The evaluation of the contribution of coal-fired thermo-electrical power plants to particulate matter (PM) is important for environmental management, for evaluation of health risks, and for its potential influence on climate. The application of receptor models, based on chemical composition of PM, is not straightforward because the chemical profile of this source is loaded with Si and Al and it is collinear with the profile of crustal particles. In this work, a new methodology, based on Positive Matrix Factorization (PMF) receptor model and Si/Al diagnostic ratio, specifically developed to discriminate the coal-fired power plant contribution from the crustal contribution is discussed. The methodology was applied to daily PM10 samples collected in central Italy in proximity of a large coal-fired power plant. Samples were simultaneously collected at three sites between 2.8 and 5.8 km from the power plant: an urban site, an urban background site, and a rural site. Chemical characterization included OC/EC concentrations, by thermo-optical method, ions concentrations (NH4+, Ca2+, Mg2+, Na+, K+, Mg2+, SO42-, NO3-, Cl-), by high performances ion chromatography, and metals concentrations (Si, Al, Ti, V, Mn, Fe, Ni, Cu, Zn, Br), by Energy dispersive X-ray Fluorescence (ED-XRF). Results showed an average primary contribution of the power plant of 2% (±1%) in the area studied, with limited differences between the sites. Robustness of the methodology was tested inter-comparing the results with two independent evaluations: the first obtained using the Chemical Mass Balance (CMB) receptor model and the second correlating the Si-Al factor/source contribution of PMF with wind directions and Calpuff/Calmet dispersion model results. The contribution of the power plant to secondary ammonium sulphate was investigated using an approach that integrates dispersion model results and the receptor models (PMF and CMB), a sulphate contribution of 1.5% of PM10 (±0.3%) as average of the three sites was observed.
Application of PMF and CMB receptor models for the evaluation of the contribution of a large coal-fired power plant to PM10 concentrations
Daniele Contini;Daniela Cesari;Marianna Conte;Antonio Donateo
2016
Abstract
The evaluation of the contribution of coal-fired thermo-electrical power plants to particulate matter (PM) is important for environmental management, for evaluation of health risks, and for its potential influence on climate. The application of receptor models, based on chemical composition of PM, is not straightforward because the chemical profile of this source is loaded with Si and Al and it is collinear with the profile of crustal particles. In this work, a new methodology, based on Positive Matrix Factorization (PMF) receptor model and Si/Al diagnostic ratio, specifically developed to discriminate the coal-fired power plant contribution from the crustal contribution is discussed. The methodology was applied to daily PM10 samples collected in central Italy in proximity of a large coal-fired power plant. Samples were simultaneously collected at three sites between 2.8 and 5.8 km from the power plant: an urban site, an urban background site, and a rural site. Chemical characterization included OC/EC concentrations, by thermo-optical method, ions concentrations (NH4+, Ca2+, Mg2+, Na+, K+, Mg2+, SO42-, NO3-, Cl-), by high performances ion chromatography, and metals concentrations (Si, Al, Ti, V, Mn, Fe, Ni, Cu, Zn, Br), by Energy dispersive X-ray Fluorescence (ED-XRF). Results showed an average primary contribution of the power plant of 2% (±1%) in the area studied, with limited differences between the sites. Robustness of the methodology was tested inter-comparing the results with two independent evaluations: the first obtained using the Chemical Mass Balance (CMB) receptor model and the second correlating the Si-Al factor/source contribution of PMF with wind directions and Calpuff/Calmet dispersion model results. The contribution of the power plant to secondary ammonium sulphate was investigated using an approach that integrates dispersion model results and the receptor models (PMF and CMB), a sulphate contribution of 1.5% of PM10 (±0.3%) as average of the three sites was observed.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
