Geographic patterns of non-carpeted floor dust loading in Syracuse, New York (USA) homes

Residential floor dust loading was measured on the smooth floor surface of 488 houses in Syracuse, New York, during the summers of 2003 and 2004. Using U.S. Environmental Protection Agency (EPA) wipe methods, pre-weighed Ghost Wipes, Lead Wipes, or Whatman Filters were employed to collect duplicate samples from (predominantly) kitchens. The collection efficiency of the various media was determined from multiple wipe tests and side-by-side comparisons. The results were normalized and aggregated at the census tract level to determine whether spatial patterns of dust loading could be observed. Loading was found to be log-normally distributed, with a geometric mean value of 0.311 g m⁻² (29 mg of dust per square foot of floor); 95% of the observations fell in the range of 0.042–2.330 g m⁻² (4–216 mg foot⁻²). The sampling for floor dust loading shows some bias for day of the week in which visits to the residential properties were made. After a first-order correction for this effect, results were aggregated by census tract and mapped in a geographic information system (GIS); strong spatial patterns can be identified in an inverse distance weighted mapping. The geographic patterns exhibit a strong correlation with socio-economic/demographic covariates extracted from the 2000 census summaries. Dust mass on the floors is positively correlated with renter-occupied properties and family size; it is negatively correlated with measures of household income.     

Waste dumpsites and public health: a case for lead exposure in Zimbabwe and potential global implications

Environmental Geochemistry and Health - Most waste sites in Zimbabwe are not sanitary landfills but open dumps that indiscriminately receive waste from municipalities, industries, commercial...     

Classification of occupations at risk in Thailand for lead exposure using blood lead levels as a biomarker

Lead (Pb) exposure is of particular concern because of the ongoing exposure of thousands of workers in industrial plants. Monitoring of Pb exposure among the at-risk workers is recommended and amongst various biomarkers, and it is well-established that blood lead (BPb) determination is utilized for biomonitoring. There are some previous reports on the BPb levels among several at-risk occupations in Thailand; however, there has been no comparison among these occupations with respect to metal levels in the blood. The aim of this study was to correlate at-risk occupations with Pb exposure and BPb levels. In order to compare between occupations, it was not possible to obtain a direct correlation as there are several confounding factors, especially for occupational conditions and lab measurement techniques. In this study, the exposure risk ratio from five previously available reports regarding BPb determination in at-risk occupations in Thailand is presented. Of interest, the high risk occupations are those in which individuals directly inhaled Pb in environmental ambient air. The cutoff median of exposure risk ratio was found to be 12.6 and this may be a useful value in determining whether exposed workers are at high risk in future studies.     

Modelling count data based on weakly dependent spatial covariates using a copula approach: application to rat sightings

Not all environmental processes are observed in a way that allows a straight forward easy modelling. Nevertheless, insights can also be gained by exploring weakly dependent covariates paying attention to details of the distribution. Using the concept of copulas, it is possible to explore the dependence of a multivariate distribution without the distortion of the marginal distribution functions acting on typical correlation measures. Furthermore, copulas turn the attention to the dependence across the entire range of the multivariate distribution and do not only summarise it in a single correlation measure. In our application, we study counts of rat sightings in the city of Madrid. The brown rat lives with mankind and adversely affects public health by transmission of diseases, bites and allergies. Better understanding behavioural and spatial correlation aspects of this species can contribute to its effective management and control. We explore weakly to moderately correlated covariates based on distances to broken sewers, feeding grounds and markets as well as population density. The use of copulas is motivated by the different dependence structures of the four covariates and the asymmetries therein. In order to deal with the discrete zero-inflated counts, we present a new approach that assigns conditional random ranks to discrete data. This way, we mimic an underlying continuous variable easing the vine copula estimation, but do not destroy the dependence as in a uniform randomisation. We show that a 5-dimensional vine copula model is able to capture the dependence in our application.     

Predictive vegetation modeling for conservation: impact of error propagation from digital elevation data.

