Application Of Air Quality Models Research Articles

Application of regional meteorology and air quality models based on the microprocessor without interlocked piped stages (MIPS) and LoongArch CPU platforms

Abstract. The microprocessor without interlocked piped stages (MIPS) and LoongArch are reduced instruction set computing (RISC) processor architectures, which have advantages in terms of energy consumption and efficiency. There are few studies on the application of MIPS and LoongArch central processing units (CPUs) in geoscientific numerical models. In this study, the Loongson 3A4000 CPU platform with the MIPS64 architecture and the Loongson 3A6000 CPU platform with the LoongArch architecture were used to establish the runtime environment for the air quality modelling system Weather Research and Forecasting–Comprehensive Air Quality Model with extensions (WRF-CAMx) in the Beijing–Tianjin–Hebei region. The results show that the relative errors for the major species (NO2, SO2, O3, CO, PNO3, and PSO4) between the MIPS and X86 benchmark platforms are within ±0.1 %. The maximum mean absolute error (MAE) of major species ranged up to 10−2 ppbV or µg m−3, the maximum root mean square error (RMSE) ranged up to 10−1 ppbV or µg m−3, and the mean absolute percentage error (MAPE) remained within 0.5 %. The CAMx takes about 195 min on the Loongson 3A4000 CPU, 71 min on the Loongson 3A6000 CPU, and 66 min on the Intel Xeon E5-2697 v4 CPU, when simulating a 24 h case with four parallel processes using MPICH. As a result, the single-core computing capability of the Loongson 3A4000 CPU for the WRF-CAMx modelling system is about one-third of the Intel Xeon E5-2697 v4 CPU, and the one of Loongson 3A6000 CPU is slightly lower than that of Intel Xeon E5-2697 v4 CPU; but, the thermal design power (TDP) of Loongson 3A4000 is 40 W, while the TDP of Loongson 3A6000 is 38 W, only about one-fourth of that of Intel Xeon E5-2697 v4, whose TDP is 145 W. The results also verify the feasibility of cross-platform porting and the scientific usability of the ported model. This study provides a technical foundation for the porting and optimization of numerical models based on MIPS, LoongArch, or other RISC platforms.

Assessment of the sensitivity of model responses to urban emission changes in support of emission reduction strategies

AbstractThe sensitivity of air quality model responses to modifications in input data (e.g. emissions, meteorology and boundary conditions) or model configurations is recognized as an important issue for air quality modelling applications in support of air quality plans. In the framework of FAIRMODE (Forum of Air Quality Modelling in Europe, https://fairmode.jrc.ec.europa.eu/) a dedicated air quality modelling exercise has been designed to address this issue. The main goal was to evaluate the magnitude and variability of air quality model responses when studying emission scenarios/projections by assessing the changes of model output in response to emission changes. This work is based on several air quality models that are used to support model users and developers, and, consequently, policy makers. We present the FAIRMODE exercise and the participating models, and provide an analysis of the variability of O3 and PM concentrations due to emission reduction scenarios. The key novel feature, in comparison with other exercises, is that emission reduction strategies in the present work are applied and evaluated at urban scale over a large number of cities using new indicators such as the absolute potential, the relative potential and the absolute potency. The results show that there is a larger variability of concentration changes between models, when the emission reduction scenarios are applied, than for their respective baseline absolute concentrations. For ozone, the variability between models of absolute baseline concentrations is below 10%, while the variability of concentration changes (when emissions are similarly perturbed) exceeds, in some instances 100% or higher during episodes. Combined emission reductions are usually more efficient than the sum of single precursor emission reductions both for O3 and PM. In particular for ozone, model responses, in terms of linearity and additivity, show a clear impact of non-linear chemistry processes. This analysis gives an insight into the impact of model’ sensitivity to emission reductions that may be considered when designing air quality plans and paves the way of more in-depth analysis to disentangle the role of emissions from model formulation for present and future air quality assessments.

