{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:04:55Z","timestamp":1782864295714,"version":"3.54.5"},"reference-count":52,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T00:00:00Z","timestamp":1780444800000},"content-version":"vor","delay-in-days":2,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100008227","name":"University of South Africa","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100008227","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Environ Geochem Health"],"published-print":{"date-parts":[[2026,6]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Mining laboratory effluents in the Democratic Republic of Congo (DRC) represent an undercharacterized source of heavy metal contamination, with direct implications for receiving watershed quality and downstream community health. A comparative time-series modeling framework is applied, incorporating ARIMA as a statistical baseline, alongside Vanilla LSTM and a dual-layer LSTM-Attention architecture, to predict effluent concentrations of pH, suspended solids (SS), and six heavy metals (As, Cu, Fe, Zn, Pb, Ni) from a mineral analysis laboratory in Haut-Katanga. The dataset comprises 43 weekly observations (January to October 2023), with 80% used for training (n\u2009=\u200934) and 20% for testing (n\u2009=\u20099). The LSTM-Attention model demonstrates the highest predictive performance (R\n                    <jats:sup>2<\/jats:sup>\n                    \u2248 0.99 for most parameters; MSE as low as 5.6\u2009\u00d7\u200910\u207b\n                    <jats:sup>1<\/jats:sup>\n                    \u2070 for arsenic), substantially outperforming both ARIMA and Vanilla LSTM, particularly for episodically variable parameters such as copper and iron. Probabilistic forecasts extending to December 2023, incorporating 95% confidence intervals under a Gaussian residual approximation, indicate an elevated risk of regulatory threshold exceedance for SS, Cu, Zn, and Fe, whereas As and Ni are projected to remain within compliant limits. These findings are site-specific and exploratory, given the limited temporal coverage and single-site design. The results highlight the need for targeted pre-treatment interventions, including alkaline neutralization and selective chemical precipitation, and support a transition from reactive compliance monitoring to proactive environmental management in alignment with SDG 6.3 (water quality) and SDG 3.9 (health impacts of pollution).\n                  <\/jats:p>","DOI":"10.1007\/s10653-026-03262-0","type":"journal-article","created":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T12:00:34Z","timestamp":1780488034000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Predictive Modeling of Mining Laboratory Effluent Contamination Using LSTM-Attention Networks: A Case Study from the Haut-Katanga Copperbelt"],"prefix":"10.1007","volume":"48","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4829-3618","authenticated-orcid":false,"given":"Mathieu Kayembe","family":"Musala","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-7223-9867","authenticated-orcid":false,"given":"Matthieu Tshanga","family":"Matthieu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Meschac Amani","family":"Mugaruka","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-8840-8910","authenticated-orcid":false,"given":"Fabrice Kalombo","family":"Mwambila","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Herv\u00e9 Ngoie","family":"Ilunga","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jerry