Spatiotemporal drivers of urban water pollution: Assessment of 102 cities across the Yangtze River Basin.

Effective management of large basins necessitates pinpointing the spatial and temporal drivers of primary index exceedances and urban risk factors, offering crucial insights for basin administrators. Yet, comprehensive examinations of multiple pollutants within the Yangtze River Basin remain scarce. Here we introduce a pollution inventory for urban clusters surrounding the Yangtze River Basin, analyzing water quality data from 102 cities during 2018-2019. We assessed the exceedance rates for six pivotal indicators: dissolved oxygen (DO), ammonia nitrogen (NH3-N), chemical oxygen demand (COD), biochemical oxygen demand (BOD), total phosphorus (TP), and the permanganate index (CODMn) for each city. Employing random forest regression and SHapley Additive exPlanations (SHAP) analyses, we identified the spatiotemporal factors influencing these key indicators. Our results highlight agricultural activities as the primary contributors to the exceedance of all six indicators, thus pinpointing them as the leading pollution source in the basin. Additionally, forest coverage, livestock farming, chemical and pharmaceutical sectors, along with meteorological elements like precipitation and temperature, significantly impacted various indicators' exceedances. Furthermore, we delineate five core urban risk components through principal component analysis, which are (1) anthropogenic and industrial activities, (2) agricultural practices and forest extent, (3) climatic variables, (4) livestock rearing, and (5) principal polluting sectors. The cities were subsequently evaluated and categorized based on these risk components, incorporating policy interventions and administrative performance within each region. The comprehensive analysis advocates for a customized strategy in addressing the discerned risk factors, especially for cities presenting elevated risk levels.

Towards the carbon neutrality of sludge treatment and disposal in China: A nationwide analysis based on life cycle assessment and scenario discovery.

Motivated by the carbon neutrality target, strategic planning for a low-carbon transition of sludge treatment and disposal in China is challenging due to the unpredictability of technical, regional, socioeconomic, and political factors affecting greenhouse gas (GHG) emissions. This study combines the use of a Life Cycle Assessment and the Patient Rule Induction Method, accounting for possibilities that could achieve net-zero carbon emissions by exploring multiple plausible future profiles of sludge treatment and disposal. Results show that reducing sludge landfill and increasing anaerobic digestion are effective methods to facilitate GHG reduction. Achieving carbon neutrality is closely linked to developing a cleaner electricity mix. Based on a cascaded scenario analysis considering regional differences for 31 Chinese provinces, results demonstrated a maximum cumulative reduction potential of 371 Mt CO2 equivalents from 2020 to 2050, equal to 59.84% of the business-as-usual scenario. Together with GHG reductions, terrestrial acidification and ecotoxicity as well as freshwater ecotoxicity are synergistically reduced. However, the shifting environmental burden results in freshwater eutrophication, human toxicity, marine ecotoxicity, marine eutrophication, and photochemical oxidant formation. This study presents a novel method for systematically identifying possible future development paths toward carbon neutrality. The findings may support policy designs for achieving target carbon reduction effects for sludge disposal.

SCC-UEFAS, an urban-ecological-feature based assessment system for sponge city construction.

An effective sponge city construction evaluation system plays a crucial role in evaluating sponge city construction schemes. The construction of a sponge city evaluation system still faces challenges related to incomplete index selection and unscientific weight division. Limited studies have focused on the comprehensive assessment of sponge city construction in the early stages. This study constructed a scientific assessment indicator system and a quantitative indicator weight at all levels by literature review and statistical analysis methods from an objective perspective. To demonstrate how to utilize our evaluation methods, three construction schemes randomly generated by MATLAB were evaluated under evaluation states of constant weight and variable weight, respectively. Scheme 3 had the highest score of 0.638 under the constant weight assessment, but it cannot practically be the final construction scheme due to the imbalance between indicators. Compared to the constant weight assessment, a variable weight assessment can effectively balance the states of the evaluation index with changes in the decision variable. Among the three schemes, Scheme 2 is the best choice with a value of 0.0355 under variable weight evaluation due to punishment and incentives in the variable weight method. The concept of "punishing" a disadvantageous indicator and "motivating" an advantageous indicator increases the relative advantages of the indices, ultimately affecting the assessment results of schemes and leading to a more balanced state. This study provides reasonable analysis and decision-making mechanisms to support decision-making and guide the scientific selection of a construction scheme.

Comparative life cycle assessment of biochar-based lignocellulosic biohydrogen production: Sustainability analysis and strategy optimization.

Lignocellulose has been considered a potential feedstock for biohydrogen production. Recently, a novel closed-loop concept of biochar approach was developed for enhanced lignocellulosic biohydrogen production. This study therefore targets to analyze the environmental impacts of the three existing lignocellulosic biohydrogen production processes, and evaluate the environmental performance of applying biochar in each process at this early stage of technological development. The results suggest that biochar dosing shows better environmental performance for all impact categories, especially in the consolidate bioprocessing case. Electricity consumption was found to be the dominant cause of environmental impact over the life cycle, while by-products generation was also found to have an effect on the life-cycle impacts. Future research focuses on the biohydrogen production scale, the electricity generation scheme transition towards renewable and cleaner energetic systems, and recovery the by-products to the maximum extent, that will make lignocellulosic biohydrogen production more environmentally sustainable.

