Assessing temporal representativeness of water quality monitoring data

The effectiveness of different monitoring methods in detecting temporal changes in water quality depends on the achievable sampling intervals, and how these relate to the extent of temporal variation. However, water quality sampling frequencies are rarely adjusted to the actual variation of the monitoring area. Manual sampling, for example, is often limited by the level of funding and not by the optimal timing to take samples. Restrictions in monitoring methods therefore often determine their ability to estimate the true mean and variance values for a certain time period or season. Consequently, we estimated how different sampling intervals determine the mean and standard deviation in a specific monitoring area by using high frequency data from in situ automated monitoring stations. Raw fluorescence measurements of chlorophyll a for three automated monitoring stations were calibrated by using phycocyanin fluorescence measurements and chlorophyll a analyzed from manual water samples in a laboratory. A moving block bootstrap simulation was then used to estimate the standard errors of the mean and standard deviations for different sample sizes. Our results showed that in a temperate, meso-eutrophic lake, relatively high errors in seasonal statistics can be expected from monthly sampling. Moreover, weekly sampling yielded relatively small accuracy benefits compared to a fortnightly sampling. The presented method for temporal representation analysis can be used as a tool in sampling design by adjusting the sampling interval to suit the actual temporal variation in the monitoring area, in addition to being used for estimating the usefulness of previously collected data.     

Evaluation and optimization of an urban PM2.5 monitoring network

The objective of this study was to evaluate the PM(2.5) monitoring network established in the Greater Cincinnati and Northern Kentucky metropolitan area for measuring the 24 h integrated PM(2.5) concentration, as well as-at selected sites-hourly PM(2.5) concentration and 24 h integrated PM(2.5) speciation. The data collected during three years at 13 measurement sites were analyzed for spatial and temporal variations. It was found that both daily and hourly concentrations of PM(2.5) have low spatial variation due to a regional influence of secondary ammonium sulfate. In contrast, the trace element concentrations had high spatial variation. Seasonal variation accounted for most of the total temporal variation (60%), while yearly, monthly, weekly and daily variations were lower. The variance components and cluster analyses were applied to optimize the number of sites for measuring the 24 h PM(2.5) concentration. It was found that the 13-site network may be optimized by reducing the number of sites to 8, which would result in a relative precision reduction of 9% and a relative cost reduction of 36%. At the same time, the data suggest that the spatial resolution of speciation monitors and real-time PM(2.5) mass monitors should be increased to better represent spatial and temporal variations of the markers of local air pollution sources.     

Sampling over time: developing a cost effective and precise exposure assessment program

Studies requiring ambient exposure assessments invariably ask: How often should measurements be taken? Answer to such questions is dictated by budgetary considerations as well as spatial and temporal variability in the data. For example, do we obtain measurements during all seasons, all months within seasons, weeks within months and days within weeks? On one hand, we can obtain a one-time snapshot sample and regard it as representing the "true" mean exposure. On the other hand, we may obtain a large number of measurements over time and then average these in order to represent this "true" mean exposure. The former estimate is the least expensive but may also be the least precise while the latter, may be very precise but prohibitively costly. In this paper, we demonstrate how a pilot study can be undertaken with a potentially promising and feasible sampling plan for the full-scale study. By applying the statistical methodology of variance component analysis (VCA) to the pilot study data and exploiting mathematical relationship between the variance of the overall mean exposure and posited variance components, we can develop a sampling design with decreased sampling costs and/or increased precision of the mean exposure. Our approach was applied to determine sampling design choices for an on-going study that aimed at assessing ambient particulate matter exposure. We conclude that a pilot study followed by the VCA analysis may often lead to sampling design choices that offer considerable cost savings and, at the same time, promise to provide relatively precise estimates of the mean exposure for the subsequent full-scale study.     

