A Variance Estimator for Constrained Estimates of Change in Relative Categorical Frequencies

Consistent estimators of change and state becomes an issue when sample data come from a mix of permanent and temporary observation units. A joint maximum likelihood estimator of state and change creates estimates of state that depend on antecedent viz. posterior survey results and may differ from estimates of state derived from a single-date analysis of the sample data. A constrained estimator of change in relative categorical frequencies that eliminates this potential inconsistency is proposed and a model based estimator of their sampling variance is developed. The performance of the constrained estimator is quantified against six criteria and a joint maximum likelihood estimator in simulated sampling from 15 populations with three combinations of permanent and temporary samples, four to six categorical class attributes, and constant size between sampling dates. Bias of the constrained estimators was negligible but larger than for joint maximum likelihood estimators. Mean absolute deviations and variances of constrained estimators were generally at par with the joint estimators. Constrained estimators of root mean square errors and achieved coverage of nominal confidence intervals of constrained estimators were occasionally better. A generalized variance function for the constrained estimates of change is provided as a computational shortcut.     

Towards a sampling strategy for the assessment of forest condition at European level: combining country estimates

A correct characterization of the status and trend of forest condition is essential to support reporting processes at national and international level. An international forest condition monitoring has been implemented in Europe since 1987 under the auspices of the International Co-operative Programme on Assessment and Monitoring of Air Pollution Effects on Forests (ICP Forests). The monitoring is based on harmonized methodologies, with individual countries being responsible for its implementation. Due to inconsistencies and problems in sampling design, however, the ICP Forests network is not able to produce reliable quantitative estimates of forest condition at European and sometimes at country level. This paper proposes (1) a set of requirements for status and change assessment and (2) a harmonized sampling strategy able to provide unbiased and consistent estimators of forest condition parameters and of their changes at both country and European level. Under the assumption that a common definition of forest holds among European countries, monitoring objectives, parameters of concern and accuracy indexes are stated. On the basis of fixed-area plot sampling performed independently in each country, an unbiased and consistent estimator of forest defoliation indexes is obtained at both country and European level, together with conservative estimators of their sampling variance and power in the detection of changes. The strategy adopts a probabilistic sampling scheme based on fixed-area plots selected by means of systematic or stratified schemes. Operative guidelines for its application are provided.     

Extending large-scale forest inventories to assess urban forests

Urban areas are continuously expanding today, extending their influence on an increasingly large proportion of woods and trees located in or nearby urban and urbanizing areas, the so-called urban forests. Although these forests have the potential for significantly improving the quality the urban environment and the well-being of the urban population, data to quantify the extent and characteristics of urban forests are still lacking or fragmentary on a large scale. In this regard, an expansion of the domain of multipurpose forest inventories like National Forest Inventories (NFIs) towards urban forests would be required. To this end, it would be convenient to exploit the same sampling scheme applied in NFIs to assess the basic features of urban forests. This paper considers approximately unbiased estimators of abundance and coverage of urban forests, together with estimators of the corresponding variances, which can be achieved from the first phase of most large-scale forest inventories. A simulation study is carried out in order to check the performance of the considered estimators under various situations involving the spatial distribution of the urban forests over the study area. An application is worked out on the data from the Italian NFI.     

Estimated distributions of personal exposure to respirable particles

A method of estimating distributions of exposure to respirable particles is presented. Using pollutant monitoring data from outdoors and indoors, time-activity data and a time-weighted exposure model, means and variances for exposure distributions are generated. Variances are estimated using Gauss' law of error propagation. The model is calibrated using data from a personal monitoring study. Estimated distributions of exposure to respirable particles for children in six cities living in homes with and without smokers are presented. The implications of these estimates for air pollution epidemiology and needs for further research are discussed.     

