Multi-Soil-Layering, the Emerging Technology for Wastewater Treatment: Review, Bibliometric Analysis, and Future Directions
Abstract
:1. Introduction
- (i)
- to conduct a bibliometric analysis in the field of MSL technology;
- (ii)
- to provide a comprehensive summary of MSL aspects (e.g., performance, removal mechanisms, etc.);
- (iii)
- to issue a comparative profile of the MSL with other eco-friendly technologies;
- (iv)
- to evaluate models applied to simulate MSL performance;
- (v)
- to highlight the MSL challenges and provide a road map for future research.
2. Materials and Methods
3. Results
3.1. Bibliometric Analysis
3.1.1. MSL Published Papers
3.1.2. MSL World Collaboration
3.1.3. Co-Occurrences Analysis
3.2. Main Pollutant Removal Mechanisms in MSL
3.2.1. Suspended Solids
3.2.2. Organic Matter
3.2.3. Fecal Indicator Bacteria and Pathogens
3.2.4. Nitrogen
3.2.5. Phosphorus
3.3. Key Efficiency Parameters
3.3.1. Temperature
3.3.2. Aeration
3.3.3. pH
3.3.4. Hydraulic Loading Rate and Clogging
3.4. Filter Media and Structure
3.5. MSL Typology and Performance
3.5.1. Single-Stage MSL
3.5.2. Combined Systems
- Two-stage MSL
- MSL with constructed wetland
- Novel hybrid combinations
3.6. Multi-Soil-Layering and Alternative Treatment Technologies
3.7. Modeling Approaches Used
3.7.1. Data-Driven Models
3.7.2. Kinetic Models
4. Recommendations and Future Consideration
4.1. MSL Treatment Efficiency
4.2. Design and Costing
4.3. MSL Modeling
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Nomenclature
Al | aluminium |
BCS | blended organic carbon source |
BOD5 | five-day biochemical oxygen demand |
CH4 | methane |
CO2 | carbon dioxide |
COD | chemical oxygen demand |
CW | constructed wetland |
DO | dissolved oxygen |
EC | electrical conductivity |
FC | fecal coliform |
Fe0 | metallic iron |
Fe2+ | ferrous ions |
Fe3+ | ferric ions |
FIB | fecal bacteria indicator |
HLR | hydraulic loading rate |
MLR | multiple linear regression |
MSL | multi-soil-layering |
N2 | diazote |
N2O | nitrous oxide |
NH3 | ammonia |
NH4+ | ammonium |
NN | neural networks |
NO3− | nitrates |
NO2− | nitrites |
ORP | oxidation-reduction potential |
PBS | polybutylene succinate |
PL | permeable layer |
Q | salinity |
SCA | stepwise-cluster analysis |
SFCW | subsurface flow constructed wetland |
SS | suspended solids |
SWI | subsurface wastewater infiltration |
TC | total coliform |
TN | total nitrogen |
TKN | total kjeldahl nitrogen |
TP | total phosphorus |
VFCW | vertical flow constructed wetland |
WoS | Web of Science |
References
- Sato, K.; Wakatsuki, T.; Iwashima, N.; Masunaga, T. Evaluation of Long-Term Wastewater Treatment Performances in Multi-Soil-Layering Systems in Small Rural Communities. Appl. Environ. Soil Sci. 2019, 2019, 11. [Google Scholar] [CrossRef]
- Moreira, F.D.; Dias, E.H.O. Constructed Wetlands Applied in Rural Sanitation: A Review. Environ. Res. 2020, 190, 110016. [Google Scholar] [CrossRef] [PubMed]
- WHO. Guidelines on Sanitation and Health; World Health Organization: Geneva, Switzerland, 2018; pp. 1–220. Available online: https://www.who.int/publications/i/item/9789241514705 (accessed on 20 June 2022).
- Yaqoob, A.A.; Parveen, T.; Umar, K.; Mohamad Ibrahim, M.N. Role of Nanomaterials in the Treatment of Wastewater: A Review. Water 2020, 12, 495. [Google Scholar] [CrossRef] [Green Version]
- Song, P.; Huang, G.; An, C.; Xin, X.; Zhang, P.; Chen, X.; Ren, S.; Xu, Z.; Yang, X. Exploring the Decentralized Treatment of Sulfamethoxazole-Contained Poultry Wastewater through Vertical-Flow Multi-Soil-Layering Systems in Rural Communities. Water Res. 2021, 188, 116480. [Google Scholar] [CrossRef]
- Sbahi, S.; Ouazzani, N.; Hejjaj, A.; Mandi, L. Neural Network and Cubist Algorithms to Predict Fecal Coliform Content in Treated Wastewater by Multi-Soil-Layering System for Potential Reuse. J. Environ. Qual. 2021, 50, 144–157. [Google Scholar] [CrossRef]
- Latrach, L.; Ouazzani, N.; Hejjaj, A.; Mahi, M.; Masunaga, T.; Mandi, L. Two-Stage Vertical Flow Multi-Soil-Layering (MSL) Technology for Efficient Removal of Coliforms and Human Pathogens from Domestic Wastewater in Rural Areas under Arid Climate. Int. J. Hyg. Environ. Health 2018, 221, 64–80. [Google Scholar] [CrossRef]
- Idris, M.O.; Yaqoob, A.A.; Ibrahim, M.N.M.; Ahmad, A.; Alshammari, M.B. Introduction of Adsorption Techniques for Heavy Metals Remediation. In Emerging Techniques for Treatment of Toxic Metals from Wastewater, 1st ed.; Elsevier: Amsterdam, The Netherlands, 2023; pp. 1–18. [Google Scholar]
