhibited significantly stronger uptake than those without biochar (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). From 2022 onward, biochar consistently enhanced CH₄ uptake relative to the no-biochar treatment across all seasons (all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The strongest treatment differences occurred in spring 2023, when CH₄ uptake in biochar-amended green roof modules reached \u0026minus;\u0026thinsp;1.91\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25 nmol\u0026middot;m⁻\u0026sup2;\u0026middot;s⁻\u0026sup1; compared with \u0026minus;\u0026thinsp;0.40\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10 nmol\u0026middot;m⁻\u0026sup2;\u0026middot;s⁻\u0026sup1; in modules without biochar (\u003cem\u003et\u003c/em\u003e = \u0026minus;\u0026thinsp;5.84, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Significant treatment contrasts also occurred in fall 2021 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.02), summer 2022 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001), and spring 2024 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007), confirming the persistence of enhanced CH₄ uptake in biochar-treated substrates. Across the five-year period, CH₄ fluxes exhibited strong temporal structure, with uptake peaks in spring and fall and weaker uptake in mid-summer, reflecting concurrent variation in substrate moisture and temperature (supplementary Figure S2). By 2024, although CH₄ uptake had slightly weakened relative to 2023, biochar-treated modules still maintained higher uptake rates (\u0026ndash;1.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17 nmol\u0026middot;m⁻\u0026sup2;\u0026middot;s⁻\u0026sup1;) than the no-biochar controls (\u0026ndash; 0.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.19 nmol\u0026middot;m⁻\u0026sup2;\u0026middot;s⁻\u0026sup1;; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) across all seasons (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; Table S3). Bare substrate fluxes were also measured but excluded from formal statistical analyses due to lower replication; their descriptive trends, showing minimal CH₄, CO₂, and H₂O exchange relative to vegetated modules, are summarized in supplementary Tables S3\u0026ndash;S5.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2 CO₂ flux varies seasonally with limited biochar effect\u003c/h2\u003e \u003cp\u003eResults from LMMs showed that biochar treatment had no significant effect on CO₂ flux (F₁,₂₂ = 0.32, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.58). In contrast, the main effect of season was highly significant (F₁₁,₂₃₃ = 37.66, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating substantial temporal variation in CO₂ flux. The interaction between treatment and season was also significant (F₁₁,₂₃₃ = 1.99, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.03), suggesting that treatment effects on CO₂ flux varied modestly across seasons (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCO₂ flux showed significant temporal variation, with moderate emissions during early establishment (2020\u0026ndash;2021) and pronounced temporal fluctuations thereafter (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea; Table S4). Biochar treatment did not exert a consistent effect on CO₂ flux (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05 for most comparisons), though significant treatment x season were evident (Figure S3). In fall 2022, modules without biochar exhibited the highest CO₂ emissions (15.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2 \u0026micro;mol\u0026middot;m⁻\u0026sup2;\u0026middot;s⁻\u0026sup1;), significantly exceeding those from biochar-amended modules (11.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6 \u0026micro;mol\u0026middot;m⁻\u0026sup2;\u0026middot;s⁻\u0026sup1;; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01). By spring 2023, both treatments showed comparable fluxes (~\u0026thinsp;9\u0026ndash;10 \u0026micro;mol\u0026middot;m⁻\u0026sup2;\u0026middot;s⁻\u0026sup1;; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.3), and this pattern persisted through 2024, with modest seasonal peaks in spring and fall (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea; Table S4). Overall, CO₂ fluxes followed a strong seasonal pattern, peaking in spring and fall and declining in summer, with interannual variability.