This study attempts to construct an econometric model using China’s natural disaster losses and macro-industry development data from 1980 to 2017 to explore the macroeconomic fluctuations caused by natural disasters. The structural vector autoregressive (SVAR) and the seemingly unrelated regression (SUR) models are employed in estimating the impact of natural disasters on China’s macroeconomy and how the disasters specifically affect the three sectors of the economy: primary, secondary, and tertiary. This study concludes that even though natural disasters in China do not significantly affect the overall real GDP, they have adverse impacts on the production in the primary industry, causing a sudden reduction in the means of production in the market and directly affecting various industries, but the impact on the secondary and tertiary industries is weak. This study also shows that the effect of natural disasters on the primary sector reduced significantly following industry restructuring after China’s accession to the World Trade Organization (WTO). The impact of natural disasters on the primary industry could be reduced by adjusting the industrial structure to deal with macroeconomic shocks caused by natural disasters in order to promote macroeconomic stability of both regional and national economies. Finally, national aid policy should focus on the primary industry since that sector is significantly affected by natural disasters shocks.

A heterotrophic nitrification-aerobic denitrification (HN-AD) mixed culture was established as a simulated aquatic ecosystem to investigate the simultaneous removal of aniline and ammonium, as well as the succession of bacterial community. Aniline and ammonium were found to be simultaneously transformed, with maximum removal efficiencies of 99.98% and 89.87%, respectively. The presence of aniline at lower concentrations (0 to 100 mg/L) slightly inhibited ammonium oxidation, whereas further increasing the aniline concentration (from 100 to 500 mg/L) yielded an apparent enhancement effect, increasing the ammonium-N removal rate from 0.33 to 1.26 mg/L/day. In contrast, ammonium-N concentrations of 100 mg/L were able to facilitate aniline removal. Ammonium was the preferred nitrogen source for aniline removal when compared with nitrification metabolites such as nitrate or nitrite. The analysis of microbial community succession using high-throughput sequencing revealed that the microbial diversity generally decreased throughout the aniline and ammonium removal process. Proteobacteria is observed to increase at the climax stage, and the average relative abundances of Pseudomonas, Massilia, and Bacillus were highest at the climax stage and were positively related to the removal rate of aniline and ammonium-N. Surprisingly, the abundance of these microbes at the end stage was almost equal to that observed at the initial stage. This study demonstrated that polluted aquatic ecosystems hold potential for simultaneous removal of pollutants (hazardous organic carbon and ammonium), and have excellent self-rehabilitation abilities.

Nonpoint source phosphorus (P) and nitrogen (N) pollution from agriculture is a global concern. Planting a cover crop after harvesting annual crops such as maize may help mitigate nutrient transport risk to surface and groundwater. Few studies have focused on the impact of a winter rye cover crop on both surface runoff (SR) and tile drainage (TD) water quality. Here, we measured N and P losses in SR and TD from maize plots grown with and without a winter rye cover crop. Four plots (46 × 23 m) in northern New York, USA, equipped with automated SR and TD flow monitoring were planted with winter rye (Secale cereal) in 2016 and 2017 after maize silage harvest. Plots were managed as typical silage fields for dairy farms in the region and received fertilizer and manure applications. Dissolved reactive P (DRP), total P (TP), nitrate-N, total N (TN), and total suspended solids (TSS) loads were monitored from 4/7/16 to 6/29/17. Cumulative SR (volumetric depth equivalent) was 1.8-fold lower for rye compared to control plots. Although runoff and loading were variable, cumulative TSS, TP, and DRP losses were approximately 3-fold lower for rye plots compared to control. Cumulative TN and nitrate-N loads for TD were similar; however, cumulative TN loss for SR was lower for rye plots. Surface runoff was the main pathway of P loss (> 90% of DRP and TP loss) with > 90% of cumulative P exported from 2017 snowmelt events. Results suggest winter rye mitigated N and P transport risk in SR compared to the common practice of leaving maize silage fields bare after harvest.

