Air pollution, especially in urban areas, is the result of a complex mixture of natural and anthropogenic emissions and their atmospheric processing. It causes millions of premature deaths worldwide and affects plant metabolism, which in turn alters the emissions of Biogenic Volatile Organic Compound (BVOCs) by plants. By taking the subtropical Metropolitan Area of São Paulo (MASP) as a natural laboratory, the BIOMASP+ project (BIOsphere-atmosphere interactions in the Metropolitan Area of São Paulo - plus ) aims to evaluate the interplay between the biosphere and secondary pollution (ozone and SOA formation and aging). The Brazilian Atlantic Forest (Mata Atlântica) is the target ecosystem as the fifth biodiversity hotspot in the world. Here we present the scientific motivations of the project, its methodology and the preliminary observations from the Special Observation Periods of year 2023 (SOP1, 2, 3 and 4). BIOMASP+ is (i) integrative, by combining in-situ/remote/laboratory observations and modeling, (ii) multidisciplinary, addressing micrometeorology, urban climate, atmospheric chemistry and biology. The project involves multiple nested scales: from leaf to above-canopy levels, from very short time (microseconds) to multi-year scale, from few millimeters (turbulence scale) to synoptic scale. In particular, the experimental effort relies on the implementation of two contrasting supersites (primary forest and urban forest) with a 30-m and 20-m flux towers, respectively, and a variety of state-of-the-art instruments. Ambient observations and the quantification of BVOC emissions have highlighted the complex interactions between meteorology, atmospheric composition of pollution, biogenic emissions of representative remnants of the Atlantic Forest and anthropogenic emissions.

Abstract The urban heat island (UHI) is a well-known consequence of urbanization on local climate. This study investigates the UHI in the Metropolitan Area of São Paulo (MASP), focusing on urban morphology and meteorological conditions. We utilized the Weather Research and Forecasting (WRF) model, incorporating the Local Climate Zones (LCZ) classification, to analyze simulations for September from 2014 to 2023. Results reveal a distinct southeast-to-northwest temperature gradient, where locations in the same LCZ class showed different temperatures based on their position. Proximity to the coast on the southeastern side provided cooling effects from the sea and valley-mountain breezes. UHI intensity was greater at night under clear skies and calm winds, peaking around 18:00 LT. The choice of rural reference point had a significant impact on the measured UHI magnitude. Furthermore, densely built-up areas (LCZ 1-3) retained more heat overnight compared to more open zones (LCZ 6, 9). These findings underscore the importance of comprehensive UHI assessments, which must consider urban morphology, meteorological conditions, regional circulation patterns, and the careful selection of rural reference points.

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.

Abstract Air quality forecasts are essential to support decision‐making in urban agglomerations, where millions of people are exposed to high levels of pollution. However, these forecasts are often limited by uncertainties in anthropogenic emissions, especially in large urban agglomerations where no dedicated operational system exists. In this study, we present an observation‐based emission scaling approach aimed at improving operational forecasts. This method derives scaling factors (SF) from the ratio of observed‐to‐modeled concentrations over the previous week and applies them to anthropogenic emissions in the forecast model, assuming that in large urban agglomerations, biases between observed and modeled concentrations are primarily driven by uncertainties in anthropogenic emission inventories. The method also derives SF for anthropogenic volatile organic compound emissions based on modeled daytime O3 biases under the assumption of a NOx‐saturated regime. We implement this method in a chemistry‐transport model using a global anthropogenic emission inventory and apply it to São Paulo for two distinct periods (February–April 2023 and July–September 2024). The results show that forecasts of CO, NO2, O3 and SO2 concentrations are significantly improved within a few weeks. For PM2.5 and PM10, improvements are more limited by the influence of secondary aerosol formation and by pollution transport from outside of the agglomeration. Overall, our results demonstrate that observation‐based emission scaling provides an efficient and transferable methodological approach for improving operational air quality forecasts in urban agglomerations without requiring model‐specific developments.

