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Formaldehyde (FA) is a ubiquitous environmental pollutant and a group 1 carcinogen. CD8+ T cells are specific tumor killers in the adaptive immune system. In this study, 2.0 mg/m3 respiratory FA ex...

In this study, using Moderate Resolution Imaging Spectroradiometer (MODIS) satellite images and environmental satellite CCD images, the spatio-temporal distribution of Ulva prolifera in the southern Yellow Sea during the period of 2011–2018 was extracted and combined with MODIS Level3 Photosynthetically Active Radiation (PAR) product data and Earth System Research Laboratory (ESRL) Sea Surface Temperature (SST) data to analyze their influences on the growth and outbreak of Ulva prolifera. The following conclusions were drawn: 1) comprehensive analysis of Ulva prolifera distribution during the eight-year period revealed that the coverage area of Ulva prolifera typically exhibited a gradually increasing trend. The coverage area of Ulva prolifera reached a maximum of approximately 1714.21 km2 during the eight-year period in late June 2015. The area affected by Ulva prolifera fluctuated. In mid-July 2014, the area affected by Ulva prolifera reached a maximum of approximately 39 020.63 km2. 2) The average growth rate of Ulva prolifera was positive in May and June but negative in July. During the outbreak of Ulva prolifera, the SST in the southern Yellow Sea tended to increase each month. The SST anomaly and average growth rate of Ulva prolifera were positively correlated in May (R2 = 0.62), but not significantly correlated in June or July. 3) The variation trends of PAR and SST were approximately the same, and the PAR during this time period maintained a range of 40–50 mol/(m2·d), providing sufficient illumination for the growth and outbreak of Ulva prolifera. In addition, the abundant nutrients and suitable temperature in the sea area near northern Jiangsu shoal resulted in a high growth rate of Ulva prolifera in May. In summary, the outbreak of Ulva prolifera was closely related to the environmental factors including SST, nutrients, and PAR. Sufficient nutrients and suitable temperatures resulted in a fast growth rate of Ulva prolifera. However, under poor nutrient conditions, even more suitable temperatures were not sufficient to trigger an outbreak of Ulva prolifera.

The Ms 6.0 earthquake in Changning, Sichuan, China, on 17 June 2019 was the largest recorded earthquake in the stable Sichuan basin. It occurred in a complicated region with salt mining and shale gas production. Whether this earthquake is induced raises concerns among the public and the scientific community. Furthermore, the relation between this earthquake and nearby industrial activities has also been of great interest. To address these questions, we estimated the nonstationary background seismicity rate and inverted for spatiotemporal stress changes. The results show that the background rate dramatically increased after hydraulic fracturing (HF) and remained at a high level until the present. Starting in 2005, the study region experienced an accelerating stress increase, and the rates of cumulative modified Coulomb stress changes were approximately 0.11 MPa/yr from January 2005 to January 2015 and 0.24 MPa/yr from January 2015 to December 2018. The 2019 Changning earthquake produced a stress step of 0.32 MPa. A clear difference between seismicity induced by salt mine injection and by HF is documented. Our results suggest that the Changning sequence might have been induced by long-term injection for salt production. Furthermore, the seismicity-stress inversion method provides a tool for using seismicity rate changes as a stress meter to monitor human-induced seismicity.

SUMMARY Ambient noise correlation has been used extensively to retrieve traveltimes of surface waves. However, studies of retrieving amplitude information and attenuation from ambient noise are limited. In this study, we develop methods and strategies to extract Rayleigh wave amplitude and attenuation from ambient noise correlation, based on theoretical derivation, numerical simulation, and practical considerations of real seismic data. The synthetic data included a numerical simulation of a highly anisotropic noise source and Earth-like temporally varying strength. Results from synthetic data validate that amplitudes and attenuations can indeed be extracted from noise correlations for a linear array. A temporal flattening procedure is effective in speeding up convergence while preserving relative amplitudes. The traditional one-bit normalization and other types of temporal normalization that are applied to each individual station separately are problematic in recovering attenuation and should be avoided. In this study, we propose an ‘asynchronous’ temporal flattening procedure for real data that does not require all stations to have data at the same time. Furthermore, we present the detailed procedure for amplitude retrieval from ambient noise. Tests on real data suggest attenuations extracted from our noise-based methods are comparable with those from earthquakes. Our study shows an exciting promise of retrieving amplitude and attenuation information from ambient noise correlations and suggests practical considerations for applications to real data.

SUMMARY Due to the partly diffuse character of ambient noise, the retrieval of amplitude information and attenuation from noise cross-correlations has been difficult. Here, we apply the temporal reweighting method proposed by Weaver & Yoritomo to seismic data from the USArray in the central-midwest US. The results show considerable improvements in retrieved Green's functions in both symmetry and causality. The reweighting is able to make the effective incident noise field more isotropic (though not yet truly isotropic). It produces more robust amplitude measurements and also makes both the causal and anticausal parts usable. This suggests that it could be widely applicable for retrieval of Green's functions from ambient noise for attenuation study. The results also suggest an alternative measure of signal-to-noise ratio that complements the conventional one.

Depth patterns of soil microbial distribution have not been well characterized and little is known about the balance between stochastic and deterministic processes in shaping microbial community th...

Spatial prediction is an important approach to obtain location-specific values of soil electrical conductivity (EC), which is a proxy of soil salinity and important for agricultural management in arid and semi-arid areas. Linear regression models assume that the relation between soil EC and environmental covariates is constant over the area to be predicted. This is problematic at the regional scale, at which some of the regression parameters may indeed be globally constant, whereas others may vary locally. Moreover, model residuals often exhibit spatial dependence, which invalidates the ordinary least squares linear regression. This study examined the combination of a mixed geographically weighted regression model with simple kriging of the residuals (MGWGK) for mapping soil EC in the Heihe River Basin, an inland river basin in arid northwest China. We compared the performance of MGWRK with those of multiple linear regression (MLR), regression kriging (RK), geographically weighted regression (GWR), geographically weighted regression kriging (GWRK) and mixed geographically weighted regression (MGWR). Environmental covariates were developed from spatial information on topography, climate, vegetation, and geographic position. A ten-fold cross-validation was applied to evaluate predictive accuracy of the various methods. Soil EC ranged from 0.031 to 182.100 dS m−1, exhibiting a contrasting distribution of soil EC in the upper, middle, and lower river reaches. The MGWRK method outperformed other methods. The effects of different environmental covariates on soil EC were revealed by the fixed and geographically varying parameters of MGWRK. The nugget-to-sill ratios of fitted variogram models all fell between 25 and 32%, exhibiting moderate spatial autocorrelation of models residuals. Predictive accuracy was improved by MGWRK, as the spatial dependence of model residuals were included in the prediction. When selecting an optimal linear regression model, covariates should be tested to see if they are constant (as in MLR) or spatially varying (as in GWR) or semi-varying (as in MGWR), and model residuals should be tested for spatial dependence (as in MGWRK).

