ORIGINAL_ARTICLE
Health risk assessment of As and Zn in canola and soybean oils consumed in Kermanshah, Iran
Due to the limited number of researches conducted globally on heavy metals in edible oil, this study was carried out for analysis and health risk assessment of As and Zn in some brands of canola and soybean oils marketed in Kermanshah City, Iran, in 2015. In this research, 18 samples of three popular brands of edible oil (canola and soybean) in the Iranian market were analyzed for levels of As and Zn after digestion with acids using atomic absorption spectroscopy (AAS) in 3 replications and the health index was obtained. In addition, all statistical analyses were performed using the SPSS statistical package. The results showed that the mean concentrations of As and Zn in oil samples were 0.06 ± 0.05 and 100.17 ± 21.94 µg/kg, respectively. Moreover, the mean concentration of As and Zn in oil samples were lower than the World Health Organization’s (WHO) maximum permissible limits (MPL). The health risk assessment showed no potential risk for children and adults by consumption of the studied vegetable oil samples. Although the results showed that the consumption of the analyzed vegetable oils did not have any adverse effects on the consumers’ health, concerning increased use of agricultural inputs by farmers and industrial development, it is very important that the appropriate measures be taken by companies during the production process and products be treated before marketing.
https://jaehr.muk.ac.ir/article_40222_bfe660a4db187dc5d091da437519d25a.pdf
2016-04-01
62
67
10.22102/jaehr.2016.40222
heavy metals
Edible Oil
Health
Risk Assessment
Iran
Soheil
Sobhan Ardakani
s_sobhan@iauh.ac.ir
1
Department of the Environment, School of Basic Sciences, Islamic Azad University, Hamedan Branch, Hamedan, Iran
LEAD_AUTHOR
1. Lo Coco F, Ceccon L, Ciraolo L, Novelli V. Determination of cadmium(II) and zinc(II) in olive oils by derivative potentiometric stripping analysis. Food Control 2003; 14(1): 55-9.
1
2. Dantas TN, Dantas Neto AA, Moura MC, Barros Neto EL, Forte KR, Leite RH. Heavy metals extraction by microemulsions. Water Res 2003; 37(11): 2709-17.
2
3. Dugo G, Pellicano TM, Pera LL, Turco VL, Tamborrino A, Clodoveo ML. Determination of inorganic anions in commercial seed oils and in virgin olive oils produced from de-stoned olives and traditional extraction methods, using suppressed ion exchange chromatography (IEC). Food Chem 2007; 102(3): 599-605.
3
4. Demirbas A. Concentrations of 21 metals in 18 species of mushrooms growing in the East Black Sea region. Food Chem 2001; 75(4): 453-7.
4
5. Pehlivan E, Arslan G, Gode F, Altun T, Ozcan M. Determination of some inorganic metals in edible vegetable oils by inductively coupled plasma atomic emission spectroscopy (ICP-AES). Grasas y Aceites 2008; 59(3): 239-44.
5
6. Jarup L. Hazards of heavy metal contamination. Br Med Bull 2003; 68: 167-82.
6
7. World Health Organization (WHO). JECFA Reports [Online]. [cited 2003]; Available from: URL:
7
http://www.who.int/foodsafety/publications/jecfa-reports/en
8
8. Tahsin N, Yankov B. Research on accumulation of zinc (Zn) and cadmium (Cd) in sunflower oil. J Tekirdag Agric Fac 2007; 4(1): 109-12.
9
9. Sobhanardakani S, Jamshidi K. Assessment of metals (Co, Ni, and Zn) content in the sediments of mighan wetland using geo-accumulation index. Iran J Toxicol 2015; 9(30): 1386-90.
10
10. Lin L, Allemekinders H, Dansby A, Campbell L, Durance-Tod S, Berger A, et al. Evidence of health benefits of canola oil. Nutr Rev 2013; 71(6): 370-85.
11
11. Barrett JR. The science of soy: what do we really
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know? Environ Health Perspect 2006; 114(6): A352-A358.
13
12. World Health Organization (WHO). International programme on chemical safety, WHO human health risk assessment toolkit: chemical hazards [Online]. [cited 2010]; Available from: URL: http://www.who.int/ipcs/methods/harmonization/areas/ra_toolkit/en/
14
13. Anwar F, Kazi TG, Saleem R, Bhanger MI. Rapid determination of some trace metals in several oils and fats. Grasas y Aceites 2004; 55(2): 160-8.
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14. Horwitz W. Official methods of analysis of the aoac international, Volume 18. Rockville, MD: Association of Official Analytical Chemists; 2005.
16
15. Omar WA, Zaghloul KH, Abdel-Khalek AA, Abo-Hegab S. Risk assessment and toxic effects of metal pollution in two cultured and wild fish species from highly degraded aquatic habitats. Arch Environ Contam Toxicol 2013; 65(4): 753-64.
17
16. Apau J, Acheampong A, Appiah JA, Ansong E. Levels and Health Risk Assessment of Heavy Metals in Tubers from Markets in the Kumasi Metropolis, Ghana. Int J Sci Technol 2014; 3(9): 534-39.
18
17. Goldhaber SB. Trace element risk assessment: essentiality vs. toxicity. Regul Toxicol Pharmacol 2003; 38(2): 232-42.
19
18. World Health Organization (WHO). Working document for information and use in discussions related to contaminants and toxins in the gsctff. Proceedings of The 5th Session Joint FAO/WHO Food Standards Programme Codex Committee on Contaminants in Foods; 2011 Mar 21-25; Hague, Netherlands.
20
19. Fu QL, Liu Y, Li L, Achal V. A survey on the heavy metal contents in Chinese traditional egg products and their potential health risk assessment. Food Addit Contam Part B Surveill 2014; 7(2): 99-105.
21
20. Farzin L, Moassesi ME. Determination of metal contents in edible vegetable oils produced in Iran using microwave-assisted acid digestion. J Appl Chem Res 2014; 8(3): 35-43.
22
21. Acar O. Evaluation of cadmium, lead, copper, iron and zinc in Turkish dietary vegetable oils and olives using electrothermal and flame atomic absorption spectrometry. Grasas Aceites 2012; 63(4): 383-93.
23
22. Zhu F, Fan W, Wang X, Qu L, Yao S. Health risk assessment of eight heavy metals in nine varieties of edible vegetable oils consumed in China. Food Chem Toxicol 2011; 49(12): 3081-5.
24
23. Mendil D, Uluozlu OD, Tuzen M, Soylak M. Investigation of the levels of some element in edible oil samples produced in Turkey by atomic absorption spectrometry. J Hazard Mater 2009; 165(1-3): 724-8.
25
24. Cindric IJ, Zeiner M, Steffan I. Trace elemental characterization of edible oils by ICP-AES and GFAAS. Microchem J 2007; 85(1): 136-9.
26
25. Dugo G, La Pera L, La Torre GL, Giuffrida D. Determination of Cd(II), Cu(II), Pb(II), and Zn(II) content in commercial vegetable oils using derivative potentiometric stripping analysis. Food Chem 2004; 87(4): 639-45.
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26. Garrido MD, Frias I, Diaz C, Hardisson A. Concentrations of metals in vegetable edible oils. Food Chem 1994; 50(3): 237-43.
28
27. Ru QM, Feng Q, He JZ. Risk assessment of heavy metals in honey consumed in Zhejiang province, southeastern China. Food Chem Toxicol 2013; 53: 256-62.
29
28. Liang Q, Xue ZJ, Wang F, Sun ZM, Yang ZX, Liu SQ. Contamination and health risks from heavy metals in cultivated soil in Zhangjiakou City of Hebei Province, China. Environ Monit Assess 2015; 187(12): 754.
