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Many studies have been conducted on model transfer in soil-plant systems. However, relatively little information is available on modeling metal transfer in soilrice system and associated risk assessment in real paddy fields. Based on a random sampling method from Nanxun, Shengzhou and Wenling in Zhejiang province, China, 15 pairs of rice and the corresponding soil samples were respectively collected for analysis of heavy metals and soil pH. The results showed that the accumulation ability of rice for different heavy metals was significantly different (p \ 0.05), and was in the order of CdThe relationships of heavy metals in rice with those in soil, along with soil pH, were well described by linear regression models. Except for Pb, the contents of most metals in rice were positively correlated with those of the soil. Soil pH was negatively correlated with the contents of Cd, Zn and Ni in rice, and positively correlated with Pb in rice; however, it had less effect on Cr and Cu. Based on risk reference dose (R f D), the risk assessment data indicated that the rice grown in Zhejiang paddy fields does pose some potential Cd and Cr contamination risk to food safety; rice in Shengzhou posed light Ni risk.

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Metal contamination in farmlands irrigated with biogas slurry is of great concern because of its potential health risks to local inhabitants. Health risks that depend heavily on multi-pathway exposure to heavy metals (e.g., Pb, Cd, Cr, Zn, Cu, and As) in water, soil, air, and local food were studied through field sampling in Taihu Basin, China. Results show that Zn, Pb, and Cd in soils irrigated with biogas slurry exceed the soil quality standard values, and grown vegetables and grains contaminated with Pb and Cd exceed the permissible limits. Food ingestion plays an important role in the total average daily dose of metals, especially for Cu and Zn, which account for 94 and 91%, respectively. Non-carcinogenic risks posed to adults mainly result from Cu, Zn, Pb, Cd, and As through food ingestion and from Cr through soil ingestion. The highest non-carcinogenic risk was determined from food ingestion, followed by soil ingestion, air inhalation, air ingestion, and dermal contact with air. Carcinogenic risks to adults are 6.68 to 7.00 times higher than the safe level and can be attributed to Cr, As, and Cd pollution. The estimated risks mainly result from As and Cd through food ingestion and from Cr through soil ingestion. Both cancer and non-cancer risks through dermal contact can be ignored. Therefore, attention should be paid to health risks imposed by adults' multi-pathway exposure to heavy metals in vegetables, grains, and related soils irrigated with biogas slurry in Taihu Basin. Effective measures should be implemented to control heavy metal pollution and protect potentially exposed adults.

In chemical batch processes with slow responses and a long duration, it is time-consuming and expensive to obtain sufficient normal data for statistical analysis. With the persistent accumulation of the newly evolving data, the modelling becomes adequate gradually and the subsequent batches will change slightly owing to the slow time-varying behavior. To efficiently make use of the small amount of initial data and the newly evolving data sets, an adaptive monitoring scheme based on the recursive Gaussian process (RGP) model is designed in this paper. Based on the initial data, a Gaussian process model and the corresponding SPE statistic are constructed at first. When the new batches of data are included, a strategy based on the RGP model is used to choose the proper data for model updating. The performance of the proposed method is finally demonstrated by a penicillin fermentation batch process and the result indicates that the proposed monitoring scheme is effective for adaptive modelling and online monitoring.

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