- Zhou, Q.; Sun, H.; Jia, L.; Zhao, L.; Wu, W. Enhanced Pollutant Removal from Rural Non-Point Source Wastewater Using a Two-Stage Multi-Soil-Layering System with Blended Carbon Sources: Insights into Functional Genes, Microbial Community Structure and Metabolic Function. Chemosphere 2021, 275, 130007. [Google Scholar] [CrossRef]
- Song, P.; Huang, G.; An, C.; Shen, J.; Zhang, P.; Chen, X.; Shen, J.; Yao, Y.; Zheng, R.; Sun, C. Treatment of Rural Domestic Wastewater Using Multi-Soil-Layering Systems: Performance Evaluation, Factorial Analysis and Numerical Modeling. Sci. Total Environ. 2018, 644, 536–546. [Google Scholar] [CrossRef]
- Sbahi, S.; Ouazzani, N.; Latrach, L.; Hejjaj, A.; Mandi, L. Predicting the Concentration of Total Coliforms in Treated Rural Domestic Wastewater by Multi-Soil-Layering (MSL) Technology Using Artificial Neural Networks. Ecotoxicol. Environ. Saf. 2020, 204, 111118. [Google Scholar] [CrossRef]
- An, C.J.; McBean, E.; Huang, G.H.; Yao, Y.; Zhang, P.; Chen, X.J.; Li, Y.P. Multi-Soil-Layering Systems for Wastewater Treatment in Small and Remote Communities. J. Environ. Inform. 2016, 27, 131–144. [Google Scholar] [CrossRef]
- Hong, Y.; Huang, G.; An, C.; Song, P.; Xin, X.; Chen, X.; Zhang, P.; Zhao, Y.; Zheng, R. Enhanced Nitrogen Removal in the Treatment of Rural Domestic Sewage Using Vertical-Flow Multi-Soil-Layering Systems: Experimental and Modeling Insights. J. Environ. Manag. 2019, 240, 273–284. [Google Scholar] [CrossRef] [PubMed]
- Latrach, L.; Masunaga, T.; Ouazzani, N.; Hejjaj, A.; Mahi, M.; Mandi, L. Removal of Bacterial Indicators and Pathogens from Domestic Wastewater by the Multi-Soil-Layering (MSL) System. Soil Sci. Plant Nutr. 2015, 61, 337–346. [Google Scholar] [CrossRef] [Green Version]
- Aba, R.P.; Mugani, R.; Hejjaj, A.; Brugerolle de Fraissinette, N.; Oudra, B.; Ouazzani, N.; Campos, A.; Vasconcelos, V.; Carvalho, P.N.; Mandi, L. First Report on Cyanotoxin (MC-LR) Removal from Surface Water by Multi-Soil-Layering (MSL) Eco-Technology: Preliminary Results. Water 2021, 13, 1403. [Google Scholar] [CrossRef]
- Sbahi, S.; Ouazzani, N.; Hejjaj, A.; Mandi, L. Nitrogen Modeling and Performance of Multi-Soil-Layering (MSL) Bioreactor Treating Domestic Wastewater in Rural Community. J. Water Process Eng. 2021, 44, 102389. [Google Scholar] [CrossRef]
- Shen, J.; Huang, G.; An, C.; Song, P.; Xin, X.; Yao, Y.; Zheng, R. Biophysiological and Factorial Analyses in the Treatment of Rural Domestic Wastewater Using Multi-Soil-Layering Systems. J. Environ. Manag. 2018, 226, 83–94. [Google Scholar] [CrossRef] [PubMed]
- Zidan, K.; Sbahi, S.; Hejjaj, A.; Ouazzani, N.; Assabbane, A.; Mandi, L. Removal of Bacterial Indicators in On-Site Two-Stage Multi-Soil-Layering Plant under Arid Climate (Morocco): Prediction of Total Coliform Content Using K-Nearest Neighbor Algorithm. Environ. Sci. Pollut. Res. 2022, 29, 75716–75729. [Google Scholar] [CrossRef]
- Sato, K.; Iwashima, N.; Wakatsuki, T.; Masunaga, T. Quantitative Evaluation of Treatment Processes and Mechanisms of Organic Matter, Phosphorus, and Nitrogen Removal in a Multi-Soil-Layering System. Soil Sci. Plant Nutr. 2011, 57, 475–486. [Google Scholar] [CrossRef]
- Song, P.; Huang, G.; An, C.; Zhang, P.; Chen, X.; Ren, S. Performance Analysis and Life Cycle Greenhouse Gas Emission Assessment of an Integrated Gravitational-Flow Wastewater Treatment System for Rural Areas. Environ. Sci. Pollut. Res. 2019, 26, 25883–25897. [Google Scholar] [CrossRef]
- Li, D.; Wang, X.; Chi, L.; Zhang, Z.; Liu, Y.; Li, X. Decentralized Domestic Sewage Treatment Using an Integrated Multi-Soil-Layering and Subsurface Wastewater Infiltration System. Water 2021, 13, 431. [Google Scholar] [CrossRef]
- Maeng, S.K.; Park, J.W.; Noh, J.H.; Won, S.Y.; Song, K.G. Dissolved Organic Matter Characteristics and Removal of Trace Organic Contaminants in a Multi-Soil-Layering System. J. Environ. Chem. Eng. 2021, 9, 105446. [Google Scholar] [CrossRef]
- Zhang, P.; Huang, G.; An, C.; Fu, H.; Gao, P.; Yao, Y.; Chen, X. An Integrated Gravity-Driven Ecological Bed for Wastewater Treatment in Subtropical Regions: Process Design, Performance Analysis, and Greenhouse Gas Emissions Assessment. J. Clean. Prod. 2019, 212, 1143–1153. [Google Scholar] [CrossRef]
