High-resolution retrospective simulations of air quality can generate O3 and PM2.5 concentrations to estimate their health effects and establish a baseline to assess the impacts of future climate and emission scenarios. To this end, the Weather Research and Forecasting Model coupled with Chemistry (WRF-Chem) is applied to a triple-nested domain over Brazil, with a high spatial resolution of 3-km over the metropolitan areas of Sao Paulo (MASP) and Rio de Janeiro (MARJ) for the period of 2012-2016. This work is presented in two-part papers. Part I describes an initial application and evaluation of WRF-Chem for August 2012 to study the impacts of improved inputs and wind speed correction parameterization options on the model performance. These simulations aim to improve the model's accuracy in reproducing the observed meteorological variables and air pollutant levels. The model results with updated land use and cover and urban fraction show a lower positive bias in temperature at 2-m. Updated elevation based on high-resolution (30-m) data reduces the positive bias on wind speed at 10-m for MARJ. The modified wind speed correction reduces the systematic bias of wind speed at 10-m for all domains. WRF-Chem using the combined emissions based on global and local inventories performs well with lower bias and better temporal and spatial representations of historical concentrations of major air pollutants than those using global emissions only. These sensitivity simulations identify the best possible model inputs and wind speed correction parameterization option for simulations for the period of 2012-2016, which will be presented in Part II paper.

Highlights What are the main findings? Ozone formation is predominantly VOC-limited in urban and coastal regions, with seasonal and spatial transitions toward NOx-limited regimes. O3 variability is mainly driven by vertical transport and chemical processes, while PM2.5 is more strongly influenced by emissions and horizontal transport. What are the implications of the main findings? Effective ozone mitigation strategies should prioritize VOC emission controls in urban/coastal areas while accounting for regime shifts across seasons and regions. Air quality management must consider land-sea circulation, topography, and transport processes to accurately represent pollutant distribution and exposure.Highlights What are the main findings? Ozone formation is predominantly VOC-limited in urban and coastal regions, with seasonal and spatial transitions toward NOx-limited regimes. O3 variability is mainly driven by vertical transport and chemical processes, while PM2.5 is more strongly influenced by emissions and horizontal transport. What are the implications of the main findings? Effective ozone mitigation strategies should prioritize VOC emission controls in urban/coastal areas while accounting for regime shifts across seasons and regions. Air quality management must consider land-sea circulation, topography, and transport processes to accurately represent pollutant distribution and exposure.Abstract Building on the regional model evaluation presented in Part I, this study investigates the processes controlling air pollutant formation and transport over West Asia, with a focus on the United Arab Emirates (UAE). Two representative months, January 2022 and June 2021, are selected for detailed analysis using Chemical Process Analysis (CPA) and Integrated Process Rate (IPR) diagnostics. The results indicate predominantly VOC-limited ozone (O3) formation across urban and coastal regions, with seasonal and spatial transitions toward NOx-limited regimes, particularly in rural and downwind areas. IPR diagnostics show that local chemistry and vertical transport are the dominant contributors to O3 variability, whereas fine particulate matter with a diameter of 2.5 & micro;m or less (PM2.5) variability is primarily driven by vertical transport and emissions. Horizontal transport and land-sea circulation play an important role in shaping the spatial distribution of both pollutants, especially along coastal zones. Comparisons among urban, coastal, and rural sites further highlight the influence of topography, land use, and meteorological conditions on pollutant dynamics. These process-based insights provide a scientific basis for refining emission control strategies, improving regional air quality management, and supporting evidence-based policies to mitigate air pollution impacts on human health and the environment in West Asia.

Global emission inventories often fail to capture the complexities of vehicular pollution in regions with unique fuel mixes, such as Brazil's extensive biofuel use, leading to significant uncertainties in atmospheric modeling. This study presents a century-long (1960-2100) bottom-up vehicular emission inventory for Brazil, leveraging locally derived emission factors. Our estimates reveal substantial discrepancies in magnitude, timing, and speciation of non-CO2 pollutants (CO, NMHC, PM2.5) compared to leading global inventories (EDGAR, CEDS, CAMS), highlighting critical inaccuracies in widely used data sets. More critically, future projections under Shared Socioeconomic Pathways (SSPs) uncover a novel positive feedback mechanism: rising temperatures significantly enhance vehicular evaporative nonmethane hydrocarbon (NMHC) emissions. This temperature-dependent increase and subsequent NMHC oxidation to CO2 suggest an overlooked pathway that could amplify climate warming and air pollution globally, particularly after a breakpoint around 2050 (p < 0.05). While historical emissions peaked in the 1990s-2000s, nonexhaust PM becomes increasingly important. Air quality simulations using our inventory in the MUSICA model show good regional PM2.5 agreement but highlight challenges in resolving local primary pollutant peaks. This comprehensive inventory provides crucial data for Brazil and uncovers globally relevant climate-chemistry interactions, urging a re-evaluation of regional specificities in global emission assessments.

