Air Quality Model Performance Research Articles

Using Regionalized Air Quality Model Performance and Bayesian Maximum Entropy data fusion to map global surface ozone concentration

Estimates of ground-level ozone concentrations have been improved through data fusion of observations and atmospheric chemistry models. Our previous global ozone estimates for the Global Burden of Disease study corrected for bias uniformly across continents and then corrected near monitoring stations using the Bayesian Maximum Entropy (BME) framework for data fusion. Here, we use the Regionalized Air Quality Model Performance (RAMP) framework to correct model bias over a much larger spatial range than BME can, accounting for the spatial inhomogeneity of bias and nonlinearity as a function of modeled ozone. RAMP bias correction is applied to a composite of 9 global chemistry-climate models, based on the nearest set of monitors. These estimates are then fused with observations using BME, which matches observations at measurement stations, with the influence of observations declining with distance in space and time. We create global ozone maps for each year from 1990 to 2017 at fine spatial resolution. RAMP is shown to create unrealistic discontinuities due to the spatial clustering of ozone monitors, which we overcome by applying a weighting for RAMP based on the number of monitors nearby. Incorporating RAMP before BME has little effect on model performance near stations, but strongly increases R2 by 0.15 at locations farther from stations, shown through a checkerboard cross-validation. Corrections to estimates differ based on location in space and time, confirming heterogeneity. We quantify the likelihood of exceeding selected ozone levels, finding that parts of the Middle East, India, and China are most likely to exceed 55 parts per billion (ppb) in 2017. About 96% of the global population was exposed to ozone levels above the World Health Organization guideline of 60 µg m−3 (30 ppb) in 2017. Our annual fine-resolution ozone estimates may be useful for several applications including epidemiology and assessments of impacts on health, agriculture, and ecosystems.

The Influence of Meteorology Initialization on Ozone Forecasting in the Great Lakes Region during MOOSE Study

This study investigates the influence of meteorology initialization on surface ozone prediction in the Great Lakes region using Canada’s operational air quality model (GEM-MACH) at a 2.5 km horizontal resolution. Two different initialization techniques are compared, and it is found that the four-dimensional incremental analysis updating (IAU) method yields improved model performance for surface ozone prediction. The IAU run shows better ozone regression line statistics (y = 0.7x + 14.9, R2 = 0.2) compared to the non-IAU run (y = 0.6x + 23.1, R2 = 0.1), with improved MB and NMB values (3.9 ppb and 8.9%, respectively) compared to the non-IAU run (4.1 ppb and 9.3%). Furthermore, analyzing ozone prediction sensitivity to model initialization time reveals that the 18z initialization leads to enhanced performance, particularly during high ozone exceedance days, with an improved regression slope of 0.9 compared to 0.7 for the 00z and 12z runs. The MB also improves to −0.2 ppb in the 18z run compared to −2.8 ppb and −3.9 ppb for the 00z and 12z runs, respectively. The analysis of meteorological fields reveals that the improved ozone predictions at 18z are linked to a more accurate representation of afternoon wind speed. This improvement enhances the transport of ozone, contributing to the overall improvement in ozone predictions.

A hyperlocal hybrid data fusion near-road PM2.5 and NO2 annual risk and environmental justice assessment across the United States.

Exposure to traffic-related air pollutants (TRAPs) has been associated with numerous adverse health effects. TRAP concentrations are highest meters away from major roads, and disproportionately affect minority (i.e., non-white) populations often considered the most vulnerable to TRAP exposure. To demonstrate an improved assessment of on-road emissions and to quantify exposure inequity in this population, we develop and apply a hybrid data fusion approach that utilizes the combined strength of air quality observations and regional/local scale models to estimate air pollution exposures at census block resolution for the entire U.S. We use the regional photochemical grid model CMAQ (Community Multiscale Air Quality) to predict the spatiotemporal impacts at local/regional scales, and the local scale dispersion model, R-LINE (Research LINE source) to estimate concentrations that capture the sharp TRAP gradients from roads. We further apply the Regionalized Air quality Model Performance (RAMP) Hybrid data fusion technique to consider the model's nonhomogeneous, nonlinear performance to not only improve exposure estimates, but also achieve significant model performance improvement. With a R2 of 0.51 for PM2.5 and 0.81 for NO2, the RAMP hybrid method improved R2 by ~0.2 for both pollutants (an increase of up to ~70% for PM2.5 and ~31% NO2). Using the RAMP Hybrid method, we estimate 264,516 [95% confidence interval [CI], 223,506-307,577] premature deaths attributable to PM2.5 from all sources, a ~1% overall decrease in CMAQ-estimated premature mortality compared to RAMP Hybrid, despite increases and decreases in some locations. For NO2, RAMP Hybrid estimates 138,550 [69,275-207,826] premature deaths, a ~19% increase (22,576 [11,288 - 33,864]) compared to CMAQ. Finally, using our RAMP hybrid method to estimate exposure inequity across the U.S., we estimate that Minorities within 100 m from major roads are exposed to up to 15% more PM2.5 and up to 35% more NO2 than their White counterparts.