Mbayo","family":"Kyongo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gbenga Olamide","family":"Adesola","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1411-8408","authenticated-orcid":false,"given":"Arthur Kaniki","family":"Tshamala","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,3]]},"reference":[{"key":"3262_CR2","doi-asserted-by":"publisher","first-page":"100546","DOI":"10.1016\/j.measen.2022.100546","volume":"24","author":"RJ Al-Bahadili","year":"2022","unstructured":"Al-Bahadili, R. J., & Drewil, G. I. (2022). Air pollution prediction using LSTM deep learning and metaheuristics algorithms. Measurement: Sensors, 24, 100546. https:\/\/doi.org\/10.1016\/j.measen.2022.100546","journal-title":"Measurement: Sensors"},{"issue":"9","key":"3262_CR3","doi-asserted-by":"publisher","first-page":"639","DOI":"10.1017\/S0016756821000182","volume":"158","author":"S Ali","year":"2021","unstructured":"Ali, S., & Alshammari, A. S. (2021). Genesis of gabbroic intrusions in the Arabian Shield, Saudi Arabia: Mineralogical, geochemical and tectonic fingerprints of the Neoproterozoic arc magmatism. Geological Magazine, 158(9), 639\u20131656. https:\/\/doi.org\/10.1017\/S0016756821000182","journal-title":"Geological Magazine"},{"key":"3262_CR4","doi-asserted-by":"publisher","first-page":"661","DOI":"10.1007\/s00500-019-04495-1","volume":"24","author":"S Al-Janabi","year":"2020","unstructured":"Al-Janabi, S., Mohammad, M., & Al-Sultan, A. (2020). A new method for prediction of air pollution based on intelligent computation. Soft Computing, 24, 661\u2013680. https:\/\/doi.org\/10.1007\/s00500-019-04495-1","journal-title":"Soft Computing"},{"issue":"7","key":"3262_CR5","doi-asserted-by":"publisher","first-page":"1235","DOI":"10.3390\/ijerph16071235","volume":"16","author":"TE Aniyikaiye","year":"2019","unstructured":"Aniyikaiye, T. E., Oluseyi, T., Odiyo, J. O., & Edokpayi, J. N. (2019). Physico-chemical analysis of wastewater discharge from selected paint industries in Lagos, Nigeria. International Journal of Environmental Research and Public Health, 16(7), 1235. https:\/\/doi.org\/10.3390\/ijerph16071235","journal-title":"International Journal of Environmental Research and Public Health"},{"key":"3262_CR6","doi-asserted-by":"publisher","DOI":"10.1007\/s00126-022-01139-7","author":"I Basson","year":"2022","unstructured":"Basson, I., McCall, M., Koegelenberg, C., & Jakaite, L. (2022). Emplacement and preservation of mineralized \u00e9caille within Roan Breccia of the Lufilian Arc, Central African Copperbelt: The Thrust Belt Boudinage model. Mineralium Deposita. https:\/\/doi.org\/10.1007\/s00126-022-01139-7","journal-title":"Mineralium Deposita"},{"key":"3262_CR9","doi-asserted-by":"publisher","first-page":"134","DOI":"10.1016\/j.jafrearsci.2005.05.005","volume":"42","author":"JLH Cailteux","year":"2005","unstructured":"Cailteux, J. L. H., Kampunzu, A. B., Lerouge, C., Kaputo, A., & Milesi, J. P. (2005). Genesis of sediment-hosted stratiform copper\u2013cobalt deposits, Central African Copperbelt. Journal of African Earth Sciences, 42, 134\u2013158. https:\/\/doi.org\/10.1016\/j.jafrearsci.2005.05.005","journal-title":"Journal of African Earth Sciences"},{"issue":"11","key":"3262_CR10","doi-asserted-by":"publisher","first-page":"5266","DOI":"10.1016\/j.csda.2006.09.032","volume":"51","author":"AX Carvalho","year":"2007","unstructured":"Carvalho, A. X., & Tanner, M. A. (2007). Modelling nonlinear count time series with local mixtures of Poisson autoregressions. Computational Statistics & Data Analysis, 51(11), 5266\u20135294. https:\/\/doi.org\/10.1016\/j.csda.2006.09.032","journal-title":"Computational