Air pollution poses significant risks to public health, ecosystems, and regional economies, particularly in rapidly developing regions. Despite its importance, the Middle East remains relatively understudied in regional air quality, with limited evaluations of pollutant transport and model performance. This study applies the WRF (Weather Research and Forecasting) model coupled with the CAMx (Comprehensive Air Quality Model with Extensions) model to simulate meteorology and air quality over West Asia, with a focus on the United Arab Emirates (UAE). Six representative months are analyzed, including three winter periods (January 2018, 2020, 2022) and three summer periods (June 2017, 2019, 2021). WRF shows good agreement with observations, reproducing near-surface temperature with an index of agreement (IOA) between 0.90 and 1.00 and generally low wind speed (MB < +/- 0.5 m s(-1)) and wind direction biases (MB < +/- 0.5), although cloud-radiative forcing is underestimated during winter. CAMx reproduces PM2.5 concentrations with moderate-to-high correlations (r = 0.44-0.65) and low bias, while AOD and O-3 column concentration show larger uncertainties. Satellite-based evaluation indicates good performance for NO2 and CO column abundances but larger discrepancies for HCHO and SO2, particularly during summer. Overall, the results demonstrate that the WRF-CAMx modeling system provides a reliable framework for regional air quality simulations over West Asia, while highlighting uncertainties associated with emissions, atmospheric chemistry, and satellite retrieval products.

In this part II paper, the Weather Research and Forecasting Model coupled with Chemistry (WRF-Chem) described in the Part I paper is applied for 2012-2016 at 36-km, 9-km, and 3-km grid resolutions over Brazil, the Southeast region, and metropolitan areas of S & atilde;o Paulo (MASP) and Rio de Janeiro (MARJ), respectively, and evaluated using meteorological and chemical observations from surface stations and satellite retrieval. Meteorological performance is acceptable, with good performance for temperature (MBs <0.2 degrees C for 9-km and 3-km) and precipitation (NMBs within +/- 15 %) and acceptable performance for most cases for absolute humidity (MBs up to 1 g/kg) and wind speed (NMBs up to 1.5 m/s). The model results show good agreement with satellite observations of radiation (GLW, GSW, OLR, and SWDOWN) and cloud fraction, however, for other cloud-related variables (LWCF, SWCF, CCN, COT, CWP, and CDNC) the model show reasonable results. The maximum 8-h average O-3 performance at 3-km is good (NMBs within +/- 6.4 %) but the model underpredicts 8-h moving average O-3 (NMB = -16.2 % for MASP and NMB = -29.9 % for MARJ). The PM2.5 performance at 3-km is in good agreement with observations in MASP in terms of composition and biases (NMBs = -6.2 % for MASP and NMB = -3.6 % for MARJ). Overall, the results show reasonably good spatiotemporal agreement with the observations for meteorology and air quality. These results will be used to study the health impact of O-3 and PM2.5 and to serve as the baseline for assessment of the impact of future emission scenarios in future work.

A multiscale modeling ensemble chain has been assembled as a first step towards an air quality analysis and forecasting (AQF) system for Latin America. Two global and three regional models were tested and compared in retrospective mode over a shared domain (120-28 degrees W, 60 degrees S-30 degrees N) for the months of January and July 2015. The objective of this experiment was to understand their performance and characterize their errors. Observations from local air quality monitoring networks in Colombia, Chile, Brazil, Mexico, Ecuador and Peru were used for model evaluation. The models generally agreed with observations in large cities such as Mexico City and S & atilde;o Paulo, whereas representing smaller urban areas, such as Bogot & aacute; and Santiago, was more challenging. For instance, in Santiago during wintertime, the simulations showed large discrepancies with observations. No single model demonstrated superior performance over others or among pollutants and sites available. In general, ozone and NO2 exhibited the lowest bias and errors, especially in S & atilde;o Paulo and Mexico City. For SO2, the bias and error were close to 200 %, except for Bogot & aacute;. The ensemble, created from the median value of all models, was evaluated as well. In some cases, the ensemble outperformed the individual models and mitigated extreme over- or underestimation. However, more research is needed before concluding that the ensemble is the path for an AQF system in Latin America. This study identified certain limitations in the models and global emission inventories, which should be addressed with the involvement and experience of local researchers.

