Determining Effective Parameters on CO Concentration in Tehran Air by Sensitivity Analysis based on Neural Network Prediction

One of the most toxic pollutant gases produced by fossil fuels is carbon monoxide. Hence, the accurate and regular estimation and control of CO in the cities such as Tehran is inevitable. In this research, for the first time, CO concentration in ambient air was predicted based on 12 important urban and meteorological parameters by neural network. Also, the sensitivity analysis of the factors that effect on the concentration of carbon monoxide in Tehran was investigated based on the pollutant concentration predictive model. In this research, the daily statistical data of Tehran metropolis over the course of five consecutive years from 12 factors affecting the amount of carbon monoxide in Tehran, such as population, density, precipitation, temperature, urban traffic, wind speed, gasoil consumption, moisture, air flow, effective vision and air pressure was used. Based on this database, the artificial neural network with the best possible algorithm had been trained to predict this contaminant and root mean square error of model was equal to 2.54. Then, sensitivity analysis was done to find the most effective factor on the concentration of carbon monoxide, urban density and air pressure. In order to control this hazardous contaminant in urban management, these parameters should be taken into account. Based on the result, by preventing the construction of high towers in Tehran, wind speed average will increase and increasing in wind speed (25%) caused to reducing in carbon monoxide concentration (about 12%). Also, prevention of urban density (25%) will cause to prevention of increasing CO concentration (about 10%)

Seasonal distribution of dominant phytoplankton in the southern Caspian Sea (Mazandaran coast) and its relationship with environmental factors

Seasonal distribution of phytoplankton and factors affecting their presence in the Mazandaran coastal ecosystems were investigated in 2012. In this study, Distribution of the phylum and dominant species of phytoplankton and water quality parameters were evaluated along 4 transects (Amirabad, Babolsar, Noshahr and Ramsar) in the different layers of the water column at final depths of 5, 10, 20 and 50m. Variation of the dominant species and environmental parameters was analyzed using Principal Components Analysis (PCA). Average annual phytoplankton density was 318687823 cubic meters, the highest density in winter and lowest in spring was determined. Totally, 5 groups and 129 species of phytoplankton were identified, including Bacillariophyta (58 species), Cyanophyta (24), Dinoflagellata (22), Chlorophyta (17) and Euglenophyta (8). The dominant species in the water body were Exuviaella cordata of the Dinoflagellata (61.25% of the species) in spring, Oscillatoria sp. of the Cyanophyta in summer and autumn (48.69 and 71.91%, respectively) and Pseudo-nitzschia seriata of the Bacillariophyta (66.12%) in winter. This study showed that thermocline, riverine transport, Mnemiopsis leidyi and opportunistic phytoplanktonic species with high competitive ability (Cyanophyta and Dinoflagellata (were the most effective factors on spatio-temporal variations of phytoplankton. Temperature, silica and inorganic nitrogen play an important role in population dynamics are diatoms, while temperature, inorganic and organic phosphorus and inorganic nitrogen for Cyanophyta and Dinoflagellata are important

Autoregressive Process Parameters Estimation under Non-Classical Error Model

Abstract Error in measuring time varying data setting is one important source of bias in estimating of time series modeling parameters. When the measurement error model is non-classic, this raises the question whether the different measurement error model strategy might differently affect the estimation of the time series modeling parameters. In this article, we investigate this in Autoregressive (AR) model parameters estimation under the non-classical measurement error model. We compare the parameters estimation of the AR model under the classical and nonclassical error models. We perform analytically this on the AR model of order p. Further, we confirm this through simulation study specifically on the AR model of order 1

Determine of Homogeneous Regions Distribution of Annual Rainfall in Golestan Province Using Clustering and L-moments

Characteristics of precipitation and the regionalization major role in the efficient use of water resources and soil and management of environmental hazards. Regionalization of rainfall can help to better use of water resources and to correct manage of environmental hazards. According to the analysis of climate phenomena such as precipitation, all data should be related to a homogeneous region, on the basis in this study, homogenous regions using data from long-term annual precipitation in Golestan province and the appropriate number of stations determined using the newer methods. Precipitation monthly data from 29 rain-gauge stations and evaporation poll in Golestan province from 1361 to 1391 were used to testing of homogeneity, the random and outlier data that 25 stations remained. Then using Wards hierarchicalclustering and with different variables was evaluated segmentation varies. Clustering in two clusters have higher average silhouette 0.48, accordingly, the province was divided into two regions. Homogeneity investigated by heterogeneity test for each region. according to investigations was performed by L- moments coefficient of skewness (τ_3^R) was smaller 0.23, The result Hosking and Wallis test was used to examine the homogeneity region. For this two region, the test statistic H11>, which is confirmed by the homogeneity of the two areas, Finally was divided into two regions. The high correlation coefficient between stations in each cluster and low correlation coefficient between two different cluster is another reason for separation of areas from each other.Characteristics of precipitation and the regionalization major role in the efficient use of water resources and soil and management of environmental hazards. Regionalization of rainfall can help to better use of water resources and to correct manage of environmental hazards. According to the analysis of climate phenomena such as precipitation, all data should be related to a homogeneous region, on the basis in this study, homogenous regions using data from long-term annual precipitation in Golestan province and the appropriate number of stations determined using the newer methods. Precipitation monthly data from 29 rain-gauge stations and evaporation poll in Golestan province from 1361 to 1391 were used to testing of homogeneity, the random and outlier data that 25 stations remained. Then using Wards hierarchicalclustering and with different variables was evaluated segmentation varies. Clustering in two clusters have higher average silhouette 0.48, accordingly, the province was divided into two regions. Homogeneity investigated by heterogeneity test for each region. according to investigations was performed by L- moments coefficient of skewness (τ_3^R) was smaller 0.23, The result Hosking and Wallis test was used to examine the homogeneity region. For this two region, the test statistic H11>, which is confirmed by the homogeneity of the two areas, Finally was divided into two regions. The high correlation coefficient between stations in each cluster and low correlation coefficient between two different cluster is another reason for separation of areas from each other

Strategy of Bayesian Propensity Score Estimation Approach in Observational Study

Abstract Estimating causal effect of a treatment on an outcome is often complicated. This is because the treatment effect may be deviated by the confounding variables. These variables affect treatment and outcome simultaneously, and the causal effect estimation thus depends upon these variables. Several methods have been proposed to reduce the attribute bias of confounding effect. In this article, we compare the traditional method of Propensity score through Stratification; and recent method of Propensity score through Bayesian in observational studies. Comparison is constructed on Mont Carlo simulation of the hypothetical binary treatment