Can we gain precision by sampling with probabilities proportional to size in surveying recent landscape changes in the Netherlands?

Seventy-two squares of 100 ha were selected by stratified random sampling with probabilities proportional to size (pps) to survey landscape changes in the period 1996–2003. The area of the plots times the urbanization pressure was used as a size measure. The central question of this study is whether the sampling with probabilities proportional to size leads to gain in precision compared to equal probability sampling. On average 1.03 isolated buildings per 100 ha have been built, while 0.90 buildings per 100 ha have been removed, leading to a net change of 0.13 building per 100 ha. The area with unspoiled natural relief has been reduced by 2.3 ha per 100 ha, and the length of linear relicts by 137 m per 100 ha. On average 74 m of linear green elements have been planted per 100 ha, while 106 m have been removed, leading to a net change of −31 m per 100 ha. For the state variables ‘unspoiled natural relief', ‘ linear relicts', ‘removed linear green elements', and ‘new – removed linear green elements' there is a gain in precision due to the pps-sampling. For the remaining state variables there is no gain or even a loss of precision (`new buildings', ‘removed buildings', ‘new – removed buildings', ‘new linear green elements'). Therefore, if many state variables must be monitored or when interest is not only in the change but also in the current totals, we recommend to keep things simple, and to select plots with equal probability.     

Uncertainty and sensitivity analysis of spatial predictions of heavy metals in wheat

Heavy metals seriously threaten the health of human beings when they enter the food chain. Therefore, policymakers require precise predictions of heavy metal concentrations in agricultural crops. In this paper we quantify the uncertainty of regression predictions of Cd and Pb in wheat (Triticum aestivum L.) and the contributions to the uncertainties in these predictions associated with inputs to the regression model. For each node of the 500- x 500-m grid covering the arable soils in The Netherlands, a latin hypercube sample size of 1000 is constructed from the uncertainty distributions of the explanatory variables (pH, soil organic matter [SOM], and heavy metal concentration in soil), the regression coefficients, and the random term of the regression model. This sample is used as input for the regression model to obtain 1000 values from the uncertainty distributions of the log(Cd) and log(Pb) concentration in wheat. There were no nodes where the recent EU quality standards for Cd and Pb (0.2 mg kg(-1) fresh wt.) in wheat were almost certain to be exceeded. For most nodes with clay soils, the quality standard for Cd in wheat almost certainly will not be exceeded; for Pb this is much less certain. The uncertainty in the Cd concentration in soil contributes most to the uncertainty in the predicted Cd concentrations in wheat (36% on the average), followed by the random term of the regression model (23%). For Pb the contribution of the random term is by far the largest (52%).     

Mapping the probability of exceeding critical thresholds for cadmium concentrations in soils in The Netherlands

The probability of exceeding critical thresholds of Cd concentrations in the soil was mapped at a national scale. The critical thresholds in soil were based on food quality criteria for Cd in crops or in organs of cattle (Bos taurus), and were calculated by inverting a regression model for the Cd concentration in the crop, with the Cd concentration in soil, soil organic matter (SOM) content, clay content, and pH as predictors. The probability of exceeding the critical threshold for Cd in soil per node of a 500- x 500-m grid was approximated by Monte Carlo simulation, using the estimated cumulative distribution functions (cdf) of SOM, clay, pH, and Cd as input. The cdfs were estimated by simple indicator kriging with local prior means. For SOM, clay, and pH, detailed maps of soil type and land use were used to define subregions with assumed constant local means of the indicators (a priori distributions). The cdfs were sampled by Latin hypercube sampling. We accounted for correlation between the actual and critical Cd concentrations in soil by drawing Cd values from cdfs conditional on SOM and clay. The estimated probability for grassland is negligible, even in areas with high Cd concentrations in soil, and for maize (Zea mays L.) land the probability is almost everywhere smaller than 5%. For arable soils, however, these probabilities commonly are larger than 5% when sugar beet (Beta vulgaris L.) or wheat (Triticum aestivum L.) is taken as a reference crop, and locally exceed 50%.     