30
ORIGINAL_ARTICLE
Forecasting of heavy metals concentration in groundwater resources of Asadabad plain using artificial neural network approach
Nowadays 90% of the required water of Iran is secured with groundwater resources and forecasting of pollutants content in these resources is vital. Therefore, this research aimed to develop and employ the feedforward artificial neural network (ANN) to forecast the arsenic (As), lead (Pb), and zinc (Zn) concentration in groundwater resources of Asadabad plain. In this research, the ANN models were developed using MATLAB R2014 software program. The artificial intelligence models were trained with the data collected from field and then utilized as prediction tool. Levenberg-Marquardt (LM) and Bayesian regularization (BR) algorithms were employed as ANN training algorithms and their performance was evaluated using determination coefficient and the root mean square error. The results showed that the ANN models could potentially forecast heavy metals concentration in groundwater resources of the studied area. Coefficients of determination for ANN models for As, Pb and Zn in testing phase were 0.9288, 0.9823 and 0.8876, respectively. Finally, based on the simulation results, it was demonstrated that ANN could be applied effectively in forecasting the heavy metals concentration in groundwater resources of Asadabad plain.
https://jaehr.muk.ac.ir/article_40223_257db8dd3e3290f6c7aee07a24dd7673.pdf
2016-04-01
68
77
10.22102/jaehr.2016.40223
Neural Networks
heavy metals
Groundwater
Forecasting
risk
Meysam
Alizamir
1
Department of Civil Engineering, Young Researchers and Elite Club, Hamadan Branch, Islamic Azad University, Hamadan, Iran
AUTHOR
Soheil
Sobhanardakani
s_sobhan@iauh.ac.ir
2
Department of the Environment, School of Basic Sciences, Hamadan Branch, Islamic Azad University, Hamadan, Iran
LEAD_AUTHOR
1. Gribble GW. The natural production of chlorinated compounds. Environ Sci Technol 1994; 28(7): 310A-9A.
1
2. Nasrabadi T. An IndexApproach to Metallic Pollution in RiverWaters. Int J Environ Res 2015; 9(1): 385-94.
2
3. Morillo J, Usero J, Gracia I. Partitioning of metals in sediments from the Odiel River (Spain). Environ Int 2002; 28(4): 263-71.
3
4. Baghvand A, Nasrabadi T, Bidhendi GN, Vosoogh A, Karbassi A, Mehrdadi N. Groundwater quality degradation of an aquifer in Iran central desert. Desalination 2010; 260(13): 264-75.
4
5. Hossein Pour M, Lashkaripour G, Dehghan P. Assessing the effect of heavy metal concentrations (Fe, Pb, Zn, Ni, Cd, As, Cu,Cr) on the quality of adjacent groundwater resources of Khorasan Steel Complex ). Int J Pl An and Env Sci 2014; 4(2): 511-8.
5
6. Prasanna MY, Chidambaram S, Hameed AS, Srinivasamoorthy K. Hydrogeochemical analysis and evaluation of groundwater quality in the Gadilam river basin, Tamil Nadu, India. J Earth Syst Sci 2011; 120(1): 85-98.
6
7. Prasad B, Kumari P, Bano S, Kumari S. Ground water quality evaluation near mining area and development of heavy metal pollution index. Appl Water Sci 2014; 4(1): 11-7.
7
8. Sobhanardakani S. Evaluation of the water quality pollution indices for groundwater resources of Ghahavand plain, Hamadan province, western Iran. Iran J Toxicol 2016; 10(3): 35-40.
8
9. Jarup L. Hazards of heavy metal contamination. Br Med Bull 2003; 68: 167-82.
9
10. Tahsin N, Yankov B. Research on accumulation of zinc (Zn) and cadmium (Cd) in sunflower oil. Journal of Tekirdag Agricultural Faculty 2007; 4(1): 109-11.
10
11. Sobhanardakani S, Jamshidi K. Assessment of Metals (Co, Ni, and Zn) Content in the Sediments of Mighan Wetland Using Geo-Accumulation Index. Iran J Toxicol 2015; 9(30): 1386-90.
11
12. Abou-Arab AAK, Ayesh AM, Amra HA, Naguib K. Characteristic levels of some pesticides and heavy metals in imported fish. Food Chem 1996; 57(4): 487-92.
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13. Dahiya S, Karpe R, Hegde AG, Sharma RM. Lead, cadmium and nickel in chocolates and candies from suburban areas of Mumbai, India. J Food Comp Anal 2005; 18(6): 517-22.
13
14. Hosseini SV, Sobhanardakani S, Tahergorabi R, Delfieh P. Selected heavy metals analysis of Persian sturgeon's (Acipenser persicus) caviar from
14
Southern Caspian Sea. Biol Trace Elem Res 2013; 154(3): 357-62.
15
15. Adekunle IM, Akinyemi MF. Lead levels of certain consumer products in Nigeria: a case study of smoked fish foods from Abeokuta. Food Chem Toxicol 2004; 42(9): 1463-8.
16
16. Haykin SS. Neural Networks: A Comprehensive Foundation. 2nd ed. Upper Saddle River, NJ: Prentice Hall; 1999.
17
17. Nikolos IK, Stergiadi M, Papadopoulou MP, Karatzas GP. Artificial neural networks as an alternative approach to groundwater numerical modelling and environmental design. Hydrol Process 2008; 22(17): 3337-48.
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18. Daliakopoulos IN, Coulibaly P, Tsanis IK. Groundwater level forecasting using artificial neural networks. J Hydrol 2005; 309(14):229 -40.
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19. Antar MA, Elassiouti I, Allam MN. Rainfall-runoff modelling using artificial neural networks technique: a Blue Nile catchment case study. Hydrolog Process 2006; 20(5): 1201-16.
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20. Shakeri Abdolmaleki A, Gholamalizadeh Ahangar A, Soltani J. Artificial neural network (ann) approach for predicting cu concentration in drinking water of chahnimeh1 reservoir in Sistan-Balochistan, Iran. Health Scope 2013; 2(1): 31-8.
21
21. Sobhanardakani S, Jamali M, Maanijou M. Evaluation of as, Zn, Cr and Mn concentrations in groundwater resources of razan plain and preparation of zoning map using gis. J Environ Sci Technol 2014; 16(2): 25-38.
22
22. Fazel Tavasol S, Vusuq BP, Manshuri M. The investigation of heavy metal (Sn-Pb) concentration in ground water resources and their environmental effects, Case study: North Chardoly Plain. Proceedings of the 1st International Applied Geological Congress; 2010 Apr 26-28; Mashhad, Iran.
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23. Clesceri LS, Greenberg AE, Eaton AD. Standard methods for the examination of water and waste water, quality assurance. 20th ed. Washington DC: American Public Health Association; 1999.
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24. Eaton AD, Franson MA. Standard methods for the examination of water & wastewater. Washington, DC: American Public Health Association; 2005.
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25. Edet A, Offiong O. Evaluation of water quality pollution indices for heavy metal contamination monitoring. A study case from Akpabuyo-Odukpani area, Lower Cross River Basin (southeastern Nigeria). Geo J 2002; 57(4): 295-304.
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26. Hornik K, Stinchcombe M, White H. Universal approximation of an unknown mapping and its derivatives using multilayer feedforward networks. Neural Net 1990; 3(5): 551-60.
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27. Hornik K, Stinchcombe M, White H. Multilayer feedforward networks are universal approximators. Neural Net 1989; 2(5): 359-66.
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28. Aziz AR, Vincent Wong KF. A Neural-Network approach to forecast metal content Approach to the Determination of Aquifer Parameters. Ground Water 1992; 30(2): 164-66.
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29. ASCE Task Committee on Application of Artificial Neural Networks in Hydrology. Artificial Neural Networks in Hydrology. I: Preliminary Concepts. J Hydrol Eng 2000; 5(2): 115-23.
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30. Amin S, Farjoud Mr, Shabani A. Groundwater contamination by heavy metals in water resources of Shiraz area. Iran Agric Res 2011; 30(1-2): 21-32.
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31. Taghipour H, Mosaferi M, Pourakbar M, Armanfar F. Heavy metals concentrations in groundwater used for irrigation. Health Promot Perspect 2012; 2(2): 205-10.
32
32. Ghosh A, Das P, Sinha K. Modeling of biosorption of Cu(II) by alkali-modified spent tea leaves using response surface methodology (RSM) and artificial neural network (ANN). Appl Water Sci (2015) 5: 191 2015; 5(2): 191-9.
33
33. Gnanasangeetha D, SaralaThambavani D. Modelling of As3+ adsorption from aqueous solution using Azadirachta indica by artificial neural network. Desalin Water Treat 2015; 56(7): 1839-54.
34
34. Yurtsever U, Yurtsever M, Sengil A, Yilmazçoban NK. Fast artificial neural network (FANN) modeling of Cd(II) ions removal by valonia resin. Desalin Water Treat 2014; 56(1): 83-96.
35
35. Marquardt DW. An Algorithm for Least-Squares Estimation of Nonlinear Parameters. J Soc Ind Appl Math 1963; 11(2): 431-41.
36
36. Levenberg K. A method for the solution of certain non-linear problems in least squares. Quart Appl Math 1944; 2(2): 164-8.
37
37. Sudheer KP, Gosain AK, Ramasastri KS. A data-driven algorithm for constructing artificial neural network rainfall-runoff models. Hydrol Process 2002; 16(6): 1325-30.
38
38. Lin J, CHENG CT, Chau KW. Using Support Vector Machines for Long-Term Discharge Prediction. Hydrol Sci J 2006; 51(4): 599-612.
39
39. Keskin TE, Dügenci M, Kaçaroglu F. Prediction of water pollution sources using artificial neural networks in the study areas of Sivas, Karabük and Bartin (Turkey). Environ Earth Sci 2015; 73(9): 5333-47.
