Effects of rainfall on surface environment and morphological characteristics in the Loess Plateau

Environmental Science and Pollution Research - Slope failure is a one of major process that causes severe landform variation and environment variation, and slope failure has become a major hidden...     

Deep learning architecture for air quality predictions

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.     

Atmospheric depositional fluxes and sources apportionment of organochlorine pesticides in the Pearl River Delta region, South China

Organochlorine pesticides (OCPs) have variously been phased out in agricultural activities, but they are still widely detected in air, water, and soil systems due to their recalcitrant nature in the environment. The purposes of this study were to assess potential OCP pollution via dry and wet deposition over the fast developing Pearl River Delta area with 41,700 km², where the main effort has been focused on emerging pollutants such as petroleum hydrocarbons and PM2.5. We quantified both the dry and wet deposition fluxes of 19 OCPs including dichlorodiphenyltrichloroethanes (DDTs), endosulfans (Endos), and hexachlorocyclohexanes (HCHs). The results showed that each year about 67.4, 42.0, 15.0, and 8.07 kg of total OCPs, DDTs, Endos, and HCHs were returned to the ground, among which 11.7, 10.4, 0.84, and 0.16 kg were in the dry deposition forms. The large spatial variations in OCP deposition fluxes indicated that OCP pollution in the air is mainly influenced on local scales because evaporation from local soil is likely the major source of the phased out OCPs. Source analysis indicated that DDTs may be still in use as antifouling agent and/or dicofol, but Endos and HCHs were mainly derived from the residual of historical usage. The study suggests that the historical OCP pollutants are persistent at high levels in this area and should not be overlooked, while we tackle emerging pollutants.     

Improving artificial forest biomass estimates using afforestation age information from time series Landsat stacks

China maintains the largest artificial forest area in the world. Studying the dynamic variation of forest biomass and carbon stock is important to the sustainable use of forest resources and understanding of the artificial forest carbon budget in China. In this study, we investigated the potential of Landsat time series stacks for aboveground biomass (AGB) estimation in Yulin District, a key region of the Three-North Shelter region of China. Firstly, the afforestation age was successfully retrieved from the Landsat time series stacks in the last 40 years (from 1974 to 2013) and shown to be consistent with the surveyed tree ages, with a root-mean-square error (RMSE) value of 4.32 years and a determination coefficient (R ²) of 0.824. Then, the AGB regression models were successfully developed by integrating vegetation indices and tree age. The simple ratio vegetation index (SR) is the best candidate of the commonly used vegetation indices for estimating forest AGB, and the forest AGB model was significantly improved using the combination of SR and tree age, with R ² values from 0.50 to 0.727. Finally, the forest AGB images were mapped at eight epochs from 1985 to 2013 using SR and afforestation age. The total forest AGB in seven counties of Yulin District increased by 20.8 G kg, from 5.8 G kg in 1986 to 26.6 G kg in 2013, a total increase of 360 %. For the persistent forest area since 1974, the forest AGB density increased from 15.72 t/ha in 1986 to 44.53 t/ha in 2013, with an annual rate of about 0.98 t/ha. For the artificial forest planted after 1974, the AGB density increased about 1.03 t/ha a year from 1974 to 2013. The results present a noticeable carbon increment for the planted artificial forest in Yulin District over the last four decades.     

改性壳聚糖复合凹凸棒土絮凝油漆废水

将壳聚糖、阳离子淀粉和二甲基二烯丙基氯化铵(DMDAAC)进行三元接枝共聚,制备了三元接枝改性壳聚糖(CTS-DMDAAC-CS),通过红外光谱和扫描电镜对其结构进行了表征。并且将三元接枝改性壳聚糖与凹凸棒土进行优化复配,对油漆废水进行了絮凝实验,得出了当pH=12,沉降时间为20 min,复合絮凝剂投加量为0.2 g,三元接枝共聚物∶凹凸棒土=1∶10时,该复合絮凝剂对油漆去除率最佳能够达到89.3%,并且絮凝效果好,是一种新型绿色环保的有机-无机复合絮凝剂。     

Physiological mechanisms of a wetland plant (Echinodorus osiris Rataj) to cadmium detoxification

Environmental Science and Pollution Research - Physiological responses of Echinodorus osiris Rataj plant under cadmium (Cd) stress (5 and 15 mg L?1) were studied by...     

