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Underground mining (as opposed to open‐cast) often causes large‐scale subsidence, leading to various types of disturbances to surface vegetation. Adequate quantitative assessment of the long‐term effects of underground mining on the growth of different plant communities is important and still lacking. To address these issues, a vegetation growth contract model (VGCM) was proposed, and six indicators including the growth trend (GT), annual growth (AG), normalized spectrum entropy (Hsn), as well as the average value of annual‐average normalized difference vegetation index (NDVI; ANDVIave), annual‐maximum NDVI (ANDVImax), and annual‐minimum NDVI (ANDVImin) were selected. The long‐term effects of underground mining (EM) on the herb, shrub, and tree communities in the Nanjiao mining area, China, from 1987 to 2017 were evaluated. The results show that the plant communities, which maintained the same type in the areas influenced and not influenced by mining, accounting for 48.07% and 46% of the total area, respectively. As for these plant communities, underground mining had a significant negative effect on the AG, ANDVIave, and ANDVImax of both the herb and tree communities, while it had a positive effect on the GT and Hsn of the shrub community. Overall, underground mining had a negative effect on these three types of plant communities, and the EMs of the herb, tree, and shrub communities were −15.10, −6.79, and −4.03%, respectively. This research could provide a reference for evaluating the long‐term effects of mining activities on vegetation, and also give more insights into the effects of underground mining on different plant communities. [ABSTRACT FROM AUTHOR]

Visible and near infrared spectroscopy is an effective method for monitoring the content of heavy metals in soil. However, due to the difference between polluted soil with phytoremediation and without phytoremediation, the common estimation model cannot meet accuracy requirements. To solve this problem, combined with an ecological restoration experiment for soil contamination using the plant Neyraudia reynaudiana, this study explored the feasibility of using a hyperspectral technology to estimate the heavy metal content (Cd, Cr, and Pb) of soil under phytoremediation. A total of 108 surface soil samples (from depths of 0–20 cm) were collected. Inversion models were established using partial least squares regression (PLSR) and the back propagation neural network optimized by a mind evolutionary algorithm (MEA-BPNN). The results revealed that: (1) modeling with derivative-transformed spectra can effectively enhance the correlation between soil spectral reflectance and heavy metal content. (2) Compared with the BP neural network model, the estimation accuracy (R2) was improved from 0.728, 0.737, and 0.675 to 0.873, 0.884, and 0.857 using the MEA-BP neural network model. The residual prediction deviation (RPD) values for the three heavy metals Cd, Cr, and Pb using the MEA-BPNN model were 2.114, 3.000, and 2.560, respectively. Among them, the estimated model of Cd was an excellent prediction. (3) Compared with PLSR, the model prediction results established by the MEA-BP neural network had higher estimation accuracy. In summary, the use of diffuse reflectance spectroscopy to predict heavy metal content provides a theoretical basis for further study of the large-scale monitoring of soil heavy-metal pollution and its remediation evaluation in the polluted area, which is of great significance. [ABSTRACT FROM AUTHOR]

The differences in near-surface CO2 concentration variations over five urban land uses within Suzhou City are investigated from January to December 2014. The selected monitoring sites include a traffic road, a residential district with ~20-story apartments, an institutional office site, an amusement park, and a cultural site at the Yuanquan Temple. The diurnal variations of CO2 show considerable fluctuation and different variable patterns at the monitoring sites controlled by the CO2 emissions and weather condition variability. On rainy days, the variation of diurnal CO2 is clearly smaller than that on sunny and cloudy days, with a reduction from 75 % at the amusement site to 41 % at the office-building site. Monthly variations indicate that the traffic and cultural sites experience the highest level of CO2 in warm months, but the other sites are exactly opposite. In each month, the mean level of CO2 at the cultural site is the lowest, but the traffic and amusement sites have the highest levels in warm months and cold months, respectively. Seasonal variations reveal that the traffic, residential, and office-building sites experience a smaller seasonal mean CO2 fluctuation, and in the spring, autumn, and winter, the amusement site has the highest mean levels compared to the other sites. The urban CO2 concentration is related to land use, local CO2 emissions, temperature, and wind; therefore, it is necessary to establish as many monitoring stations as possible to reveal the urban features of CO2 concentration variation. [ABSTRACT FROM AUTHOR]

CO2-EOR (enhanced oil recovery) has been proposed as a viable option for flooding oil and reducing anthropogenic CO2 contribution to the atmospheric pool. However, the potential risk of CO2 leakage from the process poses a threat to the ecological system. High-throughput sequencing was used to investigate the effects of CO2 emission on the composition and structure of soil bacterial communities. The diversity of bacterial communities notably decreased with increasing CO2 flux. The composition of bacterial communities varied along the CO2 flux, with increasing CO2 flux accompanied by increases in the relative abundance of Bacteroidetes and Firmicutes phyla, but decreases in the relative abundance of Acidobacteria and Chloroflexi phyla. Within the Firmicutes phylum, the genus Lactobacillus increased sharply when the CO2 flux was at its highest point. Alpha and beta diversity analysis revealed that differences in bacterial communities were best explained by CO2 flux. The redundancy analysis (RDA) revealed that differences in bacterial communities were best explained by soil pH values which related to CO2 flux. These results could be useful for evaluating the risk of potential CO2 leakages on the ecosystems associated with CO2-EOR processes. [ABSTRACT FROM AUTHOR]

