Comparing the variation and influencing factors of CO2 emission from subsidence waterbodies under different restoration modes in coal mining area.

Subsidence waterbodies play an important role in carbon cycle in coal mining area. However, little effort has been made to explore the carbon dioxide (CO2) release characteristics and influencing factors in subsidence waterbodies, especially under different restoration modes. Here, we measured CO2 release fluxes (F(CO2)) across Anguo wetland (AW), louts pond (LP), fishpond (FP), fishery-floating photovoltaic wetland (FFPV), floating photovoltaic wetland (FPV) in coal mining subsidence area, with unrestored subsidence waterbodies (SW) and unaffected normal Dasha river (DR) as the control area. We sampled each waterbody and tested which physical, chemical, and biological characteristics of water and sediment related to variability in CO2. The results indicated that F(CO2) exhibited the following patterns: FFPV > FPV > FP > SW > DR > LP > AW. Trophic lake index (TLI) and microbial biomass carbon content (MBC) in sediment had a positive impact on F(CO2). The dominant archaea Euryarchaeota and Thaumarchaeota, and dominant bacteria Proteobacteria promoted F(CO2). This study can help more accurately quantify CO2 emissions and guide CO2 future emission reduction and subsidence waterbodies estoration.

Assessment of heavy metal pollution and ecological risk in river water and sediments in a historically metal mined watershed, Northeast Japan.

Mining legacies continue to impact the geochemical cycles in historically mined watersheds after mine closure. The Hokuroku District in Northeast Japan is a famous metal mining area with a long mining history; however, studies on the distribution mechanisms and pollution characteristics of heavy metals in these historically mined watersheds after the boom period of mining activities are lacking. This study aims to provide fundamental insights into the effects of the mining activities and hydrological conditions on heavy metal pollution in the Kosaka watershed, Hokuroku District. Sampling was performed in terms of watershed segmentation, and the outlet of the tributary within each sub-watershed was also sampled to capture the diffusional pollution status. The distributions of Zn, Cu, Cd, Pb and As in river water and sediments, as well as their pollution characteristics and ecological risks, were analysed under different hydrological conditions. Our findings provide evidence of the ecological risk in surface water induced by Zn, Cu and Pb pollution in the Kosaka River system. In a high proportion of the sub-watershed, there was moderate to strong enrichment in Cd, Cu and Zn in the river sediments. The sub-watersheds with high pollution levels and ecological risk were highly consistent with the sub-watersheds encompassing abandoned mine sites. Suspended particles carried large amounts of Pb and Cu, especially on rainy days. The heavy metal contents in river water were very sensitive to occasional rainfall events; rainy days posed the most risk to organisms in the Kosaka River, followed by the low-water-level season and the high-water-level season.

A POI and LST Adjusted NTL Urban Index for Urban Built-Up Area Extraction.

Nighttime light (NTL) images have been broadly applied to extract urban built-up areas in recent years. However, the typical NTL images provided by Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) and National Polar-Orbiting Partnership's Visible Infrared Imaging Radiometer Suite (NPP/VIIRS) have the drawbacks of low resolution and blooming effect, which bring difficulty for the application of them in urban built-up area extraction. Therefore, this paper proposes the POI (point of interest) and LST (land surface temperature) adjusted NTL urban index (PLANUI) to extract the urban built-up areas with high accuracy. PLANUI is the first urban index to integrate POI and NTL for urban built-up area extraction. In this paper, NPP/VIIRS and Luojia 1-01 images were introduced as the original NTL data and the vegetation adjusted NTL urban index (VANUI) was selected as the comparison item. The threshold method was utilized to extract urban built-up areas from these data. The results show that: (1) Based on the comparison with the reference data, the PLANUI can make up the shortcoming of low resolution and the blooming effect of NTL effectively. (2) Compared with the VANUI, the PLANUI can significantly improve the accuracy of the urban built-up areas extracted and characterize urban features. (3) According to the results based on NPP/VIIRS and Luojia 1-01 images, the PLANUI has extensive applicability, both for regions with different degrees of economic development and NTL data with different resolutions. PLANUI can enhance the features of urban built-up areas with social sensing data and natural remote sensing data, which helps to weaken the NTL blooming effect and improve the extraction accuracy. PLANUI can provide an effective approach for urban built-up area extraction, which plays a certain guiding role for the study of urban structure, urban expansion, and urban planning and governance.

