Interpreting drinking water quality in the distribution system using Dempster-Shafer theory of evidence

Interpreting water quality data routinely generated for control and monitoring purposes in water distribution systems is a complicated task for utility managers. In fact, data for diverse water quality indicators (physico-chemical and microbiological) are generated at different times and at different locations in the distribution system. To simplify and improve the understanding and the interpretation of water quality, methodologies for aggregation and fusion of data must be developed. In this paper, the Dempster-Shafer theory also called theory of evidence is introduced as a potential methodology for interpreting water quality data. The conceptual basis of this methodology and the process for its implementation are presented by two applications. The first application deals with the interpretation of spatial water quality data fusion, while the second application deals with the development of water quality index based on key monitored indicators. Based on the obtained results, the authors discuss the potential contribution of theory of evidence as a decision-making tool for water quality management.     

Landform Classification for Land Use Planning in Developed Areas: An Example in Segovia Province (Central Spain)

Landform-based physiographic maps, also called land systems inventories, have been widely and successfully used in undeveloped/rural areas in several locations, such as Australia, the western United States, Canada, and the British ex-colonies. This paper presents a case study of their application in a developed semi-urban/suburban area (Segovia, Spain) for land use planning purposes. The paper focuses in the information transfer process, showing how land use decision-makers, such as governments, planners, town managers, etc., can use the information developed from these maps to assist them. The paper also addresses several issues important to the development and use of this information, such as the goals of modern physiography, the types of landform-based mapping products, the problem of data management in developed areas, and the distinctions among data, interpretations, and decisions.     

NITROGEN AND ORGANIC MATTER LOSSES IN NO‐TILL CORN CROPPING SYSTEMS1

ABSTRACT: Intensive cropping systems based on mechanical movement of soil have induced land degradation in most agricultural areas due to soil erosion and soil fertility losses. Thus, farmers have been increasing fertilization rates to maintain an economically competitive crop yield. This practice has resulted in water quality degradation and lake eutrophication in many agricultural watersheds. Research was conducted in the Patzcuaro watershed in central Mexico to develop appropriate technology that prevents nonpoint source pollution from fertilizers. Organic matter (OM) and nitrogen (N) losses in runoff and nitrate (NO₃‐N) percolation in Andisols with corn under conventional till (CT) and no‐till (NT) treatments using variable percentages of crop residue as soil cover were investigated for steep‐slope agriculture. USLE type runoff plots were used to collect water runoff, while suction tubes with porous caps at 30, 60, and 90 cm depth were used to sample soil water solutes for NO₃‐N analyses. Results indicated a significant reduction of N and OM losses in runoff as residue cover increased in the NT treatments. Inorganic N in runoff was 25 kg/ha for NT without residue cover (NT‐0) and 6 kg/ha for the NT with 100 percent residue cover (NT‐100). Organic matter losses in runoff were 157 and 24 kg/ha for the NT‐0 and NT‐100 treatments, respectively. Nitrate‐N percolation was evident in CT and NT with 100 percent residue cover (NT‐100). However, NT‐100 had higher NO₃‐N concentration at the root zone, suggesting the possibility of reducing fertilization rates with the use of NT treatments.     

Metallothionein-like metalloproteins in the Baltic clam Macoma balthica : seasonal variations and induction upon metal exposure

Metallothionein-like proteins (MTLs) have been measured by differential pulse polarography every two months during a period of two and half years in the marine bivalve Macoma balthica from two locations of the Western Scheldt Estuary. The MTL concentrations (0.85 to 7.81 mg g⁻¹ dry wt) are comparable to most values found for other marine invertebrates. These concentrations vary significantly with the seasons (higher in winter, lower in summer), which is mainly due to body weight fluctuations. Strong correlations exist between MTL and metal levels. Upon short-term exposure to a mixture of Cd, Cu and Zn, there are on average significant MTL increases, but important seasonal variations have been observed: in winter, the clams are more sensitive to metals, uptake more Cd and Cu and induce more MTL than during the warmer months. Received: 16 May 1997 / Accepted: 6 June 1997     

Evaluation of concentration efficiency of the Pseudomonas aeruginosa phage PP7 in various water matrixes by different methods

