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 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 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 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 Impact of SARS-CoV-2 on Ambient Air Quality in Northwest China (NWC)

Atmosphere ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 518
Author(s):  
Shah Zaib ◽  
Jianjiang Lu ◽  
Muhammad Zeeshaan Shahid ◽  
Sunny Ahmar ◽  
Imran Shahid
Keyword(s):  
Particulate Matter ◽  
Air Quality ◽  
Fine Particulate Matter ◽  
Temporal Distribution ◽  
Ambient Air ◽  
Northwest China ◽  
Ambient Air Quality ◽  
Coarse Particulate Matter ◽  
Air Quality Improvement ◽  
Grade Ii

SARS-CoV-2 was discovered in Wuhan (Hubei) in late 2019 and covered the globe by March 2020. To prevent the spread of the SARS-CoV-2 outbreak, China imposed a countrywide lockdown that significantly improved the air quality. To investigate the collective effect of SARS-CoV-2 on air quality, we analyzed the ambient air quality in five provinces of northwest China (NWC): Shaanxi (SN), Xinjiang (XJ), Gansu (GS), Ningxia (NX) and Qinghai (QH), from January 2019 to December 2020. For this purpose, fine particulate matter (PM2.5), coarse particulate matter (PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), and ozone (O3) were obtained from the China National Environmental Monitoring Center (CNEMC). In 2020, PM2.5, PM10, SO2, NO2, CO, and O3 improved by 2.72%, 5.31%, 7.93%, 8.40%, 8.47%, and 2.15%, respectively, as compared with 2019. The PM2.5 failed to comply in SN and XJ; PM10 failed to comply in SN, XJ, and NX with CAAQS Grade II standards (35 µg/m3, 70 µg/m3, annual mean). In a seasonal variation, all the pollutants experienced significant spatial and temporal distribution, e.g., highest in winter and lowest in summer, except O3. Moreover, the average air quality index (AQI) improved by 4.70%, with the highest improvement in SN followed by QH, GS, XJ, and NX. AQI improved in all seasons; significant improvement occurred in winter (December to February) and spring (March to May) when lockdowns, industrial closure etc. were at their peak. The proportion of air quality Class I improved by 32.14%, and the number of days with PM2.5, SO2, and NO2 as primary pollutants decreased while they increased for PM10, CO, and O3 in 2020. This study indicates a significant association between air quality improvement and the prevalence of SARS-CoV-2 in 2020.

scholarly journals Understanding Spatial Variability of Air Quality in Sydney: Part 2—A Roadside Case Study

Atmosphere ◽  
2019 ◽  
Vol 10 (4) ◽  
pp. 217 ◽  
Author(s):  
Imogen Wadlow ◽  
Clare Paton-Walsh ◽  
Hugh Forehead ◽  
Pascal Perez ◽  
Mehrdad Amirghasemi ◽  
...  
Keyword(s):  
Air Quality ◽  
Fine Particulate Matter ◽  
Temporal Variations ◽  
Subject Area ◽  
Traffic Model ◽  
Urban Landscapes ◽  
Study Region ◽  
Main Road ◽  
Agent Based ◽  
Clean Air

Motivated by public interest, the Clean Air and Urban Landscapes (CAUL) hub deployed instrumentation to measure air quality at a roadside location in Sydney. The main aim was to compare concentrations of fine particulate matter (PM2.5) measured along a busy road section with ambient regional urban background levels, as measured at nearby regulatory air quality stations. The study also explored spatial and temporal variations in the observed PM2.5 concentrations. The chosen area was Randwick in Sydney, because it was also the subject area for an agent-based traffic model. Over a four-day campaign in February 2017, continuous measurements of PM2.5 were made along and around the main road. In addition, a traffic counting application was used to gather data for evaluation of the agent-based traffic model. The average hourly PM2.5 concentration was 13 µg/m3, which is approximately twice the concentrations at the nearby regulatory air quality network sites measured over the same period. Roadside concentrations of PM2.5 were about 50% higher in the morning rush-hour than the afternoon rush hour, and slightly lower (reductions of <30%) 50 m away from the main road, on cross-roads. The traffic model under-estimated vehicle numbers by about 4 fold, and failed to replicate the temporal variations in traffic flow, which we assume was due to an influx of traffic from outside the study region dominating traffic patterns. Our findings suggest that those working for long hours outdoors at busy roadside locations are at greater risk of suffering detrimental health effects associated with higher levels of exposure to PM2.5. Furthermore, the worse air quality in the morning rush hour means that, where possible, joggers and cyclists should avoid busy roads around these times.