Statistics & Data Analysis"},{"issue":"11","key":"3262_CR11","doi-asserted-by":"publisher","first-page":"2855","DOI":"10.1162\/neco_a_01134","volume":"30","author":"Y Chen","year":"2018","unstructured":"Chen, Y., Cheng, Q., Cheng, Y., Yang, H., & Yu, H. (2018). Applications of recurrent neural networks in environmental factor forecasting: A review. Neural Computation, 30(11), 2855\u20132881. https:\/\/doi.org\/10.1162\/neco_a_01134","journal-title":"Neural Computation"},{"key":"3262_CR12","doi-asserted-by":"publisher","first-page":"e623","DOI":"10.7717\/peerj-cs.623","volume":"7","author":"D Chicco","year":"2021","unstructured":"Chicco, D., & Jurman, G. (2021). The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation. PeerJ Computer Science, 7, e623.","journal-title":"PeerJ Computer Science"},{"issue":"2","key":"3262_CR13","doi-asserted-by":"publisher","first-page":"100028","DOI":"10.1016\/j.eesus.2025.100028","volume":"1","author":"DCC Da Silva Medeiros","year":"2025","unstructured":"Da Silva Medeiros, D. C. C., Usman, M., Chelme-Ayala, P., & Gamal El-Din, M. (2025). Biochar-enhanced removal of naphthenic acids from oil sands process water. Energy & Environmental Sustainability, 1(2), 100028. https:\/\/doi.org\/10.1016\/j.eesus.2025.100028","journal-title":"Energy & Environmental Sustainability"},{"key":"3262_CR14","doi-asserted-by":"publisher","DOI":"10.2139\/ssrn.4117249","author":"Y Dai","year":"2022","unstructured":"Dai, Y., Zhou, Q., Leng, M., Yang, X., & Wang, Y. (2022). Improving the Bi-LSTM model with XGBoost and attention mechanism: A combined approach for short-term power load prediction. SSRN Electronic Journal. https:\/\/doi.org\/10.2139\/ssrn.4117249","journal-title":"SSRN Electronic Journal"},{"key":"3262_CR15","doi-asserted-by":"publisher","DOI":"10.25131\/sajg.121.0003","author":"F Delpomdor","year":"2018","unstructured":"Delpomdor, F., Schr\u00f6der, S., Pr\u00e9at, A., & Blanpied, C. (2018). Sedimentology and chemostratigraphy of the late Neoproterozoic carbonate ramp sequences of the H\u00fcttenberg Formation (northwestern Namibia) and the C5 Formation (western central Democratic Republic of Congo): Record of the late post-Marinoan marine transgression on the margin of the Congo Craton. South African Journal of Geology. https:\/\/doi.org\/10.25131\/sajg.121.0003","journal-title":"South African Journal of Geology"},{"issue":"1","key":"3262_CR16","first-page":"404","volume":"7","author":"KR Dongo","year":"2013","unstructured":"Dongo, K. R., Kambou, N. G., Kamenan, M. T., Oga, M. S., & N\u2019guessan, K. L. (2013). Impacts des effluents liquides industriels sur l\u2019environnement urbain d\u2019Abidjan (C\u00f4te d\u2019Ivoire). African Journal of Environmental Science and Technology, 7(1), 404\u2013420.","journal-title":"African Journal of Environmental Science and Technology"},{"issue":"2","key":"3262_CR17","doi-asserted-by":"publisher","first-page":"214","DOI":"10.3390\/w13020214","volume":"13","author":"E Friedler","year":"2021","unstructured":"Friedler, E., Chavez, D. F., Alfiya, Y., Gilboa, Y., & Goren, R. (2021). Impact of suspended solids and organic matter on chlorine and UV disinfection efficiency of greywater. Water, 13(2), 214. https:\/\/doi.org\/10.3390\/w13020214","journal-title":"Water"},{"issue":"6","key":"3262_CR18","doi-asserted-by":"publisher","first-page":"8455","DOI":"10.1007\/s10499-024-01574-5","volume":"32","author":"DR Gandhi","year":"2024","unstructured":"Gandhi, D. R., Govindan, H., Haq, R. A. K. P., & Bhide, A. (2024). Attention-driven