Spatial contaminant heterogeneity: quantification with scale of measurement at contrasting sites

Material within the terrestrial environment is rarely homogeneously distributed, either spatially or temporally. One consequence of heterogeneity is that uncertainty is usually generated in measurements that are taken with the aim of characterising the environment. For example, a measurement of analyte concentration within soil taken from one sampling location on contaminated land can vary substantially when compared against another sample taken at effectively the same nominal location. The measurement uncertainty arising from the heterogeneity can substantially limit the reliability of the interpretations made upon environmental investigations. The sampling uncertainty usually outweighs the analytical uncertainty from the laboratory, often by a factor of 20 or more. One approach to reducing the uncertainty is to design a more suitable sampling strategy. This might be achieved by predicting the degree of heterogeneity prior to the investigation, but this is often difficult to achieve accurately. Another approach, which was investigated here, is to actually characterise the heterogeneity prior to the main investigation using rapid and inexpensive technology, such as in situ measurement techniques. In situ portable X-ray fluorescence (PXRF) and X-ray microprobe (XMP) techniques were employed to test the feasibility of this approach. Two contrasting contaminated land sites were chosen to characterise the two-dimensional spatial heterogeneity of heavy metal contamination in topsoil at a range of scales (50 m to 0.001 m). The spatial heterogeneity of contaminants, expressed as relative standard deviations, was found to differ between the two sites by a factor of two, largely due to the mode of deposition of pollution. The study also indicated that the heterogeneity did not change systematically with the scale of measurement between sampling locations at either site.     

Assessing land take by urban development and its impact on carbon storage: Findings from two case studies in Italy

Land take due to urbanization triggers a series of negative environmental impacts with direct effects on quality of life for people living in cities. Changes in ecosystem services are associated with land take, among which is the immediate C loss due to land use conversion. Land use change monitoring represents the first step in quantifying land take and its drivers and impacts. To this end, we propose an innovative methodology for monitoring land take and its effects on ecosystem services (in particular, C loss) under multi-scale contexts. The devised approach was tested in two areas with similar sizes, but different land take levels during the time-span 1990–2008 in Central Italy (the Province of Rome and the Molise Region). The estimates of total coverage of built up areas were calculated using point sampling. The area of the urban patches including each sampling point classified as built up areas in the year 1990 and/or in the year 2008 is used to estimate total abundance and average area of built up areas. Biophysical and economic values for carbon loss associated with land take were calculated using InVEST.Although land take was 7–8 times higher in the Province of Rome (from 15.1% in 1990 to 20.4% in 2008) than in Molise region, our findings show that its relative impact on C storage is higher in the latter, where the urban growth consistently affects not only croplands but also semi-natural land uses such as grasslands and other wooded lands. The total C loss due to land take has been estimated in 1.6 million Mg C, corresponding to almost 355 million €.Finally, the paper discusses the main characteristics of urban growth and their ecological impact leading to risks and challenges for future urban planning and land use policies.     

Evaluation of the best compromise between the urban air quality monitoring resolution by diffusive sampling and resource requirements

The need to collect data representative of overall urban pollution is all-important in order to monitor the population exposure. High spatial resolution monitoring using diffusive samplers allows studying of the urban pollutant distribution, thus enabling deeper investigation of their generation and diffusion mechanisms. Nevertheless, such a monitoring campaign has a certain cost. In this study we point out how to find the best compromise between the number of necessary measurements and the affordable costs for monitoring campaigns. We also describe an innovative method for the proper design of a fixed urban monitoring network by means of preliminary high spatial resolution campaigns using diffusive samplers. Four European capital cities (Dublin, Madrid, Paris and Rome) were monitored six times, each time for seven days. Benzene, toluene, ethylbenzene, xylenes (BTEX) and NO(2) concentrations were measured at 146 sites in Dublin, 293 in Madrid, 339 in Paris and 290 in Rome. Multiscale grids have been drawn which ranged in mesh size from 500 m to 2 km. The statistical processing of data produced a twofold result: the creation of isoconcentration maps with geostatistical procedures, and an algorithm aimed at locating the minimum number of sampling sites where the fixed monitoring stations should be placed. Average urban levels estimated on the basis of these selected sites differ by less than 8% from those calculated on the whole populations of the sampled points. The aim of this work is to investigate how far the resolution of a monitoring campaign of urban pollution by diffusive sampling can be reduced, thus making the monitoring less expensive in terms of human and financial resources, while preserving the same quality of the results that could be achieved with a higher resolution. We found that there is no significant loss of information when the resolution of the monitoring grid for BTEX is lowered to a mesh size of 1.85 km, that is a sampling site each 3.4 km(2), and that the minimum number of sampling sites to be used is N = 0.29 A, where A is the urban surface to be monitored (in km(2)). As the spatial distribution of NO(2) is less sensitive to the distance from the emission source than that of BTEX, this relationship could be retained as a valid lower limit for the mesh grid size also for NO(2) monitoring.     

