Geospatial estimation of the impact of Deepwater Horizon oil spill on plant oiling along the Louisiana shorelines

Accelerated marsh erosion following the Deepwater Horizon oil spill confirmed, ameliorated by planting

Multiple studies have examined the effects of the Deepwater Horizon oil spill on coastal marsh shoreline erosion. Most studies have concluded that the spill increased shoreline erosion (linear retreat) in oiled marshes by ~ 100–200% for at least 2–3 years. However, two studies have called much of this prior research into question, due to potential study design flaws and confounding factors, primarily tropical cyclone influences and differential wave exposure between oiled (impact) and unoiled (reference) sites. Here we confirm that marsh erosion in our field experiment was substantially increased (112–233%) for 2 years in heavily oiled marsh after the spill, likely due to vegetation impacts and reduced soil shear strength attributed to the spill, rather than the influences of hurricanes or wave exposure variation. We discuss how our findings reinforce prior studies, including a wider-scale remote sensing analysis with similar study approach. We also show differences in the degree of erosion among oil spill cleanup treatments. Most importantly, we show that marsh restoration planting can drastically reduce oiled marsh erosion, and that the positive influences of planting can extend beyond the immediate impact of the spill.

Meta-analysis of salt marsh vegetation impacts and recovery: a synthesis following the Deepwater Horizon oil spill

Marine oil spills continue to be a global issue, heightened by spill events such as the 2010 Deepwater Horizon spill in the Gulf of Mexico, the largest marine oil spill in US waters and among the largest worldwide, affecting over 1,000 km of sensitive wetland shorelines, primarily salt marshes supporting numerous ecosystem functions. To synthesize the effects of the oil spill on foundational vegetation species in the salt marsh ecosystem, Spartina alterniflora and Juncus roemerianus, we performed a meta-analysis using data from 10 studies and 255 sampling sites over seven years post-spill. We examined the hypotheses that the oil spill reduced plant cover, stem density, vegetation height, aboveground biomass, and belowground biomass, and tracked the degree of effects temporally to estimate recovery time frames. All plant metrics indicated impacts from oiling, with 20-100% maximum reductions depending on oiling level and marsh zone. Peak reductions of ~70-90% in total plant cover, total aboveground biomass, and belowground biomass were observed for heavily oiled sites at the marsh edge. Both Spartina and Juncus were impacted, with Juncus affected to a greater degree. Most plant metrics had recovery time frames of three years or longer, including multiple metrics with incomplete recovery over the duration of our data, at least seven years post-spill. Belowground biomass was particularly concerning, because it declined over time in contrast with recovery trends in most aboveground metrics, serving as a strong indicator of ongoing impact, limited recovery, and impaired resilience. We conclude that the Deepwater Horizon spill had multiyear impacts on salt marsh vegetation, with full recovery likely to exceed 10 years, particularly in heavily oiled marshes, where erosion may preclude full recovery. Vegetation impacts and delayed recovery is likely to have exerted substantial influences on ecosystem processes and associated species, especially along heavily oiled shorelines. Our synthesis affords a greater understanding of ecosystem impacts and recovery following the Deepwater Horizon oil spill, and informs environmental impact analysis, contingency planning, emergency response, damage assessment, and restoration efforts related to oil spills.

A framework for the evaluation and selection of shoreline surface washing agents in oil spill response

The shorelines frequently suffer adverse impacts from oil spill accidents. As one important technique of shoreline cleanup, the application of surface washing agents (SWAs) can help achieve high oil removal from shoreline substrates with less damage to affected zone. In this study, a framework for evaluation and selection of SWAs in oil spill incidents was constructed to better understand and apply this technique. A decision tree was firstly developed to illustrate all possible scenarios which are appropriate to use SWAs in consideration of oil collectability, shoreline character, types and amount of stranded oil, and cleanup requirement. Based on literature review, theoretical modeling, and experts' suggestions, an integrated multi-criteria decision analysis (MCDA) method was then come up to select the most preferred SWA from five aspects of toxicity, effectiveness, minimal dispersion, demonstrated field test, and cost. Its suitability and rationality were proved by a hypothetical case. In addition, sensitivity analysis was performed by changing the weight of each criterion independently to check the priority rank of alternatives, and it also verified the robustness and stability of this model. The presented framework has significant implications for future research and application of SWAs in the shoreline cleanup.

Investigation into the oil removal from sand using a surface washing agent under different environmental conditions

Spilled oil frequently reaches the shorelines and affects coastal biota and communities. The application of surface washing agents is an important shoreline cleanup technique that can help remove stranded oil from substrate surfaces with the advantages of high removal efficiency, low toxicity, and strong economic viability. In this study, the investigation into the oil removal from contaminated sand using a surface washing agent under variable environmental conditions was conducted. A preliminary test was conducted to obtain the optimal combination of operating factors of surface washing agent-to-oil ratio (SOR) 2:1, mixing speed 150 rpm, and mixing time 30 min. The results of single-factor experiments showed that high temperature and humic acid concentration of flush water contributed to the performance of a surface washing agent, while salinity and kaolinite concentration could inhibit its performance. The factorial analysis revealed the main effects of temperature and salinity, and the interactive effects of temperature and salinity as well as salinity and humic acid concentration that were significant to the washing efficiency of the surface washing agent. In addition, the comprehensive assessment of a surface washing agent from the aspects of toxicity, detergency, dispersion properties, and field trials was conducted. The results have significant implications for future application of surface washing agents in the shoreline cleanup.

How geostatistics can help you find lead and galvanized water service lines: The case of Flint, MI

In the aftermath of Flint drinking water crisis, most US cities have been scrambling to locate all lead service lines (LSLs) in their water supply systems. This information, which is most often inaccurate or lacking, is critical to assess compliance with the Lead and Copper Rule and to plan the replacement of lead and galvanized service lines (GSLs) as currently under way in Flint. This paper presents the first geospatial approach to predict the likelihood that a home has a LSL or GSL based on neighboring field data (i.e., house inspection) and secondary information (i.e., construction year and city records). The methodology is applied to the City of Flint where 3254 homes have been inspected by the Michigan Department of Environmental Quality to identify service line material. GSLs and LSLs were mostly observed in houses built prior to 1934 and during World War II, respectively. City records led to the over-identification of LSLs, likely because old records were not updated as these lines were being replaced. Indicator semivariograms indicated that both types of service line are spatially clustered with a range of 1.4km for LSLs and 2.8km for GSLs. This spatial autocorrelation was integrated with secondary data using residual indicator kriging to predict the probability of finding each type of material at the tax parcel level. Cross-validation analysis using Receiver Operating Characteristic (ROC) Curves demonstrated the greater accuracy of the kriging model relative to the current approach targeting houses built in the forties; in particular as more field data become available. Anticipated rates of false positives and percentages of detection were computed for different sampling strategies. This approach is flexible enough to accommodate additional sources of information, such as local code and regulatory changes, historical permit records, maintenance and operation records, or customer self-reporting.