Pollutant concentrations and exposure variability in four urban microenvironments of London
We compared various pollutant concentrations (PM1, PM2.5, PM10, PNC, BC) at four different urban microenvironments (MEs) in London (Indoor, IN; Traffic Intersection, TI; Park, PK; and Street Canyon, SC). The physico-chemical characteristics of particles were analysed, and the respiratory deposition doses (RDD) were estimated. Field measurements were conducted over a period of 121 days. The mean PM2.5 (PNC) concentrations were found to be 9.47 ± 7.05 (16366 ± 11815), 8.09 ± 4.57 (10951 ± 6445), 5.11 ± 2.96 (7717 ± 4576), 3.88 ± 3.06 (5672 ± 2934) μg m−3 (# cm−3) at TI, SC, PK and IN, respectively. PM2.5, PM10 and PNC exhibited a trend of TI > SC > PK > IN; higher concentrations for PM1 and BC were observed at IN than PK due to the emissions from printers, producing a trend of TI > SC > IN > PK. We observed 12%–30% higher fine PM concentrations at TI and SC sites during morning peak (07:00–09:30) than the evening peak hours (16:00–19:00); while IN showed a smaller variation in fine PM concentrations compared with outdoor TI, PK and SC sites owing to their prevalence in the IN for a longer time. Fine and ultrafine PM containing potentially toxic trace transition metals including Fe, Ti, Cr, Mn, Al and Mg were detected by high resolution electron microscopy at all sites. There was a similar relative abundance of different elements at the TI, IN and PK sites, which suggests a transport of PM between MEs. RDD for PM1 was highest (2.45 ± 2.27 μg h−1) at TI for females during running; PM2.5 and PM10 were highest at SC (11.23 ± 6.34 and 37.17 ± 20.82 μg h−1, respectively). The results show that the RDD variation between MEs does not follow the PM concentration trend. RDD at PK was found to be 39%–53% lower than TI and SC during running for all the PM fractions. Overall, the study findings show the air quality variation at different MEs and reveals the exposure inequalities around the city, which enable the management of personal exposure by selecting appropriate MEs for different activities.
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