Assessing the role of atmospheric dispersion vs. emission strength in the southern Po Valley (Italy) using dispersion-normalised multi-time receptor modelling.

Saved in:
Bibliographic Details
Title: Assessing the role of atmospheric dispersion vs. emission strength in the southern Po Valley (Italy) using dispersion-normalised multi-time receptor modelling.
Authors: Crova, Federica1 (AUTHOR), Forello, Alice Corina1,2 (AUTHOR), Bernardoni, Vera1 (AUTHOR), Calzolai, Giulia2,3 (AUTHOR), Canepari, Silvia4 (AUTHOR), Argentini, Stefania5 (AUTHOR), Costabile, Francesca5 (AUTHOR), Frezzini, Maria Agostina4 (AUTHOR), Giardi, Fabio2,3 (AUTHOR), Lucarelli, Franco2,3 (AUTHOR), Massabò, Dario6,7 (AUTHOR), Massimi, Lorenzo4 (AUTHOR), Nava, Silvia2,3 (AUTHOR), Paglione, Marco8 (AUTHOR), Pazzi, Giulia2,3 (AUTHOR), Prati, Paolo6,7 (AUTHOR), Rinaldi, Matteo8 (AUTHOR), Russo, Mara8 (AUTHOR), Valentini, Sara1 (AUTHOR), Valli, Gianluigi1 (AUTHOR)
Source: Atmospheric Environment. Jan2024, Vol. 316, pN.PAG-N.PAG. 1p.
Subject Terms: *Hot spots (Pollution), *Dust, *Pollution source apportionment, *Air pollution, *Particulate matter, *Mineral dusts, *Biomass burning, Carbonaceous aerosols, Dispersion (Chemistry)
Abstract: In this paper, we applied the Dispersion Normalised Positive Matrix Factorisation (DN-PMF) approach recently proposed in the literature to provide a more realistic picture of the relative importance of emission strength vs. atmospheric dispersion conditions. The disentanglement of such effects is of great concern in pollution hot spots like the Po Valley (Italy), where particulate matter limit values are exceeded despite the existing abatement measures. To explore the potentiality of the DN-PMF approach – still scarcely applied in the literature – a well-chemically characterised PM 1 (atmospheric particles with aerodynamic diameter <1 μm) dataset comprising samples collected at different time resolutions at an urban background site (Bologna) in the southern Po Valley was used. Indeed, it is well known that shallow mixing layers promote pollutant accumulation but this observation is not enough to exclude an enhancement of emission strength which could be tackled by appropriate abatement strategies. The source apportionment of sub-micron sized aerosols having a quite long atmospheric residence time in a complex environment like the Po Valley - which is also strongly impacted by secondary aerosol formation on a basin-scale - is generally quite challenging when using receptor models. Due to the availability of a huge dataset with variables having multiple time resolutions, in this work the DN-PMF was implemented in a multi-time resolution approach (MT) to achieve a better source identification and to gain knowledge about the relative importance of atmospheric dilution vs. emissions. A comparison between results obtained by the application of the regular multi time resolution (REG-MT) vs. the DN-MT approach is presented here for the five factors identified (nitrate-dominated, sulphate-dominated, biomass burning, mineral dust, and urban aerosol). The first interesting outcome is that REG-MT and DN-MT results do not point at significant differences in temporal patterns for aerosol components and sources impacting at the basin-scale (i.e. sulphate- and nitrate-dominated aerosol, biomass burning) thus suggesting that the diel modulation of these PM 1 emissions is somehow masked by the stronger variability of the mixing layer. Conversely, contributions from local sources with more pronounced diel variation like traffic are quite well reproduced by DN-MT and the ambient concentrations are enhanced compared to REG-MT. This is an important piece of information highlighting that PM 1 concentrations from local sources have been likely underestimated by REG-MT assessments. To our knowledge, this is one of the very few applications of DN-MT and the first one at a European site where the huge effort made to implement air pollution containment measures is still not very much effective in reducing PM levels; moreover, in this paper a detailed discussion about the possible interpretation of the output of DN-MT in terms of temporal patterns is reported. [Display omitted] • High PM levels are due to atmospheric stability and emission strength; DN-PMF points out the source emission role. • Secondary aerosol-dominated factors showed a regional nature. • Urban aerosol factor was highly enhanced by dispersion-normalisation. [ABSTRACT FROM AUTHOR]
Copyright of Atmospheric Environment is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: GreenFILE
FullText Text:
  Availability: 0
Header DbId: 8gh
DbLabel: GreenFILE
An: 173692350
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Assessing the role of atmospheric dispersion vs. emission strength in the southern Po Valley (Italy) using dispersion-normalised multi-time receptor modelling.