Distinct diurnal chemical compositions and formation processes of individual organic-containing particles in Beijing winter

Organic aerosols (OA) are major components of fine particulate matter, yet their formation mechanism remains unclear, especially in polluted environments. In this study, we investigated the diurnal chemical compositions and formation processes of OA in carbonaceous particles during winter in Beijing using aerosol time-of-flight mass spectrometry. We found that 84.5% of the measured carbonaceous particles underwent aging processes, characterized by larger diameter and more secondary species compared to fresh carbonaceous particles, and presented different chemical compositions of OA in the daytime and nighttime. During the day, under high O3 concentrations, organosulfates and oligomers existed in the aged carbonaceous particles, which were mixed with a higher signal of nitrate compared with sulfate. At night, under high relative humidity, distinct spectral signatures of hydroxymethanesulfonate and organic nitrogen compounds, and minor signals of other hydroxyalkylsulfonates and high molecular weight organic compounds were present in the aged carbonaceous particles, which were mixed with a higher signal of sulfate compared with nitrate. Our results indicated that photochemistry contributed to OA formation in the daytime, while aqueous chemistry played an important role in OA formation in the nighttime. The findings can help improve the performance of air quality and climate models on OA simulation.

Urban air quality: What is the optimal place to reduce transport emissions?

We develop a linear model based on a complex network approach that predicts the effect of emission changes on air pollution exposure in urban street networks including NO–NO2–O3-chemisty. The operational air quality model SIRANE is used to create a weighted adjacency matrix A describing the relation between emissions of a passive scalar inside streets and the resulting concentrations in the street network. A case study in South Kensington (London) is used, and the adjacency matrix A0 is determined for one wind speed and eight different wind directions. The physics of the underlying problem is used to infer A for different wind speeds. Good agreement between SIRANE predictions and the model is observed for all but the lowest wind speed, despite non-linearities in SIRANE's model formulation. An indicator for exposure in the street is developed, and it is shown that the out-degree of the exposure matrix E represents the effect of a change in emissions on the exposure reduction in all streets in the network. The approach is then extended to NO–NO2–O3-chemisty, which introduces a non-linearity. It is shown that a linearised model agrees well with the fully nonlinear SIRANE predictions. The model shows that roads with large height-to-width ratios are the first in which emissions should be reduced in order to maximise exposure reduction.

An operational urban air quality model ENFUSER, based on dispersion modelling and data assimilation

An operational urban air quality modelling system ENFUSER is presented with an evaluation against measured data. ENFUSER combines several dispersion modelling approaches, uses data assimilation, and continuously extracts information from online, global open-access sources. The modelling area is described with a combination of geographic datasets. These GIS datasets are globally available with open access, and therefore the model can be applied worldwide. Urban scale dispersion is addressed with a combination of Gaussian puff and Gaussian plume modelling, and long-range transport of pollutants is accounted for via a separate regional model. The presented data assimilation method, which supports the use of AQ sensors and incorporates a longer-term learning mechanism, adjusts emission factors and the regional background values on an hourly basis. The model can be used with reasonable accuracy also in urban areas, for which detailed emissions inventories would not be available, due to the data assimilation capabilities.

