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Rapid weather phenomena, particularly sudden and intense rainfall, have become a problem in urban areas in recent years. During heavy rainfall, urban rainwater drainage systems are unable to discharge huge amounts of runoff into collecting reservoirs, which usually results in local flooding. This paper presents attempts to forecast a reduction in the load on the rainwater drainage system through the implementation of green roofs in a case study covering two selected districts of Opole (Poland)—the Old Town and the City Centre. Model tests of extensive and intensive roofs were carried out, in order to determine the reduction of rainwater runoff from the roof surface for the site under study. The potential of the roofs of the buildings to make a green roof was also determined using geographical information systems (GIS), for a case study of two central districts of Opole. It proposed a methodology to determine the rainwater drainage system load reduction by making green roofs. The analyses carried out lead to the conclusion that, in the districts selected for the study, the execution of green roofs on 25% of the of buildings with the potential to implement this type of roof solution could reduce the load on the rain water system by a degree that protects the city area from local flooding.
Alicja Kolasa-Więcek; Dariusz Suszanowicz. The green roofs for reduction in the load on rainwater drainage in highly urbanised areas. Environmental Science and Pollution Research 2021, 28, 1 -9.
AMA StyleAlicja Kolasa-Więcek, Dariusz Suszanowicz. The green roofs for reduction in the load on rainwater drainage in highly urbanised areas. Environmental Science and Pollution Research. 2021; 28 (26):1-9.
Chicago/Turabian StyleAlicja Kolasa-Więcek; Dariusz Suszanowicz. 2021. "The green roofs for reduction in the load on rainwater drainage in highly urbanised areas." Environmental Science and Pollution Research 28, no. 26: 1-9.
This study presents the results of a review of publications conducted by researchers in a variety of climates on the implementation of ‘green roofs’ and their impact on the urban environment. Features of green roofs in urban areas have been characterized by a particular emphasis on: Filtration of air pollutants and oxygen production, reduction of rainwater volume discharged from roof surfaces, reduction of so-called ‘urban heat islands’, as well as improvements to roof surface insulation (including noise reduction properties). The review of the publications confirmed the necessity to conduct research to determine the coefficients of the impact of green roofs on the environment in the city centers of Central and Eastern Europe. The results presented by different authors (most often based on a single case study) differ significantly from each other, which does not allow us to choose universal coefficients for all the parameters of the green roof’s impact on the environment. The work also includes analysis of structural recommendations for the future model green roof study, which will enable pilot research into the influence of green roofs on the environment in urban agglomerations and proposes different kinds of plants for different kinds of roofs, respectively.
Dariusz Suszanowicz; Alicja Kolasa Więcek. The Impact of Green Roofs on the Parameters of the Environment in Urban Areas—Review. Atmosphere 2019, 10, 792 .
AMA StyleDariusz Suszanowicz, Alicja Kolasa Więcek. The Impact of Green Roofs on the Parameters of the Environment in Urban Areas—Review. Atmosphere. 2019; 10 (12):792.
Chicago/Turabian StyleDariusz Suszanowicz; Alicja Kolasa Więcek. 2019. "The Impact of Green Roofs on the Parameters of the Environment in Urban Areas—Review." Atmosphere 10, no. 12: 792.
The present paper discusses a novel methodology based on neural network to determine air pollutants’ correlation with life expectancy in European countries. The models were developed using historical data from the period 1992–2016, for a set of 20 European countries. The subject of the analysis included the input variables of the following air pollutants: sulphur oxides, nitrogen oxides, carbon monoxide, particulate matters, polycyclic aromatic hydrocarbons and non-methane volatile organic compounds. Our main findings indicate that all the variables significantly affect life expectancy. Sensitivity of constructed neural networks to pollutants proved to be particularly important in the case of changes in the value of particulate matters, sulphur oxides and non-methane volatile organic compounds. The most frequent association was found for fine particle. Modelled courses of changes in the variable under study coincide with the actual data, which confirms that the proposed models generalize acquired knowledge well.
Alicja Kolasa-Więcek; Dariusz Suszanowicz. Air pollution in European countries and life expectancy—modelling with the use of neural network. Air Quality, Atmosphere & Health 2019, 12, 1335 -1345.
AMA StyleAlicja Kolasa-Więcek, Dariusz Suszanowicz. Air pollution in European countries and life expectancy—modelling with the use of neural network. Air Quality, Atmosphere & Health. 2019; 12 (11):1335-1345.
Chicago/Turabian StyleAlicja Kolasa-Więcek; Dariusz Suszanowicz. 2019. "Air pollution in European countries and life expectancy—modelling with the use of neural network." Air Quality, Atmosphere & Health 12, no. 11: 1335-1345.
