Search

The distribution of polycyclic aromatic hydrocarbons (PAHs) and halogenated PAHs (HPAHs) in surface soils from the petroleum industrial area of the Yellow River Delta (YRD) in China were investigated. The total concentrations of 16 PAHs ranged from 19.6 to 1560 ng/g, while 22 HPAHs ranged from 2.44 to 14.9 ng/g. Moreover, a high degree of spatial distribution heterogeneity was observed for both PAHs and HPAHs, which is likely attributed to the distinct industrial activities in studied area. The combustion of biomass and petroleum were identified as primary sources of soil PAHs and HPAHs in the YRD. Furthermore, benzo[ b ]fluoranthene, benzo[ k ]fluoranthene, and benzo[ g , h , i ]perylene exhibited high ecological risks (with risk quotients of 1.47, 1.44, and 1.02, respectively) in specific sites within the YRD. Considering the high toxicity of HPAHs and their potential joint environmental effects with PAHs, continuous attention should be directed towards the environmental risks associated with both PAHs and HPAHs. [Display omitted] • Co-occurrence of PAHs and HPAHs was found in soil in Yellow River Delta. • Industrial activities resulted in spatial heterogeneity of PAHs and HPAHs in soil. • Biomass and petroleum combustion are primary sources of soil PAHs and HPAHs. • Benzo[ b ]fluoranthene poses high ecological risks in studied areas. [ABSTRACT FROM AUTHOR]

The Yellow River Delta (YRD) is the second largest petrochemical base in China and the impact of human activities has been continuously increasing in recent decades, however, the contamination status of seldom monitored trace elements (SMTEs) in YRD has rarely been reported. This study evaluated the levels, vertical distributions, contamination status and sources of SMTEs in soil samples of three different land use types in YRD. The results indicated that the vertical distributions of SMTEs contents showed a gradually upward increasing trend for the soil profiles of black locust forest, while the SMTEs contents displayed a gradually upward decreasing trend for the soil profiles of cotton field. However, the SMTEs contents in the oil field area showed no significant difference among different depths. The vertical distributions of SMTEs were very likely related to the anthropogenic disturbance in the later stage. The environmental pollution status assessment of SMTEs showed obvious enrichment of Cs, Sn, and U in the soils of YRD. Moreover, the potential source analysis based on multivariate statistical methods indicated that Ga, Rb, Cs, Sc, Sn, Tl, Be, Bi, Ca and Mo were clustered together and positively correlated with Al, Fe, Mg and K, and may be mainly associated with geochemical weathering process, while the Ce, La, Th, U, Nb, Ta, and Hf may be impacted by both natural process and human activities. Though the SMTEs pollution status was not very serious, our results highlighted the non-negligible influence of anthropogenic activities on vertical distributions of SMTEs in three different land use types from YRD. Our results provide valuable information for understanding the vertical distribution and pollution status of SMTEs in YRD. • Non-negligible influence of anthropogenic activities on vertical distributions of SMTEs in YRD • SMTEs displayed an upward decreasing trend for the soil profiles of cotton field. • Obvious enrichment of Sn, Cs and U was observed in YRD soils. • Sources of most SMTEs are mainly related to the geochemical weathering process. [ABSTRACT FROM AUTHOR]

