Particulate Emissions Calculations from Fall Tillage Operations Using Point and Remote Sensors

Authors

• Kori D. Moore, Utah State UniversityFollow
• Michael D. Wojcik, Utah State University
• Randal S. Martin, Utah State University
• Christian C. Merchant, National Geospatial-Intelligence Agency
• Gail E. Bingham, Utah State University
• Richard L. Pfeiffer, USDA-ARS
• John H. Prueger, USDA-ARS
• Jerry L. Hatfield, USDA-ARSFollow

Document Type

Article

Date of this Version

2013

Citation

Published in J. Environ. Qual. (2013) 42:1029–1038. DOI:10.2134/jeq2013.01.0009

Abstract

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 mm (PM₁₀) and PM with an equivalent aerodynamic diameter ≤2.5 mm (PM_2.5), 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 PM_2.5, PM₁₀, and TSP size fractions, respectively. Implementation of this CMP provides an effective method for the reduction of PM emissions.