Search results

Filters

  • Journals
  • Authors
  • Keywords
  • Date
  • Type

Search results

Number of results: 4
items per page: 25 50 75
Sort by:
Download PDF Download RIS Download Bibtex

Abstract

The essence of the methane fermentation course is the phase nature of changes taking place during the process. The biodegradation degree of sewage sludge is determined by the effectiveness of the hydrolysis phase. Excess sludge, in the form of a flocculent suspension of microorganisms, subjected to the methane fermentation process show limited susceptibility to the biodegradation. Excess sludge is characterized by a significant content of volatile suspended solids equal about 65 ÷ 75%. Promising technological solution in terms of increasing the efficiency of fermentation process is the application of thermal modification of sludge with the use of dry ice. As a result of excess sludge disintegration by dry ice, denaturation of microbial cells with a mechanical support occurs. The crystallization process takes place and microorganisms of excess sludge undergo the so-called “thermal shock”. The aim of the study was to determine the effect of dry ice disintegration on the course of the methane fermentation process of the modified excess sludge. In the case of dry ice modification reagent in a granular form with a grain diameter of 0.6 mm was used. Dry ice was mixed with excess sludge in a volume ratio of 0.15/1, 0.25/1, 0.35/1, 0.45/1, 0.55/1, 0.65/1, 0.75/1, respectively. The methane fermentation process lasting for 8 and 28 days, respectively, was carried out in mesophilic conditions at 37°C. In the first series untreated sludge was used, and for the second and third series the following treatment parameters were applied: the dose of dry ice in a volume ratio to excess sludge equal 0.55/1, pretreatment time 12 hours. The increase of the excess sludge disintegration degree, as well as the increase of the digestion degree and biogas yield, was a confirmation of the supporting operation of the applied modification. The mixture of reactant and excess sludge in a volume ratio of 0.55/1 was considered the most favorable combination. In relation to not prepared sludge for the selected most favorable conditions of excess sludge modification, about 2.7 and 3-fold increase of TOC and SCOD values and a 2.8-fold increase in VFAs concentration were obtained respectively. In relation to the effects of the methane fermentation of non-prepared sludge, for modified sludge, about 33 percentage increase of the sludge digestion degree and about 31 percentage increase of the biogas yield was noticed.

Go to article

Authors and Affiliations

Iwona Ewa Zawieja
Download PDF Download RIS Download Bibtex

Abstract

The root-knot nematode Meloidogyne graminicola is an economically important pest in rice production. The identification of a nematode species is an important basis in nematode management to reduce yield losses by extracting nematode DNA as an early step in molecular identification. This study aimed to investigate the optimal extraction method and number of M. graminicola for nematode genomic analysis based on PCR (polymerase chain reaction) and Sanger sequencing. The DNA extraction methods used in this study were the CTAB, SDS, and commercial kit (GeneAidTM Tissue/Blood DNA Mini Kit). The results revealed that the three DNA extraction methods could be used to analyze the nematode genomics based on PCR and Sanger sequencing using one nematode, both in a second-stage juvenile and a female, equipped with the process of nematode destruction by freezing. This finding was shown by the amplification of all DNA templates with Mg-F3 and Mg-R2 primers through PCR with a size of 370 bp, while Sanger sequencing obtained 372 bp.
Go to article

Authors and Affiliations

Rendyta Morindya
1
Siwi Indarti
1
Alan Soffan
1
ORCID: ORCID
Sedyo Hartono
1

  1. Plant Protection, Faculty of Agriculture, Universitas Gadjah Mada, Yogyakarta, Indonesia
Download PDF Download RIS Download Bibtex

