Development and validation of methods for environmental monitoring of cyclophosphamide in workplaces

Methods to monitor contamination of workplaces with antineoplastic drugs have been developed and validated. Cyclophosphamide (CP) was used as a model compound as it is one of the most commonly used antineoplastic drugs. A wipe sampling method to detect contamination with CP at surfaces was developed. A personal air sampling method to sample gas and vapour on solid sorbent tubes and particles with filters was also developed. Wipe and filter samples were extracted and sorbent samples were eluted, all with ethyl acetate. The samples were analysed with liquid chromatography tandem mass spectrometry. (2)H(6)-labelled cyclophosphamide was used as an internal standard. The between-day precision was 2-5% for wipe samples, 4-6% for sorbent samples and 3-8% for filter samples. The limit of detection was 0.02 ng CP per sample for the wipe and filter methods and 0.03 ng CP per sample for the solid sorbent method. Wipe sampling on surfaces made of different materials resulted in mean recoveries between 78-106%. The desorption recovery was between 97-102% for the wipe samples, 97% for the sorbent samples and 101% for the filter samples. Samples were stable for up to 2 months at 5 degrees C and -20 degrees C and for about 2 d at room temperature. The developed methods were applied to the measurement of contamination with CP in a hospital pharmacy. Trace amounts of CP, 1.3 and 1.4 ng, were detected on surfaces in the pharmacy.     

Quality assurance and quality control data validation procedures used for the Love Canal and Dallas lead soil monitoring programs

Public awareness of soils contamination has increased in recent years due in part to the notoriety associated with the indiscriminate release, packaging, transporting and disposal of hazardous materials. In 1980, and again in 1982, the U.S. Environmental Protection Agency (EPA) was responsible for designing, implementing and conducting environmental monitoring programs at Love Canal in Niagara Falls, New York, and in Dallas, Texas, that dealt with suspected contaminated soils. Both of these monitoring programs were conducted over a relatively short time with the collection and analysis of over 4000 soil samples. The methods employed by the Environmental Protection Agency for providing soil data that was scientifically valid and of defensible quality for each of these monitoring programs are presented. Also, methods for identifying data bias, its precision and its uncertainty are identified.     

工业企业噪声监测工作中几个难点问题的探讨

通过举例对噪声监测实践工作中常易遇到的几个具有普遍性的难点问题作一探讨,以提高大家在解决实际问题时对声学理论、噪声标准及测量方法的重视。     

Using a neural network approach and time series data from an international monitoring station in the Yellow Sea for modeling marine ecosystems

The international marine ecological safety monitoring demonstration station in the Yellow Sea was developed as a collaborative project between China and Russia. It is a nonprofit technical workstation designed as a facility for marine scientific research for public welfare. By undertaking long-term monitoring of the marine environment and automatic data collection, this station will provide valuable information for marine ecological protection and disaster prevention and reduction. The results of some initial research by scientists at the research station into predictive modeling of marine ecological environments and early warning are described in this paper. Marine ecological processes are influenced by many factors including hydrological and meteorological conditions, biological factors, and human activities. Consequently, it is very difficult to incorporate all these influences and their interactions in a deterministic or analysis model. A prediction model integrating a time series prediction approach with neural network nonlinear modeling is proposed for marine ecological parameters. The model explores the natural fluctuations in marine ecological parameters by learning from the latest observed data automatically, and then predicting future values of the parameter. The model is updated in a “rolling” fashion with new observed data from the monitoring station. Prediction experiments results showed that the neural network prediction model based on time series data is effective for marine ecological prediction and can be used for the development of early warning systems.     

