Sustainable microbial water quality monitoring programme design using phage-lysis and multivariate techniques

Contamination of surface waters is a pervasive threat to human health, hence, the need to better understand the sources and spatio-temporal variations of contaminants within river catchments. River catchment managers are required to sustainably monitor and manage the quality of surface waters. Catchment managers therefore need cost-effective low-cost long-term sustainable water quality monitoring and management designs to proactively protect public health and aquatic ecosystems. Multivariate and phage-lysis techniques were used to investigate spatio-temporal variations of water quality, main polluting chemophysical and microbial parameters, faecal micro-organisms sources, and to establish ‘sentry’ sampling sites in the Ouse River catchment, southeast England, UK. 350 river water samples were analysed for fourteen chemophysical and microbial water quality parameters in conjunction with the novel human-specific phages of Bacteroides GB-124 (Bacteroides GB-124). Annual, autumn, spring, summer, and winter principal components (PCs) explained approximately 54%, 75%, 62%, 48%, and 60%, respectively, of the total variance present in the datasets. Significant loadings of Escherichia coli, intestinal enterococci, turbidity, and human-specific Bacteroides GB-124 were observed in all datasets. Cluster analysis successfully grouped sampling sites into five clusters. Importantly, multivariate and phage-lysis techniques were useful in determining the sources and spatial extent of water contamination in the catchment. Though human faecal contamination was significant during dry periods, the main source of contamination was non-human. Bacteroides GB-124 could potentially be used for catchment routine microbial water quality monitoring. For a cost-effective low-cost long-term sustainable water quality monitoring design, E. coli or intestinal enterococci, turbidity, and Bacteroides GB-124 should be monitored all-year round in this river catchment.     

Comparison of the microbiological and chemical characterization of harvested rainwater and reservoir water as alternative water resources

Rainwater harvesting (RWH) offers considerable potential as an alternative water supply. In this study, all of the harvested rainwater samples met the requirements for grey water but not for drinking water. In terms of microbiological parameters, total coliform (TC) and Escherichia coli (EC) were measured in 91.6% and 72%, respectively, of harvested rainwater samples at levels exceeding the guidelines for drinking water, consistent with rainfall events. In the case of the reservoir water samples, TC and EC were detected in 94.4% and 85.2%, respectively, of the samples at levels exceeding the guidelines for drinking water. Both indicators gradually increased in summer and fall. The highest median values of both TC and EC were detected during the fall. Chemical parameters such as common anions and major cations as well as metal ions in harvested rainwater were within the acceptable ranges for drinking water. By contrast, Al shows a notable increase to over 200 μg L^− 1 in the spring due to the intense periodic dust storms that can pass over the Gobi Desert in northern China. In terms of statistical analysis, the harvested rainwater quality showed that TC and EC exhibit high positive correlations with NO₃⁻ (ρ_TC = 0.786 and ρ_EC = 0.42) and PO₄⁻ (ρ_TC = 0.646 and ρ_EC = 0.653), which originally derive from catchment contamination, but strong negative correlations with Cl⁻ (ρ_TC = − 0.688 and ρ_EC = − 0.484) and Na⁺ (ρ_TC = − 0.469 and ρ_EC = − 0.418), which originate from seawater.     

Treated water quality assurance and description of distribution networks by multivariate chemometrics

Throughout the year 2007, 89 treated water samples from three water treatment plants (WTPs) of the Athens Water Supply and Sewerage Company (EYDAP S.A.) and 180 samples from network tanks (NWTs) were analyzed for electrical conductivity (EC), alkalinity (TA), pH, aluminium (Al), total hardness (TH), chloride (Cl⁻), residual chlorine (free Cl), calcium (Ca²⁺) and magnesium (Mg²⁺). The results regarding the WTPs were subjected to a principal component analysis (PCA) with 75% of the total variance being explained. A stepwise linear discriminant analysis (LDA) model constructed from the 89 treated water samples was used to predict class membership of the samples from the NWTs with a view to estimating the propagation of a possible water quality deterioration originating from the WTPs. The model utilized Cl⁻, Al and EC and yielded a 96% correct classification of the training dataset, whereas the cross-validation yielded a 94% correct classification. Network tank samples were 95% correctly classified with regard to their theoretically expected origin. The stepwise discriminant analysis based on separate covariance matrices of the canonical discriminant functions yielded a 98% correct classification of both the training dataset and the network tank samples. The classification and regression tree (C&RT) algorithm showed that the main parameters used in the discrimination of the WTP samples were EC and Al. The post-hoc classification of the training dataset was 99%, whereas 88% of NWT samples were correctly classified.     

