No abstract

No abstract

Concerns about drinking water (DW) quality contamination during water distribution raise a need for real-time monitoring and rapid contamination detection. Early warning systems (EWS) are a potential solution. The EWS consist of multiple conventional sensors that provide the real-time measurements and algorithms that allow the recognizing of contamination events from normal operating conditions. In most cases, these algorithms have been established with artificial data, while data from real and biological contamination events are limited. The goal of the study was the event detection performance of the Mahalanobis distance method in combination with on-line DW quality monitoring sensors and manual measurements of grab samples for potential DW biological contamination scenarios. In this study three contamination scenarios were simulated in a pilot-scale DW distribution system: untreated river water, groundwater and wastewater intrusion, which represent realistic contamination scenarios and imply biological contamination. Temperature, electrical conductivity (EC), total organic carbon (TOC), chlorine ion (Cl-), oxidation–reduction potential (ORP), pH sensors and turbidity measurements were used as on-line sensors and for manual measurements. Novel adenosine-triphosphate and flow cytometric measurements were used for biological water quality evaluation. The results showed contamination detection probability from 56% to 89%, where the best performance was obtained with manual measurements. The probability of false alarm was 5–6% both for on-line and manual measurements. The Mahalanobis distance method with DW quality sensors has a good potential to be applied in EWS. However, the sustainability of the on-line measurement system and/or the detection algorithm should be improved.

The objective of the research presented in this Research Communication was to access the environmental impact of the Latvian dairy industries. Site visits and interviews at Latvian dairy processing companies were done in order to collect site-specific data. This includes the turnover of the dairy industries, production, quality of water in various industrial processes, the flow and capacity of the sewage including their characteristic, existing practices and measures for wastewater management. The results showed that dairy industries in Latvia generated in total approximately 2263 × 103 m3 wastewater in the year 2019. The Latvian dairy effluents were characterized with high chemical oxygen demand (COD), biological oxygen demand (BOD) and total solids (TS). Few dairy plants had pre-treatment facilities for removal of contaminants, and many lacked onsite treatment technologies. Most facilities discharged dairy wastewater to municipal wastewater treatment plants. The current study gives insight into the Latvian dairy industries, their effluent management and pollution at Gulf of Riga due to wastewater discharge.

Modelling of contamination spread and location of contamination source in a water distribution network is an important task. The paper considers applicability of real-time flow direction data based model for contaminant transport for a distribution network of a city. Simulations of several contamination scenarios are made to evaluate necessary number of flow direction sensors. It is found that for a model, containing major pipes of Riga distribution system, sensor number decrease from 927 to 207 results in average 20% increase of simulated contaminated length of pipes. Simulation data suggest that optimal number of sensors for Riga model is around 200