Evaluation of Sea Water Quality in the Coastal Zone of Primor'e Using the Method of Microbial Indication

The response of marine microorganism community to variations in the concentrations of heavy metals in the environment is investigated for the coastal waters of Peter the Great Bay and the northern part of Primor'e. The fact of the existence and sensitivity of the individual response of plankton community microorganisms to variations in the concentrations of metals in the water under the conditions of water contamination with a complex of metals are confirmed on the basis of microbiological data and the results of their comparison to the data of chemical analysis. Microbial indication is shown to serve as an operative method of monitoring and short-term forecasting of changes in the environmental conditions of sea coastal waters. This method allows obtaining preliminary information at the stage of selecting environmental quality observation stations.     

Ground Water Quality In the Aquifer of the Upper Mad River Valley 1940 to 1983

More than 40 years of ground water quality monitoring data from the aquifer of the Upper Mad River Valley have been accumulated by various agencies in Ohio. The data consist of concentrations for more than 30 chemical substances found in the ground water. Evaluation of this data using statistical analysis, tables and graphs indicates that there have been moderate increases in total dissolved solids, sulfate, fluoride, calcium, magnesium and potassium. More significant increases were discovered for chloride and sodium. Iron and zinc show a general decline in concentration. The metals arsenic, barium and lead also show increases in recent years. However, large variations in the concentrations and limited data for these metals limit the reliability of the apparent trends shown in the tabulated data.
The increases in chloride and sodium are attributed to the use of road de-icing salt. Increases in sulfate and potassium may be due to use of fertilizers in a region which is largely agricultural. The most recent data may indicate that the ground water quality is improving in terms of these two parameters. Although most of the data indicate increases in concentrations with time, inconsistencies in sampling procedures and difficulties in assessing many factors which affect ground water quality preclude the broad conclusion that urbanization and industrialization have caused regional ground water quality degradation.
The inability to interpret much of the data underlies the need for an integrated environmental monitoring program. Such a program should provide a data base for assessing factors such as air and river quality and historical land use practices so that their impact on ground water quality in the Mad River Valley can be better understood.     

Bayesian chemistry-assisted hydrograph separation (BACH) and nutrient load partitioning from monthly stream phosphorus and nitrogen concentrations

A Bayesian chemistry-assisted hydrograph separation (BACH) approach was developed, based on calibration of a three-component recursive digital filter, that requires monthly water quality data only. This enables BACH to be applied to the large number of rural catchments for which continuous flow records and monthly water chemistry time series exist from ‘state of environment’ monitoring programmes, but little supplementary data required for more sophisticated analysis techniques. As well as estimating fast, medium, and slow flow components, BACH also estimates for each flow component a time-invariant concentration of the chemical tracers chosen, allowing flow path-specific loads to be calculated. The method was demonstrated using 15 years of total phosphorus (TP) and total nitrogen (TN) data from eight mesoscale catchments in the Waikato region of New Zealand’s North Island. Calibration was done separately for three 5-year data periods, and validated against data from the following 5-year period. Flow path separation and concentration predictions were consistent between data periods, indicating that the TP–TN combination contained sufficient information to reliably identify three flow paths in each catchment; an event-response near-surface flow path with high concentrations of both phosphorus and nitrogen, a seasonal shallow groundwater flow path with lower concentrations of TP but high concentrations of TN, and a deeper slower groundwater flow path characterised by generally low concentrations of both TP and TN. Based on this analysis, the catchments were able to be grouped in three hydro-types. This shows that commonly available water quality data can support robust, objective flow separation and nutrient load apportionment, even in the absence of other supporting data, provided appropriate modelling methods are used.     

Modeling Ground Water Quality Sampling Decisions

Questions such as what, where, when, and how often to sample play a central role in the development of monitoring strategies. Limited resources will not permit sampling for many contaminants at the same frequency at all well sites. Therefore, a resource allocation strategy is necessary to arrive at answers for the preceding types of questions. Such a strategy for a ground water quality monitoring program is formulated as an integer programming model (an optimization model). The model will be of use in the process of deciding what constituents to sample and where to sample them so as to maximize a given objective, subject to a set of budget, sampling, and regulatory constraints. The maximization objective in the model is defined as a weighted function of population exposure to a scaled measure of observed chemical concentrations. The sampling constraints are based on the observed variability of contaminants in the aquifer, needed precision in estimates, a chosen level of significance, the available budget for implementing the program, and selected regulatory constraints. The model is tested with field data obtained for 10 selected constituents from more than 650 wells in the Cambrian-Ordovician aquifer in Iowa. Results from two alternative formulations of the model are compared, analyzed, and discussed. Further avenues for research are briefly outlined.     

