Effects of Manure Managements on Ammonia, Hydrogen Sulfide and Greenhouse Gases Emissions from the Naturally Ventilated Dairy Barn

Joo, Hung-Soo; Ndegwa, Pius M.; Neerackal, George M.; Wang, Xiang; Harrison, Joe

doi:10.13031/aim.20131593447

Cited by 2 publications

(3 citation statements)

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“…The very long effective path length of the light in the cavity (e.g., over 20 km for 25 cm cavity; Picarro, 2017) enables a significantly higher sensitivity compared to conventional absorption spectroscopy (Berden et al, 2000;von Bobrutzki et al, 2010). There is negligible interference from VOCs on CRDS measurements, which makes CRDS ideal for measuring NH 3 concentrations in this setting (Kamp et al, 2019). A G2103 Analyzer (Picarro Inc., Sunnyvale, CA, USA) using the CRDS technique was applied in this study for the test of ammonia laboratory calibration and the effect of nontargeted VOCs on ammonia measurement.…”

Section: Instrumentation For Gas Concentrations Measurementmentioning

confidence: 99%

“…A G2103 Analyzer (Picarro Inc., Sunnyvale, CA, USA) using the CRDS technique was applied in this study for the test of ammonia laboratory calibration and the effect of nontargeted VOCs on ammonia measurement. The accuracy of the CRDS instrument is routinely checked against a certified reference gas as described by Kamp et al (2019). The CRDS analyzer was equipped with two in-line, sub-micron poly-tetrafluoroethylene (PTFE) particulate matter filters: one at the gas inlet at the back of the analyzer and one at the inlet of the cavity to protect the highly reflective mirrors.…”

Section: Instrumentation For Gas Concentrations Measurementmentioning

confidence: 99%

“…c According to the concentration calculation assumption and producer suggestion, total gas concentration should be lower than 10 ppmv; otherwise dilution is needed. d According to Kamp et al (2019). high concentrations (Shaw et al, 2007;Ngwabie et al, 2008;Howard et al, 2010;Hafner et al, 2013).…”

Section: Experiments 2: Voc Selection Testmentioning

confidence: 99%

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Photoacoustic measurement with infrared band-pass filters significantly overestimates NH<sub>3</sub> emissions from cattle houses due to volatile organic compound (VOC) interferences

et al. 2020

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Abstract. Infrared (IR) photoacoustic spectroscopy (PAS) using band-pass filters is a widely used method for measurement of NH3 and greenhouse gas emissions (CH4, N2O and CO2) especially in agriculture, but non-targeted gases such as volatile organic compounds (VOCs) from cattle barns may interfere with target gases, causing inaccurate results. This study made an estimation of NH3 interference in PAS caused by selected non-targeted VOCs which were simultaneously measured by a PAS and a PTR-MS (proton-transfer-reaction mass spectrometry). Laboratory calibrations were performed for NH3 measurement, and VOCs were selected based on a headspace test of the feeding material (maize silage). Strong interferences of VOCs were observed on NH3 and greenhouse emissions measured by PAS. Particularly, ethanol, methanol, 1-butanol, 1-propanol and acetic acid were found to have the highest interferences on NH3, giving empirical relationships in the range of 0.7 to 3.3 ppmv NH3 per ppmv VOC. A linear response was typically obtained, except for a nonlinear relation for VOCs on N2O concentration. The corrected online NH3 concentrations measured by PAS in a dairy farm (with empirical relationships 2.1±0.8 and 2.9±1.9 for Location 1 and Location 2, respectively) were confirmed to be correlated (R2=0.73 and 0.79) to the NH3 concentration measured simultaneously by the PTR-MS when the empirical corrections obtained from single VOC tests were applied.

