April L. Hiscox

DISPERSION OF FINE SPRAY FROM AERIAL APPLICATIONS IN STABLE ATMOSPHERIC CONDITIONS

A field study of aerial spray movement and dispersion was conducted at the New Mexico State University spray study site on the USDA Jornada Desert research ranch in April 2005. The purpose of the study was to measure the plume movement and in-plume spray concentrations of very fine droplets applied during calm, stable atmospheric conditions. Spray plume movement and dispersion were measured and recorded with a portable elastic-backscatter lidar. Supporting meteorology and air turbulence measurements were made simultaneously with 3-D sonic anemometers. A Cessna T188C equipped with Micronair AU5000 rotary atomizers produced small droplets (volume mean diameter of 37.3 µm). The amount of spray material remaining in the air decreased rapidly for 1 to 2 min, and thereafter remained nearly constant and drifted as a definable plume with the slight air currents.

On the Use of Lidar Images of Smoke Plumes to Measure Dispersion Parameters in the Stable Boundary Layer

Abstract In this note a methodology is presented for measuring dispersion parameters based on lidar images, which can be used as an efficient way to remotely monitor time variations of plume dispersion parameters. Lidar images of a smoke plume cross section over a forest canopy during nighttime conditions are analyzed to estimate vertical dispersion parameters and vertical meander of the plume centerline in the near field. Dispersion parameters 60 m downwind are found to have a median value of 2.31 m, with values ranging from a minimum of 0.56 m to a maximum of 5.45 m. Measurements of these parameters have not previously been made outside the restraints of a wind tunnel experiment.

A DYNAMIC LAGRANGIAN, FIELD-SCALE MODEL OF DUST DISPERSION FROM AGRICULTURE TILLING OPERATIONS

Dust exposure in and near farm fields is of increasing concern for human health and may soon be facing new emissions regulations. Dust plumes of this nature have rarely been documented due to the unpredictable nature of the dust plumes and the difficulties of accurately sampling the plumes. This article presents a dynamic random-walk model that simulates the field-scale PM10 (particle diameter <10 µm) dust dispersion from an agriculture disking operation. The major improvements over traditional plume models are that it can simulate moving sources and plume meander. The major inputs are the friction velocity (u*), wind direction in the simulation period, atmospheric stability, and source strength (µg s-1). In each time step of the model simulation, three instantaneous wind velocities (x, y, and z directions) are produced based on friction velocity, mean wind speed, and atmospheric stability. The computational time step is 0.025 times the Lagrangian time scale. The resulting instantaneous wind vectors transport all the individual particles. The particle deposition algorithm calculates if a particle is deposited based on the particle settling speed and vertical wind velocity when it touches the ground surface. The particle mass based concentration in 3-D can be obtained at any instant by counting the particle numbers in a unit volume and then converting to mass based on the particle size and density. Simulations from this model are verified by comparison with dust dispersion and plume concentrations obtained by an elastic backscatter LIDAR. The simulated plume spread parameters (s y, s z) at downplume distances up to 160 m were within ±73% of those measured with a remote aerosol LIDAR. Cross-correlations between a modeled plume and LIDAR measurements of the actual plume were as high as 0.78 near the ground and decreased to 0.65 at 9 m above ground, indicating close pattern similarity between the modeled and measured plumes at lower heights but decreasing with elevation above the ground.

Local dust emission factors for agricultural tilling operations.

Dust emission factors for regional- and local-scale simulations of particulate matter with diameters less than or equal to 10 &mgr;m (PM10) dispersion from agricultural operations are not generally available. This article presents a modification of the U.S. Environmental Protection Agency AP-42 approach to better calculate aerosol emission factors of PM10 for agricultural tilling operations. For the modification, we added the variables soil moisture, operation type, and crop type based on experimental and literature data to estimate local emission factors. Field experiments to measure the PM10 emissions from rolling, disking, listing, planting, and harvesting cotton (Gossypium hirsutum L.) were conducted. Data from these field experiments plus literature data were used to isolate the effects of soil moisture and operation type on the emissions. Literature data were then used to add different crop and operation types.

Parallel cartographic modeling: a methodology for parallelizing spatial data processing

ABSTRACT This article establishes a new methodological framework for parallelizing spatial data processing called parallel cartographic modeling, which extends the widely adopted cartographic modeling framework. Parallel cartographic modeling adds a novel component called a Subdomain, which serves as the elemental unit of parallel computation. Four operators are also added to express parallel spatial data processing, namely scheduler, decomposition, executor, and iteration. A parallel cartographic modeling language (PCML) is developed based on the parallel cartographic modeling framework, which is designed for usability, programmability, and scalability. PCML is a domain-specific language implemented in Python for the domain of cyberGIS. A key feature of PCML is that it supports automatic parallelization of cartographic modeling scripts; thus, allowing the analyst to develop models in the familiar cartographic modeling language in a Python syntax. PCML currently supports more than 70 operations and new operations can be easily implemented in as little as three lines of PCML code. Experimental results using the National Science Foundation-supported Resourcing Open Geospatial Education and Research computational resource demonstrate that PCML efficiently scales to 16 cores and can process gigabytes of spatial data in parallel. PCML is shown to support multiple decomposition strategies, decomposition granularities, and iteration strategies that be generically applied to any operation implemented in PCML.

