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

Mapping open dumps and waste dynamics in a subtropical ecoregion of Argentina

The proliferation of plastic waste since the 20th century has exacerbated pollution problems despite technological advances in waste management. In Argentina, 35% of municipal solid waste is discarded in untreated open dumps, causing environmental and public health risks. This study focused on detecting and mapping open dumps in the most densely populated province, Tucumán, using satellite imagery and unmanned aerial vehicles (UAV). Specifically, for each dump we (1) described spatio-temporal dynamics, (2) analyzed topography and hydrology characteristics, and (3) quantified waste mass. We identified and mapped forty open dumps (range size: 0.05 to 3.79 hectare). Most dumps were located within an endorheic basin, at 5 km or less from urban centers. Many have increased in size over the last decade, especially larger dumps (> than 1 hectare). The hydrological analysis showed material movement from dumps to surrounding environments, mainly watercourses and crops. Total waste mass averaged 5.72 kilotons (kt) per dump (range: 0.06 to 38.45 kilotons, where 1 kt = 1000 tons). This exhaustive survey of open dumps highlights the urgent need for sustainable waste management practices to mitigate environmental and public health risks in Tucumán and other regions facing similar challenges.

Monitoring Long-Term Waste Volume Changes in Landfills in Developing Countries Using ASTER Time-Series Digital Surface Model Data

Monitoring the amount of waste in open landfill sites in developing countries is important from the perspective of building a sustainable society and protecting the environment. Some landfill sites provide information on the amount of waste in reports and news articles; however, in many cases, the survey methods, timing, and accuracy are uncertain, and there are many sites for which this information is not available. In this context, monitoring the amount of waste using satellite data is extremely useful from the perspective of uniformity, objectivity, low cost, safety, wide coverage area, and simultaneity. In this study, we developed a method for calculating the relative volume of waste at 15 landfill sites in six developing countries using time-series digital surface model (DSM) data from the satellite optical sensor, the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), which has accumulated more than 20 years of observational data. Unnecessary variations between images were reduced by bias correction based on a reference area around the site. In addition, by utilizing various reported values, we introduced a method for converting relative volume to absolute volume and converting volume to weight, enabling a direct comparison with reported values. We also evaluated our method compared with the existing method for calculating changes in waste volume based on TanDEM-X DEM Change Map (DCM) products. The findings of this study demonstrated the efficacy of the employed method in capturing changes, such as increases and stagnation, in the amount of waste deposited. The method was found to be relatively consistent with reported values and those obtained using the DCM, though a decrease in accuracy was observed due to the depositional environment and the absence of data. The results of this study are expected to be used in the future for technology that combines an optical sensor and synthetic aperture radar (SAR) to monitor the amount of waste.

Innovative drone-based methodology for quantifying methane emissions from landfills

An accurate measurement of anthropogenic methane emissions is essential for improving the representation of greenhouse gas inventories and for mitigating the effects of climate change. Often, theoretical models overestimate actual emission values, while field measurements tend to be costly and/or labour-intensive. Landfills represent an important emission sector, necessitating continued investment in innovation and technology to limit fugitive emissions, particularly of methane. This study presents a novel method based on a mass balance approach to estimate fugitive methane emissions from landfills and has been tested at a solid waste landfill in Italy. Measurements were acquired using a drone equipped with a sensor, completed in just a few minutes and processed directly in the field. Results from two tests conducted a month apart are provided, each consisting of two downwind flights at the site. Emission rates varied from 320 ± 280 mg m-2h-1 to 578 ± 385 mg m-2h-1. The data was subsequently compared with the results obtained using the flux chamber method during the second test, highlighting values that were 2 to 4 times higher than those from the ground-based method. The findings of this study highlight the potential of UAV-based methodologies for measuring methane emissions compared to traditional methods. The speed of execution and processing is indeed crucial to providing accurate data and optimising both timings and flight models during an investigation.

