An uncertainty quantification of PM2.5 emissions from residential wood combustion in Italy

被引:8
|
作者
Rao, Marco [1 ]
D'Elia, Ilaria [1 ]
Piersanti, Antonio [1 ]
机构
[1] ENEA, Natl Agcy New Technol Energy & Sustainable Econ D, Via Lungotevere Thaon de Revel 76, Rome, Italy
关键词
PM2.5; emissions; Residential wood combustion; Uncertainty; Emission inventory; Bootstrap simulation; BIOMASS BURNING EMISSIONS; PARTICULATE-EMISSIONS; ORGANIC AEROSOL; AIR-QUALITY; EUROPE; IMPACT; URBAN; STOVES; SYSTEM; HEALTH;
D O I
10.1016/j.apr.2017.12.002
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Residential wood combustion from fireplaces and wood stoves represents a major source of aerosol emissions which can cause severe air quality deterioration, pose a serious threat to human health and climate change. Moreover, the emission estimates from this source have a high degree of uncertainty that in many emission inventories has not yet been assessed. This paper represents a first attempt to quantify the PM2.5 emission uncertainty from residential wood combustion in Italy. A bootstrap simulation analysis has been performed considering the data from the two most updated national surveys on wood consumption. The bootstrap simulations have been carried out both on a national and on a regional level varying wood consumption and emission factors of the different appliance type considered, but keeping constant their distribution in the whole national territory. The influence of the two resulting emission datasets on PM2.5 concentrations in Italy has been investigated with the MINNI model. The results show that the mean of PM2.5 total emissions for Italy is circa 120 kt, from a minimum of 97 kt to a maximum of 146 kt (-18.9%, +21.5% on the mean value). Moreover, a significant variation of annual average PM2.5 concentrations, with values higher than 10 mu g/m(3) in the most polluted areas, is shown by model results. The distribution influence of the different appliance types has been explored with a sensitivity test in the Veneto Region. This test reveals the importance of emission factors and technology share as the most important parameters influencing the uncertainty analysis.
引用
收藏
页码:526 / 533
页数:8
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