Operational Evaluation of a Wildfire Air Quality Model from a Forecaster Point of View

Abstract An evaluation of an operational wildfire air quality model (WFAQM) has been performed. Evaluation metrics were chosen through an analysis of interviews and a survey of professionals who use WFAQM forecasts as part of their daily responsibilities. The survey revealed that professional users generally focus on whether forecast air quality will exceed thresholds that trigger local air quality advisories (e.g., an event), their analysis scale is their region of responsibility, they are interested in short-term (≈24 h) guidance, missing an event is worse than issuing a false alarm, and there are two types of users—one that takes the forecast at face value, and the other that uses it as one of several information sources. Guided by these findings, model performance of Environment and Climate Change Canada’s current operational WFAQM (FireWork) was assessed over western Canada during three (2016–18) summer (May–September) wildfire seasons. Evaluation was performed at the geographic scale at which individual forecasts are issued (the forecast region) using gridded particulate matter 2.5 (PM2.5) fields developed from a machine learning–based downscaling of satellite and meteorological data. For the “at face value” user group, model performance was measured using the Peirce skill score. For the “as information source” user group, model performance was measured using the divergence skill score. For this metric, forecasts were first converted to event probabilities using binomial regression. We find when forecasts are taken at face value, FireWork cannot outperform a nearest-neighbor-based persistence model. However, when forecasts are considered as an information source, FireWork is superior to the persistence-based model.

A reflection on 10 years of the Air Quality Model Evaluation International Initiative

Editor’s Note:The authors document the progress that they made in evaluating the performance of regional air quality models within the framework of AQMEII and how this international collaborative e...

Pinpointing optimized air quality model performance over the Beijing-Tianjin-Hebei region: Mosaic approach

Mosaic approach, with certain number of tiles representing land use (LU) types in each grid cell, had been implemented into WRF-Noah model. Previous studies found mosaic approach had a better performance on meteorological parameters than only considering dominant LU in dominant approach. In this study, the impacts of mosaic approach on meteorological parameters and air quality were investigated in WRF-CMAQ over Beijing-Tianjin-Hebei (BTH) region in China in 2020. Results showed that mosaic approach improved the simulation results of WS10 (surface wind speed at 10 m), T2 (temperature at 2 m), and RH (relative humidity) especially in nighttime in winter and were available for all stations with different percent of urban area. “MOS_TOPO” scenario, which coupled with mosaic approach and “topo-wind” schemes, obtained best simulation results of WS10 and T2 in January among six scenarios, with the lower average Root Mean Square Error of WS10 (1.18 m/s) and Mean Bias of T2 (0.55 °C) for all stations. Meanwhile, mosaic approach obtained lower |NMB| of PM2.5 than dominant approach in more than 69% cities in BTH region. Cities in southern Hebei province, especially Xingtai city, were identified as the most sensitive area for PM2.5 simulation affected by mosaic approach. Although the mosaic approach has improved the simulation results of meteorological parameters, especially the nighttime simulation results of WS10, there is still some deviation in the simulation results of PM2.5. Accurate emission inventory, suitable physics option in numerical weather model and rational chemical mechanism in air quality model are the important factors for WRF-CMAQ.

Atmospheric transport over open-pit mines: The effects of thermal stability and mine depth

A key difficulty in air pollution dispersion modeling and quantifying fugitive emission fluxes of pollutants from open-pit mines is that the meteorological fields for such complex terrains cannot be reliably predicted using simplistic surface layer theory. In this study, transport phenomena over a shallow (100 m) and a deep (500 m) synthetic mine are predicted under thermally unstable, neutral, and stable conditions using CFD modelling. The skimming flow is only predicted under the neutral case, while more complex flow patterns emerge otherwise. Under the unstable case, the shallow and deep mines induce enhanced mixing downstream of the mine, resulting in substantial vertical plume transport and dilution of the pollutants released from the mine. Under the stable case, the plume from the shallow mine is restricted to the surface layer downstream of the mine. However, under the stable case, the plume from the deep mine rises into the substantial portion of the boundary layer due to formation of a standing wave over and inside the mine. The results suggest that the CFD model can predict transport phenomena over open-pit mines reliably, so that the meteorological fields may be incorporated in operational models to improve accuracy of their predictions. • CFD is used to study atmospheric transport phenomena for open-pit mines under different mine depth and thermal stability conditions. • Skimming flow occurs for the neutral case, while enhanced mixing of passive scalar, released from the mine surface, occurs for the unstable case. • Under stable condition, a shallow surface layer is predicted for the shallow mine, while a deep standing wave is predicted for the deep mine. • Such accurate meteorological predictions can be incorporated in operational air quality and emission flux models for open-pit mines.