Feasibility Study on S-Band Microwave Radiation and 3D-Thermal Infrared Imaging Sensor-Aided Recognition of Polymer Materials from End-of-Life Vehicles.

With the increase the worldwide consumption of vehicles, end-of-life vehicles (ELVs) have kept rapidly increasing in the last two decades. Metallic parts and materials of ELVs can be easily reused and recycled, but the automobile shredder residues (ASRs), of which elastomer and plastic materials make up the vast majority, are difficult to recycle. ASRs are classified as hazardous materials in the main industrial countries, and are required to be materially recycled up to 85⁻95% by mass until 2020. However, there is neither sufficient theoretical nor practical experience for sorting ASR polymers. In this research, we provide a novel method by using S-Band microwave irradiation together with 3D scanning as well as infrared thermal imaging sensors for the recognition and sorting of typical plastics and elastomers from the ASR mixture. In this study, an industrial magnetron array with 2.45 GHz irradiation was utilized as the microwave source. Seven kinds of ELV polymer (PVC, ABS, PP, EPDM, NBR, CR, and SBR) crushed scrap residues were tested. After specific power microwave irradiation for a certain time, the tested polymer materials were heated up to different extents corresponding to their respective sensitivities to microwave irradiation. Due to the variations in polymer chemical structure and additive agents, polymers have different sensitivities to microwave radiation, which leads to differences in temperature rises. The differences of temperature increase were obtained by a thermal infrared sensor, and the position and geometrical features of the tested scraps were acquired by a 3D imaging sensor. With this information, the scrap material could be recognized and then sorted. The results showed that this method was effective when the tested polymer materials were heated up to more than 30 °C. For full recognition of the tested polymer scraps, the minimum temperature variations of 5 °C and 10.5 °C for plastics and elastomers were needed, respectively. The sorting efficiency was independent of particle sizes but depended on the power and time of the microwave irradiation. Generally, more than 75% (mass) of the tested polymer materials could be successfully recognized and sorted under an irradiation power of 3 kW. Plastics were much more insensitive to microwave irradiation than elastomers. With this method, the tested mixture of the plastic group (PVC, ABS, PP) and the mixture of elastomer group (EPDM, NBR, CR, and SBR) could be fully separated with an efficiency of 100%.

Study on Impact Acoustic-Visual Sensor-Based Sorting of ELV Plastic Materials.

This paper concentrates on a study of a novel multi-sensor aided method by using acoustic and visual sensors for detection, recognition and separation of End-of Life vehicles' (ELVs) plastic materials, in order to optimize the recycling rate of automotive shredder residues (ASRs). Sensor-based sorting technologies have been utilized for material recycling for the last two decades. One of the problems still remaining results from black and dark dyed plastics which are very difficult to recognize using visual sensors. In this paper a new multi-sensor technology for black plastic recognition and sorting by using impact resonant acoustic emissions (AEs) and laser triangulation scanning was introduced. A pilot sorting system which consists of a 3-dimensional visual sensor and an acoustic sensor was also established; two kinds commonly used vehicle plastics, polypropylene (PP) and acrylonitrile-butadiene-styrene (ABS) and two kinds of modified vehicle plastics, polypropylene/ethylene-propylene-diene-monomer (PP-EPDM) and acrylonitrile-butadiene-styrene/polycarbonate (ABS-PC) were tested. In this study the geometrical features of tested plastic scraps were measured by the visual sensor, and their corresponding impact acoustic emission (AE) signals were acquired by the acoustic sensor. The signal processing and feature extraction of visual data as well as acoustic signals were realized by virtual instruments. Impact acoustic features were recognized by using FFT based power spectral density analysis. The results shows that the characteristics of the tested PP and ABS plastics were totally different, but similar to their respective modified materials. The probability of scrap material recognition rate, i.e., the theoretical sorting efficiency between PP and PP-EPDM, could reach about 50%, and between ABS and ABS-PC it could reach about 75% with diameters ranging from 14 mm to 23 mm, and with exclusion of abnormal impacts, the actual separation rates were 39.2% for PP, 41.4% for PP/EPDM scraps as well as 62.4% for ABS, and 70.8% for ABS/PC scraps. Within the diameter range of 8-13 mm, only 25% of PP and 27% of PP/EPDM scraps, as well as 43% of ABS, and 47% of ABS/PC scraps were finally separated. This research proposes a new approach for sensor-aided automatic recognition and sorting of black plastic materials, it is an effective method for ASR reduction and recycling.

