Developing high-resolution simulations of tropospheric NO2 over Flanders using WRF-Chem

Atmospheric chemistry is critical in determining air quality and thus impacts climate change. Anthropogenic species are released into the atmosphere, and undergo complex photochemical transformations leading to the production of secondary pollutants, among which ozone and particulate matter. This can induce adverse effects on human health, visibility, ecosystems and local meteorology. &#160;The combination of state-of-the-art atmospheric models with accurate atmospheric measurements of atmospheric species abundances is needed to evaluate whether atmospheric models can successfully simulate the chemical and physical processes occurring, and hopefully monitor the emissions of anthropogenic compounds and help in the implementation and verification of abatement policies.In this work, ground-based, airborne and spaceborne measuring techniques are used to evaluate the performance of the full chemistry on-line WRF-Chem model over Antwerp in Flanders, Belgium, one of the areas with the highest NO2 pollution in the world. The model is configured to allow two nested domains with spatial resolution changing from 5 to 1km, so as to pinpoint the most pollutant sources in the region, and applied to simulate the urban air quality over the Antwerp agglomeration.We will briefly discuss the choices and adaptations made regarding the physical parameterizations, emission inventories and chemical mechanism. The model performance is evaluated through comparison with various observation types. The physics parameterizations in WRF model &#160;are evaluated through comparison against ground-based data from two meteorological stations in the Antwerp region. The WRF-Chem NO2 distributions are evaluated against (1) hourly measured concentration values from monitoring stations in Flanders, (2) vertical columns measured by an airborne hyperspectral imager APEX, providing a 2-dimensional spatial mapping, on 27 and 29 June 2019, and (3) spaceborne NO2 columns over Belgium obtained from the high-resolution TROPOMI instrument aboard S5p. The consistency of the model biases across the three datasets will be discussed, and recommendations will be made for improving model performance in this region.

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First year of real-time VOC measurements at the SIRTA facility (Paris region, France): diurnal and seasonal variabilities, impact of lockdowns on air quality

10.5194/egusphere-egu21-8643 ◽

2021 ◽

Author(s):

Leïla Simon ◽

Valérie Gros ◽

Jean-Eudes Petit ◽

François Truong ◽

Roland Sarda-Esteve ◽

...

Keyword(s):

Air Quality ◽

Atmospheric Chemistry ◽

Seasonal Variability ◽

Anthropogenic Sources ◽

First Year ◽

Good Representation ◽

Atmospheric Measurements ◽

Paris Region ◽

The Impact

Volatile Organic Compounds (VOCs) have direct influences on air quality and climate. They also play a key role in atmospheric chemistry, as they are precursors of secondary pollutants, such as ozone (O3) and secondary organic aerosols (SOA).Long-term datasets of in-situ atmospheric measurements are crucial to characterize the variability of atmospheric chemical composition. Online and continuous measurements of O3, NOx and aerosols have been achieved at the SIRTA-ACTRIS facility (Paris region, France), since 2012. Regarding VOCs, they have been measured there for several years thanks to bi-weekly samplings followed by offline Gas Chromatography analysis. However, this method doesn&#8217;t provide a good representation of the temporal variability of VOC concentrations. To tackle this issue, online VOC measurements using a Proton-Transfer-Reaction Quadrupole Mass-Spectrometer (PTR-Q-MS) have been started in January 2020.The dataset acquired during the first year of online VOC measurements is analyzed, which gives insights on VOC seasonal variability. The additional long-term datasets obtained from co-located measurements (O3, NOx, aerosol physical and chemical properties, meteorological parameters) are also used for the sake of this study.Due to Covid-19 pandemic, the year 2020 notably comprised a total lockdown in France in Spring, and a lighter one in Autumn. Therefore, a focus can be made on the impact of these lockdowns on the VOC variability and sources. To this end, the diurnal cycles of VOCs considered markers for anthropogenic sources are carefully investigated. Results notably indicate that markers for traffic and wood burning sources behave quite differently during the Spring lockdown in comparison to the other periods. A source apportionment analysis using positive matrix factorization allows to further document the seasonal variability of VOC sources and the impacts on air quality associated with the lockdown measures.

