Chlorination Revisited: Does Cl- Serve as a Catalyst in the Chlorination of Phenols?

The aqueous chlorination of (chloro)phenols is one of the best-studied reactions in the environmental literature. Previous researchers have attributed these reactions to two chlorine species: HOCl (at circum-neutral and high pH) and H2OCl+ (at low pH). In this study, we seek to examine the roles that two largely overlooked chlorine species, Cl2 and Cl2O, may play in the chlorination of (chloro)phenols. Solution pH, chloride concentration, and chlorine dose were systematically varied in order to assess the importance of different chlorine species as chlorinating agents. Our findings indicate that chlorination rates at pH < 6 increase substantially when chloride is present, attributed to the formation of Cl2. At pH 6.0 and a chlorine dose representative of drinking water treatment, Cl2O is predicted to have at best a minor impact on chlorination reactions, whereas Cl2 may contribute more than 80% to the overall chlorination rate depending on the (chloro)phenol identity and chloride concentration. While it is not possible to preclude H2OCl+ as a chlorinating agent, we were able to model our low-pH data by considering Cl2 only. Even traces of chloride can generate sufficient Cl2 to influence chlorination kinetics, highlighting the role of chloride as a catalyst in chlorination reactions.

Dynamics of microbial populations mediating biogeochemical cycling in a freshwater lake.

BACKGROUND: Microbial processes are intricately linked to the depletion of oxygen in in-land and coastal water bodies, with devastating economic and ecological consequences. Microorganisms deplete oxygen during biomass decomposition, degrading the habitat of many economically important aquatic animals. Microbes then turn to alternative electron acceptors, which alter nutrient cycling and generate potent greenhouse gases. As oxygen depletion is expected to worsen with altered land use and climate change, understanding how chemical and microbial dynamics impact dead zones will aid modeling efforts to guide remediation strategies. More work is needed to understand the complex interplay between microbial genes, populations, and biogeochemistry during oxygen depletion. RESULTS: Here, we used 16S rRNA gene surveys, shotgun metagenomic sequencing, and a previously developed biogeochemical model to identify genes and microbial populations implicated in major biogeochemical transformations in a model lake ecosystem. Shotgun metagenomic sequencing was done for one time point in Aug., 2013, and 16S rRNA gene sequencing was done for a 5-month time series (Mar.-Aug., 2013) to capture the spatiotemporal dynamics of genes and microorganisms mediating the modeled processes. Metagenomic binning analysis resulted in many metagenome-assembled genomes (MAGs) that are implicated in the modeled processes through gene content similarity to cultured organism and the presence of key genes involved in these pathways. The MAGs suggested some populations are capable of methane and sulfide oxidation coupled to nitrate reduction. Using the model, we observe that modulating these processes has a substantial impact on overall lake biogeochemistry. Additionally, 16S rRNA gene sequences from the metagenomic and amplicon libraries were linked to processes through the MAGs. We compared the dynamics of microbial populations in the water column to the model predictions. Many microbial populations involved in primary carbon oxidation had dynamics similar to the model, while those associated with secondary oxidation processes deviated substantially. CONCLUSIONS: This work demonstrates that the unique capabilities of resident microbial populations will substantially impact the concentration and speciation of chemicals in the water column, unless other microbial processes adjust to compensate for these differences. It further highlights the importance of the biological aspects of biogeochemical processes, such as fluctuations in microbial population dynamics. Integrating gene and population dynamics into biogeochemical models has the potential to improve predictions of the community response under altered scenarios to guide remediation efforts.