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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. [ABSTRACT FROM AUTHOR]

Background: Lisbon has about 500,000 inhabitants and it's the capital and the main economic hub of Portugal. Studies have demonstrated that exposure to Particulate Matter with an aerodynamic diameter<2.5 μm (PM2.5) have strong association with health effects. Researchers continue to identify new harmful air pollutants effects in our health even in low levels. Objectives: This study evaluates air pollution scenarios considering a Health Impact Assessment approach in Lisbon, Portugal. Methods: We have studied abatement scenarios of PM2.5 concentrations and the health effects in the period from 2015 to 2017 using the APHEKOM tool and the associated health costs were assessed by Value of Life Year. Results: The mean concentration of PM2.5 in Lisbon was 23 μg/m3 ± 10 μg/m3 (±Standard Deviation). If we consider that World Health Organization (WHO) standards of PM2.5 (10 μg/m3) were reached, Lisbon would avoid more than 423 premature deaths (equivalent to 9,172 life years' gain) and save more than US$45 million annually. If Lisbon city could even diminish the mean of PM2.5 by 5 μg/m3, nearly 165 deaths would be avoided, resulting in a gain of US$17 million annually. Conclusion: According to our findings, if considered the worst pollution scenario, levels of PM2.5 could improve the life's quality and save a significant amount of economic resources. Implications: The manuscript addresses the health effects and costs of air pollution and constitutes an important target for improving public policies on air pollutants in Portugal. Although Portugal has low levels of air pollution, there are significant health and economic effects that, for the most part, are underreported. The health impact assessment approach associated with costs had not yet been addressed in Portugal, which makes this study more relevant in the analysis of policies aimed to drive stricter control on pollutants' emissions. Health costs are a fundamental element to support decision-making process and to orientate the trade-offs in investments for improving public policies so that to diminish health effects, which can impact the management of the local health services and the population's quality of life, especially after the pandemic period when resources are scarce. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Essential ecosystem services provided by watersheds consider quantity and quality of water supply. In the current study, we used the Avoided Cost valuation method to assess the impacts of changes in land use of Guarapiranga Reservoir watershed (Metropolitan Region of São Paulo, Southeast Brazil) on the quality of raw water and on costs of treating the public water supply. Vegetation cover area significantly correlates with the average dosage of reagents for raw water treatment. Reduction of vegetation area increased 7.1 times the cost of water treatment, generating a significant externality for users of the system that reached around US$ 6.6 million in 2010. Even whether underestimated, management of the Metropolitan reservoirs should consider this value, enabling to reinforce water conservation as a state policy and effectively implemented with the participation of society. This study highlights the potential governing processes and management strategies related to the sustainable management of the watershed. [ABSTRACT FROM AUTHOR]

Objectives: In order to assess if ambient air pollution in urban areas could be related to alterations in male/female ratio this study objectives to evaluate changes in ambient particulate matter (PM10) concentrations after implementation of pollution control programmes in São Paulo city and the secondary sex ratio (SRR). Design and methods: A time series study was conducted. São Paulo's districts were stratified according to the PM10 concentrations levels and were used as a marker of overall air pollution. The male ratio was chosen to represent the secondary sex ratio (SSR=total male birth/total births). The SSR data from each area was analysed according to the time variation and PM10 concentration areas using descriptive statistics. The strength association between annual average of PM10 concentration and SSR was performed through exponential regression, and it was adopted as a statistical significance level of p<0.05. Results: The exponential regression showed a negative and significant association between PM10 and SSR. SSR varied from 51.4% to 50.7% in São Paulo in the analysed period (2000-2007). Considering the PM10 average concentration in São Paulo city of 44.72 μg/m³ in the study period, the SSR decline reached almost 4.37%, equivalent to 30 934 less male births. Conclusions: Ambient levels of PM10 are negatively associated with changes in the SSR. Therefore, we can speculate that higher levels of particulate pollution could be related to increased rates of female births. [ABSTRACT FROM AUTHOR]

Despite the concern about climate change and the associated negative impacts, fossil fuels continue to prevail in the global energy consumption. This paper aimed to propose the first model that relates CO2 emissions of Sao Paulo, the main urban center emitter in Brazil, with gross national product and energy consumption. Thus, we investigated the accuracy of three different methods: multivariate linear regression, elastic-net regression, and multilayer perceptron artificial neural networks. Comparing the results, we clearly demonstrated the superiority of artificial neural networks when compared with the other models. They presented better results of mean absolute percentage error (MAPE = 0.76%) and the highest possible coefficient of determination (R2 = 1.00). This investigation provides an innovative integrated climate-economic approach for the accurate prediction of carbon emissions. Therefore, it can be considered as a potential valuable decision-support tool for policymakers to design and implement effective environmental policies. [ABSTRACT FROM AUTHOR]