摘要：
Zinc hydrometallurgy residue is one of the main sources of heavy metals in the environment. The chemical components and mineralogical characteristics of the reductive leaching residue was obtained by ICP-AES, XRD and SEM + EDS analysis. The results showed that the main heavy metals in the residue were Pb, Zn, Cd and As. Pb, Zn and Cd in the residue existed as PbSO4, ZnS and CdS, respectively. The three-step sequential extraction procedure study showed that Zn and Cd mainly existed as oxidizable form; Pb and As mainly existed as the residue form. The leaching toxicity results using sulfuric acid and nitric acid method showed that the leaching toxicity of Zn and Cd exceeded the reference values. The potential ecological risk assessment results of heavy metals of the residue showed that the sequence of the environmental activity and ecological risk was Cd > Zn > As > Pb. Cadmium contributed to the potential risk index more greatly than any other heavy metal in the residue and the contribution rate reached 98.9%, which showed that the cadmium in the reductive leaching residue threw the greatest threat to the ecological environment.
摘要：
Chromium is not only an essential trace element for the growth and development of living organisms; it is also a heavy metal pollutant. Excessive chromium in farmland soil will not only cause harm to crops, but could also constitute a serious threat to human health through the cumulative effect of the food chain. The determination of heavy metals in tailings of farmland soil is an essential means of soil environmental protection and sustainable development. Hyperspectral remote sensing technology has good characteristics, e.g., high speed, macro, and high resolution, etc., and has gradually become a focus of research to determine heavy metal content in soil. However, due to the spectral variation caused by different environmental conditions, the direct application of the indoor spectrum to conduct field surveys is not effective. Soil components are complex, and the effect of linear regression of heavy metal content is not satisfactory. This study builds indoor and outdoor spectral conversion models to eliminate soil spectral differences caused by environmental conditions. Considering the complex effects of soil composition, we introduce a support vector machine model to retrieve chromium content that has advantages in solving problems such as small samples, non-linearity, and a large number of dimensions. Taking a mining area in Hunan, China as a test area, this study retrieved the chromium content in the soil using 12 combination models of three types of spectra (field spectrum, lab spectrum, and direct standardization (DS) spectrum), two regression methods (stepwise regression and support vector machine regression), and two factors (strong correlation factor and principal component factor). The results show that: (1) As far as the spectral types are concerned, the inversion accuracy of each combination of the field spectrum is generally lower than the accuracy of the corresponding combination of other spectral types, indicating that field environmental interference affects the modeling accuracy. Each combination of DS spectra has higher inversion accuracy than the corresponding combination of field spectra, indicating that DS spectra have a certain effect in eliminating soil spectral differences caused by environmental conditions. (2) The inversion accuracy of each spectrum type of SVR_SC (Support Vector Regression_Strong Correlation) is the highest for the combination of regression method and inversion factor. This indicates the feasibility and superiority of inversion of heavy metals in soil by a support vector machine. However, the inversion accuracy of each spectrum type of SVR_PC (Support Vector Regression_Principal Component) is generally lower than that of other combinations, which indicates that, to obtain superior inversion performance of SVR, the selection of characteristic factors is very important. (3) Through principal component regression analysis, it is found that the pre-processed spectrum is more stable for the inversion of Cr concentration. The regression coefficients of the three types of differential spectra are roughly the same. The five statistically significant characteristic bands are mostly around 384-458 nm, 959-993 nm, 1373-1448 nm, 1970-2014 nm, and 2325-2400 nm. The research results provide a useful reference for the large-scale normalization monitoring of chromium-contaminated soil. They also provide theoretical and technical support for soil environmental protection and sustainable development.
