Richard L. Pfeiffer

Crop Wind Energy Experiment (CWEX): Observations of Surface-Layer, Boundary Layer, and Mesoscale Interactions with a Wind Farm

Perturbations of mean and turbulent wind characteristics by large wind turbines modify fluxes between the vegetated surface and the lower boundary layer. While simulations have suggested that wind farms could significantly change surface fluxes of heat, momentum, momentum, moisture, and CO2 over hundreds of square kilometers, little observational evidence exists to test these predictions. Quantifying the influences of the “turbine layer” is necessary to quantify how surface fluxes are modified and to better forecast energy production by a wind farm. Changes in fluxes are particularly important in regions of intensely managed agriculture where crop growth and yield are highly dependent on subtle changes in moisture, heat, and CO2. Furthermore, speculations abound about the possible mesoscale consequences of boundary layer changes that are produced by wind farms. To address the lack of observations to answer these questions, we developed the Crop Wind Energy Experiment (CWEX) as a multiagency, multiuniversi...

Lidar Based Emissions Measurement at the Whole Facility Scale: Method and Error Analysis

Particulate emissions from agricultural sources vary from dust created by operations and animal movement to the fine secondary particulates generated from ammonia and other emitted gases. The development of reliable facility emission data using point sampling methods designed to characterize regional, well-mixed aerosols are challenged by changing wind directions, disrupted flow fields caused by structures, varied surface temperatures, and the episodic nature of the sources found at these facilities. We describe a three-wavelength lidar-based method, which, when added to a standard point sampler array, provides unambiguous measurement and characterization of the particulate emissions from agricultural production operations in near real time. Point-sampled data are used to provide the aerosol characterization needed for the particle concentration and size fraction calibration, while the lidar provides 3D mapping of particulate concentrations entering, around, and leaving the facility. Differences between downwind and upwind measurements provide an integrated aerosol concentration profile, which, when multiplied by the wind speed profile, produces the facility source flux. This approach assumes only conservation of mass, eliminating reliance on boundary layer theory. We describe the method, examine measurement error, and demonstrate the approach using data collected over a range of agricultural operations, including a swine grow-finish operation, an almond harvest, and a cotton gin emission study.

Particulate-Matter Emission Estimates from Agricultural Spring-Tillage Operations Using LIDAR and Inverse Modeling

Abstract. 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 (η) 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 η, which were ∼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.

Derivation and Use of Simple Relationships Between Aerodynamic and Optical Particle Measurements

AbstractA simple relationship, referred to as a mass conversion factor (MCF), is presented to convert optically based particle measurements to mass concentration. It is calculated from filter-based samples and optical particle counter (OPC) data on a daily or sample period basis. The MCF allows for greater temporal and spatial mass concentration information than typical filter-based measurements. Results of MCF calculations from several field studies are summarized. Pairwise comparisons from a collocated study with multiple OPCs and mass samplers suggest the minimum variability of the MCF is 5–10%. The variability of the MCF within a sample period during a field study with distributed samplers averaged 17–21%. In addition, the precision of the Airmetrics MiniVol Portable Air Sampler for particulate matter (PM) was typically <10%. Comparisons with federal reference method (FRM) samplers showed that MiniVols yield PM2.5 concentrations essentially equivalent to FRMs with slightly greater deviations from the ...

Comparisons of Measurements and Predictions of PM Concentrations and Emission Rates from a Wind Erosion Event

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.

Application of a Backwards Lagrangian Stochastic Model to Lidar Data to Estimate Particulate Matter Emissions

A backwards Lagrangian stochastic model will be applied to particulate matter mass calibrated Lidar data in order to estimate particulate matter emissions from a nearby source. This paper describes the methodology to be used.

Aglite: A 3-Wavelength Lidar System for Quantitative Assessment of Agricultural Air Quality and Whole Facility Emissions

Ground based remote sensing technologies such as scanning lidar systems (light detection and ranging) are increasingly being used to characterize ambient aerosols due to key advantages (i.e., wide area of regard (10 km2), fast response time (s-1), high spatial resolution (<10 m) and high sensitivity). Scanning lidar allows for 3D imaging of atmospheric motion and aerosol variability, which can be used to quantitatively evaluate particulate matter (PM) concentrations and emissions. Space Dynamics Laboratory, in conjunction with USDA ARS, has developed and successfully deployed a lidar system called Aglite to characterize PM in diverse settings.

Emissions Calculated from Particulate Matter and Gaseous Ammonia Measurements from a Commercial Dairy in California, USA

Emission rates and factors for particulate matter (PM) and gaseous ammonia (NH3) were estimated from measurements taken at a dairy in June 2008. Concentration measurements were made using both point and remote sensors. Filter-based PM samplers and optical particle counters (OPCs) characterized aerodynamic and optical properties, while a scanning elastic lidar measured particles around the facility. The lidar was calibrated to PM concentration using the point measurements. NH3 concentrations were measured using 23 passive samplers and 2 open-path Fourier transform infrared spectrometers (FTS).