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The quality of drinking water is crucial for human health and the sustainable development of societies. The Aksu River Basin, a typical inland river system, has areas where groundwater arsenic levels exceed safe drinking water standards (i.e., arsenic concentrations greater than 10 μg/L). Identifying the causes of high arsenic levels in the basin’s groundwater requires further study. Analyzing the hydrogeochemical composition of the Aksu River basin helps us to understand the spatial distribution of groundwater environments and locate areas with dangerously high arsenic levels. In this research, we collected 196 groundwater samples from along the river. Out of these, 38 samples had arsenic levels above 10 μg/L, which represents 19.4% of the total samples collected. By examining the slope changes in the cumulative frequency curves of major ion ratios and employing geostatistics (specifically, the Kriging interpolation), and taking into account the environmental characteristics of the entire basin, we divided the study area into five sub-regions (Zone I through Zone V). The geostatistical analysis showed a significant spatial variability in groundwater arsenic levels, with a clear spatial correlation. Our findings demonstrate that arsenic concentrations in the Aksu River basin’s groundwater vary widely, with Zones II and III—mainly located in the northeastern part of the basin and in Awat County—being hotspots for high-arsenic water. Factors such as a weak reducing environment, intense evaporation, strong cation exchange, and the low-permeability recharge of surface water contribute to the accumulation of arsenic in the basin’s groundwater. The results of this study are vital for assessing the risk of arsenic contamination in groundwater in similar basins and for identifying critical areas for further investigation and research.

The lockdown and social distancing policies to reduce COVID-19 spread and perceived safety threats of COVID-19 significantly affected people’s travel behavior. Greece has been suffering from the COVID-19 pandemic, and people’s mobility behavior has been greatly affected. This study aims at: (1) exploring the variations in individuals’ trip frequencies by mode and purpose before and after the COVID-19 outbreak; (2) understanding the effects of individual differences (i.e., sociodemographic details) and perceptions towards COVID-19 (i.e., the perceived threats of COVID-19) on people’s mobility behavior changes after the outbreak; (3) underlining the individuals’ perceptions of the COVID-19 threat on the willingness of public transportation usage. Overall, 403 responses were collected in late 2020. A series of random parameter Probit modeling results reveal multiple individual and perception factors affecting the changes in mobility behavior in Greece. The results from structural equation modeling indicate that perceived COVID-19 threats affect the attitudes and subjective norms towards people’s intentions to use public transportation. The results from this study provide valuable insights for transportation authorities to develop effective strategies to manage traffic during the spread of disease for a possible future epidemic.

Accurate health prognostics of lithium-ion battery packs play a crucial role in timely maintenance and avoiding potential safety accidents in energy storage. To rapidly evaluate the health of newly developed battery packs, a method for predicting the future health of the battery pack using the aging data of the battery cells for their entire lifecycles and with the early cycling data of the battery pack is proposed. Firstly, health indicators (HIs) are extracted from the experimental data, and high correlations between the extracted HIs and the capacity are verified by the Pearson correlation analysis method. To predict the future health of the battery pack based on the HIs, degradation models of HIs are constructed by using an exponential function, long short-term memory network, and their weighted fusion. The future HIs of the battery pack are predicted according to the fusion degradation model. Then, based on the Gaussian process regression algorithm and battery pack data, a data-driven model is constructed to predict the health of the battery pack. Finally, the proposed method is validated with a series-connected battery pack with fifteen 100 Ah lithium iron phosphate battery cells. The mean absolute error and root mean square error of the health prediction of the battery pack are 7.17% and 7.81%, respectively, indicating that the proposed method has satisfactory accuracy.

In this work, the impact of exogenous aerobic bacteria mixture (EABM) on municipal solid waste (MSW) is well evaluated in the following aspects: biogas production, leachate analysis, organic waste degradation, EABM population, and the composition of microbial communities. The study was designed and performed as follows: the control bioreactor (R1) was filled up with MSW and the culture medium of EABM and the experimental bioreactor (R2) was filled up with MSW and EABM. The data suggests that the composition of microbial communities (bacterial and methanogenic) in R1 and R2 were similar at day 0, while the addition of EABM in R2 led to a differential abundance of Bacillus cereus, Bacillus subtilis, Staphylococcus saprophyticus, Staphlyoccus xylosus, and Pantoea agglomerans in two bioreactors. The population of exogenous aerobic bacteria in R2 greatly increased during hydrolysis and acidogenesis stages, and subsequently increased the degradation of volatile solid (VS), protein, lipid, and lignin by 59.25%, 25.68%, 60.47%, and 197.62%, respectively, compared to R1. The duration of hydrolysis and acidogenesis in R2 was 33.33% shorter than that in R1. At the end of the study, the accumulative methane yield in R2 (494.4 L) was almost three times more than that in R1 (187.4 L). In addition, the abundance of acetoclasic methanogens increased at acetogenesis and methanogenesis stages in both bioreactors, which indicates that acetoclasic methanogens (especially Methanoseata) could contribute to methane production. This study demonstrates that EABM can accelerate organic waste degradation to promote MSW biodegradation and methane production. Moreover, the operational parameters helped EABM to generate 20.85% more in accumulative methane yield. With a better understanding of how EABM affects MSW and the composition of bacterial community, this study offers a potential practical approach to MSW disposal and cleaner energy generation worldwide.

