An Efficient Landfill Surveying Method for Surface Emissions Monitoring and Cover Integrity Inspection

Spatial and temporal monitoring of the landfill surface emission and detection of cover issues

A project supported by Hinkley Center for Solid and Hazardous Waste Management

Background and Objectives

Methane (CH4) is an important greenhouse gas which has considerable consequences for climate change. A leading source of anthropogenic CH4 involves the decomposition of organic waste disposed of in landfills. In the US, landfills are the third largest (15.1%) anthropogenic CH4 emission source (US EPA 2019). Recently, the US EPA reported that three landfills in Central Florida were among the top 10 methane emitters in the US (NPR, 2021). For example, the Florida Orange County landfill emitted 32,000 metric tons of methane into the atmosphere in 2019. Estimating the amount of CH4 emitted from landfills often relies on theoretical gas generation models, applying standard conditions with respect to waste composition and surface CH4 oxidation. First-order decay models are the most widely used approach, such as USEPA's Landfill gas emissions model (LandGEM) and the Intergovernmental Panel on Climate Change (IPCC) method. Currently, EPA allows individual landfill operators to use three different ways to calculate the generated amount of methane and two different ways to calculate how much of that methane is emitted into the atmosphere for regulatory purposes. However, the estimations from these methods can vary significantly. It is also found that the commonly used emission models, including LandGEM, IPCC, and modified triangular method (MTM), predicted 46% to 228% higher emissions than actual measurements.

The main challenge for measuring landfill CH4 emissions is that there are high spatial and temporal variations. Although several measuring methods for landfill CH4 emissions quantification have been developed, the accuracy and applicability challenges for many of these methods remain. For example, surface emission monitoring (SEM) is the prevalent technique to locate emission hotspots on a landfill cover routinely (done quarterly). However, conducting SEM is often time and effort consuming, because each measurement requires a technician to walk the entire cover, which can take several days for the full survey. Other commonly used methods include flux chamber, tracer gas dispersion method, micro-meteorological method, differential absorption lidar (DiAL) method, and airborne imaging spectrometer method all of which have disadvantages associated with high cost and weather dependency. Remote sensing technology, e.g., Satellite based imaging, has also been employed with some success. However, more recently application of unmanned aerial vehicles (UAVs) remote sensing showed the advantages over satellites owing to its higher spatial resolution in surveying and inspection (e.g., 10-cm level for UAV compared to 10-meter level for satellite). UAV has been employed equipped with methane measuring sensors; however, the current equipped sensors these sensors are very expensive (e.g., UAV mounted DiAL sensor), and not capable for a routine inspection purpose. Hence, there is an urgent need for a timely and economically efficient method for the spatial and temporal monitoring of the landfill surface emission. Further, the detection of cover issues such as uneven settlement, tension cracks, vegetation loss, or leachate outbreaks also requires ground surveys and would benefit from an automated approach.

The goal of the study is to develop a timely and economically efficient method to survey landfills and inspect covers. The conceptual framework of this study is shown in Fig. 1. To achieve this goal, our objective is to develop and test an economical and efficient remote UAV-based sensing solution for landfill methane concentrations with greater accuracy than current SEM methods; we will also use the system to gather cover integrity data. The main tasks of this project are to: 1) develop an integrated sensor-UAV system, 2) assess surface methane emissions under various UAV operating (weather) conditions, 3) explore UAV abilities to inspect cover integrity, and 4) optimize the methane emission models by incorporating environmental and geotechnical-related parameters. The novelty of the proposed study compared to existing UAV-based applications in MSW landfill industry includes: the affordable sensing system development on UAV mobile platform, the integration of geotechnical and environmental measurements, the multimodal data fusion for inspect purpose, and the improvement of methane emission models using collected data.

Tentative Tasks

Task 1-Sensor-UAV Integration and System Development

The PIs will integrate a UAV system custom designed with multimodal sensing functions for MSW landfill surveying. The integrated UAV system (as shown in Fig. 1) includes an industrial quadcopter mobile platform equipped with a robust control system, Inertial Measurement Unit (IMU), and UAV-compatible sensing devices. The location is measured real-time by the Global Navigation Satellite System (GNSS) and Real-Time Kinematic (RTK) positioning technologies. LiDAR determines variable distance by targeting an object with a laser and measuring the time for the reflected light to return to the receiver. Laser pulses emitted from a LiDAR system reflect from objects both on and above the ground surface (e.g., vegetation, gas wells) causing different returns. Thus, LiDAR can be used to make digital 3-D representations of areas on the earth's surface due to differences in laser return times.

Task 2–In-field SEM and Cover Integrity Inspection

The team has selected three active landfills with daily, intermediate, and final cover systems in the Central Florida area (Seminole, Orange, and Hillsborough Counties). Field tests will be performed with the proposed UAV system on a quarterly basis. The team will receive support and insight from industry experts (PIs are currently collaborating with local landfill managers and a technical awareness group has been formed). In addition to the proposed UAV-based surveying method, conventional surveying (e.g., manual SEM, cover inspection) will be conducted to serve as data controls. In order to get the DEM, steps of data processing need to be formed after UAV-based surveying (1) the raw LiDAR 3D point cloud will be collected; (2) 3D point clouds will be then classified into ground points (e.g., landfill cover and soil) and other points (e.g., vegetation like trees and bushes); (3) the points with ground returns will be filtered out to show landfill cover; and (4) elevation contour will be overlaid onto the DEM to show the topological feature of the landfill.

Task 3-Emission Estimation and Spatiotemporal Analysis

Landfill site information (e.g., waste filling history, gas quantity/quality) will be requested from the landfill operators. A site weather station will be installed near landfill sites to capture the parameters of meteorological conditions (e.g., precipitation, temperature, solar radiation, evapotranspiration, near-surface wind speed, barometric pressure). Additionally, the parameters of soil conditions (e.g., soil water content, porosity, soil type) and landfill cover conditions (e.g., daily, intermediate, final, ponding) will be obtained at each surveying period. Methane emission rate can be estimated from measured methane concentration within plumes together with near-surface wind speed. The PIs will also employ direct correlation analyses (e.g., parameter correlation) to generate multivariate regression-based models to determine methane emission rates.

Task 4–Improving Landfill GHG Emissions Models

Major limitations of methods used to estimate landfill gas emissions using models based on first-order waste degradation are related to two major issues (1) improper input parameters that describe the ultimate methane generation and the rate of degradation and (2) the collection efficiency and extent of methane oxidation in the cover. Issue 1 may be overcome by improving the incorporation of waste composition, waste age, and climate in the model. Issue 2 may be solved by better accounting for the condition of the cover in the model. In this task, the PIs will conduct a thorough review of the relevant research for information regarding these factors and current knowledge. The PIs will link cover conditions determined by the UAV (e.g., liquid ponding, hot spots, cover type, vegetation, surface temperatures) to collection efficiency and methane oxidation factors and incorporate them into the methane generation models (e.g., LandGEM, IPCC). A collection efficiency can be estimated by measuring emissions and gas collection rates and estimating methane oxidation in the cover system. Further, the PIs will use recent waste composition studies to better estimate model input parameters. Leachate chemistry will be considered to evaluate the age of the waste. Sensitivity analysis of a series of parameters (e.g., site information, soil condition, waste composition, meteorological conditions) will be evaluated at a reasonable range revealing the site conditions.