Ground- and drone-based surface emission monitoring (SEM) campaigns were performed at two municipal solid waste landfills, during the same week as mobile tracer correlation method (TCM) testing was used to measure the total methane emissions from the same landfills. The G-SEM and the D-SEM data, along with wind data, were used as input into an inverse modeling approach combined with an optimization-based methane emission estimation method (implemented in a tool called SEM2Flux). This approach involves the use of backward dispersion modeling to estimate the whole-site methane emissions from a given landfill and the identification of locations and emission rates of major leaks. SEM2Flux is designed to exploit the measured surface methane concentration concurrently with wind data and tackle two problems: (1) inferring the estimates of methane rates from individual landfills, and (2) identifying the likely locations of the main emission sources. SEM2Flux results were also compared with emission estimates obtained using TCM. In Landfill B, the average TCM-measured methane emissions was 1178 Kg/h, with a standard deviation of 271 Kg/h. In Landfill C, the average TCM-measured emission rate was 601 Kg/h, with a standard deviation of 292 Kg/h. For both landfills, the D-SEM data yielded statistically similar estimates of methane emissions as the TCM-measured emissions. On the other hand, the G-SEM data yielded comparable estimates of emissions to TCM-measured emissions only for Landfill C, where the D-SEM and G-SEM data were statistically not different. The results of this study showcase the ability of this method using surface concentrations to provide a rapid and simple estimation of fugitive methane emissions from landfills. Such an approach can also be used to assess the effectiveness of different remedial actions in reducing fugitive methane emissions from a given landfill.

The coronavirus pandemic has resulted in major disruptions in the way municipal solid waste management systems (MSWMSs) operate due to substantial distortions in waste generation trends, along with a variety of significant operational and managerial challenges. As critical infrastructure, MSWMSs have endeavored to adapt in response to such unprecedented stresses in order to maintain their operations during the pandemic. The challenges and their relevant adaptive measures, however, have varied with the progression of the pandemic across different MSWMSs. Currently, there is a limited understanding of such time-bound and system-specific phenomena, which impedes timely and effective adaptation. This study aims to fill this knowledge gap by performing a detailed and documented investigation of the longitudinal impact of the coronavirus pandemic on different MSWMSs across the United States, along with its evolution over time, using collected qualitative and quantitative data (i.e., monthly interviews with waste management personnel, online news media, and waste tonnages). This study also develops a relational database system to facilitate the systematic recording and monitoring of the pandemic’s impact on MSWMSs, as well as guide the implementation of different adaptation strategies based on distinct systems’ characteristics. Findings of this study will help solid waste decision-makers better understand the current pandemic, along with serving as a knowledge base for future pandemic scenarios towards more resilient MSWMSs.

The concentration of surface air methane (CH₄) measured in parts per million by volume (ppmv) near the soil/atmosphere interface should, in theory, have a positive correlation with surface methane emissions fluxes, measured in grams per square meter per day (gm^-2d^-1). Some researchers suggest that CH₄ flux can be reasonably inferred from simple measurements of CH₄ concentrations near the landfill surface. Ground-based and drone-based surface emissions monitoring (SEMs) were performed at several municipal solid waste landfills as tracer correlation method (TCM) testing was being used to measure total methane emissions from the same landfills. The TCM data and SEM data were used to establish a new simple correlation to convert surface methane concentrations in ppmv to localized surface methane emission flux in gm^-2d^-1.The SEM data obtained from ten ground and drone monitoring campaigns were log-transformed and geospatially treated using inverse distance weighting to the power of 2 to predict methane surface concentrations in the entire footprint of the SEM measurements area. The developed new correlation equation was then used to convert every predicted surface methane concentration to an emissions flux. The total estimate of surface emissions from the entire landfill was obtained by integrating the predicted fluxes over the area of the footprint of the SEM measurement area. The use of the new developed correlation resulted in higher total emissions estimates than other correlations reported in the literature and should be considered more conservative. Not including other factors, the proposed approach provides estimate of total methane emissions with a coefficient of variation of 20%. This study introduces a novel approach that utilizes a developed correlation between surface methane concentrations and surface emissions fluxes to estimate total methane emissions from municipal solid waste landfills or from a specified area. This study provides an additional use of the quarterly SEM data.