A UAV-Based Thermal-Imaging Approach for the Monitoring of Urban Landfills

Progress in monitoring methane emissions from landfills using drones: an overview of the last ten years

Solid waste landfills are responsible for much of the anthropogenic methane emitted from the waste sector. The quantification of fugitive CH4 emissions from a landfill is to date characterised by high uncertainty and several methodologies have been devised to estimate emission fluxes. Unmanned Aerial Vehicles (UAVs, also known as drones) are revolutionising the way CH4 emission monitoring is conceived and offer new opportunities for quantifying emission fluxes from a landfill, mainly due to recent advances in sensor miniaturisation that make these instruments lighter and more suitable to be equipped on a drone. The paper analyses publications from the period 2014-2024 that illustrate UAV-based methods that can be used for this purpose, identifying experiences in the field and the current state of research. The review has highlighted a current research status characterised by a strong experimental focus, with few tests carried out in landfills under real emission conditions (33 % of the reviewed papers). Since 2018, there has been a growing interest in open-path sensors, tested in some controlled-release experiments according to different configurations which have given promising results, but experiences are limited and there are no experiments conducted directly in landfills. In general, the UAV-based methods identified by this systematic review are characterised by unclear uncertainties. Drones are a viable alternative to traditional monitoring methods at landfills and allow data to be acquired with a spatial and temporal resolution that can hardly be achieved by other low-cost methods. However, further studies and field trials are needed to better understand methodological aspects: especially the uncertainty of each step in the quantification process need to be properly analysed and quantified more precisely.

Biogas recovery from a state-of-the-art Italian landfill

The Fossetto landfill has operated in the municipality of Monsummano Terme (Tuscany, Italy) since 1988, being considered a state-of-the-art landfill for 35 years. Initially, Fossetto acted as a conventional sanitary landfill for mixed municipal solid waste. With changes in regulations and technology, the Fossetto landfill was gradually equipped with a biogas recovery and valorisation system, a mechanical-biological treatment (MBT) plant in 2003 and a reverse osmosis leachate treatment plant, so the concentrated leachate has been recirculated back into the landfill body since 2006. Long-term biogas monitoring, enables the calculation of the efficiency of biogas recovery using a rather simplified methodology, which was assessed as being approximately 40% over the prior ten-years period. This value was lower than expected, confirming the results of previous studies and indicating the need of attributes. Applying the USEPA LandGEM model showed that the adoption of MBT substantially reduced biogas generation yields and rates by up to approximately 90% which was facilitated by the adoption of landfill leachate recirculation transforming the conventional landfill into a bioreactor. Detailed fugitive emission monitoring has allowed the evaluation of the impact of the cover type (final or temporary) and the emissions hotspots. From these results, possible remedial actions have been suggested including the more frequent monitoring of the fugitive emissions using simple and cost-effective methods (e.g., UAVs). Approximately 50% of fugitive emissions can be attributed to emissions hotspots, which reduce biogas recovery and the efficiency of temporary covers.

Geospatial alternatives for quantification of bio-thermal influence zone in the vicinity of a solid waste dump

Owing to the release of toxic gases, leachate and thermal emissions that originate from waste dumps, these sites significantly impact environmental sustainability. The study attempts to assess the deleterious impact of municipal solid waste (MSW) dump on surrounding forested landscape by employing geospatial technologies, which are cost and time-effective. For this purpose, temporal period ranging from 2015 to 2020, having 41 valid satellite observations has been selected for study. Firstly, the radii of intense hazardous zone and hazardous zone have been measured, as two separate parameters, which are 580 ± 30 m and 1260 ± 30 m, respectively. Secondly, average spatial extent of bio-influence zone is measured to be 1262 m while the average thermal influence zone extends up to 530 m around the MSW dumping site. A detailed analysis of influence zone variations reveals that the bio-influence zone depends on multitude of meteorological parameters, whereas the thermal influence zone relies mainly on seasonal temperature fluctuations. Moreover, the level of severity of emissions from MSW decomposition directly depends upon temperature. The long-term variability analysis of these hazardous zones reveals the stationarity of their spatial extents, signifying forest resilience. This study has proved significance of geospatial techniques as an alternate of expensive and time intensive assessment methods involving in situ measurements. So the proposed technique is beneficial for environmentalists, decision-makers and municipal authorities for analysing the extent and severity of MSW pollutants for forest community to address the problem of ecological degradation.

