Brian P. Crenna

University of Alberta, Edmonton, Alberta, Canada

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Publications (13)18.06 Total impact

  • J. D. Wilson · T. K. Flesch · B. P. Crenna
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    ABSTRACT: We review the niche for Lagrangian models on the micrometeorological scale in the context of "inverse dispersion," as applied to estimate the rate of gas transfer Q from small surface sources to the atmosphere. The backward Lagrangian stochastic (bLS) method for inverse dispersion is widely used to quantify local sources of such gases as methane and ammonia, typically stemming from the agricultural sector. Data for a particular case study are given, offering interested readers a simple case illustrating the bLS method.
    AGU Chapman Conference on Advances in Lagrangian Modeling of the; 01/2012
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    ABSTRACT: Open cattle feedlots are a source of air pollutants that include particular matter (PM). Over 24 h, exposure to ambient concentrations of 50 microg m(-3) of the coarse-sized fraction PM (aerodynamic diameter <10 microm [PM(10)]) is recognized as a health concern for humans. The objective of our study was to document PM(10) concentration and emissions at two cattle feedlots in Australia over several days in summer. Two automated samplers were used to monitor the background and in-feedlot PM(10) concentrations. At the in-feedlot location, the PM(10) emission was calculated using a dispersion model. Our measurements revealed that the 24-h PM(10) concentrations on some of the days approached or exceeded the health criteria threshold of 50 microg m(-3) used in Australia. A key factor responsible for the generation of PM(10) was the increased activity of cattle in the evening that coincided with peak concentrations of PM(10) (maximum, 792 microg m(-3)) between 1930 and 2000 h. Rain coincided with a severe decline in PM(10) concentration and emission. A dispersion model used in our study estimated the emission of PM(10) between 31 and 60 g animal(-1) d(-1). These data contribute to needed information on PM(10) associated with livestock to develop results-based environmental policy.
    Journal of Environmental Quality 01/2010; 39(3):791-8. DOI:10.2134/jeq2009.0240 · 2.35 Impact Factor
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    ABSTRACT: In Canada approximately 45% of ammonia (NH3) emissions are attributed to dairy and beef cattle industries. The present study focused on NH3 emissions from a beef feedlot with a one-time capacity of 17,220 head. The aim was to improve the Canadian NH3 emission inventories and air quality forecasting capabilities. A Cessna 207, equipped with a fast-response NH3/NOy detector and a quadrupole aerosol mass spectrometer, was flown in a grid pattern covering an area of 8 × 8 km centered on a feedlot (800 × 800 m) at altitudes ranging from 30 to 300 m above ground. Stationary ground measurements of NH3 concentration and turbulence parameters were made downwind of the feedlot. Three flights were conducted under varying meteorological conditions, ranging from very calm to windy with near-neutral stratification. NH3 mixing ratios up to 100 ppbv were recorded on the calm day, up to 300 m above ground. An average feedlot NH3 emission rate of 76 ± 4 μg m−2 s−1 (equivalent to 10.2 g head−1 h−1) was estimated. Characteristics of the measured NH3 plume were compared to those predicted by a Lagrangian dispersion model. The spatially integrated pattern of NH3 concentrations predicted and measured agreed but the measured was often more complex than the predicted spatial distribution. The study suggests that the export of NH3 through advection accounted for about 90% of the emissions from the feedlot, chemical transformation was insignificant, and dry deposition accounted for the remaining 10%.
    Atmospheric Environment 12/2009; 43(38):6091-6099. DOI:10.1016/j.atmosenv.2009.08.045 · 3.28 Impact Factor
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    ABSTRACT: Inverse-dispersion calculations can be used to infer atmospheric emission rates through a combination of downwind gas concentrations and dispersion model predictions. With multiple concentration sensors downwind of a compound source (whose component positions are known) it is possible to calculate the component emissions. With this in mind, a field experiment was conducted to examine the feasibility of such multi-source inferences, using four synthetic area sources and eight concentration sensors arranged in different configurations. Multi-source problems tend to be mathematically ill-conditioned, as expressed by the condition number κ. In our most successful configuration (average κ = 4.2) the total emissions from all sources were deduced to within 10% on average, while component emissions were deduced to within 50%. In our least successful configuration (average κ = 91) the total emissions were calculated to within only 50%, and component calculations were highly inaccurate. Our study indicates that the most accurate multi-source inferences will occur if each sensor is influenced by only a single source. A “progressive” layout is the next best: one sensor is positioned to “see” only one source, the next sensor is placed to see the first source and another, a third sensor is placed to see the previous two plus a third, and so on. When it is not possible to isolate any sources κ is large and the accuracy of a multi-source inference is doubtful.
    Boundary-Layer Meteorology 07/2009; 132(1):11-30. DOI:10.1007/s10546-009-9387-1 · 2.53 Impact Factor
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    B. P. Crenna · T. K. Flesch · J. D. Wilson
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    ABSTRACT: Multi-source emission rates inferred from measured concentrations using numerical dispersion models are often extremely sensitive to measurement and model error, rendering them unusable. This sensitivity to error is quantified by the condition number of the matrix of model-derived coefficients relating source strengths to concentrations. Using a dispersion model, we examine the dependence of this condition number on source–sensor geometry, atmospheric conditions, and the amount of concentration data included in the solution. Optimal sensor arrangements are those that measure source emissions (and background concentration, if it is unknown) as independently from each other as possible under the expected range of wind directions and atmospheric stabilities. Although including more concentration measurements can improve the emission inferences, the benefit is highly contingent upon sensor placement. A set of recommendations to minimize sensitivity to error is presented. This includes arranging sensors so that each detects emissions from as few sources as possible; keeping sensors separated, both horizontally and vertically, to benefit from asymmetries in source distribution and surface layer structure; using more measurements in a given calculation, either by adding more sensors or by incorporating data from different times; and using dispersion models to assess condition number and guide sensor placement before and during a field study.
