Retrieval of physical properties of particulate emission from animal feeding operations using three-wavelength elastic lidar measurements

Zavyalov, Vladimir V.; Marchant, Christian C.; Bingham, Gail E.; Wilkerson, Thomas D.; Swasey, Jason; Rogers, Christopher; Ahlstrom, D.; Timothy, Paul

doi:10.1117/12.680967

Cited by 9 publications

(5 citation statements)

References 12 publications

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“…The statistical analysis of measurement accuracy (absolute error or bias with respect to the true value), measurement uncertainty (root mean square RMS error with respect to the true value), and measurement precision (standard deviation estimated with respect to the mean sample value) were estimated for 50 runs for each aerosol type and condition. Simulations were also conducted with a form of the retrieval algorithm [21] that permitted the retrieval of unconstrained solutions. For the single-mode log normal particle size distribution under the different scenarios of unconstrained parameters of particle size distribution, fixed mode radius (µ-constrained), and fixed width of the distribution ( -constrained), it was found that the unconstrained solution had significantly more error than the fully-constrained solution.…”

Section: Aerosol Model Sensitivity Analysismentioning

confidence: 99%

Aglite lidar: calibration and retrievals of well characterized aerosols from agricultural operations using a three-wavelength elastic lidar

Zavyalov¹,

Marchant²,

Bingham³

et al. 2009

J. Appl. Remote Sens

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Lidar (LIght Detection And Ranging) provides the means to quantitatively evaluate the spatial and temporal variability of particulate emissions from agricultural activities. AGLITE is a three-wavelength portable scanning lidar system built at the Space Dynamic Laboratory (SDL) to measure the spatial and temporal distribution of particulate concentrations around an agricultural facility. The retrieval algorithm takes advantage of measurements taken simultaneously at three laser wavelengths (355, 532, and 1064 nm) to extract particulate optical parameters, convert these parameters to volume concentration, and estimate the particulate mass concentration of a particulate plume. The quantitative evaluation of particulate optical and physical properties from the lidar signal is complicated by the complexity of particle composition, particle size distribution, and environmental conditions such as heterogeneity of the ambient air conditions and atmospheric aerosol loading. Additional independent measurements of particulate physical and chemical properties are needed to unambiguously calibrate and validate the particulate physical properties retrieved from the lidar measurements. The calibration procedure utilizes point measurements of the particle size distribution and mass concentration to characterize the aerosol and calculate the aerosol parameters. Once calibrated, the Aglite system is able to map the spatial distribution and temporal variation of the particulate mass concentrations of aerosol fractions such as TSP, PM 10 , PM 2.5 , and PM 1. This ability is of particular importance in the characterization of agricultural operations being evaluated to minimize emissions and improve efficiency, especially for mobile source activities.

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Section: Aerosol Model Sensitivity Analysismentioning

confidence: 99%

Aglite lidar: calibration and retrievals of well characterized aerosols from agricultural operations using a three-wavelength elastic lidar

Zavyalov¹,

Marchant²,

Bingham³

et al. 2009

J. Appl. Remote Sens

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“…The details of our lidar calibration and aerosol retrieval process are discussed in detail by Marchant [19] and Zavyalov [11]. The lidar return power from range z for a single scatter is shown in (1).…”

Section: Lidar Retrieval Calibrationmentioning

confidence: 99%

“…The algorithm used to retrieve aerosol physical parameters from a raw Aglite lidar signal shown schematically in Fig. 3B involves four major steps [11] [21], which account for the geometrical form factor of the telescope receiving optics and scattered sunlight background radiation. The routine then calculates the optical parameters (backscatter and extinction coefficients) of the background and source aerosols at three wavelengths.…”

Section: Lidar Retrieval Calibrationmentioning

confidence: 99%

“…Aerosol sounding techniques for the retrieval of physical aerosol parameters from multiwavelength lidar measurements have been reported since the 1980s and have made major progress in the past five years [5] [6] [7] [8][9] [10]. Unambiguous and stable retrieval of aerosol physical parameters using lidar can require up to twelve empirically derived quantities, which are not easily derived optically [11]. The instrumentation required to provide both multi-wavelength elastic scatter lidar and Raman information is expensive, complicated to operate, and often immobile.…”

Section: Introductionmentioning

confidence: 99%

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Lidar based emissions measurement at the whole facility scale: Method and error analysis

Bingham

2009

J. Appl. Remote Sens

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Particulate emissions from agricultural sources vary from dust created by operations and animal movement to the fine secondary particulates generated from ammonia and other emitted gases. The development of reliable facility emission data using point sampling methods designed to characterize regional, well-mixed aerosols are challenged by changing wind directions, disrupted flow fields caused by structures, varied surface temperatures, and the episodic nature of the sources found at these facilities. We describe a three-wavelength lidar-based method, which, when added to a standard point sampler array, provides unambiguous measurement and characterization of the particulate emissions from agricultural production operations in near real time. Point-sampled data are used to provide the aerosol characterization needed for the particle concentration and size fraction calibration, while the lidar provides 3D mapping of particulate concentrations entering, around, and leaving the facility. Differences between downwind and upwind measurements provide an integrated aerosol concentration profile, which, when multiplied by the wind speed profile, produces the facility source flux. This approach assumes only conservation of mass, eliminating reliance on boundary layer theory. We describe the method, examine measurement error, and demonstrate the approach using data collected over a range of agricultural operations, including a swine grow-finish operation, an almond harvest, and a cotton gin emission study.

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“…The process of retrieving aerosol mass concentration from the Aglite lidar data is discussed in details in our previous publications [6,10]. The retrieval process can be summarized as following (see Figure 2).…”

Section: Lidar Mass Concentration Retrievalmentioning

confidence: 99%

Integration of remote lidar and in-situ measured data to estimate particulate flux and emission from tillage operations

Zavyalov¹,

Bingham²,

Wojcik³

et al. 2010

Lidar Technologies, Techniques, and Measurements for Atmospheric Remote Sensing VI

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Agriculture, through wind erosion, tillage and harvest operations, burning, diesel-powered machinery and animal production operations, is a source of particulate matter emissions. Agricultural sources vary both temporally and spatially due to daily and seasonal activities and inhomogeneous area sources. Conventional point sampling methods originally designed for regional, well mixed aerosols are challenged by the disrupted wind flow and by the small mobile source of the emission encountered in this study. Atmospheric lidar (LIght Detection And Ranging) technology provides a means to derive quantitative information of particulate spatial and temporal distribution. In situ point measurements of particulate physical and chemical properties are used to characterize aerosol physical parameters and calibrate lidar data for unambiguous lidar data processing. Atmospheric profiling with scanning lidar allows estimation of temporal and 2D/3D spatial variations of mass concentration fields for different particulate fractions (PM1, PM 2.5 , PM 10 , and TSP) applicable for USEPA regulations. This study used this advanced measurement technology to map PM emissions at high spatial and temporal resolutions, allowing for accurate comparisons of the Conservation Management Practice (CMP) under test. The purpose of this field study was to determine whether and how much particulate emission differs from the conventional method of agricultural fall tillage and combined CMP operations.

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Retrieval of physical properties of particulate emission from animal feeding operations using three-wavelength elastic lidar measurements

Cited by 9 publications

References 12 publications

Aglite lidar: calibration and retrievals of well characterized aerosols from agricultural operations using a three-wavelength elastic lidar

Aglite lidar: calibration and retrievals of well characterized aerosols from agricultural operations using a three-wavelength elastic lidar

Lidar based emissions measurement at the whole facility scale: Method and error analysis

Integration of remote lidar and in-situ measured data to estimate particulate flux and emission from tillage operations

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