How many microplastics do you need to (sub)sample?

Analysis of microplastics in the environment requires polymer characterization as a confirmation step for suspected microplastic particles found in a sample. Material characterization is costly and can take a long time per particle. When microplastic particle counts are high, many researchers cannot characterize every particle in their sample due to time or monetary constraints. Moreover, characterizing every particle in samples with high plastic particle counts is unnecessary for describing the sample properties. We propose an a priori approach to determine the number of suspected microplastic particles in a sample that should be randomly subsampled for characterization to accurately assess the polymer distribution in the environmental sample. The proposed equation is well-founded in statistics literature and was validated using published microplastic data and simulations for typical microplastic subsampling routines. We report values from the whole equation but also derive a simple way to calculate the necessary particle count for samples or subsamples by taking the error to the power of negative two. Assuming an error of 0.05 (5 %) with a confidence interval of 95 %, an unknown expected proportion, and a sample with many particles (> 100k), the minimum number of particles in a subsample should be 386 particles to accurately characterize the polymer distribution of the sample, given the particles are randomly characterized from the full population of suspected particles. Extending this equation to simultaneously estimate polymer, color, size, and morphology distributions reveals more particles (620) would be needed in the subsample to achieve the same high absolute error threshold for all properties. The above proposal for minimum subsample size also applies to the minimum count that should be present in samples to accurately characterize particle type presence and diversity in a given environmental compartment.

Use of historical isoscapes to develop an estuarine nutrient baseline.

Coastal eutrophication is a prevalent threat to the healthy functioning of ecosystems globally. While degraded water quality can be detected by monitoring oxygen, nutrient concentrations, and algal abundance, establishing regulatory guidelines is complicated by a lack of baseline data (e.g., pre-Anthropocene). We use historical carbon and nitrogen isoscapes over ~300 years from sediment cores to reconstruct spatial and temporal changes in nutrient dynamics for a central California estuary, Elkhorn Slough, where development and agriculture dramatically enhanced nutrient inputs over the past century. We found strong contrasts between current sediment stable isotopes and those from the recent past, demonstrating shifts exceeding those in previously studied eutrophic estuaries and substantial increases in nutrient inputs. Comparisons of contemporary with historical isoscapes also revealed that nitrogen sources shifted from a historical marine-terrestrial gradient with higher δ15N near the inlet to amplified denitrification at the head and mouth of the modern estuary driven by increased N inputs. Geospatial analysis of historical data suggests that an increase in fertilizer application - rather than population growth or increases in the extent of cultivated land - is chiefly responsible for increasing nutrient loads during the 20th century. This study demonstrates the ability of isotopic and stoichiometric maps to provide important perspectives on long-term shifts and spatial patterns of nutrients that can be used to improve management of nutrient pollution.

The influence of complex matrices on method performance in extracting and monitoring for microplastics.

Previous studies have evaluated method performance for quantifying and characterizing microplastics in clean water, but little is known about the efficacy of procedures used to extract microplastics from complex matrices. Here we provided 15 laboratories with samples representing four matrices (i.e., drinking water, fish tissue, sediment, and surface water) each spiked with a known number of microplastic particles spanning a variety of polymers, morphologies, colors, and sizes. Percent recovery (i.e., accuracy) in complex matrices was particle size dependent, with ∼60-70% recovery for particles >212 µm, but as little as 2% recovery for particles <20 µm. Extraction from sediment was most problematic, with recoveries reduced by at least one-third relative to drinking water. Though accuracy was low, the extraction procedures had no observed effect on precision or chemical identification using spectroscopy. Extraction procedures greatly increased sample processing times for all matrices with the extraction of sediment, tissue, and surface water taking approximately 16, 9, and 4 times longer than drinking water, respectively. Overall, our findings indicate that increasing accuracy and reducing sample processing times present the greatest opportunities for method improvement rather than particle identification and characterization.

Quantitative assessment of visual microscopy as a tool for microplastic research: Recommendations for improving methods and reporting.

Microscopy is often the first step in microplastic analysis and is generally followed by spectroscopy to confirm material type. The value of microscopy lies in its ability to provide count, size, color, and morphological information to inform toxicity and source apportionment. To assess the accuracy and precision of microscopy, we conducted a method evaluation study. Twenty-two laboratories from six countries were provided three blind spiked clean water samples and asked to follow a standard operating procedure. The samples contained a known number of microplastics with different morphologies (fiber, fragment, sphere), colors (clear, white, green, blue, red, and orange), polymer types (PE, PS, PVC, and PET), and sizes (ranging from roughly 3-2000 µm), and natural materials (natural hair, fibers, and shells; 100-7000 µm) that could be mistaken for microplastics (i.e., false positives). Particle recovery was poor for the smallest size fraction (3-20 µm). Average recovery (±StDev) for all reported particles >50 µm was 94.5 ± 56.3%. After quality checks, recovery for >50 µm spiked particles was 51.3 ± 21.7%. Recovery varied based on morphology and color, with poorest recovery for fibers and the largest deviations for clear and white particles. Experience mattered; less experienced laboratories tended to report higher concentration and had a higher variance among replicates. Participants identified opportunity for increased accuracy and precision through training, improved color and morphology keys, and method alterations relevant to size fractionation. The resulting data informs future work, constraining and highlighting the value of microscopy for microplastics.

High nutrient loads amplify carbon cycling across California and New York coastal wetlands but with ambiguous effects on marsh integrity and sustainability.