Water Quality Monitoring of Tropical Ponds: Location and Depth Effect in Two Case Studies

The variability in water chemistry of samples taken on a monthly basis (March 1999 to February 2000) from two shallow tropical ponds was studied. The effect of location and pond depth on water chemistry was also examined. The study demonstrated that intraannual variability in nutrient concentration is high. Thus, a high annual sampling frequency is required to provide representative annual mean water quality data. Routine monitoring during the monsoons is important for studies on dissolved oxygen and macrophyte growth. Significant differences were found between the topmost and bottommost points for samples of dissolved oxygen collected from the deepest part of both ponds. For nutrient analysis (nitrogen and phosphorus), sample from any location was found to be representative of the whole pond.     

Illuminating Subsurface Microbial Water Quality Patterns Using Adenosine Triphosphate and Dynamic Time Warping Approaches

Aquifer microbial water quality evaluations are often performed by collecting groundwater samples from monitoring wells. While samples collected from continuously pumped sources are seldom disputed as representative of the aquifer, natural biofilm present in the vicinity of well screens may introduce unwanted microbial artefacts in monitoring wells that are only periodically sampled. The need for well water purging to obtain samples void of these artefacts has been widely recognized. However, purging methods are not standardized; many approaches presume that physico-chemical water quality stability achieved through the removal of 3 to 5 well volumes is indicative of the stability of target analytes. Using a data set collected from a shallow unconfined aquifer in Southern Ontario, Canada, the need for using dedicated approaches that account for the time-dependent nature of microbial water quality changes was demonstrated. Specifically, the utility of adenosine triphosphate (ATP) as a rapid, field-ready biochemical indicator of microbial water quality stability was investigated. This work shows that ATP concentrations reflect time-limited (bio)colloid transport processes that are consistent with other microbial water quality parameters monitored, but different from commonly measured physical and chemical water quality indicators of well purging adequacy. ATP concentrations occasionally fluctuated even after 3 or 4 h of purging, indicating that microbial artefacts attributable to biofilms in the vicinity of the well screen can still persist. The recurrence of characteristic ATP patterns in each well was systematically examined through the novel application of dynamic time warping (DTW), a nonparametric time series analysis approach. These patterns are believed to be linked with seasonal hydrogeological conditions, which warrant consideration in the design and interpretation of subsurface microbial water quality investigations.     

Determination of background concentrations of hydrochemical parameters and water quality assessment in the Akhuryan River Basin (Armenia)

The determination of background values of hydrochemical parameters, to distinguish between natural concentration and anthropogenically-influenced concentrations, is highly relevant. In presented study, to estimate the background values of hydrochemical parameters in Akhuryan River Basin, log-normal probability functions on the hydrochemical parameters concentrations was applied. The study is carried out on the basis of hydrochemical data of surface water quality monitoring for the period of 2010–2013. This study highlights the usefulness of application of site-specific background concentrations for the evaluation, interpretation of surface water quality and for determination of pollution sources.     

The Application of a Statistical Trend Analysis Model to Ground Water Monitoring Data from Solid Waste Landfills

A statistical trend methodology is used to compare ground water quality between eight landfill sites in western Michigan as a case study. Monitoring data were collected over a 15-year period on 36 parameters at an upgradient and downgradient well selected at each of the eight sites. This yielded a total of 576 monitoring data sets available for analysis. New trend and contamination indices are introduced that are used to compare ground water contamination between these eight sites. These indices are used to assess each landfill's relative potential for environmental harm.
Many questions remain unanswered, but what is demonstrated here is that this type of methodology has the potential to be used to assess trends of ground water chemistry concentrations at landfill sues in a region. A specific purpose of such an assessment could be to provide a quantified basis for the prioritization of funds allocated for cleanup of contaminated landfill sites. Having a technical capability to reduce large amounts of ground water monitoring data to appropriate summaries, which then can be used to assess environmental contamination between several sites, could also have important economic and health implications in other settings. Hopefully this paper will encourage further development of such technologies for these purposes.     

