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Coal seam microorganisms have attracted much attention for their special functions that are closely related to secondary biogenic gas formation, but the influence of subsurface microbial invasion on coal microorganisms remains poorly understood. In this study, subsurface microorganisms from soils at different depths were used to infect coal samples, and the abundance and composition of the microbial community were analyzed by quantitative polymerase chain reaction (PCR) and high-throughput sequencing. The results showed that methane production from the coal samples treated with soil extractions did not increase significantly nor did the abundance and transcriptional activity of archaeal 16S rRNA and mcrAgenes. In contrast, two soil extractions significantly improved the abundance and transcriptional activity of the coal bacteria. There was no significant difference in the effects of different soil extractions on bacterial abundance and transcriptional activity, but differences in the effects on α- and β-bacterial diversity between the coal samples treated by different soil extractions were found. Soil extraction from the shallow layer soil far away from the coal mine increased the bacterial α-diversity of the coal samples and changed the bacterial community composition. In addition, this soil extraction increased various bacterial groups in the coal sample and changed the predictive metagenome functional contents of the coal samples from the bacterial sequence data. In summary, this study provides basic microbial information for subsurface microbial invasion of coal seams and helps increase understanding of the source of microorganisms in in situ coal seams and the changes in the microbial community during subsurface microbial migration.

The unprecedented 2022 Yangtze River Basin (YRB) heatwave is a threat to human society and natural ecology, so the understanding of its underlying drivers is critical to regional climate adaptation and resilience. Here we conducted a multi‐method attribution analysis on the contribution of atmospheric circulation change and anthropogenic impacts to the occurrence probability and intensity of this extreme heatwave. Based on the nonstationary statistical analysis, the 2022 YRB heatwave is a 1‐in‐900‐year event and a 1‐in‐110‐year event with and without considering the 2022 YRB heatwave in the fitting, respectively. The large‐scale meteorological condition analysis shows that the 2022 YRB heatwave is featured with an anomalous high‐pressure system that favors a hot and dry atmospheric column, overlaid by anomalous subsidence and clear skies which leads to warming and greater solar heating. The ensemble constructed circulation analogue analyses show that the circulation anomaly fails to explain the observed 2022 YRB SAT anomalies fully. Specifically, 46% (0.132 ± 0.027°C decade−1) of the observed SAT trend during 1979–2022 (0.290 ± 0.048°C decade−1) is caused by anthropogenic warming and the associated thermodynamic feedback, while the remaining 54% (0.157 ± 0.038°C decade−1) of the trend is caused by changes in the large‐scale atmospheric circulation. Our findings on changes in atmospheric circulation patterns associated with YRB heatwave and anthropogenic contributions to YRB heatwave could provide valuable information for climate adaptation and mitigation strategies in the context of a warming climate. Heatwaves are major climate extremes in the context of global warming, which have inverse effects on human health, food and energy production, and ecosystems. The 2022 heatwave in the Yangtze River Basin (YRB) sets a new record in observational history with its extreme intensity, long duration, and widespread affected areas. The 2022 YRB heatwave is too exceptional to be possible without anthropogenic warming, which is a 1‐in‐900‐year event and a 1‐in‐110‐year event, respectively, with and without considering it in the nonstationary statistical fitting. An anomalous and prolonged high‐pressure system predominated the evolution of the 2022 YRB heatwave, which favors anomalous subsidence and clear skies, leading to hot and dry atmospheric conditions. Next, we applied an ensemble circulation constructed analogue approach to separate the contributions of large‐scale circulation changes and anthropogenic warming in generating the 2022 heatwave. The results reveal that circulation dynamics can only partially explain the 2022 event. When historical changes are taken into account, the changes in large‐scale circulation and anthropogenic warming, respectively, can account for 54% and 46% of the observed summer surface air temperature anomalies in the YRB region. The 2022 Yangtze River Basin (YRB) heatwave, linked to anomalous circulation backgrounds, set unprecedented records for surface air temperature (SAT)The atmospheric circulation anomalies alone cannot fully explain the SAT anomalies during the 2022 YRB heatwaveThe variation in atmospheric circulation and anthropogenic warming contributed nearly equally to the historical SAT trend of YRB The 2022 Yangtze River Basin (YRB) heatwave, linked to anomalous circulation backgrounds, set unprecedented records for surface air temperature (SAT) The atmospheric circulation anomalies alone cannot fully explain the SAT anomalies during the 2022 YRB heatwave The variation in atmospheric circulation and anthropogenic warming contributed nearly equally to the historical SAT trend of YRB

