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Article

Biochar Affects Greenhouse Gas Emissions from Urban Forestry Waste

by
Kumuduni Niroshika Palansooriya
,
Tamanna Mamun Novera
,
Dengge Qin
,
Zhengfeng An
and
Scott X. Chang
*
Department of Renewable Resources, University of Alberta, Edmonton, AB T6G 2E3, Canada
*
Author to whom correspondence should be addressed.
Land 2025, 14(8), 1605; https://doi.org/10.3390/land14081605
Submission received: 19 June 2025 / Revised: 28 July 2025 / Accepted: 1 August 2025 / Published: 6 August 2025
(This article belongs to the Special Issue Land Use Effects on Carbon Storage and Greenhouse Gas Emissions)
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Abstract

Urban forests are vital to cities because they provide a range of ecosystem services, including carbon (C) sequestration, air purification, and urban cooling. However, urban forestry also generates significant amounts of organic waste, such as grass clippings, pruned tree branches, and fallen tree leaves and woody debris that can contribute to greenhouse gas (GHG) emissions if not properly managed. In this study, we investigated the effect of wheat straw biochar (produced at 500 °C) on GHG emissions from two types of urban forestry waste: green waste (GW) and yard waste (YW), using a 100-day laboratory incubation experiment. Overall, GW released more CO2 than YW, but biochar addition reduced cumulative CO2 emissions by 9.8% in GW and by 17.6% in YW. However, biochar increased CH4 emissions from GW and reduced the CH4 sink strength of YW. Biochar also had contrasting effects on N2O emissions, increasing them by 94.3% in GW but decreasing them by 61.4% in YW. Consequently, the highest global warming potential was observed in biochar-amended GW (125.3 g CO2-eq kg−1). Our findings emphasize that the effect of biochar on GHG emissions varies with waste type and suggest that selecting appropriate biochar types is critical for mitigating GHG emissions from urban forestry waste.

1. Introduction

Urban forests are essential to cities and provide a range of ecosystem services, including carbon (C) sequestration, air purification, and urban cooling, which enhance the quality of life for urban populations [1,2]. As urbanization accelerates globally, urban forests are increasingly recognized as critical components of sustainable urban ecosystems [3]. However, urban forests also generate significant quantities of organic waste, including grass clippings, pruned branches, leaf litter, and fallen woody debris, primarily resulting from landscaping and maintenance activities. Those organic wastes, often sent to dump sites, pose significant environmental challenges if improperly managed [4]. When waste materials undergo microbial decomposition, they release greenhouse gases (GHGs), including carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O), which collectively contribute to the global warming potential (GWP). However, some GHGs have a much stronger effect than others. A study conducted in five municipalities in Paraíba state, northeast Brazil, revealed that improper disposal of urban tree pruning waste resulted in emissions of more than 1 Mt CO2 equivalent over a ten-year period [5]. These emissions contribute to global warming and exacerbate other forms of climate change, underscoring the urgent need for sustainable waste management strategies tailored to urban contexts.
While considerable research has been conducted on GHG emissions from soils or soil systems amended with organic waste [6,7], relatively few studies have directly examined GHG emissions from decomposing urban forestry waste. The decomposition dynamics of organic material in soil differ significantly from those of urban forestry waste, which is often piled at open dumping sites [8]. Soil emissions are primarily driven by microbial processes that decompose organic matter and are influenced by factors such as soil type, moisture content, temperature, and the quantity and quality of organic inputs [9,10]. In contrast, greenhouse gas emissions from urban forestry waste are mainly influenced by the chemical composition of the waste (e.g., C:N ratio, lignin content) as well as by exposure to air and fluctuating moisture and temperature conditions [11]. These factors can lead to markedly different emission profiles compared to those observed in soil systems.
Despite the growing volume of urban forestry waste, limited research has examined its direct contributions to GHG emissions or effective ways to mitigate them. This gap is critical to address, given the unique chemical composition of urban forestry waste compared to agricultural or municipal waste. For instance, urban forestry waste typically contains higher proportions of recalcitrant organic matter such as lignin, cellulose, and hemicellulose, which decompose more slowly and affect microbial metabolism and gas fluxes differently [12]. Understanding the GHG emissions and environmental impacts of this waste and identifying strategies to reduce those emissions are important. One promising approach is the use of biochar, a carbon-rich material produced through pyrolysis, which has been shown to reduce GHG emissions in various organic residues and soils [7,13]. Recent studies have shown that incorporating biochar into urban green infrastructure can enhance nutrient availability, water retention, microbial activity, plant growth, and carbon sequestration [14,15]. A mesocosm experiment found that biochar increased CH4 uptake and altered CO2 fluxes when applied to mulched urban forestry waste, likely by modifying microbial activity, pH, and C:N ratios, as well as by improving aeration and reducing water loss [16]. Biochar may also influence extracellular enzyme activity, a key driver of organic matter decomposition, by altering substrate availability and microbial dynamics. These changes can, in turn, reduce GHG emissions during waste breakdown [17]. While these findings highlight biochar’s potential to modulate GHG emissions from organic waste systems, studies on its effects on physicochemical and microbial changes during urban forestry waste decomposition remain limited.
In this study, we examined the effects of biochar on GHG emissions from two common types of urban forestry waste: green waste, which is composed primarily of grass clippings, fresh leaves, and small branches from pruning operations, and yard waste, which includes wood debris, composted urban forestry waste, and soil that has mixed in during collection or processing. We hypothesized that biochar influences GHG emissions by altering extracellular enzyme activity and the physicochemical properties of urban forestry waste, thereby reducing GHG production during decomposition. The objectives of this study were to (i) assess CO2, CH4, and N2O emissions from green waste and yard waste, (ii) examine the impact of biochar addition on extracellular hydrolytic enzyme activities and the physicochemical properties of these wastes, and (iii) evaluate how these changes influence GHG emissions and GWP. The findings can support local policymakers, urban planners, and environmental managers in identifying more sustainable strategies for managing urban forestry waste, with potential contributions to regional sustainability and climate action efforts.