[Display omitted] • A machine learning method assisted by auto hyperparameter optimization was developed. • TPE-XGBoost model has the best performance for spatial prediction of soil heavy metals. • Air quality and hyperspectral variables have the high contributions for prediction accuracy. • There is a source-receptor coupling path for the accumulation of soil heavy metals. • Soil heavy metals with high concentrations are concentrated around rivers. Machine learning (ML) models have been widely used for predicting spatial variability of soil heavy metals. However, it is impossible to explore the entire hyperparameter space of ML models by artificially trial-and-error experimentation. Here, an auto hyperparameter optimization-based machine learning (HPO-ML) method with three search algorithms and random forest (RF) and extreme gradient boosting (XGBoost) models was developed to predict the heavy metal content in soil with multiple environmental variables. The tree-structured Parzen estimator (TPE) algorithm outperformed other search algorithms in identifying the optimal hyperparameters of RF and XGBoost models. The model prediction results showed that the TPE-XGBoost had the highest accuracy for predicting the As (RMSE = 3.06 mg kg−1 and R2 = 70.35%), Cd (RMSE = 0.10 mg kg−1 and R2 = 75.43%), Cr (RMSE = 13.86 mg kg−1 and R2 = 82.11%), Ni (RMSE = 3.19 mg kg−1 and R2 = 75.20%), Pb (RMSE = 3.75 mg kg−1 and R2 = 74.79%), and Zn (RMSE = 6.83 mg kg−1 and R2 = 70.05%) contents. The TPE-XGBoost mapping result showed that areas with high concentrations of soil heavy metals were concentrated in the central and eastern areas (As), the mainstream of the Yellow River (Cd), the northeast area (Cr), the ancient watercourse of the Yellow River (Ni and Pb), and the central and northeastern areas (Zn). The SHapley additive explanation (SHAP) and structural equation model (SEM) were used to interpret the drivers of environmental variables. It is found that the variables with the highest contributions were CO, PM 2.5 , O 3 , PC3, PC1, and PC4 for predicting the As, Cd, Cr, Ni, Pb, and Zn contents, respectively, and there was a significant source-receptor coupling path. The results demonstrate the feasibility of using the HPO-ML approach in hyperparameter-limited conditions, which providing data-driven pathways and options to support the high-quality development of agriculture and the protection of farmland soil ecosystem. [ABSTRACT FROM AUTHOR]

Air quality is a vital environment variable which determines spatial accumulation of soil heavy metals. It is very important to estimate the contribution of air quality for soil heavy metals in oil mining area. For the end, we collected 116 samples from surface soil of oil mining in the Yellow River Delta (YRD) of China, and analyzed the content of As, Cr, Ni, Pb, and Zn. Furthermore, 40 monitoring stations data of air quality were collected in study area, including CO, NO 2 , SO 2 , O 3 , PM 2.5 , and PM 10. Spatial estimation and mapping of heavy metals in soil were carried out by hybrid geostatistical models, including multiple linear regression-ordinary kriging (MLROK), support vector machine-ordinary kriging (SVMOK) and random forest-ordinary kriging (RFOK). RFOK exhibited the highest estimation accuracy (R2) for As (65.76%), Cr (77.85%), Ni (61.47%), Pb (74.64%), and Zn (71.35%) in comparison with other models. And relative R2 of RFOK improved 30%, while MLROK and SVMOK increased over 100% for Zn (RI o = 121.90% and RI o = 121.64%) based on their original R2 of machine learning models. In addition, mapping results by RFOK showed the high concentrations of heavy metals were focused in the central and northeastern (As), northern (Cr), northeastern and northwestern (Ni), central and eastern (Pb), and northern (Zn). Especially, compared with vegetation index and topographic factors, PM 2.5 is the highest driving variable for As (18.34%) and Zn (12.91%), and CO is the most important variable for Cr (18.22%), Ni (14.28%). The above results indicated that there is a mechanism of sources-receptor relationship between air quality and soil heavy metals, that is, oil well and factory in study area discharge heavy metal particles into the atmosphere, and then enter the soil through atmospheric deposition and precipitation. Enlightened by this study, variable selection should be focused on important sources for the accumulation of heavy metals in study area, who must take decisions to prevent and to early warn heavy metals pollution in mine soil. [Display omitted] • Air quality data are used to predict the content of heavy metals in oil mining soils. • Random forest-ordinary kriging has the best predicted performance for soil heavy metals. • The high concentrations of soil heavy metals are the northern of study area. • Air quality variables have effective feasibility for predicting soil heavy metals. • PM (for As and Zn) and CO (for Cr and Ni) is the highest driving variable. [ABSTRACT FROM AUTHOR]