Recommendations on benchmarks for photochemical air quality model applications in China — NO2, SO2, CO and PM10

Photochemical air quality models (AQMs) are a vital tool for atmospheric pollution research and have been widely used in various applications, such as air quality prediction and evaluation of pollution control strategies. Before using these models for further studies, it is essential to thoroughly evaluate their reliability and accuracy. While previous guidelines and benchmarks have primarily focused on fine particulate matter (PM2.5) and ozone (O3), there is still a lack of benchmarks for evaluating the model performance on primary criteria pollutants such as sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), and particulate matter with aerodynamic diameter less than or equal to 10 μm (PM10). The use of air quality models in China has increased significantly in the past decades. However, there is still a lack of standardized benchmarks for the performance evaluation of these models. Building upon our previous work on PM2.5 and its chemical species, we propose a set of benchmarks for evaluating the performance of the aforementioned four air pollutants. Initially, we identified a total of 475 papers published during 2007–2019 that utilized at least one of the five commonly used AQMs in China. From these papers, we selected 164 articles that provided model performance evaluation (MPE) results of the four primary air pollutants. The three most frequently used Model Performance Evaluation (MPE) metrics were selected to analyse the impact of different model configurations on the reported statistics, including modelling region, season, and emission inventory. Lastly, three commonly used statistical indicators, including normalized mean bias (NMB), normalized mean error (NME), and correlation coefficient (R), were proposed for the validation of simulated NO2, SO2, CO, and PM10. Two sets of benchmarks are given, including the "goal" and "criteria". The "goal" represents the best range of performance that a model can be expected to achieve, and the "criteria" represents performance that the majority of studies have achieved. We recommend R values above 0.50, 0.35, 0.45, and 0.40 for NO2, SO2, PM10, and CO, respectively, in order to meet the "criteria" benchmark. If the "goal" benchmark is to be achieved, the corresponding R values are 0.60, 0.55, 0.60, and 0.60. The "goal" benchmarks of NMB for NO2, SO2, PM10, and CO are within ±20%, ±25%, ±20%, and ±25%, respectively; while the "goal" benchmarks of NME for the four pollutants are less than 40%, 45%, 45%, and 60%, respectively. These benchmarks supplement our previous benchmarks for PM2.5 and its components and provide a more comprehensive guideline for the air quality modelling community in China.

An exploratory approach to estimate point emission sources

The current atmospheric emission inventories do not fully meet the spatial and temporal resolution requirements of air quality modelling applications. Considering Portugal as a case study and focusing on combustion point emission sources (i.e., public power, refineries, manufacturers, and construction activities), this work proposes a methodological approach and dataset to estimate anthropogenic emissions suitable for different spatial scales (from regional to local). The obtained results were similar to the annual values reported by the Portuguese Environment Agency with the maximum emissions being estimated for manufacturing and construction activities. No significant differences were recorded between the temporal profiles developed in this and previous studies. However, the country-specific proxies from the developed database allowed us to better represent the temporal and spatial patterns of the Portuguese atmospheric emissions. The combination of the BigAir database with a comprehensive and standardized approach could help policymakers define mitigation and/or plan measures to reduce emissions from point sources, support countries worldwide (with a lack of data) to develop high-resolution emission inventories, and improve the current global and European inventories.

Prioritising the sources of pollution in European cities: do air quality modelling applications provide consistent responses?

Abstract. To take decisions on how to improve air quality, it is useful to perform a source allocation study that identifies the main sources of pollution for the area of interest. Often source allocation is performed with a chemical transport model (CTM) but unfortunately, even if accurate, this technique is time consuming and complex. Comparing the results of different CTMs to assess the uncertainty of source allocation results is even more difficult. In this work, we compare the source allocation (for PM2.5 yearly averages) in 150 major cities in Europe, based on the results of two CTMs (CHIMERE and EMEP), approximated with the SHERPA (Screening for High Emission Reduction Potential on Air) approach. Although contradictory results occur in some cities, the source allocation results obtained with the two SHERPA simplified models lead to similar results in most cases, even though the two CTMs use different input data and configurations.

