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Methane is a major greenhouse gas (GHG) that has contributed to one third of the warming induced by all GHGs since the preindustrial era (IPCC, 2021). Reliable quantification of methane emissions is critical for tracking progress towards methane mitigation, especially for the countries that join the Global Methane Pledge and that have firm plans for reducing methane emissions over the next decades, such as the US. In this study, we quantified US methane emissions using inverse modeling of ground- and airborne- methane measurements made from the NOAA Greenhouse Gas Reference Network for 2007 – 2021. We conducted 12 inversion runs using two high-resolution transport models (HYSPLIT-NAMs and WRF-STILT), three background estimates, and two prior emission fields. Multiple estimates of wetland emissions were considered and subtracted from our inversion-derived total emission estimates. Our derived anthropogenic emissions show a consistent increasing trend after 2015 across our inversion ensemble members and a seasonal cycle in emission magnitude that repeats every year during our study period. Both the trend and seasonal variation seem to correlate with US natural gas consumption. A similar seasonal variability has been reported previously, but only on an urban scale; this is the first time it has been derived on a national scale. Furthermore, we also compared our estimates with US EPA’s national GHG inventory reporting and investigated the impact of the COVID-19 pandemic on emissions in 2020 relative to 2019 and 2021.

Air pollutants reach high concentrations in developing countries, such as Brazil.

The current generation of Zero Emission Vehicle (ZEV) policies are designed to accelerate the transition away from conventional internal combustion engine (ICE) petrol and diesel vehicle fleets. However, the current focus on zero exhaust emissions and the lack of more detailed guidance regarding Non-Exhaust Emissions (NEEs) may mean that some of the trade-offs in transitioning to, e.g., Battery Electric Vehicle (BEV) fleets may be missed by many in the commercial sector. Here, as part of early work on the scoping of the First Bus EURO VI Diesel Vehicle (E6DV) to BEV fleet upgrades, we estimate E6DV total particulate emissions to be ca. 62–85 and 164–213 mg.veh−1.km−1 for PM2.5 and PM10, respectively, and that the majority, typically 93–97%, are NEEs. We also discuss the complex interaction between E6DV/BEV properties and estimate potential changes resulting from the transition to BEVs as ranging from a decrease of ca. 2–12% to an increase of ca. 12–50% depending on a combination of weight difference, regenerative brake performance and journey type. Finally, we propose metrics that would allow fleet operators more insight into a wider range of emission outcomes at the scoping stage of a fleet upgrade.

We evaluate the performance of the Model of Urban Network of Intersecting Canyons and Highways (MUNICH) in simulating ozone (O3) and nitrogen oxides (NOx) concentrations within the urban street canyons in the São Paulo metropolitan area (SPMA). The MUNICH simulations are performed inside the Pinheiros neighborhood (a residential area) and Paulista Avenue (an economic hub), which are representative urban canyons in the SPMA. Both zones have air quality stations maintained by the São Paulo Environmental Agency (CETESB), providing data (both pollutant concentrations and meteorological) for model evaluation. Meteorological inputs for MUNICH are produced by a simulation with the Weather Research and Forecasting model (WRF) over triple-nested domains with the innermost domain centered over the SPMA at a spatial grid resolution of 1 km. Street coordinates and emission flux rates are retrieved from the Vehicular Emission Inventory (VEIN) emission model, representing the real fleet of the region. The VEIN model has an advantage to spatially represent emissions and present compatibility with MUNICH. Building height is estimated from the World Urban Database and Access Portal Tools (WUDAPT) local climate zone map for SPMA. Background concentrations are obtained from the Ibirapuera air quality station located in an urban park. Finally, volatile organic compound (VOC) speciation is approximated using information from the São Paulo air quality forecast emission file and non-methane hydrocarbon concentration measurements. Results show an overprediction of O3 concentrations in both study cases. NOx concentrations are underpredicted in Pinheiros but are better simulated in Paulista Avenue. Compared to O3, NO2 is better simulated in both urban zones. The O3 prediction is highly dependent on the background concentration, which is the main cause for the model O3 overprediction. The MUNICH simulations satisfy the performance criteria when emissions are calibrated. The results show the great potential of MUNICH to represent the concentrations of pollutants emitted by the fleet close to the streets. The street-scale air pollutant predictions make it possible in the future to evaluate the impacts on public health due to human exposure to primary exhaust gas pollutants emitted by the vehicles.