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Biochar increases evapotranspiration through enhanced surface flux\u003c/h2\u003e \u003cp\u003eResults from LMMs showed that biochar amendment had a highly significant effect on H₂O flux in green roof substrates (F₁,₂₂ = 29.00, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The main effect of season was also highly significant (F₁₁,₂₃₄ = 12.62, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating strong seasonal variation in evapotranspiration. In addition, the interaction between treatment and season was significant (F₁₁,₂₃₄ = 3.63, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating temporal variation in the influence of biochar on H₂O flux (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eH₂O fluxes exhibited strong seasonal and interannual variation, with consistently higher fluxes in biochar-amended modules than in those without biochar (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb; Table S4). During the establishment years (2020\u0026ndash;2021), H₂O fluxes were already elevated in biochar-treated modules (e.g., summer 2020: 564\u0026thinsp;\u0026plusmn;\u0026thinsp;25 \u0026micro;mol\u0026middot;m⁻\u0026sup2;\u0026middot;s⁻\u0026sup1;) compared with controls (306\u0026thinsp;\u0026plusmn;\u0026thinsp;3 \u0026micro;mol\u0026middot;m⁻\u0026sup2;\u0026middot;s⁻\u0026sup1;; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.05). The largest treatment effect occurred in spring 2023, when mean fluxes in biochar modules (1211\u0026thinsp;\u0026plusmn;\u0026thinsp;192 \u0026micro;mol\u0026middot;m⁻\u0026sup2;\u0026middot;s⁻\u0026sup1;) exceeded those without biochar (528\u0026thinsp;\u0026plusmn;\u0026thinsp;63 \u0026micro;mol\u0026middot;m⁻\u0026sup2;\u0026middot;s⁻\u0026sup1;; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) by more than 2-fold. Fluxes generally peaked in spring and summer and declined in fall (Figure S4). By 2024, both treatments showed reduced fluxes, though biochar-amended modules maintained higher soil evaporation (390\u0026thinsp;\u0026plusmn;\u0026thinsp;178 \u0026micro;mol\u0026middot;m⁻\u0026sup2;\u0026middot;s⁻\u0026sup1;) than controls (170\u0026thinsp;\u0026plusmn;\u0026thinsp;27 \u0026micro;mol\u0026middot;m⁻\u0026sup2;\u0026middot;s⁻\u0026sup1;; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.03). Overall, biochar addition enhanced H₂O fluxes throughout the study period, especially during warm and moist seasons when evaporative potential was highest.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.4 CH₄ uptake covaries with CO₂ and H₂O fluxes\u003c/h2\u003e \u003cp\u003eCH₄ uptake increased significantly with both CO₂ flux (\u003cem\u003eR\u0026sup2;\u003c/em\u003e = 0.05, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and H₂O flux (\u003cem\u003eR\u0026sup2;\u003c/em\u003e = 0.16, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). These positive relationships were stronger in biochar-amended modules, which consistently exhibited greater CH₄ uptake than those without biochar. Linear regressions between substrate moisture, temperature, EC, and pH with CH₄ flux are shown in Supplementary Figure S5.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Integrated pathway analysis reveals controls on GHGs fluxes\u003c/h2\u003e \u003cp\u003eThe piecewise structural equation model showed an acceptable fit to the data (Fisher\u0026rsquo;s C\u0026thinsp;=\u0026thinsp;11.17, df\u0026thinsp;=\u0026thinsp;6, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.083) and explained a substantial proportion of the variance in CH₄ flux (R\u0026sup2; = 0.47) and CO₂ flux (R\u0026sup2; = 0.17), with more limited explanatory power for intermediate variables (moisture\u0026thinsp;=\u0026thinsp;0.11; temperature\u0026thinsp;=\u0026thinsp;0.03; EC\u0026thinsp;=\u0026thinsp;0.12; pH\u0026thinsp;=\u0026thinsp;0.01; H₂O flux\u0026thinsp;=\u0026thinsp;0.11; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Biochar addition exerted a significant direct effect on CH₄ flux (β = \u0026minus;0.94), indicating enhanced CH₄ uptake under biochar-amended conditions. Biochar also significantly increased substrate moisture (β\u0026thinsp;=\u0026thinsp;0.65) and reduced electrical conductivity (EC; β = \u0026minus;0.61), and marginal effects on substrate pH \u003cb\u003e(\u003c/b\u003eβ = \u0026minus;0.14; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.10; Supplementary Fig. S6\u003cb\u003e)\u003c/b\u003e. Higher substrate moisture significantly promoted H₂O flux (β\u0026thinsp;=\u0026thinsp;0.13) and increased EC (β\u0026thinsp;=\u0026thinsp;0.20), while simultaneously exerting significant suppressive effects on temperature (β = \u0026minus;0.18). Temperature positively influenced both H₂O flux (β\u0026thinsp;=\u0026thinsp;0.17) and CO₂ flux (β\u0026thinsp;=\u0026thinsp;0.32) but was negatively associated with CH₄ flux (β = \u0026minus;0.18), indicating uptake. EC had a modest positive effect on CO₂ flux (β\u0026thinsp;=\u0026thinsp;0.20) but was not directly linked to CH₄ flux. Substrate pH was weakly but negatively associated with both CO₂ flux (β = \u0026minus;0.13) and CH₄ flux (β = \u0026minus;0.13). However, given the marginal biochar\u0026ndash;pH pathway and the