- Nguyen, X.C.; Chang, S.W.; Tran, T.C.P.; Nguyen, T.T.N.; Hoang, T.Q.; Banu, J.R.; Al-Muhtaseb, A.H.; La, D.D.; Guo, W.; Ngo, H.H. Comparative Study about the Performance of Three Types of Modified Natural Treatment Systems for Rice Noodle Wastewater. Bioresour. Technol. 2019, 282, 163–170. [Google Scholar] [CrossRef] [PubMed]
- Koottatep, T.; Suksiri, P.; Pussayanavin, T.; Polprasert, C. Development of a Novel Multi-Soil Layer Constructed Wetland Treating Septic Tank Effluent with Emphasis on Organic and Ammonia Removals. Water Air Soil Pollut. 2018, 229, 258. [Google Scholar] [CrossRef]
- Luo, W.; Yang, C.; He, H.; Zeng, G.; Yan, S.; Cheng, Y. Novel Two-Stage Vertical Flow Biofilter System for Efficient Treatment of Decentralized Domestic Wastewater. Ecol. Eng. 2014, 64, 415–423. [Google Scholar] [CrossRef]
- Latrach, L.; Ouazzani, N.; Masunaga, T.; Hejjaj, A.; Bouhoum, K.; Mahi, M.; Mandi, L. Domestic Wastewater Disinfection by Combined Treatment Using Multi-Soil-Layering System and Sand Filters (MSL-SF): A Laboratory Pilot Study. Ecol. Eng. 2016, 91, 294–301. [Google Scholar] [CrossRef]
- Wu, J.; Wang, Z. A Hybrid Model for Water Quality Prediction Based on an Artificial Neural Network, Wavelet Transform, and Long Short-Term Memory. Water 2022, 14, 610. [Google Scholar] [CrossRef]
- Alnahit, A.O.; Mishra, A.K.; Khan, A.A. Stream Water Quality Prediction Using Boosted Regression Tree and Random Forest Models. Stoch. Environ. Res. Risk Assess. 2022, 36, 2661–2680. [Google Scholar] [CrossRef]
- Ho, C.C.; Wang, P.H. Efficiency of a Multi-Soil-Layering System on Wastewater Treatment Using Environment-Friendly Filter Materials. Int. J. Environ. Res. Public Health 2015, 12, 3362–3380. [Google Scholar] [CrossRef]
- Luanmanee, S.; Attanandana, T.; Masunaga, T.; Wakatsuki, T. The Efficiency of a Multi-Soil-Layering System on Domestic Wastewater Treatment during the Ninth and Tenth Years of Operation. Ecol. Eng. 2001, 18, 185–199. [Google Scholar] [CrossRef]
- Masunaga, T.; Sato, K.; Mori, J.; Shirahama, M.; Kudo, H.; Wakatsuki, T. Characteristics of Wastewater Treatment Using a Multi-Soil-Layering System in Relation to Wastewater Contamination Levels and Hydraulic Loading Rates. Soil Sci. Plant Nutr. 2007, 53, 215–223. [Google Scholar] [CrossRef]
- Khalifa, J.; Ouazzani, N.; Hejjaj, A.; Mandi, L. Remediation and Disinfection Capabilities Assessment of Some Local Materials to Be Applied in Multi-Soil-Layering (MSL) Ecotechnology. Desalin. Water Treat. 2020, 178, 53–64. [Google Scholar] [CrossRef]
- Liu, C.; Huang, G.; Song, P.; An, C.; Zhang, P.; Shen, J.; Ren, S.; Zhao, K.; Huang, W.; Xu, Y. Treatment of Decentralized Low-Strength Livestock Wastewater Using Microcurrent-Assisted Multi-Soil-Layering Systems: Performance Assessment and Microbial Analysis. Chemosphere 2022, 294, 133536. [Google Scholar] [CrossRef] [PubMed]
- Luanmanee, S.; Boonsook, P.; Attanandana, T.; Wakatsuki, T. Effect of Organic Components and Aeration Regimes on the Efficiency of a Multi-Soil-Layering System for Domestic Wastewater Treatment. Soil Sci. Plant Nutr. 2002, 48, 125–134. [Google Scholar] [CrossRef]
- Guan, Y.; Zhang, Y.; Zhong, C.N.; Huang, X.F.; Fu, J.; Zhao, D. Effect of Operating Factors on the Contaminants Removal of a Soil Filter: Multi-Soil-Layering System. Environ. Earth Sci. 2015, 74, 2679–2686. [Google Scholar] [CrossRef]
- Latrach, L.; Ouazzani, N.; Hejjaj, A.; Zouhir, F.; Mahi, M.; Masunaga, T.; Mandi, L. Optimization of Hydraulic Efficiency and Wastewater Treatment Performances Using a New Design of Vertical Flow Multi-Soil-Layering (MSL) Technology. Ecol. Eng. 2018, 117, 140–152. [Google Scholar] [CrossRef]
- Zhao, L.; Dai, T.; Qiao, Z.; Sun, P.; Hao, J.; Yang, Y. Application of Artificial Intelligence to Wastewater Treatment: A Bibliometric Analysis and Systematic Review of Technology, Economy, Management, and Wastewater Reuse. Process Saf. Environ. Prot. 2020, 133, 169–182. [Google Scholar] [CrossRef]
- Dalpé, R. Bibliometric Analysis of Biotechnology. Scientometrics 2002, 55, 189–213. [Google Scholar] [CrossRef]
- Xie, H.; Zhang, Y.; Wu, Z.; Lv, T. A Bibliometric Analysis on Land Degradation: Current Status, Development, and Future Directions. Land 2020, 9, 28. [Google Scholar] [CrossRef] [Green Version]
- Wu, L.; Wang, W.; Jing, P.; Chen, Y.; Zhan, F.; Shi, Y.; Li, T. Travel Mode Choice and Their Impacts on Environment—A Literature Review Based on Bibliometric and Content Analysis, 2000–2018. J. Clean. Prod. 2020, 249, 119391. [Google Scholar] [CrossRef]