Pollution risk assessment in sub-basins of an open dump using drones and geographic information systems

The sustainable management of municipal solid waste (MSW) presents a pressing global challenge. This study introduces an innovative methodology for analysing open dumps in Tucumán, Argentina, using unmanned aerial vehicles (UAVs) and DroneDeploy software for data collection, coupled with QGIS for estimating contamination risk at the sub-basin level. By integrating satellite imagery, ground surveys, high-resolution UAV imagery and a multi-criteria decision analysis within geographic information system, we provide a comprehensive overview of dumpsite conditions at one open dump. Commercial drone flights facilitate the rapid and cost-effective creation of digital elevation models and digital terrain models, along with orthomosaic imagery, from which waste footprints are delineated using artificial intelligence to enhance the understanding of geospatial issues. Approaching data layers, such as leachate pools, riverbanks and solar radiation, supports informed decision-making in MSW management through a replicable methodology. Field validation and the inclusion of subsurface and groundwater processes are recommended for future research to improve accuracy and maximize socio-ecological benefits.

Identification and treatment of false methane values produced by the tunable diode laser absorption spectroscopy technology equipped on unmanned aerial vehicles

Fugitive or diffuse methane emissions constitute an important source of damage to the environment, much greater even than CO2 both over a time span of 20 years and over a longer time span of 100. It is therefore of preeminent importance to undertake all the efforts necessary to implement new tools, protocols, and methods that contribute to the identification and measurement of these emissions to implement site-specific actions of mitigation, repair, and conscious management of the emitting plants. Among the remote sensing and leak detection technologies currently used, the tunable diode laser absorption spectroscopy (TDLAS) method plays a relevant role. Thanks to the study and implementation of increasingly high-performance sensors to be equipped on drones, this method is strongly promoted in the unmanned aerial vehicle sector. However, as often happens, the operational performance of a measurement method must be associated with measurement errors, which must be foreseen (where possible), and certainly detailed and corrected. The purpose of this article is to describe the procedure for identifying and processing "false-positive" values recorded by the payload during a survey flight for the measurement of methane concentrations in airborne matrix, with a TDLAS sensor. The methodology contained in this article is based on the study of scientific evidence referable to previous in-depth experiences on false positives and largely on the direct experience gained by the project team of the TALSEF laboratory (University of Bari, Italy) during numerous measurement campaigns in landfills, oil and gas sites, and cattle stables.

Heavy metal stabilization via copyrolysis of comodified vermiculite with municipal sludge/aged refuse: Comprehensive analysis of hazards and characteristics of multiple coexisting heavy metals

Mg2+ impregnation, intercalation-exfoliation, and thermal activation methods were employed to create various types of modified vermiculite (MV), with their combination creating linked-modified vermiculite (LMV). Copyrolysis of MV with municipal sludge (MS)/aged refuse (AF) in a fluidized bed improved heavy metal (HM) stability compared to copyrolysis of original vermiculite, with LMV demonstrating superior performance compared to other types of MV. The HM retention rate, potential ecological risk assessment, and form distribution analysis supported the efficacy of the approach. Doping of MS with AF was not beneficial to reducing the hazards posed by HMs. New calculation models for HM hazard assessment were developed that integrated HM concentration and form. Without additives or when only original vermiculite was added, increased pyrolysis temperature facilitated the reduction of hazards posed by HMs, indicating that HM form greatly influenced the effectiveness of the pyrolysis reaction. The characteristics of the reaction with multiple coexisting HMs and vermiculite at different pyrolysis temperatures were investigated via simulations, and the effect of interactions between HMs was explored. HMs mainly exhibited repulsive interactions, and adsorption became more unfavorable with increasing pyrolysis temperature as the repulsive force increased. The Cr, Cu, and Zn atoms in compounds formed covalent bonds with the O atoms of LMV, in contrast to the ionic bonds formed by the Pb and Cd atoms in some compounds, which may explain the differences in their reactivities with LMV.