Abstract Anchieta-Imigrantes System (AIS) is one of Brazil's primary highways, connecting São Paulo to the Port of Santos. In AIS, Operação Comboio (OC) is activated when visibility drops below 100 m. This study investigated the meteorological factors that initiated OC in AIS during 2015 using a comprehensive array of data sources, for example, meteorological stations and synoptic charts. In 2015, OC was triggered on 106 days, predominantly in spring, mostly on the Anchieta Highway, and often in the afternoon. Meteorological systems triggering OC were classified as orographic, confined to the Serra do Mar, or non-orographic, involving broader cloudiness or precipitation detected by satellite or radar. Data from Itutinga Meteorological Station helped differentiate between visibility reductions caused by fog or precipitation. Among the 87 OCs studied in 2015, sea breeze (SB) contributed to 38% of cases. Generally, moisture was provided by advection by the SB or the Post-frontal anticyclone, occasionally from precipitating systems associated with frontal passage or thermodynamic instability. Moisture influx combined with temperature drops, induced by SB, post-frontal air, or nocturnal radiation loss, triggered OC.

Dengue fever, a mosquito-borne viral disease, poses a significant public health challenge whose transmission dynamics are highly sensitive to climatic conditions. However, the effects of extreme weather events like heatwaves remain poorly understood. This study investigated the influence of climatic factors and heatwaves on dengue incidence in two key Brazilian hotspots: the subtropical megacity of São Paulo (Sao Paulo State) and the tropical coastal city of Natal (Rio Grande do Norte State). We analyzed weekly confirmed dengue cases and meteorological data (temperature, precipitation, heatwaves) from 2014 to 2023. Distributed lag non-linear models and negative binomial regression were used to assess the complex, delayed associations between meteorological variables and dengue infections. Over the study period, 149,468 dengue cases were reported in São Paulo and 80,999 in Natal. Transmission patterns differed significantly, with Natal exhibiting more regular epidemic cycles. Our models revealed that higher minimum temperatures were associated with increased dengue risk in both cities. Conversely, and perhaps counter-intuitively, higher maximum temperatures and total precipitation showed negative associations with dengue cases. The impact of heatwaves was strikingly different between the locations. In São Paulo, the occurrence of a heatwave was associated with a 70% reduction in dengue risk in subsequent weeks (Relative Risk [RR]: 0.30, 95% Confidence Interval [CI]: 0.18-0.49). In contrast, no statistically significant association between heatwaves and dengue was observed in Natal. Our findings demonstrate that the relationship between extreme heat and dengue transmission is not uniform and can be inhibitory, challenging the assumption that warming consistently favors vector proliferation. These location-specific insights are critical for developing more accurate, tailored public health early-warning systems and caution against one-size-fits-all climate adaptation strategies for vector-borne diseases.

Atmospheric CO2 concentrations in urban areas reflect a combination of fossil fuel emissions and biogenic fluxes, offering a potential approach to assess city climate policies. However, atmospheric models used to simulate urban CO2 plumes face significant uncertainties, particularly in complex urban environments with dense populations and vegetation. This study addresses these challenges by analyzing CO2 dynamics in the Metropolitan Area of S & atilde;o Paulo (MASP) using the Weather Research and Forecasting model with Chemistry (WRF-Chem). Simulations were evaluated against ground-based observations from the METROCLIMA network, the first greenhouse gas monitoring network in South America, and column concentrations (XCO2) from the OCO-2 satellite spanning February to August 2019. To improve biogenic fluxes, we optimized parameters in the Vegetation Photosynthesis and Respiration Model (VPRM) using eddy covariance flux measurements for key vegetation types, including the Atlantic Forest, Cerrado, and sugarcane. Results show that at the urban site (IAG), the model consistently underestimated CO2 concentrations, with a negative mean bias of -9 ppm throughout the simulation period, likely due to the complexity of vehicular emissions and urban dynamics. In contrast, at the vegetated site (PDJ), simulations showed a consistent positive mean bias of 5 ppm and closely matched observations. Seasonal analyses revealed higher CO2 concentrations in winter, driven by greater atmospheric stability and reduced vegetation uptake estimated by VPRM, while summer exhibited lower levels due to increased mixing and higher agricultural productivity. A comparison of biogenic and anthropogenic scenarios highlights the need for integrated emission modeling and improved representation of biogenic fluxes, anthropogenic emissions, and boundary conditions for high-resolution modeling in tropical regions.

