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Air Quality: Odor, Dust & Gaseous Emissions from CAFOs in the Southern Great Plains John Sweeten Brent Auvermann Texas Agricultural Experiment Station-Amarillo.

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Presentation on theme: "Air Quality: Odor, Dust & Gaseous Emissions from CAFOs in the Southern Great Plains John Sweeten Brent Auvermann Texas Agricultural Experiment Station-Amarillo."— Presentation transcript:

1 Air Quality: Odor, Dust & Gaseous Emissions from CAFOs in the Southern Great Plains John Sweeten Brent Auvermann Texas Agricultural Experiment Station-Amarillo David Parker, WTAMU Ronaldo Maghirang, KSU N. Andy Cole, USDA-ARS Calvin B Parnell Biological & Agricultural Engineering Department, TAMU San Antonio, Texas January 6, 2005

2 Participants Grantor--USDA-CSREES Grantor--USDA-CSREES Research Partners ( 5 univ./agencies, 23 investigators) Research Partners ( 5 univ./agencies, 23 investigators) Texas A&M University System Texas A&M University System Texas Agricultural Experiment Station—Amarillo & College Station (BAEN) Texas Agricultural Experiment Station—Amarillo & College Station (BAEN) West Texas A&M University West Texas A&M University Texas Cooperative Extension Texas Cooperative Extension Kansas State University Kansas State University USDA-ARS—Bushland TX USDA-ARS—Bushland TX Cooperators: Cooperators: Texas Cattle Feeders Association Texas Cattle Feeders Association Kansas Livestock Association Kansas Livestock Association

3 Project Funding Years 1 & 2 research-- completed. Years 1 & 2 research-- completed. Year 3 research—Began June 04. Year 3 research—Began June 04. CSREES funding levels (net): CSREES funding levels (net): Year 1-- $ 612,500 Year 1-- $ 612,500 Year 2—$ 812,631 Year 2—$ 812,631 Year 3-- $ 835,770 Year 3-- $ 835,770 Year 4-- ? Year 4-- ? Year 5--? Year 5--?

4 Objectives & Coordinators 1. Emissions characterization — David Parker, WTAMU, Canyon TX 2. Effective abatement measures — Brent Auvermann, TCE/TAES, Amarillo 3. Accurate emission factors — Calvin Parnell, BAEN/TAMU, College Station 4. Animal health effects — Andy Cole, USDA-ARS 5. Technology transfer — J. M. Sweeten, TAES; Bill Hargrove, KSU.

5 Experimental Facilities & Personnel Experimental Facilities & Personnel Air quality laboratories involved: Air quality laboratories involved: Particulate matter (PM)—TAES/Amarillo, BAEN/Texas A&M, BAEN/Kansas State. Particulate matter (PM)—TAES/Amarillo, BAEN/Texas A&M, BAEN/Kansas State. Odor—WTAMU Odor—WTAMU Odorous gases —TAES/Amarillo Odorous gases —TAES/Amarillo Ammonia —USDA-ARS/Bushland & Watkinsville GA; TAES-AMA; BAEN/CLL. Ammonia —USDA-ARS/Bushland & Watkinsville GA; TAES-AMA; BAEN/CLL. Hydrogen Sulfide —TAES/AMA; WTAMU; BAEN/CLL. Hydrogen Sulfide —TAES/AMA; WTAMU; BAEN/CLL. VOCs—TAES/AMA; BAEN/CLL. VOCs—TAES/AMA; BAEN/CLL.

6 Experimental Personnel & Facilities, con’t Experimental feedlots Experimental feedlots TAES/ARS, Bushland—380 head capacity TAES/ARS, Bushland—380 head capacity WTAMU—1,000 head capacity WTAMU—1,000 head capacity Commercial feedyards Commercial feedyards Texas Panhandle Texas Panhandle SW Kansas SW Kansas Commercial dairies Commercial dairies N. Central Texas ~ 2,000 head capacity N. Central Texas ~ 2,000 head capacity

7 Objective 1 – Emissions Measurement/Odor Characterize ambient odor concentrations & odor emissions from open-lot feedyards (Parker & Rhoades, WTAMU)

8 Odor samples— 3 beef feedyards, Texas Panhandle Sampled odor 136 times, 12-month period; Pens and holding ponds— downwind & upwind. Feedpens— Large variability Elevated odor downwind of pens ; Peak odor a ssociated with high moisture (46-69% w.b.). Runoff holding ponds— Variable odor 10% of samples showed high odors (ODT>100). Odor Measurement Dynamic forced-choice triangle olfactometer (Parker & Rhoades, WTAMU)

