Forecasting Model Validation of Particulate Air Pollution by Low Cost Sensors Data

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.

Comparison process of air quality measurement systems applied to the context of cultural heritage

“…In recent years, numerous technologies based on the Internet of Things (IoT) have been developed that allow the pollutants measurement with high spatial and temporal resolution [28]. These measurement tools are the basis of appropriately designed networks [29] capable of measuring the pollutants levels that represent the starting point for studies that can provide detailed information on air quality [30] [31] and be supportive of the implementation of policies for their reduction [32]. To further increase the spatial resolution of the measured data, real-time on-road monitoring systems have been developed in recent years which provide information on pollutants with a resolution of 1 km 2 [33] [34].…”

Comparison Process of Blood Heavy Metals Absorption Linked to Measured Air Quality Data in Areas with High and Low Environmental Impact

“…The new type of intelligent measuring device could be easily installed in many parts of the city following an optimization in the choice of places [25]. The available air quality data are the basis for developing mathematical models for data spatialization [26] and forecasting [27]. Furthermore, dispersion models support experimental measures to define the behavior of pollutants in the atmosphere and verify their long-term effects in case of accidental occurrence [28].…”

An Innovative Approach to Determining the Contribution of Saharan Dust to Pollution

“…The measured air quality data, albeit in large quantities, are discontinuous in the territory and need to be integrated into modeling systems [ 17 ] that allow their analysis, forecasting, and spatialization. In particular, the in-depth analysis of air quality data allows the study of accidental events such as fires [ 18 ], the analysis of the effects of pollutant dispersion produced by industrial activities [ 19 ], and the implementation of forecasting models that can provide detailed information on air quality over time [ 20 ] and space. Knowing the behavior of pollutants, and in particular of airborne dust, is also useful for defining the levels of healthiness in closed environments, such as homes or workplaces.…”