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Cadmium (Cd) pollution in a soil–rice system is closely related to widely concerning issues, such as food security and health risk due to exposure to heavy metals. Therefore, modeling the Cd content in a soil–rice system and identifying related controls could provide critical information for ensuring food security and reducing related health risks. To archive this goal, in this study, we collected 217 pairs of soil–rice samples from three subareas in Zhejiang Province in the Yangtze River Delta of China. All soil–rice samples were air-dried and conducted for chemical analysis. The Pearson's correlation coefficient, ANOVA, co-occurrence network, multiple regression model, and nonlinear principal component analysis were then used to predict the Cd content in rice and identify potential controls for the accumulation of Cd in rice. Our results indicate that although the mean total concentration of Cd in soil samples was higher than that of the background value in Zhejiang Province, the mean concentration of Cd in rice was higher than that of the national regulation value. Furthermore, a significant difference was detected for Cd content in rice planted in different soil groups derived from different parental materials. In addition, soil organic matter and total Cd in the soil are essential factors for predicting Cd concentrations in rice. Additionally, specific dominant factors resulting in Cd accumulation in rice planted at different subareas were identified via nonlinear principal component analysis. Our study provides new insights and essential implications for policymakers to formulate specific prevention and control strategies for Cd pollution and related health risks. [ABSTRACT FROM AUTHOR]

Purpose: Information related to spatial distribution and dominants of soil organic matter (SOM) is critical for evaluating soil quality and assessing the carbon sequestration capacity, which play essential role in soil management and climate change mitigation. Until now, no reported research has conducted an extensive survey to predict SOM content, analysed SOM spatial variability, and determined the main controls of SOM variation in areas around Dongting Lake in southern China. Therefore, this study aims to (1) explore the spatial variability of SOM content; (2) build a model to quantitatively predict SOM content with various sources of covariates and with the RF method; and (3) identify potential controls of SOM based on the relative importance of variables. Materials and methods: A total of 8040 soil samples were collected from Yueyang County in Eastern Dongting Lake Plain. Ordinary kriging was used to produce a map of SOM and then the random forest algorithms were used to predict SOM content with 17 covariates covered terrain attributes, land use, climate, soil management policies, soil properties, and geologic information. Finally, the main dominants of SOM variability were identified. Results and discussion: The SOM content in the survey region varied from 4.00 to 446.60 g kg−1 and had an average content of 33.17 g kg−1, which indicated fertile soil in the study area. SOM presented strong spatial variability in the area under study. The high SOM values were majorly located in the northeast and southwest parts of the survey regions. The R2 of our developed model was 0.74 and the RMSE was 0.16 g kg−1. The main controls of SOM variability in the study area were available phosphorus, precipitation, soil group, rotation system, available potassium, altitude, and slope. Conclusion: Our developed model showed a good performance to estimate SOM content using auxiliary variables. Soil properties and agricultural management measures played the most important roles in predicting SOM in the study area. Results obtained from this study could provide new insights for estimating SOM and contribute to the sustainable development of agriculture and better regulation of soil quality in the study area. [ABSTRACT FROM AUTHOR]

Cadmium (Cd) pollution in a soil–rice system is closely related to widely concerning issues, such as food security and health risk due to exposure to heavy metals. Therefore, modeling the Cd content in a soil–rice system and identifying related controls could provide critical information for ensuring food security and reducing related health risks. To archive this goal, in this study, we collected 217 pairs of soil–rice samples from three subareas in Zhejiang Province in the Yangtze River Delta of China. All soil–rice samples were air-dried and conducted for chemical analysis. The Pearson’s correlation coefficient, ANOVA, co-occurrence network, multiple regression model, and nonlinear principal component analysis were then used to predict the Cd content in rice and identify potential controls for the accumulation of Cd in rice. Our results indicate that although the mean total concentration of Cd in soil samples was higher than that of the background value in Zhejiang Province, the mean concentration of Cd in rice was higher than that of the national regulation value. Furthermore, a significant difference was detected for Cd content in rice planted in different soil groups derived from different parental materials. In addition, soil organic matter and total Cd in the soil are essential factors for predicting Cd concentrations in rice. Additionally, specific dominant factors resulting in Cd accumulation in rice planted at different subareas were identified via nonlinear principal component analysis. Our study provides new insights and essential implications for policymakers to formulate specific prevention and control strategies for Cd pollution and related health risks.

Greenhouse gas (GHG) emissions from reservoirs and global urbanization have gained widespread attention, yet the response of GHG emissions to the watershed urbanization is poorly understood. Meanwhile, there are millions of small reservoirs worldwide that receive and accumulate high loads of anthropogenic carbon and nitrogen due to watershed urbanization and can therefore be hotspots of GHG emissions. In this study, we assessed the GHG concentrations and fluxes in sixteen small reservoirs draining urban, agricultural and forested watersheds over a period of one year. The concentrations of p CO 2 , CH 4 and N 2 O in sampled urban reservoirs that received more sewage input were higher than those in agricultural reservoirs, and were 3, 7 and 10 times higher than those in reservoirs draining in forested areas, respectively. Accordingly, urban reservoirs had the highest estimated GHG flux rate. Regression analysis indicated that dissolved total phosphorus, dissolved organic carbon (DOC) and chlorophyll-a ( Chl-a ) had great effect on CO 2 production, while the nitrogen (N) and phosphorus (P) content of surface water were closely related to CH 4 and N 2 O production. Therefore, these parameters can act as good predictors of GHG emissions in urban watersheds. Given the rapid progress of global urbanization, small urban reservoirs play a crucial role in accounting for regional GHG emissions and cannot be ignored. [ABSTRACT FROM AUTHOR]