Assessment of sampling and analytical uncertainty of trace element contents in arable field soils

Assessment of trace element contents in soils is required in Germany (and other countries) before sewage sludge application on arable soils. The reliability of measured element contents is affected by measurement uncertainty, which consists of components due to (1) sampling, (2) laboratory repeatability (intra-lab) and (3) reproducibility (between-lab). A complete characterization of average trace element contents in field soils should encompass the uncertainty of all these components. The objectives of this study were to elucidate the magnitude and relative proportions of uncertainty components for the metals As, B, Cd, Co, Cr, Mo, Ni, Pb, Tl and Zn in three arable fields of different field-scale heterogeneity, based on a collaborative trial (CT) (standardized procedure) and two sampling proficiency tests (PT) (individual sampling procedure). To obtain reference values and estimates of field-scale heterogeneity, a detailed reference sampling was conducted. Components of uncertainty (sampling person, sampling repetition, laboratory) were estimated by variance component analysis, whereas reproducibility uncertainty was estimated using results from numerous laboratory proficiency tests. Sampling uncertainty in general increased with field-scale heterogeneity; however, total uncertainty was mostly dominated by (total) laboratory uncertainty. Reproducibility analytical uncertainty was on average by a factor of about 3 higher than repeatability uncertainty. Therefore, analysis within one single laboratory and, for heterogeneous fields, a reduction of sampling uncertainty (for instance by larger numbers of sample increments and/or a denser coverage of the field area) would be most effective to reduce total uncertainty. On the other hand, when only intra-laboratory analytical uncertainty was considered, total sampling uncertainty on average prevailed over analytical uncertainty by a factor of 2. Both sampling and laboratory repeatability uncertainty were highly variable depending not only on the analyte but also on the field and the sampling trial. Comparison of PT with CT sampling suggests that standardization of sampling protocols reduces sampling uncertainty, especially for fields of low heterogeneity.     

Passive sampling of glycol ethers and their acetates in indoor air

This study examined the performances of a thermal desorbable radial diffusive sampler for the weekly measurement of eight glycol ethers in indoor air and described the results of an application of this method carried out as part of HABIT'AIR Nord - Pas de Calais program for the air monitoring of these compounds in sixty homes located in northern France. The target compounds were the four glycol ethers banned from sale to the public in France since the 1990s (i.e. 2-methoxy ethanol, 2-ethoxy ethanol and their acetates) and four other glycol ethers derivatives of which the use have increased considerably (i.e. 1-methoxy-2-propanol, 2-butoxy ethanol and their acetates).A test program was carried out with the aim of validating the passive sampling method. It allowed the estimation of all the parameters of a method for each compound (calibration, analytical precision, desorption efficiency, sampling rate in standard conditions, detection limit and stability of sample before and after exposure), the examination of the influence of environmental factors on the sampling rate by some exposure chamber experiments and the assessment of the uncertainty of the measurements.The results of this evaluation demonstrated that the method has turned out to be suitable for six out of eight glycol ethers tested. The effect of the environmental factors on the sampling rates was the main source of measurement uncertainty. The measurements done in sixty homes revealed a relative abundance of 1-methoxy-2-propanol that was found in more than two thirds of homes at concentration levels of 4.5 microg m(-3) on average (a maximum value of 28 microg m(-3)). 1-methoxy-2-propanol acetate and 2-butoxy ethanol were also detected, but less frequently (in 19% of homes) and with the concentrations below 12 microg m(-3). The highest levels of these glycol ethers appear to be in relation to the emissions occurring at the time of cleaning tasks.     