Predicting air quality in megacities is challenging due to the diversity and variability of emission sources, as well as the specific meteorology and photochemistry occurring in the urban boundary layer. São Paulo is by far the largest city in South America, one of the biggest megacities of the world, located near the coast and on a plateau at about 800 m above sea level, in a tropical climate. A megacity such as São Paulo is therefore a challenge for regional air quality models, which must be used at a resolution high enough to sufficiently accurately represent the processes leading to the high concentrations and high diurnal variability of the main pollutants. On the other hand, the measurement network is composed of 26 stations within the metropolitan area and another 63 within the state of São Paulo mostly in or near other cities, which constitutes an excellent support for evaluating the model outputs. In this study, we assess the strengths and weaknesses of modeled concentrations of regulated pollutants (CO, O3, NO2, PM2.5, PM10), over three contrasting time periods in 2019. Four Chemistry-Transport models are involved in this intercomparison of high-resolution modeling results, less than 5 km. We study primary pollutants, meteorology, photochemistry as well as the performance of ozone and PM2.5 alerts when WHO air quality standards are not met. The results show that all models have good performance depending on the period and pollutants, and the performance of multi-model median is the best, as has already been shown for other regions. In the framework of the KLIMAPOLIS project, the perspective of our study is to build an operational air quality forecasting system for the São Paulo region based on ensemble forecasts.

Forecasting urban air quality is important for protecting public health, but current model forecasts are often limited by an inaccurate prescription of pollutant emissions from human activities. We developed a new approach that improves air quality forecasts by adjusting emission prescription based on observed concentrations in urban agglomerations for key pollutants such as nitrogen oxides, sulfur dioxide, carbon monoxide, particulate matter, and volatile organic compounds. Applying this new approach to the São Paulo metropolitan area, Brazil, we compared forecasted and observed pollutant concentrations (from 6 February to 17 April 2023). Using adjusted emission significantly improved air quality forecasts for São Paulo, especially for ozone levels after adjusting estimates of volatile organic compound emissions. However, the forecast of particulate matter concentrations remained challenging due to their links with gaseous pollutants. Our study demonstrates the potential of using observed concentrations in urban agglomerations to improve air quality forecasts. Extending this approach to other urban agglomerations can help refine emission estimates and improve regional air quality forecasts, enabling better decision making for health protection.

BIOMASP+: Biogenic Volatile Organic Compounds in the Metropolitan Area of São Paulo (MASP) is a collaboration project among different French and Brazilian institutions to investigate the critical role of the biosphere-atmosphere interactions on urban pollution conditions and to evaluate how the biogenic volatile organic compounds (BVOC) affect the secondary pollutant formation.Formaldehyde (HCHO) is the most abundant atmospheric carbonyl compound and a photochemical oxidation product of VOCs from several anthropogenic and natural sources [1]. Over São Paulo state (SP), HCHO arises from complex atmospheric interactions between a large urban area with 15 million of vehicles using four different fuel types, several industries, an extensive Atlantic Rainforest (3.9 104 km2), biomass burning and thousands of farms that cover 40% (2.5 105 km2) of the SP area [2]. The Metropolitan Area of São Paulo (MASP) is a highly polluted megacity [3], with concentrations that often exceed the World Health Organization guidelines, particularly for ozone and PM2.5, which are produced by photochemical reactions, such as the formaldehyde.Vertical formaldehyde columns data (mol/m²) were obtained from the TROPOspheric Monitoring Instrument (TROPOMI) spectrometer onboard the Sentinel-5P satellite [2], and used in the WRF-CHEM model performance evaluation. Both, the long-term simulation and the Sentinel-5P data, covered the full 2022-year.Satellite data confirmed the spatial distribution of HCHO simulated by WRF-CHEM, indicating MASP as the main formaldehyde hotspot in the state of São Paulo [2]. For the HCHO monthly averages, the normalized cross-correlation (i.e., spatial distribution) between model and satellite remains inside the range of: 0.3 < r < 0.6.Using the satellite time series, it was possible to identify a bias in the HCHO simulated concentrations, that reached up to 80% in 1-year of simulation. This reduced the model's ozone production by up to 60% in the end of simulation. Comparing the simulation results with ozone data from air quality monitoring stations of the state of São Paulo [4], the linear correlation was within the range of 0.4 < r2 < 0.7, while the error was high (RMSE < 46.5).A long-term (1-year) simulation with WRF-CHEM is quite challenging task [3], however, the TROPOMI data was crucial to identify modeling problems in areas with absence of air quality data, indicating possible adjustments and corrections in the emissions inventories. [1] Gao, S., et al., 2021, Atmospheric formaldehyde, glyoxal and their relations to ozone pollution under low- and high-NOx regimes in summertime Shanghai, China, Atmospheric Research, https://doi.org/10.1016/j.atmosres.2021.105635[2] Freitas & Fornaro, 2022, Atmospheric Formaldehyde Monitored by TROPOMI Satellite Instrument throughout 2020 over São Paulo State, Brazil, Remote Sensing, https://doi.org/10.3390/rs14133032[3] Peralta, A., et al., 2023, Future Ozone Levels Responses to Changes in Meteorological Conditions under RCP 4.5 and RCP 8.5 Scenarios over São Paulo, Brazil., Atmosphere, https://doi.org/10.3390/atmos14040626[4] CETESB < https://cetesb.sp.gov.br/ar/wp-content/uploads/sites/28/2023/07/Relatorio-de-Qualidade-do-Ar-no-Estado-de-Sao-Paulo-2022.pdf> Keywords: BIOMASP; Formaldehyde; TROPOMI; WRF-CHEM

