Publications by authors named "M Dohnal"
Article Synopsis
- Blue and Green Infrastructures (BGIs) are natural systems that help manage rainwater to prevent floods and droughts.
- Researchers created a model to understand how these systems work together in cities, using different methods to simulate how water moves and is stored.
- Testing this model on a university campus in Belgium, they found that using a mix of BGIs, like green roofs and rainwater tanks, can greatly reduce water runoff and help keep water available during dry times.
In this work, we tried to analyze dust loading behavior of polypropylene hollow fiber membranes using average pressure drop models. Hollow fiber membranes varying in fiber diameter were loaded with a standardized test dust to simulate particle-polluted air. We measured pressure drop development of the membranes at different flowrates and dust concentrations, and, after each experiment, the dust deposited on the membrane fibers was weighed to obtain dust holding capacity (DHC).
View Article and Find Full Text PDFAccess to pure water is a very topical issue today. Desalination represents a promising way of obtaining drinking water in areas of shortage. Currently, efforts are being made to replace the metal components of existing desalination units due to the high corrosivity of sea water.
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- Accurate identification of wet and dry weather periods is essential for managing processes influenced by rainfall, like urban drainage and wastewater treatment.
- Traditional methods of measuring rainfall often lack the spatial and temporal resolution needed for these transitions, leading to potential gaps in data.
- This study introduces an innovative approach using affordable electromagnetic sensors that can reliably detect precipitation events, proving to be more sensitive than standard rain gauges, although they may still produce some false negatives when not used in tandem with more reliable measuring tools.
In this paper we demonstrate that fuzzy logic can provide a better tool for predicting recycling behaviour than the customarily used linear regression. To show this, we take a set of empirical data on recycling behaviour (N=664), which we randomly divide into two halves. The first half is used to estimate a linear regression model of recycling behaviour, and to develop a fuzzy logic model of recycling behaviour.
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