Next Article in Journal
Soil Detachment Rate of a Rainfall-Induced Landslide Soil
Previous Article in Journal
Fish Size Structure as an Indicator of Fish Diversity: A Study of 40 Lakes in Türkiye
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Water
Volume 15
Issue 12
10.3390/w15122148
water-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Simulation of Biophysicochemical Characteristics of the Soils Using Geoelectrical Measurements near the Sewage Station, Assiut City, Egypt

by
Gamal Z. Abdel Aal
1,
Mohamed E. Faragallah
2,
Mohamed H. Abd-Alla
3,
Reham S. Abd El-Rhman
1,
Ahmed M. Abdel Gowad
4,
Ahmed Abdelhalim
5,
Mohamed S. Ahmed
6,
Abdelbaset M. Abudeif
7,* and
Mohammed A. Mohammed
7
1
Geology Department, Faculty of Science, Assiut University, Assiut 71515, Egypt
2
Soil and Water Department, Faculty of Agriculture, Al-Azhar University, Assiut 71515, Egypt
3
Botany and Microbiology Department, Faculty of Science, Assiut University, Assiut 71515, Egypt
4
Geology Department, Faculty of Science, South Valley University, Qena 83523, Egypt
5
Department of Civil Engineering, University of Bristol, Bristol BS8 1TR, UK
6
Geology and Geophysics Department, College of Science, King Saud University, Riyadh 11451, Saudi Arabia
7
Geology Department, Faculty of Science, Sohag University, Sohag 82511, Egypt
*
Author to whom correspondence should be addressed.
Water 2023, 15(12), 2148; https://doi.org/10.3390/w15122148
Submission received: 11 May 2023 / Revised: 30 May 2023 / Accepted: 3 June 2023 / Published: 6 June 2023
(This article belongs to the Section Wastewater Treatment and Reuse)
Browse Figures
Versions Notes

Abstract

:
Numerous farmers regularly irrigate their farms with inadequately treated sewage water pumped from the sewage system in the Arab El-Madabegh district of Assiut City, Egypt. According to previous studies, long-term irrigation with partially treated sewage water resulted in significant changes in the physicochemical properties of soil. The principal goals of this study are (1) to infer empirical equations between geoelectrical resistivity measurements and certain biophysicochemical parameters of some soil samples, and (2) to use these empirical equations to calculate the biophysicochemical parameters of the unknown samples for the same location. For this purpose, 27 soil samples at different depth levels (0 to 25, 25 to 60, and 60 to 90 cm) were collected from eleven locations at the sewage station. Physical properties including water content and particle size distribution, chemical properties including soil pH, electrical conductivity (EC), and the heavy metals concentrations, biological properties including total coliform counts, and geoelectrical resistivity measurements were estimated and analyzed for these samples. Electrical resistivity measurements and biophysicochemical properties were cross-correlated using the exponential trend line to fit the cross-correlated data, and the empirical relationships were obtained. These empirical relationships in conjunction with the measured electrical resistivity measurements were used to calculate the biophysicochemical values of the other three random soil samples. The biophysicochemical values of the former three samples were measured by the same normal procedures as 27 samples. Then, the calculated values were correlated with the measured ones. Good correlations between the estimated and the measured values for biophysicochemical features were obtained. Therefore, this method can be employed to calculate the biophysicochemical parameters for any unknown samples that have the same geological conditions for estimating and monitoring soil contamination.

1. Introduction

In arid and semi-arid areas, water scarcity promotes the recycling of water supplies. Sewage reuse is one of the initial patterns of these resources, and it unquestionably poses environmental and health risks. The usage of contaminated water for gardening and irrigation, involving wastewater, drainage, and saline water, is a result of increasing industrial and population development. The use of domestic and industrial wastewater for agricultural purposes is widespread throughout the world [1,2,3,4,5,6,7].
The important advantages of sewage irrigation are that it gives landowners a permanent supply of water while also adding organic matter and essential fertilizer to the soil. Additionally, around 30% of farmers globally rely on agriculture that uses wastewater for their livelihoods [8,9,10,11]. The typical result of sewage treatment, which is part of water regulation, is a convenient liquid waste product that can be used for irrigation with little risk to humans or the natural environment [12,13,14,15]. In sophisticated countries with environmental regulations in place, wastewater is evaluated before it is utilized for irrigation, but in other developing nations where the cost of treatment makes it prohibitively expensive, wastewater is frequently employed in agriculture untreated [14,16,17]. Various organic and inorganic materials, particularly heavy metals, can contaminate farmland as a result of improperly or insufficiently treated or untreated sewage [8,9,10,11,12,13,14,15,16,17,18,19,20,21]. As a result, prolonged wastewater use may have a significant impact on the nature of the soil and the crops that grow there [22,23,24,25]. Furthermore, uncontrolled or insufficiently treated wastewater may contain unsuitable bioactive substances as well as pathogenic bacteria (total coliform bacteria), protozoa, viruses, and other parasitic helminths, all of which can endanger the health of people, plants, and animals fed wastewater through a food contamination system [16,19,26]. Another negative impact of sewage treatment in farms involves alterations in the basic physicochemical and biological characteristics of the soil [27,28,29,30,31]. In addition to providing enough mineral macro and micronutrients for the growth of plants, they influence soil buffering capacity, soil cation exchange capacity (CEC), soil pH, and soil salinity [16]. Identifying and assessing the related alterations in the biophysicochemical properties of soils watered with untreated wastewater is crucial in light of this issue.
In general, using wastewater for irrigation has both advantages and disadvantages. The advantages include nutrient recycling, water management, reduced fertilizer treatment, a decrease in the need for pricey refrigerated transport or places to store, temporal and spatial availability of agricultural water, and the provision of an effective means of communication for aquaculture. The potential drawbacks include decreased environmental water and crop productivity, increased pollutants, and health risks [32].
Significant attempts have been recently made to create methods that are suitable for maintaining soil quality, safeguarding it, and ensuring its long-term employment. As a result, there has been a surge in attention in developing new approaches that can provide real-time data on soil parameters including porosity and bulk density, as well as compaction, moisture content, and salt. The traditional methods of soil characterization depend mainly on soil sampling and intensive laboratory analysis to investigate certain physicochemical and biological properties. Despite the fact that soil sampling and analysis provide real data and ground truth characterization of the investigated area, it has several limitations. Among these limitations, the soil analyses are expensive, labor-intensive, and provide little data. In addition, the soil sampling and analyses do not span the entire spatial and temporal scales associated with induced biophysicochemical changes. Therefore, in several cases, direct sampling only is not acceptable to precisely describe site conditions.
Geophysical approaches can be used in concert with direct techniques such as soil coring and soil pore liquid sampling to discover alterations in the biophysicochemical parameters of sewage-irrigated soils quickly and non-invasively. Electrical resistivity investigation is one of the most appealing geophysical tools for soil evaluation [33,34,35]. This method is beneficial, capable of providing precise measurements at different spatial and temporal scales, sensitive to a broad range of soil physicochemical parameters, and can be utilized as a substitute for those variables’ temporal and spatial variability.
The study area is located in Assiut governorate where the Assiut city sewage station is in the Arab El-Madabegh district, 3 km southwest of the city (Figure 1). Assiut City disembarks its wastewater into this sewage station, where it should undergo first treatment. Many farmers use the partially treated and disposed of sewage water released from the wastewater system for agricultural irrigation as a customary practice in the Arab El-Madabegh site. The biophysicochemical characteristics of irrigated soil were changed by the prolonged utilization of partially treated sewage water [29,31,36,37].
The main objectives of this work are (1) to conclude empirical equations between geoelectrical resistivity measurements and certain biophysicochemical parameters of some soil samples, and (2) to use these empirical equations to calculate the biophysicochemical parameters of the unknown samples for the same location and any other sites having the same geological conditions.

