The Impact of Catchment Characteristics and Weather Conditions on Heavy Metal Concentrations in Stormwater—Data Mining Approach
Source abstract
Abstract
The dynamics of processes affecting the quality of stormwater removed through drainage systems are highly complicated. Relatively little information is available on predicting the impact of catchment characteristics and weather conditions on stormwater heavy metal (HM). This paper reports research results concerning the concentrations of selected HM (Ni, Cu, Cr, Zn, Pb and Cd) in stormwater removed through drainage system from three catchments located in the city of Kielce, Poland. Statistical models for predicting concentrations of HM in stormwater were developed based on measurement results, with the use of artificial neural network (ANN) method (multi-layer perceptron). Analyses conducted for the study demonstrated that it is possible to use simple variables to characterise catchment and weather conditions. Simulation results showed that for Ni, Cu, Cr, Zn and Pb, the selected independent variables ensure satisfactory predictive capacities of the models (R2 > 0.78). The models offer considerable application potential in the area of development plans, and they also account for environmental aspects as stormwater and snowmelt water quality affects receiving waters.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Urban Stormwater Management Solutions · Noise Effects and Management · Hydrological Forecasting Using AI
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Geomatic and Energy Engineering, Faculty of Environmental, Kielce University of Technology, Al. Tysiąclecia Państwa Polskiego 7, 25-314 Kielce, Poland
Funding
Ministerstwo Nauki i Szkolnictwa Wyższego
References (48)
Show source-provided references
1. Ali, S.A., Rodriguez, F., Bonhomme, C., and Chebbo, G. (2018). Accounting for the Spatio-Temporal Variability of Pollutant Processes in Stormwater TSS Modeling Based on Stochastic Approaches. Water, 10.10.3390/w10121773 ↗
2. Tsai, L.Y., Chen, C.F., Fan, C.H., and Lin, J.Y. (2017). Using the HSPF and SWMM Models in a High Pervious Watershed and Estimating Their Parameter Sensitivity. Water, 9.10.3390/w9100780 ↗
3. Cho, J.H., and Lee, J.H. (2018). Multiple Linear Regression Models for Predicting Nonpoint-Source Pollutant Discharge from a Highland Agricultural Region. Water, 10.10.3390/w10091156 ↗
4. Assessment of total suspended solid emission discharged via storm sewerage system from urban areas
5. Airborne fine particulate pollution in Jinan, China: Concentrations, chemical compositions and influence on visibility impairment10.1016/j.atmosenv.2012.02.029 ↗
6. The elemental composition and origin of fine ambient particles in the largest Polish conurbation: First results from the short-term winter campaign10.1007/s00704-015-1494-y ↗
7. Garcia, J.T., Espin-Leal, P., Vigueras-Rodriguez, A., Carrillo, J.M., and Castillo, L.G. (2018). Synthetic pollutograph by prediction indices: An evaluation in several urban sub-catchnents. Sustainability, 10.10.3390/su10082634 ↗
8. Activated sludge process modelling using selected machine learning techniques10.5004/dwt.2018.22095 ↗
9. Bąk, Ł., Szeląg, B., Sałata, A., and Studziński, J. (2019). Modeling of Heavy Metal (Ni, Mn, Co, Zn, Cu, Pb, and Fe) and PAH Content in Stormwater Sediments Based on Weather and Physico-Geographical Characteristics of the Catchment-Data-Mining Approach. Water, 11.10.3390/w11030626 ↗
10. Prediction of heavy metal concentrations in urban stormwater10.1111/j.1747-6593.2008.00138.x ↗
11. Pochwat, K. (2018, January 7–8). The use of artificial neural networks for analyzing the sensitivity of a retention tank. Proceedings of the VI International Conference of Science and Technology INFRAEKO Modern Cities—E3S Web of Conferences 45, Krakow, Poland.10.1051/e3sconf/20184500066 ↗
12. Predicting combined sewer overflows chamber depth using artificial neural networks with rainfall radar data10.2166/wst.2014.024 ↗
13. Artificial neural network modeling of the river water quality—A case study10.1016/j.ecolmodel.2009.01.004 ↗
14. River Water Quality Modelling Using Artificial Neural Network Technique10.1016/j.aqpro.2015.02.135 ↗
15. Suspended Solids and Heavy Metals Content of Selected Rainwater Waves in an Urban Catchment Area: A Case Study
16. The application of SWMM software for the evaluation of stormwater treatment plant operation
17. PN-EN ISO 10523:2012 (2012). Water Quality. Determination pH Value, ISO. (In Polish).
18. PN-EN ISO 11885:2009 (2009). Determination of Selected Elements by Inductively Coupled Plasma Optical Emission Spectrometry (ICP-OES), ISO. (In Polish).