Evaluation of a Cubature Kalman Filtering-Based Phase Unwrapping Method for Differential Interferograms with High Noise in Coal Mining Areas.

Differential interferometric synthetic aperture radar has been shown to be effective for monitoring subsidence in coal mining areas. Phase unwrapping can have a dramatic influence on the monitoring result. In this paper, a filtering-based phase unwrapping algorithm in combination with path-following is introduced to unwrap differential interferograms with high noise in mining areas. It can perform simultaneous noise filtering and phase unwrapping so that the pre-filtering steps can be omitted, thus usually retaining more details and improving the detectable deformation. For the method, the nonlinear measurement model of phase unwrapping is processed using a simplified Cubature Kalman filtering, which is an effective and efficient tool used in many nonlinear fields. Three case studies are designed to evaluate the performance of the method. In Case 1, two tests are designed to evaluate the performance of the method under different factors including the number of multi-looks and path-guiding indexes. The result demonstrates that the unwrapped results are sensitive to the number of multi-looks and that the Fisher Distance is the most suitable path-guiding index for our study. Two case studies are then designed to evaluate the feasibility of the proposed phase unwrapping method based on Cubature Kalman filtering. The results indicate that, compared with the popular Minimum Cost Flow method, the Cubature Kalman filtering-based phase unwrapping can achieve promising results without pre-filtering and is an appropriate method for coal mining areas with high noise.

Provincial and regional analysis of carbon neutrality policy and the environmental Kuznets curve: examining their effect on CO2 emissions in China.

China is making a challenging effort to achieve its ambitious goal of becoming carbon-neutral by 2060. Therefore, the Chinese government should develop effective policy tools to improve its carbon neutrality plan. Due to this importance, an empirical study is required to examine the comprehensive effect of the carbon neutrality policy on CO2 emissions in China. Also, the role of economic development on the environment, the environmental Kuznets curve (EKC) hypothesis in the carbon neutrality-CO2 emissions nexus, has yet to be examined. Considering this gap, the present study investigates the impacts of carbon neutrality policy and the economy on CO2 emissions at China's provincial and regional levels from 2006 to 2017. We use a comprehensive approach to measure the carbon neutrality policy through the source control and sink increase perspectives. This study performs the panel-corrected standard errors (PCSE) and the feasible generalised least squares (FGLS) regression techniques to control the heteroscedasticity and cross-sectional correlation issues. The results show that an improvement in energy efficiency and renewable energy power generation decreases the per capita CO2 emissions at the provincial level from the source control perspective. From the sink increase perspective, only green space development affects CO2 emissions reduction; the development of carbon capture, utilisation, and storage (CCUS) does not. Also, this study shows that the EKC hypothesis holds true in China nationally and in the central region. Due to differing regional development patterns, the impact of carbon neutrality policy on CO2 emissions varies across regions. This study suggests specific policy implications such as increasing R&D investment per unit of GDP to support CCUS technologies at provincial and regional levels.

Stability of Drinking Water Distribution Systems and Control of Disinfection By-Products.

The stability of drinking water distribution systems and the management of disinfection by-products are critical to ensuring public health safety. In this paper, the interrelationships between corrosion products in the network, microbes, and drinking water quality are elucidated. This review also discusses the mechanisms through which corrosive by-products from the piping network influence the decay of disinfectants and the formation of harmful disinfection by-products. Factors such as copper corrosion by-products, CuO, Cu2O, and Cu2+ play a significant role in accelerating disinfectant decay and catalyzing the production of by-products. Biofilms on pipe walls react with residual chlorine, leading to the formation of disinfection by-products (DBPs) that also amplify health risks. Finally, this paper finally highlights the potential of peroxymonosulfate (PMS), an industrial oxidant, as a disinfectant that can reduce DBP formation, while acknowledging the risks associated with its corrosive nature. Overall, the impact of the corrosive by-products of pipe scale and microbial communities on water quality in pipe networks is discussed, and recommendations for removing DBPs are presented.

