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Particulate-matter (PM) emissions from a typical spring agricultural tillage sequence and a strip–till conservation tillage sequence in California’s San Joaquin Valley were estimated to calculate the emissions control efficiency (ηη) of the strip–till conservation management practice (CMP). Filter-based PM samplers, PM-calibrated optical particle counters (OPCs), and a PM-calibrated light detection and ranging (LIDAR) system were used to monitored upwind and downwind PM concentrations during May and June 2008. Emission rates were estimated through inverse modeling coupled with the filter and OPC measurements and through applying a mass balance to the PM concentrations derived from LIDAR data. Sampling irregularities and errors prevented the estimation of emissions from 42% of the sample periods based on filter samples. OPC and LIDAR datasets were sufficiently complete to estimate emissions and the strip–till CMP ηη, which were ∼90%∼90% for all size fractions in both datasets. Tillage time was also reduced by 84%. Calculated emissions for some operations were within the range of values found in published studies, while other estimates were significantly higher than literature values. The results demonstrate that both PM emissions and tillage time may be reduced by an order of magnitude through the use of a strip–till conservation tillage CMP when compared to spring tillage activities.

Ammonia (NH3) is an important trace gas species in the atmosphere that can have negative impacts on human, animal, and ecosystem health. Agriculture has been identified as the largest source of NH3, specifically livestock operations. NH3 emissions from a commercial dairy in California were investigated during June 2008. Cattle were held in open-lot pens, except for young calves in hutches with shelters. Solid manure was stored in the open-lot pens. Liquid manure from feed lanes was passed through a solids settling basin and stored in a holding pond. Passive sensors and open-path Fourier transform infrared spectrometers (OP-FTIR) were deployed around the facility to measure NH3 concentrations. Emissions from pens and the liquid manure system (LMS) were estimated using inverse modeling. Mean emission factors (EFs) for the entire facility were 140.5 ±42.5 g d-1 animal-1 from the passive sampler data and 199.2 ±22.0 g d-1 animal-1 from the OP-FTIR data, resulting in the facility’s summer emissions calculated at 265.2 ±80.2 kg d-1 and 375.4 ±27.1 kg d-1, respectively. These EFs are within the range of values reported in the literature. Both concentrations and emissions exhibited a strong diurnal cycle, peaking in the late afternoon. Total facility emissions exhibited significant positive correlations with temperature and wind speed. The findings of this study show that NH3 emissions from a commercial dairy can vary by a factor of 10 or more throughout the day, and EFs can vary by two orders of magnitude when compared to other U.S. dairies, based on literature values.

Wind erosion can affect agricultural productivity, soil stability, and air quality. Air quality concerns deal mainly with human health and welfare issues, but are also related to long range transport and deposition of crustal materials. Regulatory standards for ambient levels of particulate matter (PM) with equivalent aerodynamic diameters ≤ 10 μm (PM10) and ≤ 2.5 μm (PM2.5) have been established in many countries in an effort to protect the health and welfare of their citizens. Wind erosion events may lead to high PM levels that exceed air quality standards and are health hazards. Quantifying suspended wind-blown dust emissions and resulting PM concentrations from wind erosion events are, therefore, of significant interest. A high wind event causing visible soil suspension occurred on May 20, 2008 in California’s San Joaquin Valley. On this day, PM concentrations around a 10 hectare field with fine sandy loam soil were being measured as part of an agricultural tillage PM emissions study. Meteorological parameters were monitored by a weather station at 5 m and two 15.3 m towers vertically profiling wind speed, wind direction, temperature, and humidity. Point sensor PM instruments deployed were a vertical and horizontal array of optical particle counters (OPCs) and portable filter-based PM samplers. A remote sensing scanning Lidar (light detection and ranging) system with three wavelengths (1064 nm, 532 nm, and 355 nm) called Aglite was also deployed. The OPCs were used to calibrate the Lidar return signal to particle count and volume concentration. Mass concentration calibrations for both the OPCs and Lidar were calculated from OPC and filter-based PM data collected that day. The filterbased sampling was stopped upon completion of the tillage activity while the OPCs and Lidar continued to collect data during part of the wind erosion event. Since this was not designed as a wind erosion study, measurements of neither creep nor saltation were made. Emission rates (ERs) were calculated by a technique using a vertical flux equation with OPC-measured PM data, an inverse modeling technique using AERMOD with OPC-measured PM data, and through the application of a mass balancing technique to upwind and downwind vertical Lidar scans. PM values measured downwind of the field were consistently much higher than those measured upwind, showing significant suspension and vertical dispersion of soil particles from the field up to 9 m. Particle size distributions and PM levels were also consistently higher at 2 m than 9 m in both upwind and downwind locations, suggesting most particles in the wind-blown dust plumes stayed near the surface. All OPCs, especially those downwind, had high counts for particles > 1 μm relative to counts of particles < 1 μm in comparison with typical ambient atmosphere particle size distributions. The Lidar detected wind-blown dust plumes of varying size, location, and duration on the downwind field edge from 10 m to 50 m in elevation. ERs based on the vertical flux equation were 3.9 μg/s-m2 for PM2.5, 174.2 μg/s-m2 for PM10, and 872.0 μg/s-m2 for TSP while ERs from inverse modeling were 6.1 μg/s-m2 for PM2.5, 268.7 μg/s-m2 for PM10, and 1,488.9 μg/s-m2 for TSP. These PM10 ERs are similar to other values in literature. The Lidar-based ERs were three orders of magnitude lower than those from the other two methods. A minimum measurement height of ~10 m due to safety concerns prevented the Lidar from adequately detecting plumes that are close to the ground, such as the wind erosion plumes seen on this day.

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.

Agricultural operations produce a variety of particulates and gases that influence air quality. Agriculture, through wind erosion, tillage and harvest operations, burning, diesel-powered machinery and animal production operations, is a source of particulate matter that can enter human lungs and cause pulmonary problems. Animal production operations can be a source of gaseous emissions such as ammonia, odor-causing volatile organic compounds, hydrogen sulfide, greenhouse gases (methane, nitrous oxides) and airborne pathogens. These emissions can negatively impact human health, property values, and the environment. The presence of buildings and other structures often make whole facility measurement capability a requirement for understanding the source strength and characteristics. The ability to use standoff methods to determine the movement and concentrations of emissions on a whole facility basis opens new capabilities for model development and verification. An integrated system to measure whole facility emission was designed to characterize the complex structures and temporally dependent emission rates often associated with production operations. This approach combines state of the art standoff measurement techniques with standard point source monitoring equipment to provide the calibrated, high spatial and temporal frequency data required to develop and validate the models required for emission reduction and regulation. This effort includes the design, construction and operation of a new multi-wavelength lidar developed to map and track particle emissions. The lidar incorporates a laser emitting simultaneous, pulsed NdYAG laser radiation at 355, 532 and 1064 nm at a pulse frequency of 10 kHz. The system also includes open path FTS measurements for integrated chemical concentrations, and state-of-the-art point measurements of turbulence, particulate and gas concentrations. This approach was evaluated in a multidisciplinary atmospheric study at a swine production farm in Iowa. Aerosol plumes emitted from the facility were prominent phenomena, and their variations with temperature, turbulence, stability and feed cycle were studied, using arrays of particle samplers and turbulence detectors. Other lidar measurements focused on air motion as seen by long duration scans of the farm region. Successful operation of this system confirms the value of the multidimensional approach for the determination of agricultural emissions in the complex terrain often accompanying production facilities.