A Novel 4-Set Venn Diagram Model Based on High-Resolution Mass Spectrometry To Monitor Wastewater Treatment

Abstract

A 4-set Venn diagram model oriented to high-resolution mass spectrometry (HRMS) data was developed to decipher the fate of dissolved organic matters (DOM) in three-stage continuous wastewater treatment processes. In total, 24 typical wastewater treatment modes conceptualized into a combination of three stages were generalized so that this model can be applied to all common types of actual wastewater treatment processes. As a result, eight kinds of native DOM and seven kinds of wastewater-produced (WW-produced) DOM separately represented by each proper subset of the 4-set Venn diagram could be identified so as to offer a molecular profile of DOM transformation. The 15 proper subsets of the 4-set Venn diagram could then explain how different wastewater treatment units work. Transformation rates of each DOM molecular formula can be estimated as a semiquantitative result. We further discussed the relationship between the transformation rates and proper subsets. As a proof of concept, the 4-set Venn diagram model was successfully applied in a complicated full-scale mature leachate treatment process with nine treatment units. This model can help to overcome the challenging task of data mining when applying HRMS and reduce the workload of data screening in the subsequent structural annotation.

Regular Tetrahedron Model for the Assessment of High-Resolution Mass Spectrometry Data of Four-Way Fractionated Dissolved Organic Matter

“…It is currently not possible to quantitatively determine the concentrations of all DOM formulas . A compromised but common strategy is that assuming the peak area of a specific DOM formula is directly proportional to the actual concentration. ,, Accordingly, the fractionation ratios of DOM formulas can be estimated by eq . j i = A i , J · f i , J V e , J · V s , J · η i , J ∑ J ∈ { X , Y , Z , W } true A i , J · f i , J V e , J · V s , J · η i , J j i is the fractionation ratio of DOM formulas, where J can be components X , Z , Y , and W , and represents x i , y i , z i , and w i . V e,J is the recovered volume from an extraction method to isolate component J , while V s,J is the loading volume of pretreatment (e.g., solid phase extraction) for HRMS analysis.…”

“…33 A compromised but common strategy is that assuming the peak area of a specific DOM formula is directly proportional to the actual concentration. 16,34,35 Accordingly, the fractionation ratios of DOM formulas can be estimated by eq 1.…”

“…29 These gray box models are typically based on correlation analysis, which necessitates the availability of a sufficiently large sample set to ensure statistical significance. Additionally, the neutral model 30 and the Venn diagram model 16 have been proposed to illustrate the stochastic processes in natural waters and the persistence of DOM in wastewater treatment, respectively, with a sample size of n = 3, 4. The two white box models are primarily concerned with the distribution and transformation trend of persisted, removed, and produced DOM in natural water or wastewater.…”

“…High-resolution mass spectrometry (HRMS) has been a fascinating tool to monitoring lake water, , seawater, wastewater, , soil, , and activated sludge , by using Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) or Orbitrap MS . Compared with homogeneous liquid samples, DOM interacts with different components and surfaces of solid or semisolid media, for example, adsorption and redox with iron oxyhydroxides, manganese oxides, or fractionated in EPS of sludge .…”

“…Data mining of HRMS data sets is an arduous and challenging task, largely due to the high-throughput information. , Annotated formulas are commonly visualized in van Krevelen Diagram to investigate the source and transformation trend of DOM . An isotopic fine structure (e.g., D, 34 S, 37 Cl, and 81 Br) is also involved to elucidate the fate of DOM in aquatic systems according to paired mass distance, as well as by means of artificial intelligence techniques or network analysis .…”