– Name: Author
  Label: Authors
  Group: Au
  Data: &lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Crova%2C+Federica%22&quot;&gt;Crova, Federica&lt;/searchLink&gt;&lt;relatesTo&gt;1&lt;/relatesTo&gt; (AUTHOR)&lt;br /&gt;&lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Forello%2C+Alice+Corina%22&quot;&gt;Forello, Alice Corina&lt;/searchLink&gt;&lt;relatesTo&gt;1,2&lt;/relatesTo&gt; (AUTHOR)&lt;br /&gt;&lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Bernardoni%2C+Vera%22&quot;&gt;Bernardoni, Vera&lt;/searchLink&gt;&lt;relatesTo&gt;1&lt;/relatesTo&gt; (AUTHOR)&lt;br /&gt;&lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Calzolai%2C+Giulia%22&quot;&gt;Calzolai, Giulia&lt;/searchLink&gt;&lt;relatesTo&gt;2,3&lt;/relatesTo&gt; (AUTHOR)&lt;br /&gt;&lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Canepari%2C+Silvia%22&quot;&gt;Canepari, Silvia&lt;/searchLink&gt;&lt;relatesTo&gt;4&lt;/relatesTo&gt; (AUTHOR)&lt;br /&gt;&lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Argentini%2C+Stefania%22&quot;&gt;Argentini, Stefania&lt;/searchLink&gt;&lt;relatesTo&gt;5&lt;/relatesTo&gt; (AUTHOR)&lt;br /&gt;&lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Costabile%2C+Francesca%22&quot;&gt;Costabile, Francesca&lt;/searchLink&gt;&lt;relatesTo&gt;5&lt;/relatesTo&gt; (AUTHOR)&lt;br /&gt;&lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Frezzini%2C+Maria+Agostina%22&quot;&gt;Frezzini, Maria Agostina&lt;/searchLink&gt;&lt;relatesTo&gt;4&lt;/relatesTo&gt; (AUTHOR)&lt;br /&gt;&lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Giardi%2C+Fabio%22&quot;&gt;Giardi, Fabio&lt;/searchLink&gt;&lt;relatesTo&gt;2,3&lt;/relatesTo&gt; (AUTHOR)&lt;br /&gt;&lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Lucarelli%2C+Franco%22&quot;&gt;Lucarelli, Franco&lt;/searchLink&gt;&lt;relatesTo&gt;2,3&lt;/relatesTo&gt; (AUTHOR)&lt;br /&gt;&lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Massab&#242;%2C+Dario%22&quot;&gt;Massab&#242;, Dario&lt;/searchLink&gt;&lt;relatesTo&gt;6,7&lt;/relatesTo&gt; (AUTHOR)&lt;br /&gt;&lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Massimi%2C+Lorenzo%22&quot;&gt;Massimi, Lorenzo&lt;/searchLink&gt;&lt;relatesTo&gt;4&lt;/relatesTo&gt; (AUTHOR)&lt;br /&gt;&lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Nava%2C+Silvia%22&quot;&gt;Nava, Silvia&lt;/searchLink&gt;&lt;relatesTo&gt;2,3&lt;/relatesTo&gt; (AUTHOR)&lt;br /&gt;&lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Paglione%2C+Marco%22&quot;&gt;Paglione, Marco&lt;/searchLink&gt;&lt;relatesTo&gt;8&lt;/relatesTo&gt; (AUTHOR)&lt;br /&gt;&lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Pazzi%2C+Giulia%22&quot;&gt;Pazzi, Giulia&lt;/searchLink&gt;&lt;relatesTo&gt;2,3&lt;/relatesTo&gt; (AUTHOR)&lt;br /&gt;&lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Prati%2C+Paolo%22&quot;&gt;Prati, Paolo&lt;/searchLink&gt;&lt;relatesTo&gt;6,7&lt;/relatesTo&gt; (AUTHOR)&lt;br /&gt;&lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Rinaldi%2C+Matteo%22&quot;&gt;Rinaldi, Matteo&lt;/searchLink&gt;&lt;relatesTo&gt;8&lt;/relatesTo&gt; (AUTHOR)&lt;br /&gt;&lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Russo%2C+Mara%22&quot;&gt;Russo, Mara&lt;/searchLink&gt;&lt;relatesTo&gt;8&lt;/relatesTo&gt; (AUTHOR)&lt;br /&gt;&lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Valentini%2C+Sara%22&quot;&gt;Valentini, Sara&lt;/searchLink&gt;&lt;relatesTo&gt;1&lt;/relatesTo&gt; (AUTHOR)&lt;br /&gt;&lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Valli%2C+Gianluigi%22&quot;&gt;Valli, Gianluigi&lt;/searchLink&gt;&lt;relatesTo&gt;1&lt;/relatesTo&gt; (AUTHOR)
– Name: TitleSource
  Label: Source
  Group: Src
  Data: &lt;searchLink fieldCode=&quot;JN&quot; term=&quot;%22Atmospheric+Environment%22&quot;&gt;Atmospheric Environment&lt;/searchLink&gt;. Jan2024, Vol. 316, pN.PAG-N.PAG. 1p.