Sampling of Sparse Species with Probability Proportional to Prediction

In this article, a 3P sampling (Probability Proportional to Prediction) approach is presented for surveying sparse species connected to certain types of substrates. The method uses the surveyors judgement of the probability of finding the species on a substrate as the base for selection of substrates for species inventories. The method is presented together with estimators, variances and variance estimators. The method is first presented for sampling based on all substrates in a study area and then as a subsampling technique in a two-stage design in which plots or strips are selected in a first stage. The presented approach was evaluated as a subsampling technique in a strip survey of calicioid lichen species associated with coarse broadleaf trees. In comparison with a simple random subsampling without replacement, 3P subsampling was in one study area found to be an improvement with 30–55% in terms of standard errors. The improvement was more modest in the other study area, only between 10–16%. The strip survey with 3P subsampling was more cost-efficient than a strip survey without subsampling except in one case. Based on the results in the test, 3P sampling seems to have a potential for sampling sparse species.     

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.     

Kriging for estimating spatial pattern of contaminants: Potential and problems

This paper discusses and illustrates the use of kriging techniques for estimating the spatial pattern of contaminants in environmental media, particularly soil. The assumptions underlying kriging are reviewed as are some advantages and disadvantages of the method. Lognormal kriging (kriging applied to logarithmic-transformed data) is illustrated using a set of radionuclide soil concentrations at a nuclear testing area on the Nevada Test Site. This example shows how lognormal kriging can be used to estimate average concentrations at points or for blocks of land, concentration contours over space, confidence bands about these contours, and radionuclide inventory in soil. The validity of kriging estimates depends on the accurate estimation and modeling of the spatial correlation structure of the phenomenon. Accuracy is especially important when lognormal kriging is used and estimates of means and their standard deviations are required in the original, untransformed scale. This paper illustrates the bias that can result when a changing correlation structure over space is ignored.Operated for the U.S. Department of Energy by Battelle Memorial InstituteWork supported by Nevada Applied Ecology Group, U.S. Department of Energy, Nevada Operations Office under Contract DE-AC06-76RLO 1830.     

Evaluating environmental performance using new process capability indices for autocorrelated data

Traditionally, the process capability index is developed by assuming that the process output data are independent and follow normal distribution. However, in most environmental cases, the process data are autocorrelated. The autocorrelated process, if unrecognized as an independent process, can lead to erroneous decision making and unnecessary quality loss. In this paper, three new capability indices with unbiased estimators are proposed to relieve the independence assumption for the-nominal-the-best and the-smaller-the-better cases. Furthermore, we use mean squared error (MSE) and mean absolute percent error (MAPE) to compare the accuracy of our proposed indices to previous autocorrelated indices. The results show that our proposed capability indices outperform the predecessors.     

Joint versus separate estimation of state and change in category frequencies from repeat stratified two-phase sampling with a fallible classifier

Joint maximum likelihood estimates (JML) of category frequencies and change from repeat stratified two-phase samplingsurveys with a fallible classifier are often seriously biased andhave large root mean square errors when they are obtained for small populations (<5000) with three or more categories and amoderate to small phase II sample size (<1000). JML estimates of state also depend on antecedent or posterior data, a recipe for inconsistency. In these situations, a separate maximum likelihood estimation (SML) of category frequenciesat each survey date appears preferable. SML estimates of net change are obtained as the difference in states. SML standard errors of change are obtained via an estimate of the temporal correlation and variances of state. A bivariate binarylogistic model of change provided the estimate of temporal correlation. SML generally outperformed JMLsignificantly in terms of bias and root mean square errors in eight case studies.     

An urban observatory for quantifying phosphorus and suspended solid loads in combined natural and stormwater conveyances

Water quality in urban streams and stormwater systems is highly dynamic, both spatially and temporally, and can change drastically during storm events. Infrequent grab samples commonly collected for estimating pollutant loadings are insufficient to characterize water quality in many urban water systems. In situ water quality measurements are being used as surrogates for continuous pollutant load estimates; however, relatively few studies have tested the validity of surrogate indicators in urban stormwater conveyances. In this paper, we describe an observatory aimed at demonstrating the infrastructure required for surrogate monitoring in urban water systems and for capturing the dynamic behavior of stormwater-driven pollutant loads. We describe the instrumentation of multiple, autonomous water quality and quantity monitoring sites within an urban observatory. We also describe smart and adaptive sampling procedures implemented to improve data collection for developing surrogate relationships and for capturing the temporal and spatial variability of pollutant loading events in urban watersheds. Results show that the observatory is able to capture short-duration storm events within multiple catchments and, through inter-site communication, sampling efforts can be synchronized across multiple monitoring sites.     

