Heavy metal contamination has been and continues to be a worldwide phenomenon that has attracted a great deal of attention from governments and regulatory bodies. In this context, our study proposes a regression-tree model to predict the concentration level of zinc in the soils of northern Lebanon (as a case study of Mediterranean landscapes) under a GIS environment. The developed tree-model explained 88% of variance in zinc concentration using pH (100% in relative importance), surroundings of waste areas (90%), proximity to roads (80%), nearness to cities (50%), distance to drainage line (25%), lithology (24%), land cover/use (14%), slope gradient (10%), conductivity (7%), soil type (7%), organic matter (5%), and soil depth (5%). The overall accuracy of the quantitative zinc map produced (at 1:50.000 scale) was estimated to be 78%. The proposed tree model is relatively simple and may also be applied to other areas. GIS regression-tree analysis explained 88% of the variability in field/laboratory Zinc concentrations.

Determining sources of neurotoxic metals in rural and urban soils is important for mitigating human exposure. Surface soil from four areas with significant clusters of mental retardation and developmental delay (MR/DD) in children, and one control site were analyzed for nine metals and characterized by soil type, climate, ecological region, land use and industrial facilities using readily available GIS-based data. Kriging, principal component analysis (PCA) and cluster analysis (CA) were used to identify commonalities of metal distribution. Three MR/DD areas (one rural and two urban) had similar soil types and significantly higher soil metal concentrations. PCA and CA results suggested that Ba, Be and Mn were consistently from natural sources; Pb and Hg from anthropogenic sources; and As, Cr, Cu, and Ni from both sources. Arsenic had low commonality estimates, was highly associated with a third PCA factor, and had a complex distribution, complicating mitigation strategies to minimize concentrations and exposures. GIS-based data, principal component and cluster analysis identified complex distributions of metals in soils in areas with clusters of mental retardation and developmental delay.

Plastic pollution is ubiquitous throughout the marine environment, with microplastic (i.e. <5 mm) contamination a global issue of emerging concern. The lack of universally accepted methods for quantifying microplastic contamination, including consistent application of microscopy, photography, an spectroscopy and photography, may result in unrealistic contamination estimates. Here, we present and apply an analysis workflow tailored to quantifying microplastic contamination in marine waters, incorporating stereomicroscopic visual sorting, microscopic photography and attenuated total reflectance (ATR) Fourier transform infrared (FTIR) spectroscopy. The workflow outlines step-by-step processing and associated decision making, thereby reducing bias in plastic identification and improving confidence in contamination estimates. Specific processing steps include (i) the use of a commercial algorithm-based comparison of particle spectra against an extensive commercially curated spectral library, followed by spectral interpretation to establish the chemical composition, (ii) a comparison against a customised contaminant spectral library to eliminate procedural contaminants, and (iii) final assignment of particles as either natural- or anthropogenic-derived materials, based on chemical type, a compare analysis of each particle against other particle spectra, and physical characteristics of particles. Applying this workflow to 54 tow samples collected in marine waters of North-Western Australia visually identified 248 potential anthropogenic particles. Subsequent ATR-FTIR spectroscopy, chemical assignment and visual re-inspection of photographs established 144 (58%) particles to be of anthropogenic origin. Of the original 248 particles, 97 (39%) were ultimately confirmed to be plastics, with 85 of these (34%) classified as microplastics, demonstrating that over 60% of particles may be misidentified as plastics if visual identification is not complemented by spectroscopy. Combined, this tailored analysis workflow outlines a consistent and sequential process to quantify contamination by microplastics and other anthropogenic microparticles in marine waters. Importantly, its application will contribute to more realistic estimates of microplastic contamination in marine waters, informing both ecological risk assessments and experimental concentrations in effect studies.

Estimates of forest soil organic carbon (SOC) have applications in carbon science, soil quality studies, carbon sequestration technologies, and carbon trading. Forest SOC has been modeled using a regression coefficient methodology that applies mean SOC densities (mass/area) to broad forest regions. A higher resolution model is based on an approach that employs a geographic information system (GIS) with soil databases and satellite-derived landcover images. Despite this advancement, the regression approach remains the basis of current state and federal level greenhouse gas inventories. Both approaches are analyzed in detail for Wisconsin forest soils from 1983 to 2001, applying rigorous error-fixing algorithms to soil databases. Resulting SOC stock estimates are 20% larger when determined using the GIS method rather than the regression approach. Average annual rates of increase in SOC stocks are 3.6 and 1.0 million metric tons of carbon per year for the GIS and regression approaches respectively. Large differences in estimates of soil organic carbon stocks and annual changes in stocks for Wisconsin forestlands indicate a need for validation from forthcoming forest surveys.

