Modeling of soil nitric oxide (NO) emissions is highly
uncertain and may misrepresent its spatial and temporal distribution. This
study builds upon a recently introduced parameterization to improve the
timing and spatial distribution of soil NO emission estimates in the
Community Multiscale Air Quality (CMAQ) model. The parameterization
considers soil parameters, meteorology, land use, and mineral nitrogen (N)
availability to estimate NO emissions. We incorporate daily year-specific
fertilizer data from the Environmental Policy Integrated Climate (EPIC)
agricultural model to replace the annual generic data of the initial
parameterization, and use a 12 km resolution soil biome map over the
continental USA. CMAQ modeling for July 2011 shows slight differences in model
performance in simulating fine particulate matter and ozone from Interagency Monitoring of Protected Visual Environments (IMPROVE) and
Clean Air Status and Trends Network (CASTNET) sites and NO
Nitrogen oxides (NO
Soil NO
Different biome types, comprised of vegetation and soil assemblages exhibit
different NO emission factors under different soil conditions and climate
zones. One of the early attempts to stratify soil NO based on different
biomes by Davidson and Kingerlee (1997) involved compiling over 60 articles
and 100 field estimates. They clearly identified biomes associated with low
NO emissions like swamps, tundra, and temperate forests, and those with high
soil NO fluxes like tropical savanna/woodland and cultivated agriculture.
For instance, high soil NO fluxes were observed in croplands, savannahs, or
woodlands, N-rich temperate forests, and even boreal/tropical forests with
low NO
Both wet and dry deposition act as sources of nitrogen to soils (Yienger and
Levy, 1995; Hudman et al., 2012). N is deposited in both oxidized (e.g.,
nitrate) and reduced (e.g., ammonium) forms, with ammonium representing a
growing share of N deposition in the USA as anthropogenic NO
Fertilizer (organic and inorganic) application represent controllable
influences on soil N emissions (Pilegaard, 2013) and are leading sources of
reactive N worldwide (Galloway and Cowling, 2002). US
fertilizer use increased by nearly a factor of 4 from 1961 to 1999 (IFIA,
2001). Soil NO emissions increase with rising fertilizer application, with
conversion rate of applied fertilizer N to NO
Meteorological conditions influence soil NO emission rates. Soil NO pulsing events occur when water stressed nitrifying bacteria, which remain dormant during dry periods, are activated by the first rains and start metabolizing accumulated N in the soil. Large pulses of biogenic NO emissions of up to 10–100 times background levels often follow the onset of rain after a dry period and can last for 1–2 days (Davidson, 1992; Yienger and Levy, 1995; Scholes et al., 1997; Jaeglé et al., 2004; Hudman et al., 2010, 2012; Zörner et al., 2016).
Adsorption onto plant canopy surfaces can reduce the amount of soil NO emissions entering the broader atmosphere. The Yienger and Levy (1995) (YL) soil NO scheme followed a canopy reduction factor (CRF) approach (Wang et al., 1998) to account for the reduction of soil NO emission flux via stomatal or cuticle exchange as a function of dry deposition within the canopy on a global scale.
Contemporary air quality models such as the Community Multiscale Air Quality (CMAQ) model most often use an adaptation of the YL scheme to quantify soil NO emissions as a function of fertilizer application, soil moisture, precipitation, and CRF (Byun and Schere, 2006). However, YL has been found to underestimate emissions rates inferred from satellite and ground measurements by a factor ranging from 1.5 to 4.5, and to misrepresent some key spatial and temporal features of emissions (Jaeglé et al., 2005; Wang et al., 2007; Boersma et al., 2008; Zhao and Wang, 2009; Lin, 2012; Hudman et al., 2012; Vinken et al., 2014). This overall underestimation can be attributed to several uncertainties in the modeling settings, such as inaccurate emissions coefficients, poor soil moisture data, deriving soil temperatures from ground air temperatures, neglecting nitrogen deposition, and outdated fertilizer application rates (Yienger and Levy, 1995; Jaeglé et al., 2005; Delon et al., 2007; Wang et al., 2007; Boersma et al., 2008; Delon et al., 2008; Hudman et al., 2010; Steinkamp and Lawrence, 2011; Hudman et al., 2012).
The Berkley Dalhousie Soil NO Parameterization (BDSNP) scheme, originally implemented by Hudman et al. (2012) in the GEOS-Chem global chemical transport model, outperforms YL by better representing biome type, the timing of emissions, and actual soil temperature and moisture (Hudman et al., 2010).
