Risk assessment of extreme air pollution based on partial duration series: IDF approach

The occurrences of extreme pollution events have serious effects on human health, environmental ecosystems, and the national economy. To gain a better understanding of this issue, risk assessments on the behavior of these events must be effectively designed to anticipate the likelihood of their occurrence. In this study, we propose using the intensity–duration–frequency (IDF) technique to describe the relationship of pollution intensity (i) to its duration (d) and return period (T). As a case study, we used data from the city of Klang, Malaysia. The construction of IDF curves involves a process of determining a partial duration series of an extreme pollution event. Based on PDS data, a generalized Pareto distribution (GPD) is used to represent its probabilistic behaviors. The estimated return period and IDF curves for pollution intensities corresponding to various return periods are determined based on the fitted GPD model. The results reveal that pollution intensities in Klang tend to increase with increases in the length of time between return periods. Although the IDF curves show different magnitudes for different return periods, all the curves show similar increasing trends. In fact, longer return periods are associated with higher estimates of pollution intensity. Based on the study results, we can conclude that the IDF approach provides a good basis for decision-makers to evaluate the expected risk of future extreme pollution events.     

Automatic Method for Identifying Photospheric Bright Points and Granules Observed by Sunrise

In this study, we propose methods for the automatic detection of photospheric features (bright points and granules) from ultra-violet (UV) radiation, using a feature-based classifier. The methods use quiet-Sun observations at 214 nm and 525 nm images taken by Sunrise on 9 June 2009. The function of region growing and mean shift procedure are applied to segment the bright points (BPs) and granules, respectively. Zernike moments of each region are computed. The Zernike moments of BPs, granules, and other features are distinctive enough to be separated using a support vector machine (SVM) classifier. The size distribution of BPs can be fitted with a power-law slope ?1.5. The peak value of granule sizes is found to be about 0.5 arcsec². The mean value of the filling factor of BPs is 0.01, and for granules it is 0.51. There is a critical scale for granules so that small granules with sizes smaller than 2.5 arcsec² cover a wide range of brightness, while the brightness of large granules approaches unity. The mean value of BP brightness fluctuations is estimated to be 1.2, while for granules it is 0.22. Mean values of the horizontal velocities of an individual BP and an individual BP within the network were found to be 1.6 km?s^?1 and 0.9 km?s^?1, respectively. We conclude that the effect of individual BPs in releasing energy to the photosphere and maybe the upper layers is stronger than what the individual BPs release into the network.     

Gasification of sugarcane bagasse in supercritical water media for combined hydrogen and power production: a novel approach

A novel process based on supercritical water gasification has been used in this study for co-production of hydrogen and power from sugarcane bagasse as one of the main agricultural wastes of Iran. The cycle of the process was designed first, and then, the thermodynamic equilibrium model of the gasification process was simulated using ASPEN PLUS. The effects of temperature and feed concentration on molar fraction of main components of produced gas were investigated. The temperature was directly correlated with hydrogen production in which hydrogen and carbon monoxide production was favored at higher temperatures. The maximum hydrogen production occurred in the sugarcane bagasse concentrations about 20–30 wt%. Palladium membrane as a metallic dense membrane was used for separation of high-purity hydrogen. Hydrogen production of 8.55 kg/h and electrical power generation of 56 kW were obtained for the 20 wt% mixture of bagasse with a mass flow rate of 1000 kg/h, reactor pressure of 300 bars and temperature of 700 °C.     

IRG2018: A regional geoid model in Iran using Least Squares Collocation

Studia Geophysica et Geodaetica - The aim of this study is to determine an accurate geoid model for Iran based on the Least Squares Collocation method in the framework of the Remove — Compute...     