40
40. Nor AS, Faramarzi M, Yunus MA, Ibrahim S. Nitrate and sulfate estimations in water sources using a planar electromagnetic sensor array and artificial neural network method. IEEE Sens J 2015; 15(1): 497-504.
41
ORIGINAL_ARTICLE
Awareness and attitude assessment regarding toxic metal-contaminated rice based on the Health Belief Model
Given the presence of toxic metals in some local Iranian as well as some imported rice varieties, it may be of help to focus on public awareness for the implementation of educational interventions. This study aimed to assess awareness and attitudes of women in Sanandaj, Iran, regarding toxic metal-contaminated rice based on the Health Belief Model (HBM). This cross-sectional study was conducted on 1450 women aged 18 and above. The questionnaire used in the study consisted of three parts; demographic information, awareness assessment, and HBM constructs. Data were analyzed using chi-square test, t-test, ANOVA, and the logistic regression analysis in SPSS. The mean age of the study participants was 40.55 ± 13.8 years. The level of awareness regarding the presence of toxic metals in daily-consumed rice was low in 78.2% and moderate in 21.8% of the participants. Among the attitude factors, risk perception was the only one that increased the probability of falling in the group with moderate awareness instead of the group with low awareness by 1.37 times. The results support the necessity of raising public awareness and increasing risk perception in the population about the adverse effects of toxic metals.
https://jaehr.muk.ac.ir/article_40224_2eeaa541d936f7276d99c1e7a518690e.pdf
2016-04-01
78
87
10.22102/jaehr.2016.40224
awareness
Attitude
Food Contamination
Heavy Metal Toxicity
Health Belief Model
Leili
Shafiei
1
Environmental Health Research Center AND Department of Environmental Health, Kurdistan University of Medical Sciences, Sanandaj, Iran
AUTHOR
Parvaneh
Taymoori
parvaneh.tay@gmail.com
2
Environmental Health Research Center AND Department of Public Health, School of Health Kurdistan University of Medical Sciences, Sanandaj, Iran
LEAD_AUTHOR
Katayoun
Yazdanshenas
k.yazdanshenas.p@gmail.com
3
Department of Public Health, School of Health, Kurdistan University of Medical Sciences, Sanandaj, Iran
AUTHOR
1. Roca-Perez L, Gil C, Cervera ML, Gonzalvez A, Ramos-Miras J, Pons V, et al. Selenium and heavy metals content in some Mediterranean soils. J Geochem Explor 2010; 107(2): 110-6.
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2. Lam HM, Remais J, Fung MC, Xu L, Sai-Ming Sun S. Food supply and food safety issues in China. The Lancet 2013; 381(9882): 2044-53.
2
3. Zheng S, Zhang M. Effect of moisture regime on the redistribution of heavy metals in paddy soil. J Environ Sci (China) 2011; 23(3): 434-43.
3
4. Fransisca Y, Small DM, Morrison PD, Spencer MJ, Ball AS, Jones OA. Assessment of arsenic in Australian grown and imported rice varieties on sale in Australia and potential links with irrigation practises and soil geochemistry. Chemosphere
4
2015; 138: 1008-13.
5
5. Bae M, Watanabe C, Inaoka T, Sekiyama M, Sudo N, Bokul MH, et al. Arsenic in cooked rice in Bangladesh. Lancet 2002; 360(9348): 1839-40.
6
6. Malakootian M, Yaghmaeian K, Meserghani M, Mahvi AH, danesh pajouh m. Determination of pb,cd,cr and ni concentration in imported Indian rice to Iran. Iran J Health Environ 2011; 4(1): 77-84.
7
7. Jahed Khaniki GR, Zazoli MA. Cadmium and lead contents in rice (Oryza sativa) in the North of Iran.
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Int J Agri Biol 2005; 7(6): 1026-29.
9
8. Bakhtiarian A, Gholipour M, Ghazi-Khansari M. Lead and cadmium content of korbal rice in northern Iran. Iran J Public Health 2001; 30(3- 4129): 132.
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9. Ghazanfarirad N, Dehghan K, Fakhernia M. Determination of lead, cadmium and arsenic metals in imported rice into the west azerbaijan province, northwest of Iran. J Nov Appl Sci 2014; 3(5): 452-6.
11
10. Lokeshappa B, Shivpuri K, Tripathi V, Dikshit AK. Assessment of toxic metals in agricultural produce. Food and Public Health 2012; 2(1): 24-9.
12
11. Jarup L. Hazards of heavy metal contamination. Br Med Bull 2003; 68: 167-82.
13
12. Benvenuto MA, Ahuja S, Noonan GO, Duncan TV. Chemistry of food, food supplements, and food contact materials: From production to plate. Washington, DC: American Chemical Society; 2014.
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13. Zareban I, Niknami S, Hidarnia A, Rakhshani F, Shahrakipour M, Moshki M. The effect of education based on health belief model on reduction of hba1c level in diabetes type 2. J Res Health 2013; 3(2): 370-8.
15
14. Sethares KA, Elliott K. The effect of a tailored message intervention on heart failure readmission rates, quality of life, and benefit and barrier beliefs in persons with heart failure. Heart Lung 2004; 33(4): 249-60.
16
15. Glanz K, Rimer B, Viswanath K. Health behavior and health education: theory, research, and practice. New York, NY: John Wiley & Sons; 2008. p. 45-65.
17
16. Hajizadeh E, Asghari M. Statistical methods and analyses in health and biosciences. a research methodological approach. Tehran, Iran: Iranian Student Book Agency; 2011. [In Persian].
18
17. Sajadi Kaboudi P. Comparison and assessment of nutritional status of girls in state and private high schools in Babol City. Proceedings of the 5th Iranian Congress of Nutrition; 1999 Sep 13-15; Tehran, Iran. [In Persian].
19
18. Razaviye V, Sohrabi A, Pourabdollahi P, Salek Zamani M, Dastgiri S. Assessment of knowledge, attitude and practice of referred mothers to health center towards breastfeeding and complementary foods in Tabriz. Med J Tabriz Univ Med Sci 2000; 34(48): 65-70. [In Persian].
20
19. Vakili M, Joulai H. Assessment of knowledge and practice of households towards idonized-salt use in Arsanjan. Proceedings of the 7th Iranian congress of Nutrition; 2002 Sep 2-5; Tehran, Iran. [In Persian].
21
20. Kesse-Guyot E, Bertrais S, Peneau S, Estaquio C, Dauchet L, Vergnaud AC, et al. Dietary patterns and their sociodemographic and behavioural correlates in French middle-aged adults from the SU.VI.MAX cohort. Eur J Clin Nutr 2009; 63(4): 521-8.
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21. Sanchez-Villegas A, Delgado-Rodriguez M, Martinez-Gonzalez MA, De Irala-Estevez J. Gender, age, socio-demographic and lifestyle factors associated with major dietary patterns in the Spanish Project SUN (Seguimiento Universidad de Navarra). Eur J Clin Nutr 2003; 57(2): 285-92.
23
22. van Dam RM, Grievink L, Ocke MC, Feskens EJ. Patterns of food consumption and risk factors for cardiovascular disease in the general Dutch population. Am J Clin Nutr 2003; 77(5): 1156-63.
24
23. Park SY, Murphy SP, Wilkens LR, Yamamoto JF, Sharma S, Hankin JH, et al. Dietary patterns using the Food Guide Pyramid groups are associated with sociodemographic and lifestyle factors: the multiethnic cohort study. J Nutr 2005; 135(4): 843-9.
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24. Sajadi P, Bakhtiari A, Haji Ahmadi M. Assessment of Nutritional Knowledge Level of Pregnant Women Referred To Health and Therapeutic Centers of Babol. J Babol Univ Med Sci 2007;9(5): 50-5. [In Persian].
26
25. Moon JA, Yoo CH, Kim MH, Lee SM, Oh YJ, Ryu YH, et al. Knowledge, Self-Efficacy, and Perceived Barriers on the Low-Iodine Diet among Thyroid Cancer Patients Preparing for Radioactive Iodine Therapy. Clin Nutr Res 2012; 1(1): 13-22.
27
26. Yang EJ, Kerver JM, Song WO. Dietary patterns of Korean Americans described by factor analysis. J Am Coll Nutr 2005; 24(2): 115-21.
28
27. Jafari F. Evaluation of education efficacy on knowledge of mothers about prevention of Iron deficiency anemia. Proceedings of the 8th Iranian Congress of Nutrition; 2004 Sep 6-9; Tehran, Iran; 2004. p. 64-5. [In Persian].
29
28. Heshmat R, Keshtkar A, Sheykh-ol-Eslam R, Nadim A. Knowledge, Attitude and practice of households and health care staff towards nutrition and micronutrients (NUT-KAP) in provinces under the pilot study on flour fortification with Iron: study design and sampling method. Iran J Epidemiol 2005; 1(1): 9-16. [In Persian].
