Estimation of Dairy Particulate Matter Emission Rates by Lidar and Inverse Modeling

Abstract

Particulate matter (PM) emissions from agricultural operations are an important issue for air quality and human health and a topic of interest to government regulators. PM emission rates from a dairy in the San Joaquin Valley of California were investigated during June 2008. The facility had 1,885 total animals, including 950 milking cows housed in free‐stall pens with an open‐lot exercise area, and 935 dry cows, steers, bulls, and heifers housed in open lots. Point sensors, including filter‐based aerodynamic mass samplers and optical particle counters (OPC), were deployed at select points around the facility to measure optical and aerodynamic particulate concentrations. Simultaneously, vertical PM concentration profiles were measured both upwind and downwind of the facility using lidar. The lidar was calibrated to provide mass concentration information using the OPCs and filter measurements. Emission rates were estimated over this period using both an inverse modeling technique coupled with the filter‐based measurements and a mass‐balance technique applied to lidar data. Mean emission rates calculated using inverse modeling (±95% confidence interval) were 3.8 (±3.2), 24.8 (±14.5), and 75.9 (±33.2) g d‐1 AU‐1 for PM2.5, PM10, and TSP, respectively. Mean emissions rates based on lidar data were 1.3 (±0.2), 15.1 (±2.2), and 46.4 (±7.0) g d‐1 AU‐1 for PM2.5, PM10, and TSP, respectively. The PM10 findings are roughly twice as high as those reported from other dairy studies with different climatic conditions and/or housing types, but are of similar magnitude as those from a study with similar conditions, housing, and emission rate calculation technique.