Forecasting China’s primary energy demand based on an improved AI model

An improved energy demand forecasting model is built based on the autoregressive distributed lag (ARDL) bounds testing approach and an adaptive genetic algorithm (AGA) to obtain credible energy demand forecasting results. The ARDL bounds analysis is first employed to select the appropriate input variables of the energy demand model. After the existence of a cointegration relationship in the model is confirmed, the AGA is then employed to optimize the coefficients of both linear and quadratic forms with gross domestic product, economic structure, urbanization, and technological progress as the input variables. On the basis of historical annual data from 1985 to 2015, the simulation results indicate that the proposed model has greater accuracy and reliability than conventional optimization methods. The predicted results of the proposed model also demonstrate that China will demand approximately 4.9, 5.6, and 6.1 billion standard tons of coal equivalent in 2020, 2025, and 2030, respectively.     

Racemic,R-, and S-tebuconazole altered chitinase and chitobiase activity of Daphnia magna

Tebuconazole is a chiral trizole fungicide and widely used in many crops for controlling disease. Tebuconazole is potential toxic to some aquatic organisms but relative information of its isomers is scarce. To detect the endocrine disrupting effects and difference of rac-, R-, and S-tebuconazole, the chitinase activity in Daphnia magna and chitobiase activity in each test medium were used as biomonitors after a 14-day exposure. Results showed that chitinase activity was significantly reduced by rac-, R-, and S-tebuconazole. The chitobiase activity in the test medium was reduced by rac- and R-tebuconazole before day 10, and only one peak was observed at day 10 or day 12 compared with two obvious peaks in the control group (days 6 and 12). S-tebuconazole delayed and reduced the reproduction of D. magna, but did not delay the first chitobiase activity peak, whereas the second peak could not be characterized as the exposure concentration and time increased. Compared with chitinase activity, chitobiase activity can still be used as a rudimentary model for identifying molt-interfering xenobiotics, and further studies should focus on the analysis of correlations between these parameters.     

Soil surface Hg emission flux in coalfield in Wuda,Inner Mongolia,China

Hg emission flux from various land covers, such as forests, wetlands, and urban areas, have been investigated. China has the largest area of coalfield in the world, but data of Hg flux of coalfields, especially, those with coal fires, are seriously limited. In this study, Hg fluxes of a coalfield were measured using the dynamic flux chamber (DFC) method, coupled with a Lumex multifunctional Hg analyzer RA-915+ (Lumex Ltd., Russia). The results show that the Hg flux in Wuda coalfield ranged from 4 to 318 ng m^?2 h^?1, and the average value for different areas varied, e.g., coal-fire area 99 and 177 ng m^?2 h^?1; no coal-fire area 19 and 32 ng m^?2 h^?1; and backfilling area 53 ng m^?2 h^?1. Hg continued to be emitted from an underground coal seam, even if there were no phenomena, such as vents, cracks, and smog, of coal fire on the soil surface. This phenomenon occurred in all area types, i.e., coal-fire area, no coal-fire area, and backfilling area, which is universal in Wuda coalfield. Considering that many coalfields in northern China are similar to Wuda coalfield, they may be large sources of atmospheric Hg. The correlations of Hg emission flux with influence factors, such as sunlight intensity, soil surface temperature, and atmospheric Hg content, were also investigated for Wuda coalfield.

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Comparisons of three plant species in accumulating polycyclic aromatic hydrocarbons (PAHs) from the atmosphere: a review

Environmental Science and Pollution Research - Plant leaves play a key role in the accumulation of PAHs, as they are able to capture PAHs from the air. In this paper, the mechanism, including...     

The regulatory role of endogenous iron on greenhouse gas emissions under intensive nitrogen fertilization in subtropical soils of China

Environmental Science and Pollution Research - Anaerobic batch experiments were conducted to study the regulatory role of endogenous iron in greenhouse gas emissions under intensive nitrogen...     

Honeybees (<Emphasis Type="Italic">Apis mellifera</Emphasis>) as a biological barrier for contamination of honey by environmental toxic metals

The aim of the study was to investigate the transfer of toxic metals from honeybee workers (Apis mellifera L.) to bee honey in relation to the ecological state of the environment. The materials of the study consisted of samples of honeybee bodies and varietal honeys taken from the same apiary located in three areas: R1—urbanized (16), R2—ecologically clean (16) and R3—industrialized (15) of south-eastern Poland. The contents of 14 elements in all tested samples, including toxic metals (Cd, Pb, Hg, Al, Ni, Tl) as well as bioelements (K, Mg, Ca, Mn, Fe, Zn, Cu, Se), were analysed by the ICP-OES method with prior microwave mineralization. The concentrations of the majority of the studied elements, excluding aluminum and lead, were significantly higher in bee bodies than in honey samples (P?<?0.05). The pollution of bee bodies by toxic metals was dependent on the environmental cleanliness, and the most pollution was observed in the industrialized area. The bee body was the most effective barrier for Cd and Tl transfer to the honey, while the level of Ni was similar in both tested materials. The Al concentration was significantly higher in honey than bee bodies (14.81?±?24.69 and 6.51?±?5.83 mg kg^?1, respectively), which suggests the possibility of secondary contamination of honey. The greatest sensitivity to heavy metal pollution was observed in honeydew honey compared to nectar honeys (P?<?0.05). It was proved for the first time that bees work as biofilters for toxic metals and prevent honey contamination.     

