Enhanced representation of soil NO emissions in the Community Multiscale Air Quality (CMAQ) model version 5.0.2

Rasool, Quazi Z.; Zhang, Rui; Lash, Benjamin; Cohan, Daniel S.; Cooter, Ellen J.; Bash, Jesse O.; Lamsal, L. N.

doi:10.5194/gmd-9-3177-2016

Cited by 33 publications

(47 citation statements)

References 56 publications

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“…Code availability. The modified and new source code, inputs, and sample outputs along with the user manual giving details on implementing the new mechanistic module in-line with CMAQ version 5.1, as used in this work, are available on the Oak Ridge National Laboratory Distributed Active Archive Center for Bio-geochemical Dynamics (Rasool et al, 2018; https://doi.org/10.3334/ORNLDAAC/1661). Source codes for CMAQ version 5.1 and FEST-C version 1.2 are both open source and available with applicable free registration at http://www.…”

Section: Discussionmentioning

confidence: 99%

Mechanistic representation of soil nitrogen emissions in the Community Multiscale Air Quality (CMAQ) model v 5.1

2019

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Abstract. Soils are important sources of emissions of nitrogen-containing (N-containing) gases such as nitric oxide (NO), nitrous acid (HONO), nitrous oxide (N2O), and ammonia (NH3). However, most contemporary air quality models lack a mechanistic representation of the biogeochemical processes that form these gases. They typically use heavily parameterized equations to simulate emissions of NO independently from NH3 and do not quantify emissions of HONO or N2O. This study introduces a mechanistic, process-oriented representation of soil emissions of N species (NO, HONO, N2O, and NH3) that we have recently implemented in the Community Multiscale Air Quality (CMAQ) model. The mechanistic scheme accounts for biogeochemical processes for soil N transformations such as mineralization, volatilization, nitrification, and denitrification. The rates of these processes are influenced by soil parameters, meteorology, land use, and mineral N availability. We account for spatial heterogeneity in soil conditions and biome types by using a global dataset for soil carbon (C) and N across terrestrial ecosystems to estimate daily mineral N availability in nonagricultural soils, which was not accounted for in earlier parameterizations for soil NO. Our mechanistic scheme also uses daily year-specific fertilizer use estimates from the Environmental Policy Integrated Climate (EPIC v0509) agricultural model. A soil map with sub-grid biome definitions was used to represent conditions over the continental United States. CMAQ modeling for May and July 2011 shows improvement in model performance in simulated NO2 columns compared to Ozone Monitoring Instrument (OMI) satellite retrievals for regions where soils are the dominant source of NO emissions. We also assess how the new scheme affects model performance for NOx (NO+NO2), fine nitrate (NO3) particulate matter, and ozone observed by various ground-based monitoring networks. Soil NO emissions in the new mechanistic scheme tend to fall between the magnitudes of the previous parametric schemes and display much more spatial heterogeneity. The new mechanistic scheme also accounts for soil HONO, which had been ignored by parametric schemes.

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Section: Discussionmentioning

confidence: 99%

Mechanistic representation of soil nitrogen emissions in the Community Multiscale Air Quality (CMAQ) model v 5.1

2019

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“…Biogenic and lightning emissions are calculated online; biogenic emissions are calculated using BEIS v3.14. Soil NO x emissions are not included here because the CMAQ soil NO x parameterization was implemented in a newer version of the model (Rasool et al, 2016). This model simulation utilizes CB05 gas-phase chemistry.…”

Section: Cmaq Model Simulationmentioning

confidence: 99%

“…This simulation did not include NO x emissions from soils. Rasool et al (2016) has shown soil NO x emissions to be particularly important in the central US, with a lesser role in the eastern US. Excluding these emissions may have resulted in less NO x being transported from upwind regions.…”

Section: Omi_cmaq Vs Cmaqmentioning

confidence: 99%

“…It has also been used to generate and validate NO x emission estimates from source sectors such as soil (Hudman et al, 2010;Vinken et al, 2014a;Rasool et al, 2016), lightning (Allen et al, 2012;Liaskos et al, 2015;Pickering et al, 2016), power plants , aircraft (Pujadas et al, 2011), marine vessels (Vinken et al, 2014b;Boersma et al, 2015), and urban centers Canty et al, 2015;Souri et al, 2016). More recently, there has been an emphasis on analyzing emission trends because OMI has been retrieving highquality tropospheric NO 2 data for over 10 years.…”

Section: Introductionmentioning

confidence: 99%

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A high-resolution and observationally constrained OMI NO2 satellite retrieval