Performance assessment of air quality monitoring networks using principal component analysis and cluster analysis

This study aims to evaluate the performance of two statistical methods, principal component analysis and cluster analysis, for the management of air quality monitoring network of Hong Kong and the reduction of associated expenses. The specific objectives include: (i) to identify city areas with similar air pollution behavior; and (ii) to locate emission sources. The statistical methods were applied to the mass concentrations of sulphur dioxide (SO₂), respirable suspended particulates (RSP) and nitrogen dioxide (NO₂), collected in monitoring network of Hong Kong from January 2001 to December 2007.     

Evaluation of river water quality monitoring stations by principal component analysis

The development of a surface water monitoring network is a critical element in the assessment, restoration, and protection of stream water quality. This study applied principal component analysis (PCA) and principal factor analysis (PFA) techniques to evaluate the effectiveness of the surface water quality-monitoring network in a river where the evaluated variables are monitoring stations. The objective was to identify monitoring stations that are important in assessing annual variations of river water quality. Twenty-two stations used for monitoring physical, chemical, and biological parameters, located at the main stem of the lower St. Johns River in Florida, USA, were selected for the purpose of this study. Results show that 3 monitoring stations were identified as less important in explaining the annual variance of the data set, and therefore could be the non-principal stations. In addition, the PFA technique was also employed to identify important water quality parameters. Results reveal that total organic carbon, dissolved organic carbon, total nitrogen, dissolved nitrate and nitrite, orthophosphate, alkalinity, salinity, Mg, and Ca were the parameters that are most important in assessing variations of water quality in the river. This study suggests that PCA and PFA techniques are useful tools for identification of important surface water quality monitoring stations and parameters.     

Assessment of seasonal variations in surface water quality

Assessment of seasonal changes in surface water quality is an important aspect for evaluating temporal variations of river pollution due to natural or anthropogenic inputs of point and non-point sources. In this study, surface water quality data for 16 physical and chemical parameters collected from 22 monitoring stations in a river during the years from 1998 to 2001 were analyzed. The principal component analysis technique was employed to evaluate the seasonal correlations of water quality parameters, while the principal factor analysis technique was used to extract the parameters that are most important in assessing seasonal variations of river water quality. Analysis shows that a parameter that is most important in contributing to water quality variation for one season may not be important for another season except for DOC and electrical conductance, which were always the most important parameters in contributing to water quality variations for all four seasons.     

Multivariate statistical techniques for the evaluation of spatial and temporal variations in water quality of Gomti River (India)--a case study

This case study reports different multivariate statistical techniques applied for evaluation of temporal/spatial variations and interpretation of a large complex water-quality data set obtained during monitoring of Gomti River in Northern part of India. Water quality of the Gomti River, a major tributary of the Ganga River was monitored at eight different sites selected in relatively low, moderate and high pollution regions, regularly over a period of 5 years (1994-1998) for 24 parameters. The complex data matrix (17,790 observations) was treated with different multivariate techniques such as cluster analysis, factor analysis/principal component analysis (FA/PCA) and discriminant analysis (DA). Cluster analysis (CA) showed good results rendering three different groups of similarity between the sampling sites reflecting the different water-quality parameters of the river system. FA/PCA identified six factors, which are responsible for the data structure explaining 71% of the total variance of the data set and allowed to group the selected parameters according to common features as well as to evaluate the incidence of each group on the overall variation in water quality. However, significant data reduction was not achieved, as it needed 14 parameters to explain 71% of both the temporal and spatial changes in water quality. Discriminant analysis showed the best results for data reduction and pattern recognition during both temporal and spatial analysis. Discriminant analysis showed five parameters (pH, temperature, conductivity, total alkalinity and magnesium) affording more than 88% right assignations in temporal analysis, while nine parameters (pH, temperature, alkalinity, Ca-hardness, DO, BOD, chloride, sulfate and TKN) to afford 91% right assignations in spatial analysis of three different regions in the basin. Thus, DA allowed reduction in dimensionality of the large data set, delineating a few indicator parameters responsible for large variations in water quality. This study presents necessity and usefulness of multivariate statistical techniques for evaluation and interpretation of large complex data sets with a view to get better information about the water quality and design of monitoring network for effective management of water resources.     