Evaluation of Subsurface Oxygen Sensors for Remediation Monitoring

Continuous remediation monitoring using sensors is potentially a more effective and inexpensive alternative to current methods of sample collection and analysis. Gaseous components of a system are the most mobile and easiest to monitor. Continuous monitoring of soil gases such as oxygen, carbon dioxide, and contaminant vapors can provide important quantitative information regarding the progress of bioremediation efforts and the area of influence of air sparging or soil venting. Laboratory and field tests of a commercially available oxygen sensor show that the subsurface oxygen sensor provides rapid and accurate data on vapor phase oxygen concentrations. The sensor is well suited for monitoring gas flow and oxygen consumption in the vadose zone during air sparging and bioventing. The sensor performs well in permeable, unsaturated soil environments and recovers completely after being submerged during temporary saturated conditions. Calibrations of the in situ oxygen sensors were found to be stable after one year of continuous subsurface operation. However, application of the sensor in saturated soil conditions is limited. The three major advantages of this sensor for in situ monitoring arc as follows: (1) it allows data acquisition at any specified time interval; (2) it provides potentially more accurate data by minimizing disturbance of subsurface conditions; and (3) it minimizes the cost of field and laboratory procedures involved in sample retrieval and analysis.     

Rapid Assessment of Trichloroethylene in Ground Water

On-site analysis of trichloroethylene (TCE) in aqueous samples by head- space sample preparation and gas chromatography (HS/GC) provides for quick and precise concentration estimates. This analytical approach is well suited for the on-site determination of volatile organic compounds (VOCs) in a variety of sample matrices, including ground water and saturated and unsatured soils. For these reasons, HS/GC can be used to establish analyte concentrations on a near real time basis to help select appropriate casing material during monitoring well installation. This application and the collection of multiple well samples during sampling events facilitates the hydrogeological site interpretation and the formulation of remediation strategies.     

Elements in Cottonwood Trees as an Indicator of Ground Water Contaminated by Landfill Leachate

Ground water at the Norman Landfill Research Site is contaminated by a leachate plume emanating from a closed, unlined landfill formerly operated by the city of Norman, Oklahoma, Ground water contaminated by the leachate plume is known to be elevated in the concentration of many, organic and inorganic constituents. Specific conductance, alkalinity, chloride, dissolved organic carbon, boron, sodium, strontium, and deuterium in ground water are considered to be indicators of the leachate plume at this site.
Leaf samples of broad-leafed cottonwood, Populus deltoides , were collected from 57 sites around the closed landfill. Cottonwood, a phreatophyte or "well plant," functions as a & surrogate well and serves as a ground water quality sampler. The leaf samples were combusted to ash and analyzed by instrumental neutron activation for 35 elements and by prompt-gamma instrumental neutron activation, for boron. A monitoring well was located within a few meters of a sampled cottonwood tree at 15 of the 57 sites, and ground water samples were collected from these monitoring wells simultaneously with a leaf sample. The chemical analyses of the ground water and leaf samples from these 15 sites indicated that boron, bromine, sodium, and strontium concentrations in leaves were significantly correlated with leachate indicator constituents in ground water. A point-plot map of selected percentiles indicated high concentrations of boron, bromine, and sodium in leaf ash from sites downgradient of the most recent landfill and from older landfills nearby.
Data from leaf analysis greatly extended the known areal extent of the leachate plume previously determined from a network of monitoring wells and geophysical surveys. This phytosgeochemical study provided a cost-effective method for assessing the extent of a leachate plume from an old landfill. Such a method may be useful as a preliminary sampling tool to guide the design of hydrogeochemical and geophysical studies.     

Does Pumping Volume Affect the Concentration of Micropollutants in Groundwater Samples?

Information on concentrations of micropollutants (such as pharmaceuticals, pesticides, and industrial chemicals) in most highly dynamic riverbank filtration (RBF) systems is lacking, in contrast to data on standard chemical parameters. Sampling protocols have thus far been based on the stabilization of standard chemical parameters in relatively pristine environments. To determine whether groundwater samples for micropollutant analysis can be taken at a similar pumping volume as samples for testing standard chemical parameters in both environments, three groundwater monitoring wells in an RBF system were sampled at two points in time (after pumping of 3 well volumes and after pumping of 15 well volumes). Micropollutant concentrations were not significantly different between the two sampling points; therefore, appropriate samples can be drawn after pumping 3 well volumes. For a specific microbiological parameter (leucin incorporation), a statistically significant difference was found.     

