Determination of direct and fugitive PM emissions in a Mediterranean harbour by means of classic and novel tracer methods

Remodelling works are frequent in harbour areas, given that they must adapt to rapidly changing market requirements. Depending on their nature (construction, demolition, etc.), these works may be carried out during long periods of time and thus exert a significant impact on the air quality at the harbours and their surroundings. The air quality at the Valencia harbour was studied during an enlargement project. Four sampling stations aimed to cover the major dust-emitting activities in the harbour. In addition, a sampling campaign was carried out to collect dust materials at their emission sources (e.g., loading and unloading of clinker and petroleum coke, as well as the enlargement works). Differences obtained between PM levels at the monitoring sites were correlated with the distance between sampling stations and enlargement works and/or harbour operations, as well as with the preferential wind direction. In all cases, the days with the highest PM_10 levels were linked to wind directions coinciding with the location of the enlargement works or the clinker and petroleum coke stocks. Classic source apportionment methodologies (PCA and CMB) were applied to the data, but their interpretation was complex due to the similar chemical signatures of PM originating from direct and fugitive emissions from stocked materials (e.g., clinker), and the enlargement works. To overcome this difficulty, a novel non-statistical approach was used to obtain quantitative estimations of the contributions from sources (petroleum coke and clinker), based on the correlation between specific PM components (e.g., carbonaceous species) and source tracers (e.g., V). Finally, a qualitative test using phenolphthalein was devised to identify the presence of clinker on the filters, which provided positive results. This novel tracer approach is thus recommended for harbour authorities.     

Impact of data quality and model complexity on prediction of pesticide leaching

Accurate input data for leaching models are expensive and difficult to obtain which may lead to the use of "general" non-site-specific input data. This study investigated the effect of using different quality data on model outputs. Three models of varying complexity, GLEAMS, LEACHM, and HYDRUS-2D, were used to simulate pesticide leaching at a field trial near Hamilton, New Zealand, on an allophanic silt loam using input data of varying quality. Each model was run for four different pesticides (hexazinone, procymidone, picloram and triclopyr); three different sets of pesticide sorption and degradation parameters (i.e., site optimized, laboratory derived, and sourced from the USDA Pesticide Properties Database); and three different sets of soil physical data of varying quality (i.e., site specific, regional database, and particle size distribution data). We found that the selection of site-optimized pesticide sorption (Koc) and degradation parameters (half-life), compared to the use of more general database derived values, had significantly more impact than the quality of the soil input data used, but interestingly also more impact than the choice of the models. Models run with pesticide sorption and degradation parameters derived from observed solute concentrations data provided simulation outputs with goodness-of-fit values closest to optimum, followed by laboratory-derived parameters, with the USDA parameters providing the least accurate simulations. In general, when using pesticide sorption and degradation parameters optimized from site solute concentrations, the more complex models (LEACHM and HYDRUS-2D) were more accurate. However, when using USDA database derived parameters, all models performed about equally.     

Vulnerability of Rehabilitated Agricultural Production Systems to Invasion by Nontarget Plant Species

Vast areas of arable land have been retired from crop production and “rehabilitated” to improved system states through landowner incentive programs in the United States (e.g., Conservation and Wetland Reserve Programs), as well as Europe (i.e., Agri-Environment Schemes). Our review of studies conducted on invasion of rehabilitated agricultural production systems by nontarget species elucidates several factors that may increase the vulnerability of these systems to invasion. These systems often exist in highly fragmented and agriculturally dominated landscapes, where propagule sources of target species for colonization may be limited, and are established under conditions where legacies of past disturbance persist and prevent target species from persisting. Furthermore, rehabilitation approaches often do not include or successfully attain all target species or historical ecological processes (e.g., hydrology, grazing, and/or fire cycles) key to resisting invasion. Uncertainty surrounds ways in which nontarget species may compromise long term goals of improving biodiversity and ecosystem services through rehabilitation efforts on former agricultural production lands. This review demonstrates that more studies are needed on the extent and ecological impacts of nontarget species as related to the goals of rehabilitation efforts to secure current and future environmental benefits arising from this widespread conservation practice.     