30
29. Contento IR, Randell JS, Basch CE. Review and analysis of evaluation measures used in nutrition education intervention research. J Nutr Educ Behav 2002; 34(1): 2-25.
31
30. Zalilah MS, Bahaman AS, Laily P, Maznah I, Mohd Sham K, Norlijah O, et al. Nutrition education intervention improves nutrition knowledge, attitude and practices of primary school children : a pilot study. Int Electron J Health Educ
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2008; 11(1): 119-32.
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31. Farivar F, Heshmat R, Azemati B, Abbaszadeh Ahranjani SH, Keshtkar AA, Sheykholeslam R, et al. Understanding knowledge about, general attitudes toward and practice of nutrition behavior in the Iranian population. Iran J Epidemiol 2009; 5(2): 11-8. [In Persian].
34
32. Lotfi B, Rakhshani F. Knowledge and perceived threat of students in relationship with their behavior in context of consumption of breakfast and snack in primary boy schools in Zahedan. Payesh Health Monit 2014; 13(1): 61-71. [In Persian].
35
33. Perry CL, Bishop DB, Taylor G, Murray DM, Mays RW, Dudovitz BS, et al. Changing fruit and vegetable consumption among children: the 5-a- Day Power Plus program in St. Paul, Minnesota.
36
Am J Public Health 1998; 88(4): 603-9.
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34. Pawlak R, Colby S. Benefits, barriers, self-efficacy and knowledge regarding healthy foods; perception of African Americans living in eastern North Carolina. Nutr Res Pract 2009; 3(1): 56-63.
38
35. Patrick L. Toxic metals and antioxidants: Part II. The role of antioxidants in arsenic and cadmium toxicity. Altern Med Rev 2003; 8(2): 106-28.
39
36. Zazouli MA, Mohseni Bandpei A, Ebrahimi M, Izanloo H. Investigation of Cadmium and Lead Contents in Iranian Rice Cultivated in Babol Region. Chem Asian J 2010; 22(2): 1369-76.
40
37. Park JA, Part K. Text book of preventive medicine & health care service. Trans. Shojaie Tehrani H. 2nd ed. Tehran, Iran: Samat Pablications; 1997. [In Persian].
41
38. Schmidt CW. In search of "just right": the challenge of regulating arsenic in rice. Environ Health Perspect 2015; 123(1): A16-A19.
42
39. Argos M, Rathouz PJ, Pierce BL, Kalra T, Parvez F, Slavkovich V, et al. Dietary B vitamin intakes and urinary total arsenic concentration in the Health Effects of Arsenic Longitudinal Study (HEALS) cohort, Bangladesh. Eur J Nutr 2010; 49(8): 473-81.
43
ORIGINAL_ARTICLE
Vermicomposting of cow dung, kitchen waste and sewage sludge with bagasse using Eisenia fetida
The sugar cane industry produces significant amounts of cane trash and bagasse. Inappropriate disposal of agro-wastes can lead to environmental problems. Converting wastes such as cane trash and bagasse (Bg) to a fertilizer and conditioner is the aim of sustainable waste management in sugar cane industry. Cow dung (CD), kitchen waste (KW), and sewage sludge (SS) were mixed with bagasse as amendment in different proportions: Bg:CD (1:1), Bg:CD (1:2), Bg:SS (1:1), Bg:SS (1:2), Bg:KW (1:1) and Bg:KW (1:2) in triplicate treatment with Eisenia fetida. Chemical analysis of the samples showed a significant decrease in total organic carbon (TOC) (20%-54%), total Kjeldahl nitrogen (TKN) (9.5%-39.7%) and C:N ratio (12%-31.2%), while total potassium (31.4%-54%) and available phosphorus (32%-55%) contents increased during vermicomposting. A significant difference was observed among weight and number of worms in control with other treatments at the end of vermicomposting. According to obtained results vermicomposting is an efficient method for sustainable recycling different classes of waste produced in sugar cane agro-industry.
https://jaehr.muk.ac.ir/article_40225_e453c693c0fbe17deccf089aa18923e1.pdf
2016-04-01
88
94
10.22102/jaehr.2016.40225
Vermicomposting
Bagasse
Cow dung
Kitchen Waste
sewage sludge
Eisenia Fetida
Ali Akbar
Babaei
aababaei52@gmail.com
1
Environmental Technologies Research Center AND Department of Environmental Health Engineering, School of Public Health, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
AUTHOR
Gholamreza
Goudarzi
gholam_goudarzi@yahoo.com
2
Environmental Technologies Research Center AND Department of Environmental Health Engineering, School of Public Health, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
AUTHOR
Abdolkazem
Neisi
3
Environmental Technologies Research Center AND Department of Environmental Health Engineering, School of Public Health, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
AUTHOR
Zohreh
Ebrahimi
4
Environmental Technologies Research Center AND Department of Environmental Health Engineering, School of Public Health, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
AUTHOR
Nadali
Alavi
5
Environmental and Occupational Hazards Control Research Center AND Department of Environmental Health Engineering, School of Public Health, Shahid Beheshti Medical University, Tehran, Iran
LEAD_AUTHOR
1. Najafi G, Ghobadian B, Tavakoli T, Yusaf T. Potential of bioethanol production from agricultural wastes in Iran. Renew Sust Energ Rev 2009; 13(6-7): 1418-27.
1
2. Alavi N, Amir-Heidari P, Azadi R, Babaei AA. Effluent quality of ammonia unit in Razi petrochemical complex. J Advan Environ Health Res 2013; 1(1): 15-20.
2
3. Kaushik P, Garg VK. Vermicomposting of mixed solid textile mill sludge and cow dung with the epigeic earthworm Eisenia foetida. Bioresour Technol 2003; 90(3): 311-6.
3
4. Singh RP, Embrandiri A, Ibrahim MH, Esa N. Management of biomass residues generated from palm oil mill: Vermicomposting a sustainable option. Resour Conserv Recy; 2011; 55(4): 423-34.
4
5. Ravindran B, Contreras-Ramos SM, Sekaran G. Changes in earthworm gut associated enzymes and microbial diversity on the treatment of fermented tannery waste using epigeic earthworm Eudrilus eugeniae. Ecol Eng 2015; 74: 394-401.
5
6. Hait S, Tare V. Vermistabilization of primary sewage sludge. Bioresour Technol 2011; 102(3): 2812-20.
6
7. Hanc A, Chadimova Z. Nutrient recovery from apple pomace waste by vermicomposting technology. Bioresour Technol 2014; 168: 240-4.
7
8. Lim SL, Wu TY, Sim EYS, Lim PN, Clarke C. Biotransformation of rice husk into organic fertilizer through vermicomposting. Ecol Eng 2012; 41: 60-4.
8
9. Sen B, Chandra TS. Chemolytic and solid-state spectroscopic evaluation of organic matter transformation during vermicomposting of sugar industry wastes. Bioresour Technol 2007; 98(8): 1680-3.
9
10. Kumar R, Verma D, Singh BL, Kumar U, Shweta. Composting of sugar-cane waste by-products through treatment with microorganisms and subsequent vermicomposting. Bioresour Technol 2010; 101(17): 6707-11.
10
11. Sangwan P, Kaushik CP, Garg VK. Vermicomposting of sugar industry waste (press mud) mixed with cow dung employing an epigeic earthworm Eisenia fetida. Waste Manag Res 2010; 28(1): 71-5.
11
12. Pramanik P. Changes in microbial properties and nutrient dynamics in bagasse and coir during vermicomposting: quantification of fungal biomass through ergosterol estimation in vermicompost. Waste Manag 2010; 30(5): 787-91.
12
13. Pigatin LB, Atoloye IA, Obikoya OA, Borsato AV, Rezende MO. Chemical study of vermicomposted agroindustrial wastes. Int J Recycl Org Waste Agricult 2016; 5(1): 55-63.
13
14. United States Environmental Protection Agency. Soil and Waste pH [Online]. [cited 2004]; Available from: URL:
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https://www.epa.gov/sites/production/files/2015-12/documents/9045d.pdf
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15. Nelson D, Sommers LE. Total carbon and organic carbon and organic matter. In: Page AL, Miller RH, Keeney DR, Editors. Methods of soil analysis. Madison, WI: American Society of Agronomy; 1996. p. 539-79.
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18. Santhi R, Natesan R, Bhaskaran A, Murugappan V. Procedures for soil testing and water quality appraisal. Coimbatore, India: Tamil Nadu Agricultural University; 2003.
19
19. Alavi N, Azadi R, Jaafarzadeh N, Babaei A. Kinetics of Nitrogen Removal in an Anammox Up-Flow Anaerobic Bioreactor for Treating Petrochemical Industries Wastewater (Ammonia Plant). J Asian Chem 2011; 23(12): 5220-4.