Mid-crustal melting is widely documented under orogenic plateaus. However, the mechanism for its generation and its role in the evolution of orogenic plateaus remain poorly understood. Here we investigate the physical controls and consequences of crustal melting in plateau evolution using 3-D thermo-mechanical modeling method. Our simulations demonstrate that the convergence causes orogenic crust to have more upper crustal compositions and more radiogenic elements. The persistent radioactive and shear heating provides heat source required for in situ partial melting of felsic rocks at mid-crustal depths. Slower convergence rate favors earlier emergence and larger concentration of mid-crustal melt. Radioactive heating accumulated during crustal thickening plays the primary role in generating mid-crustal partial melting, which is consistent with previous 2-D model results. Shear heating promotes crustal melting, but its role is secondary to radioactive heating. Our simulations also show that basal heating produces a broad-area increase of the crust temperature and surface elevation and, to a lesser extent, promotes mid-crustal melting, because convergence-driven heat advection dominates the transport of the newly-added heat. The significant mid-crustal melting dramatically reduces the viscosity of the middle crust and eventually leads to channelized outward flow of the melt-weakened layer. This has the potential to cause mechanical decoupling of the layers above and below the melt channel and the marginal dominance of mid-crustal melting. We suggest that the widespread low velocity or resistivity zones observed in Tibet are caused by interconnected mid-crustal melting, which is an inherent feature of large hot orogenic plateaus.

Mesozoic granitoids are widely distributed in South China, but the mechanism for generating the felsic magmas is still in debate. To understand the possible origin of the granitoids, we use joint inversion methods and forward modelling of receiver functions to reveal the lithospheric structure in the Cathaysia block. The joint inversion of receiver functions and surface wave dispersion with P-velocity constraints is applied to a dense array in the Cathaysia block, and the specific crustal Vp/Vs ratios obtained from a generalized H-κ stacking method are introduced to the traditional joint inversion for passive seismic stations. The detailed structures provide valuable knowledge of the geological processes: 1) The present-day lithosphere is 60–70 km thick, which has been thinned since the Late Mesozoic with reference to geochemical data; 2) there is a lack of high Vp and Vs in the lower crust in this region. The results may have limited resolution on the localized magmatic underplating, but they do not support the extensive magmatic underplating at the Late Mesozoic. Other factors could be the mechanism for the granitoid formation in this region.

Organic carbon (OC) plays a pivotal role in earth surface systems. However, current three-dimensional (3D) mapping studies usually focus on a soil depth of 1 m rather than the depth to the bedrock. A top-down method using piecewise depth functions was proposed to fit the OC vertical decline patterns in a subtropical catchment in southern China. The vertical variation in OC was greatly affected by the heterogeneous topsoil due to natural processes and anthropogenic disturbances. Thus, topsoil OC maps were produced and utilized as covariates to indicate the OC decline rates and to benefit the 3D OC simulation. A distribution map of the underground critical zone thickness (UCZT) was applied as a lower boundary for the 3D simulation. Six widely used mapping techniques were performed to predict the spatial distribution of topsoil OC and depth function parameters. The overall cross-validation results showed a root mean squared error (RMSE) of 1.7 g kg–¹ and a ratio of performance to deviation (RPD) of 1.82. Given limited boreholes, validation showed that the depth function performed better in the lower part (>1 m) than in the upper part (

Nitrate accumulation has been reported in the top 1 m and subsurface soil (> 1 m) across arid to semi-humid regions, but not in humid regions. Nitrate inventories through the whole regolith, referred to collectively as soil and saprolite, in humid regions have received little attention to date, likely due to previously assumed low nitrification rates and large nitrogen (N) losses by severe surface runoff and erosion. In order to understand if and how reactive N exists in the below ground (soil and saprolite) in humid environment, the amount of NO3--N, NH4+-N and dissolved organic N (DON) present in the regolith to a depth of 9 m in a typical red soil Critical Zone was investigated under different land uses (upland, woodland and paddy field). The Red Soil Critical Zone Observatory is located in the subtropical Jiangxi Province, China, with a mean annual precipitation of 1795 mm and mean annual potential evapotranspiration of 1229 mm. The examined regoliths were acidic, highly weathered, and mainly clay loam in texture. Results showed that on average 92% (827 ± 97 kg N ha-1) of NO3--N and 82% (521 ± 153 kg N ha-1) of DON were stored at depth (from a depth of 1 m to the bedrock surface) in the upland regolith, while 92% (283 kg N ha-1) of NO3--N and 78% (820 kg N ha-1) of DON were stored at depth in the woodland regolith. Nitrate N significantly accumulated with depth in the upland regolith from the 1- to 4-m depth interval (p < 0.01), while the inventory (632 ± 75 kg N ha-1) in the top 3-m zone accounted for on average 71% of the total. Dissolved organic N significantly accumulated with depth in the upland regolith from the 0- to 3-m depth interval (p < 0.01), while the inventory (408 ± 75 kg N ha-1) in the top 3-m zone accounted for on average 64% of the total. There was no significant accumulation for NH4+-N throughout the upland regolith (p = 0.35). No substantial accumulation of dissolved N was measured at depth in paddy field regoliths with different cultivation ages. The finding that large reservoirs of reactive N can exist in deep regolith rather than in the routinely investigated solum of subtropical regions shows a missing part of the terrestrial N budget and raises concerns about potential 34 groundwater nitrate pollution.

The soil organic carbon (SOC) and total nitrogen (TN) of topsoil could be greatly affected by the conversion of grassland to cropland (CGC) due to vegetation harvesting and soil erosion. However, few studies have evaluated the effects of these changes in the subsoil and distinguished its main controls at the regional scale. This paper investigated the total changes in the SOC stock (SOCS) and TN stock (TNS) of soils from 0 to 30 cm and 0–100 cm in depth over the past three decades in the Songnen Plain, China, a typical Mollisol region. The CGC resulted in a moderate loss of topsoil C and N but an increase in subsoil C and N, and the soil mapping results showed that approximately 7.47 × 1011 kg C and 1.51 × 1011 kg N were accumulated during the past three decades. Prediction uncertainty was assessed by 100 model runs with random jackknife partitions, and high uncertainty was found in the areas with rapidly changing SOCS and TNS levels. Our findings indicated that subsoil should be considered in the estimation of the SOCS and TNS at the regional scale. The SOCS and TNS of the CGC areas were anticipated to increase after long-term cultivation. In view of ongoing cropland expansion, up-to-date land use and soil type data are vital for selecting monitoring sites and understanding long-term soil evolution at the regional scale.

