Health risks due to intrusion into the drinking water distribution network: hydraulic modelling and quantitative microbial risk assessment

Ageing water infrastructure is prone to increased physical deficiencies. These form pathways for pathogen intrusion into drinking water distribution networks (WDNs), hence posing major health risks to consumers. This study aimed at estimating the risk of infection from pipe breaks and intermittent water supply, which are some of the major causes of sustained low pressure within the WDN and hence the triggers for pathogen intrusion. Further, the effect of groundwater level on pathogen intrusion was investigated. Three risk scenarios were evaluated on the example of a real WDN in Sweden: (i) pipe break with no intrusion from leak holes, (ii) pipe break with intrusion due to leak holes, and (iii) insufficient water supply in the presence of leak holes. Pressure distribution from hydraulic modelling, estimated groundwater levels, and pathogen concentration in intruding water (from field study) were used to estimate the intrusion and the number of pathogens entering the WDN. Reference pathogens Campylobacter, Cryptosporidium, and norovirus were used in quantitative microbial risk assessment (QMRA) for assessing the health risks. Results indicated that the daily probability of infection exceeded an acceptable target value of 10−6 for most of the WDN and for all scenarios. The findings were consistent with the estimated annual burden of acute gastrointestinal illness in Sweden. The concentration of pathogens in intruding water and the duration of the low-pressure-causing event were observed to influence the probability of infection the most. The results from this study can be used to identify vulnerable sections in the WDN, which can be targeted for additional investment in monitoring and/or renewal

Arbitrary Steady-State Solutions with the K-epsilon Model

Widely-used forms of the K-epsilon turbulence model are shown to yield arbitrary steady-state converged solutions that are highly dependent on numerical considerations such as initial conditions and solution procedure. These solutions contain pseudo-laminar regions of varying size. By applying a nullcline analysis to the equation set, it is possible to clearly demonstrate the reasons for the anomalous behavior. In summary, the degenerate solution acts as a stable fixed point under certain conditions, causing the numerical method to converge there. The analysis also suggests a methodology for preventing the anomalous behavior in steady-state computations

Hydrodynamic modelling of the microbial water quality in a drinking water source as input for risk reduction management

To mitigate the faecal contamination of drinking water sources and, consequently, to prevent waterborne disease outbreaks, an estimation of the contribution from different sources to the total faecal contamination at the raw water intake of a drinking water treatment plant is needed. The aim of this article was to estimate how much different sources contributed to the faecal contamination at the water intake in a drinking water source, Lake Rådasjön in Sweden. For this purpose, the fate and transport of faecal indicator Escherichia coli within Lake Rådasjön were simulated by a three-dimensional hydrodynamic model. The calibrated hydrodynamic model described the measured data on vertical temperature distribution in the lake well (the Pearson correlation coefficient was 0.99). The data on the E. coli load from the identified contamination sources were gathered and the fate and transport of E. coli released from these sources within the lake were simulated using the developed hydrodynamic model, taking the decay of the E. coli into account. The obtained modelling results were compared to the observed E. coli concentrations at the water intake. The results illustrated that the sources that contributed the most to the faecal contamination at the water intake in Lake Rådasjön were the discharges from the on-site sewers and the main inflow to the lake – the river Mölndalsån. Based on the modelling results recommendations for water producers were formulated. The study demonstrated that this modelling approach is a useful tool for estimating the contribution from different sources to the faecal contamination at the water intake of a drinking water treatment plant and provided decision-support information for the reduction of risks posed to the drinking water source

The information system for LHC parameters and layouts

The construction of the Large Hadron Collider, LHC, at CERN implies both the handling of a huge amount of information and the control of the coherence of this information. The LHC machine parameters have to be maintained coherent as the design evolves from the conceptual stage to the actual, installed, machine and have to be made available to all concerned. Design data is provided in many different formats from the machine builders, drawings, technical documents, meeting notes, lattice simulation input files, etc. The World Wide Web is being used to make the information accessible both at CERN and at the external collaborating laboratories. In this paper we describe the implementation of an Oracle database as the central common repository for machine parameters and of information for the automatic generation of CAD layout drawings and WWW pages. This system is integrated in a larger context, the EDMS system for the LHC project, which encompasses both the accelerator and the experiments

Predicting grain protein concentration in winter wheat (Triticum aestivum L.) based on unpiloted aerial vehicle multispectral optical remote sensing

Prediction models for crude protein concentration (CP) in winter wheat (Triticum aestivum L.) based on multispectral reflectance data from field trials in 2019 and 2020 in southern Sweden were developed and evaluated for independent trial sites. Reflectance data were collected using an unpiloted aerial vehicle (UAV)-borne camera with nine spectral bands having similar specification to nine bands of Sentinel-2 satellite data. Models were tested for application on near-real time Sentinel-2 imagery, on the prospect that CP prediction models can be made available in satellite-based decision support systems (DSS) for precision agriculture. Two different prediction methods were tested: linear regression and multivariate adaptive regression splines (MARS). Linear regression based on the best-performing vegetation index (the chlorophyll index) was found to be approximately as accurate as the best performing MARS model with multiple predictor variables in leave-one-trial-out cross-validation (R-2 = 0.71, R-2 = 0.70 and mean absolute error 0.64%, 0.60% CP respectively). Models applied on satellite data explained to a small degree between-field variations in CP (R-2 = 0.36), however did not reproduce within-field variation accurately. The results of the different methods presented here show the differences between methods used and their potential for application in a DSS