20
20. Ndegwa PM, Thompson SA, Das KC. Effects of stocking density and feeding rate on vermicomposting of biosolids. Bioresour Technol 2000; 71(1): 5-12.
21
21. Suthar S. Vermistabilization of municipal sewage sludge amended with sugarcane trash using epigeic Eisenia fetida (Oligochaeta). J Hazard Mater 2009; 163(1): 199-206.
22
22. Plaza C, Nogales R, Senesi N, Benitez E, Polo A. Organic matter humification by vermicomposting of cattle manure alone and mixed with two-phase olive pomace. Bioresour Technol 2008; 99(11): 5085-9.
23
23. Suthar S. Nutrient changes and biodynamics of epigeic earthworm Perionyx excavatus (Perrier) during recycling of some agriculture wastes. Bioresour Technol 2007; 98(8): 1608-14.
24
24. Suthar S. Recycling of agro-industrial sludge through vermitechnology. Ecol Eng 2010; 36(8): 1028-36.
25
25. Babaei AA, Azadi R, Jaafarzadeh N, Alavi N. Application and kinetic evaluation of upflow anaerobic biofilm reactor for nitrogen removal from wastewater by Anammox process. Iranian J Environ Health Sci Eng 2013; 10(1): 20.
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26. Sanchez-Monedero MA, Roig A, Paredes C, Bernal MP. Nitrogen transformation during organic waste composting by the Rutgers system and its effects on pH, EC and maturity of the composting mixtures. Bioresour Technol 2001; 78(3): 301-8.
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27. Kumar R, Shweta. Enhancement of wood waste decomposition by microbial inoculation prior to vermicomposting. Bioresour Technol 2011; 102(2): 1475-80.
28
28. Benitez E, Nogales R, Elvira C, Masciandaro G, Ceccanti B. Enzyme activities as indicators of the stabilization of sewage sludges composting with Eisenia foetida. Bioresour Technol 1999; 67(3): 297-303.
29
29. Fernandez-Gomez MJ, Romero E, Nogales R. Feasibility of vermicomposting for vegetable
30
greenhouse waste recycling. Bioresour Technol 2010; 101(24): 9654-60.
31
30. Khwairakpam M, Bhargava R. Bioconversion of filter mud using vermicomposting employing two exotic and one local earthworm species. Bioresour Technol 2009; 100(23): 5846-52.
32
31. Deka H, Deka S, Baruah CK, Das J, Hoque S, Sarma NS. Vermicomposting of distillation waste of citronella plant (Cymbopogon winterianus Jowitt) employing Eudrilus eugeniae. Bioresour Technol 2011; 102(13): 6944-50.
33
32. Busato JG, Lima LصS, Aguiar NةO, Canellas LP, Olivares FةL. Changes in labile phosphorus forms during maturation of vermicompost enriched with phosphorus-solubilizing and diazotrophic bacteria. Bioresour Technol 2012; 110: 390-5.
34
33. Suthar S. Vermicomposting of vegetable-market solid waste using Eisenia fetida: Impact of bulking material on earthworm growth and decomposition rate. Ecol Eng 2009; 35(5): 914-20.
35
34. Suthar S, Mutiyar PK, Singh S. Vermicomposting of milk processing industry sludge spiked with plant wastes. Bioresour Technol 2012; 116: 214-9.
36
35. Xing M, Lv B, Zhao C, Yang J. Towards understanding the effects of additives on the vermicomposting of sewage sludge. Environ Sci Pollut Res Int 2015; 22(6): 4644-53.
37
36. Gupta R, Garg VK. Vermiremediation and nutrient recovery of non-recyclable paper waste employing Eisenia fetida. J Hazard Mater 2009; 162(1): 430-9.
38
37. Yadav A, Garg VK. Feasibility of nutrient recovery from industrial sludge by vermicomposting technology. J Hazard Mater 2009; 168(1): 262-8.
39
38. Suthar S. Potential utilization of guar gum industrial waste in vermicompost production. Bioresour Technol 2006; 97(18): 2474-7.
40
39. Kaushik P, Garg VK. Dynamics of biological and chemical parameters during vermicomposting of solid textile mill sludge mixed with cow dung and agricultural residues. Bioresour Technol 2004; 94(2): 203-9.
41
40. Kaviraj, Sharma S. Municipal solid waste management through vermicomposting employing exotic and local species of earthworms. Bioresour Technol 2003; 90(2): 169-73.
42
41. Pramanik P, Ghosh GK, Ghosal PK, Banik P. Changes in organic-C, N, P and K and enzyme activities in vermicompost of biodegradable organic wastes under liming and microbial inoculants. Bioresour Technol 2007; 98(13): 2485-94.
43
42. Manna MC, Jha S, Ghosh PK, Acharya CL. Comparative efficacy of three epigeic earthworms under different deciduous forest litters decomposition. Bioresour Technol 2003; 88(3): 197-206.
44
43. Garg VK, Suthar S, Yadav A. Management of food industry waste employing vermicomposting technology. Bioresour Technol 2012; 126: 437-43.
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ORIGINAL_ARTICLE
Evaluation of organic carbon, elemental carbon, and water soluble organic carbon concentration in PM2.5 in the ambient air of Sina Hospital district, Tehran, Iran
In the present study, carbon species including organic carbon (OC), elemental carbon (EC), and water-soluble organic carbon (WSOC) concentration in PM2.5 were assessed at an urban site of Tehran, Iran during March to June 2014. The PM2.5 samples were collected using an frmOMNITM Ambient Air Sampler. Thermal gravimetric analysis (TGA) was used to analyze OC and EC. The results showed that PM2.5 concentrations varied from 14.32 to 74.45 µg/m3 with an average value of 41.39 µg/m3. The results also showed that carbon species varied from 5.52 to 23.21 (15.35 ± 6.05) µg/m3 for OC and 1.03 to 4.16 (2.25 ± 0.65) µg/m3 for EC. As the findings indicated, the mean PM2.5 level in the sampling area was higher than the annual average determined by the United States Environmental Protection Agency (EPA) as the ambient air quality standard. On average, carbon species (OC, EC, and WSOC) account for almost 60% of PM2.5 mass in the atmospheric outflow from a downwind site. OC and EC concentrations in atmospheric PM2.5 collected at the sampling site were lower than the values reported for other urban areas with high or medium vehicular traffic and/or industrial sources. Moreover, the results obtained in this research can provide a valuable data base for health risk evaluation of the local residents and prioritization of control actions.
https://jaehr.muk.ac.ir/article_40221_c2f93f6a2a1fad4f8a1a88484b5baf6f.pdf
2016-04-01
95
101
10.22102/jaehr.2016.40221
Air pollution
Particulate Matter
Analysis
Standards
Hossein
Arfaeinia
1
Department of Environmental Health Engineering, School of Public Health, Bushehr University of Medical Sciences, Bushehr AND Iran University of Medical Sciences, Tehran, Iran
AUTHOR
Seyed Enayat
Hashemi
2
Department of Environmental Health Engineering, School of Public Health, Iran University of Medical Sciences, Tehran, Iran
AUTHOR
Ali Asghar
Alamolhoda
3
Institutes of Water and Energy, Sharif University of Technology, Tehran, Iran
AUTHOR
Majid
Kermani
majidkermani@yahoo.com
4
Research Center for Environmental Health Technology AND Department of Environmental Health Engineering, School of Public Health, Iran University of Medical Sciences, Tehran, Iran
LEAD_AUTHOR
1. Li X, Wang S, Duan L, Hao J, Nie Y. Carbonaceous aerosol emissions from household biofuel combustion in China. Environ Sci Technol 2009; 43(15): 6076-81.
1
2. Ram K, Sarin MM, Sudheer AK, Rengarajan R. Carbonaceous and secondary inorganic aerosols during wintertime fog and haze over urban sites in the indo-gangetic plain. Journal of Aerosol and Air Quality Research 2012; 12(3): 359-70.
2
3. Sharma SK, Rohtash MK, Saraswati NCG, Saxena M, Mandal TK. Characteristics of ambient ammonia over Delhi, India. Meteorol Atmos Phys 2014; 124(1): 67-82.
3
4. Anenberg SC, Balakrishnan K, Jetter J, Masera O, Mehta S, Moss J, et al. Cleaner cooking solutions to achieve health, climate, and economic cobenefits. Environ Sci Technol 2013; 47(9): 3944-52.
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5. Stanek LW, Sacks JD, Dutton SJ, Dubois JJB. Attributing health effects to apportioned components and sources of particulate matter: An evaluation of collective results. Atmospheric Environment 2011; 45(32): 5655-63.