Nitrate accumulated deep (>100 cm) in the regolith (soil and saprolite) threatens groundwater quality, but most studies focus only on nitrate nearer the surface (

Land use/land cover (LULC) pattern change due to human activity is one of the key components of regional and global climate change drivers. Urban green space plays a critical role in regulating urban thermal environment, and its cooling effect has received widespread attention in urban heat island (UHI) related studies. To fully understand the effects of the landscape pattern of an urban green space in regulating the urban thermal environment, it is necessary to further study the thermal effects of the landscape pattern of the urban green space and its characteristics under varied spatial&ndash, temporal scales. In this paper, we took the urban core area of Hangzhou City as the study area and analyzed the relationships between the landscape metrics of the urban green space and land surface temperature (LST) under varied spatial scales by using correlation analysis and redundancy analysis (RDA) methods. Multi-temporal Landsat 8 thermal infrared sensor data were used to retrieve the spatial and temporal dynamics of LSTs in four consecutive seasons, and the land use classification was interpreted using SPOT (Systeme Probatoire d&rsquo, Observation de la Terre) satellite imagery. The results showed that landscape dominance metrics&mdash, e.g., percentage of landscape (PLAND) and largest patch index (LPI)&mdash, were the most influential factors on urban LST. The spatial configuration of urban landscape, as represented by the fragmentation and aggregation and connectedness, also showed significant effects on LST. Furthermore, landscape pattern metrics had varied spatial scale effects on LST. Specifically, the landscape dominance metrics of urban forest showed an increased influence on LST as the spatial scale increased, while for urban water, the trend was opposite. These findings might have some practical significance for urban planning about how to spatially arrange urban green space to alleviate UHI at local and regional scales.

Soil diagnostic horizons, which each have a set of quantified properties, play a key role in soil classification. However, they are difficult to predict, and few attempts have been made to map their spatial occurrence. We evaluated and compared four machine learning algorithms, namely, the classification and regression tree (CART), random forest (RF), boosted regression trees (BRT), and support vector machine (SVM), to map the occurrence of the soil mattic horizon in the northeastern Qinghai-Tibetan Plateau using readily available ancillary data. The mechanisms of resampling and ensemble techniques significantly improved prediction accuracies (measured based on area under the receiver operator characteristic curve score (AUC)) and produced more stable results for the BRT (AUC of 0.921 ± 0.012, mean ± standard deviation) and RF (0.908 ± 0.013) algorithms compared to the CART algorithm (0.784 ± 0.012), which is the most commonly used machine learning method. Although the SVM algorithm yielded a comparable AUC value (0.906 ± 0.006) to the RF and BRT algorithms, it is sensitive to parameter settings, which are extremely time-consuming. Therefore, we consider it inadequate for occurrence-distribution modeling. Considering the obvious advantages of high prediction accuracy, robustness to parameter settings, the ability to estimate uncertainty in prediction, and easy interpretation of predictor variables, BRT seems to be the most desirable method. These results provide an insight into the use of machine learning algorithms to map the mattic horizon and potentially other soil diagnostic horizons.

Up-to-date maps of soil organic carbon (SOC) concentrations can provide vital information for monitoring global or regional soil C changes and soil quality. In this study, a national soil dataset collected in the 2010 s was applied to produce SOC maps of mainland China at soil depths of 0–5 cm, 5–15 cm, 15–30 cm, 30–60 cm, 60–100 cm and 100–200 cm. A stacking ensemble learning framework was utilized to take advantage of the optimal predictions from individual models. A voting-based ensemble learning model (VELM) was proposed with consideration of pedoclimatic zones. In this model, three machine learning models were separately trained for every pedoclimatic zone, and their predictions were selectively merged together. A weighted ensemble learning model (WELM), in which the parameterization considered all zones (i.e., the whole study area) simultaneously, was also trained for comparison. The overall R2 values of these two methods ranged from 0.16 to 0.57 and decreased with depth. Based on the independent validation, the R2 values ranged from 0.41 to 0.57 in the topsoil (0–5 cm, 5–15 cm and 15–30 cm). Overall accuracy metrics implied that the VELM and WELM yielded nearly the same prediction performances. However, model validation in the pedoclimatic zones showed that the VELM obviously outperformed the WELM, with the VELM generally improving the accuracy by 12.6%. Based on the independent validation, we also compared our predictions with other soil map products. Although the spatial patterns were similar, the predicted SOC maps outperformed two other products. The comparison of the two ensemble models should serve as a reminder that if new national or regional soil maps are generated, validation based on pedoclimatic zones or other soil-landscape units may be necessary before applying these maps.

The main aim of this paper was to calibrate and evaluate the DeNitrification-DeComposition (DNDC) model for estimating N2O emissions and crop productivity for a summer maize-winter wheat double cropping system with different N fertilizer rates in Hebei, China. The model’s performance was assessed before and after calibration and model sensitivity was investigated. The calibrated and validated DNDC performed effectively in estimating cumulative N2O emissions (coefficient of determination (1:1 relationship; r2) = 0.91; relative deviation (RD) = −13 to 16%) and grain yields for both crops (r2 = 0.91; RD = −21 to 7%) from all fertilized treatments, but poorly estimated daily N2O patterns. Observed and simulated results showed that optimal N fertilizer treatment decreased cumulative N2O flux, compared to conventional N fertilizer, without a significant impact on grain yields of the summer maize-winter wheat double cropping system. The high sensitivity of the DNDC model to rainfall, soil organic carbon and temperature resulted in significant overestimation of N2O peaks during the warm wet season. The model also satisfactorily estimated daily patterns/average soil temperature (o C; 0–5 cm depth) (r2 = 0.88 to 0.89; root mean square error (RMSE) = 4 °C; normalized RMSE (nRMSE) = 25% and index of agreement (d) = 0.89–0.97) but under-predicted water filled pore space (WFPS; %; 0–20 cm depth) (r2 = 0.3 to 0.4) and soil ammonium and nitrate (exchangeable NH4+ & NO3−; kg N ha−1; r2 = 0.97). With reference to the control treatment (no N fertilizer), DNDC was weak in simulating both N2O emissions and crop productivity. To be further improved for use under pedo-climatic conditions of the summer maize-winter wheat double cropping system we suggest future studies to identify and resolve the existing problems with the DNDC, especially with the control treatment.

SignificanceWe show high-resolution P and S images of the subducted Indian mantle lithosphere, which has been torn into four main pieces. The tearing provides a unified mechanism for (i) Late Miocene and Quaternary rifting, (ii) mantle earthquakes in southern and central Tibet, and (iii) patterns of current crustal deformation (seismicity and GPS strain rate). It suggests (iv) that the deformations of the crust and the mantle lithosphere in southern Tibet are strongly coupled and thus we need to take a “deeper view” to understand the Himalayan–Tibetan continental deformation and evolution. The geometry of the Indian mantle lithosphere and the above issues are of fundamental importance and have been debated for decades. The proposed model has implications across geoscience disciplines.

