Canopy interception, throughfall, stemflow, and runoff have received considerable attention during the study of water balance and hydrological processes in forested ecosystems. Past research has either neglected or underestimated the role of hydrological functions of litter layers, although some studies have considered the impact of various characteristics of rainfall and litter on litter interception. Based on both simulated rainfall and litter conditions in North China, the effect of litter mass, rainfall intensity and litter type on the maximum water storage capacity of litter (S) and litter interception storage capacity (C) were investigated under five simulated rainfall intensities and four litter masses for two litter types. The results indicated: 1) the S values increased linearly with litter mass, and the S values of broadleaf litter were on average 2.65 times larger than the S values of needle leaf litter; 2) rainfall intensity rather than litter mass determined the maximum interception storage capacity (Cmax); Cmax increased linearly with increasing rainfall intensity; by contrast, the minimum interception storage capacity (Cmin) showed a linear relationship with litter mass, but a poor correlation with rainfall intensity; 3) litter type impacted Cmax and Cmin; the values of Cmax and Cmin for broadleaf litter were larger than those of needle leaf litter, which indicated that broadleaf litter could intercepte and store more water than needle leaf litter; 4) a gap existed between Cmax and Cmin, indicating that litter played a significant role by allowing rainwater to infiltrate or to produce runoff rather than intercepting it and allowing it to evaporate after the rainfall event; 5) Cmin was always less than S at the same litter mass, which should be considered in future interception predictions. Vegetation and precipitation characteristics played important roles in hydrological characteristics.

The role of leaf litter in hydrological processes and soil erosion of forest ecosystems is poorly understood. A field experiment was conducted under simulated rainfall in runoff plots with a slope of 10%. Two common types of litter in North China (from Quercus variabilis, representing broadleaf litter, and Pinus tabulaeformis, representing needle leaf litter), four amounts of litter, and five rainfall intensities were tested. Results revealed that the litter reduced runoff and delayed the beginning of runoff, but significantly reduced soil loss (p<0.05). Average runoff yield was 29.5% and 31.3% less than bare-soil plot, and for Q. variabilis and P. tabulaeformis, respectively, and average sediment yield was 85.1% and 79.9% lower. Rainfall intensity significantly affected runoff (R = 0.99, p<0.05), and the efficiency in runoff reduction by litter decreased considerably. Runoff yield and the runoff coefficient increased dramatically by 72.9 and 5.4 times, respectively. The period of time before runoff appeared decreased approximately 96.7% when rainfall intensity increased from 5.7 to 75.6 mm h−1. Broadleaf and needle leaf litter showed similarly relevant effects on runoff and soil erosion control, since no significant differences (p≤0.05) were observed in runoff and sediment variables between two litter-covered plots. In contrast, litter mass was probably not a main factor in determining runoff and sediment because a significant correlation was found only with sediment in Q. variabilis litter plot. Finally, runoff yield was significantly correlated (p<0.05) with sediment yield. These results suggest that the protective role of leaf litter in runoff and erosion processes was crucial, and both rainfall intensity and litter characteristics had an impact on these processes.

With the rapid development of urbanization and industrialization, many developing countries are suffering from heavy air pollution. Governments and citizens have expressed increasing concern regarding air pollution because it affects human health and sustainable development worldwide. Current air quality prediction methods mainly use shallow models; however, these methods produce unsatisfactory results, which inspired us to investigate methods of predicting air quality based on deep architecture models. In this paper, a novel spatiotemporal deep learning (STDL)-based air quality prediction method that inherently considers spatial and temporal correlations is proposed. A stacked autoencoder (SAE) model is used to extract inherent air quality features, and it is trained in a greedy layer-wise manner. Compared with traditional time series prediction models, our model can predict the air quality of all stations simultaneously and shows the temporal stability in all seasons. Moreover, a comparison with the spatiotemporal artificial neural network (STANN), auto regression moving average (ARMA), and support vector regression (SVR) models demonstrates that the proposed method of performing air quality predictions has a superior performance.

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Activated sludge system is an important process of domestic and industrial wastewater treatment containing highly diverse microbial communities. In this study, high-throughput sequencing was applied to examine the microbial community composition and diversity of activated sludge samples from four full-scale municipal wastewater treatment plants (WWTPs) in Shanghai. A relationship between microbial communities and environmental variables was examined. Proteobacteria was the most dominant phylogenetic group, followed by Bacteroidetes and Firmicutes. A total of 166 genera were commonly shared by all seven sludge samples, including Zoogloea, Dechloromonas, Thauera, Nitrospira, Arcobacter, etc. Besides these shared populations, certain unique bacterial populations were found abundant in individual sludge sample. Canonical correspondence analysis (CCA) indicated that influent COD and pH had the greatest influence on microbial community compositions, whereas dissolved oxygen (DO) exhibited the least influence. The operating process was likely to foster diversity of the microbial communities inhabiting the wastewater treatment facilities. Alternative operation methods including a fluctuation of anoxic, oxic, and anaerobic conditions were favorable for promoting the growth of diverse microbial populations in activated sludge systems.

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