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

2019 ◽  
Vol 12 (2) ◽  
pp. 849-878 ◽  
Author(s):  
Quazi Z. Rasool ◽  
Jesse O. Bash ◽  
Daniel S. Cohan
Keyword(s):  
Air Quality ◽  
Spatial Heterogeneity ◽  
Model Performance ◽  
Soil Conditions ◽  
Soil Parameters ◽  
N Availability ◽  
Biogeochemical Processes ◽  
Mineral N ◽  
N Transformations ◽  
No Emissions

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.

scholarly journals Mechanistic representation of soil nitrogen emissions in the Community Multi-scale Air Quality (CMAQ) model v 5.1

2018 ◽  
Author(s):  
Quazi Z. Rasool ◽  
Jesse O. Bash ◽  
Daniel S. Cohan
Keyword(s):  
Air Quality ◽  
Spatial Heterogeneity ◽  
Model Performance ◽  
Soil Conditions ◽  
N Availability ◽  
Biogeochemical Processes ◽  
Mineral N ◽  
N Transformations ◽  
Multi Scale ◽  
No Emissions

Abstract. Soils are important sources of emissions of nitrogen (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 Multi-scale 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 non-agricultural soils, which was not accounted in earlier parameterizations for soil NO. Our mechanistic scheme also uses daily year-specific fertilizer use estimates from the Environmental Policy Integrated Climate (EPIC v.0509) 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.

scholarly journals Enhanced representation of soil NO emissions in the Community Multi-scale Air Quality (CMAQ) model

2016 ◽  
Author(s):  
Quazi Z. Rasool ◽  
Rui Zhang ◽  
Benjamin Lash ◽  
Daniel S. Cohan ◽  
Ellen Cooter ◽  
...  
Keyword(s):  
Air Quality ◽  
Fine Particulate Matter ◽  
Model Performance ◽  
Temporal Distribution ◽  
Soil Parameters ◽  
N Availability ◽  
Ozone Monitoring Instrument ◽  
Multi Scale ◽  
Generic Data ◽  
No Emissions

Abstract. 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 Multi-scale 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 US. CMAQ modeling for July 2011 shows slight differences in model performance in simulating fine particulate matter and ozone from IMPROVE and CASTNET sites and NO2 columns from Ozone Monitoring Instrument (OMI) satellite retrievals. We also simulate how the change in soil NO emissions scheme affects the expected O3 response to projected emissions reductions.

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

2016 ◽  
Vol 9 (9) ◽  
pp. 3177-3197 ◽  
Author(s):  
Quazi Z. Rasool ◽  
Rui Zhang ◽  
Benjamin Lash ◽  
Daniel S. Cohan ◽  
Ellen J. Cooter ◽  
...  
Keyword(s):  
Air Quality ◽  
Fine Particulate Matter ◽  
Model Performance ◽  
Temporal Distribution ◽  
Soil Parameters ◽  
N Availability ◽  
Ozone Monitoring Instrument ◽  
Generic Data ◽  
Clean Air ◽  
No Emissions

Abstract. 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 NO2 columns from Ozone Monitoring Instrument (OMI) satellite retrievals. We also simulate how the change in soil NO emissions scheme affects the expected O3 response to projected emissions reductions.

scholarly journals A multi-resolution assessment of the Community Multiscale Air Quality (CMAQ) model v4.7 wet deposition estimates for 2002–2006

2011 ◽  
Vol 4 (2) ◽  
pp. 357-371 ◽  
Author(s):  
K. W. Appel ◽  
K. M. Foley ◽  
J. O. Bash ◽  
R. W. Pinder ◽  
R. L. Dennis ◽  
...  
Keyword(s):  
Air Quality ◽  
Wet Deposition ◽  
Model Performance ◽  
Model Simulations ◽  
Western Us ◽  
Overall Performance ◽  
Primary Focus ◽  
National Trends ◽  
Horizontal Grid ◽  
No Emissions

