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Showing 3 results for Nejadkoorki
Heavy Metal Pollution in the Floor Dust of Yazd Battery Repairing Workshops in 2014
M Sabouhi , F Nejadkoorki, H.r Azimzadeh, M.s Ali Taleshi,
Volume 9, Issue 1 (6-2016)
Abstract

Background and Objectives: The presence of industrial workshops has increased urban pollution. This study aimed to investigate the heavy metal pollutants of Yazd battery repairing workshops and to identify the ecological and environmental effects resulted.

Materials and Methods: This descriptive cross-sectional study was carried out in Yazd. In this regard, the city was divided into three parts on the basis of geographical features. Then, 30 workshops were selected from each part through stratified random sampling method. Heavy metals (Pb, Cd, Cr, Zn, Cu, Fe, Mn) in the floor were measured using atomic absorption spectrophotometry (AAS). The impacts assessment of heavy metals was evaluated using environmental potential risk index (RI), cumulative pollution index (IPI), pollution coefficient factor (Cf), and the degree of modified contamination (mCd) and Pearson’s correlation statistical test.

Results: The trend of heavy metals concentrations in floor dust particles of workshops was as Fe>Cu>Pb>Zn>Mn>Cr>Cd. Therefore, the average concentrations of Fe and Cd in the samples were 27011.52 ±4721.05 and 78.25±21.07 mg/kg respectively. The results of the RI showed that heavy metal of floor dust had very high danger (2816.29). The mCd value was as 63.35 indicating these workshops were at severe contamination class. The value of Cf was as 304.17 revealing that these workshops were at very severe contamination class.

Conclusions: This research showed that the high concentration of heavy metals in battery repairing workshops is due to the interaction of heavy metals of industrial wastes components, including electrical wastes and battery with the dust having mankind origin.


Power density measurement of BTS antennas in two commercial and residential areas of the Gotvand city
Mojtaba Behdarvand, Farhad Nejadkoorki,
Volume 15, Issue 2 (8-2022)
Abstract
Background and Objective: The rise in the number of mobile phone subscribers has led to an increase in the number of BTS antennas and raised public concerns about the impact of radiation from these antennas on the health of a community. Therefore, the purpose of this study is to measure the pollution from electromagnetic waves of BTS antennas in Gotvand and to compare the emission of electromagnetic waves in commercial and residential areas.
Materials and Methods: In this study, a systematic sampling method was used to measure the power density of BTS antennas. Using a TES-593 device, 70 samples were taken from Gotvand. Data were analyzed by SPSS version 23 and statistical tests.
Results: The results showed that the most power density of electromagnetic waves is about 0.05% of the standard amount of general exposure and 0.01% of the standard amount of occupational exposure (p<0/001). Also, the power density in the commercial area is 1455.83 mw/m2 and in the residential area, it is 432.61 mw/m2, which has a significant difference between them (p<0.001), and the power density in the commercial area is higher than the residential area.
Conclusion: The power density of BTS antennas in Gotvand follows ICNIRP guidelines for occupational and general exposure. Also, the power density of the commercial area is higher due to being located in a more open space with fewer obstacles than the residential area.

Performance of adaptive neural-fuzzy inference system in predicting household waste production in Tabriz, Iran
Samira Bagheri, Farhad Nejadkoorki, Seyed Alireza Afshani, Vahid Mousavi,
Volume 15, Issue 4 (3-2023)
Abstract
Background and Objective:  One of the important environmental problems is the mass production of urban waste, which has increased per capita household waste production with the ever-increasing population growth; Therefore, nowadays, the use of intelligent systems has been expanded as a new solution in the analysis of environmental issues. Estimation of household waste through modeling, including the use of the fuzzy-neural network, leads to its better management. Therefore, the current research was conducted to investigate the socioeconomic factors on household waste production using the Adaptive Neural Fuzzy Inference System (ANFIS) in Tabriz city.
Materials and Method:   In this research, by using the adaptive neuro-fuzzy inference system (AFNIS) with the Fuzzy C-Means (FCM) method, domestic waste generation in Tabriz city has been predicted. According to the nature of the subject and the investigated indicators, the information collected in descriptive research was collected from the students of schools in Tabriz using a questionnaire. Also, socio-economic factors were statistically analyzed using SPSS version 26 software, and parameters affecting domestic waste production in Tabriz city were used for modeling in MATLAB software.
Results: The results of the study showed that the adaptive neuro-fuzzy inference system with the Fuzzy C-Means method has acceptable performance for domestic waste production in Tabriz city.
Conclusion:  According to the results obtained based on the statistical index, the forecasted model in domestic waste production in the Fuzzy C-Means method with the highest R (0.75) and the lowest error has an acceptable performance model in predicting the production of dry domestic waste in the studied area.

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