– Name: Subject
  Label: Subject Terms
  Group: Su
  Data: *&lt;searchLink fieldCode=&quot;DE&quot; term=&quot;%22Hot+spots+%28Pollution%29%22&quot;&gt;Hot spots (Pollution)&lt;/searchLink&gt;&lt;br /&gt;*&lt;searchLink fieldCode=&quot;DE&quot; term=&quot;%22Dust%22&quot;&gt;Dust&lt;/searchLink&gt;&lt;br /&gt;*&lt;searchLink fieldCode=&quot;DE&quot; term=&quot;%22Pollution+source+apportionment%22&quot;&gt;Pollution source apportionment&lt;/searchLink&gt;&lt;br /&gt;*&lt;searchLink fieldCode=&quot;DE&quot; term=&quot;%22Air+pollution%22&quot;&gt;Air pollution&lt;/searchLink&gt;&lt;br /&gt;*&lt;searchLink fieldCode=&quot;DE&quot; term=&quot;%22Particulate+matter%22&quot;&gt;Particulate matter&lt;/searchLink&gt;&lt;br /&gt;*&lt;searchLink fieldCode=&quot;DE&quot; term=&quot;%22Mineral+dusts%22&quot;&gt;Mineral dusts&lt;/searchLink&gt;&lt;br /&gt;*&lt;searchLink fieldCode=&quot;DE&quot; term=&quot;%22Biomass+burning%22&quot;&gt;Biomass burning&lt;/searchLink&gt;&lt;br /&gt;&lt;searchLink fieldCode=&quot;DE&quot; term=&quot;%22Carbonaceous+aerosols%22&quot;&gt;Carbonaceous aerosols&lt;/searchLink&gt;&lt;br /&gt;&lt;searchLink fieldCode=&quot;DE&quot; term=&quot;%22Dispersion+%28Chemistry%29%22&quot;&gt;Dispersion (Chemistry)&lt;/searchLink&gt;
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: In this paper, we applied the Dispersion Normalised Positive Matrix Factorisation (DN-PMF) approach recently proposed in the literature to provide a more realistic picture of the relative importance of emission strength vs. atmospheric dispersion conditions. The disentanglement of such effects is of great concern in pollution hot spots like the Po Valley (Italy), where particulate matter limit values are exceeded despite the existing abatement measures. To explore the potentiality of the DN-PMF approach – still scarcely applied in the literature – a well-chemically characterised PM 1 (atmospheric particles with aerodynamic diameter &lt;1 μm) dataset comprising samples collected at different time resolutions at an urban background site (Bologna) in the southern Po Valley was used. Indeed, it is well known that shallow mixing layers promote pollutant accumulation but this observation is not enough to exclude an enhancement of emission strength which could be tackled by appropriate abatement strategies. The source apportionment of sub-micron sized aerosols having a quite long atmospheric residence time in a complex environment like the Po Valley - which is also strongly impacted by secondary aerosol formation on a basin-scale - is generally quite challenging when using receptor models. Due to the availability of a huge dataset with variables having multiple time resolutions, in this work the DN-PMF was implemented in a multi-time resolution approach (MT) to achieve a better source identification and to gain knowledge about the relative importance of atmospheric dilution vs. emissions. A comparison between results obtained by the application of the regular multi time resolution (REG-MT) vs. the DN-MT approach is presented here for the five factors identified (nitrate-dominated, sulphate-dominated, biomass burning, mineral dust, and urban aerosol). The first interesting outcome is that REG-MT and DN-MT results do not point at significant differences in temporal patterns for aerosol components and sources impacting at the basin-scale (i.e. sulphate- and nitrate-dominated aerosol, biomass burning) thus suggesting that the diel modulation of these PM 1 emissions is somehow masked by the stronger variability of the mixing layer. Conversely, contributions from local sources with more pronounced diel variation like traffic are quite well reproduced by DN-MT and the ambient concentrations are enhanced compared to REG-MT. This is an important piece of information highlighting that PM 1 concentrations from local sources