Mist and fog observed at the Meteorological Station of the Institute of Astronomy, Geophysics and Atmospheric Sciences of the University of São Paulo (EM/IAG) in the southeastern part of São Paulo city in the winter of 2007 were classified according to its formation processes: advection or radiation. Meteorological data from EM/IAG, Congonhas Airport and reanalysis from the European Center for Medium-Range Weather Forecast, as well as radiosondes from Campo de Marte Airport were used. In the 1981-2015 climatology, it was observed that these phenomena are more frequent in the winter, with an average of 11 events per month. For the winter of 2007, 21 advection and 28 radiation events were detected, with radiation fog occurring more frequently in June (13) and advection fog, in August (12). The South Atlantic Subtropical High favored the formation of radiation events in 24 cases; 15 of which had the contribution of humidity advected by the sea breeze. Post-frontal anticyclone was associated with the remaining 25 cases, of which 15 were classified as advection. Four cases were chosen for a more detailed analysis. On July 2, 2007, radiation mist occurred, with the injection of moisture by the sea breeze; the next day, there was also radiation mist, however, with no sea breeze. On the 20th of July, fog was caused by advection of humidity with north/northeast winds coming from the Serra da Cantareira and on the 27th, advection mist developed due to the intensification of winds associated with the movement of a post-frontal anticyclone.

Natural disasters (NDs) have been observed more frequently and with increasing intensities in Brazil. The South Atlantic Convergence Zone (SACZ) is identified as one of the main meteorological systems responsible for the NDs, however, intense rainfall does not occur along its entire length but is restricted to some locations within the band of cloudiness that defines it. Thus, the objective of this study is to analyze occurrences of mesoscale cyclonic vortices (MCV) in SACZ events that were associated with NDs in the state of São Paulo from 2013 to 2017 using data from the ERA5 Reanalysis, as well as to analyze one case study. To account for SACZ events, surface synoptic charts, observed and estimated precipitation data were used. ND events were selected from the Integrated Disaster Information System (S2ID) database. The methodology used by Quadro (2012) was adapted to identify MCV. The results showed 62 SACZ events, of which 28 were associated with NDs, and, of these, 10 presented MCV. The MCVs were separated into two groups: 1) MCVs in the SACZ events that showed precipitation at the location of the MCV and NDs and 2) MCVs in the SACZ events that did not show precipitation at the location of the MCV and NDs. Group 1 events were characterized by convergence at low levels and divergence at high levels of the atmosphere, vorticity values lower than -8 x 10-4 s-1 predominating at low levels (850–900 hPa), demonstrating a relationship with the highest precipitation accumulations and possibly with the occurrence of NDs. In the events of group 2, there was a predominance of negative values of vorticity in medium and high levels, the lack of a pattern in the field of divergence in the atmospheric levels, as well as lower values in the accumulated precipitation compared to the events of group 1. The case study was from January 11 to 15, 2016, associated with NDs in 8 cities. As a result, it was obtained that MCV was coupled in the atmosphere and the precipitation associated with it represented more than 37% of all the precipitation of the SACZ event, making it possible to attribute to the MCV a contribution in the occurrence of NDs caused by precipitation.

