Regional-scale Air Quality Research Articles

Evaluation of local and regional air quality forecasts for London

This paper presents key results from the evaluation of a local-scale air quality forecasting system, airTEXT, alongside the CAMS regional-scale air quality forecast. The CAMS forecast, which is used to account for the long-range transport of pollutants within airTEXT, is adjusted to account for the apparent bias in concentrations predicted within the south-east of England. Forecasts of the UK Daily Air Quality Index metrics for NO2, O3, PM10 and PM2.5 in London have been assessed using the Model Evaluation Toolkit and the DELTA Tool for a five month period during 2017. For NO2, where air pollutant concentrations are primarily a result of local emissions, the local forecast performs significantly better than the regional forecast as the steep roadside concentration gradients are resolved by ADMS-Urban, the dispersion model used within airTEXT. Although regional O3 and particulate forecasts dominate the local forecasts, airTEXT also performs better than CAMS for these pollutants by accounting for emissions, dispersion and chemistry at high spatial resolution.

Influence of anthropogenic emissions and boundary conditions on multi-model simulations of major air pollutants over Europe and North America in the framework of AQMEII3.

In the framework of the third phase of the Air Quality Model Evaluation International Initiative (AQMEII3), and as contribution to the second phase of the Hemispheric Transport of Air Pollution (HTAP2) activities for Europe and North America, the impacts of a 20 % decrease of global and regional anthropogenic emissions on surface air pollutant levels in 2010 are simulated by an international community of regional-scale air quality modeling groups, using different state-of-the-art chemistry and transport models (CTMs). The emission perturbations at the global level, as well as over the HTAP2-defined regions of Europe, North America and East Asia, are first simulated by the global Composition Integrated Forecasting System (C-IFS) model from European Centre for Medium-Range Weather Forecasts (ECMWF), which provides boundary conditions to the various regional CTMs participating in AQMEII3. On top of the perturbed boundary conditions, the regional CTMs used the same set of perturbed emissions within the regional domain for the different perturbation scenarios that introduce a 20 % reduction of anthropogenic emissions globally as well as over the HTAP2-defined regions of Europe, North America and East Asia.Results show that the largest impacts over both domains are simulated in response to the global emission perturbation, mainly due to the impact of domestic emission reductions. The responses of NO2, SO2 and PM concentrations to a 20 % anthropogenic emission reduction are almost linear (~ 20 % decrease) within the global perturbation scenario with, however, large differences in the geographical distribution of the effect. NO2, CO and SO2 levels are strongly affected over the emission hot spots. O3 levels generally decrease in all scenarios by up to ~ 1 % over Europe, with increases over the hot spot regions, in particular in the Benelux region, by an increase up to ~ 6 % due to the reduced effect of NOx titration. O3 daily maximum of 8 h running average decreases in all scenarios over Europe, by up to ~ 1 %. Over the North American domain, the central-to-eastern part and the western coast of the US experience the largest response to emission perturbations. Similar but slightly smaller responses are found when domestic emissions are reduced. The impact of intercontinental transport is relatively small over both domains, however, still noticeable particularly close to the boundaries. The impact is noticeable up to a few percent, for the western parts of the North American domain in response to the emission reductions over East Asia. O3 daily maximum of 8 h running average decreases in all scenarios over north Europe by up to ~ 5 %. Much larger reductions are calculated over North America compared to Europe.In addition, values of the Response to Extra-Regional Emission Reductions (RERER) metric have been calculated in order to quantify the differences in the strengths of nonlocal source contributions to different species among the different models. We found large RERER values for O3 (~ 0.8) over both Europe and North America, indicating a large contribution from non-local sources, while for other pollutants including particles, low RERER values reflect a predominant control by local sources. A distinct seasonal variation in the local vs. non-local contributions has been found for both O3 and PM2.5, particularly reflecting the springtime long-range transport to both continents.

