Modeling of CH₄ Emission and Assessment of Energy Potential: A Case Study of Okhla Landfill, South Delhi

Abstract

Municipal solid waste (MSW) landfills are major sources of greenhouse gas (GHG) emissions, particularly methane (CH₄), which possesses a significantly higher global warming potential than carbon dioxide (CO₂). This study evaluates methane emission and energy recovery potential from the Okhla landfill site, South Delhi, India, using the Landfill Gas Emissions Model (LandGEM). Site-specific model parameters suitable for Indian landfill conditions (k = 0.032 year⁻¹ and L₀ = 70 m³ Mg⁻¹) were incorporated to improve prediction accuracy. The results showed that methane generation initiated in 1997 and is expected to continue until 2068. Peak methane emission of approximately 17.15 million m³ year⁻¹ was observed in 2020 due to rapid degradation of the biodegradable organic fraction, especially food waste. The corresponding peak total landfill gas (LFG) and CO₂ emissions were approximately 35.43 million m³ year⁻¹ and 17.71 million m³ year⁻¹, respectively. A strong correlation (R² = 0.9557) between cumulative waste deposition and methane generation confirmed model reliability. The estimated maximum energy recovery potential was approximately 46.19 million kWh year⁻¹. The study further discusses the applicability of the LandGEM under non-engineered landfill conditions commonly observed in developing countries. Overall, the findings emphasize the importance of methane recovery for greenhouse gas mitigation, sustainable waste management, and renewable energy generation in urban landfill systems.

1. Introduction

The municipal solid waste (MSW) generation rate in India exhibits a broad spectrum, ranging from 0.2 to 0.5 kg day⁻¹ person⁻¹, influenced by geographical location and prevailing lifestyles [1]. The growing population, combined with rapid industrialization and urbanization, has led to a huge production of 90 million tons (MT) of municipal solid waste annually in the country. On a global scale, a staggering 2 billion tons of waste are generated each year, with projections anticipating a surge to 9.5 billion tons by 2050 [2,3].

Presently, landfill management stands as the predominant method for processing the growing volumes of MSW in India. Notably, over 7% of MSW constitutes the biodegradable fraction, undergoing anaerobic digestion in landfills and producing significant quantities of CH₄ (40–65% v/v) and carbon dioxide (CO₂) (35–55% v/v). The severe environmental impact of CH₄, owing to its global warming potential (GWP) being approximately 30 times greater than that of CO₂, highlights the urgent need for effective waste management strategies [4,5,6]. In engineered landfills, a systematic approach involves regular compaction of deposited waste, provision of soil cover, and the installation of gas collection pipes. These measures are required towards capturing and utilizing the generated CH₄ for fuel and energy purposes. However, the reality in India reveals that many landfill systems lack proper engineering and management, leading to continuous atmospheric emissions of formed greenhouse gases (GHGs). It is estimated that India contributes approximately 20 Megagrams (Mg) of CO₂-equivalent CH₄ annually from landfill sources [7,8,9].

