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      Estimating relative contributions of primary and secondary sources of ambient nitrated and oxygenated polycyclic aromatic hydrocarbons

      , , , , , , ,
      Atmospheric Environment
      Elsevier BV

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          Spatial and temporal variations of six criteria air pollutants in 31 provincial capital cities in China during 2013-2014.

          Long-term air pollution data with high temporal and spatial resolutions are needed to support the research of physical and chemical processes that affect the air quality, and the corresponding health risks. However, such datasets were not available in China until recently. For the first time, this study examines the spatial and temporal variations of PM2.5, PM10, CO, SO2, NO2, and 8 h O3 in 31 capital cities in China between March 2013 and February 2014 using hourly data released by the Ministry of Environmental Protection (MEP) of China. The annual mean concentrations of PM2.5 and PM10 exceeded the Chinese Ambient Air Quality Standards (CAAQS), Grade I standards (15 and 40 μg/m(3) for PM2.5 and PM10, respectively) for all cities, and only Haikou, Fuzhou and Lasa met the CAAQS Grade II standards (35 and 70 μg/m(3) for PM2.5 and PM10, respectively). Observed PM2.5, PM10, CO and SO2 concentrations were higher in cities located in the North region than those in the West and the South-East regions. The number of non-attainment days was highest in the winter, but high pollution days were also frequently observed in the South-East region during the fall and in the West region during the spring. PM2.5 was the largest contributor to the air pollution in China based on the number of non-attainment days, followed by PM10, and O3. Strong correlation was found between different pollutants except for O3. These results suggest great impacts of coal combustion and biomass burning in the winter, long range transport of windblown dust in the spring, and secondary aerosol formation throughout the year. Current air pollution in China is caused by multiple pollutants, with great variations among different regions and different seasons. Future studies should focus on improving the understanding of the associations between air quality and meteorological conditions, variations of emissions in different regions, and transport and transformation of pollutants in both intra- and inter-regional contexts.
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            Source Apportionment of Atmospheric Polycyclic Aromatic Hydrocarbons Collected from an Urban Location in Birmingham, U.K.

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              Source apportionment of polycyclic aromatic hydrocarbons in the urban atmosphere: a comparison of three methods.

              Polycyclic aromatic hydrocarbons (PAHs) are ubiquitous pollutants in urban atmospheres. Several PAHs are known carcinogens or are the precursors to carcinogenic daughter compounds. Understanding the contributions of the various emission sources is critical to appropriately managing PAH levels in the environment. The sources of PAHs to ambient air in Baltimore, MD, were determined by using three source apportionment methods, principal component analysis with multiple linear regression, UNMIX, and positive matrix factorization. Determining the source apportionment through multiple techniques mitigates weaknesses in individual methods and strengthens the overlapping conclusions. Overall source contributions compare well among methods. Vehicles, both diesel and gasoline, contribute on average 16-26%, coal 28-36%, oil 15-23%, and wood/other having the greatest disparity of 23-35% of the total (gas- plus particle-phase) PAHs. Seasonal trends were found for both coal and oil. Coal was the dominate PAH source during the summer while oil dominated during the winter. Positive matrix factorization was the only method to segregate diesel from gasoline sources. These methods indicate the number and relative strength of PAH sources to the ambient urban atmosphere. As with all source apportionment techniques, these methods require the user to objectively interpret the resulting source profiles.
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                Author and article information

                Contributors
                Journal
                Atmospheric Environment
                Atmospheric Environment
                Elsevier BV
                13522310
                June 2017
                June 2017
                : 159
                : 126-134
                Article
                10.1016/j.atmosenv.2017.04.003
                c447914a-2516-4b8f-89a8-c3590a5d4601
                © 2017

                https://www.elsevier.com/tdm/userlicense/1.0/

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