Changes in fungal community during different phases in conventional and bioreactor composting systems according to metatranscriptomics analysis.

We determined the changes that occurred in fungal community structures and their functions in conventional and bioreactor composting systems. The Illumina MiSeq platform was employed to sequence cDNA by reverse transcription to conduct metatranscriptomics analysis of RNA, and the FUNGuild tool was applied. The α-diversity of fungi in the bioreactor composter increased throughout composting, especially in the initial three phases, but decreased in the conventional composting system. The three dominant phyla in the bioreactor system were Ascomycota (30.27%-68.50%), Mortierellomycota (3.81%-39.51%), and Basidiomycota (9.17%-30.86%). Ascomycota (76.96%-97.18%) was the main phylum in the conventional composting system. Mortierella, Guehomyces, Plectosphaerella, Chaetomium, Millerozyma, and Coprinopsis were the main genera in the bioreactor composter. In the same phase, significant differences in the fungal functions were found between the two composting methods. Available phosphorus was the main factor that affected the community structures and functions of fungi in the bioreactor composter.

Estimation of Soil Organic Matter in Arid Zones with Coupled Environmental Variables and Spectral Features.

The soil organic matter (SOM) content is a key factor affecting the function and health of soil ecosystems. For measurements of land reclamation and soil fertility, SOM monitoring using visible and near-infrared spectroscopy (Vis-NIR) is one approach to quantifying soil quality, and Vis-NIR is important for monitoring the SOM content in a broad and nondestructive manner. To investigate the influence of environmental factors and Vis-NIR spectroscopy in estimating SOM, 249 soil samples were collected from the Werigan-Kuqa oasis in Xinjiang, China, and their spectral reflectance, SOM content and soil salinity were measured. To classify and improve the prediction accuracy, we also take into account the soil salinity content as a variable indicator. Relevant environmental variables were extracted using remote sensing datasets (land-use/land-cover (LULC), digital elevation model (DEM), World Reference Base for Soil Resources (WRB), and soil texture). On the basis of Savitzky-Golay (S-G) smoothing and first derivative (FD) preprocessing of the original spectrum, three clusters were obtained by K-means clustering through the use of Vis-NIR and used as spectral classification variables. Using Vis-NIR as Model 1, Vis-NIR combined with spectral classification as Model 2, environmental variables as Model 3, and the combination of all the above variables (Vis-NIR, spectral classification, environmental variables, and soil salinity) as Model 4, a SOM content estimation model was constructed using partial least squares regression (PLSR). Using the 249 soil samples, the modeling set contained 166 samples and the validation set contained 83 samples. The results showed that Model 2 (validation r2 = 0.78) was better than Model 1 (validation r2 = 0.76). The prediction accuracy for Model 4 (validation r2 = 0.85) was better than Model 2 (validation r2 = 0.78). Among these, Model 3 was the worst (validation r2 = 0.39). Therefore, the combination of environmental variables with Vis-NIR spectroscopy to estimate SOM content is an important method and has important implications for improving the accuracy of SOM predictions in arid regions.

Estimation of Soil Organic Carbon Content in the Ebinur Lake Wetland, Xinjiang, China, Based on Multisource Remote Sensing Data and Ensemble Learning Algorithms.

Soil organic carbon (SOC), as the largest carbon pool on the land surface, plays an important role in soil quality, ecological security and the global carbon cycle. Multisource remote sensing data-driven modeling strategies are not well understood for accurately mapping soil organic carbon. Here, we hypothesized that the Sentinel-2 Multispectral Sensor Instrument (MSI) data-driven modeling strategy produced superior outcomes compared to modeling based on Landsat 8 Operational Land Imager (OLI) data due to the finer spatial and spectral resolutions of the Sentinel-2A MSI data. To test this hypothesis, the Ebinur Lake wetland in Xinjiang was selected as the study area. In this study, SOC estimation was carried out using Sentinel-2A and Landsat 8 data, combining climatic variables, topographic factors, index variables and Sentinel-1A data to construct a common variable model for Sentinel-2A data and Landsat 8 data, and a full variable model for Sentinel-2A data, respectively. We utilized ensemble learning algorithms to assess the prediction performance of modeling strategies, including random forest (RF), gradient boosted decision tree (GBDT) and extreme gradient boosting (XGBoost) algorithms. The results show that: (1) The Sentinel-2A model outperformed the Landsat 8 model in the prediction of SOC contents, and the Sentinel-2A full variable model under the XGBoost algorithm achieved the best results R2 = 0.804, RMSE = 1.771, RPIQ = 2.687). (2) The full variable model of Sentinel-2A with the addition of the red-edge band and red-edge index improved R2 by 6% and 3.2% over the common variable Landsat 8 and Sentinel-2A models, respectively. (3) In the SOC mapping of the Ebinur Lake wetland, the areas with higher SOC content were mainly concentrated in the oasis, while the mountainous and lakeside areas had lower SOC contents. Our results provide a program to monitor the sustainability of terrestrial ecosystems through a satellite perspective.