LSTM and GRU deep learning techniques for precise water quality prediction in smart aquaculture. Aquaculture International, 32(6), 8455\u20138478. https:\/\/doi.org\/10.1007\/s10499-024-01574-5","journal-title":"Aquaculture International"},{"issue":"5","key":"3262_CR19","doi-asserted-by":"publisher","first-page":"2786","DOI":"10.1109\/TSMC.2024.3350200","volume":"54","author":"Y Han","year":"2024","unstructured":"Han, Y., Liu, Z., Wang, H., Yang, Y., & Zhang, G. (2024). Novel long short-term memory model based on the attention mechanism for leakage detection of water supply processes. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 54(5), 2786\u20132796.","journal-title":"IEEE Transactions on Systems, Man, and Cybernetics: Systems"},{"key":"3262_CR21","doi-asserted-by":"publisher","first-page":"542","DOI":"10.1016\/j.ijhydene.2025.03.233","volume":"120","author":"K Hu","year":"2025","unstructured":"Hu, K., Liu, B., Pang, Y., et al. (2025). H\u2082 and CH\u2084 adsorption on coal: Insights from experiment and mathematical model. International Journal of Hydrogen Energy, 120, 542\u2013557. https:\/\/doi.org\/10.1016\/j.ijhydene.2025.03.233","journal-title":"International Journal of Hydrogen Energy"},{"key":"3262_CR22","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1007\/s10661-022-10904-0","volume":"195","author":"AR Jadhav","year":"2023","unstructured":"Jadhav, A. R., & Pawar, P. D. (2023). Water and wastewater quality prediction: Current trends and challenges in the implementation of artificial neural network. Environmental Monitoring and Assessment, 195, 321.","journal-title":"Environmental Monitoring and Assessment"},{"issue":"2","key":"3262_CR23","doi-asserted-by":"publisher","first-page":"285","DOI":"10.5539\/issr.2016.21.2.285","volume":"21","author":"SK Kalala","year":"2016","unstructured":"Kalala, S. K., Senga, S. C., Kavund, A. M., Twakale, L. M., Nsele, K. M., Kimpinde, M. A., & Kitwanyoka, Y. K. (2016). Impacts study of liquid discharges from Terril Company Plant (STL) on Lubumbashi River: Lubumbashi, Haut-Katanga\/DR Congo [\u00c9tude d\u2019impacts des rejets liquides de l\u2019usine STL (Soci\u00e9t\u00e9 de Terril de Lubumbashi) sur la rivi\u00e8re Lubumbashi: Lubumbashi, Haut-Katanga\/RD Congo]. International Journal of Sustainable Development and Green Economics, 21(2), 285\u2013292. https:\/\/doi.org\/10.5539\/issr.2016.21.2.285","journal-title":"International Journal of Sustainable Development and Green Economics"},{"key":"3262_CR100","unstructured":"Kipata, K., Delvaux, D., Sebagenzi, M. N., & Cailteux,J. (2013). Brittle tectonic and stress field evolution in the Pan African Lulian Arc and its foreland (Katanga, DRC): From orogenic compression to extensional collapse, transpressional inversion and transition to rifting. Journal of African Earth Sciences, 81, 182\u2013197."},{"issue":"6","key":"3262_CR24","doi-asserted-by":"publisher","first-page":"1","DOI":"10.4236\/gep.2023.116012","volume":"11","author":"AK Koblan","year":"2023","unstructured":"Koblan, A. K., Koffi, A. B., Diomand\u00e9, A., Kouakou, D. S., Kouam\u00e9, K. J., & Jourda, J. P. (2023). Hydrochemical characterization of groundwater in the sub-prefecture of Kokumbo (Ivory Coast). Journal of Geoscience and Environment Protection, 11(6), 1\u201315.","journal-title":"Journal of Geoscience and Environment Protection"},{"key":"3262_CR25","doi-asserted-by":"publisher","unstructured":"Korunoski, M., Risteska Stojkoska, B., & Trivodaliev, K. (2019). Internet of Things solution for intelligent air pollution prediction and visualization. In