Integrating Land Market Feedbacks into Conservation Planning—A Mathematical Programming Approach

Nature reserves are often designated ad hoc. Despite increasing conservation efforts, loss of biodiversity is still accelerating. Considering land scarcity and demand for alternative uses, efficiency in conservation strongly correlates with efficiency in land allocation. Systematic conservation planning can effectively prioritize conservation activities. Previous studies minimize opportunity costs for given conservation targets. However, these studies assume constant marginal costs of habitat protection. We extend this cost minimization approach by also considering a dynamic representation of marginal costs. The more land is allocated to nature reserves, the higher are opportunity costs, i.e., costs of forgone agricultural production. This increase in costs results from changes in the prices of agricultural commodities. We employ a deterministic, spatially explicit mathematical optimization model to allocate species habitats by minimizing opportunity costs for setting aside land for conservation purposes. The model is designed as a mixed integer programming problem and solved with GAMS/CPLEX. Our results show the need for integrating land market feedbacks into conservation planning. We find that ignoring land rent adjustments can lead to highly cost-ineffective solutions in reserve selection.     

Monitoring and assessment of land use status by GIS

The objectives of this study were to assess land suitability andto monitor the spatial and temporal changes in land use types (LUTs)by using GIS. The study was conducted in an irrigated area of 920ha, located in the East Mediterranean region of Turkey. Archiveddata of LUTs were utilized. Data analysis was conducted by usingArcInfo and ArcView GIS softwares. The land characteristics werecompared with land requirements of present LUTs by taking intoaccount the land evaluation results. Three major soil series andtwelve different LUTs were identified. Corn and citrus were themost planted LUTs in the assessment period. Generated mapsdemonstrated how LUTs changed spatially and temporally. Most LUTswere clustered in certain areas indicating that farmer'sselection of LUTs was affected by their habits and neighbor'sbehavior. Land suitability assessment results indicated thatCanakci and Mursel soil series were found to be highly suitable forall LUTs while Arikli soil series was determined to be moderately suitablecitrus, vegetables and watermelon. Citrus plantationmaps overlaid on soil series maps demonstrated that the citrushas not been planted completely on suitable areas. Landsuitability assessment showed that citrus plantation in Arikliseries would result in 40% yield loss and 58% of land is notused at its potential.     

Intercomparison of five PM10 monitoring devices and the implications for exposure measurement in epidemiological research

Five different instruments for the determination of the mass concentration of PM10 in air were compared side-by-side for up to 33 days in an undisturbed indoor environment: a tripod mounted BGI Inc. PQ100 gravimetric sampler with a US EPA certified Graseby Andersen PM10 inlet; an Airmetrics Minivol static gravimetric sampler; a Casella cyclone gravimetric personal sampler; an Institute of Occupational Medicine gravimetric PM10 personal sampler; and two TSI Inc. Dustrak real-time optical scattering personal samplers. For 24 h sampling of ambient PM10 concentrations around 10 microg m(-3), the estimated measurement uncertainty for the two gravimetric personal samplers was larger (approximately +/- 20%) compared with estimated measurement uncertainty for the PQ100/Graseby Andersen sampler (< +/- 5%). Measurement uncertainty for the Dustraks was lower (approximately +/- 15% on average) but calibration of the optical response against a reference PM10 method is essential since the Dustraks systematically over-read PM10 determined gravimetrically by a factor approximately 2.2. However, once calibrated, the Dustrak devices demonstrated excellent functionality in terms of ease of portability and real-time data acquisition. Estimated measurement uncertainty for PM10 concentrations determined with the Minivol were +/- 5%. The Minivol data correlated well with PQ100/Graseby Andersen data (r= 0.97, n = 18) but were, on average, 23% greater. The reason for the systematic discrepancy could not be traced. Intercomparison experiments such as these are essential for assessing measurement error and revealing systematic bias. Application of two Dustraks demonstrated the spatial and temporal variability of exposure to PM10 in different walking and transport microenvironments in the city of Edinburgh, UK. For example, very large exposures to PM10 were identified for the lower deck of a double-decker tour bus compared with the open upper deck of the same vehicle. The variability observed emphasises the need to determine truly personal exposure profiles of PM10 for quantifying exposure response relationships for epidemiological studies.     

