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Understanding benzene series (BTEX) pollutant load characteristics in the urban environment
An Liu1, Nian Hong2, Panfeng Zhu3
1College of Chemistry and Environmental Engineering, Shenzhen University, 518060 Shenzhen, China; Shenzhen Key Laboratory of Environmental Chemistry and Ecological Remediation, 518060 Shenzhen, China; Science and Engineering Faculty, Queensland University of Technology (QUT), Brisbane, Australia.
Abstract:
Benzene series (BTEX) pollutants are toxic and can pose high ecological and human health risk. BTEX pollutants can accumulate on urban road surfaces during dry periods and then be washed-off by stormwater runoff into receiving waters, degrading water quality. In this context, designing effective stormwater treatment systems to remove BTEX pollutants before entering urban water bodies is essential to safeguard urban water environment and this is closely dependent on an in-depth understanding of characteristics of pollutant loads accumulated on urban catchment surfaces. This study investigated BTEX pollutant load characteristics through collecting samples on 17 urban roads and three petrol station sites using dry and wet vacuuming method. The research outcomes showed that petrol station sites had the highest BTEX pollutant loads (mean value was 8.41μg/g) than common urban roads (6.61μg/g, 4.38μg/g and 4.60μg/g for industrial, commercial and residential roads). This highlighted a high potential of petrol station areas to export BTEX pollutants to stormwater runoff. It is also noted that industrial development plays the more important role in influencing BTEX pollutant loads than other factors including total daily traffic volume, daily heavy-duty vehicle volume, daily light-duty vehicle volume, residential development, commercial development and road texture depth. Higher industrial land use fraction led to higher BTEX pollutant loads while the spatial variability of BTEX pollutant loads generally decreased with increasing industrial land use fraction. These outcomes can assist on improving stormwater quality modelling approaches and adequately understanding the modelling results.
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