PRIORITIZING WATER MANAGEMENT PROJECTS IN SOUTHEAST ASIA: AN EXPERIMENTAL METHODOLOGY

ABSTRACT: Southeast Asian nations with extensive water management systems have numerous candidate and ongoing programs and projects. They cannot possibly all be funded and implemented simultaneously in any serious way. Choices must be made. But how to select? What criteria can assist with the screening in an acceptable manner, particularly in the near absence of useful technical and economic information across them all. This paper suggests a methodology by which assessment criteria can be constructed which conforms to any given national information base and development needs. Criteria can be weighted one against the other in their relative importance. For each criterion a ranking scale puts the “best” (most, highest) at one end and the “worst” (least, lowest) at the other. Each candidate project is then subjected to these consistent assessment methods for a weighted “score.” Key criteria normally have surrogate measures depending on information available. Criteria are of three sorts: sheer volume, e.g., of a specific use, a benefit; ratios and relations, e.g., A as percent of B; and other more subjective criteria, e.g., regional or income distribution impacts. The basic criteria include, for example, time to full benefits (minimize), OM & R as percent of investment (minimize), and permanent rural jobs created (maximize).     

An Approach Toward Understanding Wildlife-Vehicle Collisions

Among the most conspicuous environmental effects of roads are vehicle-related mortalities of wildlife. Research to understand the factors that contribute to wildlife-vehicle collisions can be partitioned into several major themes, including (i) characteristics associated with roadkill hot spots, (ii) identification of road-density thresholds that limit wildlife populations, and (iii) species-specific models of vehicle collision rates that incorporate information on roads (e.g., proximity, width, and traffic volume) and animal movements. We suggest that collision models offer substantial opportunities to understand the effects of roads on a diverse suite of species. We conducted simulations using collision models and information on Blanding’s turtles (Emydoidea blandingii), bobcats (Lynx rufus), and moose (Alces alces), species endemic to the northeastern United States that are of particular concern relative to collisions with vehicles. Results revealed important species-specific differences, with traffic volume and rate of movement by candidate species having the greatest influence on collision rates. We recommend that future efforts to reduce wildlife-vehicle collisions be more proactive and suggest the following protocol. For species that pose hazards to drivers (e.g., ungulates), identify collision hot spots and implement suitable mitigation to redirect animal movements (e.g., underpasses, fencing, and habitat modification), reduce populations of problematic game species via hunting, or modify driver behavior (e.g., dynamic signage that warns drivers when animals are near roads). Next, identify those species that are likely to experience additive (as opposed to compensatory) mortality from vehicle collisions and rank them according to vulnerability to extirpation. Then combine information on the distribution of at-risk species with information on existing road networks to identify areas where immediate actions are warranted.     

Boosted Regression Tree Models to Explain Watershed Nutrient Concentrations and Biological Condition

Boosted regression tree (BRT) models were developed to quantify the nonlinear relationships between landscape variables and nutrient concentrations in a mesoscale mixed land cover watershed during base‐flow conditions. Factors that affect instream biological components, based on the Index of Biotic Integrity (IBI), were also analyzed. Seasonal BRT models at two spatial scales (watershed and riparian buffered area [RBA]) for nitrite‐nitrate (NO₂‐NO₃), total Kjeldahl nitrogen, and total phosphorus (TP) and annual models for the IBI score were developed. Two primary factors — location within the watershed (i.e., geographic position, stream order, and distance to a downstream confluence) and percentage of urban land cover (both scales) — emerged as important predictor variables. Latitude and longitude interacted with other factors to explain the variability in summer NO₂‐NO₃ concentrations and IBI scores. BRT results also suggested that location might be associated with indicators of sources (e.g., land cover), runoff potential (e.g., soil and topographic factors), and processes not easily represented by spatial data indicators. Runoff indicators (e.g., Hydrological Soil Group D and Topographic Wetness Indices) explained a substantial portion of the variability in nutrient concentrations as did point sources for TP in the summer months. The results from our BRT approach can help prioritize areas for nutrient management in mixed‐use and heavily impacted watersheds.     