5
6. Kelly FJ, Fussell JC. Size, source and chemical composition as determinants of toxicity attributable to ambient particulate matter. Atmospheric Environment 2012; 60: 504-26.
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7. de Kok TM, Driece HA, Hogervorst JG, Briede JJ. Toxicological assessment of ambient and traffic-related particulate matter: a review of recent studies. Mutat Res 2006; 613(2-3): 103-22.
7
8. Jedynska A, Hoek G, Eeftens M, Cyrys J, Keuken M, Ampe C, et al. Spatial variations of PAH, hopanes/steranes and EC/OC concentrations within and between European study areas. Atmospheric Environment 2014; 87: 239-48.
8
9. Sharma M, Kishore S, Tripathi S, Behera SN. Role of atmospheric ammonia in the formation of inorganic secondary particulate matter: A study at Kanpur, India. Journal of Atmospheric Chemistry 2007; 58(1): 1-17.
9
10. Sharma SK, Mandal TK, Saxena M, Sharma A, Datta A, Saud T. Variation of OC, EC, WSIC and trace metals of PM10 in Delhi, India. Journal of Atmospheric and Solar-Terrestrial Physics 2014; 113: 10-22.
10
11. Sharma SK, Mandal TK, Saxena M, Rohtash R, Sharma A, Gautam R. Source apportionment of PM10 by using positive matrix factorization at an urban site of Delhi, India. Urban Climate 2014; 10(Part 4,): 656-70.
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12. Goldstein AH, Galbally IE. Known and unexplored organic constituents in the earth's atmosphere. Environ Sci Technol 2007; 41(5): 1514-21.
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13. Penner JE, Novakov T. Carbonaceous particles in the atmosphere: A historical perspective to the Fifth International Conference on Carbonaceous Particles in the Atmosphere. J Geophys Res 1996; 101(D14): 19373-8.
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14. Cyrys J, Heinrich J, Hoek G, Meliefste K, Lewne M, Gehring U, et al. Comparison between different traffic-related particle indicators: elemental carbon (EC), PM2.5 mass, and absorbance. J Expo Anal Environ Epidemiol 2003; 13(2): 134-43.
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15. Schaap M, van der Gon D. On the variability of Black Smoke and carbonaceous aerosols in the Netherlands. Atmospheric Environment 2007; 41(28): 5908-20.
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16. Keuken MP, Jonkers S, Zandveld P, Voogt M, van den Elshout S. Elemental carbon as an indicator for evaluating the impact of traffic measures on air quality and health. Atmospheric Environment 2012; 61: 1-8.
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17. Lewandowski M, Jaoui M, Kleindienst TE, Offenberg JH, Edney EO. Composition of PM2.5 during the summer of 2003 in Research Triangle Park, North Carolina. Atmospheric Environment 2007; 41(19): 4073-83.
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18. Yang H, Yu JZ, Ho SSH, Xu J, Wu WS, Wan CH, et al. The chemical composition of inorganic and carbonaceous materials in PM2.5 in Nanjing, China. Atmospheric Environment 2005; 39(20): 3735-49.
18
19. Pathak RK, Wang T, Ho KF, Lee SC. Characteristics of summertime PM2.5 organic and elemental carbon in four major Chinese cities: Implications of high acidity for water-soluble organic carbon (WSOC). Atmospheric
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Environment 2011; 45(2): 318-25.
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20. Jacob DJ. Heterogeneous chemistry and tropospheric ozone. Atmospheric Environment 2000; 34(12-14): 2131-59.
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21. Kanakidou M, Seinfeld JH, Pandis SN, Barnes I, Dentener FJD, Facchini MC, et al. Organic aerosol and global climate modelling: a review. Atmos Chem Phys 2005; 5: 1053-123.
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22. Fuzzi S, Andreae MO, Huebert BJ, Kulmala M, Bond TC, Boy M, et al. Critical assessment of the current state of scientific knowledge, terminology, and research needs concerning the role of organic aerosols in the atmosphere, climate, and global change. Atmos Chem Phys 2006; 6: 2017-38.
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23. Cooke WF, Cachier LH, Feichter J. Construction of a 1 × 1 fossil fuel emission data set for carbonaceous aerosol and implementation and radiative impact in the ECHAM4 model. J Geophys Res 1999; 104(D18): 22137-62.
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24. Masiello CA. New directions in black carbon organic geochemistry. Marine Chemistry 2004; 92(1-4): 201-13.
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25. Dachs J, Eisenreich SJ. Adsorption onto aerosol soot carbon dominates gas-particle partitioning of polycyclic aromatic hydrocarbons. Environ Sci Technol 2000; 34(17): 3690-7.
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26. Sharma SK, Singh AK, Saud T, Mandal TK, Saxena M, Singh S, et al. Study on water-soluble ionic composition of PM10 and related trace gases over Bay of Bengal during W_ICARB campaign. Meteorol Atmos Phys 2012; 118(1): 37-51.
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27. Chow JC, Watson JG, Chen LW, Arnott WP, Moosmuller H, Fung K. Equivalence of elemental carbon by thermal/optical reflectance and transmittance with different temperature protocols. Environ Sci Technol 2004; 38(16): 4414-22.
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https://www3.epa.gov/airquality/
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31. Chaloulakou A, Kassomenos P, Spyrellis N, Demokritou P, Koutrakis P. Measurements of PM10 and PM2.5 particle concentrations in Athens, Greece. Atmospheric Environment 2003; 37(5): 649-60.
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32. Koçak M, Mihalopoulos N, Kubilay N. Chemical composition of the fine and coarse fraction of aerosols in the northeastern Mediterranean. Atmospheric Environment 2007; 41(34): 7351-68.
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33. Glavas SD, Nikolakis P, Ambatzoglou D, Mihalopoulos N. Factors affecting the seasonal variation of mass and ionic composition of PM2.5 at a central Mediterranean coastal site. Atmospheric Environment 2008; 42(21): 5365-73.
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34. Leili M, Naddafi K, Nabizadeh R, Yunesian M, .Mesdaghinia A. The study of TSP and PM10 concentration and their heavy metal content in central area of Tehran, Iran. Air Qual Atmos Health 2008; 1(3): 159-66.
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35. Srinivas B, Sarin MM. PM2.5, EC and OC in atmospheric outflow from the Indo-Gangetic Plain: Temporal variability and aerosol organic carbon-to-organic mass conversion factor. Science of The Total Environment 2014; 487: 196-205.
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38. Perrino C, Tiwari S, Catrambone M, Torre SD,
40
Rantica E, Canepari S. Chemical characterization of atmospheric PM in Delhi, India, during different periods of the year including Diwali festival. Atmospheric Pollution Research 2011; 2(4): 418-27.
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39. Tarek Mohamed N, Yuji Y, Kazuhiko S, Qingyue W, Kazuhiko S. Chemical Composition of PM2.5 and PM10 and Associated Polycyclic Aromatic Hydrocarbons at a Roadside and an Urban Background Area in Saitama, Japan. Asian journal of atmospheric environment 2008; 2(2): 90-101.
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41. Park SS, Bae MS, Schauer JJ, Ryu SY, Kim YJ, Cho SY, et al. Evaluation of the TMO and TOT methods for OC and EC measurements and their characteristics in PM2.5 at an urban site of Korea during ACE-Asia. Atmospheric Environment 2005; 39(28): 5101-12.
44
42. Ram K, Sarin MM. Day–night variability of EC, OC, WSOC and inorganic ions in urban environment of Indo-Gangetic Plain: Implications to secondary aerosol formation. Atmospheric Environment 2011; 45(2): 460-8.
45
ORIGINAL_ARTICLE
A survey on fluoride, nitrate, iron, manganese and total hardness in drinking water of Fereydoonkenar city during 2008-2013
The purpose of present study was to evaluate fluoride, nitrate, iron, manganese and total hardness in drinking water of wells and reservoirs in Fereidonkenar, Mazandaran, Northern Iran and compare the results with national and international standards. This cross-sectional descriptive study was carried out on data during five years from the spring of 2008 until the autumn of 2013. Studies were performed on 430 samples in the different seasons and years taken from water and wastewater company (WWC). The results showed that the average fluoride, nitrate, iron, manganese, total hardness concentrations obtained were 0.42, 10.2, 0.136, 0.03, 382.28 mg/l, respectively. The analysis showed a negative correlation between nitrate and fluoride, iron and manganese and a positive correlation with the hardness. The mean fluoride concentration was less than the standard. Total hardness value was more than recommend standard. Nitrate was below 50 mg/l, in accordance with national and international standards. The amount of iron and manganese in drinking water were acceptable. So, except for low fluoride and high total hardness, there was no any problem in other investigated parameters.
https://jaehr.muk.ac.ir/article_40226_9bbac14267d16a93d332eaa6e94b22c3.pdf
2016-04-01
102
112
10.22102/jaehr.2016.40226
Quality of Drinking Water (F
NO3
Fe
Mn
TH)
Zeinab
Tahernezhad
1
Department of Environmental Health, School of Public Health, Mazandaran University of Medical Sciences, Sari, Iran
AUTHOR
Zabihollah
Yousefi
zyousefi2004@gmail.com
2
Department of Environmental Health Engineering, School of Health, Mazandaran University of Medical Sciences, Sari, Iran
LEAD_AUTHOR
Nouraddin
Mousavinasab
3
Department of Biostatistics, School of Public Health, Health Sciences Research Center, Mazandaran University of Medical Sciences, Sari, Iran
AUTHOR
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1
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2
3. Parvin N, Moslehi P. Heavy metals concentrations on drinking water in different Aeras of Tehran as ppb and Methods of Remal Them. Journal of Food Sciences Technology 2008; 5(1): 29-35. [In Persian].