Simulation and assessment of traffic pollutant dispersion at an urban signalized intersection using multiple platforms

Evaluating the performance of air quality models for roadside traffic exhaust dispersion is critical to sustainable environmental development and urban pollution management. This study introduces three models, namely CAL3QHC, ENVI-met and ANSYS Fluent, to model the pollutant dispersion at an isolated busy urban signalized intersection, by taking fine particulate matter (PM2.5) as the subject matter. A dynamic emission factor model was established based on Cell Transmission Model (CTM) and Portable Emission Measurement System (PEMS) experiment, which was further implemented with the User Defined Function (UDF) of ANSYS Fluent. Results from an empirical study based on the Jianchuan and Humin Road intersection, in suburb Shanghai, demonstrate that ANSYS Fluent performs better for PM2.5 concentration prediction with carefully calibrated parameter settings. Simulation results from both CAL3QHC and ANSYS Fluent are within an acceptable scale, while ENVI-met performs better in assessing the correlation relationships between PM concentrations and the intersectional and meteorological factors. The concentration of PM2.5 was found to increase significantly during the idle phase, which tends to be accumulated in front of the intersection buildings simultaneously. With the increasing of the altitude, the overall average PM2.5 concentration of the intersection decreases gradually. Street canyons with high buildings were found with comparable lower wind speeds, hindering the pollutant diffusion. Finds of this study may assist urban intersection design from a variety of perspectives, e.g., lane channelization, signal timing, and even architecture styles of the surrounding buildings.

Predicting intraurban PM2.5 concentrations using enhanced machine learning approaches and incorporating human activity patterns

Urban areas contribute substantially to human exposure to ambient air pollution. Numerous statistical prediction models have been used to estimate ambient concentrations of fine particulate matter (PM2.5) and other pollutants in urban environments, with some incorporating machine learning (ML) algorithms to improve predictive power. However, many ML approaches for predicting ambient pollutant concentrations to date have used principal component analysis (PCA) with traditional regression algorithms to explore linear correlations between variables and to reduce the dimensionality of the data. Moreover, while most urban air quality prediction models have traditionally incorporated explanatory variables such as meteorological, land use, transportation/mobility, and/or co-pollutant factors, recent research has shown that local emissions from building infrastructure may also be useful factors to consider in estimating urban pollutant concentrations. Here we propose an enhanced ML approach for predicting urban ambient PM2.5 concentrations that hybridizes cascade and PCA methods to reduce the dimensionality of the data-space and explore nonlinear effects between variables. We test the approach using different durations of time series air quality datasets of hourly PM2.5 concentrations from three air quality monitoring sites in different urban neighborhoods in Chicago, IL to explore the influence of dynamic human-related factors, including mobility (i.e., traffic) and building occupancy patterns, on model performance. We test 9 state-of-the-art ML algorithms to find the most effective algorithm for modeling intraurban PM2.5 variations and we explore the relative importance of all sets of factors on intraurban air quality model performance. Results demonstrate that Gaussian-kernel support vector regression (SVR) was the most effective ML algorithm tested, improving accuracy by 118% compared to a traditional multiple linear regression (MLR) approach. Incorporating the enhanced approach with SVR algorithm increased model performance up to 18.4% for yearlong and 98.7% for month-long hourly datasets, respectively. Incorporating assumptions for human occupancy patterns in dominant building typologies resulted in improvements in model performance by between 4% and 37%. Combined, these innovations can be used to improve the performance and accuracy of urban air quality prediction models compared to conventional approaches.