2. Materials and Methods

2.1. Study Materials

Green waste and yard waste were collected from a designated urban forestry waste dumping site located in Ambleside (53°25′35.5″ N, 113°34′20.3″ W), Edmonton, Alberta, Canada. Green waste mostly consists of grass clippings and some fresh leaves. Yard waste consists of compost, wood debris, and soil, which are primarily used for home gardens. Five samples were collected for each waste type, and the materials were homogenized separately before the incubation experiment. Subsamples were taken to determine the chemical properties of the materials. The initial moisture contents of the green waste and yard waste were 4.6% and 54.3% (w/w), respectively. The C and nitrogen (N) concentrations and C/N ratio were 36.9%, 3.5%, and 10.5, respectively, for the green waste, and 29.1%, 1.5%, and 19.3, respectively, for the yard waste. The biochar used in this study was supplied by Innotech Alberta (Vegreville, AB, Canada) and pyrolyzed from wheat (Triticum aestivum) straw at 450–500 °C. The pH, electrical conductivity, C%, N%, and C/N ratio of the biochar were 9.1, 2.4 dS m−1, 56.5%, 0.96%, and 59.1, respectively.

2.2. Experimental Design and Incubation Procedure

A completely randomized block design was implemented with four treatments and five replications. Two types of organic waste, green waste and yard waste, were used to create four treatment combinations: each waste type was incubated with and without biochar. The treatments included green waste without biochar (GW), green waste amended with 2% (w/w) wheat straw biochar (GWB), yard waste without biochar (YW), and yard waste amended with 2% (w/w) wheat straw biochar (YWB). The green waste was cut into small (1–2 cm) pieces to make them more uniform and fit into the jars used for incubation. Before being used in incubation, each waste material type was thoroughly mixed and homogenized. The initial moisture content of the green waste was very low (4.6%). Since decomposition of organic waste typically begins when the moisture content is between 40% and 60%, we adjusted the moisture content to 50% by adding distilled water. This adjustment was performed to increase microbial activity and initiate the degradation process. As the initial moisture content of the yard waste was 54.3%, the moisture content was maintained at the initial value. For the GW and YW treatments, 50 g and 100 g of fresh material, respectively, were placed into 1 L Mason jars (Figure 1). For the GWB and YWB treatments, each material was mixed with biochar at a rate of 2% (w/w) on an oven-dry weight basis before being placed into 1 L Mason jars. The materials were compacted to a specific depth (4.8 cm) in all treatments to maintain consistency across samples. The moisture content of all treatments was adjusted by adding distilled water every three days based on the weight loss observed throughout incubation. This gravimetric method ensured consistent moisture levels across treatments throughout incubation. To avoid disturbing the GHG measurement setup, two parallel incubation experiments were conducted for each waste type: one to measure GHG emissions and the other to analyze enzyme activities and physicochemical properties. For the latter, separate jars were prepared for each sampling time point (days 1, 10, 50, and 100). Mason jars were covered with aluminum foil, which was punctured with pinholes to allow ventilation while minimizing water loss. All treatments were incubated at 22 °C for 100 days (Figure 1). To maintain a humid environment and further minimize moisture loss from the incubated materials, a beaker containing 10 mL of Milli-Q water was placed inside the incubator [18,19].

2.3. Gas and Incubation Material Sampling and Analysis

Gas samples were collected from the headspace of the airtight Mason jars through a rubber septum valve using a syringe on days 1, 3, 5, 7, 10, 13, 16, 19, 22, 22, 25, 30, 35, 40, 45, 50, 55, 60, 70, 80, 90, and 100. The GHG concentrations were determined via a gas chromatograph (Varian CP-3800, Mississauga, ON, Canada) equipped with a thermal conductivity detector (for detecting CO2), an electron capture detector (for detecting N2O), and a flame ionization detector (for detecting CH4) [19,20]. The incubated waste materials were destructively sampled from the Mason jars on days 1, 10, 50, and 100 to determine their enzyme activities and physicochemical properties.