We implement BDSNP in CMAQ by using the Environmental Policy Integrated
Climate (EPIC) biogeochemical model for dynamic representation of the soil N
pool on a day-to-day basis. EPIC is a field-scale biogeochemical process
model developed by the United States Department of Agriculture (USDA) to
represent plant growth, soil hydrology, and soil heat budgets for multiple
soil layers of variable thickness, multiple vegetative systems, and crop
management practices (Cooter et al., 2012). EPIC can model up to 1 km
Baseline soil NO emission rate for each location (Hudman et al., 2012; Vinken et al., 2014), use a new soil biome map with finer-scale representation of land cover systems consistent with typical resolution of a regional model. We also built an offline version of BDSNP, which can use benchmarked inputs from the CMAQ and allows quick diagnostic based on soil NO estimates for sensitivity analysis (Supplement Sect. S2).
Our implementation of the BDSNP soil NO parameterization in CMAQ uses Pleim–Xiu Land Surface Model (Pleim and Xiu, 2003). Compared to the coarser land surface model (LSM) in GEOS-Chem (Bey et al., 2001), Pleim–Xiu provides finer-scale estimates of soil moisture and soil temperature based on solar radiation, temperature, leaf area index (LAI), vegetation coverage, and aerodynamic resistance. The rich amount of information available from the Pleim–Xiu LSM enables refined representation of soil moisture and soil temperature for implementation in soil NO parameterization.
The original implementation of BDSNP in GEOS-Chem did not provide specific
spatial–temporal variation of CRF in each modeling grid, but used a monthly
average CRF from Wang et al. (1998). Wang et al. (1998) included an updated
CRF as part of their implementation of YL into GEOS-Chem. This CRF is based
on wind speed, turbulence, canopy structure, deposition constants, and other
physical variables. In the GEOS-Chem implementation of BDSNP, this CRF
reduced the flux by
Our BDSNP implementation for CMAQ uses the same approach of integrating CRF
as used in Wang et al. (1998) with the biome categorization based on
Steinkamp and Lawrence (2011) and Köppen climate classes (Kottek et al.,
2006) in the soil NO
YL in CMAQ assumed a linear correlation between fertilizer application and
its induced emissions over the general growing season: May–August in the
Northern Hemisphere and November–February in the Southern Hemisphere
(Yienger and Levy, 1995), rather than peaking near the time of fertilization
at the beginning of the local growing season. This likely caused inaccurate
temporal representation of fertilizer-driven emissions in certain regions
(Hudman et al., 2012). The GEOS-Chem implementation of BDSNP applied a
long-term average fertilizer application with a decay term after fertilizer
is applied. Constant fertilizer emissions neglect an important phenomenon:
applying fertilizer during a dry period when neither plants nor bacteria may
have the water available to use it may result in a large pulse when the soil
is eventually re-wetted (Pilegaard, 2013). Such dry spring N fertilizer
application is common practice in the Midwest and Southern Plains in the USA
(Cooter et al., 2012). The current fertilizer data used for the BDSNP are
scaled to global 2006 emissions by Hudman et al. (2012) using a spatial
distribution for year 2000 from Potter et al. (2010). This global database
reported by Potter et al. (2010) is already 8 years out of date in magnitude
and 14 years out of date for relative distribution, and has relatively coarse
resolution based on a out-of-date long-term average (national-level
fertilizer data from 1994 to 2001). Using recent fertilizer application
information is essential to soil NO estimates given the fact that N
fertilizer is the major contributor to plant nutrient use in USA, and its
share has been increasing from 11 535 000 short tons in 2001 to
12 840 000 short tons in 2013 (USDA ERS, 2013). Our implementation of BDSNP
into CMAQ is designed to enable updates by subsequent developers to use new
year- and location-specific fertilizer data. We use the Fertilizer Emission
Scenario Tool for CMAQ (FEST-C v1.1;
YL in CMAQ neglects nitrogen deposition, which can result in a 0.5 Tg yr
Soil NO emissions modeling framework as implemented offline or in CMAQ (in-line). “Old” refers to the Hudman et al. (2012) implementation in GEOS-Chem. “New” refers to our implementation in CMAQ.
