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.     

Photochemical model evaluation of the surface ozone impact of a power plant in a heavily industrialized area of southwestern Spain

The characterization and evaluation of the impact that an industry is likely to have on the surrounding ozone levels is one of many problems confronting air quality managers and should be taken into consideration when authorizing its installation. The correct management of an environment, in terms of monitoring existing industries and planning new activities, requires adequate knowledge of the processes sustained by the industrial emissions therein.This paper explores the improvements in air quality management arising from taking into account the uncertainties involved in the photochemical modeling of the impact of an industry on surface ozone levels.For this, we evaluate the impact on ozone levels of a power plant located in an industrial area of southwestern Spain (Huelva). The evaluation takes into account the effects of both emissions' uncertainty and the non-linear chemistry between ozone and its precursors, thus providing a probable range of increase over the normative values (hourly and 8-hourly maximums) defined in the European Directive. The proposed methodology is easily applicable by air quality managers.Advanced modeling techniques were used for the power plant assessment, MM5 atmospheric modeling system, and air quality model CAMx. The results from meteorology and ozone forecasts have shown acceptable agreement with the observations.The spatial distribution of the impact is found to be strongly determined by mesoscale meteorological processes, which are reinforced by the local orography; there is also a marked temporal evolution. The industrial plume is observed to induce a decrease (or maintenance) of the ozone levels near the emission source (0–10 km), and an increase in the ozone concentrations farther away (with maximums between 10 and 50 km). In fact, in the meteorological episodes with a predominance of local breeze circulations, impacts have been detected at distances of more than 100 km from the emission source.Sensitivity of the power plant impact to variations in ozone precursor emissions is described, and the scenarios and the points in the domain presenting higher sensitivity and registering larger impacts are also identified. The results show that the largest impacts take place in emission scenarios where the NO_x has been reduced with respect to the base case scenario. In contrast, in scenarios where the VOC emissions are reduced with respect to the base case scenario, the impact is smaller or remains unchanged. This is important in areas like the study case, where there is a high percentage of biogenic VOC emissions and the industrial areas are close to natural protected areas and agricultural fields.     

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.