et al. 2017

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Abstract. This work presents a new high-resolution NO 2 dataset derived from the NASA Ozone Monitoring Instrument (OMI) NO 2 version 3.0 retrieval that can be used to estimate surface-level concentrations. The standard NASA product uses NO 2 vertical profile shape factors from a 1.25 • × 1 • (∼ 110 km × 110 km) resolution Global Model Initiative (GMI) model simulation to calculate air mass factors, a critical value used to determine observed tropospheric NO 2 vertical columns. To better estimate vertical profile shape factors, we use a high-resolution (1.33 km × 1.33 km) Community Multi-scale Air Quality (CMAQ) model simulation constrained by in situ aircraft observations to recalculate tropospheric air mass factors and tropospheric NO 2 vertical columns during summertime in the eastern US. In this new product, OMI NO 2 tropospheric columns increase by up to 160 % in city centers and decrease by 20-50 % in the rural areas outside of urban areas when compared to the operational NASA product. Our new product shows much better agreement with the Pandora NO 2 and Airborne Compact Atmospheric Mapper (ACAM) NO 2 spectrometer measurements acquired during the DISCOVER-AQ Maryland field campaign. Furthermore, the correlation between our satellite product and EPA NO 2 monitors in urban areas has improved dramatically: r 2 = 0.60 in the new product vs. r 2 = 0.39 in the operational product, signifying that this new product is a better indicator of surface concentrations than the operational product. Our work emphasizes the need to use both high-resolution and high-fidelity models in order to recalculate satellite data in areas with large spatial heterogeneities in NO x emissions. Although the current work is focused on the eastern US, the methodology developed in this work can be applied to other world regions to produce high-quality region-specific NO 2 satellite retrievals.

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“…and Europe (Huijnen et al, 2010;Curier et al, 2014). It has also been used to produce and validate NOx emission estimates from sectors such as soil (Hudman et al, 2010;Vinken et al, 2014a;Rasool et al, 2016), lightning (Allen et al, 2012;Liaskos et al, 2015;Pickering et al, 2016;Nault et al, 2017), power plants (de Foy et al, 2015), aircraft (Pujadas et al, 2011), marine vessels (Vinken et al, 2014b;, and urban centers (Lu et al, 2015;Canty et al, 2015;Souri et al, 2016).…”

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confidence: 99%

A top-down assessment using OMI NO2 suggests an underestimate in the NOx emissions inventory in Seoul, South Korea during KORUS-AQ

Goldberg¹,

Saide²,

Lamsal³

et al. 2018

Preprint

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Abstract. In this work, we investigate the NOx emissions inventory in Seoul, South Korea using a regional NASA Ozone Monitoring Instrument (OMI) NO2 product. We first develop a regional OMI NO2 product by re-calculating the air mass factors using a high-resolution (4&#8201;&#215;&#8201;4&#8201;km2) WRF-Chem model simulation, which better captures the NO2 shape profiles in urban regions. We then apply a model-derived spatial averaging kernel to further downscale the retrieval and account for the sub-pixel variability. These two modifications yield OMI NO2 values in the regional product that are 1.37 larger in the Seoul metropolitan region and >&#8201;2 times larger near large industrial sources. These two modifications also yield an OMI NO2 product that is in better agreement with the Pandora NO2 spectrometer measurements acquired during the Korea U.S.-Air Quality (KORUS-AQ) field campaign. NOx emissions are then derived for the Seoul metropolitan area during the KORUS-AQ field campaign using a top-down approach with the standard and regional NASA OMI NO2 products. We first apply the top-down approach to a model simulation to ensure that the method is appropriate: the WRF-Chem simulation utilizing the bottom-up emission inventory yields a NOx emission rate of 227&#8201;&#177;&#8201;94&#8201;kton/yr, while the bottom-up inventory itself yields a NOx emission rate of 198&#8201;kton/yr. Using the top-down approach on the regional OM NO2 product, we derive the NOx emissions rate from Seoul to be 484&#8201;&#177;&#8201;201&#8201;kton/yr, and a 353&#8201;&#177;&#8201;146&#8201;kton/yr NOx emissions rate using the standard NASA OMI NO2 product. This suggests an underestimate of 53&#8201;% and 36&#8201;% using the regional and standard NASA OMI NO2 products respectively. To supplement this finding, we compare the NO2 simulated by WRF-Chem to observations of the same quantity acquired by aircraft and find a model underestimate. When NOx emissions in the WRF-Chem model are doubled, there is better agreement with KORUS-AQ aircraft observations. Although the current work is focused on South Korea using OMI, the methodology developed in this work can be applied to other world regions using TROPOMI and future satellite datasets (e.g., GEMS and TEMPO) to produce high-quality region-specific top-down NOx emission estimates.

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Enhanced representation of soil NO emissions in the Community Multiscale Air Quality (CMAQ) model version 5.0.2

Cited by 33 publications

References 56 publications

Mechanistic representation of soil nitrogen emissions in the Community Multiscale Air Quality (CMAQ) model v 5.1

Mechanistic representation of soil nitrogen emissions in the Community Multiscale Air Quality (CMAQ) model v 5.1

A high-resolution and observationally constrained OMI NO<sub>2</sub> satellite retrieval

A top-down assessment using OMI NO2 suggests an underestimate in the NOx emissions inventory in Seoul, South Korea during KORUS-AQ

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Enhanced representation of soil NO emissions in the Community Multiscale Air Quality (CMAQ) model version 5.0.2

Cited by 33 publications

References 56 publications

Mechanistic representation of soil nitrogen emissions in the Community Multiscale Air Quality (CMAQ) model v 5.1

Mechanistic representation of soil nitrogen emissions in the Community Multiscale Air Quality (CMAQ) model v 5.1

A high-resolution and observationally constrained OMI NO&lt;sub&gt;2&lt;/sub&gt; satellite retrieval

A top-down assessment using OMI NO2 suggests an underestimate in the NOx emissions inventory in Seoul, South Korea during KORUS-AQ

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A high-resolution and observationally constrained OMI NO<sub>2</sub> satellite retrieval