The effect of digital elevation model (DEM) error on environmental variables, and subsequently on predictive habitat models, has not been explored. Based on an error analysis of a DEM, multiple error realizations of the DEM were created and used to develop both direct and indirect environmental variables for input to predictive habitat models. The study explores the effects of DEM error and the resultant uncertainty of results on typical steps in the modeling procedure for prediction of vegetation species presence/absence. Results indicate that all of these steps and results, including the statistical significance of environmental variables, shapes of species response curves in generalized additive models (GAMs), stepwise model selection, coefficients and standard errors for generalized linear models (GLMs), prediction accuracy (Cohen's kappa and AUC), and spatial extent of predictions, were greatly affected by this type of error. Error in the DEM can affect the reliability of interpretations of model results and level of accuracy in predictions, as well as the spatial extent of the predictions. We suggest that the sensitivity of DEM-derived environmental variables to error in the DEM should be considered before including them in the modeling processes.     

The spatial pattern of nitrogen cycling in the Adirondack Park, New York

Maps of canopy nitrogen obtained through analysis of high-resolution, hyperspectral, remotely sensed images now offer a powerful means to make landscape-scale to regional-scale estimates of forest N cycling and net primary production (NPP). Moreover, recent research has suggested that the spatial variability within maps of canopy N may be driven by environmental gradients in such features as historic forest disturbance, temperature, species composition, moisture, geology, and atmospheric N deposition. Using the wide variation in these six features found within the diverse forest ecosystems of the 2.5 million ha Adirondack Park, New York, USA, we examined linkages among environmental gradients and three measures of N cycling collected during the 2003 growing season: (1) field survey of canopy N, (2) field survey of soil C:N, and (3) canopy N measured through analysis of two 185 x 7.5 km Hyperion hyperspectral images. These three measures of N cycling strongly related to forest type but related poorly to all other environmental gradients. Further analysis revealed that the spatial pattern in N cycling appears to have distinct inter- and intraspecific components of variability. The interspecific component, or the proportional contribution of species functional traits to canopy biomass, explained 93% of spatial variability within the field canopy N survey and 37% of variability within the soil C:N survey. Residual analysis revealed that N deposition accounted for an additional 2% of variability in soil C:N, and N deposition and historical forest disturbance accounted for an additional 2.8% of variability in canopy N. Given our finding that 95.8% of the variability in the field canopy N survey could be attributed to variation in the physical environment, our research suggests that remotely sensed maps of canopy N may be useful not only to assess the spatial variability in N cycling and NPP, but also to unravel the relative importance of their multiple controlling factors.     

Risk assessment of children's blood lead level in some rural habitations of western Uttar Pradesh,India

The objective of this study was to determine the blood lead level (BLL) of children aged under 5 living in western Uttar Pradesh, India. A total of 100 blood samples, 100 drinking water samples, and 21 vegetable samples were assessed for lead (Pb) contamination. BLL was observed in the range of 3–11.58?µg?dL^?1 with a maximal level of 11.58?±?0.05?µg?dL^?1 in Kazipur village. Lead in water samples was detected in the range of 0.006–0.095?ppm and identified at upto 9.5-fold higher than the stated permissible limit. A significant positive correlation (+0.679) was obtained between BLL and water Pb levels. All vegetable samples were also contaminated with Pb upto 12-fold higher than the permissible limit of 2.5?µg?g^?1 of Prevention of Food Adulteration Act (PFA) 1954. Untreated industrial effluents were identified as the responsible factors for Pb occurrence in the study area. The presence of lead may be associated with the increased antisocial activities of children living in western Uttar Pradesh. Children with elevated BLL had poorer nutritional status than those with background Pb levels. There is an urgent need for a national campaign to monitor the BLL of children in India.     