Walkability has become a research topic of great concern for preserving public health, especially in the era of the COVID-19 outbreak. Today more than ever, urban and transport policies, constrained by social distancing measures and travel restrictions, must be conceptualized and implemented with a particular emphasis on sustainable walkability. Most of the walkability models apply observation and subjective methods to measure walkability, whereas few studies address walkability based on sense perception. To fill this gap, we aim at investigating the perceived neighbourhood walkability (PNW) based on sense perception in a neighbourhood of Brussels. We designed a survey that integrates 22 items grouped into 5 dimensions (cleanness, visual aesthetics, landscape and nature, feeling of pressure, feeling of safety), as well as the socio-demographic attributes of the participants. Using various statistical methods, we show that socio-demographics have almost no effects on perceived neighbourhood walkability. Nonetheless, we found significant differences between groups of different educational backgrounds. Furthermore, using a binomial regression model, we found strong associations between PNW and at least one item from each grouping dimension. Finally, we show that based on a deep neural network for classification, the items have good predictive capabilities (78% of classification accuracy). These findings can help integrate sense perception into objective measurement methods of walkable environments. Additionally, policy recommendations should be targeted based on differences of perception across socio-demographic groups.

This paper examines the environmental Kuznets curve (EKC) theory, augmenting the role of oil resources and energy consumption in carbon dioxide (CO2) emissions using the annual data of 11 African oil-producing countries from 1980 to 2014. We apply advanced panel cointegration and panel autoregressive distributive lag (ARDL) techniques coupled with Granger non-causality analysis to account for cross-sectional dependence and heterogeneity. The results of the augmented mean group (AMG) reveal that oil resources abundance degrades the environmental quality in Angola while abating CO2 emissions in Algeria, Gabon, Morocco, and Nigeria. Contrarily, energy consumption escalates pollution in the Congo Democratic Republic (COD), Côte d’Ivoire (CIV), Gabon, Morocco, and Tunisia. Our findings support the EKC hypothesis only in Cameroon, CIV, and Nigeria while exhibiting a U-shaped curve in Algeria and Morocco. Causality analysis unveils that oil resources Granger cause energy consumption, suggesting the balance between renewable and non-renewable energy sources. The current study has important policy implications for promoting green technology, economic diversification, service sector, and green investments.

In the karst area of southern China, karst water is important for supporting the sustainable production and home living for the local residents. Consequently, it is of significance to fully understand the water cycle, so as to make full use of water resources. In karst areas, epikarst and conduits are developed, participating in the hydrological cycle actively. For conventional lumped hydrologic models, it is difficult to simulate the hydrological cycle accurately. These models neglect to consider the variation of underlying surface and weather change. Meanwhile, for the original distributed hydrological model, the existence of epikarst and underground conduits as well as inadequate data information also make it difficult to achieve accurate simulation. To this end, the framework combining the advantages of lumped model–reservoir model and distributed hydrologic model–Soil and Water Assessment Tool (SWAT) model is established to simulate the water cycle efficiently in a karst area. Xianghualing karst watershed in southern China was selected as the study area and the improved SWAT model was used to simulate the water cycle. Results show that the indicators of ENS and R2 in the calibration and verification periods are both above 0.8, which is evidently improved in comparison with the original model. The improved SWAT model is verified to have better efficiency in describing the hydrological cycle in a typical karst area.

Prestressed high-strength concrete (PHC) pipe piles have been widely used in engineering fields in recent years; however, the influencing factors of their ultimate bearing capacity (UBC) in multilayer soil need to be further studied. In this paper, a static load test (SLT) and numerical analysis are performed to obtain the load transfer and key UBC factors of pipe piles. The results show that the UBC of the test pile is mainly provided by the pile shaft resistance (PSR), but the pile tip resistance (PTR) cannot be ignored. Many factors can change the UBC of pipe piles, but their effects are different. The UBC of the pipe pile is linearly related to the friction coefficient and the outer-to-inner diameter ratio. Changes in the pile length make the UBC increase sharply. Low temperatures can produce freezing stress at the pile–soil interface. The effect of changing the Young modulus of pile tip soil is relatively small.