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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.

Soil preparation for agricultural crops produces aerosols that may significantly contribute to seasonal atmospheric particulate matter (PM). Efforts to reduce PM emissions from tillage through a variety of conservation management practices (CMPs) have been made, but the reductions from many of these practices have not been measured in the field. A study was conducted in California's San Joaquin Valley to quantify emissions reductions from fall tillage CMP. Emissions were measured from conventional tillage methods and from a “combined operations” CMP, which combines several implements to reduce tractor passes. Measurements were made of soil moisture, bulk density, meteorological profiles, filter‐based total suspended PM (TSP), concentrations of PM with an equivalent aerodynamic diameter ≤10 μm (PM10) and PM with an equivalent aerodynamic diameter ≤2.5 μm (PM2.5), and aerosol size distribution. A mass‐calibrated, scanning, three‐wavelength light detection and ranging (LIDAR) procedure estimated PM through a series of algorithms. Emissions were calculated via inverse modeling with mass concentration measurements and applying a mass balance to LIDAR data. Inverse modeling emission estimates were higher, often with statistically significant differences. Derived PM10 emissions for conventional operations generally agree with literature values. Sampling irregularities with a few filter‐based samples prevented calculation of a complete set of emissions through inverse modeling; however, the LIDAR‐based emissions dataset was complete. The CMP control effectiveness was calculated based on LIDAR‐derived emissions to be 29 ± 2%, 60 ± 1%, and 25 ± 1% for PM2.5, PM10, and TSP size fractions, respectively. Implementation of this CMP provides an effective method for the reduction of PM emissions.

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