Variation and internal-external driving forces of grey water footprint efficiency in China's Yellow River Basin.

Grey water footprint (GWF) efficiency is a reflection of both water pollution and the economy. The assessment of GWF and its efficiency is conducive to improving water environment quality and achieving sustainable development. This study introduces a comprehensive approach to assessing and analyzing the GWF efficiency. Based on the measurement of the GWF efficiency, the kernel density estimation and the Dagum Gini coefficient method are introduced to investigate the spatial and temporal variation of the GWF efficiency. The Geodetector method is also innovatively used to investigate the internal and external driving forces of GWF efficiency, not only revealing the effects of individual factors, but also probing the interaction between different drivers. For demonstrating this assessment approach, nine provinces in China's Yellow River Basin from 2005 to 2020 are chosen for the study. The results show that: (1) the GWF efficiency of the basin increases from 23.92 yuan/m3 in 2005 to 164.87 yuan/m3 in 2020, showing a distribution pattern of "low in the western and high in the eastern". Agricultural GWF is the main contributor to the GWF. (2) The temporal variation of the GWF efficiency shows a rising trend, and the kernel density curve has noticeable left trailing and polarization characteristics. The spatial variation of the GWF efficiency fluctuates upwards, accompanied by a rise in the overall Gini coefficient from 0.25 to 0.28. Inter-regional variation of the GWF efficiency is the primary source of spatial variation, with an average contribution of 73.39%. (3) For internal driving forces, economic development is the main driver of the GWF efficiency, and the interaction of any two internal factors enhances the explanatory power. For external driving forces, capital stock reflects the greatest impact. The interaction combinations with the highest q statistics for upstream, midstream and downstream are capital stock and population density, technological innovation and population density, and industrial structure and population density, respectively.

Dynamics of the water environment in a water quantity-quality-soil model of China's Yellow River basin: imbalance and driving factors.

Dynamic assessment of the water environment reflects variations in water resources in a basin under the combined influence of nature and humans and is a prerequisite for rational water management. This study provides an integrated assessment of the water environment in a water quantity-quality-soil model. Using the long-term monthly data from hydrological monitoring stations, the water environment of the Yellow River basin is assessed from the year 2006 to 2019. The kernel density estimation and the Dagum Gini coefficient are used to analyze the spatial and temporal imbalances of the water environment. Geographic detectors are used to extract external driving factors of the unbalanced evolution. The study results reveal that (1) the water environment in the basin shows a fluctuating downward trend, which mainly depends on the organic pollution control indicators, with a contribution of 22.85%. Scores of the water environment in the midstream are lower than those in the upstream and downstream due to the heavy pollutant discharges. (2) The spatial imbalance shows a fluctuating downward trend. Inter-regional variation is the primary source of regional variation in the water environment, with an average contribution of 56.02%. (3) The temporal imbalance of the water environment is on the rise, with a degree of multipolarity. The significant left trailing feature of the kernel density curve suggests that there are areas within the basin where the water environment is extremely poor. (4) For the overall basin and upstream, economic development and technological innovation are the main external driving factors influencing the spatial and temporal imbalances of the water environment. For the midstream and downstream, population density and environmental regulations are the main drivers. The interaction of any two factors has a greater impact than the single one.

Spatial-temporal evolution and driving factors of grey water footprint efficiency in the Yangtze River Economic Belt.

At present, the deterioration of the water ecosystem has constituted a bottleneck for the further development of the Yangtze River Economic Belt (YREB). As a crucial indicator for evaluating the degree of water pollution, grey water footprint (GWF) is of great significance for rationally evaluating the water environment of the YREB. In this study, we calculated the GWF efficiency of the YREB based on the panel data of 9 provinces and 2 cities from 2005 to 2019. On this basis, spatiotemporal methods and Logarithmic Mean Divisia Index (LMDI) model were adopted to analyze the spatial-temporal evolution characteristics and driving factors of GWF efficiency in the YREB. This study drew the following conclusions: (1) the GWF efficiency in the YREB was on an uptrend, with the average annual growth rates of the upstream, midstream and downstream being 17.35 %, 18.31 % and 17.8 % respectively from 2005 to 2019. (2) The GWF efficiency in the YREB showed a weak trend of polarization and the gap between different regions continued to widen. Besides, it was characterized by stability and owned a positive spatial correlation in both geographic distance and economic distance. (3) The improvement of the technology level, water use efficiency, wastewater treatment capacity, economic development level and the reduction in the industrial pollution intensity contributed positively to boosting the GWF efficiency. Meanwhile, the effect of environmental regulation made a significant negative contribution to GWF efficiency. Therefore, in the process of building the YREB, while emphasizing the coordinated development of the economy, all regions should also carry out joint pollution control.