- Van Eck, N.J.; Waltman, L. Software Survey: VOSviewer, a Computer Program for Bibliometric Mapping. Scientometrics 2010, 84, 523–538. [Google Scholar] [CrossRef]
- Chen, X.; Sato, K.; Wakatsuki, T.; Masunaga, T. Effect of Structural Difference on Wastewater Treatment Efficiency in Multi-Soil-Layering Systems: Relationship between Soil Mixture Block Size and Removal Efficiency of Selected Contaminants. Soil Sci. Plant Nutr. 2007, 53, 206–214. [Google Scholar] [CrossRef]
- Paul, E. Soil Microbiology, Ecology and Biochemistry, 4th ed.; Academic Press: San Diego, CA, USA, 2006; p. 552. [Google Scholar]
- Masunaga, T.; Sato, K.; Senga, Y.; Seike, Y.; Inaishi, T.; Kudo, H.; Wakatsuki, T. Characteristics of CO2, CH4 and N2O Emissions from a Multi-Soil-Layering System during Wastewater Treatment. Soil Sci. Plant Nutr. 2007, 53, 173–180. [Google Scholar] [CrossRef] [Green Version]
- Stevik, T.K.; Aa, K.; Ausland, G.; Hanssen, J.F. Retention and Removal of Pathogenic Bacteria in Wastewater Percolating through Porous Media: A Review. Water Res. 2004, 38, 1355–1367. [Google Scholar] [CrossRef] [PubMed]
- Kadam, A.; Oza, G.; Nemade, P.; Dutta, S.; Shankar, H. Municipal Wastewater Treatment Using Novel Constructed Soil Filter System. Chemosphere 2008, 71, 975–981. [Google Scholar] [CrossRef] [PubMed]
- Song, P.; Huang, G.; Hong, Y.; An, C.; Xin, X.; Zhang, P. A Biophysiological Perspective on Enhanced Nitrate Removal from Decentralized Domestic Sewage Using Gravitational-Flow Multi-Soil-Layering Systems. Chemosphere 2020, 240, 124868. [Google Scholar] [CrossRef] [PubMed]
- Wei, C.; Wu, W. Performance of Single-Pass and by-Pass Multi-Step Multi-Soil-Layering Systems for Low-(C/N)-Ratio Polluted River Water Treatment. Chemosphere 2018, 206, 579–586. [Google Scholar] [CrossRef]
- Song, Y.; Huang, Y.T.; Ji, H.F.; Nie, X.J.; Zhang, Z.Y.; Ge, C.; Luo, A.C.; Chen, X. Treatment of Turtle Aquaculture Effluent by an Improved Multi-Soil-Layer System. J. Zhejiang Univ. Sci. B 2015, 16, 145–154. [Google Scholar] [CrossRef] [Green Version]
- Zhang, Y.; Cheng, Y.; Yang, C.; Luo, W.; Zeng, G.; Lu, L. Performance of System Consisting of Vertical Flow Trickling Filter and Horizontal Flow Multi-Soil-Layering Reactor for Treatment of Rural Wastewater. Bioresour. Technol. 2015, 193, 424–432. [Google Scholar] [CrossRef]
- Sato, K.; Masunaga, T.; Wakatsuki, T. Water Movement Characteristics in a Multi-Soil-Layering System. Soil Sci. Plant Nutr. 2005, 51, 75–82. [Google Scholar] [CrossRef]
- Zein, R.; Ningsih, Z.S.; Novita, L.; Swesty, N.; Mukhlis; Novrian, H. Treatment of Waste Water Noodle Industry with a Multi-Soil-Layering (MSL) System. Res. J. Pharm. Biol. Chem. Sci. 2016, 7, 88–94. [Google Scholar]
- Yidong, G.; Xin, C.; Shuai, Z.; Ancheng, L. Performance of Multi-Soil-Layering System (MSL) Treating Leachate from Rural Unsanitary Landfills. Sci. Total Environ. 2012, 420, 183–190. [Google Scholar] [CrossRef] [PubMed]
- Sato, K.; Masunaga, T.; Wakatsuki, T. Characterization of Treatment Processes and Mechanisms of COD, Phosphorus and Nitrogen Removal in a Multi-Soil-Layering System. Soil Sci. Plant Nutr. 2005, 51, 213–221. [Google Scholar] [CrossRef]
- Chen, X.; Luo, A.C.; Sato, K.; Wakatsuki, T.; Masunaga, T. An Introduction of a Multi-Soil-Layering System: A Novel Green Technology for Wastewater Treatment in Rural Areas. Water Environ. J. 2009, 23, 255–262. [Google Scholar] [CrossRef]
- Guo, J.; Zhou, Y.; Yang, Y.; Chen, C.; Xu, J. Effects of Hydraulic Loading Rate on Nutrients Removal from Anaerobically Digested Swine Wastewater by Multi Soil Layering Treatment Bioreactor. Int. J. Environ. Res. Public Health 2018, 15, 2688. [Google Scholar] [CrossRef] [Green Version]
- Sato, K.; Iwashima, N.; Wakatsuki, T.; Masunaga, T. Clarification of Water Movement Properties in a Multi-Soil-Layering System. Soil Sci. Plant Nutr. 2011, 57, 607–618. [Google Scholar] [CrossRef]
- Guo, J.; Zhou, Y.; Jiang, S.; Chen, C. Feasibility Investigation of a Multi Soil Layering Bioreactor for Domestic Wastewater Treatment. Environ. Technol. 2019, 40, 2317–2324. [Google Scholar] [CrossRef]
- Masunaga, T.; Sato, K.; Zennami, T.; Fujii, S.; Wakatsuki, T. Direct Treatment of Polluted River Water by the Multi-Soil-Layering Method. J. Water Environ. Technol. 2003, 1, 97–104. [Google Scholar] [CrossRef] [Green Version]
- Guan, Y.D.; Xu, D.F.; Chen, X.; Luo, A.C.; Fang, H.; Song, Y.Z. Flow Patterns of Multi-Soil-Layering Systems. Desalination Water Treat. 2014, 52, 4165–4169. [Google Scholar] [CrossRef]
