Although many studies investigate the association between land use and station ridership, few examine their nonlinear and moderating relationships. Using metro smartcard data in Shenzhen, we develop ...a gradient boosting decision trees model to estimate the relative importance of land use variables and their threshold and moderating effects on ridership. We found that station betweenness centrality has the largest predictive power, followed by employment density and commercial floor area ratio (FAR). Results suggest that employment density, commercial FAR, and aggregate residential density should be set at 40,000 jobs/km2, 2, and 77,000 persons/km2, respectively, for maximizing ridership. The moderating effects show that population densification is more effective at terminal stations, whereas the policies intensifying nonresidential use work better at middle stations. These findings help planners prioritize land use strategies, identify effective ranges of land use metrics, and propose land use guidelines adaptive to the network position of stations.
Employing second-generation econometric procedures that consider multiple structural breaks in the series, this article examines Turkey as a case study in the investigation of the moderating role of ...financial development in a conventional environmental Kuznets curve (EKC). The study uses two separate models for this purpose: (1) the main effects model and (2) the interaction effects model. The results of this investigation suggest a long-term equilibrium relationship between financial development and the EKC in Turkey, using both model options. Financial development has been found to moderate the effect of real output on carbon dioxide emissions in the shorter periods negatively, which signifies successful environmental performance and energy management. In comparison, financial development moderates the effect of real output on carbon dioxide emissions in the longer periods positively, and in which this finding again signifies that policies for energy savings and green house targets need to be established to target longer periods and energy management policies at higher levels of economic activity. The present study did not confirm a significant moderating effect of financial development on the impact of energy consumption on carbon dioxide emissions in the case of Turkey.
Addressing context dependence in ecology Catford, Jane A.; Wilson, John R.U.; Pyšek, Petr ...
Trends in ecology & evolution,
February 2022, 2022-02-00, 20220201, Letnik:
37, Številka:
2
Journal Article
Recenzirano
Odprti dostop
Context dependence is widely invoked to explain disparate results in ecology. It arises when the magnitude or sign of a relationship varies due to the conditions under which it is observed. Such ...variation, especially when unexplained, can lead to spurious or seemingly contradictory conclusions, which can limit understanding and our ability to transfer findings across studies, space, and time. Using examples from biological invasions, we identify two types of context dependence resulting from four sources: mechanistic context dependence arises from interaction effects; and apparent context dependence can arise from the presence of confounding factors, problems of statistical inference, and methodological differences among studies. Addressing context dependence is a critical challenge in ecology, essential for increased understanding and prediction.
‘Context dependence’ is widely used to describe disparate results in ecology, but the term is poorly defined and inconsistently used.Context dependence arises when ecological relationships vary in magnitude or sign, depending on the conditions under which they are observed.Context dependence can result from multiple factors and processes, so, unless the underlying causes are identified, concluding that relationships are context dependent provides limited understanding.We distinguish between apparent and mechanistic context dependence, with the former an artefact of study design and approach and the latter reflecting ecological interaction effects.Recognising and addressing the different sources of context dependence should facilitate increased understanding, prediction, and generalisation in ecology.
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•Co-pyrolysis behaviors of durian shell and textile dyeing sludge were characterized.•(Co-)pyrolysis in CO2 atmosphere promoted CO and CH4 emissions.•75% durian shell and 25% textile ...dyeing sludge had the most positive interaction.•Co-pyrolysis enriched the productions of furan and acid compounds.•(Co-)pyrolysis in CO2 atmosphere reduced NOx release.
Textile dyeing sludge (TDS) is a highly toxic solid waste whose co-pyrolysis can jointly achieve waste reduction and recovery of value-added products. This study aimed to fill the knowledge gaps about the co-pyrolysis mechanisms and products (gases and solids) and their dynamics in response to the atmosphere type, blend ratio, heating rate, temperature, and their interactions. The high temperature pyrolysis (>720 °C) in the CO2 atmosphere appeared as the best option for the waste reduction. The (co-)pyrolysis in the CO2 atmosphere enhanced S-containing air pollutants, CO, and CH4 but reduced NOx. The interaction effect between TDS and durian shell (DS) residues promoted the productions of furan and acid compounds and inhibited the productions of aromatic, phenolic, and N-containing compounds. The atmosphere type affected the type and strength of the reactions involved in the production of biochars. Our findings provide practical and new insights into the optimization of energy generation, product recovery, and emission control during the (co-)pyrolysis.