Ecological network analysis of industrial wastes metabolism based on input-output model for Jiangsu, China.

The enormous discharge of industrial waste seriously hinders the sustainable development of cities. However, most studies only involve a single or limited category of industrial pollutants, ignoring the environmental pressure caused by multiple resources and environmental factors. This paper combines input-output analysis and ecological network analysis to construct an industrial waste metabolic input-output (IWMIO) model, which explores the industrial waste discharge and discharge relationships among different sectors in Jiangsu Province from the three aspects of industrial wastewater, industrial waste gas, and industrial solid waste. The results show that the indirect discharge of industrial waste is greater than the direct discharge in the industrial waste metabolism system. TI (Tertiary industry), CI (Chemical industry), SPM (Smelting and pressing of metals), and PSEH (Production and supply of electricity and heat) dominate the industrial waste metabolism system. In addition, MWC (Mining and washing of coal), MNMP (Manufacture of non-metallic mineral products), SPM (Smelting and pressing of metals) have more mutualism and competition relationships with other sectors, so the control of industrial waste discharge in these sectors contributes to achieving emission reduction targets. Based on the research results, this paper proposes corresponding policy recommendations such as considering both direct and indirect emissions of sectors when formulating waste reduction policies and developing pertinent industrial waste reduction programs based on the characteristics of the identified sectors. The results of this paper are helpful to identify the dependence and influence relationships of various sectors in the industrial waste metabolism system, promote industrial waste discharge control, and provide theoretical support for the adjustment of industrial structure and the formulation of related policies in Jiangsu Province.

Measurement and driving factors of grey water footprint efficiency in Yangtze River Basin.

Water shortages and poor water quality have become an urgent problem that is constraining the sustainable development of China. Grey water has been found to bring greater stress on the water supply than freshwater consumption, and the grey water footprint (GWF) has received significant attention as a comprehensive indicator to assess wastewater pollution. In this study, we analysed the grey water footprint in the Yangtze River Basin from 2003 to 2017 and established a Logarithmic mean divisia index (LMDI) model to decompose the grey water footprint efficiency into six key factors. Our findings are as follows: (1) The average grey water footprint (AGWF) in the central regions was 40% higher than eastern region and 172% higher than western region; (2) Economic effects and capital deepening effects are the main factors affecting positive changes in grey water footprint efficiency; (3) Based on an analysis of the driving factors of greywater footprint efficiency in each province, we conducted a territorial classification according to the primary driving factors in each province. Our results reflect the spatial distribution characteristics of the influencing factors on the grey water footprint effect in the Yangtze River Basin and will enable the government to formulate relevant policies for each subregion.

Which sample type is better for Xpert MTB/RIF to diagnose adult and pediatric pulmonary tuberculosis?

OBJECTIVE: This review aimed to identify proper respiratory-related sample types for adult and pediatric pulmonary tuberculosis (PTB), respectively, by comparing performance of Xpert MTB/RIF when using bronchoalveolar lavage (BAL), induced sputum (IS), expectorated sputum (ES), nasopharyngeal aspirates (NPAs), and gastric aspiration (GA) as sample. METHODS: Articles were searched in Web of Science, PubMed, and Ovid from inception up to 29 June 2020. Pooled sensitivity and specificity were calculated, each with a 95% confidence interval (CI). Quality assessment and heterogeneity evaluation across included studies were performed. RESULTS: A total of 50 articles were included. The respective sensitivity and specificity were 87% (95% CI: 0.84-0.89), 91% (95% CI: 0.90-0.92) and 95% (95% CI: 0.93-0.97) in the adult BAL group; 90% (95% CI: 0.88-0.91), 98% (95% CI: 0.97-0.98) and 97% (95% CI: 0.95-0.99) in the adult ES group; 86% (95% CI: 0.84-0.89) and 97% (95% CI: 0.96-0.98) in the adult IS group. Xpert MTB/RIF showed the sensitivity and specificity of 14% (95% CI: 0.10-0.19) and 99% (95% CI: 0.97-1.00) in the pediatric ES group; 80% (95% CI: 0.72-0.87) and 94% (95% CI: 0.92-0.95) in the pediatric GA group; 67% (95% CI: 0.62-0.72) and 99% (95% CI: 0.98-0.99) in the pediatric IS group; and 54% (95% CI: 0.43-0.64) and 99% (95% CI: 0.97-0.99) in the pediatric NPA group. The heterogeneity across included studies was deemed acceptable. CONCLUSION: Considering diagnostic accuracy, cost and sampling process, ES was a better choice than other sample types for diagnosing adult PTB, especially HIV-associated PTB. GA might be more suitable than other sample types for diagnosing pediatric PTB. The actual choice of sample types should also consider the needs of specific situations.