A Laboratory Evaluation of Ground Water Sampling Mechanisms

The reliability of ground water monitoring information can be assured by careful selection of sample handling and analytical procedures. Sampling mechanism selection has been studied far less than analytical methodologies (Scalf et al. 1981, Nacht 1983). This study has as its primary goal the identification of reliable sampling mechanisms for purgeable organic compounds and gas-sensitive chemical parameters in ground water. Carefully controlled sampling experiments were run to investigate the error contributed to chemical results due to sampling mechanism alone. Fourteen commercial sampling devices in five mechanistic categories were evaluated for their performance in sample collection for solution parameters, dissolved gases and purgeable organic compounds. Systematic errors related to sampling mechanism can reduce the accuracy of monitoring data by factors of two to three times that involved in analytical procedures.     

The effects of sample scheduling and sample numbers on estimates of the annual fluxes of suspended sediment in fluvial systems

Since the 1970s, there has been both continuing and growing interest in developing accurate estimates of the annual fluvial transport (fluxes and loads) of suspended sediment and sediment‐associated chemical constituents. This study provides an evaluation of the effects of manual sample numbers (from 4 to 12 year^?1) and sample scheduling (random‐based, calendar‐based and hydrology‐based) on the precision, bias and accuracy of annual suspended sediment flux estimates. The evaluation is based on data from selected US Geological Survey daily suspended sediment stations in the USA and covers basins ranging in area from just over 900 km² to nearly 2 million km² and annual suspended sediment fluxes ranging from about 4 Kt year^?1 to about 200 Mt year^?1. The results appear to indicate that there is a scale effect for random‐based and calendar‐based sampling schemes, with larger sample numbers required as basin size decreases. All the sampling schemes evaluated display some level of positive (overestimates) or negative (underestimates) bias. The study further indicates that hydrology‐based sampling schemes are likely to generate the most accurate annual suspended sediment flux estimates with the fewest number of samples, regardless of basin size. This type of scheme seems most appropriate when the determination of suspended sediment concentrations, sediment‐associated chemical concentrations, annual suspended sediment and annual suspended sediment‐associated chemical fluxes only represent a few of the parameters of interest in multidisciplinary, multiparameter monitoring programmes. The results are just as applicable to the calibration of autosamplers/suspended sediment surrogates currently used to measure/estimate suspended sediment concentrations and ultimately, annual suspended sediment fluxes, because manual samples are required to adjust the sample data/measurements generated by these techniques so that they provide depth‐integrated and cross‐sectionally representative data. Published 2014. This article is a U.S. Government work and is in the public domain in the USA.     

Using the precision of the mean to estimate suitable sample sizes for monitoring total phosphorus in Australian catchments

The effect of the sample size on prediction quality is well understood. Recently, studies have assessed this relationship using near‐continuous water quality samples. However, this is rarely possible because of financial constraints, and therefore, many studies have relied on simulation‐based methods utilizing more affordable surrogates. A limitation of simulation‐based methods is the requirement of a good relationship, which is often not present. Therefore, catchment managers require a direct method to estimate the effect of sample size on the mean using historical water quality data. One measure of prediction quality is the precision with which a mean is estimated; this is the focus of this work. By characterizing the effect of sample size on the precision of the mean, it is possible for catchment managers to adjust the sample size in relation to both the cost and the precision. Historical data are often sparse and generally collected using several different sampling schemes, all without inclusion probabilities. This means that an approach is needed to obtain unbiased estimates of the variance of the mean using a model‐based approach. With the use of total phosphorus data from 17 sub‐catchments in southeastern Australia, the ability of a model‐based approach to estimate the effect of sample size on the precision of event and base‐flow mean concentrations. The results showed that for estimating annual base‐flow mean concentration, little gain in precision was achieved above 12 observations per year. Sample sizes greater than 12 samples per event improved event‐based estimates; however, the inclusion of more than 12 samples per event did not greatly reduce the event mean concentration uncertainties. The precision of the base‐flow estimates was most correlated to percentage urban cover, whereas the precision of the event mean estimates was most correlated with catchment size. The method proposed in this work could be readily applied to other water quality variables and other monitoring sites. Copyright © 2014 John Wiley & Sons, Ltd.     

