Seawater desalination is considered a technique with high water supply potential and has become an emerging alternative for freshwater supply in China. The increase of the capacity also increases energy consumption and greenhouse gases (GHG) emissions, which has not been well investigated in studies. This study has analyzed the current development of seawater desalination in China, including the capacity, distribution, processes, as well as the desalted water use. Energy consumption and GHG emissions of overall desalination in China, as well as for the provinces, are calculated covering the period of 2006–2016. The unit product cost of seawater desalination plants specifying processes is also estimated. The results showed that 1) The installed capacity maintained increased from 2006 to 2016, and reverse osmosis is the major process used for seawater desalination in China. 2) The energy consumption increased from 81 MWh/y to 1,561 MWh/y during the 11 years. The overall GHG emission increase from 85 Mt CO_2eq/y to 1,628 Mt CO_2eq/y. Tianjin had the largest GHG emissions, following are Hebei and Shandong, with emissions of 4.1 Mt CO_2eq/y, 2.2 Mt CO_2eq/y. and 1.0 Mt CO_2eq/y. 3) The unit product cost of seawater desalination is higher than other water supply alternatives, and it differentiates the desalination processes. The average unit product cost of the reverse osmosis process is 0.96 USD and 2.5 USD for the multiple-effect distillation process. The potential for future works should specify different energy forms, e.g. heat and power. Alternatives of process integration should be investigated—e.g. efficiency of using the energy, heat integration, and renewables in water desalination, as well as the utilization of total site heat integration.

The increasing issue of water quality degradation has affected the availability of water. The consideration of water quality is becoming more important for water minimisation. There is a need to integrate water quality into the current water assessment framework. This study tries to involve the water quality into the widely used water footprint assessment framework in order to quantify the water usability changes during the water use process. Based on water footprint concepts from international standard ISO 14046, water availability is further interpreted to emphasize the impact of water quality on the usability. An effective water availability footprint is defined as the quantitative and qualitative extent of a certain body of water which meets the needs of a certain purpose of water use. A water quality index is proposed to quantify the contribution of water quality on water availability, and two approaches of calculating water quality index are discussed, in order to explore the possibility of involving water quality into the water availability footprint assessment. Based on the definitions and framework, a case study is conducted to illustrate the features of this framework, and 3 outflows with different water quality are set to discuss the impact of different water quality profiles on the calculation of water availability footprint. It shows that water quality profiles can have a remarkable influence on the calculation. For the case with an outflow of F2-1, the water availability footprints with minimum water quality index, average water quality index and the volumetric water footprints as 1,600 m 3 , 1,277 m 3 , and 1,000 m 3. This indicator can determine the consumptive water use and also quantify the exploitation of water quality. The involvement of water quality regarding multiple contaminants in water footprint assessment should be further investigated in future studies.

With the development of human society, the urban population and the urban building stock have been continuously increasing. Environmental issues such as greenhouse gases emissions, air pollution, and construction waste have gradually emerged. Due to the lack of an urban functional area database, it is very time-consuming to manually identify building functional areas. As a result, most of the current research on urban building functions are estimated at a large regional scale or only detailed calculations of individual buildings. The building functions classification method needs to be further improved. Based on the traditional methods, this paper proposes a building function classification method with higher recognition accuracy and is less time-consuming. The method is then applied to a certain area of Chaoyang District, Beijing, for validation and verification. The results show that the urban building function classification method in this paper has a recognition rate of 96.18%, an overall classification accuracy of 94.37%, and a kappa coefficient of 0.9089. The classification results are in good agreement with the virtual interpretation. In addition, automatic classification of building functions is implemented using ArcPy in ArcGIS, which significantly improves the classification efficiency.