A Space/Time Data Fusion Method for Accurately Estimating Wildfire Smoke Concentrations During the October 2017 California Fires to Inform Population-Level Exposure

Background. Exposure to wildfire smoke causes a range of adverse health outcomes, suggesting the importance of accurately estimating wildfire smoke concentrations. While chemical transport models (CTMs) and spatial interpolation of observations are often used to assess smoke exposure, geostatistical methods can combine surface observations with modeled and satellite-derived concentrations to produce more accurate exposure estimates.Methods. Here we estimate ground-level PM2.5 concentrations during the October 2017 California wildfires, using the Constant Air Quality Model Performance (CAMP) Method and the Bayesian Maximum Entropy (BME) Framework to bias-correct and fuse together three PM2.5 datasets: concentrations from permanent and temporary monitoring stations, a CTM, and satellite observations. Four different BME space/time (s/t) kriging and data fusion methods using these three datasets were evaluated for accuracy.Results. All four BME methods produced more accurate estimates of ground-level PM2.5 than the standalone CTM and satellite products, with the addition of temporary monitoring station data further improving accuracy. While BME s/t kriging on observations performed best near monitoring stations, the BME data fusion of observations with the CAMP-corrected CTM provided the best overall estimate, especially in smoke-impacted regions. Using these smoke concentrations, we estimate more than 60,000 people were exposed to very unhealthy air, PM2.5 concentrations greater than 150.5 µg/m3, during the 2017 wildfires.Conclusions. We show that the BME framework, used in combination with the CAMP correction method, can be used to accurately estimate ground-level PM2.5 concentrations during a wildfire event. Our results emphasize the importance of combining multiple data sources to characterize population-level exposure during extreme air pollution events.

Estimating Wildfire Smoke Concentrations during the October 2017 California Fires through BME Space/Time Data Fusion of Observed, Modeled, and Satellite-Derived PM2.5.

Exposure to wildfire smoke causes adverse health outcomes, suggesting the importance of accurately estimating smoke concentrations. Geostatistical methods can combine observed, modeled, and satellite-derived concentrations to produce accurate estimates. Here, we estimate daily average ground-level PM2.5 concentrations at a 1 km resolution during the October 2017 California wildfires, using the Constant Air Quality Model Performance (CAMP) and Bayesian Maximum Entropy (BME) methods to bias-correct and fuse three concentration datasets: permanent and temporary monitoring stations, a chemical transport model (CTM), and satellite-derived estimates. Four BME space/time kriging and data fusion methods were evaluated. All BME methods produce more accurate estimates than the standalone CTM and satellite products. Adding temporary station data increases the R2 by 36%. The data fusion of observations with the CAMP-corrected CTM and satellite-derived concentrations provides the best estimate (R2 = 0.713) in fire-impacted regions, emphasizing the importance of combining multiple datasets. We estimate that approximately 65,000 people were exposed to very unhealthy air (daily average PM2.5 ≥ 150.5 μg/m3).

Systematic bias of WRF-CMAQ PM10 simulations for Seoul, Korea

For evaluating the performance of the Korean operational air quality model (Weather Research and Forecasting-Community Multiscale Air Quality), we compared the simulated concentrations of particulate matter with diameters ≤ 10 μm (PM10) against observations in Seoul, Korea for the cold seasons (October through March) of 2015–2018. The simulations yield about 70% hit rate for PM10 concentrations within 10 μg m−3 from the observations. For the 50–90 μg m−3 range, the simulations systematically overestimate the PM10 frequency mainly due to overestimation/underestimation of PM10 concentrations in the moderate-PM10 (30 < PM10 ≤ 60 μg m−3)/high-PM10 (60 < PM10 ≤ 100 μg m−3) range. Considering that the model simulates PM10 transports using input meteorological fields and emissions amounts, 72-h back-trajectories from Seoul (37.51°N, 126.99°E) at the 500 m above ground level are analyzed to investigate the cause of the systematic bias. The 410 days when PM10 concentrations are within the 30–100 μg m−3 range are grouped into three major wind flows: westerly (163 days), northerly (99 days), and northwesterly (148 days). Overestimations of moderate-PM10 days frequently occur for the westerly group associated with a synoptic pattern characterized by calm anticyclonic systems over China and Korea, especially when the emission amounts were overestimated in eastern China, the main PM10 source region for Seoul for the westerly group. Underestimations of high-PM10 days frequently occur for the northerly and northwesterly groups associated with climatological patterns or strong windy cyclonic systems along with some small inflow from underestimated emission in northeastern China. To improve the model performance, statistical models trained for each back-trajectory group along with improved estimations of emission amounts may prove helpful.

