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Lung cancer remains the primary cause of death globally. Studies have increasingly explored the role of greenspace in mitigating lung cancer risks, yet research gaps persist. First, while the direct spatial effect of greenspace has received attention, its potential spillover effects, driven by human mobility and air pollution dispersion, remain underexamined. Second, despite prevalent assertions of greenspace as an air purifier, the extent to which it moderates the air pollution-lung cancer association has yet to be fully understood. Third, the evaluation of greenspace's effects, predominately analyzed linearly a priori , demands exploration into their potential nonlinearity. We utilize three-year lung cancer datasets from 228 counties in China, to investigate the spatial, moderating, and threshold effects of greenspaces on lung cancer incidence in relation to air pollution. Employing spatial econometric and threshold models, our findings indicate that greenspace reduces lung cancer incidence in both local and neighboring counties. We also observe a diminution in the detrimental impact of air pollution on lung cancer incidence in areas with higher greenspace, especially when the Normalized Difference Vegetation Index surpasses a given threshold. These insights contribute to an enhanced understanding of greenspace's role in lung cancer prevention and could inform policies on greenspace expansion prioritization. • Exposure to greenspaces can effectively mitigate the risk of lung cancer in both local and neighboring regions. • Greenspace can moderate the PM 2.5 -lung cancer association, especially when the NDVI surpasses a specific threshold. • The greenspace-related interventions aimed at enhancing lung cancer resilience at the regional level should be implemented. • The critical value of greenspace could be regarded as the essential requirement for regional urban planning policies. [ABSTRACT FROM AUTHOR]

As a component of both urban transport infrastructures and green spaces, urban greenways play a vital role in connecting urban public spaces, promoting active travel, and facilitating population-level health outcomes. Recently, there has been considerable interest in illustrating the determinants of greenway use in various contexts. Nevertheless, most studies have failed to identify the different aspects of greenway use. Meanwhile, the potential significance of residential neighborhood environments in utilizing greenways has largely been overlooked. In this study, we analyzed data collected from 1020 residents living around the East Lake Greenway in Wuhan, China, to discern three aspects of greenway use: frequency, time, and intensity. In addition, we investigated the moderate effect of neighborhood environmental characteristics on the association between greenway proximity and different aspects of use. After controlling for covariates, multi-level regression models showed that greenway proximity and neighborhood environmental characteristics were significantly associated with greenway use, while the specific associations varied across different aspects of greenway use. Furthermore, proportion of residential land, floor area ratio, and street connectivity moderated the relationship between greenway proximity and greenway use. In summary, the findings of this study contributed to the planning and management of greenways in high-density cities. • Among one of the first studies that distinguished different aspects of greenway use (e.g., frequency, time, and intensity). • We extended the influence of residential neighborhood environment on greenway use. • We investigated the moderate effect of neighborhood characteristics on the association between greenway proximity and use. [ABSTRACT FROM AUTHOR]

• Soil water content could be measured accurately at a hectometer scale using CRNS. • Temporally variable extent of CRNS footprint was identified. • Relative contribution of land uses to the signals was less affected by the types than the positions. • Occasional sampling in the dry season is a recommended strategy. Cosmic-ray neutron sensing (CRNS) is one of the few techniques that can directly acquire information about the temporal dynamics of soil water content (SWC) at a hectometer scale. The performance of CRNS depends on the climate, soil and conditions of the underlying surface. The region of red soil in China is characterized by heterogeneous land uses (combination of upland and paddy fields), distinct dry-wet periods and low contents of soil organic carbon. The applicability of CRNS has not yet been evaluated under the subtropical hydroclimatic conditions of this region. We therefore compared spatially averaged SWC measured using CRNS with point-scale data measured using time domain reflectometry (TDR) at five locations and oven-dried soil samples collected at 27 locations across the CRNS footprint in an agricultural ecosystem of the red-soil region between March 2019 and February 2020. The footprint of CRNS varied with time. The horizontal radius of CRNS varied between 175 m in July and 215 m in October, with a mean of 194 m, and the measuring depth varied between 16 cm in March and 23 cm in August, with a mean of 19 cm. The relative contribution of rainfed cropland, citrus land and paddy fields were 86.7, 10.7 and 2.6%, respectively. The differences of the relative contribution among land uses were determined by the distance to the sensor and the area, which were less affected by the types of land use. The neutron count rate (N 0 , counts/h) was weakly temporally variable, with a mean coefficient of variation of 3%. The influence of its variation on the accuracy of CRNS, however, should not be ignored. Both the timing and frequency of sampling affected the calculation of N 0. Occasional sampling in the dry season is a recommended strategy to acquire an accurate N 0 of a region. CRNS performed well for determining the dynamics of spatially averaged SWC in heterogeneous landscapes. More specifically, the accuracy of CRNS was comparable with those using the oven-drying method, with R 2, root mean square error and Nash-Sutcliffe model efficiency coefficient of 0.86, 0.026 cm3/cm3 and 0.81, respectively. This study evaluated the applicability of CRNS in a region with complex climatic, hydrological and underlying-surface conditions. These results are useful for developing a reasonable sampling strategy for acquiring an accurate N 0 and calibrating the CRNS. Future studies, however, should focus on the application of the CRNS technique to resolve the eco-environmental issues of the region. [ABSTRACT FROM AUTHOR]