Effects of coal spoil amendment on heavy metal accumulation and physiological aspects of ryegrass (<Emphasis Type="Italic">Lolium perenne</Emphasis> L.) growing in copper mine tailings

Copper mine tailings pose many threats to the surrounding environment and human health, and thus, their remediation is fundamental. Coal spoil is the waste by-product of coal mining and characterized by low levels of metals, high content of organic matter, and many essential microelements. This study was designed to evaluate the role of coal spoil on heavy uptake and physiological responses of Lolium perenne L. grown in copper mine tailings amended with coal spoil at rates of 0, 0.5, 1, 5, 10, and 20%. The results showed that applying coal spoil to copper mine tailings decreased the diethylenetriaminepentaacetic acid (DTPA)-extractable Cd, Cu, Pb, and Zn contents in tailings and reduced those metal contents in both roots and shoots of the plant. However, application of coal spoil increased the DTPA-extractable Cr concentration in tailings and also increased Cr uptake and accumulation by Lolium perenne L. The statistical analysis of physiological parameters indicated that chlorophyll and carotenoid increased at the lower amendments of coal spoil followed by a decrease compared to their respective controls. Protein content was enhanced at all the coal spoil amendments. When treated with coal spoil, the activities of superoxide dismutases (SOD), peroxidase (POD), and catalase (CAT) responded differently. CAT activity was inhibited, but POD activity was increased with increasing amendment ratio of coal spoil. SOD activity increased up to 1% coal spoil followed by a decrease. Overall, the addition of coal spoil decreased the oxidative stress in Lolium perenne L., reflected by the reduction in malondialdehyde (MDA) contents in the plant. It is concluded that coal spoil has the potential to stabilize most metals studied in copper mine tailings and ameliorate the harmful effects in Lolium perenne L. through changing the physiological attributes of the plant grown in copper mine tailings.     

An automated (novel) algorithm for estimating green vegetation cover fraction from digital image: UIP-MGMEP

Green vegetation cover fraction (VCF) is an important indicator of vegetation status in ecology and agronomy. Digital image analysis (DIA) has been widely accepted as a new VCF measurement technique. In this study, we present a novel fully automatic threshold segmentation algorithm for VCF measurements, which is named as upper inflection point plus mean gradient magnitude of edge pixels (UIP-MGMEP). The algorithm performs VCF estimation upon the vegetation index Excess Green (EXG). UIP-MGMEP optimizes the EXG threshold by searching the upper inflection point (UIP) of the M-Et curve (mean gradient magnitude of edge pixels (MGMEP) vs. EXG threshold), based on the assumption that EXG variance of the boundary pixels between vegetation and background is larger than the variance of the background. Five typical sample images are used to illustrate how ground complexity reduces the distinctness of the UIP. Three controlled experiments are illustrated to test the robustness of UIP-MGMEP to resolution, exposure, and ground complexity. The results show that UIP-MGMEP is a promising algorithm for automatic VCF estimation upon digital images. Compared to broad-leaved grass, narrow-leaved grass is more sensitive to resolution and exposure. To reduce ground complexity, smaller footprint size while more images to cover the same area may be better than one image with large footprint size. UIP-MGMEP is fully automatic, making it promising for batch processing of VCF measurements that is very difficult in any wide-range field survey in the past. UIP-MGMEP algorithm can only extract green vegetation and is not suitable for non-green (even grayish-green) vegetation, due to the limits of vegetation index EXG. In addition, UIP-MGMEP is not recommended for images with VCF less than 0.5% or greater than 99.5%.     

Spatial distribution characteristics of soil organic carbon in subtropical forests of mountain Lushan,China

The study on the spatial distribution of forest soil organic carbon (SOC) is of great significance for accurate assessment of carbon storage in forest ecosystems. In the present study, by taking eight kinds of forest soils of Mountain Lushan in the subtropical area as the research object, we studied the spatial distribution characteristics of SOC in this mountainous area. The results showed that the SOC content and SOC density (SOCD) of main forest types in the Mountain Lushan were lower than the national and the world average. The soil layer of Lushan forest was thinner, and the SOC and active SOC (ASOC) contents of different forest types and SOCDs are the highest in the surface soil. SOCD of the topsoil accounts for 32.64–54.03% of the total SOCD in the whole soil profile. Surface litter is an important source of SOC, and the different vegetation types are the important reason for the different spatial distribution of SOC in this area. Soil SOC contents in the high-altitude forest (bamboo forest, deciduous broadleaf forest, Pinus taiwanensis forest, evergreen-deciduous forest, and coniferous-broadleaved mixed forest) were higher than those in the low-altitude forest (evergreen broadleaf forest, shrub, and Pinus massoniana forest). However, the difference in SOC content exhibited at the altitude gradient is significantly lower than that in SOC in the soil profile. This indicates that both soil depth and elevation are the important factors that affected SOC distribution. However, the influence of soil depth on spatial distribution of SOC may be more complex than that of altitude. Vegetation types and soil properties are the main reasons for the large differences of reduction rate in the contents of SOC and ASOC.     

含氟废气对农业环境的影响

叙述了某厂氢氟酸生产过程排出的含氟废气对厂区周围水稻和蔬菜的污染及其与气象条件的关系，并提出了防止农业环境氟污染的措施。