Patent Description:
Methane (CH<NUM>) is an odorless and colorless naturally occurring organic molecule, which is present in the atmosphere at average ambient levels of approximately <NUM> ppm as of <NUM> and is projected to continually climb. While methane is found globally in the atmosphere, a significant amount is collected or "produced" through anthropogenic processes including exploration, extraction, and distribution of petroleum in the form of natural gas. Natural gas, an odorless and colorless gas, is a primary source of energy used to produce electricity and heat. The main component of natural gas is methane (<NUM> mol % CH<NUM> typ. While extraction of natural gas is a large source of methane released to atmosphere, major contributors of methane also include livestock farming (enteric fermentation), and solid waste and wastewater treatment (anaerobic digestion). <CIT> discloses an apparatus and method for detecting the location of sources of one or more target molecule, the apparatus comprising: a molecule detector; and a processor connected to the molecule detector and to a global position system, wherein the processor calculates the presence of the one or more target molecules, runs a computer code that determines a dynamic reverse gas stack model for the one or more target molecules, and triangulates the possible position for a source or effluent of the one or more target molecules based on the dynamic reverse gas stack model. The determined reverse gas stack model may have a Gaussian dispersion over one or more sampled locations. <CIT> discloses a process for detecting media dispersion. The process detects the dispersion of contaminants through distributed remote sensor platforms that connect one or more sensors on a remote device. The process generates a plume model in response to the detection data and meteorological data that models dispersion plumes and activates and deactivates selected sensors in response to a forecasted to dispersion area.

According to a first aspect of the invention a system is provided as recited in claim <NUM>.

Additional system embodiments may include: a display in communication with the processor, where the display may be configured to show the determined methane emission rate of the methane source on a map. In additional system embodiments, the map may be at least one of: a satellite image, an aerial image, a two-dimensional color map, a two-dimensional contour map, and a three-dimensional topographical surface.

Additional system embodiments may include: a payload of a UAV, where the payload may include one or more gas concentration sensors configured to generate the methane concentration data along the UAV flight path. In additional system embodiments, the UAV information along the UAV flight path may include at least one of: a location of the UAV, a time corresponding to the location of the UAV, a barometric pressure, an altitude, a relative altitude, and an orientation of the UAV, and where the UAV information along the UAV flight path corresponds to the generated methane concentration data along the UAV flight path. In additional system embodiments, the location of the UAV may be determined by at least one of: a global positioning system (GPS), an onboard avionics, and a location sensor. In additional system embodiments, the relative altitude of the UAV may be determined by at least one of: an altitude of a global positioning system (GPS), a LIDAR, a Sonar, a radar, and a barometric pressure sensor. In additional system embodiments, the orientation of the UAV may be determined by at least one of: an inertial measurement unit (IMU) and an orientation sensor.

Additional system embodiments may include: one or more weather stations, where each weather station generates the Meteorological data packet. In additional system embodiments, the Meteorological Data Packet may include data from at least one of: an anemometer, one or more pressure sensors, a pryanometer, a ground temperature sensor, an air temperature sensor, and a current atmospheric condition sensor. In additional system embodiments, at least one of: a ground control station (GCS), a cloud server, the UAV, and the weather station may include the processor. In additional system embodiments, the determined methane emission rate may be stored by at least one of: a ground control station (GCS) and a cloud server.

According to a second aspect of the invention a method is provided as recited in claim <NUM>.

Additional method embodiments may include: measuring, by a payload of a UAV, the methane concentration data along the UAV flight path, where the payload comprises one or more gas concentration sensors; generating, by the UAV, the UAV data packet, where the UAV data packet comprises a spatial position of the UAV at each methane concentration data measurement; and generating, by a weather station of one or more weather stations, the Meteorological data packet; where the UAV data packet comprises data from at least one of: a weather sensor, an onboard avionics, a barometric pressure sensor, an orientation sensor, an intertial measurement unit (IMU), a wireless radio, a global positioning system (GPS), a time measurement device, an altitude sensor, a location sensor, a radar, a lidar, an anemometer, an a Sonar; and where the Meteorological data packet comprises data from at least one of: an anemometer, one or more pressure sensors, a pryanometer, a ground temperature sensor, an air temperature sensor, and a current atmospheric condition sensor.

There is also disclosed as an illustrative example not forming part fo the invention a system including: an unmanned aerial vehicle configured to generate a UAV data packet; a payload of the UAV, where the payload comprises one or more gas concentration sensors configured to generate the methane concentration data along a UAV flight path; one or more sensors of the UAV, where the one or more sensors of the UAV are configured to generate UAV information; one or more weather stations, where each weather station generates a Meteorological data packet, where the Meteorological data packet comprises weather data from one or more sensors of the weather station; and a processor having addressable memory, the processor in communication with the UAV and the one or more weather stations, where the processor configured to: receive the UAV data packet, where the UAV data packet comprises methane concentration data from the payload and UAV information the one or more sensors of the UAV; receive at least one Meteorological data packet; combine the UAV data packet with a nearest.

Meteorological data packet; determine a methane emission rate of a methane source based on the combined UAV data packet and the nearest Meteorological data packet; and show the determined methane emission rate of the methane source on a map via a display in communication with the processor.

The components in the figures are not necessarily to scale, emphasis instead being placed upon illustrating the principals of the invention. Like reference numerals designate corresponding parts throughout the different views. Embodiments are illustrated by way of example and not limitation in the figures of the accompanying drawings, in which:.