Critical to the effective operation and maintenance (O&M) of landfill sites is the accurate design, estimation, and prediction of CH₄ generation rates, as CH₄ is one of the major GHGs released during the anaerobic decomposition of municipal solid waste. Reliable prediction of CH₄ emissions is essential not only for evaluating landfill performance and environmental impacts but also for planning landfill gas (LFG) collection, energy recovery systems, and mitigation strategies [5]. In recent years, considerable research efforts have been directed toward estimating CH₄ emissions from landfill sites to better quantify the contribution of solid waste disposal practices to national and global GHG inventories. The modeling of CH₄ generation generally considers several important parameters, including waste composition, organic and biodegradable fraction, moisture content, climatic conditions, landfill operational practices, and the degradation rate of waste materials over time. Since landfill waste undergoes complex biological and physicochemical transformations, different mathematical and empirical models have been developed to estimate CH₄ generation under varying environmental and operational conditions [10]. Among the most widely used models are the first-order decay (FOD) model, triangular model, Intergovernmental Panel on Climate Change (IPCC) model, and Landfill Gas Emissions Model (LandGEM) [11,12,13]. The FOD model assumes that the degradation of organic waste and CH₄ generation occur progressively over time following first-order kinetics, making it one of the most commonly adopted approaches worldwide. The triangular model represents CH₄ production through a simplified triangular distribution pattern and incorporates the temporal characteristics of waste degradation. The IPCC model, recommended for GHG inventory estimation, integrates factors such as waste composition, degradable organic carbon content, climate conditions, and landfill management practices to provide standardized emission estimates [11,13]. In contrast, the LandGEM developed by the United States Environmental Protection Agency (USEPA) offers a more detailed estimation approach. These models collectively contribute to a more comprehensive understanding of CH₄ emission dynamics from landfill systems and support policymakers, researchers, and environmental engineers in developing effective waste management and GHG strategies [14]. LandGEM is considered superior to other methods for CH₄ gas emission estimation due to its comprehensive approach. The model is widely recognized for providing comparatively reliable CH₄ emission predictions. Previous studies reported that LandGEM predictions generally agree with field-monitored CH₄ emissions within an uncertainty range of approximately ±10–20%, whereas simplified empirical methods and default inventory approaches may show deviations exceeding 30–50% due to insufficient consideration of waste degradation kinetics and landfill operational conditions [10,13]. Kumar et al. [15] estimated CH₄ emissions from Indian landfill sites between 0.5 and 1.2 Tg CH₄ year⁻¹ using FOD modeling and demonstrated improved prediction accuracy when site-specific parameters were applied. More recently, Bhuiya et al. [16] compared LandGEM, IPCC-ZODM, IPCC-FODM, and modified triangular models for CH₄ estimation and reported that LandGEM produced more stable predictions with over 92% explained variability in statistical validation analyses, while some conventional models exhibited significantly higher sensitivity to input uncertainty. Similarly, Dey and Ashok [17] highlighted that LandGEM-integrated machine learning models produced CH₄ emission predictions closely matching observed landfill emission trends and improved prediction reliability by approximately 25–40% over conventional empirical approaches. Furthermore, USEPA reports that LFG generally contains 45–60% CH₄, and LandGEM remains one of the most extensively validated and regulatory-accepted tools for estimating LFG emissions and energy recovery potential (https://www.epa.gov/; accessed on 26 May 2026) [18]. Therefore, the comparatively lower prediction uncertainty, integration of site-specific variables, and successful validation across diverse landfill conditions support the use of LandGEM over several conventional estimation methods.

However, a major challenge encountered in most LFG emission studies is the lack of reliable and accurate data regarding MSW generation rates and the actual composition of disposed waste. The quantity and characteristics of MSW vary significantly depending on population growth, socioeconomic conditions, seasonal variations, consumption patterns, and waste management practices, making precise estimation difficult [19,20]. In addition, inconsistent waste segregation and limited long-term monitoring further increase uncertainties in CH₄ emission prediction models. Since the accuracy of LFG estimation largely depends on input parameters such as waste composition, degradable organic carbon content, moisture conditions, and waste deposition rates, it is essential to incorporate country-specific and site-specific parameters for improving model reliability and prediction accuracy [3,11]. Among the various available LFG prediction models, the LandGEM has gained considerable attention and widespread acceptance due to its simplicity, reliability, and effectiveness in estimating CH₄ generation from landfill sites under varying waste disposal conditions. The present study focuses on the Okhla landfill site in Delhi, India, with the objective of estimating the generation of LFG, including CH₄, CO₂, and non-methane organic compounds (NMOC), using the LandGEM. The study further aims to evaluate the temporal variation and emission characteristics of LFG based on historical waste deposition data. Another important objective of this research is to assess the energy recovery potential of the generated CH₄ gas and examine its suitability as a renewable energy resource.

2. Materials and Methodology

2.1. Site Description and Waste Composition

Okhla landfill (28°30′42″ N, 77°16′59″ E) is a dump yard situated in south Delhi with an area of 40 acres (Figure 1). The site was used in the dumping process from 1996 and exhausted in 2018. Okhla receive precipitation of 706 mm year⁻¹ and near the closing period approx. 1800 Tons per day (TPD) waste was being dumped in the landfill. In the current study, annual solid waste deposition data were collected from records of the Delhi Pollution Control along with published research articles to ensure more realistic estimation of LFG emissions [9,13,21]. Waste composition is a major factor for total LFG emission. The biodegradable waste at Okhla landfill contains readily degradable waste (35%), slowly degradable waste (20%), very slowly degradable waste (10%), and other waste. The details of the waste composition are given in

Table 1. Components of Okhla landfill waste.

