The effluent total nitrogen (TN) is one of the key indicators for assessing the biological denitrification performance of wastewater treatment plants(WWTPs). To mitigate the prevalent issue of excessive TN discharges from WTTPs, we proposed a real-time prediction model based on long short-term memory (LSTM) networks. We performed Pearson correlation analysis to determine model inputs and used grid search algorithm to optimize model hyperparameters. Then, we used the proposed model to predict the actual effluent TN in a WWTP in Chongqing and compared its predictive performance with that of traditional time-series models. Results indicate that the proposed model can effectively predict effluent TN with an average absolute error of 0.911 mg/L, an average root mean square error of 1.074 mg/L, and an average absolute percentage error of 11.28%. All of these performance indicators surpass those of the recurrent neural network and ARIMA models. The proposed model can serve as the foundation for effective monitoring of effluent TN.

Based on the monthly average global precipitation data from January 1982 to April 2022, this study discusses the spatial distribution and time series of mode one and mode two using the cyclostationary empirical orthogonal function (CSEOF) method; analyzes the spatial distribution, seasonal variation characteristics, and interannual variation characteristics of global marine precipitation at low and middle latitudes; and discusses the possible causes of these variations. Results show that the interannual variation of precipitation exhibits periodic characteristics, with the main areas experiencing variations being distributed in the tropical Pacific region; additionally, the spatial field of mode one demonstrates an east-west inverse phase distribution, which is stronger in winter than in summer. The seasonal variation of the spatial field of mode two is more complex than that of mode one, with the high-value positive and negative variability regions demonstrating an east-west inverse phase distribution in winter and negative variability regions dominating in summer with weaker intensity. ENSO has an important impact on the interannual variations of precipitation. The spatiotemporal variation characteristics of mode one are primarily affected by the ENSO phenomenon, while those of mode two are mainly affected by the El Niño Modoki phenomenon. The main precipitation variation characteristics are affected by the superposition of these two phenomena.

To ascertain the current status of plant resources and biodiversity in the Yellow River, Yishu River, Nansi Lake Basin and Weifang in Shandong Province, a comprehensive and detailed investigation was conducted by combining field investigation, specimen collection, indoor classification, and anatomical identification. The results identified 1 194 species of vascular plants belonging to 587 genera and 155 families. Among these, 6 species, namely Teucrium japonicum, Euphorbia heyneana, Echinochloa colona, Commelina diffusa, Braya humilis, and Bidens maximowicziana, were newly recorded in Shandong Province. The key identification characteristics of these species are described in this study, and the distribution status and application value are also discussed. The discovery of these plants not only enriches the background data of plant resources and plant diversity in Shandong, but also is significancant to the study of the systematic classification, floristic plant geography, and distribution patterns of related families and genera.

As the second most abundant element on earth, silicon plays an important role in soil biogeochemical processes. However, the geochemical characteristics of soil silicon in different forms in coastal wetlands still need further investigation. In this study, we selected four typical coastal wetlands (nonflooding Phragmites australis, tidal P. australis, freshwater P. australis, and tidal Suaeda salsa wetlands) as sampling sites and collected soils from 0 to 20 cm depth. Furthermore, we determined oxalate-extractable silicon, dithionite-citrate-extractable silicon, pyrophosphate-extractable silicon and analyzed their soil physical and chemical properties, distribution patterns, and influencing factors in typical coastal wetlands. Results showed that dithionite-citrate-extractable silicon and pyrophosphate-extractable silicon showed no significant differences among four wetlands (p>0.05), while oxalate-extractable silicon in nonflooding P. australis wetlands was significantly lower than tidal P. australis wetlands (p<0.05). As for the profile distribution, the three types of extractable silicon in soils from 0 to 10 cm were generally higher than in soils from 10 cm to 20 cm. Additionally, the correlation analysis revealed that soil organic matter, total nitrogen, bulk density, pH, silt and moisture were important factors influencing these three types of extractable silicon.

Based on MOD17A3 product data for Shandong Province from 2010 to 2022, this study uses univariate regression trend analysis, the coefficient of variation method, partial correlation analysis, and the Hurst index method to investigate the spatiotemporal distribution of vegetation net primary productivity (NPP) for the ecosystem of Shandong Province and analyze the impact of climate factors. Results demonstrate the fluctuating upward trend of vegetation NPP for Shandong Province in recent years, with an annual average of (398.03±150.20) g/(m²·a), higher than the national average and comparable with that of the Beijing-Tianjin-Hebei region. Vegetation NPP varies considerably across different areas. The vegetation NPP in the eastern coastal hilly area is higher than that in the inland plain area. With respect to interannual variation, the overall vegetation NPP in Shandong Province is relatively stable. With regard to the variation trend of vegetation NPP, the areas of positive and negative trends are equivalent. The trend analysis shows that 27.86% of the areas show an increasing trend, whereas 33.49% show a decreasing trend. However, the areas that have shifted from the increasing trend to the decreasing trend are mostly in woodland areas with high vegetation NPP levels, and further research is needed. In general, a positive correlation exists between vegetation NPP and climate factors. Temperature has a wider and more considerable impact on vegetation NPP than precipitation; moreover, the correlation between vegetation NPP and climate factors is poor in plain agricultural areas.