摘要：
A comprehensive mathematical model to simulate a serial composite process for biomass and coal co-gasification has been built. The process is divided into combustion stage and gasification stage in the same gasifier, it is a new process for the co-gasification of biomass and coal. The model is based on reaction kinetic, hydrodynamics, mass and energy balances, it is a one-dimensional, K-L three-phase, unsteady state model. The model is divided into two sub-models, one is the combustion sub-model, the other is the coal-biomass serial gasification sub-model. Combustion sub-model includes coal pyrolysis, dense phase combustion, and dilute phase combustion model. Gasification sub-model includes biomass pyrolysis, dense phase coal gasification, dense phase biomass gasification, and dilute phase gasification model. The model studies the effects of key parameters on gasification properties, including gasification temperature, S/B, B/C, and predicts the composition of product gas and gas calorific value along the reactor's axis at different time. The model predictions agree well with experimental results and can be used to study and optimize the operation of the process.
作者机构：
[Wang, Aihe; Deng, Yumei; Zhang, Chun; Jiang, Haiyan] Hunan City Univ, Sch Municipal & Mapping Engn, Yiyang 413000, Peoples R China.
通讯机构：
[Zhang, Chun] H;Hunan City Univ, Sch Municipal & Mapping Engn, Yiyang 413000, Peoples R China.
摘要：
Antimony pollution resulting from industrial production is a great threat to the environment, ecology and the human body. Zero-valent iron powder is low-cost and easy to obtain. Nevertheless, the lower adsorption capacity limits its application when it is used as an adsorbent. In the present study, ball-milling and acid modification were developed to change its surface characteristics and gamma-Fe2O3, gamma-FeOOH and Fe3O4 were obtained after treatment, which promoted its adsorption capacity. Compared with the raw iron powder, the adsorption capacities for Sb(iii) and Sb(v) using the modified material were increased from 12.93 mg g(-1) and 5.47 mg g(-1) to 17.96 mg g(-1) and 10.58 mg g(-1), respectively. The study showed that the experimental data fitted the Langmuir model and the pseudo-first-order kinetic model better; the adsorption process was monolayer and chemically controlled at pH 5.0 +/- 0.2. XPS and FT-IR analysis showed that Fe-O-Sb bonds arose during the adsorption process. The effect of pH on the adsorption capacity was also studied and the pH affected the adsorption of Sb(v) more than the adsorption of Sb(iii). In addition, the modified iron powder presented better efficiency when applied to the removal of low levels of antimony in drinking water. Based on the increase of adsorption capacity and low cost, iron powder should be a promising adsorbent for aqueous antimony removal.
摘要：
The effects of Phanerochaete chrysosporium on the bioavailability of multiple heavy metals (Pb, Cd, Cu, and Zn) in river sediments were investigated by co-composting with the agricultural waste. The results showed that the Phanerochaete chrysosporium inoculation can greatly enhance the passivation on Cu, Pb and Cd during 60 days co-composting. The effects in the three metals followed the order: Cu>Cd>Pb. There were no differences for Zn whether inoculation with P. chrysosporium or not. Redundancy analysis (RDA) implied that more than 4/5 of the variation of all fractions data for all heavy metals was explained by all significant canonical axes. P. chrysosporium can change the significant parameters for each metal and enhance the explanatory power of RDA model. The inoculation can strengthen the effect of OM (organic matter) on the bioavailability of heavy metals, but weaken the contribution of pH.
摘要：
In view of the low accuracy of traditional adjustment, an improved multiple-group adjustment method in indirect adjustment is proposed in this paper. In the process of improved multiple-group adjustment, the first group is first adjusted, and then the adjustment result of the first group and the observation value of the second group are adjusted together, which makes the results in the first-group adjustment meet the overall adjustment results. Finally, the posteriori unit-weight variance value, the coordinated factor matrix of the adjustment result, and the unknown function weight reciprocal are calculated. The experimental results show that the accuracy of multiple-group adjustment in the indirect grouping adjustment will be more accurate than the traditional indirect adjustment method. Moreover, this work provides important ideas and techniques for handling the goniometric triangular network of control surveys.
期刊：
Transactions of the Indian Institute of Metals,2019年72(10):2591-2597 ISSN：0972-2815
通讯作者：
Zhang, Chun
作者机构：
[Wang, Aihe; Deng, Yumei; Zhang, Chun; Jiang, Haiyan] Hunan City Univ, Sch Municipal & Mapping Engn, Yiyang 413000, Hunan, Peoples R China.
