Aerosol source apportionment uncertainty linked to the choice of input chemical components
For a Positive Matrix Factorization (PMF) aerosol source apportionment (SA) studies there is no standard procedure to select the most appropriate chemical components to be included in the input dataset for a given site typology, nor specific recommendations in this direction. However, these choices...
| Main Authors: | , , , , , , , , , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024-02-01
|
| Series: | Environment International |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S0160412024000278 |
| _version_ | 1827346867645579264 |
|---|---|
| author | F. Amato B.L. van Drooge J.L. Jaffrezo O. Favez C. Colombi E. Cuccia C. Reche F. Ippolito S. Ridolfo R. Lara G. Uzu T.V.D. Ngoc P. Dominutti S. Darfeuil A. Albinet D. Srivastava A. Karanasiou G. Lanzani A. Alastuey X. Querol |
| author_facet | F. Amato B.L. van Drooge J.L. Jaffrezo O. Favez C. Colombi E. Cuccia C. Reche F. Ippolito S. Ridolfo R. Lara G. Uzu T.V.D. Ngoc P. Dominutti S. Darfeuil A. Albinet D. Srivastava A. Karanasiou G. Lanzani A. Alastuey X. Querol |
| author_sort | F. Amato |
| collection | DOAJ |
| description | For a Positive Matrix Factorization (PMF) aerosol source apportionment (SA) studies there is no standard procedure to select the most appropriate chemical components to be included in the input dataset for a given site typology, nor specific recommendations in this direction. However, these choices are crucial for the final SA outputs not only in terms of number of sources identified but also, and consequently, in the source contributions estimates. In fact, PMF tends to reproduce most of PM mass measured independently and introduced as a total variable in the input data, regardless of the percentage of PM mass which has been chemically characterized, so that the lack of some specific source tracers (e.g. levoglucosan) can potentially affect the results of the whole source apportionment study. The present study elaborates further on the same concept, evaluating quantitatively the impact of lacking specific sources’ tracers on the whole source apportionment, both in terms of identified sources and source contributions. This work aims to provide first recommendations on the most suitable and critical components to be included in PMF analyses in order to reduce PMF output uncertainty as much as possible, and better represent the most commons PM sources observed in many sites in Western countries. To this aim, we performed three sensitivity analyses on three different datasets across EU, including extended sets of organic tracers, in order to cover different types of urban conditions (Mediterranean, Continental, and Alpine), source types, and PM fractions. Our findings reveal that the vehicle exhaust source resulted to be less sensitive to the choice of analytes, although source contributions estimates can deviate significantly up to 44 %. On the other hand, for the detection of the non-exhaust one is clearly necessary to analyze specific inorganic elements. The choice of not analysing non-polar organics likely causes the loss of separation of exhaust and non-exhaust factors, thus obtaining a unique road traffic source, which provokes a significant bias of total contribution. Levoglucosan was, in most cases, crucial to identify biomass burning contributions in Milan and in Barcelona, in spite of the presence of PAHs in Barcelona, while for the case of Grenoble, even discarding levoglucosan, the presence of PAHs allowed identifying the BB factor. Modifying the rest of analytes provoke a systematic underestimation of biomass burning source contributions. SIA factors resulted to be generally overestimated with respect to the base case analysis, also in the case that ions were not included in the PMF analysis. Trace elements were crucial to identify shipping emissions (V and Ni) and industrial sources (Pb, Ni, Br, Zn, Mn, Cd and As). When changing the rest of input variables, the uncertainty was narrow for shipping but large for industrial processes. Major and trace elements were also crucial to identify the mineral/soil factor at all cities. Biogenic SOA and Anthropogenic SOA factors were sensitive to the presence of their molecular tracers, since the availability of OC alone is unable to separate a SOA factor. Arabitol and sorbitol were crucial to detecting fungal spores while odd number of higher alkanes (C27 to C31) for plant debris. |
| first_indexed | 2024-03-07T23:38:58Z |
| format | Article |
| id | doaj.art-0f55379985de4fcdb55523d75dc40ae6 |
| institution | Directory Open Access Journal |
| issn | 0160-4120 |
