Journal of Advances in Environmental Health Research

Journal of Advances in Environmental Health Research

Modeling Greenhouse Gas Emissions in a Full-Scale Activated Sludge Unit Based on Benchmark Simulation Model – A Case Study of Isfahan Wastewater Treatment Plant

Document Type : Original Article

Authors
Hamidreza Shiran 1
Gholamreza Nabi Bidhendi 2
Nasser Mehrdadi 2
1 Department of Environmental Engineering, Aras International Campus, University of Tehran, Jolfa, Iran
2 Faculty of Environment, University of Tehran, Tehran, Iran
10.34172/jaehr.1386
Abstract
Background: The objective of this study was to establish a facility-level modeling tool to assess the performance of internal operational strategies and external emissions.
Methods: The biological process model in Benchmark Simulation Model No. 2 (BSM2) was upgraded to include two-stage and four-stage nitrification, as well as denitrification processes, to effectively simulate nitrous oxide (N2O) production. Emissions of carbon dioxide (CO2), methane (CH4), and N2O were also incorporated, taking into account digestion, cogeneration, and sludge storage processes. The refined model was utilized in a case study at the Isfahan Wastewater Treatment Plant (WWTP) in Iran to assess various operational strategies and explore potential challenges related to emission management.
Results: The simulation results, based on the model’s structural assumptions, indicate a negative impact on greenhouse gas (GHG) emissions associated with regional energy upgrades in the ventilation system and activated sludge sector. For example, variations in the biogenic and non-biogenic CO2​ proportions were observed when the total suspended solids (TSS) removal efficiency was altered, decreasing or increasing to 30/70 and 20/80, respectively. While off-site CO2​ emissions can be mitigated, this reduction is offset by a significant rise in N2​O emissions. This is particularly concerning given that N2​O exhibits a greenhouse effect nearly 300 times greater than that of CO2​.
Conclusion: It should be pointed out that numerous studies are needed to evaluate plant-wide control strategies in WWTPs for informed and effective decision-making regarding performance optimization.
Keywords
GHG emissions
Modeling
N2O
WWTP

