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

Year 2: Odor Detection and Distribution Mapping

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

Background and Objectives

Municipal solid waste (MSW) landfills are potential sources of offensive odors creating annoyance to surrounding areas. Odor issues commonly exist near MSW landfills due to the complex biological and physicochemical processes involved in the waste degradation of MSW, which also makes the residents in the vicinity of landfills worry about potential emissions of toxic compounds [e.g., toxic volatile organic compounds (VOC)] (Palmiotto et al., 2014). The annoying odors released to the atmosphere from landfills may cause decreased quality of life and reduce property values in the surrounding communities (Sarkar et al., 2003) as well as other negative consequences on human health and welfare (ATSDR, 2023). Although the non-methane organic compounds generated by waste decomposition account for less than 1% (v/v) of the landfill gas (LFG) (Dincer et al., 2006), they can produce offensive odorous smells even at low concentrations because of their low odor thresholds (USEPA, 1990). The offensiveness of an odor is a unique characteristic that does not vary with intensity and odor intensity is the relative measure of the perceived concentration (Meeroff & Rahman, 2023). The odors emitted from landfills may cause temporary health symptoms (e.g., nausea, headache). People with respiratory ailments (e.g., asthma) can be sensitive to VOCs and VCs emitted from landfills.

Although MSW landfills or construction and demolition debris landfills are typically planned to be far away from residency community, with growing population and urbanization, communities are encroaching these landfill sites, both active and closed. Odor is becoming a major source of complaints by the residents living around landfills and some landfill operators have online forms for residences to report issues. Generally, unpleasant odors in landfill sites are associated with VOCs [e.g., benzene, toluene (Meeroff & Rahman, 2023; Urase et al., 2008) (Urase et al. 2008, Meeroff and Rahman 2023)], and VCs [hydrogen sulfide (H2S) and ammonia (NH3) (Jiang et al., 2021; Lou et al., 2015)]. While hydrogen sulfide is detectable at low concentrations by the human nose, low levels of exposure are not usually harmful, according to the Occupational Health and Safety Administration (OSHA). However, we lack an odor detection method that can efficiently cover the entire boundary of a landfill. Conducting odor detection or mapping manually is time and effort consuming and not practical for a large area (100-200 acres) and large perimeter (8,000-12,000 feet). Hence, there is an urgent need for an automated odor measurement and mapping method to monitor odorous gases dispersing from landfills into surrounding communities. In addition, localizing the hotspots of odor concentration can help future step of odor mitigation.

The objectives of this project are to develop an automated odor measurement by extending our Year-1 product (UAV-based methane sensing) to map odorous gases dispersing from landfills into the surrounding communities using a dispersion model. The conceptual framework of this study consists of VOC/VC odor sensor integration to UAV platform, efficient odor measurement using cascaded steps of remote sensing, odor dispersion model using calibration from in-field measurements, odor mapping and concentration estimation in community. We believe an efficient, periodical odor monitoring can proactively detect unpleasant odor issues and allow landfill management to take prompt solutions.

Tentative Tasks

Task 1 - Odor Assessment Methods and Sensor-UAV Integration

The team will summarize current odor assessment technologies used at MSW landfills and study their successes/failures. Based on the survey studies, the developed UAV sensing platform will be expanded with the new odor detection and distribution mapping technology. The team will integrate affordable VOC sensors and VC sensors to the well-developed UAV sensing system (completed in Year-1 project) for odor detection. For example, suitable VOC sensors can be photoionization detection (PID)-based sensors with wide-range of sensing (e.g., 0-50ppm) and with a fine measurement resolution (e.g., 4ppb). While candidate VC sensors can be affordable electrochemistry-based sensor to measure H2S (with detection range of 0-90ppm and resolution of 4ppb) and NH3 (with detection range of 0-100ppm and resolution of less than 0.2ppm). Individual lab evaluations on the additional VC sensing units will be conducted and calibrated before the actual field measurement. The UAV-based VC sensor will be compared and calibrated with a handheld VC measurement device [e.g., H2S (Shaha & Meeroff, 2020)]. An evaluation of the system-level performance on data collection will be conducted at lab and in field the to ensure coherent functionality, such as flight stability, data transmission, positioning accuracy.

Task 2 – Spatial-Temporal Odor Measurement within and at Landfill Boundary

The team will select multiple landfill cells (e.g., active/closed, intermediate/final cover) and perform field tests using the developed UAV sensing system. The intensity of odor emissions depends on the landfill operations (e.g., excavating, dumping). Multiple data collection campaigns will be conducted over the entire project (once per month) to cover different atmospheric conditions and to appraise possible seasonal variability on the measurements. The team will receive support (please see Letter of Support from Orange County Utility) and insight from industry experts (PIs have been collaborating with local landfill managers and a technical awareness group has been formed.) In addition, route planning based on 3D topological information of landfill site (surveyed in Year 1 project) will be studied to find an energy-efficient choice of routes with large landfill area (e.g., 100-200 acres) and perimeter (e.g., 8,000-12,000 feet).

Task 3- Odor Dispersion Model and Model Updating

For a given landfill, the dispersion of odorous compounds depends on the geographic and atmospheric conditions around the landfills. Landfill site information (e.g., waste filling history, gas quantity/quality) will be requested from the landfill operator. A site weather station will be installed near landfill site to capture the meteorology conditions (e.g., precipitation, temperature, solar radiation, evapotranspiration, near-surface wind speed). Additionally, the parameters of soil conditions (e.g., soil water content, porosity, soil type) and landfill cover design (e.g., collection efficiency) will be obtained at each surveying period. Odor emissions rate can be estimated from measured odor concentration together with near-surface wind speed. Plume map and plume characteristics can help calculate the emissions (R. M. Duren et al., 2019). These parameters will be used in the dispersion models, such as Gaussian model and California Puff (CALPUFF) model. In addition, the dispersion models will be updated using the actual measurements in Task 2, especially when the outer measurements at the landfill boundary (2nd step in data collection) are not enough to update the dispersion models, inner measurements on the landfills (3rd step in data collection) are usefully in the model updating.

Task 4 – Odor Hotspot Detection and Odor Mapping

The landcover types and the operation on the landfill can infer the potential locations of the odor emission hotspot. For example, detected active operations on the working face of a landfill show a strong possibility of odor emission. The 3rd step in the proposed cascaded data collection can measure the odor concentration at the specific locations. The landcover classification (e.g., vegetation, bare soil, mulch cover) can be obtained using UAV-based images and orthomosaic map. For example, the landfill cover type is classified into three categories (i.e., more vegetation less soil, more soil less vegetation, mulch). In addition, the operation types (e.g., dumping, excavating) can also be identified using collected UAV-images to perform AI-based object detection (Sun et al., 2022) and the changing working faces can be localized using the orthomosaic maps during different periods.