In the second quarter of 2021, the companies at the Capuava Petrochemical Complex (CPC, Santo André, Brazil) carried out a 50-day scheduled shutdown for the maintenance and installation of new industrial equipment. This process resulted in severe uncontrolled emissions of particulate matter (PM) and volatile organic compounds (VOCs) in a densely populated residential area (~3400 inhabitants/km2). VOCs can be emitted directly into the atmosphere in urban areas by vehicle exhausts, fuel evaporation, solvent use, emissions of natural gas, and industrial processes. PM is emitted by vehicle exhausts, mainly those powered by diesel, industrial processes, and re-suspended soil dust, in addition to that produced in the atmosphere by photochemical reactions. Our statistical analyses compared the previous (2017–2020) and subsequent (2021–2022) periods from this episode (April–May 2021) from the official air quality monitoring network of the PM10, benzene, and toluene hourly data to improve the proportion of this period of uncontrolled emissions. Near-field simulations were also performed to evaluate the dispersion of pollutants of industrial origin, applying the Gaussian plume model AERMOD (steady-state plume model), estimating the concentrations of VOC and particulate matter (PM10) in which the population was exposed in the region surrounding the CPC. The results comparing the four previous years showed an increase in the mean concentrations by a factor of 2 for PM10, benzene, and toluene, reaching maximum values during the episode of 174 µg m−3 (PM10), 79.1 µg m−3 (benzene), and 58.7 µg m−3 (toluene). Meanwhile, these higher concentrations continued to be observed after the episode, but their variation cannot be fully explained yet. However, it is worth highlighting that this corresponds to the post-pandemic period and the 2022 data also correspond to the period from January to June, that is, they do not represent the annual variation. A linear correlation indicated that CPC could have been responsible for more than 60% of benzene measured at the Capuava Air Quality Station (AQS). However, the PM10 behavior was not fully explained by the model. AERMOD showed that the VOC plume had the potential to reach a large part of Mauá and Santo André municipalities, with the potential to affect the health of more than 1 million inhabitants.

AbstractThe main features of low‐level jet (LLJ) in the metropolitan region of São Paulo (MRSP), Brazil, are assessed using rawinsondes carried out: (a) every 3‐hr during 10‐consecutive days in summer and winter field campaigns of the MCITY BRAZIL Project in 2013, (b) at 0900 and 2100 local time, from September 2009 to August 2013. These observations indicate that the LLJ is a typical feature of the MRSP, observed in 85% of the 20 days of the field‐campaigns and 77.6% of the 1,446 days of regular rawinsonde period. The fine temporal and spatial resolution soundings indicate that most of the LLJs occur during nighttime and early in the morning, with mean intensity of 8.5 ± 0.3 m s−1, height of 539 ± 26 m, and mostly (52.5%) from east and north. The coarse resolution soundings indicate the LLJ display a seasonal variation with maximum intensity in October (8.6 ± 0.3 m s−1) and a minimum in February (7.1 ± 0.2 m s−1), a maximum height in March (703 ± 151 m) and a minimum in June (577 ± 151 m). During MCITY campaigns about 76.4% of LLJ events show inertial oscillation and 35.3% of them are combined with sea breeze. Only 17.6% of LLJ events are associated with cold fronts and post‐frontal high pressure system. The urban heat island intensity, surface inversion layer strength, particulate matter 2.5 and carbon monoxide concentrations are negatively correlated with the LLJ intensity, suggesting the jet‐induced turbulent mixing may contribute to reduce them.

Abstract Air pollution is associated to poor urban mobility conditions, as in the megacity of São Paulo. Three scenarios of policies of driving restrictions were simulated using the WRF/Chem model, to assess the impacts on ozone pollution and the consequences for environmental justice: urban toll, sustainable transition, and vehicle free zone. Results show that the urban toll leads to a decrease of 20 µg.m-3 in maximum 8-hour ozone averages and the prevention of six hours of exceedances of the air quality standard. Sustainable transition was the most promising scenario, with a decrease of 50 µg.m-3 and avoiding 22 hours of exceedances. The vehicle free zone did not show any advantages. For the implementation of adequate public transport systems, massive investments are required, in order to achieve better air quality with the greatest environmental justice possible, aiming towards the protection of human health along with a more democratic access to the urban spaces.

Vehicular emissions are one of the most important source of pollution in urban centers, impacting air quality with a deleterious effect on human health and ecosystems. Air quality managers rely on emissions inventories to characterize pollution and sources. In this study we predicted vehicular emissions, using three sources of traffic data: 1) travel demand model outputs consisting of traffic simulations of light-duty vehicles, trucks, 2) and urban buses, and 3) a massive data set of real-time GPS coordinates of light-duty vehicles and trucks. The study area comprises the metropolitan areas of Sao Paulo, Santos, Vale de Paraiba, Sorocaba, and Campinas, which have a population of more than 30 million inhabitant. Once we generated hourly traffic flows, we used the Vehicular Emissions INventory Model (VEIN) to predict fuel consumption and emissions. Emissions using travel demand model for the metropolitan area of Sao Paulo are CO 177406 t/y, NOX 73554 t/y, NMHC 33999 t/y and PM2.5 2281 t/y. The emissions using GPS data were higher than using travel demand outputs, because GPS average speeds were lower, producing higher emission factors.