This study delves into the photochemical atmospheric changes reported globally during the pandemic by analyzing the change in emissions from mobile sources and the contribution of local meteorology to ozone (O3) and particle formation in Bogotá (Colombia), Santiago (Chile), and São Paulo (Brazil). The impact of mobility reductions (50%–80%) produced by the early coronavirus-imposed lockdown was assessed through high-resolution vehicular emission inventories, surface measurements, aerosol optical depth and size, and satellite observations of tropospheric nitrogen dioxide (NO2) columns. A generalized additive model (GAM) technique was also used to separate the local meteorology and urban patterns from other drivers relevant for O3 and NO2 formation. Volatile organic compounds, nitrogen oxides (NOx), and fine particulate matter (PM2.5) decreased significantly due to motorized trip reductions. In situ nitrogen oxide median surface mixing ratios declined by 70%, 67%, and 67% in Bogotá, Santiago, and São Paulo, respectively. NO2 column medians from satellite observations decreased by 40%, 35%, and 47%, respectively, which was consistent with the changes in mobility and surface mixing ratio reductions of 34%, 25%, and 34%. However, the ambient NO2 to NOx ratio increased, denoting a shift of the O3 formation regime that led to a 51%, 36%, and 30% increase in the median O3 surface mixing ratios in the 3 respective cities. O3 showed high sensitivity to slight temperature changes during the pandemic lockdown period analyzed. However, the GAM results indicate that O3 increases were mainly caused by emission changes. The lockdown led to an increase in the median of the maximum daily 8-h average O3 of between 56% and 90% in these cities.

Brazil, the country most impacted by the coronavirus disease 2019 (COVID-19) on the southern hemisphere, use intensive care admissions per day, mobility and other indices to monitor quarantines and prevent the transmissions of SARS-CoV-2. In this study we quantified the associations between residential mobility index (RMI), air pollution, meteorology, and daily cases and deaths of COVID-19 in São Paulo, Brazil. We applied a semiparametric generalized additive model (GAM) to estimate: 1) the association between RMI and COVID-19, accounting for ambient particulate matter (PM2.5), ozone (O3), relative humidity, temperature and delayed exposure between 3-21 days, and 2) the association between COVID-19 and exposure to for ambient particulate matter (PM2.5), ozone (O3), accounting for relative humidity, temperature and mobility. We found that an RMI of 45.28% results in 1,212 cases (95% CI: 1,189 to 1,235) and 44 deaths (95% CI: 40 to 47). Increasing the isolation from 45.28% to 50% would avoid 438 cases and 21 deaths. Also, we found that an increment of 10 μg·m-3of PM2.5results in a risk of 1.140 (95% CI: 1.021 to 1.274) for cases and 1.086 (95% CI: 1.008 to 1.170) for deaths, while O3produces a relative risk of 1.075 (95% CI: 1.006 to 1.150) for cases and 1.063 (95% CI: 1.006 to 1.124) for deaths, respectively. We compared our results with observations and literature review, finding well agreement. Policymakers can use such mobility indices as tools to control social distance activities. Spatial distancing is an important factor to control COVID-19. Small increments of air pollution result in an increased number of COVID-19 cases and deaths.