Global plastics production is increasing exponentially and contributing to significant pollution of the marine environment. Of particular concern is ingestion and entanglement risks for marine wildlife, including when items such as rope are incorporated into nest structures. These events are commonly documented using photographic and visual surveys, and each presents a number of challenges and benefits for species conservation and monitoring. Here we compare an invasive (i.e., removing debris from nests) and non-invasive (i.e., photographs) sampling method for quantifying nest debris using the silver gull (Chroicocephalus novahollandiae) as a case study. Overall, 17 debris items were detected in 9% of gull nests. While the use of photographs to monitor nest debris is increasingly popular, the invasive method detected one additional debris item not identified using photography. We therefore recommend caution for nest debris and other monitoring programs where identifying small or cryptic items may require a high level of skill.

The abundance of marine debris was quantified for a total of sixty-two inaccessible beaches in the western Saronikos Gulf, Greece. High resolution images were obtained through vessel-based photography survey, merged into seamless photomosaics, and manually processed to quantify beach litter abundance. A sample of four selected beaches were subjected to detailed photography followed by beach macro-litter (≥ 2.5 cm) in-situ sampling surveys over a period of one year, to calibrate and validate the proposed method. Regression analysis between photographic and in-situ data showed a significant correlation, hence providing a highly accurate regression model to assess the real number of beach litter stranded on the rest of the investigated beaches, exhibiting clear correlations to the hydrodynamic status of the area and, provide an indication of the main litter sources. The proposed method is an easily applicable and useful tool for fast and low-cost macro-litter monitoring in extended, remote coastlines, when only photographic data are available.

Sampling the sea bottom surface remains difficult because of the surface hydraulic shock due to water flowing through the gear (i.e., the bow wave effect) and the loss of epifauna organisms due to the gear’s closing mechanism. Slow-moving mobile epifauna, such as the ophiuroid Ophiothrix fragilis, form high-density patches in the English Channel, not only on pebbles like in the Dover Strait or offshore Brittany but also on gravel in the Bay of Seine (>5000indm⁻²). Such populations form high biomasses and control the water transfer from the water column to the sediment. Estimating their real density and biomass is essential for the assessment of benthic ecosystem functioning using trophic web modelling. In this paper, we present and discuss the patch patterns and sampling efficiency of the different methods for collecting in the dense beds of O. fragilis in the Bay of Seine. The large Hamon grab (0.25m⁻²) highly under-estimated the ophiuroid density, while the Smith McIntyre appeared adequate among the tested sampling grabs. Nowadays, diving sampling, underwater photography and videos with remote operated vehicle appear to be the recommended alternatives to estimate the real density of such dense slow-moving mobile epifauna.

The combined effects of the herbicide glyphosate and elevated temperature were studied on the tropical staghorn coral Acropora formosa, in Nha Trang bay, Vietnam. The corals were collected from two different reefs, one close to a polluted fish farm and one in a marine-protected area (MPA). In the laboratory, branches of the corals were exposed to the herbicide glyphosate at ambient (28 °C) and at 3 °C elevated water temperatures (31 °C). Effects of herbicide and elevated temperature were studied on coral bleaching using photography and digital image analysis (new colorimetric method developed here based on grayscale), chlorophyll a analysis, and symbiotic dinoflagellate (Symbiodinium, referred to as zooxanthellae) counts. All corals from the MPA started to bleach in the laboratory before they were exposed to the treatments, indicating that they were very sensitive, as opposed to the corals collected from the more polluted site, which were more tolerant and showed no bleaching response to temperature increase or herbicide alone. However, the combined exposure to the stressors resulted in significant loss of color, proportional to loss in chlorophyll a and zooxanthellae. The difference in sensitivity of the corals collected from the polluted site versus the MPA site could be explained by different symbiont types: the resilient type C3u and the stress-sensitive types C21 and C23, respectively. The additive effect of elevated temperatures and herbicides adds further weight to the notion that the bleaching of coral reefs is accelerated in the presence of multiple stressors. These results suggest that the corals in Nha Trang bay have adapted to the ongoing pollution to become more tolerant to anthropogenic stressors, and that multiple stressors hamper this resilience. The loss of color and decrease of chlorophyll a suggest that bleaching is related to concentration of chloro-pigments. The colorimetric method could be further fine-tuned and used as a precise, non-intrusive tool for monitoring coral bleaching in situ.