Spatial distribution and source apportionment of polycyclic aromatic hydrocarbons in typical oasis soil of north-western China and the bacterial community response.

Oases environments in oases to be sensitive to anthropogenic activity because of ecological fragility. Polycyclic aromatic hydrocarbon (PAH) pollution resulting from anthropogenic activity leads to ecological degradation in oases. To examine the impact of anthropogenic activity on the oasis ecological environment, the present study focused on the spatial distribution and source apportionment of soil PAHs and bacterial community responses in typical oases in Xinjiang, China. The results showed that the soil PAH level were higher in the city centres of Urumqi (9-6340 µg kg-1), Aksu (8-957 µg kg-1) and Korla (8-1103 µg kg-1) and lower in the centres of Hotan city (11-268 µg kg-1) and Qira county (7-163 µg kg-1). Source apportionment suggested that gasoline emissions, diesel emissions, vehicle emissions, coal combustion, coke processing and biomass burning were the sources of soil PAHs. The integrated lifetime cancer risks of soil PAH exceeding the guideline safety values (10-6) recommended by United States Environmental Protection Agency. The ingestion and dermal exposure pathways caused the greatest health risk (contribution ≤82%). Additionally, in the soil with low PAH concentrations, the richness and evenness of the soil bacterial community were great, and the molecular ecological network (MEN) structure was complex. Among populations, Proteobacteria and Actinobacteria (relative abundance ≥17%) are the main dominant species in the bacterial communities and the keystone species in the MEN.

Assessing landscape fragmentation in a desert-oasis region of Northwest China: patterns, driving forces, and policy implications for future land consolidation.

Landscape fragmentation is considered a serious threat to eco-environmental integrity and socioeconomic development. Although many studies have focused on landscape fragmentation resulting from agricultural production and urbanization, landscape fragmentation from the aspects of patterns, driving forces, and the policy perspective of ecosystems has rarely been investigated. Oases, as a unique landscape, face severe fragmentation in arid and semiarid regions. This study applied a combination of approaches, including remote sensing image interpretations, landscape fragmentation metrics, and community surveys, to analyze patterns and their driving forces, as well as the policy implications for future land consolidation, in the Hotan oasis of Northwest China from the space and time perspectives. Results show that the frequent occurrence of summer flood events changes the patch number, density, size, and splitting degree of oasis-desert ecotone vegetation. The socioeconomic factors including total population and irrigation area are more important driving forces on oasis landscape fragmentation, compared with natural factors such as temperature and precipitation. Rural expansion, road and canal system developments caused by population growth, and the rising number of households increase oasis landscape fragmentation. Rapid economic development, such as agricultural expansion and urbanization, has imposed the intensification of landscape fragmentation. Fragmentation reaches peak when agricultural development makes up 40-50% of study area. Rural residential reconstruction and farmland transfer policies facilitate the intensive utilization of land toward oasis fragmentation solutions, but many factors, such as landholders' household characteristics and living conditions, are partly responsible for the challenges in land consolidation. This study also demonstrates that intense human activities pose a great threat for land consolidation and sustainable development of oasis landscape.

Oasis sustainability assessment in arid areas using GRACE satellite data.

An oasis is an important habitat for humans, plants, and wildlife in arid desert areas. The sustainability of an oasis is crucial for a smooth regional ecological functioning and healthy economic development. However, the overexploitation of groundwater will result in unsustainable oasis development. Due to the lack of long-term groundwater monitoring data, the impact of groundwater level changes on the sustainability of an oasis has not been studied extensively. In the present study, we used the ground water storage anomaly (GWSA) in combination with the Gravity Recovery and Climate Experiment (GRACE) and the Global Land Data Assimilation System (GLDAS) for the rapid identification of oasis sustainability, which has been tested and evaluated in Hotan and Qira oasis located in arid areas. The results showed that (1) the GWSA is a suitable and reliable indicator for trend change analysis in small-scale oasis and, (2) additionally, M-K test results for long-term trend change of GWSA showed a positive correlation with water resource carrying capacity (WRCC). These results suggest that GWSA can be used as a reliable index for the rapid assessment of oasis sustainability status in arid areas. Moreover, the potential applicability of GRACE satellite data in evaluating the groundwater sustainability in arid areas lacking proper data has also been proved in this study. These findings have provided a foundation to evaluate the sustainability status of an oasis and set a reference point to formulate future policies for the oasis.