A Comparison of Lagrangian Model Estimates to Light Detection and Ranging (LIDAR) Measurements of Dust Plumes from Field Tilling

Abstract A Lagrangian particle model has been adapted to examine human exposures to particulate matter ≤ 10 µm (PM10) in agricultural settings. This paper reports the performance of the model in comparison to extensive measurements by elastic LIDAR (light detection and ranging). For the first time, the LIDAR measurements allowed spatially distributed and time dynamic measurements to be used to test the predictions of a field-scale model. The model outputs, which are three-dimensional concentration distribution maps from an agricultural disking operation, were compared with the LIDAR-scanned images. The peak cross-correlation coefficient and the offset distance of the measured and simulated plumes were used to quantify both the intensity and location accuracy. The appropriate time averaging and changes in accuracy with height of the plume were examined. Inputs of friction velocity, Monin–Obukhov length, and wind direction (1 sec) were measured with a three-axis sonic anemometer at a single point in the field (at 1.5-m height). The Lagrangian model of Wang et al. predicted the near-field concentrations of dust plumes emitted from a field disking operation with an overall accuracy of approximately 0.67 at 3-m height. Its average offset distance when compared with LIDAR measurements was approximately 38 m, which was 6% of the average plume moving distance during the simulation periods. The model is driven by weather measurements, and its near-field accuracy is highest when input time averages approach the turbulent flow time scale (3–70 sec). The model accuracy decreases with height because of smoothing and errors in the input wind field, which is modeled rather than measured at heights greater than the measurement anemometer. The wind steadiness parameter (S) can be used to quantify the combined effects of wind speed and direction on model accuracy.

Effect of Atmospheric Conditions on Coverage of Fogger Applications in a Desert Surface Boundary Layer

Near-ground aerosol fogs were applied in the Chihuahua Desert of New Mexico, which has widely spaced, low shrub vegetation. Near-ground fog dispersion was measured remotely with a light detection and ranging (lidar) system. Local atmospheric turbulence and stability were continuously measured with 3-axis sonic anemometers during aerosol treatments. Lidar-measured plume area coverage and spread were related to the simultaneous local-scale weather, including both convective boundary layers (CBL) and stable boundary layers (SBL). A modified bulk stability ratio (SRm) was used to characterize the stability conditions near the ground. Time averages appropriate to the SBL were determined using the multidimensional decomposition technique and matched to the short spray time periods in the CBL. The widest, most effective, near-ground coverage was obtained from insect fogger applications conducted during relatively high wind speeds: U > 1 m s-1 in stable conditions, and U > 3 m s-1 in unstable conditions. In general, spraying during SBLs was more efficient than during CBLs, with less material wasted and better consistency of coverage in the target zone nearest the ground. There was no significant difference in spray coverage or plume dispersion between the handheld thermal fogger and the ultra-low volume (cold fogger) applicator used.

Simulated Regional PM10 Dispersion from Agricultural Tilling Operations Using HYSPLIT

Particulate matter (PM) of aerodynamic diameter less than or equal to 10 µm (PM10) is regulated by the U.S. Environmental Protection Agency (EPA) as part of the National Ambient Air Quality Standards (NAAQS). This article reports on the calibration and evaluation of the HYSPLIT (Hybrid Single-Particle Lagrangian Integrated Trajectory) version 4.9 model to simulate regional dust dispersion from a disking operation. Disking operations in a cotton field in Las Cruces, New Mexico, were conducted, and boundary layer PM10 concentrations were sampled using a DustTrak sampler on an airplane flown at altitudes between 200 and 500 m and several kilometers downwind. Using North American Mesoscale (NAM) forecast meteorological data (NAM12km, 12 km resolution) with vertical profiles, the model is capable of reasonably simulating regional PM10 dispersion (simulated data = 1.048 × measured data with R2 = 0.85).

POSITION ERRORS CAUSED BY GPS HEIGHT OF INSTRUMENT BLUNDERS

Abstract Height of instrument (HI) blunders in GPS measurements cause position errors. These errors can be pure vertical, pure horizontal, or a mixture of both. There are different error regimes depending on whether both the base and the rover both have HI blunders, if just the base has an HI blunder, or just the rover has an HI blunder. The resulting errors are on the order of 30 cm for receiver separations of 1000 km for an HI blunder of 2 m. Given the complicated nature of the errors, we believe it would be difficult, if not impossible, to detect such errors by visual inspection. This serves to underline the necessity to enter GPS HIs correctly.

Lidar Measurement Techniques for Understanding Smoke Plume Dynamics in Sugarcane Production

Elastic backscatter lidar has proven to be a valuable tool for measuring plumes from agricultural sources. This paper discusses the use of a ground-based scanning system for plume measurements in sugarcane burning operations.