low variance explained, pH appears to act as a significant secondary chemical correlate rather than a primary biochar-mediated control on gas exchange. Residual correlations among H₂O, CO₂, and CH₄ fluxes (grey two-headed arrows) indicate shared environmental or biological drivers not explicitly resolved as causal pathways in the model, rather than direct flux\u0026ndash;flux regulation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Biochar promotes sustained methane uptake in engineered green roof substrates\u003c/h2\u003e \u003cp\u003eOver five years of field measurements on extensive green roofs, biochar amendment substantially enhanced CH₄ uptake relative to non-amended control substrates. Although the magnitude of uptake varied with season and substrate age, the direction of the biochar effect remained consistent, supporting the hypothesis that biochar strongly promotes conditions favorable for methanotrophy in organic-rich engineered media. Biochar addition also increased surface evaporation but did not elevate CO₂ emissions, indicating that enhanced CH₄ uptake was not accompanied by increased carbon losses. The sustained enhancement of CH₄ uptake across years was closely associated with higher substrate moisture and greater H₂O flux, suggesting that biochar improved gas diffusivity and maintained aerobic microsites conducive to microbial CH₄ oxidation.\u003c/p\u003e \u003cp\u003eThe CH₄ uptake rates observed in biochar-amended substrates (up to \u0026minus;\u0026thinsp;1.9 nmol\u0026sdot;m⁻\u0026sup2;\u0026sdot;s⁻\u0026sup1;) exceeded those typically reported for agricultural soils (\u0026minus;\u0026thinsp;0.1 to \u0026minus;\u0026thinsp;1.0 nmol\u0026sdot;m⁻\u0026sup2;\u0026sdot;s⁻\u0026sup1;) (Le Mer and Roger \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) and urban soils, often negligible to \u0026minus;\u0026thinsp;1.0 nmol\u0026sdot;m⁻\u0026sup2;\u0026sdot;s⁻\u0026sup1;; (Groffman and Pouyat \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), though they remained lower than growing season uptake rates reported for intact upland temperate forest soils in the region (\u0026ndash;2.4 to \u0026minus;\u0026thinsp;4.2 nmol\u0026sdot;m⁻\u0026sup2;\u0026sdot;s⁻\u0026sup1;) (Priem\u0026eacute; and Christensen \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Steinkamp et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). These comparisons highlight the potential for biochar-amended green roofs to function as meaningful CH₄ sinks within urban environments, despite their shallow profiles and engineered constraints.\u003c/p\u003e \u003cp\u003eOnly a limited number of studies have quantified GHG fluxes from green roof substrates, with most reporting minimal CH₄ uptake (Halim et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). While biochar application in urban green infrastructure has gained attention, its effect on CH₄ flux in extensive green roof systems had not been previously tested. Prior research in agricultural and urban soils largely focused on short-term experiments, where biochar generally reduced CH₄ emissions (Wu et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Kayes et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), but evidence for long-term persistence has remained scarce. Our results provide the first long-term field demonstration that biochar can substantially strengthen the CH₄ sink capacity of green roof substrates, adding a previously unquantified climate co-benefit to urban green infrastructure. Together, these results indicate that biochar promotes sustained methane uptake in engineered green roof substrates by stabilizing moisture availability and enhancing gas diffusivity, thereby maintaining aerobic conditions favorable for methanotrophy over time.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Biochar-mediated methane uptake pathways\u003c/h2\u003e \u003cp\u003eOur results showed that biochar enhanced CH₄ uptake through both direct effects and significant indirect effects mediated by higher substrate moisture and H₂O flux, together creating conditions favorable for methanotrophy. Similar enhancements of CH₄ uptake following biochar application have been reported in paddy fields (Wu et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), urban soils (Kayes et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) and landfill cover materials (Chetri and Reddy \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and are commonly attributed to biochar\u0026rsquo;s porous structure and large surface area, which can support methane-oxidizing microbial habitat and improve aeration. Our findings are consistent with these mechanisms, as biochar\u0026rsquo;s high porosity likely facilitated gas exchange and supported microbial activity conducive to CH₄ oxidation in engineered substrates.