• Energy Research

An important component in improving the quality of forests is to study the interference intensity of forest fires, in order to describe the intensity of the forest fire and the vegetation recovery, and to improve the monitoring ability of the dynamic change of the forest. Using a forest fire event in Bilahe, Inner Monglia in 2017 as a case study, this study extracted the burned area based on the BAIS2 index of Sentinel-2 data for 2016–2018. The leaf area index (LAI) and fractional vegetation cover (FVC), which are more suitable for monitoring vegetation dynamic changes of a burned area, were calculated by comparing the biophysical and spectral indices. The results showed that patterns of change of LAI and FVC of various land cover types were similar post-fire. The LAI and FVC of forest and grassland were high during the pre-fire and post-fire years. During the fire year, from the fire month (May) through the next 4 months (September), the order of areas of different fire severity in terms of values of LAI and FVC was: low > moderate > high severity. During the post fire year, LAI and FVC increased rapidly in areas of different fire severity, and the ranking of areas of different fire severity in terms of values LAI and FVC was consistent with the trend observed during the pre-fire year. The results of this study can improve the understanding of the mechanisms involved in post-fire vegetation change. By using quantitative inversion, the health trajectory of the ecosystem can be rapidly determined, and therefore this method can play an irreplaceable role in the realization of sustainable development in the study area. Therefore, it is of great scientific significance to quantitatively retrieve vegetation variables by remote sensing.

Currently, large quantities of municipal solid waste (MSW) in many cities of the developing countries are being dumped in informal or formal but unregulated dumpsites that threaten the ecological environment and general public health. The situation in Dar es Salaam, Tanzania is of particular concern and is further challenged by a rapidly growing population and urbanization without adequate waste management systems. Current MSW treatment options have been selected based on the judgment and the experience of individuals with authority while underestimating the role of scientifically derived techniques. This study analyzes the most efficient waste treatment options, particularly scenarios with the lowest economic and environmental costs (EcC and EnC, respectively). It uses 12 years (2006–2017) of MSW management data and compares potential waste treatment options for the identified waste streams. A total of 108 different scenarios were designed, and a multi-criteria analysis method was applied to enable the identification of 11 scenarios with acceptable EcCs and EnCs. These formed an initial decision matrix of aggregation dominance that was then categorized into four groups, each represented by the most ideal point. Finally, the dominant scenario that formed the core for all considered options was found. It costs around $274,100 USD while saving about 1585 metric tons (MT) of CO2 emissions daily. This suggests that after all the MSW generated in the city is collected and segregated, organic waste should be composted whilst plastic, paper, glass, and ferrous metal should be recycled. After treatment, other waste will go to some form of landfill. Sustainable management of MSW in this city and others with similar conditions should consider particular local conditions and could use the methods and the findings of this study as a starting point.

Residents in industrial cities may be exposed to potentially toxic elements (PTEs) in soil that increase chronic disease risks. In this study, six types of PTEs (Zn, As, Cr, Ni, Cu, and Pb) in 112 surface soil samples from three land use types—industrial land, residential land, and farmland—in Tonghua City, Jilin Province were measured. The geological accumulation index and pollution load index were calculated to assess the pollution level of metal. Meanwhile, the potential ecological risk index, hazard index, and carcinogenic risk were calculated to assess the environmental risks. The spatial distribution map was determined by the ordinary kriging method, and the sources of PTEs were identified by factor analysis and cluster analysis. The average concentrations of Zn, As, Cr, Ni, Cu, and Pb were 266.57, 15.72, 72.41, 15.04, 20.52, and 16.30 mg/kg, respectively. The results of the geological accumulation index demonstrated the following: Zn pollution was present in all three land use types, As pollution in industrial land cannot be neglected, Cr pollution in farmland was higher than that in the other two land use types. The pollution load index decreased in the order of industrial land > farmland > residential land. Multivariate statistical analysis divided the six PTEs into three groups by source: Zn and As both originated from industrial activities; vehicle emissions were the main source of Pb; and Ni and Cu were derived from natural parent materials. Meanwhile, Cr was found to come from a mixture of artificial and natural sources. The soil environment in the study area faced ecological risk from moderate pollution levels mainly contributed by As. PTEs did not pose a non-carcinogenic risk to humans; however, residents of the three land use types all faced estimated carcinogenic risks caused by Cr, and As in industrial land also posed high estimated carcinogenic risk to human health. The conclusion of this article provides corresponding data support to the government’s policy formulation of remediating different types of land and preventing exposure and related environmental risks.