通讯机构：
[Zhang, Chun] H;Hunan City Univ, Sch Municipal & Mapping Engn, Yiyang 413000, Hunan, Peoples R China.
关键词：
Iron recovery;Oxydrolysis;Goethite;Zinc neutral leaching residue;Magnetic seeds
摘要：
The recovery of iron is the key step for zinc recycling by electrolysis in zinc hydrometallurgy industry. Iron exists mainly as ferrites in the zinc neutral leaching residue and is difficult to decompose. In this paper, the reductive leaching process using sulfur dioxide was adopted to replace the traditional hot-acid leaching method, and the magnetic seed-assisted iron precipitation was developed. Under the optimum reductive leaching conditions, the initial concentration of sulfuric acid, the temperature and the partial pressure were determined as: 80 g/L H2SO4, 90 degrees C, 200 kPa at the fixed liquid/solid ratio of 10:1 and the stirring speed of 400 rpm. The most suitable iron precipitation reaction conditions were as follows: the pH: 3.0-3.5; the temperature: 95 degrees C. The recovery of iron mainly depended on the oxydrolysis of the ferrous. The ferrous precipitated as goethite and its crystallinity noticeably decreased with decreasing pH. The addition of the magnetic seeds shortened the reaction time from 4.5 to 3.0 h when the iron precipitation rate reached more than 99%. Simultaneously, the loss rates of zinc and cadmium in the iron process were also reduced from 10.96% and 9.27% to 4.23% and 3.73%, respectively. Besides, the sedimentation and filtration performance were greatly improved after the addition of magnetic seeds. The adsorption and inclusion were the main reasons for better sedimentation and filtration performance and higher metal loss rate.
摘要：
Multifractal detrended fluctuation analysis (MFDFA) method can examine higher-dimensional fractal and multifractal characteristics hidden in time series. However, removal of local trends in MFDFA is based on discontinuous polynomial fitting, resulting in pseudo-fluctuation errors. In this paper, we propose a two-stage modified MFDFA for multifractal analysis. First, an overlap moving window (OMW) algorithm is introduced to divide time series of the classic MFDFA method. Second, detrending by polynomial fitting local trend in traditional MFDFA is replaced by ensemble empirical mode decomposition (EEMD)-based local trends. The modified MFDFA is named OMW-EEMD-MFDFA. Then, the performance of the OMW-EEMD-MFDFA method is assessed by extensive numeric simulation experiments based on a p-model of multiplicative cascading process. The results show that the modified OMW-EEMD-MFDFA method performs better than conventional MFDFA and OMW-MFDFA methods. Lastly, the modified OMW-EEMD-MFDFA method is applied to explore multifractal characteristics and multifractal sources of daily precipitation time series data at the Mapoling and Zhijiang stations in Dongting Lake Basin. Our results showed that the scaling properties of the daily precipitation time series at the two stations presented a long-range correlation, showing a long-term persistence of the previous state. The strong q-dependence of indicated strong multifractal characteristics in daily precipitation time series data at the two stations. Positive values demonstrate that precipitation may have a local increasing trend. Comparing the generalized Hurst exponent and the multifractal strength of the original precipitation time series data with its shuffled and surrogate time series data, we found that the multifractal characteristics of the daily precipitation time series data were caused by both long-range correlations between small and large fluctuations and broad probability density function, but the broad probability density function was dominant. This study may be of practical and scientific importance in regional precipitation forecasting, extreme precipitation regulation, and water resource management in Dongting Lake Basin.
期刊：
International Journal of Environmental Research and Public Health,2018年15(5):1032- ISSN：1661-7827
通讯作者：
Zhang, Qiuwen;Zhang, Gui
作者机构：
[Zhang, Xike; Zhang, Qiuwen] Huazhong Univ Sci & Technol, Sch Hydropower & Informat Engn, Wuhan 430074, Hubei, Peoples R China.;[Zhang, Xike; Nie, Zhiping] Hunan City Univ, Sch Municipal & Mapping Engn, Yiyang 413000, Peoples R China.;[Zhang, Gui; Que, Huafei] Cent South Univ Forestry & Technol, Key Lab Digital Dongting Lake Basin Hunan Prov, Changsha 410004, Hunan, Peoples R China.;[Gui, Zifan] Shenzhen Garden Management Ctr, Shenzhen 518000, Peoples R China.