\u003c/p\u003e \u003cp\u003eThe structural equation model revealed a strong relationship between substrate moisture and CH₄ uptake, and between biochar and substrate moisture, indicating that biochar improved water retention and thereby enhanced CH₄ uptake. Soil moisture strongly regulates biogeochemical processes: excessive moisture limits air-filled pore space and creates anaerobic microsites, while low moisture imposes water stress on methanotrophs (Castro et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Borken et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Maximum CH₄ uptake rates have been recorded in forest and grassland soils at 20\u0026ndash;60% saturation (Bowden et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Feng et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). While green roof substrates are unique, we found moisture generally remained within this optimal range in both treatments but was consistently higher in biochar-amended modules, indicating that biochar maintains conditions favorable for CH₄ uptake under dry conditions. We observed a positive relationship between substrate temperature and CH₄ uptake (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), suggesting that higher temperatures stimulated methane oxidation. Structural equation modeling indicated that biochar exerted a positive but non-significant effect on temperature, possibly due to its low albedo increasing heat absorption, offset by greater evaporative cooling. This thermal balance resembles prior observations of biochar effects on green roof microclimates (Chen et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). We also observed non-significant relationships between CH₄ uptake and substrate EC but a significant effect of pH (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The average pH in biochar-treated modules (6.57\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04 to 7.53\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05; Table S5) was near-neutral, within the optimal range (6\u0026ndash;8) for CH₄ oxidation (Hanson and Hanson \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). Although biochar commonly has a liming effect on acid soils (Zhang et al. 2025), we did not detect any effect in the present study; instead, biochar-induced pH buffering appears to have contributed to favorable conditions for methanotrophic activity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Coupling of CH₄ uptake, evaporation, and CO₂ efflux\u003c/h2\u003e \u003cp\u003eWe found a significant relationship between CH₄ and H₂O flux (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), with biochar also consistently enhancing H₂O flux across seasons (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In rooftop environments characterized by high atmospheric demand of water flux compared to ground environment, biochar likely enhances capillary continuity and pore connectivity (Lehmann and Joseph \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) facilitating vertical water transport in substrate, thereby improving gas diffusivity in near-surface oxic layers and facilitating microbial CH₄ oxidation (Or et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Smith et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This dual effect of greater water storage coupled with improved gaseous exchange appears to sustain moisture while simultaneously enhancing oxygen diffusion, promoting long-term CH₄ uptake in biochar-amended substrates.\u003c/p\u003e \u003cp\u003eThe observed relationships among CH₄, CO₂, and H₂O fluxes further indicate strong coupling between CH\u003csub\u003e4\u003c/sub\u003e uptake, evaporation, and carbon dioxide efflux in engineered green roof substrates. During Stage-I evaporation, removal of surface water increases air-filled porosity while capillary continuity maintains moisture supply to the surface (Or et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), creating oxic microsites immediately above wetter layers where CH₄ oxidation can proceed efficiently. Because methanotrophic activity is highly sensitive to oxygen availability, even modest increases in air-filled pore space can substantially enhance CH₄ uptake (Smith et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In biochar-amended substrates, improved pore connectivity and sustained moisture likely amplified these effects, strengthening the coupling between evaporation and CH\u003csub\u003e4\u003c/sub\u003e uptake.\u003c/p\u003e \u003cp\u003eThe negative relationship between CH₄ uptake and CO₂ efflux further supports this interpretation. Because methanotrophs oxidize CH₄ to CO₂, a fraction of the measured CO₂ efflux likely originated from subsurface CH₄ oxidation (Hanson and Hanson \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Conrad \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), in addition to microbial and root respiration. Collectively, these findings demonstrate that biochar modifies CH₄ flux not through a single control variable, but through interacting physical and biological processes that link water movement, gas diffusion, and microbial metabolism.