2.4. Analysis of Enzyme Activities and Physicochemical Properties of the Incubated Materials

The activities of extracellular hydrolytic enzymes, including β-1,4-glucosidase (BG) and β-1,4-N-acetylglucosaminidase (NAG), were analyzed in moist waste samples using a fluorometric method, following the methods described in Sinsabaugh et al. [21]. BG and NAG are key enzymes involved in the degradation of cellulose and chitin, respectively, which are key components of plant and microbial biomass in organic waste. Their activities provide insight into microbial decomposition and nutrient cycling that affect GHG emissions during waste breakdown [22]. For each sample, enzyme activity was determined using fluorogenic substrates: 4-methylumbelliferyl-β-D-glucopyranoside for BG and 4-methylumbelliferyl-N-acetyl-β-D-glucosaminide for NAG. Upon enzymatic hydrolysis, these substrates released 4-methylumbelliferone (MUB), a fluorescent product. Fluorescence in the aliquots was measured using a microplate reader (Synergy HT, Bio-Tek Instruments, Winooski, VT, USA) with 365 nm excitation and 450 nm emission filters. Fresh waste samples were oven-dried at 105 °C for ~24 h to determine the moisture content. The pH and electrical conductivity of the waste materials were measured at a 1:10 (w:v) waste-to-water ratio via a pH meter (DMP-2 mV, Thermo Orion, Boston, MA, USA). To determine the available N content in the waste materials, the samples were extracted with 0.01 M K2SO4 solution at a 1:20 (w:v) ratio and analyzed for NO3− and NH4+ via a colorimetric method [23,24]. The samples were extracted with 0.01 M K2SO4 solution at a 1:20 (w:v) ratio to determine the extractable C and N concentrations using a TOC-VCSN analyzer (Shimadzu, Kyoto, Japan). The extractable C concentration represents dissolved organic C (DOC), and the difference between the extractable N and available N (total inorganic N: TIN = NO3− + NH4+) represents dissolved organic N (DON) [25]. The total C (TC) and total N (TN) contents of the waste materials were determined via a CHN elemental analyzer (Vario MICRO cube, Elementar Analysensysteme GmbH, Hannover, Germany). The C/N ratio was determined as the mass ratio of TC to TN.

2.5. Statistical Analyses

All data analyses were performed using SPSS v.26 software (IBM Corp., Armonk, NY, USA). The effects of biochar on enzyme activity and physicochemical properties were analyzed separately for each sampling day (days 1, 10, 50, and 100). Before statistical analysis, data were tested for normality of distribution using the Shapiro–Wilk test and for homogeneity of variance using Levene’s test. When necessary, enzyme activity and physicochemical property data were log- or square root-transformed to meet the assumptions of normality. All statistical tests, including one-way ANOVA and Tukey’s post hoc test (α = 0.05), were conducted on the transformed data. For clarity, results are presented as back-transformed means, with significance groupings derived from the analysis of the transformed data.

