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Distribution And Source Apportionment Of N-Alkanes In Atmospheric Particle In Taiyuan

Posted on:2014-04-30Degree:MasterType:Thesis
Country:ChinaCandidate:D M HuFull Text:PDF
GTID:2271330482460894Subject:Environmental Science and Engineering
Abstract/Summary:PDF Full Text Request
N-alkanes, a kind of important trace organic pollutants in ambient air, mainly comes from the burning of fossil fuels and natural sources, such as higher plants, with great harm to human body, even causing cancer. Information on pollution sources reflected through the composition, distribution, major peak carbon and other associated diagnostic parameters has provided extremely important scientific significance and application value for atmospheric environment.N-alkanes in PM10 and typical emission sources samples collected during heating and non-heating period in Taiyuan, were determined with GC-MS. Meanwhile, distribution characteristics and source identification of n-alkanes were investigated with diagnostic parameters and principal component analysis, such as distribution, major peak carbon, carbon preference index, odd-even dominance, plant wax, ratio of Pristane to Phytane and so on. Results of the research would be the effective way for lower concentration in the corresponding n-alkanes.1. Concentrations of n-alkanes ranged from 213.74 to 573.32 ng·m-3 and 22.69 to 150.82 ng·m-3, with average of 338.56 and 48.43 ng·m-3 in heating and non-heating period, respectively. N-alkanes concentrations in suburban districts including JY, JCP, XD and SL were higher than those in urban sites in heating period, and the relative concentration in JS was 7 times more than that in SL in the other period. The correlation of the total n-alkanes in PM10 with that derived from fossil fuel was better than plant in heating period, while the opposite result was detected in the other period, manifesting higher contribution of fossil fuel in heating days.2. In heating period, n-C22 or n-C23 was the main carbon, a unimodal distribution with high middle and low at both ends. CPI value fluctuated around 1.1 and%WNA was 5.57%to 14.26%; the abundance of high carbon number alkanes (n-C22-n-C31) were significantly higher than low ones (n-C15-n-C22), also great odd-carbon advantage was obvious at high carbon range. The CPI value was up to 2.27 and%WNA swayed between 11.69%and 40.180, accordingly, the average was 2 times than that of the heating season. CPI and%WNA values showed that the contribution from plant wax in non-heating period was higher than that in heating one. Information on higher organic matter maturity was fetched during heating periods by Cmax and OEP and existence of UCM bulge confirmed vehicles were the significant contributor to n-alkanes concentration in the whole year.3. Concentrations of wax n-alkanes were 25.06-45.76 ng-m-3 and .87-17.64 ng·m-3 for heating and non-heating period, respectively, and n-alkanes production rate elevated along with the increase in environmental pressures. OEP plots of individual n-alkanes for PM10 and sources showed that coal ashes and vehicles were the main source during heating period, while higher plants and vehicles were the prime during non-heating period.4. The sum of n-alkanes were 47.78-305.56 μg·g-1,0.35-20.94 μg·g-1 and 3.87-351.06 μg·g-31 for higher plants, coal ashes and vehicle exhausts, respectively, with remarkable differences of source profiles. For these sources, the predominant carbon numbers were low carbon numbers(≤n-C20), high carbon numbers(≥n-C25) and the middle ones(n-C20-n-C25), showing that the contribution of Cmax from higher plants was significantly higher than the burning of fossil fuels.5. Two major components were yielded accounting for 51.28% and 43.14% of the total variances, which represented a mixture of vehicle emission and higher plant and coal dust, respectively. Cooperating control of emissions from coal combustions and vehicles would be the effective way for lower concentration in the corresponding n-alkanes.
Keywords/Search Tags:PM10, n-alkanes, distribution characteristic, principal component analysis, source apportionment
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