A gaseous mixture of methanol (M), α-pinene (P), and hydrogen sulfide (H) was treated in one/two-liquid phase biotrickling filters (OLP/TLP-BTFs) at varying inlet concentrations and at an empty bed ...residence time (EBRT) of 57 s. The performance of TLP-BTF BTF (A) improved significantly in terms of M and P removal due to the presence of silicone oil at 5% v/v. The maximum elimination capacities (ECs) of M, P, and H in BTF (A) were obtained as 309, 73, and 56 g m−3 h−1, respectively. While, the maximum ECs achieved in the BTF operated without silicone oil BTF (B) were 172, 28, and 21 g m−3 h−1 for M, P, and H removal, respectively. Increasing the inlet concentration of H from 32 to 337 ppm inhibited P removal in both the BTFs. The presence of silicone oil enhanced gas-liquid mass transfer, prevented the BTF from experiencing substrate inhibition effects and allowed reaching high ECs for M and P. The experiments showed promising results for the long-term operation of removal of M, P, and H mixture in a one-stage TLP-BTF with the decreasing negative effects of M and H on P degradation.
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•Two-liquid phase BTF (TLP-BTF) worked for long-term removal of mixture of compounds.•Presence of 5% v/v silicone oil overcame mass transfer limitations for α-pinene (P).•Inhibition of methanol concentration (M) for microbial species decreased in TLP-BTF.•Negative effect of H2S concentration on M and P degradation decreased in TLP-BTF.•H2S abatement was not significantly affected by the presence of oil.
The replication crisis has seen increased focus on best practice techniques to improve the reliability of scientific findings. What remains elusive to many researchers and is frequently misunderstood ...is that predictions involving interactions dramatically affect the calculation of statistical power. Using recent papers published in Personality and Social Psychology Bulletin (PSPB), we illustrate the pitfalls of improper power estimations in studies where attenuated interactions are predicted. Our investigation shows why even a programmatic series of six studies employing 2 × 2 designs, with samples exceeding N = 500, can be woefully underpowered to detect genuine effects. We also highlight the importance of accounting for error-prone measures when estimating effect sizes and calculating power, explaining why even positive results can mislead when power is low. We then provide five guidelines for researchers to avoid these pitfalls, including cautioning against the heuristic that a series of underpowered studies approximates the credibility of one well-powered study.
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•SUHI in different time and different global climate zones have significant differences.•The top three most important drivers of SUHI for daytime are PREC, POP, and PRES.•The top ...three most important drivers of SUHI for nighttime are PREC, POP, and AHE.•Natural-anthropogenic drivers interactively affect SUHI in multiple urban grids.•Greening is more effective to mitigate SUHI in arid climate zones when EVI > 0.38.
The inconstant climate change and rapid urbanization substantially disturb the global thermal balance and induce severe urban heat island (UHI) effect, adversely impacting human development and health. Existing literature has revealed the UHI characteristics and driving factors at an urban scale, but interactions between the main factors of a global grid scale assessment on the context of climate zones remain unclear. Therefore, based on the multidimensional climatic and socio-economic statistical datasets, the multi-time scale of surface urban heat island intensity (SUHI) characteristics was investigated in this study to analyze how natural-anthropogenic drivers affect the variance of SUHI and vary in their importance for the changes of other interaction factors. The results show that the mean value of SUHI in summer is higher than in winter, and in daytime is higher than in nighttime on a seasonal and daily scale. SUHIs in different global climate zones have significant differences. When analyzing drivers’ contributions and interactions with LightGBM model and SHAP algorithm, we know that monthly precipitation (PREC), the estimated population (POP) and surface pressure (PRES) are the three major drivers of daytime SUHI. The nighttime SUHI is mainly PREC, POP and anthropogenic heat emission (AHE), the influence rules of the natural driversare mostly opposite to that of daytime. This study highlights the fundamental role of background climate for designing strategies. Irrigation or artificial rainfall will be effective to mitigate SUHI in low rainfall areas, while it is more effective to reduce AHE in high rainfall areas. In where greening can be difficult in the most developed cities, reducing AHE, increasing per capita GDP and controlling the population scale may also contribute to alleviating the SUHI. This study provides ideas for developing responsive urban heat island mitigation policies in a more realistic setting.