    Atmospheric Environment 10/2008; 42(32):7373-7383. DOI:10.1016/j.atmosenv.2008.06.019 · 3.28 Impact Factor
  • S M McGinn · T. Coates · T K Flesch · B. Crenna
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    ABSTRACT: It is recognized that volatilized ammonia (NH3) from intensive livestock production can be a significant pathway for nitrogen (N) pollution to land and water, and can contribute to poor air quality. The objectives of our study were to document NH3 emissions from a dairy lagoon and to assess the influence of meteorology on NH3 emissions. Ammonia emissions were determined using a backward Lagrangian Stochastic approach using WindTrax software, an open-path NH3 laser and a sonic anemometer. Results indicate that an average 5.1 ± 1.6 g NH3 m -2 d-1 was released over the summer; however, the emission varied typically over 24 h between 3.6 and 8.6 g NH3 m-2 d-1. Wind speed and surface temperature of the lagoon had similar influences on the magnitude of the release, where their direct impact on NH 3 emission accounted for 28 and 31% of the variability, respectively. The main implication of this study is that NH3 losses are significant from dairy lagoons, contributing to the issue of N pollution. As well, NH3 emissions are a loss of valuable N for manure used as fertilizer, which in our study amounted to approximately 13% of the total ammoniacal N content of the manure in the lagoon.
    Canadian Journal of Soil Science 08/2008; 88(4):611-615. DOI:10.4141/CJSS08002 · 1.00 Impact Factor
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    S M McGinn · T K Flesch · B P Crenna · K A Beauchemin · T Coates
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    ABSTRACT: Livestock manure is a significant source of ammonia (NH3) emissions. In the atmosphere, NH3 is a precursor to the formation of fine aerosols that contribute to poor air quality associated with human health. Other environmental issues result when NH3 is deposited to land and water. Our study documented the quantity of NH3 emitted from a feedlot housing growing beef cattle. The study was conducted between June and October 2006 at a feedlot with a one-time capacity of 22,500 cattle located in southern Alberta, Canada. A backward Lagrangian stochastic (bLS) inverse-dispersion technique was used to calculate NH3 emissions, based on measurements of NH3 concentration (open-path laser) and wind (sonic anemometer) taken above the interior of the feedlot. There was an average of 3146 kg NH3 d(-1) lost from the entire feedlot, equivalent to 84 microg NH3 m(-2) s(-1) or 140 g NH3 head(-1) d(-1). The NH3 emissions correlated with sensible heat flux (r2 = 0.84) and to a lesser extent the wind speed (r2 = 0.56). There was also evidence that rain suppressed the NH3 emission. Quantifying NH3 emission and dispersion from farms is essential to show the impact of farm management on reducing NH3-related environmental issues.
    Journal of Environmental Quality 11/2007; 36(6):1585-90. DOI:10.2134/jeq2007.0167 · 2.35 Impact Factor
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    ABSTRACT: We use an inverse–dispersion technique to diagnose gas emissions (ammonia) from a swine farm. A backward Lagrangian stochastic (bLS) model gives the emission-concentration relationship, so that downwind gas concentration establishes emissions. The bLS model takes as input the average wind velocity and direction, surface roughness, and atmospheric stability. Despite ignoring wind complexity and assuming a simplified source configuration in the model calculations, we argue that with concentration and wind measured sufficiently far from the farm the errors can be relatively small. An important part of our analysis was identifying periods likely to give erroneous results. The resulting emission calculations (6.5 and 16 g animal−1 day−1 in March and July, respectively) are plausible in the light of comparative figures.
    Atmospheric Environment 09/2005; 39(27-39):4863-4874. DOI:10.1016/j.atmosenv.2005.04.032 · 3.28 Impact Factor
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    ABSTRACT: The gas emission rate Q from an artificial 36-m2 surface area source was inferred from line-average concen- tration CL measured by an open-path laser situated up to 100 m downwind. Using a backward Lagrangian stochastic (bLS) model, a theoretical CL/Q relationship was established for each experimental trial by simulating an ensemble of fluid-element paths arriving in the laser beam under the prevailing micrometeorological conditions. The diagnosed emission rates (QbLS) were satisfactory for trials done when Monin-Obukhov similarity theory gave a good description of the surface layer, but were poor during periods of extreme atmospheric stability ( | L | # 2 m) and transition periods in stratification. With such periods eliminated, the average value of the 15-min ratios QbLS/Q over n 5 77 fifteen-minute trials spanning 6 days was 1.02. Individual 15-min estimates, however, exhibited sizable variability about the true rate, with the standard deviation in QbLS/Q being sQ/Q 5 0.36. This variability is lessened (sQ/Q 5 0.22, n 5 46) if one excludes cases in which the detecting laser path lay above or immediately downwind from the source—a circumstance in which the laser path lies at the edge of the gas plume.
    Journal of Applied Meteorology 04/2004; 43(3):487-502. DOI:10.1175/1520-0450(2004)043<0487:DGEFOT>2.0.CO;2
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    John D. Wilson · Thomas K. Flesch · Brian P. Crenna
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    Thomas Flesch · John Wilson · Brian Crenna
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    T K Flesch · J D Wilson · L A Harper · R R Sharpe · B P Crenna

Publication Stats

268 Citations
18.06 Total Impact Points

Institutions

  • 2002–2012
    • University of Alberta
      • Department of Earth and Atmospheric Sciences
      Edmonton, Alberta, Canada
  • 2007
    • Agriculture and Agri-Food Canada
      • Dairy and Swine Research and Development Centre (DSRDC)
      Ottawa, Ontario, Canada