Contaminated lead environments of man: reviewing the lead isotopic evidence in sediments,peat, and soils for the temporal and spatial patterns of atmospheric lead pollution in Sweden

Clair Patterson and colleagues demonstrated already four decades ago that the lead cycle was greatly altered on a global scale by humans. Moreover, this change occurred long before the implementation of monitoring programs designed to study lead and other trace metals. Patterson and colleagues also developed stable lead isotope analyses as a tool to differentiate between natural and pollution-derived lead. Since then, stable isotope analyses of sediment, peat, herbaria collections, soils, and forest plants have given us new insights into lead biogeochemical cycling in space and time. Three important conclusions from our studies of lead in the Swedish environment conducted over the past 15 years, which are well supported by extensive results from elsewhere in Europe and in North America, are: (1) lead deposition rates at sites removed from major point sources during the twentieth century were about 1,000 times higher than natural background deposition rates a few thousand years ago (~10 mg Pb m^?2 year^?1 vs. 0.01 mg Pb m^?2 year^?1), and even today (~1 mg Pb m^?2 year^?1) are still almost 100 times greater than natural rates. This increase from natural background to maximum fluxes is similar to estimated changes in body burdens of lead from ancient times to the twentieth century. (2) Stable lead isotopes (²⁰⁶Pb/²⁰⁷Pb ratios shown in this paper) are an effective tool to distinguish anthropogenic lead from the natural lead present in sediments, peat, and soils for both the majority of sites receiving diffuse inputs from long range and regional sources and for sites in close proximity to point sources. In sediments >3,500 years and in the parent soil material of the C-horizon, ²⁰⁶Pb/²⁰⁷Pb ratios are higher, 1.3 to >2.0, whereas pollution sources and surface soils and peat have lower ratios that have been in the range 1.14–1.18. (3) Using stable lead isotopes, we have estimated that in southern Sweden the cumulative anthropogenic burden of atmospherically deposited lead is ~2–5 g Pb m^?2 and ~1 g Pb m^?2 in the “pristine” north. Half of this cumulative total was deposited before industrialization. (4) In the vicinity of the Rönnskär smelter in northern Sweden, a major point source during the twentieth century, there is an isotopic pattern that deviates from the general trends elsewhere, reflecting the particular history of ore usage at Rönnskär, which further demonstrates the chronological record of lead loading recorded in peat and in soil mor horizons.     

Identifying 120 years of decline in ecosystem structure and maturity of Great South Bay, New York using the Ecopath modelling approach

Mass balanced models yield valuable information regarding ecological function and delivery of ecosystem services, but often rely on data collected well before many species were reduced to fractions of their original abundance. Lagoonal systems, such as Great South Bay (GSB), NY, sit on the interface of terrestrial and marine ecosystems and are prone to anthropogenic stressors but proximity to land also makes the presence of data regarding historic populations and structure more likely. To quantify over a century of ecosystem change, Ecopath models were developed for GSB at each of four time periods where commercial and scientific data exist: 1880s, 1930s, 1980s and 2000s. The results indicated that the GSB has experienced a decline in ecosystem maturity, loss of top keystone predators, a decline in connectivity to the ocean though the reduction of migratory species and increasing dominance of low trophic level organisms. These changes undermine the delivery of ecosystem services, increase conflicts over limited resources and suggest that present day restoration targets fail to recognize appropriate baselines. We discuss the role of stochastic events, which result in state changes that could be defined as regime shifts, and ecosystem connectivity to the long-term stability of lagoonal systems.     

Analysis of spatial and temporal patterns in a large reservoir using water quality and hydrodynamic modeling

Effective management of reservoir water resources demands a good command of ecological processes in the waterbody. In this work the three-dimensional finite element hydrodynamic model RMA10 was coupled to an eutrophication model. The models were used together with a methodology for loads estimation to foster the understanding of such processes in the largest reservoir in Western Europe—the Alqueva. Nutrient enrichment and eutrophication are water quality concerns in this man-made impoundment. A total phosphorus and nitrogen loads quantification methodology was developed to estimate the inputs in the reservoir, using point and non-point source data.Field data (including water temperature, wind, water elevation, chlorophyll-a, nutrient concentration and dissolved oxygen) and estimated loads were used as forcing for simulations.The analysis of the modeling results shows that spatial and temporal distributions for water temperature, chlorophyll-a, dissolved oxygen and nutrients are consistent with measured in situ data.Modeling results allowed the identification of likely key impact factors on the water quality of the Alqueva reservoir. It is shown that the particular geomorphological and hydrological characteristics of the reservoir together with local climate features are responsible for the existence of distinct ecological regions within the reservoir.     