Enteric viruses monitoring in surface waters requires the concentration of viruses before detection assays. The aim of this study was to evaluate different methods in terms of recovery efficiencies of bacteriophage PP7 of Pseudomonas aeruginosa, measured by real-time PCR, using it as a viral control process in water analysis. Different nucleic acid extraction methods (silica–guanidinium thiocyanate, a commercial kit (Qiagen Viral RNA Kit) and phenol–chloroform with alcohol precipitation) exhibited very low recovery efficiencies (0.08–4.18 %), being the most efficient the commercial kit used for subsequent experiments. To evaluate the efficiency of three concentration methods, PBS (as model for clean water) and water samples from rivers were seeded to reach high (HC, 10⁶ pfu ml^?1) and low concentrations (LC, 10⁴ pfu ml^?1) of PP7. Tangential ultrafiltration proved to be more efficient (50.36?±?12.91, 17.21?±?9.22 and 12.58?±?2.35 % for HC in PBS and two river samples, respectively) than adsorption–elution with negatively charged membranes (1.00?±?1.34, 2.79?±?2.62 and 0.05?±?0.08 % for HC in PBS and two river samples, respectively) and polyethylene glycol precipitation (15.95?±?7.43, 4.01?±?1.12 and 3.91?±?0.54 %, for HC in PBS and two river samples, respectively), being 3.2–50.4 times more efficient than the others for PBS and 2.7–252 times for river samples. Efficiencies also depended on the initial virus concentration and aqueous matrixes composition. In consequence, the incorporation of an internal standard like PP7 along the process is useful as a control of the water concentration procedure, the nucleic acid extraction, the presence of inhibitors and the variability of the recovery among replicas, and for the calculation of the sample limit of detection. Thus, the use of a process control, as presented here, is crucial for the accurate quantification of viral contamination.     

Bacterial toxicity testing and antibacterial activity of parabens

Parabens are used because of their antimicrobial effects, yet they have multiple adverse effects. The aim of this work was to evaluate the bactericidal activity and bacterial toxicity of the most common parabens: methylparaben, ethylparaben, propylparaben, isopropylparaben, butylparaben, isobutylparaben and phenylparaben. The bactericidal action was evaluated by means of the dilution-neutralization method against Escherichia coli and Staphylococcus aureus, and the biotest of inhibition of growth of E. coli was applied for the bacterial toxicity assay. Bactericidal concentrations were reached only for propylparaben, butylparaben and isobutylparaben. The toxicity study showed methylparaben to be the least toxic, whereas the most toxic one was phenylparaben. It can be concluded that the disinfectant activity of the parabens studied is insignificant against S. aureus and E. coli. Acute toxicity is very low, and the inhibitory effect is weak. Our results provide information about the risk–benefit balance of parabens use.     

Integrating intraseasonal grassland dynamics in cross-scale distribution modeling to support waterbird recovery plans

Despite much discussion about the utility of remote sensing for effective conservation, the inclusion of these technologies in species recovery plans remains largely anecdotal. We developed a modeling approach for the integration of local, spatially measured ecosystem functional dynamics into a species distribution modeling (SDM) framework in which other ecologically relevant factors are modeled separately at broad scales. To illustrate the approach, we incorporated intraseasonal water-vegetation dynamics into a cross-scale SDM for the Common Snipe (Gallinago gallinago), which is highly dependent on water and vegetation dynamics. The Common Snipe is an Iberian grassland waterbird characteristic of European agricultural meadows and a member of one of the most threatened bird guilds. The intraseasonal dynamics of water content of vegetation were measured using the standard deviation of the normalized difference water index time series computed from bimonthly images of the Sentinel-2 satellite. The recovery plan for the Common Snipe in Galicia (northwestern Iberian Peninsula) provided an opportunity to apply our modeling framework. Model accuracy in predicting the species’ distribution at a regional scale (resulting from integration of downscaled climate projections with regional habitat–topographic suitability models) was very high (area under the curve [AUC] of 0.981 and Boyce's index of 0.971). Local water-vegetation dynamic models, based exclusively on Sentinel-2 imagery, were good predictors (AUC of 0.849 and Boyce's index of 0.976). The predictive power improved (AUC of 0.92 and Boyce's index of 0.98) when local model predictions were restricted to areas identified by the continental and regional models as priorities for conservation. Our models also performed well (AUC of 0.90 and Boyce's index of 0.93) when projected to updated water-vegetation conditions. Our modeling framework enabled incorporation of key ecosystem processes closely related to water and carbon cycles while accounting for other factors ecologically relevant to endangered grassland waterbirds across different scales, allowed identification of priority areas for conservation, and provided an opportunity for cost-effective recovery planning by monitoring management effectiveness from space.     