Figure 1 provides the flow chart of the BDSNP scheme implementation, which has the option to run in-line with CMAQ, or as an offline emissions parameterization. Static input files in the Hudman et al. (2012) BDSNP implementation (labeled as “old” in Fig. 1), such as those giving soil biome type with climate zone and global fertilizer pool, are needed to determine the soil base emission value at each modeling grid. The Meteorology–Chemistry Interface Processor (MCIP) (Otte and Pleim, 2010) takes outputs from a meteorological model such as Weather Research and Forecasting (WRF) model (Skamarock et al., 2008) to provide a complete set of meteorological data needed for emissions and air quality simulations.
There are seven key input environment variables and two key output environment variables in our implementation of BDSNP. Table S1 lists their names and corresponding functionalities.
Our implementation of the BDSNP soil NO
BDSNP uses a Poisson function to represent the dependence of emission rates
on soil moisture (
The pulsing term for emissions when rain follows a dry period is
Biomes from GEOS-Chem (0.25
Beyond this basic implementation of the above stated BDSNP framework into CMAQ, there were major modifications (highlighted as “new” in Fig. 1) in the form of (a) updating biome map consistent with CMAQ, (b) incorporating year- and location-specific fertilizer data using EPIC outputs, and (c) development of an offline BDSNP module. Our work focuses on those developments discussed in detail in the sections to follow.
The original implementation of BDSNP used the global soil biome data from
the GEOS-Chem, with emission factors for each biome under dry/wet conditions
taken from Steinkamp and Lawrence (2011) (Appendix Table A1). Our
implementation in CMAQ uses a finer resolution (12 km) soil biome map over
CONUS. The map is generated from the 30 arcsec (approximately 1 km) NLCD40 (National Land Cover Dataset) for 2006, with 40 land
cover/land use classifications. A mapping algorithm table (see Appendix
Table A2) was created to connect the land use category to soil biome type
(Table A1) based on best available knowledge. For the categories with
identical names, such as “evergreen needleleaf forest”, “deciduous
needleleaf forest”, “mixed forest”, “savannas”, and “grassland”, the mapping
is direct. Categories in NLCD40, which are subsets of the corresponding
biome category, are consolidated into one category by addition. For example,
“permanent snow and ice” and “perennial ice-snow” in NLCD40 are combined to
form “snow and ice”; “developed open space”, “developed low intensity”,
“developed medium intensity”, and “developed high intensity” are added to
form “urban and built-up lands”. For the categories appearing only in
NLCD40, the mapping algorithm is determined by referring to the CMAQ mapping
scheme, available in cross section and quantum yield (CSQY) data files in
the CMAQ coding. One such case is to map “lichens” and “moss” in NLCD40 to
the category “grassland” in soil biome. Furthermore, a model resolution
compatible Köppen climate zone classification (Kottek et al., 2006) was
added to allocate different emission factor for the same biome type, e.g., to
account for different altitudes of “grassland” at different locations. There
are five climate zone classifications, namely A: equatorial, B: arid, C:
warm temperature, D: snow, and E: polar. A 12 km CONUS model resolution climate
zone classification map (see Fig. 2) was created using the Spatial Allocator based on the county level climate zone definition as the surrogate
based on a dominant land use (
Modeling framework for obtaining total soil N from EPIC using FEST-C.
Figure 2 compares the 24-soil biome map with 0.25
The classification of simulation domain into arid and non-arid region with
consistent resolution is also included in our implementation. Figure B1
shows the distribution of arid (red) and non-arid (blue) regions. For the
modeling grid classified as “arid” region, the maximum moisture scaling
factor corresponds to the water-filled pore space (
We implemented two approaches for representing fertilizer N. The first
approach re-grids fertilizer data from the global GEOS-Chem BDSNP
implementation (Hudman et al., 2012) to our 12 km resolution CONUS domain.
That scheme uses the global fertilizer database from Potter et al. (2010)
and assumed 37 % of fertilizer and manure N is available (1.8 Tg N yr
Our second approach (Fig. 3) uses the EPIC model as implemented in the FEST-C
tool (Cooter et al., 2012) to provide a dynamic representation of fertilizer
applications for a specific growing season. FEST-C (v1.1) generates
model-ready fertilizer input files for CMAQ. Use of FEST-C/EPIC instead of
soil emissions from YL scheme has been shown to improve CMAQ performance for
nitrate and ammonia in CONUS (Bash et al., 2013). The BELD4 tool in the
FEST-C system was used to provide the crop usage fraction over our domain. We
summed FEST-C data for ammonia, nitrate and organic (T1_ANH3, T1_ANO3, and
T1_AON, respectively) in kg N ha
Figure 4 compares the FEST-C-derived N fertilizer map and the default coarser resolution long-term average fertilizer map from Potter et al. (2010). While the spatial patterns are similar, EPIC provides finer resolution and more up-to-date information.
