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Photometric Measurement of Ground-Level

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Photometric Measurement of Ground-Level

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For more: http://www.extension.org/67676 Fugitive dust from confined livestock operations is a primary air quality issue associated with impaired visibility, nuisance odor, and other quality-of-life factors. Particulate matter has conventionally been measured using costly scientific instruments such as transmissometers, nephelometers, or tapered-element, oscillating microbalances (TEOMs). The use of digital imaging and automated data-acquisition systems has become a standard practice in some locations to track visibility conditions on roadways; however, the concept of using photometry to measure fugitive dust concentrations near confined livestock operations is relatively new. We have developed a photometric method to estimate path-averaged particulate matter (PM10) concentrations using digital SLR cameras and high-contrast visibility targets. Digital imaging, followed by automated image processing and interpretation, would be a plausible, cost-effective alternative for operators of confined livestock facilities to monitor on-site dust concentrations. We report on the development and ongoing evaluation of such a method for use by cattle feeders and open-lot dairy producers.

For more: http://www.extension.org/67676 Fugitive dust from confined livestock operations is a primary air quality issue associated with impaired visibility, nuisance odor, and other quality-of-life factors. Particulate matter has conventionally been measured using costly scientific instruments such as transmissometers, nephelometers, or tapered-element, oscillating microbalances (TEOMs). The use of digital imaging and automated data-acquisition systems has become a standard practice in some locations to track visibility conditions on roadways; however, the concept of using photometry to measure fugitive dust concentrations near confined livestock operations is relatively new. We have developed a photometric method to estimate path-averaged particulate matter (PM10) concentrations using digital SLR cameras and high-contrast visibility targets. Digital imaging, followed by automated image processing and interpretation, would be a plausible, cost-effective alternative for operators of confined livestock facilities to monitor on-site dust concentrations. We report on the development and ongoing evaluation of such a method for use by cattle feeders and open-lot dairy producers.

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Photometric Measurement of Ground-Level

  1. 1. Photometric Measurement of Ground-Level Fugitive Dust Emissions from Open-Lot Animal Feeding Operations S. L. M. Preece, B. W. Auvermann, T. Kwon, G. W. Marek, K. Heflin, K. J. Bush Texas A&M AgriLife Research, Amarillo TX; University of Minnesota-Duluth, Duluth MN
  2. 2. Photo: J. Sweeten
  3. 3. Photo: S. Sakirkin
  4. 4. Photo: S. Preece
  5. 5. Photo: S. Preece
  6. 6. © 2009, Google Earth
  7. 7. Upwind Upwind Upwind Upwind Upwind Upwind Upwind Upwind Upwind Downwind Downwind Downwind Downwind Downwind Downwind Downwind Downwind Downwind 0 20 40 60 80 100 120 140 160 180 200 1 2 3 4 5 6 7 8 9 InherentTargetContrast Image Pairs Contrast in Visually Assessed Dust Conditions Moderate Dust High DustLow Dust
  8. 8. Honing the Approach • Sky glare • Polarized light • Wide-spectrum CCD • Dust on targets • Number of TEOMs relative to path-length • TEOM-associated errors • Image resolution • RGB to Grayscale conversion equation • Night Measurements
  9. 9. Sky Glare
  10. 10. Polarized Light
  11. 11. Wide Spectrum Camera
  12. 12. Dust on Targets
  13. 13. TEOM Data • Number of TEOMs • Placement of TEOMs • R2 associated with TEOM data
  14. 14. Image Resolution / Path Length
  15. 15. RGB to Grayscale Conversion I = (0.2989R) + (0.5870G) + (0.1140B)
  16. 16. Night
  17. 17. Automation
  18. 18. ACKNOWLEDGMENT The authors gratefully acknowledge the funding support of USDA-CSREES under National Research Initiative grant number 2009-55112-05235. DISCLAIMER References to specific, commercial brand names in this paper are not exclusive of other, functionally equivalent brands or products and should not be construed as an endorsement of those brand names by Texas AgriLife Research or any of its research partners.
  19. 19. REFERENCES Adobe. 2007. Photoshop CS3 Extended. Ver. 10.0.1. San Jose, CA.: Adobe Systems Inc. CAMO. 2007. The Unscrambler. Version 9.7. CAMO Software AS. Oslo, Norway. Kwon, T. M. 1998. An automatic visibility measurement system based on video cameras. University of Minnesota Duluth Report No. MN/RC-1998-25. Report to the Minnesota Department of Transportation. Springfield, VA.: National Technical Information Services. Kwon, T. M. 2004. Atmospheric visibility measurements using video cameras: relative visibility. University of Minnesota Duluth Report No. CTS 04-03. Report to the Minnesota Department of Transportation. Springfield, VA.: National Technical Information Services. Kwon, T. M. 2006. Video camera-based visibility measurement system. U. S. patent No. 7,016,045 B2. Mathworks. 2008. MATLAB and Simulink. Ver. R2008b. Natick, MD.: The Mathworks, Inc. MacVean, D. W., Franzen, D. K., Keefe, T. J., and Bennett, B. W. 1986. Airborne particle concentration and meteorological conditions associated with pneumonia incidence in feedlot cattle. American Journal of Veterinary Research. 47(12):2676-2682. Von Essen, S. G., and B. W. Auvermann. 2006. Health effects from breathing air near CAFOs for feeder cattle or hogs. Journal of Agromedicine. 10(4):55-64. Wyatt, T. A., R. E. Slager, J. D. DeVasure, B. W. Auvermann, M. L. Mulhern, S. Von Essen, T. Mathisen, A. A. Floreani, and D. J. Romberger. 2007. Feedlot dust stimulation of interleukin-6 and -8 requires protein kinase Cε in human bronchial epithelial cells. American Journal of Physiology. Lung, Cellular, and Molecular Physiology. 293(5):L1163-L1170.

