Particulate Emissions Calculations from Fall Tillage Operations Using Point and Remote Sensors
| Autor: | John H. Prueger, Michael Wojcik, Randal S. Martin, Christian C. Marchant, Richard L. Pfeiffer, Gail E. Bingham, Kori D. Moore, Jerry L. Hatfield |
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| Rok vydání: | 2013 |
| Předmět: |
Environmental Engineering
Soil science Management Monitoring Policy and Law Mass concentration (chemistry) Particle Size Waste Management and Disposal Water content Water Science and Technology Vehicle Emissions Air Pollutants Conventional tillage remote sensors Point sensors Agriculture Particulates Pollution Bulk density Aerosol Tillage Particulate Emissions Calculations Lidar Fall Tillage operations Environmental science Particulate Matter Seasons Environmental Monitoring |
| Zdroj: | Space Dynamics Lab Publications |
| Popis: | 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 (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. |
| Databáze: | OpenAIRE |
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