We have modelled the possible antibiotics concentrations at different nodes along the Volga River using a system dynamics model developed for the purpose. The antibiotics concentrations in the river estimated using the model are far above the proposed no effect concentrations (PNEC) limits suggested by the WHO and EU European Environmental Agency at 0.1 μg/l total antibiotics water content. Concentrations in the range of 0.1 to more than 4 μg/l have been simulated with the model. A part of this comes from use in the agricultural sector. The simulations were done with a system dynamics model built for the purpose. The Volga model simulations are uncertain because of lack of measurements in the river and lack of accurate estimates of antibiotics loads from medical and agricultural use. The picture is consistent with observations in earlier international studies from various rivers in the world. To comply with the suggested PNEC limit, the medical pollution to Volga needs to be reduced by 90%.

Vigorously developing high-tech industry has been considered to be an effective way to coordinate economic growth with excessive carbon dioxide (CO₂) emissions. However, previous studies have not explored the heterogeneous impacts of high-tech industry on CO₂ emissions in regions with different levels of high-tech industry development, and not distinguished the direct and indirect impacts. Based on STIRPAT model, this study investigates the impacts of high-tech industry development on CO₂ emissions in China between 2005 and 2016. Adopting the K-medians cluster method, effects in regions with high, middle, and low levels of high-tech industry development are considered. Indirect effects of high-tech industry development on CO₂ emissions by affecting industry structure upgrades and economic growth are explored. Empirical results illustrate a positive U-shaped nonlinear link between the level of high-tech industry development and CO₂ emissions at the national level and regional (high, middle, and low) level. In terms of indirect impacts, high-tech industry development attenuates the reduction of CO₂ emissions due to industry structure upgrades, and promotes economic growth to increase CO₂ emissions slightly. The indirect impact intensity gradually decreases as the level of high-tech industry development decreases across three regions. Reasonable implications of our findings are proposed.

This study aimed to decrease chemical costs and increase productivity and environmental performance by applying various practices for chemical minimization and substitution in an integrated textile mill producing woolen textile fabric. Detailed on-site process investigations and data collection studies were carried out in the mill. Process-based specific auxiliary chemical and dyestuff consumptions were calculated. Process and composite wastewater samples were collected at different periods and analyzed. The chemical loads of wastewaters were also calculated. The specific dyestuff and auxiliary chemical consumptions of the mill were compared with the data of a similar textile mill in the literature and the Integrated Pollution Prevention and Control (IPPC), Textile Best Available Techniques Reference (BREF) document. Thus, the chemical saving potential of the mill was evaluated. A detailed chemical inventory study was also carried out in the mill. The material safety data sheets (MSDSs) of 371 chemicals were examined in terms of biodegradation ratio, toxicity, and micropollutant content. As a result, 23 chemicals were proposed to be replaced with environmentally friendly substitutes. A total of 10 minimization and substitution practices were identified for the mill according to the investigation and analysis results. After the implementation of the suggested practices, reductions of 15–32 and 13–37% are estimated to be achieved in total chemical consumption and chemical oxygen demand (COD) load of wastewater, respectively. The potential payback periods of the suggested practices were calculated to range between 4 and 36 months. The employed methodology and the findings of this study may be useful for similar textile mills, stakeholders, and regulators. This study may also provide a road map to the textile industry for their sustainable and green production applications.

Efficient removal of heavy metals from water and wastewater is a necessity for human and environmental well-being. Agricultural, domestic, and industrial waste discharges increase with the increase in the global population. Discharges are loaded with toxic metallic substances that inevitably reach water sources. Conventional treatment methods are in many cases inadequate in their removal efficiencies. Alternatively, recently developed advanced treatment approaches, such as nanotechnologies, offer advantages in water treatment. Nanotechnology brought about materials with high specific surface areas and adsorption capacities for the removal of undesirable heavy metals present in water. A detailed review of the use of nanotechnologies and nanomaterials for the removal of heavy metals from aqueous solutions is presented in this study. Limitations, research gaps, and suitability of nanotechnology in water treatment for the removal of heavy metals at a large scale are discussed, and relevant conclusions are accordingly deduced.

