scholarly journals Inverse modeling of Texas NO<sub>x</sub> emissions using space-based and ground-based NO<sub>2</sub> observations

2013 ◽  
Vol 13 (21) ◽  
pp. 11005-11018 ◽  
Author(s):  
W. Tang ◽  
D. S. Cohan ◽  
L. N. Lamsal ◽  
X. Xiao ◽  
W. Zhou
Keyword(s):  
Inverse Modeling ◽  
Direct Method ◽  
A Priori ◽  
Model Performance ◽  
Ground Level ◽  
Nox Emissions ◽  
Emission Inventories ◽  
Air Quality Model ◽  
Quality Model ◽  
Ground Level Ozone

Abstract. Inverse modeling of nitrogen oxide (NOx) emissions using satellite-based NO2 observations has become more prevalent in recent years, but has rarely been applied to regulatory modeling at regional scales. In this study, OMI satellite observations of NO2 column densities are used to conduct inverse modeling of NOx emission inventories for two Texas State Implementation Plan (SIP) modeling episodes. Addition of lightning, aircraft, and soil NOx emissions to the regulatory inventory narrowed but did not close the gap between modeled and satellite-observed NO2 over rural regions. Satellite-based top-down emission inventories are created with the regional Comprehensive Air Quality Model with extensions (CAMx) using two techniques: the direct scaling method and discrete Kalman filter (DKF) with decoupled direct method (DDM) sensitivity analysis. The simulations with satellite-inverted inventories are compared to the modeling results using the a priori inventory as well as an inventory created by a ground-level NO2-based DKF inversion. The DKF inversions yield conflicting results: the satellite-based inversion scales up the a priori NOx emissions in most regions by factors of 1.02 to 1.84, leading to 3–55% increase in modeled NO2 column densities and 1–7 ppb increase in ground 8 h ozone concentrations, while the ground-based inversion indicates the a priori NOx emissions should be scaled by factors of 0.34 to 0.57 in each region. However, none of the inversions improve the model performance in simulating aircraft-observed NO2 or ground-level ozone (O3) concentrations.

scholarly journals Inverse modeling of Texas NO<sub>x</sub> emissions using space-based and ground-based NO<sub>2</sub> observations

2013 ◽  
Vol 13 (7) ◽  
pp. 17479-17517
Author(s):  
W. Tang ◽  
D. Cohan ◽  
L. N. Lamsal ◽  
X. Xiao ◽  
W. Zhou
Keyword(s):  
Inverse Modeling ◽  
Direct Method ◽  
A Priori ◽  
Model Performance ◽  
Ground Level ◽  
Nox Emissions ◽  
Emission Inventories ◽  
Air Quality Model ◽  
Quality Model ◽  
Ground Level Ozone

Abstract. Inverse modeling of nitrogen oxide (NOx) emissions using satellite-based NO2 observations has become more prevalent in recent years, but has rarely been applied to regulatory modeling at regional scales. In this study, OMI satellite observations of NO2 column densities are used to conduct inverse modeling of NOx emission inventories for two Texas State Implementation Plan (SIP) modeling episodes. Addition of lightning, aircraft, and soil NOx emissions to the regulatory inventory narrowed but did not close the gap between modeled and satellite observed NO2 over rural regions. Satellite-based top-down emission inventories are created with the regional Comprehensive Air Quality Model with extensions (CAMx) using two techniques: the direct scaling method and discrete Kalman filter (DKF) with Decoupled Direct Method (DDM) sensitivity analysis. The simulations with satellite-inverted inventories are compared to the modeling results using the a priori inventory as well as an inventory created by a ground-level NO2 based DKF inversion. The DKF inversions yield conflicting results: the satellite-based inversion scales up the a priori NOx emissions in most regions by factors of 1.02 to 1.84, leading to 3–55% increase in modeled NO2 column densities and 1–7 ppb increase in ground 8 h ozone concentrations, while the ground-based inversion indicates the a priori NOx emissions should be scaled by factors of 0.34 to 0.57 in each region. However, none of the inversions improve the model performance in simulating aircraft-observed NO2 or ground-level ozone (O3) concentrations.

scholarly journals Influence of satellite-derived photolysis rates and NO<sub>x</sub> emissions on Texas ozone modeling