As a significant application of CO2geological storage technology, CO2-enhanced oil recovery (CO2-EOR) considers impacts on ecosystems due to the risk of CO2leakage from oil-producing wells. It is critical to clarify the relationship between CO2leakage and vegetation growth, because large numbers of wells are situated in the midst of cropland. In this study, we calculated the net primary productivity (NPP) of crops to examine the correlation between oil-producing wells and crop growth using Pleiades’ high-spatial resolution remote-sensing imaging in a CO2-EOR experimental area in Shandong Province, China, basing our methods on the improved Carnegie–Ames–Stanford approach (CASA) model. Atmospheric CO2concentrations at varying distances from one of the producing wells were collected by wireless sensors and hand-held equipment, and analysis was conducted in regard to the impact of CO2leakage from producing wells on crop growth according to a simple sampling method. The results indicated that increased CO2concentration resulting from leakage from producing wells has a slightly positive influence on crop productivity, with the influence ranges correlated with wind direction and velocity. The variance of NPP resulting from increased CO2concentration and wind direction ranged from 5.0 to 33.7%. [ABSTRACT FROM PUBLISHER]

Carbon stocks of the farmland ecosystem are rapidly changing by human mining activity, which not only changes the soil fertility and the crop yield, but also has great influence on the global environment. In fact, it is a primary way to maintain the stability of the carbon pool. This paper takes the Jiuli Mining Area, in the city of Xuzhou, as the research area to study the effect of human mining activity on the regional carbon pool of the farmland ecosystem. Firstly, we studied the evaluation thinking: we studied the difference in storage of the carbon pool of the farmland ecosystem which is in the same agriculture zones (the general affecting factors of the carbon pool are almost the same) under two conditions: the carbon pool of the farmland ecosystem is affected by mining, and it is not affected after several decades of change. Secondly, this research studied the effect of human mining activity through sampling and experiments in the farmland area, model construction and carbon storage estimates of the carbon pool of the farmland ecosystem, the carbon density profile, and carbon storage of the farmland ecosystem in the Jiuli Mining Area. The carbon density profile and carbon storage can then be obtained in a scenario simulation where the regional farmland ecosystem is not disturbed by human mining activity until today. Finally, the effect on the regional carbon pool of the farmland ecosystem by the human mining activity can be obtained by careful contrasting the carbon density profile and carbon storage of the two scenarios mentioned above. We find that the Kriging Interpolation method is the most suitable for spatial interpolation of the vegetation carbon density in the mining subsidence area, and the accuracy evaluation of the spatial interpolation R2 is 0.818. Lastly, the conclusion of this paper is that there are 1.3×104 tons of carbon lost in the subsidence area, 0.2 ×104 tons of carbon lost in the seasonal water-logged area, 0.4×104 tons of carbon lost in the perennial water-logged area, and 0.5×104 tons of carbon lost in the subsidence water-logged area. If not affected by mining, the vegetation carbon pool storage of the farmland ecosystem in the subsidence region of the Jiuli Mining Area is 5.8×104 tons, and this figure becomes 3.4×104 tons after mining. So, there is 2.4×104 tons of carbon loss because of human mining activity. Furthermore, on the rising parts of the mining subsidence area, human mining activity has a significant effect on the carbon pool of the farmland ecosystem, and has little effect in the seasonal hydrops areas. In the whole Jiuli Mining Subsidence Area, the carbon effect on the farmland vegetation carbon pool by mining is a carbon loss (carbon source) effect. This research can provide insight into evaluating the effect on the regional carbon pool of the farmland ecosystem by human mining activity. [ABSTRACT FROM AUTHOR]

Land consolidation of dryland-to-paddy conversion for improving tillage conditions and grain production capacity is widely implemented throughout the world. The conversion affects soil ecological stability, especially the most active soil microorganisms. However, the impacts of the dryland-to-paddy conversion has paid little attention in recent decades. In this study, a pot experiment was used to explore the responses of the microbial community and their interactions with soil properties after rice in the first season (five months). The results indicated that a significant decrease in the topsoil pH, organic matter content, nitrate nitrogen, and ammonical nitrogen, and an increase in soil electrical conductivity (EC) was observed (p < 0.05) after the dryland-to-paddy conversion. The richness and diversity of bacteria and fungi decreased in the short term. The composition of the soil microbial community and the soil microbial dominant bacteria had considerably changed after the conversion. Actinobacteria, Firmicutes, and Olpidiomycota were found to be highly sensitive to the dryland-to-paddy conversion. The soil microbial community structure had extremely significant positive correlations with soil pH, EC, organic matter, nitrate nitrogen, and ammonical nitrogen (p < 0.05). Microorganisms are the most important component of soil nutrient cycling. Converting a large area of dryland to paddy may lead to an imbalance in the soil carbonitride cycle and should be further examined in North China. [ABSTRACT FROM AUTHOR]