The following description is made for the purpose of illustrating the general principles of the embodiments discloses herein and is not meant to limit the concepts disclosed herein. Further, particular features described herein can be used in combination with other described features in each of the various possible combinations and permutations. Unless otherwise specifically defined herein, all terms are to be given their broadest possible interpretation including meanings implied from the description as well as meanings understood by those skilled in the art and/or as defined in dictionaries, treatises, etc..

The present system and method disclosed herein allow for determining a methane emission rate of a methane source based on measurements from one or more sensors mounted on an unmanned aerial vehicle (UAV), UAV data, and one or more sensors from one or more weather stations. The UAV flies in a raster grid pattern flight path downwind of the methane source. The path of the flight pattern is substantially perpendicular to a ground surface and an average wind direction to measure methane emissions downwind of the methane source. Data from the one or more UAV sensors, the UAV data, and the one or more sensors from the one or more weather stations may be combined, stored, processed, and/or filtered to determine the methane emission rate of the methane source.

The goal of the natural gas production and supply chain is to deliver gas from source production areas to endpoint users without undue loss. Product loss in this context amounts to flaring or venting, intentional or otherwise, of natural gas to the atmosphere. Undue product loss results in uncaptured revenue, an increased environmental footprint, and possible safety hazards for vented emissions. There are many opportunities throughout the natural gas production and supply chain for gas to be released from containment and lost, such as pneumatic component venting, maintenance blowdowns, component failures, accidental release, and the like. Natural gas production and distribution infrastructure are spatially distributed. Efficient, wide area survey methods are needed to identify, localize, and quantify natural gas releases throughout these spatially distributed systems.

The disclosed unmanned aerial system (UAS) measures methane concentration along the chosen UAV flight path at high frequency to detect anomalies associated with natural gas releases. Data from the UAV may be reconciled with atmospheric conditions to identify and quantify the mass flow rate of natural gas sources within an inspection area.

The disclosed method for emission rate quantification is based on an engineering control volume model. The UAS has a fast response, in situ methane sensor payload and flies downwind of potential emission sources on transects that are nearly perpendicular to the average wind direction approximately +/- <NUM> degrees. The disclosed sensors measure the crosswind and vertical profile of methane concentration and maps out the spatial profile of methane emissions from upwind sources as well as the characteristics of the background concentration variability.

<FIG> depicts an illustration of an unmanned aerial system (UAS) emissions measurement process and flight path <NUM>, according to one embodiment. A representative flight path <NUM> of an unmanned aerial vehicle (UAV) <NUM> is downwind of a point source <NUM>. In this example, the point source <NUM> is a pump jack, but the point source <NUM> may be any equipment having a potential to emit gas. The UAV <NUM> measures a cross-section of an emissions plume <NUM> from the point source <NUM> on a vertical plane. In addition to concentration, emission rate estimates are determined by wind velocity along the UAV flight path <NUM>.

To capture a downwind "control surface" for the emissions estimate, the disclosed UAV <NUM> flies a raster grid pattern flight path <NUM> along a vertical plane that is perpendicular to a ground surface <NUM> and the average wind direction +/- <NUM> degrees. The position of the UAV <NUM> and corresponding natural gas concentration measurement are recorded, such as by a global positioning system (GPS) position. The altitude of the UAV <NUM> relative to the ground <NUM> may be further quantified using a range-finding LIDAR, Sonar, radar, GPS altitude, and/or barometric pressure sensor.

<FIG> depicts a high-level block diagram of a UAS emissions measurement system <NUM>, according to one embodiment. The system <NUM> may include a UAV <NUM>. In some embodiments, the UAV <NUM> may be a quadcopter-style aerial vehicle capable of hovering and flying the raster pattern flight path, as shown in <FIG>. In other embodiments, the UAV <NUM> may be a winged aerial vehicle. The UAV <NUM> may have any number of rotors, motors <NUM>, wings, or the like to sustain flight and fly the determined UAV flight path. The UAV <NUM> may have the ability to fly in a three-dimensional flight path in the vicinity of a potential methane source (<NUM>, <FIG>). The UAV <NUM> may fly ≤<NUM> from a <NUM> SCFH emissions point.

Embodiments of the unmanned aerial vehicle <NUM> may include any number of sensors shown in <FIG> based on the desired data. Embodiments of the weather station <NUM> may include any number of sensors shown in <FIG> based on the desired data. In some embodiments, the weather station <NUM> may only include an anemometer <NUM>. In other embodiments, the weather station <NUM> may be integrated into the unmanned aerial vehicle <NUM>. For example, the anemometer <NUM> may be integrated on the unmanned aerial vehicle <NUM>. In another embodiment, the weather station <NUM> may be located on another aerial vehicle or unmanned aerial vehicle. For example, the system may include two or more unmanned aerial vehicles where at least one unmanned aerial vehicle is recording methane gas concentrations and at least one unmanned aerial vehicle is recording meteorological data. The weather station <NUM> may be stationary or mobile. The weather station <NUM> may be in relatively close proximity to the unmanned aerial vehicle <NUM>. In some embodiments, the weather station <NUM> may record meteorological data. In some embodiments, the weather station <NUM> may be from a third-party source, such as a third-party sensor. In some embodiments, the weather station <NUM> may predict future meteorological measurements. The nearest temporal Meteorological (MET) data packet <NUM> may be combined with the UAV data packet <NUM>. The frequency of each of the MET data packet <NUM> and the UAV data packet <NUM> may be different but close in some embodiments. The frequency of each of the MET data packet <NUM> and the UAV data packet <NUM> may be substantially the same in some embodiments.