Converting CO 2 to highly demanded fuels or chemicals is able to achieve substantial economic and environmental benefits. Herein, a series of Pd/P–CeO 2 –Al 2 O 3 coatings on foam ceramics with particular morphologies including mesh, rod, plate, as well as mixed mesh and plate, are successfully prepared. The mesh Pd/P–CeO 2 –Al 2 O 3 coatings (MC) supported on ceramic displayed much higher catalytic activity and better selectivity than those of catalysts with other morphological Pd/P–CeO 2 –Al 2 O 3 coatings. The CO 2 conversion and CH 4 selectivity of MC were up to 85.9% and 100%, respectively. The superior catalytic performance was ascribed to the strong MSI effect, high hydrogen storage capacity, abundant oxygen vacancies, and metal sites, as well as moderately basic sites, which were characterized by TEM, XPS, CO 2 -TPD, and H 2 -TPD. More importantly, the intermediates and possible reaction pathways were systematically investigated by in situ FTIR. And the high performance of MC mainly comes from the accelerated conversion of carbonate and the hydrogenation of the generated monodentate formate. Overall, the catalytic performance of Pd/P–CeO 2 –Al 2 O 3 coatings supported on ceramic can be facilely regulated and optimized by accurately devising the morphology of the Pd/P–CeO 2 –Al 2 O 3 coatings, which offers a novel perspective for high-performance CO 2 methanation. [ABSTRACT FROM AUTHOR]

[Display omitted] Structural information of grassland changes on the Tibetan Plateau is essential for understanding alterations in critical ecosystem functioning and their underlying drivers that may reflect environmental changes. However, such information at the regional scale is still lacking due to methodological limitations. Beyond remote sensing indicators only recognizing vegetation productivity, we utilized multivariate data fusion and deep learning to characterize formation-based plant community structure in alpine grasslands at the regional scale of the Tibetan Plateau for the first time and compared it with the earlier version of Vegetation Map of China for historical changes. Over the past 40 years, we revealed that (1) the proportion of alpine meadows in alpine grasslands increased from 50% to 69%, well-reflecting the warming and wetting trend; (2) dominances of Kobresia pygmaea and Stipa purpurea formations in alpine meadows and steppes were strengthened to 76% and 92%, respectively; (3) the climate factor mainly drove the distribution of Stipa purpurea formation, but not the recent distribution of Kobresia pygmaea formation that was likely shaped by human activities. Therefore, the underlying mechanisms of grassland changes over the past 40 years were considered to be formation dependent. Overall, the first exploration for structural information of plant community changes in this study not only provides a new perspective to understand drivers of grassland changes and their spatial heterogeneity at the regional scale of the Tibetan Plateau, but also innovates large-scale vegetation study paradigm. [ABSTRACT FROM AUTHOR]