Regional-scale modelling for the assessment of atmospheric particulate matter concentrations at rural background locations in Europe

Abstract. The application of regional-scale air quality models is an important tool in air quality assessment and management. For this reason, the understanding of model abilities and performances is mandatory. The main objective of this research was to investigate the spatial and temporal variability of background particulate matter (PM) concentrations, to evaluate the regional air quality modelling performance in simulating PM concentrations during statically stable conditions and to investigate processes that contribute to regionally increased PM concentrations with a focus on eastern and central Europe. The temporal and spatial variability of observed PM was analysed at 310 rural background stations in Europe during 2011. Two different regional air quality modelling systems (offline coupled European Monitoring and Evaluation Programme, EMEP, and online coupled Weather Research and Forecasting with Chemistry) were applied to simulate the transport of pollutants and to further investigate the processes that contributed to increased concentrations during high-pollution episodes. Background PM measurements from rural background stations, wind speed, surface pressure and ambient temperature data from 920 meteorological stations across Europe, classified according to the elevation, were used for the evaluation of individual model performance. Among the sea-level stations (up to 200 m), the best modelling performance, in terms of meteorology and chemistry, was found for both models. The underestimated modelled PM concentrations in some cases indicated the importance of the accurate assessment of regional air pollution transport under statically stable atmospheric conditions and the necessity of further model improvements.

Spatiotemporal imputation of MAIAC AOD using deep learning with downscaling

Aerosols have adverse health effects and play a significant role in the climate as well. The Multiangle Implementation of Atmospheric Correction (MAIAC) provides Aerosol Optical Depth (AOD) at high temporal (daily) and spatial (1 km) resolution, making it particularly useful to infer and characterize spatiotemporal variability of aerosols at a fine spatial scale for exposure assessment and health studies. However, clouds and conditions of high surface reflectance result in a significant proportion of missing MAIAC AOD. To fill these gaps, we present an imputation approach using deep learning with downscaling. Using a baseline autoencoder, we leverage residual connections in deep neural networks to boost learning and parameter sharing to reduce overfitting, and conduct bagging to reduce error variance in the imputations. Downscaled through a similar auto-encoder based deep residual network, Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA-2) GMI Replay Simulation (M2GMI) data were introduced to the network as an important gap-filling feature that varies in space to be used for missingness imputations. Imputing weekly MAIAC AOD from 2000 to 2016 over California, a state with considerable geographic heterogeneity, our full (non-full) residual network achieved mean R2 = 0.94 (0.86) [RMSE = 0.007 (0.01)] in an independent test, showing considerably better performance than a regular neural network or non-linear generalized additive model (mean R2 = 0.78–0.81; mean RMSE = 0.013–0.015). The adjusted imputed as well as combined imputed and observed MAIAC AOD showed strong correlation with Aerosol Robotic Network (AERONET) AOD (R = 0.83; R2 = 0.69, RMSE = 0.04). Our results show that we can generate reliable imputations of missing AOD through a deep learning approach, having important downstream air quality modeling applications.

The challenges of air quality modelling when crossing multiple spatial scales

Multiscale air quality assessment implies understanding the interaction among atmospheric processes and scales. In this perspective, the use of air quality models has a fundamental role and the way they address these interactions is very important for the quality of results. However, in multiscale air quality modelling, the relationship between models, simulation domains and resolutions remains a challenging research issue. This paper presents a state-of-the-art review on multiscale air quality modelling applications from the regional to the street level, identifying which models are used, the methodological principles and the required input datasets. Based on the findings, an analysis of the current limitations associated with the integration of different models and multiple spatial scales in a single modelling system is presented and discussed. Lastly, taking as support the reviewed contents, a set of guidelines for strengthening the synergy among scales and harmonizing different types of models within a system is proposed. Unsuitable or non-existent urban canopy parametrizations (UCP), limited grid resolutions, discarded microscale impacts on upper domains, inadequate input data and simplified chemistry are some important aspects contributing to large uncertainties when analyzing multiscale air quality results. To overcome these shortcomings in current multiscale modelling systems, more research aimed at improving the modelling performance is required, focusing on (i) UCP coupled to mesoscale models, (ii) developing modelling systems with two-way nesting capability to boost feedbacks among domains and (iii) promoting the use, improvement and development of multiscale online modelling systems, in order to have a comprehensive multiscale understanding of all atmospheric processes that govern the transport, dispersion, transformation and deposition of air pollutants.