The UAV <NUM> may have a global positioning system <NUM>, an onboard avionics <NUM>, and/or a location sensor <NUM> to track a spatial position of the UAV <NUM> as it travels along the flight path (<NUM>, <FIG>). The UAV <NUM> may track its spatial position as it measures gas concentrations along the flight path such that each gas measurement of the UAV <NUM> corresponds to a spatial position where that gas measurement was taken. The global positioning system <NUM>, onboard avionics <NUM>, and/or location sensor <NUM> may be in communication with a UAV processor <NUM> having addressable memory <NUM>. In some embodiments, the location of the UAV <NUM> may be determined by the onboard avionics <NUM>. The onboard avionics <NUM> may include a triangulation system, a beacon, a spatial coordinate system, or the like. The onboard avionics <NUM> may be used with the GPS <NUM> and/or location sensor <NUM> in some embodiments. In other embodiments, the UAV <NUM> may use only one of the GPS <NUM>, the onboard avionics <NUM>, and/or the location sensor <NUM>.

The UAV <NUM> may include a payload <NUM> in communication with the UAV processor <NUM>. The payload <NUM> may include one or more gas concentration sensors. The payload <NUM> may be detachably attached to the UAV <NUM>. In other embodiments, the payload <NUM> may be fixedly attached to the UAV <NUM>. The payload <NUM> may be in communication with the UAV processor <NUM>. In one embodiment, the payload <NUM> may be an ultra-lightweight, low power, Part per Billion (ppb) sensitivity, mid-Infrared (λ = <NUM> - 8µm), open path methane concentration sensor with sampling rate > <NUM>.

The UAV processor <NUM> may also be in communication with an orientation sensor <NUM>, an inertial measurement unit (IMU) <NUM>, an altitude sensor <NUM>, a radar <NUM>, a LIDAR <NUM>, and/or a Sonar <NUM> for generating additional information on the spatial position of the UAV <NUM> during each gas measurement by the payload <NUM>. The orientation sensor <NUM> may be used to determine an orientation of the UAV <NUM> relative to ground. The IMU <NUM> may be used to determine attitude, velocity and/or position of the UAV <NUM>. The altitude sensor <NUM> may be used to determine an altitude of the UAV <NUM>. The LIDAR <NUM>, Sonar <NUM>, and/or radar <NUM> may be used to determine a relative altitude of the UAV <NUM>.

In some embodiments, the UAV processor <NUM> may also be in communication with an anemometer <NUM>, one or more weather sensors <NUM>, and/or a barometric pressure sensor <NUM>. The anemometer <NUM> may be used to measure the speed of the wind. The anemometer <NUM> may be attached to the UAV <NUM> at a point so as to ensure an accurate wind measurement without interfering with the propulsion from the motors <NUM> or sensors of the payload <NUM>. The weather sensor <NUM> may measure weather and/or atmospheric conditions. The barometric pressure sensor <NUM> may measure a barometric pressure. The anemometer <NUM>, weather sensor <NUM>, and/or barometric pressure sensor <NUM> may be used to record data at each gas measurement from the payload <NUM>.

In some embodiments, the UAV processor <NUM> may also be in communication with a time measurement device <NUM>. The time measurement device <NUM> may be used to record the time for each gas measurement measured by the payload <NUM> of the UAV <NUM>. Each gas measurement, position measurement, orientation measurement, weather measurement, and/or relative altitude measurement may be 'time-stamped' so as to be combined by the processor <NUM> and/or the UAV processor <NUM>.

The UAV processor <NUM> may also be in communication with a transceiver <NUM> and/or a wireless radio <NUM>. The transceiver <NUM> and/or wireless radio <NUM> may be used to communicate between the UAV <NUM> and the processor <NUM>, the ground control station (GCS) <NUM>, and/or a cloud server <NUM>.

The processor determines a flight path for the UAV <NUM> having the payload <NUM>. In some embodiments, the flight path may be determined on a site-specific basis. In other embodiments, the flight path may be self-determined, autonomous control. The flight path is used to measure gas concentration along a crosswind transect, and vertical profile, in the vicinity of a possible gas emissions point. This flight plane of the flight path is designed to capture the atmospheric methane background as well as emissions signature, i.e., elevated ambient concentration, from all potential sources at an inspection site.

The UAV <NUM> may have the UAV processor <NUM> in communication with addressable memory <NUM>, a GPS <NUM>, one or more motors <NUM>, and a power supply <NUM>. The UAV <NUM> may communicate gathered payload <NUM> data to the UAV processor <NUM>. The power supply <NUM> may be a battery in some embodiments. In some embodiments, the processor <NUM> may be a part of the UAV <NUM>, the cloud server <NUM>, the GCS <NUM> used to control the UAV <NUM>, or the like.

The UAV processor <NUM> may receive gas data from the one or more gas sensors of the payload <NUM>. The UAV processor <NUM> may also receive spatial position data from the GPS <NUM>, altitude sensor <NUM>, location sensor <NUM>, radar <NUM>, LIDAR <NUM>, Sonar <NUM>, orientation sensor <NUM>, IMU <NUM>, and/or onboard avionics <NUM>. In some embodiments, the UAV processor <NUM> may also receive weather data from the weather sensor <NUM>, the barometric pressure sensor <NUM>, and/or the anemometer. The UAV processor <NUM> may also receive the time from the time measurement device <NUM>. The UAV processor <NUM> may fuse the gas data from the payload <NUM> with the UAV <NUM> spatial position data, weather data, and/or time to form a UAV Data Packet <NUM>.