Modelling Environmental Risk Using the Upper Partial Moment: a Safety-First Approach

The objective of the paper is to model economic-environmental trade-offs using a safety-first approach. The novelty of the approach is in the use of the empirically estimated flexible production risk and environmental risk functions that create a clear link between the source of the pollution, the production risk due to input use and the environmental outcome. Simulated production data and an environmental indicator were used to estimate a production function and environmental response function for every production year. The response functions were incorporated into an upper partial moment model, which ensures compliance to the user-specified environmental goal by enforcing the environmental constraint. Production decisions are significantly impacted by the presence of an environmental constraint due to the substantial compliance costs. Any changes in the intensive and extensive margin made by risk-averse decision-makers impact the size of the compliance cost faced by producers. The presence of an environmental constraint impact production decisions significantly with substantial compliance costs. While, fertiliser application technique have very little effect on the size of compliance costs. The greatest difference in compliance cost is therefore due to changes in the use of fixed resources.     

Development of a calibration system to evaluate VOC losses in a branch enclosure

Considerable uncertainties are associated with the experimental estimates of emission rates of different volatile organic compound (VOC) species from the biosphere to the atmosphere. Some of this uncertainty derives from the sampling and analytical procedures used in emission rate measurements. A calibration system was developed in order to evaluate possible errors in the measurements of biogenic emission rates using a branch enclosure system. Two types of calibration procedures were tested, a standard additions technique and an internal standard procedure. Both techniques were used to evaluate possible losses while sampling isoprene and monoterpenes, which are the most abundant VOCs of biogenic origin. The losses to Teflon lines and the empty sampling system were tested and losses to the branch enclosure system installed on two VOC emitting plant species were evaluated. A considerable loss of isoprene (approximately 18% of inflow concentration 65 ng l(-1)) to the empty enclosure system and to the system installed on the plant was measured, but no losses of monoterpenes were observed.     

A Robust Optimization Model for an Invasive Species Management Problem

Invasive species pose a significant threat to global biodiversity. Managing invasive species often involves modeling the species’ spread pattern, estimating control costs and damage costs due to the invasion, designing control efforts, and accounting for uncertainties in model parameters. Dealing with uncertainty is arguably the most important part of the process, since biological, environmental, and economic factors can cause parameter values to vary greatly. Managers need decision tools that are robust to such limited or variable information. Here, we present a robust spatial optimization model to select treatment sites in a way that maximally reduces the size of an invasive population, given a constraint on financial resources. We develop an integer programming model that includes population dynamics and management costs over space and time. The model incorporates uncertainty in the available budget and the invasive spread rate as sets of discrete scenarios to determine a robust, cost-effective management plan in a novel way.     

原子荧光度法测定城市污泥总砷的不确定度评定

根据《测量不确定度评定与表示》（JJF1059—1999）,建立了原子荧光度法测定污泥泥质中砷不确定度的数学模型,分析了测试过程中不确定度的来源,并对各不确定度分量进行评定及合成,并计算得出合成不确定度和扩展不确定度。本次测量的合成相对不确定度值为0．023,其中由消化样浓度引起的相对合成不确定度为0．021;最大的不确定度分量是样品消化重复测定的不确定度,分量值为O．0152。本次测定结果为19．97±0．92mg／kg,k=2（置信水平约为95％）。     

Developing an environmental water quality monitoring program for Haraz River in Northern Iran