Resource recovery in life cycle assessment of sludge treatment: Contribution, sensitivity, and uncertainty.

This study focused on the resource recovery of sludge treatment by quantifying the environmental contributions, identifying the influential factors, and comparing different scenarios. Life cycle assessment (LCA) of sewage sludge treatment was carried out to estimate the environmental impacts of six scenarios: (1) co-digestion of sludge and food waste; (2) co-gasification of sludge and woody waste; (3) co-incineration of sludge and used oil; (4) landfilling; (5) incineration; and (6) anaerobic digestion combined with incineration. Results demonstrate that the resource recovery had a substantial contribution to the environmental performance of the sludge treatment, while the degree of contribution was largely affected by various treatment scenarios and diverse impact categories. To gain deep insight into the parameters related to resource recovery, sensitivity analysis was performed to investigate the influence of the parameters on the LCA results, including the organic content, conversion efficiency of organic matter to methane, and other energy conversion efficiencies. After integrating the inventory variation of those parameters into the decision process via the Monte Carlo simulation, results indicate that no obviously superior scenario could be identified. Conversely, when parameter uncertainty was not considered, co-gasification of sludge and woody waste exhibited the most preferable environmental performance. Overall, this study demonstrates that considering the parameter uncertainty of resource recovery will contribute to a more transparent evaluation process, but will inevitably increase the complexity of the decision-making process based on LCA results because it is difficult to determine a sludge treatment scenario that decisively outperforms the others.

Economical evaluation of sludge reduction and characterization of effluent organic matter in an alternating aeration activated sludge system combining ozone/ultrasound pretreatment.

An ozone/ultrasound lysis-cryptic growth technology combining a continuous flow anaerobic-anoxic-microaerobic-aerobic (AAMA+O3/US) system was investigated. Techno-economic evaluation and sludge lyses return ratio (r) optimization of this AAMA+O3/US system were systematically and comprehensively discussed. Economic assessment demonstrated that this AAMA+O3/US system with r of 30% (AAMA+O3/US2# system) was more economically feasible that can give a 14.04% saving of costs. In addition to economic benefits, a 55.08% reduction in sludge production, and respective 21.17% and 5.45% increases in TN and TP removal efficiencies were observed in this AAMA+O3/US2# system. Considering the process performances and economic benefits, r of 30% in AAMA+O3/US2# system was recommended. Excitation-emission matrix and Fourier transform infrared spectra analyses also proved that less refractory soluble microbial products were generated from AAMA+O3/US2# system. Improvement in 2,3,5-triphenyltetrazolium chloride electron transport system (TTC-ETS) activity in AAMA+O3/US2# further indicated that a lower sludge lyses return ratio stimulated the microbial activity.

Changes in BMI before and during economic development and subsequent risk of cardiovascular disease and total mortality: a 35-year follow-up study in China.

OBJECTIVE: It is unclear whether changes in BMI during rapid economic development influence subsequent mortality. RESEARCH DESIGN AND METHODS: We analyzed whether BMI in 1976 and 1994 and changes in BMI during 1976-1994 predict cardiovascular disease (CVD) and all-cause mortality in a 35-year follow-up cohort of 1,696 Chinese (1,124 men and 572 women, aged 35-65 years) in Xi'an, China. Participants were categorized as underweight (<18.5 kg/m(2)), normal weight (18.5-24.9 kg/m(2)), and overweight (≥25.0 kg/m(2)). RESULTS: During 51,611 person-years of follow-up, we identified 655 deaths from all causes and 234 from CVD. From 1976 to 1994, the prevalence of overweight rose from 9.2 to 27.8%. With each unit increment in 1976 BMI, multivariate hazard ratios (HRs) (95% CI) were 0.78 (0.72-0.84) for CVD and 0.91 (0.87-0.95) for all-cause mortality. In contrast, corresponding HRs were 1.14 (1.08-1.19) and 1.05 (1.01-1.08) in 1994 BMI. The HRs for each unit increment in BMI change from 1976 to 1994 were 1.35 (1.25-1.41) for CVD and 1.09 (1.05-1.13) for all-cause mortality. Compared with participants with stable normal weight in 1976 and 1994, HRs of all-cause mortality for those who had normal weight in 1976 but became overweight in 1994 and for those who were persistently overweight during 1976-1994 were 1.42 (1.12-1.80) and 1.80 (1.04-3.14), respectively. CONCLUSIONS: Gaining weight with increased BMI at middle age in Chinese during economic development was associated with elevated risks of all-cause and CVD mortality. Higher BMI measured before economic development was associated with lower mortality risk, whereas BMI measured afterward was associated with increased mortality.