Anisotropy of Earth’s inner core provides a key role to understand its evolution and the Earth’s magnetic field. Recently, using autocorrelations from earthquake’s coda, we found an equatorial anisotropy of the inner-inner core (IIC), in apparent contrast to the polar anisotropy of the outer-inner core (OIC). To reduce the influence of the polar anisotropy and reduce possible contaminations from the large Fresnel zone of the PKIKP 2 and PKIIKP 2 phases at low frequencies, we processed coda noise of large earthquakes (10,000–40,000 s after magnitude ≥7.0) from stations at low latitudes (within ±35°) during 1990–2013. Using a number of improved procedures of both autocorrelation and cross-correlation, we extracted 52 array-stacked high-quality empirical Green’s functions (EGFs), an increase of over 60% from our previous study. The high-quality data allow us to measure the relative arrival times by automatic waveform cross correlation. The results show large variation (∼10.9 s) in the differential times between the PKIKP 2 and PKIIKP 2 phases. The estimated influence of the Fresnel zone is insignificant (

The variation in Arctic sea ice has significant implications for climate change due to its huge influence on the global heat balance. In this study, we quantified the spatio-temporal variation of Arctic sea ice distribution using Advanced Microwave Scanning Radiometer (AMSR-E) sea-ice concentration data from 2003 to 2013. The results found that, over this period, the extent of sea ice reached a maximum in 2004, whereas in 2007 and 2012, the extent of summer sea ice was at a minimum. It declined continuously from 2010 to 2012, falling to its lowest level since 2003. Sea-ice extent fell continuously each summer between July and mid-September before increasing again. It decreased most rapidly in September, and the summer reduction rate was 1.35 × 105 km2/yr, twice as fast as the rate between1979 and 2006, and slightly slower than from 2002 to 2011. Area with >90% sea-ice concentration decreased by 1.32 × 107 km2/yr, while locations with >50% sea-ice concentration, which were mainly covered by perennial ice, were near the North Pole, the Beaufort Sea, and the Queen Elizabeth Islands. Perennial Arctic ice decreased at a rate of 1.54 × 105 km2 annually over the past 11 years.

The waterlogging is a serious agrometeorological disaster caused by excessive soil water during crop growth stages. The middle and lower reaches of the Yangtze River are one of the major winter wheat growing regions in China and at the same time they are waterlogging-prone due to their specific climatic conditions. In this study, we integrated a set of tools to analyze the spatiotemporal features of winter wheat waterlogging in this region. We proposed a waterlogging precipitation index (WPI) based on winter wheat yield loss rate and precipitation anomaly percentage and analyzed the frequency, scope, and intensity of winter wheat waterlogging. The results showed that the spring rainfall had a direct and significant effect on winter wheat yield, and the meteorological yield of winter wheat was negatively correlated with precipitation abnormal event from jointing to maturity stages (March to May) across the whole study area. The matching between the waterlogging severity identified by the WPI and historical winter wheat waterlogging records was relatively high. We also discussed the influences of the other nonmeteorological factors, for example, soil texture, topographic and geomorphic conditions, and local disaster-resisting ability, on the extent of waterlogging damage.

To deal with the global and regional issues including food security, climate change, land degradation, biodiversity loss, water resource management, and ecosystem health, detailed accurate spatial soil information is urgently needed. This drives the worldwide development of digital soil mapping. In recent years, significant progresses have been made in different aspects of digital soil mapping. The main purpose of this paper is to provide a review for the major progresses of digital soil mapping in the last decade. First, we briefly described the rise of digital soil mapping and outlined important milestones and their influence, and main paradigms in digital soil mapping. Then, we reviewed the progresses in legacy soil data, environmental covariates, soil sampling, predictive models and the applications of digital soil mapping products. Finally, we summarized the main trends and future prospect as revealed by studies up to now. We concluded that although the digital soil mapping is now moving towards mature to meet various demands of soil information, challenges including new theories, methodologies and applications of digital soil mapping, especially for highly heterogeneous and human-affected environments, still exist and need to be addressed in the future.

Reasonable regional energy benchmarking of public buildings can assist the energy supervision and planning from the government point of view, especially when faced with the uncertain features of planning stage. With 195 samples of three public building types, the Lorenz curve was introduced to present the distribution inequality and diversity of energy consumption within a building type, and thus a target-oriented regional energy benchmarking model was established, by accumulation between the pre-set upper and lower EUI limitations along with the corresponding part of Lorenz curve. Besides an applied case study, the adaptability of Lorenz curves in different cities and the flexible responding ability to dynamic energy strategies were also tested respectively. This paper attempts to offer a reliable resilient energy benchmarking model at regional scale as the corresponding demand basis of resilient energy planning.

Formaldehyde (FA) is widely used in chemical industry, which is also known as a common indoor air pollutant. Exposure of FA has been associated with multiple detrimental health effects. Our previous study showed that FA could inhibit the development of T lymphocytes in mice, leading to impaired immune functions. Macrophages are important innate immune cells which trigger inflammatory responses in tissues. In the present study, FA exposure at 2.0 mg/m3 was found to enhance the pro-inflammatory responses of macrophages in male BALB/c mice, which was confirmed by elevated pro-inflammatory cytokine release and NO secretion in macrophages isolated from the FA-exposed mice and in vitro macrophage models upon lipopolysaccharide stimulation. Glycolysis is the key metabolic process for the classical activation of macrophages, which was found to be elevated in the in vitro macrophage models treated with FA at 50 and 100 μM concentrations for 18 h. HIF-1α and the associated proteins in its signaling cascade, which are known to mediate glycolytic metabolism and inflammatory responses, were found to be upregulated by 50 and 100 μM FA in THP-1 derived and RAW264.7 macrophage models, and the enhanced pro-inflammatory responses induced by 100 μM FA were reversed by inhibitory compounds interfering with glucose metabolism or suppressing HIF-1α activity. Collectively, the results in this study revealed that FA could enhance the pro-inflammatory responses of macrophages through the induction of glycolysis, which outlined the FA-triggered metabolic and functional alterations in immune cells.

We introduced a P-velocity model into the traditional joint inversion of P receiver function (RF) and surface wave dispersions to reduce model ambiguity. The method was implemented using a global search-based algorithm and a flexible parameterization of a sedimentary layer and spline-based parameterization that can represent sharp discontinuities. We applied the method to a dense array in SE Tibet (longitude ~97.5 °E to 107 °E, latitude ~25.3 °N). Extensive tests using synthetic and real data suggest that the method is suitable and robust for a variety of velocity structures and Moho discontinuities, and can simultaneously provide the crustal Vp/Vs profile and better-constrained Moho depth. The flexibility of the parameterization and the inclusion of the Vp constraint are crucial in the improved model recovery. Artifacts may be created without including the sedimentary layer. Even when it is less perfect, a reasonable Vp model is valuable in such a joint inversion. We showed that crustal multiples in RFs may bias the traditional H-k results when the crust structure is complex and should be avoided in a joint inversion before appropriate corrections can be made. The results from the joint inversion show two low-velocity zones (LVZs) reported previously and were identified as channels of crustal flow. A prominent isolated LVZ is observed in the mid-lower crust under the Xiaojiang fault area, which correlates with anomalously high Vp/Vs ratios, indicating possible partial melting. However, the other LVZ is imaged to be in the brittle shallow upper crust without very high Vp/Vs ratios, which is likely associated with crustal fault zones rather than partial melting. We observe clear low-velocity structures in the mantle beneath the two crustal LVZs, which also correlate with zones of low resistivity. The crust-mantle correlation may suggest influence of mantle processes on crustal deformation.