9 LocationnMeanODTRangeODTMedianODT Upwind13640 7 - 362 23 Downwind Feed Pens 13659 8 – 665 25 Downwind Holding Pond 13678 8 - 1223 25 Dynamic Olfactometry--Summary Odor Concentrations, ODT (OU/m 3 or OU) 3 beef cattle feedyards, 12 ‑ month period (Parker & Rhoades, WTAMU)

10 Outli er No. Feedyard & Date Upwind ODT Downwind ODT Manure Moisture Content (% wb) 7-Day 7-Day Precip. Precip. (in) (in) 1FY-B7/24/02 11 11 114 114 55 55 0.8 0.8 2FY-B8/29/02 18 18 144 144 69 69 1.5 1.5 3FY-C10/30/02 16 16 665 665 46 46 2.4 2.4 Dynamic Olfactometry Feedpen Odor vs. Weather (Parker & Rhoades, WTAMU)

11 Estimating Maximum N-Volatilization Using N:P Ratios N-Losses--as NH 3, N 2, NOx. Lowered N:P ratios approximates ~ N-losses to atmosphere-- Cattle ration ~ 5:1 Fresh feces ~ 5:1 Urine ~ 20:1 Pen surface manure~ 3:1 Compost ~ 2:1 Estimated N-losses  Surface manure –30-48 % Compost – 17-20 %. Cole & Todd. USDA-ARS, Bushland TX

12 Pen Surface Chemistry Pen Surface Chemistry Cole & Todd: USDA-ARS, Bushland TX Pen surface is major source of NH 3 losses. Major losses may occur from urine spots Surface chemistry partially regulates these losses.ItemSummerWinterpH7.47.9 N % db 2.673.37 P 0.861.16 NOx, ppm db 78 NHx, 1,3297,487

13 Feedyard Playa/Runoff Retention Pond 9924 NHx, mg/L 67260 P, mg/L 506686 TKN, mg/L 923Temp 7.98.6pHWinterSummer Item Item Runoff retention ponds -- a minor ammonia source. Ammonium-- accumulates during winter; due to cold temperatures. Cole & Todd, USDA-ARS, Bushland

14 Ammonia Monitoring Todd & Cole, USDA-ARS/Bushland Parameters: Parameters: Wind speed. Wind speed. Air temperature Air temperature Ammonia concentration. Ammonia concentration. Flux-gradient method: Flux-gradient method: 10-m tower. 10-m tower. NH 3 concentration--Acid trapping. NH 3 concentration--Acid trapping. Measured at 5 heights; Measured at 5 heights; 3-hr integration time. 3-hr integration time. Ammonia Flux: Ammonia Flux: Concentration profiles Concentration profiles Estimated using micrometeorological flux- gradient method. Estimated using micrometeorological flux- gradient method.

15 Daily Ammonia Flux, Feedyard C, Summer 2003 Todd & Cole, USDA-ARS/Bushland

16 Preliminary Results Ammonia Emissions Estimated by Flux-Gradient Method (Todd & Cole, ARS, Bushland)

17 NH 3, H 2 S & VOCs flux measurements, Feedyard C (Koziel, Baek, Spinhirne, TAES-AMA; Todd & Cole, ARS) Flux-Chamber methods vs. Micromet.-based methods Q air

18 Diurnal variations of NH 3 -N and H 2 S-S flux from cattle pens (Koziel et al., TAES-AMA) NH 3 -N H 2 S-S

19 NH 3 -N and H 2 S-S flux vs. manure pack temperature (Koziel, Baek, Spinhirne, TAES-AMA) NH 3 -N H 2 S-S

20 NH 3 -N and H 2 S-S emission rates Feedyard C (individual cattle pen) (Koziel, Baek, Spinhirne, TAES-AMA) Concen- trations Concen-trations Emission Rates Period Period NH 3, ppm H 2 S, ppb NH3-Ng/hd/dayH2S-Sg/hd/day Summer, 2002 16.1(1.3-97.0)10.2(1.1-77.6)34.50.027 Winter, 2002 3.3(0.6-15.8)2.0(0.1-34.5)8.90.006 Spring, 2003 19.9(1.2-99.0)9.2(0.5-50.4)30.90.026