Graphical abstract, Nowadays, many smart-phones and vehicles are equipped with Global Position System (GPS) for tracking and navigation purposes, providing an opportunity to derive highly representative local vehicular flow and estimate vehicular emissions information. Here, we report and discuss methods used to handle large volumes of such activity data, namely 124 million GPS recordings from the web page Maplink.com.br, extract high spatial resolution vehicular flow information for a vast area in South-east Brazil, and correct for bias using traffic counts observations for the same area. The method consists in filter speed and accelerations, assign buffers to the road network, aggregate speed by street, fill missing number of lanes, generate traffic flow. Methods presented here were used to inform traffic-related air quality modelling and used as part of local air pollution management activities but are also amenable to any work that would be enhanced by more locally representative or time-resolved inputs for traffic flow, e.g. traffic network management, and demand modelling. • 124 million GPS observations from electronic devices were used to generate traffic flow. • Spatial bias was investigated and accounted for using independent local traffic count data. • Traffic count rescaled GPS traffic flow provide a robust description of spatial and quantitative traffic patterns.

Since 2001, four emission measurement campaigns have been conducted in multiple traffic tunnels in the megacity of Sao Paulo, Brazil, an area with a fleet of more than 7 million vehicles running on fuels with high biofuel contents: gasoline + ethanol for light-duty vehicles (LDVs) and diesel + biodiesel for heavy-duty vehicles (HDVs). Emission factors for LDVs and HDVs were calculated using a carbon balance method, the pollutants considered including nitrogen oxides (NOx), carbon monoxide (CO), and sulfur dioxide, as well as carbon dioxide and ethanol. From 2001 to 2018, fleet-average emission factors for LDVs and HDVs, respectively, were found to decrease by 4.9 and 5.1% per year for CO and by 5.5 and 4.2% per year for NOx. These reductions demonstrate that regulations for vehicle emissions adopted in Brazil in the last 30 years improved air quality in the megacity of Sao Paulo significantly, albeit with a clear delay. These findings, especially those for CO, indicate that official emission inventories underestimate vehicle emissions. Here, we demonstrated that the adoption of emission factors calculated under real-world conditions can dramatically improve air quality modeling in the region.

We evaluate the performance of the Model of Urban Network of Intersecting Canyons and Highways (MUNICH) in simulating Ozone (O3) and Nitrogen Oxides (NOx) concentrations within the urban street canyons in the Sao Paulo Metropolitan Area (SPMA). The MUNICH simulations are performed inside Pinheiros neighborhood (a residential area) and Paulista Avenue (an economic hub), which are representative urban canyons in the SPMA. Both zones have air quality stations maintained by the Sao Paulo Environmental Agency (CETESB), providing data (both pollutants concentrations and meteorological) for model evaluation. Meteorological inputs for MUNICH are produced by a simulation with the Weather Research and Forecasting model (WRF) over triple-nested domains with the innermost domain centered over the SPMA at a spatial grid resolution of 1 km. Street links coordinates and emission flux rates are retrieved from the Vehicular Emission Inventory (VEIN) emission model, representing the real fleet of the region. The VEIN model has an advantage to spatially represent emissions and present compatibility with MUNICH. Building height is estimated from the World Urban Database and Access Portal Tools (WUDAPT) Local Climate Zone map for SPMA. Background concentrations are obtained from the Ibirapuera air quality station located in an urban park. Finally, volatile organic compounds (VOCs) speciation is approximated using information from Sao Paulo air quality forecast emission file and non-methane hydrocarbons concentration measurements. Results show an overprediction of O3 concentrations in both study cases. NOx concentrations are underpredicted in Pinheiros but are better simulated in Paulista Avenue. Compared to O3, NO2 is better simulated in both urban zones. The O3 prediction is highly dependent on the background concentration, which is the main cause for the model O3 overprediction. The MUNICH simulations satisfy the performance criteria when emissions are calibrated. The results show the great potential of MUNICH to represent the concentrations of pollutants emitted by the fleet close to the streets. The street-scale air pollutant predictions make it possible in the future to evaluate the impacts on public health due to human exposure to primary exhaust gases pollutants emitted by the vehicles.