| language | English |
| last_indexed | 2024-03-07T23:38:58Z |
| publishDate | 2024-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Environment International |
| spelling | doaj.art-0f55379985de4fcdb55523d75dc40ae62024-02-20T04:18:19ZengElsevierEnvironment International0160-41202024-02-01184108441Aerosol source apportionment uncertainty linked to the choice of input chemical componentsF. Amato0B.L. van Drooge1J.L. Jaffrezo2O. Favez3C. Colombi4E. Cuccia5C. Reche6F. Ippolito7S. Ridolfo8R. Lara9G. Uzu10T.V.D. Ngoc11P. Dominutti12S. Darfeuil13A. Albinet14D. Srivastava15A. Karanasiou16G. Lanzani17A. Alastuey18X. Querol19Institute of Environmental Assessment and Water Research (IDAEA), Spanish National Research Council (CSIC), 08034 Barcelona, Spain; Corresponding author.Institute of Environmental Assessment and Water Research (IDAEA), Spanish National Research Council (CSIC), 08034 Barcelona, SpainUniv. Grenoble Alpes, CNRS, IRD, Grenoble INP, INRAE, IGE, 38000 Grenoble, FranceInstitut national de l’environnement industriel et des risques (Ineris), 60550 Verneuil en Halatte, FranceEnvironmental Monitoring Sector, Arpa Lombardia, Via Rosellini 17, Milan, 20124, ItalyEnvironmental Monitoring Sector, Arpa Lombardia, Via Rosellini 17, Milan, 20124, ItalyInstitute of Environmental Assessment and Water Research (IDAEA), Spanish National Research Council (CSIC), 08034 Barcelona, SpainInstitute of Environmental Assessment and Water Research (IDAEA), Spanish National Research Council (CSIC), 08034 Barcelona, SpainInstitute of Environmental Assessment and Water Research (IDAEA), Spanish National Research Council (CSIC), 08034 Barcelona, SpainInstitute of Environmental Assessment and Water Research (IDAEA), Spanish National Research Council (CSIC), 08034 Barcelona, SpainUniv. Grenoble Alpes, CNRS, IRD, Grenoble INP, INRAE, IGE, 38000 Grenoble, FranceUniv. Grenoble Alpes, CNRS, IRD, Grenoble INP, INRAE, IGE, 38000 Grenoble, FranceUniv. Grenoble Alpes, CNRS, IRD, Grenoble INP, INRAE, IGE, 38000 Grenoble, FranceUniv. Grenoble Alpes, CNRS, IRD, Grenoble INP, INRAE, IGE, 38000 Grenoble, FranceInstitut national de l’environnement industriel et des risques (Ineris), 60550 Verneuil en Halatte, FranceInstitut national de l’environnement industriel et des risques (Ineris), 60550 Verneuil en Halatte, FranceInstitute of Environmental Assessment and Water Research (IDAEA), Spanish National Research Council (CSIC), 08034 Barcelona, SpainEnvironmental Monitoring Sector, Arpa Lombardia, Via Rosellini 17, Milan, 20124, ItalyInstitute of Environmental Assessment and Water Research (IDAEA), Spanish National Research Council (CSIC), 08034 Barcelona, SpainInstitute of Environmental Assessment and Water Research (IDAEA), Spanish National Research Council (CSIC), 08034 Barcelona, SpainFor a Positive Matrix Factorization (PMF) aerosol source apportionment (SA) studies there is no standard procedure to select the most appropriate chemical components to be included in the input dataset for a given site typology, nor specific recommendations in this direction. However, these choices are crucial for the final SA outputs not only in terms of number of sources identified but also, and consequently, in the source contributions estimates. In fact, PMF tends to reproduce most of PM mass measured independently and introduced as a total variable in the input data, regardless of the percentage of PM mass which has been chemically characterized, so that the lack of some specific source tracers (e.g. levoglucosan) can potentially affect the results of the whole source apportionment study. The present study elaborates further on the same concept, evaluating quantitatively the impact of lacking specific sources’ tracers on the whole source apportionment, both in terms of identified sources and source contributions. This work aims to provide first recommendations on the most suitable and critical components to be included in PMF analyses in order to reduce PMF output uncertainty as much as possible, and better represent the most commons PM sources observed in many sites in Western countries. To this aim, we performed three sensitivity analyses on three different datasets across EU, including extended sets of organic tracers, in order to cover different types of urban conditions (Mediterranean, Continental, and Alpine), source types, and PM fractions. Our findings reveal that the vehicle exhaust source resulted to be less sensitive to the choice of analytes, although source contributions estimates can deviate significantly up to 44 %. On the other hand, for the detection of the non-exhaust one is clearly necessary to