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Introduction Oil contamination affects the biological, physical, and chemical properties of soil. The abundance and diversity of soil microbial communities can significantly be influenced by petroleum hydrocarbons. Soil biological indicators including microbial population and enzyme activity, are highly sensitive to environmental stresses and respond to them quickly. Measuring the microbial population is one of the most common biological indicators which is used to study the quality and health of the soil. Also, measuring the activity of enzymes such as urease is one of the most sensitive indicators of oil-contaminated soils. There are some studies on the effects of oil contamination on microbial population and soil enzyme activity. Most of the studies have tested non-natural and short-term oil pollution and reported the adverse effects of oil hydrocarbons on microbial activities in soil. While the soil sample used in this research had natural and long-term contamination and the microorganisms are compatible with polluted conditions. The aim of this study was to investigate changes in the microbial population and urease activity in the presence of different levels of oil contamination, and how petroleum hydrocarbons can affect them. Petroleum hydrocarbons are toxic and persistent in soil, so it is necessary to study the pattern of changes in soil biological characteristics in effective soil management. Material and Methods In this study, 120 samples of oil-contaminated soils were collected from the oil-rich area of Naft-Shahr (located in the west of Kermanshah province) which had natural and long-term oil pollution. A nested design was used to analysis data in this research. The test factors included locations (4 locations) and 3 different levels of oil pollution: low (L), moderate (M), and high (H). Also, 10 replications were considered in the three levels of oil contamination. The collected soils were analyzed for physico-chemical (pH, EC, 0m, CCE, OC, soil texhure) and biological properties (including urease activity, BR and SIR) using standard methods, and the concentration of oil pollutants was determined by the Soxhlet extractor. To determine the abundance of the culturable microbial population, bacterial counting was performed using nutrient agar (NA) and carbon-free minimal medium (CFMM) supplemented with crude oil as the media. Urease activity was measured by the indophenol blue method and finally, the results of measuring chemical, physical and biological properties were analyzed using principal component analysis (PCA). Results and Discussion The average percentage of oil measured by Soxhlet method was 4.03%, 9.95% and 22.50% respectively for L, M and H levels. The results showed that the microbial population increased with the increase of contamination intensity. The highest microbial population counted in NA clhrre medium was 9.54 x105 CFU/g in H soils and the lowest population was 3.25 x 105 CFU/g in L soils. In the CFMM culture medium, the highest population in H soils was 11.3 x 105 CFU/g and the lowest population in L soils was 11.8 x 104 CFU/g. For both NA and CFMM mediums, location 1 had the highest population and location 4 had the lowest microbial population. Oil contamination of soil samples led to a decrease in urease activity in such a way that the highest enzyme activity in soils was obtained with low contamination (594.90 jrgNHgh) and the lowest activity in heavily contaminated soils (176.11 gNHgh). Also, the lowest urease activity was observed in location 1 and the highest in location 4. Principal components analysis (PCA) was also performed and 71% of the variance of the samples could be explained by the first two components (biochemical component and physical component). The results of this research indicated an increase in the microbial population with an increasing of the intensity of oil pollution. It seems that the results obtained from the studies conducted on man-made pollution and natural pollution have differences in terms of the type of biological responses. Aged, long-term and natural oil pollution has caused the selection of oil-resistant microbial community, and therefore we see their positive response to the presence of oil compounds. Conversely, urease enzyme activity was found to be higher in soils with low pollution. This suggests that microbial activity, while influential, is not the sole determinant of urease activity, and various factors contribute to Soil Enzyme Activity (SEA). The type of petroleum pollutant, the direct effect of petroleum compounds on urease-producing microorganisms, as well as the non-microbial origin of urease in soil can be possible reasons for reducing urease activity in contaminated soils. Conclusion In areas where petroleum pollutants are naturally and long-term present in the soil, some oil-decomposing microbial groups use petroleum hydrocarbons as a source of carbon for their nutrition, so the abundance of oildecomposing communities increases. The results showed an increase in the microbial population with an increase in the intensity of oil pollution. On the other hand, the activity of urease enzyme measured in soils with low pollution was higher because non-microbial factors may affect the activity of this enzyme and the increase in the microbial population is not related to the increase in the population of urease-producing microbes. [ABSTRACT FROM AUTHOR]

This paper introduces a novel continuous-time dynamic average consensus algorithm for networks whose interaction is described by a strongly connected and weight-balanced directed graph. The proposed distributed algorithm allows agents to track the average of their dynamic inputs with some steady-state error whose size can be controlled using a design parameter. This steady-state error vanishes for special classes of input signals. We analyze the asymptotic correctness of the algorithm under time-varying interaction topologies and characterize the requirements on the stepsize for discrete-time implementations. We show that our algorithm naturally preserves the privacy of the local input of each agent. Building on this analysis, we synthesize an extension of the algorithm that allows individual agents to control their own rate of convergence towards agreement and handle saturation bounds on the driving command. Finally, we show that the proposed extension additionally preserves the privacy of the transient response of the agreement states and the final agreement value from internal and external adversaries. Numerical examples illustrate the results. [ABSTRACT FROM AUTHOR]