Waste Type	Weight Avg. (%)
Metals	0.8
Construction and demolition waste	30.0
Wooden waste	0
Paper	11.8
Plastics	5.0
Food	35.7
Garden waste	6.3
Rubber, leather	5.0
Textiles	5.0
Glass and ceramics	0.40
Total	100.0

. The waste composition of the Okhla landfill indicates that food waste (35.7%) constitutes the largest fraction, followed by construction and demolition (C&D) waste (30.0%). The high proportion of food waste suggests a substantial biodegradable organic content, which can undergo anaerobic decomposition and generate significant quantities of landfill gas, particularly methane (CH₄). Other notable components include paper (11.8%), garden waste (6.3%), plastics (5.0%), rubber and leather (5.0%), and textiles (5.0%), while metals (0.8%) and glass and ceramics (0.4%) are present in smaller quantities. The dominance of biodegradable materials (food, paper, garden waste, textiles, and leather) indicates a high methane generation potential, making the landfill a significant source of GHG emissions as well as a potential resource for energy recovery through landfill gas utilization.

2.2. LandGEM

LandGEM tool (V3.1) is developed by USEPA and used for calculation of total LFG, CH₄, CO₂ and NMOCs emission from landfill site. The model has parameters related to Clean Air Act (CAA) defaults and inventory defaults [14]. Default parameters can be used for modeling purposes when no site-specific data are available. The model estimate CH₄ emissions from landfill sites based on the anaerobic decomposition behavior of biodegradable MSW. The rationale for using these equations lies in the fact that organic waste deposited in landfills decomposes gradually over time through microbial activity, producing LFG mainly composed of CH₄ and CO₂. Since CH₄ generation does not occur uniformly and varies depending on waste age, composition, moisture content, climatic conditions, and landfill operational practices, mathematical equations are essential for accurately predicting gas generation trends [13,14,15]. The first-order decay approach effectively represents the exponential pattern of waste degradation, where CH₄ production initially increases, reaches a peak, and subsequently decreases as the biodegradable organic fraction becomes depleted. The LandGEM equations incorporate important parameters such as CH₄ generation potential, CH₄ generation rate constant, annual waste acceptance rate, and waste age to estimate annual and cumulative CH₄ emissions during both operational and post-closure phases of landfill sites. The formula used for emission calculation is given in Equation (1) [10,22]:

$$Q_{C{H}_4}={\displaystyle {\sum }_{i=1}^n{\displaystyle {\sum }_{j=0.1}^{1}k{L}_0\left(\frac{M_i}{10}\right)e^{-k{t}_{ij}}}}$$

(1)

• $Q_{C{H}_4}$ = CH₄ generation in the particular year (m³ year⁻¹)
• i = 1-year time increment
• j = 0.1 year time increment
• n = (Year of the calculation) − (Initial year of waste acceptance)
• k = CH₄ generation rate (Default value: 0.050 year⁻¹)
• Lo = Potential CH₄ generation capacity (170 m³ Mg⁻¹)
• M_i = Mass of waste accepted in the year (Mg)
• t_ij = Age of the jth sector of waste mass M_i accepted in the ith year

Lo is the Potential CH₄ Generation Capacity (PMGC), i.e., the amount of CH₄ (m³) generated per million grams (Mg) of MSW decomposed. Waste mass with high cellulose content will have high Lo value, with high lignin content will have low Lo value. k is the CH₄ Generation Constant (year⁻¹) i.e., rate of waste decay and CH₄ production. The value of k depends on following properties of waste mass such as pH, temperature, moisture content and nutrient availability to microorganisms [12,23]. If the biodegradable content of waste mass is high, CH₄ generation rate will be high too, so the k value. In current study the considered Lo and k value are 70 m³ Mg⁻¹ and 0.032 year⁻¹, respectively, as evaluated by Kumar and Sharma (2014) [6] (

Table 2. LandGEM parameters.

Variable	Unit	Symbol	Rate (CAA) *	Present Study
CH4 generation rate	Year−1	k	0.05	0.032
Potential CH4 generation capacity	m3 Mg−1	Lo	170	70
NMOC concentration	ppmv as hexane		4000	4000
CH4 content	% by volume		50	50

* Model parameters according to Clean Air Act (CAA) regulations.

).