analyze specific inorganic elements. The choice of not analysing non-polar organics likely causes the loss of separation of exhaust and non-exhaust factors, thus obtaining a unique road traffic source, which provokes a significant bias of total contribution. Levoglucosan was, in most cases, crucial to identify biomass burning contributions in Milan and in Barcelona, in spite of the presence of PAHs in Barcelona, while for the case of Grenoble, even discarding levoglucosan, the presence of PAHs allowed identifying the BB factor. Modifying the rest of analytes provoke a systematic underestimation of biomass burning source contributions. SIA factors resulted to be generally overestimated with respect to the base case analysis, also in the case that ions were not included in the PMF analysis. Trace elements were crucial to identify shipping emissions (V and Ni) and industrial sources (Pb, Ni, Br, Zn, Mn, Cd and As). When changing the rest of input variables, the uncertainty was narrow for shipping but large for industrial processes. Major and trace elements were also crucial to identify the mineral/soil factor at all cities. Biogenic SOA and Anthropogenic SOA factors were sensitive to the presence of their molecular tracers, since the availability of OC alone is unable to separate a SOA factor. Arabitol and sorbitol were crucial to detecting fungal spores while odd number of higher alkanes (C27 to C31) for plant debris.http://www.sciencedirect.com/science/article/pii/S0160412024000278Sensitivity analysisPMFIonsElementsCarbonMolecular markers |
| spellingShingle | F. Amato B.L. van Drooge J.L. Jaffrezo O. Favez C. Colombi E. Cuccia C. Reche F. Ippolito S. Ridolfo R. Lara G. Uzu T.V.D. Ngoc P. Dominutti S. Darfeuil A. Albinet D. Srivastava A. Karanasiou G. Lanzani A. Alastuey X. Querol Aerosol source apportionment uncertainty linked to the choice of input chemical components Environment International Sensitivity analysis PMF Ions Elements Carbon Molecular markers |
| title | Aerosol source apportionment uncertainty linked to the choice of input chemical components |
| title_full | Aerosol source apportionment uncertainty linked to the choice of input chemical components |
| title_fullStr | Aerosol source apportionment uncertainty linked to the choice of input chemical components |
| title_full_unstemmed | Aerosol source apportionment uncertainty linked to the choice of input chemical components |
| title_short | Aerosol source apportionment uncertainty linked to the choice of input chemical components |
| title_sort | aerosol source apportionment uncertainty linked to the choice of input chemical components |
| topic | Sensitivity analysis PMF Ions Elements Carbon Molecular markers |
| url | http://www.sciencedirect.com/science/article/pii/S0160412024000278 |
| work_keys_str_mv | AT famato aerosolsourceapportionmentuncertaintylinkedtothechoiceofinputchemicalcomponents AT blvandrooge aerosolsourceapportionmentuncertaintylinkedtothechoiceofinputchemicalcomponents AT jljaffrezo aerosolsourceapportionmentuncertaintylinkedtothechoiceofinputchemicalcomponents AT ofavez aerosolsourceapportionmentuncertaintylinkedtothechoiceofinputchemicalcomponents AT ccolombi aerosolsourceapportionmentuncertaintylinkedtothechoiceofinputchemicalcomponents AT ecuccia aerosolsourceapportionmentuncertaintylinkedtothechoiceofinputchemicalcomponents AT creche aerosolsourceapportionmentuncertaintylinkedtothechoiceofinputchemicalcomponents AT fippolito aerosolsourceapportionmentuncertaintylinkedtothechoiceofinputchemicalcomponents AT sridolfo aerosolsourceapportionmentuncertaintylinkedtothechoiceofinputchemicalcomponents AT rlara aerosolsourceapportionmentuncertaintylinkedtothechoiceofinputchemicalcomponents AT guzu aerosolsourceapportionmentuncertaintylinkedtothechoiceofinputchemicalcomponents AT tvdngoc aerosolsourceapportionmentuncertaintylinkedtothechoiceofinputchemicalcomponents AT pdominutti aerosolsourceapportionmentuncertaintylinkedtothechoiceofinputchemicalcomponents AT sdarfeuil aerosolsourceapportionmentuncertaintylinkedtothechoiceofinputchemicalcomponents AT aalbinet aerosolsourceapportionmentuncertaintylinkedtothechoiceofinputchemicalcomponents AT dsrivastava aerosolsourceapportionmentuncertaintylinkedtothechoiceofinputchemicalcomponents AT akaranasiou aerosolsourceapportionmentuncertaintylinkedtothechoiceofinputchemicalcomponents AT glanzani aerosolsourceapportionmentuncertaintylinkedtothechoiceofinputchemicalcomponents AT aalastuey aerosolsourceapportionmentuncertaintylinkedtothechoiceofinputchemicalcomponents AT xquerol aerosolsourceapportionmentuncertaintylinkedtothechoiceofinputchemicalcomponents |