Abstract
Urban stormwater runoff is considered worldwide as one of the most critical diffuse pollutions since it transports contaminants that threaten the quality of receiving water bodies and represent a harm to the aquatic ecosystem. Therefore, a thorough analysis of nutrient build-up and wash-off from impervious surfaces is crucial for effective stormwater-treatment design. In this study, the self-organizing map (SOM) method was used to simplify a complex dataset that contains precipitation, flow rate, and water-quality data, and identify possible patterns among these variables that help to explain the main features that impact the processes of nutrient build-up and wash-off from urban areas. Antecedent dry weather, among the rainfall-related characteristics, and sediment transport resulted in being the most significant factors in nutrient urban runoff simulations. The outcomes of this work will contribute to facilitating informed decision making in the design of management strategies to reduce pollution impacts on receiving waters and, consequently, protect the surrounding ecological environment.
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References
Bernhardt, E.S., Palmer, M.A.: Restoring streams in an urbanizing world. Freshw. Biol. 52, 738–751 (2007)
Jiang, S.C., Lim, K.-Y., Huang, X., McCarthy, D., Hamilton, A.J.: Human and environmental health risks and benefits associated with use of urban stormwater. Wiley Interdiscip. Rev. Water 2, 683–699 (2015)
Gorgoglione, A., Gioia, A., Iacobellis, V.: A framework for assessing modeling performance and effects of rainfall-catchment-drainage characteristics on nutrient urban runoff in poorly gauged watersheds. Sustainability 11, 4933 (2019)
Brett, M.T., et al.: Non-point-source impacts on stream nutrient concentrations along a forest to urban gradient. Environ. Manage. 35, 330–342 (2005)
Lee, H., Lau, S.L., Kayhanian, M., Stenstrom, M.K.: Seasonal first flush phenomenon of urban stormwater discharges. Water Res. 38(19), 4153–4163 (2004)
Gobel, P., Dierkes, C., Coldewey, W.C.: Storm water runoff concentration matrix for urban areas. J. Contam. Hydrol. 91(1–2), 26–42 (2007)
Nguyen, H.L., Leermakers, M., Elskens, M., De Ridder, F., Doan, T.H., Baeyens, W.: Correlations, partitioning and bioaccumulation of heavy metals between different compartments of Lake Balaton. Sci. Total Environ. 341(1–3), 211–226 (2005)
Surbeck, C.Q., Jiang, S.C., Ahn, J.H., Grant, S.B.: Flow fingerprinting fecal pollution and suspended solids in stormwater runoff from an urban coastal watershed. Environ. Sci. Technol. 40(14), 4435–4441 (2006)
Ki, S.J., et al.: Advancing assessment and design of stormwater monitoring programs using a self-organizing map: characterization of trace metal concentration profiles in stormwater runoff. Water Res. 45, 4183–4197 (2011)
Varol, M.: Spatio-temporal changes in surface water quality and sediment phosphorus content of a large reservoir in Turkey. Environ. Pollut. 259, 113860 (2020)
Batur, E., Maktav, D.: Assessment of surface water quality by using satellite images fusion based on PCA method in the Lake Gala, Turkey. IEEE Trans. Geosci. Remote Sens. 57(5), 2983–2989 (2019)
Kohonen, T.: Automatic formation of topological maps of patterns in a self-organizing system. In: Oja, E., Simula, O. (eds.) Processing 2nd Scandinavian Conference on Image Analysis, pp. 214–220 (1981)
Sengorur, B., Koklu, R., Ates, A.: Water quality assessment using artificial intelligence techniques: SOM and ANN—a case study of Melen River Turkey. Water Qual. Exposure Health 7(4), 469–490 (2015)
Jiang, M., Wang, Y., Tang, Q., Meng, F., Yao, Z., Cheng, P.: Assessment of surface water quality using a growing hierarchical self-organizing map: a case study of the Songhua River Basin, northeastern China, from 2011 to 2015. Environ. Monit. Assess. 190, 260 (2018). Regional geographical information system: SIT Puglia: Available online: http://www.sit.puglia.it/. Accessed 8 Jan 2020
Di Modugno, M., et al.: Build-up/wash-off monitoring and assessment for sustainable management of first flush in an urban area. Sustainability 7, 5050–5070 (2015)
Veneziano, D., Iacobellis, V.: Multiscaling pulse representation of temporal rainfall. Water Resour. Res. 38, 131–1313 (2002)
Veneziano, D., Furcolo, P., Iacobellis, V.: Multifractality of iterated pulse processes with pulse amplitudes generated by a random cascade. Fractals 10, 209–222 (2002)
Gorgoglione, A., Gioia, A., Iacobellis, V., Piccinni, A.F., Ranieri, E.: A rationale for pollutograph evaluation in ungauged areas, using daily rainfall patterns: Case studies of the Apulian region in Southern Italy. Appl. Environ. Soil Sci. 2016 (2016)
Regional Regulation: Stormwater runoff and first flush regulations (implementation of article 13 of Legislative Decree no 152/06 and subsequent amendments), no 26, 9 December 2013. https://www.indicenormativa.it/sites/default/files/R_26_09_12_2013.pdf. Accessed 6 Jan 2020
Rossman, L.A.: Storm water management model user’s manual version 5.0; EPA/600/R-05/040; National Risk Management Research Laboratory-Office of Research and Development, U.S. Environmental Protection Agency, Cincinnati, OH, USA (2010)
Gorgoglione, A., Bombardelli, F.A., Pitton, B.J.L., Oki, L.R., Haver, D.L., Young, T.M.: Uncertainty in the parameterization of sediment build-up and wash-o processes in the simulation of sediment transport in urban areas. Environ. Model. Softw. 111, 170–181 (2019)
Horton, R.E.: An approach toward a physical interpretation of infiltration capacity. Soil Sci. Soc. Am. 5, 399–417 (1940)
Kohonen, T.: Self-organizing Maps, 3rd edn. Springer, Heidelberg (2001). https://doi.org/10.1007/978-3-642-56927-2
Jain, A.K., Murty, M.N., Flynn, P.J.: Data clustering: a review. ACM Comput. Surv. 31, 264–323 (1999)
Rousseeuw, P.J.: Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 20, 53–65 (1987)
Wei, C., Gao, C., Han, D., Zhao, W., Lin, Q., Wang, G.: Spatial and temporal variations of water quality in Songhua River from 2006 to 2015: implication for regional ecological health and food safety. Sustainability 9, 1502 (2017)
Hofmann, H., Kafadar, K., Wickham, H.: Letter-value plots: boxplots for large data. J. Comput. Graph. Stat. 26(3), 469–477 (2017)
Vesanto, J.: SOM implementation in SOM toolbox. SOM toolbox online help. http://www.cis.hut.fi/projects/somtoolbox/documentation/somalg.shtml. Accessed Feb 2020
Vettigli, G.: Minisom: minimalistic and numpy-based implementation of the self organizing map. https://github.com/JustGlowing/minisom. Accessed Feb 2020
Waskom, M., Botvinnik, O., O’Kane, D., Hobson, P., Ostblom, J., Lukauskas, S., et al.: mwaskom/seaborn: v0.9.0, July 2018. https://www.doi.org/10.5281/zenodo.1313201. Accessed Feb 2020
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Gorgoglione, A., Castro, A., Gioia, A., Iacobellis, V. (2020). Application of the Self-organizing Map (SOM) to Characterize Nutrient Urban Runoff. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2020. ICCSA 2020. Lecture Notes in Computer Science(), vol 12252. Springer, Cham. https://doi.org/10.1007/978-3-030-58811-3_49
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