The LandGEM assumes relatively homogeneous landfill conditions, including uniform waste composition, consistent moisture distribution, stable anaerobic degradation conditions, and constant CH₄ generation parameters throughout the modeling period [10,14]. However, in actual landfill environments, particularly under non-engineered landfill conditions such as the Okhla landfill site, waste characteristics and environmental conditions may vary spatially and temporally due to irregular waste deposition, seasonal climatic fluctuations, varying organic content, differential compaction, and uncontrolled aeration [9,13]. These variations can influence microbial degradation behavior and introduce uncertainties in CH₄ emission predictions. The use of constant parameter values may simplify the complex biodegradation processes occurring in landfills and may therefore lead to some deviation between predicted and actual LFG generation. Nevertheless, site-specific values of k = 0.032 year⁻¹ and L_O = 70 m³ Mg⁻¹, obtained from previous studies under Indian landfill conditions, were incorporated in the present study to improve model reliability and reduce uncertainty [6].

The model estimates the CO₂ emission (Q_CO2) rate from the value of CH₄ emission (Q_CH4) and % CH₄ content (P_CH4) in total LFG using Equation (2) [9]:

$$Q_{C{O}_2}=Q_{C{H}_4}\left[\left\{{\displaystyle \frac{1}{\raisebox{1ex}{$P_{C{H}_4}$}\!\left/ \!\raisebox{-1ex}{$100$}\right.}}\right\}-1\right]$$

(2)

Equation (2) estimates CO₂ emission rates based on the calculated CH₄ emission rate and the percentage of methane (P_CH4) present in the total LFG composition because LFG is primarily composed of CH₄ and CO₂ generated simultaneously during the anaerobic decomposition of organic waste. The rationale for using this equation is founded on the biological and chemical relationship between CH₄ and CO₂ production in landfill environments, where both gases are produced as major end products of microbial degradation processes [24]. Since CH₄ generation is generally predicted more accurately through first-order decay modeling, the corresponding CO₂ emission can be estimated proportionally using the methane emission rate (Q_CH4) and the methane fraction (P_CH4) in the LFG mixture [25]. This approach simplifies the estimation process while maintaining reasonable accuracy because the composition of LFG typically remains within a known range under stable anaerobic conditions. The equation enables researchers to quantify CO₂ emissions efficiently without requiring separate complex degradation modeling for CO₂ generation.

2.3. Energy Potential Estimation

CH₄ generated from landfill can be utilized as energy source and its energy generation potential (Ep in kWh year⁻¹) can be calculated using Equation (3) [24]:

$$E_p={\displaystyle \frac{0.9\times Q_{C{H}_4}\times LH{V}_{C{H}_4}\times \eta \times \lambda }{3.6}}$$

(3)

• $Q_{C{H}_4}$ = Emitted CH₄ gas (m³) from landfill in a particular year
• $LH{V}_{C{H}_4}$ = Lower Heating Value of CH₄ (37.2 MJ m⁻³)
• η = Electrical conversion efficiency for the IC engine (33%)
• λ = Collection efficiency of CH₄ from landfill (75%)
• 0.9 = Empirical coefficient
• 3.6 = Conversion factor (MJ to kWh)

The estimation of energy potential from landfill CH₄ generation is essential because CH₄ is a combustible gas with significant calorific value and can serve as an alternative renewable energy source. The rationale for using above equation is based on the direct relationship between the quantity of CH₄ generated from landfill waste and the amount of recoverable energy that can be produced through CH₄ combustion. Since LFG primarily contains CH₄ as its energy-bearing component, the energy generation potential (E_p) is calculated using the CH₄ emission rate along with its heating value and appropriate conversion efficiency factors [26,27,28]. This equation enables the conversion of CH₄ generation data into electrical energy potential expressed in kilowatt-hours per year (kWh year⁻¹), thereby helping researchers and policymakers evaluate the feasibility of landfill gas-to-energy projects.

3. Results and Discussion

CH₄ gas emissions from landfills present a dual environmental impact and energy potential. It is a potent GHG, contributing to climate change; however, it also offers an opportunity for harnessing electrical energy. CH₄ can be captured and utilized as a renewable energy source [29]. This approach not only addresses environmental concerns but also taps into the sustainable energy potential inherent in LFG, contributing to an eco-friendly and energy-efficient waste management strategy. The current results focus on the rate of CH₄ generation from the Okhla landfill and its electrical energy generation potential.