3. Results and Discussion

3.1. Biochar Effects on Physicochemical Properties

The TC in GW and GWB decreased over time (Figure 2a), reflecting microbial decomposition of C and subsequent C loss. GWB showed a more rapid decline, suggesting biochar may enhance microbial activity or alter decomposition dynamics by increasing nutrient availability or serving as a microbial habitat [26,27]. TN increased in both treatments (Figure 2b), likely due to microbial N immobilization or the formation of stable N compounds [28,29]. GWB consistently had a higher C/N ratio than GW (Figure 2c), possibly because biochar slowed decomposition or retained N during early stages. As decomposition progressed, the C/N ratios of both treatments gradually converged [30]. In contrast, TC remained relatively stable in YW and YWB (Figure 2a), indicating slower decomposition, possibly due to more resistant C or lower microbial activity. TN increased slightly (Figure 2b) and C/N ratios remained stable (Figure 2c), suggesting consistent nutrient dynamics. These patterns indicate that biochar had a lesser influence on yard waste than on green waste. Yard waste’s high fixed C content [31] and larger proportion of slowly hydrolysable C in leaves and branches [32] contribute to its slower decomposition. Green waste, including grass clippings, decomposes more rapidly due to its lower proportion of recalcitrant C.
The DOC levels were significantly higher (p < 0.05) in GWB than GW on day 1 (Figure 3a), indicating that biochar stimulated early soluble C release. DOC peaked at day 10 in both treatments, followed by a steady decline due to microbial consumption and stabilization of labile organic matter [33,34]. DON also peaked on day 10 (Figure 3b), reflecting active microbial decomposition and N release [35,36]. Despite early differences, DON levels in GW and GWB were similar by day 100, suggesting biochar had a limited long-term effect on total DON dynamics. In YW and YWB, DOC significantly increased (p < 0.05) on days 50 and 100 (Figure 3c), and DON peaked on day 50 (Figure 3d). These increases likely resulted from the gradual decomposition of more complex organic matter and the release of soluble C and N [37]. Biochar did not significantly affect the trends in DOC or DON in yard waste, indicating a limited role in influencing the release of C and N from more recalcitrant materials. The GW and GWB showed stable pH values throughout the incubation period (Table 1), indicating consistent alkalinity. Electrical conductivity (EC) increased gradually due to the release of mineral salts during decomposition [38,39]. In GW, NH4+ levels declined significantly (p < 0.05), consistent with ammonification followed by volatilization or nitrification [40,41]. In GWB, NH4+ initially increased and then declined slightly, suggesting that biochar helped retain NH4+. NO3− concentrations in both treatments increased significantly by day 100, indicating nitrification. TIN was consistently higher in GWB, indicating that biochar enhanced overall N retention. In YW and YWB, pH remained stable with minimal fluctuations (Table 2), while EC steadily increased, reflecting ongoing mineralization. NH4+ levels declined, and NO3− levels increased progressively, a typical pattern of aerobic decomposition in organic matter [42]. These changes reflect the conversion of organic N to NH4+ and its subsequent oxidation to NO3−. The observed trends align with the composting literature, showing a reduction in NH4+ and an increase in NO3− over time [43]. Biochar had little impact on these patterns in yard waste, possibly due to the more stable nature of its organic matter.

3.2. Biochar Effects on Enzyme Activities

In GW, BG activity declined significantly over time (Figure 4a), dropping by 42.9% on day 10, 80.4% on day 50, and 84.1% on day 100 relative to day 1. This suggests reduced cellulose availability and microbial activity as decomposition progressed [44,45]. GWB showed a more gradual decline, with significant reductions (p < 0.05) only after day 10. In YW, BG activity was relatively stable, with a significant decrease observed only on day 50 (Figure 4b), indicating slower decomposition and more resilient enzyme dynamics. In YWB, a significant reduction occurred only on day 100.
For NAG, GW showed a significant decline by days 50 and 100 (Figure 4c), reflecting the diminishing availability of N-containing substrates over time [46]. GWB peaked on day 10 but declined significantly thereafter, suggesting that biochar may temporarily stimulate enzyme activity before decomposition advances [47,48]. YW displayed consistently low NAG activity (Figure 4d), while YWB showed minimal activity on day 10 and a peak on day 50. These trends emphasize the influence of substrate quality and biochar on enzyme activity and nitrogen cycling during decomposition.

3.3. Biochar Effects on Greenhouse Gas Emissions and Global Warming Potential

Compared with YW, GW consistently emitted more CO2 throughout the 100-day incubation (Figure 5a), likely due to its higher content of easily degradable compounds such as cellulose (25.3%) and hemicellulose (46.3%) [49], as well as its higher nitrogen content. In contrast, yard waste contained more lignin [50], which is more resistant to microbial decomposition. As a result, GW emitted over three times more CO2 than YW on day 1 and continued to emit substantially more by day 100. Adding biochar significantly reduced CO2 emissions in both waste types, suggesting a stabilizing effect on organic matter [51,52]. GW also produced notable CH4 emissions, peaking at day 30 (Figure 5b). Interestingly, CH4 emissions were even higher in GWB, suggesting that biochar may stimulate methanogenic activity in GW [53]. In contrast, both YW and YWB showed negative cumulative CH4 emissions (Figure 5b), indicating net CH4 uptake driven by methanotrophic activity. Similarly, Li et al. [19] reported a net CH4 uptake (i.e., negative CH4 emissions) in soils amended with biochar. N2O emissions were substantially higher in GWB than in GW (Figure 5c), possibly due to biochar inhibiting N2O-reducing microbes and increasing nitrate and ammonium availabilities for N2O production through nitrification and denitrification [54,55,56]. Conversely, biochar addition reduced N2O emissions in YWB compared to YW (Figure 5c), emphasizing that biochar’s effects vary with waste type. As a result of elevated CH4 and N2O emissions, GWB exhibited the highest global warming potential (GWP: 125.3 g CO2-eq kg−1) (Figure 5d). In contrast, YW and YWB showed significantly lower (p < 0.05) GWPs (29.7 and 23.6 g CO2-eq kg−1, respectively) compared to GWB, with no significant difference between YW and YWB. These findings underscore that biochar’s impact on GHG emissions and GWP is highly dependent on the type of waste material.