Despite a growing literature for complex air quality models, scientific evidence lacks of the influences of varying exposure assessments and air quality data sources on the estimated mortality risks. This case-crossover study estimated cardiovascular mortality risks from fine particulate matter (PM2.5) and ozone (O3) exposures, using varying exposure methods, to aid understanding of the impact of exposure methods in the health risk estimation. We used individual-level cardiovascular mortality data in the city of Rio de Janeiro, 2012-2016. PM2.5 and O3 exposure levels (from the date of death to seven prior days [lag0-7]) were estimated at the individual level or district level using either the WRF-Chem modeling data or monitoring data, resulting in a total of 10 exposure methods. The exposure-response relationships were estimated using multiple logistic regressions. The changes in cardiovascular mortality were represented as an odds ratio (OR) and 95% confidence intervals (CIs) for an interquartile range (IQR) increase in the exposures. Results showed that socioeconomically more advantaged populations had lower access to the stationary monitoring networks. Higher variance in the estimated exposure levels across the 10 exposure methods was found for PM2.5 than O3. PM2.5 exposure was not associated with mortality risk in any exposure methods. WRF-Chem-based O3 exposure estimated for each individual of the entire population found a significant mortality risk (OR = 1.06, 95% CI: 1.01, 1.11), but not the other exposure methods. Higher risks for females and older populations were suggested for O3 estimates estimated for each individual using the WRF-Chem data. Findings indicate that decisions on exposure methods and data sources can lead to substantially varying implications for air pollution risks and highlight the need for comprehensive exposure and health impact assessments to aid local decision-making for air pollution and public health.

Numerous studies have used air quality models to estimate pollutant concentrations in the Metropolitan Area of São Paulo (MASP) by using different inputs and assumptions. Our objectives are to summarize these studies, compare their performance, configurations, and inputs, and recommend areas of further research. We examined 29 air quality modeling studies that focused on ozone (O3) and fine particulate matter (PM2.5) performed over the MASP, published from 2001 to 2023. The California Institute of Technology airshed model (CIT) was the most used offline model, while the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) was the most used online model. Because the main source of air pollution in the MASP is the vehicular fleet, it is commonly used as the only anthropogenic input emissions. Simulation periods were typically the end of winter and during spring, seasons with higher O3 and PM2.5 concentrations. Model performance for hourly ozone is good with half of the studies with Pearson correlation above 0.6 and root mean square error (RMSE) ranging from 7.7 to 27.1 ppb. Fewer studies modeled PM2.5 and their performance is not as good as ozone estimates. Lack of information on emission sources, pollutant measurements, and urban meteorology parameters is the main limitation to perform air quality modeling. Nevertheless, researchers have used measurement campaign data to update emission factors, estimate temporal emission profiles, and estimate volatile organic compounds (VOCs) and aerosol speciation. They also tested different emission spatial disaggregation approaches and transitioned to global meteorological reanalysis with a higher spatial resolution. Areas of research to explore are further evaluation of models' physics and chemical configurations, the impact of climate change on air quality, the use of satellite data, data assimilation techniques, and using model results in health impact studies. This work provides an overview of advancements in air quality modeling within the MASP and offers practical approaches for modeling air quality in other South American cities with limited data, particularly those heavily impacted by vehicle emissions.

In this work, we report the ongoing implementation of online-coupled aerosol–cloud microphysical–radiation interactions in the Brazilian global atmospheric model (BAM) and evaluate the initial results, using remote-sensing data for JFM 2014 and JAS 2019. Rather than developing a new aerosol model, which incurs significant overheads in terms of fundamental research and workforce, a simplified aerosol module from a preexisting global aerosol–chemistry–climate model is adopted. The aerosol module is based on a modal representation and comprises a suite of aerosol microphysical processes. Mass and number mixing ratios, along with dry and wet radii, are predicted for black carbon, particulate organic matter, secondary organic aerosols, sulfate, dust, and sea salt aerosols. The module is extended further to include physically based parameterization for aerosol activation, vertical mixing, ice nucleation, and radiative optical properties computations. The simulated spatial patterns of surface mass and number concentrations are similar to those of other studies. The global means of simulated shortwave and longwave cloud radiative forcing are comparable with observations with normalized mean biases ≤11% and ≤30%, respectively. Large positive bias in BAM control simulation is enhanced with the inclusion of aerosols, resulting in strong overprediction of cloud optical properties. Simulated aerosol optical depths over biomass burning regions are moderately comparable. A case study simulating an intense biomass burning episode in the Amazon is able to reproduce the transport of smoke plumes towards the southeast, thus showing a potential for improved forecasts subject to using near-real-time remote-sensing fire products and a fire emission model. Here, we rely completely on remote-sensing data for the present evaluation and restrain from comparing our results with previous results until a complete representation of the aerosol lifecycle is implemented. A further step is to incorporate dry deposition, in-cloud and below-cloud scavenging, sedimentation, the sulfur cycle, and the treatment of fires.

