Particulate emissions calculations from fall tillage operations using point and remote sensors

Area of Science:

• Agricultural Science
• Environmental Science
• Atmospheric Science

Background:

• Soil preparation for agriculture is a significant source of atmospheric particulate matter (PM).
• Conservation management practices (CMPs) aim to reduce tillage-related PM emissions, but field validation is often lacking.
• Quantifying PM reductions from specific CMPs is crucial for effective air quality management.

Purpose of the Study:

• To quantify PM emission reductions from a specific fall tillage CMP in California's San Joaquin Valley.
• To compare emissions from conventional tillage with a "combined operations" CMP designed to reduce tractor passes.
• To assess the effectiveness of the CMP in reducing different size fractions of particulate matter.

Main Methods:

• Conducted field measurements of soil properties, meteorological profiles, and various PM size fractions (TSP, PM10, PM2.5).
• Utilized a mass-calibrated scanning light detection and ranging (LIDAR) system to estimate PM concentrations.
• Employed inverse modeling and mass balance calculations using both filter-based and LIDAR data to determine emission rates.

Main Results:

• LIDAR-derived emissions data provided a complete dataset for analysis.
• The combined operations CMP demonstrated significant control effectiveness: 29 ± 2% for PM10, 60 ± 1% for PM2.5, and 25 ± 1% for TSP.
• Inverse modeling emission estimates were generally higher than other methods, with some statistically significant differences.

Conclusions:

• The "combined operations" CMP is an effective strategy for reducing agricultural PM emissions.
• Field-validated data confirm substantial reductions in PM2.5, PM10, and TSP from this conservation tillage practice.
• This study provides critical data for implementing effective agricultural air quality management strategies.