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Biochar effects on CO₂ emissions and carbon balance\u003c/h2\u003e \u003cp\u003eDespite enhancing CH₄ uptake, in this study, biochar application did not significantly increase CO₂ emissions (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). The tested green roof substrate was already rich in organic matter, and any transient CO₂ pulse from labile carbon likely occurred before monitoring (modules installed in 2019; measurements began 2020). CO₂ efflux showed strong seasonal patterns, higher in spring and summer, lower in fall, with temperature exerting a significant positive effect (Osanai et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Chen et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Seasonal variation of CO\u003csub\u003e2\u003c/sub\u003e flux also reflects plant root respiration during active growth (Ben-Noah and Friedman \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe absence of a sustained biochar-induced increase in CO₂ emissions suggests that enhanced CH₄ uptake did not come at the expense of greater carbon loss from the system. Instead, biochar appears to shift gas exchange dynamics toward increased methane consumption while maintaining overall carbon balance. When considered alongside the positive coupling between CH₄ uptake, evaporation, and CO₂ efflux, these results highlight the importance of integrated physical\u0026ndash;biological controls on GHG fluxes in engineered substrates. Understanding and leveraging these interactions offers a promising pathway for designing green roof substrates that simultaneously support hydrological function, plant performance, and greenhouse gas mitigation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e4.5 Limitations and Future Perspectives\u003c/h2\u003e \u003cp\u003eThis study evaluated a single biochar type (sugar maple) applied at a fixed dosage, whereas biochar properties vary widely with feedstock and pyrolysis conditions. Establishing dose\u0026ndash;response relationships across biochar types will be essential to identify thresholds at which benefits to methane uptake plateau or unintended effects emerge. Future work should also link observed flux responses to microbial population dynamics using molecular approaches, such as functional gene assays targeting methanotroph abundance and activity, to directly resolve biological mechanisms underlying biochar-induced CH₄ uptake. Beyond these methodological gaps, our findings have clear implications for engineered substrate design. The strong coupling among biochar addition, substrate moisture, evaporation, and methane uptake suggests that biochar should be considered as a multifunctional design component, rather than a passive carbon amendment. Incorporating biochar into green roof substrates may allow designers to enhance gas diffusivity and moisture retention simultaneously, supporting methane uptake while maintaining hydrological and plant-performance objectives. Because extensive green roofs are often managed to balance drainage and aeration under shallow substrate depths, biochar offers a practical means of stabilizing these properties over time.\u003c/p\u003e \u003cp\u003eWhile this study focused on extensive stonecrop-dominated systems, future research should evaluate biochar amendments in native plant green roofs, where greater rooting depth, rhizosphere complexity, and plant\u0026ndash;microbe interactions may further influence CH₄ sink strength and broader ecosystem functions. Finally, our results connect biochar use in green roofs to urban wood-waste management strategies. Converting forestry and urban tree residues into biochar for engineered substrates could provide durable carbon storage while simultaneously enhancing greenhouse gas mitigation, stormwater regulation, long-term substrate performance relative to conventional organic amendments, and facilitates circular economy approaches to urban biomass management.