RBC agglutination was determined by the number of protonated amine groups on chitosan and its derivatives.

How to effectively use clean renewable energy to improve the capacity of the power grid to absorb new energy and optimize the power grid structure has become one of China's current issues. The Yalong River Wind-PV-Hydro complementary clean energy base was chosen as the research object from which to analyze the output complementarity principle and characteristics of wind farms, photovoltaic power plants, and hydropower stations. Then, an optimization scheduling model was established with the objective of minimizing the amount of abandoned wind and photovoltaic power and maximizing the stored energy in cascade hydropower stations. A Progress Optimality Algorithm (POA) was used for the short-term optimal operation of Wind-PV-Hydro combinations. The results show that use of cascaded hydropower storage capacity can compensate for large-scale wind power and photovoltaic power, provide a relatively sustained and stable power supply for the grid. Wind-PV-Hydro complementary operation not only promotes wind power and photovoltaic power consumption but also improves the efficiency of using the original transmission channel of hydropower. This is of great significance to many developing countries in formatting a new green approach, realizing low-carbon power dispatch and trade and promoting regional economic development.

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Ground-received solar radiation is affected by several meteorological and air pollution factors. Previous studies have mainly focused on the effects of meteorological factors on solar radiation, but research on the influence of air pollutants is limited. Therefore, this study aimed to analyse the effects of air pollution characteristics on solar radiation. Meteorological data, air quality index (AQI) data, and data on the concentrations of six air pollutants (O3, CO, SO2, PM10, PM2.5, and NO2) in nine cities in China were considered for analysis. A city model (model-C) based on the data of each city and a unified model (model-U) based on national data were established, and the key pollutants under these conditions were identified. Correlation analysis was performed between each pollutant and the daily global solar radiation. The correlation between O3 and daily global solar radiation was the highest (r = 0.575), while that between SO2 and daily global solar radiation was the lowest. Further, AQI and solar radiation were negatively correlated, while some pollution components (e.g., O3) were positively correlated with the daily global solar radiation. Different key pollutants affected the solar radiation in each city. In Shenyang and Guangzhou, the driving effect of particles on the daily global solar radiation was stronger than that of pollutants. However, there were no key pollutants that affect solar radiation in Shanghai. Furthermore, the prediction performance of model-U was not as good as that of model-C. The model-U showed a good performance for Urumqi (R2 = 0.803), while the difference between the two models was not particularly significant in other areas. This study provides significant insights to improve the accuracy of regional solar radiation prediction and fill the gap regarding the absence of long-term solar radiation monitoring data in some areas.