PBDW: A non-intrusive Reduced Basis Data Assimilation method and its application to an urban dispersion modeling framework

The challenges of understanding the impacts of air pollution require detailed information on the state of air quality. While many modeling approaches attempt to treat this problem, physically-based deterministic methods are often overlooked due to their costly computational requirements and complicated implementation. In this work we extend a non-intrusive Reduced Basis Data Assimilation method (known as PBDW state estimation) to large pollutant dispersion case studies relying on equations involved in chemical transport models for air quality modeling. This, with the goal of rendering methods based on parameterized partial differential equations (PDE) feasible in air quality modeling applications requiring quasi-real-time approximation and correction of model error in imperfect models. Reduced basis methods (RBM) aim to compute a cheap and accurate approximation of a physical state using approximation spaces made of a suitable sample of solutions to the model. One of the keys of these techniques is the decomposition of the computational work into an expensive one-time offline stage and a low-cost parameter-dependent online stage. Traditional RBMs require modifying the assembly routines of the computational code, an intrusive procedure which may be impossible in cases of operational model codes. We propose a less intrusive reduced order method using data assimilation for measured pollution concentrations, adapted for consideration of the scale and specific application to exterior pollutant dispersion as can be found in urban air quality studies. Common statistical techniques of data assimilation in use in these applications require large historical data sets, or time-consuming iterative methods. The method proposed here avoids both disadvantages. In the case studies presented in this work, the method allows to correct for unmodeled physics and treat cases of unknown parameter values, all while significantly reducing online computational time.

Weather research and forecasting model simulations over the Pearl River Delta Region

Pearl River Delta (PRD), located in south-eastern coast of mainland China, is one of the regions affected by heavy particulate matter (PM) levels. Notwithstanding the potential use of meteorological and air quality modelling to characterize the air pollution problems, little attention has been paid to meteorological model configuration and its impact on air quality modelling applications over the region. Aiming to find the most suitable set of parameterization schemes of the Advanced Research Weather Research and Forecasting (WRF-ARW) model for air quality modelling applications over the PRD region, a performance experiment was performed. Three tests, with different combinations of parameterization schemes, were created and evaluated. For the best configuration modelling setup, meteorological simulations for a winter (i.e. January) and summer (i.e. July) periods are provided. The meteorological model showed a clockwise deviation for the wind direction and tends to overestimate the temperature and wind speed. It is expected that the present work could reduce the meteorological and air quality modelling uncertainty over the PRD region.

Identification Source of Variation on Regional Impact of Air Quality Pattern using Chemometric Techniques in Kuching, Sarawak

Air pollution has been considered a devastating environmental issue that can negatively impact human health and the environment. Kuching as one of the capital cities in Malaysia is also affected by air pollution and unsatisfactory air quality condition. Thus, the main objective is to identify the source of variation on regional impact of air quality pattern in Kuching, Sarawak. A seven-year (2009-2015) database was acquired from the Malaysia Department of Environment (DOE). The data were analysed using several Chemometric Techniques. The findings demonstrated strong positive correlation of Particulate Matter below than 10 microns (PM10) and API (r = 0.994). In addition, Principal Component Analysis (PCA) and Artificial Neural Network (ANN) revealed that Carbon Monoxide (CO), Ozone (O3) and PM10 were the most significant air pollutants in Kuching. Based on the results in the Statistical Process Chart (SPC) analysis, PM10,COand O3 values exceeded the Control Limit (CL) of SPC. This study concluded that the application of air quality model in this study is relevant for mitigating action plan of air quality in Kuching, Sarawak, as it is of paramount importance to continuously monitor and manage the quality of air for the sustainability of the environment and human health.