Geographic variability of Escherichia coli ribotypes from animals in Idaho and Georgia

Several genotypic methods have been developed for determining the host origin of fecal bacteria in contaminated waters. Some of these methods rely on a host origin database to identify environmental isolates. It is not well understood to what degree these host origin isolates are geographically variable (i.e., cosmopolitan or endemic). This is important because a geographically limited host origin database may or may not be universally applicable. The objective of our study was to use one genotypic method, ribotyping, to determine the geographic variability of the fecal bacterium, Escherichia coli, from one location in Idaho and three locations in Georgia for cattle (Bos taurus), horse (Equus caballus), swine (Sus scrofa), and chicken (Gallus gallus domesticus). A total of 568 fecal E. coli isolates from Kimberly, ID (125 isolates), Athens, GA (210 isolates), Brunswick, GA (102 isolates), and Tifton, GA (131 isolates), yielded 213 ribotypes. The percentage of ribotype sharing within an animal species increased with decreased distance between geographic locations for cattle and horses, but not for swine and chicken. When the E. coli ribotypes among the four host species were compared at one location, the percent of unshared ribotypes was 86, 89, 81, and 79% for Kimberly, Athens, Brunswick, and Tifton, respectively. These data suggest that there is good ribotype separation among host animal species at each location. The ability to match environmental isolates to a host origin database may depend on a large number of environmental and host origin isolates that ideally are not geographically separated.     

Simulated wetland conservation-restoration effects on water quantity and quality at watershed scale

Wetlands are one of the most important watershed microtopographic features that affect hydrologic processes (e.g., routing) and the fate and transport of constituents (e.g., sediment and nutrients). Efforts to conserve existing wetlands and/or to restore lost wetlands require that watershed-level effects of wetlands on water quantity and water quality be quantified. Because monitoring approaches are usually cost or logistics prohibitive at watershed scale, distributed watershed models such as the Soil and Water Assessment Tool (SWAT), enhanced by the hydrologic equivalent wetland (HEW) concept developed by Wang [Wang, X., Yang, W., Melesse, A.M., 2008. Using hydrologic equivalent wetland concept within SWAT to estimate streamflow in watersheds with numerous wetlands. Trans. ASABE 51 (1), 55–72.], can be a best resort. However, there is a serious lack of information about simulated effects using this kind of integrated modeling approach. The objective of this study was to use the HEW concept in SWAT to assess effects of wetland restoration within the Broughton's Creek watershed located in southwestern Manitoba, and of wetland conservation within the upper portion of the Otter Tail River watershed located in northwestern Minnesota. The results indicated that the HEW concept allows the nonlinear functional relations between watershed processes and wetland characteristics (e.g., size and morphology) to be accurately represented in the models. The loss of the first 10–20% of the wetlands in the Minnesota study area would drastically increase the peak discharge and loadings of sediment, total phosphorus (TP), and total nitrogen (TN). On the other hand, the justifiable reductions of the peak discharge and loadings of sediment, TP, and TN in the Manitoba study area may require that 50–80% of the lost wetlands be restored. Further, the comparison between the predicted restoration and conservation effects revealed that wetland conservation seems to deserve a higher priority while both wetland conservation and restoration may be equally important.     

