Selecting the optimum plot size for a California design-based stream and wetland mapping program

Accurate estimates of the extent and distribution of wetlands and streams are the foundation of wetland monitoring, management, restoration, and regulatory programs. Traditionally, these estimates have relied on comprehensive mapping. However, this approach is prohibitively resource-intensive over large areas, making it both impractical and statistically unreliable. Probabilistic (design-based) approaches to evaluating status and trends provide a more cost-effective alternative because, compared with comprehensive mapping, overall extent is inferred from mapping a statistically representative, randomly selected subset of the target area. In this type of design, the size of sample plots has a significant impact on program costs and on statistical precision and accuracy; however, no consensus exists on the appropriate plot size for remote monitoring of stream and wetland extent. This study utilized simulated sampling to assess the performance of four plot sizes (1, 4, 9, and 16 km²) for three geographic regions of California. Simulation results showed smaller plot sizes (1 and 4 km²) were most efficient for achieving desired levels of statistical accuracy and precision. However, larger plot sizes were more likely to contain rare and spatially limited wetland subtypes. Balancing these considerations led to selection of 4 km² for the California status and trends program.     

External drift kriging of NOx concentrations with dispersion model output in a reduced air quality monitoring network

In the mid nineteen eighties the Dutch NO_x air quality monitoring network was reduced from 73 to 32 rural and city background stations, leading to higher spatial uncertainties. In this study, several other sources of information are being used to help reduce uncertainties in parameter estimation and spatial mapping. For parameter estimation, we used Bayesian inference. For mapping, we used kriging with external drift (KED) including secondary information from a dispersion model. The methods were applied to atmospheric NO_x concentrations on rural and urban scales. We compared Bayesian estimation with restricted maximum likelihood estimation and KED with universal kriging. As a reference we also included ordinary least squares (OLS). Comparison of several parameter estimation and spatial interpolation methods was done by cross-validation. Bayesian analysis resulted in an error reduction of 10 to 20% as compared to restricted maximum likelihood, whereas KED resulted in an error reduction of 50% as compared to universal kriging. Where observations were sparse, the predictions were substantially improved by inclusion of the dispersion model output and by using available prior information. No major improvement was observed as compared to OLS, the cause presumably being that much good information is contained in the dispersion model output, so that no additional spatial residual random field is required to explain the data. In all, we conclude that reduction in the monitoring network could be compensated by modern geostatistical methods, and that a traditional simple statistical model is of an almost equal quality.

Spatial statistics for modeling of abundance and distribution of wildlife species in the Masai Mara ecosystem, Kenya

This study illustrates the use of modern statistical procedures for better wildlife management by addressing three key issues: determination of abundance, modeling of animal distributions and variability of diversity in space and time. Prior information in Markov Chain Monte Carlo (MCMC) methods is used to improve estimates of abundance. Measures of autocorrelation are included when modeling distributions of animal counts, and a diversity index to indicate species abundance and richness for large herbivores is developed. Data from the Masai Mara ecosystem in Kenya are used to develop and demonstrate these procedures. The new abundance estimates are up to 35% more accurate than those obtained by existing methods. Significant temporal changes in spatial patterns are found from a space-time analysis of elephant counts over a 20-year period, with strong interactions over 5 km and 6 months space and time separations, respectively. The new diversity index is sensitive to both high abundance and species richness and is also able to capture year to year variation. It indicates an overall marginal decrease in diversity for large herbivores in the Mara ecosystem. The space-time analyses and diversity index can easily be computed thereby providing tools for rapid decision making.     

Incinerator Conference Covers Emissions Control Aspects

The success of the application of computer modeling to decision-making will depend on the degree to which the scientifically valid “cause-and-effect” features of the air pollution system are represented. For this reason, dynamic simulation models are to be preferred to statistical and empirical models. A digital simulation model based on a stoichiometrically logical chemical mechanism and trajectory estimating routines was constructed, using Los Angeles source, meteorological and geographic input. The basic physical concept underlying the simulation model is the process of evolution of photochemical pollution in a parcel of air as it moves in a dynamic urban emission/meteorological environment along a given urban wind trajectory. Both the photochemical evolution and the trajectory are numerically integrated by a standard linear multistep predictor-corrector method. Concentrations of photochemical reactants and products (i.e., primary and secondary contaminants) are determined by this numerical integration, which also includes appropriate terms for relevant effects. In five preliminary validation runs, simulated NO₂, NO, and O₃ values were within 20% or 0.05 ppm of those observed at air monitoring stations located near the termini of the runs. The trajectories were plotted on the basis of hourly meteorological data for 22 stations. Six control strategy exercises were conducted to illustrate the application of the model to problem-solving situations.     