EURODELTA-Trends, a multi-model experiment of air quality hindcast in Europe over 1990–2010

Abstract. The EURODELTA-Trends multi-model chemistry-transport experiment has been designed to facilitate a better understanding of the evolution of air pollution and its drivers for the period 1990–2010 in Europe. The main objective of the experiment is to assess the efficiency of air pollutant emissions mitigation measures in improving regional-scale air quality. The present paper formulates the main scientific questions and policy issues being addressed by the EURODELTA-Trends modelling experiment with an emphasis on how the design and technical features of the modelling experiment answer these questions. The experiment is designed in three tiers, with increasing degrees of computational demand in order to facilitate the participation of as many modelling teams as possible. The basic experiment consists of simulations for the years 1990, 2000, and 2010. Sensitivity analysis for the same three years using various combinations of (i) anthropogenic emissions, (ii) chemical boundary conditions, and (iii) meteorology complements it. The most demanding tier consists of two complete time series from 1990 to 2010, simulated using either time-varying emissions for corresponding years or constant emissions. Eight chemistry-transport models have contributed with calculation results to at least one experiment tier, and five models have – to date – completed the full set of simulations (and 21-year trend calculations have been performed by four models). The modelling results are publicly available for further use by the scientific community. The main expected outcomes are (i) an evaluation of the models' performances for the three reference years, (ii) an evaluation of the skill of the models in capturing observed air pollution trends for the 1990–2010 time period, (iii) attribution analyses of the respective role of driving factors (e.g. emissions, boundary conditions, meteorology), (iv) a dataset based on a multi-model approach, to provide more robust model results for use in impact studies related to human health, ecosystem, and radiative forcing.

Dynamic evaluation of two decades of WRF-CMAQ ozone simulations over the contiguous United States

Dynamic evaluation of the fully coupled Weather Research and Forecasting (WRF)– Community Multi-scale Air Quality (CMAQ) model ozone simulations over the contiguous United States (CONUS) using two decades of simulations covering the period from 1990 to 2010 is conducted to assess how well the changes in observed ozone air quality are simulated by the model. The changes induced by variations in meteorology and/or emissions are also evaluated during the same timeframe using spectral decomposition of observed and modeled ozone time series with the aim of identifying the underlying forcing mechanisms that control ozone exceedances and making informed recommendations for the optimal use of regional-scale air quality models. The evaluation is focused on the warm season's (i.e., May–September) daily maximum 8-hr (DM8HR) ozone concentrations, the 4th highest (4th) and average of top 10 DM8HR ozone values (top10), as well as the spectrally-decomposed components of the DM8HR ozone time series using the Kolmogorov-Zurbenko (KZ) filter. Results of the dynamic evaluation are presented for six regions in the U.S., consistent with the National Oceanic and Atmospheric Administration (NOAA) climatic regions. During the earlier 11-yr period (1990–2000), the simulated and observed regional average trends are not statistically significant. During the more recent 2000–2010 period, all observed trends are statistically significant and WRF-CMAQ captures the observed downward trend in the Southwest and Midwest but under-predicts the downward trends in observations for the other regions. Observational analysis reveals that it is the magnitude of the long-term forcing that dictates the maximum ozone exceedance potential; there is a strong linear relationship between the long-term forcing and the 4th highest or the average of the top10 ozone concentrations in both observations and model output. This finding indicates that improving the model's ability to reproduce the long-term component will also enable better simulation of ozone extreme values that are of interest to regulatory agencies.

Evaluating unintended outcomes of regional smart-growth strategies: Environmental justice and public health concerns

Many urban areas are perusing infill, transit oriented, and other “smart-growth” strategies to address a range of important regional goals. Denser and more mixed use urban development may increase sustainability and improve public health by reducing vehicle travel and increasing the share of trips made by transit, walking and bicycling. Fewer vehicle trips results in fewer greenhouse gas and toxic vehicle emissions, and more trips made by walking and bicycle increases physical activity. Prior research has largely focused on modeling and estimating the potential size of these and other smart-growth strategy benefits. A largely overlooked area is the potential for unexpected public health costs and environmental justice concerns that may result from increasing density. We evaluate regional land-use and transportation planning scenarios developed for the year 2040 by a metropolitan planning organization with a newly developed regional air quality modeling framework. Our results find that a set of regional plans designed by the MPO to promote smart-growth that are estimated to result in less vehicle use and fewer vehicle emissions than a more typical set of plans results in higher population exposure to toxic vehicle emissions. The smart-growth plans also result in greater income-exposure inequality, raising environmental justice concerns. We conclude that a more spatially detailed regional scale air quality analysis can inform the creation of smarter smart-growth plans.