A risk index methodology for potentially polluting marine sites

Attempting to assess the risk of a release from a potentially polluting marine site (PPMS) can be a very subjective process. The Marine Site Risk Index (MaSiRI) is designed to provide a more objective approach to this process by adopting a table-based evaluation scheme, while still allowing for the inevitable unknown conditions by including a subjective ‘expert correction’ in a suitably controlled manner. Building on a geographic database of PPMS records, the MaSiRI algorithm applies data filters to remove PPMS records for which it is not applicable and then estimates a basic risk index based on core data that almost all sites would contain. It can then refine the results for those sites that have auxiliary data, varying the assessed risk as appropriate, according to standard rule-sets. A risk level of confidence is computed and adjusted to express dynamic confidence in the risk value (e.g., due to reliance on estimates rather than measured values), and where appropriate an upper and lower bound of risk can be used to assess the range of values associated with an estimated parameter. This information can be visualized by a composite quality symbol proposed here. MaSiRI is demonstrated on three illustrative shipwrecks and then compared against the DEvelopment of European guidelines for Potentially Polluting (DEEPP) project database from the Pelagos Sanctuary in the western Mediterranean. The aggregate results of the comparison are broadly similar to DEEPP, within the limits of the comparison, but provide a more detailed analysis in the case of estimated pollutant volume and ubiquitous assessment of levels of confidence.     

MANAGEMENT AND ANALYSIS OF WATER-USE DATA USING A GEOGRAPHIC INFORMATION SYSTEM1

ASSTRACT: As part of its mission, the U.S. Geological Survey conducts water-resources research. Site-specific and aggregate water-use data are used in the Survey's National Water-Use Information Program and in various hydrologic investigations. Both types of activities have specific requirements in terms of water-use data access, analysis, and display. In Kansas, the Survey obtains water-use information from several sources. Trpically, this information is in a format that is not readily usable by the Survey. Geographic information system (GIS) technology is being used to restructure the available water-use data into a format that allows users to readily access and summarize site-specific water-use data by source (i.e., surface or ground water), type of use, and user-defined area.     

Assessment of water quality and identification of pollution sources of plateau lakes in Yunnan (China)

The plateau lakes of Yunnan are important both ecologically and economically in China. Nevertheless, the human impact on water quality in these lakes has become increasingly highlighted. The water quality of 10 plateau lakes was monitored regularly over the period of 2000 through 2004 for 24 parameters. Multivariate statistical techniques, including cluster analysis (CA), factor analysis (FA), and principal component analysis (PCA), were employed to better interpret information about the water quality and its pollution sources. No obvious data reduction from CA/FA was found because three principal components (PCs) needed 14 variables to explain 85.01% of the total variance. However, three latent factors accounted for pollution mainly from the following sources: agricultural activities, residential activities and anthropogenic-toxic pollution from industrial effluents, or other special activities. Box-whiskers plots were employed to visually interpret the spatiotemporal variations of water quality variables, which were highly correlated with three PCs. Three types of water quality (i.e., low-, medium-, and high-polluted lakes) were determined through CA based on the similarity of water quality variables. Our results may provide helpful information for the authorities to effectively manage the water quality and make sound policies.     

Arc StormSurge: Integrating Hurricane Storm Surge Modeling and GIS

Arc StormSurge is a data model that integrates geographic information systems and the hurricane wave and surge model SWAN+ADCIRC, which is the coupling of the Simulating WAves Nearshore (SWAN) wave model and the Advanced Circulation (ADCIRC) hydrodynamic model. The Arc StormSurge data model is a geodatabase, which is a relational database that can contain georeferenced information. It includes feature classes in feature datasets and tables, all related among them through relationship classes, and raster catalogs and grids. In addition to the data model schema, Arc StormSurge includes a number of pre‐ and post‐processing tools that help integrate spatial data and numerical modeling. As an illustration, Arc StormSurge was used to support the modeling of Hurricane Bret, which made landfall in the Corpus Christi area in Texas in 1999. By using Arc StormSurge, it was possible to take advantage of already available geo‐referenced information (e.g., base maps, land cover datasets, and monitoring station locations) for the model setup, and for identifying spatial patterns in the model results by presenting them in map format.     