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•Pyrolytic performance of microalgae residue (CSR) and polymeric waste (PS/WT) were quantified.•The addition of PS/WT improved the pyrolysis performance of CSR.•The average activation ...energy decreased (124.57 kJ/mol) with the addition of WT.•Temperature-dependent behavior of evolved gaseous products were evaluated.•Fuels interaction promoted hydrocarbons and impeded O- and N- containing compounds.
Biomass and polymeric wastes are becoming more problematic economically and environmentally. Their co-pyrolysis could be a cost-effective and environmentally preferable alternative for reducing waste quantities and gas emissions, as well as recovering valuable oils, chars, and gases. This study aimed to dynamically characterize the co-pyrolysis of hydrothermally treated Chlorella sorokiniana residue (CSR) and waste polymers (polystyrene (PS) and waste tires (WT)) based on their pyrolytic behaviors, kinetics, interaction effects, bio-chars, bio-oils, and in-situ evolved gasses characteristics. The devolatilization of CSR mainly occurred between 150 and 500 °C, while the decomposition of WT and PS appeared between 200–550 °C and 300–550 °C, respectively. The average activation energy was 179.25, 222.82, and 223.12 kJ/mol for the CSR, WT, and PS devolatilization, respectively. The TG-FTIR-detected functional groups included CH (stretching vibration), CH (in-plane & out-of-plane bending vibration), CC, CO2, OH, CO, COOH, CO, and NH3. The TG-MS-detected pyrolytic products were CHO+, CH3O+, C2H3O+, C3H3O+ C3H6O+, C4H3O+, C5H6O+, COOH+, NH3+, aliphatic, and aromatic hydrocarbons. Moreover, the pyrolytic oil was found to be more stable, with significantly fewer N- and O-containing compounds. It seemed that the inclusion of PS and WT in CSR pyrolysis reduce the additional fuel processing requirements to transform liquid yields into useful fuel. This research findings provide theoretical and practical insights into the control and application potential as well as the limitations of the high-value energy and products derived from the co-pyrolysis of CSR, PS, and WT.
High-dimensional accelerated failure time (AFT) models are commonly used regression models in survival analysis. Feature selection problem in high-dimensional AFT models is addressed, considering ...scenarios involving solely main effects or encompassing both main and interaction effects. A rank-based sequential feature selection (RankSFS) method is proposed, the selection consistency is established and illustrated by comparing it with existing methods through extensive numerical simulations. The results show that RankSFS achieves a higher Positive Discovery Rate (PDR) and lower False Discovery Rate (FDR). Additionally, RankSFS is applied to analyze the data on Breast Cancer Relapse. With a remarkable short computational time, RankSFS successfully identifies two crucial genes.
An objective and accurate forecast of carbon emissions can provide the government with an important baseline for the implementation of the Green Economic Development Strategy. This paper considers ...the time-lag effect and the interaction effect of the influencing factors on carbon emissions simultaneously and establishes a new grey multivariate coupled model (CTGM(1,N)) for carbon emission projection by introducing the Choquet fuzzy integral and grey multivariate delay model. To further promote the prediction performance, the time-lag number of each influencing factor is determined by time-delay grey correlation analysis, and the whale optimization algorithm is designed to acquire the optimal parameters and accuracy of the model. The new model is designed to fitting carbon emissions data in three countries and compare it to six reference models. The performance test shows that the CTGM(1,N) model has high stability. The results of the forecasts show that China's carbon emissions are expected to rise by 1.17% by 2025 from 2020 levels. Meanwhile, emissions will decrease by 5.08% (US) and 0.88% (Japan). The prediction results were consistent with the development status of the three countries. According to the results, we can grasp the development trend of carbon emissions and formulate targeted strategies to achieve sustainable development.
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•A new prediction mechanism is developed by complex effects.•A grey multivariate coupled time-delay model is proposed.•The model parameters are optimized by the whale optimization algorithm.•Comparative study shows that new model is superior to other various models.
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