2015 ◽  
Vol 15 (4) ◽  
pp. 1601-1619 ◽  
Author(s):  
W. Tang ◽  
D. S. Cohan ◽  
A. Pour-Biazar ◽  
L. N. Lamsal ◽  
A. T. White ◽  
...  
Keyword(s):  
Direct Method ◽  
Model Performance ◽  
State Level ◽  
Nox Emissions ◽  
Air Quality Model ◽  
Ozone Monitoring Instrument ◽  
Quality Model ◽  
Vertical Column ◽  
Joint Application ◽  
Photolysis Rates

Abstract. Uncertain photolysis rates and emission inventory impair the accuracy of state-level ozone (O3) regulatory modeling. Past studies have separately used satellite-observed clouds to correct the model-predicted photolysis rates, or satellite-constrained top-down NOx emissions to identify and reduce uncertainties in bottom-up NOx emissions. However, the joint application of multiple satellite-derived model inputs to improve O3 state implementation plan (SIP) modeling has rarely been explored. In this study, Geostationary Operational Environmental Satellite (GOES) observations of clouds are applied to derive the photolysis rates, replacing those used in Texas SIP modeling. This changes modeled O3 concentrations by up to 80 ppb and improves O3 simulations by reducing modeled normalized mean bias (NMB) and normalized mean error (NME) by up to 0.1. A sector-based discrete Kalman filter (DKF) inversion approach is incorporated with the Comprehensive Air Quality Model with extensions (CAMx)–decoupled direct method (DDM) model to adjust Texas NOx emissions using a high-resolution Ozone Monitoring Instrument (OMI) NO2 product. The discrepancy between OMI and CAMx NO2 vertical column densities (VCDs) is further reduced by increasing modeled NOx lifetime and adding an artificial amount of NO2 in the upper troposphere. The region-based DKF inversion suggests increasing NOx emissions by 10–50% in most regions, deteriorating the model performance in predicting ground NO2 and O3, while the sector-based DKF inversion tends to scale down area and nonroad NOx emissions by 50%, leading to a 2–5 ppb decrease in ground 8 h O3 predictions. Model performance in simulating ground NO2 and O3 are improved using sector-based inversion-constrained NOx emissions, with 0.25 and 0.04 reductions in NMBs and 0.13 and 0.04 reductions in NMEs, respectively. Using both GOES-derived photolysis rates and OMI-constrained NOx emissions together reduces modeled NMB and NME by 0.05, increases the model correlation with ground measurement in O3 simulations, and makes O3 more sensitive to NOx emissions in the O3 non-attainment areas.

scholarly journals Influence of satellite-derived photolysis rates and NO<sub>x</sub> emissions on Texas ozone modeling

2014 ◽  
Vol 14 (17) ◽  
pp. 24475-24522 ◽  
Author(s):  
W. Tang ◽  
D. S. Cohan ◽  
A. Pour-Biazar ◽  
L. N. Lamsal ◽  
A. White ◽  
...  
Keyword(s):  
Direct Method ◽  
Model Performance ◽  
State Level ◽  
Nox Emissions ◽  
Air Quality Model ◽  
Ozone Monitoring Instrument ◽  
Quality Model ◽  
Vertical Column ◽  
Joint Application ◽  
Photolysis Rates

Abstract. Uncertain photolysis rates and emission inventory impair the accuracy of state-level ozone (O3) regulatory modeling. Past studies have separately used satellite-observed clouds to correct the model-predicted photolysis rates, or satellite-constrained top-down NOx emissions to identify and reduce uncertainties in bottom-up NOx emissions. However, the joint application of multiple satellite-derived model inputs to improve O3 State Implementation Plan (SIP) modeling has rarely been explored. In this study, Geostationary Operational Environmental Satellite (GOES) observations of clouds are applied to derive the photolysis rates, replacing those used in Texas SIP modeling. This changes modeled O3 concentrations by up to 80 ppb and improves O3 simulations by reducing modeled normalized mean bias (NMB) and normalized mean error (NME) by up to 0.1. A sector-based discrete Kalman filter (DKF) inversion approach is incorporated with the Comprehensive Air Quality Model with extensions (CAMx)-Decoupled Direct Method (DDM) model to adjust Texas NOx emissions using a high resolution Ozone Monitoring Instrument (OMI) NO2 product. The discrepancy between OMI and CAMx NO2 vertical column densities (VCD) is further reduced by increasing modeled NOx lifetime and adding an artificial amount of NO2 in the upper troposphere. The sector-based DKF inversion tends to scale down area and non-road NOx emissions by 50%, leading to a 2–5 ppb decrease in ground 8 h O3 predictions. Model performance in simulating ground NO2 and O3 are improved using inverted NOx emissions, with 0.25 and 0.04 reductions in NMBs and 0.13 and 0.04 reductions in NMEs, respectively. Using both GOES-derived photolysis rates and OMI-constrained NOx emissions together reduces modeled NMB and NME by 0.05 and increases the model correlation with ground measurement in O3 simulations and makes O3 more sensitive to NOx emissions in the O3 non-attainment areas.

