The application and optimal operation of nanoparticle adsorbents in fixed-bed columns or industrial-scale water treatment applications are limited. This limitation is generally due to the tendency of nanoparticles to aggregate, the use of non-sustainable and inefficient polymeric resins as supporting materials in fixed-bed columns, or low adsorption capacity. In this study, magnesium-doped amorphous iron oxide nanoparticles (IONPs) were synthesized and immobilized on the surface of cellulose nanofibrils (CNFs) within a lightweight porous aerogel for arsenic removal from water. The IONPs had a specific surface area of 165 m² g⁻¹. The IONP-containing CNF aerogels were stable in water and under constant agitation due to the induced crosslinking using an epichlorohydrin crosslinker. The adsorption kinetics showed that both As(III) and As(V) adsorption followed a pseudo second-order kinetic model, and the equilibrium adsorption isotherm was best fitted using the Langmuir model. The maximum adsorption capacities of CNF-IONP aerogel for As(III) and As(V) were 48 and 91 mg As g-IONP⁻¹, respectively. The optimum IONP concentration in the aerogel was 12.5 wt.%, which resulted in a maximum arsenic removal, minimal mass loss, and negligible leaching of iron into water.

Temporal Secchi depth trends are used in lake assessment to evaluate lake condition and possible shifts in trophic state. For accurate lake assessments, it is important to differentiate regional trends from lake-specific trends, but this can be confounded by interacting factors. We present a divergent trend analysis which uses temporal patterns of Secchi depth water clarity from 1999 to 2018 for five different types of reference lakes from minimally disturbed watersheds to create dynamic baselines against which we evaluate Secchi depth trends from nonreference lakes in Maine, USA. We used mixed-effect generalized additive models to generate smoothed curves of the expected baseline Secchi depth for each reference lake type to account for the nonlinear dynamics of lake condition through time. The majority of nonreference lakes (74%) showed no difference between measured trend (actual Secchi depth data) and divergent trend (residual Secchi depth from baseline trends). The most common difference in lakes with inconsistent trend test results showed stability in measured trends but apparent declining trends in divergent Secchi depth clarity. We used a Dynamic Factor Analysis (DFA) model to help interpret the variation and shifts observed in baseline reference lake trends. The best DFA model identified two common trends in water clarity among lake types and precipitation during the primary stratification season as the most informative covariable. Because precipitation amount and intensity are expected to increase according to predictive climate models for the Northeast US, our results suggest that baseline lake clarity in Maine will decrease with climate change.

Lake morphometric, chemical, and watershed land-use attributes control lake phosphorus concentration. Lake depth, sediment chemical makeup, DOC, and watershed agricultural extent are the most important controlling variables.