3.1. Landfill Site Results

Over the past 23 years, the quantities of municipal solid waste disposed at the Okhla landfill site have been systematically documented and are presented in

Table 3. Input datasheet to the LandGEM software for Okhla [9,13,21].

Year	Waste Accepted	Waste-In-Place
	(Mg Year−1)	(Mg Year−1)
1996	2.47 × 105	0
1997	4.29 × 105	2.47 × 105
1998	5.70 × 105	6.76 × 105
1999	4.91 × 105	12.47 × 105
2000	6.09 × 105	17.39 × 105
2001	6.15 × 105	23.48 × 105
2002	4.22 × 105	29.64 × 105
2003	5.21 × 105	33.86 × 105
2004	5.79 × 105	39.08 × 105
2005	3.96 × 105	44.87 × 105
2006	5.21 × 105	48.84 × 105
2007	3.66 × 105	54.05 × 105
2008	3.66 × 105	57.72 × 105
2009	5.08 × 105	61.39 × 105
2010	4.86 × 105	66.47 × 105
2011	5.02 × 105	71.34 × 105
2012	5.46 × 105	76.37 × 105
2013	4.86 × 105	81.83 × 105
2014	4.53 × 105	86.69 × 105
2015	4.09 × 105	91.22 × 105
2016	5.80 × 105	95.32 × 105
2017	5.84 × 105	101.12 × 105
2018	6.57 × 105	106.96 × 105

. The term “waste accepted” refers to the quantity of waste transported and deposited at the landfill before compaction, whereas “waste-in-place” represents the cumulative quantity of compacted waste occupying the landfill after settlement and compaction processes [9]. The waste-in-place values were estimated using the LandGEM software by incorporating annual waste acceptance data and landfill operational assumptions. Initially, the waste quantities were recorded in megagrams per year (Mg/year) and later converted into short tons/year during the modeling process because the LandGEM tool operates primarily using imperial units. In 1996, approximately 2.47 × 10⁵ Mg year⁻¹ of municipal solid waste was disposed at the Okhla landfill site, which gradually increased to about 6.57 × 10⁵ Mg year⁻¹ by 2018, indicating a substantial rise in waste generation and disposal over the years. The observed trend demonstrates a progressive increase in waste deposition during the early operational years, followed by fluctuations and a sharp rise near the final years of landfill operation. Such variability in waste disposal rates may be attributed to changes in waste management policies, diversion of waste to alternative landfill facilities, and variations in municipal waste collection practices within Delhi. The significant increase in waste accumulation is primarily associated with rapid population growth, urban expansion, industrial development, and changing consumption patterns in the metropolitan region [30]. The deposited waste stream consisted of diverse components including food waste, paper, plastics, garden waste, textiles, rubber, leather, construction debris, and other biodegradable and non-biodegradable materials. Among these, biodegradable organic fractions such as food and garden waste played a major role in LFG generation due to their high decomposition potential under anaerobic conditions [26]. The continuous accumulation of such heterogeneous waste materials over the years significantly influenced CH₄ generation behavior and LFG emission characteristics at the Okhla landfill site.

Table 4. Production of gases based on LandGEM.

Year	Total Landfill Gas (Mg Year−1)	CO2 (Mg Year−1)	CH4 (Mg Year−1)	NMOC (Mg Year−1)
1996	0	0	0	0
1997	1.37 × 103	1.00 × 103	3.66 × 102	1.57 × 101
1998	3.70 × 103	2.71 × 103	9.88 × 102	4.25 × 101
1999	6.74 × 103	4.94 × 103	1.80 × 103	7.74 × 101
2000	9.25 × 103	6.78 × 103	2.47 × 103	1.06 × 102
2001	1.23 × 104	9.03 × 103	3.29 × 103	1.42 × 102
2002	1.53 × 104	1.12 × 104	4.10 × 103	1.76 × 102
2003	1.72 × 104	1.26 × 104	4.59 × 103	1.97 × 102
2004	1.95 × 104	1.43 × 104	5.22 × 103	2.24 × 102
2005	2.21 × 104	1.62 × 104	5.91 × 103	2.54 × 102
2006	2.36 × 104	1.73 × 104	6.31 × 103	2.71 × 102
2007	2.58 × 104	1.89 × 104	6.88 × 103	2.96 × 102
2008	2.70 × 104	1.98 × 104	7.21 × 103	3.10 × 102
2009	2.82 × 104	2.06 × 104	7.52 × 103	3.23 × 102
2010	3.01 × 104	2.21 × 104	8.04 × 103	3.45 × 102
2011	3.18 × 104	2.33 × 104	8.50 × 103	3.65 × 102
2012	3.36 × 104	2.46 × 104	8.98 × 103	3.86 × 102
2013	3.56 × 104	2.61 × 104	9.50 × 103	4.08 × 102
2014	3.71 × 104	2.72 × 104	9.92 × 103	4.26 × 102
2015	3.85 × 104	2.82 × 104	1.03 × 104	4.42 × 102
2016	3.95 × 104	2.90 × 104	1.06 × 104	4.54 × 102
2017	4.15 × 104	3.04 × 104	1.11 × 104	4.76 × 102
2018	4.34 × 104	3.18 × 104	1.16 × 104	4.98 × 102

