Heavy metal resistance genes (MRGs) and antibiotic resistance genes (ARGs) in bacteria can respond to the inducement of heavy metals. However, the co-occurrence of MRGs and ARGs in the long-term heavy metal contaminated area is still poorly understood. Here, we investigated the relationship between the abundance of soil bacteria MRGs, ARGs and heavy metal pollution in a copper tailing dam area of northern China. We found that arsC and ereA genes coding for resistance mechanisms to arsenic and to macrolides, respectively, are the most abundant MRG and ARG in the study area. The abundance of MRGs is positively correlated with cadmium (Cd) concentration, and this indicates the importance of Cd in the selection of MRGs. The network analysis results show that sulII and MRGs co-occur and copB occur with ARGs, which suggests that MRGs and ARGs can be co-selected in the soil contaminated by heavy metal. The network analysis also reveals the co-occurrence of Cd and MRGs, and thus heavy metal with a high ‘toxic-response’ factor can be used as the indicator of MRGs. This study improves the understanding of the relationship between bacterial resistance and multi-metal contamination, and underlies the exploration of the adaptive mechanism of microbes in the multi-metal contaminated environment.

Many studies indicated that industrialization and urbanization caused serious soil heavy metal pollution from industrialized age. However, fewer previous studies have conducted a combined analysis of the landscape pattern, urbanization, industrialization, and heavy metal pollution. This paper was aimed at exploring the relationships of heavy metals in the soil (Pb, Cu, Ni, As, Cd, Cr, Hg, and Zn) with landscape pattern, industrialisation, urbanisation in Taiyuan city using multivariate analysis. The multivariate analysis included correlation analysis, analysis of variance (ANOVA), independent-sample T test, and principal component analysis (PCA). Geographic information system (GIS) was also applied to determine the spatial distribution of the heavy metals. The spatial distribution maps showed that the heavy metal pollution of the soil was more serious in the centre of the study area. The results of the multivariate analysis indicated that the correlations among heavy metals were significant, and industrialisation could significantly affect the concentrations of some heavy metals. Landscape diversity showed a significant negative correlation with the heavy metal concentrations. The PCA showed that a two-factor model for heavy metal pollution, industrialisation, and the landscape pattern could effectively demonstrate the relationships between these variables. The model explained 86.71% of the total variance of the data. Moreover, the first factor was mainly loaded with the comprehensive pollution index (P), and the second factor was primarily loaded with landscape diversity and dominance (H and D). An ordination of 80 samples could show the pollution pattern of all the samples. The results revealed that local industrialisation caused heavy metal pollution of the soil, but such pollution could respond negatively to the landscape pattern. The results of the study could provide a basis for agricultural, suburban, and urban planning.

Soil microorganisms play vital roles in ecosystem functions, and soil microbial communities might be affected by heavy metal contamination caused by the anthropogenic activities associated with the coal-based industry. This study explored the effects of heavy metal contamination on soil bacterial and fungal communities surrounding different coal-based industrial fields (the coal mining industry, coal preparation industry, coal-based chemical industry, and coal-fired power industry) in Shanxi province, North China. Moreover, soil samples from farmland and parks away from all the industrial plants were collected as references. The results showed that the concentrations of most heavy metals were greater than the local background values, particularly for arsenic (As), lead (Pb), cadmium (Cd), and mercury (Hg). There were significant differences in soil cellulase and alkaline phosphatase activities among sampling fields. The composition, diversity, and abundance of soil microbial communities among all sampling fields were significantly different, particularly for the fungal community. Actinobacteria, Proteobacteria, Chloroflexi, and Acidobacteria were the predominant bacterial phyla, while Ascomycota, Mortierellomycota, and Basidiomycota dominated the studied fungal community in this coal-based industrially intensive region. A redundancy analysis, variance partitioning analysis, and Spearman correlation analysis revealed that the soil microbial community structure was significantly affected by Cd, total carbon, total nitrogen, and alkaline phosphatase activity. This study profiles the basic features of the soil physicochemical properties, the multiple heavy metal concentrations, and the microbial communities in a coal-based industrial region in North China.

Cropping system models can be useful tools for assessing tillage systems, which are both economically and environmentally viable. The objectives of this study were to evaluate the decision support system for agrotechnology transfer (DSSAT) CERES-Maize model’s ability to predict maize growth and yield, as well as soil water dynamics, and to apply the evaluated model to predict evapotranspiration processes under conventional tillage (CT) and no-tillage (NT) systems in a semi-arid loess plateau area of China from 2014 to 2016. The field experiment results showed that NT increased the surface soil bulk density and water-holding capacity but decreased the total porosity for the surface soil and the maize grain yield. Model calibration for maize cultivar was achieved using grain yield measurements from 2014 to 2016 for CT, and model evaluation was achieved using soil and crop measurements from both CT and NT for the same 3 yr period. Good agreement was reached for CT grain yields for model calibration (nRMSE = 4.02%; d = 0.87), indicating that the model was successfully calibrated. Overall, the results of model evaluation were acceptable, with good agreement for NT grain yields (nRMSE = 4.26%; d = 0.86); the agreement for LAI ranged from good to moderate (RMSE = 0.30‒0.31; d = 0.84‒0.85); the agreement for soil water content was good for NT (RMSE = 0.03‒0.08; d = 0.81‒0.95), but ranged from good to poor for CT (RMSE = 0.06‒0.09; d = 0.42‒0.88); the overall agreement between measured and simulated soil water varied from poor to good depending on soil depth and tillage. It was concluded that the DSSAT CERES-Maize model provided generally good-to-moderate simulations of continuous maize production (yield and LAI) for a short-term tillage experiment in the loess plateau, China, but generally good-to-poor simulations of soil water content.