Beata Czader

Radical precursors and related species from traffic as observed and modeled at an urban highway junction

Nitrous acid (HONO) and formaldehyde (HCHO) are important precursors for radicals and are believed to favor ozone formation significantly. Traffic emission data for both compounds are scarce and mostly outdated. A better knowledge of todays HCHO and HONO emissions related to traffic is needed to refine air quality models. Here the authors report results from continuous ambient air measurements taken at a highway junction in Houston, Texas, from July 15 to October 15, 2009. The observational data were compared with emission estimates from currently available mobile emission models (MOBILE6; MOVES [MOtor Vehicle Emission Simulator]). Observations indicated a molar carbon monoxide (CO) versus nitrogen oxides (NOx) ratio of 6.01 ± 0.15 (r 2 = 0.91), which is in agreement with other field studies. Both MOBILE6 and MOVES overestimate this emission ratio by 92% and 24%, respectively. For HCHO/CO, an overall slope of 3.14 ± 0.14 g HCHO/kg CO was observed. Whereas MOBILE6 largely underestimates this ratio by 77%, MOVES calculates somewhat higher HCHO/CO ratios (1.87) than MOBILE6, but is still significantly lower than the observed ratio. MOVES shows high HCHO/CO ratios during the early morning hours due to heavy-duty diesel off-network emissions. The differences of the modeled CO/NOx and HCHO/CO ratios are largely due to higher NOx and HCHO emissions in MOVES (30% and 57%, respectively, increased from MOBILE6 for 2009), as CO emissions were about the same in both models. The observed HONO/NOx emission ratio is around 0.017 ± 0.0009 kg HONO/kg NOx which is twice as high as in MOVES. The observed NO2/NOx emission ratio is around 0.16 ± 0.01 kg NO2/kg NOx, which is a bit more than 50% higher than in MOVES. MOVES overestimates the CO/CO2 emission ratio by a factor of 3 compared with the observations, which is 0.0033 ± 0.0002 kg CO/kg CO2. This as well as CO/NOx overestimation is coming from light-duty gasoline vehicles. Implications: Nitrous acid (HONO) and formaldehyde (HCHO) are important precursors for radicals that ultimately contribute to ozone formation. There still exist uncertainties in emission sources of HONO and HCHO and thus regional air quality modeling still tend to underestimate concentrations of free radicals in the atmosphere. This paper demonstrates that the latest U.S. Environmental Protection Agency (EPA) traffic emission model MOVES still shows significant deviations from observed emission ratios, in particular underestimation of HCHO/CO and HONO/NOx ratios. Improving the performance of MOVES may improve regional air quality modeling.

The impact of observation nudging on simulated meteorology and ozone concentrations during DISCOVER-AQ 2013 Texas campaign

Accurate meteorological fields are imperative for correct chemical transport modeling. Observation nudging, along with objective analysis, is generally considered a lowcost and effective technique to improve meteorological simulations. However, the meteorological impact of observation nudging on chemistry has not been well characterized. This study involved two simulations to analyze the impact of observation nudging on simulated meteorology and ozone concentrations during the 2013 Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) Texas campaign period, using the Weather Research and Forecasting (WRF) and Community Multiscale Air Quality (CMAQ) models. The results showed improved correlations between observed and simulated parameters. For example, the index of agreement (IOA) improved by about 9 % for surface temperature and 6–11 % for surface zonal (U-WIND) and meridional (V-WIND) winds when observation nudging was employed. Analysis of a cold front event indicated that nudging improved the timing of wind transition during the front passage. Observation nudging also reduced the model biases for the planetary boundary layer height predictions. Additionally, the IOA for CMAQ simulated surface ozone improved by 6 % during the simulation period. The high-ozone episode on 25 September was a post-front ozone event in Houston. The small-scale morning wind shifts near the Houston Ship Channel combined with higher aloft ozone early morning likely caused the day’s ozone exceedance. While observation nudging did not recreate the wind shifts on that day and failed to reproduce the observed high ozone, analyses of surface and aircraft data found that observation nudging helped the model yield improved ozone predictions. In a 2 h period during the event, substantially better winds in the sensitivity case noticeably improved the ozone. The average IOA for ozone in the period increased from just over 0.4 to near 0.7. Further work on improving the capability of nudging to reproduce local meteorological events such as stagnations and wind reversals could enhance a chemical transport model’s skill for predicting high-ozone events.

Simulation of Atmospheric Oxidation Capacity in Houston, Texas

Air quality model simulations are performed and evaluated for Houston using the Community Multiscale Air Quality (CMAQ) model. The simulations use two different emissions estimates: the EPA 2005 National Emissions Inventory (NEI) and the Texas Commission on Environmental Quality (TCEQ) Emissions Inventory. A comparison of predictions with observed data from the 2006 TexAQS-II Radical and Aerosol Measurement Project (TRAMP) suggest that while the predicted oxides of nitrogen are greater than observations, predicted volatile organics (e.g., ethane, acetone) are substantially lower than the observations. Predicted hydroxyl radical predictions are in good agreement with the observations. Hydroperoxy radical predictions, however, are substantially lower than the observations.

Efficacy of Incremental Reduction of Input Uncertainties to Improve Air Quality Prediction

Performance of an air quality prediction may suffer with problems due to uncertainties in meteorological forecasts, lack of event-based real-life emissions variations such as forest fires, and inaccurate dynamic boundary conditions representing effects of long-range transport events. The main objective of this article is to demonstrate the combined synergistic effect of incremental reduction of uncertainties in the model inputs for improving air quality predictions. Effects of improved meteorological inputs, emissions, and initial and boundary conditions, through the use of in-situ measurements and satellite-retrieved information are studied for the continental US domains utilizing the Community Multiscale Air Quality (CMAQ) model. In this extended abstract, only the impacts of improved initial and boundary conditions are discussed.