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[87690] Artykuł:

The Impact of Catchment Characteristics and Weather Conditions on Heavy Metal Concentrations in Stormwater—Data Mining Approach

(Wpływ charakterystyk zlewni i warunków atmosferycznych na zawartość metali ciężkich w wodach deszczowych - podejście data mining)
Czasopismo: Applied Sciences   Tom: 9(11), Zeszyt: 2210, Strony: 1-15
ISSN:  2076-3417
Opublikowano: Maj 2019
Liczba arkuszy wydawniczych:  1.50
 
  Autorzy / Redaktorzy / Twórcy
Imię i nazwisko Wydział Katedra Do oświadczenia
nr 3		 Grupa
przynależności		 Dyscyplina
naukowa		 Procent
udziału		 Liczba
punktów
do oceny pracownika		 Liczba
punktów wg
kryteriów ewaluacji
Jarosław Górski orcid logo WiŚGiEKatedra Geotechniki, Geomatyki i Gospodarki Odpadami*Niezaliczony do "N"Inżynieria środowiska, górnictwo i energetyka2525.0033.33  
Łukasz Bąk orcid logo WiŚGiEKatedra Geotechniki, Geomatyki i Gospodarki Odpadami*Niezaliczony do "N"Inżynieria środowiska, górnictwo i energetyka2525.0033.33  
Bartosz Szeląg orcid logo WiŚGiEKatedra Geotechniki, Geomatyki i Gospodarki Odpadami*Niezaliczony do "N"Inżynieria środowiska, górnictwo i energetyka2525.0033.33  
Katarzyna Górska orcid logo WiŚGiEKatedra Technologii Wody i ŚciekówNiespoza "N" jednostkiInżynieria środowiska, górnictwo i energetyka2525.00.00  

Grupa MNiSW:  Publikacja w czasopismach wymienionych w wykazie ministra MNiSzW (część A)
Punkty MNiSW: 100

Pełny tekstPełny tekst     DOI LogoDOI    
Keywords:

stormwater  heavy metals  artificial neural network method 


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.


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