通讯机构：
[Zhang, Qiuwen] H;[Zhang, Gui] C;Huazhong Univ Sci & Technol, Sch Hydropower & Informat Engn, Wuhan 430074, Hubei, Peoples R China.;Cent South Univ Forestry & Technol, Key Lab Digital Dongting Lake Basin Hunan Prov, Changsha 410004, Hunan, Peoples R China.
关键词：
daily land surface temperature;forecasting;data-driven;hybrid model;Ensemble Empirical Mode Decomposition (EEMD);Long Short-Term Memory (LSTM);Neural Network (NN);Dongting Lake basin
摘要：
Daily land surface temperature (LST) forecasting is of great significance for application in climate-related, agricultural, eco-environmental, or industrial studies. Hybrid data-driven prediction models using Ensemble Empirical Mode Composition (EEMD) coupled with Machine Learning (ML) algorithms are useful for achieving these purposes because they can reduce the difficulty of modeling, require less history data, are easy to develop, and are less complex than physical models. In this article, a computationally simple, less data-intensive, fast and efficient novel hybrid data-driven model called the EEMD Long Short-Term Memory (LSTM) neural network, namely EEMD-LSTM, is proposed to reduce the difficulty of modeling and to improve prediction accuracy. The daily LST data series from the Mapoling and Zhijaing stations in the Dongting Lake basin, central south China, from 1 January 2014 to 31 December 2016 is used as a case study. The EEMD is firstly employed to decompose the original daily LST data series into many Intrinsic Mode Functions (IMFs) and a single residue item. Then, the Partial Autocorrelation Function (PACF) is used to obtain the number of input data sample points for LSTM models. Next, the LSTM models are constructed to predict the decompositions. All the predicted results of the decompositions are aggregated as the final daily LST. Finally, the prediction performance of the hybrid EEMD-LSTM model is assessed in terms of the Mean Square Error (MSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), Pearson Correlation Coefficient (CC) and Nash-Sutcliffe Coefficient of Efficiency (NSCE). To validate the hybrid data-driven model, the hybrid EEMD-LSTM model is compared with the Recurrent Neural Network (RNN), LSTM and Empirical Mode Decomposition (EMD) coupled with RNN, EMD-LSTM and EEMD-RNN models, and their comparison results demonstrate that the hybrid EEMD-LSTM model performs better than the other five models. The scatterplots of the predicted results of the six models versus the original daily LST data series show that the hybrid EEMD-LSTM model is superior to the other five models. It is concluded that the proposed hybrid EEMD-LSTM model in this study is a suitable tool for temperature forecasting.
关键词：
GB-RAR;static clutter;accuracy validation;SQP-GA;resonance frequencies;high-rise building
摘要：
Dynamic vibration characteristics monitoring of high-rise buildings is of great significance for evaluating their safety operation conditions, verifying structural design parameters and updating numerical models. A ground-based real-aperture radar (RAR) has been applied to a high-rise building in Wuhan, China. In the case of RAR measurements, in which several points in the same range bins can add unexpected multiplicity contributions due to spatial resolution varying with distance, the static clutter effect must be removed. However, only a few studies have analyzed it. In this paper, we introduced the least squares fitting circle method to eliminate the static clutter. On this basis, the accuracy of instrument deformation detection is verified by a precise stepping mobile platform in laboratory. Subsequently, we established a sequential quadratic programming-genetic algorithm (SQP-GA) to identify the dynamic vibration characteristics of buildings under natural environment excitation. The SQP-GA method not only accurately identifies the resonance frequencies, but also directly extracts the amplitudes of sine and cosine components of the building vibration signals under the resonance frequencies response compared with the traditional spectrum analysis based on fast Fourier transform.