Multi-U-Net: Residual Module under Multisensory Field and Attention Mechanism Based Optimized U-Net for VHR Image Semantic Segmentation.

As the acquisition of very high resolution (VHR) images becomes easier, the complex characteristics of VHR images pose new challenges to traditional machine learning semantic segmentation methods. As an excellent convolutional neural network (CNN) structure, U-Net does not require manual intervention, and its high-precision features are widely used in image interpretation. However, as an end-to-end fully convolutional network, U-Net has not explored enough information from the full scale, and there is still room for improvement. In this study, we constructed an effective network module: residual module under a multisensory field (RMMF) to extract multiscale features of target and an attention mechanism to optimize feature information. RMMF uses parallel convolutional layers to learn features of different scales in the network and adds shortcut connections between stacked layers to construct residual blocks, combining low-level detailed information with high-level semantic information. RMMF is universal and extensible. The convolutional layer in the U-Net network is replaced with RMMF to improve the network structure. Additionally, the multiscale convolutional network was tested using RMMF on the Gaofen-2 data set and Potsdam data sets. Experiments show that compared to other technologies, this method has better performance in airborne and spaceborne images.

Using MODIS data to analyse the ecosystem water use efficiency spatial-temporal variations across Central Asia from 2000 to 2014.

It is important to understand the carbon-water cycle, which accurately reflects the temporal and spatial variabilities in ecosystem water use efficiency (WUE). In this study, the Mann-Kendall (MK) test was used to study the variabilities in the spatial patterns of the gross primary production (GPP), evapotranspiration and WUE across Central Asia [the Xinjiang Uyghur Autonomous Region (XJ) in China (CHN), Kazakhstan (KAZ), Turkmenistan (TKM), Uzbekistan (UZB), Kyrgyzstan (KGZ), and Tajikistan (TJK)] from 2000 to 2014. We compared the change results by country, land cover type, population density, and human influence. In addition, the results of GPP, evapotranspiration (ET), and WUE parameter tests were combined and classified to analyse the causes of the changes in WUE. The results showed that (1) the time series of GPP, ET and WUE exhibited no significant changes. The spatial distribution of the WUE exhibited significant increases in the northern part of KAZ, the Ili Valley and the alpine region in KGZ and exhibited decreases in south Xinjiang and the irrigated area of UZB. (2) The main land cover types that exhibited changes in WUE were farmlands and grasslands, and areas with a medium population density exhibited large WUE changes. (3) The increased WUE resulted from an increased GPP and decreased ET. The increased GPP was because of increased precipitation and the Green for Grain Project, and the decreased ET was due to the response of vegetation to drought stress; the decreased WUE was mainly caused by changes in the crops planted and unreasonable water use practices in the irrigated agricultural areas in Central Asia. This study, which is based on the variabilities in WUE spatial patterns, should provide a theoretical basis for ecosystems in arid land areas.

Assessing the Response of Ecosystem Water Use Efficiency to Drought During and after Drought Events across Central Asia.