Abstract. This paper examines the operational performance of the Community Multiscale Air Quality (CMAQ) model simulations for 2002–2006 using both 36-km and 12-km horizontal grid spacing, with a primary focus on the performance of the CMAQ model in predicting wet deposition of sulfate (SO4=), ammonium (NH4+) and nitrate (NO3−). Performance of the wet deposition estimates from the model is determined by comparing CMAQ predicted concentrations to concentrations measured by the National Acid Deposition Program (NADP), specifically the National Trends Network (NTN). For SO4= wet deposition, the CMAQ model estimates were generally comparable between the 36-km and 12-km simulations for the eastern US, with the 12-km simulation giving slightly higher estimates of SO4= wet deposition than the 36-km simulation on average. The result is a slightly larger normalized mean bias (NMB) for the 12-km simulation; however both simulations had annual biases that were less than ±15 % for each of the five years. The model estimated SO4= wet deposition values improved when they were adjusted to account for biases in the model estimated precipitation. The CMAQ model underestimates NH4+ wet deposition over the eastern US, with a slightly larger underestimation in the 36-km simulation. The largest underestimations occur in the winter and spring periods, while the summer and fall have slightly smaller underestimations of NH4+ wet deposition. The underestimation in NH4+ wet deposition is likely due in part to the poor temporal and spatial representation of ammonia (NH3) emissions, particularly those emissions associated with fertilizer applications and NH3 bi-directional exchange. The model performance for estimates of NO3− wet deposition are mixed throughout the year, with the model largely underestimating NO3− wet deposition in the spring and summer in the eastern US, while the model has a relatively small bias in the fall and winter. Model estimates of NO3− wet deposition tend to be slightly lower for the 36-km simulation as compared to the 12-km simulation, particularly in the spring. The underestimation of NO3− wet deposition in the spring and summer is due in part to a lack of lightning generated NO emissions in the upper troposphere, which can be a large source of NO in the spring and summer when lightning activity is the high. CMAQ model simulations that include production of NO from lightning show a significant improvement in the NO3− wet deposition estimates in the eastern US in the summer. Overall, performance for the 36-km and 12-km CMAQ model simulations is similar for the eastern US, while for the western US the performance of the 36-km simulation is generally not as good as either eastern US simulation, which is not entire unexpected given the complex topography in the western US.

scholarly journals A multi-resolution assessment of the Community Multiscale Air Quality (CMAQ) Model v4.7 wet deposition estimates for 2002–2006

2010 ◽  
Vol 3 (4) ◽  
pp. 2315-2360 ◽  
Author(s):  
K. W. Appel ◽  
K. M. Foley ◽  
J. O. Bash ◽  
R. W. Pinder ◽  
R. L. Dennis ◽  
...  
Keyword(s):  
Air Quality ◽  
Wet Deposition ◽  
Model Performance ◽  
Grid Spacing ◽  
Model Simulations ◽  
Western Us ◽  
Primary Focus ◽  
National Trends ◽  
Horizontal Grid ◽  
No Emissions

Abstract. This paper examines the operational performance of the Community Multiscale Air Quality (CMAQ) model simulations for 2002–2006 using both 36-km and 12-km horizontal grid spacing with a primary focus on the performance of the CMAQ model in predicting wet deposition of sulfate (SO4=), ammonium (NH4+) and nitrate (NO3−). Performance of the wet deposition species is determined by comparing CMAQ predicted concentrations to concentrations measured by the National Acid Deposition Program (NADP), specifically the National Trends Network (NTN). For SO4= wet deposition, the CMAQ model estimates were generally comparable between the 36-km and 12-km simulations for the eastern US, with the 12-km simulation giving slightly higher estimates of SO4= wet deposition than the 36-km simulation on average. The normalized mean bias (NMB) was slightly higher for the 12-km simulation, however, both simulations had annual biases that were less than ±15% for each of the five years. The model estimated SO4= wet deposition values improved when they were adjusted to account for biases in the model estimated precipitation. The CMAQ model underestimates NH4+ wet deposition over the eastern US using both the 36-km and 12-km horizontal grid spacing, with a slightly larger underestimation in the 36-km simulation. The largest underestimations occur during the winter and spring periods, while the summer and fall have slightly smaller underestimations of NH4+ wet deposition. Annually, the NMB generally ranges between −10% and −16% for the 12-km simulation and −12% to −18% for the 36-km simulation over the five-year period for the eastern US. The underestimation in NH4+ wet deposition is likely due, in part, to the poor temporal and spatial representation of ammonia (NH3) emissions, particularly those emissions associated with fertilizer applications and NH3 bi-directional exchange. The model performance for estimates of NO3− wet deposition are mixed throughout the year, with the model largely underestimating NO3− wet deposition in the spring and summer in the eastern US, while the model has a relatively small bias in the fall and winter. Model estimates of NO3− wet deposition tend to be slightly lower for the 36-km simulation as compared to the 12-km simulation, particularly in the spring. Annually for the eastern US, the NMB ranges from roughly −12% to −20% for the 12-km simulation and −18% to −26% for the 36-km simulation. The underestimation of NO3− wet deposition in the spring and summer is due, in part, to a lack of lightning generated NO emissions in the upper troposphere, which can be a large source of NO in the spring and summer when lightning activity is the high. CMAQ model simulations that include the production of NO from lightning show a significant improvement in the NO3− wet deposition estimates in the eastern US in the summer. Model performance for the western US was generally not as good as that for the eastern US for all three wet deposition species.