21 (Koziel, Baek, Spinhirne, TAES-AMA) Mean NH 3 -N and H 2 S-S flux (µg/m 2 /sec) Flux Chamber Method, Feedyard C (Koziel, Baek, Spinhirne, TAES-AMA) NH 3 -N flux H 2 S-S flux Mean(SD)Mean(SD) Summer 2002* 28(27)0.021(0.02) Winter2003 5 (4) (4) 0.005 0.005 (0.01) (0.01) Spring 2003 30(25)0.025(0.03) * Flux, as % of N and S fed in cattle ration: NH 3 -N ~16% H 2 S-S ~ 0.14%

22 Summertime VFA flux from cattle pens (Koziel, Baek, Spinhirne, TAES-AMA) Volatile Fatty Acids (VFA) Concentration Mean, ppb Concentration Range, ppb Mean flux (  g/m 2 /min) (+/-Std) Acetic acid 47 47 35 – 55 35 – 55 12.1 (2.3) Propionic acid 23 23 17 – 26 17 – 26 7.2 (1.3) Isobutyric acid 10 10 7 – 13 7 – 13 3.8 (1.0) Butyric acid 13 13 10 – 15 10 – 15 5.0 (0.9) Isovaleric acid 5.6 5.6 3.9 – 6.5 3.9 – 6.5 2.5 (0.5) Valeric acid 6.2 6.2 3.8 – 8.4 3.8 – 8.4 1.7 (0.6) Hexanoic acid 0.66 0.66 0.43 – 0.81 0.43 – 0.81 0.3 (0.1) Flux comparison : Hydrogen sulfide = 1.28 (+/- 1.06) Ammonia = 1,666 (+/- 1,642)

23 SPME sampling of air downwind from 15,000-head beef cattle feedlot (Koziel, Baek, Spinhirne, TAES-AMA) ~400 m downwind ~2,000 m downwind ~50 m downwind SPME

24 MDGC-MS-O analysis of odor samples collected with SPME (Koziel, TAES-AMA) SPME MDGC-MS-PID-FID Sniff port Separated components of livestock odor

25 Analysis of beef cattle odor (aromagrams) ~400 m downwind ~50 m downwind ~2,000 m downwind p-cresol isovaleric acid butyric acid p-ethyl-phenol DMTS acetic acid methyl mercaptan trimethyl amine P-cresol—an important odorants (also a HAP?). Odorous VOCs and semi- VOCs undergo dispersion and chemical reactions. Need to include chemical reactions with OH, NO 3, O 3 to model “odor”.

26 Odors NH 3 Dust N, C, H 2 O, pH Quantify atmospheric losses Reduce losses Modify diets & management Effectively manage manure N.A. Cole, USDA-ARS-CPRL

27 In vitro experiments tested odor reduction products applied to simulated feedlot surfaces. None of these products have shown much promise. Objective 2—Abatement Measures/Odor Reduction (Parker, Cole, Koziel) Preliminary experiments underway to test odor reduction products in simulated holding ponds. Initial experiments have shown 3 additives tested increased odor emissions.

28 Objective 2 – Abatement Measures/Ammonia Develop cost-effective measures for reducing odor and ammonia emissions (Parker, Cole, Koziel).  Ammonia emissions reduced 70% in the lab using urease inhibitor (NBPT). Unable to distinguish statistical differences in the field due to extreme pen variation. 

29 Diurnal Cattle Behavior & Dust Emissions—Hot dry weather

30 Objective 2 --Abatement Measures Feedlot Dust/PM-- Apparent Drivers Feedlot Dust/PM-- Apparent Drivers (Auvermann, Parnell, Sweeten, others) Dry manure surface Dry manure surface Evening cattle activity/aggressive behavior Evening cattle activity/aggressive behavior Nighttime inversions Nighttime inversions Moisture recovery—humidity/hygroscopicity. Moisture recovery—humidity/hygroscopicity. Evaporation rate, feedlot surface— Evaporation rate, feedlot surface— Lysimeter research, simulated feedlot surfaces. Lysimeter research, simulated feedlot surfaces. Auvermann & Marek, TAES-AMA Auvermann & Marek, TAES-AMA Physical models, dust generation -- Physical models, dust generation -- Weight-drop test chambers—TAES & KSU. Weight-drop test chambers—TAES & KSU. Simulated hoof energy/ shear; Simulated hoof energy/ shear; Effects of depth & moisture content. Effects of depth & moisture content.

31 Objective 2--Abatement Measures (Auvermann & Marek, TAES/TAMUS-AMA)

32 Objective 2--Abatement Measures Feedlot Evaporation Rate (lysimetry) (Auvermann et al., TAES-AMA) Surface manure moisture Diurnal trend, summer Afternoon-- Rapid evaporation Rising vapor pressure Minimum ~ 9 pm. Night-- Falling vapor pressure Rising humidity Hygroscopic adsorption.