Estimating evapotranspiration by coupling Bayesian model averaging methods with machine learning algorithms.

Evapotranspiration (ET) is one of the most important components of global hydrologic cycle and has significant impacts on energy exchange and climate change. Numerous models have been developed to estimate ET so far; however, great uncertainties in models still require considerations. The aim of this study is to reduce model errors and uncertainties among multi-models to improve daily ET estimate. The Bayesian model averaging (BMA) method is used to assemble eight ET models to produce ET with Landsat 8 satellite data, including four surface energy balance models (i.e., SEBS, SEBAL, SEBI, and SSEB) and four machine learning algorithms (i.e., polymars, random forest, ridge regression, and support vector machine). Performances of each model and BMA method were validated through in situ measurements of semi-arid region. Results indicated that the BMA method outperformed all eight single models. The four most important models obtained by the BMA method were ranked by random forest, SVM, SEBS, and SEBAL. The BMA method coupled with machine learning can significantly improve the accuracy of daily ET estimate, reducing uncertainties among models, and taking different intrinsic benefits of empirically and physically based models to obtain a more reliable ET estimate.

Comparison of the groundwater microbial community in a salt-freshwater mixing zone during the dry and wet seasons.

To gain a better understanding of the microbial community in salt-freshwater mixing zones, in this study, the influence of seasonal variation on the groundwater microbial community was evaluated by high throughput 16S rDNA gene sequencing. The results showed that notable changes in microbial community occurred in a salt-freshwater mixing zone and the groundwater samples in the dry season were more saline than those in the wet season. The increase in precipitation during the wet season relieved local seawater intrusion. Microbial diversity varied greatly with seasons, while no obvious change pattern was found. Proteobacteria was identified as the dominant phylum in all samples. The genus Hydrogenophaga dominated in the dry season, while the genus Acidovorax dominated in the wet season. Dissolved oxygen affected the diversity of the microbial communities during the dry and wet season, while groundwater level had a strong influence on the structure of microbial communities. Phylogenetic molecular network analysis of the microbial communities indicated that increased seawater intrusion led to a more compact microbial network and strengthening the groundwater microbial interactions.

Assessing water distribution and agricultural expansion in the Cele Oasis, China.

Oases support 90% of the province's inhabitants and produce more than 95% of the social wealth in Xinjiang Province of China. Oases' dependency on water availability from mountainous regions plays a critical factor in the sustainability of agricultural practices and oasis expansion. In this study, we have chosen the Cele Oasis located in the south rim of the Taklimakan Desert, typical of oases in the region, as a case study to examine water availability. With over 97% of Cele's economy tied to agriculture, unfettered expansion of the oasis into the desert has raised concern on water availability. A spatial and temporal analysis of water availability is performed using newly available data to determine whether agricultural production within the Cele Oasis has overexploited available water resources or if feasible expansion of agricultural production is feasible beyond its current boundary. Transferability of the methodology for assessing water availability spatially and temporally will be beneficial to other oases in the arid region that face similar concerns.

Acceleration of soil salinity accumulation and soil degradation due to greenhouse cultivation: a survey of farmers' practices in China.