presents the estimated annual generation of CH₄, CO₂, total LFG, and non-methane organic compounds (NMOC) from the Okhla landfill site using the LandGEM. The model incorporates waste composition characteristics, CH₄ generation parameters, and annual waste deposition history, thereby providing more realistic and reliable emission estimates compared to simplified empirical methods. The waste stream at the Okhla landfill contains a substantial proportion of biodegradable organic matter, particularly food waste, which constitutes approximately 35.7% of the total waste composition as shown in Table 1. The presence of such highly biodegradable and moisture-rich waste significantly accelerates anaerobic decomposition processes and enhances CH₄ generation within the landfill environment [7]. In the initial operational year (1996), LFG generation was considered negligible because freshly deposited waste requires a sufficient acclimatization period for microbial communities to establish anaerobic degradation conditions. As waste accumulation increased over the years, microbial decomposition activity intensified, resulting in gradual increases in LFG production. CH₄ generation initiated at approximately 3.66 × 10² Mg year⁻¹ in 1997 and subsequently increased rapidly to approximately 1.16 × 10⁴ Mg year⁻¹ by 2018. Similarly, total LFG and CO₂ emissions exhibited continuous increases due to the cumulative deposition of biodegradable waste and enhanced microbial degradation activity. During the landfill closure year (2018), total LFG and CO₂ emissions reached approximately 4.34 × 10⁴ Mg year⁻¹ and 3.18 × 10⁴ Mg year⁻¹, respectively. The observed increase in gas generation clearly reflects the influence of waste accumulation, organic fraction content, climatic conditions, and long-term anaerobic decomposition processes occurring within the landfill mass.

The temporal variation in total LFG, CH₄, and CO₂ emissions over the study period is illustrated in Figure 2. According to the predicted emission trends, CH₄ generation commenced in 1997 and is expected to continue until approximately 2068, even after landfill closure. This prolonged CH₄ generation behavior is typical of municipal solid waste landfills because biodegradable organic matter continues to decompose under anaerobic conditions for several decades. The highest CH₄ generation of approximately 17.15 million m³ year⁻¹ was predicted in 2020, nearly two years after landfill closure. This delayed peak emission phenomenon can be attributed to the stabilization and intensified anaerobic degradation of the large accumulated biodegradable organic fraction, particularly food waste, present in the landfill [25]. Furthermore, the increasing trend in CH₄ generation corresponds closely with the continuous rise in municipal waste disposal caused by rapid urbanization, population growth, industrial development, and changing consumption patterns in Delhi. The CH₄ emission curve shown in Figure 2 clearly demonstrates the relationship between cumulative waste deposition and CH₄ production potential.