Multi-state Markov model in outcome of mild cognitive impairments among community elderly residents in Mainland China.

BACKGROUND: Although knowledge of established risk factors for Alzheimer's disease (AD) can logically contribute to the search for predictors of the progression of cognitive impairment, it has not yet been firmly established where in the cognitive impairment process these risk factors exert their effects and how to predict quantitatively for the progression of mild cognitive impairments (MCI) to AD. This study aimed to determine whether known risk factors increased the risk of progression from MCI to AD and to make prediction based on transition probabilities. METHODS: Based on ten examinations of 600 community-dwelling MCI residents and cognitive assessments to classify individuals into MCI, global impairment, and AD, a multi-state Markov Cox's regression model was used and the hazard ratios with their confidence intervals and transition probabilities were estimated. RESULTS: Multivariate analysis showed that gender, age, and hypertension were statistically significant predictors of transition from MCI to global impairment; age, education, and reading statistically influenced transition from global impairment to MCI; gender, age, hypertension, diabetes, and apolipoprotein E geneε4 status were statistically associated with transition from global impairment to AD. Subjects at MCI were more likely (67%) to remain in that cognitive state at the next cognitive assessment than to transition to cognitive deterioration. For global impairment, probability of remaining in the same state was only 18% and that of forward transition was three times more likely than that of backward transition. CONCLUSIONS: Known risk factors influenced differently for different transitions. Transition from global impairment was more likely to worsen to severe cognitive deterioration than transition from MCI.

[Multi-state model in the evaluation of outcome on mild cognitive impairment to Alzheimer's disease].

OBJECTIVE: The aim of this study was to introduce the multi-state Markov model for the prediction of mild cognitive impairment (MCI) to Alzheimer's disease (AD) and to find out the related factors for AD prevention and early intervention among the elderly. METHODS: MCI, moderate to severe cognitive impairment, and AD were defined as state 1, 2 and 3, respectively. A three-state homogeneous model with discrete states and discrete times from data on six follow-up visits was constructed to explore factors for various progressive stages from MCI to AD. Transition probability and survival curve were made after the model fit assessment. RESULTS: At the level of 0.05, data from the multivariate analysis showed that gender (HR=1.23, 95%CI: 1.12-1.38), age (HR=1.37, 95%CI: 1.07-1.72), hypertension (HR=1.54, 95%CI: 1.31-2.19) were statistically significant for the transition from state 1 to state 2, while age (HR=0.78, 95%CI: 0.69-0.98), education level (HR=1.35, 95%CI: 1.09-1.86) and reading (HR=1.20, 95%CI: 1.01-1.41) were statistically significant for transition from state 2 to state 1, and gender (HR=1.59, 95%CI: 1.33-1.89), age (HR=1.33, 95%CI: 1.02-1.64), hypertension (HR=1.22, 95%CI: 1.11-1.43), diabetes (HR=1.52, 95%CI: 1.12-2.00), ApoEe4 (HR=1.44, 95%CI: 1.09-1.68) were statistically significant for transition from state 2 to state 3. Based on the fitted model, the three-year transition probabilities during each state at average covariate level were estimated. CONCLUSION: To delay the disease progression of MCI, phase by phase prevention measures could be adopted based on the main factors of each stage. Multi-state Markov model could imitate the natural history of disease and showed great advantage in dynamically evaluating the development of chronic diseases with multi-states and multi-factors.

[Multi-state Markov model in expressing the outcome of mild cognitive impairment among community-based elderly residents].

OBJECTIVE: To introduce the Multi-state Markov model in studying the outcome prediction of mild cognitive impairment (MCI). METHODS: Based on the intelligence quotient (IQ) changes that reflecting the trends in cognitive function in the patients under follow-up program, we constructed a four states model and used Multi-state Markov model to analyze the patients. RESULTS: Among 600 MCI patients, there were 174 (29.0%) males and 426 (71.0%) females, with age range of 65-90 years-old (average 69.7 ± 6.6). For univariate analysis, gender, age, education level, marital status, smoking, household income, cerebral hemorrhage, hypertension, high cholesterol, diabetes, LDL-C, SBP and DBP were found to be associated with cognitive function. For multivariate analysis, female, older age, cerebral hemorrhage and higher SBP were shown to be the risk factors for transition from the state of cognitive stability to the state of severe deterioration, and their coefficients were 0.762, 0.366, 0.885, and 0.069, respectively. Age (0.038) could influence the transition from the state of cognitive stability to slight deterioration. Higher education level was shown to be the protective factor for these transitions (-0.219 and -0.297). Transition intensity from the state of cognitive stability to the state of slight and severe deterioration was 1.2 times that of transition to the state of improving. Transition intensity from state of slight deterioration to cognitive stability was 11.4 times that of transition to severe deterioration. CONCLUSION: Multi-state Markov model was an effective tool in dealing with longitudinal data.