The frequency and intensity of drought are expected to increase worldwide in the future. However, it is still unclear how ecosystems respond to drought. Ecosystem water use efficiency (WUE) is an essential ecological index used to measure the global carbon-water cycles, and is defined as the carbon absorbed per unit of water lost by the ecosystem. In this study, we applied gross primary productivity (GPP), evapotranspiration (ET), land surface temperature (LST), and normalized difference vegetation index (NDVI) data to calculate the WUE and drought index (temperature vegetation dryness index (TVDI)), all of which were retrieved from moderate resolution imaging spectroradiometer (MODIS) data. We compared the mean WUE across different vegetation types, drought classifications, and countries. The temporal and spatial changes in WUE and drought were analyzed. The correlation between drought and WUE was calculated and compared across different vegetation types, and the differences in WUE between drought and post-drought periods were compared. The results showed that (1) ecosystems with a low (high) productivity had a high (low) WUE, and the mean ecosystem WUE of Central Asia showed vast differences across various drought levels, countries, and vegetation types. (2) The WUE in Central Asia exhibited an increasing trend from 2000 to 2014, and Central Asia experienced both drought (from 2000 to 2010) and post-drought (from 2011 to 2014) periods. (3) The WUE showed a negative correlation with drought during the drought period, and an obvious drought legacy effect was found, in which severe drought affected the ecosystem WUE over the following two years, while a positive correlation between WUE and drought was found in the post-drought period. (4) A significant increase in ecosystem WUE was found after drought, which revealed that arid ecosystems exhibit high resilience to drought stress. Our results can provide a specific reference for understanding how ecosystems will respond to climate change.

Soil Moisture Retrival Based on Sentinel-1 Imagery under Sparse Vegetation Coverage.

Soil moisture is an important aspect of heat transfer process and energy exchange between land-atmosphere systems, and it is a key link to the surface and groundwater circulation and land carbon cycles. In this study, according to the characteristics of the study area, an advanced integral equation model was used for numerical simulation analysis to establish a database of surface microwave scattering characteristics under sparse vegetation cover. Thus, a soil moisture retrieval model suitable for arid area was constructed. The results were as follows: (1) The response of the backscattering coefficient to soil moisture and associated surface roughness is significantly and logarithmically correlated under different incidence angles and polarization modes, and, a database of microwave scattering characteristics of arid soil surface under sparse vegetation cover was established. (2) According to the Sentinel-1 radar system parameters, a model for retrieving spatial distribution information of soil moisture was constructed; the soil moisture content information was extracted, and the results were consistent with the spatial distribution characteristics of soil moisture in the same period in the research area. (3) For the 0⁻10 cm surface soil moisture, the correlation coefficient between the simulated value and the measured value reached 0.8488, which means that the developed retrieval model has applicability to derive surface soil moisture in the oasis region of arid regions. This study can provide method for real-time and large-scale detection of soil moisture content in arid areas.

[Soil Salinity Modelling Study with Salinity Inference Model Based on the Integration of Soil and Vegetation Spectrum in Arid Land].

Only using soil spectrum to model soil salinity is not enough to meet the actual demands because of the complicated soil context. As a remotely sensed indicator, the vegetation type and its growing condition can provide a spatial overview of salinity distribution. Based on the synergistic relationship between soil salinity and vegetation in arid land, this paper tries to combine the spectrum of soil and vegetation to quantitatively estimate the salt content with the help of the concept of two-dimensional feature space. After the analysis of scatter diagram, the soil salinity detecting model was constructed to improve reasoning precision. However, because the impact of soil reflectance on the quantification of vegetation parameters under the individual pixel, the Normalized Difference Vegetation Index (NDVI) was difficult to accurately obtain sparse vegetation cover in arid areas. Therefore, in order to avoid the limitations of NDVI, the Combined Vegetation Indicative Factor(CVIF)was created and supported by Linear Spectral Unmixing Model (LSUM). Then, the study constructed the feature space based on the CVIF and salinity index (SI) and analyzed the response relationship between soil salinity and the trend of scattered points. Finally, a new and operational model termed Salinity Inference Model (SID) was developed. The results showed that the CVIF (R2>0.84, RMSE=3.92) performed better than NDVI(R2>0.66, RMSE=13.77), which means the CVIF was more appropriate for analyzing variations in vegetation cover (particularly halophytes) than NDVI in the study area. The SID was then compared to the Combined Cpectral Response Index (COSRI)(NDVI-based) from field measurements with respect to the soil salt content. The results indicated that the SID values are highly correlated with soil salinity, in contrast to the performance of COSRI. Strong exponential relationships were observed between soil salinity and SID (R2>0.86, RMSE<6.86) compared to COSRI (R2=0.71, RMSE=16.21). These results suggested that the feature space related to biophysical properties combined with CVIF and SI can effectively provide information on soil salinity.