Evaluation of CMAQ modeling sensitivity to planetary boundary layer parameterizations for gaseous and particulate pollutants over a fjord valley

Abstract Three-dimensional chemical transport models are useful for spatial and temporal analyses of outdoor air quality. However, the suitability of boundary-layer parameterizations for air pollution modeling over deep, coastal valleys has seldom been tested. An evaluation of the Community Multiscale Air Quality (CMAQ) model performance for five planetary boundary-layer schemes (PBL) with the Weather Research and Forecasting (WRF) meteorological driver was conducted at 1-km horizontal resolution for fine particulate matter (PM2.5), sulfur dioxide (SO2) and nitrogen dioxide (NO2) over the Terrace-Kitimat valley of northwestern British Columbia, Canada. The top-ranked schemes were Mellor-Yamada-Nakanishi-Niino Level 3 (MYNN3) for PM2.5 and Mellor-Yamada-Janjic for NO2. Both schemes ranked high for absolute SO2 levels, but the MYJ and Asymmetric Convective Model, version 2 (ACM2) schemes qualitatively emulated peak summertime diurnal concentrations in the near field of elevated point sources. Greater nighttime SO2 concentrations with MYNN3 and Yonsei University PBL schemes, in less agreement with station monitoring 8 km downwind of emissions from tall stacks, suggested sustained pollutant mixing and downward transport within the nocturnal boundary layer. Consequently, for these two schemes with representations of nonlocal mass flux transfers between model layers, inland penetrations of pollutant plumes were farther than those of ACM2, MYJ, and University of Washington schemes. For NO2 and PM2.5 that mainly discharged passively from fugitive, ground-level sources, hence are less accurately quantified than SO2 emissions, the fully local MYJ, and semi-local MYNN3 PBL schemes more reasonably reproduced peak season concentrations than other schemes. It is concluded that for air pollution modeling in rugged, remote areas, the mode of pollutant emissions is important for the choice of a PBL scheme. PM2.5 was consistently underestimated by the various PBL schemes, and aspects for improving CMAQ simulations for a complex environment are discussed.

Comparison of PM2.5 Chemical Components over East Asia Simulated by the WRF-Chem and WRF/CMAQ Models: On the Models’ Prediction Inconsistency

High levels of atmospheric concentration of PM2.5 (particulate matters less than 2.5 μm in size) are one of the most urgent societal issues over the East Asian countries. Air quality models have been used as an essential tool to predict spatial and temporal distribution of the PM2.5 and to support relevant policy making. This study aims to investigate the performance of high-fidelity air quality models in simulating surface PM2.5 chemical composition over the East Asia region in terms of a prediction consistency, which is a prerequisite for accurate air quality forecasts and reliable policy decision. The WRF-Chem (Weather Research and Forecasting-Chemistry) and WRF/CMAQ (Weather Research and Forecasting/Community Multiscale Air Quality modeling system) models were selected and uniquely configured for a one-month simulation by controlling surface emissions and meteorological processes (model options) to investigate the prediction consistency focusing the analyses on the effects of meteorological and chemical processes. The results showed that the surface PM2.5 chemical components simulated by both the models had significant inconsistencies over East Asia ranging fractional differences of 53% ± 30% despite the differences in emissions and meteorological fields were minimal. The models’ large inconsistencies in the surface PM2.5 concentration were attributed to the significant differences in each model’s chemical responses to the meteorological variables, which were identified from the multiple linear regression analyses. Our findings suggest that the significant models’ prediction inconsistencies should be considered with a great caution in the PM2.5 forecasts and policy support over the East Asian region.

