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The China seismic instrumental intensity can be used to measure the level of destruction and serve as the foundation of earthquake early warning (EEW) systems. To indirectly develop the instrumental intensity estimation and its application to EEW, we estimated the on-site filtered peak ground motion velocity (PGV) of the intensity using a support vector machine (SVM)-based model with eight P-wave features at a 3-s time window. Alert thresholds were set when the PGV was ≥ 8.18 cm/s (VII on the instrumental intensity scale). Compared with two linear estimation models (IV2 and Pd), the mean absolute error (MAE) and standard deviation of the error of the SVM estimation model were less, 0.241 and 0.298, respectively, with better performance on the PGV estimation. To evaluate the feasibility of transforming the SVM estimation for EEW by issuing alerts based on the intensity scale, we used the accuracy, precision, recall, F1 score, and false-negative rate (FNR) as evaluation metrics, achieving values of 99.62%, 95.68%, 79.90%, 87.08%, and 20.10%, respectively, using 11,970 records. We also provided the ratio, maximum, and average of the true positives to evaluate the lead time performance. Meanwhile, we used six earthquakes to evaluate the performance of our approach in detail. The approach performed well on EEW applications by issuing alerts based on the China seismic instrumental intensity. The analysis of the feature importance and data balance strategy can provide the basis for improving the performance of the SVM-based PGV estimation model. [ABSTRACT FROM AUTHOR]

By considering the impacts of cleaner production (CP) on China's pulp industry, the synergistic effects of the capacity replacement policy on the focal sector and supply chain (SC) in China have been evaluated by taking into account its economic, eco-environmental, and social dimensions. The obtained results demonstrated that, when only focal enterprises were analysed on the nationwide scale, the capacity replacement policy was practical from economic and environmental perspectives, but sustainable problems manifest when we considered the five sustainable characteristics of sustainable supply chain management (SSCM). Furthermore, CP become not 'clean' when the SC of the reed pulp production was considered. The closure of small reed pulp mills (the focal sector) had destructive effects on reed harvesting (the supplier sectors), which intensifies the internal pollutant cycle (especially eutrophic pollutants) and accelerated the dying process of the lake and wetland. Hence, CP optimisation must necessarily include the SC and sustainability. From the obtained data, a win-win solution was proposed to apply the principles of SSCM to the optimisation of CP performance, which could be expressed as CP + SSCM→CPSSCM. CPSSCM not only provided a theoretical basis for CP implementation that current issues pertaining to SSCM. In addition, a conceptual model of CPSSCM was suggested and specific recommendations were provided for the underdeveloped countries by taking into account the main challenges posed by small-medium enterprises during CPSSCM implementation. It was also shown that implementing the proposed strategy might have wider implications in terms of controlling both the external discharge of pollutants and internal pollution of lakes. Image 1 • CP policy may be not "clean" when supply chain of reed pulp production is considered. • "Sustainable" principle and "supply chain" should be considered in CP strategy. • Integration of CP and SSCM is recommended: CP + SSCM→CPSSCM. • CPSSCM can be a win-win solution for both CP and SSCM. • CPSSCM can solve contradicts between reed pulp production and lake eco-environment. [ABSTRACT FROM AUTHOR]

• We propose a method combining neural network and transfer learning for China. • This method has better performance on PGA and PGV prediction than baseline models. • Robust on-site instrumental intensity prediction at 3 s after P-wave arrival. • High percentage of successful alarms for the area nearby the epicenter. China is a seismically active country. Rapidly and accurately predicting instrumental seismic intensity at recording sites is important for China to mitigate earthquake disasters. According to the peak ground acceleration (PGA) and peak ground velocity (PGV) at recording stations, instrumental seismic intensity for China is measured. Here, for the robust and rapid on-site instrumental seismic intensity prediction, we propose a method combining recurrent neural network (RNN) and transfer learning to predict on-site PGA and PGV for China. For the same test dataset from China, our results indicate that at 3 s after P-wave arrival, the RNN models using transfer learning have better performance on PGA and PGV prediction than the baseline models, which include traditional methods based on the single parameter and RNN models without using transfer learning. Additionally, according to the predicted PGA and PGV of the RNN models using transfer learning, we statistically analyze the alarm performance based on the predicted on-site instrumental seismic intensity. Meanwhile, according to the proposed method in this paper, we test five destructive earthquake events (M ≥ 6.6) occurred in China. The results show that at 3 s after the P-wave arrival, the predicted instrumental seismic intensity is almost consistent with the observed instrumental seismic intensity, the predicted instrumental seismic intensity error is mainly within ± 1, and the mean absolute error is 0.78. Meanwhile, for the area near the epicenter, the percentage of successful alarms reaches 90%, and the percentage of false alarms is 0%. [ABSTRACT FROM AUTHOR]

Nitrogen accumulation in sediments, and the subsequent migration and transformations between sediment and the overlying water, plays an important role in the lake nitrogen cycle. However, knowledge of these processes are largely confined to ice-free seasons. Recent research under ice has mainly focused on the water eco-environmental effects during winter. Sediment N accumulation during the ice-on season and its associated eco-environmental impacts have never been systematically investigated. To address these knowledge gaps, we chose Wuliangsu Lake in China as a case study site, taking advantage of the spatial disparity between the 13 semi-separated sub-lakes. Based on samples of 35 sampling sites collected before, in the middle, and at the end of ice-on season separately, we performed a quantitative analysis of under-ice lake N accumulation and water-sediment N exchange by analyzing N fraction variations. Hierarchical Cluster Analysis and Relevance Analysis were used to help elucidate the main causes and implications of under-ice N variation. Our results clearly show that existing studies have underestimated the impact of under-ice N accumulation on the lake ecology throughout year: 1) Sediment N accumulated 2-3 times more than that before winter; 2) residual nitrogen (Res-N) contributed to the majority of the accumulated sediment N and was mainly induced by the debris of macrophytes; 3) total available nitrogen (TAN) was the most easily exchanged fractions between sediment and water, and it mainly affected the water environment during winter; 4) the Res-N accumulation during the ice-on season may have a strong impact on the eco-environment in the subsequent seasons. Our research is valuable for understanding the mechanism of internal nutrient cycle and controlling the internal nitrogen pollution, especially in shallow seasonally-frozen lakes that have long suffered from macrophyte-phytoplankton co-dominated eutrophication., (Copyright © 2019 Elsevier Ltd. All rights reserved.)