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  1. AU="Muwu Xu"
  2. AU="Sacchi, Diana"
  3. AU="Romain Berraud-Pache"
  4. AU=Johnson Paul D R
  5. AU="Sarigül-Yildirim, Figen"
  6. AU=Chang Yu-Ting
  7. AU="Xu, Ivana"
  8. AU="Linde, Lauren"
  9. AU="Brewer, Katlyn K"
  10. AU="Prow, Natalie A"
  11. AU=Venkatesan Arun
  12. AU="Russcher, H."
  13. AU="Chambino, Beatriz"
  14. AU="L'Abbé, Ericka N."
  15. AU=Moore Stephen M.
  16. AU="Gabriel, Berteșteanu Șerban Vifor" AU="Gabriel, Berteșteanu Șerban Vifor"
  17. AU="Gallo, Eduado"
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  20. AU="Rajakumar, Gopal Suseela" AU="Rajakumar, Gopal Suseela"
  21. AU="Gutierrez, M. N"
  22. AU=Zhuo Jia L.
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  27. AU="Berns, Lauren"
  28. AU="Elena A. Deshevaya"
  29. AU=Zhang Ruijuan
  30. AU="Mueller, Luke"
  31. AU=Barzon Luisa
  32. AU="Karunakaran, Denuja"
  33. AU="Figueroa-Rivera, Ivonne M"
  34. AU="Blackburn, Fran"
  35. AU="Lee, Hee-Kyung"
  36. AU=Kinoshita J H
  37. AU="Hernesniemi, Juha"
  38. AU="Evans, Matthew L"
  39. AU=Payne Thomas
  40. AU="Brown, Dexter"

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  1. Artikel ; Online: Estimating the Impact of COVID-19 on the PM 2.5 Levels in China with a Satellite-Driven Machine Learning Model

    Qiulun Li / Qingyang Zhu / Muwu Xu / Yu Zhao / K. M. Venkat Narayan / Yang Liu

    Remote Sensing, Vol 13, Iss 1351, p

    2021  Band 1351

    Abstract: China implemented an aggressive nationwide lockdown procedure immediately after the COVID-19 outbreak in January 2020. As China emerges from the impact of COVID-19 on national economic and industrial activities, it has become the site of a large-scale ... ...

    Abstract China implemented an aggressive nationwide lockdown procedure immediately after the COVID-19 outbreak in January 2020. As China emerges from the impact of COVID-19 on national economic and industrial activities, it has become the site of a large-scale natural experiment to evaluate the impact of COVID-19 on regional air quality. However, ground measurements of fine particulate matters (PM 2.5 ) concentrations do not offer comprehensive spatial coverage, especially in suburban and rural regions. In this study, we developed a machine learning method with satellite aerosol remote sensing data, meteorological fields and land use parameters as major predictor variables to estimate spatiotemporally resolved daily PM 2.5 concentrations in China. Our study period consists of a reference semester (1 November 2018–30 April 2019) and a pandemic semester (1 November 2019–30 April 2020), with six modeling months in each semester. Each period was then divided into subperiod 1 (November and December), subperiod 2 (January and February) and subperiod 3 (March and April). The reference semester model obtained a 10-fold cross-validated R 2 (RMSE) of 0.79 (17.55 μg/m 3 ) and the pandemic semester model obtained a 10-fold cross-validated R 2 (RMSE) of 0.83 (13.48 μg/m 3 ) for daily PM 2.5 predictions. Our prediction results showed high PM 2.5 concentrations in the North China Plain, Yangtze River Delta, Sichuan Basin and Xinjiang Autonomous Region during the reference semester. PM 2.5 levels were lowered by 4.8 μg/m 3 during the pandemic semester compared to the reference semester and PM 2.5 levels during subperiod 2 decreased most, by 18%. The southeast region was affected most by the COVID-19 outbreak with PM 2.5 levels during subperiod 2 decreasing by 31%, followed by the Northern Yangtze River Delta (29%) and Pearl River Delta (24%).
    Schlagwörter PM 2.5 ; air pollution ; COVID-19 ; MAIAC AOD ; random forest ; machine learning ; Science ; Q
    Thema/Rubrik (Code) 333
    Sprache Englisch
    Erscheinungsdatum 2021-04-01T00:00:00Z
    Verlag MDPI AG
    Dokumenttyp Artikel ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  2. Artikel ; Online: Characteristics of school children's personal exposure to ultrafine particles in Heshan, Pearl River Delta, China – A pilot study

    Mandana Mazaheri / Weiwei Lin / Samuel Clifford / Dingli Yue / Yuhong Zhai / Muwu Xu / Valeria Rizza / Lidia Morawska

    Environment International, Vol 132, Iss , Pp - (2019)

    2019  

    Abstract: Background: There is a significant lack of scientific knowledge on population exposure to ultrafine particles (UFP) in China to date. This paper quantifies and characterises school children's personal UFP exposure and exposure intensity against their ... ...

    Abstract Background: There is a significant lack of scientific knowledge on population exposure to ultrafine particles (UFP) in China to date. This paper quantifies and characterises school children's personal UFP exposure and exposure intensity against their indoor and outdoor activities during a school day (home, school and commuting) in the city of Heshan within the Pearl River Delta (PRD) region, southern China. Methods: Time-series of UFP number concentrations and average size were measured over 24 h for 24 children (9–13 years old), using personal monitors over two weeks in April 2016. Time-activity diaries and a questionnaire on the general home environment and potential sources of particles at home were also collected for each participating child. The analysis included concurrently measured size distributions of ambient UFP at a nearby fixed reference site (Heshan Supersite). Results: Hourly average UFP concentrations exhibited three peaks in the morning, midday and evening. Time spent indoors at home was found to have the highest average exposure (1.26 × 104 cm−3 during sleeping) and exposure intensity (2.41). While there is always infiltration of outdoor particles indoors (from nearby traffic and general urban background sources), indoor exposure at home was significantly higher than outdoor exposure. Based on the collected questionnaire data, this was considered to be driven predominantly by adults smoking and the use of mosquito repellent incense during the night. Outdoor activities at school were associated with the lowest average exposure (6.87 × 102 cm−3) and exposure intensity (0.52). Conclusion: Despite the small sample size, this study characterised, for the first time, children's personal UFP exposure in a city downwind of major pollution sources of the PRD region in China. Particularly, the results highlighted the impact of smoking at home on children's exposure. While the study could not apportion the specific contributions of second hand-smoking and mosquito coil burning, considering the prevalence ...
    Schlagwörter Environmental sciences ; GE1-350
    Thema/Rubrik (Code) 333
    Sprache Englisch
    Erscheinungsdatum 2019-11-01T00:00:00Z
    Verlag Elsevier
    Dokumenttyp Artikel ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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