High-resolution air quality modeling in a medium-sized city in the tropical Andes: Assessment of local and global emissions in understanding ozone and PM10 dynamics

Application of high-resolution air quality models in the Andes Region is scarce, especially in medium-sized cities of South America, which are undergoing a fast urban growth, increasing the risk associated with air pollution episodes. In this study, the WRF-Chem model was applied for analyzing the influence of local and global emission inventories (EI) in the representation of O3 and PM10 concentrations in the medium-sized Andean city of Manizales, Colombia. Furthermore, the relative impact of anthropogenic emissions on O3 and PM10 dynamics was evaluated. The use of local emission fluxes allowed significant improvements in O3 representation, characterized by better performance metrics (MGE, RMSE and r coefficient of 7.6 ppb, 8.8 ppb, and 0.81 respectively) than that obtained from the use of the EDGAR-HTAP global database (MGE = 11.6 ppb, RMSE = 13.9 ppb and r = 0.64). In terms of PM10 concentrations, better metrics were obtained using the local EI (MGE = 10 μg/m3 and RMSE = 11.5 μg/m3), compared with those from the global EI (MGE = 28 μg/m3 and RMSE = 28.8 μg/m3). Analysis of the relative impact of anthropogenic emissions suggests that PM10 levels and ozone chemistry in the urban area of Manizales were controlled by emissions of its precursors from on-road vehicular sources and possible transport of O3 at a regional scale from near rural zones. Results obtained highlight the importance of estimating and improving local EIs in medium-sized cities, for a more realistic analysis of emission impacts.

Strengths and weaknesses of the FAIRMODE benchmarking methodology for the evaluation of air quality models

The Forum of Air Quality Modelling in Europe (FAIRMODE) was launched in 2007 to bring together air quality modellers and users in order to promote and support the harmonised use of models by EU Member States, with emphasis on model application under the European Air Quality Directive. In this context, a methodology for evaluating air quality model applications has been developed. This paper presents an analysis of the strengths and weaknesses of the FAIRMODE benchmarking approach, based on users’ feedback. European wide, regional and urban scale model applications, developed by different research groups over Europe, have been taken into account. The analysis is focused on the main pollutants under the Air Quality Directive, namely PM10, NO2 and O3. The different case studies are described and analysed with respect to the methodologies applied for model evaluation and quality assurance. This model evaluation intercomparison demonstrates the potential of a harmonised evaluation and benchmarking methodology. A SWOT analysis of the FAIRMODE benchmarking approach is performed based on feedback from users of the tool. This analysis helps to identify the main advantages and value of this model evaluation benchmarking approach compared with other methodologies, in addition to highlighting requirements for future development.

Application of a Line Source Air Quality Model to The Study of Traffic Carbon Monoxide in Brunei Darussalam

Application of a Line Source Air Quality Model to The Study of Traffic Carbon Monoxide in Brunei Darussalam

Persistence of initial conditions in continental scale air quality simulations

This study investigates the effect of initial conditions (IC) for pollutant concentrations in the atmosphere and soil on simulated air quality for two continental-scale Community Multiscale Air Quality (CMAQ) model applications. One of these applications was performed for springtime and the second for summertime. Results show that a spin-up period of ten days commonly used in regional-scale applications may not be sufficient to reduce the effects of initial conditions to less than 1% of seasonally-averaged surface ozone concentrations everywhere while 20 days were found to be sufficient for the entire domain for the spring case and almost the entire domain for the summer case. For the summer case, differences were found to persist longer aloft due to circulation of air masses and even a spin-up period of 30 days was not sufficient to reduce the effects of ICs to less than 1% of seasonally-averaged layer 34 ozone concentrations over the southwestern portion of the modeling domain. Analysis of the effect of soil initial conditions for the CMAQ bidirectional NH3 exchange model shows that during springtime they can have an important effect on simulated inorganic aerosols concentrations for time periods of one month or longer. The effects are less pronounced during other seasons. The results, while specific to the modeling domain and time periods simulated here, suggest that modeling protocols need to be scrutinized for a given application and that it cannot be assumed that commonly-used spin-up periods are necessarily sufficient to reduce the effects of initial conditions on model results to an acceptable level. What constitutes an acceptable level of difference cannot be generalized and will depend on the particular application, time period and species of interest. Moreover, as the application of air quality models is being expanded to cover larger geographical domains and as these models are increasingly being coupled with other modeling systems to better represent air-surface-water exchanges, the effects of model initialization in such applications needs to be studied in future work.