Slow-Mode Oscillations and Damping of Hot Solar Coronal Loops

The effect of temperature inhomogeneity on the periods, their ratios (fundamental versus first overtone), and the damping times of the standing slow modes in gravitationally stratified solar coronal loops are studied. The effects of optically thin radiation, compressive viscosity, and thermal conduction are considered. The linearized one-dimensional magnetohydrodynamic (MHD) equations (under low-?? condition) were reduced to a fourth-order ordinary differential equation for the perturbed velocity. The numerical results indicate that the periods of nonisothermal loops (i.e., temperature increases from the loop base to apex) are smaller compared to those of isothermal loops. In the presence of radiation, viscosity, and thermal conduction, an increase in the temperature gradient is followed by a monotonic decrease in the periods (compared with the isothermal case), while the period ratio turns out to be a sensitive function of the temperature gradient and the loop lengths. We verify that radiative dissipation is not a main cooling mechanism in both isothermal and nonisothermal hot coronal loops and has a small effect on the periods. Thermal conduction and compressive viscosity are primary mechanisms in the damping of slow modes of the hot coronal loops. The periods and damping times in the presence of compressive viscosity and/or thermal conduction dissipation are consistent with the observed data in specific cases. By tuning the dissipation parameters, the periods and the damping times could be made consistent with the observations in more general cases.     

Contamination and ecological risk assessment of heavy metals in street dust of Tehran,Iran

Heavy metals are introduced in human tissue through breathing air, food chain and human skin. They can cause damage to the nervous system and internal organs. In the present study, sixty street dust samples were collected from the central area of Tehran and were digested in the laboratory to determine the content of Zn, Ni, Cd, Cr, Cu and Pb, using inductively coupled plasma optical emission spectrometry (ICP-OES). The level of contamination with the analyzed metals was determined according to the following indices: geo-accumulation index (I _geo), enrichment factor (EF), pollution index (PI), integrated pollution index (IPI) and potential ecological risk index (RI). The average concentration of heavy metals found was in the order of Zn > Cu > Pb > Ni > Cr > Cd. The average I _geo values for Cd, Cr, Cu, Ni, Pb and Zn were 1.53, ?1.88, 2.68, ?0.67, 1.62 and 2.70, respectively. Among the investigated heavy metals, zinc and copper had the maximum average EF values and were placed into the “very severe enrichment” class. Potential ecological risk factor (E _r) also indicated that Cd had the highest risk, and it was classified as of considerable potential ecological risk. Therefore, it is necessary to pay more attention to the appearance of Cd in the human environment. The calculated potential ecological risk index values also illustrated that the street dust samples presented a “moderate ecological risk.” The calculated IPI values showed that the pollution levels of the street dust samples ranged from high to extremely high.     

Temperature Analysis of Flaring(AR 11283)and Non-flaring(AR 12194)Coronal Loops

Here,we study the temperature structure of flaring and non-flaring coronal loops,using extracted loops from images taken in six extreme ultraviolet channels rec...     

Comparison of two inverse analysis techniques for learning deep excavation response

Performance observation is a necessary part of the design and construction process in geotechnical engineering. For deep urban excavations, empirical and numerical methods are used to predict potential deformations and their impacts on surrounding structures. Two inverse analysis approaches are described and compared for an excavation project in downtown Chicago. The first approach is a parameter optimization approach based on genetic algorithm (GA). GA is a stochastic global search technique for optimizing an objective function with linear or non-linear constraints. The second approach, self-learning simulations (SelfSim), is an inverse analysis technique that combines finite element method, continuously evolving material models, and field measurements. The optimization based on genetic algorithm approach identifies material properties of an existing soil model, and SelfSim approach extracts the underlying soil behavior unconstrained by a specific assumption on soil constitutive behavior. The two inverse analysis approaches capture well lateral wall deflections and maximum surface settlements. The GA optimization approach tends to overpredict surface settlements at some distance from the excavation as it is constrained by a specific form of the material constitutive model (i.e. hardening soil model); while the surface settlements computed using SelfSim approach match the observed ones due to its ability to learn small strain non-linearity of soil implied in the measured settlements.