- Koottatep, T.; Pussayanavin, T.; Khamyai, S.; Polprasert, C. Performance of Novel Constructed Wetlands for Treating Solar Septic Tank Effluent. Sci. Total Environ. 2021, 754, 142447. [Google Scholar] [CrossRef]
- Tang, W.; Li, X.; Liu, H.; Wu, S.; Zhou, Q.; Du, C.; Teng, Q.; Zhong, Y.; Yang, C. Sequential Vertical Flow Trickling Filter and Horizontal Flow Multi-Soil-Layering Reactor for Treatment of Decentralized Domestic Wastewater with Sodium Dodecyl Benzene Sulfonate. Bioresour. Technol. 2020, 300, 122634. [Google Scholar] [CrossRef]
- Ait-Hmane, A.; Mandi, L.; Ouazzani, N.; Ait Hammou, H.; Hejjaj, A.; Alahiane, S.; Assabbane, A. Combined Treatment of Olive Mill Wastewater by Multi-Soil-Layering Ecotechnology and Adsorption on Activated Carbon/Lime. Desalination Water Treat. 2021, 233, 253–260. [Google Scholar] [CrossRef]
- Wang, J.; Zhang, Z.; Guo, Y.; Zhang, L.; Liu, J. Experimental Study on the Treatment of Rural Domestic Wastewater Using the Multi-Soil-Layering System Filled with Sludge-Based Biochar. Ann. Chim. Sci. Mater. 2021, 45, 161–165. [Google Scholar] [CrossRef]
- Sy, S.; Sofyan, S.; Ardinal, A.; Kasman, M. Reduction of Pollutant Parameters in Textile Dyeing Wastewater by Gambier (Uncaria gambir Roxb) Using the Multi Soil Layering (MSL) Bioreactor. Mater. Sci. Eng. 2019, 546, 22–32. [Google Scholar] [CrossRef]
- Xiao, M.; Hu, R.; Cui, X.; Gwenzi, W.; Noubactep, C. Understanding the Operating Mode of Fe0/Fe-Sulfide/H2O Systems for Water Treatment. Processes 2020, 8, 409. [Google Scholar] [CrossRef] [Green Version]
- Chen, Y.C.; Pat, H.W. Comparing Natural Red Soil and Irons for Removal of Phosphorus from Wastewater Using the Multi-Soil-Layering System and Its Economic Analysis. J. Environ. Manag. 2021, 296, 113252. [Google Scholar] [CrossRef]
- Liu, D.; Li, J.; Li, C.; Deng, Y.; Zhang, Z.; Ye, Z.; Zhu, S. Poly(Butylene Succinate)/Bamboo Powder Blends as Solid-Phase Carbon Source and Biofilm Carrier for Denitrifying Biofilters Treating Wastewater from Recirculating Aquaculture System. Sci. Rep. 2018, 8, 3289. [Google Scholar] [CrossRef] [Green Version]
- Pattnaik, R.; Yost, R.S.; Porter, G.; Masunaga, T.; Attanandana, T. Improving Multi-Soil-Layer (MSL) System Remediation of Dairy Effluent. Ecol. Eng. 2008, 32, 1–10. [Google Scholar] [CrossRef]
- Colella, C. Natural zeolites in environmentally friendly processes and applications. Stud. Surf. Sci. Catal. 1999, 125, 641–655. [Google Scholar]
- Boonsook, P.; Luanmanee, S.; Attanandana, T.; Kamidouzono, A.; Masunaga, T.; Wakatsuki, T.; Luanmanee, S.; Kamidouzono, A.; Masunaga, T.; Wakatsuki, T. A Comparative Study of Permeable Layer Materials and Aeration Regime on Efficiency of Multi-Soil-Layering System for Domestic Wastewater Treatment in Thailand. Soil Sci. Plant Nutr. 2003, 49, 873–882. [Google Scholar] [CrossRef]
- Chen, X.; Sato, K.; Wakatsuki, T.; Masunaga, T. Effect of Aeration and Material Composition in Soil Mixture Block on the Removal of Colored Substances and Chemical Oxygen Demand in Livestock Wastewater Using Multi-Soil-Layering Systems. Soil Sci. Plant Nutr. 2007, 53, 509–516. [Google Scholar] [CrossRef]
- Attanandana, T.; Saitthiti, B.; Thongpae, S.; Kritapirom, S.; Luanmanee, S.; Wakatsuki, T. Multi-Media-Layering System for Food Service Wastewater Treatment. Ecol. Eng. 2000, 15, 133–138. [Google Scholar] [CrossRef]
- Luanmanee, S.; Boonsook, P.; Attanandana, T.; Saitthiti, B.; Panichajakul, C.; Wakatsuki, T. Effect of Intermittent Aeration Regulation of a Multi-Soil-Layering System on Domestic Wastewater Treatment in Thailand. Ecol. Eng. 2002, 18, 415–428. [Google Scholar] [CrossRef]
- Tang, W.; Wu, M.; Lou, W.; Yang, C. Role of Extracellular Polymeric Substances and Enhanced Performance for Biological Removal of Carbonaceous Organic Matters and Ammonia from Wastewater with High Salinity and Low Nutrient Concentrations. Bioresour. Technol. 2021, 326, 124764. [Google Scholar] [CrossRef] [PubMed]
- Abourida, A. Approche Hydrogéologique de la Nappe du Haouz (Maroc) par Télédétection, Isotopie, SIG et Modélisation. Ph.D. Thesis, Université Cadi Ayyad Faculté des Sciences et Techniques, Marrakech, Morocco, 2007; p. 146. [Google Scholar]
- Zema, D.A.; Calabro, P.S.; Folino, A.; Tamburino, V.; Zappia, G.; Zimbone, S.M. Wastewater Management in Citrus Processing Industries: An Overview of Advantages and Limits. Water 2019, 11, 2481. [Google Scholar] [CrossRef] [Green Version]