Hydrochemical characteristics and water quality assessment of surface water and groundwater in Songnen plain, Northeast China

Water quality is the critical factor that influence on human health and quantity and quality of grain production in semi-humid and semi-arid area. Songnen plain is one of the grain bases in China, as well as one of the three major distribution regions of soda saline-alkali soil in the world. To assess the water quality, surface water and groundwater were sampled and analyzed by fuzzy membership analysis and multivariate statistics. The surface water were gather into class I, IV and V, while groundwater were grouped as class I, II, III and V by fuzzy membership analysis. The water samples were grouped into four categories according to irrigation water quality assessment diagrams of USDA. Most water samples distributed in category C1-S1, C2-S2 and C3-S3. Three groups were generated from hierarchical cluster analysis. Four principal components were extracted from principal component analysis. The indicators to water quality assessment were Na, HCO₃, NO₃, Fe, Mn and EC from principal component analysis. We conclude that surface water and shallow groundwater are suitable for irrigation, the reservoir and deep groundwater in upstream are the resources for drinking. The water for drinking should remove of the naturally occurring ions of Fe and Mn. The control of sodium and salinity hazard is required for irrigation. The integrated management of surface water and groundwater for drinking and irrigation is to solve the water issues.     

再生水水质综合评价的主成分分析方法

采用主成分分析方法评价再生水综合水质,利用R软件强大的统计功能,对某再生水厂供水实测数据进行了定量化评价。结果表明,主成分分析方法是一种切实可行的水质综合评价方法,值得进一步研究,并能为其他相关行业的质量评价提供参考。     

Rooftop runoff as a source of contamination: A review

Scientific reports concerning chemical and microbiological contaminant levels of rainwater runoff from rooftop collection in both urban and rural areas are reviewed. This alternative source of water has been documented to often contain substantial amounts of contaminants. Studies describing levels of heavy metal contamination specific to runoff from rooftop catchment areas containing exposed metal surfaces are discussed. Depending upon the intended use, scientific evidence is also accumulating that various treatments and disinfections will be required prior to release of roof-runoff water either into surface waters or for more direct consumer usage. For microbial contamination, current proposed standards and guidelines regarding this type of water source are shown to vary widely worldwide. Scientific literature reveals a lack of clarity regarding water quality guidelines and health related standards for certain types of rooftop runoff. Studies suggests that rainwater collection systems which are properly designed, maintained, and treated may provide a valuable supplement to existing water supplies by reducing demand on community water supplies/infrastructure costs, enhancing effective management of storm water runoff, and increasing restoration of underground reservoirs through controlled infiltration.     

Pesticides in rain and roof runoff waters from an urban region

The levels of organonitrogen, organochlorine and organophosphorus pesticides were monitored in rain and roof runoff waters in Gdańsk (Poland) over a period of 6 months (October 2000 – March 2001). Buildings included in the study were of different ages, and covered with a variety of roofing materials. Samples were collected during the precipitation events. Pesticides were determined by gas chromatography combined with NPD and ECD detection. Bromofos, heptachlor epoxide and o,p′-DDE were detected the most often in the samples collected. In general, pesticide concentrations were higher in roof runoff than in rainwater. The type of the roofing material had a significant effect on the pesticide levels in runoff waters. The highest analyte levels were recorded in October and November, when the temperature was the highest.     

Long-term water quality monitoring of the Sejnane reservoir in northeast Tunisia

The Sejnane reservoir in northeast Tunisia provides drinking and irrigation water. Long-term water quality monitoring data including precipitation, evaporation, temperature, pH, conductivity, dissolved oxygen, turbidity, total suspended solids, major anions and cations, fluoride, BOD₅, NO₃ ⁻, NO₂ ⁻, NH₄ ⁺, P _tot, fecal coliform bacteria, boron and heavy metals (Fe, Zn, Cu, Ni, Pb, Cr and Cd) are reported. The appropriateness for irrigation was estimated by the SAR and Na percentage and the water quality assessed using the Canadian Water Quality Index as good to excellent, which confirmed its suitability for drinking, aquatic life and irrigation purposes.      