Effects of Climate Change on Land Cover Change and Vegetation Dynamics in Xinjiang, China.

Since the Silk-road Economic belt initiatives were proposed, Xinjiang has provided a vital strategic link between China and Central Asia and even Eurasia. However, owing to the weak and vulnerable ecosystem in this arid region, even a slight climate change would probably disrupt vegetation dynamics and land cover change. Thus, there is an urgent need to determine the Normalized Difference Vegetation Index (NDVI) and Land-use/Land-cover (LULC) responses to climate change. Here, the extreme-point symmetric mode decomposition (ESMD) method and linear regression method (LRM) were applied to recognize the variation trends of the NDVI, temperature, and precipitation between the growing season and other seasons. Combining the transfer matrix of LULC, the Pearson correlation analysis was utilized to reveal the response of NDVI to climate change and climate extremes. The results showed that: (1) Extreme temperature showed greater variation than extreme precipitation. Both the ESMD and the LRM exhibited an increased volatility trend for the NDVI, with the significant improvement regions mainly located in the margin of basins. (2) Since climate change had a warming trend, the permanent snow has been reduced by 20,436 km2. The NDVI has a higher correlation to precipitation than temperature. Furthermore, the humid trend could provide more suitable conditions for vegetation growth, but the warm trend might prevent vegetation growth. Spatially, the response of the NDVI in North Xinjiang (NXC) was more sensitive to precipitation than that in South Xinjiang (SXC). Seasonally, the NDVI has a greater correlation to precipitation in spring and summer, but the opposite occurs in autumn. (3) The response of the NDVI to extreme precipitation was stronger than the response to extreme temperature. The reduction in diurnal temperature variation was beneficial to vegetation growth. Therefore, continuous concentrated precipitation and higher night-time-temperatures could enhance vegetation growth in Xinjiang. This study could enrich the understanding of the response of land cover change and vegetation dynamics to climate extremes and provide scientific support for eco-environment sustainable management in the arid regions.

Geographic distribution of heavy metals and identification of their sources in soils near large, open-pit coal mines using positive matrix factorization.

Mining activities are considered the most important factor causing heavy metal accumulation in surface soil and it is important to understand the spatial distribution and source of heavy metals in typical steppes. In this study, the contents, spatial distribution, and sources of heavy metals were determined using geostatistical analyses, multivariate statistical analyses, and a positive matrix factorization (PMF) model using 152 soil samples collected from a grassland near the Sheng-Li coal base. The results shows that the mean concentration of heavy metals is low and does not threaten the quality of the local soil. However, the concentrations of eight heavy metals (Cu 15.04 mg kg-1, Zn 49.30 mg kg-1, Cd 0.11 mg kg-1, Pb 20.00 mg kg-1, Se 0.12 mg kg-1, Ge 1.45 mg kg-1, As 9.06 mg kg-1, and Sn 2.52 mg kg-1) are higher than their mean background values in soil in Inner Mongolia. High coefficients of variation for the heavy metals, especially Ge (1.03), and As (0.56), indicate that the concentrations of the elements are affected by the presence of the open-pit mines. Multivariate statistical and geo-statistical analyses show that Ge and As are highly correlated (R2 = 0.67, P < 0.01), suggesting that they have the same source. Using geostatistical and PMF models, we identified five potential pollution sources in the study area: 1) Industrial pollution (21.2 %), which includes smelting activity and open-pit coal mines, as suggested by elevated levels of Zn, Cd, Ge, and Cu; 2) Germanium mining (7.6 %), as indicated by higher levels of Ge and As; 3) Natural sources (37.2 %), as indicated by higher levels of Mn and Ni; 4) Coal mining activity (8.5 %), as indicated by higher levels of Sn and Cr; 5) Coal conveyor belts and high vehicular traffic, as indicated by elevated levels of Pb and Se. Taken together, the results of this study indicate that the coal base has a significant effect on the heavy metal concentration in the grassland. Therefore, the identification of the spatial distribution of heavy metals in the area may be key to controlling the pollution in the grassland. The results of this study can help to reduce pollution sources, cut down on pollution transport. So that zonal pollution control and ecological protection in the typical steppe region is achieved.

