The main aim of this study was a survey of micropollutants in stormwater runoff of Berlin (Germany) and its dependence on land-use types. In a one-year monitoring program, event mean concentrations were measured for a set of 106 parameters, including 85 organic micropollutants (e.g., flame retardants, phthalates, pesticides/biocides, polycyclic aromatic hydrocarbons (PAH)), heavy metals and standard parameters. Monitoring points were selected in five catchments of different urban land-use types, and at one urban river. We detected 77 of the 106 parameters at least once in stormwater runoff of the investigated catchment types. On average, stormwater runoff contained a mix of 24 µg L−1 organic micropollutants and 1.3 mg L−1 heavy metals. For organic micropollutants, concentrations were highest in all catchments for the plasticizer diisodecyl phthalate. Concentrations of all but five parameters showed significant differences among the five land-use types. While major roads were the dominant source of traffic-related substances such as PAH, each of the other land-use types showed the highest concentrations for some substances (e.g., flame retardants in commercial area, pesticides in catchment dominated by one family homes). Comparison with environmental quality standards (EQS) for surface waters shows that 13 micropollutants in stormwater runoff and 8 micropollutants in the receiving river exceeded German quality standards for receiving surface waters during storm events, highlighting the relevance of stormwater inputs for urban surface waters.
Deterioration models can be successfully deployed only if decision-makers trust the modelling outcomes and are aware of model uncertainties. Our study aims to address this issue by developing a set of clearly understandable metrics to assess the performance of sewer deterioration models from an end-user perspective. The developed metrics are used to benchmark the performance of a statistical model, namely, GompitZ based on survival analysis and Markov-chains, and a machine learning model, namely, Random Forest, an ensemble learning method based on decision trees. The models have been trained with the extensive CCTV dataset of the sewer network of Berlin, Germany (115,258 inspections). At network level, both models give satisfactory outcomes with deviations between predicted and inspected condition distributions below 5%. At pipe level, the statistical model does not perform better than a simple random model, which attributes randomly a condition class to each inspected pipe, whereas the machine learning model provides satisfying performance. 66.7% of the pipes inspected in bad condition have been predicted correctly. The machine learning approach shows a strong potential for supporting operators in the identification of pipes in critical condition for inspection programs whereas the statistical approach is more adapted to support strategic rehabilitation planning.
The effect of combined sewer overflow (CSO) control measures should be validated during operation based on monitoring of CSO activity and subsequent comparison with (legal) requirements. However, most CSO monitoring programs have been started only recently and therefore no long-term data is available for reliable efficiency control. A method is proposed that focuses on rainfall data for evaluating the effectiveness of CSO control measures. It is applicable if a sufficient time-series of rainfall data and a limited set of data on CSO discharges are available. The method is demonstrated for four catchments of the Berlin combined sewer system. The analysis of the 2000-2007 data shows the effect of CSO control measures, such as activation of in-pipe storage capacities within the Berlin system. The catchment, where measures are fully implemented shows less than 40% of the CSO activity of those catchments, where measures have not yet or not yet completely been realised.
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