Chemometric approach to validating faecal sterols as source tracer for faecal contamination in water

Faecal sterols detection is a promising method for identifying sources of faecal pollution. In this study, faecal contamination in water samples from point source (sewage treatment plants, chicken farms, quail farms and horse stables) was extracted using the solid phase extraction (SPE) technique. Faecal sterols (coprostanol, cholesterol, stigmasterol, β-sitosterol and stigmastanol) were selected as parameters to differentiate the source of faecal pollution. The results indicated that coprostanol, cholesterol and β-sitosterol were the most significant parameters that can be used as source tracers for faecal contamination. Chemometric techniques, such as cluster analysis, principal component analysis and discriminant analysis were applied to the data set on faecal contamination in water from various pollution sources in order to validate the faecal sterols’ profiles. Cluster analysis generated three clusters: coprostanol was in cluster 1, cholesterol and β-sitosterol formed cluster 2, while cluster 3 contained stigmasterol and stigmastanol. Discriminant analysis suggested that coprostanol, cholesterol and β-sitosterol were the most significant parameters to discriminate between the faecal pollution source. The use of chemometric techniques provides useful and promising indicators in tracing the source of faecal contamination.     

Physicochemical and microbiological quality of water from a pilot domestic rainwater harvesting facility in Ireland

Dublin Institute of Technology personnel were commissioned in 2005 by the Department of Environment, Heritage and Local Government in Ireland to assess the feasibility of utilizing harvested rainwater to replace treated mains water, for nonpotable uses. A pilot project was set up. The project involved the design, installation, commissioning and monitoring of rainwater harvesting facilities in a rural housing development. A monitoring programme was carried out to examine the physicochemical and microbiological quality of the harvested rainwater. Harvested rainwater was sampled monthly and tested. Analysis of the harvested rainwater quality showed a consistently high quality of raw water in general compliance with the requirements of the European Communities (Quality of Bathing Water) Regulations for 100% of samples and the European Communities (Drinking Water) Regulations, 2007 for 37% of sampling dates.     

The effect of roofing material on the quality of harvested rainwater

Due to decreases in the availability and quality of traditional water resources, harvested rainwater is increasingly used for potable and non-potable purposes. In this study, we examined the effect of conventional roofing materials (i.e., asphalt fiberglass shingle, Galvalume^® metal, and concrete tile) and alternative roofing materials (i.e., cool and green) on the quality of harvested rainwater. Results from pilot-scale and full-scale roofs demonstrated that rainwater harvested from any of these roofing materials would require treatment if the consumer wanted to meet United States Environmental Protection Agency primary and secondary drinking water standards or non-potable water reuse guidelines; at a minimum, first-flush diversion, filtration, and disinfection are recommended. Metal roofs are commonly recommended for rainwater harvesting applications, and this study showed that rainwater harvested from metal roofs tends to have lower concentrations of fecal indicator bacteria as compared to other roofing materials. However, concrete tile and cool roofs produced harvested rainwater quality similar to that from the metal roofs, indicating that these roofing materials also are suitable for rainwater harvesting applications. Although the shingle and green roofs produced water quality comparable in many respects to that from the other roofing materials, their dissolved organic carbon concentrations were very high (approximately one order of magnitude higher than what is typical for a finished drinking water in the United States), which might lead to high concentrations of disinfection byproducts after chlorination. Furthermore the concentrations of some metals (e.g., arsenic) in rainwater harvested from the green roof suggest that the quality of commercial growing media should be carefully examined if the harvested rainwater is being considered for domestic use. Hence, roofing material is an important consideration when designing a rainwater catchment.     