Dynamics of colony development in Polistes dominulus: a modeling approach

A simple model based on feedback mechanisms is developed to describe the dynamics of brood production and colony development of primitively eusocial paper wasps. The presence of pupae and empty cells stimulate egg laying, which varies between a basic rate and a physiological maximum. Newly hatched larvae are fed eggs, causing fluctuations in brood demography and forming cohorts of offspring. The basic feedback mechanisms produce emergent colony-level properties such as synchronized development of the brood and mature nest size. Results suggest that it is incorrect to imply colony decline from lack of nest growth, and that production of waves of offspring can be interpreted as the inevitable result of these simple feedback mechanisms rather than the solution to ultimate optimality criteria. Simulations using the parameters estimated in Polistes dominulus Christ are compared to studies of live wasps to test the validity of the model. Comparing simulated results with a perturbation experiment in nature suggests that feedback relationships establish a system that is robust and resilient against severe disturbance. Received: 20 January 1996/Accepted after revision: 27 April 1996     

Protective values for soil lead with respect to child health: a critique of UK guidelines

In 2002, The (UK) Department for Environment, Food and Rural Affairs (DEFRA) together with the Environment Agency published document SGV10 “Contaminants in soil: collation of toxicological data and intake values for humans. Lead”. This publication sets out the derivation of Soil Guideline Values for lead contamination. Values for soil lead that are protective of the health of children and also for adults are derived using the model recommended by an international task force working under the auspices of the Society for Environmental Geochemistry and Health (SEGH) (Wixson and Davies 1993, 1994). Detailed examination of the DEFRA publication shows it uses unrealistic values for two variables in the SEGH model. The consequence is both actual and has potential adverse societal impacts. These impacts could be avoided if the appropriate scientific community were regarded as stakeholders in the legislative outcome and not simply the providers of the research data.     

PyLUR: Efficient software for land use regression modeling the spatial distribution of air pollutants using GDAL/OGR library in Python

• PyLUR comprises four modules for developing and applying a LUR model. • It considers both conventional and novel potential predictor variables. • GDAL/OGR libraries are used to do spatial analysis in the modeling and prediction. • Developed on Python platform, PyLUR is rather efficient in data processing. Land use regression (LUR) models have been widely used in air pollution modeling. This regression-based approach estimates the ambient pollutant concentrations at un-sampled points of interest by considering the relationship between ambient concentrations and several predictor variables selected from the surrounding environment. Although conceptually quite simple, its successful implementation requires detailed knowledge of the area, expertise in GIS, statistics, and programming skills, which makes this modeling approach relatively inaccessible to novice users. In this contribution, we present a LUR modeling and pollution-mapping software named PyLUR. It uses GDAL/OGR libraries based on the Python platform and can build a LUR model and generate pollutant concentration maps efficiently. This self-developed software comprises four modules: a potential predictor variable generation module, a regression modeling module, a model validation module, and a prediction and mapping module. The performance of the newly developed PyLUR is compared to an existing LUR modeling software called RLUR (with similar functions implemented on R language platform) in terms of model accuracy, processing efficiency and software stability. The results show that PyLUR out-performs RLUR for modeling in the Bradford and Auckland case studies examined. Furthermore, PyLUR is much more efficient in data processing and it has a capability to handle detailed GIS input data.     