The UAV data packet <NUM> may be sent to the processor <NUM>, ground control station <NUM>, and/or cloud server <NUM> via the transceiver <NUM> and/or wireless radio <NUM>. In some embodiments, the wireless radio <NUM> or cellular connection may be used for remote data transfer between the UAV <NUM>, the GCS <NUM>, the processor <NUM>, and/or the cloud server <NUM>. The wireless interface or cellular connection between the UAV <NUM>, the GCS <NUM>, the processor <NUM>, and/or the cloud server <NUM> may be used to performing advanced data analysis functions. Direct, bidirectional data transfer may occur between the UAV <NUM> and the GCS <NUM>, between the UAV <NUM> and the cloud server <NUM>, and/or between the GCS <NUM> and the cloud server <NUM>.

The processor <NUM> may be a part of the UAV <NUM>, the GCS <NUM>, the cloud server <NUM>, and/or the weather station <NUM> in some embodiments. While multiple sensors and devices are depicted for the UAV <NUM>, any number of sensors and/or devices may be used based on the system <NUM>, desired accuracy, time limitations, weight limitations, and the like.

One or more weather stations <NUM>, <NUM>, <NUM> may provide local weather information to the UAV <NUM>, payload <NUM>, GCS <NUM>, and/or cloud server <NUM>. The weather stations <NUM>, <NUM>, <NUM> may also receive information from the UAV <NUM>, payload <NUM>, GCS <NUM>, and/or cloud server <NUM>.

The first weather station <NUM> may include one or more anemometers <NUM>, one or more pressure sensors <NUM>, one or more pyranometers <NUM>, one or more ground temperature sensors <NUM>, one or more air temperature sensors <NUM>, one or more atmospheric condition sensors <NUM>, and one or more location sensors <NUM>. The anemometer may be used to measure wind speed. The pressure sensor <NUM> may measure a pressure. The pyranometer may be used to measure solar irradiance. The ground temperature sensor <NUM> may be used to measure a temperature of the ground. The air temperature sensor <NUM> may be used to measure a temperature of the air. An atmospheric condition sensor <NUM> may be used to measure data relating to the atmosphere. The location sensor <NUM> may be used to measure the location of the weather station <NUM>. Each weather station <NUM>, <NUM>, <NUM> may include any number of sensors and/or devices based on the system <NUM>, desired accuracy, number of weather stations over a geographical area, and the like.

In some embodiments, sensors and/or devices of the weather station <NUM> may be located and/or duplicated on the UAV <NUM>. High resolution (<<NUM>/s), high-frequency measurements (> <NUM>) of wind speed and direction may be recorded using one or more wind sensors, and one or more additional weather/micro-meteorological sensors including, air temperature, humidity, atmospheric pressure, solar irradiance, ground surface temperature - from the ground via a weather station <NUM>, <NUM>, <NUM> and/or from the UAV <NUM> as disclosed herein. For example, both the weather station <NUM> and the UAV <NUM> may include respective anemometers <NUM>, <NUM>, which may be used to generate wind speed data. The weather station data may be associated with a time the data was collected and/or generated. The weather station data may be used to generate a Meteorological (MET) data packet <NUM>. The Meteorological data packet <NUM> may be sent to the processor <NUM>, ground control station <NUM>, cloud server <NUM>, and/or UAV <NUM>. The Meteorological data packet <NUM> may include measurements and/or predictions of the atmosphere, weather, temperature, wind patterns, or the like.

Each UAV Data Packet <NUM> may be combined with the nearest temporal Meteorological Data Packet <NUM> by the processor <NUM> and saved on the GCS <NUM> and/or cloud server <NUM>. The data may be uploaded to the cloud server <NUM> in real-time, near real-time, or at a later time. The combined UAV data packet <NUM> and Meteorological data packet <NUM> may be used to determine a methane emission rate of the methane source by the processor <NUM>, GCS <NUM>, and/or cloud server <NUM>. The methane emission rate may be determined based on a control volume model that combines concentration measurements from the UAV flight plane with measured wind speed, direction and spatial gradient to determine the mass flow rate emissions from sources in the inspection area.

This determined methane emission rate may be stored by the processor <NUM>, GCS <NUM>, and/or cloud server <NUM>. In some embodiments, the determined emission rate may be shown on a display <NUM>. The display <NUM> may show source emissions data on a map, satellite image, aerial image, two-dimensional color map, two-dimensional contour map, and/or three-dimensional topographical surface / mesh.

<FIG> depicts a high-level flowchart of a method <NUM> embodiment of determining emissions measurements via unmanned aerial vehicle (UAV) data and weather data, according to one embodiment. The method <NUM> includes determining, by a processor, a flight path of a UAV downwind of a methane source (step <NUM>). The method <NUM> may then include generating a UAV data packet include a measured methane concentration and UAV information from the determined flight path (step <NUM>). The method <NUM> may then include generating, by a weather station, a Meteorological data packet including data measured by the weather station <NUM>. The method <NUM> includes receiving, by the processor, the UAV data packet and the Meteorological data packet (step <NUM>). The method includes combining, by the processor, the received UAV data packet with a nearest temporal, i.e., in time, received Meteorological data packet (step <NUM>). The method <NUM> includes determining, by the processor, a methane emission rate of the methane source based on the combined received UAV data packet and the Meteorological data packet (step <NUM>). The method <NUM> may then include sending, by the processor, the determined emission rate of the methane source to a cloud server (step <NUM>).

<FIG> depicts a data flow <NUM> in a single sensor and unmanned aerial vehicle (UAV) <NUM> configuration with a ground control station (GCS) <NUM> as a point of interface between the UAV <NUM> and the cloud-connected processor, local server processor, and/or database <NUM>, according to one embodiment. The general flow of data is from one or more gas concentration sensors, i.e., payload <NUM>, affixed to one or more UAVs <NUM> and wirelessly transmitted to the centralized GCS <NUM> and then transferred to the cloud-connected Server, Processer, Local Server Processor, and/or Database <NUM>.

