Polysiloxane-Based Polyurethanes with High Strength and Recyclability.

Polysiloxanes have attracted considerable attention in biomedical engineering, owing to their inherent properties, including good flexibility and biocompatibility. However, their low mechanical strength limits their application scope. In this study, we synthesized a polysiloxane-based polyurethane by chemical copolymerization. A series of thermoplastic polysiloxane-polyurethanes (Si-TPUs) was synthesized using hydroxyl-terminated polydimethylsiloxane containing two carbamate groups at the tail of the polymer chains 4,4'-dicyclohexylmethane diisocyanate (HMDI) and 1,4-butanediol as raw materials. The effects of the hard-segment content and soft-segment number average molecular weight on the properties of the resulting TPUs were investigated. The prepared HMDI-based Si-TPUs exhibited good microphase separation, excellent mechanical properties, and acceptable repeatable processability. The tensile strength of SiTPU-2K-39 reached 21.5 MPa, which is significantly higher than that of other flexible polysiloxane materials. Moreover, the tensile strength and breaking elongation of SiTPU-2K-39 were maintained at 80.9% and 94.6%, respectively, after three cycles of regeneration. The Si-TPUs prepared in this work may potentially be used in gas separation, medical materials, antifouling coatings, and other applications.

Global pattern and drivers of water scarcity research: a combined bibliometric and geographic detector study.

Water scarcity, which refers to a deficit of freshwater resources availability in meeting anthropogenic and environmental water needs, is nowadays a growing concern in many countries around the world. Because water scarcity is often poor management induced, research is critical to advance knowledge and provide technical and policy support for water scarcity adaptation and solutions. Here, we address global water scarcity research pattern and underlying drivers, using the bibliometric analysis combined with geographic detector. The results indicate that water scarcity research exhibits great temporal and spatial variations. Predominant factors that control the numbers of water scarcity publications are gross domestic products (GDP) and population, which altogether explain 30-52% of the variance of the number of publications in different countries. Water scarcity research is biased in a few populated and affluent countries. Other factors, including physical water scarcity, research and development expenditure, and governance indicators can also be linked to water scarcity research. Keywords mining reveals that hotspots of research domains on causes, approaches, types, and effects of water scarcity show continental difference. The results have policy implications for guiding future water scarcity research. Research in developing countries suffering from physical and economic water scarcity should be enhanced to improve adaptive capacity and reduce vulnerability to water scarcity.

PM2.5 mitigation in China: Socioeconomic determinants of concentrations and differential control policies.

Elucidating the key impact factors on PM2.5 concentrations is crucial to formulate effective mitigation policies. In this study, we employed an extended Stochastic Impacts by Regression on Population Affluence and Technology (STIRPAT) model to identify the socioeconomic determinants of PM2.5 concentrations for 12 different regions and across China. The evaluation was based on a balanced panel dataset integrating long-term satellite-derived PM2.5 concentrations and socio-economic data in China from 1999 to 2011. Empirical results indicate that the influencing factors can be ranked in descending order of importance as: proportion of secondary sector of the economy, GDP per capita, urbanization, population, energy intensity, and proportion of tertiary sector. Proportion of secondary sector is the greatest contribution to increasing PM2.5 concentrations, especially for heavily polluted regions. GDP per capita is secondary in importance, and its impact is weakened by the existence of an EKC relationship between GDP per capita and PM2.5 concentrations. Therefore, PM2.5 pollution is an economic development mode problem, rather than a general economic development problem. The impact of urbanization varies across regions; while promoting urbanization will be conducive to decreased PM2.5 concentrations in Northwest China and Northeast China, it will contribute to increased PM2.5 concentrations in other regions. Population and energy intensity are significant in most regions, but neither are decisive factors because of the small absolute value of their coefficients. Finally, different combinations of mitigation policies are proposed for different regions in this study to meet the mitigation targets.

The Effect of Economic Growth, Urbanization, and Industrialization on Fine Particulate Matter (PM2.5) Concentrations in China.