Subjects

  1. Zangeneh A, Sabzalipour S, Takdastan A, Jalilzadeh Yengejeh R, Abdullatif Khafaie M. Mass transfer coefficient of ammonia in the air stripping process for municipal wastewater: an experimental study. Adv Environ Technol. 2021;7(4):289-304. doi: 22104/aet.2022.5345.1447.
  2. Nikpour B, Jalilzadeh Yengejeh R, Takdastan A, Hassani AH, Zazouli MA. Studying the effluent quality of Enhanced Modified Ludzack Ettinger-Oxic Settling Anaerobic process (E-MLE-OSA) for treating real municipal wastewater. J Adv Environ Health Res. 2021;9(4):345-60. doi: 32598/jaehr.9.4.1243.
  3. Nikpour B, Jalilzadeh Yengejeh R, Takdastan A, Hassani AH, Zazouli MA. The investigation of biological removal of nitrogen and phosphorous from domestic wastewater by inserting anaerobic/anoxic holding tank in the return sludge line of MLE-OSA modified system. J Environ Health Sci Eng. 2020;18(1):1-10. doi: 1007/s40201-019-00419-1.
  4. Nabi Bid Hendi G, Daryabeigi Zand A, Rabiee Abyaneh M. Assessing the life-cycle greenhouse gas (GHG) emissions of renewable and fossil fuel energy sources in Iran. Environ Energy Econ Res. 2021;5(2):1-9. doi: 22097/eeer.2021.258370.1176.
  5. Heidari Farsani M, Jalilzadeh Yengejeh R, Hajiseyed Mirzahosseini A, Monavari M, Hassani AH, Mengelizadeh N. Study of the performance of bench-scale electro-membranes bioreactor in leachate treatment. Adv Environ Technol. 2021;7(3):209-20. doi: 22104/aet.2022.5266.1426.
  6. Newhart KB, Holloway RW, Hering AS, Cath TY. Data-driven performance analyses of wastewater treatment plants: a review. Water Res. 2019;157:498-513. doi: 1016/j.watres.2019.03.030.
  7. Verijkazemi K, Jalilzadeh Yengejeh R. Simulation of an industrial wastewater treatment plant by up-flow anaerobic fixed bed bioreactor based on an artificial neural network. Avicenna J Environ Health Eng. 2022;9(1):1-8. doi: 34172/ajehe.2022.01.
  8. Shakiba A, Aliasghar A, Moazeni K, Pazoki M. Hydrothermal carbonization of sewage sludge with sawdust and corn stalk: optimization of process parameters and characterization of hydrochar. Bioenergy Res. 2023:1-12. doi: 1007/s12155-022-10552-9.
  9. Zeng S, Chen X, Dong X, Liu Y. Efficiency assessment of urban wastewater treatment plants in China: Considering greenhouse gas emissions. Resour Conserv Recycl. 2017;120:157-65. doi: 1016/j.resconrec.2016.12.005.
  10. Masuda S, Sano I, Hojo T, Li YY, Nishimura O. The comparison of greenhouse gas emissions in sewage treatment plants with different treatment processes. Chemosphere. 2018;193:581-90. doi: 1016/j.chemosphere.2017.11.018.
  11. Delre A, Mønster J, Scheutz C. Greenhouse gas emission quantification from wastewater treatment plants, using a tracer gas dispersion method. Sci Total Environ. 2017;605-606:258-68. doi: 1016/j.scitotenv.2017.06.177.
  12. Koutsou OP, Fountoulakis MS, Matsoukas C, Fyllas NM, Stasinakis AS. Estimation of N2O emissions from wastewater characteristics in constructed wetlands. J Environ Chem Eng. 2021;9(6):106632. doi: 1016/j.jece.2021.106632.
  13. Nguyen TK, Ngo HH, Guo W, Chang SW, Nguyen DD, Nghiem LD, et al. Insight into greenhouse gases emissions from the two popular treatment technologies in municipal wastewater treatment processes. Sci Total Environ. 2019;671:1302-13. doi: 1016/j.scitotenv.2019.03.386.
  14. Blomberg K, Kosse P, Mikola A, Kuokkanen A, Fred T, Heinonen M, et al. Development of an extended ASM3 model for predicting the nitrous oxide emissions in a full-scale wastewater treatment plant. Environ Sci Technol. 2018;52(10):5803-11. doi: 1021/acs.est.8b00386.
  15. Li P, Peng Y, Wang S, Liu Y. N2O emission from partial nitrification and full nitrification in domestic wastewater treatment process. Water. 2022;14(20):3195. doi: 3390/w14203195.
  16. Monteith HD, Sahely HR, MacLean HL, Bagley DM. A rational procedure for estimation of greenhouse-gas emissions from municipal wastewater treatment plants. Water Environ Res. 2005;77(4):390-403. doi: 2175/106143005x51978.
  17. Gémar G, Gómez T, Molinos-Senante M, Caballero R, Sala-Garrido R. Assessing changes in eco-productivity of wastewater treatment plants: the role of costs, pollutant removal efficiency, and greenhouse gas emissions. Environ Impact Assess Rev. 2018;69:24-31. doi: 1016/j.eiar.2017.11.007.
  18. Kyung D, Kim M, Chang J, Lee W. Estimation of greenhouse gas emissions from a hybrid wastewater treatment plant. J Clean Prod. 2015;95:117-23. doi: 1016/j.jclepro.2015.02.032.
  19. Mannina G, Rebouças TF, Cosenza A, Chandran K. A plant-wide wastewater treatment plant model for carbon and energy footprint: model application and scenario analysis. J Clean Prod. 2019;217:244-56. doi: 1016/j.jclepro.2019.01.255.
  20. Yapıcıoğlu P, Demir Ö. Minimizing greenhouse gas emissions of an industrial wastewater treatment plant in terms of water–energy nexus. Appl Water Sci. 2021;11(12):180. doi: 1007/s13201-021-01484-4.
  21. Domingo-Félez C, Smets BF. Modelling N2O dynamics of activated sludge biomass: uncertainty analysis and pathway contributions. Chem Eng J. 2020;379:122311. doi: 1016/j.cej.2019.122311.
  22. Maktabifard M, Blomberg K, Zaborowska E, Mikola A, Mąkinia J. Model-based identification of the dominant N2O emission pathway in a full-scale activated sludge system. J Clean Prod. 2022;336:130347. doi: 1016/j.jclepro.2021.130347.
  23. Khalil M, AlSayed A, Liu Y, Vanrolleghem PA. Machine learning for modeling N2O emissions from wastewater treatment plants: aligning model performance, complexity, and interpretability. Water Res. 2023;245:120667. doi: 1016/j.watres.2023.120667.
  24. Hwangbo S, Al R, Chen X, Sin G. Integrated model for understanding N2O emissions from wastewater treatment plants: a deep learning approach. Environ Sci Technol. 2021;55(3):2143-51. doi: 1021/acs.est.0c05231.
  25. Mehrani MJ, Bagherzadeh F, Zheng M, Kowal P, Sobotka D, Mąkinia J. Application of a hybrid mechanistic/machine learning model for prediction of nitrous oxide (N2O) production in a nitrifying sequencing batch reactor. Process Saf Environ Prot. 2022;162:1015-24. doi: 1016/j.psep.2022.04.058.
  26. Valkova T, Parravicini V, Saracevic E, Tauber J, Svardal K, Krampe J. A method to estimate the direct nitrous oxide emissions of municipal wastewater treatment plants based on the degree of nitrogen removal. J Environ Manage. 2021;279:111563. doi: 1016/j.jenvman.2020.111563.
  27. Barbu M, Vilanova R, Meneses M, Santin I. On the evaluation of the global impact of control strategies applied to wastewater treatment plants. J Clean Prod. 2017;149:396-405. doi: 1016/j.jclepro.2017.02.018.
  28. Santín I, Barbu M, Pedret C, Vilanova R. Fuzzy logic for plant-wide control of biological wastewater treatment process including greenhouse gas emissions. ISA Trans. 2018;77:146-66. doi: 1016/j.isatra.2018.04.006.
  29. Massara TM, Malamis S, Guisasola A, Baeza JA, Noutsopoulos C, Katsou E. A review on nitrous oxide (N2O) emissions during biological nutrient removal from municipal wastewater and sludge reject water. Sci Total Environ. 2017;596-597:106-23. doi: 1016/j.scitotenv.2017.03.191.
  30. Arnell M, Rahmberg M, Oliveira F, Jeppsson U. Multi-objective performance assessment of wastewater treatment plants combining plant-wide process models and life cycle assessment. J Water Clim Chang. 2017;8(4):715-29. doi: 2166/wcc.2017.179.
Journal of Advances in Environmental Health Research
Volume 13, Issue 2
Winter 2025
Pages 96-103