Mattic epipedon (ME), a special diagnostic surface horizon for soils in alpine meadow, plays an important role in carbon storage, soil water retention, and indication of alpine meadow degradation. At the global scale, it mainly distributes in the Qinghai-Tibetan Plateau and other similar alpine environments. However, its spatial patterns and the relations with environmental conditions remain unknown. This study attempts to explore key environmental variables responsible for the occurrence of the special surface soil horizon and based on those factors to predict and map digitally the spatial distribution of ME. By combining a variable selection procedure and the Random Forest (RF) algorithm, the variables extracted from Landsat 5 TM mosaic image, ASTER GDEM, and climate data were optimized and their importance was measured. The classification accuracy was compared with that obtained from binary logistic regression algorithm. In addition, a land use/land cover (LULC) map-based modification was conducted to further improve the classification accuracy. Results showed that the variable selection procedure had little effect in improving prediction accuracy. However, the number of used variables markedly reduced from 26 to 6 with a 77% decrease, which could speed up the training of the RF model (about one-third of the computation time could be saved). Analysis of variable importance showed that band 3 of TM and normalized difference vegetation index were the most important environmental variables influencing the occurrence of ME. The final overall accuracy of the ME map was predicted to be 84%. Our results demonstrate that the proposed procedure, which combined the proposed variables (derived from remote sensing, GDEM, and climate data), the variable selection approach, the RF algorithm, and the LULC map-based modification method can identify key environmental variables influencing the occurrence of ME and map the spatial distribution of ME effectively on the Qinghai-Tibetan Plateau.

Local terrain attributes, which are derived directly from the digital elevation model, have been widely applied in digital soil mapping. This study aimed to evaluate the mapping accuracy of soil organic carbon (SOC) concentration in 2 zones of the Heihe River in China, by combining prediction methods with local terrain attributes derived from different polynomial models. The prediction accuracy was used as a benchmark for those who may be more concerned with how accurately the variability of soil properties is modeled in practice, rather than how morphometric variables and their geomorphologic interpretations are understood and calculated. In this study, 2 neighborhood types (square and circular) and 6 representative algorithms (Evans-Young, Horn, Zevenbergen-Thorne, Shary, Shi, and Florinsky algorithms) were applied. In general, 35 combinations of first- and second-order derivatives were produced as candidate predictors for soil mapping using two mapping methods (i.e., kriging with an external drift and geographically weighted regression). The results showed that appropriate local terrain attribute algorithms could better capture the spatial variation of SOC concentration in a region where soil properties are strongly influenced by the topography. Among the different combinations of first- and second-order derivatives used, there was a best combination with a more accurate estimate. For different prediction methods, the relative improvement in the two zones varied between 0.30% and 9.68%. The SOC maps resulting from the higher-order algorithms (Zevenbergen-Thorne and Florinsky) yielded less interpolation errors. Therefore, it was concluded that the performance of predictive methods, which incorporated auxiliary variables, could be improved by attempting different terrain analysis algorithms.

The parental material of soils in the Qilian Mountains of northwest China is mainly aeolian loess containing CaCO3 which may remain in soils under the semiarid-arid climate. To disclose the CaCO3 characteristics change with the altitude and the terrain attributes, we surveyed 18 soil profiles in an altitude sequence from 3076 m to 4510 m in the Hulugou Watershed in the Qilian Mountains, measured CaCO3 contents of all genetic horizon samples, analyzed the densities, illuviation modes and depths of CaCO3 in the profiles, extracted values of the terrain attributes of the profiles including altitude slope, aspect, plane curvature, profile curvature and terrain wetness index (TWI) from the 90 m resolution SRTM3 DEM data on ArcGIS 9.3 platform. We found that CaCO3 weighted content of the profiles ranged from 1.30 g·kg-1 to 93.09 g·kg-1, CaCO3 density from 0.05 kg/m2 to 75.69 kg/m2, CaCO3 illuviation depth from 12 cm to 54 cm. CaCO3 illuviation modes could be divided into three types, i.e., no illuviation mode in which the profile has only A horizon or CaCO3 content -1, middle illuviation mode in which CaCO3 accumulated in a middle horizon, and down illuviation mode in which CaCO3 content increases with the depth. CaCO3 weighted content, density and illuviation depth had significant correlation with certain terrain attributes. In general, the altitude sequence is an effective way to study CaCO3 characteristics in the alpine region, and the data of terrain attributes which can influence the precipitation and its redistribution in soil are potential in predicting soil CaCO3 characteristics in the alpine region.

We validated moderate resolution imaging spectroradiometer MODIS Level 2 aerosol products with ground-based sun photometer CE-318 measurements over the Pearl River Delta PRD region. MODIS aerosol products are also used to investigate the temporal and spatial variations of aerosol optical thickness AOT. The results show that MODIS AOT is validated quantitatively with a higher correlation coefficient r = 0.88, 0.80 at Guangzhou and r = 0.95, 0.92 at Hong Kong and lower root mean square errors RMSE = 0.15, 0.16 at Guangzhou and RMSE = 0.07, 0.08 at Hong Kong, while the Angstrom exponent α is still in doubt r = 0.09. The MODIS AOT values are generally higher than those of the CE-318 values in Guangzhou and smaller than those in Hong Kong. The regional multi-year monthly July 2002–December 2012 mean AOT values are 0.66 ± 0.20 and 0.64 ± 0.18 for Terra-and Aqua-MODIS, respectively. From month to month, the values of Terra-MODIS AOT are larger than those of Aqua-MODIS during most of the month. This implies that AOT in the morning is generally larger than that in the afternoon. The largest monthly AOT occurred in April at 0.85 ± 0.16 and 0.88 ± 0.17 for Terra-MODIS and Aqua-MODIS, respectively, and the smallest occurred in November for both Terra-and Aqua-MODIS at 0.47 ± 0.13 and 0.47 ± 0.10, respectively. The spatial distribution of AOT in spring and summer shows more variation than in autumn and winter. This can be partially attributed to the cleansing effect of precipitation which clears aerosol particles over the whole region in spring and summer and results in a lower AOT outside urban areas, while AOT in urban areas is higher where anthropogenic aerosols build up quickly despite the cleansing effect of the rain.