Proceedings of the 18th International Conference on Smart Technologies (EUROCON 2019). IEEE. https:\/\/doi.org\/10.1109\/EUROCON.2019.8861609","DOI":"10.1109\/EUROCON.2019.8861609"},{"key":"3262_CR26","first-page":"1","volume":"134","author":"B Kumar","year":"2023","unstructured":"Kumar, B., & Yadav, S. N. (2023). A novel hybrid model combining SARMA and LSTM for time series forecasting. Expert Systems with Applications, 134, 1\u201315.","journal-title":"Expert Systems with Applications"},{"issue":"6","key":"3262_CR27","doi-asserted-by":"publisher","first-page":"907","DOI":"10.3390\/w17060907","volume":"17","author":"I Le\u0161\u010de\u0161en","year":"2025","unstructured":"Le\u0161\u010de\u0161en, I., Tanhapour, M., Pek\u00e1rov\u00e1, P., Mikl\u00e1nek, P., & Bajtek, Z. (2025). Long short-term memory (LSTM) networks for accurate river flow forecasting: A case study on the Morava River Basin (Serbia). Water, 17(6), 907. https:\/\/doi.org\/10.3390\/w17060907","journal-title":"Water"},{"key":"3262_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.tibtech.2025.08.013","author":"Y Li","year":"2025","unstructured":"Li, Y., Li, Y., Ni, B., et al. (2025). Towards scalable anammox: Mechanistic insights and emerging strategies. Trends in Biotechnology. https:\/\/doi.org\/10.1016\/j.tibtech.2025.08.013","journal-title":"Trends in Biotechnology"},{"key":"3262_CR29","doi-asserted-by":"publisher","first-page":"104832","DOI":"10.1016\/j.apor.2025.104832","volume":"165","author":"Q Liu","year":"2025","unstructured":"Liu, Q., Bao, S., Yan, H., et al. (2025). Enhancing sea surface salinity short-term prediction using physically informed deep learning. Applied Ocean Research, 165, 104832. https:\/\/doi.org\/10.1016\/j.apor.2025.104832","journal-title":"Applied Ocean Research"},{"issue":"18","key":"3262_CR30","doi-asserted-by":"publisher","first-page":"2607","DOI":"10.3390\/polym16182607","volume":"16","author":"I Malashin","year":"2024","unstructured":"Malashin, I., Tynchenko, V., Gantimurov, A., Nelyub, V., & Borodulin, A. (2024). Applications of long short-term memory (LSTM) networks in polymeric sciences: A review. Polymers, 16(18), 2607. https:\/\/doi.org\/10.3390\/polym16182607","journal-title":"Polymers"},{"key":"3262_CR33","doi-asserted-by":"publisher","first-page":"392","DOI":"10.1007\/s12665-024-11618-x","volume":"83","author":"H Moayedi","year":"2024","unstructured":"Moayedi, H., Salari, M., Ali, S. A. J., et al. (2024). Modeling the total hardness (TH) of groundwater in aquifers using novel hybrid soft computing optimizer models. Environmental Earth Sciences, 83, 392. https:\/\/doi.org\/10.1007\/s12665-024-11618-x","journal-title":"Environmental Earth Sciences"},{"key":"3262_CR34","doi-asserted-by":"publisher","first-page":"2961","DOI":"10.1007\/s00477-023-02429-w","volume":"37","author":"H Moayedi","year":"2023","unstructured":"Moayedi, H., Salari, M., Dehrashid, A. A., et al. (2023). Groundwater quality evaluation using hybrid model of the multi-layer perceptron combined with neural-evolutionary regression techniques: Case study of Shiraz Plain. Stochastic Environmental Research and Risk Assessment, 37, 2961\u20132976. https:\/\/doi.org\/10.1007\/s00477-023-02429-w","journal-title":"Stochastic Environmental Research and Risk Assessment"},{"issue":"13","key":"3262_CR36","doi-asserted-by":"publisher","first-page":"1946","DOI":"10.3390\/w17131946","volume":"17","author":"RN Muniz","year":"2025","unstructured":"Muniz, R. N., Buratto, W. G., Cardoso, R., Barros, CFdeO., Nied, A., & Villarrubia Gonzalez, G. (2025). State-of-the-art