Tropospheric ozone threatens human health and crop yields, exacerbates global warming, and fundamentally changes atmospheric chemistry. Evidence has pointed toward widespread ozone increases in the troposphere, and particularly surface ozone is chemically complex and difficult to abate. Despite past successes in some regions, a solution to new challenges of ozone pollution in a warming climate remains unexplored. In this perspective, by compiling surface measurements at similar to 4,300 sites worldwide between 2014 and 2019, we show the emerging global challenge of ozone pollution, featuring the unintentional rise in ozone due to the uncoordinated emissions reduction and increasing climate penalty. On the basis of shared emission sources, interactive chemical mechanisms, and synergistic health effects between ozone pollution and climate warming, we propose a synergistic ozone-climate control strategy incorporating joint control of ozone and fine particulate matter. This new solution presents an opportunity to alleviate tropospheric ozone pollution in the forthcoming low-carbon transition.

The planetary-boundary-layer (PBL) flow and above is investigated for the central north coast of Brazil, an equatorial region spanning from 8° to 2°S. The daily PBL flow is controlled by vertical entrainment of horizontal momentum from a southerly large-scale flow associated with the Hadley cell, and by a mesoscale pressure gradient force (PGF) created by the differential heating between land and ocean. Near the coast, the flow is from the north-east quadrant comprising a small rotation, probably caused by a weak mesoscale PGF and a weak Coriolis force. Inland it is north-easterly in the morning, but deep mixing during the afternoon brings down momentum from above causing it to become south-easterly. The mesoscale PGF executes a daily 360° rotation at most of the stations. In the afternoon it points to land due to continuous heating of the land, and a sea breeze develops in the presence of the background flow. Once convection dies out, the transfer of horizontal momentum is reduced, and the marine-air layer can flow faster into the continent as a nocturnal jet. As the stable boundary layer grows thicker, this flow tends to be eliminated at the surface. By morning, the mesoscale PGF points north, forcing the inland flow to become south-easterly, while on the coast flow becomes almost easterly. This scenario repeats during dry and wet seasons and can be understood as a consequence of the south–north propagation of an atmospheric circulation resembling a helix with its rotation axis oriented parallel to the shoreline.

Online-coupled meteorology-chemistry models provide powerful tools for more realistically simulation of current and future air quality with feedbacks between atmospheric composition and meteorology that cannot be considered in offline-coupled models. In this work, several state-of-science online-coupled models are applied to generate the best possible predictions of surface ozone (O3) and fine particulate matter (PM2.5) concentrations under current emission and climate conditions. Two ensemble methods are used to further reduce the model biases and errors including a simple ensemble mean based on an average of ensemble members, and a weighted ensemble mean based on the multi-linear regression. The skills of individual models and their ensembles are evaluated using available surface network data. Compared to individual models and the simple ensemble mean, the weighted ensemble predictions based on the multi-linear regression perform the best overall for both O3 and PM2.5. The model with best performance is selected to apply for future years to project the changes in air quality under various energy transition scenarios to support the development of emission control strategies. These results illustrate the current capability of the online-coupled models and the potential of weighted ensemble in generating the best possible estimates of air pollutant concentrations under current and future atmospheric conditions.

Brazil, one of the world’s fastest-growing economies, is the fifth most populous country and is experiencing accelerated urbanization. This combination of factors causes an increase in urban population that is exposed to poor air quality, leading to public health burdens. In this work, the Weather Research and Forecasting Model with Chemistry is applied to simulate air quality over Brazil for a short time period under three future emission scenarios, including current legislation (CLE), mitigation scenario (MIT), and maximum feasible reduction (MFR) under the Representative Concentration Pathway 4.5 (RCP4.5), which is a climate change scenario under which radiative forcing of greenhouse gases (GHGs) reach 4.5 W m−2 by 2100. The main objective of this study is to determine the sensitivity of the concentrations of ozone (O3) and particulate matter with aerodynamic diameter 2.5 µm or less (PM2.5) to changes in emissions under these emission scenarios and to determine the signal and spatial patterns of these changes for Brazil. The model is evaluated with observations and shows reasonably good agreement. The MFR scenario leads to a reduction of 3% and 75% for O3 and PM2.5 respectively, considering the average of grid cells within Brazil, whereas the CLE scenario leads to an increase of 1% and 11% for O3 and PM2.5 respectively, concentrated near urban centers. These results indicate that of the three emission control scenarios, the CLE leads to poor air quality, while the MFR scenario leads to the maximum improvement in air quality. To the best of our knowledge, this work is the first to investigate the responses of air quality to changes in emissions under these emission scenarios for Brazil. The results shed light on the linkage between changes of emissions and air quality.