Statistical models for evaluating suspected artefacts in long-term environmental monitoring data

Long-term water quality monitoring is of high value for environmental management as well as for research. Artificial level shifts in time series due to method improvements, flaws in laboratory practices or changes in laboratory are a common limitation for analysis, which, however, are often ignored. Statistical estimation of such artefacts is complicated by the simultaneous existence of trends, seasonal variation and effects of other influencing factors, such as weather conditions. Here, we investigate the performance of generalised additive mixed models (GAMM) to simultaneously identify one or more artefacts associated with artificial level shifts, longitudinal effects related to temporal trends and seasonal variation, as well as to model the serial correlation structure of the data. In the same model, it is possible to estimate separate residual variances for different periods so as to identify if artefacts not only influence the mean level but also the dispersion of a series. Even with an appropriate statistical methodology, it is difficult to quantify artificial level shifts and make appropriate adjustments to the time series. The underlying temporal structure of the series is especially important. As long as there is no prominent underlying trend in the series, the shift estimates are rather stable and show less variation. If an artificial shift occurs during a slower downward or upward tendency, it is difficult to separate these two effects and shift estimates can be both biased and have large variation. In the case of a change in method or laboratory, we show that conducting the analyses with both methods in parallel strongly improves estimates of artefact effects on the time series, even if certain problems remain. Due to the difficulties of estimating artificial level shifts, posterior adjustment is problematic and can lead to time series that no longer can be used for trend analysis or other analysis based on the longitudinal structure of the series. Before carrying out a change in analytic method or laboratory, it should be considered if this is absolutely necessary. If changes cannot be avoided, the analysis of the two methods considered, or the two laboratories contracted, should be run in parallel for a considerable period of time so as to enable a good assessment of changes introduced to the data series.     

Applying parcel-specific land-use data for improved monitoring of semi-natural grassland in Denmark

A major obstacle to the conservation and management of semi-natural grassland (SNG) is the general lack of consistent and precise maps showing the extent and quality of this habitat type. SNG is related to, and influenced by, agricultural land use. Both intensive land use as well as a lack of extensive land use can reduce the extent and quality of SNG. For Denmark, this paper demonstrates how parcel-specific land-use data can be applied to detect localities with spatial convergences and conflicts between SNG and agricultural land use. Based on their respective influence on SNG, land use is aggregated into five major classes and spatially overlaid with existing nationally registered SNG. Results show that almost 11 % of all SNG is mapped either on cropped land or on rotational grassland, indicating a conflict between mapped SNG and land use. Thirty percent of SNG is mapped outside any agricultural land use, pointing to a lack of management. Fifty nine percent of SNG is mapped either on land receiving payments for grazing/mowing or on other land under extensive management, indicating a convergence between SNG and land use. Finally, 30 % of land receiving payments for grazing/mowing and 62 % of other land under extensive management does not contain any semi-natural habitat. Potential, as yet unmapped, SNG might exist in these localities. Based on these results it is argued that the application of parcel-specific land-use data could significantly improve monitoring of SNG.     

A gas-phase biosensor for environmental monitoring of formic acid: laboratory and field validation

In order to encourage more exposure measurements to be performed, a formic acid gas-phase biosensor has been developed for this purpose. In the present paper, an enzyme based biosensor has been validated with respect to analyte selectivity and on-site use. To ensure that the sampler developed measures the compound of interest the biosensor was exposed to three near structural homologues to formic acid, i.e. acetic acid, methanol and formaldehyde. These vapours were generated with and without formic acid and the only compound that was found to have an effect on the performance of the biosensor, albeit a small one, was acetic acid. The field test was performed in a factory using formic acid-containing glue for glulam products. In parallel to the measurements with the biosensor a well defined reference method was used for sampling and analysing formic acid. It was found that the biosensor worked satisfactorily in this environment when used in a stationary position. It was also shown that the biosensor could determine formic acid vapour concentrations down to 0.03 mg m(-3).     

相似分析在空气连续监测缺测资料插补处理中的应用

空气连续自动监测中缺测资料的插补处理是环境监测与科研的一项基础性的技术工作,文章应用数理统计中的相似分析原理进行探讨,取得了较理想的结果.该方法对解决和处理因采样时间不足而造成的污染物浓度日均值缺失问题是十分有效的,对目前我国开展的城市空气质量日报工作有一定的实际意义.     