Self-Organising Feature Map (SOFM) neural models and the Learning Vector Quantization (LVQ) algorithm were used to produce a classifier identifying the quality classes of compost, according to the degree of its maturation within a period of time recorded in digital images. Digital images of compost at different stages of maturation were taken in a laboratory. They were used to generate an SOFM neural topological map with centres of concentration of the classified cases. The radial neurons on the map were adequately labelled to represent five suggested quality classes describing the degree of maturation of the composted organic matter. This enabled the creation of a neural separator classifying the degree of compost maturation based on easily accessible graphic information encoded in the digital images. The research resulted in the development of original software for quick and easy assessment of compost maturity. The generated SOFM neural model was the kernel of the constructed IT system.
Piotr Boniecki; Małgorzata Idzior-Haufa; Agnieszka A. Pilarska; Krzysztof Pilarski; Alicja Kolasa-Wiecek. Neural Classification of Compost Maturity by Means of the Self-Organising Feature Map Artificial Neural Network and Learning Vector Quantization Algorithm. International Journal of Environmental Research and Public Health 2019, 16, 3294 .
AMA StylePiotr Boniecki, Małgorzata Idzior-Haufa, Agnieszka A. Pilarska, Krzysztof Pilarski, Alicja Kolasa-Wiecek. Neural Classification of Compost Maturity by Means of the Self-Organising Feature Map Artificial Neural Network and Learning Vector Quantization Algorithm. International Journal of Environmental Research and Public Health. 2019; 16 (18):3294.
Chicago/Turabian StylePiotr Boniecki; Małgorzata Idzior-Haufa; Agnieszka A. Pilarska; Krzysztof Pilarski; Alicja Kolasa-Wiecek. 2019. "Neural Classification of Compost Maturity by Means of the Self-Organising Feature Map Artificial Neural Network and Learning Vector Quantization Algorithm." International Journal of Environmental Research and Public Health 16, no. 18: 3294.
Animal waste, including chicken manure, is a category of biomass considered for application in the energy industry. Poland is leading poultry producer in Europe, with a chicken population assessed at over 176 million animals. This paper aims to determine the theoretical and technical energy potential of chicken manure in Poland. The volume of chicken manure was assessed as 4.49 million tons per year considering three particular poultry rearing systems. The physicochemical properties of examined manure specimens indicate considerable conformity with the data reported in the literature. The results of proximate and ultimate analyses confirm a considerable effect of the rearing system on the energy parameters of the manure. The heating value of the chicken manure was calculated for the high moisture material in the condition as received from the farms. The value of annual theoretical energy potential in Poland was found to be equal to around 40.38 PJ. Annual technical potential of chicken biomass determined for four different energy conversion paths occurred significantly smaller then theoretical and has the value from 9.01 PJ to 27.3 PJ. The bigger energy degradation was found for heat and electricity production via anaerobic digestion path, while fluidized bed combustion occurred the most efficient scenario.
Mariusz Tańczuk; Robert Junga; Alicja Kolasa-Więcek; Patrycja Niemiec. Assessment of the Energy Potential of Chicken Manure in Poland. Energies 2019, 12, 1244 .
AMA StyleMariusz Tańczuk, Robert Junga, Alicja Kolasa-Więcek, Patrycja Niemiec. Assessment of the Energy Potential of Chicken Manure in Poland. Energies. 2019; 12 (7):1244.
Chicago/Turabian StyleMariusz Tańczuk; Robert Junga; Alicja Kolasa-Więcek; Patrycja Niemiec. 2019. "Assessment of the Energy Potential of Chicken Manure in Poland." Energies 12, no. 7: 1244.
The present paper discusses a novel methodology based on neural network to determine agriculture emission model simulations. Methane and nitrous oxide are the key pollutions among greenhouse gases being a major contribution to climate changes because of their high potential global impact. Using statistical clustering (k-means and Ward’s method), five meaningful clusters of countries with similar level of greenhouse gases emission were identified. Neural modeling using multi-layer perceptron networks was performed for countries placed in particular groups. The parameters that characterize the quality of a network are the predictive errors (mainly validation and test) and they are high (0.97–0.99). The use of sensitivity analysis allowed for identifying the variables that have a significant influence on the greenhouse gases emissions. The sensitivity analysis of the designed artificial neural network models shows a few dominant variables, affecting emissions with varied intensity: cattle and buffaloes, sheep and goat populations, afforestation as well as electricity consumption. The observed values were compared with those predicted by the models. The forecasted course of changes in the variable test is identical with the real data, which proves that the model highly matches to the observed data.
Alicja Kolasa-Więcek. Neural Modeling of Greenhouse Gas Emission from Agricultural Sector in European Union Member Countries. Water, Air, & Soil Pollution 2018, 229, 205 .
AMA StyleAlicja Kolasa-Więcek. Neural Modeling of Greenhouse Gas Emission from Agricultural Sector in European Union Member Countries. Water, Air, & Soil Pollution. 2018; 229 (6):205.
Chicago/Turabian StyleAlicja Kolasa-Więcek. 2018. "Neural Modeling of Greenhouse Gas Emission from Agricultural Sector in European Union Member Countries." Water, Air, & Soil Pollution 229, no. 6: 205.