Soil environment and water quality face large pressure due to the rapid expansion of greenhouse cultivation in China. However, studies rarely provide the linkage between farmers' practices and soil degradation in greenhouse cultivation field. In this study, a field survey and sampling of greenhouse cultivation soil were conducted in five regions of China to investigate the accumulation and variation characteristics of soil ion compositions in the field. First, the pH, ion compositions, and electrical conductivity (EC) of 132 composite soil samples were analyzed. Second, farmers' practices with regard to fertilizer, crop yield, and soil degradation processes were surveyed. Lastly, soil nutrient status was evaluated by different grades, and the principal component analysis method was used to analyze the main sources of soil ion compositions. Results of the study reveal the following: (1) Enrichment of greenhouse soil nutrient was mainly caused by excessive fertilization, which introduced the secondary salinization phenomenon for 3-5 years in plastic greenhouse and 1-3 years in multispan greenhouse. (2) Significant changes between the EC and salt ion composition of open soil and greenhouse cultivated soil were observed. The contents of nitrate nitrogen and ammonium nitrogen in the greenhouse soil were high. (3) After a certain period of cultivation in the greenhouse, salt accumulation, pH decline, and varying degrees of acidification were observed in the soil profile. The relationship between soil pH and EC values indicated that the balance of soil compositions was broken. The recommended methods for sustaining greenhouse cultivation include balanced fertilization, rotation practices, and reasonable water utilization in the field.

Stoichiometry in aboveground and fine roots of Seriphidium korovinii in desert grassland in response to artificial nitrogen addition.

Nitrogen (N) input by atmospheric deposition and human activity enhances the availability of N in various ecosystems, which may further affect N and phosphorus (P) cycling and use by plants. However, the internal use of N, P, and N:P stoichiometry by plants in response to N supply, particularly for grass species in a desert steppe ecosystem, remains unclear. In this work, a field experiment was conducted at an infertile area in a desert steppe to investigate the effects of N fertilizer addition rates on the stoichiometry of N and P in a dominant grass species, Seriphidium korovinii. Results showed that for both aboveground and fine roots of S. korovinii, N inputs exponentially increased the N concentration and N:P ratios while P concentrations decreased. Meanwhile, the relationships between N and P concentrations for both aboveground and fine roots were significantly negative. Furthermore, while the N concentrations in the plants were relatively low, P concentrations were higher than the global means, resulting in a relatively low N:P ratio. These results suggest that the stoichiometric characteristics of N were different from that of P for this desert plant species. Results also show that the intraspecific variations in the main element traits (N, P, and N:P ratios) were consistent at the whole-plant level. Our results also suggest that N should be part of any short-term fertilization plan that is part of a management strategy designed to restore degraded desert grassland. These findings highlight that nutrient addition by atmospheric N deposition and human activity can have significant effects on the internal use of N and P by plants. Therefore, establishing a nutrient-conservation strategy for desert grasslands is important.

A high-precision oasis dataset for China from remote sensing images.

High-resolution oasis maps are imperative for understanding ecological and socio-economic development of arid regions. However, due to the late establishment and relatively niche nature of the oasis discipline, there are no high-precision datasets related to oases in the world to date. To fill this gap, detailed visual interpretation of remote sensing images on Google Earth Professional or Sentinel-2 was conducted in summer 2020, and for the first time, a high-precision dataset of China's oases (abbreviation HDCO) with a resolution of 1 meter was constructed. HDCO comprises 1,466 oases with a total area of 277,375.56 km2. The kappa coefficient for this dataset validated by the field survey was 0.8686 and the AUC value for the ROC curve was 0.935. In addition, information on the geographic coordinates, climatic conditions, major landforms, and hydrological features of each oasis was added to the attribute table of the dataset. This dataset enables researchers to quantitatively monitor location and area of oases, fosters exploration of the relationship between oases and human under climate change and urbanization.

Automatedly identify dryland threatened species at large scale by using deep learning.

Dryland biodiversity is decreasing at an alarming rate. Advanced intelligent tools are urgently needed to rapidly, automatedly, and precisely detect dryland threatened species on a large scale for biological conservation. Here, we explored the performance of three deep convolutional neural networks (Deeplabv3+, Unet, and Pspnet models) on the intelligent recognition of rare species based on high-resolution (0.3 m) satellite images taken by an unmanned aerial vehicle (UAV). We focused on a threatened species, Populus euphratica, in the Tarim River Basin (China), where there has been a severe population decline in the 1970s and restoration has been carried out since 2000. The testing results showed that Unet outperforms Deeplabv3+ and Pspnet when the training samples are lower, while Deeplabv3+ performs best as the dataset increases. Overall, when training samples are 80, Deeplabv3+ had the best overall performance for Populus euphratica identification, with mean pixel accuracy (MPA) between 87.31 % and 90.2 %, which, on average is 3.74 % and 11.29 % higher than Unet and Pspnet, respectively. Deeplabv3+ can accurately detect the boundaries of Populus euphratica even in areas of dense vegetation, with lower identification uncertainty for each pixel than other models. This study developed a UAV imagery-based identification framework using deep learning with high resolution in large-scale regions. This approach can accurately capture the variation in dryland threatened species, especially those in inaccessible areas, thereby fostering rapid and efficient conservation actions.

