A simplified geospatial model to rank LID solutions for urban runoff management.

Studies have shown the usefulness of low impact development (LID) in runoff management in urban areas; however, there is a limited number of systematic decision-making models for ranking LID solutions (i.e., the location and type of LID required). This research proposes a physics-based GIS Multi Criteria Decision Making model (GIS-MCDM), which we refer to as the LID Solution Evaluation and Ranking ApproacH (SERAH). This model integrates the hydrological and socioeconomic-environmental benefits of LID with the subcatchment-level demand of LIDs - this has been traditionally overlooked in previous research. Specifically, SERAH integrates key the contributing criteria, including LID benefits, cost, feasibility, and subcatchment demand to rank LID solutions. To demonstrate the applicability of SERAH, a highly urbanized catchment in Toronto was used as a case-study and three types of LID: rain gardens, infiltration trenches, and porous pavements were considered. The hydrological performance of the ranked solutions was estimated using the stormwater management model, PCSWMM. The resulting LID ranking from SERAH corresponded to the best hydrological performance and LID co-benefits. Runoff volumes were reduced by 8.9-11.3%, and peak runoff values were reduced by 1.3-19.9% compared to the base scenario. The infiltration trench was ranked the highest in 16 of the 19 subcatchments where the cost was identified as a key factor. For the remaining three subcatchments, the rain garden was ranked the highest due to its socioeconomic-environmental benefits outweighing the higher cost. The effect of different rainfall durations, frequencies, and temporal patterns on the performance of the highest-ranked LID solution suggested that LID provide higher performance (runoff volume reduction) in more severe events. SERAH is useful for strategic planning for sustainable infrastructure. Future research is needed to better quantify the socioeconomic and environmental benefits of LID to improve SERAH.

Subsurface transport of carboxymethyl cellulose (CMC)-stabilized nanoscale zero valent iron (nZVI): Numerical and statistical analysis.

Subsurface remediation using nanoscale zero valent iron (nZVI) is a promising in-situ technology that can transform certain groundwater contaminants into non-toxic compounds. However, field scale implementation of nZVI technology has faced major challenges due to poor subsurface mobility, limited longevity and well clogging, all leading to a shorter nZVI travel distance. This distance nZVI travels in the subsurface is an important parameter since it influences the amount of contaminants that can be reached and thereby remediated. There are several factors which may affect nZVI travel distance such as groundwater velocity, injection concentration and rate, lag period (duration when nZVI injection is stopped), solution viscosity, and subsurface heterogeneity. Although various studies have been performed to reveal the effect of different factors on nZVI transport in homogeneous domains, few studies have focused on heterogeneous media, which is more representative of field conditions. In this study, a statistical analysis was performed using a two-dimensional numerical model which simulated carboxymethyl cellulose (CMC) stabilized nZVI transport in randomly distributed soil permeability fields of two aquifers to examine the factors that have the greatest impact on nZVI travel distance. Among all possible factors, field scale solution viscosity and injection rate had a statistically significant effect on nZVI travel distance in both the horizontal and vertical directions, as well as, on the attached mass. Additionally, the lag period between injections had a statistically significant effect on the attached mass, but not the travel distance. These results suggest that having a long injection period followed by a short lag phase during field deployment may result in less nZVI attachment. Lastly, aquifer heterogeneity impacted the nZVI spread while the impact of intrinsic groundwater velocity and injection concentration was found not to be statistically significant. Results from this numerical study can aid in field-scale CMC-nZVI injection by identifying key factors for remediation optimization.

Response of green roof performance to multiple hydrologic and design variables: a laboratory investigation.

Multiple factors affect green roof performance and their effects might vary at different stages of operation. This paper aimed to link green roof performance to hydrologic variables (antecedent moisture condition (AMC) and rainfall intensity) and design variables (growing medium (GM) type and depth) under multiple dimensions at the early stage of operation using laboratory experiment data. The results showed that the AMC is the most influential factor of hydrologic performance, whereas the GM type appeared to primarily affect the nutrient levels of the outflow. The significant main effects of other variables and interaction effects between two variables point to challenges in green roof design.

River flood prediction using fuzzy neural networks: an investigation on automated network architecture.

Urban floods are one of the most devastating natural disasters globally and improved flood prediction is essential for better flood management. Today, high-resolution real-time datasets for flood-related variables are widely available. These data can be used to create data-driven models for improved real-time flood prediction. However, data-driven models have uncertainty stemming from a number of issues: the selection of input data, the optimisation of model architecture, estimation of model parameters, and model output. Addressing these sources of uncertainty will improve flood prediction. In this research, a fuzzy neural network is proposed to predict peak flow in an urban river. The network uses fuzzy numbers to account for the uncertainty in the output and model parameters. An algorithm that uses possibility theory is used to train the network. An adaptation of the automated neural pathway strength feature selection (ANPSFS) method is used to select the input features. A search and optimisation algorithm is used to select the network architecture. Data for the Bow River in Calgary, Canada are used to train and test the network.