A decision framework for estimating the cost of marine plastic pollution interventions

Marine plastic pollution has emerged as one of the most pressing environmental challenges of our time. Although there has been a surge in global investment for implementing interventions to mitigate plastic pollution, there has been little attention given to the cost of these interventions. We developed a decision support framework to identify the economic, social, and ecological costs and benefits of plastic pollution interventions for different sectors and stakeholders. We calculated net cost as a function of six cost and benefit categories with the following equation: cost of implementing an intervention (direct, indirect, and nonmonetary costs) minus recovered costs and benefits (monetary and nonmonetary) produced by the interventions. We applied our framework to two quantitative case studies (a solid waste management plan and a trash interceptor) and four comparative case studies, evaluating the costs of beach cleanups and waste-to-energy plants in various contexts, to identify factors that influence the costs of plastic pollution interventions. The socioeconomic context of implementation, the spatial scale of implementation, and the time scale of evaluation all influence costs and the distribution of costs across stakeholders. Our framework provides an approach to estimate and compare the costs of a range of interventions across sociopolitical and economic contexts.     

Analysis of per- and polyfluoroalkyl substances in Houston Ship Channel and Galveston Bay following a large-scale industrial fire using ion-mobility-spectrometry-mass spectrometry

Per- and polyfluoroalkyl substances (PFAS) are persistent organic pollutants of concern because of their ubiquitous presence in surface and ground water; analytical methods that can be used for rapid comprehensive exposure assessment and fingerprinting of PFAS are needed. Following the fires at the Intercontinental Terminals Company (ITC) in Deer Park, TX in 2019, large quantities of PFAS-containing firefighting foams were deployed. The release of these substances into the Houston Ship Channel/Galveston Bay (HSC/GB) prompted concerns over the extent and level of PFAS contamination. A targeted liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based study of temporal and spatial patterns of PFAS associated with this incident revealed presence of 7 species; their levels gradually decreased over a 6-month period. Because the targeted LC-MS/MS analysis was focused on about 30 PFAS molecules, it may have missed other PFAS compounds present in firefighting foams. Therefore, we utilized untargeted LC-ion mobility spectrometry-mass spectrometry (LC-IMS-MS)-based analytical approach for a more comprehensive characterization of PFAS in these water samples. We analyzed 31 samples from 9 sites in the HSC/GB that were collected over 5 months after the incident. Our data showed that additional 19 PFAS were detected in surface water of HSC/GB, most of them decreased gradually after the incident. PFAS features detected by LC-MS/MS correlated well in abundance with LC-IMS-MS data; however, LC-IMS-MS identified a number of additional PFAS, many known to be components of firefighting foams. These findings therefore illustrate that untargeted LC-IMS-MS improved our understanding of PFAS presence in complex environmental samples.     

Model for Prioritizing Best Management Practice Implementation: Sediment Load Reduction

Understanding the best way to allocate limited resources is a constant challenge for water quality improvement efforts. The synoptic approach is a tool for geographic prioritization of these efforts. It uses a benefit-cost framework to calculate indices for functional criteria in subunits (watersheds, counties) of a region and then rank the subunits. The synoptic approach was specifically designed to incorporate best professional judgment in cases where information and resources are limited. To date, the synoptic approach has been applied primarily to local or regional wetland restoration prioritization projects. The goal of this work was to develop a synoptic model for prioritizing watersheds within which suites of agricultural best management practices (BMPs) can be implemented to reduce sediment load at the watershed outlets. The model ranks candidate watersheds within an ecoregion or river basin so that BMP implementation within the highest ranked watersheds will result in the most sediment load reduction per conservation dollar invested. The model can be applied anywhere and at many scales provided that the selected suite of BMPs is appropriate for the evaluation area’s biophysical and climatic conditions. The model was specifically developed as a tool for prioritizing BMP implementation efforts in ecoregions containing watersheds associated with the USDA-NRCS conservation effects assessment project (CEAP). This paper presents the testing of the model in the little river experimental watershed (LREW) which is located near Tifton, Georgia, USA and is the CEAP watershed representing the southeastern coastal plain. The application of the model to the LREW demonstrated that the model represents the physical drivers of erosion and sediment loading well. The application also showed that the model is quite responsive to social and economic drivers and is, therefore, best applied at a scale large enough to ensure differences in social and economic drivers across the candidate watersheds. The prioritization model will be used for planning purposes. Its results are visualized as maps which enable resource managers to identify watersheds within which BMP implementation would result in the most water quality improvement per conservation dollar invested.     

Support for First Nations' Land Claims amongst Members of the Wilderness Preservation Movement: The Potential for an Environmental Justice Movement in British Columbia

Abstract

In this paper we examine the relationship between identification with the environmental movement and support for First Nations' land claims in order to determine the potential for an environmental justice movement in British Columbia. The findings are based on survey data collected from members of a wilderness preservation movement organization based on Vancouver Island. The findings demonstrate that the stronger an individual identifies with the environmental movement, the more s/he supports linking First Nations' land claims to conservation campaigns. We conclude by proposing that the wilderness preservation movement could increase its mobilization potential and widen the scope of the movement by including First Nations' issues in their campaigns. It could do this by expanding its frame to include issues of environmental justice, thereby connecting environmental protection and fair access to resources.     

Spatial statistical models that use flow and stream distance

We develop spatial statistical models for stream networks that can estimate relationships between a response variable and other covariates, make predictions at unsampled locations, and predict an average or total for a stream or a stream segment. There have been very few attempts to develop valid spatial covariance models that incorporate flow, stream distance, or both. The application of typical spatial autocovariance functions based on Euclidean distance, such as the spherical covariance model, are not valid when using stream distance. In this paper we develop a large class of valid models that incorporate flow and stream distance by using spatial moving averages. These methods integrate a moving average function, or kernel, against a white noise process. By running the moving average function upstream from a location, we develop models that use flow, and by construction they are valid models based on stream distance. We show that with proper weighting, many of the usual spatial models based on Euclidean distance have a counterpart for stream networks. Using sulfate concentrations from an example data set, the Maryland Biological Stream Survey (MBSS), we show that models using flow may be more appropriate than models that only use stream distance. For the MBSS data set, we use restricted maximum likelihood to fit a valid covariance matrix that uses flow and stream distance, and then we use this covariance matrix to estimate fixed effects and make kriging and block kriging predictions. Received: July 2005 / Revised: March 2006