Increased public health risk caused by pathogen contamination in streams is a serious issue, and mitigating the risk requires improvement in existing microbial monitoring of streams. To improve understanding of microbial contamination in streams, we monitored Escherichia coli in stream water columns and streambed sediment. Two distinct streams and their subwatersheds were studied: (i) a mountain stream (Merced River, California), which represents pristine and wild conditions, and (ii) an agricultural stream (Squaw Creek, Iowa), which represents an agricultural setting (i.e., crop, manure application, cattle access). Stream water column and sediment samples were collected in multiple locations in the Merced River and Squaw Creek watersheds. Compared with the mountain stream, water column E. coli concentrations in the agricultural stream were considerably higher. In both mountain and agriculturalstreams, E. coli concentrations in bed sediment were higher than the water column, and principal component analysis indicates that land use affected water column E. coli levels significantly (p < 0.05). The cluster analysis showed grouping of subwatersheds for each basin, indicating unique land use features of each watershed. In general, water column E. coli levels in the mountain stream were lower than the USEPA’s existing waterquality criteria for bacteria. However, the E. coli levels in the agricultural stream exceeded the USEPA’s microbialwaterquality criteria by several fold, which substantiated that increased agricultural activities, use of animal waste as fertilizers, and combined effect of rainfall and temperature may act as potential determining factors behind the elevated E. coli levels in agriculture streams.
Beyond the immediate issues of spatial and temporal quality of his- torical datasets and associated sampling capability, there still exist a number of gaps in fundamental understanding of FIO processes in the environment. Progress is being made but issues remain, for example, in determining good quantitative data on wildlife contributions to FIO loading in, and export from, agricultural catchments (Coffey et al., 2015; Guber et al., 2015), securing a robust library of FIO die-off in fresh faeces of younger livestock (Oliver et al., 2012a), obtaining robust parameters to enable prediction of manure release and removal in site- speci ﬁ c conditions (Blaustein et al., 2015a, 2015b, 2016), incorporating bed sediment reservoirs of FIOs into existing modelling frameworks (Coffey et al., 2014) and also in understanding the contributions of sep- tic tanks to the impairment of microbialwaterquality. Septic tank in- puts of P to watercourses are now gaining signi ﬁ cant attention (Withers et al., 2014) and in many ways this has accelerated the focus of their potential role on FIO contributions to the aquatic environment — helping to ‘ bridge the gap ’ between these two aspects of diffuse pol- lution research. This represents an idea (based on anecdotal associa- tion) linking nutrient to FIO research that has had a positive impact. However, it has progressed because of the testing of hypothesised links so that the cross pollination across research disciplines contributed inspiration rather than allowing anecdote to prevail over evidence.
It is evident from the analysis of the different parameters that the waterquality of UMR and RR are deteriorating due to coal mine drainage which found their way into the streams. The pollution level in some of them has reached the toxic level, making their waters unfit for human use. This can also be indicated by the decline of fungal and bacterial species diversity when compared to the reference site. The rapid and unscientific mining activities in and around the streams of Jaintia Hills, the corresponding rise of untreated mine wastes which flows into the water bodies, the streams and also rivers may be subjected to even more pronounced effects than those described in this study, limiting their future use for domestic, recreational, agricultural and industrial purposes. Any remediation efforts will have to consider the implementation of anti-pollution measures in the form of solid waste disposal at designated sites away from water courses and slopes, and the development of educational programs aimed at raising awareness of the pollution problem and the need for its prevention. Our findings suggest that further studies characterizing the streams in the polluted sites and a more detail study on microbial taxa may help to develop scientific criteria for pollution monitoring and in developing remediation strategies in the coal mine affected sites.
try to redress some of the ﬁner-scale issues of management impacts
on FIO dynamics (e.g. Aitken, 2003). Key data that can be extracted
from such sources for onward use in FIO modelling include: information on yard management (clean and dirty water separation); frequency of livestock stream fording; and manure management including com- puted ages of manures and slurries at time of application to land. Access to, and knowledge of, such sources of supplementary data can be en- hanced by the involvement of stakeholders and anticipated end-users throughout the entire modelling process, from development to evalua- tion (Hamilton et al., 2015). Further research is needed to enable suc- cessful and reliable integration of mitigation and best management practices (BMPs) into model space but there are large collections of sup- plementary data that can be explored in order to strengthen FIO model- ling for some purposes. However, it is likely that FIO source apportionment or model calibration can be especially sensitive to biased assumptions about practices (e.g. cattle access to streams and duration of manure storage) in particular catchments, based on the down- scaling of regional or national surveys of farm practices. Therefore im- provements in farm activity data collection will always be welcome to better inform FIO modelling.
