Climate impacts of landfill gas emissions: Analysis for 20-year and 100-year time horizons.

Climate impacts of landfill gas emissions were investigated for 20- and 100-year time horizons to identify the effects of atmospheric lifetimes of short- and long-lived drivers. Direct and indirect climate impacts were determined for methane and 79 trace species. The impacts were quantified using global warming potential, GWP (direct and indirect); atmospheric degradation (direct); tropospheric ozone forming potential (indirect); secondary aerosol forming potential (indirect) and stratospheric ozone depleting potential (indirect). Effects of cover characteristics, landfill operational conditions, and season on emissions were assessed. Analysis was conducted at five operating municipal solid waste landfills in California, which collectively contained 13% of the waste in place in the state. Climate impacts were determined to be primarily due to direct emissions (99.5 to 115%) with indirect emissions contributing -15 to 0.5%. Methane emissions were 35 to 99% of the total emissions and the remainder mainly greenhouse gases (hydro)chlorofluorocarbons (up to 42% of total emissions) and nitrous oxide. Cover types affected emissions, where the highest emissions were generally from intermediate covers with the largest relative landfill surface areas. Landfill-specific direct emissions varied between 683 and 103,411 and between 381 and 37,925 Mg CO2-eq./yr for 20- and 100-yr time horizons, respectively. Total emissions (direct + indirect) were 680 to 103,600 (20-yr) and were 374 to 38,108 (100-yr) Mg CO2-eq./yr. Analysis time horizon significantly affected emissions. The 20-yr direct and total emissions were consistently higher than the 100-yr emissions by up to 2.5 times. Detailed analysis of time-dependent climate effects can inform strategies to mitigate climate change impacts of landfill gas emissions.

Disaster reconnaissance framework for sustainable post-disaster materials management.

A first foundational assessment is provided for disaster debris reconnaissance that includes identifying tools and techniques for reconnaissance activities, identifying challenges in field reconnaissance, and identifying and developing preliminary guidelines and standards based on advancements from a workshop held in 2022. In this workshop, reconnaissance activities were analyzed in twofold: in relation to post-disaster debris and waste materials and in relation to waste management infrastructure. A four-phase timeline was included to capture the full lifecycle of management activities ranging from collection to temporary storage to final management route: pre-disaster or pre-reconnaissance, post-disaster response (days/weeks), short-term recovery (weeks/months), and long-term recovery (months/years). For successful reconnaissance, objectives of field activities and data collection needs; data types and metrics; and measurement and determination methods need to be identified. A reconnaissance framework, represented using a 3x2x2x4 matrix, is proposed to incorporate data attributes (tools, challenges, guides), reconnaissance attributes (debris, infrastructure; factors, actions), and time attributes (pre-event, response, short-term, long-term). This framework supports field reconnaissance missions and protocols that are longitudinally based and focused on post-disaster waste material and infrastructure metrics that advance sustainable materials management practices. To properly frame and develop effective reconnaissance activities, actions for all data attributes (tools, challenges, guides) are proposed to integrate sustainability and resilience considerations. While existing metrics, tools, methods, standards, and protocols can be adapted for sustainable post-disaster materials management reconnaissance, development of new approaches are needed for addressing unique aspects of disaster debris management.

Application of cavity ring-down spectroscopy and a novel near surface Gaussian plume estimation approach to inverse model landfill methane emissions.

Fugitive methane emissions from municipal solid waste landfills impact global climate change and reliable emissions quantification is of increasing importance. Ground-based cavity ring-down spectrometer (CRDS) measurements were used to determine methane concentrations and isotopic compositions of carbon in CH4. Then, CH4 oxidation through various cover materials was assessed using the Keeling plot method. A novel inverse modeling approach using Gaussian dispersion analysis, termed near-surface Gaussian plume estimation (NSGPE), was developed to predict whole-site landfill methane emissions. The concentration data obtained around the landfill perimeter with the mobile ground-based CRDS were used. Methane concentration data were integrated to parameterize discretized point source emissions from a Gaussian dispersion model. Post-processing algorithms were applied to refine modeling predictions to account for the influence of topographical and meteorological conditions on methane transport. Results indicate spatially resolved and consistent emissions estimates among multiple optimization simulations, with refinements increasing the resolution and spatial trends of emissions. Post-processing algorithms resolve consistent overestimation of emissions commonly observed using conventional Gaussian dispersion models.•Ground-based CRDS used to obtain methane concentration and oxidation data.•Novel inverse Gaussian dispersion modeling approach developed to predict methane emissions from landfills accounting for site-specific topography and meteorology.•Post-processing algorithms refine emissions estimates.