3.4. Factors Affecting GHG Emissions from Green Waste Under Biochar Application

On Day 100, Pearson correlation analysis revealed key relationships between GHG emissions and several variables (Table 3). CO2 emissions were positively correlated with TC (r = 0.73, p < 0.05) and TN (r = 0.76, p < 0.05), but negatively correlated with NH4+ (r = −0.83, p < 0.01) and TIN (r = −0.73, p < 0.05). Cumulative CO2 emissions were lower in GWB than in GW (Figure 5a), suggesting biochar moderated emissions by stabilizing organic matter and slowing decomposition [57,58]. Wu et al. [25] similarly observed that increased TIN availability reduced CO2 emissions. Biochar’s retention of NH4+ and NO3− may suppress microbial activity responsible for CO2 production by altering N availability and microbial communities [59,60,61].
CH4 emissions were negatively correlated with TC (r = −0.79, p < 0.01) and TN (r = −0.78, p < 0.01), but positively correlated with NO3− (r = 0.77, p < 0.01) and TIN (r = 0.89, p < 0.01). CH4 emissions were higher in GWB, possibly due to elevated TIN (1083.1 mg N g−1), which may have promoted methanogenesis. The positive correlation with NO3− suggests that N availability supported methanogenic microbial activity. Biochar’s ability to store nutrients may indirectly facilitate CH4 production [62,63]. While some studies report CH4 suppression by biochar through enhanced methanotrophy [64,65], high organic N in green waste may override this effect.
The rate of N2O emissions was negatively correlated with TC (r = −0.69, p < 0.05) and TN (r = −0.72, p < 0.05), but positively correlated with NH4+ (r = 0.705, p < 0.05), NO3− (r = 0.69, p < 0.05), and TIN (r = 0.88, p < 0.01). GWB emitted significantly more N2O than GW (Figure 5c), likely due to the increased availability of NH4+ and NO3− (Table 1), which are key substrates for nitrification and denitrification [66]. These findings align with previous studies, which have demonstrated that biochar can increase N2O emissions under high N availability and labile C conditions [67,68].
GWP was positively correlated with NO3− (r = 0.71, p < 0.05) and TIN (r = 0.86, p < 0.01), and negatively with TN (r = −0.65, p < 0.05). It also showed a negative correlation with CO2 emissions (r = −0.66, p < 0.01) and strong positive correlations with CH4 (r = 0.90, p < 0.01) and N2O (r = 0.98, p < 0.01), highlighting the dominant role of these gases in determining the total GWP. Although biochar application led to a reduction in CO2 emissions, likely due to its stabilization of labile carbon and suppression of microbial respiration, the concurrent increases in CH4 and N2O emissions resulted in an overall rise in GWP. This is primarily due to the much higher global warming potentials of CH4 and N2O, which are 25 and 298 times greater than that of CO2, respectively [69]. These findings suggest that the benefits of CO2 mitigation through biochar could be offset by enhanced emissions of more potent greenhouse gases. Therefore, while biochar has potential as a carbon management strategy, its use in organic waste systems may pose a trade-off, and strategies to mitigate CH4 and N2O emissions must be considered to optimize its environmental performance.

3.5. Factors Affecting GHG Emissions from Yard Waste Under Biochar Application

Carbon dioxide emissions were positively correlated with NAG activity (r = 0.68, p < 0.05) (Table 3), indicating that higher CO2 emissions are associated with increased microbial N cycling. NAG is involved in chitin degradation, contributing to N mineralization and stimulating microbial respiration, which releases CO2 [70,71]. Methane emissions were positively correlated with NO3− (r = 0.74, p < 0.05) and TIN (r = 0.71, p < 0.05) and negatively with DON (r = −0.65, p < 0.05). These results suggest that greater N availability promotes methanogenesis as microbial activity increases with available N [72,73]. In contrast, higher DON may suppress CH4 production by promoting denitrifiers or limiting labile C for methanogens [74,75,76].
Nitrous oxide emissions were negatively correlated with EC (r = −0.77, p < 0.05) and TIN (r = −0.74, p < 0.05). The increased EC from biochar amendment may inhibit microbial processes involved in N2O production, thereby reducing emissions [77,78]. Enhancing biochar’s electrochemical properties, such as specific capacitance and electrical conductivity, has been shown to mitigate N2O emissions [78]. The negative correlation with TIN suggests that biochar may alter N cycling by shifting denitrification pathways or reducing excess N availability [79]. On the other hand, N2O emissions were positively correlated with BG activity (r = 0.68, p < 0.05), indicating that biochar’s influence on C degradation also affects N transformations. Yan et al. [80] reported similar findings linking N2O emissions with BG activity. Reduced BG activity due to biochar likely limited cellulose decomposition and glucose release, decreasing energy availability for microbial growth. Since denitrification relies on both NO3− and labile C [81], lower C availability restricts this process, thereby reducing N2O emissions [82,83].
GWP was negatively correlated with pH (r = −0.66, p < 0.05). Biochar’s alkalinity can buffer acidity, influencing microbial community structure and enzymatic activity related to GHG emissions [84,85]. These results support previous findings that increasing soil pH through biochar can suppress GHG production by altering N cycling pathways [67,86]. GWP also showed a strong positive correlation with CO2 emissions (r = 0.99, p < 0.01), underscoring the dominant role of CO2 in contributing to global warming potential.
This study provides practical insights into sustainable urban forestry waste management by evaluating the effects of biochar amendment on GHG emissions during the waste decomposition. The findings highlight that the impact of biochar is dependent on the type of waste, showing contrasting effects in green waste and yard waste. These results have direct implications for real-world applications, particularly in urban settings where green and yard waste are commonly generated. Urban ecosystems often experience challenges related to the accumulation of organic waste, inefficient composting, and elevated GHG emissions. To improve the current condition of urban ecosystems, we recommend promoting decentralized composting with biochar addition, especially for waste types that show reduced GHG emissions or improved stability with biochar. This approach can be further supported by municipal policies that encourage waste source separation, local composting, and GHG monitoring. These actions may contribute to climate change mitigation and the restoration of urban ecosystem health.