Peak emissions of total LFG and CH₄ were observed approximately 24 years after landfill initiation, after which the emission rate gradually declined due to depletion of the biodegradable organic fraction available for microbial decomposition. Figure 2 indicates that the maximum total LFG and CO₂ generation occurred in 2020, reaching approximately 35.43 million m³ year⁻¹ and 17.71 million m³ year⁻¹, respectively, followed by a gradual reduction in emission rates over time. The declining trend after the emission peak reflects the reduction in biodegradable carbon content and progressive stabilization of landfill waste. The predicted CH₄ emission trend obtained from the LandGEM showed close agreement with earlier studies on Delhi landfill sites reported by Chakraborty et al. [9] and Ghosh et al. [13], which also identified rapid CH₄ generation due to the high biodegradable and food waste fraction in Delhi municipal solid waste. The present study estimated peak CH₄ generation of approximately 17.15 million m³ year⁻¹ in 2020, whereas Ghosh et al. [13] reported CH₄ generation within a comparable range for major landfill sites in Delhi using field-supported modeling approaches. Furthermore, the cumulative CH₄ generation showed a strong correlation with cumulative waste deposition (R² = 0.9557), indicating good consistency between waste input and predicted CH₄ generation trends (Figure 3). The obtained CH₄ generation behavior and post-closure emission pattern also align with the findings of Gollapalli and Kota [22] and Naveen and Fard [25], who reported that CH₄ emissions generally peak shortly after landfill closure and gradually decline due to depletion of biodegradable organic matter. Additionally, elevated temperature and moisture conditions prevailing at the Okhla landfill site likely promoted enhanced microbial activity and accelerated biodegradation of organic waste fractions, consequently increasing CH₄ generation rates throughout the operational period of the landfill.

3.2. Electrical Energy Generation Potential

The predicted CH₄ emission values from the LandGEM were used to estimate the electrical energy recovery potential from the Okhla landfill site. The energy generation estimation mainly depends on the CH₄ generation rate constant (k) and CH₄ generation potential (L₀) [24,26]. Since CH₄ is a combustible gas with high calorific value, its recovery and utilization can provide a sustainable renewable energy source while reducing GHG emissions from landfill sites. Figure 4 illustrates the temporal variation in energy generation potential in kWh year⁻¹, which closely follows the CH₄ emission trend. The maximum energy generation potential was estimated at approximately 46.19 million kWh year⁻¹ in 2020, corresponding to the peak CH₄ generation period after landfill closure. The high energy potential is primarily attributed to the large biodegradable organic fraction, especially food waste, present in the landfill. Utilization of landfill CH₄ for electricity generation can significantly reduce atmospheric GHG emissions and dependence on fossil fuels [29,30]. According to the model prediction, CH₄ generation and associated energy recovery potential at the Okhla landfill site are expected to continue for nearly 30 years after landfill closure, although the energy generation rate will gradually decline with the depletion of biodegradable organic matter.

3.3. Application of LandGEM Under Non-Engineered Landfill Conditions

The LandGEM can also be effectively applied under non-engineered landfill conditions, particularly in developing countries where open dumping and poorly managed waste disposal practices are common. Although the model was originally developed for engineered sanitary landfills, several studies have demonstrated its applicability in non-engineered landfill sites when appropriate site-specific parameters such as waste composition, moisture content, climatic conditions, and waste deposition rates are incorporated [12,14,22]. In non-engineered landfills, factors such as irregular waste compaction, absence of leachate collection systems, uncontrolled aeration, and heterogeneous waste characteristics may influence CH₄ generation behavior and introduce higher uncertainty in emission predictions. Nevertheless, the first-order decay approach used in LandGEM remains useful for estimating long-term CH₄ emissions, GHG inventories, and energy recovery potential under such conditions. Researchers have reported that, despite certain limitations, LandGEM provides reasonably acceptable CH₄ generation estimates for unmanaged dumpsites and non-sanitary landfills, especially when local calibration factors are applied [9,10]. Therefore, the model serves as a practical and cost-effective tool for preliminary assessment of LFG emissions and renewable energy potential in regions lacking advanced landfill infrastructure and continuous monitoring systems.

4. Conclusions

The present study demonstrates that the Okhla landfill site is a major source of CH₄ emissions and possesses considerable potential for renewable energy recovery through LFG utilization. The application of the LandGEM provided valuable insight into CH₄ generation trends under Indian landfill conditions and highlighted the influence of biodegradable waste, particularly food waste, on GHG emissions. The results indicate that CH₄ generation continues even after landfill closure, emphasizing the long-term environmental impact of unmanaged municipal solid waste disposal. The estimated energy recovery potential suggests that landfill CH₄ can serve as an alternative renewable energy source while reducing GHG emissions and dependence on fossil fuels. Although uncertainties remain due to heterogeneous waste composition, climatic variations, and assumptions associated with first-order decay modeling, the incorporation of site-specific parameters improved prediction reliability.