Integration of target analyses, non-target screening and effect-based monitoring to assess OMP related water quality changes in drinking water treatment.

The ever-increasing production and use of chemicals lead to the occurrence of organic micro-pollutants (OMPs) in drinking water sources, and consequently the need for their removal during drinking water treatment. Due to the sheer number of OMPs, monitoring using targeted chemical analyses alone is not sufficient to assess drinking water quality as well as changes thereof during treatment. High-resolution mass spectrometry (HRMS) based non-target screening (NTS) as well as effect-based monitoring using bioassays are promising monitoring tools for a more complete assessment of water quality and treatment performance. Here, we developed a strategy that integrates data from chemical target analyses, NTS and bioassays. We applied it to the assessment of OMP related water quality changes at three drinking water treatment pilot installations. These installations included advanced oxidation processes, ultrafiltration in combination with reverse osmosis, and granular activated carbon filtration. OMPs relevant for the drinking water sector were spiked into the water treated in these installations. Target analyses, NTS and bioassays were performed on samples from all three installations. The NTS data was screened for predicted and known transformation products of the spike-in compounds. In parallel, trend profiles of NTS features were evaluated using multivariate analysis methods. Through integration of the chemical data with the biological effect-based results potential toxicity was accounted for during prioritization. Together, the synergy of the three analytical methods allowed the monitoring of OMPs and transformation products, as well as the integrative biological effects of the mixture of chemicals. Through efficient analysis, visualization and interpretation of complex data, the developed strategy enabled to assess water quality and the impact of water treatment from multiple perspectives. Such information could not be obtained by any of the three methods alone. The developed strategy thereby provides drinking water companies with an integrative tool for comprehensive water quality assessment.

A mathematical model for removal of human pathogenic viruses and bacteria by slow sand filtration under variable operational conditions.

Slow sand filtration (SSF) in drinking water production removes pathogenic microorganisms, but detection limits and variable operational conditions complicate assessment of removal efficiency. Therefore, a model was developed to predict removal of human pathogenic viruses and bacteria as a function of the operational conditions. Pilot plant experiments were conducted, in which bacteriophage MS2 and Escherichia coli WR1 were seeded as model microorganisms for pathogenic viruses and bacteria onto the filters under various temperatures, flow rates, grain sizes and ages of the Schmutzdecke. Removal of MS2 was 0.082-3.3 log10 and that of E. coli WR1 0.94-4.5 log10 by attachment to the sand grains and additionally by processes in the Schmutzdecke. The contribution of the Schmutzdecke to the removal of MS2 and E. coli WR1 increased with its ageing, with sticking efficiency and temperature, decreased with grain size, and was modelled as a logistic growth function with scale factor f0 and rate coefficient f1. Sticking efficiencies were found to be microorganism and filter specific, but the values of f0 and f1 were independent of microorganism and filter. Cross-validation showed that the model can be used to predict log removal of MS2 and ECWR1 within ±0.6 log. Within the range of operational conditions, the model shows that removal of microorganisms is most sensitive to changes in temperature and age of the Schmutzdecke.