4. Conclusions

Biochar exerts contrasting effects on GHG emissions and waste decomposition in urban forestry waste. Biochar addition reduced CO2 emissions in both green waste and yard waste, highlighting biochar’s potential for C stabilization. However, increased CH4 and N2O emissions in GWB underscore the complex interplay between biochar and changes in microbial communities and substrate availability. YW and YWB exhibited negative cumulative CH4 emissions, and YWB exhibited lower N2O emissions than YW. GWP was highest in GWB (125.3 g CO2-eq kg−1), indicating that biochar application exacerbated the overall GHG impact of green waste. These findings highlight the critical role of waste composition in shaping the effectiveness of biochar as a GHG mitigation strategy. Green waste, rich in labile organic carbon, decomposed rapidly, and the addition of biochar further accelerated this process, stimulating microbial activity and increasing CH4 and N2O production. In contrast, yard waste, rich in recalcitrant compounds, responded more favorably to biochar treatment with net reductions in GHG emissions. Overall, the results emphasize the need for a tailored approach to biochar application, as well as the importance of considering the specific properties of the waste material. Further research should explore strategies such as combining biochar with complementary amendments or fine-tuning application rates to maximize the potential of biochar for sustainable waste management.

Author Contributions

Conceptualization, K.N.P. and S.X.C.; methodology, K.N.P., T.M.N., D.Q. and Z.A.; formal analysis, K.N.P.; resources, S.X.C.; writing—original draft preparation, K.N.P.; writing—review and editing, Z.A. and S.X.C.; supervision, S.X.C. All authors have read and agreed to the published version of the manuscript.

Funding

This work was funded by an NSERC (Natural Science and Engineering Research Council of Canada) Alliance grant (grant No. ALLRP 577155-2022).