Guidelines for evaluating selenium data from aquatic monitoring and assessment studies

It is now possible to formulate diagnostic selenium concentrations in four distinct ecosystem-level components; water, food-chain, predatory fish (consuming fish or invertebrate prey), and aquatic birds. Waterborne selenium concentrations of 2 µg/l or greater (parts per billion; total recoverable basis in 0.45 filtered samples) should be considered hazardous to the health and long-term survival of fish and wildlife populations due to the high potential for food-chain bioaccumulation, dietary toxicity, and reproductive effects. In some cases, ultra-trace amounts of dissolved and particulate organic selenium may lead to bioaccumulation and toxicity even when total waterborne concentrations are less than 1 µg/l.Food-chain organisms such as zooplankton, benthic invertebrates, and certain forage fishes can accumulate up to 30 µg/g dry weight selenium (some taxa up to 370 µg/g) with no apparent effect on survival or reproduction. However, the dietary toxicity threshold for fish and wildlife is only 3 µg/g; these food organisms would supply a toxic dose of selenium while being unaffected themselves. Because of this, food-chain organisms containing 3 µg/g (parts per million) dry weight or more should be viewed as potentially lethal to fish and aquatic birds that consume them.Biological effects thresholds (dry weight) for the health and reproductive success of freshwater and anadromous fish are: whole body=4 µg/g; skeletal muscle=8 µg/g; liver=12 µg/g; ovaries and eggs=10 µg/g. Effects thresholds for aquatic birds are: liver=10 µg/g; eggs=3 µg/g. The most precise way to evaluate potential reproductive impacts to adult fish and aquatic bird populations is to measure selenium concentrations in gravid ovaries and eggs. This single measure integrates waterborne and dietary exposure, and allows an evaluation based on the most sensitive biological endpoint. Resource managers and aquatic biologists should obtain measurements of selenium concentrations present in water, food-chain organisms, and fish and wildlife tissues in order to formulate a comprehensive and conclusive assessment of the overall selenium status and health of aquatic ecosystems.     

实验室监测数据的合理性检验

通过比较总含盐量与溶解性总固体的关系、阴阳离子的平衡关系、总硬度与钙镁离子的关系及三氧之间的关系,进而检验实验监测数据的合理性和可靠性。     

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.     

Qualitative time trend analysis of ground water monitoring networks

The detection of significant (short-term) time trends is one of the major goals of ground water monitoring networks. These trends can be used to recognize active geochemical processes and potential environmental threats. This paper presents a case history of time trend analysis on macrochemical parameters of ground water quality. It shows the difficulties and traps that are generally encountered in such studies. The data used originated from the Dutch National Groundwater Quality Monitoring Network. This network is operative since 1979, and keeps track of the ground water composition at 350 locations at two depths (ca. 10 and 25 m below surface; general density, one location per 100 km²). Prior to the trend analysis the data set was divided into geochemically homogeneous groups using fuzzy c-means clustering. Each group represents a specific ground water type, characterized by a distinct source (seawater, surface water or precipitation) and a unique combination of dominant geochemical processes (e.g. mineralization of organic matter, carbonate dissolution and cation exchange).To study trends qualitatively, the concentrations of the various macro-constituents in ground water are correlated with time of sampling. The nonparametric and outlier insensitive Spearman rank correlation coefficient is computed per well screen. A frequency distribution of correlation coefficients is formed by combining the Spearman correlation coefficients of all individual wells within a homogeneous group. This distribution is tested for trends against the appropriate theoretical distribution of zero correlation by use of the Kolmogorov-Smirnov one-sample test. The type of trend is derived from the shape of the distribution.Most ground water types show statistically significant qualitative trends, of which many, however, are caused by changes in the sampling and analytical procedures over the monitoring period. After elimination of differences in limits of detection for NO₃, total-P, and NH₄, most trends in these compounds disappeared. In some water types trends for alkalinity, apparent trends for pH, Ec, and total-P are caused by variations in the laboratory practice, e.g. varying storage procedures, leading to erroneous analyses. Other parameters showed statistically significant trends, related to geochemical processes.The most interesting and most substantial trends are observed in the water type characterized by infiltrating rainwater with agricultural pollutants. In this water type the lowering ground water table induces lower rates of evapotranspiration, giving lower concentrations in time of conservative parameters (Cl, Na, Ca). The aerated zone is enlarged, resulting in increased oxidation of organic material, less efficient nutrient (NO₃, K) uptake by plant roots, leading to increased ground water concentrations of nutrients. In other water types trends are quantitatively small. However, trends are not necessarily linear, and all should be closely monitored in future.     