scholarly journals Sensitivity and Performance Analyses of the Distributed Hydrology–Soil–Vegetation Model Using Geomorphons for Landform Mapping

Water ◽  
2021 ◽  
Vol 13 (15) ◽  
pp. 2032
Author(s):  
Pâmela A. Melo ◽  
Lívia A. Alvarenga ◽  
Javier Tomasella ◽  
Carlos R. Mello ◽  
Minella A. Martins ◽  
...  
Keyword(s):  
Complex Terrain ◽  
Overland Flow ◽  
Hydrological Model ◽  
Model Performance ◽  
Soil Parameters ◽  
Vegetation Model ◽  
Landform Classification ◽  
Physically Based ◽  
And Performance ◽  
Streamflow Simulation

Landform classification is important for representing soil physical properties varying continuously across the landscape and for understanding many hydrological processes in watersheds. Considering it, this study aims to use a geomorphology map (Geomorphons) as an input to a physically based hydrological model (Distributed Hydrology Soil Vegetation Model (DHSVM)) in a mountainous headwater watershed. A sensitivity analysis of five soil parameters was evaluated for streamflow simulation in each Geomorphons feature. As infiltration and saturation excess overland flow are important mechanisms for streamflow generation in complex terrain watersheds, the model’s input soil parameters were most sensitive in the “slope”, “hollow”, and “valley” features. Thus, the simulated streamflow was compared with observed data for calibration and validation. The model performance was satisfactory and equivalent to previous simulations in the same watershed using pedological survey and moisture zone maps. Therefore, the results from this study indicate that a geomorphologically based map is applicable and representative for spatially distributing hydrological parameters in the DHSVM.

scholarly journals Urban Air Quality Modeling Using Low-Cost Sensor Network and Data Assimilation in the Aburrá Valley, Colombia

Atmosphere ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 91
Author(s):  
Santiago Lopez-Restrepo ◽  
Andres Yarce ◽  
Nicolás Pinel ◽  
O.L. Quintero ◽  
Arjo Segers ◽  
...  
Keyword(s):  
Air Quality ◽  
Data Assimilation ◽  
Transport Model ◽  
Cost Model ◽  
Low Cost ◽  
Model Performance ◽  
Monitoring Network ◽  
Correlation Factor ◽  
Chemical Transport Model ◽  
High Quality

The use of low air quality networks has been increasing in recent years to study urban pollution dynamics. Here we show the evaluation of the operational Aburrá Valley’s low-cost network against the official monitoring network. The results show that the PM2.5 low-cost measurements are very close to those observed by the official network. Additionally, the low-cost allows a higher spatial representation of the concentrations across the valley. We integrate low-cost observations with the chemical transport model Long Term Ozone Simulation-European Operational Smog (LOTOS-EUROS) using data assimilation. Two different configurations of the low-cost network were assimilated: using the whole low-cost network (255 sensors), and a high-quality selection using just the sensors with a correlation factor greater than 0.8 with respect to the official network (115 sensors). The official stations were also assimilated to compare the more dense low-cost network’s impact on the model performance. Both simulations assimilating the low-cost model outperform the model without assimilation and assimilating the official network. The capability to issue warnings for pollution events is also improved by assimilating the low-cost network with respect to the other simulations. Finally, the simulation using the high-quality configuration has lower error values than using the complete low-cost network, showing that it is essential to consider the quality and location and not just the total number of sensors. Our results suggest that with the current advance in low-cost sensors, it is possible to improve model performance with low-cost network data assimilation.

scholarly journals Ensemble Predictions of Air Pollutants in China in 2013 for Health Effects Studies Using WRF/CMAQ Modeling System with Four Emission Inventories