A poluição do ar é influenciada por fatores naturais e antropogênicos. Quatro pontos de monitoramento (veicular, comercial, residencial e background urbano (BGU))da poluição do ar em São Paulo foram avaliados durante 16 anos, revelando diferenças significativas devidoao uso do solo em todas as escalas temporais. Na escala diurna, as concentrações de poluentes primários são duas vezes mais altas nos pontos veicular e residencial do que no ponto BGU, onde a concentração de ozonio (O3) é 50% mais alta. Na escala sazonal, as concentrações de monóxido de carbono(CO) variaram em 80% devido ao uso do solo, e 55% pela sazonalidade.As variações sazonais ede uso do solo exercem impactos similares nas concentrações de O3 e monóxido de nitrogênio (NO). Para o material particulado grosso (MP10) e o dióxido de nitrogênio(NO2), as variações sazonais são mais intensas do que as por uso do solo. Na série temporal de 16 anos, o ponto BGU apresentou correlações mais fortes e significativas entre a média mensal de ondas longas (ROL) e o O3 (0,48) e o MP10 (0,37), comparadas ao ponto veicular (0,33 e 0,22, respectivamente). Estes resultados confirmam que o uso do solo urbano tem um papel significativo na concentração de poluentes em todas as escalas de análise, embora a sua influência se torne menos pronunciada em escalas maiores, conforme a qualidade do ar transita de um sistema antropogênico para um sistema natural. Isto poderá auxiliar decisões sobre políticas públicas em megacidades envolvendo a modificação do uso do solo.

The changing composition of the atmosphere, driven by anthropogenic emissions, is the cause of anthropogenic climate change as well as deteriorating air quality. Emission inventories are essential to understand the contribution of various human activities, model and predict the changing atmospheric composition, and design cost-effective mitigation measures. At present, national emission inventories in South America (SA) focus on Greenhouse Gases (GHG) as part of their obligation to the United Nations Framework Convention for Climate Change (UNFCC) within the framework of their national communications. Emission inventories other than GHG in SA focus mainly on growing urban areas and megacities. Therefore, studies examining air quality at national, regional or continental scales in SA depend on (down-scaled) global emission inventories. This paper examines the emission estimates of air pollutants from various global inventories for five SA countries, namely Argentina, Brazil, Chile, Colombia and Peru. A more detailed analysis is conducted for the EDGAR and ECLIPSE emission inventories, in particular comparing local city-scale inventories of a major city in each country. Although total emissions between down-scaled global inventories and local city inventories are often comparable, large discrepancies exist between the sectoral contributions. This is critical, as the mitigation of poor air quality will depend on addressing the right sources. Potential sources of discrepancies between global and local inventories include the spatial distribution proxies, difference in emission factors used and/or the use of generic statistical country data when estimating emissions. This highlights the importance of using local information when generating national emission inventories, especially for air quality modeling and development of effective mitigation measures. This study represents the first step towards an increased understanding of the strengths and weaknesses of emissions information in SA.

Great efforts have been made over the years to assess the effectiveness of air pollution controls in place in the metropolitan area of São Paulo (MASP), Brazil. In this work, the community multiscale air quality (CMAQ) model was used to evaluate the efficacy of emission control strategies in MASP, considering the spatial and temporal variability of fine particle concentration. Seven different emission scenarios were modeled to assess the relationship between the emission of precursors and ambient aerosol concentration, including a baseline emission inventory, and six sensitivity scenarios with emission reductions in relation to the baseline inventory: a 50% reduction in SO2 emissions; no SO2 emissions; a 50% reduction in SO2, NOx, and NH3 emissions; no sulfate (PSO4) particle emissions; no PSO4 and nitrate (PNO3) particle emissions; and no PNO3 emissions. Results show that ambient PM2.5 behavior is not linearly dependent on the emission of precursors. Variation levels in PM2.5 concentrations did not correspond to the reduction ratios applied to precursor emissions, mainly due to the contribution of organic and elemental carbon, and other secondary organic aerosol species. Reductions in SO2 emissions are less likely to be effective at reducing PM2.5 concentrations at the expected rate in many locations of the MASP. The largest reduction in ambient PM2.5 was obtained with the scenario that considered a reduction in 50% of SO2, NOx, and NH3 emissions (1 to 2 μg/m3 on average). It highlights the importance of considering the role of secondary organic aerosols and black carbon in the design of effective policies for ambient PM2.5 concentration control.