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4. Nabizadeh R, Faezi Razi D. Guidelines for drinking water quality. Tehran, Iran: Nas Publication; 2016. [In Persian].
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5. Harrison PTC. Fluoride in water: A UK perspective. J Fluor Chem 2005; 126(11-12): 1448-56.
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6. United States Environmental Protection Agency. Estimated national occurrence and exposure to Nitrate and Nitrite in Public drinking water supplies. Washington, DC: UAEPA; 1997.
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7. Espejo-Herrera N, Cantor KP, Malats N, Silverman DT, Tardon A, Garcia-Closas R, et al. Nitrate in drinking water and bladder cancer risk in Spain. Environ Res 2015; 137: 299-307.
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8. Liu H, Gao Y, Sun L, Li M, Li B, Sun D. Assessment of relationship on excess fluoride intake from drinking water and carotid atherosclerosis development in adults in fluoride endemic areas, China. Int J Hyg Environ Health 2014; 217(2-3): 413-20.
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11. Mohammadi H, Yazdanbakhsh A, Sheykh Mohammadi A, Bonyadinejad G, Alinejad A, Ghanbari G. Investigation of nitrite and nitrate in drinking water of regions under surveillance of Shahid Beheshti University of Medical Sciences in Tehran Province, Iran. J Health Syst Res 2011; 7(6): 782-9. [In Persian].
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15. Nazemi S, Raei M. Fluoride concentration in drinking water in Shahroud (Northern Iran) and determination of DMF index in 7 year old children. J Occu Health Epidemiol 2012; 1(1): 50-5. [In Persian].
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16. Rafati L, Mokhtari M, Fazelinia F, Momtaz SM, Mahvi AH. Evaluation of ground water fluoride concentration in Hamadan province west of Iran 2012. Iran J Health Sci 2013; 1(3): 71-6. [In Persian].
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18. Seifi-Nigje Gheshlagh F, Ziarati P, Arbabi Bidgoli S. Seasonal Fluctuation of Heavy Metal and Nitrate Pollution in ground water of Farmlands in Talesh, Gilan, Iran. Intl J Farm & Alli Sci 2013; 2(20): 836-41.
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20. Fakhri Y, Amirhajelo LR, Moradi B, Langaridadeh Gh, Zandsalimi Y, Jafarzadeh S, et al. Concentration of nitrate in drinking water of the distribution network of Minab city, Iran. International Journal of Innovative Science, Engineering & Technology 2014; 2(4): 469-74. [In Persian].
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21. Ziarati P, Zendehdel T, Bidgoli SA. Nitrate content in drinking water in Gilan and Mazandaran Provinces, Iran. J Environ Anal Toxicol 201; 4: 219.
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chemical water quality of Ilam water treatment plant. World Appl Sci J 2009; 6(12): 1661-4.
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24. Fahiminia M, Jafari Mansoorian H, Ansari M, Saifour Mofrad A, Majidi G, Ansari Tadi R, et al. Evaluation of trends for iron and manganese concentrationsin wells, reservoirs, and water distribution networks, Qom city, Iran. Environmental Health Engineering and Management Journal 2015; 2(2): 67-72. [In Persian].
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27. Alighadr M, Hasani AH, Ghanbari M. Measurement of heavy metals concentration in drinking water sources in Ardebil city. Proceeding of the 16th National Conference Environmental health; 2007 Nov 8-10; Hamadan, Iran; 2007. p.16-24. [In Persian].
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28. Sakizadeh M, Mirzaei R. Health risk assessment of Fe, Mn, Cu, Cr in drinking water in some wells and springs of Shush and Andimeshk, Khuzestan Province, Southern Iran. Iranian Journal of Toxicology 2016; 10(2): 29-35. [In Persian].
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29. Sepehrnia B, Nabizadeh R, Mahvi AH, Naseri S. Water quality analysis of drinking water distribution systems of rey township using IWQIS software. Iran J Health Environ 2016; 9(1): 103-14. [In Persian].
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37. Yousefii Z, Hanafi B. Fluoride level in drinking water supplies of Gonbad-e Qabus, 2008-2012. J Mazandaran Univ Med Sci 2013; 23(101): 112-6. [In Persian].
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38. Yousefi Z, Barafrashteh Pour M, Taghavi M, MashayekhSalehi A, Sedaghat F. Survey on Temporal and spatial variation of nitrate and nitrite in drinking water of Gachsaran by using Geographic Information System (GIS). J Mazandaran Univ Med Sci 2013; 22(2): 158-62. [In Persian].
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39. Yousefi Z, Naeej O. Study on nitrate value in rural area in Amol City. J Mazandaran Univ Med Sci 2007; 17(61): 161-5. [In Persian].
40
ORIGINAL_ARTICLE
Application of geostatistical methods for mapping groundwater phosphate construction in Eyvan plain, Ilam Province, Iran
The purpose of this study was to evaluate the spatial changes of groundwater phosphate concentrations using geostatistical methods based on data from 10 groundwater wells. One of the conventional tools in decision making on the groundwater management is geostatistical method. To evaluate the spatial changes of phosphate concentrations in groundwater, the universal kriging method with cross-validation was used for mapping and estimating groundwater phosphate concentrations in Eyvan Plain, Iran. Phosphate concentration followed a log-normal distribution and demonstrated a moderate spatial dependence according to the nugget ratio (60%). The experimental variogram of groundwater phosphate concentration was best-fitted by a spherical model. Cross-validation errors were within an acceptable level. According to the spatial distribution map, phosphate pollution in the groundwater occurred mostly in the west of the plain because of the phosphate discharge from the industrial effluents.
https://jaehr.muk.ac.ir/article_40227_b6724f5e0462e748ab433e7019e93f82.pdf
2016-04-01
113
119
10.22102/jaehr.2016.40227
Decision-making
Groundwater
phosphate
spatial analysis
Water
Iran
Milad
Ahmadi
ahmadi.m.9090@gmail.com
1
Department of Environment, School of Basic Sciences, Islamic Azad University, Hamedan Branch, Hamedan, Iran
AUTHOR
Behzad
Shahmoradi
bshahmorady@gmail.com
2
Environmental Health Research Center, Kurdistan University of Medical Sciences, sanandaj, Iran
LEAD_AUTHOR
Maryam
Kiani-Sadr
mkianysadr@gmail.com
3
Department of Environment, School of Basic Sciences, Islamic Azad University, Hamedan Branch, Hamadan, Iran
AUTHOR
1. Piccini C, Marchetti A, Farina R, Francaviglia R. Application of indicator kriging to evaluate the probability of exceeding nitrate contamination thresholds. Int J Environ Res 2012; 6(4): 853-62.
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3. Hudak PF. Nitrate and chloride concentrations in groundwater beneath a portion of the trinity group outcrop zone, Texas. Int J Environ Res 2012; 6(3): 663-8.
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4. Li H, Wang Y, Shi LQ, Mi J, Song D, Pan XJ. Distribution and fractions of phosphorus and nitrogen in surface sediments from dianchi lake, China. Int J Environ Res 2012; 6(1): 195-208.
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5. Domagalski JL, Johnson H. Phosphorus and groundwater: establishing links between agricultural use and transport to streams. New Jersey, NJ: U.S. Geological Survey Fact Sheet 2012-3004; 2012.
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6. Jarvis SC. Nitrogen dynamics in natural and agricultural ecosystem. In: Ball AS, Wilson WS, Hinton R, Editors. Managing risks of nitrates to humans and the environment. Sawston, Cambridge: Woodhead Publishing; 1999.
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7. Harrison RM. Pollution: causes, effects, and control. London, UK: Royal Society of Chemistry; 1990.
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8. McLay CD, Dragten R, Sparling G, Selvarajah N. Predicting groundwater nitrate concentrations in a region of mixed agricultural land use: a comparison of three approaches. Environ Pollut 2001; 115(2): 191-204.
