Southeastern Brazil, the most populous and developed region of the country, faces various environmental problems associated with the growth of its population in urban areas. It is the most industrialized area in the country, comprising the metropolitan areas of São Paulo, Rio de Janeiro, Belo Horizonte, and other major cities. Air quality is a major concern, because the reported concentrations of certain regulated pollutants, typically ozone and fine particulate, have exceeded national standards. Due to the difficulty in taking measurements over many different areas, air quality modeling is a useful tool to estimate air pollutant concentrations. For southeastern Brazil, air quality modeling has been performed mostly with the Brazilian Regional Atmospheric Modeling System with Simplified Photochemical Module and the Weather Research and Forecast with Chemistry models. One of the main objectives was to study the evolution of air quality associated with improved vehicle emission factors in urban areas, the impact of climate change on air quality, and the relationship between pollutant concentrations and health. Knowledge of mobile source emission factors has been continuously expanded by in-tunnel measurements and dynamometer protocols, which provide accurate data as inputs to photochemical air quality models. The spatial distribution of the mobile source emissions was constructed based on open access data related to the streets and traffic distribution. The mobile emission module was combined to the chemistry modeling and this implementation can be an example to be applied to other places that do not have a spatial distribution of this kind of source. Forecasts of pollutant concentrations can inform public policies, including those addressing the effects of pollutants on health of the general population, and studies of the impacts of using different fuels and implementation of emissions regulations programs.

The objective of this work is to evaluate the impact of vehicular emissions on the formation of fine particles (PM_2.5; ≤ 2.5 µm in diameter) in the Sao Paulo Metropolitan Area (SPMA) in Brazil, where ethanol is used intensively as a fuel in road vehicles. The Weather Research and Forecasting with Chemistry (WRF-Chem) model, which simulates feedbacks between meteorological variables and chemical species, is used as a photochemical modelling tool to describe the physico-chemical processes leading to the evolution of number and mass size distribution of particles through gas-to-particle conversion. A vehicular emission model based on statistical information of vehicular activity is applied to simulate vehicular emissions over the studied area. The simulation has been performed for a 1-month period (7 August–6 September 2012) to cover the availability of experimental data from the NUANCE-SPS (Narrowing the Uncertainties on Aerosol and Climate Changes in Sao Paulo State) project that aims to characterize emissions of atmospheric aerosols in the SPMA. The availability of experimental measurements of atmospheric aerosols and the application of the WRF-Chem model made it possible to represent some of the most important properties of fine particles in the SPMA such as the mass size distribution and chemical composition, besides allowing us to evaluate its formation potential through the gas-to-particle conversion processes. Results show that the emission of primary gases, mostly from vehicles, led to a production of secondary particles between 20 and 30 % in relation to the total mass concentration of PM_2.5 in the downtown SPMA. Each of PM_2.5 and primary natural aerosol (dust and sea salt) contributed with 40–50 % of the total PM₁₀ (i.e. those ≤ 10 µm in diameter) concentration. Over 40 % of the formation of fine particles, by mass, was due to the emission of hydrocarbons, mainly aromatics. Furthermore, an increase in the number of small particles impaired the ultraviolet radiation and induced a decrease in ozone formation. The ground-level O₃ concentration decreased by about 2 % when the aerosol-radiation feedback is taken into account.

The Green Ocean Amazon experiment – GoAmazon 2014–2015 – explored the interactions between natural biogenic forest emissions from central Amazonia and urban air pollution from Manaus. Previous GoAmazon 2014–2015 studies showed that nitrogen oxide (NO_x = NO + NO₂) and sulfur oxide (SO_x) emissions from Manaus strongly interact with biogenic volatile organic compounds (BVOCs), affecting secondary organic aerosol (SOA) formation. In previous studies, ground-based and aircraft measurements provided evidence of SOA formation and strong changes in aerosol composition and properties. Aerosol optical properties also evolve, and their impacts on the Amazonian ecosystem can be significant. As particles age, some processes, such as SOA production, black carbon (BC) deposition, particle growth and the BC lensing effect change the aerosol optical properties, affecting the solar radiation flux at the surface. This study analyzes data and models SOA formation using the Weather Research and Forecasting with Chemistry (WRF-Chem) model to assess the spatial variability in aerosol optical properties as the Manaus plumes interact with the natural atmosphere. The following aerosol optical properties are investigated: single scattering albedo (SSA), asymmetry parameter (g_aer), absorption Ångström exponent (AAE) and scattering Ångström exponent (SAE). These simulations were validated using ground-based measurements at three experimental sites, namely the Amazon Tall Tower Observatory – ATTO (T0a), downtown Manaus (T1), Tiwa Hotel (T2) and Manacapuru (T3), as well as the U.S. Department of Energy (DOE) Gulfstream 1 (G-1) aircraft flights. WRF-Chem simulations were performed over 7 d during March 2014. Results show a mean biogenic SOA (BSOA) mass enrichment of 512 % at the T1 site, 450 % in regions downwind of Manaus, such as the T3 site, and 850 % in areas north of the T3 site in simulations with anthropogenic emissions. The SOA formation is rather fast, with about 80 % of the SOA mass produced in 3–4 h. Comparing the plume from simulations with and without anthropogenic emissions, SSA shows a downwind reduction of approximately 10 %, 11 % and 6 % at the T1, T2 and T3 sites, respectively. Other regions, such as those further downwind of the T3 site, are also affected. The g_aer values increased from 0.62 to 0.74 at the T1 site and from 0.67 to 0.72 at the T3 site when anthropogenic emissions are active. During the Manaus plume-aging process, a plume tracking analysis shows an increase in SSA from 0.91 close to Manaus to 0.98 160 km downwind of Manaus as a result of SOA production and BC deposition.

Smoke particles ejected into the atmosphere from biomass burning can modify the atmospheric composition around and even far from the sources. In late winter and early spring, biomass burning emissions from inland regions can be efficiently transported to urban areas in southeastern South America, thus affecting air quality in those areas. In this study, the Weather Research and Forecasting with Chemistry model was applied in order to investigate the impact of biomass burning sources on aerosol loadings and properties over the São Paulo Metropolitan Area (SPMA), in southeastern Brazil, during the period from 19 August to 3 September 2014. The model performance was evaluated using available aerosol measurements from the Narrowing the Uncertainties on Aerosol and Climate Change in São Paulo State project. The combined application of aerosol data and Weather Research and Forecasting with Chemistry simulations made it possible to represent some of the most important aerosol properties, such as particle number concentration and cloud condensation nuclei activation, in addition to evaluation of the impact of biomass burning by analyzing a 5‐day transport event, from 22 to 26 August 2014. During this transport event, differences in the average predicted PM_2.5 concentration reached 15 μg/m³ (peaking at 20 μg/m³ during the nighttime hours) over the SPMA, compared with 35 μg/m³ over inland areas northwest and north of the SPMA. Biomass burning accounted for up to 20% of the baseline particle number concentration‐ and cloud condensation nuclei‐weighted relative differences over the SPMA (2,300 and 1,400 cm⁻³, respectively).