ESP v2.0: enhanced method for exploring emission impacts of future scenarios in the United States – addressing spatial allocation

Abstract. The Emission Scenario Projection (ESP) method produces future-year air pollutant emissions for mesoscale air quality modeling applications. We present ESP v2.0, which expands upon ESP v1.0 by spatially allocating future-year non-power sector emissions to account for projected population and land use changes. In ESP v2.0, US Census division-level emission growth factors are developed using an energy system model. Regional factors for population-related emissions are spatially disaggregated to the county level using population growth and migration projections. The county-level growth factors are then applied to grow a base-year emission inventory to the future. Spatial surrogates are updated to account for future population and land use changes, and these surrogates are used to map projected county-level emissions to a modeling grid for use within an air quality model. We evaluate ESP v2.0 by comparing US 12 km emissions for 2005 with projections for 2050. We also evaluate the individual and combined effects of county-level disaggregation and of updating spatial surrogates. Results suggest that the common practice of modeling future emissions without considering spatial redistribution over-predicts emissions in the urban core and under-predicts emissions in suburban and exurban areas. In addition to improving multi-decadal emission projections, a strength of ESP v2.0 is that it can be applied to assess the emissions and air quality implications of alternative energy, population and land use scenarios.

Dynamic evaluation of air quality models over European regions

Chemistry-transport models are increasingly used in Europe for estimating air quality or forecasting changes in pollution levels. But with this increased use of modeling arises the need of harmonizing the methodologies to determine the quality of air quality model applications. This is complex for planning applications, i.e. when models are used to assess the impact of realistic or virtual emission scenarios. In this work, the methodology based on the calculation of potencies proposed by Thunis and Clappier (2014) to analyze the model responses to emission reductions is applied on three different domains in Europe (Po valley, Southern Poland and Flanders). This methodology is further elaborated to facilitate the inter-comparison process and bring in a single diagram the possibility of differentiating long-term from short-term effects. This methodology is designed for model users to interpret their model results but also for policy-makers to help them defining intervention priorities. The methodology is applied to both daily PM10 and 8 h daily maximum ozone.

Impact of updated traffic emissions on HONO mixing ratios simulated for urban site in Houston, Texas

Abstract. Recent measurements in Houston show that HONO traffic emissions are 1.7% of NOx emissions, which is about twice the previously estimated value of 0.8% based on tunnel measurements in 2001. The 0.8% value is widely used to estimate mobile emissions of HONO for air quality modeling applications. This study applies the newly estimated HONO / NOx ratio in the WRF–SMOKE–CMAQ modeling system and estimates the impact of higher HONO traffic emissions on its mixing ratios. Since applied emission inventory resulted in overestimates of NOx mixing ratios and because HONO emissions and chemical formation depend on the magnitude of NOx, thus, before proceeding with HONO emission modifications emissions of NOx were adjusted to reflect current emission trends. The modeled mixing ratios of NOx were evaluated against measured data from a number of sites in the Houston area. Overall, the NOx mean value dropped from 11.11 ppbv in the base case to 7.59 ppbv in the NOx-adjusted case becoming much closer to the observed mean of 7.76 ppbv. The index of agreement (IOA) is improved in the reduced NOx case (0.71 vs. 0.75) and the absolute mean error (AME) is lowered from 6.76 to 4.94. The modeled mixing ratios of HONO were evaluated against the actual observed values attained at the Moody Tower in Houston. The model could not reproduce the morning HONO peaks when the low HONO / NOx ratio of 0.008 was used to estimate HONO emissions. Doubling HONO emissions from mobile sources resulted in higher mixing ratios, and the mean value increased from 0.30 to 0.41 ppbv becoming closer to the observed mean concentrations of 0.69 but still low; AME was slightly reduced from 0.46 to 0.43. IOA for simulation that used the 2001 emission values is 0.63 while for simulation with higher HONO emission it increased to 0.70. Increased HONO emissions from mobile sources resulted in a 14% increase in OH during morning time at the location of the Moody Tower and 3% when averaged over an urban area. The increase calculated for daytime was 7 and 1% for the Moody Tower and the urban area, respectively. The impact on ozone was found to be marginal. This study results shed light on the underestimated HONO and OH in the morning from global/regional chemical transport models with the typical emission of 0.8% HONO emission out of the total NOx emissions.