Impacts of wildfire smoke plumes on regional air quality

Recent trends in increased frequency and severity of large fires necessitate an improved understanding of smoke plume impacts on regional-scale air quality and public health. We examine the impact ...

Source apportionment of PM2.5 at multiple Northwest U.S. sites: Assessing regional winter wood smoke impacts from residential wood combustion

Wood smoke from residential wood combustion is a significant source of elevated PM2.5 in many communities across the Northwest U.S. Accurate representation of residential wood combustion in source-oriented regional scale air quality models is challenging because of multiple uncertainties. As an alternative to source-oriented source apportionment, this work provides, through receptor-oriented source apportionment, an assessment of winter residential wood combustion impacts at multiple Northwest U.S. locations. Source apportionment was performed on chemically speciated PM2.5 from 19 monitoring sites using the Positive Matrix Factorization (PMF) receptor model. Each site was modeled independently, but a common data preparation and modeling protocol was used so that results were as comparable as possible across sites. Model solutions had from 4 to 8 PMF factors, depending on the site. PMF factors at each site were associated with a source classification (e.g., primary wood smoke), a dominant chemical composition (e.g., ammonium nitrate), or were some mixture. 15 different sources or chemical compositions were identified as contributing to PM2.5 across the 19 sites. The 6 most common were; aged wood smoke and secondary organic carbon, motor vehicles, primary wood smoke, ammonium nitrate, ammonium sulfate, and fugitive dust. Wood smoke was identified at every site, with both aged and primary wood smoke identified at most sites. Wood smoke contributions to PM2.5 were averaged for the two winter months of December and January, the months when wood smoke in the Northwest U.S. is mainly from residential wood combustion. The total contribution of residential wood combustion, that from primary plus aged smoke, ranged from 11.4% to 92.7% of average December and January PM2.5 depending on the site, with the highest percent contributions occurring in smaller towns that have fewer expected sources of winter PM2.5. Receptor modeling at multiple sites, such as that conducted in this work, provided some significant advantages over modeling a single or small number of sites. Analysis at multiple sites allowed common factor chemical compositions to be identified, making it easier to evaluate when a PMF factor at a particular site represents a mix of sources versus a single source. The identification of similar PMF factors across multiple sites also allowed average chemical profiles to be established for the 6 the most commonly identified PM2.5 sources or compositions in this study. These average profiles have the potential to be used as source profile inputs in future Chemical Mass Balance receptor modeling, when a limited number of samples may restrict the ability to conduct PMF receptor modeling, or when the availability of local source profiles is limited. Receptor modeling results spanning a range of community sizes and source compositions, as in this study, could be used to evaluate and improve the representation of wood smoke and other specific sources in source-oriented regional scale air quality models by providing an independent source impact assessment.

Overview of current regional and local scale air quality modelling practices: Assessment and planning tools in the EU

The 2008 European Air Quality Directive (AQD) (2008/50/EC) encourages the use of models in combination with monitoring in a range of applications. It also requires Member States to design appropriate air quality plans for zones where the air quality does not comply with the AQD limit values. In order to cope with these various elements, a wide range of different modeling methods have been developed and applied by EU Member States in the last decade to assess the effects of local and regional emission abatement policy options on air quality and human health. However, an overall review of the methodologies that are used in different countries to compile local and regional air quality plans has not been performed so far. Such a review has been the objective of the APPRAISAL EU FP7 project with the main goal to identify methodologies and their limitations and to propose possible key areas to be addressed by research and innovation on the basis of this review. To fulfill these objectives, a structured online database of methodologies has been developed in collaboration with experts involved in the design of air quality plans (AQP). The current work relies on the APPRAISAL database which currently totals 59 contributions from 13 Member States. In this paper we summarize the outcome of the APPRAISAL project with respect to the review of current Integrated Assessment Modeling practices.