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 Low modeled ozone production suggests underestimation of precursor emissions (especially NO<sub><i>x</i></sub>) in Europe

2018 ◽  
Vol 18 (3) ◽  
pp. 2175-2198 ◽  
Author(s):  
Emmanouil Oikonomakis ◽  
Sebnem Aksoyoglu ◽  
Giancarlo Ciarelli ◽  
Urs Baltensperger ◽  
André Stephan Henry Prévôt
Keyword(s):  
Air Quality ◽  
Surface Ozone ◽  
Model Performance ◽  
Ozone Production ◽  
Nox Emissions ◽  
Air Quality Model ◽  
Quality Model ◽  
Voc Emissions ◽  
Mixing Ratios ◽  
The Impact

Abstract. High surface ozone concentrations, which usually occur when photochemical ozone production takes place, pose a great risk to human health and vegetation. Air quality models are often used by policy makers as tools for the development of ozone mitigation strategies. However, the modeled ozone production is often not or not enough evaluated in many ozone modeling studies. The focus of this work is to evaluate the modeled ozone production in Europe indirectly, with the use of the ozone–temperature correlation for the summer of 2010 and to analyze its sensitivity to precursor emissions and meteorology by using the regional air quality model, the Comprehensive Air Quality Model with Extensions (CAMx). The results show that the model significantly underestimates the observed high afternoon surface ozone mixing ratios (≥ 60 ppb) by 10–20 ppb and overestimates the lower ones (< 40 ppb) by 5–15 ppb, resulting in a misleading good agreement with the observations for average ozone. The model also underestimates the ozone–temperature regression slope by about a factor of 2 for most of the measurement stations. To investigate the impact of emissions, four scenarios were tested: (i) increased volatile organic compound (VOC) emissions by a factor of 1.5 and 2 for the anthropogenic and biogenic VOC emissions, respectively, (ii) increased nitrogen oxide (NOx) emissions by a factor of 2, (iii) a combination of the first two scenarios and (iv) increased traffic-only NOx emissions by a factor of 4. For southern, eastern, and central (except the Benelux area) Europe, doubling NOx emissions seems to be the most efficient scenario to reduce the underestimation of the observed high ozone mixing ratios without significant degradation of the model performance for the lower ozone mixing ratios. The model performance for ozone–temperature correlation is also better when NOx emissions are doubled. In the Benelux area, however, the third scenario (where both NOx and VOC emissions are increased) leads to a better model performance. Although increasing only the traffic NOx emissions by a factor of 4 gave very similar results to the doubling of all NOx emissions, the first scenario is more consistent with the uncertainties reported by other studies than the latter, suggesting that high uncertainties in NOx emissions might originate mainly from the road-transport sector rather than from other sectors. The impact of meteorology was examined with three sensitivity tests: (i) increased surface temperature by 4 ∘C, (ii) reduced wind speed by 50 % and (iii) doubled wind speed. The first two scenarios led to a consistent increase in all surface ozone mixing ratios, thus improving the model performance for the high ozone values but significantly degrading it for the low ozone values, while the third scenario had exactly the opposite effects. Overall, the modeled ozone is predicted to be more sensitive to its precursor emissions (especially traffic NOx) and therefore their uncertainties, which seem to be responsible for the model underestimation of the observed high ozone mixing ratios and ozone production.