A weather station <NUM> may provide local weather information to the UAV <NUM>, payload <NUM>, and/or GCS <NUM>. The weather station <NUM> may also receive information from the UAV <NUM>, GCS <NUM>, and/or payload <NUM>. The UAV <NUM> vehicle state and other information may be transmitted by the UAV <NUM> and received by the GCS <NUM>. The GCS may send command and control information to the UAV <NUM>. The payload <NUM> may provide and/or receive payload data between the payload <NUM> and the UAV <NUM> and/or the GCS <NUM>.

<FIG> depicts a data flow <NUM> in a single sensor and UAV <NUM> configuration with the UAV <NUM> directly interfacing with the cloud-connected processor, local server processor, and/or database <NUM>, according to one embodiment. In another embodiment, the payload(s) <NUM>, UAV(s) <NUM>, and/or weather station(s) <NUM> communicate directly with a cloud server processor, local server processor, and/or database <NUM>. In all cases, each subsystem, i.e., UAV <NUM>; payload <NUM>; GCS (see <FIG>); cloud server processor, local server processor, and/or database <NUM>; and weather station <NUM>, may or may not have the ability to directly communicate with each other subsystem, as shown in <FIG>. At the GCS (see <FIG>), and/or cloud server processor, local server processor, and/or database <NUM>, the data from the payload <NUM> is coupled with local weather station <NUM> data through local private networks and/or publicly available over the Internet. The data can then be post-processed on the GCS (see <FIG>), on a local server, and/or on a cloud-hosted server <NUM>.

<FIG> depicts a detailed data transfer <NUM> from a single sensor with a single UAV <NUM>, where this combination of devices comprise a UAS, according to one embodiment. Data from the payload <NUM> transfers to the UAV <NUM> and directly to an autopilot <NUM> via a serial connection. In some embodiments, the data transfer from the UAV <NUM> to the autopilot <NUM> may be any connection hardwire or wireless. Then, the data transfer is fused with GPS location and time, barometric pressure, altitude, relative altitude from LiDAR, Sonar, Radar, and/or UAV orientation, which forms a UAV Data Packet. The UAV Data Packet may be transferred to the GCS and/or cloud processor <NUM> via a 500mW <NUM> Frequency Hopping Spread Spectrum (FHSS) transceiver. In some embodiments, the UAV Data Packet may be transferred via any wireless radio. In parallel, a weather station <NUM> having at least an anemometer, and may contain one or more pressure sensors, pyranometers, i.e., for solar irradiance, ground temperature sensors, air temperature sensors, and/or any sensor necessary for quantifying current atmospheric conditions, may form a Meteorological Data Packet. The GCS and/or cloud processor <NUM> receives both the Meteorological Data Packet and UAV Data Packet at a frequency greater than <NUM>. Each UAV Data Packet is fused with the nearest temporal Meteorological Data Packet and saved on the GCS and/or a cloud server, local server, and/or database <NUM> in an ASCII, binary, or any file necessary. The data may be uploaded to a cloud server in real-time, near real-time, or at a later time.

<FIG> depicts a background gas concentration workflow <NUM>, according to one embodiment. The first step in the emissions estimate model is to calculate the control volume in-flow condition. The in-flow condition will determine total emissions for the source area that is of interested, by accounting for and subtracting any emissions from upwind sources. Typically, it is assumed that the background concentration measured on the upwind side of the source inspection area is a good representation of the local background concentration and provides an estimate of the upwind in-flow condition. The procedure for calculating the background concentration starts with selecting data for the appropriate time period (step <NUM>). Then, the GPS coordinates, i.e., longitude and latitude, are framed to along a path distance (step <NUM>). Third, a statistical filter is applied to the concentration data (step <NUM>).

<FIG> depicts a graph <NUM> showing raw concentration data <NUM>, filtered/interpolated data <NUM>, and a background concentration estimation <NUM>, according to one embodiment. <FIG> depicts a graph <NUM> showing a concentration enhancement data resolved utilizing a sliding window median filter, according to one embodiment. The raw concentration data as a function of distance, i.e., spatial coordinate, is filtered using a sliding window median filter. The filter window scale is determined based on a typical, or expected, gas plume width. For example, if the maximum plume width is expected to be on the order of <NUM>, the filter scale would be set to three to five times the max plume width. The median filter also removes infrequent transients, or dropouts, in the concentration measurement caused by communication interference, or platform vibrations. The background concentration is subtracted from the total concentration to obtain the concentration enhancement. The concentration enhancement signal represents the signature of an upwind emission source and is used to quantify the emissions released by the local source.

Spike detection on the concentration enhancement signal is performed as part of the emissions calculation to determine if an emission source is present upwind of the flight path. This is a binary determination step, after performing spike detection on the concentration enhancement signal the remaining portion of the emissions algorithm only continues if an upwind emission source is present.

A statistical filter is then applied to the concentration enhancement signal to identify "spikes" in the data that indicate methane plumes from nearby sources. The statistical filter is determined by analysis of the Cumulative Distribution Function (CDF) for the concentration enhancement, and targets extremum data points based on a prescribed percentile threshold. The selected points are then analyzed for contiguity and consolidated to form spatially continuous events. Each spike event may be further analyzed according to other metrics such as spatial extent, amplitude, magnitude, variance, and waveform shape. Individual spike events may be included or excluded through a selection process based on these derived quantities.