[Study on Estimation of Deserts Soil Total Phosphorus Content by Vis-NIR Spectra with Variable Selection].

In this paper, 300 samples of desert soil collected in the Ebinur Lake Wetland Nature Reserve of Xinjiang were used as the research subject, and the visible/near-infrared spectra data about the soil obtained with the ASD Field Spec 3 HR spectrometer and the data about total phosphorus in the soil obtained through chemical analysis were used as the data sources; following Savizky-Golay smoothing, standard normal variation transformation and the first-order differential pretreatment, the combination of ant colony optimization interval partial least squares (ACO-iPLS) and genetic algorithm interval partial least squares (GA-iPLS) were employed to extract the characteristic wavelengths of the total phosphorus content in desert soil, before the partial least squares regression model for predicting the total-phosphorus content in soil was constructed; and this model was compared with the full-spectrum partial least squares model, ACO-iPLS and GA-iPLS. According to the results: through filte- ring with ACO-iPLS, the total-phosphorus characteristic wavebands in the desert soil were 500-700, 1 101-1 300, 1 501-1 700, and 1 901-2 100 nm; through further variable selection with GA-iPLS, 13 effective wavelengths with the minimum colinearity were selected, which were respectively: 1621, 546, 1259, 573, 1572, 1527, 564, 1 186, 1 988, 1541, 2024, 1 118, and 1 191 nm. According to the comparison of modeling methods, the most accurate model was the one based on the characteristic variables selected with the combination of ACO-iPLS and GA-iPLS, followed by the ones with genetic algorithm, ant colony optimization algorithm and the full spectrum method. For the total phosphorus content in soil model established with the combination of ACO-iPLS and GA-iPLS, the root mean square error of cross validation (RMSECV) and the root mean square error of prediction (RMSEP) were respectively 0.122 and 0.108 mg x g(-1), and the related coefficient for cross validation (R(c)) and the related coefficient for prediction (R(p)) were 0.535 7 and 0.555 9, respectively. Therefore, it can be seen that the model constructed through Savizky-Golay smoothing, standard normal variation transformation and the first-order differential pretreatment and by using the combination of AGO-iPLS and GA-iPLS has simple structure, high prediction accuracy and good robustness, and can be used for estimating the total phosphorus content in desert soil.

The influence of natural and human factors in the shrinking of the Ebinur Lake, Xinjiang, China, during the 1972-2013 period.

The Ebinur Lake is a closed inland lake located within the arid region of the Xinjiang Autonomous Region in the northwestern part of China, near the Kazakhstan border. The shrinkage of the lake area is believed to be caused by ecological environmental deterioration and has become an important restraining factor for the social development of the local population. Of all the lakes in the Xinjiang Autonomous Region, the Ebinur Lake is the most severely impacted water body. The lake has undergone change in size naturally for over thousands of years due to natural causes. However, the authors observed the dramatic changes in the freshwater resources of this region from the aerial images from 1972 to 2013. Thus, this paper traces and analyzes the change in the Ebinur Lake surface area in the past 41 years. A set of six satellite images acquired between 1972 and 2013 was employed to map the change in the surface area of the Ebinur Lake using the water index approach. The authors applied the traditional normalized difference water index (NDWI) and the modified normalized difference water index (MNDWI) to quantify the change in the water body area of the Ebinur Lake during the study period. The results indicate that the lake area has experienced a dramatic decrease of 31.4% from 1972 to 2013. The paper also examines the natural processes and human activities that may have contributed to the decrease in the lake area. The results show that the decrease in total lake area appears to coincide with periods of rapid land reclamation in the study area. Moreover, the uncontrolled land reclamation activities, such as irrigation, can increase the sedimentation in the Ebinur Lake thereby reducing the lake size. Reduction of the lake area has a negative ecological impact on the environment and on human life and property. The lake area is the most important factor to ensure the environment of the watershed and the key index to measure the environment balance.

