Predicting Air Quality by Integrating a Mesoscopic Traffic Simulation Model and Simplified Air Pollutant Estimation Models

© 2018, Springer Science+Business Media, LLC, part of Springer Nature. Continuous growth in traffic demand has led to a decrease in the air quality in various urban areas. More than ever, local authorities for environmental protection and urban planners are interested in performing detailed investigations using traffic and air pollution simulations for testing various urban scenarios and raising citizen awareness where necessary. This article is focused on the traffic and air pollution in the eco-neighbourhood “Nancy Grand Cœur”, located in a medium-size city from north-eastern France. The main objective of this work is to build an integrated simulation model which would predict and visualize various environmental changes inside the neighbourhood such as: air pollution, traffic flow or meteorological information. Firstly, we conduct a data profiling analysis on the received data sets together with a discussion on the daily and hourly traffic patterns, average nitrogen dioxide concentrations and the regional background concentrations recorded in the eco-neighbourhood for the study period. Secondly, we build the 3D mesoscopic traffic simulation model using real data sets from the local traffic management centre. Thirdly, by using reliable data sets from the local air-quality management centre, we build a regression model to predict the evolution of nitrogen dioxide concentrations, as a function of the simulated traffic flow and meteorological data. We then validate the estimated results through comparisons with real data sets, with the purpose of supporting the traffic engineering decision-making and the smart city sustainability. The last section of the paper is reserved for further regression studies applied to other air pollutants monitored in the eco-neighbourhood, such as sulphur dioxide and particulate matter and a detailed discussion on benefit and challenges to conduct such studies

Serological detection of antibodies against Paracoccidioides brasiliensis in dogs with leishmaniasis

The aim of this study was to detect antibodies against Paracoccidioides brasiliensis in dogs seropositive and seronegative for leishmaniasis. Sera from 836 dogs (449 positive and 387 negative to leishmaniasis) were analysed by ELISA and the immunodiffusion test using gp43 and exoantigen, respectively. The analysis of the 836 serum samples by ELISA and the immunodiffusion test showed a positivity of 67.8 % and 7.3%, respectively, for P. brasiliensis infection. The dogs positive to leishmaniasis showed a higher reactivity to gp43 (79.9%) and exoantigen (12.7%) than the negative ones (54.0% and 1.0%, respectively). The higher reactivity to P. brasiliensis antigens may be due to cross-reactivity or a co-infection of dogs by Leishmania and P. brasiliensis. The lower correlation (0.187) observed between reactivity to gp43 and Leishmania antigen reinforces the latter hypothesis