Influence of water quality on the presence of off-flavour compounds (geosmin and 2-methylisoborneol)

Geosmin and 2-methylisoborneol are off-flavour compounds related to poor organoleptics and a decrease in consumer satisfaction with drinking water. The relationship between these off-flavour compounds and 39 parameters of water quality (chemical, physical and biological) for three different surface waters from lotic systems that supply potable water to Quebec City and Lévis (Canada) was studied using principal component analysis. The objective of this study was to show that a multidimensional approach with principal component analysis using the component matrix serves to differentiate the processes involved in the appearance of the olfactory compounds from those not involved or little involved and to extract the most representative data of these processes. Our study shows that the presence of geosmin can be explained, in the case of the water studied, by a process associated primarily with the eutrophication of water and, to a lesser degree, by an allochthonous origin. However, the presence of 2-methylisoborneol may be linked to two processes with different origins, but of equal importance: a winter origin – most likely allochthonous – involving anthropic contributions and an autochthonous bacterial origin.     

Assessment of the water quality and identification of pollution sources of Kaduna River in Niger State (Nigeria) using exploratory data analysis

Exploratory data analysis such as hierarchical cluster analysis and principal component analysis were applied to water quality dataset of the Kaduna River, obtained during 3 years (2008–2010), monthly monitoring of eight key different sampling sites for 19 parameters to extract correlations and similarities between variables and to classify river sampling sites in groups of similar quality. Hierarchical cluster analysis grouped eight sampling sites into three statistically significant clusters of similar water composition. Six varifactors were obtained after varimax rotation of initial principal components using principal component analysis. These techniques gave an insight into the sources of pollution. Anthropogenic influence (municipal, industrial wastewater and agricultural run‐off) was the major source of river water pollution.     

Evaluation of the salt accumulation process during inundation in water resource of Contas river basin (Bahia-Brazil) applying principal component analysis

The high salinization in some reservoirs of the Contas river basin (Bahia-Brazil) has been erroneously attributed only to concentration by evaporation. However, recent studies of this basin have shown that in period of intense rainfalls, occur an increase of the saline concentration in the flowing rivers of the reservoirs. The application of statistical methods (cluster and principal components analysis) have shown that this fact can be attributed to the discharge of saline waters from the small reservoirs of every drained area, provoked by inundation, is also an important factor in the salinization process. Thus the study of the geochemical variables: Na(+), K(+), Ca(2+), Mg(2+), Cl(-), SO(4)(2-) and CO(3)(2-), showed one group formed by Na(+) and Cl(-), attributed to the discharge of saline water provoked by inundation from a small reservoir, and a second group constituted by Ca(2+), Mg(2+), K(+) and SO(4)(2-), due to an increase provoked by the evaporation in the salinization process.     

Monitoring for close proximity tunneling effects on an existing tunnel using principal component analysis technique with limited sensor data

A realistic field monitoring application to evaluate close proximity tunneling effects of a new tunnel on an existing tunnel is presented. A Principal Component Analysis (PCA)-based monitoring framework was developed using sensor data collected from the existing tunnel while the new tunnel was excavated. The developed monitoring framework is particularly useful to analyze underdetermined systems due to insufficient sensor data for explicit relations between force and deformation as the system input and output, respectively. The analysis results show that the eigen-parameters obtained from the correlation matrix of raw sensor data can be used as excellent indicators to assess the tunnel structural behaviors during the excavation with powerful visualization capability of tunnel lining deformation. Since the presented methodology is data-driven and not limited to a specific sensor type, it can be employed in various proximity excavation monitoring applications.     

Identifying fouling events in a membrane-based drinking water treatment process using principal component analysis of fluorescence excitation-emission matrices

The identification of key foulants and the provision of early warning of high fouling events for drinking water treatment membrane processes is crucial for the development of effective countermeasures to membrane fouling, such as pretreatment. Principal foulants include organic, colloidal and particulate matter present in the membrane feed water. In this research, principal component analysis (PCA) of fluorescence excitation-emission matrices (EEMs) was identified as a viable tool for monitoring the performance of pre-treatment stages (in this case biological filtration), as well as ultrafiltration (UF) and nanofiltration (NF) membrane systems. In addition, fluorescence EEM-based principal component (PC) score plots, generated using the fluorescence EEMs obtained after just 1 hour of UF or NF operation, could be related to high fouling events likely caused by elevated levels of particulate/colloid-like material in the biofilter effluents. The fluorescence EEM-based PCA approach presented here is sensitive enough to be used at low organic carbon levels and has potential as an early detection method to identify high fouling events, allowing appropriate operational countermeasures to be taken.