- Almasia, A.; Mohammadib, M.; Salehniac, S.; Azizib, M.H.; Pirsahebb, M.; Zolfagharid, M.R. Removal of Parasitic Particles, Protozoa Cysts, and Thermotolerant Coliforms in the Integrated Aeration Lagoon, Case Study: Iran. Desalin. Water Treat. 2019, 137, 221–225. [Google Scholar] [CrossRef]
- Kauppinen, A.; Martikainen, K.; Matikka, V.; Veijalainen, A.-M.; Pitkänen, T.; Heinonen-Tanski, H.; Miettinen, I.T. Sand Filters for Removal of Microbes and Nutrients from Wastewater during a One-Year Pilot Study in a Cold Temperate Climate. J. Environ. Manag. 2014, 133, 206–213. [Google Scholar] [CrossRef]
- Chen, S.; Chen, Z.; Dougherty, M.; Zuo, X.; He, J. The Role of Clogging in Intermittent Sand Filter (ISF) Performance in Treating Rural Wastewater Retention Pond Effluent. J. Clean. Prod. 2021, 294, 126309. [Google Scholar] [CrossRef]
- Guo, L.K.; Yang, L.; Ren, Y.X.; Dou, J.W.; Cui, S.; Lan, J.; Li, X.T.; Wang, J.; Wang, Y.C. Enhanced Biofilm Formation and Denitrification in Slow Sand Filters for Advanced Nitrogen Removal by Powdery Solid Carbon Sources Addition. J. Water Process Eng. 2022, 50, 103192. [Google Scholar] [CrossRef]
- Jia, Y.; Zheng, F.; Maier, H.R.; Ostfeld, A.; Creaco, E.; Savic, D.; Langeveld, J.; Kapelan, Z. Water Quality Modelling in Sewer Networks: Review and Future Research Directions. Water Res. 2021, 202, 117419. [Google Scholar] [CrossRef]
- Kuhn, M.; Johnson, K. Applied Predictive Modeling, 1st ed.; Springer: New York, NY, USA, 2013; p. 615. [Google Scholar]
- Wang, X.; Huang, G.; Lin, Q.; Nie, X.; Cheng, G.; Fan, Y.; Li, Z.; Yao, Y.; Suo, M. A Stepwise Cluster Analysis Approach for Downscaled Climate Projection–A Canadian Case Study. Environ. Model. Softw. 2013, 49, 141–151. [Google Scholar] [CrossRef]
- Zhuang, X.W.; Li, Y.P.; Huang, G.H.; Liu, J. Assessment of Climate Change Impacts on Watershed in Cold-Arid Region: An Integrated Multi-GCM-Based Stochastic Weather Generator and Stepwise Cluster Analysis Method. Clim. Dyn. 2016, 47, 191–209. [Google Scholar] [CrossRef]
- Elmaz, F.; Yücel, Ö.; Mutlu, A.Y. Predictive Modeling of Biomass Gasification with Machine Learning-Based Regression Methods. Energy 2020, 191, 116541. [Google Scholar] [CrossRef]
- Guthery, F.S.; Bingham, R.L. A Primer on Interpreting Regression Models. J. Wildl. Manag. 2007, 71, 684–692. [Google Scholar] [CrossRef]
- Shrestha, N. Detecting Multicollinearity in Regression Analysis. Am. J. Appl. Math. Stat. 2020, 8, 39–42. [Google Scholar] [CrossRef]
- Wang, Y.M.; Elhag, T.M.S. A Comparison of Neural Network, Evidential Reasoning and Multiple Regression Analysis in Modelling Bridge Risks. Expert Syst. Appl. 2007, 32, 336–348. [Google Scholar] [CrossRef]
- Zhou, J.; Li, E.; Wei, H.; Li, C.; Qiao, Q.; Armaghani, D.J. Random Forests and Cubist Algorithms for Predicting Shear Strengths of Rockfill Materials. Appl. Sci. 2019, 9, 1621. [Google Scholar] [CrossRef] [Green Version]
- Zhang, Q.; Wu, Z.; Guo, G.; Zhang, H.; Tarolli, P. Explicit the Urban Waterlogging Spatial Variation and Its Driving Factors: The Stepwise Cluster Analysis Model and Hierarchical Partitioning Analysis Approach. Sci. Total Environ. 2021, 763, 143041. [Google Scholar] [CrossRef]
- Liu, Y.R.; Li, Y.P.; Sun, J. Statistical Downscaling of Temperature Using Stepwise Cluster Analysis Method—A Case Study in Nur Sultan, Kazakhstan. IOP Conf. Ser. Earth Environ. Sci. 2020, 435, 12019. [Google Scholar] [CrossRef]
- Sun, W.; Shi, Q.; Huang, Y.; Lv, Y. Ensemble Learning Enhanced Stepwise Cluster Analysis for River Ice Breakup Date Forecasting. J. Environ. Inf. Lett. 2019, 1, 37–47. [Google Scholar] [CrossRef]
- Huang, G.H.; Huang, Y.F.; Wang, G.Q.; Xiao, H.N. Development of a Forecasting System for Supporting Remediation Design and Process Control Based on NAPL-biodegradation Simulation and Stepwise-cluster Analysis. Water Resour. Res. 2006, 42, W06413. [Google Scholar] [CrossRef]
- Shokrzadeh, S.; Jozani, M.J.; Bibeau, E. Wind Turbine Power Curve Modeling Using Advanced Parametric and Nonparametric Methods. IEEE Trans. Sustain. Energy. 2014, 5, 1262–1269. [Google Scholar] [CrossRef]
- Békés, G.; Kézdi, G. Data Analysis for Business, Economics, and Policy; Cambridge University Press: Cambridge, UK, 2021; p. 738. [Google Scholar]
- Sahoo, S.; Jha, M.K. Groundwater-Level Prediction Using Multiple Linear Regression and Artificial Neural Network Techniques: A Comparative Assessment. Hydrogeol. J. 2013, 21, 1865–1887. [Google Scholar] [CrossRef]