The impact of terrorism and FDI on environmental pollution: Evidence from Afghanistan,Iraq, Nigeria,Pakistan, Philippines,Syria, Somalia,Thailand and Yemen

In this study, the relationships among environmental pollution, terrorism, foreign direct investments (FDI), energy consumption and economic growth is investigated for Afghanistan, Iraq, Nigeria, Pakistan, Philippines, Syria, Somalia, Thailand and Yemen covering the 1975–2017 period utilizing Panel cointegration tests, ANOVA tests, long-run estimators and panel trivariate Causality tests. ANOVA results are in favor of evidence of homogeneity between the selected countries. Long-run estimators reveal that terrorism, FDI, energy consumption and economic growth have statistically significant effects on environmental pollution. Panel trivariate Causality test determines the causal relationship between the variables. Accordingly, one-way causal nexus from terrorism to Carbon dioxide (CO₂) emissions and from FDI inflows to CO₂ emissions are found in the short-run. In the long-run, with strong causality results, the evidence of bi-directional causality between CO₂ emissions and other variables, namely, terrorism, FDI inflow energy consumption and economic growth are detected.     

The magnitude of variability produced by methods used to estimate annual stormwater contaminant loads for highly urbanised catchments

Stormwater contaminant loading estimates using event mean concentration (EMC), rainfall/runoff relationship calculations and computer modelling (Model of Urban Stormwater Infrastructure Conceptualisation—MUSIC) demonstrated high variability in common methods of water quality assessment. Predictions of metal, nutrient and total suspended solid loadings for three highly urbanised catchments in Sydney estuary, Australia, varied greatly within and amongst methods tested. EMC and rainfall/runoff relationship calculations produced similar estimates (within 1 SD) in a statistically significant number of trials; however, considerable variability within estimates (～50 and ～25 % relative standard deviation, respectively) questions the reliability of these methods. Likewise, upper and lower default inputs in a commonly used loading model (MUSIC) produced an extensive range of loading estimates (3.8–8.3 times above and 2.6–4.1 times below typical default inputs, respectively). Default and calibrated MUSIC simulations produced loading estimates that agreed with EMC and rainfall/runoff calculations in some trials (4–10 from 18); however, they were not frequent enough to statistically infer that these methods produced the same results. Great variance within and amongst mean annual loads estimated by common methods of water quality assessment has important ramifications for water quality managers requiring accurate estimates of the quantities and nature of contaminants requiring treatment.     

The exposure commitment method in environmental pollutant assessment

The exposure commitment method is a time independent approach to pollutant assessment. The exposure commitment is a measure of the intensity and duration of a pollutant's presence in an environmental reservoir. The method is a particularly convenient means of comparing contributions to intake and exposure from various pathways and in expressing source-receptor relationships. The concepts and definitions of the method are presented and application shown for the transfer of lead and cadmium from general atmospheric sources to man.     

Marginal Abatement Costs and Marginal Welfare Costs for Greenhouse Gas Emissions Reductions: Results from the EPPA Model

Marginal abatement cost (MAC) curves, relationships between tonnes of emissions abated and the CO₂ (or greenhouse gas (GHG)) price, have been widely used as pedagogic devices to illustrate simple economic concepts such as the benefits of emissions trading. They have also been used to produce reduced-form models to examine situations where solving the more complex model underlying the MAC is difficult. Some important issues arise in such applications: (1) Are MAC relationships independent of what happens in other regions?, (2) are MACs stable through time regardless of what policies have been implemented in the past?, and (3) can one approximate welfare costs from MACs? This paper explores the basic characteristics of MAC and marginal welfare cost (MWC) curves, deriving them using the MIT Emissions Prediction and Policy Analysis model. We find that, depending on the method used to construct them, MACs are affected by policies abroad. They are also dependent on policies in place in the past and depend on whether they are CO₂-only or include all GHGs. Further, we find that MACs are, in general, not closely related to MWCs and therefore should not be used to derive estimates of welfare change. We also show that, as commonly constructed, MACs may be unreliable in replicating results of the parent model when used to simulate GHG policies. This is especially true if the policy simulations differ from the conditions under which the MACs were simulated.     