赵县地震台地表及深井地震计观测数据对比分析

由于城市化进程加快,人为干扰因素逐渐增多,地震观测数据质量受到严重的影响。通过对赵县地震台地表及深井地震计记录的数据进行幅频、功率谱分析,并对比二者的地震监测能力等,分析发现:与地面观测相比,深井地震计在避免噪声及提高地震监测能力范围方面具有更大优势,观测数据精度能提高1—2个数量级。通过总结分析地表及深井地震计记录的优缺点,为测震台网今后的发展方向提供依据。     

Monitoring and Mathematical Modeling of Contaminated Ground-Water Plumes in Fluvial Environments

Ground-water monitoring to delineate a contaminant plume in fluvial hydrostratigraphic units often is uncertain. Fluvial deposits consist typically of interbedded layers of sands, silts and clays, with buried stream channel deposits of sands or gravels. The channel deposits are often interpreted erroneously to be discontinuous between test holes and in cross section due to their sinuosity. Erroneous conclusions pertaining to the areal continuity of these geometrically complex deposits are inevitable unless the investigator thoroughly understands the depositional environment(s). The hydraulic conductivity of buried stream channel deposits may be several orders of magnitude higher than the matrix materials in which they are enclosed. The higher hydraulic conductivity of buried stream channel deposits has potentially significant ramifications with respect to ground-water monitoring to delineate the geometry of a contaminant plume migrating through these deposits. Ground-water monitoring at uranium mill waste disposal sites located in fluvial environments began on a significant scale in about 1977. A uranium mill tailing disposal site located in such an environment in central Wyoming is among the first sites monitored. Thirty-seven monitor wells were constructed at the site to delineate a seepage plume originating from one of the tailing ponds. This case history illustrates the need for a detailed under—standing of the hydrostratigraphy at a waste disposal site in order to interpret the meaning of ground-water quality data effectively. Water quality data from monitor wells located on a hit or miss basis often are misleading. The hydrostratigraphic horizon from which a water quality sample is collected must be well defined before the sample analyses can be interpreted quantitatively.     

Attributes and uncertainty of dissolved zinc data from a mined catchment

This study evaluated the attributes and uncertainty of non‐point source pollution data derived from synoptic surveys in a catchment affected by inactive metal mines in order to help to identify and select appropriate methods for data analysis/reporting and information use. Dissolved zinc data from the Upper Animas River Basin, Colorado, USA, were the focus of the study. Zinc was evaluated because concentrations were highest relative to national water quality criteria for brown trout, and zinc had the greatest frequency of criteria exceedances compared with other metals. Data attributes evaluated included measurement and model error, sample size, non‐normality, seasonality and uncertainty. The average measurement errors for discharges, concentrations and loadings were 0·15, 0·1 and 0·18, respectively. The 90 and 95% coefficients of confidence intervals for mean concentrations based on a sample size of four were 0·48 and 0·65, respectively, and ranged between 0·15 and 0·23 for sample sizes greater than 40. Aggregation of data from multiple stations decreased the confidence intervals significantly, but additional aggregation of all data increased them as a result of increasing spatial variability. Unit area loading data were approximately log‐normal. Concentration data were right‐skewed but not log‐normal. Differences in median concentrations were appreciable between snowmelt and both storm flow and baseflow, but not between storm flow and baseflow. Differences in unit area loadings between all flow events were large. It was determined that the average concentration and unit area loading values should be estimated for each flow event because of significant seasonality. Time weighted values generally should be computed if annual information is required. The confidence in average concentrations and unit area loadings is dependent on the computation method used. Both concentrations and loadings can be significantly underestimated on an annual basis when using data from synoptic surveys if the first flush of contaminants during the initial snowmelt runoff period is not sampled. The ambient standard for dissolved zinc for all events was estimated as 1600 μg l⁻¹ using the 85th percentile of observed concentration data, with a 90% confidence interval width of 200 μg l⁻¹. Copyright © 1999 John Wiley & Sons, Ltd.     

地震地球物理台网典型观测环境干扰源管理模块

随着地震数据跟踪分析工作的深入开展,发现环境干扰成为影响地球物理台网观测数据质量的主要因素之一,为此设计干扰源管理模块,为监测人员提供工作平台,对典型干扰信息进行有效管理.随着干扰源记录的不断补充,充实了干扰源数据库,可为今后干扰事件的大数据分析提供研究基础.利用该模块对干扰源进行管理,可有效提高地球物理台网数据质量,...