Evaluation of an operational air quality model using large-eddy simulation

Abstract The large-eddy simulation (LES) model uDALES is used to evaluate the predictive skill of the operational air quality model SIRANE. The use of LES in this study presents a novel approach to air quality model evaluation, avoiding sources of uncertainty and providing numerical control that permits systematic analysis of targeted parametrisations and assumptions. A case study is conducted over South Kensington, London with the morphology, emissions, meteorological conditions and boundary conditions carefully matched in both models. The dispersion of both inert (NOx) and reactive (NO, NO2 and O3) pollutants under neutral, steady-state conditions is simulated for a south-westerly and westerly wind direction. A quantitative comparison between the two models is performed using statistical indices (the fractional bias, FB, the normalised mean squared error, NMSE, and the fraction in a factor of 2, FAC2). SIRANE is shown to successfully capture the dominant trends with respect to canyon-averaged concentrations of inert NOx (FB = −0.08, NMSE = 0.08 and FAC2 = 1.0). The prediction of along-canyon velocities is shown to exhibit sources of systematic error dependant on the angle of incidence of the mean wind (FB = −0.18). The assumption of photostationarity within SIRANE (deviations from equilibrium of up to 170% exist close to busy roads) is also identified as a significant source of systematic bias resulting in over- and underpredictions of NO2 (FB = −0.18) and O3 (FB = 0.14) respectively. The validity of the assumed uniform in-canyon concentration is assessed by analysing the pedestrian, leeward and windward concentrations resolved in uDALES. The use of canyon-averaged concentrations to predict pedestrian level exposure is shown to result in significant underestimations. Linear regression is used to effectively capture the relationship between pedestrian- and canyon-averaged concentrations in uDALES. Correction factors are derived ( m ≈ 1.62 and R 2 = 0.92 for inert NOx) that are capable of significantly improving SIRANE's ability to assess pedestrian level exposure for the conditions investigated in this study. The prevalence of intersections and advective nature of the shear layer are highlighted as important differences between modelling real heterogeneous urban morphology and idealised infinite canyons.

One-way coupling of WRF with a Gaussian dispersion model: a focused fine-scale air pollution assessment on southern Mediterranean.

Numerous uncertainty factors in dispersion models should be taken into account in order to improve the reliability of predictions. The ability of a mesoscale meteorological model to assimilate observational data is an efficient way to improve operational air quality model forecasts. In this study, local weather data assimilation based on a flux-adjusting surface data assimilation system (FASDAS) is introduced to a Gaussian atmospheric dispersion model for a period with reported stable meteorological conditions. After evaluating the vulnerabilities of FASDAS, a combined data assimilation method is proposed to simultaneously improve the model weather prediction and retrieve the representation of accurate concentration distributions for short-range dispersion modeling against a control run. The two main uncertainty parameters considered are the wind speed and direction. A twin experiment demonstrates that the combined technique effectively improves the distribution of simulated concentrations. Comparison between results before and after the implement of data assimilation demonstrates that discrepancies between the reference simulation and the model forecast are mitigated after introducing the combined method, with more than 70 % of the predictions within a factor of two of the measurements. The errors in wind predictions in the FASDAS influenced the dispersion calculations, and the implementation of wind data assimilation in conjunction with the FASDAS has an indirect effect on further alleviating pollutant transport modeling errors.

A Dynamic Dust Emission Allocation Method and Holiday Profiles Applied to Emission Processing for Improving Air Quality Model Performance

ABSTRACT An accurate depiction of temporal and spatial variations in emissions is critical in simulating air quality with atmospheric chemical transport models. Most emission processing systems typically use prescribed profiles to allocate anthropogenic emissions based on the assumptions that the temporal variance is periodical and spatial variance is time-independent. However, these assumptions are not applicable to emission sources heavily influenced by meteorology and holiday activity. In this study, we improved the temporal and spatial allocation of anthropogenic emissions by, first of all, developing a dynamic allocation method for fugitive dust that uses the negative correlation between dust emissions and precipitation, based on hourly rainfall data generated by the Weather Research and Forecasting model. Second, we employed holiday-specific profiles that were established using continuous emission monitoring system and traffic flow monitoring data to allocate power plant and on-road mobile emissions during the Spring Festival period, when human activity differs considerably from that of non-holiday periods. The new dynamic allocation method and holiday-specific profiles were applied to emissions in the Pearl River Delta region as a demonstration. Validated using a chemical transport model, this method obviously improved the model performance for periods with rainfall, with the normalized mean bias (NMB) decreasing by 6.27% for PM10 (particulate matter with a diameter of ≤ 10 µm) and 4.33% for PM2.5 (particulate matter with a diameter of ≤ 2.5 µm). The holiday simulations revealed that the holiday-specific profiles mitigated overestimations of NO2, SO2, and PM10 for the Spring Festival period, with the NMBs decreasing by 37.95%, 18.56%, and 20.83%, respectively. Hence, refining the allocation of emissions improved model simulation and air quality forecasting.