have been likely underestimated by REG-MT assessments. To our knowledge, this is one of the very few applications of DN-MT and the first one at a European site where the huge effort made to implement air pollution containment measures is still not very much effective in reducing PM levels; moreover, in this paper a detailed discussion about the possible interpretation of the output of DN-MT in terms of temporal patterns is reported. [Display omitted] • High PM levels are due to atmospheric stability and emission strength; DN-PMF points out the source emission role. • Secondary aerosol-dominated factors showed a regional nature. • Urban aerosol factor was highly enhanced by dispersion-normalisation. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: &lt;i&gt;Copyright of Atmospheric Environment is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder&#39;s express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.&lt;/i&gt; (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=8gh&AN=173692350
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1016/j.atmosenv.2023.120168
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 1
        StartPage: N.PAG
    Subjects:
      – SubjectFull: Hot spots (Pollution)
        Type: general
      – SubjectFull: Dust
        Type: general
      – SubjectFull: Pollution source apportionment
        Type: general
      – SubjectFull: Air pollution
        Type: general
      – SubjectFull: Particulate matter
        Type: general
      – SubjectFull: Mineral dusts
        Type: general
      – SubjectFull: Biomass burning
        Type: general
      – SubjectFull: Carbonaceous aerosols
        Type: general
      – SubjectFull: Dispersion (Chemistry)
        Type: general
    Titles:
      – TitleFull: Assessing the role of atmospheric dispersion vs. emission strength in the southern Po Valley (Italy) using dispersion-normalised multi-time receptor modelling.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Crova, Federica
      – PersonEntity:
          Name:
            NameFull: Forello, Alice Corina
      – PersonEntity:
          Name:
            NameFull: Bernardoni, Vera
      – PersonEntity:
          Name:
            NameFull: Calzolai, Giulia
      – PersonEntity:
          Name:
            NameFull: Canepari, Silvia
      – PersonEntity:
          Name:
            NameFull: Argentini, Stefania
      – PersonEntity:
          Name:
            NameFull: Costabile, Francesca
      – PersonEntity:
          Name:
            NameFull: Frezzini, Maria Agostina
      – PersonEntity:
          Name:
            NameFull: Giardi, Fabio
      – PersonEntity:
          Name:
            NameFull: Lucarelli, Franco
      – PersonEntity:
          Name:
            NameFull: Massabò, Dario
      – PersonEntity:
          Name:
            NameFull: Massimi, Lorenzo
      – PersonEntity:
          Name:
            NameFull: Nava, Silvia
      – PersonEntity:
          Name:
            NameFull: Paglione, Marco
      – PersonEntity:
          Name:
            NameFull: Pazzi, Giulia
      – PersonEntity:
          Name:
            NameFull: Prati, Paolo
      – PersonEntity:
          Name:
            NameFull: Rinaldi, Matteo
      – PersonEntity:
          Name:
            NameFull: Russo, Mara
      – PersonEntity:
          Name:
            NameFull: Valentini, Sara
      – PersonEntity:
          Name:
            NameFull: Valli, Gianluigi
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 01
              Text: Jan2024
              Type: published
              Y: 2024
          Identifiers:
            – Type: issn-print
              Value: 13522310
          Numbering:
            – Type: volume
              Value: 316
          Titles:
            – TitleFull: Atmospheric Environment
              Type: main
ResultId 1