Skip to main content

Lund University Publications

LUND UNIVERSITY LIBRARIES

Sources for PM air pollution in the Po Plain, Italy: II. Probabilistic uncertainty characterization and sensitivity analysis of secondary and primary sources

Larsen, B. R. ; Gilardoni, S. ; Stenström, Kristina LU ; Niedzialek, J. ; Jimenez, J. and Belis, C. A. (2012) In Atmospheric Environment 50. p.203-213
Abstract
Very high levels of ambient particulate matter (PM) are frequently encountered in the north of Italy and air quality limits are regularly exceeded. To obtain quantitative information on the pollution sources and to gain understanding of the dynamics of pollution episodes in this populated area PM10 and/or PM2.5 samples were collected daily at nine urban to regional sites distributed over the central Po Plain and one site in the Valtelline Valley. In total, 23 five-week winter campaigns and one comparative summer/ autumn campaign (2007-2009) were organized. The PM was analyzed for 61 chemical constituents and a data-base was built up consisting of approx. 70000 records of the concentrations and their associated uncertainty. In addition... (More)
Very high levels of ambient particulate matter (PM) are frequently encountered in the north of Italy and air quality limits are regularly exceeded. To obtain quantitative information on the pollution sources and to gain understanding of the dynamics of pollution episodes in this populated area PM10 and/or PM2.5 samples were collected daily at nine urban to regional sites distributed over the central Po Plain and one site in the Valtelline Valley. In total, 23 five-week winter campaigns and one comparative summer/ autumn campaign (2007-2009) were organized. The PM was analyzed for 61 chemical constituents and a data-base was built up consisting of approx. 70000 records of the concentrations and their associated uncertainty. In addition C-14/C-12 ratios were determined in PM10 from four sites. Primary and secondary sources were quantified using macro-tracer methods in combination with chemical mass balance modelling and positive matrix factorization and the combined results were computed by probability-and sensitivity analysis. Monte Carlo simulations yielded probability distributions for seven source categories contributing to the carbonaceous fraction of PM and five major source categories contributing to the PM10 and PM2.5 mass. Despite large uncertainties in the combined source contribution estimates the paper demonstrates that secondary aerosol formed simultaneously over the Po Plain is the main responsible for the typical, rapid build-up of air pollution after clean-air episodes. Next to secondary sources, the most important sources are primary emissions from road transport followed by biomass burning (BB). In the Valtelline Valley, higher contributions from BB and lower contributions from secondary sources were observed. (C) 2012 Elsevier Ltd. All rights reserved. (Less)
Please use this url to cite or link to this publication:
author
; ; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Source apportionment, Receptor models, Macro-tracers, C-14, Carbonaceous, aerosol
in
Atmospheric Environment
volume
50
pages
203 - 213
publisher
Elsevier
external identifiers
  • wos:000301561100024
  • scopus:84856645133
ISSN
1352-2310
DOI
10.1016/j.atmosenv.2011.12.038
language
English
LU publication?
yes
id
3d85c8e5-59f7-4c2d-a4c8-4beac2cab621 (old id 2571075)
date added to LUP
2016-04-01 13:31:59
date last changed
2022-04-06 05:37:47
@article{3d85c8e5-59f7-4c2d-a4c8-4beac2cab621,
  abstract     = {{Very high levels of ambient particulate matter (PM) are frequently encountered in the north of Italy and air quality limits are regularly exceeded. To obtain quantitative information on the pollution sources and to gain understanding of the dynamics of pollution episodes in this populated area PM10 and/or PM2.5 samples were collected daily at nine urban to regional sites distributed over the central Po Plain and one site in the Valtelline Valley. In total, 23 five-week winter campaigns and one comparative summer/ autumn campaign (2007-2009) were organized. The PM was analyzed for 61 chemical constituents and a data-base was built up consisting of approx. 70000 records of the concentrations and their associated uncertainty. In addition C-14/C-12 ratios were determined in PM10 from four sites. Primary and secondary sources were quantified using macro-tracer methods in combination with chemical mass balance modelling and positive matrix factorization and the combined results were computed by probability-and sensitivity analysis. Monte Carlo simulations yielded probability distributions for seven source categories contributing to the carbonaceous fraction of PM and five major source categories contributing to the PM10 and PM2.5 mass. Despite large uncertainties in the combined source contribution estimates the paper demonstrates that secondary aerosol formed simultaneously over the Po Plain is the main responsible for the typical, rapid build-up of air pollution after clean-air episodes. Next to secondary sources, the most important sources are primary emissions from road transport followed by biomass burning (BB). In the Valtelline Valley, higher contributions from BB and lower contributions from secondary sources were observed. (C) 2012 Elsevier Ltd. All rights reserved.}},
  author       = {{Larsen, B. R. and Gilardoni, S. and Stenström, Kristina and Niedzialek, J. and Jimenez, J. and Belis, C. A.}},
  issn         = {{1352-2310}},
  keywords     = {{Source apportionment; Receptor models; Macro-tracers; C-14; Carbonaceous; aerosol}},
  language     = {{eng}},
  pages        = {{203--213}},
  publisher    = {{Elsevier}},
  series       = {{Atmospheric Environment}},
  title        = {{Sources for PM air pollution in the Po Plain, Italy: II. Probabilistic uncertainty characterization and sensitivity analysis of secondary and primary sources}},
  url          = {{http://dx.doi.org/10.1016/j.atmosenv.2011.12.038}},
  doi          = {{10.1016/j.atmosenv.2011.12.038}},
  volume       = {{50}},
  year         = {{2012}},
}