Understanding high wintertime ozone pollution events in an oil- and natural gas-producing region of the western US

Abstract. Recent increases in oil and natural gas (NG) production throughout the western US have come with scientific and public interest in emission rates, air quality and climate impacts related to this industry. This study uses a regional-scale air quality model (WRF-Chem) to simulate high ozone (O3) episodes during the winter of 2013 over the Uinta Basin (UB) in northeastern Utah, which is densely populated by thousands of oil and NG wells. The high-resolution meteorological simulations are able qualitatively to reproduce the wintertime cold pool conditions that occurred in 2013, allowing the model to reproduce the observed multi-day buildup of atmospheric pollutants and the accompanying rapid photochemical ozone formation in the UB. Two different emission scenarios for the oil and NG sector were employed in this study. The first emission scenario (bottom-up) was based on the US Environmental Protection Agency (EPA) National Emission Inventory (NEI) (2011, version 1) for the oil and NG sector for the UB. The second emission scenario (top-down) was based on estimates of methane (CH4) emissions derived from in situ aircraft measurements and a regression analysis for multiple species relative to CH4 concentration measurements in the UB. Evaluation of the model results shows greater underestimates of CH4 and other volatile organic compounds (VOCs) in the simulation with the NEI-2011 inventory than in the case when the top-down emission scenario was used. Unlike VOCs, the NEI-2011 inventory significantly overestimates the emissions of nitrogen oxides (NOx), while the top-down emission scenario results in a moderate negative bias. The model simulation using the top-down emission case captures the buildup and afternoon peaks observed during high O3 episodes. In contrast, the simulation using the bottom-up inventory is not able to reproduce any of the observed high O3 concentrations in the UB. Simple emission reduction scenarios show that O3 production is VOC sensitive and NOx insensitive within the UB. The model results show a disproportionate contribution of aromatic VOCs to O3 formation relative to all other VOC emissions. The model analysis reveals that the major factors driving high wintertime O3 in the UB are shallow boundary layers with light winds, high emissions of VOCs from oil and NG operations compared to NOx emissions, enhancement of photolysis fluxes and reduction of O3 loss from deposition due to snow cover.

Predicting the effects of nanoscale cerium additives in diesel fuel on regional-scale air quality.

Diesel vehicles are a major source of air pollutant emissions. Fuel additives containing nanoparticulate cerium (nCe) are currently being used in some diesel vehicles to improve fuel efficiency. These fuel additives also reduce fine particulate matter (PM2.5) emissions and alter the emissions of carbon monoxide (CO), nitrogen oxides (NOx), and hydrocarbon (HC) species, including several hazardous air pollutants (HAPs). To predict their net effect on regional air quality, we review the emissions literature and develop a multipollutant inventory for a hypothetical scenario in which nCe additives are used in all on-road and nonroad diesel vehicles. We apply the Community Multiscale Air Quality (CMAQ) model to a domain covering the eastern U.S. for a summer and a winter period. Model calculations suggest modest decreases of average PM2.5 concentrations and relatively larger decreases in particulate elemental carbon. The nCe additives also have an effect on 8 h maximum ozone in summer. Variable effects on HAPs are predicted. The total U.S. emissions of fine-particulate cerium are estimated to increase 25-fold and result in elevated levels of airborne cerium (up to 22 ng/m3), which might adversely impact human health and the environment.

Simulating smoke transport from wildland fires with a regional-scale air quality model: Sensitivity to spatiotemporal allocation of fire emissions

Air quality forecasts generated with chemical transport models can provide valuable information about the potential impacts of fires on pollutant levels. However, significant uncertainties are associated with fire-related emission estimates as well as their distribution on gridded modeling domains. In this study, we explore the sensitivity of fine particulate matter concentrations predicted by a regional-scale air quality model to the spatial and temporal allocation of fire emissions. The assessment was completed by simulating a fire-related smoke episode in which air quality throughout the Atlanta metropolitan area was affected on February 28, 2007. Sensitivity analyses were carried out to evaluate the significance of emission distribution among the model's vertical layers, along the horizontal plane, and into hourly inputs. Predicted PM2.5 concentrations were highly sensitive to emission injection altitude relative to planetary boundary layer height. Simulations were also responsive to the horizontal allocation of fire emissions and their distribution into single or multiple grid cells. Additionally, modeled concentrations were greatly sensitive to the temporal distribution of fire-related emissions. The analyses demonstrate that, in addition to adequate estimates of emitted mass, successfully modeling the impacts of fires on air quality depends on an accurate spatiotemporal allocation of emissions.

