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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.

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.

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.

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.