Mechanoluminescence (ML) is the nonthermal luminescence generated in the process of force-to-light conversion, which has broad prospects in stress sensing, wearable devices, biomechanics, and multiple information anticounterfeiting. Multivalence emitter ions utilize their own self-reduction process to realize multiband ML without introducing another dopant, such as Eu3+/Eu2+, Sm3+/Sm2+, and Mn4+/Mn2+. However, self-reduction-induced ML in bismuth-activated materials has rarely been reported so far. In this work, a novel visible-to-near-infrared (vis–NIR) ML induced by the self-reduction of Bi3+to Bi2+in the spinel-type compound (MgGa2O4) is reported. The photoluminescence (PL) spectra, PL excitation (PLE) spectra, and PL lifetime curves demonstrate that Bi3+/Bi2+ions are the main luminescence centers. Notably, the possible self-reduction model is proposed, where a magnesium vacancy (VMg″) is considered as the driving force for the self-reduction of Bi3+to Bi2+. Furthermore, an oxygen vacancy (VO••) is confirmed by electron paramagnetic resonance (EPR) spectroscopy. Combined with thermoluminescence (TL) glow curves and ML spectra, a plausible trap-controlled ML mechanism is illustrated, where electron–hole (VO••/VMg″) pairs play a significant role in capturing electrons and holes. It is worth noting that the proof-of-concept dual-mode electronic signature application is implemented based on the flexible ML film, which improves the capabilities of signature anticounterfeiting for high-level security applications. Besides, multistimulus-responsive luminescence behaviors of the ML film are realized under the excitation of a 254 nm UV lamp, thermal disturbance, 980 nm laser, and mechanical stimuli. In general, this study provides new insights into designing vis–NIR ML materials toward wider application possibilities.

During the process of using directed energy deposition (DED) to prepare functionally graded material (FGM), there is a coupling problem involving multiple materials and parameters. The generation of intermetallic compounds and mismatched thermal property parameters often leads to the occurrence of defects such as pores and cracks at the microscopic level, as well as poor surface quality at the macroscopic level. In response to the issues, a hybrid prediction-multi-objective optimization method is proposed for the process parameter optimization of DED-fabricated thin-walled structures with SS 316 L/IN718 FGM. The aim is to achieve optimal density and surface morphology. The study investigates the influence of process parameters on the density and surface morphology of SS 316 L/IN718 functionally graded thin-walled structures during the preparation process and analyzes the reasons for defect generation and surface morphology changes. Based on the results, it can be inferred that interlayer lifting has the most prominent impact on density. The density first increases and then decreases as interlayer lifting increases. On the other hand, scan speed has a significant effect on surface morphology, with an increase leading to a decrease in surface roughness. The presence of ceramic oxides and intermetallic compounds in gradient materials induces cracking, and the accumulation of Nb- and Mo-rich phases, as well as columnar to equiaxed transition (CET), causes thermal stress and residual stress concentration, leading to the expansion of defects. Hybrid prediction-multi-objective optimization achieved optimal density and surface roughness of 8001 kg/m3and 180.96 μm, respectively. The model's reliability was validated experimentally.