长光程大气自动监测中异常数据的分析及处置

长光程大气自动监测系统部件较多,结构复杂,中心站对子站监控通过在线方式完成,因此对系统中故障的判断和排除有一定难度。通过对中心站调出的异常数据进行诊断和分析,帮助操作者提升对系统的了解和对异常数据处理的技能。     

Comparison of data from two vegetation monitoring methods in semi-natural grasslands

Two vegetation-monitoring methods were compared: subplot frequency analysis (SF) and visual estimation of percentage cover (VE). Two independent observers collected data from two semi-natural, species-rich grasslands on three different occasions during the growth-season. During the last data collection period, survey times were also recorded. The two different data sets from the two methods were compared using partial Redundancy Analyses. The purpose of the comparison was to identify the method that explains most of the relevant variation in biodiversity-monitoring (inter-and intra-site variation), and the variation irrelevant when evaluating data (systematic inter-observer variation and variation due to phenological changes). Compared with VE data, more variation in SF data could be explained by spatial variables, while less variation depended on the observer and time of year surveyed. SF also found more species per plot but took on average five times longer to complete than VE. In conclusion, the different methods are suitable for different purposes: SF is more suitable for purposes demanding high accuracy and high precision, such as long-term biodiversity-monitoring when the identification of small changes has high priority, while VE might be more suitable for a one-time mapping of a large area.     

Long-term environmental monitoring for assessment of change: measurement inconsistencies over time and potential solutions

The importance of long-term environmental monitoring and research for detecting and understanding changes in ecosystems and human impacts on natural systems is widely acknowledged. Over the last decades, a number of critical components for successful long-term monitoring have been identified. One basic component is quality assurance/quality control protocols to ensure consistency and comparability of data. In Norway, the authorities require environmental monitoring of the impacts of the offshore petroleum industry on the Norwegian continental shelf, and in 1996, a large-scale regional environmental monitoring program was established. As a case study, we used a sub-set of data from this monitoring to explore concepts regarding best practices for long-term environmental monitoring. Specifically, we examined data from physical and chemical sediment samples and benthic macroinvertebrate assemblages from 11 stations from six sampling occasions during the period 1996–2011. Despite the established quality assessment and quality control protocols for this monitoring program, we identified several data challenges, such as missing values and outliers, discrepancies in variable and station names, changes in procedures without calibration, and different taxonomic resolution. Furthermore, we show that the use of different laboratories over time makes it difficult to draw conclusions with regard to some of the observed changes. We offer recommendations to facilitate comparison of data over time. We also present a new procedure to handle different taxonomic resolution, so valuable historical data is not discarded. These topics have a broader relevance and application than for our case study.     

