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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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 PM 10 and PM 2.5 ambient air samplers, multistage impactors, and an aerosol mass spectrometer.

We report on the design, construction and operation of a new multiwavelength lidar developed for the Agricultural Research Service of the United States Department of Agriculture and its program on particle emissions from animal production facilities. The lidar incorporates a laser emitting simultaneous, pulsed Nd laser radiation at 355, 532 and 1064 nm at a PRF of 10 kHz. Lidar backscatter and extinction data are modeled to extract the aerosol information. Allreflective optics combined with dichroic and interferometric filters permit all the wavelength channels to be measured simultaneously, day or night, using photon counting by PMTs, an APD, and high speed scaling. The lidar is housed in a transportable trailer for all-weather operation at any accessible site. The laser beams are directed in both azimuth and elevation to targets of interest. We describe application of the lidar in a multidisciplinary atmospheric study at a swine production farm in Iowa. Aerosol plumes emitted from the hog barns were prominent phenomena, and their variations with temperature, turbulence, stability and feed cycle were studied, using arrays of particle samplers and turbulence detectors. Other lidar measurements focused on air motion as seen by long duration scans of the farm region. Successful operation of this lidar confirms the value of multiwavelength, eye-safe lidars for agricultural aerosol measurements.We report on a collaborative research program between the Space Dynamics Laboratory (SDL), a unit of the Utah State University Research Foundation, and the United States Department of Agriculture, Agriculture Research Service (USDA/ARS), under Cooperative Agreement 58-3625-4-12 1, to measure the emissions and dispersion of gases and particulates from agricultural operations. We plan to define the operations that generate the emissions and to identify the practices that can help mitigate those emissions. The objective of this collaboration is to work jointly to provide the data and analysis required to make increasing large scale agriculture production operations less objectionable. Specific objectives include the following: 1) develop new methods and improve existing methods of measuring emissions of particulate matter and gases from animal feeding operations, 2) develop and determine the effectiveness of management practices and control technologies to reduce emissions, and 3) develop tools to predict emissions and their dispersion across a wide range of animal production systems, management practices, and environmental conditions. APPROACHSDL and ARS are addressing these objectives in a multi-faceted program that includes a range of experimental and theoretical studies. These studies will help determine the particulate emission from livestock and cropping systems as a model system to evaluate micro meteorological models applicable to limited fetch conditions. These observations will be used to refine existing dispersion models for more realistic conditions in agriculture. We will study and support the development of technolo...

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