Residual enrofloxacin (ENR) exposed in aqueous environments is challenging to the ecosphere. In this work, a layered double hydroxide CoAl-LDH was used to activate the common oxidizing agent persulfate (PS) for photodegradation of ENR, and the degradation pathways of ENR were scrutinized and elucidated. The results indicated that, under the optimal conditions obtained through orthogonal experiments, even though the degradation rate of ENR was as high as 97.72%, the removal of total organic carbon (TOC) from the system was only about 30%. Eleven probable reaction pathways were categorized, and thirty-one types of intermediates were identified in participating in the complex degradation process. The major products of ENR were P4 (C17H20FN3O3), P22 (C19H22FN3O4), P19 (C17H18FN3O3), which are mainly derived from the cleavage of the piperazine groups and quinolone rings. Density functional theory (DFT) calculations of the Fukui index for ENR revealed that the two N atoms in the piperazine ring were the core reactive sites in triggering the degradation chains, which were sensitive for electrophilic attack by the dominant radicals (•OH and SO4•-) generated from the composite PS-UV-CoAl-LDH system.

Microorganisms play vital roles in the biogeochemical processes, such as biomethane production, which is an important source of coal bed methane (CBM), that occur in coal beds. However, little information is available regarding the microbial communities in in situ coal beds of different ranks from Permian mining areas. In this study, anaerobic Biolog ECO microplates were used to determine the microbial metabolic activity, and high-throughput sequencing was used to investigate the bacteria and archaea communities in three typical mining areas. Anaerobic Biolog ECO microplates showed that the peak value of average well color development (AWCD) in low-rank coal seams from Anhui Province (A-L) was 0.46 higher than that of medium-rank coal from Shanxi Province (S-M). Phylogenetic analysis indicated that the bacterial phyla Proteobacteria, Firmicutes, and Actinobacteria were the predominant lineages in all samples, in particular, the family Oxalobacteraceae accounted for the overwhelming majority of the bacterial sequences (from 47.20 to 92.67%). Most of the identified genera, Herbaspirillum, Pseudomonas, and Acinetobacter, were fermentative bacteria that can convert coal macromolecules into methanol, acetic acid, and CO2. In addition, the archaea in A-L were dominated by the phyla Euryarchaeota, Thaumarchaeota, and Woesearchaeota. Additionally, Euryarchaeota was the dominant archaeal lineage in S-M, and Thaumarchaeota was predominant in the Guizhou Province group of high-rank coals (G-H). Canonical correspondence analysis (CCA) showed that there were significant correlations between the microbial community and porosity, hydrogen content, and oxygen content (p< 0.05). The KEGG pathway analysis indicated that biomethane production mainly depended on archaea and that the methane metabolism capacity of A-L coals was the highest. Overall, the results suggest that the diversities of the microbial communities were related to the porosity and chemical elements of the different ranks of coal.

The skill of global climate model (GCM) forecasts is usually indicated by the anomaly correlation between ensemble mean and observation. For GCM forecasts, anomaly correlation does not steadily improve with decreasing lead time but oscillates instead. This paper aims to address the oscillation and illustrate the relationship between anomaly correlation and lead time. We formulate the anomaly correlation of forecasts at different initialization times as a vector and pool anomaly correlation vectors across grid cells in the analysis. We propose two patterns to characterize the spatial and temporal variation of anomaly correlation in the three‐dimensional space of latitude, longitude, and initialization time. The first pattern suggests that the anomaly correlation at different initialization times is at a similar level. The second pattern indicates that the anomaly correlation linearly increases with decreasing lead time. These two patterns are tested using the eigenvectors through principal component analysis. They are first illustrated using the GFDL‐CM2p1‐aer04 forecasts of summer precipitation in China. They are further verified by another nine sets of North‐American Multi‐Model Ensemble (NMME) forecasts. Overall, the first pattern explains more variation than the second pattern. In total, the two patterns explain 42% of the variation of anomaly correlation for CanCM3, 59% for CanCM4, 42% for COLA‐RSMAS‐CCSM3), 45% for COLA‐RSMAS‐CCSM4, 59% for GFDL‐CM2p1, 67% for GFDL‐CM2p1‐aer04, 65% for GFDL‐CM2p5‐FLOR‐A06, 57% for GFDL‐CM2p5‐FLOR‐B01, 48% for NCAR‐CESM1, and 60% for NCEP‐CFSv2. The percentage of explained variation demonstrates the effectiveness of the two patterns as exploratory tools to analyze the predictive performance of GCM forecasts. We investigate two conceptual patterns of the variation of anomaly correlation of GCM forecastsThe patterns explain 40% to 70% of the variation for 10 sets of NMME precipitation forecastsThe patterns are useful exploratory tools for investigating the predictive performance of GCM forecasts

