This study investigated the causes behind the nighttime ozone biases in southeast Texas, including background ozone, the land use/land cover (LULC) change and the minimum nighttime eddy diffusivity coefficient (Kzmin) setting. It also tried to shed light on a historical issue of large ozone biases by tracing the history of Community Multiscale Air Quality (CMAQ) model's treatment of Kzmin. The ozone biases associated with the low and active ozone days are distinctively different through examination of their diurnal cycles. Hence the model background ozone bias is likely responsible for ozone bias at the regional level. Significant differences in PBL height, vertical mixing, dry deposition and spatial patterns of ozone biases were found in two LULC cases. Spatial patterns of nighttime eddy diffusivity and dry deposition are consistent with the underlying LULC data. By using the default Kzmin setting which lowering the nighttime vertical mixing, the average ozone bias is reduced as compared to the alternative Kzmin setting. The reduction is 3.7 ppb or 31% when the latest National Land Cover Database (NLCD) 2011 is used. Still biases as high as 20 ppb exist at a number of sites. There are minimal changes in ozone biases in inner city and coastal sites while the changes in some rural/suburban sites are substantial. Also higher sensitivity was observed when preceding day ozone was high. The results showed that both downward mixing of ozone above and dilution of surface NO are important processes for nighttime ozone. By comparing the spatial bias pattern of 2006 and 2015, we are fairly certain the high nighttime eddy diffusivity in the old simulation played a significant role in the large nighttime biases. The impacts from the uncertainties in dry deposition can be medium while uncertainties caused by model horizontal advection are likely less significant. Factors other than background ozone mostly contribute to the local level biases.

Houston is recognized for its large petrochemical industrial facilities providing abundant radicals for tropospheric ozone formation. Fortunately, maximum daily 8-h average (MDA8) surface ozone concentrations have declined in Houston (−0.6±0.3ppbvyr−1) during the summers (i.e., May to September) of 2000 to 2014, possibly due to the reductions in precursor emissions by effective control policies. However, it is also possible that changes in meteorological variables have affected ozone concentrations. This study focused on the impact of long-term wind patterns which have the highest impact on ozone in Houston. The analysis of long-term wind patterns can benefit surface ozone studies by 1) providing wind patterns that distinctly changed ozone levels, 2) investigating the frequency of patterns and the respective changes and 3) estimating ozone trends in specific wind patterns that local emissions are mostly involved, thus separating emissions impacts from meteorology to some extent. To this end, the 900-hPa flow patterns in summers of 2000 to 2014 were clustered in seven classes (C1–C7) by deploying an unsupervised partitioning method. We confirm the characteristics of the clusters from a backward trajectory analysis, monitoring networks, and a regional chemical transport model simulation. The results indicate that Houston has experienced a statistically significant downward trend (−0.6±0.4dayyr−1) of the cluster of weak easterly and northeasterly days (C4), when the highest fraction of ozone exceedances (MDA8>70ppbv) occurred. This suggests that the reduction in ozone precursors was not the sole reason for the decrease in ozone exceedance days (−1.5±0.6dayyr−1). Further, to examine the efficiency of control policies intended to reduce the amount of ozone, we estimated the trend of MDA8 ozone in C4 and C5 (weak winds) days when local emissions are primarily responsible for high ambient ozone levels. Both C4 and C5 show a large reduction in the 95th percentile and summertime trends mainly due to effective control strategies. Based on the 5th percentile daytime ozone for C1 (strong southeasterly wind) in coastal sites, this study found that the cleanest air masses that Houston received became more polluted during the summer of 2000–2014 by 1–3ppbv. Though this study focused on Houston, the analysis method presented could generally be used to estimate ozone trends in other regions where surface ozone is dominantly influenced by both wind patterns and local emissions.

Accurate meteorological fields are imperative for correct chemical transport modeling. Observation nudging, along with objective analysis, is generally considered a low-cost 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.