Data Availability Statement

The data presented in this article are available on request from the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. A schematic illustration of the experimental setup.
Figure 1. A schematic illustration of the experimental setup.
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Figure 2. The effects of biochar on (a) total carbon, (b) total nitrogen, and (c) C/N ratio in green waste and yard waste. Error bars denote means ± standard errors (n = 5). Treatment codes: GW = green waste without biochar; GWB = green waste + biochar; YW = yard waste without biochar; YWB = yard waste + biochar.
Figure 2. The effects of biochar on (a) total carbon, (b) total nitrogen, and (c) C/N ratio in green waste and yard waste. Error bars denote means ± standard errors (n = 5). Treatment codes: GW = green waste without biochar; GWB = green waste + biochar; YW = yard waste without biochar; YWB = yard waste + biochar.
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Figure 3. The effects of biochar amendment on dissolved organic carbon in (a) green waste and (b) yard waste, and on dissolved organic nitrogen in (c) green waste and (d) yard waste. Treatment codes: GW = green waste without biochar; GWB = green waste + biochar; YW = yard waste without biochar; YWB = yard waste + biochar. Error bars denote means ± standard errors (n = 5). Different lowercase letters indicate significant differences among incubation days within GW/YW treatments, whereas uppercase letters indicate significant differences among incubation days within GWB/YWB treatments (p < 0.05). Asterisks (*) indicate significant differences between biochar-treated and untreated waste for each time point (p < 0.05).
Figure 3. The effects of biochar amendment on dissolved organic carbon in (a) green waste and (b) yard waste, and on dissolved organic nitrogen in (c) green waste and (d) yard waste. Treatment codes: GW = green waste without biochar; GWB = green waste + biochar; YW = yard waste without biochar; YWB = yard waste + biochar. Error bars denote means ± standard errors (n = 5). Different lowercase letters indicate significant differences among incubation days within GW/YW treatments, whereas uppercase letters indicate significant differences among incubation days within GWB/YWB treatments (p < 0.05). Asterisks (*) indicate significant differences between biochar-treated and untreated waste for each time point (p < 0.05).
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Figure 4. The effects of biochar amendment on BG (β-1,4-glucosidase) activities in (a) green waste and (b) yard waste, and on (NAG) β-1,4-N-acetyl glucosaminidase activities in (c) green waste and (d) yard waste. Error bars denote means ± standard errors (n = 5). Treatment codes: GW = green waste without biochar; GWB = green waste + biochar; YW = yard waste without biochar; YWB = yard waste + biochar. Different lowercase letters indicate significant differences among incubation days within GW/YW treatments, whereas uppercase letters indicate significant differences among incubation days within GWB/YWB treatments (p < 0.05). Asterisks (*) indicate significant differences between biochar-treated and untreated waste for each time point (p < 0.05).
Figure 4. The effects of biochar amendment on BG (β-1,4-glucosidase) activities in (a) green waste and (b) yard waste, and on (NAG) β-1,4-N-acetyl glucosaminidase activities in (c) green waste and (d) yard waste. Error bars denote means ± standard errors (n = 5). Treatment codes: GW = green waste without biochar; GWB = green waste + biochar; YW = yard waste without biochar; YWB = yard waste + biochar. Different lowercase letters indicate significant differences among incubation days within GW/YW treatments, whereas uppercase letters indicate significant differences among incubation days within GWB/YWB treatments (p < 0.05). Asterisks (*) indicate significant differences between biochar-treated and untreated waste for each time point (p < 0.05).
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Figure 5. Effects of biochar amendment on cumulative emissions of (a) CO2, (b) CH4, and (c) N2O, and (d) global warming potential (GWP) from green waste and yard waste. The inset in (c) shows the effect of biochar on cumulative N2O emissions from yard waste. Error bars denote means ± standard errors (n = 5). Treatment codes: GW = green waste without biochar; GWB = green waste + biochar; YW = yard waste without biochar; YWB = yard waste + biochar. Different lowercase letters in (d) indicate significant differences among treatments (p < 0.05).
Figure 5. Effects of biochar amendment on cumulative emissions of (a) CO2, (b) CH4, and (c) N2O, and (d) global warming potential (GWP) from green waste and yard waste. The inset in (c) shows the effect of biochar on cumulative N2O emissions from yard waste. Error bars denote means ± standard errors (n = 5). Treatment codes: GW = green waste without biochar; GWB = green waste + biochar; YW = yard waste without biochar; YWB = yard waste + biochar. Different lowercase letters in (d) indicate significant differences among treatments (p < 0.05).