Ce–P/γ-Al 2 O 3 coatings applied with the freeze-drying technique were deposited on foam ceramics before being further prepared as Pd catalysts. According to the research, phosphoric acid replaced the hydroxyl on the surface of γ-Al 2 O 3 to produce polyhydroxy P-containing sites that readily attracted cerium oxide. The palladium oxide was then embedded into the cerium oxide vacancy. The impacts of the drying technique on the catalysts were investigated, and the results indicated that freeze drying created a special coating with a large specific surface area and uniform mesoporous structure by inhibiting nanoparticle agglomeration. H 3 PO 4 's altering action was augmented to boost the number of active sites and the percentage of weakly acidic sites. Due to better P–Ce interactions, more Ce3+ was added into CeO 2 , which created numerous oxygen vacancies. By adjusting the P-containing sites, the dispersion of cerium oxide and the active phase could be controlled, which aided the activation and spillover of hydrogen. Furthermore, the capacity for oxygen adsorption and desorption was enhanced, and the amount of adsorbed oxygen species increased. As a result, the freeze-drying technique improved the advantages of the Ce–P/γ-Al 2 O 3 coating, increasing the conversion and selectivity of CO 2 methanation to 81% and 94%, respectively, and lowering the complete conversion temperature of CO oxidation to 116 °C. This research has a guiding influence on the development of foam ceramic-loaded palladium catalysts in the fields of energy and the environment. [ABSTRACT FROM AUTHOR]

[Display omitted] • Single-atom catalysts M 1 /2DMs could presented high 1,3-butadiene conversions. • Single-atom catalysts M 1 /2DMs could presented high 1-butene selectivity. • Ni 1 /GR and Ni 1 /BN are the ideal catalysts for 1,3-butadiene hydrogenation. Selective hydrogenating 1,3-butadiene to butene is an important tool for the removal of 1,3-butadiene which is a by-product of olefin production. Traditional noble metal catalysts are expensive and relatively low selective for 1-butene. In order to decrease the catalyst cost and improve the 1-butene selective, single-atom catalysts were designed by doping non-noble metals single atom (Pd, Ru, Fe, Co, Ni) on two-dimensional material carriers, hexagonal BN and graphene. It is found that 1,3-butadiene tends to be dominated by mono-π-adsorption on single-atom catalysts, resulting in dissociated H atom preferentially attack the one end C C bond of 1,3-butadiene. Unlike the metallic nanomaterials, low energy barriers for the hydrogenation of 1,3-butadiene to 1-butene were obtained (Ru 1 /BN: 0.20 eV, Fe 1 /GR: 0.23 eV, Co 1 /BN: 0.36 eV, Ru 1 /GR: 0.44 eV, Co 1 /GR: 0.59 eV, Ni 1 /GR: 0.61 eV, Ni 1 /BN: 0.62 eV). The 1-butene desorption energies are smaller than the further hydrogenation energy barriers on the Pd 1 /GR, Ni 1 /GR, Pd 1 /BN Co 1 /BN and Ni 1 /BN surfaces, which indicated better selectivity on these catalysts' surfaces. By contrast, Ni 1 /GR and Ni 1 /BN single-atom catalysts would be the best catalysts among all the studied catalyst in terms of adsorption energies of feed gas, dissociation ability of H 2 , hydrogenation energy barrier and 1-butene desorption energy. [ABSTRACT FROM AUTHOR]

The complexity and uncertainty of pore structures, hinder the understanding of the water infiltration process along a soil profile. In this study, soil pore structures quantified by X-ray computed tomography (CT) scanning and infiltration processes traced using stable hydrogen and oxygen isotopes were lin k ed under three land uses (forestland, citrus land, and rainfed cropland) in the red soil region of southern China. Our results showed that 95% of the soil pore diameters in the surface layer (0–20 cm) in forestland, citrus land, and rainfed cropland were concentrated in the ranges of 0.05–2.46, 0.05–2.24 and 0.05–1.04 mm, respectively. Rainfed cropland had the most soil micropores, along with a high porosity and poor connectivity, while there were more large, well-connected tubular pore channels in the forestland and citrus land. Hydrogen and oxygen stable isotopes were effective in tracing soil water infiltration processes under saturated and near-saturated conditions. The soil infiltration depths of citrus land, forestland, and rainfed cropland were 45, 40, and 25 cm, with "new water" volumes of 37.2, 26.4, and 22.3 mm, respectively. CT-based porosity and the soil texture were considered crucial indicators in the evaluation of the exchange between "old water" and "new water" during infiltration processes. More specifically, the soil structure of the limiting layer not only controlled the infiltration rate but also played an extremely important role in the infiltration mechanism, reflected by the different ratios of "new water". The soil pore characteristics mainly affected soil water transport, while other soil properties played more important roles in soil water retention. • Land use affected the profile distribution of soil pore characteristics. • More information came from the stable isotope abundance of soil water than its contents. • The exchange ratio of "new water" and "old water" was obtained by stable isotopes. • Soils with well-connected structures had a higher proportion of "new water". • The pore structure of the limiting layer controlled the infiltration rate and mechanism. [ABSTRACT FROM AUTHOR]

