Submitted as: model experiment description paper 16 Feb 2021

Submitted as: model experiment description paper | 16 Feb 2021

Review status: a revised version of this preprint is currently under review for the journal GMD.

Comparison of source apportionment approaches and analysis of non-linearity in a real case model application

Claudio A. Belis1, Guido Pirovano2, Maria Gabriella Villani3, Giuseppe Calori4, Nicola Pepe4, and Jean Philippe Putaud1 Claudio A. Belis et al.
  • 1European Commission, Joint Research Centre, via Fermi 2748, 21027 Ispra (VA), Italy
  • 2RSE Spa, via Rubattino 54, 20134, Milan, Italy
  • 3ENEA Laboratory of Atmospheric Pollution, via Fermi 2748, 21027 Ispra (VA), Italy
  • 4ARIANET s.r.l. via Gilino, 9 - 20128 Milan (MI) – Italy

Abstract. The response of particulate matter (PM) concentrations to emission reductions was analysed by assessing the results obtained with two different source apportionment approaches. The brute force (BF) method source impacts, computed at various emission reduction levels using two chemical transport models (CAMx and FARM), were compared with the contributions obtained with the tagged species (TS) approach (CAMx with PSAT module). The study focused on the main sources of secondary inorganic aerosol precursors in the Po Valley (Northern Italy): agriculture, road transport, industry and residential combustion. The interaction terms between different sources obtained from a factor decomposition analysis were used as indicators of non-linear PM10 concentration responses to individual source emission reductions. Moreover, such interaction terms were analysed in the light of the free ammonia/total nitrate gas ratio to determine the relationships between the chemical regime and the non-linearity at selected sites. The impacts of the different sources were not proportional to the emission reductions and such non-linearity was most relevant for 100 % emission reduction levels compared with smaller reduction levels (50 % and 20 %). Such differences between emission reduction levels were connected to the extent to which they modify the chemical regime in the base case. Non-linearity was mainly associated with agriculture and the interaction of this source with road transport and, to a lesser extent, with industry. Actually, the mass concentration of PM10 allocated to agriculture by TS and BF approaches were significantly different when a 100 % emission reduction was applied. However, in many situations the non-linearity in PM10 annual average source allocation was negligible and the TS and the BF approaches provided comparable results. PM mass concentrations attributed to the same sources by TS and BF were highly comparable in terms of spatial patterns and quantification of the source allocation for industry, transport and residential combustion. The conclusions obtained in this study for PM10 are also applicable to PM2.5.

Claudio A. Belis et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2020-410 --> very good comparison', Richard Kranenburg, 24 Mar 2021
  • RC2: 'Comment on gmd-2020-410', Anonymous Referee #2, 21 Apr 2021
  • AC1: 'Comment on gmd-2020-410', Claudio Belis, 19 May 2021

Claudio A. Belis et al.

Data sets

PM10 scenarios in Northern Italy Belis, C. A., Pirovano, G., Villani, M. G., Calori, G., and Pepe, N.

Model code and software

PM10 scenarios in Northern Italy Belis, C. A., Pirovano, G., Villani, M. G., Calori, G., and Pepe, N.

Claudio A. Belis et al.


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Short summary
The study presents an in-depth analysis of the implications that using different CTM source apportionment approaches (tagged species and brute force) have on the source allocation of secondary inorganic aerosol, an important component of PM10 and PM2.5. A set of runs combining different emissions levels and models were carried out aiming to describe the situations in which strong non-linearity may lead the two approaches to deliver different results and when they are expected to be comparable.