2. Materials and Methods

Eleven locations adjacent to the sewage station were selected for soil sampling at three depths (0 to 25, 25 to 60, and 60 to 90 cm) (Figure 1). Aseptic bags were used to collect these soil samples, stored at 4 °C, and immediately used for the total coliform count within 24 h. The samples were measured for some physical properties (water content and particle size distribution), chemical properties (soil pH, EC, and some heavy metals), and biological properties (total coliform counting). Geoelectrical resistivities were measured using a laboratory resistivity meter.

2.1. The Biophysicochemical Analysis

Suitable procedures were used for physical properties determination. The sand content was determined using a dry sieving method [38,39,40], and the silt and clay fractions were obtained by the pipetting technique [41,42,43,44]. After determining the percentage of sand, clay, and silt, the texture grade of the sample was determined by the USDA-FAO textural triangle [45,46,47,48,49]. The oven-drying technique (gravimetric technique) was used to estimate the water content [50,51,52].
PH and conductivity meters were utilized to measure the soil pH and electrical conductivity (EC), respectively [53,54,55]. The diethylene triamine pentaacetic acid (DTPA) technique is employed to determine the concentrations of the heavy metals (Fe, Zn, Cu, Pb, Mn, and Co) utilizing a GBC atomic absorption spectrometer, model 906 AA [56,57,58,59]. The pour plate procedure was used to calculate the total coliform counting in the soil samples [60,61,62].

2.2. Geoelectrical Measurements

The ABEM Terrameter (SAS 300) and the usual four electrodes method were employed for the laboratory measuring electrical resistivity of the collected samples [63,64,65]. Figure 2 depicts the instrumentation used for this task. The sample holder (laboratory conductivity cell) was cylindrical in shape with four electrodes, two of them for current (I) and the others for potential (∆V). The soil sample was put in the sample holder, then the holder was conducted with the resistivity meter (ABEM Terrameter, Sundbyberg, Sweden). The soil sample was subjected to an electrical current, and the voltage was measured between the two potential electrodes. The sample’s resistance (R) is derived from Ohm’s law (R = V/I) and displays the resistance in ohms (Ω). The average of four readings was recorded. The resistivity (ρ) was calculated using this formula (ρ) = (∆V/I × A/L), where (A) is the holder cross-sectional area and (L) is the length of the measured sample (the distance between the potential electrodes).

3. Empirical Relationship and Validation

Cross correlations between the measured biophysicochemical and electrical resistivity values of the soil samples were plotted, and from the fitted trend lines, empirical equations were deduced. Additional three randomly chosen sample locations (A, B, and C, Figure 1) at the study site were selected at the same depths in order to verify the deduced results of the empirical equations. The electrical resistivity and biophysicochemical characteristics of these three samples were measured using laboratory instruments and then the biophysicochemical characteristics were calculated from the empirical equations for comparison. Then, throughout the linear relationship between measured and calculated values, a correlation coefficient (R2) was identified to detect the validity of the empirical relationships and can be used in estimating and monitoring soil contamination.