A GIS-based approach for assessing the regional conservation status of genetic diversity: an example from the southern Appalachians

Conserving genetic diversity requires an assessment of the distribution of genetic variants in relation to patterns of land use and environmental variation at a regional scale. This assessment requires a novel approach to integrating and analyzing the genetic and environmental data across spatial scales. To explore the integration of genetic data with other geospatial data sets, we developed a GIS-based approach for examining patterns of genetic diversity for several species of salamanders in southern Appalachians. The genetic data, from allozyme surveys in the genetics literature, were integrated into a GIS database along with related attributes including population identifications and spatial locations. Using existing geospatial data, we classified sample locations as being either protected from anthropogenic disturbance (e.g., National Parks, Wilderness Areas) or as unprotected (e.g., private lands, multiple-use lands in National Forests). We used multidimensional scaling of allelic frequencies and contributions of populations to interpopulation differences in allelic richness to determine which populations had genetic characteristics most different from other populations in the sample. Measures of genetic differentiation were integrated into the GIS database to facilitate spatial analysis and visualization of the indices in relation to land use. This approach was useful for both identification of populations with components of genetic variation that were not well represented at protected sites and for identifying areas of species distributions where more genetic sampling would be necessary to make informed management decisions. Our approach could be readily adapted for use by managers and geneticists working with other species and types of genetic markers.     

Factors Affecting Stream Nutrient Loads: A Synthesis of Regional SPARROW Model Results for the Continental United States1

Preston, Stephen D., Richard B. Alexander, Gregory E. Schwarz, and Charles G. Crawford, 2011. Factors Affecting Stream Nutrient Loads: A Synthesis of Regional SPARROW Model Results for the Continental United States. Journal of the American Water Resources Association (JAWRA) 47(5):891‐915. DOI: 10.1111/j.1752‐1688.2011.00577.x Abstract: We compared the results of 12 recently calibrated regional SPARROW (SPAtially Referenced Regressions On Watershed attributes) models covering most of the continental United States to evaluate the consistency and regional differences in factors affecting stream nutrient loads. The models – 6 for total nitrogen and 6 for total phosphorus – all provide similar levels of prediction accuracy, but those for major river basins in the eastern half of the country were somewhat more accurate. The models simulate long‐term mean annual stream nutrient loads as a function of a wide range of known sources and climatic (precipitation, temperature), landscape (e.g., soils, geology), and aquatic factors affecting nutrient fate and transport. The results confirm the dominant effects of urban and agricultural sources on stream nutrient loads nationally and regionally, but reveal considerable spatial variability in the specific types of sources that control water quality. These include regional differences in the relative importance of different types of urban (municipal and industrial point vs. diffuse urban runoff) and agriculture (crop cultivation vs. animal waste) sources, as well as the effects of atmospheric deposition, mining, and background (e.g., soil phosphorus) sources on stream nutrients. Overall, we found that the SPARROW model results provide a consistent set of information for identifying the major sources and environmental factors affecting nutrient fate and transport in United States watersheds at regional and subregional scales.     

MODELING INSTREAM RECREATIONAL BENEFITS1

ABSTRACT: A basic problem in the management of rivers has been how to balance the tradeoffs between instream and out-of-stream uses. Traditionally, the problem has been addressed by optimizing the economic benefits of flow diversions and regulated releases with instream uses as a flow constraint. An alternative method is to model the effect different river flows have on various recreational uses (e.g., boating, fishing) and then use the results as an additional function or piece of information to determine river project operations and benefits. A methodology that is based on multiobjective decision theory and that relates instream recreational preferences to river flow is proposed. The methodology consists of determining, standardizing, and combining recreational benefit functions, and incorporating potential sources of uncertainty into an estimate of total instream benefits. Thus different types of flow patterns, resulting from reservoir regulation (out-of-stream water uses), can be analyzed to determine their potential instream impact. The methodology is applied to the New River Gorge, West Virginia, which has been designated as a National River.     