We have evaluated the spread of SARS-CoV-2 through Latin America and the Caribbean (LAC) region by means of a correlation between climate and air pollution indicators, namely, average temperature, minimum temperature, maximum temperature, rainfall, average relative humidity, wind speed, and air pollution indicators PM10, PM2.5, and NO2 with the COVID-19 daily new cases and deaths. The study focuses in the following LAC cities: Mexico City (Mexico), Santo Domingo (Dominican Republic), San Juan (Puerto Rico), Bogotá (Colombia), Guayaquil (Ecuador), Manaus (Brazil), Lima (Perú), Santiago (Chile), São Paulo (Brazil) and Buenos Aires (Argentina). The results show that average temperature, minimum temperature, and air quality were significantly associated with the spread of COVID-19 in LAC. Additionally, humidity, wind speed and rainfall showed a significant relationship with daily cases, total cases and mortality for various cities. Income inequality and poverty levels were also considered as a variable for qualitative analysis. Our findings suggest that and income inequality and poverty levels in the cities analyzed were related to the spread of COVID-19 positive and negative, respectively. These results might help decision-makers to design future strategies to tackle the spread of COVID-19 in LAC and around the world., Graphical abstract Image 1, Highlights • The study analyzes the impact of climate and pollution indicators on COVID-19 pandemic in Latin America and the Caribbean. • The low wind speed generates less ventilation, then a higher concentration of pollutants or air contaminated with the SARS-CoV-2 virus can be inhaled in various cities of Latin America and the Caribbean. • The climate and pollution indicators are one of the factors that triggered the spread of Covid-19 through Latin America and the Caribbean. • The COVID-19 spread is significantly correlated with PM10, PM2.5 and NO2 in several cities of Latin America and the Caribbean. • The income inequality and poverty levels of the cities analyzed in Latin America and the Caribbean are related to the spread of COVID-19.

Social distancing policies put in place during COVID-19 epidemic in addition to helping to limit the spread of the disease also contributed to improving urban air quality. Here we show a decrease in air pollutant concentration as a consequence of mobility reduction in São Paulo during the containment measure which began on 22nd March 2020. When comparing to foregoing weeks to equivalent periods of 2019, the concentration of most air pollutants sharply decreased in the first days of mobility restriction, to then increase again after government officials downplayed the threat of the disease. This trend is also followed by a decrease in hospital admissions by SARS-influenza. Therefore, despite the great economic and social unrest caused by the pandemic, this unique situation shows that large-scale mobility reduction policy had a significant impact on air quality, benefiting, directly and indirectly, the public health system.

This chapter introduces conventional emissions monitoring and modeling practices used in the on-road transportation sector. It describes both laboratory-based dynamometer and real-world emissions monitoring practices including portable emissions measurement systems (PEMS) and vehicle emissions remote sensing systems (VERSS) and identifies key events that helped to shape conventional practices. It also describes commonly used macro, meso, and microscale emissions modeling methods and the changing nature of the relationship between monitoring and modeling activities. Finally, it considers concerns regarding the effectiveness of existing practices to support ongoing efforts to reduced vehicle fleet emissions, and opportunities to do better in future.