4. Results and Discussion

The results of the measured electrical resistivity and biophysicochemical characteristics are displayed in Figure 3. Generally, the distribution of the particle sizes exhibited that the percentage of sand contents of the soil samples decreases with the depth and varies from 85.96% to 93.91%, 79.68% to 94.5%, and 64.93% to 98.46% at depths of 0 to 25, 25 to 60, and 60 to 95 cm, respectively (Figure 3a). At the same previously mentioned depths, the percentages of clay and silt contents increase with depth and range from 0.45% to 1.3%, 0.71% to 2.21%, and 1.24% to 2.34%, respectively (Figure 3b). The percentage of the silt contents varies from 4.25% to 18%, 4.13% to 22.2%, and 0.46% to 34.56%, also at the same depths, respectively (Figure 3c). Generally, increases in the clay contents of sandy soils are due to prolonged irrigation with sewage effluent. In general, the study area is typically distinguished by sandy and loamy sand textures within the range of the investigated depth (0–95 cm). Few soil layers show sandy loam and silt loam textures [36,66].
The percentage of the water contents of the studied soil samples increases in general with the depth and varies from 1.286% to 6.868%, 0.494% to 9.88%, and 0.227% to 10.799% at depths of 0 to 25, 25 to 60, and 60 to 95 cm, respectively (Figure 3d). This has been interpreted as a result of water infiltration from surface sandy soils to clay and silty soils at depth, as well as the ability of clay and silty soils to retain water in comparison to sand.
Soil samples reveal electrical conductivities (EC) ranging from 0.66 to 1.94, 0.42 to 1.26, and 0.2 to 1.2 ds/m at depths of 0 to 25, 25 to 60, and 60 to 95 cm, respectively (Figure 3e). The measured soil samples are considered slightly saline (EC < 8 ds/m) [36,67]. As a consequence of the greater amounts of salts in the surface samples from sewage water, the surface samples had higher EC values than the deeper ones.
The pH of the samples ranges from 7.44 to 8.08, 7.73 to 8.49, and 7.98 to 8.92, also at the same depths previously mentioned, respectively (Figure 3f). The pH values of the surface soil samples are lower than those of the relatively deeper ones. In regard to the acidity classes, the majority of the soil samples analyzed are mildly alkaline soils [9,68,69]. Decreased pH values of the surface layers may be produced from mineralization and nitrification processes of organic matters in sewage water. Organic acids, nitrates and CO2 caused by microbial activity may all have a role in the soils’ pH decline [70].
Figure 3g explains the relationship between the DTPA extractable heavy metals and the distances from the sewage station in relation to the same previously mentioned depths. The results reveal that extractable metals in the shallower depths have greater concentrations than those in the deeper ones. The soil’s concentrations of the DTPA-extractable metals decrease in the following sequence: Fe > Mn > Zn > Cu > Pb > Co. The total amounts of heavy metals in the soil samples range from 7.912 to 34.719 ppm, 7.922 to 23.33 ppm, and 3.628 to 15.726 ppm at depths of 0 to 25, 25 to 60, and 60 to 95 cm, respectively, as seen in this graph. These results are compatible with the results of previous studies [70,71].
The counts of total coliform in soil samples varied from 70 to 265 CFU/g, 55 to 202 CFU/g, and 27 to 105 CFU/g, also at the same depths previously mentioned, respectively (Figure 3h). The cumulative coliform counts are greater in the shallower samples than in the deeper ones. This result is due to the concentrations of the organic material at the shallower depths being slightly greater than those in the deeper ones. While water passes through the soils, the soil surface-suspended particles, involving bacteria, act as a filter, limiting bacteria’s path through the soil [19,72].
The electrical resistivities (ER) of the samples range from 21.243 to 163.404 ohm·m, 35.263 to 273.39 ohm·m, and 95.39 to 391.867 ohm·m, also at the same depths previously mentioned, respectively (Figure 3i). Because of the electrical resistivity is a result of several soil parameters, comprising particle size distribution, EC conductivity, water content, and solute concentration, there is no general trend for the increasing or decreasing of ER of the analyzed soil samples with depth. From the obtained results of measured biophysicochemical analysis of the studied soil profiles, it was observed that the electrical resistivity values in the soil samples decrease with increasing the water content, electrical conductivity, clay content, concentration of metals, and bacterial count.
Figure 4 illustrates the cross correlation between the measured biophysicochemical characteristics and electrical resistivities. The cross-correlated data are fitted extremely well with an exponential trend line based on the Boltzmann’s distribution law [73], except for pH values of the soil samples which were fitted with a linear trend line with the electrical resistivity measurements. ER measurements revealed an inverse exponential relationship with the water content, EC, sum of heavy metals, and total coliform, with correlation coefficients (R2) of 0.79, 0.61, 0.57, and 0.58, respectively (Figure 4d,e,g,h). ER measurements have a strong linear and direct proportional relationship with pH values, with R2 equal to 0.73 (Figure 4f).
The results of the clay and silt composition of the examined soil samples showed a direct proportional and exponential relationship with the measured electrical resistivity values (Figure 4b,c). However, the electrical resistivity measurements of the soil samples displayed an inverse exponential relationship with the sand content (Figure 4a). By using only the measured electrical resistivity, the identified empirical relationships (exponential equations) can be utilized to predict the biophysicochemical characteristics at any point within this site.
Table 1 summarizes the empirical equations inferred from the exponential growth between the measured physical, chemical, and biological characteristics of the studied soil samples and the measured electrical resistivities, in addition to the correlation coefficients (R2) for each property.
To validate the acquired empirical relationships, samples were retrieved from random three locations at the investigated site and both the electrical resistivity and biophysicochemical features were measured. Then, the previously defined empirical relationships for each property were used to calculate the biophysicochemical characteristics of the soil samples that were randomly chosen. The measured and calculated values of the measured electrical resistivities, physical, chemical, and biological characteristics of the soil samples are shown in Table 2, Table 3, Table 4 and Table 5, respectively. The measured and calculated biophysicochemical characteristics of the sand, silt, and clay contents, water content, EC, pH, sum of heavy metals concentrations, and total coliform were linearly cross-correlated and displayed strong correlation coefficients of 0.95, 0.96, 0.95, 0.96, 0.83, 0.68, 0.98, and 0.98, respectively (Figure 5).
The strong correlation coefficients prove that the acquired empirical equations can be utilized to calculate the biophysicochemical characteristics at the study site and sites with similar geological conditions.