Environmental health in minority and other underserved populations: Benign methods for identifying lead hazards at day care centres of New Orleans

This study tests the hypothesis that exterior sources of lead dust are more important than interior sources in the route of exposure of children. Benign field methods were used to distinguish between potential and actual lead exposure problems. Utilising hand wipe and surface wipe techniques, hand and environmental samples were obtained from selected day care centres at different locations within New Orleans. Previous research has shown that soil lead is determined by location within the city. Private and public day care centres were selected from inner and outer city areas to estimate the extent of hand lead exposure. To measure and identify the extent of environmental lead exposure, hand wipes were taken before and after playing outdoors. Results of preliminary findings show that outdoor lead dust is a more potent contaminant of hands than indoor lead dust. An association was found between the amount of lead on children's hands after playing outdoors and the lead content in the exterior dust and soil. Although two girls out of forty children had exceptionally high hand lead quantities after playing outdoors, in general, boys have higher hand lead levels than girls. The private inner-city day care centre had a severe contamination problem in its outdoor play area. By contrast, the outdoor play area of the public inner city day care centre is of such a high quality that the quantity of lead dust is independent of location in the city.     

Multimethod,multistate Bayesian hierarchical modeling approach for use in regional monitoring of wolves

In many cases, the first step in large‐carnivore management is to obtain objective, reliable, and cost‐effective estimates of population parameters through procedures that are reproducible over time. However, monitoring predators over large areas is difficult, and the data have a high level of uncertainty. We devised a practical multimethod and multistate modeling approach based on Bayesian hierarchical‐site‐occupancy models that combined multiple survey methods to estimate different population states for use in monitoring large predators at a regional scale. We used wolves (Canis lupus) as our model species and generated reliable estimates of the number of sites with wolf reproduction (presence of pups). We used 2 wolf data sets from Spain (Western Galicia in 2013 and Asturias in 2004) to test the approach. Based on howling surveys, the naïve estimation (i.e., estimate based only on observations) of the number of sites with reproduction was 9 and 25 sites in Western Galicia and Asturias, respectively. Our model showed 33.4 (SD 9.6) and 34.4 (3.9) sites with wolf reproduction, respectively. The number of occupied sites with wolf reproduction was 0.67 (SD 0.19) and 0.76 (0.11), respectively. This approach can be used to design more cost‐effective monitoring programs (i.e., to define the sampling effort needed per site). Our approach should inspire well‐coordinated surveys across multiple administrative borders and populations and lead to improved decision making for management of large carnivores on a landscape level. The use of this Bayesian framework provides a simple way to visualize the degree of uncertainty around population‐parameter estimates and thus provides managers and stakeholders an intuitive approach to interpreting monitoring results. Our approach can be widely applied to large spatial scales in wildlife monitoring where detection probabilities differ between population states and where several methods are being used to estimate different population parameters.     

Sediment budget as a driver for sediment management at plumb beach,New York,USA: vectors of change and impacts

Persistent erosion at Plumb Beach, New York, USA, has been countered by a series of isolated actions to stabilize the shoreline. Sediment placed at the erosion site has quickly been removed and re-deposited in downdrift locations to the detriment of the existing habitats. In a renewed effort to better understand the dimensions of change at Plumb Beach, detailed spatial analysis of shoreline displacement, dune-beach profile evolution, and sediment budget has been undertaken through intensive surveying and comparisons of Digital Elevation Models. Products derived from these surveys establish that the sector of maximum erosion has shifted downdrift and that the scale of the changes is twice the volume exchange as previously estimated. Improved knowledge of the dimensions and spatial distributions of the changes is used to create a focused approach that is functioning within the scale of the local sediment budget and reducing its impacts.     

Prospective spatial prediction of infectious disease: experience of New York State (USA) with West Nile Virus and proposed directions for improved surveillance

Infectious disease surveillance has become an international top priority due to the perceived risk of bioterrorism. This is driving the improvement of real-time geo-spatial surveillance systems for monitoring disease indicators, which is expected to have many benefits beyond detecting a bioterror event. West Nile Virus surveillance in New York State (USA) is highlighted as a working system that uses dead American Crows (Corvus brachyrhynchos) to prospectively indicate viral activity prior to human onset. A cross-disciplinary review is then presented to argue that this system, and infectious disease surveillance in general, can be improved by complementing spatial cluster detection of an outcome variable with predictive “risk mapping” that incorporates spatiotemporal data on the environment, climate and human population through the flexible class of generalized linear mixed models.