Citation

Liu Z, Yin H, Ma S, Jin G, Gao J, Ding W. Environ. Pollut. 2019; 247: 626-637.

Affiliation

Laboratory of Environment and Health, College of Life Sciences, University of Chinese Academy of Sciences, No.19A Yuquan Road, Beijing, 100049, China.

Copyright

(Copyright © 2019, Elsevier Publishing)

DOI

10.1016/j.envpol.2019.01.095

PMID

30711818

Abstract

There have been an increasing number of automobile vehicles in cities, so that newly developed residential areas are mostly designed with underground parking garages (UPGs). For naturally ventilated UPGs, the ventilation performance may be insufficient to discharge totally vehicle-induced pollutants out of the enclosed underground spaces, which consequently results in threats to residents' health. This study, therefore, aims at examining the patterns of pollutant concentrations in naturally ventilation UPGs as well as their sensitivities to traffic volume. In particular, the naturally ventilated UPGs' weekday particulate matters (PM_2.5 and PM₁₀), CO₂ and TVOC concentration as well as their relationships between traffic volume were quantitively evaluated based on field measurements in eight residential areas in Baoding, China.

RESULTS indicated that daily average PM_2.5, PM₁₀, CO₂ and TVOC concentrations in studied UPGs were 105.81 μg/m³, 464.17 μg/m³, 571 ppm and 24 ppb, respectively. The PM_2.5 concentrations in UPGs were slightly higher than that in ambient environments, while the PM₁₀ concentrations in UPGs were significantly higher. Furthermore, both PM₁₀ and TVOC concentrations in UPGs were in significant relationships with traffic volume at the p < 0.01 level, while the concentration of UPG PM_2.5 generally exhibited a significant correlation (p < 0.01) with that of the ambient. Nevertheless, a combination of traffic volume, the ambient and accumulative effect was much better to explain the hourly PM₁₀ concentration in UPGs. These findings will be conducive to instruct engineers with fundamental knowledge of UPG ventilation design.

Copyright © 2019 Elsevier Ltd. All rights reserved.

Language: en

Keywords

Correlation analysis; Daily variation characteristics; Indoor pollutant concentrations; Multiple regression model; Natural ventilation; Underground parking garage