5. Conclusions

This work focuses on determining the physical, chemical, and biological properties of soils irrigated with sewage water using unconventional procedures. The main targets of this work are to infer empirical equations between laboratory electric resistivity and biophysicochemical measurements of the collected soil samples at different depths, and then use random samples to verify these equations that were deduced. Eleven locations for sampling were chosen from the investigated area at different depths (0 to 25, 25 to 60, and 60 to 90 cm). Using laboratory probes and electrical resistivity measurements, the soil samples were measured for fundamental biophysicochemical and geoelectrical resistivity parameters. Other three random samples in the same site were collected to validate the results of the empirical equations where the measured and the calculated values were compared numerically and with linear plots. From the obtained results of biophysicochemical analysis of the studied soil sample locations, it was observed that the electrical resistivity decreased in the studied soil samples when the water content, electrical conductivity, clay content, concentration of metals, and bacterial count increased.
The cross-correlation between the biophysicochemical features and electrical resistivity of the observed soil samples fit very well with an exponential growth trend line. Based only on the measured electrical resistivity, the gained empirical relationships can be employed to determine the biophysicochemical characteristics at any location at the investigation site. The measured and calculated values of biophysicochemical characteristics showed good consistency where linear correlations between the measured and the calculated values with high correlation coefficients (more than 95% for most of these properties) were detected. Consequently, these empirical equations are valid and can be applied to calculate the soil parameters at this site and any other sites that have the same geological conditions. In addition, these empirical equations have the potential to be used to give accurate geologic information, assist subsurface sampling and excavation, and contribute real-time monitoring.

Author Contributions

Methodology, G.Z.A.A., M.E.F., M.H.A.-A., R.S.A.E.-R., A.M.A.G., A.A., M.S.A., A.M.A. and M.A.M.; Software, G.Z.A.A., M.E.F., A.M.A.G., M.S.A. and M.A.M.; Validation, M.H.A.-A., R.S.A.E.-R., A.M.A. and M.A.M.; Formal analysis, A.A. and A.M.A.; Investigation, G.Z.A.A., M.E.F., M.H.A.-A., R.S.A.E.-R., A.A., M.S.A. and M.A.M.; Writing—original draft, G.Z.A.A., M.E.F., M.H.A.-A., R.S.A.E.-R., A.M.A.G., M.S.A., A.M.A. and M.A.M. All authors have read and agreed to the published version of the manuscript.

Funding

This work is funded by Researchers Supporting Project number (RSP2023R455), King Saud University, Riyadh, Saudi Arabia.

Data Availability Statement

The data is available upon request from the authors.