Climate change impact on the olive pollen season in Mediterranean areas of Italy: air quality in late spring from an allergenic point of view

Recent studies have shown that there are many effects of climate change on aeroallergens, and thus on allergic diseases in humans. In the Mediterranean region, despite the importance of the olive tree for production, there is high allergenicity of olive pollen and related risks to human health. Aerobiological sampling techniques can be used to analyse the pollinosis phenomenon through determination of mean daily pollen concentrations per cubic metre of air. The present study was carried out from 1999 to 2008 in 16 olive-growing areas in Italy, to update the information on the pollinosis characteristics of Olea europaea in the study areas. The analysis of the average flowering season over the study period highlights a temporal scaling of pollen in the atmosphere that depends on the different climatic characteristics. This is mainly dependent on temperature, and in part, determined by latitude. Generally, the levels of O. europaea pollen in the atmosphere are higher from mid-April to the end of June, with the period of greatest risk to human health due to this olive pollen in this area currently limited primarily to the last 10 days of May. However, the pollen season can move, depending on the climate scenario considered, and data here can be used to determine potential time shifts in pollinosis that might cause more precocious asthma and allergy problems. The allergy season for this type of pollen might be significantly precocious in future decades (20–30 days earlier in the year), which will impact on the severity and duration of allergies attributable to olive tree pollen.     

Estimation of nonpoint source loadings with data obtained from limited sampling programs

The feasibility of estimating nonpoint source loadings with data obtained from limited sampling programs was analyzed in conjunction with a study of sediment and nutrient loadings in a Swedish river basin. The study showed that different loading estimation methods can yield significantly different results, even if sampling during events (e.g. peak flows) occurs. This was particularly true for the temporal distribution of the estimated loadings. The estimated spatial distribution of loadings in the monitored subbasins was more independent of the applied estimation technique. Theoretical calculations showed that sampling strategies with evenly spaced sampling intervals may systematically over- or underestimate the true loading.The study basin was characterized by a pronounced snowmelt period and partly erosion-controlled nutrient loadings. Guidelines for the estimation of nonpoint loadings in such basins are summarized in a matrix. Factors influencing the choice of estimation method include the characteristics of the collected data, the relative influence of point sources, and the desired detail of loading estimates. Possible correlations between flow and concentration, and the presence of extreme events (and whether or not the events were sampled), also determine the appropriateness of the different methods.     

Network design factors for assessing temporal variability in ground-water quality

Benchmark major ions and nutrients data were collected biweekly for about two years at 12 wells at two sites in a shallow sand and gravel aquifer in west-central Illinois. The purpose of the study was to explore the time series properties of ground-water quality data collected at a relatively high sampling frequency. A secondary purpose was to determine the relative magnitudes of natural and sampling-related sources of variance in ground-water quality time series. The absence of this kind of information has severely hindered the design of ground-water sampling programs in the past.An autocorrelation analysis showed that the median sampling frequency for which the predicted ratio of effective independent sample size to total sample size was 0.5 (50% sampling redundancy) ranged from 6 to 14 samples per year. For a predicted ratio of effective independent sample size to total sample size of 0.9 (10% sampling redundancy) the sampling frequency ranged from 3 to 6 samples per year. This suggests that, for the wells sampled, sampling frequencies much higher than monthly can result in considerable loss of information, and may not be cost effective. Care was taken in the design of the field and laboratory sampling protocol to minimize the effects of measurement error. The data analysis confirmed that this goal was accomplished. In most cases considerably less than five percent of the total variability could be attributed to sampling and analytical error. Because of the relatively short duration of the study (42 biweekly sampling occasions at most wells) it was not possible to identify the magnitude of seasonal variations reliably.     

Economics of place-based monitoring under the safe drinking water act, part II: Design and development of place-based monitoring strategies

