In this work, we studied a novel algae cultivation strategy, mixotrophic microalgae biofilm, to improve the productivity and cost-efficiency of algal biofuel production. In contrast to previous methods, this improved approach can achieve high productivity at low cost by harnessing the benefits of mixotrophic growth’s high efficiency, i.e., capable of subsisting on inorganic and organic carbons thus unaffected by limited light, and microalgae biofilm’s low harvesting cost. Our results, as one of the first studies of this type, proved that microalgae biofilms under mixotrophic condition exhibited significantly higher productivity and quality of biofuel feedstock: 2–3 times higher of biomass yield, 2–10 times higher of lipid accumulation, and 40–60% lower of ash content when compared to microalgae biofilms under autotrophic condition. In addition, we investigated the impact of cell-surface properties (hydrophobicity and roughness) on the growth activities of microalgae biofilms and found that the productivity of mixotrophic biofilms was significantly correlated with the surface hydrophobicity. Finally, our work demonstrated the applicability of integrating this novel cultivation method with wastewater for maximum efficiency. This study opens a new possibility to solve the long-lasting challenges of algal biofuel feedstock production, i.e., low productivity and high cost of algal cultivation.

This study characterizes potential soil lead (Pb) exposure risk at the household scale in Greensboro, North Carolina, using an innovative combination of field sampling, statistical analysis, and machine-learning techniques. Soil samples were collected at the dripline, yard, and street side at 462 households (total sample size = 2310). Samples were analyzed for Pb and then combined with publicly available data on potential historic Pb sources, soil properties, and household and neighborhood demographic characteristics. This curated data set was then analyzed with statistical and machine-learning techniques to identify the drivers of potential soil Pb exposure risks and to build predictive models. Among all samples, 43% exceeded current guidelines for Pb in residential gardens. There were significant racial disparities in potential soil Pb exposure risk; soil Pb at the dripline increased by 19% for every 25% increase in the neighborhood population identifying as Black. A machine-learned Bayesian network model was able to classify residential parcels by risk of exceeding residential gardening standards with excellent reproducibility in cross validation. These findings underscore the need for targeted outreach programs to prevent Pb exposure in residential areas and demonstrate an approach for prioritizing outreach locations.

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