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Table 1. Physicochemical properties of green waste (GW) and biochar-treated green waste (GWB) at four incubation intervals. The values are means with standard errors in parentheses (n = 5). Different lowercase letters indicate significant differences among incubation days within each treatment (p < 0.05). An asterisk (*) represents a significant difference between GW and GWB at each sampling time (p < 0.05).
Table 1. Physicochemical properties of green waste (GW) and biochar-treated green waste (GWB) at four incubation intervals. The values are means with standard errors in parentheses (n = 5). Different lowercase letters indicate significant differences among incubation days within each treatment (p < 0.05). An asterisk (*) represents a significant difference between GW and GWB at each sampling time (p < 0.05).
PropertyTreatmentIncubation Time
Day 1Day 10Day 50Day 100
pHGW7.69 (0.1) a7.8 (0.1) a7.3 (0.0) b7.73 (0.0) a
GWB7.65 (0.1) ab7.94 (0.1) a7.47 (0.0) b7.79 (0.0) ab
EC
(dS m−1)		GW2.38 (0.1) c3.36 (0.1) bc3.86 (0.1) ab4.04 (0.2) a
GWB2.24 (0.1) c3.6 (0.1) b4.08 (0.1) a3.94 (0.1) ab
NH4+
(mg kg−1)		GW552.13 (43) a *534.95 (28) a *100.75 (2.2) b93.46 (3.8) b *
GWB732.53 (35) a *835.58 (53) a *205.34 (4.2) b263.47 (8.9) b *
NO3−
(mg kg−1)		GW237.83 (15) c298.91 (6.8) b282.74 (11) b696.52 (16) a
GWB231.86 (18) c235.67 (15) c372.63 (5.7) b819.62 (38) a
TIN
(mg kg−1)		GW789.96 (43) a *833.86 (26) a *383.49 (13) b *789.98 (15) a *
GWB964.39 (47) a *1071.25 (49) a *577.97 (8.9) b *1083.09 (33) a *
Abbreviations: EC = electrical conductivity, NH4+ = ammonium, NO3− = nitrate, and TIN = total inorganic N = NH4+ + NO3−.
Table 2. Physicochemical properties of yard waste (YW) and biochar-treated yard waste (YWB) at four incubation intervals. The values are means with standard errors in parentheses (n = 5). Different lowercase letters indicate significant differences among incubation days within each treatment (p < 0.05). An asterisk (*) represents a significant difference between YW and YWB for each incubation day (p < 0.05).
Table 2. Physicochemical properties of yard waste (YW) and biochar-treated yard waste (YWB) at four incubation intervals. The values are means with standard errors in parentheses (n = 5). Different lowercase letters indicate significant differences among incubation days within each treatment (p < 0.05). An asterisk (*) represents a significant difference between YW and YWB for each incubation day (p < 0.05).
PropertyTreatmentIncubation Days
Day 1Day 10Day 50Day 100
pHYW7.62 (0.0) a7.55 (0.0) a7.38 (0.1) a7.5 (0.0) a
YWB7.69 (0.0) a7.73 (0.0) a7.43 (0.0) b7.61 (0.1) ab
EC
(dS m−1)		YW0.33 (0.0) b *0.36 (0.0) ab0.38 (0.0) a0.38 (0.0) a
YWB0.41 (0.0) b *0.41 (0.0) b0.42 (0.0) ab0.44 (0.0) a
NH4+
(mg kg−1)		YW160.09 (2.3) a104.23 (6.1) b40.69 (1.4) c20.78 (0.2) d *
YWB168.15 (6.9) a131.33 (4.4) b71.75 (6.5) c38.73 (2.5) d *
NO3−
(mg kg−1)		YW24.65 (2.5) d38.86 (4.3) c102.82 (4.7) b143.01 (6.7) a
YWB31.95 (1) c42.81 (4.3) c114.1 (6.5) b183.43 (15.9) a
TIN
(mg kg−1)		YW184.74 (4.1) a143.08 (9.2) b143.51 (6) b *163.79 (6.8) ab *
YWB200.1 (6.5) ab174.14 (7) b185.86 (4.7) ab *222.16 (14.7) a *
Abbreviations: EC = electrical conductivity, NH4+ = ammonium, NO3− = nitrate, and TIN = total inorganic N = NH4+ + NO3−.
Table 3. Pearson correlation coefficient and significance among greenhouse gas emissions and selected chemical properties in green waste and yard waste on day 100 of incubation. Only statistically significant correlations are presented.
Table 3. Pearson correlation coefficient and significance among greenhouse gas emissions and selected chemical properties in green waste and yard waste on day 100 of incubation. Only statistically significant correlations are presented.
Green waste
TCTNNH4+NO3−TINCO2CH4N2O
CO20.73 * 0.76 *−0.83 **−0.46−0.73 *
CH4−0.79 **−0.78 **0.650.77 **0.89 **−0.69 *
N2O−0.69 *−0.72 *0.705 *0.69 *0.88 **−0.80 **0.91 **
GWP−0.63−0.65 *0.610.71 *0.86 **−0.66 *0.90 **0.98 **
Yard waste
pHECNO3−TINDONBGNGCO2CH4N2O
CO2−0.63−0.35−0.22−0.350.280.560.68 *
CH4−0.200.560.74 *0.71 *−0.65 *−0.57−0.30−0.05
N2O−0.41−0.77 *−0.63−0.74 *0.530.68 *0.260.69 *−0.33
GWP−0.66 *−0.29−0.13−0.260.200.480.630.99 **0.100.65
TC = total carbon; TN = total nitrogen; NH4+ = ammonium; NO3− = nitrate; TIN = total inorganic nitrogen; EC = electrical conductivity; DON = dissolved organic nitrogen; BG: β-1,4-glucosidase; NAG: β-1,4-N-acetyl glucosaminidase; GWP = global warming potential. * = p < 0.05; ** = p < 0.01.
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Palansooriya, K.N.; Novera, T.M.; Qin, D.; An, Z.; Chang, S.X. Biochar Affects Greenhouse Gas Emissions from Urban Forestry Waste. Land 2025, 14, 1605. https://doi.org/10.3390/land14081605

AMA Style

Palansooriya KN, Novera TM, Qin D, An Z, Chang SX. Biochar Affects Greenhouse Gas Emissions from Urban Forestry Waste. Land. 2025; 14(8):1605. https://doi.org/10.3390/land14081605

Chicago/Turabian Style

Palansooriya, Kumuduni Niroshika, Tamanna Mamun Novera, Dengge Qin, Zhengfeng An, and Scott X. Chang. 2025. "Biochar Affects Greenhouse Gas Emissions from Urban Forestry Waste" Land 14, no. 8: 1605. https://doi.org/10.3390/land14081605

APA Style

Palansooriya, K. N., Novera, T. M., Qin, D., An, Z., & Chang, S. X. (2025). Biochar Affects Greenhouse Gas Emissions from Urban Forestry Waste. Land, 14(8), 1605. https://doi.org/10.3390/land14081605

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