<FIG> depicts a plane projection workflow <NUM>, according to one embodiment. Due to variability in the flight trajectory caused by wind and GPS uncertainty, it is not generally possible to fly perfect transects along the same line at various heights. Therefore, an automatic detection of the nominal flight path, or plane, orientation may be performed using a least-squares fitting method to the data. First, data is selected for the appropriate time period (step <NUM>). Then, a GPS coordinate, i.e., longitude and latitude, frame is converted to a Cartesian frame, i.e., x and y (step <NUM>). Then, the nominal flight path is calculated, e.g., least squares (step <NUM>). Finally, the data is projected onto the nominal control surface coordinate, i.e., y (step <NUM>).

<FIG> depicts formulas <NUM> for an orthogonal vector projection, according to one embodiment. The projection is accomplished utilizing a linear algebra vector projection equation. Other projected vector formulas are possible and contemplated to determine the vector projection. Gamma (γ) is the vector norm, which is the length of the vector, as shown in <FIG>.

<FIG> depicts a graph <NUM> of an overhead view of the flux plane flight trajectory with derived nominal flight plane trajectory used for projection, according to one embodiment. <FIG> depicts a graph <NUM> of a flight trajectory projected onto the (y,z) plane, according to one embodiment. The line <NUM> in the <FIG> represents the best estimate for the nominal flight plane trajectory, onto which the measurement coordinates are projected. <FIG> shows the vertical profile of concentration measurements as a function of the projected flight plane coordinate, i.e., gamma, and the altitude.

<FIG> depicts a graph <NUM> of projected values along the (y, z) plane, according to one embodiment. <FIG> depicts a graph <NUM> of an averaged and interpolated flux plane, according to one embodiment. Once the background concentration value is measured or estimated, the value is subtracted at each point along the flight path to obtain the concentration enhancement, and the measurements are averaged and interpolated on a continuous grid across the flight plane. A grid is created with a specified resolution over the range of coordinates in the projected flight plane. The concentration measurements within each grid cell are averaged, if no measurements exist for a particular grid cell then the value is left empty or replaced with null. After the grid is populated with the average measurements, a linear interpolation is applied to fill the grid with continuous concentration values. Some values on the grid have a negative concentration enhancement, this is important to ensure that the calculation returns a zero value for the flux when no upwind emission source is present.

<FIG> depicts a diagram <NUM> illustrating the primary surface level sublayers in the Atmospheric Boundary Layer, according to one embodiment. An important input to the Emissions Estimate model disclosed herein is wind speed. Most of the measurements made by the disclosed UAS will be performed within the Roughness Sublayer portion of the atmospheric sublayer, i.e., the region from the surface to just above the average obstruction height or the local topography. In this region, the standard log law vertical wind gradient model provides a reasonable approximation of the vertical wind profile. Vertical wind profile may be approximated using a log law. Other ways of determining the vertical wind profile given the wind at a certain height and projected altitude are possible and contemplated. In some embodiments, the vertical wind profile may be measured directly. The vertical wind gradient or the vertical wind model may be determined based on the meteorological data measured, recorded, and/or predicted.

<FIG> depicts a graph <NUM> of a flux plane average flight path vector <NUM>, average wind vector <NUM>, and derived normal wind component <NUM>, according to one embodiment. <FIG> depicts a graph <NUM> of a modeled wind profile for the total wind <NUM> and the derived orthogonal wind <NUM>. The Emissions Estimate algorithm determines the surface wind speed and direction utilizing measurements from the onsite 2D or 3D sonic anemometer. Due to local wind variability, it is necessary to derive the average and/or median normal wind component to the flown flux plane. The orthogonal wind component is utilized to model the local wind profile. The derived orthogonal wind profile <NUM> is utilized in the emissions calculation.

<FIG> depicts a schematic drawing <NUM> of an Emissions Estimate Model, according to one embodiment. The Emissions Estimate algorithm is based on an engineering control volume model. This means that the two key inputs to the model are the concentration profile on the outflow surface, and the wind speed through the outflow. The concentration enhancement is measured by the disclosed sensor in volumetric concentration units, i.e., ppmv. To determine the source emission rate, it is necessary to convert from volumetric concentration to mass concentration, this is accomplished through the ideal gas law. The ideal gas law is used to calculate the air density based on the local air temperature and atmospheric pressure. At or below about <NUM>% methane in air, the contribution of methane to the overall mixture density is negligible, i.e. the change in specific gravity of the mixture is small. For methane concentrations less than <NUM>,<NUM> ppmv, the mass concentration of methane can be obtained by multiplying the volumetric concentration measurement by the air density to yield kgCH4/m<NUM>. In the ideal gas law, the state of an amount of gas is determined by its pressure, volume, and temperature.

After converting the concentration enhancement to mass units and multiplying the concentration plane by the corresponding wind speed at each height, a 2D integration over the entire plane is performed to arrive at the mass flux through the plane (Eq. <NUM>) in units of kg/s. The velocity (u) is at a given altitude (z). A concentration (c) is at the distance Gamma (γ) and altitude (z). The concentration (c) is a function of Gamma (γ) along the flight path and altitude (z).

<FIG> depicts a <NUM>-dimensional illustration of a UAS system Emissions measurement process <NUM>, according to one embodiment. The UAV <NUM> flies vertical, crosswind transects downwind of potential methane sources <NUM> and measures ambient methane concentration. Elevated methane concentration is the signature of an upwind source. The measurements are interpolated onto a continuous grid <NUM>, multiplied by the corresponding wind vector, and spatially integrated to quantify the source emission rates.