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The Eulerian urban dispersion model EPISODE. Part II: Extensions to the source dispersion and photochemistry for EPISODE-CityChem v1.2 and its application to the city of Hamburg

10.5194/gmd-2018-325 ◽

2019 ◽

Cited By ~ 1

Author(s):

Matthias Karl ◽

Sam-Erik Walker ◽

Sverre Solberg ◽

Martin O. P. Ramacher

Keyword(s):

Air Quality ◽

Atmospheric Chemistry ◽

Urban Areas ◽

Dispersion Model ◽

Model Performance ◽

Pollutant Dispersion ◽

Lower Latitude ◽

Urban Dispersion ◽

Grid Model ◽

The City

Abstract. This paper describes the CityChem extension of the Eulerian urban dispersion model EPISODE. The development of the CityChem extension was driven by the need to apply the model in lower latitude cities with higher insolation than in northern European cities. The CityChem extension offers a more advanced treatment of the photochemistry in urban areas and entails specific developments within the sub-grid components for a more accurate representation of the dispersion in the proximity of urban emission sources. The WMPP (WORM Meteorological Pre-Processor) is used in the point source sub-grid model to calculate the wind speed at plume height. The simplified street canyon model (SSCM) is used in the line source sub-grid model to calculate pollutant dispersion in street canyons. The EPISODE-CityChem model integrates the CityChem extension in EPISODE, with the capability of simulating photochemistry and dispersion of multiple reactive pollutants within urban areas. The main focus of the model is the simulation of the complex atmospheric chemistry involved in the photochemical production of ozone in urban areas. EPISODE-CityChem was evaluated with a series of tests and with a first application to the air quality situation in the city of Hamburg, Germany. A performance analysis with the FAIRMODE DELTA Tool for the air quality in Hamburg showed that the model fulfils the model performance objectives for NO2 (hourly), O3 (daily max. of the 8-h running mean) and PM10 (daily mean) set forth in the Air Quality Directive, qualifying the model for use in policy applications. Observed levels of annual mean ozone at the five urban background stations in Hamburg are captured by the model within 15 %. Envisaged applications of the EPISODE-CityChem model are urban air quality studies, emission control scenarios in relation to traffic restrictions and the source attribution of sector-specific emissions to observed levels of air pollutants at urban monitoring stations.

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Sensitivity of the Community Multiscale Air Quality (CMAQ) model v4.7 results for the eastern United States to MM5 and WRF meteorological drivers

Geoscientific Model Development ◽

10.5194/gmd-3-169-2010 ◽

2010 ◽

Vol 3 (1) ◽

pp. 169-188 ◽

Cited By ~ 38

Author(s):

K. W. Appel ◽

S. J. Roselle ◽

R. C. Gilliam ◽

J. E. Pleim

Keyword(s):

United States ◽

Air Quality ◽

Mesoscale Model ◽

Model Performance ◽

Wrf Model ◽

Total Carbon ◽

Eastern United States ◽

Model Simulations ◽

Mixing Ratios ◽

Vegetation Fraction

Abstract. This paper presents a comparison of the operational performances of two Community Multiscale Air Quality (CMAQ) model v4.7 simulations that utilize input data from the 5th-generation Mesoscale Model (MM5) and the Weather Research and Forecasting (WRF) meteorological models. Two sets of CMAQ model simulations were performed for January and August 2006. One set utilized MM5 meteorology (MM5-CMAQ) and the other utilized WRF meteorology (WRF-CMAQ), while all other model inputs and options were kept the same. For January, predicted ozone (O3) mixing ratios were higher in the Southeast and lower Mid-west regions in the WRF-CMAQ simulation, resulting in slightly higher bias and error as compared to the MM5-CMAQ simulations. The higher predicted O3 mixing ratios are attributed to less dry deposition of O3 in the WRF-CMAQ simulation due to differences in the calculation of the vegetation fraction between the MM5 and WRF models. The WRF-CMAQ results showed better performance for particulate sulfate (SO42−), similar performance for nitrate (NO3−), and slightly worse performance for nitric acid (HNO3), total carbon (TC) and total fine particulate (PM2.5) mass than the corresponding MM5-CMAQ results. For August, predictions of O3 were notably higher in the WRF-CMAQ simulation, particularly in the southern United States, resulting in increased model bias. Concentrations of predicted particulate SO42− were lower in the region surrounding the Ohio Valley and higher along the Gulf of Mexico in the WRF-CMAQ simulation, contributing to poorer model performance. The primary causes of the differences in the MM5-CMAQ and WRF-CMAQ simulations appear to be due to differences in the calculation of wind speed, planetary boundary layer height, cloud cover and the friction velocity (u∗) in the MM5 and WRF model simulations, while differences in the calculation of vegetation fraction and several other parameters result in smaller differences in the predicted CMAQ model concentrations. The performance for SO42−, NO3− and NH4+ wet deposition was similar for both simulations for January and August.

