Measuring N<sub>2</sub>O Emissions from Multiple Sources Using a Backward Lagrangian Stochastic Model

Nitrous oxide (N<sub>2</sub>O) emissions from agricultural soil are substantially influenced by nitrogen (N) and field management practices. While routinely soil chambers have been used to measure emissions from small plots, measuring field-scale emissions with micrometeorological method...

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Main Authors: Cheng-Hsien Lin, Richard H. Grant, Cliff T. Johnston
Format: Article
Language:English
Published: MDPI AG 2020-11-01
Series:Atmosphere
Subjects:
Online Access:https://www.mdpi.com/2073-4433/11/12/1277
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author Cheng-Hsien Lin
Richard H. Grant
Cliff T. Johnston
author_facet Cheng-Hsien Lin
Richard H. Grant
Cliff T. Johnston
author_sort Cheng-Hsien Lin
collection DOAJ
description Nitrous oxide (N<sub>2</sub>O) emissions from agricultural soil are substantially influenced by nitrogen (N) and field management practices. While routinely soil chambers have been used to measure emissions from small plots, measuring field-scale emissions with micrometeorological methods has been limited. This study implemented a backward Lagrangian stochastic (bLS) technique to simultaneously and near-continuously measure N<sub>2</sub>O emissions from four adjacent fields of approximately 1 ha each. A scanning open-path Fourier-transform infrared spectrometer (OP-FTIR), edge-of-field gas sampling and measurement, locally measured turbulence, and bLS emissions modeling were integrated to measure N<sub>2</sub>O emissions from four adjacent fields of maize production using different management in 2015. The maize N management treatments consisted of 220 kg NH<sub>3</sub>-N ha<sup>−1</sup> applied either as one application in the fall after harvest or spring before planting or split between fall after harvest and spring before planting. The field preparation treatments evaluated were no-till (NT) and chisel plow (ChP). This study showed that the OP-FTIR plus bLS method had a minimum detection limit (MDL) of ±1.2 µg m<sup>−2</sup> s<sup>−1</sup> (3σ) for multi-source flux measurements. The average N<sub>2</sub>O emission of the four treatments ranged from 0.1 to 2.3 µg m<sup>−2</sup> s<sup>−1</sup> over the study period of 01 May to 11 June after the spring fertilizer application. The management of the full-N rate applied in the fall led to higher N<sub>2</sub>O emissions than the split-N rates applied in the fall and spring. Based on the same N application, the ChP practice tended to increase N<sub>2</sub>O emissions compared with NT. Advection of N<sub>2</sub>O from adjacent fields influenced the estimated emissions; uncertainty (1σ) in emissions was 0.5 ± 0.3 µg m<sup>−2</sup> s<sup>−1</sup> if the field of interest received a clean measured upwind background air, but increased to 1.1 ± 0.5 µg m<sup>−2</sup> s<sup>−1</sup> if all upwind sources were advecting N<sub>2</sub>O over the field of interest. Moreover, higher short-period emission rates (e.g., half-hour) were observed in this study by a factor of 1.5~7 than other micrometeorological studies measuring N<sub>2</sub>O-N loss from the N-fertilized cereal cropping system. This increment was attributed to the increase in N fertilizer input and soil temperature during the measurement. We concluded that this method could make near-continuous “simultaneous” flux comparisons between treatments, but further studies are needed to address the discrepancies in the presented values with other comparable N<sub>2</sub>O flux studies.