Eutrophic conditions in estuaries are a globally important stressor to coastal ecosystems and have been suggested as a driver of coastal salt marsh loss. Potential mechanisms in marshes include disturbance caused by macroalgae accumulations, enhanced soil sulfide levels linked to high labile carbon inputs, accelerated decomposition, and declines in belowground biomass that contribute to edge instability, erosion, and slumping. However, results of fertilization studies have been mixed, and it is unclear the extent to which local environmental conditions, such as soil composition and nutrient profiles, help shape the response of salt marshes to nutrient exposure. In this study, we characterized belowground productivity and decomposition, organic matter mineralization rates, soil respiration, microbial biomass, soil humification, carbon and nitrogen inventories, nitrogen isotope ratios, and porewater profiles at high and low marsh elevations across eight marshes in four estuaries in California and New York that have strong contrasts in nutrient inputs. The higher nutrient load marshes were characterized by faster carbon turnover, with higher belowground production and decomposition and greater carbon dioxide efflux than lower nutrient load marshes. These patterns were robust across marshes of the Atlantic and Pacific coasts that varied in plant species composition, soil flooding patterns, and soil texture. Although impacts of eutrophic conditions on carbon cycling appeared clear, it was ambiguous whether high nutrient loads are causing negative effects on long-term marsh sustainability in terms of studied metrics. While high nutrient exposure marshes had high rates of decomposition and soil respiration rates, high nutrient exposure was also associated with increased belowground production, and reduced levels of sulfides, which should lead to greater marsh sustainability. While this study does not resolve the extent to which nutrient loads are negatively affecting these salt marshes, we do highlight functional differences between Atlantic and Pacific wetlands which may be useful for understanding coastal marsh health and integrity.

Concentration Depth Profiles of Microplastic Particles in River Flow and Implications for Surface Sampling.

River flow is a major conveyance of microplastic (1-5000 µm) pollution from land to marine systems. However, the current approaches to monitoring and modeling fluvial transport of microplastic pollution have primarily relied on sampling the surface of flow and assumptions about microplastic concentration depth profiles to estimate the depth-averaged concentration. The Rouse profile was adapted to show that fluvial transport of microplastic pollution includes all traditional domains of transport (bed load, settling suspended load, and wash load), as well as additional domains specific to low-density materials with rising velocities in water (rising suspended load and surface load). The modified Rouse profile was applied to describe the positively buoyant particle concentration depth profiles and compared to field observations to showcase the utility of this approach. A procedure was developed for assessing the uncertainty and bias from using a surface sample to estimate the depth-averaged concentration while assuming either surface load or wash load concentration depth profiles. Both assumptions may introduce a large amount of uncertainty due to the range of suspended microplastic concentration depth profiles. Monitoring microplastic pollution and estimating the depth-averaged concentration of microplastics in fluvial systems would further benefit from broader adoption of depth-integrated sampling, characterization of particle concentration depth profiles, and estimation of uncertainties in depth-averaged concentration based on the sampling approach.

Unexpected nitrogen sources in a tropical urban estuary.

Tropical urban estuaries are severely understudied. Little is known about the basic biogeochemical cycles and dominant ecosystem processes in these waterbodies, which are often low-lying and heavily modified. The San Juan Bay Estuary (SJBE) in San Juan, Puerto Rico is an example of such a system. Over the past 80 years, a portion of the estuary has filled in, changing the hydrodynamics and negatively affecting water quality. Here we sought to document these changes using ecological and biogeochemical measurements of surface sediments and bivalves. Measurements of sediment physical characteristics, organic matter content, and stable isotope ratios (δ13C, δ15N, δ34S) illustrated the effects of the closure of the Caño Martín Peña (CMP) on the hydrology and water quality of the enclosed and semienclosed parts of the estuary. The nitrogen stable isotope (δ15N) values were lowest in the CMP, the stretch of the SJBE that is characterized by waters with low dissolved oxygen and high fecal coliform concentrations. Despite this, the results of this study indicate that nitrogen (N) contributions from N-fixing, sulfate-reducing microbes may meet or even exceed contributions from urban runoff and sewage. While the importance of sulfate reducers in contributing N to mangrove ecosystems is well documented, this is the first indication that such processes could be dominant in an intensely urban system. It also underscores just how little we know about tropical coastal ecosystems in densely populated areas throughout the globe.

Anthropogenic litter cleanups in Iowa riparian areas reveal the importance of near-stream and watershed scale land use.

Volunteer cleanup operations collect large datasets on anthropogenic litter that are seldom analyzed. Here we assess the influence of land use in both near-stream and watershed scale source domains on anthropogenic litter concentration (standing stock, kg km-1) in riparian zones of Iowa, USA. We utilized riparian litter concentration data on four classes of anthropogenic litter (metal, recyclable, garbage, and tires) from volunteer cleanup operations. Anthropogenic litter data were tested for correlation with near-stream and watershed scale land uses (developed, road density, agricultural, and open lands). Road density (road length/area) and developed land use (% area) were significantly correlated to anthropogenic litter, but agricultural (% area) and open lands (% area) were not. Metal objects correlated to near-stream road density (r = 0.79, p = 0.02), while garbage and recyclable materials correlated to watershed scale road density (r = 0.69, p = 0.06 and r = 0.71, p = 0.05 respectively). These differences in the important spatial scales of land use may be related to differences in transport characteristics of anthropogenic litter. Larger, denser metal objects may be transported more slowly through the watershed/channelized system and thus, dependent on more proximal sources, whereas smaller, less dense garbage and recyclable material are likely transported more rapidly, resulting in concentrations that depend more on watershed scale supply. We developed a linear regression model that used near-stream road density and the total amount of observed litter to predict an average anthropogenic litter density of 188 kg km-1 and a standing stock of 946 t in all Iowa streams (>4th Strahler order). The techniques employed in this study can be applied to other professional and volunteer litter datasets to develop prevention and cleanup efforts, inform investigations of process, and assess management actions.