Potter et al. (2010) (left) and EPIC (right) annual fertilizer
applications
(kg N ha
The CMAQ domain settings for CONUS as provided by the EPA were used to simulate the whole month of July in 2011. July corresponds to the month of peak flux for soil nitrogen emissions in the USA (Williams et al., 1992; Cooter et al., 2012; Bash et al., 2013) and is an active period for ozone photochemistry (Cooper et al., 2014; Strode et al., 2015).
A 10-day (21–30 June 2011) spin-up time was used to minimize the influence from initial conditions. The domain consisted of 396 columns, 246 rows, 26 vertical layers, and 12 km rectangular cells using a Lambert Conformal Projection over North America. This configuration was consistent throughout the WRF–BDSNP–CMAQ modeling framework (see Fig. 1). Meteorology data were produced through the WRF Model nudged to National Centers for Environmental Prediction (NCEP) and National Center for Atmospheric Research Reanalysis (NARR) data, which is comprised of historical observations and processed to control quality and consistency across years by National Oceanic and Atmospheric Administration (NOAA). Emissions were generated using the Sparse Matrix Operator Kernel Emissions (SMOKE) model (CMAS, 2014) and 2011NEIv1. CMAQ was applied with bi-directional exchange of ammonia between soils and atmosphere.
We applied CMAQ with three sets of soil NO emissions: (a) standard YL soil NO
scheme, (b) BDSNP scheme with Potter et al. (2010) fertilizer data set and
biome mappings from GEOS-Chem, and (c) BDSNP scheme with EPIC 2011 data and
new biome mappings (see Appendix Table A3). Within these three cases, we
simulated the impact of anthropogenic NO
Model simulations were evaluated against the following in situ and
satellite-based data: 16 USEPA Clean Air Status and Trends Network (CASTNET)
sites for MDA8 O
CASTNET (forest/national park and agricultural sites) and IMPROVE
sites in continental USA for comparison of modeled and observed ozone and
PM
We also simulated three sensitivity cases for the same time period and domain with the offline soil NO module: (a) NLCD40-based (new) biome vs. GEOS-Chem-based (old) biome (using EF1 in Table A1), (b) EPIC 2011 vs. Potter et al. (2010) data, and (c) global mean biome emission factor (EF1 in Table A1) vs. North American mean emission factor (EF3 in Table A1) (Supplement Sect. S3).
Modeling configuration used for the WRF–BDSNP–CMAQ CONUS domain runs.
We demarcated the CONUS domain into six sub-domains (Fig. 6) to analyze model outputs. The updated BDSNP model and EPIC fertilizer result in higher soil NO emission rates than YL and Potter et al. (2010). Emissions increase by a factor ranging from 1.8 to 2.8 in shifting from YL to BDSNP, even while retaining the Potter et al. (2010) fertilizer data and original biome map, indicating that the shift from the YL to the BDSNP scheme is the largest driver of the increase in emissions estimates. EPIC and the new biome data set further increase emissions over most of CONUS, except for the southwest region. In the Midwest and western USA, the new biome map identified more cropland and shifted some grasslands to other land cover types such as forests, savannah, and croplands, which exhibit higher soil NO emissions (Fig. 2; Table A1). The Midwest region is characterized with the highest emission rate due to its abundant agricultural lands with high fertilizer application rates (Fig. 4).
Soil NO (t day
Spatial average for Tropospheric NO
The standard (version 2.1) OMI tropospheric NO
We compared CMAQ simulated tropospheric NO
Model results are compared with observational data from IMPROVE monitors for
PM
Changes in modeled daily average PM
Changes in modeled maximum daily 8 h ozone (MDA8) when switching from (a) YL to BDSNP ( et al. (2010) fertilizer data with original biome map) (left) and (b) BDSNP ( et al. (2010) with original biomes) to BDSNP (EPIC with new biomes) (right).
Aggregated performance statistics of CMAQ modeled daily average
PM
Statistical comparisons of modeled and observed daily average PM
Comparison of the three in-line BDSNP–CMAQ cases with IMPROVE
PM
Performance statistics of CMAQ modeled MDA8 Ozone for 16 CASTNET
remote sites grouped into two categories: (a) 11 sites with moist or wet soil
condition (monthly mean soil moisture (m
In contrast to the PM
Comparison of the three in-line BDSNP–CMAQ cases with CASTNET MDA8
O
At the California CASTNET sites, BDSNP enhances model performance in simulating observed MDA8 ozone (Table 3). This can be seen in the NMB, NME, MAGE, and RMSE comparisons between YL and BDSNP, though updating BDSNP to the newer inputs does not enhance performance (Table 3).