Monitoring landscape metrics by point sampling: accuracy in estimating Shannon’s diversity and edge density

Environmental monitoring of landscapes is of increasing interest. To quantify landscape patterns, a number of metrics are used, of which Shannon’s diversity, edge length, and density are studied here. As an alternative to complete mapping, point sampling was applied to estimate the metrics for already mapped landscapes selected from the National Inventory of Landscapes in Sweden (NILS). Monte-Carlo simulation was applied to study the performance of different designs. Random and systematic samplings were applied for four sample sizes and five buffer widths. The latter feature was relevant for edge length, since length was estimated through the number of points falling in buffer areas around edges. In addition, two landscape complexities were tested by applying two classification schemes with seven or 20 land cover classes to the NILS data. As expected, the root mean square error (RMSE) of the estimators decreased with increasing sample size. The estimators of both metrics were slightly biased, but the bias of Shannon’s diversity estimator was shown to decrease when sample size increased. In the edge length case, an increasing buffer width resulted in larger bias due to the increased impact of boundary conditions; this effect was shown to be independent of sample size. However, we also developed adjusted estimators that eliminate the bias of the edge length estimator. The rates of decrease of RMSE with increasing sample size and buffer width were quantified by a regression model. Finally, indicative cost–accuracy relationships were derived showing that point sampling could be a competitive alternative to complete wall-to-wall mapping.     

A bioassessment approach for mid-continent great rivers: the Upper Mississippi, Missouri, and Ohio (USA)

The objectives of the Environmental Monitoring and Assessment Program for Great River Ecosystems (EMAP-GRE) are to (1) develop and demonstrate, in collaboration with states, an assessment program yielding spatially unbiased estimates of the condition of mid-continent great rivers; (2) evaluate environmental indicators for assessing great rivers; and (3) assess the current condition of selected great river resources. The purpose of this paper is to describe EMAP-GRE using examples based on data collected in 2004-2006 with emphasis on an approach to determining reference conditions. EMAP-GRE includes the Upper Mississippi River, the Missouri River, and the Ohio River. Indicators include biotic assemblages (fish, macroinvertebrates, plankton, algae), water chemistry, and aquatic and riparian physical habitat. Reference strata (river reaches for which a single reference expectation is appropriate) were determined by ordination of the fish assemblage and examination of spatial variation in environmental variables. Least disturbed condition of fish assemblages for reference strata was determined by empirical modeling in which we related fish assemblage metrics to a multimetric stressor gradient. We inferred least disturbed condition from the y-intercept, the predicted condition when stress was least. Thresholds for dividing the resource into management-relevant condition classes for biotic indicators were derived using predicted least disturbed condition to set the upper bound on the least disturbed condition class. Also discussed are the outputs of EMAP-GRE, including the assessment document, multimetric indices of condition, and unbiased data supporting state and tribal Clean Water Act reporting, adaptive management, and river restoration.     

Appraising the role of relationships between regulators and consultants for effective EIA

The role of relationships between regulators and consultants engaged in environmental impact assessment (EIA) is explored. A Partnering Agreement between Western Australian regulators and consultants gave rise to a survey and interviews with representatives of these EIA practitioners to understand levels of cooperation between them and ways to improve EIA practice locally. A mixture of quantitative and qualitative responses are presented with an emphasis on practitioner comments reproduced in their own words and ‘voice’. The results suggest that while relationships are strained because of staff resources and levels of expertise brought about by a major resource boom in recent years, there is a great desire for cooperation and collaboration. Greater clarification and understanding of each stakeholder's purpose and role in the EIA process along with opportunities for EIA practitioners to interact, communicate and socialise are identified as positive ways forward. The value of establishing the relatively simple Partnering Agreement approach is demonstrated and is put forward as something that practitioners internationally might equally benefit from as a means of improving the effectiveness of EIA practice.     