33 Weight Drop Test Chambers (WDTC) Auvermann, TAES/TAMUS-AMA; Maghirang & Murphy, KSU

34 Objective 2--Abatement Measures/PM Weight Drop Test Chambers ( TAES/TAMUS & KSU)

35 Objective 2 --Abatement Measures/PM (Auvermann, TAES/TAMUS; Maghirang & Murphy, KSU) WDTC results--1, 2 or 4” depth, dry un-compacted manure. WDTC results--1, 2 or 4” depth, dry un-compacted manure. At given impact energy & moisture-- At given impact energy & moisture-- PM 10 increases with depth PM 10 increases with depth Recommendation— frequent scraping. Recommendation— frequent scraping. Greatest benefit: 2”  1” Greatest benefit: 2”  1” Marginal benefit: Marginal benefit: 4 “  2” 4 “  2”

36 Objective 2 --Abatement Measures/PM (Auvermann,TAES/TAMUS; Maghirang & Murphy, KSU) Moisture content vs. PM 10 emission, mg. Moisture content vs. PM 10 emission, mg. Test conditions: Test conditions: 3 reps. 3 reps. 10 cm depth 10 cm depth Drop energy = 54 J Drop energy = 54 J 95% confidence interval. 95% confidence interval. Result—moisture critical ~10 x increase in PM 10 from 20 %  6%. Result—moisture critical ~10 x increase in PM 10 from 20 %  6%.

37 Objective 2—PM Abatement Measures Manure harvesting/scraping—reduce depth * Manure harvesting/scraping—reduce depth * Surface sprinkling–Replace evaporated moisture * Surface sprinkling–Replace evaporated moisture * Water requirement ~> cattle drinking water needs. Water requirement ~> cattle drinking water needs. Scheduling vs. moisture balance –ET Network (?) Scheduling vs. moisture balance –ET Network (?) Cattle stocking density — focus excreted moisture. Cattle stocking density — focus excreted moisture. Water curtain — Water curtain — Edge-of-feedlot inverted sprinkler/“scrubber”. Edge-of-feedlot inverted sprinkler/“scrubber”. Feeding schedules ? Feeding schedules ?------------ * Denotes USDA-NRCS-EQIP/TX practices, 2003-04.

38 Objective 2—PM Abatement Measures/ Water Curtain (Auvermann, TAES/TAMUS-AMA) Edge-of-feedlot PM capture Pilot testing mode 40,000 hd feedyard 300 ft. x 40 ft. Inverted spray nozzles Evaluation—PM 10, TSP, VOC. ~ 30 % PM 10 reduction.

39 Objective 3 --Emission Factor Development Biological & Agricultural Engineering Dept., TAMU Biological & Agricultural Engineering Dept., TAMU Calvin Parnell Calvin Parnell Bryan Shaw Bryan Shaw Ron Lacey Ron Lacey Saqib Mukhtar Saqib Mukhtar Center for Agricultural Air Quality Engineering Science, TAMUS Center for Agricultural Air Quality Engineering Science, TAMUS

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48 Tower Conc. Comparison 0 1000 2000 3000 4000 5000 6000 7000 16111621 test# Concentration 2m(ground) 5m(tower) 7m(tower) 9m(tower)

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52 Table 1.Dairy Ammonia concentrations and emission rates (ER) for 2003-winter. GLASConc (ppm ) E. Flux (µg / m2/ s) Area(m2)ER(kg/day) Compost 17.4 ±23.5 5.3 ±7.1 21000 9.5 ±12.9 Free Stall Free Stall 36.4 ±23.3 11.0 ±7.0 9790 9.3 ±6.0 Dry Open Lot 6.5 ±8.8 2±2.726000 4.4 ±6.0 Wet Open Lot 14.1 ±5.4 4.3±1.61400 0.5 ±0.2 Sep. Solids 9.3 ±7.9 2.8 ±2.4 110 0.03 ±0.02 Lagoon 1 2.0 ±0.5 0.6 ±0.2 14000 0.7 ±0.2 Lagoon 2 0.4 ±0.3 0.1 ±0.1 16000 0.2 ±0.1 88300 24.7a ±25.4

53 Objective 3 FINDINGS 1. NH 3 and H 2 S losses using flux chambers are NOT losses – response functions. 2. Flux chamber protocols – 3 per TEI 3. PM 10 EF for dairies and feedyards 4. TEI calibration and measurements 5. Co-located PM 10 and TSP samplers 6. Modeling Gaussian and BLS – 10X