We analyzed how the choice of soil NO parameterization affects the
responsiveness of ozone to reductions in anthropogenic NO
Emission perturbation factors applied to anthropogenic NO
Difference in monthly mean MDA8 O
Shifting from YL to the BDSNP soil NO scheme reduces the sensitivity of MDA8
O
Our BDSNP implementation represents a substantial update from the YL scheme for estimating soil NO in CMAQ. Compared to the previous implementation of BDSNP in global GEOS-Chem model, our implementation in CMAQ incorporated finer-scale representation of its dependence on land use, soil conditions, and N availability. This finer resolution and updated biome and fertilizer data set resulted in higher sensitivity of soil NO to biome emission factors. Our updated BDSNP scheme (EPIC and new biome) predicts slightly higher soil NO than the inputs used in GEOS-Chem, primarily due to the use of 2011 daily EPIC/FEST-C fertilizer data and fine resolution NLCD40 biomes (Fig. 6).
Sensitivities to different input data sets were examined using our offline BDSNP module to reduce computational cost. Switching from GEOS-Chem biome to new NLCD40 biome drops soil NO in the northwest and southwest portions of our domain due to the finer resolution biome map exhibiting lower emission factors in those regions. Replacing fertilizer data from Potter et al. (2010) with an EPIC 2011 data set increased soil NO mostly in the Midwest (Supplement Fig. S4).
We compared CMAQ tropospheric NO
We examined the performance of CMAQ under each of the soil NO
parameterizations. Regions where soil NO parameterizations most impacted
MDA8 ozone and PM
For PM
Over-predictions of soil NO emissions especially in wet conditions may result from EPIC not properly accounting for on-farm nitrogen management practices like tile drainage. Crops such as alfalfa, hay, grass, and rice experience soil N loss due to tile drainage in wet soils (Gast et al., 1978; Randall et al., 1997). Recent updates to FEST-C (v. 1.2) include tile drainage for some crops but not hay, rice, grass, and alfalfa (CMAS, 2015). Tile drainage results in loss of fertilizer N to water run-off from wet or moist soils.
We analyzed how the soil NO schemes affect the sensitivity of MDA8 ozone to
anthropogenic NO
This work represents crucial advancement toward enhanced representation of soil NO in a regional model. Although possible wet biases and using dominant land cover rather than fractional in soil biome classification, may have over-predicted NO in agricultural regions in the present study. The EPIC simulation used here lacks complete representation of farming management practices like tile, which can reduced soil moisture and soil NO fluxes. Inclusion of biogeochemistry influencing different reactive N species encompassing the entire N cycling could enable more mechanistic representation of emissions. For future work, there is a need for more accurate representation of actual farming practices and internalizing updated soil reactive N bio-geochemical schemes. More field observations are needed as well in order to increase the sample size for evaluation of modeled estimate soil emissions of reactive N species beyond NO.
The modified and new scripts used for implementation of BDSNP in CMAQ
version 5.0.2 are in the Supplement. Also provided as Supplement is the user
manual giving details on implementing BDSNP module in-line with CMAQ, as used
in this work. Source codes for CMAQ version 5.0.2 and FEST-C version 1.1 are
both open source, available with applicable free registration at
List of 24 soil biome emission factors (EFs) from Steinkamp and Lawrence (2011).
Mapping table to create the “new” soil biome map based on NLCD40 MODIS land cover categories.
Summary of differences between YL, and the two applications of BDSNP. See Table 1 for other aspects of model configuration.
Arid (red) and non-arid (blue) region over continental USA (12 km resolution).
Daily variation of total N from fertilizer application (from Potter et al., 2010) processed from BDSNP to establish timing over continental USA throughout 2011.
Difference of OMI NO
Although this work was reviewed by EPA and approved for publication, it may not necessarily reflect official agency policy.
This work was supported by NASA's Air Quality Applied Sciences Team through a tiger team project grant for DYNAMO: DYnamic Inputs of Natural Conditions for Air Quality Models and by the Texas Air Quality Research Program. Edited by: J. Williams Reviewed by: K.-J. Liao and one anonymous referee