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. Resumo A poluição atmosférica pode ser agravada devido à precárias condições de mobilidade urbana, como ocorre em São Paulo. Visando avaliar os impactos na poluição por ozônio e as consequências para a justiça ambiental, 3 cenários de políticas de restrição veicular foram simulados com o modelo WRF/Chem: pedágio urbano, transição sustentável e centro sem veículos. Os resultados indicaram que o pedágio urbano levou a uma redução de 20 µg.m-3 nas médias das máximas de 8 horas, e evitou seis ultrapassagens do padrão de qualidade do ar. A transição sustentável teve máximas até 50 µg.m-3 mais baixas e evitou 22 ultrapassagens. O centro sem veículos não demonstrou vantagens. Para a implementação de um sistema de transporte público adequado, são necessários expressivos investimentos para se atingir melhorias na qualidade do ar com a maior justiça ambiental possível, buscando a proteção da saúde da população juntamente à democratização do acesso aos espaços urbanos. Resumen La contaminación atmosférica puede agravarse debido à la movilidad urbana ineficiente, como en São Paulo. Visando avaliar los impactos em la contaminación por ozono y las consecuencias para la justicia ambiental, se simularan 3 escenarios de restricción vehicular con en el modelo WRF/Chem: peaje urbano, transición sustentable y centro sin vehículos. Los resultados indicaron que el peaje urbano condujo a una reducción de 20 µg.m-3 en las concentraciones medias máximas de 8 horas, y evito 6 excedencias de las normas de calidad del aire. La transición sustentable tuvo un máximo de hasta 50 µg.m-3 más bajo e 22 excedencias evitadas. El centro sin vehículos no presento ventajas. Para la implementación de un transporte público adecuado, se necesitan inversiones significativas para lograr mejoras en la calidad del aire con la mayor justicia ambiental posible, buscando proteger la salud de la población junto con la democratización del acceso a los espacios urbanos.

Emissions inventorying is a complex task with regulatory and/or scientific environmental purposes. In South American cities, when the task is performed, the common denominator is lack of data and documentation, and vehicles are usually the main source of pollutant of emerging and consolidated megacities. Therefore, emissions inventories is becoming more important, especially for mobile sources. In this manuscript we present the model REMI (R-EMssions-Inventory) for developing bottom-up emissions inventory for vehicles in cities with lack of data (Ibarra & Ynoue, 2016). The program was written in R (R CORE TEAM 2016) using several libraries. The program consists in several R scripts organized in folders with Inputs& Outputs. For traffic inputs uses counts or simulations, and also, it can be as a top-down method with statistical traffic information. REMI classifies vehicule data by fuel, size of motor, use and gross weight anually up to 50 years, according to EEA/EMEP guidelines and Copert (Ntziachristos, 2014). REMI has several options for emission factors, 1) Emission factors from Ntziachristos (2014), 2) local emission factors or 3) mixed emission factors. In the future REMI will include HBEFA emission factors. REMI also incorporates deterioration factors. Currently REMI estimate hot-engine emissions of 27 pollutants. Keywords: REMI, vehicular, emissions inventory, R, bottom-up.

In this paper, we analyze the variability of the ozone concentration over São Paulo Macrometropolis, as well the factors, whichdetermined the tendency observed in the last two decades. Time series of hourly ozone concentrations measured at 16 automatedstations from an air quality network from 1996 to 2017 were analyzed. The temporal variability of ozone concentrations exhibitswell-defined daily and seasonal patterns. Ozone presents a significant positive correlation between the number of cases (thresh-olds of 100–160μgm−3) and the fuel sales of gasohol and diesel. The ozone concentrations do not exhibit significant long-termtrends, but some sites present positive trends that occurs in sites in the proximity of busy roads and negative trends that occurs insites located in residential areas or next to trees. The effect of atmospheric process of transport and ozone formation was analyzedusing a quantile regression model (QRM). This statistical model can deal with the nonlinearities that appear in the relationship ofozone and other variables and is applicable to time series with non-normal distribution. The resulting model explains 0.76% ofthe ozone concentration variability (with global coefficient of determinationR1= 0.76) providing a better representation than anordinary least square regression model (with coefficient of determinationR2= 0.52); the effect of radiation and temperature arethe most critical in determining the highest ozone quantiles