Acknowledgments

This work is funded by Researchers Supporting Project number (RSP2023R455), King Saud University, Riyadh, Saudi Arabia.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Miller-Robbie, L.; Ramaswami, A.; Amerasinghe, P. Wastewater treatment and reuse in urban agriculture: Exploring the food, energy, water, and health nexus in Hyderabad, India. Environ. Res. Lett. 2017, 12, 075005. [Google Scholar] [CrossRef] [Green Version]
  2. Belhaj, D.; Jerbi, B.; Medhioub, M.; Zhou, J.; Kallel, M.; Ayadi, H. Impact of treated urban wastewater for reuse in agriculture on crop response and soil ecotoxicity. Environ. Sci. Pollut. Res. 2016, 23, 15877–15887. [Google Scholar] [CrossRef] [PubMed]
  3. Jeong, H.; Kim, H.; Jang, T. Irrigation water quality standards for indirect wastewater reuse in agriculture: A contribution toward sustainable wastewater reuse in South Korea. Water 2016, 8, 169. [Google Scholar] [CrossRef] [Green Version]
  4. Yadav, K.; Singh, P.; Purohit, R. Impacts of wastewater reuse on peri-urban agriculture: Case study in Udaipur city, India. In Balanced Urban Development: Options and Strategies for Liveable Cities; Springer: Berlin/Heidelberg, Germany, 2016; pp. 329–339. [Google Scholar]
  5. Michailidis, A.; Papadaki-Klavdianou, A.; Apostolidou, I.; Lorite, I.J.; Pereira, F.A.; Mirko, H.; Buhagiar, J.; Shilev, S.; Michaelidis, E.; Loizou, E. Exploring treated wastewater issues related to agriculture in Europe, employing a quantitative swot analysis. Procedia Econ. Financ. 2015, 33, 367–375. [Google Scholar] [CrossRef] [Green Version]
  6. Kihila, J.; Mtei, K.M.; Njau, K.N. Wastewater treatment for reuse in urban agriculture; the case of Moshi Municipality, Tanzania. Phys. Chem. Earth Parts ABC 2014, 72, 104–110. [Google Scholar] [CrossRef]
  7. Amerasinghe, P.; Bhardwaj, R.M.; Scott, C.; Jella, K.; Marshall, F. Urban Wastewater and Agricultural Reuse Challenges in India; IWMI: Colombo, Sri Lanka, 2013; Volume 147. [Google Scholar]
  8. Bao, Z.; Wu, W.; Liu, H.; Chen, H.; Yin, S. Impact of long-term irrigation with sewage on heavy metals in soils, crops, and groundwater—A case study in Beijing. Pol. J. Environ. Stud. 2014, 23, 309–318. [Google Scholar]
  9. Liu, W.; Zhao, J.; Ouyang, Z.; Söderlund, L.; Liu, G. Impacts of sewage irrigation on heavy metal distribution and contamination in Beijing, China. Environ. Int. 2005, 31, 805–812. [Google Scholar] [CrossRef]
  10. Abd El-Salam, A.; El-Sheemy, H.; Minaisy, F. Impact of Irrigation Regional Seminar; Waste Water Reclamation and Reuse; FOA: Cairo, Egypt, 2004; pp. 11–16. [Google Scholar]
  11. Horswell, J.; Speir, T.; Van Schaik, A. Bio-indicators to assess impacts of heavy metals in land-applied sewage Sludge. Soil Biol. Biochem. 2003, 35, 1501–1505. [Google Scholar] [CrossRef]
  12. Drechsel, P.; Evans, A.E. Wastewater use in irrigated agriculture. Irrig. Drain. Syst. 2010, 24, 1–3. [Google Scholar] [CrossRef]
  13. Ensink, J.H.; Mahmood, T.; Van der Hoek, W.; Raschid-Sally, L.; Amerasinghe, F.P. A nationwide assessment of wastewater uses in Pakistan: An obscure activity or a vitally important one? Water Policy 2004, 6, 197–206. [Google Scholar] [CrossRef]
  14. Hussain, I.; Raschid, L.; Hanjra, M.A.; Marikar, F.; van der Hoek, W. A Framework for Analyzing Socioeconomic, Health and Environmental Impacts of Wastewater Use in Agriculture in Developing Countries; International Water Management Institute (IWMI): Colombo, Sri Lanka, 2001; Volume 26. [Google Scholar]
  15. Blumenthal, U.; Peasey, A.; Ruiz-Palacios, G.; Mara, D. Guidelines for Wastewater Reuse in Agriculture and Aquaculture: Recommended Revisions Based on New Research Evidence; Task No. 68, Part 1; Water and Environmental Health at London and Loughborough (WELL): London, UK, 2000; Available online: https://www.ircwash.org/resources/guidelines-wastewater-reuse-agriculture-and-aquaculture-recommended-revisions-based-new (accessed on 11 May 2023).
  16. Mohammad, M.J.; Mazahreh, N. Changes in soil fertility parameters in response to irrigation of forage crops with secondary treated wastewater. Commun. Soil Sci. Plant Anal. 2003, 34, 1281–1294. [Google Scholar] [CrossRef]
  17. Friedel, J.; Langer, T.; Siebe, C.; Stahr, K. Effects of long-term waste water irrigation on soil organic matter, soil microbial biomass and its activities in central Mexico. Biol. Fertil. Soils 2000, 31, 414–421. [Google Scholar] [CrossRef]
  18. Al-Rashidi, R.; Rusan, M.; Obaid, K. Changes in plant nutrients, and microbial biomass in different soil depths after long-term surface application of secondary treated wastewater. Environ. Clim. Technol. 2013, 11, 28–33. [Google Scholar] [CrossRef] [Green Version]
  19. Manios, T.; Moraitaki, G.; Mantzavinos, D. Survival of total coliforms in lawn irrigated with secondary wastewater and chlorinated effluent in the Mediterranean region. Water Environ. Res. 2006, 78, 330–335. [Google Scholar] [CrossRef] [PubMed]
  20. Malkawi, H.; Mohammad, M. Survival and accumulation of microorganisms in soils irrigated with secondary treated wastewater. J. Basic Microbiol. 2003, 43, 47–55. [Google Scholar] [CrossRef] [PubMed]
  21. Wang, X.; Tao, S. Spatial structures and relations of heavy metal content in wastewater irrigated agricultural soil of Beijing’s eastern farming regions. Bull. Environ. Contam. Toxicol. 1998, 61, 261–268. [Google Scholar] [CrossRef] [PubMed]
  22. Sinha, S.; Gupta, A.; Bhatt, K.; Pandey, K.; Rai, U.; Singh, K. Distribution of metals in the edible plants grown at Jajmau, Kanpur (India) receiving treated tannery wastewater: Relation with physico-chemical properties of the soil. Environ. Monit. Assess. 2006, 115, 1–22. [Google Scholar] [CrossRef] [PubMed]
  23. Sinha, S.; Pandey, K.; Gupta, A.; Bhatt, K. Accumulation of metals in vegetables and crops grown in the area irrigated with river water. Bull. Environ. Contam. Toxicol. 2005, 74, 210–218. [Google Scholar] [CrossRef]