Probabilistic Characterization of Atmospheric Transport and Diffusion

2007 ◽  
Vol 46 (7) ◽  
pp. 980-993 ◽  
Author(s):  
John S. Irwin ◽  
William B. Petersen ◽  
Steven C. Howard
Keyword(s):  
Atmospheric Transport ◽  
Atmospheric Dispersion ◽  
Model Performance ◽  
Ground Level ◽  
Air Quality Model ◽  
Quality Model ◽  
Probabilistic Characterization ◽  
Transport And Diffusion ◽  
Statistical Designs ◽  
Release Location

Abstract The observed scatter of observations about air quality model predictions stems from a combination of naturally occurring stochastic variations that are impossible for any model to simulate explicitly and variations arising from limitations in knowledge and from imperfect input data. In this paper, historical tracer experiments of atmospheric dispersion were analyzed to develop algorithms to characterize the observed stochastic variability in the ground-level crosswind concentration profile. The algorithms were incorporated into a Lagrangian puff model (“INPUFF”) so that the consequences of variability in the dispersion could be simulated using Monte Carlo methods. The variability in the plume trajectory was investigated in a preliminary sense by tracking the divergence in trajectories from releases adjacent to the actual release location. The variability in the near-centerline concentration values not described by the Gaussian crosswind profile was determined to be on the order of a factor of 2. The variability in the trajectory was determined as likely to be larger than the plume width, even with local wind observations for use in characterizing the transport. Two examples are provided to illustrate how estimates of variability 1) can provide useful information to inform decisions for emergency response and 2) can provide a basis for sound statistical designs for model performance assessments.

scholarly journals Impact of inert organic nitrate formation on ground-level ozone in a regional air quality model using the Carbon Bond Mechanism 4

1997 ◽  
Vol 24 (24) ◽  
pp. 3205-3208 ◽  
Author(s):  
P. Kasibhatla ◽  
W. L. Chameides ◽  
B. Duncan ◽  
M. Houyoux ◽  
C. Jang ◽  
...  
Keyword(s):  
Air Quality ◽  
Ground Level ◽  
Organic Nitrate ◽  
Air Quality Model ◽  
Quality Model ◽  
Carbon Bond ◽  
Ground Level Ozone ◽  
Regional Air Quality ◽  
Bond Mechanism

scholarly journals Air Quality Modeling with WRF-Chem v3.5 in East and South Asia: sensitivity to emissions and evaluation of simulated air quality

2015 ◽  
Vol 8 (10) ◽  
pp. 9373-9413 ◽  
Author(s):  
M. Zhong ◽  
E. Saikawa ◽  
Y. Liu ◽  
V. Naik ◽  
L. W. Horowitz ◽  
...  
Keyword(s):  
Air Quality ◽  
South Asia ◽  
Model Development ◽  
Model Performance ◽  
Ground Level ◽  
Performance Criteria ◽  
Weather Research And Forecasting ◽  
Air Quality Model ◽  
Quality Model ◽  
Mixing Ratios

Abstract. We conducted simulations using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) version 3.5 to study air quality in East and South Asia at a spatial resolution of 20 km × 20 km. We find large discrepancies between two existing emissions inventories: the Regional Emission Inventory in Asia version 2 (REAS) and the Emissions Database for Global Atmospheric Research version 4.2 (EDGAR) at the provincial level in China, with maximum differences up to 500 % for CO emissions, 190 % for NO, and 160 % for primary PM10. Such differences in the magnitude and the spatial distribution of emissions for various species lead to 40–70 % difference in surface PM10 concentrations, 16–20 % in surface O3 mixing ratios, and over 100 % in SO2 and NO2 mixing ratios in the polluted areas of China. Our sensitivity run shows WRF-Chem is sensitive to emissions, with the REAS-based simulation reproducing observed concentrations and mixing ratios better than the EDGAR-based simulation for July 2007. We conduct further model simulations using REAS emissions for January, April, July, and October in 2007 and evaluate simulations with available ground-level observations. The model results show clear regional variations in the seasonal cycle of surface PM10 and O3 over East and South Asia. The model meets the air quality model performance criteria for both PM10 (mean fractional bias, MFB &amp;leq; ± 60 %) and O3 (MFB &amp;leq; ± 15 %) in most of the observation sites, although the model underestimates PM10 over Northeast China in January. The model predicts the observed SO2 well at sites in Japan, while it tends to overestimate SO2 in China in July and October. The model underestimates most observed NO2 in all four months. These findings suggest that future model development and evaluation of emission inventories and models are needed for particulate matter and gaseous pollutants in East and South Asia.

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