The goals of environmental legislation and associated regulations are to protect public health, natural resources, and ecosystems. In this context, monitoring programs should provide timely and relevant information so that the regulatory community can implement legislation in a cost-effective and efficient manner. The Safe Drinking Water Act (SDWA) of 1974 attempts to ensure that public water systems (PWSs) supply safe water to its consumers. As is the case with many other federal environmental statutes, SDWA monitoring has been implemented in relatively uniform fashion across the United States. In this three part series, spatial and temporal patterns in water quality data are utilized to develop, compare, and evaluate the economic performance of alternative place-based monitoring approaches to current monitoring practice. Part II: Several factors affect the performance of monitoring strategies, including: measurable objectives, required precision in estimates, acceptable confidence levels of such estimates, available budget for sampling. In this paper, we develop place-based monitoring strategies based on extensive analysis of available historical water quality data (1960-1994) of 19 Iowa community water systems. These systems supply potable water to over 350,000 people. In the context of drinking water, the objective is to protect public health by utilizing monitoring resources to characterize contaminants that are detectable, and are close to exceeding health standards. A place-based monitoring strategy was developed in which contaminants were selected based on their historical occurrence, rather than their appearance on the SDWA contaminant list. In a subset of the water systems, the temporal frequency of monitoring for one ubiquitous contaminant, nitrate, was tailored to patterns in its historical occurrence and concentration. Three sampling allocation models (linear, quadratic, and cubic) based on historic patterns in peak occurrence were developed and evaluated. Random and fixed-interval sampling strategies within the context of such models were also developed and evaluated. Strategies were configured to incorporate a variety of options for frequency and number of samples (depending on budget and the desired precision in estimate of peak concentrations).     

A cost-precision model for marine environmental monitoring,based on time-integrated averages

Ongoing marine monitoring programs are seldom designed to detect changes in the environment between different years, mainly due to the high number of samples required for a sufficient statistical precision. We here show that pooling over time (time integration) of seasonal measurements provides an efficient method of reducing variability, thereby improving the precision and power in detecting inter-annual differences. Such data from weekly environmental sensor profiles at 21 stations in the northern Bothnian Sea was used in a cost-precision spatio-temporal allocation model. Time-integrated averages for six different variables over 6 months from a rather heterogeneous area showed low variability between stations (coefficient of variation, CV, range of 0.6–12.4%) compared to variability between stations in a single day (CV range 2.4–88.6%), or variability over time for a single station (CV range 0.4–110.7%). Reduced sampling frequency from weekly to approximately monthly sampling did not change the results markedly, whereas lower frequency differed more from results with weekly sampling. With monthly sampling, high precision and power of estimates could therefore be achieved with a low number of stations. With input of cost factors like ship time, labor, and analyses, the model can predict the cost for a given required precision in the time-integrated average of each variable by optimizing sampling allocation. A following power analysis can provide information on minimum sample size to detect differences between years with a required power. Alternatively, the model can predict the precision of annual means for the included variables when the program has a pre-defined budget. Use of time-integrated results from sampling stations with different areal coverage and environmental heterogeneity can thus be an efficient strategy to detect environmental differences between single years, as well as a long-term temporal trend. Use of the presented allocation model will then help to minimize the cost and effort of a monitoring program.     

稀释倍数法测定污水色度的方法

研究选择了有代表性的6种色调的污水样品为研究对象,系统比较了不同色调、不同人员、不同稀释方法对测定结果的影响。选用红、橙、黄、绿、蓝、紫6种不同色调的样品进行测定,绿色、蓝色和黄色的测定结果分散在2个倍数之间,其中绿色的集中度高达84%。红色、橙色、紫色分散在3个倍数之间,紫色的集中度高达100%。对比自然倍数稀释法、偶数倍稀释法和2n倍稀释法,这3种方法同时测定红色调的样品,其测定结果的区间是相同的,集中区域内的人员分别占总人数的71%、83%和94%。结果表明:用1名实验人员使用自然倍数稀释法对样品进行单次分析作为色度测定的方法是有效的。     

Geostatistical analysis as applied to two environmental radiometric time series

This article details the results of an investigation into the application of geostatistical data analysis to two environmentalradiometric time series. The data series employed consist of ⁹⁹Tc values for seaweed (Fucus vesiculosus) and seawater samples taken as part of a marine monitoring program conducted on the coast of northern Norway by the Norwegian Radiation Protection Authority. Geostatistical methods were selected in order to provide information on values of the variables at unsampled times and to investigate the temporalcorrelation exhibited by the data sets. This information is ofuse in the optimisation of future sampling schemes and for providing information on the temporal behaviour of the variablesin question that may not be obtained during a cursory analysis.The results indicate a high degree of temporal correlation withinthe data sets, the correlation for the seawater and seaweed databeing modelled with an exponential and linear function,respectively. The semi-variogram for the seawater data indicatesa temporal range of correlation of approximately 395 days with noapparent random component to the overall variance structure and was described best by an exponential function. The temporal structure of the seaweed data was best modelled by a linear function with a small nugget component. Evidence of drift was present in both semi-variograms. Interpolation of the data setsusing the fitted models and a simple kriging procedure were compared, using a cross-validation procedure, with simple linearinterpolation. Results of this exercise indicate that, for theseawater data, the kriging procedure outperformed the simpleinterpolation with respect to error distribution andcorrelation of estimates with actual values. Using theunbounded linear model with the seaweed data produced estimatesthat were only marginally better than those produced by thesimple interpolation.     