Hydrotransport-Oriented Zn, Cu, and Pb Behavior Assessment and Source Identification in the River Network of a Historically Mined Area in the Hokuroku Basin, Northeast Japan.

Aquatic ecosystems continuously receive potentially hazardous heavy metals from natural and anthropogenic sources. Focusing on the origin of heavy metals, this study aims to estimate the load contribution of tributaries from individual watershed and human drainage and to dissect the source of heavy metals, as commonly required for environmental impact assessment. Using integrated water dynamics, Geographic Information System (GIS), and chemical analysis, we identified and evaluated the heavy metal sources of the Kosaka river system in Hokuroku basin, which is a historically mined area in Northeast Japan, both in the high-water and low-water seasons. The migration and diffusion behaviors of heavy metals along with hydro-transport were analyzed, and the effects of mining activities on regional water quality both in the high-water and low-water seasons were clarified. The results indicate that Zn pollution was obvious in the Kosaka River network, especially in the downstream area. The spatial heterogeneity of heavy metal outflows from tributary watersheds was obvious, and the variations had strong correlations with mine site locations. The heavy metal flows in the mainstream increased sharply in the vicinity downstream of the Kosaka refinery drainage outlets. Compared to the low-water season, the influences of human drainage were slighter in high-water season, with lower contribution rates due to the dilution effect of the greater water discharge. Downscale sampling is effective to identify pollutant sources in regional basins.

Assessment of Landscape Ecological Health: A CaseStudy of a Mining City in a Semi-Arid Steppe.

The ecological status of the semi-arid steppes in China is fragile. Under the long-term and high-intensity development of mining, the ecological integrity and biodiversity of steppe landscapes have been destroyed, causing soil pollution, grassland degradation, landscape function defect, and so on. Previous studies have mainly focused on ecosystem health assessment in mining areas. Landscape ecological health (LEH) pays more attention to the interactions between different ecosystems. Therefore, the ecological assessment of mining cities is more suitable on a landscape scale. Meanwhile, the existing LEH assessment index systems are not applicable in ecologically fragile areas with sparse population, underdeveloped economy, and in relatively small research areas. The purpose of this study was to construct a LEH assessment index system and evaluate the LEH of a mining city located in a semi-arid steppe. Xilinhot is a typical semi-arid steppe mining city in China. The contradictions between the human, land and ecological environment are serious. A new model Condition, Vigor, Organization, Resilience, and Ecosystem (CVORE) model was constructed that integrated five subsystems (services) from the perspectives of ecology, landscape ecology, mining science, and geography. This study used the CVORE model to systematically evaluate the LEH in Xilinhot city in terms of five LEH levels, including very healthy, healthy, sub-healthy, unhealthy and morbid landscape. Research results show that the areas of the very healthy, healthy, sub-healthy, unhealthy and morbid landscapes are 13.23, 736.35, 184.5, 66.76 and 20.63 km², respectively. The healthy landscapes area accounts for 72.08% and most grasslands are healthy. The sub-healthy landscapes are mainly located around areas with higher disturbances due to human activities. The morbid or unhealthy landscapes are concentrated in the mining areas. The proposed CVORE model can enrich the foundations for the quantitative assessment of Landscape Ecological Health of Mining Cities in Semi-arid Steppe (LEHMCSS). This study provided a new LEH assessment approach (CVORE model), which can support landscape ecological restoration, ecological environmental protection and urban planning of the semi-arid steppe mining cities.

The challenges of reusing mining and mineral-processing wastes.

Mining and mineral-processing wastes are one of the world's largest chronic waste concerns. Their reuse should be included in future sustainable development plans, but the potential impacts on a number of environmental processes are highly variable and must be thoroughly assessed. The chemical composition and geotechnical properties of the source rock determine which uses are most appropriate and whether reuse is economically feasible. If properly evaluated, mining waste can be reused to reextract minerals, provide additional fuel for power plants, supply construction materials, and repair surface and subsurface land structures altered by mining activities themselves.