2,4-dichlorophenoxyacetic acid (2,4-D) micropollutant herbicide removing from water using granular and powdered activated carbons: a comparison applied for water treatment and health safety

Abstract

Activated carbons are well-known porous materials as an effective adsorbent used for the removal of emerging contaminants, such as herbicides, which are increasingly present in water bodies. Most water treatment plants, specially in Brazil, are unable to completely remove such contaminants by the conventional process and advanced treatment using activated carbons is required. The aim of this paper was to verify the influence of the activated carbons granulometry and specific surface area on the 2,4-dichlorophenoxyacetic acid (2,4-D) herbicide removal efficiency using distilled-deionized water and filtered water collected from a conventional Water Treatment Plant. Commercial activated carbons samples used in this work were obtained from two different manufacturers. Activated carbons were analyzed by the specific surface area, pore size and volume distribution, nuclear magnetic resonance, infrared and x-ray spectroscopy, moisture, volatile matter and ash contents. Batch adsorption isotherms experiments were used and performed by Langmuir and Freundlich models. Granular and powdered activated carbons removed over 99% of 2,4-D in distilled water and near to 99% using filtered water. The activated carbons evaluated in this work presented high performance and played a key role in water treatment by removing 2,4-D herbicide, ensuring the protection of human health and the ecosystem.     

Addressing social attitudes toward lethal control of wildlife in national parks

The extraordinary population growth of certain ungulate species is increasingly a concern in agroforestry areas because overabundance may negatively affect natural environments and human livelihoods. However, society may have negative perceptions of killing wildlife to reduce their numbers and mitigate damage. We used an online survey that included a choice experiment to determine Spanish citizens’ (n = 190) preferences toward wildlife population control measures related to negative effects of ungulate overabundance (negative impacts on vegetation and other wildlife species and disease transmission to livestock) in 2 agroforestry national parks in Spain. We used latent-class and willingness-to-pay in space models to analyze survey results. Two percent of respondents thought a national park should have no human intervention even if lack of management may cause environmental degradation, whereas 95% of respondents favored efforts to reduce damage caused by overabundant ungulate species. We estimated human well-being losses of survey respondents when sustainable effects of deer overabundance on the environment became unsustainable effects and well-being gains when sustainable effects transitioned to no visible effects. We found that the type of wildlife-control program was a very relevant issue for the respondents; indirect control in which killing was avoided was the preferred action. Sixty-six percent of respondents agreed with the option of hunters paying for culling animals to reduce ungulate impacts rather than management cost coming out of taxes, whereas 19% of respondents were against this option and willing to pay for other solutions in national parks. Our results suggest that killing wildlife in national parks could be a socially acceptable tool to manage overabundance problems in certain contexts, but it could also generate social conflicts.     

Accounting for multiple testing in the analysis of spatio-temporal environmental data

The statistical analysis of environmental data from remote sensing and Earth system simulations often entails the analysis of gridded spatio-temporal data, with a hypothesis test being performed for each grid cell. When the whole image or a set of grid cells are analyzed for a global effect, the problem of multiple testing arises. When no global effect is present, we expect $$ \alpha $$% of all grid cells to be false positives, and spatially autocorrelated data can give rise to clustered spurious rejections that can be misleading in an analysis of spatial patterns. In this work, we review standard solutions for the multiple testing problem and apply them to spatio-temporal environmental data. These solutions are independent of the test statistic, and any test statistic can be used (e.g., tests for trends or change points in time series). Additionally, we introduce permutation methods and show that they have more statistical power. Real-world data are used to provide examples of the analysis, and the performance of each method is assessed in a simulation study. Unlike other simulation studies, our study compares the statistical power of the presented methods in a comprehensive simulation study. In conclusion, we present several statistically rigorous methods for analyzing spatio-temporal environmental data and controlling the false positives. These methods allow the use of any test statistic in a wide range of applications in environmental sciences and remote sensing.