Comparing universal kriging and land-use regression for predicting concentrations of gaseous oxides of nitrogen (NOx) for the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air)

BACKGROUND: Epidemiological studies that assess the health effects of long-term exposure to ambient air pollution are used to inform public policy. These studies rely on exposure models that use data collected from pollution monitoring sites to predict exposures at subject locations. Land use regression (LUR) and universal kriging (UK) have been suggested as potential prediction methods. We evaluate these approaches on a dataset including measurements from three seasons in Los Angeles, CA. METHODS: The measurements of gaseous oxides of nitrogen (NOx) used in this study are from a "snapshot" sampling campaign that is part of the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air). The measurements in Los Angeles were collected during three two-week periods in the summer, autumn, and winter, each with about 150 sites. The design included clusters of monitors on either side of busy roads to capture near-field gradients of traffic-related pollution. LUR and UK prediction models were created using geographic information system (GIS)-based covariates. Selection of covariates was based on 10-fold cross-validated (CV) R(2) and root mean square error (RMSE). Since UK requires specialized software, a computationally simpler two-step procedure was also employed to approximate fitting the UK model using readily available regression and GIS software. RESULTS: UK models consistently performed as well as or better than the analogous LUR models. The best CV R(2) values for season-specific UK models predicting log(NOx) were 0.75, 0.72, and 0.74 (CV RMSE 0.20, 0.17, and 0.15) for summer, autumn, and winter, respectively. The best CV R(2) values for season-specific LUR models predicting log(NOx) were 0.74, 0.60, and 0.67 (CV RMSE 0.20, 0.20, and 0.17). The two-stage approximation to UK also performed better than LUR and nearly as well as the full UK model with CV R(2) values 0.75, 0.70, and 0.70 (CV RMSE 0.20, 0.17, and 0.17) for summer, autumn, and winter, respectively. CONCLUSION: High quality LUR and UK prediction models for NOx in Los Angeles were developed for the three seasons based on data collected for MESA Air. In our study, UK consistently outperformed LUR. Similarly, the 2-step approach was more effective than the LUR models, with performance equal to or slightly worse than UK.     

A hybrid micro-scale model for transport in connected macro-pores in porous media

This paper presents a hybrid model for transport in connected macro-pores in porous media. A pore-scale model is used to parameterize the hybrid model. The hybrid model explicitly models the advection and diffusion of species in the connected macro-pores and treats the porous media around the connected macro-pores as a continuum with effective transport properties. The pore-scale model is used to calculate the effective transport properties of the porous continuum. This approach negates the need to calibrate the hybrid model against experimental data, which is common for continuum-scale models of porous media, and allows an arbitrary microstructure to be considered. The paper presents the multi-scale modeling approach along with the details of the hybrid and pore-scale models. Validation of the model is also presented along with several case studies investigating the applicability of the multi-scale modeling approach to different geometries and transport conditions. The case studies show that the multi-scale modeling approach is accurate for various connected macro-pore geometries given that the porosity of the porous medium around the connected macro-pores is sufficiently small. The accuracy of the hybrid model decreases with increasing porosity of the matrix.     

Robust population management under uncertainty for structured population models.

Structured population models are increasingly used in decision making, but typically have many entries that are unknown or highly uncertain. We present an approach for the systematic analysis of the effect of uncertainties on long-term population growth or decay. Many decisions for threatened and endangered species are made with poor or no information. We can still make decisions under these circumstances in a manner that is highly defensible, even without making assumptions about the distribution of uncertainty, or limiting ourselves to discussions of single, infinitesimally small changes in the parameters. Suppose that the model (determined by the data) for the population in question predicts long-term growth. Our goal is to determine how uncertain the data can be before the model loses this property. Some uncertainties will maintain long-term growth, and some will lead to long-term decay. The uncertainties are typically structured, and can be described by several parameters. We show how to determine which parameters maintain long-term growth. We illustrate the advantages of the method by applying it to a Peregrine Falcon population. The U.S. Fish and Wildlife Service recently decided to allow minimal harvesting of Peregrine Falcons after their recent removal from the Endangered Species List. Based on published demographic rates, we find that an asymptotic growth rate lambda > 1 is guaranteed with 5% harvest rate up to 3% error in adult survival if no two-year-olds breed, and up to 11% error if all two-year-olds breed. If a population growth rate of 3% or greater is desired, the acceptable error in adult survival decreases to between 1% and 6% depending of the proportion of two-year-olds that breed. These results clearly show the interactions between uncertainties in different parameters, and suggest that a harvest decision at this stage may be premature without solid data on adult survival and the frequency of breeding by young adults.     

Convergent and Contingent Community Responses to Grass Source and Dominance During Prairie Restoration Across a Longitudinal Gradient

Restoring prairie on formerly cultivated land begins by selecting propagule seed sources and the diversity of species to reintroduce. This study examined the effects of dominant grass propagule source (cultivar vs. non-cultivar) and sown propagule diversity (grass:forb sowing ratio) on plant community structure. Two field experiments were established in Kansas and Illinois consisting of identical split plot designs. Dominant grass source was assigned as the whole-plot factor, and sown dominance of grasses (five levels of seeded grass dominance) as the subplot factor. Species density, cover, and diversity were quantified for 5 years. The effect of dominant grass source on the cover of focal grasses, sown species, and volunteer species was contingent upon location, with variation between dominant grass sources observed exclusively in Kansas. Species density and diversity showed regionally convergent patterns in response to dominant grass source. Contrary to our hypotheses, total species density and diversity were not lower in the presence of grass cultivars, the grass source we had predicted would be more competitive. Sown grass dominance effects on the cover of the focal grass species were contingent upon location resulting from establishment corresponding better to the assigned treatments in Illinois. All other cover groups showed regionally convergent patterns, with lower cover of volunteers and higher cover of sown forbs, diversity, and species density in the lowest sown grass dominance treatment in both sites. Thus, decisions regarding the diversity of propagules to reintroduce had more consequence for plant community structure than cultivar or non-cultivar source of dominant grasses.     