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Long-term particulate matter modeling for health effect studies in California – Part 1: Model performance on temporal and spatial variations

Atmospheric Chemistry and Physics ◽

10.5194/acp-15-3445-2015 ◽

2015 ◽

Vol 15 (6) ◽

pp. 3445-3461 ◽

Cited By ~ 28

Author(s):

J. Hu ◽

H. Zhang ◽

Q. Ying ◽

S.-H. Chen ◽

F. Vandenberghe ◽

...

Keyword(s):

Air Quality ◽

Health Effect ◽

Model Performance ◽

Wrf Model ◽

Temporal Variations ◽

Performance Criteria ◽

Spatial And Temporal Variations ◽

Model Predictions ◽

Mobile Emissions ◽

Temporal And Spatial

Abstract. For the first time, a ~ decadal (9 years from 2000 to 2008) air quality model simulation with 4 km horizontal resolution over populated regions and daily time resolution has been conducted for California to provide air quality data for health effect studies. Model predictions are compared to measurements to evaluate the accuracy of the simulation with an emphasis on spatial and temporal variations that could be used in epidemiology studies. Better model performance is found at longer averaging times, suggesting that model results with averaging times ≥ 1 month should be the first to be considered in epidemiological studies. The UCD/CIT model predicts spatial and temporal variations in the concentrations of O3, PM2.5, elemental carbon (EC), organic carbon (OC), nitrate, and ammonium that meet standard modeling performance criteria when compared to monthly-averaged measurements. Predicted sulfate concentrations do not meet target performance metrics due to missing sulfur sources in the emissions. Predicted seasonal and annual variations of PM2.5, EC, OC, nitrate, and ammonium have mean fractional biases that meet the model performance criteria in 95, 100, 71, 73, and 92% of the simulated months, respectively. The base data set provides an improvement for predicted population exposure to PM concentrations in California compared to exposures estimated by central site monitors operated 1 day out of every 3 days at a few urban locations. Uncertainties in the model predictions arise from several issues. Incomplete understanding of secondary organic aerosol formation mechanisms leads to OC bias in the model results in summertime but does not affect OC predictions in winter when concentrations are typically highest. The CO and NO (species dominated by mobile emissions) results reveal temporal and spatial uncertainties associated with the mobile emissions generated by the EMFAC 2007 model. The WRF model tends to overpredict wind speed during stagnation events, leading to underpredictions of high PM concentrations, usually in winter months. The WRF model also generally underpredicts relative humidity, resulting in less particulate nitrate formation, especially during winter months. These limitations must be recognized when using data in health studies. All model results included in the current manuscript can be downloaded free of charge at http://faculty.engineering.ucdavis.edu/kleeman/ .

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Evaluation and Error Apportionment of an Ensemble of Atmospheric Chemistry Transport Modelling Systems: Multi-variable Temporal and Spatial Breakdown

10.5194/acp-2016-682 ◽

2016 ◽

Cited By ~ 1

Author(s):

Efisio Solazzo ◽

Roberto Bianconi ◽

Christian Hogrefe ◽

Gabriele Curci ◽

Ummugulsum Alyuz ◽

...

Keyword(s):