Water quality management plans are an indispensable strategy for conservation and utilization of water resources in a sustainable manner. One common industrial use of water is aquaculture. The present study is an attempt to use statistical analyses in order to prepare an environmental water quality monitoring program for Haraz River, in Northern Iran. For this purpose, the analysis of a total number of 18 physicochemical parameters was performed at 15 stations during a 1-year sampling period. According to the results of the multivariate statistical methods, the optimal monitoring would be possible by only 3 stations and 12 parameters, including NH₃, EC, BOD, TSS, DO, PO₄, NO₃, TDS, temperature, turbidity, coliform, and discharge. In other words, newly designed network, with a total number of 36 measurements (3 stations × 12 parameters = 36 parameters), could achieve exactly the same performance as the former network, designed based on 234 measurements (13 stations × 18 parameters = 234 parameters). Based on the results of cluster, principal component, and factor analyses, the stations were divided into three groups of high pollution (HP), medium pollution (MP), and low pollution (LP). By clustering the stations, it would be possible to track the water quality of Haraz River, only by one station at each cluster, which facilitates rapid assessment of the water quality in the river basin. Emphasizing on three main axes of monitoring program, including measurement parameters, sampling frequency, and spatial pattern of sampling points, the water quality monitoring program was optimized for the river basin based on natural conditions of the study area, monitoring objectives, and required financial resources (a total annual cost of about US $2625, excluding the overhead costs).     

The spatial statistics formalism applied to mapping electromagnetic radiation in urban areas

Determining the electromagnetic radiation levels in urban areas is a complicated task. Various approaches have been taken, including numerical simulations using different models of propagation, sampling campaigns to measure field values with which to validate theoretical models, and the formalism of spatial statistics. In the work, we present here that this latter technique was used to construct maps of electric field and its associated uncertainty from experimental data. For this purpose, a field meter and a broadband probe sensitive in the 100-kHz–3-GHz frequency range were used to take 1,020 measurements around buildings and along the perimeter of the area. The distance between sampling points was 5 m. The results were stored in a geographic information system to facilitate data handling and analysis, in particular, the application of the formalism of spatial statistical to the analysis of the distribution of the field levels over the study area. The spatial structure was analyzed using the variographic technique, with the field levels at non-sampled points being interpolated by kriging. The results indicated that, in the urban area analyzed in the present work, the linear density of sampling points could be reduced to a distance which coincides with the length of the blocks of buildings without the statistical parameters varying significantly and with the field level maps being reproduced qualitatively and quantitatively.     

Variations of measured and simulated soil-loss amounts in a semiarid area in Turkey

The main goal of this research was soil-loss determination and comparison of the plot measurement results with simulation model (universal soil loss equation (USLE)) results in different land use and slope classes. The research took place in three different land-use types (Scotch pine forest, pasture land, and agricultural land) and in two different slope classes (15–20%, 35–40%). Within six measurement stations (for each land-use type and slope class-one station), totally 18 measurement plots have been constituted, and soil-loss amount measurements have been investigated during the research period (3 years along). USLE simulation model is used in these measurement plots for calculation the soil-loss amounts. The results pointed out that measured (in plots) and simulated (with USLE) soil-loss amounts differ significantly in each land-use type and slope class.     

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.     

Review of monitoring and assessing ground vegetation biodiversity in national forest inventories

Ground vegetation (GV) is an important component from which many forest biodiversity indicators can be estimated. To formulate policies at European level, taking into account biodiversity, European National Forest Inventories (NFIs) are one of the most important sources of forest information. However, for monitoring GV, there are several definitions, data collection methods, and different possible indicators. Even though it must be considered that natural conditions in different countries form very different understory types, each one has its own cost-efficient monitoring design, and they can hardly be compared. Therefore, the development of general guidelines is a particularly complex issue. This paper is a review of data collection methods and consequently a selection of the best available methods for the set of indicators with an emphasis on GV sampling methodologies in NFIs. As a final result, recommendations on GV definitions and classifications, sampling methodologies, and indicators are formulated for NFIs. Different sampling areas are recommended for each life form (shrubs, herbs, etc.). Inventory cycles and sampling seasons (depending on the phonological stages) should be specially considered and evaluated in the results. The proposed indicators are based on composition at different levels of sampling intensity for each life form and on coverage measurements.     