The research design required three sites on each river, consisting of one upper stream near pristine (fynbos) (with no or low densities of woody alien invasive plants, and free of human disturbance), a mid-stream cleared riparian site (a site that was previously invaded by Acacia mearnsii and cleared ±10 years ago) and invaded (invaded primarily by A. mearnsii for at least ten years). Due to the lack of near-pristine fynbos systems (reaches) in similar longitudinal zones of river systems of the Western Cape, and the extensive transformations and developments in most of these systems, fieldwork was carried out in a combination of mountain stream and mountain stream transitional zones of two perennial rivers within the Western Cape, South Africa. The surface water resource (WR90) quaternary catchments (sub catchments) were delineated in 1990 for South Africa’s principal water management areas (Midgley et al. 1994).The sub catchments which were suitable for inclusion in the study were selected using five basic criteria. First, rivers should have the following treatment sequence from upstream to downstream: a) near pristine (fynbos), b) cleared and c) A. mearnsii invaded. Second, candidate catchments had to be free of land use (i.e. agricultural, industrial and residential) from the recent past in order to compare documented changes in vegetation structure composition with any estimated changes in the relative sediment and nutrients contributions derived from individual source types. Third, no significant tributaries originating in other catchments were to be entering the main stream at or near the sites used, but fitted the criteria if these entered the main stem a significant distance above or below the sites. Fourth, sites had to be reasonably similar in physical characteristics (e.g. same or similar geology, gradients and topology). Fifth, sites should be in close proximity to Stellenbosch University and accessible. The study sites and their respective characteristics are summarized in Table 2.1.
with the mandatory performance-based policy, farmers can employ any practice or strategy they choose to achieve their numeric limit and be in compliance.
To improve waterquality in an impaired watershed, both practice- and performance-based mandatory NPS pollution policies require patience and prolonged policy support. First patience is required to see results: due to time lags in the movements of nutrients from land to water, the results of management changes on the land today will most likely not be seen for decades (Meals et al., 2010; Morgenstern et al., 2015), though this varies by soil type and landscape. Additionally, internal nutrient cycling in lakes can impede the alleviation of eutrophication symptoms once external nutrient loads are reduced (Carpenter et al., 1998; Roy, Martin, Irwin, Conroy, & Culver, 2010). Second, prolonged support is required to ensure that behavior changes endure to provide sustained improvements in waterquality in the future. This suggests that two different elements of policy success are particularly important for the long-term success of agricultural NPS pollution policy: achievement and maintenance of the desired outcome and attracting support for the goals of the policy and means of achieving them to ensure long-term viability (McConnell, 2010).
Advisor: Dr. Paul A. Bukaveckas, Professor, VCU Department of Biology and Center for Environmental Studies
The objective of this study was to characterize waterquality and hydrologic properties of urban streams in the Richmond metropolitan area. Waterquality data were analyzed for six urban sites and two non-urban sites. Geomorphological surveys and conservative tracer studies were performed at four urban sites and one non-urban site to describe intra- and inter- site variability in transient storage, channel geomorphology, and related hydrologic parameters. Urban sites showed elevated concentrations of nitrogen and more variable TSS concentrations relative to reference sites. Urban channels were deeply incised with unstable banks and low sinuosity. Little Westham Creek exhibited the greatest transient storage. This site was
al. 2006, Gurnell et al. 2006, Wharton et al. 2006), they constitute a significant store of fine sediment.