<FIG> is a high-level block diagram <NUM> showing a computing system comprising a computer system useful for implementing an embodiment of the system and process, disclosed herein. Embodiments of the system may be implemented in different computing environments. The computer system includes one or more processors <NUM>, and can further include an electronic display device <NUM> (e.g., for displaying graphics, text, and other data), a main memory <NUM> (e.g., random access memory (RAM)), storage device <NUM>, a removable storage device <NUM> (e.g., removable storage drive, a removable memory module, a magnetic tape drive, an optical disk drive, a computer readable medium having stored therein computer software and/or data), user interface device <NUM> (e.g., keyboard, touch screen, keypad, pointing device), and a communication interface <NUM> (e.g., modem, a network interface (such as an Ethernet card), a communications port, or a PCMCIA slot and card). The communication interface <NUM> allows software and data to be transferred between the computer system and external devices. The system further includes a communications infrastructure <NUM> (e.g., a communications bus, cross-over bar, or network) to which the aforementioned devices/modules are connected as shown.

Information transferred via communications interface <NUM> may be in the form of signals such as electronic, electromagnetic, optical, or other signals capable of being received by communications interface <NUM>, via a communication link <NUM> that carries signals and may be implemented using wire or cable, fiber optics, a phone line, a cellular/mobile phone link, an radio frequency (RF) link, and/or other communication channels. Computer program instructions representing the block diagram and/or flowcharts herein may be loaded onto a computer, programmable data processing apparatus, or processing devices to cause a series of operations performed thereon to produce a computer implemented process.

Embodiments have been described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments. Each block of such illustrations/diagrams, or combinations thereof, can be implemented by computer program instructions. The computer program instructions when provided to a processor produce a machine, such that the instructions, which execute via the processor, create means for implementing the functions/operations specified in the flowchart and/or block diagram. Each block in the flowchart/block diagrams may represent a hardware and/or software module or logic, implementing embodiments. In alternative implementations, the functions noted in the blocks may occur out of the order noted in the figures, concurrently, etc..

Computer programs (i.e., computer control logic) are stored in main memory and/or secondary memory. Computer programs may also be received via a communications interface <NUM>. Such computer programs, when executed, enable the computer system to perform the features of the embodiments as discussed herein. In particular, the computer programs, when executed, enable the processor and/or multi-core processor to perform the features of the computer system. Such computer programs represent controllers of the computer system.

<FIG> shows a block diagram of an example system <NUM> in which an embodiment may be implemented. The system <NUM> includes one or more client devices <NUM> such as consumer electronics devices, connected to one or more server computing systems <NUM>. A server <NUM> includes a bus <NUM> or other communication mechanism for communicating information, and a processor (CPU) <NUM> coupled with the bus <NUM> for processing information. The server <NUM> also includes a main memory <NUM>, such as a random access memory (RAM) or other dynamic storage device, coupled to the bus <NUM> for storing information and instructions to be executed by the processor <NUM>. The main memory <NUM> also may be used for storing temporary variables or other intermediate information during execution or instructions to be executed by the processor <NUM>. The server computer system <NUM> further includes a read only memory (ROM) <NUM> or other static storage device coupled to the bus <NUM> for storing static information and instructions for the processor <NUM>. A storage device <NUM>, such as a magnetic disk or optical disk, is provided and coupled to the bus <NUM> for storing information and instructions. The bus <NUM> may contain, for example, thirty-two address lines for addressing video memory or main memory <NUM>. The bus <NUM> can also include, for example, a <NUM>-bit data bus for transferring data between and among the components, such as the CPU <NUM>, the main memory <NUM>, video memory and the storage <NUM>. Alternatively, multiplex data/address lines may be used instead of separate data and address lines.

The server <NUM> may be coupled via the bus <NUM> to a display <NUM> for displaying information to a computer user. An input device <NUM>, including alphanumeric and other keys, is coupled to the bus <NUM> for communicating information and command selections to the processor <NUM>. Another type or user input device comprises cursor control <NUM>, such as a mouse, a trackball, or cursor direction keys for communicating direction information and command selections to the processor <NUM> and for controlling cursor movement on the display <NUM>.

According to one embodiment, the functions are performed by the processor <NUM> executing one or more sequences of one or more instructions contained in the main memory <NUM>. Such instructions may be read into the main memory <NUM> from another computer-readable medium, such as the storage device <NUM>. Execution of the sequences of instructions contained in the main memory <NUM> causes the processor <NUM> to perform the process steps described herein. One or more processors in a multi-processing arrangement may also be employed to execute the sequences of instructions contained in the main memory <NUM>. In alternative embodiments, hard-wired circuitry may be used in place of or in combination with software instructions to implement the embodiments. Thus, embodiments are not limited to any specific combination of hardware circuitry and software.

The terms "computer program medium," "computer usable medium," "computer readable medium", and "computer program product," are used to generally refer to media such as main memory, secondary memory, removable storage drive, a hard disk installed in hard disk drive, and signals. These computer program products are means for providing software to the computer system. The computer readable medium allows the computer system to read data, instructions, messages or message packets, and other computer readable information from the computer readable medium. The computer readable medium, for example, may include non-volatile memory, such as a floppy disk, ROM, flash memory, disk drive memory, a CD-ROM, and other permanent storage. It is useful, for example, for transporting information, such as data and computer instructions, between computer systems. Furthermore, the computer readable medium may comprise computer readable information in a transitory state medium such as a network link and/or a network interface, including a wired network or a wireless network that allow a computer to read such computer readable information. Computer programs (also called computer control logic) are stored in main memory and/or secondary memory. Computer programs may also be received via a communications interface. Such computer programs, when executed, enable the computer system to perform the features of the embodiments as discussed herein. In particular, the computer programs, when executed, enable the processor multi-core processor to perform the features of the computer system. Accordingly, such computer programs represent controllers of the computer system.

