RESUMO
Limited studies have reported on in-vitro analysis of PM2.5 but as far as the authors are aware, bioaccessibility of PM2.5 in artificial lysosomal fluid (ALF) has not been linked to urban development models before. The Brazilian cities Manaus (Amazon) and Curitiba (South region) have different geographical locations, climates, and urban development strategies. Manaus drives its industrialization using the free trade zone policy and Curitiba adopted a services centered economy driven by sustainability. Therefore, these two cities were used to illustrate the influence that these different models have on PM2.5 in vitro profile. We compared PM2.5 mass concentrations and the average total elemental and bioaccessible profiles for Cu, Cr, Mn, and Pb. The total average elemental concentrations followed Mn > Pb > Cu > Cr in Manaus and Pb > Mn > Cu > Cr in Curitiba. Mn had the lowest solubility while Cu showed the highest bioaccessibility (100%) and was significantly higher in Curitiba than Manaus. Cr and Pb had higher bioaccessibility in Manaus than Curitiba. Despite similar mass concentrations, the public health risk in Manaus was higher than in Curitiba indicating that the free trade zone had a profound effect on the emission levels and sources of airborne PM. These findings illustrate the importance of adopting sustainable air quality strategies in urban planning.
Assuntos
Poluentes Atmosféricos/análise , Poluição do Ar/análise , Monitoramento Ambiental , Metais Pesados/análise , Material Particulado/análise , Reforma Urbana , Brasil , Cidades , Desenvolvimento Industrial , Exposição por Inalação , Medição de RiscoRESUMO
Understanding the impact on human health during peak episodes in air pollution is invaluable for policymakers. Particles less than PM2.5 can penetrate the respiratory system, causing cardiopulmonary and other systemic diseases. Statistical regression models are usually used to assess air pollution impacts on human health. However, when there are databases missing, linear statistical regression may not process well and alternative data processing should be considered. Nonlinear Artificial Neural Networks (ANN) are not employed to research environmental health pollution even though another advantage in using ANN is that the output data can be expressed as the number of hospital admissions. This research applied ANN to assess the impact of air pollution on human health. Three well-known ANN were tested: Multilayer Perceptron (MLP), Extreme Learning Machines (ELM) and Echo State Networks (ESN), to assess the influence of PM2.5, temperature, and relative humidity on hospital admissions due to respiratory diseases. Daily PM2.5 levels were monitored, and hospital admissions for respiratory illness were obtained, from the Brazilian hospital information system for all ages during two sampling campaigns (2008-2011 and 2014-2015) in Curitiba, Brazil. During these periods, the daily number of hospital admissions ranged from 2 to 55, PM2.5 concentrations varied from 0.98 to 54.2⯵gâ¯m-3, temperature ranged from 8 to 26⯰C, and relative humidity ranged from 45 to 100%. Of the ANN used in this study, MLP gave the best results showing a significant influence of PM2.5, temperature and humidity on hospital attendance after one day of exposure. The Anova Friedman's test showed statistical difference between the appliance of each ANN model (pâ¯<â¯.001) for 1 lag day between PM2.5 exposure and hospital admission. ANN could be a more sensitive method than statistical regression models for assessing the effects of air pollution on respiratory health, and especially useful when there is limited data available.
Assuntos
Poluentes Atmosféricos/análise , Poluição do Ar/estatística & dados numéricos , Exposição Ambiental/estatística & dados numéricos , Redes Neurais de Computação , Material Particulado/análise , Doenças Respiratórias/epidemiologia , Poluição do Ar/análise , Brasil/epidemiologia , Hospitalização , Humanos , Umidade , Modelos Lineares , Modelos Estatísticos , Análise de Regressão , Transtornos Respiratórios/epidemiologia , TemperaturaRESUMO
Analysis of fuel emissions is crucial for understanding the pathogenesis of mortality because of air pollution. The objective of this study is to assess cardiovascular and inflammatory toxicity of diesel and biodiesel particles. Mice were exposed to fuels for 1 h. Heart rate (HR), heart rate variability, and blood pressure were obtained before exposure, as well as 30 and 60 min after exposure. After 24 h, bronchoalveolar lavage, blood, and bone marrow were collected to evaluate inflammation. B100 decreased the following emission parameters: mass, black carbon, metals, CO, polycyclic aromatic hydrocarbons, and volatile organic compounds compared with B50 and diesel; root mean square of successive differences in the heart beat interval increased with diesel (p < 0.05) compared with control; low frequency increased with diesel (p < 0.01) and B100 (p < 0.05) compared with control; HR increased with B100 (p < 0.05) compared with control; mean corpuscular volume increased with B100 compared with diesel (p < 0.01), B50, and control (p < 0.001); mean corpuscular hemoglobin concentration decreased with B100 compared with B50 (p < 0.001) and control (p < 0.05); leucocytes increased with B50 compared with diesel (p < 0.05); platelets increased with B100 compared with diesel and control (p < 0.05); reticulocytes increased with B50 compared with diesel, control (p < 0.01), and B100 (p < 0.05); metamyelocytes increased with B50 and B100 compared with diesel (p < 0.05); neutrophils increased with diesel and B50 compared with control (p < 0.05); and macrophages increased with diesel (p < 0.01), B50, and B100 (p < 0.05) compared with control. Biodiesel was more toxic than diesel because it promoted cardiovascular alterations as well as pulmonary and systemic inflammation.