A method to combine non-probability sample data with probability sample data in estimating spatial means of environmental variables

In estimating spatial means of environmental variables of a region from datacollected by convenience or purposive sampling, validity of the results canbe ensured by collecting additional data through probability sampling. Theprecision of the estimator that uses the probability sample can beincreased by interpolating the values at the nonprobability sample points tothe probability sample points, and using these interpolated values as anauxiliary variable in the difference or regression estimator. Theseestimators are (approximately) unbiased, even when the nonprobability sampleis severely biased such as in preferential samples. The gain in precisioncompared to the estimator in combination with Simple Random Samplingis controlled by the correlation between the target variable andinterpolated variable. This correlation is determined by the size (density)and spatial coverage of the nonprobability sample, and the spatialcontinuity of the target variable. In a case study the average ratio of thevariances of the simple regression estimator and estimator was 0.68for preferential samples of size 150 with moderate spatial clustering, and0.80 for preferential samples of similar size with strong spatialclustering. In the latter case the simple regression estimator wassubstantially more precise than the simple difference estimator.     

National baseline survey of soil quality in the Netherlands

To determine background values of the 252 chemical compounds listed in Dutch legislation, a survey was designed with the aim of estimating percentiles of the cumulative frequency distributions and areal fractions exceeding the former, legislative reference values. Sampling locations were selected by probability sampling, so that in estimating the target quantities no model assumptions on the spatial variation were required, and valid estimates could be obtained by design-based inference. Strata in random sampling were formed by overlaying an aggregated soil map and land use map. For most of the heavy metals the areal fraction with concentrations in the topsoil (0-10 cm) exceeding the reference value was smaller than the allowable maximum of 5%. For these compounds a background value was determined smaller than the reference value. Exceptions were V, Co, Ba and Cu, for which a background value was defined (slightly) larger than the reference value.     

Simulation of wind-driven dispersion of fire pollutants in a street canyon using FDS

Air quality in urban areas attracts great attention due to increasing pollutant emissions and their negative effects on human health and environment. Numerous studies, such as those by Mouilleau and Champassith (J Loss Prevent Proc 22(3): 316–323, 2009), Xie et al. (J Hydrodyn 21(1): 108–117, 2009), and Yassin (Environ Sci Pollut Res 20(6): 3975–3988, 2013) focus on the air pollutant dispersion with no buoyancy effect or weak buoyancy effect. A few studies, such as those by Hu et al. (J Hazard Mater 166(1): 394–406, 2009; J Hazard Mater 192(3): 940–948, 2011; J Civ Eng Manag (2013)) focus on the fire-induced dispersion of pollutants with heat buoyancy release rate in the range from 0.5 to 20 MW. However, the air pollution source might very often be concentrated and intensive, as a consequence of the hazardous materials fire. Namely, transportation of fuel through urban areas occurs regularly, because it is often impossible to find alternative supply routes. It is accompanied with the risk of fire accident occurrences. Accident prevention strategies require analysis of the worst scenarios in which fire products jeopardize the exposed population and environment. The aim of this article is to analyze the impact of wind flow on air pollution and human vulnerability to fire products in a street canyon. For simulation of the gasoline tanker truck fire as a result of a multivehicle accident, computational fluid dynamics large eddy simulation method has been used. Numerical results show that the fire products flow vertically upward, without touching the walls of the buildings in the absence of wind. However, when the wind velocity reaches the critical value, the products touch the walls of the buildings on both sides of the street canyon. The concentrations of carbon monoxide and soot decrease, whereas carbon dioxide concentration increases with the rise of height above the street canyon ground level. The longitudinal concentration of the pollutants inside the street increases with the rise of the wind velocity at the roof level of the street canyon.     