Generally, the term "computer-readable medium" as used herein refers to any medium that participated in providing instructions to the processor <NUM> for execution. Such a medium may take many forms, including but not limited to, non-volatile media, volatile media, and transmission media. Non-volatile media includes, for example, optical or magnetic disks, such as the storage device <NUM>. Volatile media includes dynamic memory, such as the main memory <NUM>. Transmission media includes coaxial cables, copper wire and fiber optics, including the wires that comprise the bus <NUM>. Transmission media can also take the form of acoustic or light waves, such as those generated during radio wave and infrared data communications.

Common forms of computer-readable media include, for example, a floppy disk, a flexible disk, hard disk, magnetic tape, or any other magnetic medium, a CD-ROM, any other optical medium, punch cards, paper tape, any other physical medium with patterns of holes, a RAM, a PROM, an EPROM, a FLASH-EPROM, any other memory chip or cartridge, a carrier wave as described hereinafter, or any other medium from which a computer can read.

Various forms of computer readable media may be involved in carrying one or more sequences of one or more instructions to the processor <NUM> for execution. For example, the instructions may initially be carried on a magnetic disk of a remote computer. A modem local to the server <NUM> can receive the data on the telephone line and use an infrared transmitter to convert the data to an infrared signal. An infrared detector coupled to the bus <NUM> can receive the data carried in the infrared signal and place the data on the bus <NUM>. The bus <NUM> carries the data to the main memory <NUM>, from which the processor <NUM> retrieves and executes the instructions. The instructions received from the main memory <NUM> may optionally be stored on the storage device <NUM> either before or after execution by the processor <NUM>.

The server <NUM> also includes a communication interface <NUM> coupled to the bus <NUM>. The communication interface <NUM> provides a two-way data communication coupling to a network link <NUM> that is connected to the world wide packet data communication network now commonly referred to as the Internet <NUM>. The Internet <NUM> uses electrical, electromagnetic or optical signals that carry digital data streams. The signals through the various networks and the signals on the network link <NUM> and through the communication interface <NUM>, which carry the digital data to and from the server <NUM>, are exemplary forms or carrier waves transporting the information.

In another embodiment of the server <NUM>, interface <NUM> is connected to a network <NUM> via a communication link <NUM>. For example, the communication interface <NUM> may be an integrated services digital network (ISDN) card or a modem to provide a data communication connection to a corresponding type of telephone line, which can comprise part of the network link <NUM>. As another example, the communication interface <NUM> may be a local area network (LAN) card to provide a data communication connection to a compatible LAN. In any such implementation, the communication interface <NUM> sends and receives electrical electromagnetic or optical signals that carry digital data streams representing various types of information.

The network link <NUM> typically provides data communication through one or more networks to other data devices. For example, the network link <NUM> may provide a connection through the local network <NUM> to a host computer <NUM> or to data equipment operated by an Internet Service Provider (ISP). The ISP in turn provides data communication services through the Internet <NUM>. The local network <NUM> and the Internet <NUM> both use electrical, electromagnetic or optical signals that carry digital data streams. The signals through the various networks and the signals on the network link <NUM> and through the communication interface <NUM>, which carry the digital data to and from the server <NUM>, are exemplary forms or carrier waves transporting the information.

The server <NUM> can send/receive messages and data, including e-mail, program code, through the network, the network link <NUM> and the communication interface <NUM>. Further, the communication interface <NUM> can comprise a USB/Tuner and the network link <NUM> may be an antenna or cable for connecting the server <NUM> to a cable provider, satellite provider or other terrestrial transmission system for receiving messages, data and program code from another source.

The example versions of the embodiments described herein may be implemented as logical operations in a distributed processing system such as the system <NUM> including the servers <NUM>. The logical operations of the embodiments may be implemented as a sequence of steps executing in the server <NUM>, and as interconnected machine modules within the system <NUM>. The implementation is a matter of choice and can depend on performance of the system <NUM> implementing the embodiments. As such, the logical operations constituting said example versions of the embodiments are referred to for e.g., as operations, steps or modules.

Similar to a server <NUM> described above, a client device <NUM> can include a processor, memory, storage device, display, input device and communication interface (e.g., e-mail interface) for connecting the client device to the Internet <NUM>, the ISP, or LAN <NUM>, for communication with the servers <NUM>.

The system <NUM> can further include computers (e.g., personal computers, computing nodes) <NUM> operating in the same manner as client devices <NUM>, wherein a user can utilize one or more computers <NUM> to manage data in the server <NUM>.

Claim 1:
A system, comprising:
a processor (<NUM>) having addressable memory, the processor configured to:
determine an unmanned aerial vehicle, UAV (<NUM>), flight path (<NUM>), wherein the UAV flight path is a raster grid flight path downwind of a methane source (<NUM>) and wherein the UAV flight path forms a flight plane substantially perpendicular to a ground surface (<NUM>) and an average wind direction;
generate a UAV data packet (<NUM>), wherein the UAV data packet comprises
methane concentration data and UAV information from the UAV flight path (<NUM>);
receive at least one Meteorological data packet (<NUM>), wherein the Meteorological data packet comprises weather data;
combine the UAV data packet with a nearest temporal Meteorological data packet; and
determine a methane emission rate of the methane source (<NUM>) based on the combined UAV data packet and the nearest Meteorological data packet.