Air Quality ◽

Atmospheric Chemistry ◽

Model Comparison ◽

Regional Scale ◽

Model Development ◽

Model Performance ◽

Strong Impact ◽

Air Quality Model ◽

Quality Model ◽

Transport Modelling

Abstract. Through the comparison of several regional-scale chemistry transport modelling systems that simulate meteorology and air quality over the European and American continents, this study aims at i) apportioning the error to the responsible processes using time-scale analysis, ii) helping to detect causes of models error, and iii) identifying the processes and scales most urgently requiring dedicated investigations. The analysis is conducted within the framework of the third phase of the Air Quality Model Evaluation International Initiative (AQMEII) and tackles model performance gauging through measurement-to-model comparison, error decomposition and time series analysis of the models biases for several fields (ozone, CO, SO2, NO, NO2, PM10, PM2.5, wind speed, and temperature). The operational metrics (magnitude of the error, sign of the bias, associativity) provide an overall sense of model strengths and deficiencies, while apportioning the error to its constituent parts (bias, variance and covariance) can help to assess the nature and quality of the error. Each of the error components is analysed independently and apportioned to specific processes based on the corresponding timescale (long scale, synoptic, diurnal, and intra-day) using the error apportionment technique devised in the former phases of AQMEII. The application of the error apportionment method to the AQMEII Phase 3 simulations provides several key insights. In addition to reaffirming the strong impact of model inputs (emissions and boundary conditions) and poor representation of the stable boundary layer on model bias, results also highlighted the high inter-dependencies among meteorological and chemical variables, as well as among their errors. This indicates that the evaluation of air quality model performance for individual pollutants needs to be supported by complementary analysis of meteorological fields and chemical precursors to provide results that are more insightful from a model development perspective. The error embedded in the emissions is dominant for primary species (CO, PM, NO) and largely outweighs the error from any other source. The uncertainty in meteorological fields is most relevant to ozone. Some further aspects emerged whose interpretation requires additional consideration, such as, among others, the uniformity of the synoptic error being region and model-independent, observed for several pollutants; the source of unexplained variance for the diurnal component; and the type of error caused by deposition and at which scale.

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Free radical modelling studies during the UK TORCH Campaign in Summer 2003

Atmospheric Chemistry and Physics ◽

10.5194/acp-7-167-2007 ◽

2007 ◽

Vol 7 (1) ◽

pp. 167-181 ◽

Cited By ~ 115

Author(s):

K. M. Emmerson ◽

N. Carslaw ◽

D. C. Carslaw ◽

J. D. Lee ◽

G. McFiggans ◽

...

Keyword(s):

Hydroxyl Radical ◽

Atmospheric Chemistry ◽

Model Performance ◽

Peroxy Radical ◽

Chemical Mechanism ◽

Ozone Production ◽

Peroxy Radicals ◽

North East ◽

Hydroperoxy Radical ◽

Modelling Studies

Abstract. The Tropospheric ORganic CHemistry experiment (TORCH) took place during the heatwave of summer 2003 at Writtle College, a site 2 miles west of Chelmsford in Essex and 25 miles north east of London. The experiment was one of the most highly instrumented to date. A combination of a large number of days of simultaneous, collocated measurements, a consequent wealth of model constraints and a highly detailed chemical mechanism, allowed the atmospheric chemistry of this site to be studied in detail. Between 25 July and 31 August, the concentrations of the hydroxyl radical and the hydroperoxy radical were measured using laser-induced fluorescence at low pressure and the sum of peroxy radicals was measured using the peroxy radical chemical amplifier technique. The concentrations of the radical species were predicted using a zero-dimensional box model based on the Master Chemical Mechanism version 3.1, which was constrained with the observed concentrations of relatively long-lived species. The model included a detailed parameterisation to account for heterogeneous loss of hydroperoxy radicals onto aerosol particles. Quantile-quantile plots were used to assess the model performance in respect of the measured radical concentrations. On average, measured hydroxyl radical concentrations were over-predicted by 24%. Modelled and measured hydroperoxy radical concentrations agreed very well, with the model over-predicting on average by only 7%. The sum of peroxy radicals was under-predicted when compared with the respective measurements by 22%. Initiation via OH was dominated by the reactions of excited oxygen atoms with water, nitrous acid photolysis and the ozone reaction with alkene species. Photolysis of aldehyde species was the main route for initiation via HO2 and RO2. Termination, under all conditions, primarily involved reactions with NOx for OH and heterogeneous chemistry on aerosol surfaces for HO2. The OH chain length varied between 2 and 8 cycles, the longer chain lengths occurring before and after the most polluted part of the campaign. Peak local ozone production of 17 ppb hr−1 occurred on 3 and 5 August, signifying the importance of local chemical processes to ozone production on these days. On the whole, agreement between model and measured radicals is good, giving confidence that our understanding of atmospheres influenced by nearby urban sources is adequate.

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High resolution air quality simulation over Europe with the chemistry transport model CHIMERE

Geoscientific Model Development Discussions ◽

10.5194/gmdd-6-4137-2013 ◽

2013 ◽

Vol 6 (3) ◽

pp. 4137-4187 ◽

Cited By ~ 5

Author(s):

E. Terrenoire ◽

B. Bessagnet ◽

L. Rouïl ◽

F. Tognet ◽

G. Pirovano ◽

...

