The influence that different urban development models has on PM2.5 elemental and bioaccessible profiles.

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.

Assessing the impact of PM2.5 on respiratory disease using artificial neural networks.

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 µg 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 (p < .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.

Effects of reducing exposure to air pollution on submaximal cardiopulmonary test in patients with heart failure: Analysis of the randomized, double-blind and controlled FILTER-HF trial.

BACKGROUND: Air pollution exposure could mitigate the health benefits of exercise in patients with heart failure (HF). We tested the effects of a respiratory filter on HF patients exposed to air pollution during exercise. METHODS AND RESULTS: Ancillary analysis of the FILTER-HF trial, focused on the exercise outcomes. In a randomized, double-blind, 3-way crossover design, 26 HF patients and 15 control volunteers were exposed to clean air, unfiltered dilute diesel engine exhaust (DE), or filtered DE for 6min during a submaximal cardiopulmonary testing in a controlled-exposure facility. Prospectively collected data included six-minute walking test [6mwt], VO2, VE/VCO2 Slope, O2Pulse, pulmonary ventilation [VE], tidal volume, VD/Vt, oxyhemoglobin saturation and CO2-rebreathing. Compared to clean air, DE adversely affected VO2 (11.0±3.9 vs. 8.4±2.8ml/kg/min; p<0.001); 6mwt (243.3±13.0 vs. 220.8±13.7m; p=0.030); and O2Pulse (8.9±1.0 vs. 7.8±0.7ml/beat; p<0.001) in HF patients. Compared to DE, filtration reduced the particulate concentration from 325±31 to 25±6µg/m(3), and was associated with an increase in VO2 (10.4±3.8ml/kg/min; p<0.001 vs. DE) and O2Pulse (9.7±1.1ml/beat; p<0.001 vs. DE) in patients with HF. Filtration was associated with higher VE and CO2-rebreathing in both groups. VE/VCO2 Slope was higher among patients with HF. CONCLUSION: DE adversely affects exercise capacity in patients with HF. A simple respiratory filter can reduce the adverse effects of pollution on VO2 and O2Pulse. Given the worldwide prevalence of exposure to traffic-related air pollution, these findings are relevant for public health especially in this highly susceptible population. The filter intervention holds great promise that needs to be tested in future studies.

Respiratory Filter Reduces the Cardiovascular Effects Associated With Diesel Exhaust Exposure: A Randomized, Prospective, Double-Blind, Controlled Study of Heart Failure: The FILTER-HF Trial.

OBJECTIVES: The goal of this study was to test the effects of a respiratory filter intervention (filter) during controlled pollution exposure. BACKGROUND: Air pollution is considered a risk factor for heart failure (HF) decompensation and mortality. METHODS: This study was a double-blind, randomized to order, controlled, 3-way crossover, single-center clinical trial. It enrolled 26 patients with HF and 15 control volunteers. Participants were exposed in 3 separate sessions to clean air, unfiltered diesel exhaust exposure (DE), or filtered DE. Endpoints were endothelial function assessed by using the reactive hyperemia index (RHi), arterial stiffness, serum biomarkers, 6-min walking distance, and heart rate variability. RESULTS: In patients with HF, DE was associated with a worsening in RHi from 2.17 (interquartile range [IQR]: 1.8 to 2.5) to 1.72 (IQR: 1.5 to 2.2; p = 0.002) and an increase in B-type natriuretic peptide (BNP) from 47.0 pg/ml (IQR: 17.3 to 118.0 pg/ml) to 66.5 pg/ml (IQR: 26.5 to 155.5 pg/ml; p = 0.004). Filtration reduced the particulate concentration (325 ± 31 µg/m(3) vs. 25 ± 6 µg/m(3); p < 0.001); in the group with HF, filter was associated with an improvement in RHi from 1.72 (IQR: 1.5 to 2.2) to 2.06 (IQR: 1.5 to 2.6; p = 0.019) and a decrease in BNP from 66.5 pg/ml (IQR: 26.5 to 155.5 pg/ml) to 44.0 pg/ml (IQR: 20.0 to 110.0 pg/ml; p = 0.015) compared with DE. In both groups, DE decreased the 6-min walking distance and arterial stiffness, although filter did not change these responses. DE had no effect on heart rate variability or exercise testing. CONCLUSIONS: To our knowledge, this trial is the first to show that a filter can reduce both endothelial dysfunction and BNP increases in patients with HF during DE. Given these potential benefits, the widespread use of filters in patients with HF exposed to traffic-derived air pollution may have beneficial public health effects and reduce the burden of HF. (Effects of Air Pollution Exposure Reduction by Filter Mask on Heart Failure; NCT01960920).