Drone technology in municipal solid waste management and landfilling: A comprehensive review

The paper discusses the experience of using unmanned aerial vehicles (UAV) in the management of municipal solid waste landfills and dumpsites. Although the use of drones at waste disposal sites (WDS) has a more than ten-year history, the active application of these technologies has increased in the last 3-4 years. The paper analyzes scientific publications of 2010-2021 (July) and identifies the main WDS management task groups for which the solution of UAV can be used. It illustrates that most of the research is devoted to studying spatial and volumetric characteristics of landfills, which is connected with the practical needs. About a quarter of the publications focus on monitoring the emissions of landfill gas or its individual components, mainly methane. Issues of a comprehensive assessment of the technological and environmental safety of landfills and dumps are covered in the scientific literature fragmentarily and insufficiently. At the same time, the current level of technologies for collecting and processing remote sensing air data (UAV, sensors for aerial imagery, software for photogrammetric processing of aerial imagery data, geographic information systems (GIS)) makes it possible to identify and assess many environmental effects of landfills and dumps and to monitor compliance with the standards for the landfills operation, which could bring management of these facilities to a fundamentally different level. Promising areas of further research in the field of UAV application at WDS are indicated: development of processes for automatic interpretation of aerial imagery materials; product analysis of photogrammetric data processing in a GIS environment, etc.

Advanced Leak Detection and Quantification of Methane Emissions Using sUAS

Detecting and quantifying methane emissions is gaining an increasingly vital role in mitigating emissions for the oil and gas industry through early detection and repair and will aide our understanding of how emissions in natural ecosystems are playing a role in the global carbon cycle and its impact on the climate. Traditional methods of measuring and quantifying emissions utilize chamber methods, bagging individual equipment, or require the release of a tracer gas. Advanced leak detection techniques have been developed over the past few years, utilizing technologies, such as optical gas imaging, mobile surveyors equipped with sensitive cavity ring down spectroscopy (CRDS), and manned aircraft and satellite approaches. More recently, sUAS-based approaches have been developed to provide, in some ways, cheaper alternatives that also offer sensing advantages to traditional methods, including not being constrained to roadways and being able to access class G airspace (0–400 ft) where manned aviation cannot travel. This work looks at reviewing methods of quantifying methane emissions that can be, or are, carried out using small unmanned aircraft systems (sUAS) as well as traditional methods to provide a clear comparison for future practitioners. This includes the current limitations, capabilities, assumptions, and survey details. The suggested technique for LDAQ depends on the desired accuracy and is a function of the survey time and survey distance. Based on the complexity and precision, the most promising sUAS methods are the near-field Gaussian plume inversion (NGI) and the vertical flux plane (VFP), which have comparable accuracy to those found in conventional state-of-the-art methods.

High-Resolution Aerial Detection of Marine Plastic Litter by Hyperspectral Sensing

An automatic custom-made procedure is developed to identify macroplastic debris loads in coastal and marine environment, through hyperspectral imaging from unmanned aerial vehicles (UAVs). Results obtained during a remote-sensing field campaign carried out in the seashore of Sassari (Sardinia, Italy) are presented. A push-broom-sensor-based spectral device, carried onboard a DJI Matrice 600 drone, was employed for the acquisition of spectral data in the range 900−1700 nm. The hyperspectral platform was realized by assembling commercial devices, whereas algorithms for mosaicking, post-flight georeferencing, and orthorectification of the acquired images were developed in-house. Generation of the hyperspectral cube was based on mosaicking visible-spectrum images acquired synchronously with the hyperspectral lines, by performing correlation-based registration and applying the same translations, rotations, and scale changes to the hyperspectral data. Plastics detection was based on statistically relevant feature selection and Linear Discriminant Analysis, trained on a manually labeled sample. The results obtained from the inspection of either the beach site or the sea water facing the beach clearly show the successful separate identification of polyethylene (PE) and polyethylene terephthalate (PET) objects through the post-processing data treatment based on the developed classifier algorithm. As a further implementation of the procedure described, direct real-time processing, by an embedded computer carried onboard the drone, permitted the immediate plastics identification (and visual inspection in synchronized images) during the UAV survey, as documented by short video sequences provided in this research paper.