Abstract

This study focuses on mapping the groundwater’s vulnerability to pollution in the region of Ouargla, located in the North-East of the northern Sahara, exposed to potential risks of alteration. By applying the methods (GOD, DRASTIC, and SINTACS), coupled with a Geographic Information System (GIS), we were able to identify a medium to high vulnerability trend. In light of the results recorded, the DRASTIC and SINTACS methods prove to be more suitable for our study region. This makes it possible to highlight the recharge zones and land use as being the most vulnerable in the territory studied. The GOD method presents a strong vulnerability trend over 77.02% of the study area. Such a result is directly related to the depth of the water table. It can therefore be argued that this method is far from being representative of the reality on the ground because of these very heterogeneous characteristics.
Go to article

Bibliography

  1. Abunada, Z., Kishawi, Y., Alslaibi, T. M., Kaheil, N. & Mittelstet, A. (2021). The application of SWAT-GIS tool to improve the recharge factor in the DRASTIC framework: Case study. Journal of Hydrology, 592, [125613]. DOI:10.1016/j.jhydrol.2020.125613
  2. ANRH. (2018). Données des fiches techniques des forages de la Wilaya de Ouargla.
  3. ANRH. (2022). Inventaire des forages de la Wilaya de Ouargla.
  4. Awawdeh, M., Al-Kharabsheh, N., Obeidat M. & Awawdeh, M. (2020) Groundwater vulnerability assessment using modified SINTACS model in Wadi Shueib, Jordan, Annals of GIS, 26:4, 377-394. DOI:10.1080/19475683.2020.1773535
  5. Bera, A., Mukhopadhyay, B. P., Chowdhury, P., Ghosh, A. & Biswas, S. (2021). Groundwa-ter vulnerability assessment using GIS-based DRASTIC model in Nangasai River Basin, India with special emphasis on agricultural contamination. Ecotoxicology and Environmental Safety, 214, 112085. DOI:10.1016/j.ecoenv.2021.112085
  6. Chakraborty, B., Roy, S., Bera, A., Adhikary, P. P., Bera, B., Sengupta, D., Bhunia, G. S. & Shit, P. K. (2022). Groundwater vulnerability assessment using GIS-based DRASTIC model in the upper catchment of Dwarakeshwar river basin, West Bengal, India. Environmental Earth Sciences, 81,1, pp.1–15. DOI:10.1007/s12665-021-10002-3
  7. Charikh, M., Slimani, R., Hamdi-aïssa, B., Bouadjila, O. & Hassaine, A. (2022). Evaluation of Arid Soil Landscapes Permeability in Algerian Sahara. Al-Qadisiyah Journal for Agricul-ture Sciences (QJAS), 12,2, pp. 12–18. DOI:10.33794/qjas.2022.134247.1050
  8. El Baba, M. & Kayastha, P. (2022). Groundwater vulnerability, water quality, and risk assessment in a semi-arid region: a case study from the Dier al-Balah Governorate, Gaza Strip. Modeling Earth Systems and Environment, pp.1–16. DOI:10.3390/w12010262
  9. Elzain, H. E., Chung, S. Y., Senapathi, V., Sekar, S., Lee, S. Y., Roy, P. D., Hassan, A. & Sabarathinam, C. (2022). Comparative study of machine learning models for evaluating groundwater vulnerability to nitrate contamination. Ecotoxicology and Environmental Safety, 229, 113061. DOI:10.1016/j.ecoenv.2021.113061
  10. Fannakh, A. & Farsang, A. (2022). DRASTIC, GOD, and SI approaches for assessing groundwater vulnerability to pollution: a review. Environ Sci Eur 34, 77. DOI:10.1186/s12302-022-00646-8
  11. Gao, Y.Y., Qian, H., Zhou, Y.H., Chen, J. Wang, H.K., Ren, W.H. & Qu, W.G. (2022). Cumulative health risk assessment of multiple chemicals in groundwater based on determinis-tic and Monte Carlo models in a large semiarid basin. J. Clean. Prod., 352. DOI:10.1016/j.jclepro.2022.131567.
  12. Gharekhani, M., Nadiri, A. A., Khatibi, R., Sadeghfam, S. & Moghaddam, A. A. (2022). A study of uncertainties in groundwater vulnerability modelling using Bayesian model averaging (BMA). Journal of Environmental Management, 303, 114168. DOI:10.1016/j.jenvman.2021.114168
