Rapid changes in microbial water quality in surface waters pose challenges for production of safe drinking water. If not treated to an acceptable level, microbial pathogens present in the drinking water can result in severe consequences for public health. The aim of this paper was to evaluate the suitability of data-driven models of different complexity for predicting the concentrations of E. coli in the river Göta älv at the water intake of the drinking water treatment plant in Gothenburg, Sweden. The objectives were to (i) assess how the complexity of the model affects the model performance; and (ii) identify relevant factors and assess their effect as predictors of E. coli levels. To forecast E. coli levels one day ahead, the data on laboratory measurements of E. coli and total coliforms, Colifast measurements of E. coli, water temperature, turbidity, precipitation, and water flow were used. The baseline approaches included Exponential Smoothing and ARIMA (Autoregressive Integrated Moving Average), which are commonly used univariate methods, and a naive baseline that used the previous observed value as its next prediction. Also, models common in the machine learning domain were included: LASSO (Least Absolute Shrinkage and Selection Operator) Regression and Random Forest, and a tool for optimising machine learning pipelines - TPOT (Tree-based Pipeline Optimization Tool). Also, a multivariate autoregressive model VAR (Vector Autoregression) was included. The models that included multiple predictors performed better than univariate models. Random Forest and TPOT resulted in higher performance but showed a tendency of overfitting. Water temperature, microbial concentrations upstream and at the water intake, and precipitation upstream were shown to be important predictors. Data-driven modelling enables water producers to interpret the measurements in the context of what concentrations can be expected based on the recent historic data, and thus identify unexplained deviations warranting further investigation of their origin.

Microplastics (MP) are transported from land-based sources from rivers to marine waters. However, there is currently little knowledge about MP fate from land sources to marine waters. Traffic is estimated to be one of the largest sources of MP; hence, stormwater is expected to be an important transportation route of MP to marine waters. The aim of this study was to investigate the effect of the size and density of tyre wear particles in road run-off on their fate in the Göta River in Sweden using hydrodynamic modelling. The model of the stretch of Göta River, Sweden’s largest river, passing through Gothenburg (Sweden’s second largest city) and out to the sea, was set up using MIKE 3 FM software. Literature data were used to define the MP characteristics: concentrations in stormwater, prevalent particle sizes, density of MP commonly occurring in road run-off and settling velocities. Results show that higher concentrations of MP are found on the south side of the river, compared with the north side, due to higher annual average daily traffic loads along the south side of the river. The mixing processes in the river and the MP concentrations were generally influenced by the vertical water density gradient caused by saline water from the Kattegat strait. While most MP with higher density and larger size settle in the river, smaller MP with density close to 1.0 g/cm³ do not settle in the river and therefore reach the Kattegat strait and the marine environments. Further research is needed to describe the fate and transport of microplastics in the stormwater system, including treatment facilities, i.e. biofouling, aggregation, degradation and/or further fragmentation and settling.