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Section: Instrumentation For Gas Concentrations Measurementmentioning

confidence: 99%

Section: Instrumentation For Gas Concentrations Measurementmentioning

confidence: 99%

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Photoacoustic measurement with infrared band-pass filters significantly overestimates NH<sub>3</sub> emissions from cattle houses due to volatile organic compound (VOC) interferences

et al. 2020

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Sampling Duration and Frequency for Determining Emission Rates from Naturally Ventilated Dairy Barns

Kafle¹,

Joo²,

Ndegwa³

2018

Transactions of the ASABE

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Abstract. The ideal practice for determining gas emission rates from concentrated animal feeding operations (CAFOs) is continuous measurement throughout the year to capture diurnal and seasonal variations. However, data that meet this criterion are scarce because this approach is costly and technically challenging. A practical approach with reduced sampling time and frequency without compromising integrity is thus necessary. In this study, we examined five reduced sampling protocols for determining emission rates from naturally ventilated dairy barns: (1) six sampling events, during even months, each event running continuously for 24 h (144 hourly data points); (2) six sampling events, during odd months, each event running continuously for 24 h (144 hourly data points); (3) six sampling events, during even months, each event running continuously for 7 d (1,008 hourly data points); (4) six sampling events, during odd months, each event running continuously for 7 d (1,008 hourly data points); and (5) 12 sampling events, one event every month, each event running continuously for 24 h (288 hourly data points). These five reduced sampling protocols were evaluated against baseline emission rates obtained from a protocol consisting of 12 sampling events, one event every month, each event running continuously for 7 d (2,016 hourly data points). The average baseline emission rates for carbon dioxide (CO2), ammonia (NH3), and hydrogen sulfide (H2S) ranged from 7.0 to 10.8 g d-1 AU-1, from 16.9 to 24.3 g d-1 AU-1, and from 0.40 to 0.63 g d-1 AU-1, respectively. Emission rates for CO2 and NH3 obtained from all five reduced sampling protocols had relative biases of less than 20% from the respective baseline emission rates. This implied that even the most reduced sampling protocol (six sampling events per year, each event running continuously for 24 h) would be adequate for determining CO2 and NH3 emission rates. However, for H2S, relative biases of the reduced sampling protocols ranged from 2% to 45%, with a 50% chance of emission rates falling outside ±20% of the baseline emission rates. These protocols, however, are only applicable for quantifying emission rates from barns with steady-state live animal mass. Keywords: Air quality, Emission rates, Air sampling protocol, CAFO, Emission inventories.

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Effects of Manure Managements on Ammonia, Hydrogen Sulfide and Greenhouse Gases Emissions from the Naturally Ventilated Dairy Barn

Cited by 2 publications

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Photoacoustic measurement with infrared band-pass filters significantly overestimates NH<sub>3</sub> emissions from cattle houses due to volatile organic compound (VOC) interferences

Photoacoustic measurement with infrared band-pass filters significantly overestimates NH<sub>3</sub> emissions from cattle houses due to volatile organic compound (VOC) interferences

Sampling Duration and Frequency for Determining Emission Rates from Naturally Ventilated Dairy Barns

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Effects of Manure Managements on Ammonia, Hydrogen Sulfide and Greenhouse Gases Emissions from the Naturally Ventilated Dairy Barn

Cited by 2 publications

References 0 publications

Photoacoustic measurement with infrared band-pass filters significantly overestimates NH&lt;sub&gt;3&lt;/sub&gt; emissions from cattle houses due to volatile organic compound (VOC) interferences

Photoacoustic measurement with infrared band-pass filters significantly overestimates NH&lt;sub&gt;3&lt;/sub&gt; emissions from cattle houses due to volatile organic compound (VOC) interferences

Sampling Duration and Frequency for Determining Emission Rates from Naturally Ventilated Dairy Barns

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Photoacoustic measurement with infrared band-pass filters significantly overestimates NH<sub>3</sub> emissions from cattle houses due to volatile organic compound (VOC) interferences

Photoacoustic measurement with infrared band-pass filters significantly overestimates NH<sub>3</sub> emissions from cattle houses due to volatile organic compound (VOC) interferences