2017 ◽  
Author(s):  
Jianlin Hu ◽  
Xun Li ◽  
Lin Huang ◽  
Qi Ying ◽  
Qiang Zhang ◽  
...  
Keyword(s):  
Air Quality ◽  
Health Effects ◽  
Air Pollutants ◽  
Emission Inventory ◽  
Model Performance ◽  
Observation Data ◽  
Emission Inventories ◽  
Air Quality Model ◽  
Quality Model ◽  
Future Health

Abstract. Accurate exposure estimates are required for health effects analyses of severe air pollution in China. Chemical transport models (CTMs) are widely used tools to provide detailed information of spatial distribution, chemical composition, particle size fractions, and source origins of pollutants. The accuracy of CTMs' predictions in China is largely affected by the uncertainties of public available emission inventories. The Community Multi-scale Air Quality model (CMAQ) with meteorological inputs from the Weather Research and Forecasting model (WRF) were used in this study to simulate air quality in China in 2013. Four sets of simulations were conducted with four different anthropogenic emission inventories, including the Multi-resolution Emission Inventory for China (MEIC), the Emission Inventory for China by School of Environment at Tsinghua University (SOE), the Emissions Database for Global Atmospheric Research (EDGAR), and the Regional Emission inventory in Asia version 2 (REAS2). Model performance was evaluated against available observation data from 422 sites in 60 cities across China. Model predictions of O3 and PM2.5 with the four inventories generally meet the criteria of model performance, but difference exists in different pollutants and different regions among the inventories. Ensemble predictions were calculated by linearly combining the results from different inventories under the constraint that sum of the squared errors between the ensemble results and the observations from all the cities was minimized. The ensemble annual concentrations show improved agreement with observations in most cities. The mean fractional bias (MFB) and mean fractional errors (MFE) of the ensemble predicted annual PM2.5 at the 60 cities are −0.11 and 0.24, respectively, which are better than the MFB (−0.25–−0.16) and MFE (0.26–0.31) of individual simulations. The ensemble annual 1-hour peak O3 (O3-1 h) concentrations are also improved, with mean normalized bias (MNB) of 0.03 and mean normalized errors (MNE) of 0.14, compared to MNB of 0.06–0.19 and MNE of 0.16–0.22 of the individual predictions. The ensemble predictions agree better with observations with daily, monthly, and annual averaging times in all regions of China for both PM2.5 and O3-1 h. The study demonstrates that ensemble predictions by combining predictions from individual emission inventories can improve the accuracy of predicted temporal and spatial distributions of air pollutants. This study is the first ensemble model study in China using multiple emission inventories and the results are publicly available for future health effects studies.

scholarly journals Carbon losses from pyrolysed and original wood in a forest soil under natural and increased N deposition

2014 ◽  
Vol 11 (18) ◽  
pp. 5199-5213 ◽  
Author(s):  
B. Maestrini ◽  
S. Abiven ◽  
N. Singh ◽  
J. Bird ◽  
M. S. Torn ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Dissolved Organic Carbon ◽  
Forest Soil ◽  
N Deposition ◽  
Main Process ◽  
N Availability ◽  
N Losses ◽  
Mineral N ◽  
C Cycling ◽  
Carbon Losses

Abstract. Pyrogenic organic matter (PyOM) plays an important role as a stable carbon (C) sink in the soils of terrestrial ecosystems. However, uncertainties remain about in situ turnover rates of fire-derived PyOM in soil, the main processes leading to PyOM-C and nitrogen (N) losses from the soil, and the role of N availability on PyOM cycling in soils. We measured PyOM and native soil organic carbon losses from the soil as carbon dioxide and dissolved organic carbon (DOC) using additions of highly 13C-labelled PyOM (2.03 atom %) and its precursor pinewood during 1 year in a temperate forest soil. The field experiment was carried out under ambient and increased mineral N deposition (+60 kg N-NH4NO3 ha−1 year−1). The results showed that after 1 year: (1) 0.5% of PyOM-C and 22% of wood-C were mineralized as CO2, leading to an estimated turnover time of 191 and 4 years, respectively; (2) the quantity of PyOM and wood lost as dissolved organic carbon was negligible (0.0004 ± 0.0003% and 0.022 ± 0.007% of applied-C, respectively); and (3) N additions decreased cumulative PyOM mineralization by 43%, but did not affect cumulative wood mineralization and did not affect the loss of DOC from PyOM or wood. We conclude that mineralization to CO2 was the main process leading to PyOM losses during the first year of mineralization in a forest soil, and that N addition can decrease PyOM-C cycling, while added N showed no effect on wood C cycling.

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