Assessment of methane emissions from a California landfill using concurrent experimental, inventory, and modeling approaches.

Methane flux and emissions were obtained at a California landfill concurrently using field measurements, inventory analyses, and modeling. Measured fluxes ranged from -3.7 to 828 g/m2-day and generally decreased from daily to intermediate to final covers. Soil covers with high-plasticity clay had the lowest fluxes. Whole-site emissions ranged from 406 to 47,414 tonnes/year (11,368 to 1,327,592 tonnes CO2-eq./year), and were dominated by intermediate covers with high relative surface area. Emissions estimates from flux chamber tests and California Landfill Methane Inventory Model (CALMIM) with oxidation were similar and low, whereas emissions from aerial measurements and CALMIM without oxidation were similar and high. The inventory analyses provided intermediate emissions and a new Gaussian plume model based on ground cavity ring-down spectrometer measurements provided the highest emissions. The assumptions used and the inherent strengths and limitations of the different approaches resulted in the flux and emissions differences. With varied attributes (experimental/modeling; flux/emissions; whole-site/cover-specific, top-down/bottom-up), the approaches provide envelopes of methane emissions and can be used selectively for the two main purposes of landfill methane emissions analysis: to mechanistically determine the factors that control/limit surface emissions and to provide data for atmospheric methane analysis. To reduce emissions, progression from temporary to permanent cover areas can be accelerated and covers with coarser materials can be amended with plastic fines.

Accurate and reliable estimation of kinetic parameters for environmental engineering applications: A global, multi objective, Bayesian optimization approach.

Accurate and reliable predictions of bacterial growth and metabolism from unstructured kinetic models are critical to the proper operation and design of engineered biological treatment and remediation systems. As such, parameter estimation has progressed into a routine challenge in the field of Environmental Engineering. Among the main issues identified with parameter estimation, the model-data calibration approach is a crucial, yet an often overlooked and difficult optimization problem. Here, a novel and rigorous global, multi objective, and fully Bayesian optimization approach that overcomes challenges associated with multi-variate, sparse and noisy data, as well as highly non-linear model structures commonly encountered in Environmental Engineering practice is presented. This optimization approach allows an improved definition and targeting of the compromise solution space for all multivariate problems, allowing efficient convergence, and a Bayesian component to thoroughly explore parameter and model prediction uncertainty. This global optimization approach outperformed, in terms of parameter accuracy and precision, standard, local non-linear regression routines and overcomes issues associated with premature convergence and addresses overfitting of different variables in the calibration process. •A sequential single, multi-objective, and Bayesian optimization workflow was developed to accurately and reliably estimate unstructured kinetic model parameters.•The global, single objective approach defines the global optimum (the best compromise solution) and "extreme" parameter solutions for each variable, while the global, multi-objective approach confirms the "best" compromise solution space for the Bayesian search to target and convergence is assessed using the single objective results.•The Approximate Bayesian Computational approach fully explores parameter and model prediction uncertainty targeting the compromise solution space previously identified.

Application of unstructured kinetic models to predict microcystin biodegradation: Towards a practical approach for drinking water treatment.

Biological drinking water treatment technologies offer a cost-effective and sustainable approach to mitigate microcystin (MC) toxins from harmful algal blooms. To effectively engineer these systems, an improved predictive understanding of the bacteria degrading these toxins is required. This study reports an initial comparison of several unstructured kinetic models to describe MC microbial metabolism by isolated degrading populations. Experimental data was acquired from the literature describing both MC removal and cell growth kinetics when MC was utilized as the primary carbon and energy source. A novel model-data calibration approach melding global single-objective, multi-objective, and Bayesian optimization in addition to a fully Bayesian approach to model selection and hypothesis testing were applied to identify and compare parameter and predictive uncertainties associated with each model structure. The results indicated that models incorporating mechanisms of enzyme-MC saturation, affinity, and cooperative binding interactions of a theoretical single, rate limiting reaction accurately and reliably predicted MC degradation and bacterial growth kinetics. Diverse growth characteristics were observed among MC degraders, including moderate to high maximum specific growth rates, very low to substantial affinities for MC, high yield of new biomass, and varying degrees of cooperative enzyme-MC binding. Model predictions suggest that low specific growth rates and MC removal rates of degraders are expected in practice, as MC concentrations in the environment are well below saturating levels for optimal growth. Overall, this study represents an initial step towards the development of a practical and comprehensive kinetic model to describe MC biodegradation in the environment.