Assessment of urban sustainability efficiency based on general data envelopment analysis: a case study of two cities in western and eastern China

Sustainable urban development focuses on enhancing urban well-being, while also balancing the demands of urban social and economic development, natural resource consumption, and environmental pollution. This work used general data envelopment analysis to assess the urban sustainability efficiency (USE) and sustainability potential (SP) in Lanzhou and Xiamen, two cities that are characteristic of urban areas in western and eastern China. The assessment indicator system included important natural and urban welfare factors as input and output indices, respectively. The results showed that overall urban sustainability efficiency increased in Lanzhou and Xiamen from 1985 to 2010, but that the sustainability of natural resources clearly decreased. The urban sustainability efficiency of Xiamen was higher than that of Lanzhou, and the sustainability potential of Xiamen was lower than that of Lanzhou; this indicates that Xiamen performed better in terms of urban sustainable development. The urban sustainability efficiency in Xiamen has increased with increasing urban population, and the rate and scale of economic development have been higher than in Lanzhou. The assessment and analysis performed in this study show that cities with different natural resources and development characteristics have different forms, patterns, and trajectories of sustainable development.     

厌氧膨胀床处理低浓度污水的污泥颗粒和生物活性变化

污泥的聚集形态和活性，是影响厌氧反应器处理效率的关键因素。通过对厌氧膨胀床反应器（anaerobicex—pandedblanketreactor，AEBR）处理低浓度城镇污水在启动和稳定运行期的污泥活性研究，AEBR在启动运行期内，接种颗粒污泥为适应低浓度基质条件，污泥粒径经历从大变小，再重新颗粒化粒径变大的过程。在运行期第103天，粒径小于1000μm污泥的体积比达到44．7％，平均粒径为952μm，到运行期第173天，粒径小于1000μm污泥的体积比降为28％，平均粒径达1179μm，污泥重新颗粒化完成。颗粒污泥适应新的环境后，单位重量污泥的最大比产甲烷活性（specificmetha．nogensisactivity，SMA）值和胞外聚合物含量增加，分别达到112mLCH4／（gVSS·d）和215mg／gVSS。在处理实际城镇污水的AEBR反应器内，辅酶F420含量可以有效指示污泥样品的产甲烷活性，AEBR反应器不同高度位置的污泥活性不一样，反应器底部污泥活性低于中上部区域污泥的活性。     

牡蛎壳生物滤池处理含盐污水中的硝化特性

采用牡蛎壳为曝气生物滤池填料,以含NaCl的生活污水为处理对象,在SBR操作条件下,系统考察进水NaCl浓度、曝气时间及进水pH值等对硝化性能的影响。结果表明,进水NaCl浓度为10~15 g/L时,平均氨氮去除率可稳定在97%以上;较高浓度NaCl对亚硝酸化菌活性影响较弱,对硝酸化菌活性影响较强,特别是在日曝气时间少于12 h时,其出水中亚硝氮的含率大于50%;当进水pH值在6~9变化时,反应器内pH值可稳定在6.5~7.5,硝化性能良好,表明牡蛎壳填料可为硝化反应提供碱度。     

Assessment of heterogeneity of metal composition of fine particulate matter collected from eight U.S. counties using principal component analysis

The main objectives of this study are to (1) characterize chemical constituents of particulate matter (PM) and (2) compare overall differences in PM collected from eight US. counties. This project was undertaken as a part of a larger research program conducted by the Johns Hopkins Particulate Matter Research Center (JHPMRC). The goal of the JHPMRC is to explore the relationship between health effects and exposure to ambient PM of differing composition. The JHPMRC collected weekly filter-based ambient fine particle samples from eight US. counties between January 2008 and January 2010. Each sampling effort consisted of a 5-6-week sampling period. Filters were analyzed for 25 metals using inductively coupled plasma mass spectrometry (ICP-MS). Overall compositional differences were ranked by principal component analysis (PCA). The results showed that weekly concentrations of each element varied 3-40 times between the eight counties. PCA showed that the first five principal components explained 85% of the total variance. The authors found significant overall compositional differences in PM as the average of standardized principal component scores differed between the counties. These findings demonstrate PCA is a useful tool to identify the differences in PM compositional mixtures by county. These differences will be helpful for epidemiological and toxicological studies to help explain why health risks associated with PM exposure are different in locations with similar mass concentrations of PM.