Editor's Notes

  • The second through seventh slides are introductory and background material only, and can easily be discarded without loss to the overall content. Beginning with the eighth slide then, the opener might be “in previous, preliminary work…”All slides have detailed notes with information that can be delivered orally.
  • Particulate matter emitted from feedyards not only affects air quality and human health, but the health and performance of livestock as well.
  • The cattle feeding industry is taking steps to reduce dust (and odor) emissions, and feedyards are adopting methods best suited to their own particular situation. However, to determine the effectiveness of dust mitigation strategies a feedyard must be able to estimate its fugitive dust emission rates.
  • Dust in the atmosphere (atmospheric particulate matter) has conventionally been measured using transmissometers, nephelometers, or tapered element oscillating microbalances (TEOMs) like the one shown here. All of these instruments are quite expensive and require someone whoknows what they are doing to collect and interpret the data the instruments provide. As such they are impractical for use by commercial feedyards. To our knowledge, there exists no affordable, reasonably accurate, quantitative way for feedyards to self-monitor their own dust emissions or assess the effectiveness of the dust management practices they implement. Filling this void may pave the way for the widespread adoption of dust control practices amongst feedyards and further, allow for yard-specific improvements.(Direct attenuation, or transmissometry, is used to determine visibility through light-absorbing particles such as smoke, dust, or sand and other aerosols having particle sizes greater than the wavelength of light, and nephelometry, or scatter sensing, is used in rain, fog, snow, or ice and with other small aerosols having a size smaller than the wavelength of light.)
  • In a related field, Dr. Taek Kwon’s research on road traffic visibility measured with a surveillance video camera, an image digitizer, and multiple targets positioned at specific distances from the camera has proven successful. He has developed an image-processing algorithm to accurately determinevisibility based on the distance at which targets individual targets in an array of targets becomeno longer distinguishable.
  • After seeing this we wondered – can we measure, with reasonable accuracy, ground level fugitive dust emissions photometrically? Could we do it with an array of high-contrast targets similar to the ones Dr. Kwon used for roadway visibility, but using a digital still camera? To find out, we got our hands on two Nikon D80 dSLR cameras and constructed two arrays of visibility targets and installed them alongside several TEOMs on the upwind and downwind edges of a feedyard in the Texas Panhandle. We hypothesized that as PM10 concentrations along the path length between the camera and the targets increased, that the contrast between the black and white regions of the visibility targets would decrease, that is to say as their luminosities would move away from black or white towards gray. (The point of extinction being the luminosity at which the two converge.)
  • We installed two visibility arrays (blue lines) on the upwind and downwind (green arrows) edges of a typical feedyard in the Texas Panhandle. Several TEOMs were also installed to sample PM10 every five minutes over a two week period. We photographed the targets during the expected evening dust peak and compared the results with the corresponding PM10 concentrations collected by the TEOMs.
  • Next we compared the contrast information with the weighted* mass concentration data from the TEOMs. For our preliminary comparison we grouped the photographs into light, moderate, and heavy dust conditions based on visual on-site assessment. The results demonstrated that in low-dust conditions the downwind image contrast was high in relation to the upwind image, and in high-dust conditions the inherent contrast was very low. Further, the difference in contrast corresponded very well to the apparent fugitive dust concentrations.*Weights corresponded with the path length from the camera to each of the four targets. For every image this produced four weighted mass concentration values.
  • One major improvement to our method was a new target design. First, we redesigned our targets to be constructed from high-grade aluminum (as opposed to ¾ inch plywood) with uniform and reflective surfaces similar to that used in traffic signs. Second, we upgraded the mounts to perforated steel posts with secure ground ties. The new targets are smaller, lighter, more easily adjusted on their mounts, and safer. The borders around each of the black and white rectangles create edges which will facilitate automated image processing using an edge-finder algorithm in MATLAB.
  • Top image: relative sizes of targetsMiddle image: targets occupy equal areas in image, stock and wide-spectrum cameras in positionBottom image: schematic of target array, camera, and TEOM placement
  • This plot does not include the most recent photographs – I need data from that unresponsive TEOM.
  • Each of these points is expanded upon in the following slides.
  • Top row L-R: Three degree tilt backwards resulted in maximum sky glare, Vertical positioning caused moderate but interfering glare, Three degree tilt forwards eliminated all glare.Bottom frame: Two 400m targets with shim at five degrees forward.
