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The emergence of vegetable greenhouses greatly improves the yield and diversity of vegetable crops, but has a negative impact on the soil. This paper briefly introduced the vegetable greenhouse and the methods used for evaluating the soil ecological risk and took a vegetable planting base in Yichang City of Hubei Province as the subject to carry out the soil pollution management and ecological risk analysis in the greenhouse planting area. The results showed that the content of heavy metals in the soil and the ecological risk increased with the continuous use of the greenhouse; after using organic fertiliser instead of chemical fertiliser, the content of heavy metals in the soil of both open-air farmland and greenhouse farmland were reduced, especially in the greenhouse farmland, in which the ecological risk was also reduced. Then, according to the test items and results, several suggestions were put forward for the soil pollution management of greenhouse vegetable planting.

Oxygen-deficiency is a cause of fatalities in confined-space workplaces. Most research and projects have been conducted to reduce work-related accidents by external measurements, while works addressing the early warning of workers’ hypoxia state through a bio-electrical signal have rarely been conducted. In this paper, we present a hypoxia detection system based on non-invasive photoplethysmograph (PPG) measurement using machine-learning (ML) algorithms. The PPG signals obtained from 22 subjects underwent preprocessing and features extraction steps. Time-domain features, rising and falling slopes, and amplitude parameters were adopted to train and test the ML algorithms. In addition, an Internet of Things- (IoT) based smart wearable device and monitoring system were developed to measure the vital parameters of workers in confined spaces. Cardiac cycle time of all the participants except subject 8 decreased significantly (P< 0.01) throughout the oxygen-deficiency trial. The PPG signal complexity essentially decreased (P= 0.006) when the concentration of environmental oxygen declined. The DT algorithm with nine input parameters outperformed the other algorithms in prediction accuracy (Acc = 0.9). The results show that the features extracted from the PPG signal can be adopted as important indicators to revealing the hypoxia state of workers. The device and system automatically measure and analyze the PPG signal with the location details of caregivers and notify the monitoring staff in the case of an emergency.