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Comprehensive assessment of heavy metals and environmental factors on soil bacterial communities in YRD are still limited. In this study, the heavy metals and bacterial community compositions of soil samples from different land use types in YRD were analyzed. The EF, Igeo and PLI assessment results indicated that severe heavy metal pollution was observed in crude oil contaminated soil samples. Moreover, the highest bacterial community richness was observed in soil samples of cotton field, while the richness was lowest for soils around a chemical factory. The bacterial community composition and structure were different among different land use types in YRD, which was consistent with beta-diversity indices. Results showed that the dominant bacterial phyla in YRD soils were Proteobacteria, Chloroflexi, Actinobacteria and Acidobacteria. Redundancy analysis (RDA) showed that the nutrients (SOM, TC, TN and TK) and heavy metals (Cd, Zn, Cu and As) had important effects on soil bacterial communities. Furthermore, the resistant bacteria strains may have great potential for remediation of contaminated soils. Therefore, the present study contributes to a deeper insight into the crucial influencing factors on bacterial community structure in soil samples of YRD, which may provide early warnings of environmental pollution and ecological destruction of ecosystems. [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]