ALBERT

Description: During the second Texas Air Quality Study 2006 (TexAQS II), a full range of pollutants was measured by aircraft in eastern Texas during successive transects of power plant plumes (PPPs). A regional photochemical model is applied to simulate the physical and chemical evolution of the plumes. The observations reveal that SO2 and NOy were rapidly removed from PPPs on a cloudy day but not on the cloud-free days, indicating efficient aqueous processing of these compounds in clouds. The model reasonably represents observed NOx oxidation and PAN formation in the plumes, but fails to capture the rapid loss of SO2 (0.37 h−1) and NOy (0.24 h−1) in some plumes on the cloudy day. Adjustments to the cloud liquid water content (QC) and the default metal concentrations in the cloud module could explain some of the SO2 loss. However, NOy in the model was insensitive to QC. These findings highlight cloud processing as a major challenge to atmospheric models. Model-based estimates of ozone production efficiency (OPE) in PPPs are 20–50 % lower than observation-based estimates for the cloudy day.

Description: 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.

Description: During the second Texas Air Quality Study 2006 (TexAQS II), a full range of pollutants was measured by aircraft in eastern Texas during successive transects of power plant plumes (PPPs). A regional photochemical model is applied to simulate the physical and chemical evolution of the plumes. The observations reveal that SO2 and NOy were rapidly removed from PPPs on a cloudy day but not on the cloud-free days, indicating efficient aqueous processing of these compounds in clouds. The model reasonably represents observed NOx oxidation and PAN formation in the plumes, but fails to capture the rapid loss of SO2 (0.37 h−1) and NOy (0.24 h−1) in some plumes on the cloudy day. Adjustments to the cloud liquid water content (QC) and the default metal concentrations in the cloud module could explain some of the SO2 loss. However, NOy in the model was insensitive to QC. These findings highlight cloud processing as a major challenge to atmospheric models. Model-based estimates of ozone production efficiency (OPE) in PPPs are 20–50 % lower than observation-based estimates. Possible explanations for this discrepancy include the observed rapid NOy loss which biases high some observation-based OPE estimates, and the model's under-prediction of isoprene emissions.

Description: Airborne measurements from two Texas Air Quality Study (TexAQS) field campaigns have been used to investigate changes of ozone production in Houston, Texas, from 2000 to 2006, a period of major emission reduction measures at petrochemical and other sources. Simultaneous declines in nitrogen oxides (NOx=NO+NO2) and highly reactive volatile organic compounds (HRVOCs) were observed between the two periods. We simulated HOx (OH and HO2) and organic radicals with a box model, the Dynamically Simple Model of Atmospheric Chemical Complexity, constrained by available airborne observations. Parameters such as total radical production, total OH reactivity of VOCs and ozone production rate (P(O3) are computed to characterize the change of ozone production between 2000 and 2006 in the Houston area. The reduction in HRVOCs led to a decline in total radical production by 20–50%. Ozone production rates in the Houston area declined by 40–50% from 2000 to 2006, to which the reduction in NOx and HRVOCs had large contributions. Despite the significant decline in P(O3), ozone production efficiency held steady, and VOC-sensitive conditions dominated during times of most rapid ozone formation. Our results highlight the importance of ongoing HRVOC controls to further reduce O3 levels in the Houston area.

Description: 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.

Description: 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.

Description: Airborne measurements from two Texas Air Quality Study (TexAQS) field campaigns have been used to investigate changes of ozone production in Houston, Texas, from 2000 to 2006, a period of major emission reduction measures for petrochemical and other sources. Simultaneous declines in nitrogen oxides (NOx = NO + NO2) and highly reactive volatile organic compounds (HRVOCs) were observed between the two periods. We simulate HOx (OH and HO2) and organic radicals with a box model, the Dynamically Simple Model of Atmospheric Chemical Complexity, constrained by available airborne observations. Parameters such as total radical production, total OH reactivity of VOCs and ozone production rate (OPR) are computed to characterize the change of ozone production between 2000 and 2006 in the Houston area. The reduction in HRVOCs led to a decline in total radical production by 20–50%. Ozone production rates in the Houston area declined by 40–50% from 2000 to 2006, to which the reduction in NOx and HRVOCs made large contributions. Despite the significant decline in OPR, ozone production efficiency held steady, and VOC-sensitive conditions dominated during times of most rapid ozone formation, while the slow ozone formation continued to be NOx-limited. Our results highlight the importance of a balanced approach of ongoing HRVOC controls with NOx controls to further reduce O3 levels in the Houston area.

Description: 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.

Description: 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. Satellitebased 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.