Estimating the Variability and Confidence of Land Use and Imperviousness Relationships at a Regional Scale1

Abstract: Impervious cover is a commonly used metric to help explain or predict anthropogenic impacts on aquatic resources; often it is used as a surrogate for intensity of human impacts when evaluating effects on aquatic resources. The most common way to estimate imperviousness is based on relationships with land use. Few studies have evaluated how the relationship between impervious surface and land use varies among geographies with different levels of development and between types of imagery used to assign land use type. In this study, we assess variability in estimates of imperviousness based on two locally available land use datasets: one based on aerial imagery (2‐m resolution) and another based on satellite imagery (30‐m resolution). The ranges and variability in imperviousness within land use categories were assessed at several spatial scales, including within counties, between counties, and between watersheds. Results indicate that there was considerable variability for all developed land use types. Estimated impervious cover often varied over a range of 20‐40% points within a land use category. Furthermore, there were clear spatial patterns both between and within counties, with impervious cover for a given land use type being higher near the urban centers and lower at the margins of development. Estimates of imperviousness for 12 study watersheds indicated that variability increased with increasing watershed development, making it difficult to confidently set management or regulatory targets based on impervious cover. This study suggests that locally derived, high resolution satellite or aerial imagery should be used to estimate imperviousness when a high level of accuracy and precision is required for regulatory or management decisions. Furthermore, the error associated with impervious land use relationships should be accounted for when using impervious cover in runoff or water quality models, or when making management decisions regarding stream health.     

Levels and chemical composition of PM in a city near a large Cu-smelter in Spain

A long-term series (2001-2008) of chemical analysis of atmospheric particulate matter (PM(10) and PM(2.5)) collected in the city of Huelva (SW Spain) is considered in this study. The impact of emission plumes from one of the largest Cu-smelters in the world on air quality in the city of Huelva is evidenced by the high daily and hourly levels of As, other potentially toxic elements (e.g. Cu, Zn, Cd, Se, Bi, and Pb) in particulate matter, as well as the high levels of some gaseous pollutants (NO(2) and SO(2)). Mean arsenic levels in the PM10 fraction were higher than the target value set by European Directive 2004/107/EC (6 ngAs m(-3)) for 1(st) January 2013. Hourly peak concentrations of As and other metals and elements (Zn, Cu, P and Se) analyzed by PIXE can reach maximum hourly levels as high as 326 ngAs m(-3), 506 ngZn m(-3), 345 ngCu m(-3), 778 ngP m(-3) and 12 ngSe m(-3). The contribution of Cu-smelter emissions to ambient PM is quantified on an annual basis in 2.0-6.7 μg m(-3) and 1.8-4.2 μg m(-3) for PM(10) and PM(2.5), respectively. High resolution outputs of the HYSPLIT dispersion model show the geographical distribution of the As ambient levels into the emission plume, suggesting that the working regime of the Cu-smelter factory and the sea breeze circulation are the main factors controlling the impact of the Cu-smelter on the air quality of the city. The results of this work improve our understanding of the behaviour of industrial emission plumes and their impact on air quality of a city, where the population might be exposed to very high ambient concentrations of toxic metals during a few hours.     

The New England Air Quality Forecasting Pilot Program: Development of an Evaluation Protocol and Performance Benchmark

Abstract

The National Oceanic and Atmospheric Administration recently sponsored the New England Forecasting Pilot Program to serve as a “test bed” for chemical forecasting by providing all of the elements of a National Air Quality Forecasting System, including the development and implementation of an evaluation protocol. This Pilot Program enlisted three regional-scale air quality models, serving as prototypes, to forecast ozone (O₃) concentrations across the northeastern United States during the summer of 2002. A suite of statistical metrics was identified as part of the protocol that facilitated evaluation of both discrete forecasts (observed versus modeled concentrations) and categorical forecasts (observed versus modeled exceedances/nonexceedances) for both the maximum 1-hr (125 ppb) and 8-hr (85 ppb) forecasts produced by each of the models. Implementation of the evaluation protocol took place during a 25-day period (August 5–29), utilizing hourly O₃ concentration data obtained from over 450 monitors from the U.S. Environment Protection Agency’s Air Quality System network.