2.2.4 Storage within the channel
River channel beds are not generally considered a major depositional environment over geologic time-scales (Leeder 1999). However, significant fine sediment deposits can form in areas where water velocities and turbulence decrease, either naturally (e.g. downstream fining, fluvial lakes) or due to anthropogenic influences (e.g. dams, reservoirs, and weirs). In these areas, sedimentation can pose a significant problem to navigation, the functioning of engineering structures, as well as environment/human health because of contaminants (Mehta et al. 1989b, Luoma & Rainbow 2008, Droppo et al. 2009). In small streams they can still represent a significant temporary sink/source of fine sediment over shorter time periods (i.e. months or years). Estimates for the amount of fine sediment stored within the gravel beds of chalk rivers are 920 g m -2 for the River Frome, 1290 g m -2 for the River Lambourn, 1590 g m -2 for the River Piddle, and 2390 g m -2 for the River Tern (Collins & Walling 2007a, b). These represent 19 - 57% of the rivers’ annual fine sediment loads. When the deposits within aquatic macrophytes are included in the analysis, estimates of fine sediment storage increase significantly. Heppell et al. (2009) calculated fine sediment storage between 11.6 and 66.8 kg m -2 for the Bere Stream, and 0.9 and 23.5 kg m -2 in the River Frome. Storage was strongly correlated to the percent cover of macrophytes, and it is macrophyte cover which is responsible for the substantial temporal variations in fine sediment storage. For sediment stored within macrophytes, the permanence of the storage is tied to macrophyte stand dynamics. If the plants die back in the winter, leaving the sediment exposed to bed shear stress and turbulence, it will more likely to be transported away from the reach (Kleeberg et al. 2010). However, if macrophyte stands persist for greater than a year, deposition within the stands will have a more significant impact on sediment transport dynamics in these systems.
Drinkability of water is determined based on its physical, chemical, and microbial properties. Undesirable changes in these parameters could threaten the health of consumers. The present study aimed to assess the physical, chemical, and microbial parameters of the drinking water sources in Sanandaj, Iran and compare them with the national standard values. This descriptive, cross-sectional study was conducted for 24 months. Samples were collected via simple random sampling from 116 stations in accordance with the principles of water sampling, including 51 stations of Sanandaj water distribution system, 15 reservoir stations, 25 stations for the outlet of the water treatment plant, and 25 stations for raw water. In total, 2,784 samples were obtained from the stations and transferred to the laboratory in standard conditions. Residual chlorine, pH parameters, turbidity, total coliforms, thermophilic coliforms, and heterotrophic plate count (HPC) were measured. Data analysis was performed in SPSS version 18 using t-test and ANOVA. According to the results, the mean values for the physical parameters of turbidity and pH in the water distribution system were 0.9522 NTU and 7.9644, respectively. With regard to the chemical parameters, the mean residual chlorine in the water distribution system was 0.5548 mg/L, and the microbial parameters of total coliforms, thermophilic coliforms, and HPC were 0 MPN/100 mL, 0 MPN/100 mL, and 107.6533 CFU/ml, respectively. Our findings indicated that the mean concentrations of the measured parameters in the water distribution system of Sanandaj were within the national standard limits.
Table 5 summarizes the correlations of number of families and biotic indices and physicochemical variables. It was found that the number of families and BMWP have significant correlation with nitrate-nitrogen, while ASPT was significantly correlated with turbidity of water. However, an increase in the results in BMWP and ASPT scores systems shows good ecological quality . Benthic macroinvertebrate species are differentially sensitive to many biotic and abiotic factors in their environment . In many studies diversity indices are also used for assessing waterquality but the biotic index and score systems are better for assessing organic pollution and eutrophication .
Out of 23,705 lives lost to different disasters from 1983 to 2013 in Nepal, epidemics alone were responsible for 11,507 deaths (Disaster Review 2013, Department of Water Induced Disaster Prevention), most of which are water related vector borne diseases. The severity of epidemics can be illustrated by the INSEC record of diarrheal case when the death toll of diarrhea rose to 464 in 18 districts of Mid and Far Western Region during April 30 to September 18, 2009. ((http://www.inseconline.org/). ICIMOD, a regional organization working for the Hindu Kush-Himalayan region reports that around 16,000 deaths occurring in Nepal every year in an average is linked to poor quality of water (http://www.icimod.org/?q=64). Additionally, waterquality is also a major factor linked with agriculture productivity, health of livestock and quality of industrial products.