Rapid economic growth, industrialization, and urbanization in China have led to extremely severe air pollution that causes increasing negative effects on human health, visibility, and climate change. However, the influence mechanisms of these anthropogenic factors on fine particulate matter (PM2.5) concentrations are poorly understood. In this study, we combined panel data and econometric methods to investigate the main anthropogenic factors that contribute to increasing PM2.5 concentrations in China at the prefecture level from 1999 to 2011. The results showed that PM2.5 concentrations and three anthropogenic factors were cointegrated. The panel Fully Modified Least Squares and panel Granger causality test results indicated that economic growth, industrialization, and urbanization increased PM2.5 concentrations in the long run. The results implied that if China persists in its current development pattern, economic growth, industrialization and urbanization will inevitably lead to increased PM2.5 emissions in the long term. Industrialization was the principal factor that affected PM2.5 concentrations for the total panel, the industry-oriented panel and the service-oriented panel. PM2.5 concentrations can be reduced at the cost of short-term economic growth and industrialization. However, reducing the urbanization level is not an efficient way to decrease PM2.5 pollutions in the short term. The findings also suggest that a rapid reduction of PM2.5 concentrations relying solely on adjusting these anthropogenic factors is difficult in a short-term for the heavily PM2.5-polluted panel. Moreover, the Chinese government will have to seek much broader policies that favor a decoupling of these coupling relationships.

Unequal prefecture-level water footprints in China: The urban-rural divide.

Water is vital for inclusive human well-being and economic growth, but water and its benefits are not equally distributed to all. The water gap between city dwellers and rural folks was not well understood. In this paper we assessed prefecture-level urban and rural water footprints (WFs) in China, using an improved multi-region input-output (MRIO) table with resolved urban and rural final consumption data. The assessment provided a quantitative foundation for evaluating and explaining urban and rural water use inequality from the consumption perspective. The results showed that per capita urban WF was on average 2.1 times per capita rural WF. The urban-rural WF divide constituted an important contribution to spatial WF inequality, in addition to provincial-level and prefecture-level differences. Compared to previous provincial-level WF analyses, this high-resolution prefecture-level urban and rural analysis showed clear evidence of economically developed urban areas as hotspots of large WFs. Specifically, our results provided a quantitative assessment revealing that 10 % China's population (urban residents in 51 prefectures) appropriated 25.8 % of the national WF. The dominant driving factor for urban-rural per capita WF disparity in all the prefectures was the consumption level, accounting for on average about 84 % of the disparity. There is an urgent need to leverage socio-economic development and urbanization against equitable and sustainable water use. The results have implications to equitable and sustainable water management from a broader macro-economic view.

How many people can the Qinghai-Tibet Plateau hold, and how large cities can be built in recent hundred years?

The Qinghai-Tibet Plateau (QTP) serves as a vital barrier for both national security and ecological preservation. Overpopulation and urban sprawl pose threats to its ecological security, while underpopulation and small urban cities also undermine national security. Hence, optimizing population distribution and urban development on the QTP is crucial for bolstering the national security perimeter and ensuring basic modernisation across China. Nonetheless, understanding the population carrying capacity (CC) of the QTP and how large cities can safeguard both national security and ecological stability remains limited. To address this research gap, we utilised various model algorithms and methodologies to assess the population CC and urban scale of the QTP from seven different perspectives. The results indicate that the permanent population CC of the QTP in 2050 will be 26.2 million people, with an urbanisation level of 57.25 %, thereby allowing 15 million people to enter cities. Thus, the QTP can add 13.07 million people to its permanent population in the future, with a newly added urban population of 8.75 million, increasing the urbanisation level by 9.67 %. The future permanent population will mainly be distributed in the Xining, Lhasa, and Qaidam metropolitan areas. Combined, the permanent and urban populations will account for 38.54 % and 49.84 % of the QTP, respectively. Moreover, these populations will be moderately dispersed in 11 important node cities and more widely dispersed in key border towns. These findings provide a scientific basis for the sustainable development and high-quality urbanisation of the QTP, which have important implications for achieving sustainable development goals, offering crucial references for governments to formulate resource management policies and achieve sustainable resource utilisation.

Input variable selection and calibration data selection for storm water quality regression models.

