Purpose There has been a growing concern about air quality because in recent years, industrial and vehicle emissions have resulted in unsatisfactory human health conditions. There is an urgent need for the measurements and estimations of particulate pollutants levels, especially in urban areas. As a contribution to this issue, the purpose of this paper is to use data from measured concentrations of particulate matter and meteorological conditions for the predictions of PM10. Design/methodology/approach The procedure included daily data collection of current PM10 concentrations for the city of São Carlos-SP, Brazil. These data series enabled to use an estimator based on artificial neural networks. Data sets were collected using the high-volume sampler equipment (VFA-MP10) in the period ranging from 1997 to 2006 and from 2014 to 2015. The predictive models were created using statistics from meteorological data. The models were developed using two neural network architectures, namely, perceptron multilayer (MLP) and non-linear autoregressive exogenous (NARX) inputs network. Findings It was observed that, over time, there was a decrease in the PM10 concentration rates. This is due to the implementation of more strict environmental laws and the development of less polluting technologies. The model NARX that used as input layer the climatic variables and the PM10 of the previous day presented the highest average absolute error. However, the NARX model presented the fastest convergence compared with the MLP network. Originality/value The presentation of a given PM10 concentration of the previous day improved the performance of the predictive models. This paper brings contributions with the NARX model applications.

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Nowadays, most of the world’s population lives in urban centres, where air quality levels are not strictly checked; citizens are exposed to air quality levels over the limits of the World Health Organization. The interaction between the issuing and atmospheric sources influences the air quality or level. The local climate conditions (temperature, humidity, winds, rainfall) determine a greater or less dispersion of the pollutants present in the atmosphere. In this sense, this work aimed to build a math modelling prediction to control the air quality around the campus of IPBeja, which is in the vicinity of a car traffic zone. The researchers have been analysing the data from the last months, particle matter (PM10 and PM2.5), and meteorological parameters for prediction using NARX. The results show a considerable increase in particles in occasional periods, reaching average values of 135 μg/m3 for PM10 and 52 μg/m3 for PM2.5. Thus, the monitoring and prediction serve as a warning to perceive these changes and be able to relate them to natural phenomena or issuing sources in specific cases.

RESUMO -Pesquisas mostram que a exposição ao material particulado fino (MP 2, 5 ) e grosso (MP 10 ) pode causar mortes prematuras, doenças mutagênicas e problemas respiratórios como a asma. O perigo da inalação de partículas depende da forma, do tamanho, da composição química e do lugar no qual elas foram depositadas no sistema respiratório. O objetivo deste trabalho foi caracterizar o material particulado (MP 10 ) na cidade de São Carlos, SP. Um Amostrador de Grandes Volumes (AGV) está sendo utilizado para coleta do MP e foram realizadas análises químicas por meio das técnicas de Fluorescência de Raio-X (XRF). As concentrações encontradas no período da amostragem, não ultrapassaram os limites estabelecidos pelas legislações vigentes, CONANA e CETESB. O XFR foi utilizado para fazer um levantamento qualiquantitativo e os elementos encontrados foram coerentes com outros estudos em áreas urbanas, como silício, alumínio, enxofre.

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