Optimizing two- and three-stage designs for spatial inventories of natural resources by simulated annealing

This paper reviews design-based estimators for two- and three-stage sampling designs to estimate the mean of finite populations. This theory is then extended to spatial populations with continuous, infinite populations of sampling units at the latter stages. We then assume that the spatial pattern is the result of a spatial stochastic process, so the sampling variance of the estimators can be predicted from the variogram. A realistic cost function is then developed, based on several factors including laboratory analysis, time of fieldwork, and numbers of samples. Simulated annealing is used to find designs with minimum sampling variance for a fixed budget. The theory is illustrated with a real-world problem dealing with the volume of contaminated bed sediments in a network of watercourses. Primary sampling units are watercourses, secondary units are transects perpendicular to the axis of the watercourse, and tertiary units are points. Optimal designs had one point per transect, from one to three transects per watercourse, and the number of watercourses varied depending on the budget. However, if laboratory costs are reduced by grouping all samples within a watercourse into one composite sample, it appeared to be efficient to sample more transects within a watercourse.     

Cadmium,zinc, lead,and copper in livers and kidneys of cattle in the neighbourhood of zinc refineries

Cattle in the Kempen area (in the province North-Brabant, the Netherlands) were investigated for cadmium, lead, zinc, and copper in livers and kidneys. The animals originated from farms located within a 20 km radius around several zinc refinery plants. The local soil is polluted with zinc and cadmium because of a thermal refining process used in the past.Mean cadmium organ concentrations were 2.5 times, and mean lead organ concentrations were 1.5 times higher than the concentrations found in controls. Copper levels tended to be decreased, but zinc levels did not differ from controls.The observed cadmium and lead organ concentrations did not indicate intoxication of the animals, but 22% of the kidneys and 3% of the livers investigated trespassed the maximum tolerance limit of cadmium with regard to human consumption.Continuous control of cadmium organ contents in organs from slaughtercattle kept in cadmium-polluted areas is recommended.     

Propagation of uncertainties in soil and pesticide properties to pesticide leaching

In the new Dutch decision tree for the evaluation of pesticide leaching to groundwater, spatially distributed soil data are used by the GeoPEARL model to calculate the 90th percentile of the spatial cumulative distribution function of the leaching concentration in the area of potential usage (SP90). Until now it was not known to what extent uncertainties in soil and pesticide properties propagate to spatially aggregated parameters like the SP90. A study was performed to quantify the uncertainties in soil and pesticide properties and to analyze their contribution to the uncertainty in SP90. First, uncertainties in the soil and pesticide properties were quantified. Next, a regular grid sample of points covering the whole of the agricultural area in the Netherlands was randomly selected. At the grid nodes, realizations from the probability distributions of the uncertain inputs were generated and used as input to a Monte Carlo uncertainty propagation analysis. The analysis showed that the uncertainty concerning the SP90 is 10 times smaller than the uncertainty about the leaching concentration at individual point locations. The parameters that contribute most to the uncertainty about the SP90 are, however, the same as the parameters that contribute most to uncertainty about the leaching concentration at individual point locations (e.g., the transformation half-life in soil and the coefficient of sorption on organic matter). Taking uncertainties in soil and pesticide properties into account further leads to a systematic increase of the predicted SP90. The important implication for pesticide regulation is that the leaching concentration is systematically underestimated when these uncertainties are ignored.     

Validation of regression models for nitrate concentrations in the upper groundwater in sandy soils

For Dutch sandy regions, linear regression models have been developed that predict nitrate concentrations in the upper groundwater on the basis of residual nitrate contents in the soil in autumn. The objective of our study was to validate these regression models for one particular sandy region dominated by dairy farming. No data from this area were used for calibrating the regression models. The model was validated by additional probability sampling. This sample was used to estimate errors in 1) the predicted areal fractions where the EU standard of 50 mg l⁻¹ is exceeded for farms with low N surpluses (ALT) and farms with higher N surpluses (REF); 2) predicted cumulative frequency distributions of nitrate concentration for both groups of farms.Both the errors in the predicted areal fractions as well as the errors in the predicted cumulative frequency distributions indicate that the regression models are invalid for the sandy soils of this study area.