Storm water quality models are useful tools in storm water management. Interest has been growing in analyzing existing data for developing models for urban storm water quality evaluations. It is important to select appropriate model inputs when many candidate explanatory variables are available. Model calibration and verification are essential steps in any storm water quality modeling. This study investigates input variable selection and calibration data selection in storm water quality regression models. The two selection problems are mutually interacted. A procedure is developed in order to fulfil the two selection tasks in order. The procedure firstly selects model input variables using a cross validation method. An appropriate number of variables are identified as model inputs to ensure that a model is neither overfitted nor underfitted. Based on the model input selection results, calibration data selection is studied. Uncertainty of model performances due to calibration data selection is investigated with a random selection method. An approach using the cluster method is applied in order to enhance model calibration practice based on the principle of selecting representative data for calibration. The comparison between results from the cluster selection method and random selection shows that the former can significantly improve performances of calibrated models. It is found that the information content in calibration data is important in addition to the size of calibration data.

China's enhanced urban wastewater treatment increases greenhouse gas emissions and regional inequality.

Sustainable water pollution control requires understanding of historical trajectories and spatial characteristics of greenhouse gas (GHG) emissions from wastewater treatment plants (WWTPs), which remains inadequately studied. Here, we establish plant-level monthly operational emissions inventories of China's WWTPs in 2009-2019. We show that urban wastewater treatment has been enhanced with 80% more chemical oxygen demand being removed annually. However, this progress is associated with 180% more GHG emissions at the national level, up to 58.3 Mt CO2 eq in 2019. We found significant seasonality in GHG emissions. Increasing sludge yield and electricity intensity became primary drivers after 2015 because of stricter standards, causing GHG emissions increase 12.9 and 8.3% until 2019. GHG emissions from urban wastewater treatment show high spatial difference at province, city and plant levels, with different sludge disposal and energy mix approaches combined with different influent and effluent conditions in WWTPs across China. Stricter effluent standard resulted in similar GHG emissions growth pattern in cities. We argue WWTPs focus on resource recovery in developed areas and higher operational efficiency in developing areas.

Water transfer infrastructure buffers water scarcity risks to supply chains.

Inter-basin water transfer (IBWT) infrastructure has been expanding to deliver water across China to meet water demands in populated and industrial areas. Water scarcity may threaten the ability to produce and distribute goods through supply chains. Yet, it is not clear if IBWTs transmit or buffer water scarcity throughout supply chains. Here we combine a national database of IBWT projects and multi-region input-output analysis to trace water transferred by IBWT and virtual scarce water (scarcity weighted water use) from IBWT sourcing basins to production sites then to end consumers. The results indicate that production and final consumption of sectoral products have been increasingly supported by IBWT infrastructure, with physically transferred water volumes doubling between 2007 and 2017. Virtual scarce water is about half of the virtual water supporting the supply chain of the nation. IBWT effectively reduced virtual scarce water supporting the supply chains of most provinces, with the exposure to water scarcity reduced by a maximum of 56.7% and 15.0% for production and final consumption, respectively. IBWT Infrastructure development can thus buffer water scarcity risk to the supply chain and should be considered in water management and sustainable development policy decisions.

The role of deep learning in urban water management: A critical review.

Deep learning techniques and algorithms are emerging as a disruptive technology with the potential to transform global economies, environments and societies. They have been applied to planning and management problems of urban water systems in general, however, there is lack of a systematic review of the current state of deep learning applications and an examination of potential directions where deep learning can contribute to solving urban water challenges. Here we provide such a review, covering water demand forecasting, leakage and contamination detection, sewer defect assessment, wastewater system state prediction, asset monitoring and urban flooding. We find that the application of deep learning techniques is still at an early stage as most studies used benchmark networks, synthetic data, laboratory or pilot systems to test the performance of deep learning methods with no practical adoption reported. Leakage detection is perhaps at the forefront of receiving practical implementation into day-to-day operation and management of urban water systems, compared with other problems reviewed. Five research challenges, i.e., data privacy, algorithmic development, explainability and trustworthiness, multi-agent systems and digital twins, are identified as key areas to advance the application and implementation of deep learning in urban water management. Future research and application of deep learning systems are expected to drive urban water systems towards high intelligence and autonomy. We hope this review will inspire research and development that can harness the power of deep learning to help achieve sustainable water management and digitalise the water sector across the world.

Public perceptions of physical and virtual water in China.