Unmanned Aerial Vehicle Photogrammetry for Monitoring the Geometric Changes of Reclaimed Landfills

Monitoring reclaimed landfills is essential for ensuring their stability and monitoring the regularity of facility settlement. Insufficient recognition of the magnitude and directions of these changes can lead to serious damage to the body of the landfill (landslides, sinkholes) and, consequently, threaten the environment and the life and health of people near landfills. This study focuses on using UAV photogrammetry to monitor geometric changes in reclaimed landfills. This approach highlights the advantages of UAVs in expanding the monitoring and providing precise information critical for decision-making in the reclamation process. This study presents the result of annual photogrammetry measurements at the Słabomierz-Krzyżówka reclaimed landfill, located in the central part of Poland. The Multiscale Model to Model Cloud Comparison (M3C2) algorithm was used to determine deformation at the landfill. The results were simultaneously compared with the landfill's reference (angular-linear) measurements. The mean vertical displacement error determined by the photogrammetric method was ±2.3 cm. The results showed that, with an appropriate measurement methodology, it is possible to decide on changes in geometry reliably. The collected 3D data also gives the possibility to improve the decision-making process related to repairing damage or determining the reclamation direction of the landfill, as well as preparing further development plans.

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.

Evaluating methods for marine fuel sulfur content using microsensor sniffing systems on ocean-going vessels

Establishing emission control areas (ECAs) can effectively reduce air pollution from marine emissions, making efficient monitoring of the fuel sulfur content (FSC) of ocean-going vessels (OGVs) key to ECA policy enforcement. Various FSC detection approaches, including oil sample analysis, sniffing method, and optical remote sensing, have been proposed, each with its benefits and drawbacks. Among these, the sniffing method appears promising but requires further improvement in field operation protocol and data analysis processes. This study aims to develop a comprehensive methodology, including sensor calibration, field operations, and data analysis, to enhance the performance of an Unmanned Aerial Vehicle (UAV)-based Microsensor Sniffing System (MSS) for real-time FSC monitoring. Hong Kong has a cap of 0.5 % m/m FSC for OGVs, and hence Hong Kong waters served as the "real-world" monitoring location to evaluate the MSS system through land-based and sea-based measurements. Three different FSC calculation methods were employed and verified against bunker delivery note (BDN) data through blind testing. Results confirm that the MSS is effective in field settings, though it has an underestimation tendency, demonstrating an absolute error of 0.06 % m/m, 0.11 % m/m, and 0.10 % m/m for the Crest, Slope, and Area methods, respectively, compared to BDN data. However, high errors were possible with low CO2 and SO2 peak heights, and single-peak samples compared to multi-peak samples. Over 16 successful trips, the FSC of 125 valid OGVs (Mean FSC = 0.39 % m/m) exhibited a lognormal distribution pattern, with the distribution tail approaching the 0.5 % m/m regulatory cap. This investigation highlights the potential of a UAV-based MSS for monitoring and enforcing FSC regulations within ECAs, providing a systematic protocol to guide future research direction and enforcement practices.

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.

Estimation of total landfill surface methane emissions using geospatial approach combined with measured surface ambient air methane concentrations

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.Implications: The proposed approach provides an occasion for additional use of the easily obtainable quarterly SEMs data that can be performed by most landfills. The SEMs data are the most abundant landfill methane concentrations data. This approach gives them more benefit for the user. It is intended to convert ambient air concentrations to some estimates of surface emissions that can help landfill owners with decision making such as remediation activities or adjustments of their gas collection a systems.

Exploring the industrial dynamics of waste management and recycling: A call for research and a proposed agenda

The waste management sector is undergoing profound transformations that challenge its structures and institutions. The function and position of waste management and recycling companies have been changing, and this process accelerates as the circular economy consolidates as part of the strategy to implement green shifts. This article argues that scholars, practitioners and policymakers interested in waste management could benefit from building bridges with the field of industrial dynamics. Industrial dynamics is concerned with the driving forces of economic transformation, with focus on not just outcomes but processes and structures. This type of research is crucial in face of transformations going on in the sector. Three crucial themes for cross-disciplinary investigation are: (i) industry evolution and institutions, (ii) business organization and management, and (iii) technological change, innovation and entrepreneurship. Waste management is a lively, complex and diverse sector, whose process of reinvention present the opportunity to research profound industrial transformations in real time. By systematically investigating the industrial dynamics of waste management, it becomes possible to uncover the structural changes underpinning the transformation of waste into resources, their driving forces and the directions to which they point, while mindful of the evolving discourses and the wider technological and institutional landscape.

