Is the expansion of the subway network alone capable of improving local air quality? A study case in São Paulo, Brazil.

One of the policies adopted to reduce vehicular emissions is subway network expansion. This work fitted interrupted regression models to investigate the effects of the inauguration of subway stations on the mean, trend, and seasonality of the NO, NO2, NOx, and PM10 local concentrations. The regions investigated in the city of São Paulo (Brazil) were Pinheiros, Butantã, and St. Amaro. In Pinheiros, after the inauguration of the subway station, there were downward trends for all pollutants. However, these trends were not significantly different from the trends observed before. In Butantã, only regarding NO, there was a significant reduction and seasonal change after the subway station's inauguration. In St. Amaro, no trend in the PM10 concentration was noted. The absence of other transportation and land use policies in an integrative way to the subway network expansion may be responsible for the low air quality improvement. This study highlights that the expansion of the subway network must be integrated with other policies to improve local air quality.

Does air pollution explain COVID-19 fatality and mortality rates? A multi-city study in São Paulo state, Brazil.

Since air pollution compromise the respiratory system and COVID-19 disease is caused by a respiratory virus, it is expected that air pollution plays an important role in the current COVID-19 pandemic. Exploratory studies have observed positive associations between air pollution and COVID-19 cases, deaths, fatality, and mortality rate. However, no study focused on Brazil, one of the most affected countries by the pandemic. Thus, this study aimed to understand how long-term exposure to PM10, PM2.5, and NO2 contributed to COVID-19 fatality and mortality rates in São Paulo state in 2020. Air quality data between 2015 and 2019 in 64 monitoring stations within 36 municipalities were considered. The COVID-19 fatality was calculated considering cases and deaths from the government's official data and the mortality rate was calculated considering the 2020 population. Linear regression models were well-fitted for PM2.5 concentration and fatality (R2 = 0.416; p = 0.003), NO2 concentration and fatality (R2 = 0.232; p = 0.005), and NO2 concentration and mortality (R2 = 0.273; p = 0.002). This study corroborates other authors' findings and enriches the discussion for having considered a longer time series to represent long-term exposure to the pollutants and for having considered one of the regions with the highest incidence of COVID-19 in the world. Thus, it reinforces measures to reduce the concentration of air pollutants which are essential for public health and will increase the chance to survive in future respiratory disease epidemics.

A Multi-Pollutant and Meteorological Analysis of Cardiorespiratory Mortality among the Elderly in São Paulo, Brazil-An Artificial Neural Networks Approach.

Traditionally, studies that associate air pollution with health effects relate individual pollutants to outcomes such as mortality or hospital admissions. However, models capable of analyzing the effects resulting from the atmosphere mixture are demanded. In this study, multilayer perceptron neural networks were evaluated to associate PM10, NO2, and SO2 concentrations, temperature, wind speed, and relative air humidity with cardiorespiratory mortality among the elderly in São Paulo, Brazil. Daily data from 2007 to 2019 were considered and different numbers of neurons on the hidden layer, algorithms, and a combination of activation functions were tested. The best-fitted artificial neural network (ANN) resulted in a MAPE equal to 13.46%. When individual season data were analyzed, the MAPE decreased to 11%. The most influential variables in cardiorespiratory mortality among the elderly were PM10 and NO2 concentrations. The relative humidity variable is more important during the dry season, and temperature is more important during the rainy season. The models were not subjected to the multicollinearity issue as with classical regression models. The use of ANNs to relate air quality to health outcomes is still very incipient, and this work highlights that it is a powerful tool that should be further explored.

Independent influence of thermoregulatory cost on the lower and upper set-points of a heliothermic lizard.

Studies on ectothermic vertebrates generally lead to average indicators of thermal preferences measured in the laboratory, which do not say about responses to natural environmental change and may not inform about individual variation and its triggering mechanisms. We studied whether and how changes in costs of thermoregulation influence the preferred temperature (Tp) of individual lizards and their energetic investment in thermoregulation by exposing specimens to three treatments of increasing costs, recording body temperature (Tb) and distance walked (energetic investment). Moderate costs induced an investment trade-off between energy-investment in thermoregulation and Tb, and highlighted individual variation that decreased with higher costs. Lower average Tp's were observed in the high costs trials as a result of the decreased values of the lower and upper voluntary Tb's. As costs increased, lizards walked a shorter overall distance and accepted lower Tb's, but lizards still engaged in costly thermoregulation. Individual variation resulted in two main thermoregulatory patterns that in certain ecological contexts may have an important role in decision-making and adjusting to temperatures that are far from optimal temperatures for performance and physiological processes.