  24. Singh, K.P.; Mohan, D.; Sinha, S.; Dalwani, R. Impact assessment of treated/untreated wastewater toxicants discharged by sewage treatment plants on health, agricultural, and environmental quality in the wastewater disposal area. Chemosphere 2004, 55, 227–255. [Google Scholar] [CrossRef]
  25. Madyiwa, S.; Chimbari, M.; Nyamangara, J.; Bangira, C. Cumulative effects of sewage sludge and effluent mixture application on soil properties of a sandy soil under a mixture of star and kikuyu grasses in Zimbabwe. Phys. Chem. Earth Parts ABC 2002, 27, 747–753. [Google Scholar] [CrossRef]
  26. Sidhu, J.; Hanna, J.; Toze, S. Survival of enteric microorganisms on grass surfaces irrigated with treated effluent. J. Water Health 2008, 6, 255–262. [Google Scholar] [CrossRef] [PubMed]
  27. Zhan, X.; Wu, W.; Zhou, L.; Liang, J.; Jiang, T. Interactive effect of dissolved organic matter and phenanthrene on soil enzymatic activities. J. Environ. Sci. 2010, 22, 607–614. [Google Scholar] [CrossRef] [PubMed]
  28. Marcato-Romain, C.-E.; Guiresse, M.; Cecchi, M.; Cotelle, S.; Pinelli, E. New direct contact approach to evaluate soil genotoxicity using the Vicia Faba micronucleus test. Chemosphere 2009, 77, 345–350. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  29. Chen, Y.; Wang, C.; Wang, Z.; Huang, S. Assessment of the contamination and genotoxicity of soil irrigated with wastewater. Plant Soil 2004, 261, 189–196. [Google Scholar] [CrossRef] [Green Version]
  30. Aleem, A.; Malik, A. Genotoxic hazards of long-term application of wastewater on agricultural soil. Mutat. Res. Toxicol. Environ. Mutagen. 2003, 538, 145–154. [Google Scholar] [CrossRef] [PubMed]
  31. Aziz, O.; Inam, A.; Samiullah, A. Utilization of petrochemical industry waste water for agriculture. Water Air Soil Pollut. 1999, 115, 321–335. [Google Scholar] [CrossRef]
  32. Qadir, M.; Wichelns, D.; Minhas, P.; McCornick, P.; Abaidoo, R.; Attia, F.; El-Guindy, S.; Ensink, J.; Jiménez, B.; Kijne, J.; et al. Agricultural Use of Marginal-Quality Water—Opportunities and Challenges; International Water Management Institute (IWMI): Colombo, Sri Lanka, 2007. [Google Scholar]
  33. Corwin, D.L. Past, Present, and Future Trends of Soil Electrical Conductivity Measurements Using Geophysical Methods; CRC Press: Boca Raton, FL, USA; Taylor & Francis Group: New York, NY, USA, 2008. [Google Scholar]
  34. Friedman, S.P. Soil properties influencing apparent electrical conductivity: A review. Comput. Electron. Agric. 2005, 46, 45–70. [Google Scholar] [CrossRef]
  35. Corwin, D.; Lesch, S. Apparent soil electrical conductivity measurements in agriculture. Comput. Electron. Agric. 2005, 46, 11–43. [Google Scholar] [CrossRef]
  36. El-Desoky, M.; Faragallah, M.; Khalifa, E.; El-Ansary, M. Soil fabric change induced by prolonge irrigation with sewage effluent at Assiut, Egypt. J. Agric. Sci. Assiut Univ. 2010, 15, 297–321. [Google Scholar]
  37. Tbassum, D.; Azad, S.; Inam, A. Utility of city wastewater as a source of irrigation water for mustard. J. Ind. Pollut. Cont 2007, 23, 391–396. [Google Scholar]
  38. Ramsdale, T.M.; Fairweather, P.G. A calibration equation for combining dry-sieving and laser-diffraction techniques for assessing grain-size distributions of beach sands. J. Coast. Res. 2016, 32, 206–212. [Google Scholar]
  39. Rodríguez, J.G.; Uriarte, A. Laser diffraction and dry-sieving grain size analyses undertaken on fine-and medium-grained sandy marine sediments: A note. J. Coast. Res. 2009, 251, 257–264. [Google Scholar] [CrossRef]
  40. Lindholm, R.C. A Practical Approach to Sedimentology, Grain Size; Springer: Berlin/Heidelberg, Germany, 1987; p. 276. [Google Scholar]
  41. Eshel, G.; Levy, G.; Mingelgrin, U.; Singer, M. Critical evaluation of the use of laser diffraction for particle-size distribution analysis. Soil Sci. Soc. Am. J. 2004, 68, 736–743. [Google Scholar] [CrossRef]
  42. Beuselinck, L.; Govers, G.; Poesen, J.; Degraer, G.; Froyen, L. Grain-size analysis by laser diffractometry: Comparison with the sieve-pipette method. Catena 1998, 32, 193–208. [Google Scholar] [CrossRef]
  43. Konert, M.; Vandenberghe, J. Comparison of laser grain size analysis with pipette and sieve analysis: A solution for the underestimation of the clay fraction. Sedimentology 1997, 44, 523–535. [Google Scholar] [CrossRef] [Green Version]
  44. Indorante, S.; Hammer, R.; Koenig, P.; Follmer, L. Particle-size analysis by a modified pipette procedure. Soil Sci. Soc. Am. J. 1990, 54, 560–563. [Google Scholar] [CrossRef]
  45. Schoeneberger, P.J. Field Book for Describing and Sampling Soils; Government Printing Office: Washington, DC, USA, 2012. [Google Scholar]
  46. Rice, A.K. Predicting Hydraulic Response: Comparison of Textural and Response Clustering Approaches to Soil Classification; The University of Arizona: Tucson, AZ, USA, 2009. [Google Scholar]
  47. Metternicht, G.; Stott, J. Trivariate spectral encoding: A prototype system for automated selection of colours for soil maps based on soil textural composition. In Proceedings of the 21st International Cartographic Conference, Durban, South Africa, 10–16 August 2003. [Google Scholar]
  48. Minasny, B.; McBratney, A.B. The australian soil texture boomerang: A comparison of the Australian and Usda/Fao soil particle-size classification systems. Soil Res. 2001, 39, 1443–1451. [Google Scholar] [CrossRef]
  49. FAO. Guidelines for Soil Profile Description. Soil Resources, Management and Conservation Service, Land and Water Development Division; FAO: Rome, Italy, 1990. [Google Scholar]
  50. Lang, L.Z.; Xiang, W.; Huang, W.; Schanz, T. An experimental study on oven-drying methods for laboratory determination of water content of a calcium-rich bentonite. Appl. Clay Sci. 2017, 150, 153–162. [Google Scholar] [CrossRef]
  51. O’Kelly, B.C.; Sivakumar, V. Water content determinations for peat and other organic soils using the oven-drying method. Dry. Technol. 2014, 32, 631–643. [Google Scholar] [CrossRef]