Assessing the water budget closures and source of water budget imbalances is fundamental to improving the understanding of changes in the hydrological system and their associated impacts. We analyzed the long‐term (1982–2016) water budget for 1,561 watersheds by using various observed data sets for precipitation (P), evapotranspiration (ET), observed streamflow (Q), and total water storage change (∆TWS). The results show that 93%, 79%, 44%, and 20% of watersheds show water imbalances ratio less than 30%, 20%, 10%, and 5% of their corresponding precipitation. The average absolute water imbalance ratio for all watersheds is 14.2% of P. Watersheds showing large water imbalance ratio values are mostly located in biomes of Tropical and Subtropical Moist Broadleaf Forests, Boreal Forests/Taiga, and Tundra. Different P, ET, and Qdata set combinations result in different degrees of water imbalance. The water budget imbalance ratio shows a significant negative relationship with humidity index and vegetation coverage, while a positive relationship with the proportions of irrigation area and watershed area. Showing small water imbalances for most watersheds, reanalysis precipitation data set (ERA5 and MSWEP), MTE evapotranspiration data set performed better than other data sets in water budget analyses in most biomes. The uncertainties of P, ET, Q, and ∆TWSGRACEcontribute to 40.1%, 14.3%, 26.6%, and 19% of the water budget imbalance on average, respectively. Improving the accuracy of Pand ETestimates, and streamflow measurements are critical to better understanding the water budget and improves modeling of hydrological processes. Water budget imbalances are less than 30%, 20%, 10%, and 5% of precipitation for 93%, 79%, 44%, and 20% of all watershedsReanalysis precipitation (ERA5 and MSWEP) and MTE evapotranspiration perform better than other data sets in the water budget in most biomesMoist Broadleaf Forests, Boreal Forests/Taiga, and Tundra show the largest water imbalance ratio due to bias in hydroclimatic data sets Water budget imbalances are less than 30%, 20%, 10%, and 5% of precipitation for 93%, 79%, 44%, and 20% of all watersheds Reanalysis precipitation (ERA5 and MSWEP) and MTE evapotranspiration perform better than other data sets in the water budget in most biomes Moist Broadleaf Forests, Boreal Forests/Taiga, and Tundra show the largest water imbalance ratio due to bias in hydroclimatic data sets