Evaluating performance of stormwater sampling approaches using a dynamic watershed model

Accurate quantification of stormwater pollutant levels is essential for estimating overall contaminant discharge to receiving waters. Numerous sampling approaches exist that attempt to balance accuracy against the costs associated with the sampling method. This study employs a novel and practical approach of evaluating the accuracy of different stormwater monitoring methodologies using stormflows and constituent concentrations produced by a fully validated continuous simulation watershed model. A major advantage of using a watershed model to simulate pollutant concentrations is that a large number of storms representing a broad range of conditions can be applied in testing the various sampling approaches. Seventy-eight distinct methodologies were evaluated by "virtual samplings" of 166 simulated storms of varying size, intensity and duration, representing 14 years of storms in Ballona Creek near Los Angeles, California. The 78 methods can be grouped into four general strategies: volume-paced compositing, time-paced compositing, pollutograph sampling, and microsampling. The performances of each sampling strategy was evaluated by comparing the (1) median relative error between the virtually sampled and the true modeled event mean concentration (EMC) of each storm (accuracy), (2) median absolute deviation about the median or "MAD" of the relative error or (precision), and (3) the percentage of storms where sampling methods were within 10% of the true EMC (combined measures of accuracy and precision). Finally, costs associated with site setup, sampling, and laboratory analysis were estimated for each method. Pollutograph sampling consistently outperformed the other three methods both in terms of accuracy and precision, but was the most costly method evaluated. Time-paced sampling consistently underestimated while volume-paced sampling over estimated the storm EMCs. Microsampling performance approached that of pollutograph sampling at a substantial cost savings. The most efficient method for routine stormwater monitoring in terms of a balance between performance and cost was volume-paced microsampling, with variable sample pacing to ensure that the entirety of the storm was captured. Pollutograph sampling is recommended if the data are to be used for detailed analysis of runoff dynamics.     

Trend, seasonal and multivariate modelling study of wet precipitation data from the Austrian Monitoring Network (1990-1997)

The aim of the present study was to analyse the data structure of a large data set from rainwater samples collected during a long-term interval (1990-1997) by the Austrian Precipitation Monitoring Network. Eleven sampling sites from the network were chosen as data sources (chemical concentrations of major ions only) covering various location characteristics (height above sea level, rural and urban sampling positions, Alpine rim and Alpine valley disposition, etc.). The analytical results were treated by the application of already classical environmetric approaches, such as linear regression analysis, time-series analysis and principal components analysis (PCA). For most of the sampling sites, a distinct trend of acidity decrease of the wet precipitation was observed. An overall decrease in sulfate concentration for the whole period and all sites of 3.9% year(-1) (2.0 muequiv. L(-1) year(-1)) was found. The free acidity decrease for most of the sites was between 3.5 and 10.9% year(-1). No significant linear trends were found for nitrate. Base cations either decreased (mean percentage decrease for calcium was 5.4% year(-1) and for magnesium 4.4% year(-1)) or did not show any significant change (sodium, potassium). The overall decrease in ammonium concentration was 2.3% year(-1). Further, some typical "rural" (summer minima and winter maxima) and "urban" (winter minima and spring maxima) seasonal behaviour for the majority of the sites in consideration could be defined, indicating the influence of local emission sources. Several latent factors, named "anthropogenic", "crustal" and "mixed salt", were revealed by the multivariate modelling procedure (PCA) possessing a similar structure for most of the sites. The unavoidable exceptions observed were indications of the influence of sporadic local events (construction and agricultural activities, secondary emission sources, etc.), and an effort was made to explain these exceptions.     