[EEMD de-noising of reflecting spectrum in soil profiles].

In the present paper, based on the multi-resolution attribute of EEMD (ensemble empirical mode decomposition) method, we presented a new de-noising method for analyzing spectrum, and applied it to process the reflecting spectrum data of 33 soil profiles in the typical oasis located in the middle reaches of the Tarim River. To explore the de-noising effect of EEMD threshold method for reflecting spectrum in soil profiles; we compared EEMD threshold method with wavelet transform method. The results showed that compared with traditional wavelet transform method, the signal to noise ratio (SNR) was improved from 14. 8366 to 34. 2757 dB, and the root mean square error (RMSE) was reduced to 7. 2406 X 10(-6) from 6. 7861 X 10(-5) and the correlation coefficient (r) increased from 0. 9825 to 0. 9998. Therefore, three de-noising effect indicators of EEMD threshold method are better than those of wavelet transform method. This proved that the EEMD threshold method can effectively eliminate the noise of soil-profile spectrum and also preserve the detailed information of the original spectra well. Thus, the analysis precision of the spectrum will be improved. In addition, by contrast with the wavelet threshold method, the EEMD threshold method is adaptive and is fairly reliable. As a new method for spectral pretreatment, the EEMD threshold method will have a good application prospect in spectra de-noising.

[Study on the soil salinization monitoring based on measured hyperspectral and HSI data].

The present paper selects the Kuqa Oasis as the study area, studies spectrum characteristics of soil salinity, and establishes soil spectrum library. Through transforming and analyzing varying degrees of soil salinization reflectance spectra data in the typical study area, and selecting the most sensitive spectral bands in response to salinization, we established the measured hyperspectral soil salinity monitoring model, and by correcting the soil salinity monitoring model established by HIS image through scale effect conversion improved the model accuracy under the conditions of a regional-scale monitoring of soil salinization. The results show that both measured hyperspectral soil salinity monitoring model and HSI image soil salinity inversion model have good accuracy, model determination coefficient (R2) is higher than 0.57 and the model stability is better. Compared with the corrected HSI image soil salinity inversion model and uncorrected HSI image soil salinity inversion model, the coefficient of determination has been greatly improved, which increased from 0.571 to 0.681, and through the 0.01 significance level, the root mean square error (RMSE) value is 0.277. The correction HIS image soil salinization monitoring model can better improve the model accuracy under the condition of regional scale soil salinization monitoring, and using this method to carry out the soil salinization quantitative remote sensing monitoring is feasible, and also can provide scientific reference for future research.

Responses of vegetation cover to hydro-climatic variations in Bosten Lake Watershed, NW China.

Amidst the backdrop of global climate change, it is imperative to comprehend the intricate connections among surface water, vegetation, and climatic shifts within watersheds, especially in fragile, arid ecosystems. However, these relationships across various timescales remain unclear. We employed the Ensemble Empirical Mode Decomposition (EEMD) method to analyze the multifaceted dynamics of surface water and vegetation in the Bosten Lake Watershed across multiple temporal scales. This analysis has shed light on how these elements interact with climate change, revealing significant insights. From March to October, approximately 14.9-16.8% of the areas with permanent water were susceptible to receding and drying up. Both the annual and monthly values of Bosten Lake's level and area exhibited a trend of initial decline followed by an increase, reaching their lowest point in 2013 (1,045.0 m and 906.6 km2, respectively). Approximately 7.7% of vegetated areas showed a significant increase in the Normalized Difference Vegetation Index (NDVI). NDVI volatility was observed in 23.4% of vegetated areas, primarily concentrated in the southern part of the study area and near Lake Bosten. Regarding the annual components (6 < T < 24 months), temperature, 3-month cumulative NDVI, and 3-month-leading precipitation exhibited the strongest correlation with changes in water level and surface area. For the interannual components (T≥ 24 months), NDVI, 3-month cumulative precipitation, and 3-month-leading temperature displayed the most robust correlation with alterations in water level and surface area. In both components, NDVI had a negative impact on Bosten Lake's water level and surface area, while temperature and precipitation exerted positive effects. Through comparative analysis, this study reveals the importance of temporal periodicity in developing adaptive strategies for achieving Sustainable Development Goals in dryland watersheds. This study introduces a robust methodology for dissecting trends within scale components of lake level and surface area and links these trends to climate variations and NDVI changes across different temporal scales. The inherent correlations uncovered in this research can serve as valuable guidance for future investigations into surface water dynamics in arid regions.