  13. Goyal, D., Haritash, A. K. & Singh, S. K. (2021). A comprehensive review of groundwater vulnerability assessment using index-based, modelling, and coupling methods. Journal of Environmental Management, 296, 113161. DOI:10.1016/j.jenvman.2021.113161
  14. Griffel, L. M., Toba, A-L., Paudel, R., Lin, Y., Hartley D. S. & Langholtz, M. (2022). A multi-criteria land suitability assessment of field allocation decisions for switchgrass, I, 136, 108617. DOI:10.1016/j.ecolind.2022.108617.
  15. Hamdi-Aïssa, B., & Girard, M.-C. (2000). Utilisation de la télédétection en régions sahariennes, pour l’analyse et l’extrapolation spatiale des pédopaysages. Science et Changements Planétaires/Sécheresse, 11,3, pp. 179–188.
  16. Hamza, M.H. & Chmit, M. (2022). "GIS-Based Planning and Web/3D Web GIS Applications for the Analysis and Management of MV/LV Electrical Networks (A Case Study in Tuni-sia)" Applied Sciences 12, no. 5: 2554. DOI:10.3390/app12052554
  17. Kirlas, M.C., Karpouzos, D.Κ., Georgiou, P.E. & Katsifarakis, K. L. (2022). A comparative study of groundwater vulnerability methods in a porous aquifer in Greece. Appl Water Sci 12, 123.DOI:10.1007/s13201-022-01651-1.
  18. Qian, H. Chen, J. & Howard, K. W.F. (2020). Assessing groundwater pollution and potential remediation processes in a multi-layer aquifer system. Environ. Pollut., 263. DOI:10.1016/j.envpol.2020.114669.
  19. Saranya, T. & Saravanan, S. (2022). Assessment of groundwater vulnerability using analytical hierarchy process and evidential belief function with DRASTIC parameters, Cuddalore, India. Int. J. Environ. Sci. Technol. DOI:10.1007/s13762-022-03944-z
  20. Sarkar, M. & Pal, S.C. (2021). Application of DRASTIC and Modified DRASTIC Models for Model-ing Groundwater Vulnerability of Malda District in West Bengal. J Indian Soc Remote Sens, 49, pp. 1201–1219. DOI:10.1007/s12524-020-01176-7
  21. Slimani, R, & Guendouz, A. (2015). Groundwater vulnerability and risk mapping for the Phreatic aquifer in the Ouargla Oasis of Algerian Sahara using GIS and GOD method. Inter-national Journal of AgriculturalScience and Research (IJASR). ISSN(P): 2250-0057; ISSN(E): 2321-0087. Vol. 5, Issue 3, Jun 2015, pp. 149-158 © TJPRC Pvt. Ltd.
  22. Slimani, Rabia, Guendouz, A., Trolard, F., Moulla, A. S., Hamdi-Aïssa, B. & Bourrié, G. (2017). Identification of dominant hydrogeochemical processes for groundwaters in the Alge-rian Sahara supported by inverse modeling of chemical and isotopic data. Hydrology and Earth System Sciences, 21, 3, pp.1669–1691. DOI:10.5194/hess-21-1669-2017, 2017.
  23. Stigter, T. Y., Ribeiro, L., & Dill, A. M. M. C. (2006). Application of a groundwater quality index as an assessment and communication tool in agro-environmental policies–Two Portu-guese case studies. Journal of Hydrology, 327, 3–4, pp. 578–591. DOI:10.1016/j.jhydrol.2005.12.001
  24. UNESCO. (2020). Rapport mondial des Nations Unies sur la mise en valeur des ressources en eau 2020: l’eau et les changements climatiques. UNESCO. https://unesdoc.unesco.org/notice?id=p::usmarcdef_0000372941
  25. United Nations. (2022). The United Nations World Water Development Report 2022: groundwater: making the invisible visible. UNESCO. https://unesdoc.unesco.org/notice?id=p::usmarcdef_0000380721.
  26. Zhang, Q., Qian, H., Xu, P., Li, W., Feng, W., & Liu, R. (2021). Effect of hydrogeological conditions on groundwater nitrate pollution and human health risk assessment of nitrate in Jiaokou Irrigation District. Journal of Cleaner Production, 298, 126783. DOI:10.1016/j.jclepro.2021.126783.
Go to article

