Assessing spatiotemporal sources of biogenic and anthropogenic sedimentary organic matter from the mainstream Haihe River, China: Using n-alkanes as indicators

Identification of organic chemical indicators for tracking pollution sources in groundwater by machine learning from GC-HRMS-based suspect and non-target screening data

In this study, the strong analytical power of gas chromatography coupled to a high resolution mass spectrometry (GC-HRMS) in suspect and non-target screening (SNTS) of organic micropollutants was combined with machine learning tools for proposing a novel and robust systematic environmental forensics workflow, focusing on groundwater contamination. Groundwater samples were collected from four different regions with diverse contamination histories (namely oil [OC], agricultural [AGR], industrial [IND], and landfill [LF]), and a total of 252 organic micropollutants were identified, including pharmaceuticals, personal care products, pesticides, polycyclic aromatic hydrocarbons, plasticizers, phenols, organophosphate flame retardants, transformation products, and others, with detection frequencies ranging from 3 % to 100 %. Amongst the SNTS identified compounds, a total of 51 chemical indicators (i.e., OC: 13, LF: 12, AGR: 19, IND: 7) which included level 1 and 2 SNTS identified chemicals were pinpointed across all sampling regions by integrating a bootstrapped feature selection method involving the bootfs algorithm and a partial least squares discriminant analysis (PLS-DA) model to determine potential prevalent contamination sources. The proposed workflow showed good predictive ability (Q2) of 0.897, and the suggested contamination sources were gasoline, diesel, and/or other light petroleum products for the OC region, anthropogenic activities for the LF region, agricultural and human activities for the AGR region, and industrial/human activities for the IND region. These results suggest that the proposed workflow can select a subset of the most diagnostic features in the chemical space that can best distinguish a specific contamination source class.

Source identification of sedimentary organic carbon in coastal wetlands of the western Bohai Sea

Coastal wetlands play a vital role in mitigating climate change, yet the characteristics of buried organic carbon (OC) and carbon cycling are limited due to difficulties in assessing the composition of OC from different sources (allochthonous vs. autochthonous). In this study, we analyzed the total organic carbon (TOC) to total nitrogen (TN) ratio (C/N), stable carbon isotope (δ13C) composition, and n-alkane content to distinguish different sources of OC in the surface sediments of the coastal wetlands on the western coast of the Bohai Sea. The coupling of the C/N ratio with δ13C and n-alkane biomarkers has been proved to be an effective tool for revealing OC sources. The three end-member Bayesian mixing model based on coupling C/N ratios with δ13C showed that the sedimentary OC was dominated by the contribution of terrestrial particulate organic matter (POM), followed by freshwater algae and marine phytoplankton, with relative contributions of 47 ± 21 %, 41 ± 18 % and 12 ± 17 %, respectively. The relative contributions of terrestrial plants, aquatic macrophytes and marine phytoplankton assessed by n-alkanes were 56 ± 8 %, 35 ± 9 % and 9 ± 5 % in the study area, respectively. The relatively high salinity levels and strong hydrodynamic conditions of the Beidagang Reservoir led to higher terrestrial plants source and lower aquatic macrophytes source than these of Qilihai Reservoir based on the assessment of n-alkanes. Both methods showed that sedimentary OC was mainly derived from terrestrial sources (plant-dominated), suggesting that vegetation plays a crucial role in storing carbon in coastal wetlands, thus, the coastal vegetation management needs to be strengthened in the future. Our findings provide insights into the origins and dynamics of OC in coastal wetlands on the western coast of the Bohai Sea and a significant scientific basis for future monitoring of the blue carbon budget balance in coastal wetlands.

Source identification and ecological risks of parent and substituted polycyclic aromatic hydrocarbons in river surface sediment-pore water systems: Effects of multiple factors

Substituted polycyclic aromatic hydrocarbons (SPAHs) have shown higher health and ecological risks than the corresponding parent PAHs (PPAHs) from laboratory studies, their variations in freshwater system, especially in surface sediment and pore water, remain inadequate understanding. In this study, we revealed the coexistence, ecological risk, and multiple factors affecting variations and sources of PPAHs and SPAHs (nitrated PAHs (NPAHs), oxygenated PAHs (OPAHs)) in the surface sediment-pore water system from a typical urban river in Northern China. The concentration ranges of Σ₂₆PPAHs, Σ₁₀NPAHs, and Σ₄OPAHs in the surface sediments were 153.0-5367.4, not detected (N.D.)-105.4, and 42.2-1177.0 ng·g^-1 dry weight, and fell within 0.6-38.8, N.D.-297.9, and N.D.-212.6 ng·mL^-1 in the pore waters. The t-distributed stochastic neighbor embedding (SNE) coupled with the partitioning around medoids (PAM) elucidated spatiotemporal the variations in PAHs, emphasizing the impacts of industrial activities and sewage discharges. Besides the geochemical and hydrochemical conditions, SPAHs were affected by the potential secondary formation, especially during the wet season. The method comparisons indicated the advantages of principal component analysis-multivariate linear regression (PCA-MLR) and n-alkanes model on source identification. PAHs mainly originated from fossil fuel combustion and vehicular exhaust. The top risk quotient (RQ) values for PAHs occurred in the urban and industrial sections. A majority of the surface sediment samples emerged with low to moderate exposure risks, while all the pore water samples showed high exposure risks. The RQs of OPAHs were significantly higher (p < 0.01) than those of PPAHs. The results suggested the secondary formation of SPAHs as an important role in ecological risks of PAHs in the urban river system.