In the coming era of big data, many advances and attempts have been witnessed in deep learning based remaining useful life (RUL) prediction methods which can construct the mapping relation between the massive information and the RUL. Existing studies leverage advanced deep networks for RUL prediction mainly generating the point estimates for the RUL while the prognostic uncertainty quantification is often difficult. However, it is well admitted that such prognostic uncertainty quantification is important and cannot be neglected for health management of degrading products. The purpose of this paper is to develop an adaptive prognostic method towards both the massive data and prognostics uncertainty by leveraging the advantages of deep learning methods in processing massive data and stochastic methods in the uncertainty representation. To do so, a continuous deep belief network (CDBN) is first utilized to extract the deep hidden features behind the massive information, and then, we determine the health index via the self-organizing map (SOM) neural network based on the extracted features. Next, the diffusion process is applied to construct the health index evolving model. The parameters in the diffusion process are estimated online by combining Bayesian method and Expectation Maximization (EM) algorithm. Consequently, the probability density function (PDF) of the RUL can be obtained and updated adaptively. Finally, a practical case study for bearings is provided to substantiate the effectiveness and superiority of the proposed method. Experimental results indicate that the proposed method can provide more accurate RUL predictions. [ABSTRACT FROM AUTHOR]

[Display omitted] • The introduction of Ni/Cu lower the 1,3-butadiene hydrogenation energy barriers. • Higher butene selectivity could obtained on AgCuPd 2 and Ag 2 NiPd surfaces. • Cu is benefit of forming 2-butene and Ni is favorable of generating 1-butene. Selective hydrogenation of 1,3-butadiene to form butene is an important tool for the removal of 1,3-butadiene which is a by-product of olefin production. AgPd bimetallic catalysts improved the butene selective, but its expensive cost is still an urgent problem. In this study, we introduced a third inexpensive metal Ni or Cu to replace part of the noble metal Pd or Ag in a common AgPd catalyst and studied the influence of the introduction on the hydrogenation reaction by DFT. Compared with the Ag 3 Pd and AgPd 3 alloys, AgCuPd 2 and Ag 2 NiPd hardly changed the adsorption energy of 1,3-butadiene and hydrogen but lowered the butene adsorption energy, which indicates that the addition of Cu or Ni metals can improve the butene selectivity. By calculating the reaction pathways, it was found that the energy barriers of 1,3-butadiene hydrogenation to butene on AgCuPd 2 and Ag 2 NiPd was lower than that of Ag 3 Pd and AgPd 3 alloys, while the energy barriers of butene further hydrogenation to butane was much larger than the desorption energy of butene. The partial replacement of Ag or Pd atom of AgPd alloys with Cu or Ni not only reduces the catalyst cost but also improves the conversion and selectivity of the hydrogenation reaction. [ABSTRACT FROM AUTHOR]

