The size of airborne particles is a key air quality parameter that is related to their composition, transport properties and effects on human health and the environment. Optical particle counters (OPCs) are increasingly used to dynamically characterize the size of ambient air particles. Monitoring campaigns lasting several months or even years generate millions of individual data values that must be effectively processed to extract information. Data mining algorithms as Self-Organizing Map (SOM) can support exploratory data analysis and pattern recognition in aerosol science. The use of SOMs, which offer powerful visualization features using 2D maps, allows us to interpret a large amount of data while avoiding any loss of information on variability from pre-treatments, such as compacting data recorded every minute to hourly or daily means. In the present study, we processed the data collected with an OPC during a long-term monitoring campaign (almost 3 years) conducted near residential buildings positioned very close to a steel plant and used them to assess and compare particulate matter (PM) profiles. About 12 million individual recorded values in total were handled. The current approach enabled us to identify four main PM profiles, follow their variation over time, and relate the differences to changes in the plant management and processes. Furthermore, it is potentially broadly applicable in high-frequency, long-term air quality monitoring campaigns employing different types of instruments to characterize the particle size and chemical composition of both PM and gases.

Assessment and comparison of multi-annual size profiles of particulate matter monitored at an urban-industrial site by an optical particle counter with a chemometric approach

Licen S.
;
Cozzutto S.;Barbieri P.
2020

Abstract

The size of airborne particles is a key air quality parameter that is related to their composition, transport properties and effects on human health and the environment. Optical particle counters (OPCs) are increasingly used to dynamically characterize the size of ambient air particles. Monitoring campaigns lasting several months or even years generate millions of individual data values that must be effectively processed to extract information. Data mining algorithms as Self-Organizing Map (SOM) can support exploratory data analysis and pattern recognition in aerosol science. The use of SOMs, which offer powerful visualization features using 2D maps, allows us to interpret a large amount of data while avoiding any loss of information on variability from pre-treatments, such as compacting data recorded every minute to hourly or daily means. In the present study, we processed the data collected with an OPC during a long-term monitoring campaign (almost 3 years) conducted near residential buildings positioned very close to a steel plant and used them to assess and compare particulate matter (PM) profiles. About 12 million individual recorded values in total were handled. The current approach enabled us to identify four main PM profiles, follow their variation over time, and relate the differences to changes in the plant management and processes. Furthermore, it is potentially broadly applicable in high-frequency, long-term air quality monitoring campaigns employing different types of instruments to characterize the particle size and chemical composition of both PM and gases.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11368/2966592
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