A summer campaign in Central Italy was carried out to study the impact of fire emissions on the mixing ratios of surface trace gases. Observations with a selective and sensitive instrument that uses the laser induced fluorescence technique for direct measurements of nitrogen dioxide (NO2), show a significant increase of NO2 mixing ratios, in the evening, when a fire plume reached the observations site. The increase of NO2 mixing ratios is well correlated (R=0.83) with that of particulate matter (PM), which is one of the primary product of forest and grassland fires. The tight correlation between NO2 and PM is used to improve the performance of a statistical regression model to simulate the observed O3, and to highlight the effect of fire emissions on the O3 mixing ratios. The statistical regression model of O3 improves in terms of performance (bias reduction of 77% and agreement enhancement of 10% for slope and correlation coefficient) when PM2.5 is included as additional input and proxy of the fire emissions among the usual input parameters (meteorological data and NO2 mixing ratios). A case study, comparing observed and modeled O3 in different days (with and without fire plume), suggests an impact of fire emissions on the O3 mixing ratios of about 10%.

Wildfires impact on surface nitrogen oxides and ozone in Central Italy

DI CARLO, PIERO;PITARI, Giovanni;TUCCELLA, PAOLO;
2015

Abstract

A summer campaign in Central Italy was carried out to study the impact of fire emissions on the mixing ratios of surface trace gases. Observations with a selective and sensitive instrument that uses the laser induced fluorescence technique for direct measurements of nitrogen dioxide (NO2), show a significant increase of NO2 mixing ratios, in the evening, when a fire plume reached the observations site. The increase of NO2 mixing ratios is well correlated (R=0.83) with that of particulate matter (PM), which is one of the primary product of forest and grassland fires. The tight correlation between NO2 and PM is used to improve the performance of a statistical regression model to simulate the observed O3, and to highlight the effect of fire emissions on the O3 mixing ratios. The statistical regression model of O3 improves in terms of performance (bias reduction of 77% and agreement enhancement of 10% for slope and correlation coefficient) when PM2.5 is included as additional input and proxy of the fire emissions among the usual input parameters (meteorological data and NO2 mixing ratios). A case study, comparing observed and modeled O3 in different days (with and without fire plume), suggests an impact of fire emissions on the O3 mixing ratios of about 10%.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11697/13889
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