  • We investigated the usefulness of a polarization filter when photographing the targets. The two photos here demonstrate the effect of a polarization filter on Nikon D80 set to minimize and maximize effect (ninety degrees rotational difference) on glare and reflectivity (notwithstanding dust).There was a negligible effect on photographs of targets when polarizing filters were used. This was likely because the targets are close to 180 from the sun at sunset where polarizing effects are minimal.Polarizing notes: Polarizing filters reduce reflections and glare, are a neutral color, and can be used for color or black & white imaging.Unpolarized light from the sun becomes partially polarized due to scattering by particles in the atmosphere. The scattering reduces the transparency of the air and creates haze in the distance and blue skies on a sunny day. Polarization also causes light to reflect off surfaces in different amounts or colors. A polarizing filter reduces the bluish cast in landscapes and increases the blue color of the sky. The effect is strongest at 90° to the sun. Polarizing filters suppress surface reflections often eliminating them and enhance color saturation in images. The effect can be tested with the eye as well as through the camera lens. Note number where effect is maximum, the minimum effect will be 90° to that number (±13 on the index). Heliopan-Lichtfilter-Technik, Summer GMBH and Co KG, D-82166 Gräfelfing / München, www.heliopan.de
  • The charge coupled device (CCD) in a standard dSLR camera senses light intensity (luminosity) at wavelengths between approximately 200 to 1200 nm on the electromagnetic spectrum with the most useful wavelengths lying between 325 to 1100 nm. Most dSLR cameras feature filters (Bayer, ultraviolet (UV), infrared (IR), anti-aliasing (AA)) which drastically reduce the camera’s dynamic range. When data acquisition is the goal, there may be little practical justification for reducing a camera’s optical density with filters that serve only to improve image aesthetics. Removing the IR and UV filters triples the CCD sensitivity from approximately 400 – 700 to 200 – 1200 nm and allows UV and IR light to pass to the photosensors. The Bayer filter is inseparable from the CCD and cannot be removed. In addition to evaluating the capability of a stock dSLR camera to discern low, medium, and high dust conditions at a feedyard, we also compared the performance of a modified dSLR camera to that of a stock dSLR camera in estimating concentrations ofground-level PM10 at confined livestock facilities.
  • The wide-spectrum camera consistently produced lower predictive abilities than the stock camera, likely due to increased noise in the images. (Related to wavelength of light and dust particle-size relationship perhaps?)
  • Throughout the project we have wiped the right-hand side target clean but allowed the left to remain dusty prior to photographing. The image shows dust on a target. The chart shows negligible difference in prediction ability between the clean and dusty targets at the longest path length (400m). Both trend lines have similar R2s.
  • Errors associated with the TEOM data may be reducing the predictive ability of the method. Two aspects we may be able to control include (1) increasing the number of TEOMs may provide better path-averaged PM10 values to incorporate in the models, and/or (2) strategically placing TEOMs at longer path lengths or adjacent to targets may have an effect. A third aspect we cannot control is the error associated with the TEOM method itself.
  • This is an actual photo showing the image composition an proportion of image area occupied by the target faces. The entire image measures 3872 x 2592 pixels, with much of that area unoccupied by the visibility targets. To maximize the number of useful pixels, the area occupied by the target array in the image must be maximized. This might be accomplished by using larger targets or increasing the zoom factor of the camera lens, Telephoto lenses can get extremely expensive. We don’t know at this point what target area, in terms of pixels, maximizes accuracy in luminosity information. The ideal combination of maximized accuracy and minimized cost is yet to be determined.Further, longer path lengths have shown to be more predictive of PM10 concentrations. Moving our current targets away from the camera to lengthen the camera path may improve predictive performance, but would result in the targets occupying smaller areas in the images. Again, we are interested in knowing what the ideal combination of path-length, target size, and lens zoom factor is.
  • We use the National Television System Committee (NTSC) standard equation (also the default ISO and Adobe Photoshop equation) to convert the RGB images to grayscale mode before calculating luminosity values. It may be possible to optimize the information contributed in each of the R, G, and B channels by adjusting their respective coefficients in the conversion equation to further improve PM10 prediction.I = (0.2989R) + (0.5870G) + (0.1140B)
  • Night issues include interference from artificial lighting, such as is illustrated by illumination from a cheese factory located approximately 2.5 kilometers east of the dairy we were working on, and also a small security light on the scale house door which strongly illuminated the 400m target in the center of the images. This brings up another notion we are considering: illuminated visibility targets for use at night.
  • We are developing means to automate the process of image acquisition with the use of controllers for the cameras, and custom code written in MATLAB to identify and isolate visibility targets in images, calculate the inherent contrast information, and report expected PM10 values.The images above shows actual output of preliminary MATLAB code which successfully reads an RGB image, converts it to binary form, identifies and isolates visibility targets, and calculates contrast values based on the grayscale luminosity values of the original RGB image.

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