• Grassland degradation is predictable on a large scale by Random Forest. • Specific microbial groups, mainly Solirubrobacter, were screened as credible biomarkers. • Grassland degradation exhibited a longitudinal pattern and was correlated with current Net primary productivity. Extensive grassland degradation under climate change and intensified human activities has threatened ecological security and caused a variety of environmental problems. However, it is still challenging to predict the grassland degradation status on a large scale because it is a multi-factorial phenomenon with complex changes in ecosystem structure and function, which is hard to be fully characterized through mechanism models. The emergence of machine learning algorithms provides a potential to model complex systems and mine information from multi-source data without elucidating underlying mechanisms. Here, we utilized random forest and neural network algorithms to predict the grassland degradation represented by the net primary productivity (NPP) changing rate based on multi-source data including geographic, meteorological, plant traits, land use type and microbial variables in the Chinese Northern grassland. Particularly, the microbial roles in determining the degradation status were concerned. Results show that a high prediction precision was achieved by random forest model, rather than by neural network model, with a mean relative error of 16.9% and a mean square error of 9.273e-05. Besides identified longitude, arid index and current NPP state, specific soil microbial groups, mainly Solirubrobacter, were screened as credible biomarkers. Regarding model fitting, geographic, meteorological and plant variables explained 61.8% of the total variance, which was enhanced up to 72.8% by the rest microbial markers. These findings provide a theoretical basis to establish a pre-warning system for grassland management and policy-making. [ABSTRACT FROM AUTHOR]

• Non-linear patterns of microbial N genes occurred along precipitation gradient. • Breaking points were clearly related to certain soil types. • Attribution of different environmental factors varied by ecosystem. Despite the importance of microorganisms in soil nitrogen (N) cycling, studies on spatial patterns of microbial N-cycling gene abundances in temperate grasslands are still lacking, whose productivity is limited by N. Here, we investigated N functional genes in soil microorganisms from 60 sites in temperate grasslands across 1661 km in Inner Mongolia, China. Abundances of all N functional genes and 16S rDNA tended to decrease from northeast to southwest, consistent with the changing tend of precipitation but contrary to that of temperature. Non-linear saturation curves dominated patterns for abundances of most N functional genes and 16S rDNA along precipitation as they increased with the rising precipitation when <288–343 mm, but remained stable after these breaking points. Interestingly, these breaking points were clearly related to certain soil types, e.g. nifH did not change with precipitation in Chernozems and Gleysols, but increased with it in other soil types. Non-linear saturation patterns were also observed for most N functional gene and 16S rDNA abundances with temperature and soil total organic carbon. In contrast, no consistent spatial pattern for ratios of N functional genes: 16S rDNA was observed, which varied greatly by different N functional genes. Moreover, decay relationships were discovered for the abundance-based matrix of N functional genes over geographic distance. From the temperate meadows to typical steppes and to temperate desert steppes, the influence of relatively long-term environmental variables increased, but that of short-term ones decreased. Overall, we revealed non-linear patterns of N functional gene abundances along precipitation with a clear relationship with soil type, and discovered that attributions of geographic distance, plant community, historical-contingency and contemporary-disturbance to N functional gene community similarity decay were ecosystem–dependent. [ABSTRACT FROM AUTHOR]

• Soil microbial community profiles were significantly altered in the degraded patches. • Soil microbial abundance remained unaffected by the degraded path formation. • Degraded path formation increased soil nitrifier and plant pathogen proportions. • Degraded path formation altered the soil microbial co-occurrence pattern. • Degraded path formation impeded the recovery of degraded alpine meadows. Approximately half of global grasslands are degraded. Although soil microbes play a key role in ecosystem functioning, their response to grassland degradation has not been fully investigated. In particular, degraded patch formation is the main feature of alpine meadow degradation, but little is known about its effect on soil microbes. In this study, soils were collected from three patch-degraded Tibetan alpine meadows to examine the effects of degraded patch formation on soil microbial communities. The alpine meadows at the three sites were in the third to fifth stages of degradation, respectively. Soil microbial abundance and community structure were determined through real-time PCR and MiSeq sequencing, respectively. The results showed that the degraded patch formation significantly decreased microbial respiration rates, changed the interaction patterns among microbial taxa, and increased fungal diversity, but did not significantly affect microbial abundance. Additionally, both prokaryotic and fungal community composition was significantly altered by the degraded patch formation. The functional predictions based on FAPROTAX and FUNGuild suggested that degraded patch formation significantly increased the proportion of nitrifiers, plant pathogenic fungi, and saprotrophic fungi, especially when mattic epipedons were eroded. The increased relative abundance of nitrifiers and pathogenic fungi can aggravate the risk of nitrogen leaching and plant diseases, respectively. Therefore, degraded patch formation can impede the recovery of degraded alpine meadows by changing soil microbial community composition. [ABSTRACT FROM AUTHOR]