- Strutz, J.; Martin, J.; Greene, J.; Broadbelt, L.; Tyo, K. Metabolic Kinetic Modeling Provides Insight into Complex Biological Questions, but Hurdles Remain. Curr. Opin. Biotechnol. 2019, 59, 24–30. [Google Scholar] [CrossRef] [PubMed]
- Srinivasan, S.; Cluett, W.R.; Mahadevan, R. Constructing Kinetic Models of Metabolism at Genome-scales: A Review. Biotechnol. J. 2015, 10, 1345–1359. [Google Scholar] [CrossRef]
- Bellier, N.; Chazarenc, F.; Comeau, Y. Phosphorus Removal from Wastewater by Mineral Apatite. Water Res. 2006, 40, 2965–2971. [Google Scholar] [CrossRef]
- Zhang, W.; Ding, Y.; Boyd, S.A.; Teppen, B.J.; Li, H. Sorption and Desorption of Carbamazepine from Water by Smectite Clays. Chemosphere 2010, 81, 954–960. [Google Scholar] [CrossRef]
Combination Units | Scale | Type of Wastewater | Flow (L/day) | SS (%) | BOD5 (%) | COD (%) | NH4+ (%) | TN (%) | TP (%) | Coliforms (log Units) | References |
---|---|---|---|---|---|---|---|---|---|---|---|
Two stage MSL | |||||||||||
two vertical flow MSL in series | pilot-scale | domestic | 1000 | 97 | 96 | 91 | 95 | 96 | 95 | 3.15 | [7] |
polluted river | 1000 | - | - | >71 | 99 | 70 | 82 | - | [49] | ||
MSL with CW | |||||||||||
MSL + Constructed wetland | pilot scale | solar septic tank effluent | 743 | >70 | 80 | 72 | >70 | >70 | >70 | 2.00 | [62] |
MSL + subsurface flow CW | full scale | agritainment | 30,000 | - | - | 78 | - | - | 70 | - | [23] |
MSL + subsurface flow CW | full scale | domestic | 5000 | - | 93 | 92 | - | 76 | 92 | - | [20] |
Integrated hybrid system | pilot scale | rice noodle | 50 | 80.5 | - | 73.2 | 60.6 | - | 54 | 4.80 | [24] |
MSL+ vertical flow CW | lab scale | academic building effluent | - | 84.7 | 85.5 | 84 | - | - | - | - | [25] |
MSL with trickling filter | |||||||||||
vertical flow trickling filter + horizontal flow MSL | lab scale | mariculture | 660 | - | - | 84 | 83 | - | - | - | [76] |
domestic | - | - | 92 | 82 | - | 96 | - | [63] | |||
- | - | 94 | 96 | 93 | 92 | - | [51] | ||||
MSL + iron modified zeolite trickling filter | domestic | 920 | - | - | 93 | 86 | 61 | 93 | - | [26] | |
Other combinations | |||||||||||
MSL + blended carbon sources | pilot scale | rural non-point source | 800 | - | - | 30.3 | >50 | 64 | 60 | - | [9] |
MSL + subsurface wastewater infiltration | domestic | 300 | - | - | 93 | - | 74 | 98 | - | [21] | |
MSL + sand filters | lab scale | black water | 100 | 94 | 80 | 90 | 93 | 54 | 78 | 4.50 | [27] |
Methods | Basic Substrates | Principle | Advantages | Disadvantages | Reference |
---|---|---|---|---|---|
MSL | Soil Iron Charcoal Sawdust Zeolite (Gravel) | Exert soil filtration, adsorption, and biodegradation functions supported by adequate aeration and HLR | Small land requirement Low-cost Low energy No odors, no insects Easy operation and maintenance Adaptation to high pollutant loads Support high HLR | Moderate sanitary efficiency Risk of clogging at high HLR | [12,18] |
CW | Soil Sand Clay Gravel Plants | Benefiting from the combined effect of the physical and biochemical properties of soil, artificial media, and microorganisms | Low-cost Low energy Simple operation | High land requirement Plants are subject to the effects of the seasons Low denitrification rates Periodic maintenance Odor and insects | [12,77] |
Lagoon | Microorganisms Plants | Transformation of organic matter into mineral elements that can be assimilated by plants | Low-cost Minimal energy Simple operation Resistance to HLR variations High sanitary efficiency | High land requirement Risk of evaporation High residence time Odor and insect | [12,78,79] |
Sand filter | Rocks Gravel Sand | Infiltration and purification of wastewater by sand-attached microorganisms | Low-cost Small land requirement Easy operation and maintenance High sanitary efficiency | Risk of clogging Low denitrification rates Odor and insect | [80,81,82] |
References | Modeling Approach | Input Variables | Output Variables | R2 | Conditions | Limitations |
---|---|---|---|---|---|---|
Data-driven models | ||||||
