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Linking of ‘consumption-industrial production-surface water deterioration’ is essential for industrialised economies to understand the mechanism of industrial water pollution. However, such a connection may mislead policy decisions if sectoral details are lacking. This study investigated excess pollution discharge from 11,094 industrial enterprises comprising 22 economic sectors through setting discharge thresholds on 1338 water function zones in Jiangsu Province, the most industrialised province in China. We further evaluated the contribution of final consumption in Chinese provinces to excess pollution discharge in Jiangsu via a national multi-region input–output table. Notably, despite typically heavy polluting sectors contributing the maximum excess pollution discharge, high-tech manufacturing sectors had a higher level of risk for excess pollution discharge. This was attributed to the spatial agglomeration of these sectors, with enterprises typically located in industrial parks. The increasing final consumption of specific sectors in both Jiangsu and other provinces may further drive excess pollution discharge in Jiangsu.

Microplastics pollution has been threatening the global environmental security, in which agricultural activities are considered as a main source of microplastics occurrence in soils. However, little is known about the occurrence characteristics of microplastics in agricultural soils with long-term plastic film mulching. Therefore, the abundance, distribution, and composition of microplastics were investigated by analyzing 225 soil samples collected from typical maize (Zea mays L.) planting zones with and without long-term (>20 years) plastic film mulching in northern China. Microplastics abundance in mulched soils (754 ± 477 items kg–1) was significantly higher than that in non-mulched soils (376 ± 149 items kg–1), which indicated that plastic film mulching contributed half of microplastics in soils. Moreover, microplastics abundance was significantly positively related to the length of time with film mulching applied. The percentage of microplastics

Flood disasters are one of the most serious meteorological disasters in China. With the rapid development of information technology, individual monitoring tools could not meet the need for flood disaster monitoring. Therefore, a new integrated air-space-ground method, based on combined satellite remote sensing, unmanned aerial vehicle remote sensing and field measurement technology, has been proposed to monitor and assess flood disasters caused by a dam failure in Poyang County, Jiangxi Province. In this paper, based on an air-space-ground investigation system, the general flooded areas, severely affected areas, and more severely affected areas were 53.18 km2, 12.61 km2 and 6.98 km2, respectively. The size of the dam break gap was about 65 m and 34.7 m on 22 and 23 June. The assessment precision was better than 98%, and the root mean square error (RMSE) was 0.86 m. The method could meet the needs for flood disaster information at different spatiotemporal scales, such as macro scale, medium scale and local small scale. The integrated monitoring of flood disasters was carried out to provide the whole process and all-round information on flood evolution dynamics, the disaster development process for flood disaster monitoring and emergency assessment, and holographic information for emergency rescue and disaster reduction, as well as to meet the need for different temporal and spatial scales of information in the process of disaster emergencies.

Multi-hazard risk assessment is a major concern in risk analysis, but most approaches do not consider all hazard interactions when calculating possible losses. We address this problem by developing an improved quantitative model—Model for multi-hazard Risk assessment with a consideration of Hazard Interaction (MmhRisk-HI). This model calculates the possible loss caused by multiple hazards, with an explicit consideration of interaction between those hazards. There are two main components to the model. In the first, based on the hazard-forming environment, relationships among hazards are classified into four types for calculation of the exceedance probability of multiple hazards occurrence. In the second, a Bayesian network is used to calculate possible loss caused by multiple hazards with different exceedance probabilities. A multi-hazard risk map can then be drawn addressing the probability of multi-hazard occurrence and corresponding loss. This model was applied in northeast Zhejiang, China and validated by comparison against an observed multi-hazard sequence. The validation results show that the model can more effectively represent the real world, and that the modelled outputs, possible loss caused by multiple hazards, are reliable. The outputs can additionally help to identify areas at greatest risk, and allows a determination of the factors that contribute to that risk, and hence the model can provide useful further information for planners and decision-makers concerned with risk mitigation.

Risk assessment plays an important role in disaster risk management. Existing multi-hazard risk assessment models are often qualitative or semi-quantitative in nature and used for comparative study of regional risk levels. They cannot estimate directly probability of disaster losses from the joint impact of several hazards. In this paper, a quantitative approach of multi-hazard risk assessment based on vulnerability surface and joint return period of hazards is put forward to assess the risk of crop losses in the Yangtze River Delta region of China. The impact of strong wind and flood, the two most prominent agricultural hazards in the area, is analyzed. The multi-hazard risk assessment process consists of three steps. First, a vulnerability surface, which denotes the functional relationship between the intensity of the hazards and disaster losses, was built using the crop losses data for losses caused by strong wind and flood in the recent 30 years. Second, the joint probability distribution of strong wind and flood was established using the copula functions. Finally, risk curves that show the probability of crop losses in this multi-hazard context at four case study sites were calculated according to the joint return period of hazards and the vulnerability surface. The risk assessment result of crop losses provides a useful reference for governments and insurance companies to formulate agricultural development plans and analyze the market of agricultural insurance. The multi-hazard risk assessment method developed in this paper can also be used to quantitatively assess multi-hazard risk in other regions.

In recent years, greater attention has been given to advancing the theory and practice of assessing risk from multiple hazards. Most approaches calculate multi-hazard risk by aggregating risk scores for individual hazards and ignore the combined exceedance probability of multiple hazards. We address this problem by developing a simple and practicable multi-hazard risk assessment method that uses information diffusion theory to overcome the difficulty posed by a lack of historical or spatial data on natural hazard-induced loss. China’s Yangtze River Delta region is used as a demonstrative example, and the exceedance probability distribution of multi-hazard risk to human life was calculated using natural hazard disaster life loss data for 1950–2010. Multi-hazard risk to human life is mapped as exceedance probability at different mortality rates and loss at different risk return periods using a geographical information system. Results show that Hangzhou and Ningbo are at a relatively high risk from multiple natural hazards, and Shanghai is at a relatively low risk. For scenarios of 10-, 20- and 50-year risk return periods, there are no significant changes in the risk rank of the cities; Hangzhou, Ningbo and Zhoushan are at a relatively high risk, while Shanghai is at a relatively low risk.

In recent decades, society has been greatly affected by natural disasters (e.g. floods, droughts, earthquakes), and losses and effects caused by these disasters have been increasing. Conventionally, risk assessment focuses on individual hazards, but the importance of addressing multiple hazards is now recognised. Two approaches exist to assess risk from multiple hazards: the risk index (addressing hazards, and the exposure and vulnerability of people or property at risk) and the mathematical statistics method (which integrates observations of past losses attributed to each hazard type). These approaches have not previously been compared. Our application of both to China clearly illustrates their inconsistency. For example, from 31 Chinese provinces assessed for multi-hazard risk, Gansu and Sichuan provinces are at low risk of life loss with the risk index approach, but high risk using the mathematical statistics approach. Similarly, Tibet is identified as being at almost the highest risk of economic loss using the risk index, but lowest risk under the mathematical statistics approach. Such inconsistency should be recognised if risk is to be managed effectively, whilst the practice of multi-hazard risk assessment needs to incorporate the relative advantages of both approaches.