Enhancement and mechanisms of micron-pyrite driven autotrophic denitrification with different pretreatments for treating organic-limited waters

Pyrite-driven autotrophic denitrification (PAD) represents a cheap and promising way for nitrogen removal from organic-limited wastewater, which has obtained increasing attention in recent years. However, the limited denitrification rate and unclear mechanism underlying the process have hindered the engineered application of PAD. This study aims to shed light on the impacts of different pretreatments (i.e., ultrasonication, acid-washing and calcination) on micron-pyrite surface characteristics, denitrification performance and biofilm formation during PAD in batch reactors. A series of solid-phase analyses revealed that all pretreatments could significantly promote biofilm attachment on pyrite granules, but impacted the proportion, distribution and chemical oxidation state of sulfur (S) and iron (Fe) at varying degrees. Batch tests showed that ultrasonication and acid-washing could enhance the total nitrogen reduction rate by 14% and 99%, and decrease the sulfate production rate by 51% and 42%, respectively, when compared with untreated pyrite. Microbial community analysis indicated that Thiobacillus and Rhodanobacter dominated in PAD systems. Two types of indirect mechanisms (i.e., contact and non-contact) for pyrite leaching may co-occur in PAD system, resulting in ferrous iron (Fe2+), thiosulfate (S2O32-) and sulfide (S2-) as the main electron donors for denitrification. A PAD mechanism model was proposed to describe the PAD electron transfer pathway with the aim to optimize the engineered application of PAD for nitrogen removal.

Automated summary

This study investigates the effects of pretreatments on pyrite-driven autotrophic denitrification (PAD) and finds that ultrasonication and acid-washing can improve denitrification performance and biofilm formation. Microbial community analysis reveals the dominance of Thiobacillus and Rhodanobacter in PAD systems. A PAD mechanism model is proposed to optimize nitrogen removal.