In this paper, we analyze the variability of the ozone concentration over São Paulo Macrometropolis, as well the factors, which determined the tendency observed in the last two decades. Time series of hourly ozone concentrations measured at 16 automated stations from an air quality network from 1996 to 2017 were analyzed. The temporal variability of ozone concentrations exhibits well-defined daily and seasonal patterns. Ozone presents a significant positive correlation between the number of cases (thresholds of 100–160 μg m−3) and the fuel sales of gasohol and diesel. The ozone concentrations do not exhibit significant long-term trends, but some sites present positive trends that occurs in sites in the proximity of busy roads and negative trends that occurs in sites located in residential areas or next to trees. The effect of atmospheric process of transport and ozone formation was analyzed using a quantile regression model (QRM). This statistical model can deal with the nonlinearities that appear in the relationship of ozone and other variables and is applicable to time series with non-normal distribution. The resulting model explains 0.76% of the ozone concentration variability (with global coefficient of determination R1 = 0.76) providing a better representation than an ordinary least square regression model (with coefficient of determination R2 = 0.52); the effect of radiation and temperature are the most critical in determining the highest ozone quantiles.

One risk associated with rocket‐launching activities is the inhalation of exhaust gases, which can occur either in the vicinity of the launch pad or, depending on the way these gases are transported and dispersed through the atmosphere, at locations far from the launch pad. Immediately after the ignition of the rocket motors, a large, hot cloud is formed near the ground, composed of carbon monoxide (CO) and carbon dioxide (CO 2 ), hydrogen chloride (HCl) and also particulate material composed of aluminium oxide (Al 2 O 3 ) in the case of rockets driven by a solid propellant. The Weather Research and Forecasting Model (WRF), an atmospheric mesoscale model, was modified in the present study to simulate the transport, dispersion and chemistry mechanisms (with the addition of reactions involving HCl and other chlorinated compounds) of these gases released during a launch. Simulations of the dispersion of effluents were performed for different rainfall regimes (dry and rainy periods) as well as for atmospheric thermal stability (day‐ and night‐time conditions) for a specific rocket (the Veículo Lançador de Satélites, VLS) at Brazil's Alcântara Launch Center (Centro de Lançamento de Alcântara, CLA). The results show that the most critical levels of HCl and CO occurred on the launch pad (Setor de Preparação de Lançamento, SPL) from the time of the launch ( t 0 ) to 10 min. The village of Alcântara, which is 9 km from the launch pad, could be affected according to the wind direction. The concentration of HCl reached a level in the range of 2200–3800 ppmv for both dry and wet periods respectively, and these concentrations can be very hazardous to humans.

Air quality models are tools capable to predict the physical and chemical processes that occur in atmosphere affecting the atmospheric composition, such as wind advection, turbulent diffusion, wet and dry deposition, chemical reactions, photolysis, anthropogenic and biogenic emission processes. These models need input data containing information about atmosphere (usually from a global atmospheric model), terrestrial data (usually for the models maintainer) and emissions (that comes from air quality pollution inventories). EmissV is a code written to create emissions input for these atmospheric models.

The flow patterns over a finite square cylinder of aspect ratio of 3 were analyzed experimentally in a subsonic wind tunnel using the time-resolved particle image velocimetry (TR - PIV) techniques. The near wake flow structures and vortex shedding characteristics were investigated using mean flow analysis, spectral analysis and proper orthogonal decomposition (POD). The cylinders were fixed on a elliptical leading edge flat plate, creating a boundary layer which interacted with the cylinder wake. The 2D PIV measurements were conducted at a low horizontal plane, z/h = 0.3, to investigate possible boundary layer interactions. Due to the complexity of the phenomena, the flow was characterized both in terms of average behavior and time-resolved velocity fields. Both symmetrical and anti-symmetrical vortices structures occur in the cylinder wake, which can be identified based on the coefficients of the first four POD modes. The results indicated that the alternating Karman vortex structures are dominant, described by the first two POD modes.