Concerted efforts needed to standardise data collection, invest in modern waste management infrastructure, develop region-specific models
Methane emissions contribute to climate change and air pollution, and are thus a major environmental concern. Estimating these emissions accurately is essential for effective mitigation and management strategies.
Methane estimation models play a crucial role in assessing the environmental impact of landfills and devising effective mitigation strategies. However, when it comes to Indian landfills, these models often produce variable results, posing challenges for policymakers and environmentalists. There are two ways of estimating methane emissions from landfills or dumpsites: Theoretically and experimentally.
Various theoretical models such as the Intergovernmental Panel on Climate Change models, the Landfill Gas Emissions Model and the Modified Triangular Method are commonly used to predict annual methane emissions. They continue to receive criticism due to their poor accuracy and insufficient validation.
Also, there is a huge disparity in methane emission estimates from Indian landfills / dumpsites. This can be attributed to factors such as inconsistent data collection, variable landfill practices, informal waste disposal, methodological differences, changes over time and regulatory variations.
Methane emission studies done on Delhi dumpsites (in gigagrams)
Source: Compiled from multiple studies
As a potent greenhouse gas and contributor to air quality degradation, methane emissions from dumpsites require comprehensive studies and estimation methods.
Methane emission estimates from Delhi dumpsites, like many other locations, can vary for several reasons, resulting in different results among different studies.
Here are some of the key factors contributing to the variation in methane emission estimates in Indian dumpsites:
Heterogeneity and waste composition: One of the primary reasons for the variability in methane estimation models is the heterogeneous nature of waste composition in Indian landfills. Unlike controlled environments in developed countries, Indian landfills receive a wide variety of waste, including biodegradable organic matter, plastic waste, and construction debris. The complex mix of materials and varying decomposition rates make it challenging to predict methane generation accurately.
Lack of comprehensive data: Accurate estimation models rely on comprehensive data, which is often lacking in Indian landfill settings. Inadequate waste characterisation, inconsistent record-keeping and limited access to data can hinder the development and calibration of methane models. This data scarcity makes it challenging to create models that reflect the true conditions in Indian landfills.
Climate variability: India’s diverse climate, with regions experiencing monsoons, tropical conditions and desert climates, adds another layer of complexity to methane estimation models. Climate influences the rate of decomposition, with higher temperatures and humidity levels typically accelerating methane production. Accurate models must account for these regional variations.
Varying waste management practices: India’s waste management practices vary significantly across regions and municipalities. Some areas have adopted improved waste management systems including infrastructure for scientific treatment of waste and sanitary landfill management techniques, including gas collection systems and engineered caps, which can effectively capture and mitigate methane emissions. In contrast, many others continue to rely on open dumping, leading to uncontrolled methane release. These differences in waste management practices directly affect methane generation and escape rates.
Population density and urbanisation: Population density and urbanisation rates impact waste generation and landfill operations. High-density urban areas produce more waste, while rapidly urbanising regions may lack adequate landfill infrastructure. Both factors influence methane emissions and, consequently, the accuracy of estimation models.
Regulatory and policy frameworks: In conclusion, the variability in methane estimation models for Indian landfills arises from a combination of factors, including waste composition, data availability, climate variability, waste management practices, population density and regulatory frameworks.
To improve the accuracy of these models, concerted efforts are needed to standardise data collection, invest in modern waste management infrastructure and develop region-specific models that account for India’s diverse environmental and socioeconomic factors.
Additionally, increased collaboration between researchers, policymakers and waste management authorities can help address these challenges and pave the way for more effective methane mitigation strategies in Indian landfills.
To reconcile differing results, it is crucial to critically assess the methodologies, data sources and assumptions used in different studies. Combining multiple approaches and conducting ongoing monitoring can provide a more accurate picture of methane emissions from dumpsites in Delhi or any other area.
Additionally, improved waste management practices including biodegradable waste treatment and legacy waste dumpsite remediation can help reduce emissions from these sites.
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