Simultaneous statistical bias correction of multiple PM2.5 species from a regional photochemical grid model

In recent years environmental epidemiologists have begun utilizing regional-scale air quality computer models to predict ambient air pollution concentrations in health studies instead of or in addition to data from fixed-site ambient monitors. The advantages of using such models include better spatio-temporal coverage and the capability to predict concentrations of unmonitored pollutants. However, there are also drawbacks, chief among them being that these models can exhibit systematic spatial and temporal biases. In order to use these models in epidemiological investigations it is very important to bias-correct the model surfaces. We present a novel statistical method of spatio-temporal bias correction for the Community Multi-scale Air Quality (CMAQ) model that allows simultaneous bias adjustment of PM2.5 mass and its major constituent species using publically available speciated data from ambient monitors. The method uses mass conservation and the more widespread unspeciated PM2.5 mass observations to constrain the sum of the PM2.5 species' concentrations in locations without speciated monitors. We develop the model in the context of an epidemiological study investigating the association between PM2.5 species’ ambient concentrations and birth outcomes throughout the state of New Jersey. Since our exposures of interest are multi-month averages we focus specifically on modeling seasonal bias trends rather than daily biases. Using a cross-validation study we find that our bias-corrected CMAQ results are more accurate than either the original CMAQ output or a spline fit without CMAQ. More interestingly, we find that our model clearly performs better when mass conservation is enforced, and furthermore that our model is competitive with Kriging in a comparison in which the latter has the advantage.

Improved satellite retrievals of NO<sub>2</sub> and SO<sub>2</sub> over the Canadian oil sands and comparisons with surface measurements

Abstract. Satellite remote sensing is increasingly being used to monitor air quality over localized sources such as the Canadian oil sands. Following an initial study, significantly low biases have been identified in current NO2 and SO2 retrieval products from the Ozone Monitoring Instrument (OMI) satellite sensor over this location resulting from a combination of its rapid development and small spatial scale. Air mass factors (AMFs) used to convert line-of-sight "slant" columns to vertical columns were re-calculated for this region based on updated and higher resolution input information including absorber profiles from a regional-scale (15 km × 15 km resolution) air quality model, higher spatial and temporal resolution surface reflectivity, and an improved treatment of snow. The overall impact of these new Environment Canada (EC) AMFs led to substantial increases in the peak NO2 and SO2 average vertical column density (VCD), occurring over an area of intensive surface mining, by factors of 2 and 1.4, respectively, relative to estimates made with previous AMFs. Comparisons are made with long-term averages of NO2 and SO2 (2005–2011) from in situ surface monitors by using the air quality model to map the OMI VCDs to surface concentrations. This new OMI-EC product is able to capture the spatial distribution of the in situ instruments (slopes of 0.65 to 1.0, correlation coefficients of >0.9). The concentration absolute values from surface network observations were in reasonable agreement, with OMI-EC NO2 and SO2 biased low by roughly 30%. Several complications were addressed including correction for the interference effect in the surface NO2 instruments and smoothing and clear-sky biases in the OMI measurements. Overall these results highlight the importance of using input information that accounts for the spatial and temporal variability of the location of interest when performing retrievals.

Exposure prediction approaches used in air pollution epidemiology studies: Key findings and future recommendations

