Nature based solutions for contaminated land remediation and brownfield redevelopment in cities: A review.

Urban industrialization has caused severe land contamination at hundreds of thousands of sites in cities all around the world, posing a serious health risk to millions of people. Many contaminated brownfield sites are being left abandoned due to the high cost of remediation. Traditional physical and chemical remediation technologies also require high energy and resource input, and can result in loss of land functionality and cause secondary pollution. Nature-based solutions (NBS) including phytoremediation and conversion of brownfield sites to public greenspaces, holds much promise in maximizing a sustainable urban renaissance. NBS is an umbrella concept that can be used to capture nature based, cost effective and eco-friendly treatment technologies, as well as redevelopment strategies that are socially inclusive, economically viable, and with good public acceptance. The NBS concept is novel and in urgent need of new research to better understand the pros and cons, and to enhance its practicality. This review article summarizes NBS's main features, key technology choices, case studies, limitations, and future trends for urban contaminated land remediation and brownfield redevelopment.

Radar-raingauge data combination techniques: a revision and analysis of their suitability for urban hydrology.

The applicability of the operational radar and raingauge networks for urban hydrology is insufficient. Radar rainfall estimates provide a good description of the spatiotemporal variability of rainfall; however, their accuracy is in general insufficient. It is therefore necessary to adjust radar measurements using raingauge data, which provide accurate point rainfall information. Several gauge-based radar rainfall adjustment techniques have been developed and mainly applied at coarser spatial and temporal scales; however, their suitability for small-scale urban hydrology is seldom explored. In this paper a review of gauge-based adjustment techniques is first provided. After that, two techniques, respectively based upon the ideas of mean bias reduction and error variance minimisation, were selected and tested using as case study an urban catchment (∼8.65 km(2)) in North-East London. The radar rainfall estimates of four historical events (2010-2012) were adjusted using in situ raingauge estimates and the adjusted rainfall fields were applied to the hydraulic model of the study area. The results show that both techniques can effectively reduce mean bias; however, the technique based upon error variance minimisation can in general better reproduce the spatial and temporal variability of rainfall, which proved to have a significant impact on the subsequent hydraulic outputs. This suggests that error variance minimisation based methods may be more appropriate for urban-scale hydrological applications.

Impact of rainfall temporal resolution on urban water quality modelling performance and uncertainties.

A key control on the response of an urban drainage model is how well the observed rainfall records represent the real rainfall variability. Particularly in urban catchments with fast response flow regimes, the selection of temporal resolution in rainfall data collection is critical. Furthermore, the impact of the rainfall variability on the model response is amplified for water quality estimates, as uncertainty in rainfall intensity affects both the rainfall-runoff and pollutant wash-off sub-models, thus compounding uncertainties. A modelling study was designed to investigate the impact of altering rainfall temporal resolution on the magnitude and behaviour of uncertainties associated with the hydrological modelling compared with water quality modelling. The case study was an 85-ha combined sewer sub-catchment in Bogotá (Colombia). Water quality estimates showed greater sensitivity to the inter-event variability in rainfall hyetograph characteristics than to changes in the rainfall input temporal resolution. Overall, uncertainties from the water quality model were two- to five-fold those of the hydrological model. However, owing to the intrinsic scarcity of observations in urban water quality modelling, total model output uncertainties, especially from the water quality model, were too large to make recommendations for particular model structures or parameter values with respect to rainfall temporal resolution.

Monitoring and modelling to support wastewater system management in developing mega-cities.

Urban drainage system models can be useful to assess and manage system performance and to plan its development. However, due to data and computational costs, sophisticated, high-resolution contemporary models of the sewer system may not be applicable. This constraint is particularly marked in developing country mega-cities where catchments can be large, data tend to be scarce, and there are many unknowns, for example regarding sources, losses and wrong connections. This paper presents work undertaken over the last 7 years to develop a suitable monitoring and modelling framework to support operation and development of the wastewater system of Bogotá (Colombia). Components of the framework covered here are: (a) the flow and water quality database, (b) a wastewater pollution load generator, and (c) a semi-distributed sewer network model, which aims at a complexity that matches the information available from the previous two components. Results from a catchment within Bogotá, area 150 km(2) and with 2.5 million inhabitants, show that the model outputs capture the scale and dynamics of the observed concentrations and loads at various points on the sewer system. However uncertainty is high because much of variability of observed dry weather flow profiles is apparently random. Against this variability, the effects of in-sewer processes were not identifiable except where backwaters caused particularly high retention times. Hence the work has resulted in an operational model with a scientifically justified, yet useful, level of complexity for Bogotá. More generally, the work demonstrates the value of monitoring and modelling programmes, including having modellers actively involved in monitoring specification and operations; and the insights into suitable level of model complexity that may be gained by uncertainty and sensitivity analysis.

A database and model to support proactive management of sediment-related sewer blockages.

Due to increasing customer and political pressures, and more stringent environmental regulations, sediment and other blockage issues are now a high priority when assessing sewer system operational performance. Blockages caused by sediment deposits reduce sewer system reliability and demand remedial action at considerable operational cost. Consequently, procedures are required for identifying which parts of the sewer system are in most need of proactive removal of sediments. This paper presents an exceptionally long (7.5 years) and spatially detailed (9658 grid squares--0.03 km² each--covering a population of nearly 7.5 million) data set obtained from a customer complaints database in Bogotá (Colombia). The sediment-related blockage data are modelled using homogeneous and non-homogeneous Poisson process models. In most of the analysed areas the inter-arrival time between blockages can be represented by the homogeneous process, but there are a considerable number of areas (up to 34%) for which there is strong evidence of non-stationarity. In most of these cases, the mean blockage rate increases over time, signifying a continual deterioration of the system despite repairs, this being particularly marked for pipe and gully pot related blockages. The physical properties of the system (mean pipe slope, diameter and pipe length) have a clear but weak influence on observed blockage rates. The Bogotá case study illustrates the potential value of customer complaints databases and formal analysis frameworks for proactive sewerage maintenance scheduling in large cities.

Design of on-line river water quality monitoring systems using the entropy theory: a case study.

The design of a water quality monitoring network is considered as the main component of water quality management including selection of the water quality variables, location of sampling stations and determination of sampling frequencies. In this study, an entropy-based approach is presented for design of an on-line water quality monitoring network for the Karoon River, which is the largest and the most important river in Iran. In the proposed algorithm of design, the number and location of sampling sites and sampling frequencies are determined by minimizing the redundant information, which is quantified using the entropy theory. A water quality simulation model is also used to generate the time series of the concentration of water quality variables at some potential sites along the river. As several water quality variables are usually considered in the design of water quality monitoring networks, the pair-wise comparison is used to combine the spatial and temporal frequencies calculated for each water quality variable. After selecting the sampling frequencies, different components of a comprehensive monitoring system such as data acquisition, transmission and processing are designed for the study area, and technical characteristics of the on-line and off-line monitoring equipment are presented. Finally, the assessment for the human resources needs, as well as training and quality assurance programs are presented considering the existing resources in the study area. The results show that the proposed approach can be effectively used for the optimal design of the river monitoring systems.