scholarly journals The impact of US wildland fires on ozone and particulate matter: a comparison of measurements and CMAQ model predictions from 2008 to 2012

2018 ◽  
Vol 27 (10) ◽  
pp. 684 ◽  
Author(s):  
Joseph L. Wilkins ◽  
George Pouliot ◽  
Kristen Foley ◽  
Wyat Appel ◽  
Thomas Pierce
Keyword(s):  
Particulate Matter ◽  
Air Quality ◽  
Wildland Fire ◽  
Fine Particulate Matter ◽  
Model Performance ◽  
Grid Cell ◽  
Emissions Inventory ◽  
Fire Emissions ◽  
The Mean ◽  
The Impact

Wildland fire emissions are routinely estimated in the US Environmental Protection Agency’s National Emissions Inventory, specifically for fine particulate matter (PM2.5) and precursors to ozone (O3); however, there is a large amount of uncertainty in this sector. We employ a brute-force zero-out sensitivity method to estimate the impact of wildland fire emissions on air quality across the contiguous US using the Community Multiscale Air Quality (CMAQ) modelling system. These simulations are designed to assess the importance of wildland fire emissions on CMAQ model performance and are not intended for regulatory assessments. CMAQ ver. 5.0.1 estimated that fires contributed 11% to the mean PM2.5 and less than 1% to the mean O3 concentrations during 2008–2012. Adding fires to CMAQ increases the number of ‘grid-cell days’ with PM2.5 above 35 µg m−3 by a factor of 4 and the number of grid-cell days with maximum daily 8-h average O3 above 70 ppb by 14%. Although CMAQ simulations of specific fires have improved with the latest model version (e.g. for the 2008 California wildfire episode, the correlation r = 0.82 with CMAQ ver. 5.0.1 v. r = 0.68 for CMAQ ver. 4.7.1), the model still exhibits a low bias at higher observed concentrations and a high bias at lower observed concentrations. Given the large impact of wildland fire emissions on simulated concentrations of elevated PM2.5 and O3, improvements are recommended on how these emissions are characterised and distributed vertically in the model.

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 An inversion of fine particulate matter (PM<sub>2.5</sub>) mass concentrations based on the air quality index (AQI) during dust prone periods in Hotan oasis, Sinkiang

2018 ◽  
Author(s):  
Ju Chunyan ◽  
Zhang Zili ◽  
Zhou Xu ◽  
He Qing
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Correlation Coefficient ◽  
Fine Particulate Matter ◽  
Temporal Distribution ◽  
Ambient Air ◽  
Good Choice ◽  
Ambient Air Pollution ◽  
Observation Data ◽  
Air Quality Monitoring Stations

Abstract. Ambient air pollution has been implicated as a major environmental problem in Urban development process. The objective of this publication is to analyse deeply the correlation coefficient of PM2.5 and AOD and aerosol optical depth (AOD). Surface PM2.5 observation data and AOD were investigated from March to June in 2015 and 2016. Hourly PM2.5 data are sampled from air quality monitoring stations in Hotan oasis. The AOD data are derived from Terra and Aqua at 10 km resolution. The satellite passed the area at about 13:30 AM and 15:30 PM，respectively．By using the matched PM2.5 and AOD data，the spatial and temporal distribution characteristics are discussed, and the correlation coefficient of PM2.5 versus AOD are estimated. The results show that PM2.5 mass concentration and AOD vary greatly in different pollution weather. This phenomenon may be associated with data collection time, and other meteorological factors. Regression analysis based on typical air pollution show subsection fitting effect is relatively good choice, and regression is relatively well in Hazardous and serious pollution weather. Fitting analysis is good for PM2.5 in different level of air pollution, and sources and pollutants transmission have difference.

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