Many epidemiologic studies of the health effects of exposure to ambient air pollution use measurements from central-site monitors as their exposure estimate. However, measurements from central-site monitors may lack the spatial and temporal resolution required to capture exposure variability in a study population, thus resulting in exposure error and biased estimates. Articles in this dedicated issue examine various approaches to predict or assign exposures to ambient pollutants. These methods include combining existing central-site pollution measurements with local- and/or regional-scale air quality models to create new or "hybrid" models for pollutant exposure estimates and using exposure models to account for factors such as infiltration of pollutants indoors and human activity patterns. Key findings from these articles are summarized to provide lessons learned and recommendations for additional research on improving exposure estimation approaches for future epidemiological studies. In summary, when compared with use of central-site monitoring data, the enhanced spatial resolution of air quality or exposure models can have an impact on resultant health effect estimates, especially for pollutants derived from local sources such as traffic (e.g., EC, CO, and NO(x)). In addition, the optimal exposure estimation approach also depends upon the epidemiological study design. We recommend that future research develops pollutant-specific infiltration data (including for PM species) and improves existing data on human time-activity patterns and exposure to local source (e.g., traffic), in order to enhance human exposure modeling estimates. We also recommend comparing how various approaches to exposure estimation characterize relationships between multiple pollutants in time and space and investigating the impact of improved exposure estimates in chronic health studies.

Estimating the contribution of traffic-related pollutants to urban air quality for applying a hybrid modeling approach

Traffic-related pollutants such as NO, NO2, PM and O3 which is indirectly produced through photochemical reaction are widely known as a serious hazard for human health. In this study, we estimate the contribution of traffic-related pollutants to ambient concentrations using CMAQ model and examine the “double count” of the same traffic emissions for applying a hybrid modeling approach, which is to combine the regional-scale air quality model and local dispersion model for linking the model-predicted gridded concentrations with population exposure model. Busan, the target area in this study, is the second largest metropolis located on the southeastern tip of the Korean Peninsula with a population of around 3.6 million. The main emission sources in the city are on-road mobile sources (50.5%), followed by non-road mobile sources such as shipping around port areas (17.6%) and solvent utilization (17.4%). In this study, to evaluate the impact of the traffic-related pollutants on outdoor ambient concentrations were quantified by analyzing the difference between model-predicted concentrations derived from two sets of simulation scenarios : (1) the WRF-SMOKE-CMAQ simulation for a 1 month (May, 2011) with (i.e., BASE case) and (2) without the traffic emissions (i.e., CNTL case). To evaluate the model validation for the BASE case, a statistical comparison was made between the observed and simulated values of both meteorological variables (air temperature, wind speed and direction) and air pollutants (O3 and NO2) at several monitoring sites. Overall, the simulated values show good correlation with the observations and indicate that O3-NO2 dynamics are generally well captured by the CMAQ (MBE of -7.4 ppb and 3.4 ppb for NO2 and O3, respectively). The differences between two simulations (BASE case minus CNTL case) are the range from -4 ppb to 60 ppb for NO2 and -44 ppb to 17 ppb for O3. This result reveals that traffic-related pollutants contribute negatively to O3 concentrations due to NOx increase under the NOx-saturated environment.

Canopy Controls on the Forest-Atmosphere Exchange of Biogenic Ozone and Aerosol Precursors

Vegetation supports life on Earth, from supplying oxygen to the atmosphere to playing a role in the cycling of water. However, plants also emit natural or biogenic volatile organic compounds (BVOCs). BVOCs are an important precursor to the formation of tropospheric ozone— a compound that can adversely affect air quality, human health, and the environment— yet there are many outstanding questions regarding the emission and flux of BVOCs from the forest to the atmosphere. This review synthesizes recent research results pertaining to forestatmosphere exchange, including the formation and fate of primary biogenic ozone precursors and the key physical and chemical processes occurring within and above vegetation canopies that control the efficient exchange of gases. Knowledge of these processes can improve our understanding of the forested environment and its role in sustainable air quality. We conclude from this review that, while we have a deep understanding of a vast array of processes that regulate BVOC emissions to the atmosphere on a local level, how those processes feedback to regionalscale air quality and climate is less understood. A synthesis of data from a wide variety of forest sites may help improve our understanding of the role of forests on air quality and climate and allow policy makers to make informed decisions with respect to maintaining sustainable air quality and climate.