Ecological Linkages Between Headwaters and Downstream Ecosystems: Transport of Organic Matter,Invertebrates, and Wood Down Headwater Channels1

Abstract: Headwater streams make up a large proportion of the total length and watershed area of fluvial networks, and are partially characterized by the large volume of organic matter (large wood, detritus, and dissolved organic matter) and invertebrate inputs from the riparian forest, relative to stream size. Much of those inputs are exported to downstream reaches through time where they potentially subsidize river communities. The relative rates, timing, and conversion processes that carry inputs from small streams to downstream reaches are reasonably well quantified. For example, larger particles are converted to smaller particles, which are more easily exported. Also, dissolved organic matter and surface biofilms are converted to larger particles which can be more easily intercepted by consumers. However, the quality of these materials as it affects biological activity downstream is not well known, nor is the extent to which timing permits biological use of those particles. These ecological unknowns need to be resolved. Further, land uses may disrupt and diminish material transport to downstream reaches by removing sources (e.g., forest harvest), by affecting transport and decomposition processes (e.g., flow regulation, irrigation, changes in biotic communities), and by altering mechanisms of storage within headwaters (e.g., channelization). We present conceptual models of energy and nutrient fluxes that outline small stream processes and pathways important to downstream communities, and we identify informational gaps that, if filled, could significantly advance the understanding of linkages between headwater streams and larger rivers. The models, based on empirical evidence and best professional judgment, suggest that navigable waters are significantly influenced by headwater streams through hydrological and ecological connectivities, and land use can dramatically influence these natural connectivities, impacting downstream riverine ecosystems.     

A Generic Bio-Economic Farm Model for Environmental and Economic Assessment of Agricultural Systems

Bio-economic farm models are tools to evaluate ex-post or to assess ex-ante the impact of policy and technology change on agriculture, economics and environment. Recently, various BEFMs have been developed, often for one purpose or location, but hardly any of these models are re-used later for other purposes or locations. The Farm System Simulator (FSSIM) provides a generic framework enabling the application of BEFMs under various situations and for different purposes (generating supply response functions and detailed regional or farm type assessments). FSSIM is set up as a component-based framework with components representing farmer objectives, risk, calibration, policies, current activities, alternative activities and different types of activities (e.g., annual and perennial cropping and livestock). The generic nature of FSSIM is evaluated using five criteria by examining its applications. FSSIM has been applied for different climate zones and soil types (criterion 1) and to a range of different farm types (criterion 2) with different specializations, intensities and sizes. In most applications FSSIM has been used to assess the effects of policy changes and in two applications to assess the impact of technological innovations (criterion 3). In the various applications, different data sources, level of detail (e.g., criterion 4) and model configurations have been used. FSSIM has been linked to an economic and several biophysical models (criterion 5). The model is available for applications to other conditions and research issues, and it is open to be further tested and to be extended with new components, indicators or linkages to other models.     

Two-stage planning for sustainable water-quality management under uncertainty

In water-quality management problems, uncertainties may exist in a number of impact factors and pollution-related processes (e.g., the volume and strength of industrial wastewater and their variations can be presented as random events through identifying a statistical distribution for each source); moreover, nonlinear relationships may exist among many system components (e.g., cost parameters may be functions of wastewater-discharge levels). In this study, an inexact two-stage stochastic quadratic programming (ITQP) method is developed for water-quality management under uncertainty. It is a hybrid of inexact quadratic programming (IQP) and two-stage stochastic programming (TSP) methods. The developed ITQP can handle not only uncertainties expressed as probability distributions and interval values but also nonlinearities in the objective function. It can be used for analyzing various scenarios that are associated with different levels of economic penalties or opportunity losses caused by improper policies. The ITQP is applied to a case of water-quality management to deal with uncertainties presented in terms of probabilities and intervals and to reflect dynamic interactions between pollutant loading and water quality. Interactive and derivative algorithms are employed for solving the ITQP model. The solutions are presented as combinations of deterministic, interval and distributional information, and can thus facilitate communications for different forms of uncertainties. They are helpful for managers in not only making decisions regarding wastewater discharge but also gaining insight into the tradeoff between the system benefit and the environmental requirement.