Air quality simulations were conducted using the Community Multiscale Air Quality (CMAQ) model for nocturnal isoprene in September 2013 using the United States Environmental Protection Agency's (EPA's) National Emissions Inventory of 2011 (NEI, 2011). The results were evaluated against measurements collected at eight Texas Commission on Environmental Quality (TCEQ) Automated Gas Chromatographs (AutoGCs) monitoring stations. The comparisons demonstrated two distinctive behaviors: overestimation before midnight (20:00–23:00p.m. local time) versus underestimation after midnight (00:00–06:00a.m.). Analyses identify the uncertainties in nitrate radical (NO3) concentration and vertical mixing as the possible minor factors contributing to the underestimation, and the underestimated wind speed as the major factor contributing to the overestimation. Further analysis links isoprene underestimation to the uncertainties in the nocturnal isoprene anthropogenic emissions in the NEI (2011) over industrial areas in Houston. This can be substantiated by the fact that the observed nighttime isoprene concentrations increased when the wind direction veered back from southeast to northeast, placing the stations downwind of industrial facilities. A sensitivity run with adjusted anthropogenic isoprene emissions in the later part of the night (i.e., the emissions were multiplied by the hourly underestimation factors ranging from 3.81 to 14.82) yielded closer isoprene predictions after midnight with slightly improved model mean (0.15 to 0.20ppb), mean error (−0.10 to −0.04ppb), mean absolute error (0.18 to 0.15ppb), root mean squared error (RMSE, 0.27 to 0.25ppb), and index of agreement (IOA, 0.66 to 0.68). The insignificant improvement was likely due to the uncertainties in the location of the high-peaked anthropogenic emissions. The impacts of the nighttime-adjusted isoprene emissions on the isoprene oxidation products, organic nitrate and ozone, were found to be minimal. This study, however, shows that more in-situ surface nighttime measurement data is critical to constrain the underestimated nocturnal isoprene emissions in Houston.

Reliable emission inventories are key to precisely model air pollutant concentrations. The relatively large reduction in NOx emissions that is well corroborated by satellite and in-situ observations over southeast Texas has resulted in discrepancies between observations and regional model simulations based on the National Emission Inventory (NEI) provided every three years in U.S. In this study, a Bayesian inversion of OMI tropospheric NO2 is conducted to update anthropogenic sources of NEI-2011 and soil-biogenic sources from BEIS3 (Biogenic Emission Inventory System version 3) over southeast Texas and west Louisiana during the 2013 DISCOVER-AQ Texas campaign. Results reveal that influences of the a priori profile used in OMI NO2 retrieval play a significant role in inconsistencies between model and satellite observations, which should be mitigated. A posteriori emissions are produced using the regional Community Multiscale Air Quality (CMAQ) model associated with Decoupled Direct Method (DDM) sensitivity analysis. The inverse estimate suggests a reduction in area (44%), mobile (30%), and point sources (60%) in high NOx areas (ENOx > 0.2 mol/s), and an increase in soil (similar to 52%) and area emissions (37%) in low NOx regions (ENOx < 0.02 mol/s). The reductions in anthropogenic sources in high NOx regions are attributed to both uncertainty of the priori and emissions policies, while increases in area and soil-biogenic emissions more likely resulted from under-estimation of ships emissions, and the Yienger-Levy scheme used in BEIS respectively. In order to validate the accuracy of updated NOx emissions, CMAQ simulation was performed and results were evaluated with independent surface NO2 measurements. Comparing to surface monitoring sites, we find improvements (before and after inverse modeling) for MB (1.95, -0.30 ppbv), MAB (3.65, 2.60 ppbv), RMSE (6.13, 4.37 ppbv), correlation (0.68, 0.69), and IOA (0.76, 0.82). The largest improvement is seen for morning time surface NO2 for which RMSE and MB are reduced considerably by similar to 60% (5.05 to 2.07 ppbv) and 109% (3.49 to -0.3 ppbv). However, under prediction of model NO2 in Houston on 09125-09/26 likely resulting from unprecedented local NOx sources from the Houston Ship Channel (HSC) becomes more evident. Overall, results show that use of OMI tropospheric NO2 columns can substantially reduce the uncertainty of bottom-up emissions for a regional study which should be extended to larger domains (e.g. entire U.S. or North America). (C) 2016 Elsevier Ltd. All rights reserved.