Evaluating the capability of regional-scale air quality models to capture the vertical distribution of pollutants

Abstract. This study is conducted in the framework of the Air Quality Modelling Evaluation International Initiative (AQMEII) and aims at the operational evaluation of an ensemble of 12 regional-scale chemical transport models used to predict air quality over the North American (NA) and European (EU) continents for 2006. The modelled concentrations of ozone and CO, along with the meteorological fields of wind speed (WS) and direction (WD), temperature (T), and relative humidity (RH), are compared against high-quality in-flight measurements collected by instrumented commercial aircraft as part of the Measurements of OZone, water vapour, carbon monoxide and nitrogen oxides by Airbus In-service airCraft (MOZAIC) programme. The evaluation is carried out for five model domains positioned around four major airports in NA (Portland, Philadelphia, Atlanta, and Dallas) and one in Europe (Frankfurt), from the surface to 8.5 km. We compare mean vertical profiles of modelled and measured variables for all airports to compute error and variability statistics, perform analysis of altitudinal error correlation, and examine the seasonal error distribution for ozone, including an estimation of the bias introduced by the lateral boundary conditions (BCs). The results indicate that model performance is highly dependent on the variable, location, season, and height (e.g. surface, planetary boundary layer (PBL) or free troposphere) being analysed. While model performance for T is satisfactory at all sites (correlation coefficient in excess of 0.90 and fractional bias ≤ 0.01 K), WS is not replicated as well within the PBL (exhibiting a positive bias in the first 100 m and also underestimating observed variability), while above 1000 m, the model performance improves (correlation coefficient often above 0.9). The WD at NA airports is found to be biased in the PBL, primarily due to an overestimation of westerly winds. RH is modelled well within the PBL, but in the free troposphere large discrepancies among models are observed, especially in EU. CO mixing ratios show the largest range of modelled-to-observed standard deviations of all the examined species at all heights and for all airports. Correlation coefficients for CO are typically below 0.6 for all sites and heights, and large errors are present at all heights, particularly in the first 250 m. Model performance for ozone in the PBL is generally good, with both bias and error within 20%. Profiles of ozone mixing ratios depend strongly on surface processes, revealed by the sharp gradient in the first 2 km (10 to 20 ppb km−1). Modelled ozone in winter is biased low at all locations in the NA, primarily due to an underestimation of ozone from the BCs. Most of the model error in the PBL is due to surface processes (emissions, transport, photochemistry), while errors originating aloft appear to have relatively limited impact on model performance at the surface. Suggestions for future work include interpretation of the model-to-model variability and common sources of model bias, and linking CO and ozone bias to the bias in the meteorological fields. Based on the results from this study, we suggest possible in-depth, process-oriented and diagnostic investigations to be carried out next.

Data assimilation of surface air pollutants (O3 and NO2) in the regional-scale air quality model AURORA

In the present work, a bias-aware optimal interpolation in conjunction with the Hollingsworth–Lönnberg method to estimate error covariance matrices was applied as data assimilation algorithm in the regional scale air quality model AURORA to assimilate ground level O3 and NO2 concentrations. The study was conducted over the domain Belgium including part of its neighbouring areas with grid resolution of 3 × 3 km2. Data assimilation was carried out for the retrospective simulation in post-processing (offline) mode for a summer and a winter month. Observations were provided by the AIRBASE data archive. Since the air quality model AURORA is presumed to represent background conditions, only the background stations within the domain have been taken into account. The validation of the proposed method was carried out by leaving observations of ten monitoring stations out in one run of the data assimilation process and another ten stations out in the next run and so on (a “leave ten out approach”). The proposed method has been evaluated in both spatial as well temporal domain against various statistical indicators such as correlation coefficient (CORR), root mean square error (RMSE), index of agreement (IOA) and mean fractional bias (MFB). For both the O3 and NO2, the extensive validation results have clearly shown substantial improvement in the data assimilation results over AURORA free run in both the seasons. The results over 70 validation stations show that CORR increased from 0.4 to 0.8 for O3, 0.3 to 0.6 for NO2 while average RMSE reduced from 27.9 to 12.6 for O3 and from 17.4 to 11.0 for NO2 for the month of June. Similar improvements have been observed for the month of Dec as well. Spatial CORR, IOA for monthly means of both the O3 and NO2 concentrations were also increased considerably. The results clearly indicate that the applied bias aware optimal interpolation in conjunction with Hollingsworth–Lönnberg method is a very promising candidate for the statistical correction of regional scale air quality modelling results for the retrospective simulation.