Understanding of the influencing factors of industrial sector carbon dioxide emissions is essential to reduce natural and anthropogenic greenhouse gas emissions. In this paper, we applied the Logarithmic Mean Divisia Index (LMDI) decomposition method based on the extended Kaya identity to analyze the changes in industrial carbon dioxide emissions resulting from 39 industrial sectors in Inner Mongolia northeast of China over the period 2003–2012. The factors were divided into five types of effects i.e., industrial growth effect, industrial structure effect, energy effect, energy intensity effect, population effect and comparative analysis of differential influences of various factors on industrial sector. Our results clearly show that (1) Industrial sector carbon dioxide emissions have increased from 134.00 million ton in 2003 to 513.46 million ton in 2012, with an annual average growth rate of 16.097%. The industrial carbon dioxide emissions intensity has decreased from 0.99 million ton/billion yuan to 0.28 million ton/billion yuan. Also, the energy structure has been dominated by coal; (2) Production and supply of electric power, steam and hot water, coal mining and dressing, smelting and pressing of ferrous metals, petroleum processing, coking and nuclear fuel processing, and raw chemical materials and chemical products account for 89.74% of total increased industrial carbon dioxide emissions; (3) The industrial growth effect and population effect are found to be a critical driving force for increasing industrial sector carbon dioxide emissions over the research period. The energy intensity effect is the crucial drivers of the decrease of carbon dioxide emissions. However, the energy structure effect and industrial structure effect have considerably varied over the study years without displaying any clear trend.

Urban seismic hazards are natural disasters caused by earthquakes in urban areas, which may lead to serious casualties, the collapse of buildings, infrastructure damage, and other impacts, require huge social resources for recovery and reconstruction, and even affect the security stability and sustainable development of the area. This paper adopts the research idea of “Risk = Hazard × Exposure × Vulnerability ÷ Emergency response and recovery capability” and constructs an evaluation system containing 24 representative indicators on this basis. The CF-logistic regression model is applied in the study to calculate the seismic hazard, while the combined weight-TOPSIS model is used to assess the vulnerability of urban hazard-bearing body. Lastly, the study conducts multi-criteria seismic risk evaluation using the GIS platform. The results show that the overall seismic risk in Songyuan is moderate, with 18.66% of the medium-risk area, 37.68% of the very low risk area, 33.96% of the low-risk area, 8.47% of the high-risk area, and 1.23% of the very high-risk area. The significance of this study is to provide a scientific basis for formulating corresponding disaster prevention and mitigation measures and emergency plans, improving urban disaster prevention and emergency response capabilities, reducing urban earthquake disaster losses, and helping to achieve safe and stable urban development.

We report a recycling bioresource involving harvesting of Microcystis aeruginosa using the bioflocculant (MBF-32) produced by Enterobacter aerogenes followed by the recovery of the harvested M. aeruginosa as the main substrate for the sustainable production of MBF-32 and biohydrogen. The experimental results indicate that the efficiency of bioflocculation exceeded 90% under optimal conditions. The harvested M. aeruginosa was further recycled as the main substrate for the supply of necessary elements. The highest yield (3.6±0.1g/L) of MBF-32 could be obtained from 20g/L of wet biomass of M. aeruginosa with an additional 20g/L of glucose as the extra carbon source. The highest yield of biohydrogen was 35mL of H2/g (dw) algal biomass, obtained from 20g/L of wet biomass of M. aeruginosa with an additional 10g/L of glycerol. Transcriptome analyses indicated that MBF-32 was mainly composed of polysaccharide and tyrosine/tryptophan proteins. Furthermore, NADH synthase and polysaccharide export-related genes were found to be up-regulated.

Actinidia arguta (Siebold and Zucc.) Planch.ex Miq, called “hardy kiwifruit”, “baby kiwi” or “kiwi berry”, has a unique taste, is rich in nutrients and has high economic value and broad market prospects. Active research on the potential geographic distribution of A. arguta in China aims to provide a reference basis for its resource investigation, conservation, development and utilization and introduction of cultivation. In this study, the Maxent model was used to combine climatic factors, soil factors and geographical factors (elevation, slope and aspect) to predict the current and future (2041–2060 and 2081–2100) potential distribution of A. arguta and to analyze the impact of climate change on it. The results showed that the suitable distribution range of A. arguta in China was 23–43 N and 100–125 E, with a total area of about 3.4451 × 106 km2. The highly suitable area of A. arguta was mainly concentrated in the middle and low mountain areas of the south of Shaanxi, the east of Sichuan, the middle and west of Guizhou and the west of Yunnan, presenting a circular distribution. The Jackknife test was used to calculate the main environmental factors affecting the distribution of A. arguta. The first four main factors were annual mean temperature (bio_1), precipitation of the warmest quarter (bio_18), elevation (ELE) and mean temperature of the warmest quarter (bio_10), which provided a contribution up to 81.7%. Under the scenarios of three representative concentrations (SSP1_2.6, SSP2_4.5 and SSP5_8.5) in the future, the area of low and moderate suitable habitat decreased, while the area of highly suitable habitat increased. The migration direction of the centroid in the highly suitable habitat moved to the southwest in the future scenario period.