UAV hyperspectral analysis of secondary salinization in arid oasis cotton fields: effects of FOD feature selection and SOA-RF.

Secondary salinization is a crucial constraint on agricultural progress in arid regions. The specific mulching irrigation technique not only exacerbates secondary salinization but also complicates field-scale soil salinity monitoring. UAV hyperspectral remote sensing offers a monitoring method that is high-precision, high-efficiency, and short-cycle. In this study, UAV hyperspectral images were used to derive one-dimensional, textural, and three-dimensional feature variables using Competitive adaptive reweighted sampling (CARS), Gray-Level Co-occurrence Matrix (GLCM), Boruta Feature Selection (Boruta), and Brightness-Color-Index (BCI) with Fractional-order differentiation (FOD) processing. Additionally, three modeling strategies were developed (Strategy 1 involves constructing the model solely with the 20 single-band variable inputs screened by the CARS algorithm. In Strategy 2, 25 texture features augment Strategy 1, resulting in 45 feature variables for model construction. Strategy 3, building upon Strategy 2, incorporates six triple-band indices, totaling 51 variables used in the model's construction) and integrated with the Seagull Optimization Algorithm for Random Forest (SOA-RF) models to predict soil electrical conductivity (EC) and delineate spatial distribution. The results demonstrated that fractional order differentiation highlights spectral features in noisy spectra, and different orders of differentiation reveal different hidden information. The correlation between soil EC and spectra varies with the order. 1.9th order differentiation is proved to be the best order for constructing one-dimensional indices; although the addition of texture features slightly improves the accuracy of the model, the integration of the three-waveband indices significantly improves the accuracy of the estimation, with an R2 of 0.9476. In contrast to the conventional RF model, the SOA-RF algorithm optimizes its parameters thereby significantly improving the accuracy and model stability. The optimal soil salinity prediction model proposed in this study can accurately, non-invasively and rapidly identify excessive salt accumulation in drip irrigation under membrane. It is of great significance to improve the growing conditions of cotton, increase the cotton yield, and promote the sustainable development of Xinjiang's agricultural economy, and also provides a reference for the prevention and control of regional soil salinization.

[Research on remote sensing monitoring of soil salinization based on measured hyperspectral and EM38 data].

In the present study, the delta oasis between the Weigan River and the Kuqa River was selected as our study area. Firstly, the measured hyperspectral data related to different soil salinization extent was combined with electromagnetic induction instrument (EM38) in order to establish a soil salinization monitoring model; Secondly, by using the scaling transformation method, the model was adopted to calibrate the soil salinity index calculated from Landsat-TM images. Thirdly, the calibrated Landsat-TM images were used for the retrieval of regional soil salinity, and the retrieved data was verified based on the measured data. We found that at wavelengths of 456, 533, 686 and 1 373 nm, the interpretated data of EM38 were highly correlated with soil spectral reflectance (obtained via first order differentiation transformation of the spectra). Additionally, the soil salinity index model constructed from the combination of 456, 686 and 1 373 nm waveband was the best model among the different saliniza tion monitoring models. The authors' conclusion is that with R2 = 0.799 3 (p < 0.01), extracting the salinity information at regional scale by combining the electromagnetic and multispectral data performed better than those monitoring models with only salinity index extracted from multispectral remote sensing method (R2 = 0.587 4, p < 0 01). Our findings provides scientific bases for the future studies related to more accurate monitoring and prediction of soil salinization.

Identification of dust aerosols, their sources, and the effect of soil moisture in Central Asia.