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spelling doaj.art-6ebd3be12c7b4cd89e846e746c8c674c2023-11-20T22:27:06ZengMDPI AGAtmosphere2073-44332020-11-011112127710.3390/atmos11121277Measuring N<sub>2</sub>O Emissions from Multiple Sources Using a Backward Lagrangian Stochastic ModelCheng-Hsien Lin0Richard H. Grant1Cliff T. Johnston2Department of Agronomy, Purdue University, West Lafayette, IN 47907, USADepartment of Agronomy, Purdue University, West Lafayette, IN 47907, USADepartment of Agronomy, Purdue University, West Lafayette, IN 47907, USANitrous oxide (N<sub>2</sub>O) emissions from agricultural soil are substantially influenced by nitrogen (N) and field management practices. While routinely soil chambers have been used to measure emissions from small plots, measuring field-scale emissions with micrometeorological methods has been limited. This study implemented a backward Lagrangian stochastic (bLS) technique to simultaneously and near-continuously measure N<sub>2</sub>O emissions from four adjacent fields of approximately 1 ha each. A scanning open-path Fourier-transform infrared spectrometer (OP-FTIR), edge-of-field gas sampling and measurement, locally measured turbulence, and bLS emissions modeling were integrated to measure N<sub>2</sub>O emissions from four adjacent fields of maize production using different management in 2015. The maize N management treatments consisted of 220 kg NH<sub>3</sub>-N ha<sup>−1</sup> applied either as one application in the fall after harvest or spring before planting or split between fall after harvest and spring before planting. The field preparation treatments evaluated were no-till (NT) and chisel plow (ChP). This study showed that the OP-FTIR plus bLS method had a minimum detection limit (MDL) of ±1.2 µg m<sup>−2</sup> s<sup>−1</sup> (3σ) for multi-source flux measurements. The average N<sub>2</sub>O emission of the four treatments ranged from 0.1 to 2.3 µg m<sup>−2</sup> s<sup>−1</sup> over the study period of 01 May to 11 June after the spring fertilizer application. The management of the full-N rate applied in the fall led to higher N<sub>2</sub>O emissions than the split-N rates applied in the fall and spring. Based on the same N application, the ChP practice tended to increase N<sub>2</sub>O emissions compared with NT. Advection of N<sub>2</sub>O from adjacent fields influenced the estimated emissions; uncertainty (1σ) in emissions was 0.5 ± 0.3 µg m<sup>−2</sup> s<sup>−1</sup> if the field of interest received a clean measured upwind background air, but increased to 1.1 ± 0.5 µg m<sup>−2</sup> s<sup>−1</sup> if all upwind sources were advecting N<sub>2</sub>O over the field of interest. Moreover, higher short-period emission rates (e.g., half-hour) were observed in this study by a factor of 1.5~7 than other micrometeorological studies measuring N<sub>2</sub>O-N loss from the N-fertilized cereal cropping system. This increment was attributed to the increase in N fertilizer input and soil temperature during the measurement. We concluded that this method could make near-continuous “simultaneous” flux comparisons between treatments, but further studies are needed to address the discrepancies in the presented values with other comparable N<sub>2</sub>O flux studies.https://www.mdpi.com/2073-4433/11/12/1277N<sub>2</sub>Oa backward Lagrangian stochastic (bLS) dispersion techniqueopen-path Fourier-transform infrared spectrometer (OP-FTIR)multiple emission sources
spellingShingle Cheng-Hsien Lin
Richard H. Grant
Cliff T. Johnston
Measuring N<sub>2</sub>O Emissions from Multiple Sources Using a Backward Lagrangian Stochastic Model
Atmosphere
N<sub>2</sub>O
a backward Lagrangian stochastic (bLS) dispersion technique
open-path Fourier-transform infrared spectrometer (OP-FTIR)
multiple emission sources
title Measuring N<sub>2</sub>O Emissions from Multiple Sources Using a Backward Lagrangian Stochastic Model
title_full Measuring N<sub>2</sub>O Emissions from Multiple Sources Using a Backward Lagrangian Stochastic Model
title_fullStr Measuring N<sub>2</sub>O Emissions from Multiple Sources Using a Backward Lagrangian Stochastic Model
title_full_unstemmed Measuring N<sub>2</sub>O Emissions from Multiple Sources Using a Backward Lagrangian Stochastic Model
title_short Measuring N<sub>2</sub>O Emissions from Multiple Sources Using a Backward Lagrangian Stochastic Model
title_sort measuring n sub 2 sub o emissions from multiple sources using a backward lagrangian stochastic model
topic N<sub>2</sub>O
a backward Lagrangian stochastic (bLS) dispersion technique
open-path Fourier-transform infrared spectrometer (OP-FTIR)
multiple emission sources
url https://www.mdpi.com/2073-4433/11/12/1277
work_keys_str_mv AT chenghsienlin measuringnsub2suboemissionsfrommultiplesourcesusingabackwardlagrangianstochasticmodel
AT richardhgrant measuringnsub2suboemissionsfrommultiplesourcesusingabackwardlagrangianstochasticmodel
AT clifftjohnston measuringnsub2suboemissionsfrommultiplesourcesusingabackwardlagrangianstochasticmodel