Spatio-temporal variability of solid, total dissolved and labile metal: passive vs. discrete sampling evaluation in river metal monitoring

In order to obtain representative dissolved and solid samples from the aquatic environment, a spectrum of sampling methods are available, each one with different advantages and drawbacks. This article evaluates the use of discrete sampling and time-integrated sampling in illustrating medium-term spatial and temporal variation. Discrete concentration index (CI) calculated as the ratio between dissolved and solid metal concentrations in grab samples are compared with time-integrated concentration index (CI) calculated from suspended particulate matter (SPM) collected in sediment traps and labile metals measured by the diffusive gel in thin films (DGT) method, collected once a month during one year at the Seine River, upstream and downstream of the Greater Paris Region. Discrete CI at Bougival was found to be significantly higher than at Triel for Co, Cu, Mn, Ni and Zn, while discrete metal partitioning at Marnay was found to be similar to Bougival and Triel. However, when using time-integrated CI, not only was Bougival CI significantly higher than Triel CI, CI at Marnay was also found to be significantly higher than CI at Triel which was not observed for discrete CI values. Since values are time-averaged, dramatic fluctuations were smoothed out and significant medium-term trends were enhanced. As a result, time-integrated concentration index (CI) was able to better illustrate urbanization impact between sites when compared to discrete CI. The impact of significant seasonal phenomenon such as winter flood, low flow and redox cycles was also, to a certain extent, visible in time-integrated CI values at the upstream site. The use of time-integrated concentration index may be useful for medium- to long-term metal studies in the aquatic environment.     

Concentration gradient patterns of aerosol particles near interstate highways in the Greater Cincinnati airshed

The objective of this study was to determine if there is an exposure gradient in particulate matter concentrations for people living near interstate highways, and to determine how far from the highway the gradient extends. Air samples were collected in a residential area of Greater Cincinnati in the vicinity of two major highways. The measurements were conducted at different distances from the highways by using ultrafine particle counters (measurement range: 0.02-1 microm), optical particle counters (0.3-20 microm), and PM2.5 Harvard Impactors (0.02-2.5 microm). The collected PM2.5 samples were analyzed for mass concentration, for elemental and organic carbon, and for elemental concentrations. The results show that the aerosol concentration gradient was most clearly seen in the particle number concentration measured by the ultrafine particle counters. The concentration of ultrafine particles decreased to half between the sampling points located at 50 m and 150 m downwind from the highway. Additionally, elemental analysis revealed a gradient in sulfur concentrations up to 400 m from the highway in a residential area that does not have major nearby industrial sources. This gradient was qualitatively attributed to the sulfate particle emissions from diesel engine exhausts, and was supported by the concentration data on several key elements indicative of traffic sources (road dust and diesel exhaust). As different particulate components gave different profiles of the diesel exposure gradient, these results indicate that no single element or component of diesel exhaust can be used as a surrogate for diesel exposure, but more comprehensive signature analysis is needed. This characterization is crucial especially when the exposure data are to be used in epidemiological studies.     

Is PM10 mass measurement a reliable index for air quality assessment? An environmental study in a geographical area of north-eastern Italy

The aim of this study was to measure the concentration of some metals (Cd, Cr, Cu, Fe, Mn, Ni, Pb, and Ti) in PM(10) samples collected in one urban and one industrial site and to assess that PM(10) total mass measurement may be not sufficient as air quality index due to its complex composition. Metals were determined by inductively coupled plasma-atomic emission spectroscopy (ICP-AES) and differential pulsed anodic stripping voltammetry (DPASV). The measured concentrations were used to calculate the content of metals in the PM(10) total mass, and to estimate the enrichment factors and the correlations between PM(10), metal concentrations and meteorological data for the two sites. The mean PM10 concentration during the sampling period in the urban site exceeded the annual European Union (EU) standard (40 microg/m(3)) and, for some sampling days, the daily EU standard (50 microg/m(3)) was also exceeded. In opposite, both EU standards were never exceeded in the industrial site. The overall metal content was nearly double in the industrial site compared to the urban one, and the mean Ni concentration exceeded the EU annual limit value (10 ng/m(3)). The metals with the highest enrichment factor were Cd, Cu, Ni and Pb for both sites, suggesting a dominant anthropogenic source for these metals. Metal concentrations were very low and typical of rural background during Christmas holidays, when factories were closed. PM(10) total mass measurement is not a sufficient air quality index since the metal content of PM(10) is not related to its total mass, especially in sites with industrial activities. This measurement should be associated with the analysis of toxic metals.     