54 Objective 4 –Animal Health Effects (Cole, ARS; Auvermann, TCE/TAES; Brown/Loneragan, WTAMU; Pickrell, KSU) Exposure chambers (Auvermann, TAMUS ) Exposure chambers (Auvermann, TAMUS ) Prototype—designed & built. Prototype—designed & built. Calibrated for PM delivery. Calibrated for PM delivery. Test chambers under construction. Test chambers under construction. Cattle exposure experiments  Year 3 Cattle exposure experiments  Year 3 Health effect indicators Health effect indicators Blood antioxidant (Chirase, TAES) Blood antioxidant (Chirase, TAES) Lung lavage fluid (Pickrell, KSU) Lung lavage fluid (Pickrell, KSU) Performance feeding trials, etc. (Cole, ARS; Brown et al., WTAMU). Performance feeding trials, etc. (Cole, ARS; Brown et al., WTAMU).

55 Objective 5 –Technology Transfer Project Industry Advisory Committee Project Industry Advisory Committee TCFA & KLA TCFA & KLA Graduate students recruited. Graduate students recruited. QA/QC plan devloped. QA/QC plan devloped. Scientific outputs, to date: Scientific outputs, to date: 46 professional papers. 46 professional papers. 6 refereed journal articles. 6 refereed journal articles. 35 presentations at scientific meetings 35 presentations at scientific meetings Co-funding recruited > $1.25 million to date. Co-funding recruited > $1.25 million to date.

56 Significant Findings—Years 1 & 2 Odor concentrations Odor concentrations ODT varied widely; ODT varied widely; Increased by wet weather. Increased by wet weather. Multidimensional olfactometry vs. GC/MS Multidimensional olfactometry vs. GC/MS p-cresol--major constituent of downwind odor. p-cresol--major constituent of downwind odor. Ammonia flux from cattle feedyard Ammonia flux from cattle feedyard Diurnal patterns—winter & summer; Diurnal patterns—winter & summer; NH 3 ~1000 X H 2 S emissions. NH 3 ~1000 X H 2 S emissions. Flux gradient vs. flux chamber methods--similar results Flux gradient vs. flux chamber methods--similar results Flux chamber methods improved. Flux chamber methods improved. Ammonia reduction w/urease inhibitor: Ammonia reduction w/urease inhibitor: ~70 % in-vitro (lab) ~ 0 % in preliminary field tests. 1

57 Significant Findings—Years 1 & 2 Particulate matter/”feedlot dust” Particulate matter/”feedlot dust” Intrinsic susceptibility increased with Intrinsic susceptibility increased with Greater manure depth Greater manure depth Decreased moisture content <20% w.b. Decreased moisture content <20% w.b. Evaporation rates (lysimeters) Evaporation rates (lysimeters) Winter ~ 50-70% of ETo. Winter ~ 50-70% of ETo. Summer < 30% of ETo; hygroscopic effects? Summer < 30% of ETo; hygroscopic effects? Summer PM emissions Summer PM emissions Diurnal patterns Diurnal patterns Evening/nighttime peaks ~ 10-20 X daytime concentrations. Evening/nighttime peaks ~ 10-20 X daytime concentrations. 2

58 Significant Findings—Years 1 & 2 Dispersion models Dispersion models Back-calculated emission flux from concentration data Back-calculated emission flux from concentration data ISC vs. BLS ~ 10 X ISC vs. BLS ~ 10 X Development of accurate emission factors Development of accurate emission factors Continuing -- open-lot feedlots & dairies. Continuing -- open-lot feedlots & dairies. PM, NH 3, H 2 S, VOC, odor. PM, NH 3, H 2 S, VOC, odor.

59 Year 3 Focus Project review —expert panel & industry advisory committee. Project review —expert panel & industry advisory committee. “Continuous” monitoring, feedyard “Continuous” monitoring, feedyard Mega-flux chamber. Mega-flux chamber. PM abatement methods PM abatement methods GLAS model development GLAS model development Emission factors development—feedlot & dairy. Emission factors development—feedlot & dairy. Animal health effects experiments. Animal health effects experiments. Peer-reviewed articles. Peer-reviewed articles.

60 Project Values Sound science & engineering. Sound science & engineering. Industry cooperation Industry cooperation Multidisciplinary & multi-agency Multidisciplinary & multi-agency “Seek first to understand, then be understood.” “Seek first to understand, then be understood.” Steven Covey Steven Covey


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