Dust aerosols in Central Asia are an important factor in global climate change and attribution studies. Identifying the source of dust in Central Asia is crucial for understanding the ecological environment and climate, locally and globally. In this study, daily dust aerosol data were calculated and extracted for Central Asia from 2003 to 2018. The multi-year trends of dust aerosols were analyzed, dust sources were identified, the characteristics of dust aerosols in dust sources were analyzed, and the influence of soil moisture on sand initiation was explored. The results show that there are distinct seasonal characteristics in the spatial distribution of dust aerosols in Central Asia. The proportion of the area in the zone of high dust aerosols was the greatest in spring. Nearly half of the dust aerosol areas exhibited an increasing trend. A high incidence of dust sources was mainly distributed in the southern Xinjiang region. The trend of change in the dust area first increased and then decreased. With the increase in soil moisture under different wind speed conditions, the aerosols from dust sources all showed an exponentially decreasing trend, and the increase in soil moisture led to an increase in the wind speed threshold of sand initiation. This study provides basic data support for the study of dust aerosols, identifies dust sources, and provides a basis for studying the radiative forcing and climate effects of dust aerosols in Central Asia.

Effects of the Rainfall Intensity and Slope Gradient on Soil Erosion and Nitrogen Loss on the Sloping Fields of Miyun Reservoir.

Environmental loss is primarily caused by soil, water, and nutrient loss, and runoff is associated with nutrient transport and sediment loss. Most existing studies have focused on one influencing factor, namely slope gradient or rainfall intensity, for slope erosion and nutrient loss, but the joint effects of the two factors have rarely been researched. In this context, the impact of slope gradients (0°, 5°, 10°, and 15°) and rainfall intensities (30, 40, 50, 60, 70, and 80 mm/h) on soil erosion and nutrient loss on the sloping fields of Miyun Reservoir were explored using the indoor artificial rainfall simulation testing system. Based on the results of the study, the variation of runoff coefficient with slope gradient was not noticeable for rainfall intensities <40 mm/h; however, for rainfall intensities >40 mm/h, the increased range of runoff coefficient doubled, and the increase was the fastest under 0° among the four slope gradients. The slope surface runoff depth and runoff rate showed positive correlations with the rainfall intensity (r = 0.875, p < 0.01) and a negative correlation with the slope gradient. In addition, the cumulative sediment yield was positively related to the slope gradient and rainfall intensity (r > 0.464, p < 0.05). Moreover, the slope surface runoff-associated and sediment-associated loss rates of total nitrogen (TN) rose as the rainfall intensity or slope gradient increased, and significant linear positive correlations were found between the runoff-associated TN loss rate (NLr) and the runoff intensity and between the sediment-associated NLr and the erosion intensity. In addition, there were positive linear correlations between slope runoff-associated or sediment-associated TN loss volumes and rainfall intensity, surface runoff, and sediment loss volumes, which were highly remarkable. The slope gradient had a significant positive correlation with the slope surface runoff-associated TN loss at 0.05 (r = 0.452) and a significant positive correlation with the sediment-associated TN loss at the level of 0.01 (r = 0.591). The rainfall intensity exhibited extremely positive correlations with the slope surface runoff-associated and sediment-associated TN loss at 0.01 (r = 0.717 and 0.629) Slope gradients have less effect on nitrogen loss on sloped fields than rainfall intensity, mainly because rainfall intensity affects runoff depth. Based on the findings of this study, Miyun Reservoir may be able to improve nitrogen loss prevention and control.

Laws Governing Nitrogen Loss and Its Numerical Simulation in the Sloping Farmland of the Miyun Reservoir.

Surface flow (SF) and subsurface flow (SSF) are important hydrological processes occurring on slopes, and are driven by two main factors: rainfall intensity and slope gradient. To explore nitrogen (N) migration and loss from sloping farmland in the Miyun Reservoir, the characteristics of total nitrogen (TN) migration and loss via SF and SSF under different rainfall intensities (30, 40, 50, 60, 70, and 80 mm/h) and slope gradients (5°, 10°, and 15°) were studied using indoor stimulated rainfall tests and mathematical models. Nitrogen loss via SF and SSF was found to increase exponentially and linearly with time, respectively, with SSF showing 14-78 times higher loss than SF. Under different rainfall intensities, SSF generally had larger TN loss loading than SF, thereby indicating that SSF was the main route for TN loss. However, the TN loss loading proportion via SF increasing from 14.03% to 35.82% with increasing rainfall intensity is noteworthy. Furthermore, compared with the measurement data, the precision evaluation index Nash-Suttcliffe efficient (NSE) and the determination coefficient (R2) of the effective mixing depth model in the numerical simulation of TN loss through SF in the sloping farmland in the Miyun Reservoir were 0.74 and 0.831, respectively, whereas those of the convection-dispersion equation for SSF were 0.81 and 0.811, respectively, thus indicating good simulation results. Therefore, this paper provides a reference for studying the mechanism of N migration and loss in sloping farmland in the Miyun Reservoir.