(1.19 km 2 ) rural catchment in southern Brazil, and evalu- ated two calibration methods by comparing the estimates of SSC obtained from the calibrated turbidity readings with direct measurements obtained using a suspended sediment sampler. Meral et al. [ 11 ] used two practical and relatively cheap alternative methods (namely turbidity sensor and Imhoff cone method) to estimate SSC. Williamson and Crawford [ 1 ] aimed to quantify the potential for estimating SSC using two surrogate sediment parameters (TSS and turbidity) in order to enable regional and site-specific modeling of sediment concentrations in Kentucky streams. Recently, the neural networks approach has been applied to many branches of science [ 12 – 16 ]. The approach is becoming a strong tool for providing hydraulic and environmental engineers with sufficient details for design purposes and management practices. The technique has a growing body of applications for river engineering and water resources [ 17 – 19 ]. ANNs employment in suspended sediment estimation and prediction has been worked out. Nagy et al. [ 9 ] developed an ANN model to estimate SSC in rivers, achieved by training the ANN model to extrap- olate several stream data collected from reliable sources. The network was set up using several parameters, such as Froude number, stream width ratio, mobility number and Reynolds number, as the input pattern and the SSC as the output pattern. Kisi et al. [ 20 ] analyzed and discussed the performance of adaptive neuro-fuzzy technique, radial basis neural network, feed-forward neural network, and generalized regression neural network in the prediction of suspended sediment. Rajaee et al. [ 21 ] considered ANNs, neuro-fuzzy, multi-linear regression, and conventional SRC models for time series modeling of SSC in rivers. The models are trained using daily river discharge and SSC data belonging to Little Black River and Salt River gauging stations in the USA. Wang et al. [ 22 ] investigated the potential of two algorithm networks, the feed-forward back propagation and generalized regression neural network, in comparison with the classical regression for modeling the event-based SSC at Jiasian diversion weir in Southern Taiwan. Mount and Abrahart [ 23 ] reported a comprehen- sive set of single-input single-output neural network sus- pended sediment modeling experiments performed on two catchments in Puerto Rico. Bayram et al. [ 24 ] evaluated whether the turbidity can produce a satisfactory prediction of the SSC, and improve an ANN method estimating the SSC based on in situ turbidity measurements in the stream Harsit, Eastern Black Sea Basin, Turkey. Demirci and Baltaci [ 25 ] proposed a fuzzy logic approach to estimate SSC from streamflow.
Direct bedload measurements have been performed in the field since the beginning of the 20 th century, deploying “portable” basket collectors or sediment traps directly digged within the channel bed, such as in the seminal work of Muellhofer (1933) in the River Inn. These methods have been developed until recent years, such as in Bunte and Abt (2009), where Helley-Smith sampler and sediment traps have been used to measure bedload and compare measured rates obtained using both techniques. Direct methods of sampling are usually limited to one cross section at a specific time, and are difficult to take during high- magnitude events; furthermore the sampling instruments have been highlighted to be not as accurate as proposed by theory (Habersack et al., 2001; Laronne et al., 2003; Vericat et al., 2006; Liebault et al., 2012). Direct methods do not allow to measure the specific travel distances travelled by clasts during flood events. To overcome this problem, tracers have been deployed in fluvial geomorphology enabling displacement length of tagged particles to be tracked with different methods. The advantages of using tracers are that they are recovered after flood events, and the range and distribution of their size can adequately mimic that of reach natural conditions. Furthermore information on sediment sorting by particle size or shape can be obtained by tracers, as well as thresholds for particle entrainment, depth of the active layer (better when complemented with scour chains), sediment sources and deposition areas, and relative sediment transport rates. The largest limitation is primarily due to the recovery rate, that can affect the accuracy of measurements, especially in unusual conditions as might occur in high-elevation reaches. The first tracers used were painted rock (e.g. Einstein, 1937; Takayama, 1965; Leopold et al., 1966), used to record the track of those visible in the channel bed after flood events. This kind of tracers showed low recovery rate and buried and superficial clasts exhibit a different behaviour (Hassan and Church, 1992). A first improvement was represented by using ferric coating (Nir, 1964) or metal strips placed on the surface of clasts (Butler, 1977), but the recovery rates were generally below 35 %. To determine the movement of tracers, rivers were periodically surveyed to locate their position, revealed through a metal detector. From the comparison of the current position with the previous, the distance moved by a single particle was derived; additional information such as channel morphology and positions could be recorded, and the relationship between particle size and mobility could be inferred.