Keyword(s):

Air Quality ◽

High Resolution ◽

Mean Squared Error ◽

Transport Model ◽

Model Performance ◽

Horizontal Resolution ◽

Eastern European ◽

Rural And Urban ◽

Good Reproduction ◽

Eastern European Countries

Abstract. A high resolution air quality simulation (0.125° × 0.0625° horizontal resolution) performed over Europe for the year 2009 has been evaluated using both rural and urban background stations available over most of the domain. Using seasonal and yearly mean statistical indicators such as the correlation index, the fractional bias and the root mean squared error; we interpret objectively the performance of the simulation. Positive outcomes are: a very good reproduction of the daily variability at UB sites for O3 (R =0.73) as well as for NO2 (R =0.61); a very low bias calculated at UB stations for PM2.5 (FB = −6.4%) and PM10 concentrations (FB = −20.1%). Conversely, main weaknesses in model performance include: the underestimation of the NO2 daily maxima at UB site (FB = −53.6%); an overall underestimation of PM10 and PM2.5 concentrations observed over Eastern European countries (e.g. Poland); the overestimation of sulphates concentrations at spring time (FB = 53.7%); finally, over the year, total nitrate and ammonia concentrations are better reproduced than nitrate and ammonium aerosol phase compounds. Obtained results suggest that, in order to improve the model performances, efforts should focus on the improvement of the emission inventory quality for Eastern Europeans countries and the improvement of a specific parameterisation in the model to better account for the urban effect on meteorology and air pollutants concentrations.

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Effect of land cover on atmospheric processes and air quality over the continental United States – a NASA unified WRF (NU-WRF) model study

Atmospheric Chemistry and Physics Discussions ◽

10.5194/acpd-13-5429-2013 ◽

2013 ◽

Vol 13 (2) ◽

pp. 5429-5475 ◽

Cited By ~ 1

Author(s):

Z. Tao ◽

J. A. Santanello ◽

M. Chin ◽

S. Zhou ◽

Q. Tan ◽

...

Keyword(s):

United States ◽

Soil Moisture ◽

Air Quality ◽

Land Cover ◽

Atmospheric Chemistry ◽

Land Surface ◽

Vegetation Type ◽

Sensible Heat Flux ◽

Wrf Model ◽

Atmospheric Processes

Abstract. The land surface plays a crucial role in regulating water and energy fluxes at the land–atmosphere (L–A) interface and controls many processes and feedbacks in the climate system. Land cover and vegetation type remains one key determinant of soil moisture content that impacts air temperature, planetary boundary layer (PBL) evolution, and precipitation through soil moisture–evapotranspiration coupling. In turn it will affect atmospheric chemistry and air quality. This paper presents the results of a modeling study of the effect of land cover on some key L–A processes with a focus on air quality. The newly developed NASA Unified Weather Research and Forecast (NU-WRF) modeling system couples NASA's Land Information System (LIS) with the community WRF model and allows users to explore the L–A processes and feedbacks. Three commonly used satellite-derived land cover datasets, i.e. from the US Geological Survey (USGS) and University of Maryland (UMD) that are based on the Advanced Very High Resolution Radiometer (AVHRR) and from the Moderate Resolution Imaging Spectroradiometer (MODIS), bear large differences in agriculture, forest, grassland, and urban spatial distributions in the continental United States, and thus provide an excellent case to investigate how land cover change would impact atmospheric processes and air quality. The weeklong simulations demonstrate the noticeable differences in soil moisture/temperature, latent/sensible heat flux, PBL height, wind, NO2/ozone, and PM2.5 air quality. These discrepancies can be traced to associate with the land cover properties, e.g. stomatal resistance, albedo and emissivity, and roughness characteristics. It also implies that the rapid urban growth may have complex air quality implications with reductions in peak ozone but more frequent high ozone events.

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Long-term particulate matter modeling for health effects studies in California – Part 1: Model performance on temporal and spatial variations

Atmospheric Chemistry and Physics Discussions ◽

10.5194/acpd-14-20997-2014 ◽

2014 ◽

Vol 14 (14) ◽

pp. 20997-21036

Author(s):

J. Hu ◽

H. Zhang ◽

Q. Ying ◽

S.-H. Chen ◽

F. Vandenberghe ◽

...