Quantification of non-linearities as a function of time averaging in regional air quality modeling applications

Air quality models which are nowadays used for a wide range of scopes (i.e. assessment, forecast, planning) see their intrinsic complexity progressively increasing as better knowledge of the atmospheric chemistry processes is gained. As a result of this increased complexity potential non-linearities are implicitly and/or explicitly incorporated in the system. These non-linearities represent a key and challenging aspect of air quality modeling, especially to assess the robustness of the model responses. In this work the importance of non-linear effects in air quality modeling is quantified, especially as a function of time averaging. A methodology is proposed to decompose the concentration change resulting from an emission reduction over a given domain into its linear and non-linear contributions for each precursor as well as in the contribution resulting from the interactions among precursors. Simulations with the LOTOS-EUROS model have been performed by TNO over three regional geographical areas in Europe for this analysis. In all three regions the non-linear effects for PM10 and PM2.5 are shown to be relatively minor for yearly and monthly averages whereas they become significant for daily average values. For Ozone non-linearities become important already for monthly averages in some regions. An approach which explicitly deals with monthly variations seems therefore more appropriate for O3. In general non-linearities are more important at locations where concentrations are the lowest, i.e. at urban locations for O3 and at rural locations for PM10 and PM2.5. Finally the impact of spatial resolution (tested by comparing coarse and fine resolution simulations) on the degree of non-linearity has been shown to be minor as well. The conclusions developed here are model dependent and runs should be repeated with the particular model of interest but the proposed methodology allows with a limited number of runs to identify where efforts should be focused in order to include the relevant terms into a simplified surrogate model for integrated assessment purposes.

A comparative analysis of two highly spatially resolved European atmospheric emission inventories

A reliable emissions inventory is highly important for air quality modelling applications, especially at regional or local scales, which require high resolutions. Consequently, higher resolution emission inventories have been developed that are suitable for regional air quality modelling.This research performs an inter-comparative analysis of different spatial disaggregation methodologies of atmospheric emission inventories. This study is based on two different European emission inventories with different spatial resolutions: 1) the EMEP (European Monitoring and Evaluation Programme) inventory and 2) an emission inventory developed by the TNO (Netherlands Organisation for Applied Scientific Research). These two emission inventories were converted into three distinct gridded emission datasets as follows: (i) the EMEP emission inventory was disaggregated by area (EMEParea) and (ii) following a more complex methodology (HERMES-DIS – High-Elective Resolution Modelling Emissions System – DISaggregation module) to understand and evaluate the influence of different disaggregation methods; and (iii) the TNO gridded emissions, which are based on different emission data sources and different disaggregation methods. A predefined common grid with a spatial resolution of 12 × 12 km2 was used to compare the three datasets spatially.The inter-comparative analysis was performed by source sector (SNAP – Selected Nomenclature for Air Pollution) with emission totals for selected pollutants. It included the computation of difference maps (to focus on the spatial variability of emission differences) and a linear regression analysis to calculate the coefficients of determination and to quantitatively measure differences.From the spatial analysis, greater differences were found for residential/commercial combustion (SNAP02), solvent use (SNAP06) and road transport (SNAP07). These findings were related to the different spatial disaggregation that was conducted by the TNO and HERMES-DIS for the first two sectors and to the distinct data sources that were used by the TNO and HERMES-DIS for road transport.Regarding the regression analysis, the greatest correlation occurred between the EMEParea and HERMES-DIS because the latter is derived from the first, which does not occur for the TNO emissions. The greatest correlations were encountered for agriculture NH3 emissions, due to the common use of the CORINE Land Cover database for disaggregation. The point source emissions (energy industries, industrial processes, industrial combustion and extraction/distribution of fossil fuels) resulted in the lowest coefficients of determination. The spatial variability of SOx differed among the emissions that were obtained from the different disaggregation methods.In conclusion, HERMES-DIS and TNO are two distinct emission inventories, both very well discretized and detailed, suitable for air quality modelling. However, the different databases and distinct disaggregation methodologies that were used certainly result in different spatial emission patterns. This fact should be considered when applying regional atmospheric chemical transport models. Future work will focus on the evaluation of air quality models performance and sensitivity to these spatial discrepancies in emission inventories. Air quality modelling will benefit from the availability of appropriate resolution, consistent and reliable emission inventories.