Ensuring access to water is one of the United Nations' Sustainable Development Goals. Water demand management, which has emerged as an important approach to secure water supply, should be underpinned by a good understanding of how the public perceive their use of water. In this study, we investigated public perceptions of physical and virtual water in China through online surveys using the multi-level regression models (two-level models). Based on 3262 responses, we found that overall, participants underestimated water uses and differences between water uses (daily potable water of an adult, shower, toilet flushing, etc.). Most participants did not possess the knowledge of virtual water embedded in their daily consumed products. Individuals showed rather different perceptions in water use, which were affected by gender, age, education, resource and environmental attitude, water saving behaviors, water price and residential water source. In combination with previous findings in the United States, we concluded that despite different natural water endowment and socio-economic and cultural conditions, underestimation of water use is commonly shared by Chinese and Americans. This highlights a need of strengthening public knowledge of water use. The results are useful in informing policies to enhance the public's awareness of water use towards improved water demand management.

Unprecedented Strength Polysiloxane-Based Polyurethane for 3D Printing and Shape Memory.

Thermoplastic polysiloxane-based polyurethane (Si-TPU) has been attracting a great deal of attention because of the dual advantages of polysiloxane and polyurethane. However, the strength of Si-TPU with a traditional structure is low, and improvement is urgently needed for diverse applications. Herein, we design a polysiloxane-based soft segment (SS) with two urethane groups at the end of the polysiloxane chain, and then we prepare a series of Si-TPUs through a designed SS, isophorone diisocyanate and 1,4-butanediol. Such structural design improves the polarity of the SS and endows more regular hydrogen bonds to the polymer molecular chain. As a result, the prepared Si-TPUs exhibit a good microphase separation structure, unprecedentedly high strength, repeatable processing, noncytotoxicity, shape memory properties, and three-dimensional printing capabilities. Moreover, a maximum tensile strength of Si-TPUs can reach 20.3 MPa, exceeding that of other existing Si-based polymer materials. Si-TPUs show great potential for biomedical applications.

A Comparative Study of Urban Park Preferences in China and The Netherlands.

Urban parks play an important role in tackling several urban challenges such as air pollution, urban heat, physical inactivity, social isolation, and stress. In order to fully seize the benefits of urban parks, it is important that they are attractive for various groups of residents. While several studies have investigated residents' preferences for urban park attributes, most of them have focused on a single geographical context. This study aimed to investigate differences in park preferences, specifically between Dutch and Chinese park users. We collected data in the Netherlands and China using an online stated choice experiment with videos of virtual parks. The data were analyzed with a random parameter mixed logit model to identify differences in preferences for park attributes between Chinese and Dutch citizens, controlling for personal characteristics. Although the results showed a general preference for parks with many trees, several differences were found between the Dutch and Chinese respondents. These differences concerned vegetation (composition of trees and flowers), the presence of benches and play facilities, and could probably be explained by differences in park use, values of nature, and landscape preferences. The findings of this study can be used as design guidelines by urban planners and landscape designers to design attractive and inclusive parks for different target groups.

Short- and Long-Term Recovery after Moderate/Severe AKI in Patients with and without COVID-19.

Background: Severe AKI is strongly associated with poor outcomes in coronavirus disease 2019 (COVID-19), but data on renal recovery are lacking. Methods: We retrospectively analyzed these associations in 3299 hospitalized patients (1338 with COVID-19 and 1961 with acute respiratory illness but who tested negative for COVID-19). Uni- and multivariable analyses were used to study mortality and recovery after Kidney Disease Improving Global Outcomes Stages 2 and 3 AKI (AKI-2/3), and Machine Learning was used to predict AKI and recovery using admission data. Long-term renal function and other outcomes were studied in a subgroup of AKI-2/3 survivors. Results: Among the 172 COVID-19-negative patients with AKI-2/3, 74% had partial and 44% complete renal recovery, whereas 12% died. Among 255 COVID-19 positive patients with AKI-2/3, lower recovery and higher mortality were noted (51% partial renal recovery, 25% complete renal recovery, 24% died). On multivariable analysis, intensive care unit admission and acute respiratory distress syndrome were associated with nonrecovery, and recovery was significantly associated with survival in COVID-19-positive patients. With Machine Learning, we were able to predict recovery from COVID-19-associated AKI-2/3 with an average precision of 0.62, and the strongest predictors of recovery were initial arterial partial pressure of oxygen and carbon dioxide, serum creatinine, potassium, lymphocyte count, and creatine phosphokinase. At 12-month follow-up, among 52 survivors with AKI-2/3, 26% COVID-19-positive and 24% COVID-19-negative patients had incident or progressive CKD. Conclusions: Recovery from COVID-19-associated moderate/severe AKI can be predicted using admission data and is associated with severity of respiratory disease and in-hospital death. The risk of CKD might be similar between COVID-19-positive and -negative patients.

