Christian C. Marchant

Aglite lidar: a portable elastic lidar system for investigating aerosol and wind motions at or around agricultural production facilities

The Aglite Lidar is a portable scanning lidar that can be quickly deployed at agricultural and other air quality study sites. The purpose of Aglite is to map the concentration of PM 10 and PM 2.5 in aerosol plumes from agricultural and other sources. Aglite uses a high-repetition rate low-pulse energy 3-wavelength YAG laser with photon-counting detection together with a steerable pointing mirror to measure aerosol concentration with high spatial and temporal resolution. Aglite has been used in field campaigns in Iowa, Utah and California. The instrument is described, and performance and lidar sensitivity data are presented. The value of the lidar in aerosol plume mapping is demonstrated, as is the ability to extract wind-speed information from the lidar data.

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.

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

Agricultural operations produce a variety of particulates and gases that influence ambient air quality. Lidar (LIght Detection And Ranging) technology provides a means to derive quantitative information of particulate spatial distribution and optical/physical properties over remote distances. A three-wavelength scanning lidar system built at the Space Dynamic Laboratory (SDL) is used to extract optical parameters of particulate matter and to convert these optical properties to physical parameters of particles. This particulate emission includes background aerosols, emissions from the agricultural feeding operations, and fugitive dust from the road. Aerosol optical parameters are retrieved using the widely accepted solution proposed by Klett. The inversion algorithm takes advantage of measurements taken simultaneously at three lidar wavelengths (355, 532, and 1064 nm) and allows us to estimate the particle size distribution. A bimodal lognormal particle size distribution is assumed and mode radius, width of the distribution, and total number density are estimated, minimizing the difference between calculated and measured extinction coefficients at the three lidar wavelengths. The results of these retrievals are then compared with simultaneous point measurements at the feeding operation site, taken with standard equipment including optical particle counters, portable PM10 and PM2.5 ambient air samplers, multistage impactors, and an aerosol mass spectrometer.

An Iterative Least Square Approach to Elastic-Lidar Retrievals for Well-Characterized Aerosols

An iterative least square method is presented for estimating the solution to the lidar equation. The method requires knowledge of the backscatter values at a boundary point for all channels and a priori defined relationships between backscatter, extinction, and mass-fraction concentration for all scattering components. The lidar equation is formulated in vector form, and a solution is computed using an iterative least square technique. The solution is stable for signals with extremely low signal-to-noise ratios and for signals at ranges far beyond the boundary point. The solution can be applied to lidar signals with an arbitrary number of wavelengths and scattering components.

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 ...

Estimation of Aerosol Effective Radius by Multiwavelength Elastic Lidar

A new lidar algorithm is presented as part of a technique for estimating aerosol concentration and particle-size distribution (PSD). This technique uses a form of the extended Kalman filter (EKF), wherein the target aerosol is represented as a linear combination of basis-aerosols, so that the estimated PSD of the aerosol is a linear combination of the PSD of the individual basis-aerosols. The state vector of the filter contains the amplitudes of the basis-aerosols, eliminating the need for an intermediate step of estimating scattering coefficients. Point-sensor instruments and Mie scattering theory are used to establish the relationship between basis-aerosols and measured power. The algorithm is demonstrated using both synthetic test data and field measurements of biological and nonbiological aerosols. The estimated PSD allows straightforward calculation of parameters such as volume-fraction concentration and effective radius.

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.