Implications of Spatial Data Variations for Protected Areas Management: An Example from East Africa

Geographic information systems and remote sensing technologies have become an important tool for visualizing conservation management and developing solutions to problems associated with conservation. When multiple organizations separately develop spatial data representations of protected areas, implicit error arises due to variation between data sets. We used boundary data produced by three conservation organizations (International Union for the Conservation of Nature, World Resource Institute, and Uganda Wildlife Authority), for seven Ugandan parks, to study variation in the size represented and the location of boundaries. We found variation in the extent of overlapping total area encompassed by the three data sources, ranging from miniscule (0.4 %) differences to quite large ones (9.0 %). To underscore how protected area boundary discrepancies may have implications to protected area management, we used a landcover classification, defining crop, shrub, forest, savanna, and grassland. The total area in the different landcover classes varied most in smaller protected areas (those less than 329 km²), with forest and cropland area estimates varying up to 65 %. The discrepancies introduced by boundary errors could, in this hypothetical case, generate erroneous findings and could have a significant impact on conservation, such as local-scale management for encroachment and larger-scale assessments of deforestation.     

Spectroscopic speciation and quantification of lead in phosphate-amended soils

The immobilization of Pb in contaminated soils as pyromorphite [Pb(5)(PO(4))(3)Cl, OH, F] through the addition of various phosphate amendments has gained much attention in the remediation community. However, it is difficult to fully determine the speciation and amount of soil Pb converted to pyromorphite by previously employed methods, such as selective sequential extraction procedures and scanning electron microscopy coupled with energy dispersive X-ray spectroscopy, which often lead to erroneous results in these non-equilibrated and heterogeneous systems. Statistical analysis by linear combination fitting (LCF) applied to X-ray absorption fine structure (XAFS) spectroscopic data of Pb-contaminated soil samples relative to known Pb reference material provides direct, in situ evidence of dominate Pb species in the amended soils without chemical or physical disruption to the Pb species as well as a weighted quantification output. The LCF-XAFS approach illustrated that pyromorphite concentration ranged from 0% (control soil) to 45% (1% phosphoric acid amendment, residence time of 32 mo) relative to the total Pb concentration. The Pb speciation in the nonamended control soil included Pb-sulfur species (galena + angelsite = 53%), adsorbed Pb (inner-sphere + outer-sphere + organic-bound = 45%), and Pb-carbonate phases (cerussite + hydrocerussite = 2%). The addition of P promoted pyromorphite formation and the rate of formation increased with increasing P concentration (up to 45%). The supplemental addition of an iron amendment as an iron-rich byproduct with triple superphosphate (TSP) enhanced pyromorphite formation relative to independent TSP amendment of like concentrations (41 versus 29%). However, the amendment of biosolids and biosolids plus TSP observed little pyromorphite formation (1-16% of total Pb), but a significant increase of sorbed Pb was determined by LCF-XAFS.     

Stream Restoration and Enhancement Projects: Is Anyone Monitoring?

Declines in salmon stocks and general watershed health in Washington State, USA, have led to an increase in stream restoration and enhancement projects initiated throughout the state. The increasing number of projects has also raised questions regarding the monitoring of these efforts. Project managers receiving hydraulic project approvals (HPAs) were surveyed to determine whether monitoring was taking place on their projects. About half the project managers surveyed reported the collection of baseline data and the use of biological, physical, chemical, or other water quality measures for their projects. Of those who reported collection of monitoring data, only 18% indicated that monitoring was required. Respondents were also asked to rank the importance of various project goals on a Likert scale. Project managers with projects focusing on “engineering” goals (e.g., roadbed stabilization) were less likely than other project managers to collect baseline monitoring data. Project managers with projects focusing on “restoration/ecological” or “fisheries” goals were more likely than other project managers to collect monitoring measures. Although monitoring appears to be taking place in slightly more than half of the projects surveyed, the nature of the data collected varies widely across projects, and in most cases the monitoring effort is voluntary. This suggests that project sponsors, funders, and managers must consider the issues involved in requiring appropriate monitoring, establishing standardized monitoring guidelines, the time frames in which to monitor, providing other incentives for conducting monitoring, and ensuring adequate funding for monitoring efforts.     

MODELING THE IMPACTS OF WATER LEVEL CHANGES ON A GREAT LAKES COMMUNITY1

ABSTRACT: Recent research that couples climate change scenarios based on general circulation models (GCM) with Great Lakes hydrologic models has indicated that average water levels are projected to decline in the future. This paper outlines a methodology to assess the potential impact of declining water levels on Great Lakes waterfront communities, using the Lake Huron shoreline at Goderich, Ontario, as an example. The methodology utilizes a geographic information system (GIS) to combine topographic and bathymetric datasets. A digital elevation surface is used to model projected shoreline change for 2050 using water level scenarios. An arbitrary scenario, based on a 1 m decline from February 2001 lake levels, is also modeled. By creating a series of shoreline scenarios, a range of impact and cost scenarios are generated for the Goderich Harbor and adjacent marinas. Additional harbor and marina dredging could cost as much as CDN $7.6 million. Lake freighters may experience a 30 percent loss in vessel capacity. The methodology is used to provide initial estimates of the potential impacts of climate change that can be readily updated as more robust climate change scenarios become available and is adaptable for use in other Great Lakes coastal communities.