The processes that affect sediment quality in drainage systems show high dynamics and complexity. However, relatively little information is available on the influence of both catchment characteristics and meteorological conditions on sediment chemical properties, as those issues have not been widely explored in research studies. This paper reports the results of investigations into the content of selected heavy metals (Ni, Mn, Co, Zn, Cu, Pb, and Fe) and polycyclic aromatic hydrocarbons (PAHs) in sediments from the stormwater drainage systems of four catchments located in the city of Kielce, Poland. The influence of selected physico-geographical catchment characteristics and atmospheric conditions on pollutant concentrations in the sediments was also analyzed. Based on the results obtained, statistical models for forecasting the quality of stormwater sediments were developed using artificial neural networks (multilayer perceptron neural networks). The analyses showed varied impacts of catchment characteristics and atmospheric conditions on the chemical composition of sediments. The concentration of heavy metals in sediments was far more affected by catchment characteristics (land use, length of the drainage system) than atmospheric conditions. Conversely, the content of PAHs in sediments was predominantly affected by atmospheric conditions prevailing in the catchment. The multilayer perceptron models developed for this study had satisfactory predictive abilities; the mean absolute error of the forecast (Ni, Mn, Zn, Cu, and Pb) did not exceed 21%. Hence, the models show great potential, as they could be applied to, for example, spatial planning for which environmental aspects (i.e., sediment quality in the stormwater drainage systems) are accounted.
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.
The paper presents measurements of the amount, spatial distribution, grain size structure, density and the content of organic matter in sediments of Suchedniów reservoir. After 33 years of exploitation, the primary volume of the reservoir decreased by 78 thousand m3. Catchment area covered in 45% by forests is 83 km2. Mean annual water flow is estimated at 0.63 m3·s-1. Primary volume of the reservoir was 303 thousand m3 and its surface area - 21.4 ha. The greatest width of the reservoir is 490 m, distance from the river inlet to the dam is 655 m and the length along assumed water course is 740 m. Mean and maximum depths are 1.42 and 4 m, respectively. The analyses of vertical and horizontal distribution of sediments revealed that in the deepest parts of the water body, where depth exceed 2.6 m, only 16% of sediment volume were deposited. In the inlet part of a relative volume of 0.2, 32% of sediment volume were deposited and in the outlet part - 16%. The reservoir accumulates material of a grain size <1.0 mm whose density varies between 2586 and 2758 kg·m-3. Percent of organic parts in sediments ranged from 0.24 to 18.97%. The existing methods of description of sediment distribution in retention reservoirs do not allow for accurate predicting of this distribution. They do not account for many factors affecting the distribution of sediments in reservoirs and for the time of exploitation. None of the dimensionless curves of the Annandale’s nomograph [ANNANDALE 1984] describes the distribution of sediments in Suchedniów Reservoir. The curve closest to the actual distribution curve corresponds to the value dP/dx = 1.2 while for Suchedniów Reservoir dP/dx is 0.0014 (P - wetted perimeter of the reservoir’s cross section, x - distance from the dam). Sediments in vertical profiles have laminar structure and bottom material in layers largely differs in grain size and colour which is an evidence of different conditions of sedimentation in different hydrological periods and exploita
Contaminants bound to suspended solids transported by stormwater pose quantity and quality problems to the water ecosystem. Therefore, it is necessary to carry out an ecological risk assessment. Currently, Polish legal regulations do not require that stormwater sediments should be tested for hazardous substances prior to their removal. Ecological evaluation was performed for polycyclic aromatic hydrocarbons (PAHs) and heavy metals in the sediments from three selected stormwater sedimentation tanks located in urban catchment areas of Kielce city. The purpose this study was to analyze sediments for chemical contaminants and to demonstrate the relationship between contaminant levels and physical attributes of the catchment, including land use, drainage area, age of catchment and impervious surface in the watershed. Levels of PAHs and heavy metals (Cd, Cr, Ni, Pb and Zn) in stormwater sediments were then compared with Polish regulations and ecological screening values to determine their potential risk to environment were established. The results of the current study suggest that stormwater sediments are moderately contaminated with PAHs and heavy metals according to domestic regulations. They are, however, highly contaminated according to ecological benchmarks, which are exceeded several times.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.