Kori Moore

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

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.

Lidar Based Emissions Measurement at the Whole Facility Scale: Method and Error Analysis

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.

Particulate-Matter Emission Estimates from Agricultural Spring-Tillage Operations Using LIDAR and Inverse Modeling

Abstract. Particulate-matter (PM) emissions from a typical spring agricultural tillage sequence and a strip–till conservation tillage sequence in California’s San Joaquin Valley were estimated to calculate the emissions control efficiency (η) of the strip–till conservation management practice (CMP). Filter-based PM samplers, PM-calibrated optical particle counters (OPCs), and a PM-calibrated light detection and ranging (LIDAR) system were used to monitored upwind and downwind PM concentrations during May and June 2008. Emission rates were estimated through inverse modeling coupled with the filter and OPC measurements and through applying a mass balance to the PM concentrations derived from LIDAR data. Sampling irregularities and errors prevented the estimation of emissions from 42% of the sample periods based on filter samples. OPC and LIDAR datasets were sufficiently complete to estimate emissions and the strip–till CMP η, which were ∼90% for all size fractions in both datasets. Tillage time was also reduced by 84%. Calculated emissions for some operations were within the range of values found in published studies, while other estimates were significantly higher than literature values. The results demonstrate that both PM emissions and tillage time may be reduced by an order of magnitude through the use of a strip–till conservation tillage CMP when compared to spring tillage activities.

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

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, PM2.5, PM10, 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.

Derivation and Use of Simple Relationships Between Aerodynamic and Optical Particle Measurements

AbstractA simple relationship, referred to as a mass conversion factor (MCF), is presented to convert optically based particle measurements to mass concentration. It is calculated from filter-based samples and optical particle counter (OPC) data on a daily or sample period basis. The MCF allows for greater temporal and spatial mass concentration information than typical filter-based measurements. Results of MCF calculations from several field studies are summarized. Pairwise comparisons from a collocated study with multiple OPCs and mass samplers suggest the minimum variability of the MCF is 5–10%. The variability of the MCF within a sample period during a field study with distributed samplers averaged 17–21%. In addition, the precision of the Airmetrics MiniVol Portable Air Sampler for particulate matter (PM) was typically <10%. Comparisons with federal reference method (FRM) samplers showed that MiniVols yield PM2.5 concentrations essentially equivalent to FRMs with slightly greater deviations from the ...

Multi Path FTIR Agriculture Air Pollution Measurement System

This paper details the design and validation of a Multiple Path OP-FTIR system with elevation and radial scanning ability and demonstrates its capabilities to quantify and monitor gaseous ammonia emitted from agricultural facilities.

Comparisons of Measurements and Predictions of PM Concentrations and Emission Rates from a Wind Erosion Event

Wind erosion can affect agricultural productivity, soil stability, and air quality. Air quality concerns deal mainly with human health and welfare issues, but are also related to long range transport and deposition of crustal materials. Regulatory standards for ambient levels of particulate matter (PM) with equivalent aerodynamic diameters = 10 µm (PM10) and = 2.5 µm (PM2.5) have been established in many countries in an effort to protect the health and welfare of their citizens. Wind erosion events may lead to high PM levels that exceed air quality standards and are health hazards. Quantifying suspended wind-blown dust emissions and resulting PM concentrations from wind erosion events are, therefore, of significant interest.

Development and calibration of an eye-safe elastic backscatter lidar for chemical and biological aerosol tracking

Dugway Proving Grounds (DPG) plays a key role in the open-air field-testing of systems used in defense against chemical and biological threats. The performance of systems under test are benchmarked against a suite of wellcharacterized point and standoff instrumentation. Elastic-backscatter lidar systems with large power-apertures operating at 1.06 μm provide standoff detection, quantification, and location of aerosol plumes. The accuracy and sensitivity these systems provide comes at the cost of a large NOHD (>5 km) which limits their utility. To this end, Space Dynamics Lab (SDL) developed an eye-safe system following system requirements from DPG. The system provides a standoff capability for field tests where a NOHZ and required PPE would be an undue burden. CELiS (Compact Eye-Safe Lidar System) is an elastic-backscatter lidar that operates at 1.57 μm, using a commercial 30 Hz Nd:YAG laser and OPO combination. The short pulse length and low repetition rate give the system an advantage in range resolution and daytime operation over a similarly sized system based on a fiber laser. CELiS uses LidarView, an SDL-developed lidar display package, for data acquisition and hardware control. The Joint Ambient Breeze Tunnel (JABT) is used to perform calibration and sensitivity measurements of the various lidar systems at DPG. The JABT provides confinement of an aerosol plume and allows for comparison of TSI APS (Aerodynamic Particle Sizer) concentrations to the lidar backscatter values over an extended period. CELiS was used to support a recent JABT test and the data analysis and performance results from the test are described.

Single-wavelength lidar retrieval algorithm of particulate matter concentration using CELiS (compact eyesafe lidar system) a 1.5 μm elastic lidar system

CELiS (Compact Eyesafe Lidar System) is an elastic backscatter light detection and ranging (lidar) system developed for monitoring air quality environmental compliance regarding particulate matter (PMk) generated from off-road use of wheeled and tracked vehicles as part of the SERDP (Strategic Environmental Research and Development Program) Measurement and Modeling of Fugitive Dust Emission from Off-Road DoD Activities program. CELiS is small, lightweight and easily transportable for quick setup and measurement of PMk concentration and emissions. CELiS operates in a biaxial configuration at the 1.5μm eyesafe wavelength with a working range of better than 6 km and range resolution of 5 m. In this paper, we describe an algorithm that allows for semi-quantitative PMk determination under a set of guiding assumptions using a single wavelength lidar. Meteorological and particle measurements are used to estimate the total extinction (α) and backscatter (β) at a calibration point located at the end range of the lidar. These α and β values are used in conjunction with the Klett inversion to estimate α and β over the lidar beam path. A relationship between β, α and PMk mass concentrations at calibration points is developed, which then allows the β and α values derived to be converted to PMk at each lidar bin over the lidar beam path. CELiS can be used to investigate PMk concentrations and emissions over a large volume, a task that is very difficult to accomplish with typical PMk sensors.

Application of a Backwards Lagrangian Stochastic Model to Lidar Data to Estimate Particulate Matter Emissions

A backwards Lagrangian stochastic model will be applied to particulate matter mass calibrated Lidar data in order to estimate particulate matter emissions from a nearby source. This paper describes the methodology to be used.