A WRF-SMOKE-CMAQ modeling system was used to study Volatile Organic Compound (VOC) emissions and their impact on surface VOC and ozone concentrations in southeast Texas during September 2013. The model was evaluated against the ground-level Automated Gas Chromatograph (Auto-GC) measurement data from the Texas Commission on Environmental Quality (TCEQ). The comparisons indicated that the model over-predicted benzene, ethylene, toluene and xylene, while under-predicting isoprene and ethane. The mean biases between simulated and observed values of each VOC species showed clear daytime, nighttime, weekday and weekend variations. Adjusting the VOC emissions using simulated/observed ratios improved model performance of each VOC species, especially mitigating the mean bias substantially. Simulated monthly mean ozone showed a minor change: a 0.4 ppb or 1.2% increase; while a change of more than 5 ppb was seen in hourly ozone data on high ozone days, this change moved model predictions closer to observations. The CMAQ model run with the adjusted emissions better reproduced the variability in the National Aeronautics and Space Administration (NASA)'s Ozone Monitoring Instrument COMO formaldehyde (HCHO) columns. The adjusted model scenario also slightly better reproduced the aircraft HCHO concentrations from NASA's DISCOVER-AQ campaign conducted during the simulation episode period; Correlation, Mean Bias and RMSE improved from 0.34, 1.38 ppb and 2.15 ppb to 0.38, 1.33 ppb and 2.08 ppb respectively. A process analysis conducted for both industrial/ urban and rural areas suggested that chemistry was the main process contributing to ozone production in both areas, while the impact of chemistry was smaller in rural areas than in industrial and urban areas. For both areas, the positive chemistry contribution increased in the sensitivity simulation largely due to the increase in emissions. Nudging VOC emissions to match the observed concentrations shifted the ozone hotspots outside the industrial/urban region and enhanced the peaked ozone in the outflow region with consistent southerly/southeasterly winds during the afternoon time (1-5 pm). This study helps in the understanding of these processes which are critical to constrain high peak ozone values in the outflow regions. The results indicate that formation of ozone in the outflow could complicate attainment status in neighboring counties. (C) 2015 Elsevier Ltd. All rights reserved.

A WRF–SMOKE–CMAQ modeling system was used to simulate vertical profiles of ozone, temperature and wind as well as surface ozone and meteorology during the Study of Houston Atmospheric Radical Precursors (SHARP) in May 2009. WRF and CMAQ sensitivity studies were conducted to define a proper combination of model configurations.

Air quality forecasting requires atmospheric weather models to generate accurate meteorological conditions, one of which is the development of the planetary boundary layer (PBL). An important contributor to the development of the PBL is the land–air exchange captured in the energy budget as well as turbulence parameters. Standard and surface energy variables were modeled using the fifth-generation Penn State/National Center for Atmospheric Research mesoscale model (MM5), version 3.6.1, and the Weather Research and Forecasting (WRF) model, version 3.5.1, and compared to measurements for a southeastern Texas coastal region. The study period was 28 August–1 September 2006. It also included a frontal passage. The results of the study are ambiguous. Although WRF does not perform as well as MM5 in predicting PBL heights, it better simulates energy budget and most of the general variables. Both models overestimate incoming solar radiation, which implies a surplus of energy that could be redistributed in either the partitioning of the surface energy variables or in some other aspect of the meteorological modeling not examined here. The MM5 model consistently had much drier conditions than the WRF model, which could lead to more energy available to other parts of the meteorological system. On the clearest day of the study period, MM5 had increased latent heat flux, which could lead to higher evaporation rates and lower moisture in the model. However, this latent heat disparity between the two models is not visible during any other part of the study. The observed frontal passage affected the performance of most of the variables, including the radiation, flux, and turbulence variables, at times creating dramatic differences in the r2 values.

AbstractAir quality simulations were performed for the Houston‐Galveston‐Brazoria area for springtime conditions in May and June of 2009. Meteorological parameters predicted by Weather Research and Forecasting (WRF) model, for which data assimilation with recursive objective analysis was performed, are well simulated most of the time. The Community Multiscale Air Quality (CMAQ) model driven by meteorology from WRF simulates ozone and many other trace species, including radical precursors such as HCHO and HONO, with a satisfactory agreement with observations. While CMAQ satisfactorily captures the daily variations of the OH radical, it sometimes underestimates its high daytime values. Concentrations of HO2 are often underpredicted in polluted air masses and persistently severely underpredicted at low NOx conditions, when the Houston air is affected by marine air masses. In contrast, concentrations of H2O2 and CH3OOH are almost always overpredicted by the model, the overprediction occurs frequently in the polluted air and occurs always when marine air is encountered. Those mispredictions are consistent despite day‐to‐day variations in meteorological conditions and emissions and bring into question current representation of radical‐related chemistry in the model as radical production and recirculation in the model is overtaken by termination processes and creation of more stable compounds, such as H2O2 and CH3OOH. Smaller model biases of H2O2 and peroxides are associated with lower humidity. The relative importance of various photolysis processes as radical sources in the Houston atmosphere was also elucidated. Morning HOx formation is dominated by HONO while ozone contributes the most during midday. HONO contribution to HOx formation is more pronounced at the surface layer where most of it is formed, radical production from ozone is more important at elevated levels where higher concentrations of ozone are observed. Formaldehyde contributes up to 40% and also peaks during midday, but on days when high morning concentrations of formaldehyde are observed, its contribution to HOx in the morning exceeds that of ozone. Photolysis of H2O2 is a minor contributor to radical levels.