decarbonization in sludge thermal treatments for electrical power generation considering sensors and the application of artificial intelligence. Water, 17(13), 1946. https:\/\/doi.org\/10.3390\/w17131946","journal-title":"Water"},{"key":"3262_CR37","doi-asserted-by":"publisher","first-page":"110985","DOI":"10.1016\/j.clinbiochem.2025.110985","volume":"110","author":"NL Nwobi","year":"2025","unstructured":"Nwobi, N. L., Anetor, G. O., Nwobi, J. C., Igharo, G. O., Adeyemi, A. V., Badrick, T., & Anetor, J. I. (2025). Waste management and environmental health impact: Sustainable laboratory medicine as a mitigating response. Clinical Biochemistry, 110, 110985. https:\/\/doi.org\/10.1016\/j.clinbiochem.2025.110985","journal-title":"Clinical Biochemistry"},{"issue":"13","key":"3262_CR38","doi-asserted-by":"publisher","first-page":"4738","DOI":"10.1073\/pnas.1118574109","volume":"109","author":"MJ Olascoaga","year":"2012","unstructured":"Olascoaga, M. J., & Haller, G. (2012). Forecasting sudden changes in environmental pollution patterns. Proceedings of the National Academy of Sciences, 109(13), 4738\u20134743. https:\/\/doi.org\/10.1073\/pnas.1118574109","journal-title":"Proceedings of the National Academy of Sciences"},{"key":"3262_CR39","first-page":"138","volume":"25","author":"AG Omisore","year":"2018","unstructured":"Omisore, A. G. (2018). Attaining sustainable development goals in Sub-Saharan Africa: The need to address environmental challenges. Science of the Total Environment, 25, 138\u2013145.","journal-title":"Science of the Total Environment"},{"key":"3262_CR40","doi-asserted-by":"publisher","unstructured":"Patthi, S., Paruchuri, I., Padhy, N. (2024). Data-Driven Air Quality Prediction with Batch Normalization in Long Short-Term Memory Networks. In: Swaroop, A., Kansal, V., Fortino, G., Hassanien, A.E. (eds) Proceedings of Fifth Doctoral Symposium on Computational Intelligence. DoSCI 2024. Lecture Notes in Networks and Systems, vol 1085. Springer, Singapore. https:\/\/doi.org\/10.1007\/978-981-97-6726-7_22","DOI":"10.1007\/978-981-97-6726-7_22"},{"issue":"4","key":"3262_CR41","doi-asserted-by":"publisher","first-page":"896","DOI":"10.2166\/wpt.2025.046","volume":"20","author":"A Rahman","year":"2025","unstructured":"Rahman, A., Syeed, M. M. M., Fatema, K., Karim, M. R., Khan, R. H., Hossain, M. S., & Uddin, M. F. (2025). An enhanced multi-head attention-based LSTM model for forecasting the surface water quality index. Water Practice and Technology, 20(4), 896\u2013914. https:\/\/doi.org\/10.2166\/wpt.2025.046","journal-title":"Water Practice and Technology"},{"key":"3262_CR43","unstructured":"Rouessac, F., Rouessac, A., & Cruch\u00e9, S. (2004). Chemical analysis: Modern instrumental methods and techniques (6th ed., with the collaboration of D. Cruch\u00e9). Dunod"},{"issue":"3","key":"3262_CR44","doi-asserted-by":"publisher","first-page":"608","DOI":"10.3390\/en14030608","volume":"14","author":"J Runge","year":"2021","unstructured":"Runge, J., & Zmeureanu, R. (2021). A review of deep learning techniques for forecasting energy use in buildings. Energies, 14(3), 608. https:\/\/doi.org\/10.3390\/en14030608","journal-title":"Energies"},{"key":"3262_CR45","doi-asserted-by":"crossref","unstructured":"Salari, M., Abdolmaleki, M. J., Shahid, E. S., & B Akhoundi, (2025). Dispersion of Particulate Matter Using the AERMOD Model and Health Risk Assessment (Case Study: Saveh Gypsum Industry). Iranica Journal of Energy & Environment, e234793","DOI":"10.5829\/ijee.2026.17.02.12"},{"issue":"2","key":"3262_CR46","doi-asserted-by":"publisher","first-page":"91","DOI":"10.4236\/ajac.2014.52012","volume":"5","author":"ZM