Emission factors for a biofuel impacted fleet in South America's largest metropolitan area.

The Metropolitan Area of São Paulo (MASP) is among the largest urban areas in the Southern Hemisphere. Vehicular emissions are of great concern in metropolitan areas and MASP is unique due to the use of biofuels on a large scale (sugarcane ethanol and biodiesel). In this work, tunnel measurements were employed to assess vehicle emissions and to calculate emission factors (EFs) for heavy-duty and light-duty vehicles (HDVs and LDVs). The EFs were determined for particulate matter (PM) and its chemical compounds. The EFs obtained for 2018 were compared with previous tunnel experiments performed in the same area. An overall trend of reduction of fine and coarse PM, organic carbon (OC), and elemental carbon (EC) EFs for both LDVs and HDVs was observed if compared to those observed in past years, suggesting the effectiveness of vehicular emissions control policies implemented in Brazil. A predominance of Fe, Cu, Al, and Ba emissions was observed for the LDV fleet in the fine fraction. Cu presented higher emissions than two decades ago, which was associated with the increased use of ethanol fuel in the region. For HDVs, Zn and Pb were mostly emitted in the fine mode and were linked with lubricating oil emissions from diesel vehicles. A predominance in the emission of three- and four-ring polycyclic aromatic hydrocarbons (PAHs) for HDVs and five-ring PAHs for LDVs agreed with what was observed in previous studies. The use of biofuels may explain the lower PAH emissions for LDVs (including carcinogenic benzo[a]pyrene) compared to those observed in other countries. The tendency observed was that LDVs emitted higher amounts of carcinogenic species. The use of these real EFs in air quality modeling resulted in more accurate simulations of PM concentrations, showing the importance of updating data with real-world measurements.

Health Risk Assessment of Exposure to Air Pollutants Exceeding the New WHO Air Quality Guidelines (AQGs) in São Paulo, Brazil.

We applied the AirQ+ model to analyze the 2021 data within our study period (15 December 2020 to 17 June 2022) to quantitatively estimate the number of specific health outcomes from long- and short-term exposure to atmospheric pollutants that could be avoided by adopting the new World Health Organization Air Quality Guidelines (WHO AQGs) in São Paulo, Southeastern Brazil. Based on temporal variations, PM2.5, PM10, NO2, and O3 exceeded the 2021 WHO AQGs on up to 54.4% of the days during sampling, mainly in wintertime (June to September 2021). Reducing PM2.5 values in São Paulo, as recommended by the WHO, could prevent 113 and 24 deaths from lung cancer (LC) and chronic obstructive pulmonary disease (COPD) annually, respectively. Moreover, it could avoid 258 and 163 hospitalizations caused by respiratory (RD) and cardiovascular diseases (CVD) due to PM2.5 exposure. The results for excess deaths by RD and CVD due to O3 were 443 and 228, respectively, and 90 RD hospitalizations due to NO2. Therefore, AirQ+ is a useful tool that enables further elaboration and implementation of air pollution control strategies to reduce and prevent hospital admissions, mortality, and economic costs due to exposure to PM2.5, O3, and NO2 in São Paulo.

Evolution of Vehicle Emission Factors in a Megacity Affected by Extensive Biofuel Use: Results of Tunnel Measurements in São Paulo, Brazil.

Since 2001, four emission measurement campaigns have been conducted in multiple traffic tunnels in the megacity of São 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 São 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.

How socio-economic and atmospheric variables impact COVID-19 and influenza outbreaks in tropical and subtropical regions of Brazil.

COVID-19 has been disturbing human society with an intensity never seen since the Influenza epidemic (Spanish flu). COVID-19 and Influenza are both respiratory viruses and, in this study, we explore the relations of COVID-19 and Influenza with atmospheric variables and socio-economic conditions for tropical and subtropical climates in Brazil. Atmospheric variables, mobility, socio-economic conditions and population information were analyzed using a generalized additive model for daily COVID-19 cases from March 1st to May 15th, 2020, and for daily Influenza hospitalizations (2017-2019) in Brazilian states representing tropical and subtropical climates. Our results indicate that temperature combined with humidity are risk factors for COVID-19 and Influenza in both climate regimes, and the minimum temperature was also a risk factor for subtropical climate. Social distancing is a risk factor for COVID-19 in all regions. For Influenza and COVID-19, the highest Relative Risks (RR) generally occurred in 3 days (lag = 3). Altogether among the studied regions, the most important risk factor is the Human Development Index (HDI), with a mean RR of 1.2492 (95% CI: 1.0926-1.6706) for COVID-19, followed by the elderly fraction for both diseases. The risk factor associated with socio-economic inequalities for Influenza is probably smoothed by Influenza vaccination, which is offered free of charge to the entire Brazilian population. Finally, the findings of this study call attention to the influence of socio-economic inequalities on human health.

Short-Term Responses of Air Quality to Changes in Emissions under the Representative Concentration Pathway 4.5 Scenario over Brazil.

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.

Generating traffic flow and speed regional model data using internet GPS vehicle records.

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.

A two decades study on ozone variability and trend over the main urban areas of the São Paulo state, Brazil.

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.