157 6.2 Discussion of the results from sites in the lower Buffalo River catchment
6.2.1 Site R2Buff-Laing
This site is downstream of Laing Dam. This monitoring site indicated water physico-chemical improvements compared with its upstream site R2Buff-Kwami, as demonstrated by the nutrient concentration decrease. Seasonal patterns were observed in all water physico-chemical parameters. A significant increase in turbidity was recorded in spring 2007, compared to the rest of the sampling period. High nutrient concentrations were recorded in spring, which then significantly decreased during other seasons. No seasonal patterns were detected in microbial cell counts in both biofilm and water samples. Watermicrobial cell counts were high in January 2008, coinciding with a turbidity increase that was however lower than the spring measurement reported earlier. High suspended microbe concentrations thus contributed to increased suspended particulates in water resulting to high turbidity as nutrient concentrations decreased during the same period, thus indicating that dissolved organic matter was not responsible for higher turbidity. Biofilm microbial cell counts indicated no response to water physico-chemical changes. Higher microbial sulphate reductions suggested the presence of sulphate reducing prokaryotes (Garrity et al., 2005). These organisms were probably stimulated to grow by elevated sulphate concentrations in the water column. This site recorded microbial cell counts that were comparable to the upper catchment reference sites R2Buff-Maden and R2Mgqa-Pirie and were different to its specific reference site R2Buff-Umtiz. The cell counts were lower than the counts recorded from the lower catchment reference site R2Buff-Umtiz and also an upstream monitoring site R2Buff-Kwami. It is important to note that the Yellowwoods River joins the Buffalo River upstream of Laing Dam, thus contributing to poor water physico-chemistry of this river. Hence, the knowledge of poor water physico- chemistry that was recorded in site R2Buff-Kwami and R2Yello-Fortm of the Yellowwoods River showed that water physico-chemistry at this site had significantly improved, possibly as a result of suspended matter settling in Laing Dam, as reported by Palmer and O’Keeffe (1989). Though water physico-chemistry improvements were recorded in this site, measured results for selected water physico-chemical parameters indicated that site R2Buff-Laing is experiencing serious impairment, indicated by nutrients being categorized as Poor. There were no existing data from previous studies to compare the present state of water physico-chemistry.
Closed and open types of check dams have been installed in order to capture debris flows in the mountain areas. However, the closed type always has to be kept empty to trap a large amount of sedi- ment during a debris flow event. On the other hand, the open type allows finer sediment to pass through at lower discharge and coarser sediment to be trapped at higher discharge such as debris flow. From these characteristics of each type of check dams, the open type becomes more popular than the closed type. However, designing an appropriate opening becomes a difficult subject (e.g. Ashida and Takahashi, 1980; Ashida et al., 1987; Armanini, 2001).