Investigation on the climatic effects of irrigation is of great significance to fully understand the impact of water management on the Earth's environment and hydrological cycle. To comprehensively explore the effects of irrigation across different climatic regions over China, we propose a novel combined irrigation scheme (including dynamic and fixed irrigation schemes) into the Weather Research and Forecasting (WRF)‐Noah‐mosaic model. Two experiments (with and without irrigation) are designed to simulate the impacts of irrigation over China on regional climate. As irrigation increases the latent heat flux but decreases the sensible heat flux and surface temperature, and the magnitudes of corresponding change are highly dependent on the irrigation amounts and irrigation fraction, the evaporative cooling effects significantly reduce the warm bias over extensive irrigation area during crop growing seasons. Meanwhile, irrigation increases soil moisture during both irrigated and non‐irrigated seasons. Compared with the dynamic irrigation scheme, the fixed irrigation scheme results in relatively higher subsurface runoffs due to the continuous infiltration. In addition, the irrigation‐induced changes on precipitation during spring (March–May) are weaker than that during summer (June–August). The opposed effects of irrigation cooling and wetting on generating convective precipitation, and the irrigation‐induced changes in large‐scale circulation jointly give rise to the heterogeneous changes of precipitation. Besides, the subgrid‐scale irrigation scheme can capture the climatic effects of irrigation in some grids where the dominant land use types are not cropland, indicating that in mesoscale simulation, climate model coupled with subgrid‐scale irrigation scheme may improve climate variables trends attribution studies. China is a large agricultural country, wherein irrigation is the largest water consumption across both arid and humid area. Fully understanding the specific climatic effects of irrigation over arid and humid area is crucial for providing a sustainable climatic adapation to ensure food security. Here, we propose a combined subgrid‐scale irrigation scheme which can mimic irrigation processes of upland and lowland crops to a certain extent, and add it into Weather Research and Forecasting (WRF) model to assess the impacts of irrigation on regional climate. The WRF model experiments clearly support that irrigation altered local climate and hydrological cycles though surface cooling and wetting effects. In addition, the climatic effects of irrigation in humid area are similar compared with that in arid area, despite that the irrigation water use in humid area is clearly larger than that in arid area. A novel combined irrigation scheme respectively satisfied for lowland and upland crops is incorporated into Weather Research and Forecasting (WRF) modelWith the modified WRF model, irrigation leads to significant cooling effects, and reduces warm bias over extensive irrigated areaIrrigation can influence local and remote precipitation through modifying large‐scale circulation in spring and summer A novel combined irrigation scheme respectively satisfied for lowland and upland crops is incorporated into Weather Research and Forecasting (WRF) model With the modified WRF model, irrigation leads to significant cooling effects, and reduces warm bias over extensive irrigated area Irrigation can influence local and remote precipitation through modifying large‐scale circulation in spring and summer

Understanding the impacts of climate change and human activities on hydrological processes, especially the baseflow, is vital for sustainable water resource management. We analyzed the global changes in baseflow and baseflow index (BFI) for 2,374 global streamflow stations from 1970 to 2016 to examine their associations with precipitation, temperature, terrestrial water storage (TWS), normalized difference vegetation index (NDVI) representing the vegetation regimes, potential evapotranspiration (PET), and a humidity index (HI)‐precipitation/PET. Results showed that changes in both baseflow and BFI are significantly region dependent in continents, due to spatial differences of changes in climate and vegetation that determines the baseflow generation. Seasonal baseflow changed slightly, while seasonal BFI varied greatly, partly due to the shift from snowfall to rainfall and warming effects on glacial retreat and the timing of snowmelt. The multibasin baseflow elasticity analyses show that baseflow (BFI) was highly sensitive to changes in NDVI, precipitation, and temperature (temperature and PET). The multivariate regression analyses show that globally, changes in precipitation and TWS contributed to the majority (64.8% and 20.2%) of changes in baseflow, while changes in HI, PET, and precipitation contributed to the majority (30.3%, 28.9%, and 27.5%) of changes in BFI. The spatially varying interaction between climate, vegetation, and baseflow implies that regional adaptation of water resources utilization to climate change should consider the regional shifts in vegetation. This study quantified the elasticity and relative contribution of changes in geographic factors to changes in baseflow and BFI around the world and could inform practices for sustainable water management. Impacts of changes in terrestrial water storage, normalized difference vegetation index, temperature, precipitation, potential evapotranspiration, and humidity index on changes in baseflow are assessedChanges in baseflow and its index are significantly region dependent in continents due to differences of changes in climate and vegetationBaseflow changes are most sensitive to changes in vegetation changes and mainly attributed to changes in precipitation and TWS