19. Arbeitsblatt DWA-A 118 (2006). Hydraulische Bemessung und Nachweis von Entwässerungssystemen, Deutsche Vereinigung für Wasserwirtschaft, Abwasser und Abfall.
20. Hydrodynamic and probabilistic modelling of storm overflow discharges
21. An analysis of transformations10.1111/j.2517-6161.1964.tb00553.x ↗
22. Murphy, L.U. (2015). Quantifying Spatial and Temporal Deposition of Atmospheric Pollutants in Runoff from Different Pavement Types. [Ph.D. Thesis, University of Canterbury Christchurch].
23. Rutkowski, L. (2006). Artificial Intelligence Methods and Techniques, PWN. (In Polish).
24. Hecht-Nielsen, R. (1987, January 21–24). Kolmogorov’s Mapping Neural Network Existence Theorem. Proceedings of the IEEE First Annual International Conference on Neural Networks, San Diego, CA, USA.
25. Demuth, H., and Beale, M. (2002). Neural Network Toolbox. For Use with MATLAB, The MathWorks.
26. Fach, S., Sitzenfrei, R., and Rauch, W. (September, January 31). Assessing the relationship between water level and combined sewer overflow with computational fluid dynamics. Proceedings of the 11th International Conference on Urban Drainage, Edinburgh, UK.
27. Królikowski, A., Garbarczyk, K., Gwoździej-Mazur, J., and Butarewicz, A. (2005). Sediments Formed in Stormwater Sewer Facilities, Polish Academy of Sciences. (In Polish).
28. Occurrence of heavy metals in rain wastewater on example of urban catchment in Kielce
29. The size distribution and origin of elements bound to ambient particles: A case study of a Polish urban area10.1007/s10661-015-4450-5 ↗
30. Build-up and wash-off dynamics of atmospherically derived Cu, Pb, Zn and TSS in stormwater runoff as a function of meteorological characteristics10.1016/j.scitotenv.2014.11.094 ↗
31. Modelling heavy metals build-up on urban road surfaces for effective stormwater reuse strategy implementation10.1016/j.envpol.2017.08.056 ↗
32. Further insight into the mechanism of heavy metals partitioning in stormwater runoff10.1016/j.jenvman.2015.11.035 ↗
33. Stormwater pollution as the basis of choice the method of their management
34. Risk prioritisation of stormwater pollutant sources10.1016/j.watres.2011.10.039 ↗
35. Wastewater quality and pollutant loads in combined sewers during dry weather periods10.1080/03087290802201415 ↗
36. Priority pollutants in urban stormwater: Part 1—Case of separate storm sewers10.1016/j.watres.2011.12.012 ↗
37. Storm water pollution in the urban environment of Genoa, Italy10.1016/j.atmosres.2004.10.017 ↗
38. The sources, impact and management of car park runoff pollution: A review10.1016/j.jenvman.2014.05.041 ↗
39. Quality characterization and impact assessment of highway runoff in urban and rural area of Guangzhou, China10.1007/s10661-007-9856-2 ↗
40. Brombach, H., and Fuchs, S. (2001). Datenpool Gemessener Verschmutzungskonzentrationen von Trocken—und Regenwetterabflüssen in Misch—und Trennkanalisationen, GFA. ATV-DVWK—Forschungsfonds, Projekt 1-01.
41. Untreated runoff quality from roof and road surfaces in a low intensity rainfall climate10.1016/j.scitotenv.2016.01.093 ↗
42. The Effects of Urbanization on Runoff Pollutant Concentrations, Loadings and Their Seasonal Patterns Under Cold Climate10.1007/s11270-014-1977-y ↗
43. Metal emissions from road traffic and the influence of resuspension—Results from two tunnel studies10.1016/S1352-2310(02)00561-7 ↗
44. Urban Metal Flows—A Case Study of Stockholm. Review and Conclusions10.1023/A:1017531532576 ↗
45. Assessment of spatial distribution and potential ecological risk of the heavy metals in relation to granulometric contents of Veeranam lake sediments India10.1016/j.ecoenv.2012.06.027 ↗
46. Sources and transport pathways of common heavy metals to urban road surfaces10.1016/j.ecoleng.2015.01.023 ↗
47. Hydrocarbons and metals in atmospheric deposition and roof runoff in central Paris10.1023/B:WATE.0000049165.12410.98 ↗
48. Atmospheric deposition and storm induced runoff of heavy metals from different impermeable urban surfaces10.1039/C1EM10643K ↗