  52. Gardner, W. Water content. In Methods of Soil Analysis, Part 1. Physical and Mineralogical Methods—Agronomy Monograph; American Society of Agronomy: Madison, WI, USA; Soil Science Society of America: Madison, WI, USA, 1986; Volume 1, pp. 493–544. [Google Scholar]
  53. Acquarone, C.; Buera, P.; Elizalde, B. Pattern of ph and electrical conductivity upon honey dilution as a complementary tool for discriminating geographical origin of honeys. Food Chem. 2007, 101, 695–703. [Google Scholar] [CrossRef]
  54. Jones, J.B., Jr. Laboratory Guide for Conducting Soil Tests and Plant Analysis; CRC Press: Boca Raton, FL, USA, 2001. [Google Scholar]
  55. McLean, E. Soil pH and lime requirement. In Methods of Soil Analysis. Part 2. Chemical and Microbiological Properties; Soil Science Society of America: Madison, WI, USA, 1982; pp. 199–224. [Google Scholar]
  56. Zahedifar, M.; Dehghani, S.; Moosavi, A.A.; Gavili, E. Temporal variation of total and dtpa-extractable heavy metal contents as influenced by sewage sludge and perlite in a calcareous soil. Arch. Agron. Soil Sci. 2017, 63, 136–149. [Google Scholar] [CrossRef]
  57. Yadava, N.; Malik, R.S.; Shivakumar, L. Kinetic release behavior of DTPA-extractable manganese in soils of different cropping systems and total manganese content associated with soil texture. Indian J. Agric. Sci. 2017, 87, 603–606. [Google Scholar]
  58. Gonçalves da Silva, M.A.; Bull, L.T.; Miggiolaro, A.E.; Antonangelo, J.A.; Muniz, A.S. Heavy metals extracted by DTPA and organic acids from soil amended with urban or industrial residues. Commun. Soil Sci. Plant Anal. 2013, 44, 3216–3230. [Google Scholar] [CrossRef]
  59. Muhlbachova, G. The availability of dtpa extracted heavy metals during laboratory incubation of contaminated soils with glucose amendments. Rostl. Vyrob. 2002, 48, 536–542. [Google Scholar] [CrossRef] [Green Version]
  60. Jericho, K.; Kozub, G.; Loewen, K.; Ho, J. Comparison of methods to determine the microbiological contamination of surfaces of beef carcasses by hydrophobic grid membrane filters, standard pour plates or flow cytometry. Food Microbiol. 1996, 13, 303–309. [Google Scholar] [CrossRef]
  61. AS/NZS 4276; Water Microbiology. Heterotrophic Colony Count Methods–Pour Plate Method Using Plate Count Agar. Standards Australia Int.: Strathfield, NSW, Australia, 1995.
  62. Boetcher, S.; Hildebrandt, G. The precision of colony count techniques. 1. Literature study about the comparison of Koch’s pour plate method with surface plating techniques. Fleischwirtschaft 1991, 71, 596–599. [Google Scholar]
  63. Mironov, V.; Kim, J.; Park, M.; Lim, S.; Cho, W. Comparison of electrical conductivity data obtained by four-electrode and four-point probe methods for graphite-based polymer composites. Polym. Test. 2007, 26, 547–555. [Google Scholar] [CrossRef]
  64. Shea, P.; Luthin, J. An investigation of the use of the four-electrode probe for measuring soil salinity in situ. Soil Sci. 1961, 92, 331–339. [Google Scholar] [CrossRef]
  65. Edlefsen, N.; Anderson, A. The four-electrode resistance method for measuring soil-moisture content under field conditions. Soil Sci. 1941, 51, 367–376. [Google Scholar] [CrossRef]
  66. Gomah, H. Assessment and Evaluation of Certain Heavy Metals in Soils and Plants in Assiut Governorate. Ph. D. Thesis, Faculty of Agriculture, Assiut University, Asyut, Egypt, 2001. [Google Scholar]
  67. Sys, C.; Verheye, W. Attempt to the Evaluation of Physical Land Characteristics for Irrigation according to the Fao Framework for Land Evaluation; State University of Ghent: Ghent, Belgium, 1978. [Google Scholar]
  68. Weil, R.R.; Brady, N.C.; Weil, R.R. The Nature and Properties of Soils; Prentice Hall: Upper Saddle River, NJ, USA, 2016. [Google Scholar]
  69. US Soil Conservation Service. Soil Survey Manual; US Government Printing Office: Washington, DC, USA, 1962. [Google Scholar]
  70. Roshdy, N. Distribution and Forms of Some Heavy Metals in a Contaminated Soil at Assiut; Faculty of Agriculture, Assiut University: Asyut, Egypt, 2009. [Google Scholar]
  71. El-Ameen, M.; Farragallah, A.; Essa, M.A. Physical, chemical and macro-micromorphological characteristics of some alluvial soils irrigated with different water resources. Ass. Univ. Bull. Environ. Res. 2005, 8, 51–69. [Google Scholar]
  72. Gerba, C.P.; Bitton, G. Microbial pollutants: Their survival and transport pattern to groundwater. In Groundwater Pollution Microbiology; John Wiley and Sons: New York, NY, USA, 1984; pp. 65–88. [Google Scholar]
  73. Pozdnyakova, L.A. Electrical Properties of Soils; University of Wyoming: Laramie, WY, USA, 1999. [Google Scholar]
Water 15 02148 g001 550
Figure 1. The map shows the locations of the soil samples that were gathered at the study area.
Figure 1. The map shows the locations of the soil samples that were gathered at the study area.
Water 15 02148 g001
Water 15 02148 g002 550
Figure 2. The laboratory apparatus employed for measuring electrical resistivity.
Figure 2. The laboratory apparatus employed for measuring electrical resistivity.
Water 15 02148 g002
Water 15 02148 g003a 550Water 15 02148 g003b 550
Figure 3. (a–i) The measured physical, chemical, bacteriological, and geoelectrical of the investigated soil profiles at various depths where (a) % sand content, (b) % clay content, (c) % silt content, (d) Water content, (e) Electrical conductivity (EC), (f) soil pH, (g) Concentration of DTPA-extractable heavy metals, (h) Total coliform count, and (i) Electrical resistivity (ER).
Figure 3. (a–i) The measured physical, chemical, bacteriological, and geoelectrical of the investigated soil profiles at various depths where (a) % sand content, (b) % clay content, (c) % silt content, (d) Water content, (e) Electrical conductivity (EC), (f) soil pH, (g) Concentration of DTPA-extractable heavy metals, (h) Total coliform count, and (i) Electrical resistivity (ER).
Water 15 02148 g003aWater 15 02148 g003b
