@article{fdi:010091032,
  title = {{U}nveiling the optimal regression model for source apportionment of the oxidative potential of {PM}10},
  author = {{T}huy, {V}. {D}. {N}. and {J}affrezo, {J}. {L}. and {H}ough, {I}. and {D}ominutti, {P}. {A}. and {M}oreton, {G}. {S}. and {G}ille, {G}. and {F}rancony, {F}. and {P}atron-{A}nquez, {A}. and {F}avez, {O}. and {U}zu, {G}a{\¨e}lle},
  editor = {},
  language = {{ENG}},
  abstract = {{T}he capacity of particulate matter ({PM}) to generate reactive oxygen species ({ROS}) in vivo leading to oxidative stress is thought to be a main pathway in the health effects of {PM} inhalation. {E}xogenous {ROS} from {PM} can be assessed by acellular oxidative potential ({OP}) measurements as a proxy of the induction of oxidative stress in the lungs. {H}ere, we investigate the importance of {OP} apportionment methods for {OP} distribution by {PM}10 sources in different types of environments. {PM}10 sources derived from receptor models (e.g., {EPA} positive matrix factorization ({EPA} {PMF})) are coupled with regression models expressing the associations between {PM}10 sources and {PM}10 {OP} measured by ascorbic acid ({OPAA}) and dithiothreitol assay ({OPDTT}). {T}hese relationships are compared for eight regression techniques: ordinary least squares, weighted least squares, positive least squares, {R}idge, {L}asso, generalized linear model, random forest, and multilayer perceptron. {T}he models are evaluated on 1 year of {PM}10 samples and chemical analyses at each of six sites of different typologies in {F}rance to assess the possible impact of {PM} source variability on {PM}10 {OP} apportionment. {PM}10 source-specific {OPDTT} and {OPAA} and out-of-sample apportionment accuracy vary substantially by model, highlighting the importance of model selection according to the datasets. {R}ecommendations for the selection of the most accurate model are provided, encompassing considerations such as multicollinearity and homoscedasticity.},
  keywords = {{FRANCE}},
  booktitle = {},
  journal = {{A}tmospheric {C}hemistry and {P}hysics},
  volume = {24},
  numero = {12},
  pages = {7261--7282},
  ISSN = {1680-7316},
  year = {2024},
  DOI = {10.5194/acp-24-7261-2024},
  URL = {https://www.documentation.ird.fr/hor/fdi:010091032},
}