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Prediction of organophosphorus pesticide adsorption by biochar using ensemble learning algorithms

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Abstract

Machine learning (ML) models have become a potent tool for advancing environmentally conscious research in materials science, allowing the prediction of wastewater treatment efficacy using eco-materials. In this study, we showcase the potential of an advanced decision tree–based ensemble learning algorithm to model the eviction of emerging organophosphate-based pesticidal pollutants in aqueous systems. The model is trained using laboratory-based biochar adsorption data, and it establishes the relationship between independent experimental factors and the % organophosphate pesticide adsorption efficiency as the output parameter. We classified the experimental dataset into input and output parameters to build the model. The input parameters included pyrolysis temperature, solution pH, surface area, pore volume, and initial pesticide concentration. Grid search optimization in Python was employed to train the model using sets of input-output patterns. The results indicated that the XGBoost-based ensemble ML framework provides the best forecast for pesticide adsorption on the biochar matrix, achieving high scores for the regularization coefficient (R2train = 0.998, R2test = 0.981). The concentration of the organophosphorus compound and the morphology of biochar significantly influenced the pesticide adsorption behavior. These findings demonstrate the potential of using ensemble learning algorithms for the balanced design of carbon-enriched biomaterials to remove emerging micropollutants from water effectively.

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Data availability

The data supporting the findings of this study are not publicly available due to their use in ongoing advanced studies. However, interested individuals can request access to the data from the corresponding author. Please contact the corresponding author directly for further information regarding data availability.

References

  • Feng, C., Xu, Q., Qiu, X., Ji, J., Lin, Y., Le, S., She, J., Lu, D., & Wang, G. (2021). Evaluation and application of machine learning-based retention time prediction for suspect screening of pesticides and pesticide transformation products in LC-HRMS. Chemosphere, 271, 129447.

    Article  CAS  Google Scholar 

  • Finckh, S., Beckers, L.-M., Busch, W., Carmona, E., Dulio, V., Kramer, L., Krauss, M., Posthuma, L., Schulze, T., & Slootweg, J. (2022). A risk based assessment approach for chemical mixtures from wastewater treatment plant effluents. Environment International, 164, 107234.

    Article  CAS  Google Scholar 

  • Ganaie, M. A., & Hu, M. (2021). Ensemble deep learning: A review. ArXiv Preprint ArXiv:2104.02395.

  • He, M., Xu, Z., Hou, D., Gao, B., Cao, X., Ok, Y. S., Rinklebe, J., Bolan, N. S., & Tsang, D. C. (2022). Waste-derived biochar for water pollution control and sustainable development. Nature Reviews Earth & Environment, 3(7), 444–460.

    Article  CAS  Google Scholar 

  • Ke, M., Xu, N., Zhang, Z., Qiu, D., Kang, J., Lu, T., Wang, T., Peijnenburg, W., Sun, L., & Hu, B. (2022). Development of a machine-learning model to identify the impacts of pesticides characteristics on soil microbial communities from high-throughput sequencing data. Environmental Microbiology, 24(11), 5561–5573.

    Article  CAS  Google Scholar 

  • Liang, L., Xi, F., Tan, W., Meng, X., Hu, B., & Wang, X. (2021). Review of organic and inorganic pollutants removal by biochar and biochar-based composites. Biochar, 3(3), 255–281. https://doi.org/10.1007/s42773-021-00101-6

    Article  CAS  Google Scholar 

  • Mandal, A., Kumar, A., & Singh, N. (2021). Sorption mechanisms of pesticides removal from effluent matrix using biochar: Conclusions from molecular modelling studies validated by single-, binary and ternary solute experiments. Journal of Environmental Management, 295, 113104. https://doi.org/10.1016/j.jenvman.2021.113104

    Article  CAS  Google Scholar 

  • Mrozik, W., Minofar, B., Thongsamer, T., Wiriyaphong, N., Khawkomol, S., Plaimart, J., Vakros, J., Karapanagioti, H., Vinitnantharat, S., & Werner, D. (2021). Valorisation of agricultural waste derived biochars in aquaculture to remove organic micropollutants from water–experimental study and molecular dynamics simulations. Journal of Environmental Management, 300, 113717.

    Article  CAS  Google Scholar 

  • Paíga, P., Sousa, S., Vera, J., Bitencourt, L., Vieira, J., Jorge, S., Silva, J. G., Correia, M., Domingues, V. F., & Delerue-Matos, C. (2021). Multi-residue analysis of fifty pesticides in river waters and in wastewaters. Environmental Science and Pollution Research, 28(47), 66787–66803.

    Article  Google Scholar 

  • Parra-Arroyo, L., González-González, R. B., Castillo-Zacarías, C., Martínez, E. M. M., Sosa-Hernández, J. E., Bilal, M., Iqbal, H. M., Barceló, D., & Parra-Saldívar, R. (2022). Highly hazardous pesticides and related pollutants: Toxicological, regulatory, and analytical aspects. Science of The Total Environment, 807, 151879.