Fertilization altered co-occurrence patterns and microbial assembly process of ammonia-oxidizing microorganisms.

Ammonia-oxidizing archaea and bacteria (AOA and AOB, respectively) are important intermediate links in the nitrogen cycle. Apart from the AOA and AOB communities in soil, we further investigated co-occurrence patterns and microbial assembly processes subjected to inorganic and organic fertilizer treatments for over 35 years. The amoA copy numbers and AOA and AOB communities were found to be similar for the CK and organic fertilizer treatments. Inorganic fertilizers decreased the AOA gene copy numbers by 0.75-0.93-fold and increased the AOB gene copy numbers by 1.89-3.32-fold compared to those of the CK treatment. The inorganic fertilizer increased Nitrososphaera and Nitrosospira. The predominant bacteria in organic fertilizer was Nitrosomonadales. Furthermore, the inorganic fertilizer increased the complexity of the co-occurrence pattern of AOA and decreased the complexity pattern of AOB comparing with organic fertilizer. Different fertilizer had an insignificant effect on the microbial assembly process of AOA. However, great difference exists in the AOB community assembly process: deterministic process dominated in organic fertilizer treatment and stochastic processes dominated in inorganic fertilizer treatment, respectively. Redundancy analysis indicated that the soil pH, NO3-N, and available phosphorus contents were the main factors affecting the changes in the AOA and AOB communities. Overall, this findings expanded our knowledge concerning AOA and AOB, and ammonia-oxidizing microorganisms were more disturbed by inorganic fertilizers than organic fertilizers.

Effects of Biochar on the Fluorescence Spectra of Water-Soluble Organic Matter in Black Soil Profile after Application for Six Years.

At present, extracting water-soluble organic matter (WSOM) from agricultural organic waste is primarily used to evaluate soil organic matter content in farmland. However, only a few studies have focused on its vertical behavior in the soil profile. This study aims to clarify the three-dimensional fluorescence spectrum characteristics of the WSOM samples in 0-60 cm black soil profile before and after different chemical fertilizer treatments after six years of fertilization. Fluorescence spectroscopy combined with fluorescence and ultraviolet-visible (UV-Vis) spectroscopies are used to divide four different fertilization types: no fertilization (T0), nitrogen phosphorus potassium (NPK) (T1), biochar (T2), biochar + NPK (T3), and biochar + N (T4) in a typical black soil area. The vertical characteristics of WSOC are also analyzed. The results showed that after six years of nitrogen application, T2 had a significant effect on the fluorescence intensity of Zone II (decreasing by 9.6% in the 0-20 cm soil layer) and Zone V (increasing by 8.5% in the 0-20 cm soil layer). The fluorescent components identified in each treatment group include ultraviolet radiation A humic acid-like substances (C1), ultraviolet radiation C humic acid-like substances (C2), and tryptophan-like substance (C3). As compared with the land with T1, the content of C2 in the 20-60 cm soil layer with T2 was lower, while that of C2 in the surface and subsoil with T3 was higher. In addiiton, there were no significant differences in the contents of C1, C2, and C3 by comparing the soils applied with T3 and T4, respectively. The composition of soil WSOM was found to be significantly influenced by the addition of a mixture of biochar and chemical fertilizers. The addition of biochar alone exerted a positive effect on the humification process in the surface soil (0-10 cm). NPK treatment could stimulate biological activity by increasing biological index values in deeper soil layers (40-50 cm). Nitrogen is the sovereign factor that improves the synergism effect of chemical fertilizer and biochar during the humification process. According to the UV-Vis spectrum and optical index, soil WSOM originates from land and microorganisms. This study reveals the dynamics of WSOC in the 0-60 cm soil layer and the biogeochemical effect of BC fertilizer treatment on the agricultural soil ecosystem.