Water 15 02148 g004a 550Water 15 02148 g004b 550
Figure 4. (a–h) Cross correlation between the resistivity measurements and (a) sand content; (b) clay content; (c) silt content; (d) water content; (e) EC; (f) pH; (g) sum of heavy metal concentrations; and (h) total coliform using an exponential growth fitting. The correlation coefficients (R2) for these equations were derived.
Figure 4. (a–h) Cross correlation between the resistivity measurements and (a) sand content; (b) clay content; (c) silt content; (d) water content; (e) EC; (f) pH; (g) sum of heavy metal concentrations; and (h) total coliform using an exponential growth fitting. The correlation coefficients (R2) for these equations were derived.
Water 15 02148 g004aWater 15 02148 g004b
Water 15 02148 g005a 550Water 15 02148 g005b 550
Figure 5. (a–h). Cross correlation between calculated and measured (a) sand content; (b) clay content; (c) silt content; (d) water content; (e) EC; (f) pH; (g) sum of heavy metals concentrations; and (h) total coliform. These relationships exhibit strong correlation coefficients (R2).
Figure 5. (a–h). Cross correlation between calculated and measured (a) sand content; (b) clay content; (c) silt content; (d) water content; (e) EC; (f) pH; (g) sum of heavy metals concentrations; and (h) total coliform. These relationships exhibit strong correlation coefficients (R2).
Water 15 02148 g005aWater 15 02148 g005b
Table
Table 1. The empirical correlation between the biophysicochemical characteristics of the 27 soil samples and electrical resistivity (ER) with the driven correlation coefficients (R2) for each one.
Table 1. The empirical correlation between the biophysicochemical characteristics of the 27 soil samples and electrical resistivity (ER) with the driven correlation coefficients (R2) for each one.
PropertyRelation Coefficient Value (R2)Empirical Relationship
Water content0.785ER = 209.4e−0.17water content
EC0.613ER = 258e−1.32EC
pH0.61ER = 276.4(pH) − 2108
Sand content0.654ER = 0.115e0.074sand content
Silt content0.563ER = 159.8e−0.04silt content
Clay content0.531ER = 129.3e−0.46clay content
Summation of heavy metals0.566ER = 221.1e−0.06sum.of heavy metals
Total coliform0.584ER = 287.7e−0.01TC
Table
Table 2. The measured resistivities and the calculated and measured values of some physical properties of random soil samples.
Table 2. The measured resistivities and the calculated and measured values of some physical properties of random soil samples.
ProfileDepthResistivity% Water ContentParticle Size Distribution
% Clay Content% Silt Content% Sand Content
No.(cm)(ohm·m)Cal.Meas.Cal.Meas.Cal.Meas.Cal.Meas.
A0–2550.38298.3765.8572.041.5828.8516.9782.2882.54
25–6045.99528.9126.9642.241.6931.1222.3780.9777.49
60–9545.54138.9717.2962.261.7331.3719.9380.8479.15
B0–2547.20568.7596.8952.181.7730.4718.8981.3280.43
25–6042.51539.3767.6512.41.8433.122.4179.9177.75
60–9536.009410.3539.7692.772.1537.2530.2377.6668.67
C0–2540.24589.77.7972.532.0534.4724.7879.1675.14
25–6036.614610.2599.5082.742.1936.8529.0577.8870.67
Table
Table 3. The calculated and measured values of some chemical features of the soil samples for validation.
Table 3. The calculated and measured values of some chemical features of the soil samples for validation.
ProfileDepthECpH
No.(cm)Cal.Meas.Cal.Meas.
A0–251.200.967.817.84
25–601.301.057.798.08
60–951.301.207.798.17
B0–251.281.007.807.79
25–601.361.157.787.91
60–951.491.397.768.24
C0–251.411.187.778.19
25–601.481.287.768.31
Table
Table 4. The calculated and measured values of DTPA extractable heavy metals of the random soil samples for validation.
Table 4. The calculated and measured values of DTPA extractable heavy metals of the random soil samples for validation.
ProfileDepthDTPA-Extractable Heavy Metals (ppm)
FeMnCuZnCoPbSum.
No.(cm)Cal.Meas.Cal.Meas.Cal.Meas.Cal.Meas.Cal.Meas.Cal.Meas.Cal.Meas.
A0–2511.2310.435.535.4022.591.742.281.6760.350.2942.792.9224.6422.462
25–6011.9310.565.795.4372.791.972.421.9680.370.3083.05 2.9826.1623.223
60–951210.5385.825.3292.822.0312.442.000.370.3283.083.0226.3323.246
B0–2511.7310.645.725.262.742.1362.381.8530.360.2862.982.8725.7323.045
25–6012.9210.8796.035.6832.972.3862.552.170.380.3423.293.0127.4824.47
60–9513.8211.6246.56.233.342.9592.832.4060.410.3863.773.5230.2427.125
C0–2512.9610.9856.195.7093.092.642.642.1760.390.3363.453.2628.3925.116
25–6013.6911.2396.476.0523.33.002.82.4050.410.3733.733.5829.9826.649
Table
Table 5. The calculated and measured total coliform of the random soil samples for validation.
Table 5. The calculated and measured total coliform of the random soil samples for validation.
Profile No.DepthTotal Coliform
No.(cm)Cal.Meas.
A0–25174158
25–60183166
60–95184170
B0–25181168
25–60191178
60–95208198
C0–25197190
25–60206200
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Abdel Aal, G.Z.; Faragallah, M.E.; Abd-Alla, M.H.; Abd El-Rhman, R.S.; Abdel Gowad, A.M.; Abdelhalim, A.; Ahmed, M.S.; Abudeif, A.M.; Mohammed, M.A. Simulation of Biophysicochemical Characteristics of the Soils Using Geoelectrical Measurements near the Sewage Station, Assiut City, Egypt. Water 2023, 15, 2148. https://doi.org/10.3390/w15122148

AMA Style

Abdel Aal GZ, Faragallah ME, Abd-Alla MH, Abd El-Rhman RS, Abdel Gowad AM, Abdelhalim A, Ahmed MS, Abudeif AM, Mohammed MA. Simulation of Biophysicochemical Characteristics of the Soils Using Geoelectrical Measurements near the Sewage Station, Assiut City, Egypt. Water. 2023; 15(12):2148. https://doi.org/10.3390/w15122148

Chicago/Turabian Style

Abdel Aal, Gamal Z., Mohamed E. Faragallah, Mohamed H. Abd-Alla, Reham S. Abd El-Rhman, Ahmed M. Abdel Gowad, Ahmed Abdelhalim, Mohamed S. Ahmed, Abdelbaset M. Abudeif, and Mohammed A. Mohammed. 2023. "Simulation of Biophysicochemical Characteristics of the Soils Using Geoelectrical Measurements near the Sewage Station, Assiut City, Egypt" Water 15, no. 12: 2148. https://doi.org/10.3390/w15122148

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop