Urban ambient air concentrations of 39 aromatic (including benzene, toluene, and xylenes) and aliphatic volatile organic compounds (VOCs) were measured in Yokohama city, Japan. Yokohama city was selected as a case study to assess the amount of VOC released from Industrial area to characterize the ambient air quality with respect to VOC as well as to know the impact of petrochemical storage facilities on local air quality. For this purpose, ambient air samples were collected (from June 2007 to November 2008) at six selected locations which are designated as industrial, residential, or commercial areas. To find out the diurnal variations of VOC, hourly nighttime sampling was carried out for three nights at one of the industrial locations (Shiohama). Samples were analyzed using gas chromatographic system (GC-FID). Results show strong variation between day and nighttime concentrations and among the seasons. Aliphatic fractions were most abundant, suggesting petrochemical storage facilities as the major source of atmospheric hydrocarbons. High concentrations of benzene, toluene, ethyl benzene, and xylene (BTEX) were observed at industrial locations. BTEX showed strong diurnal variation which is attributed to change in meteorology. During our campaign, low ambient VOC concentrations were observed at the residential site.

This study describes polycyclic aromatic hydrocarbon (PAH) concentrations, patterns, and possible sources from atmospheric filter samples collected from three different areas in the city of Nairobi, Kenya. Total median concentrations for the 25 PAHs detected were higher in the traffic area (201 ng/m −3 ), followed by the residential area (141 ng/m −3 ), and lowest in the industrial area (128 ng/m −3 ). Results from the three sampled areas show that the percentage contributions of carcinogenic PAHs were approximately 30% of the total PAH concentrations reported. Some PAH isomer ratios differentiated traffic sources from non-traffic sources. Principal component analysis showed four significant principal components accounting for 82% of the variance. The first principal component (35%) was associated with fuel burning. The second principal component (27%) was associated with traffic emissions (diesel and gasoline). The other two principal components, which accounted for 12% and 8%, could not be interpreted with certainty. In order to interpret all the principal components in relation to sources, further collection of data is needed. More data points would have helped in further resolving the sources because data analysis models recommend more than 30 data sets.

No abstract

No abstract

No abstract