Subsurface soil bacterial community composition and the controlling factors remain largely unknown, especially the micro-zone differentiation of community composition within a horizon. We studied a plinthic horizon to determine how different micro-zones in a horizon affect the bacterial community. The plinthic horizon is a net-like horizon characterized by the segregation of iron forms as shown by contrasting red matrix and white veins, which share common macro-environmental conditions such as climate and land use but differ only in physical and chemical compositions. The studied horizon is typical of the red soils of southeastern China and is an important layer in the red soil Critical Zone. The plinthite is considered to have been formed in the Quaternary and thus is a record of the paleo-environment. We evaluated the difference in the bacterial community composition between the red matrix and white veins and explored the possible assembly mechanisms of their co-occurrence patterns. Compared to the eutrophic environments of a red matrix, higher relative abundances of Acidobacteria and Nitrospirae were observed in the white veins. Similarly, more niches led to a higher density of bacterial co-occurrence patterns in the red matrix. The differences in the bacterial community composition and association networks are due to environmental selection, including the legacy of the paleoclimate that is represented by major element contents and contemporary hydrological properties that are mainly controlled by the soil texture. Our study shows that micro-zones even within a same plinthic horizon can provide different habitats and thus select for specific bacterial communities. Furthermore, this study could improve our understanding of the differentiation of bacterial communities among microenvironments caused by both historical and contemporary processes and help to predict how these communities may respond to future environmental changes.

Environmental exposure to arsenite (As+3) is known to induce immunotoxicity. Natural killer (NK) cells are innate lymphoid cells act as professional killers of tumor cells. Our previous report indicated that 500 ppb As+3 drinking water exposure induced significant DNA damage in the NK cells of C57BL/6 mice. Myricetin is a plant-derived flavonoid known as a strong antioxidant. In this study, daily administration of myricetin at 20 mg/kg was found to alleviate the cell population decrease and DNA damage in the NK cells of BALB/c mice exposed to 500 and 1000 ppb As+3 via drinking water. Oxidative stress and poly(ADP-ribose) polymerase 1 (PARP-1) inhibition were induced by As+3 at 1 and 2 μM in isolated mouse NK cells in vitro, which were attenuated by 20 μM myricetin. The mitigatory effect of myricetin on the PARP-1 inhibition in NK cells treated with As+3 was also found to be the result of its prevention of the zinc loss induced by As+3 on PARP-1. Collectively, these results demonstrated, for the first time, that myricetin could protect NK cells from As+3 induced DNA through attenuating oxidative stress and retaining PARP-1 activity, indicating that myricetin may be utilized for the prevention of the immunotoxicity induced by As+3 in NK cells.

Nitrate contamination of water bodies is a serious concern in regions with intensive agriculture. Previous studies, which only considered surface water and groundwater but not the regolith, do not provide a comprehensive accounting of nitrate sources and their proportional contributions in the Critical Zone, which extends from the top of the vegetation canopy down to the bottom of the fresh groundwater. In this study, dual nitrate isotopes, hydrochemical compositions and a Bayesian isotope mixing model were used to investigate nitrate sources, processes and individual source contributions across surface water, regolith and groundwater at the Red Soil Critical Zone Observatory of China. Denitrification was found to be weak in the Critical Zone. Dilution processes were important in eliminating nitrate from streamflow and groundwater. More than 90% of the nitrate inventory was stored at depths from 1 m to the bedrock surface in the upland regolith. Nitrate with light nitrogen (14N) isotopes was preferentially adsorbed by the variable charge soils with the help of weak electrostatic attraction. The contribution of soil organic N (SON) to the nitrate in the upland catchment streamflow was found to be highest with a mean of 56%, followed by manure and sewage (MS, 26%), NH4+ in chemical fertilizers (CF, 17%). For the paddy subcatchment streamflow, the contribution of SON (68%) was highest, followed by CF (19%) and MS (13%). In contrast, for the groundwater, the contributions of CF and SON were 60% and 36%, respectively. For the regolith, the contributions of CF and SON were 72% and 25%, respectively. Thus the most important sources of nitrate were SON for the streamflow and CF for the groundwater and regolith. These findings show that the different partitioning and isotopic fractionation within the regolith strongly affect the sources of nitrate reaching groundwater, compared to the sources reaching surface water.

Fractures in carbonate rocks make the reservoir and its elastic properties complex, which creates challenges in the geophysical exploration of oil and gas resources. To study the elastic properties of fractured carbonate rocks, pore-scale numerical simulations and microscopic analyses are necessary for theoretically comprehensive analysis. First, a digital core of carbonate rock was established from X-ray CT scan using medium filtering and threshold segmentation method technologies. Then, some carbonate rock digital cores with different types of fractures were constructed by inserting fractures into scanning slices. Based on the theory of elastic mechanics, the elastic properties of carbonate rock cores with fractures were calculated using the finite-element method, and the effects of the microscopic factors on the rock elastic properties were studied in detail. The calculated stiffness-matrix elements of the digital core are consistent with the Gassmann fluid-substitution theory in isotropic rock. For the digital cores with coin-shaped and penetrating fractures, the influence of fracture density, fracture opening, and fracture length on the stiffness-matrix elements and the wave velocity were studied in detail through pore-scale simulations. This work provides a new perspective for carbonate rock physics and lays a foundation for multi-scale analysis of acoustic properties in carbonate rock reservoirs.

In this reply, we address the main issues raised by Yao et al. (2020) and conduct additional analyses, particularly on the seismic station clock problems and the influence of earthquake depth. We show that although the depth is an important issue to consider, the depth difference of our doublets is too small to be significant. Extensive analyses reaffirm our previous conclusions that (1) the inner core (IC) temporal changes come mostly from the interior (rather than its surface) and the proposal that the IC surface as the sole source of the temporal changes can be ruled out; (2) the most reasonable and simplest explanation so far for the temporal changes is an IC differential rotation; and (3) absolute-time based method in previous studies should be avoided for studies of temporal changes of less than 0.15 s unless the clock issue is resolved.

The hydro-acoustic noise radiating from underwater tunnels during vehicle passage may be harmful to aquatic fauna, and this is a particular concern for endangered species. Therefore, the effects of underwater noise radiation and propagation on aquatic biodiversity must be investigated. In this study, the dynamic response of the sediment and tunnel structure in the Yangtze River in China was explored by conducting a field test, and the associated noise radiation from the tunnel was recorded and investigated. A three-dimensional numerical model was then developed to simulate the vibration of the tunnel-sediment coupling system induced by random traffic-flow models. Next, a modal acoustic transfer vector-based method was used to predict underwater noise radiation by use of a three-dimensional finite-element acoustic model. Finally, the accuracy of the simulated results was verified by comparison with measurements. The results showed that the noise radiation induced by passing vehicles was approximately 14 dB greater than the background noise, with a main frequency range of 12–25 Hz. The random traffic-flow model had obvious influence of the simulated noise level above 20 Hz. Vehicle-induced underwater noise may thus have a direct effect on fish species that can perceive low-frequency sound pressure. The proposed method can be used for further investigation of methods to reduce the effect of underwater noise on aquatic fauna, especially endangered species.