Cement has been widely used for low- to intermediate-level radioactive waste management; however, the long-term modelling of multiple mineral transfer between the cement leachate and the host rock of a geological disposal facility remains a challenge due to the strong physical-chemical interactions within the chemically disturbed zone. This paper presents a modelling study for a 15-year experiment simulating the reaction of crystalline basement rock with evolved near-field groundwater (pH = 10.8). A mixed kinetic equilibrium (MKE) modelling approach was employed to study the dolomite-rich fracture-filling assemblage reacting with intermediate cement leachate. The study found that the mineralogical and geochemical transformation of the system was driven by the kinetically controlled dissolution of the primary minerals (dolomite, calcite, quartz, k-feldspar and muscovite). The initial high concentration of calcium ions appeared to be the main driving force initiating the dedolomitization process, which played a significant role in the precipitation of secondary talc, brucite and Mg-aluminosilicate minerals. The modelling study also showed that most of the initially precipitated calcium silicon hydrate phases redissolved and formed more stable calcium silicon aluminium hydrate phases. The findings highlight the importance of a deep and insightful understanding of the geochemical transformations based on the type and characteristics of the host rock, where the system is under out of equilibrium conditions, and the rates of mineral reactions.

Concerns on health effects from uranium (U) mining still represent a major issue of debate. Any typology of active job in U mines is associated with exposure to U and its decay products, such as radon (Rn), thorium (Th), and radium (Ra) and its decay products with alpha-emission and gamma radiation. Health effects in U miners have been investigated in several cohort studies in the USA, Canada, Germany, the Czech Republic, and France. While public opinion is particularly addressed to pay attention to the safety of nuclear facilities, health hazard associated with mining is poorly debated. According to the many findings from cohort studies, the most significant positive dose-response relationship was found between occupational U exposure and lung cancer. Other types of tumors associated with occupational U exposure are leukemia and lymphoid cancers. Furthermore, it was found increased but not statistically significant death risk in U miners due to cancers in the liver, stomach, and kidneys. So far, there has not been found a significant association between U exposure and increased cardiovascular mortality in U miners. This review tries to address the current state of the art of these studies.

Urban street dust constitutes important intermediate products for the transmission of solid organic and inorganic pollutants in the urban environment. In this study, 133 street dust samples were collected from Xinyang to explore their magnetic characteristics, spatial distribution, and environmental implications using magnetic measurements. The results are as follows. (1) There were ferrimagnetic, antiferrimagnetic, and paramagnetic (e.g., lepidocrocite) minerals in the dust. Among these, the dominant magnetic carriers were ferrimagnetic minerals. Furthermore, magnetite was a first-order ferrimagnetic carrier. (2) The magnetic domains of the dust were pseudo single-domain to multi-domain. (3) The magnetic concentration (χ and SIRM) of dust were 2.6 and 4.1 times higher than those of background samples that were not polluted by urban and anthropogenic activities, respectively. Therefore, we conclude that the dust consisted of high concentration of ferrimagnetic minerals and coarse magnetic particles. (4) The magnetic distribution was spatially different. The industrial area, which was the most polluted sampling area, had the highest magnetic concentration and the coarsest magnetic particles. This was attributable to industrial emissions, fossil fuel combustion, and exhaust emissions from heavy-laden trucks. Residential and commercial areas, which were the second most polluted areas, had higher concentration and coarser particles. This was primarily due to the high population density and traffic activities of mini-cars (i.e., high flux and exhaust emissions). Hence, the conclusion is that the magnetic characteristics, spatial distribution, and the sources of dust are dictated by anthropogenic activities. Our results indicate that the magnetic method is a highly effective tool to monitor urban environmental pollution.