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.

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.