[16] | Neural network (NN) | HLR, NH4+, DO, BOD5, and EC (influent) | NH4+ (removal) | >0.93 | NN = Large data, input and output variables, activation function, hidden layers and neurons, weight decay, optimizer [84]. Cubist = Large data, input and output variables, committees, instance, pruning, or combining operations [84]. SCA = Large data, input and output variables, continuous and/or discrete variables, nodes, leaf nodes, cutting or merging operations [13,85,86]. QPF = ≥2 input variables, continuous output variable, no multicollinearity [87,88]. MLR = ≥2 input variables, continuous output variable, no multicollinearity [87,88,89]. | NN = Difficult to describe connection weights; subjectivity in determining optimal parameters; time consuming; high computational complexity [90]. Cubist = Sensitive to a small dataset; sensitive to the fitness of the dataset; overfitted condition often occurred; high computation time [91]. SCA = High requirements for the predictor; high computational requirements; sensitive to its inputs and internal parameters; usually not well described [92,93,94,95]. QPF = Sensitive to outlier data points; difficulty in interpreting its coefficients; Perform poorly on the predictor’s extremes [96,97]. MLR = Sensitive to outliers data points; fails to capture nonlinear relationship; low performance in large datasets; cannot be used to model data with numerous inputs and outputs; requires numerical values; sensitive to a large number of input variables [84,90,98]. |
HLR, TKN, TC, DO, and NH4+ (influent) | TKN (removal) | |||||
HLR, TN, TC, BOD5, and DO (influent) | TN (removal) | |||||
[5] | Stepwise cluster analysis (SCA) | Time, DO, ORP, pH, removal of COD, NH3, NO3−, and TN | sulfamethoxazole removal | 0.82 (SCA) 0.62 (MLR) | ||
Multiple linear regression (MLR) | ||||||
[6] | Neural network (NN) Cubist | SS, pH, EC, DO, and BOD5 | FC (effluent) | 0.95 (NN) 0.94 (Cubist) 0.48 (MLR) | ||
Multiple linear regression (MLR) | ||||||
[11] | Neural network (NN) | HLR, pH, SS, orthophosphates, TC, TN, TKN of the influent | TC (total coliform effluent) | 0.97 (NN) 0.58 (MLR) | ||
Multiple linear regression (MLR) | ||||||
[24] | Linear model | COD (influent loading rates) | COD (removal/MSL) | 0.88 | ||
COD (effluent/MSL) | 0.88 | |||||
NH4+ (influent loading rates) | NH4+ (removal/MSL) | 0.75 | ||||
NH4+ (effluent/MSL) | 0.59 | |||||
[13] | Stepwise cluster analysis (SCA) | Time, DO, pH, ORP, removal of NH4+, NO3−, and TN | TN (removal) | 0.94 | ||
Quadratic polynomial function (QPF) | PBS, activated sludge, and submerged height | DO, pH, ORP, removal of COD, TP, NH4+, NO3−, and TN | 0.89–0.99 | |||
[10] | Quadratic polynomial function (QPF) | bottom submersion, microbial amendment, aeration | removal of COD, BOD5, TP, TN, NH4+ and NO3− | 0.77–0.99 | ||
[10] | Stepwise cluster analysis (SCA) | Time, removal of BOD5, TP, TN, NH4+, and NO3− | COD (removal) | 0.98 | ||
Time, removal of COD, TP, TN, NH4+, and NO3− | BOD5 (removal) | 0.95 | ||||
Time, removal of BOD5, COD, TP, TN, and NO3− | NH4+ (removal) | 0.95 | ||||
Time, removal of BOD5, COD, NH4+, TN, and NO3− | TP (removal) | 0.85 | ||||
Time, removal of BOD5, COD, TP, NH4+, and NO3− | TN (removal) | 0.98 | ||||
Time, removal of BOD5, COD, TP, TN, and NH4+ | NO3−(removal) | 0.89 | ||||
[72] | Linear regression | pH (effluent) | TN (removal) | 0.32 | ||
Kinetic Model | ||||||
[76] | Logistic kinetic model (LKM) | Q (salinity) | COD (effluent) | 0.98 | Kinetic = Kinetic constant, input and output variables, reaction rate coefficient, temperature coefficient [25]. | Kinetic = Large models result in computational intractability; cannot be used to model data with numerous inputs and outputs; non-linearity; computational tractability; parameter identifiability [99,100]. |
BOD load, NH4+ load, Iv (media surface area), and T (filter effluent temperature) | NH4+ (effluent) | 0.94 | ||||
[25] | Kinetic Model | Influent concentration, kinetic coefficients | COD, BOD5, and NH3 removal | ≥0.94 |
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Sbahi, S.; Mandi, L.; Masunaga, T.; Ouazzani, N.; Hejjaj, A. Multi-Soil-Layering, the Emerging Technology for Wastewater Treatment: Review, Bibliometric Analysis, and Future Directions. Water 2022, 14, 3653. https://doi.org/10.3390/w14223653
Sbahi S, Mandi L, Masunaga T, Ouazzani N, Hejjaj A. Multi-Soil-Layering, the Emerging Technology for Wastewater Treatment: Review, Bibliometric Analysis, and Future Directions. Water. 2022; 14(22):3653. https://doi.org/10.3390/w14223653
Chicago/Turabian StyleSbahi, Sofyan, Laila Mandi, Tsugiyuki Masunaga, Naaila Ouazzani, and Abdessamad Hejjaj. 2022. "Multi-Soil-Layering, the Emerging Technology for Wastewater Treatment: Review, Bibliometric Analysis, and Future Directions" Water 14, no. 22: 3653. https://doi.org/10.3390/w14223653