Traffic noise assessment based on mobile measurements

Continuous noise monitoring based on mobile systems could provide a quick feedback to assess the effect of policies implemented by authorities to mitigate noise pollution. The present research verifies that mobile measurements taken along a main street and aggregated in time and space can accurately estimate noise levels at static points. As a consequence, mobile sensors would be suitable to build, and continuously update, noise maps. Furthermore, the experiment computes the optimum aggregation distance of the mobile measurements.To perform the mobile noise measurements, a low-cost noise sensor with an integrated GPS was mounted on a bicycle. One hour worth of measurements was taken along a main avenue with the mobile receiver simultaneously to 6 static measurement points. For the mobile receiver, the L_Aeq was computed aggregating samples within a radius from 1 m to 100 m around the static measurement points. Then, the error between the aggregated L_Aeq of the mobile and the static receivers for the same time period was computed. It is observed that the RMSE and the measurement uncertainties decrease as the aggregation distance increases, having a minimum at an aggregation radius of 33 m and reaching a stabilization due to the constant traffic of the studied street.     

Uncertainty based optimal monitoring network design for a chlorinated hydrocarbon contaminated site

An application of a newly developed optimal monitoring network for the delineation of contaminants in groundwater is demonstrated in this study. Designing a monitoring network in an optimal manner helps to delineate the contaminant plume with a minimum number of monitoring wells at optimal locations at a contaminated site. The basic principle used in this study is that the wells are installed where the measurement uncertainties are minimum at the potential monitoring locations. The development of the optimal monitoring network is based on the utilization of contaminant concentration data from an existing initial arbitrary monitoring network. The concentrations at the locations that were not sampled in the study area are estimated using geostatistical tools. The uncertainty in estimating the contaminant concentrations at such locations is used as design criteria for the optimal monitoring network. The uncertainty in the study area was quantified by using the concentration estimation variances at all the potential monitoring locations. The objective function for the monitoring network design minimizes the spatial concentration estimation variances at all potential monitoring well locations where a monitoring well is not to be installed as per the design criteria. In the proposed methodology, the optimal monitoring network is designed for the current management period and the contaminant concentration data estimated at the potential observation locations are then used as the input to the network design model. The optimal monitoring network is designed for the consideration of two different cases by assuming different initial arbitrary existing data. Three different scenarios depending on the limit of the maximum number of monitoring wells that can be allowed at any period are considered for each case. In order to estimate the efficiency of the developed optimal monitoring networks, mass estimation errors are compared for all the three different scenarios of the two different cases. The developed methodology is useful in coming up with an optimal number of monitoring wells within the budgetary limitations. The methodology also addresses the issue of redundancy, as it refines the existing monitoring network without losing much information of the network. The concept of uncertainty-based network design model is useful in various stages of a potentially contaminated site management such as delineation of contaminant plume and long-term monitoring of the remediation process.     

Continuous, intermittent and passive sampling of airborne VOCs

Long sampling periods are often advantageous or required for measuring air quality and characterizing exposures. However, sampling periods exceeding 8 to 24 h using thermally desorbable adsorbent tube (TDT) samplers for the measurement of airborne volatile organic compounds (VOCs) face several challenges, including maintaining stable and low flow rates, and avoiding breakthrough of the adsorbent. These problems may be avoided using intermittent sampling; however, the literature contains few if any reports that have evaluated this technique in environmental, occupational or other applications. The purpose of this study is to evaluate continuous, intermittent and passive sampling methods using both laboratory and real-world tests. Laboratory tests compared continuous and intermittent (active) samplers in a controlled dynamic test gas generation system. Field tests used side-by-side active and passive samplers in an office, home workshop and four smokers' homes. All samples were analyzed for a wide range of VOCs by GC-MS. In most instances, intermittent sampling yielded better reproducibility (duplicate precision of 10 +/- 6%) than continuous low-flow sampling (18 +/- 5%), in part due to difficulty maintaining low flows. Concentrations obtained using intermittent sampling agreed with those for continuous sampling, with downward biases resulting primarily from errors in flow rate measurements. In the field, more VOC species were detected using active rather than passive sampling. Passive measurements were 12% lower than continuous measurements, a difference attributed to declining uptake rates at higher concentrations over the 3 to 4 d sampling period. Overall, most measurements obtained using the three sampling methods agreed within 20% for a wide range of concentrations (0.1 to 230 microg m(-3)). Both passive and intermittent sampling approaches are suitable for long sampling periods, but intermittent sampling provides greater flexibility with respect to sampling period, and permits the use of multi-bed adsorbents that can capture a wider range of VOCs.