Accurately mapping crop area using coarse spatial resolution remote sensing imageries is challenging due to the existence of various spatial heterogeneities. The objective of this study is to analyze the accuracy of crop classification and area estimation affected by spatial heterogeneities, especially for sample impurity and landscape heterogeneity. The Normalized Difference Vegetation Index (NDVI) time series calculated from Moderate Resolution Imaging Spectroradiometer (MODIS) MOD09Q1 8-day composites and the derived phenology metrics were used to classify crop areas over Manitoba, Canada. The Classification and Regression Trees (CART) approach was applied in the classification. The Agriculture and Agri-Food Canada (AAFC) Land Cover Dataset with 30 m spatial resolution was used as the base map to determine the study regions and training and validation samples. The results allowed to conclude that: (1) the classification accuracy of MODIS imagery is sensitive to both sample impurity and landscape heterogeneity. Purity limitations in samples can have a large impact on the classification accuracy. Regions with more homogenous pixels are more likely to be accurately classified and vice versa; (2) the crop area estimation error is less sensitive to sample impurity. It is not only determined by the purity of training samples but also by the actual purity condition of the crop type. The purest training sample group does not correspond well with the lowest error; (3) the impact of configurational heterogeneity on the area estimation is more significant than that of the compositional heterogeneity. Overall, both the sample impurity and landscape heterogeneities can largely affect the classification accuracy while only configurational heterogeneity has significant influence on crop area estimation.

The present study aims at exploring whether aspects of urban form (compactness ratio and elongation ratio) are associated with urban smog (particulate matter) in China. Quantitative indicators relating to urban form and urban smog were selected and quantified for 30 Chinese cities, for the reference years 2000, 2007, and 2010, by using a combination of compiled statistical data, remote sensing, and geographical information system data. Panel data analysis was used to evaluate the degree of association between measures of urban form and urban smog, while controlling for urban population, built-up area green coverage rate, power consumption, SO2 emissions, gross value of industrial output, gross industrial output, and buses per capita. The results indicate that urban compactness and urban elongation were positively correlated to urban particulate matter. It is therefore recommended to consider the implication of urban form on smog as part of urban planning and as part of ongoing strategies to mitigate the deleterious consequences of air pollution.

Conventional soil maps generally contain one or more soil types within a single soil polygon. But their geographic locations within the polygon are not specified. This restricts current applications of the maps in site-specific agricultural management and environmental modelling. We examined the utility of legacy pedon data for disaggregating soil polygons and the effectiveness of similarity-based prediction for making use of the under- or over-sampled legacy pedon data for the disaggregation. The method consisted of three steps. First, environmental similarities between the pedon sites and each location were computed based on soil formative environmental factors. Second, according to soil types of the pedon sites, the similarities were aggregated to derive similarity distribution for each soil type. Third, a hardening process was performed on the maps to allocate candidate soil types within the polygons. The study was conducted at the soil subgroup level in a semi-arid area situated in Manitoba, Canada. Based on 186 independent pedon sites, the evaluation of the disaggregated map of soil subgroups showed an overall accuracy of 67% and a Kappa statistic of 0.62. The map represented a better spatial pattern of soil subgroups in both detail and accuracy compared to a dominant soil subgroup map, which was commonly used in practice. Incorrect predictions mainly occurred in the agricultural plain area and the soil subgroups that are very similar in taxonomy, indicating that new environmental covariates need to be developed. We concluded that the combination of legacy pedon data with similarity-based prediction is an effective solution for soil polygon disaggregation.

Soil moisture content (SMC) is a key hydrological parameter in agriculture, meteorology and climate change, and understanding of spatio-temporal distributions of SMC in farmlands is important to address the precise irrigation scheduling. However, the hybrid interaction of static and dynamic environmental parameters makes it particularly difficult to accurately and reliably model the distribution of SMC. At present, deep learning wins numerous contests in machine learning and hence deep belief network (DBN), a breakthrough in deep learning is trained to extract the transition functions for the simulation of the cell state changes. In this study, we used a novel macroscopic cellular automata (MCA) model by combining DBN to predict the SMC over an irrigated corn field (an area of 22 km2) in the Zhangye oasis, Northwest China. Static and dynamic environmental variables were prepared with regard to the complex hydrological processes. The widely used neural network, multi-layer perceptron (MLP), was utilized for comparison to DBN. The hybrid models (MLP-MCA and DBN-MCA) were calibrated and validated on SMC data within four months, i.e. June to September 2012, which were automatically observed by a wireless sensor network (WSN). Compared with MLP-MCA, the DBN-MCA model led to a decrease in root mean squared error (RMSE) by 18%. Thus, the differences of prediction errors increased due to the propagating errors of variables, difficulties of knowing soil properties and recording irrigation amount in practice. The sequential Gaussian simulation (sGs) was performed to assess the uncertainty of soil moisture estimations. Calculated with a threshold of SMC for each grid cell, the local uncertainty of simulated results in the post processing suggested that the probability of SMC less than 25% will be difference in different areas at different time periods. The current results showed that the DBN-MCA model performs better than the MLP-MCA model, and the DBN-MCA model provides a powerful tool for predicting SMC in highly non-linear forms. Moreover, because modeling soil moisture by using environmental variables is gaining increasing popularity, DBN techniques could contribute a lot to enhancing the calibration of MCA-based SMC estimations and hence provide an alternative approach for SMC monitoring in irrigation systems on the basis of canals.

Mid-subtropical forests are the main vegetation type of global terrestrial biomes, and are critical for maintaining the global carbon balance. However, estimates of forest biomass increment in mid-subtropical forests remain highly uncertain. It is critically important to determine the relative importance of different biotic and abiotic factors between plants and soil, particularly with respect to their influence on plant regrowth. Consequently, it is necessary to quantitatively characterize the dynamic spatiotemporal distribution of forest carbon sinks at a regional scale. This study used a large, long-term dataset in a boosted regression tree (BRT) model to determine the major components that quantitatively control forest biomass increments in a mid-subtropical forested region (Wuyishan National Nature Reserve, China). Long-term, stand-level data were used to derive the forest biomass increment, with the BRT model being applied to quantify the relative contributions of various biotic and abiotic variables to forest biomass increment. Our data show that total biomass (t) increased from 4.62 × 106 to 5.30 × 106 t between 1988 and 2010, and that the mean biomass increased from 80.19 ± 0.39 t ha−1 (mean ± standard error) to 94.33 ± 0.41 t ha−1 in the study region. The major factors that controlled biomass (in decreasing order of importance) were the stand, topography, and soil. Stand density was initially the most important stand factor, while elevation was the most important topographic factor. Soil factors were important for forest biomass increment but have a much weaker influence compared to the other two controlling factors. These results provide baseline information about the practical utility of spatial interpolation methods for mapping forest biomass increments at regional scales.