Vehicles are one of the most significant sources of air pollutant emissions in urban areas, and their real contribution always needs to be updated to predict impacts on air quality. Radar databases and traffic counts using statistical modeling is an alternative and low-cost approach to produce traffic activities data in each urban street to be used as input to predict vehicular emissions. In this work, we carried out a spatial statistical analysis of local radar data and calculated traffic flow using local radar data combined with different statistical models. Future scenarios about vehicle emission inventory to define public policies were also proposed and analyzed for Belo Horizonte (BH), a Brazilian State capital, with the third-largest metropolitan region in the country. The Normal-Neighborhood Model (i.e., the mixed effect model with random effect in the neighborhood, radar type, and in the regional area) was used to calculate traffic flow in each urban street. Results showed average reductions in CO (4.5%), NMHC (3.0%), NOx (3.0%) and PM2.5 (6.2%) emissions even with an increase in fleet composition (25% in average). The decrease is a result of the implementation of emission control programs by the government, improvements vehicles technologies, and the quality of fuels. Prediction of traffic data from radar databases has proven to be useful for avoiding the high costs of performing origin-destination surveys and traffic modeling using commercial software. Radar databases can provide many potential benefits for research and analysis in environmental and transportation planning. These findings can be incorporated in future investigations to implement public policies on vehicular emission reduction in urban areas and to advance environmental health effects research and human health risk assessment.

Northeast China has experienced a dramatic increase in vehicle population, along with economic development. However, information about the emissions from the transport sector is still limited. A comprehensive, detailed, and high-resolution vehicular emissions inventory for Northeast China was developed in this study. We implemented the methodology of the Chinese emissions guidelines into the open-source model VEIN to obtain emission factors adjusted by temperature, humidity, altitude, speed, fuel quality, and activity. Furthermore, we split the emission factors to obtain specific pollutant compounds with data from the European emissions guidelines. Our estimates were calibrated to match fuel consumption data from the National Statistical Yearbook (NSY) and International Energy Agency (IEA). The emissions for CO, HC, NOX, PM2.5, BC, CO2 were 1,420,212 [t·y−1], 123,861 [t·y−1], 518,181 [t·y−1], 17,118 [t·y−1], 10,645 [t·y−1] and 108,249,047 [t·y−1], respectively. The passenger cars emit approximately 80% of CO and HC, while trucks the 72% of NOX and PM2.5. The inventory was gridded with a resolution of 0.1° revealing that emissions are highly concentrated in urban centers. The results with NSY data produce a better agreement with other studies. The city with the most emissions is Shenyang, followed by Harbin, Changchun, and Dalian. The peak emissions of CO, HC, isopentane and toluene occurs at 11 years of vehicle use, while the peak for BC, CH4, PM2.5, and NOX occurs at six years of vehicle use. To the best of our knowledge, this is the first emissions inventory from the transport sector covering 133 pollutants.

Vehicles are commonly the main source of outdoor air pollution in urban areas and vehicular emission inventory is a tool to identify the emissions contribution from mobile sources. In this study, we developed an emission inventory to Belo Horizonte, a densely populated urban city in Brazil, with approximately 2.0 million vehicles. The vehicular emission inventory was developed applying the National Vehicle Emission Inventory model (VEIN) using emission factor from Sao Paulo State Environmental Protection Agency, different traffic behavior profile (constant and different diurnal cycle per vehicle type) established from local radar data and kriging interpolation method considering four different scenarios with reductions in fleet composition. The scenarios were described as according the combination between traffic behavior profiles, vehicle flow, vehicle type and a fuel consumption. The comparison between scenarios showed reductions of emissions around 8.5 % (CO), 8.8 % (CO2), when it was considered 10 % of reduction in fleet composition of passenger cars and light commercial vehicles. Considering 20 % reduction in diesel fleet composition (trucks and buses), a decrease of 8.4 % (NOx) and 8.6 % (PM) was observed. Furthermore, this work presented that the kriging method to define a spatial/temporal distributing using radar traffic data is an alternative low- cost method to investigate the effect of real traffic data on the vehicular emissions modeling. This study is pioneer in Brazil and reinforced the importance of detailing traffic activities using real data to estimate vehicular emissions in an urban area. Transportation management strategies to reduce air pollution and to assist users to reduce air pollution exposure are mandatory to create a collaborative network and build sustainable cities in the future. It is necessary more investigations methods to generate an accuracy spatial distribution aggregated coupled with different traffic behavior profile to develop actions to reduce vehicular emissions in urban areas, investigate air pollution exposure, perform project-level emissions and hot-spot analysis.

