Assessment of Small-Settlement Wastewater Discharges on the Irtysh River Using Tracer-Based Mixing Diagnostics and Regularized Predictive Models
Artykuł w czasopiśmie
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| Status: | |
| Autorzy: | Anapyanova Samal, Kolpakova Valentina, Kulisz Monika, Nabiollina Madina, Yeremeyeva Yuliya, Nurbayeva Nailya, Sherov Anvar |
| Dyscypliny: | |
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| Rok wydania: | 2026 |
| Wersja dokumentu: | Drukowana | Elektroniczna |
| Język: | angielski |
| Numer czasopisma: | 2 |
| Wolumen/Tom: | 18 |
| Numer artykułu: | 232 |
| Strony: | 1 - 24 |
| Impact Factor: | 3,0 |
| Scopus® Cytowania: | 0 |
| Bazy: | Scopus |
| Efekt badań statutowych | NIE |
| Materiał konferencyjny: | NIE |
| Publikacja OA: | TAK |
| Licencja: | |
| Sposób udostępnienia: | Witryna wydawcy |
| Wersja tekstu: | Ostateczna wersja opublikowana |
| Czas opublikowania: | W momencie opublikowania |
| Data opublikowania w OA: | 15 stycznia 2026 |
| Abstrakty: | angielski |
| An integrated field–analytical framework was applied to quantify the impact of two small-settlement treatment facilities (TF1 and TF2) on the Irtysh River (East Kazakhstan). The main objective of this study is to quantify effluent-driven dilution and non-conservative changes in key water-quality indicators downstream of TF1 and TF2 and to evaluate parsimonious models for predicting effluent-outlet BOD and COD from upstream measurements. Paired upstream–downstream control sections are sampled in 2024–2025 for 22 indicators, and plant influent–effluent records are compiled for key wastewater variables. Chloride-based conservative mixing indicated very strong dilution (approximately D ≈ 2.0 × 10 3 for TF1 and D ≈ 4.2 × 10 2 for TF2). Deviations from the mixing line were summarized using a transformation diagnostic θ. At TF1, several constituents exceeded mixing expectations (θ ≈ 13 for COD, θ ≈ 42 for ammonium, and θ ≈ 6 for phosphates), while nitrate shows net attenuation θ < 0. At TF2, θ values cluster near unity, indicating modest deviations. Under a small-sample regime (N = 10) and leave-one-out validation, regularized regression provided accurate forecasts of effluent-outlet BOD and COD. Lasso under LOOCV performed best (BOD_after: RMSE = 0.626, MAE = 0.459, and R2 = 0.976; COD_after: RMSE = 0.795, MAE = 0.634, and R2 = 0.997). The results reconcile strong reachscale dilution with constituent-specific local departures and support targeted modernization and operational forecasting for water-quality management in small facilities. |