Complex social-ecological systems models typically need to consider deeply uncertain long run future conditions. The influence of this deep (i.e. incalculable, uncontrollable) uncertainty on model parameter sensitivities needs to be understood and robustly quantified to reliably inform investment in data collection and model refinement. Using a variance-based global sensitivity analysis method (eFAST), we produced comprehensive model diagnostics of a complex social-ecological systems model under deep uncertainty characterised by four global change scenarios. The uncertainty of the outputs, and the influence of input parameters differed substantially between scenarios. We then developed sensitivity indicators that were robust to this deep uncertainty using four criteria from decision theory. The proposed methods can increase our understanding of the effects of deep uncertainty on output uncertainty and parameter sensitivity, and incorporate the decision maker's risk preference into modelling-related activities to obtain greater resilience of decisions to surprise. We performed global sensitivity analyses of a land use model under deep uncertainty.Deep uncertainty was characterised by internally consistent global change scenarios.The influence of scenarios on output uncertainty and parameter sensitivity was significant.Sensitivity indicators robust to deep uncertainty were calculated using four decision criteria.Our methods can better inform efforts to improve model outputs under deep uncertainty.

This paper presents an approach to predicting three-dimensional (3D) variation of soil organic matter (SOM) concentration by integrating a similarity-based method with depth functions. It was tested in a small hilly landscape. A depth function model was constructed to fit SOM profile distribution using a linear relation in the topsoil and a power function in the subsoil. Then, under the assumption that similar environmental conditions at two sites would lead to the development of similar profile morphologies and thus similar depth function parameters, the similarity-based method was used to spatially interpolate the depth function parameters based on their relationships with environmental variables. With the values of the parameters for every location, a 3D map of SOM distribution was generated. The predicted SOM pattern well reproduced the statistical distribution of the pedon dataset used in this study. The overall mean error (ME) was 0.06 g kg− 1 and ratio of performance to deviation (RPD) was 2.34. We conclude that the proposed approach is effective and accurate for 3D SOM prediction. It overcomes two drawbacks of the frequently used pseudo 3D soil mapping approach: (1) the neglect of vertical soil pattern when performing horizontal soil predictions, and (2) the repeated applications of depth function fittings in the mapping process, both of which may lead to prediction errors. Moreover, the similarity-based method is a transparent and traceable prediction process, allowing for easy interpretation of its results. This is useful for understanding soil–environmental relationships and processes. The method thus is an attractive alternative to the commonly used non-linear “black-box” techniques such as artificial neural networks.

The earthquake coda is the residual vibration energy long after the main arrivals of an earthquake. Previous studies of coda waves have focused on high frequencies and small earthquakes, which are generally attributed to the scattering from the Earth's internal heterogeneities. Here we examined the early coda of great earthquakes at long periods (50 to 300 s) at time window of 10,000 to 50,000 s after the origin time. We used 10 major earthquakes (magnitude > 8.0) in recent decades that were recorded at global seismic networks. We found that the coda energy can be well represented by a simple exponential decay model for the early coda. The variation of the initial coda energy with distance is dominated by the effect of geometric spreading, suggesting that the scattering of the Earth's internal heterogeneities contributes little to the long-period early coda. The energy decay rate is largely insensitive to earthquake source (location, magnitude, focal mechanism, or rupture process), station location, or 3D variation of the earth's internal structure, making it ideal to constrain the average global attenuation structure. A small modification of the quality factor based on a global reference model can fit the coda energy decay well. The long-period coda energy may potentially provide a new way to determine the seismic moment of a major earthquake.

Nitrate accumulation at depth in variable charge soils in humid tropics and subtropics affects nitrogen (N) leaching to groundwater. However, the mechanisms on the nitrate accumulation in relation to regolith (soil and saprolite) properties are poorly understood. In this study, four upland regoliths were selected in a typical red soil Critical Zone of humid subtropical China. These regoliths with a thickness of 6–8 m, which developed from Quaternary red clay underlain by sandstone bedrock, were acidic, highly weathered, and clay loam to clay in texture. We analyzed the relationships between nitrate concentrations and the regolith physiochemical properties. The results showed that regolith pH was negatively correlated with nitrate concentrations, whereas crystalline iron (Fe) and aluminum (Al) and anion exchange capacity (AEC) were positively correlated with nitrate concentrations at depths below both soil surface and 1 m. Bulk density (BD) was significantly related to nitrate concentrations only at depths below soil surface, while depth, clay, amorphous Fe and Al, total organic carbon (C), and total N were significantly related to nitrate concentrations only at depths below 1 m. There was no significant correlation between nitrate concentrations and sand, silt, silicate bound Fe or Al, C/N, or moisture at depths below both soil surface and 1 m. Based on the random forest analysis, the relative contributions of pH, crystalline Fe, depth, AEC, BD, crystalline Al and amorphous Fe to explaining nitrate concentration variations were 28.3%, 9.5%, 4.3%, 3.4%, 2.9%, 2.8% and 2.6%, respectively. The concentration of crystalline Fe was ten times that of crystalline Al, which would account for its larger contribution to the nitrate variations. Nitrate accumulation at depth was most likely controlled both by electrostatic adsorption on crystalline Fe and Al oxides and low hydraulic conductivity of the regolith at depth, which appeared to be largely influenced by regolith pH and competitive anions. Our findings shed light on regolith internal property controls on nitrate legacy in the subtropical vadose zone and may help control nitrate contamination of groundwater in intensified agricultural regions.

Intense human activities and drastic land use changes in rapidly urbanized areas may cause serious water quality degradation. In this study, we explored the effects of land use on water quality from a landscape perspective. We took a rapidly urbanized area in Hangzhou City, China, as a case study, and collected stream water quality data and algae biomass in a field campaign. The results showed that built-up lands had negative effects on water quality and were the primary cause of stream water pollution. The concentration of total phosphorus significantly correlated with the areas of residential, industrial, road, and urban greenspace, and the concentration of chlorophyll a also significantly correlated with the areas of these land uses, except residential land. At a landscape level, the correlation analysis showed that the landscape indices, e.g., dominance, shape complexity, fragmentation, aggregation, and diversity, all had significant correlations with water quality parameters. From the perspective of land use, the redundancy analysis results showed that the percentages of variation in water quality explained by the built-up, forest and wetland, cropland, and bareland decreased in turn. The spatial composition of the built-up lands was the main factor causing stream water pollution, while the shape complexities of the forest and wetland patches were negatively correlated with stream water pollution.

Temporal changes of inner-core (IC) seismic phases have been confirmed with high-quality waveform doublets. However, the nature of the temporal changes is still controversial. We investigated systematically the temporal changes of IC refracted (PKIKP) and reflected (PKiKP) waves with a large data set of waveform doublets. We used non-IC reference phase (mainly SKP), which eliminated ambiguity where the temporal changes come from. We found that the temporal changes have always started at refracted PKIKP and the travel time changes correlate better with PKIKP. Changes in reflected PKiKP can be easily contaminated by the strong and time-varying PKIKP and coda wave trains and therefore are not reliable indicators for IC boundary changes. Combining with previous observations, we conclude that the temporal changes come mostly (if not all) from the IC interior and IC surface changes as the sole source suggested previously can be ruled out. The differential rotation of the IC shifting its heterogeneous uppermost structures is the simplest and most reasonable explanation for the origin of the time-varying IC waves. A rotation rate of about 0.05-0.1° per year with possible decadal fluctuation can reconcile all temporal change observations from body waves, IC scattering, and normal mode data.