\u003c/p\u003e \u003c/div\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003ePre-grown stonecrop green roofs showed low but detectable rates of CH\u003csub\u003e4\u003c/sub\u003e uptake; biochar amendment greatly enhanced CH₄ uptake, with effects persisting over the five years of the study. Although uptake declined slightly in the fifth year, it remained consistently higher than in unamended substrates. Biochar-mediated enhancement of CH₄ uptake was primarily associated with improved substrate moisture retention and gas diffusivity, highlighting the importance of coupled aeration\u0026ndash;evaporation processes in sustaining methanotrophic activity in engineered substrates. These findings demonstrate that a single biochar amendment can maintain elevated CH\u003csub\u003e4\u003c/sub\u003e uptake over multiple years while simultaneously improving hydrological function. Collectively, our results show that biochar-enhanced CH₄ oxidation can be integrated with the pronounced stormwater-regulation and plant-performance benefits of green roofs, supporting their role as multifunctional and climate-resilient components of urban green infrastructure.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors gratefully acknowledge\u0026nbsp;\u003cstrong\u003eProfessors Jennifer Drake\u003c/strong\u003e and\u0026nbsp;\u003cstrong\u003eLiat Margolis\u003c/strong\u003e, co-principal investigators on the broader research project, for their valuable leadership and logistical support. We are grateful to\u0026nbsp;\u003cstrong\u003eMalaika Mitra\u003c/strong\u003e,\u0026nbsp;\u003cstrong\u003eKatie Monat\u003c/strong\u003e,\u0026nbsp;\u003cstrong\u003eJennifer Barrett\u003c/strong\u003e,\u0026nbsp;\u003cstrong\u003eLiam Douglas\u003c/strong\u003e, and\u0026nbsp;\u003cstrong\u003eJovana Shrestha\u003c/strong\u003e for their assistance in fieldwork. We thank\u0026nbsp;\u003cstrong\u003eTony Ung\u003c/strong\u003e for his support in maintaining the green-roof experiment. We also acknowledge\u0026nbsp;\u003cstrong\u003eHaliburton Forest and Wildlife Reserve Ltd.\u003c/strong\u003e,\u0026nbsp;\u003cstrong\u003eGro-Bark Inc.\u003c/strong\u003e, and\u0026nbsp;\u003cstrong\u003eBioroof Systems Inc.\u003c/strong\u003e for providing materials and product information.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor\u0026rsquo;s contribution\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eImrul Kayes and Sean C. Thomas contributed to the conceptualization of the research. Imrul Kayes, Md Abdul Halim, Wenxi Liao, Md Rezaul Karim, and Melanie A. Sifton participated in field data collection and data curation. Imrul Kayes conducted the formal data analysis and prepared the original manuscript draft. Sean C. Thomas provided validation and supervision and contributed to project administration and funding acquisition. All authors reviewed and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by the Natural Sciences and Engineering Research Council of Canada (\u003cstrong\u003eNSERC)-CREATE Discovery Grant\u003c/strong\u003e awarded to\u0026nbsp;\u003cstrong\u003eSean C. Thomas\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe source data used to generate all graphs and charts presented in this study are publicly available via the Scholars Portal Dataverse repository at: https://doi.org/10.5683/SP3/CANT7Q\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclarations\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor details\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImrul Kayes\u003c/strong\u003e:\u0026nbsp;Institute of Forestry and Conservation, John H. Daniels Faculty of Architecture, Landscape and Design, University of Toronto, 33 Willcocks Street, Toronto, ON M5S 3B3 Canada\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSchool of the Environment, University of Toronto, 5 Bancroft Ave, Toronto, ON M5S 3J1, Canada\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMd Abdul Halim\u003c/strong\u003e: Institute of Forestry and Conservation, John H. Daniels Faculty of Architecture, Landscape and Design, University of Toronto, 33 Willcocks Street, Toronto, ON M5S 3B3 Canada\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWenxi Liao\u003c/strong\u003e: School of Environmental Sciences, University of Guelph, 50 Stone Road East, Guelph, ON, N1G 2W1, Canada\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMd Rezaul Karim\u003c/strong\u003e: Institute of Forestry and Conservation, John H. Daniels Faculty of Architecture, Landscape and Design, University of Toronto, 33 Willcocks Street, Toronto, ON M5S 3B3 Canada\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMelanie A. Sifton\u003c/strong\u003e: Institute of Forestry and Conservation, John H. Daniels Faculty of Architecture, Landscape and Design, University of Toronto, 33 Willcocks Street, Toronto, ON M5S 3B3 Canada\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSean C. Thomas\u003c/strong\u003e: Institute of Forestry and Conservation, John H. Daniels Faculty of Architecture, Landscape and Design, University of Toronto, 33 Willcocks Street, Toronto, ON M5S 3B3 Canada\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbeysinghe AMSN, Lakshani MMT, Amarasinghe UDHN, et al (2022) Soil-Gas Diffusivity-Based Characterization of Variably Saturated Agricultural Topsoils. 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Biogeosciences 15:6621\u0026ndash;6635. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5194/bg-15-6621-2018\u003c/span\u003e\u003cspan address=\"10.5194/bg-15-6621-2018\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":false,"email":"","identity":"carbon-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Carbon Research","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"VoR Journals","inReviewEnabled":false,"inReviewRevisionsEnabled":false},"keywords":"Biochar, methane flux, engineered substrates, green roof substrates, greenhouse gas fluxes, urban soils","lastPublishedDoi":"10.21203/rs.3.rs-8971619/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8971619/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBiochar is increasingly applied to soils to enhance carbon sequestration and mitigate greenhouse gas emissions, yet its role in regulating methane (CH₄), a potent greenhouse gas, in engineered substrates remains poorly understood. In particular, there is a lack of field data on how biochar amendments influence CH₄ fluxes from engineered substrates in urban environments, where substrate composition, shallow profiles, and intensive management fundamentally differ from natural soils. Here, we present a long-term (2020\u0026ndash;2024) field study examining the effects of biochar amendment on CH₄ fluxes from engineered substrates used in extensive green roofs dominated by stonecrop species (\u003cem\u003ePhedimus kamschaticus\u003c/em\u003e and \u003cem\u003eSedum\u003c/em\u003e spp.). Biochar-amended modules (~\u0026thinsp;5% v/v; 20 t ha⁻\u0026sup1;) consistently exhibited greater CH\u003csub\u003e4\u003c/sub\u003e uptake than unamended controls across seasons, with peak uptake rates in spring 2023 nearly fivefold higher (\u0026minus;\u0026thinsp;1.91\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25 vs. \u0026minus;0.40\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10 nmol\u0026sdot;m⁻\u0026sup2;\u0026sdot;s⁻\u0026sup1;). Importantly, biochar addition did not increase carbon dioxide (CO₂) emissions, indicating enhanced CH\u003csub\u003e4\u003c/sub\u003e uptake without increased carbon losses. Variability in CH\u003csub\u003e4\u003c/sub\u003e uptake was strongly associated with substrate moisture and water vapour flux, suggesting that biochar modifies gas diffusivity and oxygen availability through moisture-mediated physical controls that favour microbial CH\u003csub\u003e4\u003c/sub\u003e oxidation. These results demonstrate that biochar can substantially enhance CH\u003csub\u003e4\u003c/sub\u003e uptake in engineered green roof substrates and extend the application of biochar-based mitigation strategies to engineered systems beyond conventional soils. The mechanistic insights provided here are broadly relevant to understanding CH\u003csub\u003e4\u003c/sub\u003e cycling in biochar-amended engineered substrates.\u003c/p\u003e","manuscriptTitle":"Biochar enhances methane uptake in engineered green roof substrate","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-24 17:06:39","doi":"10.21203/rs.3.rs-8971619/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-14T09:05:04+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-13T16:35:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"163296087735738353277570345268887694707","date":"2026-04-29T14:55:01+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-29T07:39:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"326529443328972740973851392264130197673","date":"2026-04-21T11:38:06+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-19T07:52:08+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-26T01:14:16+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-26T01:13:35+00:00","index":"","fulltext":""},{"type":"submitted","content":"Carbon Research","date":"2026-02-25T22:50:32+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":false,"email":"","identity":"carbon-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Carbon Research","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"VoR Journals","inReviewEnabled":false,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"13c9e7f4-39cc-4512-bd5c-330d497caf8c","owner":[],"postedDate":"March 24th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-14T09:05:04+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-13T16:35:58+00:00","index":58,"fulltext":""},{"type":"reviewerAgreed","content":"163296087735738353277570345268887694707","date":"2026-04-29T14:55:01+00:00","index":56,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-05-14T09:13:11+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-24 17:06:39","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8971619","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8971619","identity":"rs-8971619","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}