Using ‘found’ data to augment a probability sample: Procedure and case study

While probability sampling has the advantage of permitting unbiased population estimates, many past and existing monitoring schemes do not employ probability sampling. We describe and demonstrate a general procedure for augmenting an existing probability sample with data from nonprobability-based surveys (found data). The procedure, first proposed by Overton (1990), uses sampling frame attributes to group the probability and found samples into similar subsets. Subsequently, this similarity is assumed to reflect the representativeness of the found sample for the matching subpopulation. Two methods of establishing similarity and producing estimates are described: pseudo-random and calibration. The pseudo-random method is used when the found sample can contribute additional information on variables already measured for the probability sample, thus increasing the effective sample size. The calibration method is used when the found sample contributes information that is unique to the found observations. For either approach, the found sample data yield observations that are treated as a probability sample, and population estimates are made according to a probability estimation protocol. To demonstrate these approaches, we applied them to found and probability samples of stream discharge data for the southeastern US.     

A proficiency testing scheme for aromatic hydrocarbons in air by the manual thermal desorption-GC method: a comparison of laboratory performance with the uncertainty requirements of the European Union Ambient Air Directive

The Workplace Analysis Scheme for Proficiency (WASP) is a proficiency testing scheme for the analysis of occupational hygiene and environmental air samples and is operated in the UK by the Health and Safety Laboratory. Since 1997, WASP has offered samples of benzene, toluene and m-xylene, at environmental levels on Tenax, and has about 35 laboratories participating, mostly from industry, local government and consultancy organisations in the UK. The results reported cover the first 10 rounds of the environmental analytes (1997-1999) and demonstrate the important role of proficiency testing in assessing the quality of laboratory performance. Estimates are obtained for within-laboratory precision and the total variability at each analyte level. The estimates of within-laboratory precision suggest that laboratories have more difficulty analysing toluene and m-xylene than benzene. Linear relationships for the reproducibility relative standard deviation (RSDT) with loading level are evident for the analytes at occupational levels. At environmental levels, the relationship between loading level and reproducibility is much less well defined. The standard deviation for the proficiency testing assessment for all three analytes at the environmental level is 14%, as derived from the benzene data. Expanded uncertainty estimates (k = 1.96), for the analysis of samples since the scheme started, are obtained from the average total variance, and are 27% for benzene, 39% for toluene and 36% for m-xylene. Although the linear trend of performance against round number was not significant at the 95% level of confidence (p = 0.23 for benzene, p = 0.3 for toluene and p = 0.32 for m-xylene), there was a general improvement in RSDT from 26-34% to about 8-13% 10 rounds later. Currently, for a laboratory to meet one of the data quality objectives in the Ambient Air Directive (indicative measurement of benzene, expanded uncertainty +/- 30% or less), it would have to achieve a level of analytical performance to satisfy the category 1 (best performance) limit of better than +/- 8.8%. In the last proficiency testing round, discussed in this paper, only 58% of laboratories obtained performance scores that indicated that they were able to consistently achieve this level of performance.     

Assessing uncertainty in annual nitrogen,phosphorus, and suspended sediment load estimates in three agricultural streams using a 21-year dataset

Accurate estimation of constituent loads is important for studies of ecosystem mass balance or total maximum daily loads. In response, there has been an effort to develop methods to increase both accuracy and precision of constituent load estimates. The relationship between constituent concentration and stream discharge is often complicated, potentially leading to high uncertainty in load estimates for certain constituents, especially at longer-term (annual) scales. We used the loadflex R package to compare uncertainty in annual load estimates from concentration vs. discharge relationships in constituents of interest in agricultural systems, including ammonium as nitrogen (NH₄-N), nitrate as nitrogen (NO₃-N), soluble reactive phosphorus (SRP), and suspended sediments (SS). We predicted that uncertainty would be greatest in NO₃-N and SS due to complex relationships between constituent concentration and discharge. We also predicted lower uncertainty with a composite method compared to regression or interpolation methods. Contrary to predictions, we observed the lowest uncertainty in annual NO₃-N load estimates (relative error 1.5–23%); however, uncertainty was greatest in SS load estimates, consistent with predictions (relative error 19–96%). For all constituents, we also generally observed reductions in uncertainty by up to 34% using the composite method compared to regression and interpolation approaches, as predicted. These results highlight differences in uncertainty among different constituents and will aid in model selection for future studies requiring accurate and precise estimates of constituent load.