Keyword(s):

Air Quality ◽

Health Effects ◽

Model Performance ◽

Wrf Model ◽

Temporal Variations ◽

Performance Criteria ◽

Spatial And Temporal Variations ◽

Model Predictions ◽

Mobile Emissions ◽

Temporal And Spatial

Abstract. For the first time, a decadal (9 years from 2000 to 2008) air quality model simulation with 4 km horizontal resolution and daily time resolution has been conducted in California to provide air quality data for health effects studies. Model predictions are compared to measurements to evaluate the accuracy of the simulation with an emphasis on spatial and temporal variations that could be used in epidemiology studies. Better model performance is found at longer averaging times, suggesting that model results with averaging times ≥ 1 month should be the first to be considered in epidemiological studies. The UCD/CIT model predicts spatial and temporal variations in the concentrations of O3, PM2.5, EC, OC, nitrate, and ammonium that meet standard modeling performance criteria when compared to monthly-averaged measurements. Predicted sulfate concentrations do not meet target performance metrics due to missing sulfur sources in the emissions. Predicted seasonal and annual variations of PM2.5, EC, OC, nitrate, and ammonium have mean fractional biases that meet the model performance criteria in 95%, 100%, 71%, 73%, and 92% of the simulated months, respectively. The base dataset provides an improvement for predicted population exposure to PM concentrations in California compared to exposures estimated by central site monitors operated one day out of every 3 days at a few urban locations. Uncertainties in the model predictions arise from several issues. Incomplete understanding of secondary organic aerosol formation mechanisms leads to OC bias in the model results in summertime but does not affect OC predictions in winter when concentrations are typically highest. The CO and NO (species dominated by mobile emissions) results reveal temporal and spatial uncertainties associated with the mobile emissions generated by the EMFAC 2007 model. The WRF model tends to over-predict wind speed during stagnation events, leading to under-predictions of high PM concentrations, usually in winter months. The WRF model also generally under-predicts relative humidity, resulting in less particulate nitrate formation especially during winter months. These issues will be improved in future studies. All model results included in the current manuscript can be downloaded free of charge at http://faculty.engineering.ucdavis.edu/kleeman/.

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Evaluation of NO2, O3, PM10, and PM2.5 in the city of Buenos Aires, Argentina using WRF-Chem model

10.5194/egusphere-egu21-1514 ◽

2021 ◽

Author(s):

Ana Isabel Lopez-Noreña ◽

Lucas Berná ◽

María Florencia Tames ◽

Emmanuel Millán ◽

Enrique Puliafito ◽

...

Keyword(s):

Air Quality ◽

High Resolution ◽

Buenos Aires ◽

Model Performance ◽

Performance Criteria ◽

Quality Analysis ◽

Modeling Tools ◽

Pm10 And Pm2.5 ◽

The Mean ◽

The Impact

The online-coupled Weather Research and Forecasting model with Chemistry (WRF-Chem v4.0), was applied to evaluate the impact of using different anthropogenic emissions inventories on regional air quality in Argentina. For this purpose, we couple the Argentinian high-resolution emissions inventory (GEAA-AHRI) and the Emissions Database for Global Atmospheric Research &#8211; Hemispheric Transport of Air Pollution (EDGAR-HTAP) and introduce them into the model, with a local optimized configuration considering 3 nested domains with a horizontal grid size of 20 x 20 km, 4 x 4 km, and 1.3 x 1.3 km and the MOZART chemical scheme. The model output for NO2, PM10, PM2.5, and O3 concentrations over the innermost domain was compared against the existing surface and satellite-derived observations for the Buenos Aires Metropolitan Area (AMBA) during austral fall 2018. We found an overall good model performance for all simulations, and large discrepancies between the emission inventories, obtaining an improved urban-scale spatio-temporal representation when the high resolution GEAA-AHRI dataset is considered. Our results show that the daytime concentrations of air pollutants are strongly influenced by the shape and shift of the hourly emissions profile before sunrise and after sunset, especially for NO2 where the inclusion of the temporal profile decreased the mean bias by ~80%. Performance criteria for modeled PM10 and PM2.5 were in general satisfied, despite having an average underestimation of observations. When compared to NO2 tropospheric columns derived from TROPOMI, The general magnitude and spatial pattern of the NO2 tropospheric column is in agreement with the mean TROPOMI columns during the modeled period, obtaining correlation coefficients higher than 0.6 for all simulations. Our results highlight the benefits of using a time-dependent and high-resolution local inventory for addressing the background air quality in AMBA. The implementation and validation of local emissions and static fields with high spatial and temporal resolution carried out in this work, establishes a benchmark for forthcoming studies in other regions of South America where different modeling tools for air quality analysis are currently being used to complement the usually sparse and discontinuous air quality networks.

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