Shakhsher","year":"2014","unstructured":"Shakhsher, Z. M., Abu Shqair, I., Qasim, H. R., & Odeh, I. (2014). Polyvinylbenzyl tris-aminodicarboxylate microspheres for the optical sensing of Cu2\u207a ions. American Journal of Analytical Chemistry, 5(2), 91\u2013100. https:\/\/doi.org\/10.4236\/ajac.2014.52012","journal-title":"American Journal of Analytical Chemistry"},{"key":"3262_CR47","doi-asserted-by":"publisher","first-page":"122348","DOI":"10.1016\/j.oceaneng.2025.122348","volume":"340","author":"R Su","year":"2025","unstructured":"Su, R., Zhao, J., Bao, S., et al. (2025). Deep learning approach for internal tide prediction based on China ocean reanalysis data. Ocean Engineering, 340, 122348. https:\/\/doi.org\/10.1016\/j.oceaneng.2025.122348","journal-title":"Ocean Engineering"},{"key":"3262_CR48","unstructured":"Sun, M. (2022). Stock trend prediction based on graph convolutional networks and LSTM (Master\u2019s thesis, University of Sherbrooke). Sherbrooke, Qu\u00e9bec, Canada"},{"key":"3262_CR49","doi-asserted-by":"crossref","unstructured":"Vatsa, A., Sharma, H., Patil, K. M., & Panchal, C. (2024). Residual LSTM-based short duration forecasting of polarization current for effective assessment of transformers insulation. Scientific Reports, 14, Article 1369","DOI":"10.1038\/s41598-023-50641-z"},{"key":"3262_CR101","unstructured":"Van Langendonck, S., Muchez, P., Dewaele, S., Kaputo Kalubi, A., & Cailteux, J. (2013). Petrographic and mineralogical study of the sediment-hosted Cu\u2013Co ore deposit at Kambove West in the central part of the Katanga Copperbelt (DRC). Geologica Belgica, 16(1\u20132), 91\u2013104."},{"key":"3262_CR50","doi-asserted-by":"publisher","unstructured":"Wang, R., Kon, H., & Madnick, S. (1993). Data quality requirements analysis and modeling. In Proceedings of the 9th International Conference on Data Engineering (ICDE) (pp. 670\u2013677). https:\/\/doi.org\/10.1109\/ICDE.1993.344012","DOI":"10.1109\/ICDE.1993.344012"},{"key":"3262_CR51","first-page":"1","volume":"324","author":"W Wang","year":"2018","unstructured":"Wang, W., & Liu, Y. (2018). Analysis of the mean absolute error (MAE) and the root mean square error (RMSE). Statistical Analysis, 324, 1\u20139.","journal-title":"Statistical Analysis"},{"key":"3262_CR52","doi-asserted-by":"crossref","unstructured":"Wen, X., & Liu, W. (2023). Time series prediction based on LSTM-attention-LSTM model. Computational Intelligence and Neuroscience, 2023, Article 1361","DOI":"10.1109\/ACCESS.2023.3276628"},{"key":"3262_CR53","doi-asserted-by":"publisher","DOI":"10.3233\/JIFS-224598","author":"Z Yao","year":"2023","unstructured":"Yao, Z., Zeng, B., Hu, H., & Wei, P. (2023). The implicit mathematical reasoning model combining self-attention and convolution. Journal of Intelligent & Fuzzy Systems. https:\/\/doi.org\/10.3233\/JIFS-224598","journal-title":"Journal of Intelligent & Fuzzy Systems"},{"issue":"1","key":"3262_CR54","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1007\/s10666-006-9048-4","volume":"12","author":"K Yetilmezsoy","year":"2007","unstructured":"Yetilmezsoy, K., & Saral, A. (2007). Stochastic modeling approaches based on neural network and linear\u2013nonlinear regression techniques for the determination of single droplet collection efficiency of countercurrent spray towers. Environmental Modeling & Assessment, 12(1), 13\u201326. https:\/\/doi.org\/10.1007\/s10666-006-9048-4","journal-title":"Environmental Modeling & Assessment"},{"key":"3262_CR55","doi-asserted-by":"publisher","first-page":"104107","DOI":"10.1016\/j.eti.2025.104107","volume":"38","author":"H Yin","year":"2025","unstructured":"Yin, H., Jin, D., Hong, H., Moon, J., & Gu, Y. H. (2025). IAQ-STL-ML: A novel indoor air quality prediction pipeline using meta-learning framework with STL decomposition. Environmental Technology & Innovation, 38, 104107. https:\/\/doi.org\/10.1016\/j.eti.2025.104107","journal-title":"Environmental Technology & Innovation"},{"issue":"4","key":"3262_CR57","doi-asserted-by":"publisher","first-page":"3769","DOI":"10.2166\/ws.2022.022","volume":"22","author":"J Zhang","year":"2022","unstructured":"Zhang, J. (2022). Approaches for estimating mixing time in a water storage tank. Water Supply, 22(4), 3769\u20133783. https:\/\/doi.org\/10.2166\/ws.2022.022","journal-title":"Water Supply"},{"key":"3262_CR58","doi-asserted-by":"publisher","first-page":"150856","DOI":"10.1016\/j.scitotenv.2021.150856","volume":"772","author":"Z Zhang","year":"2021","unstructured":"Zhang, Z., Malik, M. Z., Khan, A., Ali, N., Malik, S., & Bilal, M. (2021). Environmental impacts of hazardous waste, and management strategies to reconcile circular economy and eco-sustainability. The Science of the Total Environment, 772, 150856. https:\/\/doi.org\/10.1016\/j.scitotenv.2021.150856","journal-title":"The Science of the Total Environment"},{"issue":"10","key":"3262_CR59","doi-asserted-by":"publisher","first-page":"4417","DOI":"10.3390\/su17104417","volume":"17","author":"J Zheng","year":"2025","unstructured":"Zheng, J., Suzuki, G., & Shioya, H. (2025). Sustainable sewage treatment prediction using integrated KAN\u2013LSTM with multi-head attention. Sustainability, 17(10), 4417. https:\/\/doi.org\/10.3390\/su17104417","journal-title":"Sustainability"}],"container-title":["Environmental Geochemistry and Health"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10653-026-03262-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10653-026-03262-0","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10653-026-03262-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T23:39:02Z","timestamp":1782862742000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10653-026-03262-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6]]},"references-count":52,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2026,6]]}},"alternative-id":["3262"],"URL":"https:\/\/doi.org\/10.1007\/s10653-026-03262-0","relation":{},"ISSN":["0269-4042","1573-2983"],"issn-type":[{"value":"0269-4042","type":"print"},{"value":"1573-2983","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6]]},"assertion":[{"value":"16 January 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 May 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 June 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This study did not involve human participants; therefore, consent to participate was not required.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to Participate"}},{"value":"This study does not include any individual person\u2019s data in any form; therefore, consent to publish is not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to Publish"}},{"value":"This research was conducted following ethical guidelines and received ethical clearance from the University of Lubumbashi. The study did not involve any experiments on humans or animals.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Approval"}},{"value":"The authors reported no potential conflict of interest.","order":6,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure statement"}}],"article-number":"386"}}