With over 6 million inhabitants the Houston metropolitan area is the fourth-largest in the United States. Ozone concentration in this southeast Texas region frequently exceeds the National Ambient Air Quality Standard (NAAQS). For this reason our study employed the Weather Research and Forecasting model with Chemistry (WRF/Chem) to quantify meteorological prediction differences produced by four widely used PBL schemes and analyzed its impact on ozone predictions. The model results were compared to observational data in order to identify one superior PBL scheme better suited for the area. The four PBL schemes include two first-order closure schemes, the Yonsei University (YSU) and the Asymmetric Convective Model version 2 (ACM2); as well as two turbulent kinetic energy closure schemes, the Mellor-Yamada-Janjic (MYJ) and Quasi-Normal Scale Elimination (QNSE). Four 24 h forecasts were performed, one for each PBL scheme. Simulated vertical profiles for temperature, potential temperature, relative humidity, water vapor mixing ratio, and the u-v components of the wind were compared to measurements collected during the Second Texas Air Quality Study (TexAQS-II) Radical and Aerosol Measurements Project (TRAMP) experiment in summer 2006. Simulated ozone was compared against TRAMP data, and air quality stations from Continuous Monitoring Station (CAMS). Also, the evolutions of the PBL height and vertical mixing properties within the PBL for the four simulations were explored. Although the results yielded high correlation coefficients and small biases in almost all meteorological variables, the overall results did not indicate any preferred PBL scheme for the Houston case. However, for ozone prediction the YSU scheme showed greatest agreements with observed values. (C) 2014 Elsevier Ltd. All rights reserved.

In jurisdictions including the US and the EU ground transportation and marine fuels have recently been required to contain lower concentrations of sulfur, which has resulted in reduced atmospheric SO(x) emissions. In contrast, the maximum sulfur content of aviation fuel has remained unchanged at 3000 ppm (although sulfur levels average 600 ppm in practice). We assess the costs and benefits of a potential ultra-low sulfur (15 ppm) jet fuel standard ("ULSJ"). We estimate that global implementation of ULSJ will cost US$1-4bn per year and prevent 900-4000 air quality-related premature mortalities per year. Radiative forcing associated with reduction in atmospheric sulfate, nitrate, and ammonium loading is estimated at +3.4 mW/m(2) (equivalent to about 1/10th of the warming due to CO(2) emissions from aviation) and ULSJ increases life cycle CO(2) emissions by approximately 2%. The public health benefits are dominated by the reduction in cruise SO(x) emissions, so a key uncertainty is the atmospheric modeling of vertical transport of pollution from cruise altitudes to the ground. Comparisons of modeled and measured vertical profiles of CO, PAN, O(3), and (7)Be indicate that this uncertainty is low relative to uncertainties regarding the value of statistical life and the toxicity of fine particulate matter.

This paper explores the use of statistical downscaling of General Circulation Model (GCM) results for the purpose of regional climate change analysis. The strong correlation between surface observations and GCM upper air predictions is used in an approach very similar to the Model Output Statistics approach used in numerical weather prediction. The primary assumption in this analysis is that the statistical relationships remain unchanged under conditions of climatic change. These relations are applied to GCM upper atmosphere predictions for future (2*CO{sub 2}) climate predictions. The result is a set of regional climate change predictions conceptually valid at the scale of cities. The downscaling for specific cities within a GCM grid cell reveals some of the anticipated variability within the grid cell. In addition, multiple linear regression analysis may indicate warming that is significantly higher or lower for a particular region than the raw data from the GCM runs. 3 refs., 3 figs., 2 tabs.