Authors and Affiliations

Rabia Slimani
1
Messaouda Charikh
1 2
Mohammad Aljaradin
3
ORCID: ORCID

  1. Laboratory of Biogeochemistry of desert environments, Faculty of Natural and Life Sciences, Kasdi Marbah University, Ouargla, Algeria
  2. Ouargla Higher Normal School, Algeria
  3. School of Health and Environmental Studies, Hamdan Bin Mohammed Smart University, Dubai, UAE
Download PDF Download RIS Download Bibtex

Abstract

Phytolith-occluded carbon (PhytOC) is highly stable, and constitutes an important source of long-term C storage in agrosystems. This stored carbon is resistant to the processes of oxidation of carbon compounds. In our research phytolith content in barley (Estonia) and oat (Poland) grain and straw was assessed at field trials, with Si as a liquid immune stimulant OPTYSIL and compost fertilisation. We showed that cereals can produce relatively high amounts of phytoliths. PhytOC plays a key role in carbon sequestration, particularly for poor, sandy Polish and Estonian soils. The phytolith content was always higher in straw than in grain regardless of the type of cereals. The phytolith content in oat grains varied from 18.46 to 21.28 mg∙g−1 DM, and in straw 27.89–38.97 mg∙g−1 DM. The phytolith content in barley grain ranged from 17.24 to 19.86 mg∙g−1 DM, and in straw from 22.06 to 49.08 mg∙g−1 DM. Our results suggest that oat ecosystems can absorb from 14.94 to 41.73 kg e-CO2∙ha−1 and barley absorb from 0.32 to 1.60 kg e-CO2∙ha−1. The accumulation rate of PhytOC can be increased 3-fold in Polish conditions through foliar application of silicon, and 5-fold in Estonian conditions. In parallel, the compost fertilisation increased the phytolith content in cereals.
Go to article

Authors and Affiliations

Beata Rutkowska
1
ORCID: ORCID
Peter Schröder
2
ORCID: ORCID
Michel Mench
3 4
ORCID: ORCID
Francois Rineau
5
ORCID: ORCID
Witold Szulc
6
ORCID: ORCID
Wiesław Szulc
1
ORCID: ORCID
Jarosław Pobereżny
7
ORCID: ORCID
Kristjan Tiideberg
8
ORCID: ORCID
Tomasz Niedziński
1
ORCID: ORCID
Evelin Loit
8
ORCID: ORCID

  1. Warsaw University of Life Sciences – SGGW, Institute of Agriculture, Nowoursynowska St, 166, 02-787 Warsaw, Poland
  2. Helmholtz Center for Environmental Health, German Research Center for Environmental Health, Research Unit Environmental Simulation, Ingolstädter Landstraße 1, D-85764 Neuherberg, Munich, Germany
  3. University of Bordeaux, Amphithéâtre 3 à 12, 33000, Bordeaux, France
  4. INRAE – National Research Institute for Agriculture, Food and the Environment, 147 rue de l’Université 75338, Paris, France
  5. Hasselt University, Martelarenlaan 42, 3500, Hasselt, Belgium
  6. Fire University, Słowackiego St, 52/54, 01-629 Warsaw, Poland
  7. University of Science and Technology, Kaliskiego Ave., 7, 85-796 Bydgoszcz, Poland
  8. Estonian University of Life Sciences, Institute of Agricultural and Environmental Sciences, Fr. R. Kreutzwaldi 1, 51006, Tartu, Estonia

This page uses 'cookies'. Learn more