Abstract
Environmental pollution in urban areas may be mainly attributed to the rapid industrialization and increased growth of vehicular traffic. As a consequence of air quality deterioration, the health and welfare of human beings are compromised. Air quality monitoring networks usually are used not only to assess the pollutant trend but also in the effective set-up of preventive measures of atmospheric pollution. In this context, monitoring can be a valid action to evaluate different emission control scenarios; however, installing a high space-time resolution monitoring network is still expensive. Merge of observations data from low-cost air quality monitoring networks with forecasting models can contribute to improving significantly emission control scenarios. In this work, a validation algorithm of the forecasting model for the concentration of small particulates (PM10 and PM2.5) is proposed. Results showed a satisfactory agreement between the PM concentration forecast values and the measured data from 3 air quality monitoring stations. Final average RMSE values for all monitoring stations are equal to about 4.5 µg/m3.
Highlights
Particulate Matter (PM) causes acute and chronic effects, at the respiratory level since they can penetrate deep into the lungs
The monitoring stations are an efficient solution in collecting vast amounts of pollutant concentrations in real-time in urban and extra-urban areas and they are a support for citizens who can know the pollution status of their city [2]
The concentration of small particulates (PM10 and PM2.5) and meteorological parameters have been collected for all days of August 2018 by three monitoring stations installed in Battipaglia city (Italy) in the position located between the industrial area and the urban center (Figure 1)
Summary
Particulate Matter (PM) causes acute and chronic effects, at the respiratory level since they can penetrate deep into the lungs. It is crucial to make many efforts to monitor and control air pollution in an urban context [1]. A model is a simplified representation of the reality and it gives an approximate description of the modeled phenomenon. One of the main purposes of modeling is the phenomenon explanation, sometimes it can be used to describe the mechanism behind the reality we are investigating. Considerable relative humidity usually causes increases in PM concentrations due to the hygroscopic effect of aerosols, but not for PM10 in spring and summer, mainly due to the suppression of dust emissions under wet air conditions in spring and the impact of wet scavenging under high summer rainfall [5]
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