Simulation of urban and regional air pollution in Bangladesh

We have developed a regional scale air quality simulation using the Weather Research and Forecasting (WRF) – Community Multiscale Air Quality Model (CMAQ) to assess the suitability of such an advanced modeling system for predicting the air quality of Bangladesh and its surrounding region. The Regional Emission Inventory in Asia (REAS) was used as the emission input in this modeling approach. Both meteorological and chemical model performance were evaluated with observations including satellite data. Comparison between simulated and observed meteorological parameters revealed that the WRF can generate the necessary meteorological inputs for CMAQ. Comparison of observed and simulated concentrations of different air pollutants revealed that CMAQ greatly underestimates the concentrations of key pollutants. Comparison with satellite observations revealed that CMAQ reproduces the spatial distribution of NO2with some underestimation in Bangladesh and India. The simulated AOD and satellite‐retrieved AOD showed good temporal and spatial agreement mutually, with a correlation coefficient of 0.58. Sensitivity simulation using higher horizontal resolution emission data made by re‐gridding the REAS inventory with the population distribution improved the CMAQ performance. Nevertheless, CMAQ underestimated the pollutant concentrations in Dhaka. Uncertainties in the emission inventory and in the lack of time variation in emissions input mainly contributed to the model underestimation. Model predictions show that 36–72% PM10 and 15–60% PM2.5 in Dhaka might be contributed from brick kiln emissions in monthly average of January 2004. The chemical composition of PM2.5showed that the considerable amounts of secondary aerosols in Dhaka and carbonaceous components (particularly organic carbon) are most responsible for the model underestimation. Results suggest that improvements of emission inputs and more detailed sensitivity analysis of CMAQ model are important to assess the reliability and applicability of WRF‐CMAQ modeling system in this region.

Regional scale photochemical model evaluation of total mercury wet deposition and speciated ambient mercury

Methylmercury is a known neurotoxin with deleterious health effects on humans and wildlife. Atmospheric deposition is the largest source of mercury loading to most terrestrial and aquatic ecosystems. Regional scale air quality models are needed to quantify mercury deposition resulting from complex emissions sources and physical and chemical processes that govern the fate of mercury in the atmosphere. Total mercury wet deposition estimates from multiple regional photochemical transport models applied at 12 km grid resolution over the continental United States compare well with observations (CAMx fractional error = 45%, CMAQ fractional error = 33%) despite uncertainties in global mercury emissions inventories and certain chemical transformation pathways. In addition, both CMAQ and CAMx well represent observed diel and seasonal patterns of Hg(0) and tend to exaggerate the diel patter of Hg(II) at AMNet monitor locations. The observed fraction of particulate mercury to total oxidized mercury (sum of particulate mercury and Hg(II)) is generally greater in colder months and during overnight hours. The modeling systems tend to capture these patterns but have a systematically lower fraction of particulate mercury to total oxidized mercury, especially in winter months. Annual total mercury deposition from wet and dry processes is 65% greater in CMAQ compared to CAMx over the entire modeling domain. This is largely due to higher wet deposition in CMAQ and higher dry deposition of Hg(0), which is treated as equilibrium with mercury re-emissions and not modeled in CAMx. A sensitivity using CAMx with Hg(0) dry deposition treated similar to CMAQ resulted in more comparable total mercury deposition estimates. Modeled dry deposition velocities for Hg(II) compare well with the limited experimental data, while Hg(0) dry deposition velocities are lower than published experimental data. A mercury bi-directional flux sensitivity application in CMAQ had the overall effect of reducing total mercury dry deposition and slightly improving ambient Hg(0) performance. The range of the domain wide total deposition from all model sensitivities was within 25% of the mean but exhibited larger deviations in the individual wet and dry deposition budgets. The contribution of mercury initial conditions and lateral boundary inflow conditions were tracked separately using CAMx source apportionment. Initial contribution to total mercury deposition for the entire model domain falls below 5% after 2 weeks. Boundary contribution to total mercury deposition vary considerably across the continental United States, but ranges between 20 and 99% at MDN monitor locations.