Do the persisting populations undergo adaptive evolution in response to the decreasing energy availability with sediment depth, or are they already adapted to live under extreme energy limitation as previously hypothesized (9)? To answer this central question, we used metagenomics and single-cell genomics to assess the extent and nature of genomic change in four dominant persisting lineages with sediment depth and age. We extracted cells from the upper part of the sulfate reduction zone (0.25 m depth) and from the methanogenic zone (1.75 m) at station M5 ( SI Appendix, Table S1 ), sorted individual cells randomly by flow cytometry, and amplified and sequenced each of their genomes. We obtained 17 single-cell amplified genomes (SAGs) repre- senting four different taxonomic lineages, which all harbor per- sisting populations ( SI Appendix, Table S3 and Fig. S6 ). We also constructed metagenomic DNA sequence libraries from 0.25, 0.75, 1.25, and 1.75 m sediment depth of station M5 ( SI Ap- pendix, Table S1 ). We then mapped the reads of each library onto predicted genes in the assembled SAGs with a conservative nucleotide identity cutoff of 95%, which represents the recog- nized species boundary in prokaryotic systematics (21). From these data we could measure the genetic diversity in the per- sisting lineages represented by the SAGs and evaluate their mode of evolution. We first calculated genomic nucleotide di- versity (π) as the average number of single-nucleotide poly- morphism positions (SNPs) per base pair for each lineage and depth. The observed nucleotide diversity was low [≤0.0075 SNPs bp −1 ; notably, the sequencing error rate was >10-fold lower (Materials and Methods)] and mostly varied within less than twofold over depth depending on the population (Fig. 3A). These results indicate that absolute levels of diversity in per- sisting populations are similar across sediment depth. We also found that, within lineages, individuals from different sediment depths shared a high proportion of identical SNPs within their genomes (40–60%; SI Appendix, Fig. S7A ). It is highly unlikely that populations in different subsurface zones have exchanged cells or genes because the clay sediments show no advective fluid
PVC pipe with an attached outlet tube (3.2 mm ID × 20 cm vinyl tube) at the downstream end. As water flowed through the pipe, we capped the downstream end with a stopper. Lift- ing from the stream, water flowed through outlet tube to > triple overflow a 12 mL Exetainer vial. These vials were capped immediately without bubbles. We did not use preser- vative because we analyzed samples within a week and we found no change in concentration of these nearly inert gases in this time period using laboratory tests. We measured spe- cific conductivity using a handheld conductivity sensor or conductivity and temperature using a Onset HOBO conduc- tivity logger and converted the values to specific conductiv- ity at each sampling location. We also recorded the stream temperature using a reference Thermapen (ThermoWorks, American Fork, UT) and barometric pressure in millimeters of mercury (mmHg) using a handheld barometer (Extech, Nashua, NH, USA) to calculate saturated dissolved gas con- centrations. We assumed SF 6 concentration was 0 before the
waterquality in surface drainage. This study used intolerant, tolerant and resistant benthic species as bioindicators of changes in waterquality of two streams, taking in account particle size of sediments and volatile solids. Eight points were sampled in the stream Bandeirinha and four points in the stream Josefa Gomes in January and September 2013 in the municipality of Formosa-GO. The two streams were chosen in order to study two different environments. The study used the diversity indexes of Shannon (H), Equitability (E) and Bray-Curtis Similarity and the grouping analysis method UPGMA (Unweighted Pair Group Method with Arithmetic Mean). The results of particle size and volatile solids study of Bandeirinha stream located in rural areas showed bioindicator species intolerant of changes in waterquality. The stream Josefa Gomes, completely inserted in an urban environment, showed alteration identified by tolerant and resistant species. The results demonstrate differences in waterquality in the rural and urban environments and the relationship of bioindicators with sediments. The study aids the interpretation of changes in the waterquality of the two drainages areas. Analysis of waterquality based upon benthic biological indicators associated with physical-chemical and geochemical analyses of water and sediments provide a better interpretation of the results.
of pollution, forconducting further investigations on the eco-biological impacts and also for initiating necessary steps for remedial actions in case of polluted water bodies . In India, many researchers have worked on physicochemical and biological characteristics of reservoirs and rivers [4, 14, 15, 16, 17, 18, 19, 20]. Although statistics vary, the World Health Organization (WHO) reports that approximately 36% of urban and 65% of rural Indian’s were without access to safe drinking water . Ground water is an essential and vital component of our life support system. The ground water resources are being utilized for drinking, irrigation and industrial purposes. There is growing concern on the deterioration of ground waterquality due to geogenic and anthropogenic activities. Thus, Freshwater has become a scare commodity due to over exploitation and pollution . Uncontrolled domestic wastewater discharge into pond has resulted in eutrophication of ponds as evidence by substantial algal bloom, dissolve oxygen depletion in the subsurface water leads to large fish kill and other oxygen requiring organism . The natural and human activities over the years have contributed towards continuous built up of toxic metals in water bodies. Human activities such as mining and smelting of metals, electroplating, gas exhaust, energy and fuel production, fertilizers, sewage and pesticides, municipal waste generations are contributing for heavy metal pollution  which has become one of the most severe environmental problems today. The content of heavy metals in river bottom sediments is often used as an indicator of theiranthropogenic pollution . In most of the rivers contaminated sediment has become one of the most environmental issues