Global impacts of future urban expansion on terrestrial vertebrate diversity.

Rapid urban expansion has profound impacts on global biodiversity through habitat conversion, degradation, fragmentation, and species extinction. However, how future urban expansion will affect global biodiversity needs to be better understood. We contribute to filling this knowledge gap by combining spatially explicit projections of urban expansion under shared socioeconomic pathways (SSPs) with datasets on habitat and terrestrial biodiversity (amphibians, mammals, and birds). Overall, future urban expansion will lead to 11-33 million hectares of natural habitat loss by 2100 under the SSP scenarios and will disproportionately cause large natural habitat fragmentation. The urban expansion within the current key biodiversity priority areas is projected to be higher (e.g., 37-44% higher in the WWF's Global 200) than the global average. Moreover, the urban land conversion will reduce local within-site species richness by 34% and species abundance by 52% per 1 km grid cell, and 7-9 species may be lost per 10 km cell. Our study suggests an urgent need to develop a sustainable urban development pathway to balance urban expansion and biodiversity conservation.

Global assessment of future sectoral water scarcity under adaptive inner-basin water allocation measures.

Water scarcity has become a major threat to sustainable development under climate change. To reduce the population exposure to water scarcity and improve universal access to safe drinking water are important targets of the Sustainable Development Goal (SDG) 6 in the near future. This study aims to examine the potential of applying adaptive inner-basin water allocation measures (AIWAM), which were not explicitly considered in previous studies, for mitigating water scarcity in the future period (2020-2050). By incorporating AIWAM in water scarcity assessment, nonagricultural water uses are assumed to have high priority over agricultural water use and thus would receive more water supply. Results show that global water deficit is projected to be ~3241.9 km3/yr in 2050, and severe water scarcity is mainly found in arid and semi-arid regions, e.g. Western US, Northern China, and the Middle East. Future warming climate and socioeconomic development tend to aggravate global water scarcity, particularly in Northern Africa, Central Asia, and the Middle East. The application of AIWAM could significantly mitigate water scarcity for nonagricultural sectors by leading to a decrease of global population subject to water scarcity by 12% in 2050 when compared to that without AIWAM. However, this is at the cost of reducing water availability for agricultural sector in the upstream areas, resulting in an increase of global irrigated cropland exposed to water scarcity by 6%. Nevertheless, AIWAM provides a useful scenario that helps design strategies for reducing future population exposure to water scarcity, particularly in densely populated basins and regions. Our findings highlight increasing water use competition across sectors between upstream and downstream areas, and the results provide useful information to develop adaptation strategies towards sustainable water management.

Unraveling the effect of inter-basin water transfer on reducing water scarcity and its inequality in China.

Securing water supply in the face of increasing water scarcity is one important challenge faced by humanity in sustainable development. Inter-basin water transfer is widely applied to provide water supply security in regions where water demand exceeds water availability. However, the effect of inter-basin water transfer on alleviating water scarcity and its inequality is poorly understood especially at the national scale. Based on a newly compiled database of inter-basin water transfer projects in China, here we report a first national assessment of their effect on securing water supply in different basins. We developed a number of indices to facilitate quantifying the effect of water transfer on water scarcity and its inequality. The capacity of inter-basin transfer projects has been steadily increased, which achieved ~48.5 billion m3 yr-1 by 2016 (equivalent to ~8% of the national water use). The results indicate that water transfer has impacted water supply of 43 sub-basins out of a total of 76 sub-basins, but it hardly changes a basin's water scarcity level (e.g., from water scarcity to low water scarcity). Approximately three quarters of people in China are affected by water transfer. More than a half of the national population (705 million) benefit from alleviated water scarcity, leading to the inequality coefficient reduced from 0.64 under natural water availability condition to 0.59 considering water transfer in 2016. However, 357 million people in water transfer source basins are subject to increased water scarcity, in which ~21% are from water stressed sub-basins. This study reveals for the first time water transfer induced water scarcity and inequality change across sub-basins in China, and highlights the challenges to secure water supply across basins.

