@article{,
    author = {Liu, Xiansheng; Hadiatullah, Hadiatullah; Khedr, Mohamed; Zhang, Xun; Schnelle-Kreis, Jürgen; Zimmermann, Ralf; Adam, Thomas},
    title = {Personal exposure to various size fractions of ambient particulate matter during the heating and non-heating periods using mobile monitoring approach : A case study in Augsburg, Germany},
    editor = {},
    booktitle = {},
    series = {},
    journal = {Atmospheric Pollution Research},
    address = {},
    publisher = {},
    edition = {},
    year = {2022},
    isbn = {},
    volume = {13},
    number =  {7},
    pages = {101483},
    url = {https://doi.org/10.1016/j.apr.2022.101483},
    doi = {10.1016/j.apr.2022.101483},
    keywords = {aerosol ; personal exposure},
    abstract = {In this study, the exposure to ambient particulate matter metrics (PM1, PM2.5, PM10, black carbon (BC), brown carbon (BrC), ultraviolet particulate matter (UVPM), particle number concentration (PNC), and lung deposited surface area (LDSA)) were measured along a fixed walking route with specific focus on three typical micro-environments (park, central business district (CBD), and traffic) in different time of day during the non-heating (May–Oct.) and heating (Nov.–Apr.2nd year) periods from 2018 to 2020 in the downtown Augsburg, Germany. The spatio-temporal exposure to ambient PM metrics exhibited substantial heterogeneity during the observation period, with park environment having lowest exposure and traffic area having the highest exposure. Generally, the higher LDSA concentrations were found in traffic area and CBD during the observation periods, while the lower concentrations were found in the park, which is similar with other ambient PM metrics (PMX, eUVPM, eBC, and PNC). The correlations between LDSA and other ambient PM metrics were higher during the heating than non-heating period in most of investigated environments, indicating the different PM sources. Overall, this study provides a comprehensive assessment of personal exposure that complements fixed-site ambient PM metrics measurements in the context of health risk assessment and epidemiological studies.},
    note = {},
    institution = {Universität der Bundeswehr München, Fakultät für Maschinenbau, MB 6 - Institut für Chemie und Umwelttechnik, Professur: Adam, Thomas},
    
    
}