Estimation of underwater visibility in coastal and inland waters using remote sensing data

An optical method is developed to estimate water transparency (or underwater visibility) in terms of Secchi depth (Z _sd ), which follows the remote sensing and contrast transmittance theory. The major factors governing the variation in Z _sd , namely, turbidity and length attenuation coefficient (1/(c + K _d ), c = beam attenuation coefficient; K _d  = diffuse attenuation coefficient at 531 nm), are obtained based on band rationing techniques. It was found that the band ratio of remote sensing reflectance (expressed as (R _rs (443) + R _rs (490))/(R _rs (555) + R _rs (670)) contains essential information about the water column optical properties and thereby positively correlates to turbidity. The beam attenuation coefficient (c) at 531 nm is obtained by a linear relationship with turbidity. To derive the vertical diffuse attenuation coefficient (K _d ) at 531 nm, K _d (490) is estimated as a function of reflectance ratio (R _rs (670)/R _rs (490)), which provides the bio-optical link between chlorophyll concentration and K _d (531). The present algorithm was applied to MODIS-Aqua images, and the results were evaluated by matchup comparisons between the remotely estimated Z _sd and in situ Z _sd in coastal waters off Point Calimere and its adjoining regions on the southeast coast of India. The results showed the pattern of increasing Z _sd from shallow turbid waters to deep clear waters. The statistical evaluation of the results showed that the percent mean relative error between the MODIS-Aqua-derived Z _sd and in situ Z _sd values was within ±25%. A close agreement achieved in spatial contours of MODIS-Aqua-derived Z _sd and in situ Z _sd for the month of January 2014 and August 2013 promises the model capability to yield accurate estimates of Z _sd in coastal, estuarine, and inland waters. The spatial contours have been included to provide the best data visualization of the measured, modeled (in situ), and satellite-derived Z _sd products. The modeled and satellite-derived Z _sd values were compared with measurement data which yielded RMSE = 0.079, MRE = ?0.016, and R ²  = 0.95 for the modeled Z _sd and RMSE = 0.075, MRE = 0.020, and R ²  = 0.95 for the satellite-derived Z _sd products.     

The analysis of monitoring data with the aid of time-series analysis

Methods derived from time-series analysis are proposed for processing monitoring data. The necessity for the use of these methods is demonstrated. In a case study time-series analysis was applied to assess the impact of the closure of the Grevelingen estuary (S. W. Netherlands) in 1971 on a local wintering population of Oystercatchers (Haematopus ostralegus) in the adjacent Oosterschelde estuary.Communication No. 328 of the Delta Institute for Hydrobiological Research.     

计算机在环境监测数据辅助审核中的应用

以地面水环境监测数据为例 ,对实现监测数据计算机辅助审核的可行性以及审核的方法等进行了研究。指出实现监测数据计算机辅助审核是完全可行的。监测数据计算机辅助审核不仅将大大减轻数据审核技术人员的工作压力 ,也有利于提高监测数据审核工作的技术水平和工作质量     

Biological effects monitoring of the North Sea employing fish embryological data

Using the reproductive capacity of fish appears to be a suitable approach for risk assessment in the aquatic environment since fish are a typical representative thereof and in addition they are of considerable societal value. Generally the early embryonic stages are considered to be one of the most sensitive parts of a fish's life cycle. A method has been developed to use the state of health of live, naturally spawned fish embryos from plankton samples for biological effects monitoring. During the years 1985–1987 in the southern North Sea and in 1991–1992 in the whole of the North Sea fish eggs were sampled from surface waters and examined for developmental abnormalities. Elevated embryo malformation rates were detected in the plume of the major rivers Elbe and Rhine as well as along the eastern coast of England. Occurring malformations are thought to be pollution-related and may be used to define areas of environmental deterioration. The method is discussed in view of its suitability for biological effects monitoring using malformations in fish embryos as biomarkers.     

降雨对噪声自动监测数据的影响分析

选取北京市近5年夏半年(4—9月)的降雨数据及相关噪声自动监测小时等效声级,利用数学统计软件进行有雨-无雨声级差异性分析、声级-降雨相关性分析及平均声级-雨量变化趋势分析等,提出降雨确实对噪声自动监测小时数据有一定贡献,不同雨量对不同功能区噪声影响不同,建议噪声自动监测系统建设时应考虑雨噪声影响,以保证对声环境质量评价的科学性和准确性。