Long-term spatiotemporal variation of drought patterns over the Greater Horn of Africa.

Understanding historical patterns of changes in drought is essential for drought adaptation and mitigation. While the negative impacts of drought in the Greater Horn of Africa (GHA) have attracted increasing attention, a comprehensive and long-term spatiotemporal assessment of drought is still lacking. Here, we provided a comprehensive spatiotemporal drought pattern analysis during the period of 1964-2015 over the GHA. The Standardised Precipitation-Evapotranspiration Index (SPEI) at various timescales (1 month (SPEI-01), 3 month (SPEI-03), 6 month (SPEI-06), and 12 month (SPEI-12)) was used to investigate drought patterns on a monthly, seasonal, and interannual basis. The results showed that despite regional differences, an overall increasing tendency of drought was observed across the GHA over the past 52 yr, with trends of change of -0.0017 yr-1, -0.0036 yr-1, -0.0031 yr-1, and -0.0023 yr-1 for SPEI-01, SPEI-03, SPEI-06, and SPEI-12, respectively. Droughts were more frequent, persistent, and intense in Sudan and Tanzania, while more severe droughts were found in Somalia, Ethiopia, and Kenya. Droughts occurred frequently before the 1990 s, and then became intermittent with large-scale impacts occurred during 1973-1974, 1984-1985, and 2010-2011. A turning point was also detected in 1989, with the SPEI showing a statistically significant downward trend during 1964-1989 and a non-statistically significant downward trend from 1990 to 2015. Seasonally, droughts exhibited an increasing trend in winter, spring, and summer, but a decreasing trend in autumn. The research findings have significant implications for drought adaptation and mitigation strategies through identifying the hotspot regions across the GHA at various timescales. Area-specific efforts are required to alleviate environmental and societal vulnerabilities to drought events.

Detecting Miscoded Diabetes Diagnosis Codes in Electronic Health Records for Quality Improvement: Temporal Deep Learning Approach.

BACKGROUND: Diabetes affects more than 30 million patients across the United States. With such a large disease burden, even a small error in classification can be significant. Currently billing codes, assigned at the time of a medical encounter, are the "gold standard" reflecting the actual diseases present in an individual, and thus in aggregate reflect disease prevalence in the population. These codes are generated by highly trained coders and by health care providers but are not always accurate. OBJECTIVE: This work provides a scalable deep learning methodology to more accurately classify individuals with diabetes across multiple health care systems. METHODS: We leveraged a long short-term memory-dense neural network (LSTM-DNN) model to identify patients with or without diabetes using data from 5 acute care facilities with 187,187 patients and 275,407 encounters, incorporating data elements including laboratory test results, diagnostic/procedure codes, medications, demographic data, and admission information. Furthermore, a blinded physician panel reviewed discordant cases, providing an estimate of the total impact on the population. RESULTS: When predicting the documented diagnosis of diabetes, our model achieved an 84% F1 score, 96% area under the curve-receiver operating characteristic curve, and 91% average precision on a heterogeneous data set from 5 distinct health facilities. However, in 81% of cases where the model disagreed with the documented phenotype, a blinded physician panel agreed with the model. Taken together, this suggests that 4.3% of our studied population have either missing or improper diabetes diagnosis. CONCLUSIONS: This study demonstrates that deep learning methods can improve clinical phenotyping even when patient data are noisy, sparse, and heterogeneous.

Pollution exacerbates China's water scarcity and its regional inequality.

Inadequate water quality can mean that water is unsuitable for a variety of human uses, thus exacerbating freshwater scarcity. Previous large-scale water scarcity assessments mostly focused on the availability of sufficient freshwater quantity for providing supplies, but neglected the quality constraints on water usability. Here we report a comprehensive nationwide water scarcity assessment in China, which explicitly includes quality requirements for human water uses. We highlight the necessity of incorporating water scarcity assessment at multiple temporal and geographic scales. Our results show that inadequate water quality exacerbates China's water scarcity, which is unevenly distributed across the country. North China often suffers water scarcity throughout the year, whereas South China, despite sufficient quantities, experiences seasonal water scarcity due to inadequate quality. Over half of the population are affected by water scarcity, pointing to an urgent need for improving freshwater quantity and quality management to cope with water scarcity.