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, NO X 73554 t/y, NMHC 33999 t/y and PM 2.5 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.

Emission inventories are the quantification of pollutants from different sources. They provide important information not only for climate and weather studies but also for urban planning and environmental health protection. We developed an open-source model (called Vehicular Emissions Inventory – VEIN v0.2.2) that provides high-resolution vehicular emissions inventories for different fields of studies. We focused on vehicular sources at street and hourly levels due to the current lack of information about these sources, mainly in developing countries.The type of emissions covered by VEIN are exhaust (hot and cold) and evaporative considering the deterioration of the factors. VEIN also performs speciation and incorporates functions to generate and spatially allocate emissions databases. It allows users to load their own emission factors, but it also provides emission factors from the road transport model (Copert), the United States Environmental Protection Agency (EPA) and Brazilian databases. The VEIN model reads, distributes by age of use and extrapolates hourly traffic data, and it estimates emissions hourly and spatially. Based on our knowledge, VEIN is the first bottom–up vehicle emissions software that allows input to the WRF-Chem model. Therefore, the VEIN model provides an important, easy and fast way of elaborating or analyzing vehicular emissions inventories under different scenarios. The VEIN results can be used as an input for atmospheric models, health studies, air quality standardizations and decision making.

Emission inventories are the quantification of pollutants from different sources. They provide important information not only for climate and weather studies, but also for urban planning and environmental health protection. We developed an open source model (named VEIN v0.2.2) that provides high resolution vehicular emissions inventories for different fields of studies. We focused on vehicular sources at street and hourly levels % they are the major source of air pollution in megacities. due to the current lack of information about these sources, mainly in developing countries. The type of emissions covered by VEIN are: exhaust (hot and cold) and evaporative considering the deterioration of the factors. VEIN also performs speciation and incorporates functions to generate and spatially allocate emissions databases. It allows users to load their own emissions factors, but it also provides emissions factors from the road transport model (Copert), the United States Environmental Protection Agency (EPA) and Brazilian databases. The VEIN model reads, distributes by age of use and extrapolates hourly traffic data, and estimates hourly and spatially emissions. Based on our knowledge, VEIN is the first bottom-up vehicle emissions software that allows input to the WRF-Chem model. Therefore, the VEIN model provides an important, easy and fast way of elaborating or analyzing vehicular emissions inventories, under different scenarios. The VEIN results can be used as an input for atmospheric models, health studies, air quality standardizations and decision making.

Ozone events in South America might be triggered by increasing air temperatures and dry conditions, leading to vulnerable population exposure. The current air quality standards and attention levels in S&atilde, o Paulo state, Brazil, are 40% higher and 25% higher, respectively, than the limits recommended by the World Health Organization (WHO). We simulated an extreme ozone event in the S&atilde, o Paulo megacity using the Weather Research and Forecast/Chemistry model during an extreme event characterized by positive anomalies of air temperature and solar radiation. Results were evaluated using the different air quality limits from S&atilde, o Paulo state and the WHO, also with socioeconomic vulnerability data from the Brazilian census and cost analysis for the public health system from the extreme episode. More than 3 million people in vulnerability conditions, such as low income and families with an above-average percentage of children, live in areas where ozone concentrations exceeded the attention levels of the WHO during the episode, which is ignored by the lenient SP state environmental laws. WHO air quality guidelines must be adopted urgently in developing nations in order to provide a more accurate basis for cost analysis and population exposure, particularly the for vulnerable population groups.