    Article  CAS  Google Scholar 

  • Ponnuchamy, M., Kapoor, A., Senthil Kumar, P., Vo, D.-V. N., Balakrishnan, A., Mariam Jacob, M., & Sivaraman, P. (2021). Sustainable adsorbents for the removal of pesticides from water: A review. Environmental Chemistry Letters, 19(3), 2425–2463.

    Article  CAS  Google Scholar 

  • Qian, Y., Wang, X., Wu, G., Wang, L., Geng, J., Yu, N., & Wei, S. (2021). Screening priority indicator pollutants in full-scale wastewater treatment plants by non-target analysis. Journal of Hazardous Materials, 414, 125490.

    Article  CAS  Google Scholar 

  • Rashid, R., Shafiq, I., Akhter, P., Iqbal, M. J., & Hussain, M. (2021). A state-of-the-art review on wastewater treatment techniques: The effectiveness of adsorption method. Environmental Science and Pollution Research, 28(8), 9050–9066. https://doi.org/10.1007/s11356-021-12395-x

    Article  CAS  Google Scholar 

  • Richardson, A. K., Chadha, M., Rapp-Wright, H., Mills, G. A., Fones, G. R., Gravell, A., Stürzenbaum, S., Cowan, D. A., Neep, D. J., & Barron, L. P. (2021). Rapid direct analysis of river water and machine learning assisted suspect screening of emerging contaminants in passive sampler extracts. Analytical Methods, 13(5), 595–606.

    Article  CAS  Google Scholar 

  • Shen, Y., Zhao, E., Zhang, W., Baccarelli, A. A., & Gao, F. (2022). Predicting pesticide dissipation half-life intervals in plants with machine learning models. Journal of Hazardous Materials, 436, 129177.

    Article  CAS  Google Scholar 

  • Tun, W. S. T., Talodthaisong, C., Daduang, S., Daduang, J., Rongchai, K., Patramanon, R., & Kulchat, S. (2022). A machine learning colorimetric biosensor based on acetylcholinesterase and silver nanoparticles for the detection of dichlorvos pesticides. Materials Chemistry Frontiers, 6(11), 1487–1498.

    Article  CAS  Google Scholar 

  • Wang, L., Ding, J., Shi, P., Fu, L., Pan, L., Tian, J., Cao, D., Jiang, H., & Ding, X. (2021). Ensemble machine learning to evaluate the in vivo acute oral toxicity and in vitro human acetylcholinesterase inhibitory activity of organophosphates. Archives of Toxicology, 95(7), 2443–2457.

    Article  CAS  Google Scholar 

  • Water, U. N. (2018). 2018 UN world water development report, nature-based solutions for water.

    Google Scholar 

  • Zahedi, L., Mohammadi, F. G., Rezapour, S., Ohland, M. W., & Amini, M. H. (2021). Search algorithms for automated hyper-parameter tuning. ArXiv Preprint ArXiv:2104.14677.

  • Zhang, Y., Zhang, H., Wang, J., Yu, Z., Li, H., & Yang, M. (2021). Suspect and target screening of emerging pesticides and their transformation products in an urban river using LC-QTOF-MS. Science of The Total Environment, 790, 147978.

    Article  CAS  Google Scholar 

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Author notes

  1. All authors contributed equally to the work.

Authors and Affiliations

  1. Department of Metallurgical and Materials Engineering, Defence Institute of Advanced Technology (DU), Pune, India

    Amrita Nighojkar

  2. Mechanical Engineering Department, Indian Institute of Technology Jodhpur, Jodhpur, India

    Amrita Nighojkar & Anand Plappally

  3. Computer Science and Engineering Department, Malaviya National Institute of Technology Jaipur, Jaipur, India

    Jyoti Nagpal

  4. Mechanical Engineering Department, Worcester Polytechnic Institute, Worcester, Massachusetts, USA

    Winston Soboyejo

  5. Computer Science Department, Pandit Deendayal Energy University, Gandhinagar, Gujarat, India

    Shilpa Pandey

Authors
  1. Amrita Nighojkar
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  2. Jyoti Nagpal
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  3. Winston Soboyejo
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  4. Anand Plappally
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  5. Shilpa Pandey
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Contributions

The manuscript was written with the contributions of all authors. All authors have given approval for the final version of the manuscript. Amrita Nighojkar and Shilpa Pandey designed and executed the model simulations and wrote the original draft. Jyoti Nagpal prepared the data and reviewed and edited the manuscript. Winston Soboyejo and Anand Plappally conceptualized the research work and performed the formal analysis.

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Correspondence to Amrita Nighojkar or Shilpa Pandey.

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Nighojkar, A., Nagpal, J., Soboyejo, W. et al. Prediction of organophosphorus pesticide adsorption by biochar using ensemble learning algorithms. Environ Monit Assess 195, 984 (2023). https://doi.org/10.1007/s10661-023-11599-7

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