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9. Ghaderi AA, Abduli MA, Karbassi AR, Nasrabadi T, Khajeh M. Evaluating the effects of fertilizers on bioavailable metallic pollution of soils, case study of Sistan farms, Iran. Int J Environ Res 2012; 6(2): 565-70.
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10. Schullehner J, Hansen B, Sigsgaard T. Nitrate in drinking water. Proceedings of the 6th International Conference on Medical Geology. MedGeo; 2015 Jul 26 Aug 1; Aveiro, Portugal.
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11. Barbieri S. Direttiva Nitrati: aspetti della sua applicazione in Friuli Venezia Giulia [Online]. [cited 2016]; Available from: URL: http://www.ersa.fvg.it/informativa/notiziarioersa/anno/2012/notiziario-ersa-n-2-2012/direttiva-nitrati-aspetti-della-sua-applicazione-in-friuli-venezia-giulia/fss_download/file
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12. Poshtmasari HK, Tahmasebi Sarvestani Z, Kamkar B, Shataei S, Sadeghi S. Comparison of interpolation methods for estimatingbpH and EC in agricultural fields of Golestan province (north of Iran). Intl J Agri Crop Sci 2012; 4(4): 157-67.
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13. Gundogdu KS, Guney I. Spatial analyses of groundwater levels using universal kriging. J Earth Syst Sci 2007; 116(1): 49-55.
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29
ORIGINAL_ARTICLE
Experimental investigation, modeling, and optimization of combined electro-(fenton/coagulation/flotation) process: design of experiments and artificial intelligence systems
In this study, a combined electro-(Fenton/coagulation/flotation) (EF/EC/El) process was studied via degradation of Disperse Orange 25 (DO25) organic dye as a case study. Influences of seven operational parameters on the dye removal efficiency (DR%) were measured: initial pH of the solution (pH0), applied voltage between the anode and cathode (V), initial ferrous ion concentration (CFe), initial hydrogen peroxide concentration (CH2O2), initial DO25 concentration (C0), applied aeration flow rate (FAir), and process time (tP). Combined design of experiments (DOE) was applied, and experiments were conducted in accordance with the design. The experimental data were collected in a hand-made laboratory-scaleglass cylindrical batch reactor equipped with four graphite barcathodes, an aluminum sheet anode, an aeration pump equipped with an air filter and air distributer, a 150-rpm mixer, and a DC power supply. A DR% of 98 was achieved with a pH0 of 4, V of 10, CFe of 7.5, CH2O2 of 0, C0 of 140, and FAir of 0. The data were used for modeling using normal and reduced multiple regression models (MLR & r-MLR) and artificial neural networks (ANN & r-ANN). Further statistical tests were applied to determine the models’ goodness and to compare the models. Based on statistical comparison, ANN models clearly outperformed the stepwise multiple linear regression (SMLR) models. Finally, an optimization process was carried out using a genetic algorithm (GA) over the outperformed ANN model. The optimization procedure was used to determine the optimal operating conditions of the combined process.
https://jaehr.muk.ac.ir/article_40228_ab9ab2da3b42c1f6390f98ef32527f6b.pdf
2016-04-01
120
128
10.22102/jaehr.2016.40228
Fenton Reagents Concentration
Artificial Neural Network
Genetic algorithm
Dye Removal Efficiency
Electro-(Fenton/Coagulation/Flotation)
Gilas
Hosseini
hossinegilas@yahoo.com
1
Environmental Health Research Center, Kurdistan University of Medical Sciences, Sanandaj, Iran
AUTHOR
Snur
Ahmadpour
2
Environmental Health Research Center, Kurdistan University of Medical Sciences, Sanandaj, Iran
AUTHOR
Maryam
Khosravi
3
Environmental Health Research Center, Kurdistan University of Medical Sciences, Sanandaj, Iran
AUTHOR
Amir Hossein
Mahvi
amahvi@yahoo.com
4
School of Public Health and Center for Solid Waste Research, Institute for Environmental Research, Tehran University of Medical Sciences, Tehran, Iran
AUTHOR
Sang
Joo
5
School of Mechanical Engineering, Yeungnam University, Gyeongsan 712-749, Republic of Korea
AUTHOR
Hiua
Daraei
hiua.daraei@gmail.com
6
Environmental Health Research Center, Kurdistan University of Medical Sciences, Sanandaj, Iran
LEAD_AUTHOR
1. Asgarzadeh S, Rostamian R, Faez E, Maleki A, Daraei H. Biosorption of Pb(II), Cu(II), and Ni(II) ions onto novel lowcost P. eldarica leaves-based biosorbent: isotherm, kinetics, and operational parameters investigation. Desalination Water Treat 2016; 57(31): 14544-51.
1
2. Maleki A, Daraei H, Khodaei F, Aghdam KB, Faez E. Direct blue 71 dye removal probing by potato peel-based sorbent: applications of artificial intelligent systems. Desalination Water Treat 2016; 57(26): 12281-6.
2
3. Maleki A, Daraei H, Khodaei F, Bayazid-Aghdam K, Rezaee R, Naghizadeh A. Investigation of potato peel-based bio-sorbent efficiency in reactive dye removal: Artificial neural network modeling and
3
genetic algorithms optimization. J Adv Environ Health Res 2013; 1(1): 21-8.
4
4. Maleki A, Daraii H, Mahvi AH, Rezaee R, Ebrahimi R. Fluoride adsorption from aqueous systems using barley husk and barley husk ash. Proceedings of the 30th Conference of the. International Society for Fluoride Research, which will be held. 2012 Sep 5-8; Szczecin, Poland; 2012.
5
5. Maleki A, Safari M, Shahmoradi B, Zandsalimi Y, Daraei H, Gharibi F. Photocatalytic degradation of humic substances in aqueous solution using Cu-doped ZnO nanoparticles under natural sunlight irradiation. Environ Sci Pollut Res Int 2015; 22(21): 16875-80.
6
6. Maleki A, Daraei H, Hosseini EA, zizi S, aez E, haribi F. Azo Dye DB71 Degradation Using Ultrasonic-Assisted Fenton Process: Modeling and Process Optimization. Arab J Sci Eng (2015) 40: 295 2015; 40(2): 295-301.
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9. Raghu S, Ahmed BC. Chemical or electrochemical techniques, followed by ion exchange, for recycle of textile dye wastewater. J Hazard Mater 2007; 149(2): 324-30.
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11. Isarain-Chavez E, Garrido JA, Rodriguez RM, Centellas F, Arias C, Cabot PL, et al. Mineralization of metoprolol by electro-Fenton and photoelectro-Fenton processes. J Phys Chem A 2011; 115(7): 1234-42.
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12. Ilhan F, Kurt U, Apaydin O, Gonullu MT. Treatment of leachate by electrocoagulation using aluminum and iron electrodes. J Hazard Mater 2008; 154(1-3): 381-9.
13
13. Hosseini G, Maleki A, Daraei H. Electrochemical Process for Diazinon Removal from Aqueous Media: Design of Experiments, Optimization, and DLLME-GC-FID Method for Diazinon Determination. Arab J Sci Eng 2015; 40(11): 3041-6.
14
14. Maleki A, Daraei H, Alaei L, Izadi LA. Dye Removal probing by electrocoagulation process: modeling by MLR and ANN methods. J Chem Soc Pak 2012; 34(5): 1056-69.
15
15. Inan H, Dimoglo A, Simsek H, Karpuzcu M. Olive oil mill wastewater treatment by means of electro-coagulation. Sep Purif Technol 2004; 36(1): 23-31.
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16. Mollah MY, Gomes JA, Das KK, Cocke DL. Electrochemical treatment of Orange II dye solution--use of aluminum sacrificial electrodes and floc characterization. J Hazard Mater 2010; 174(1-3): 851-8.
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17. Chen G. Electrochemical technologies in wastewater treatment. Sep Purif Technol 2004; 38(1): 11-41.
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18. Chacon JM, Teresa Leal M, Sanchez M, Bandala ER. Solar photocatalytic degradation of azo-dyes by photo-Fenton process. Dyes Pigm 2006; 69(3): 144-50.
19
19. Deng Y, Englehardt JD. Treatment of landfill leachate by the Fenton process. Water Res 2006; 40(20): 3683-94.
20
20. Maleki A, Mahvi A, Daraei H, Zandi S. Influence of selected anions on fluoride removal in electrocoagulation/electroflotation. Fluoride 2015; 48(1): 37-47.
21
21. Maleki A, Teymouri P, Rahimi R, Rostami M, Amini H, Daraei H, et al. Assessment of chemical quality of drinking water in rural area of Qorveh city, Kurdistan province, Iran. J Adv Environ Health Res 2014; 2(1): 22-9.
22