Estimating elemental composition of personal PM 2.5 by a modeling approach in two megacities, China
Personal exposure to PM , and the elemental composition therein, may vary greatly from ambient measurements at fixed monitoring sites. Here, we characterized the differences between personal, indoor, and outdoor concentrations of PM -bound elements, and predicted personal exposures to 21 PM -bound e...
Gespeichert in:
Veröffentlicht in: | The Science of the total environment 2023-09, Vol.892, p.164751 |
---|---|
Hauptverfasser: | , , , , , , , , , |
Format: | Artikel |
Sprache: | eng |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
Zusammenfassung: | Personal exposure to PM
, and the elemental composition therein, may vary greatly from ambient measurements at fixed monitoring sites. Here, we characterized the differences between personal, indoor, and outdoor concentrations of PM
-bound elements, and predicted personal exposures to 21 PM
-bound elements. Personal-indoor-outdoor PM
filter samples were collected for five consecutive days across two seasons from 66 healthy non-smoking retired adults in Beijing (BJ) and Nanjing (NJ), China. Personal element-specific models were developed using liner mixed effects models and evaluated by R
and root mean square error (RMSE). The mean (SD) concentrations of personal exposures varied by element and city, ranging from 2.5 (1.4) ng/m
for Ni in BJ to 4271.2 (1614.8) ng/m
for S in NJ. Personal exposures to PM
and most elements were significantly correlated with both indoor and outdoor (except Ni in BJ) measurements, but frequently exceeded indoor levels and fell below outdoor levels. Indoor and outdoor PM
elemental concentrations were the strongest determinants of most personal elemental exposures, with R
ranging from 0.074 to 0.975 for indoor and from 0.078 to 0.917 for outdoor levels, respectively. Home ventilation conditions (especially window opening behavior), time-activity patterns, meteorological factors, household characteristics, and season were also key factors influencing personal exposure levels. The final models accounted for 24.2 %-94.0 % (RMSE: 0.135-0.718) of the variance in personal PM
elemental exposures. By incorporating these crucial determinants, the modeling approach used here can improve PM
-bound elemental exposure estimates and better associate compositionally dependent PM
exposures and health risks. |
---|---|
ISSN: | 1879-1026 |