Environmental science and pollution research international
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Environ Sci Pollut Res Int · Oct 2017
ReviewA review on the mechanism, risk evaluation, and prevention of coal spontaneous combustion in China.
In recent years, the ecology, security, and sustainable development of modern mines have become the theme of coal mine development worldwide. However, spontaneous combustion of coal under conditions of oxygen supply and automatic exothermic heating during coal mining lead to coalfield fires. Coal spontaneous combustion (CSC) causes huge economic losses and casualties, with the toxic and harmful gases produced during coal combustion not only polluting the working environment, but also causing great damage to the ecological environment. ⋯ Furthermore, the main methods for CSC fire prevention and control and their advantages and disadvantages are analyzed. To eventually construct CSC prevention and control integration system, future developmental direction of CSC was given from five aspects. Our results can present a reference for the development of CSC fire prevention and control technology and promote the protection of ecological environment in China.
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Environ Sci Pollut Res Int · Oct 2017
Research on the influencing factors of reverse logistics carbon footprint under sustainable development.
With the concerns of ecological and circular economy along with sustainable development, reverse logistics has attracted the attention of enterprise. How to achieve sustainable development of reverse logistics has important practical significance of enhancing low carbon competitiveness. In this paper, the system boundary of reverse logistics carbon footprint is presented. ⋯ The quantitative research methodology using ADF test, Johansen co-integration test, and impulse response is utilized to interpret the relationship between reverse logistics carbon footprint and the influencing factors more accurately. This research finds that energy efficiency, energy structure, and product remanufacturing rate are more capable of inhibiting reverse logistics carbon footprint. The statistical approaches will help practitioners in this field to structure their reverse logistics activities and also help academics in developing better decision models to reduce reverse logistics carbon footprint.
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Environ Sci Pollut Res Int · Aug 2017
Ferruginous compounds in the airborne particulate matter of the metropolitan area of Belo Horizonte, Minas Gerais, Brazil.
Samples of soil, iron ore, and airborne particulate matter (size <10 μm) were analyzed with the main goal of investigating the differentiating physicochemical properties of their ferruginous compounds. These data were used to identify whether the sources of airborne particulate matter in the metropolitan area of Belo Horizonte, Minas Gerais, Brazil, are either from natural origin, as, for instance, re-suspension of particles from soil, or due to anthropogenic activities, meaning that it would be originated from the many iron ore minings surrounding the metropolitan area. Numerical simulations were used to model the atmospheric dispersion of the airborne particulate matter emitted by iron mining located at the Iron Quadrangle geodomain, Minas Gerais. ⋯ The structural characteristics of the hematite of these particulate materials were further explored. The direct influence of the iron ore mining on the composition of the airborne particulate matter was clearly evidenced based on the trace ability of hematite to its source of emission. Even the atmospheric air on regions relatively far away from the mining activities is also significantly influenced.
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Environ Sci Pollut Res Int · Jul 2017
Extreme learning machines: a new approach for modeling dissolved oxygen (DO) concentration with and without water quality variables as predictors.
In this paper, several extreme learning machine (ELM) models, including standard extreme learning machine with sigmoid activation function (S-ELM), extreme learning machine with radial basis activation function (R-ELM), online sequential extreme learning machine (OS-ELM), and optimally pruned extreme learning machine (OP-ELM), are newly applied for predicting dissolved oxygen concentration with and without water quality variables as predictors. Firstly, using data from eight United States Geological Survey (USGS) stations located in different rivers basins, USA, the S-ELM, R-ELM, OS-ELM, and OP-ELM were compared against the measured dissolved oxygen (DO) using four water quality variables, water temperature, specific conductance, turbidity, and pH, as predictors. For each station, we used data measured at an hourly time step for a period of 4 years. ⋯ Fourthly, and finally, we compared the results obtained from different ELM models with those obtained using multiple linear regression (MLR) and multilayer perceptron neural network (MLPNN). Results obtained using MLPNN and MLR models reveal that: (i) using water quality variables as predictors, the MLR performed the worst and provided the lowest accuracy in all stations; (ii) MLPNN was ranked in the second place at two stations, in the third place at four stations, and finally, in the fourth place at two stations, (iii) for predicting DO without water quality variables, MLPNN is ranked in the second place at five stations, and ranked in the third, fourth, and fifth places in the remaining three stations, while MLR was ranked in the last place with very low accuracy at all stations. Overall, the results suggest that the ELM is more effective than the MLPNN and MLR for modelling DO concentration in river ecosystems.
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Environ Sci Pollut Res Int · Nov 2016
Concentrations, properties, and health risk of PM2.5 in the Tianjin City subway system.
A campaign was conducted to assess and compare the personal exposure in L3 of Tianjin subway, focusing on PM2.5 levels, chemical compositions, morphology analysis, as well as the health risk of heavy metal in PM2.5. The results indicated that the average concentration of the PM2.5 was 151.43 μg/m3 inside the train of the subway during rush hours. PM2.5 concentrations inside car under the ground are higher than those on the ground, and PM2.5 concentrations on the platform are higher than those inside car. ⋯ For small Fe metal particles, iron oxide can be formed easily. With regard to their sources, Fe-containing particles are generated mainly from mechanical wear and friction processes at the rail-wheel-brake interfaces. The non-carcinogenic risk to metals Cr, Ni, Cu, Zn and Pb, and carcinogenic hazard of Cr and Ni were all below the acceptable level in L3 of Tianjin subway.