Satellite Data Potentialities in Solid Waste Landfill Monitoring: Review and Case Studies

Remote sensing can represent an important instrument for monitoring landfills and their evolution over time. In general, remote sensing can offer a global and rapid view of the Earth's surface. Thanks to a wide variety of heterogeneous sensors, it can provide high-level information, making it a useful technology for many applications. The main purpose of this paper is to provide a review of relevant methods based on remote sensing for landfill identification and monitoring. The methods found in the literature make use of measurements acquired from both multi-spectral and radar sensors and exploit vegetation indexes, land surface temperature, and backscatter information, either separately or in combination. Moreover, additional information can be provided by atmospheric sounders able to detect gas emissions (e.g., methane) and hyperspectral sensors. In order to provide a comprehensive overview of the full potential of Earth observation data for landfill monitoring, this article also provides applications of the main procedures presented to selected test sites. These applications highlight the potentialities of satellite-borne sensors for improving the detection and delimitation of landfills and enhancing the evaluation of waste disposal effects on environmental health. The results revealed that a single-sensor-based analysis can provide significant information on the landfill evolution. However, a data fusion approach that incorporates data acquired from heterogeneous sensors, including visible/near infrared, thermal infrared, and synthetic aperture radar (SAR), can result in a more effective instrument to fully support the monitoring of landfills and their effect on the surrounding area. In particular, the results show that a synergistic use of multispectral indexes, land surface temperature, and the backscatter coefficient retrieved from SAR sensors can improve the sensitivity to changes in the spatial geometry of the considered site.

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.

UAV-Based Landfill Land Cover Mapping: Optimizing Data Acquisition and Open-Source Processing Protocols

Earth observation technologies offer non-intrusive solutions for monitoring complex and risky sites, such as landfills. In particular, unmanned aerial vehicles (UAVs) offer the ability to acquire data at very high spatial resolution, with full control of the temporality required for the desired application. The versatility of UAVs, both in terms of flight characteristics and on-board sensors, makes it possible to generate relevant geodata for a wide range of landfill monitoring activities. This study aims to propose a robust tool and to provide data acquisition guidelines for the land cover mapping of complex sites using UAV multispectral imagery. For this purpose, the transferability of a state-of-the-art object-based image analysis open-source processing chain was assessed and its sensitivity to the segmentation approach, textural and contextual information, spectral and spatial resolution was tested over the landfill site of Hallembaye (Wallonia, Belgium). This study proposes a consistent open-source processing chain for the land cover mapping using UAV data with accuracies of at least 85%. It shows that low-cost red-green-blue standard sensors are sufficient to reach such accuracies and that spatial resolution of up to 10 cm can be adopted with limited impact on the performance of the processing chain. This study also results in the creation of a new operational service for the monitoring of the active landfill sites of Wallonia.

A Deep Learning-Based Intelligent Garbage Detection System Using an Unmanned Aerial Vehicle

A population explosion has resulted in garbage generation on a large scale. The process of proper and automatic garbage collection is a challenging and tedious task for developing countries. This paper proposes a deep learning-based intelligent garbage detection system using an Unmanned Aerial Vehicle (UAV). The main aim of this paper is to provide a low-cost, accurate and easy-to-use solution for handling the garbage effectively. It also helps municipal corporations to detect the garbage areas in remote locations automatically. This automation was derived using two Convolutional Neural Network (CNN) models and images of solid waste were captured by the drone. Both models were trained on the collected image dataset at different learning rates, optimizers and epochs. This research uses symmetry during the sampling of garbage images. Homogeneity regarding resizing of images is generated due to the application of symmetry to extract their characteristics. The performance of two CNN models was evaluated with the state-of-the-art models using different performance evaluation metrics such as precision, recall, F1-score, and accuracy. The CNN1 model achieved better performance for automatic solid waste detection with 94% accuracy.