水、土环境中锑污染与控制研究进展

随着工业的发展,锑作为一种具有潜在毒性和致癌性的类金属元素,已经较为广泛地存在于水体和土壤环境中,显现出不可忽视的环境问题,并引起国际科学界的高度关注。文章综述了锑在水体和土壤环境中的污染现状、化学行为形态以及污染控制方面的研究进展。由采矿业、制造业带来的锑水土污染问题较为突出,目前的处理处置方法难以满足需求。今后需要在锑的水土环境化学方面加强研究,探讨锑的迁移转化规律;高效吸附材料的开发制备也是一个重要内容;植物组合修复土壤中的锑污染将是一个重要的突破口。     

地震灾区河谷滑坡检测的遥感分析——以北川县滑坡为例

震区滑坡易堵塞河道形成高危型堰塞湖,一旦坝堤溃决,将释放巨大能量,造成严重的地震次生水灾。本文利用多源遥感数据,旨在研究一种检测河道滑坡的快速有效的方法,迅速判断出滑坡位置,为救援减灾提供重要信息。首先,对于灾前CBERS-02B星CCD数据,通过归一化水体指数(NDWI)来提取灾前水体信息主要为河道;由于灾后震区天气状况恶劣,故采用Radarsat-1SAR数据。利用水体在SAR影像上的特性,经过多次试验后使用阈值法来提取河道范围。再对两幅影像提取结果中的河道区域进行交集运算,检测出河道范围内的变化部分。通过上述方法提取的河道信息较为模糊、零散,这些模糊、零散并非都由滑坡堵塞河道造成,而是受噪声影响,在此对河道交集影像做膨胀处理,达到突出滑坡信息的目的,进而得以提取河道真实堵塞范围。最后进行人机交互判读,提取滑坡区域。实验以四川省北川县滑坡灾害为例,证明了本文方法的高效性与可靠性。     

新型高压脉冲等离子体反应器处理荧光增白剂废水

以浙江传化集团的荧光增白剂废水为研究对象,进行了新型高压脉冲等离子体反应器处理该废水的试验研究。试验表明,新型高压脉冲等离子体反应器处理该废水的COD、BOD5和NH3-N的去除率分别达到85.7%、80.5%和56.8%。     

Comparison of hybrid machine learning methods for the prediction of short-term meteorological droughts of Sakarya Meteorological Station in Turkey

Drought is a harmful natural disaster with various negative effects on many aspects of life. In this research, short-term meteorological droughts were predicted with hybrid machine learning models using monthly precipitation data (1960–2020 period) of Sakarya Meteorological Station, located in the northwest of Turkey. Standardized precipitation index (SPI), depending only on precipitation data, was used as the drought index, and 1-, 3-, and 6-month time scales for short-term droughts were considered. In the prediction models, drought index was predicted at t?+?1 output variable by using t, t???1, t???2, and t???3 input variables. Artificial neural networks (ANNs), adaptive neuro-fuzzy inference system (ANFIS), Gaussian process regression (GPR), support vector machine regression (SVMR), k-nearest neighbors (KNN) algorithms were employed as stand-alone machine learning methods. Variation mode decomposition (VMD), discrete wavelet transform (DWT), and empirical mode decomposition (EMD) were utilized as pre-processing techniques to create hybrid models. Six different performance criteria were used to assess model performance. The hybrid models used together with the pre-processing techniques were found to be more successful than the stand-alone models. Hybrid VMD-GPR model yielded the best results (NSE?=?0.9345, OI?=?0.9438, R²?=?0.9367) for 1-month time scale, hybrid VMD-GPR model (NSE?=?0.9528, OI?=?0.9559, R²?=?0.9565) for 3-month time scale, and hybrid DWT-ANN model (NSE?=?0.9398, OI?=?0.9483, R²?=?0.9450) for 6-month time scale. Considering the entire performance criteria, it was determined that the decomposition success of VMD was higher than DWT and EMD.

     

Solid waste composition and COVID-19-induced changes in an inland water ecosystem in Turkey

Environmental Science and Pollution Research - The composition and abundance of solid waste and the effect of COVID-19 measures were studied in an inland water ecosystem in Turkey. Solid waste...