10 and Phe/Ant ratios Ratio of Fluoranthen/Pyren is one of the ratios that widely employed as characteristic tools. Similarly, Flr/Pyr ratios >1 and The benzo(a)anthracene/chrysene (BaA/Chr) ratio has also been suggested to identify PAH origins, this ratio tended to increase as petrogenic contribution decreased. In present study, the mean value of this ratio was 0.22. PAHs of molecular mass 178 and 202 are widely used to identify between petrogenic and pyrogenic sources. For mass178, ratio of anthracene to anthracene plus phenanthrene (An/ An+Ph) are employed. Ratio< 0.10 and >0.10 indicate petrogenic and pyrogenic sources, respectively. In present study, the value of this ratio was 0.05 that strongly indicated major source of PAHs in this regain is petrogenic. For mass 202, ratio of ﬂuoranthene to ﬂuoranthene plus pyrene (Fl/Fl+Py) has been suggested to characterize the source of PAHs. The value 0.40 for this ratio, specified as the petrogenic/pyrogenic transition point. Most petroleum samples have (Fl/Fl+Pyr) ratio below 0.40 while those of most combustion produced PAHs are above 0.40. In this investigation mean value of this ratio was 0.35 that indicated petrogenic source. Among PAHs with molecular mass of 228, the ratio of benzo(a)anthracene to the sum of benzo(a)anthracene and chrysene, (BaA/BaA+Chr) is also declarative of the PAHs sources. Values lower than 0.20 for this ratio suggests a petrogenic source, whereas values from 0.20 to 0.35 indicates a petroleum or combustion source, and any values higher than 0.35 signify a combustion source. For present study the mean value of this ratio was 0.16. likewise, petrogenic sources may be idetified by a ratio of indeno(1,2,3-cd) pyrene to the sum of indeno(1,2,3-cd) pyrene and benzo[g,h,i] perylene, IP/(IP+Bghi), lower than 0.20. A ratio between 0.20 and 0.50 may suggest liquid fossil fuel combustion, and a ratio higher than 0.50 indicates biomass and coal combustion. In this study the mean value of this ratio was 0.17. The samples were also calculated using methylphenanthrene/phenanthrene (MP/P) ratio to determine the source of PAHs. The value of less than 1 is the combustion sources and more than 1 consists of petroleum sources. In present study the mean value of this ratio was 2. Also ratio of LMW/HMW is employed for indentification of PAHs source. The high amount of this ratio strongly indicated petrogenic source. The mean value of this ratio was 19.78, that this amount is rather high. We use of some ratios to source identification of n-alkanes in Khuran strait, too. Wide range of CPI, TAR and U/R for sediment samples of Khuran strait have indicated that there are combined sources (biogenic and petrogenic sources) for organic matter of surface sediments. Therefore predominate petrogenic source in some of the Middle part stations and biogenic in some others could be explained for these reasons. Pr/Ph, Pr/n-C17 and Ph/n-C18 ratio is close to 1 indicating background petrogenic source in surface sediments of Persian Gulf mangrove forests. In summary, results showed that the main source of hydrocarbons in this regain is mixed source of biogenic and petrogenic origin.]]>
0.36), low (L: 0. 36≥X>0.52), medium (M: 0.52≥X>0.68), high (H: 0.68≥X>0.84) and very high (VH: 0.84≥X>1). Fuzzy logic study method In the fuzzy logic method, above mentioned mathematical matrix indices were considered as fuzzy inference system's input. Criteria got fuzzificationed and after determination of membership functions similar to the groups of mathematical matrix classification, and forming rule base center the importance of impact calculated by using the center of gravity method as Defuzzzification approach. The output of the fuzzy logic inference actually is the effect of each activity on the environment and ultimately, the efficiency of two mentioned methods was compared for assessment of effect importance. These two methods have quite similar inputs and finally classified outputs which actually is the importance of the impact, were compared. To do this, in mathematical matrix method and fuzzy logic, 6 criteria for 2 indices (complementary index & basic index) were used (magnitude (Mij), duration (Dij) & occurrence Time (Tij) as basic index parameters, and synergy effects (Sij), cumulative effects (Aij) & probability of occurrence (Pij) as complementary index parameters). In the method of fuzzy inference system using Matlab Ver R2012a software and applying Mamadani implication method and use the same mathematical matrix indices as system input was implemented. Discussion of Results and Conclusions According to the below chart review (Fig.1), the difference in the number of linguistic variables in mathematical matrix and fuzzy methods is obvious. These differences arise from the decision making method in Aristotelian logic and fuzzy logic. In mathematical matrix if the number is placed in border area (high or low range), still belongs to the same range. The importance of the impact calculated based on a mathematical matrix class can create Uncertainty, which is more important in borders of classification (where X is increasing along with the value of impact from very low to very high). i.e. as we move towards increasing the variable X, the value of linguistic variable have increased. This can be seen as several classes in output matrix. For example, if the variable is X=0.53, belongs to medium-class and if variable is X=0.67, still belongs to the same class, even though there has been a major numerical increase; on the other hand, with the increase of 0.01 at 0.67 point, the importance of impact will change from medium to high. Figure 1. Comparing the numeric summation of whole impacts importance (positive and negative) in mathematical matrix and fuzzy logic methods But fuzzy logic approach solved this problem and its output defined based on membership grade. For example, if the output of fuzzy logic is 0.67, then the fuzzy logic determines a degree of membership for two membership functions, and thus the uncertainty in the mathematical matrix classification, which is acting as a binary logic, would improve. Impact importance of Ȳ=0.67 in fuzzy logic belongs to two membership functions with different membership levels, moderate linguistic variable with 0.06 degree of membership and high linguistic variable with 0.94 membership degree. The concept of environmental impact Assessment is unambiguous and ecological effects cannot be explicitly defined, for this reason the fuzzy logic has a very high performance in formulating the importance of each impact in an appropriate manner. Fuzzy logic is capable of using qualitative criteria or linguistic variables for assessment and solves the problem of the variables formulation and simultaneously is capable to use and synthesis both qualitative and quantitative data derived from environmental assessors. As a result, the fuzzy logic method leads to modification of uncertainty which always is a problem in unambiguous and complicated matters such as EIA. Since one of the main issues in environmental impact assessment (regarding project approval and determination of appropriate corrective solutions) is to define the impact significance correctly; the fuzzy logic with its spectacular capabilities is an appropriate method. Determining the importance of environmental impacts is one of the main issues in the process of environmental impact assessment (EIA). Ecological impacts assessment is very complicated and always associated with uncertainty because the assessment data are often qualitative and common EIA methods are incapable of managing these kind of data.]]>