环境空气颗粒物来源解析受体采样频率优选方法初探

环境空气颗粒物来源解析受体样品化学组成的时空差异,除受采样点位分布制约外,主要还受采样时间的制约。如何选取时间段及采样周期显得尤为重要。通过青岛市空气自动监测系统近几年获取的颗粒物连续时均值数据,采用统计优化组合选择法,筛选出日均值、年均值的最佳采样时段,可供类似研究参考。     

Optimization of sampling for the determination of mean radium-226 concentration in surface soil

There are thousands of properties in the United States on which the soil has been contaminated to some degree with uranium mill tailings. An effort is now underway by the United States Department of Energy to identify sites contaminated with tailings and to perform remedial action when ²²⁶Ra levels exceed current guidelines. Because of the large number of sites involved, it is imperative that sample collection be performed in a cost-effective manner. In this paper we describe the results of a study in which we compared the efficiencies of different methods of sample collection in order to determine an optimal method for estimating the mean ²²⁶Ra concentration in soil. The study involved a field experiment in which extensive sampling was performed on sites known to be contaminated with uranium tailings. The experiment was designed to identify the advantages and limitations of composite sampling, the relative merits of random and uniformly spaced sample collection, the use of field gamma measurements for supplementing and reducing soil sample collection, and practical levels of accuracy and precision that can be obtained. Conclusions regarding gamma measurements are unique to ²²⁶Ra contamination. On the other hand, conclusions concerning composite sampling and random versus uniformly spaced sampling may depend primarily on the way the contamination was spread by man and hence may not be unique to ²²⁶Ra.Research sponsored by the Department of Remedial Action Projects, Office of Terminal Waste Disposal, U. S. Department of Energy under contract DE-AC05-84OR21400 with the Martin Marietta Energy Systems, Inc.ORNL—Grand Junction Office, Grand Junction, Colorado.     

Honeybees and their products as potential bioindicators of heavy metals contamination

The concentrations of three representative heavy metals(cadmium, chromium and lead) were measured by atomic absorptionspectroscopy in honeybees and in apiary's products (honey,pollen, propolis, and wax). Samples were collected from fivedifferent sampling points: four from areas surrounding the cityof Rome, and the fifth in the city center which receives intensevehicular traffic. All apiaries employed for this study werespecifically constructed without any metal part in order toavoid the risk of contamination of the assayed materials.Sample collection was conducted over a 3-month period (6samplings for honey and pollen, 3 sampling for propolis and wax,2 samplings for honeybees, all of which were collected in duplicate). Experimental data revealed, in general,statistically significant differences between the backgroundlevels of heavy metals recorded from the reference sites and thelevels measured in the site located in the center of the city ofRome.These results indicate that honeybees and, to a lesser extent,some of their products (pollen, propolis, wax, but not honey),can be considered representative bioindicators of environmentalpollution.     

三峡库区大宁河回水段水华暴发时空分布特征分析

对三峡库区一级支流大宁河巫山段2004年到2008年常规监测、水华期间加密监测以及单个测点水华垂线24小时监测数据进行了统计、分析及评价。结果发现,该河段水华暴发具有明显的时空分布特征,时间上,全年各月份均有出现,但主要集中在3～6月,占观测频次的79%;24小时内叶绿素a含量在下午16:00～18:00时间段内达到最大值;空间上,总体呈现出离入长江口越近暴发的频次越高,回水河湾较平直河道,宽阔河段较狭窄河段更重的趋势,主要集中在大宁河的开阔河段(小三峡三个峡谷之间及其上、下游开阔河段);24小时内水华垂线分布明显,0.5～5m的叶绿素a含量上层高于下层,水面下30m及以下区域叶绿素a含量变化不显著。