Effects of Human Activities on Evapotranspiration and Its Components in Arid Areas.

With the increasing impact of human activities on the environment, evapotranspiration (ET) has changed in arid areas, which further affects the water resources availability in the region. Therefore, understanding the impact of human activities on ET and its components is helpful to the management of water resources in arid areas. This study verified the accuracy of Fisher's model (PT-JPL model) for ET estimation in southern Xinjiang, China by using the evaporation complementarity theory dataset (AET dataset). The ET and the evapotranspiration components (T:E) of six land-use types were estimated in southern Xinjiang from 1982 to 2015, and the impact of human activities on ET was analyzed. In addition, the impact of four environmental factors (temperature (Temp), net radiation (Rn), relative humidity (RH), and NDVI) on ET were evaluated. The results showed that the calculated ET values of the PT-JPL model were close to the ET values of the AET dataset. The correlation coefficient (R2) was more than 0.8, and the NSE was close to 1. In grassland, water area, urban industrial and mining land, forest land, and cultivated land, the ET values were high, and in unused land types, the ET values were the lowest. The T:E values varied greatly in urban industrial and mining land, forest land, and cultivated land, which was due to the intensification of human activities, and the values were close to 1 in summer in recent years. Among the four environmental factors, temperature largely influenced the monthly ET. These findings suggest that human activities have significantly reduced soil evaporation and improved water use efficiency. The impact of human activities on environmental factors has caused changes in ET and its components, and appropriate oasis expansion is more conducive to regional sustainable development.

Spectral reflectance properties of major objects in desert oasis: a case study of the Weigan-Kuqa river delta oasis in Xinjiang, China.

Aiming at the remote sensing application has been increasingly relying on ground object spectral characteristics. In order to further research the spectral reflectance characteristics in arid area, this study was performed in the typical delta oasis of Weigan and Kuqa rivers located north of Tarim Basin. Data were collected from geo-targets at multiple sites in various field conditions. The spectra data were collected for different soil types including saline-alkaline soil, silt sandy soil, cotton field, and others; vegetations of Alhagi sparsifolia, Phragmites australis, Tamarix, Halostachys caspica, etc., and water bodies. Next, the data were processed to remove high-frequency noise, and the spectral curves were smoothed with the moving average method. The derivative spectrum was generated after eliminating environmental background noise so that to distinguish the original overlap spectra. After continuum removal of the undesirable absorbance, the spectrum curves were able to highlight features for both optical absorbance and reflectance. The spectrum information of each ground object is essential for fully utilizing the multispectrum data generated by remote sensing, which will need a representative spectral library. In this study using ENVI 4.5 software, a preliminary spectral library of surface features was constructed using the data surveyed in the study area. This library can support remote sensing activities such as feature investigation, vegetation classification, and environmental monitoring in the delta oasis region. Future plan will focus on sharing and standardizing the criteria of professional spectral library and to expand and promote the utilization of the spectral databases.

Extraction of multi-scale features enhances the deep learning-based daily PM2.5 forecasting in cities.

Characterising the daily PM2.5 concentration is crucial for air quality control. To govern the status of the atmospheric environment, a novel hybrid model for PM2.5 forecasting was proposed by introducing a two-stage decomposition technology of complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and variational mode decomposition (VMD); subsequently, a deep learning approach of long short-term memory (LSTM) was proposed. Five cities with unique meteorological and economic characteristics were selected to assess the predictive ability of the proposed model. The results revealed that PM2.5 pollution was generally more severe in inland cities (66.98 ± 0.76 µg m-3) than in coastal cities (40.46 ± 0.40 µg m-3). The modelling comparison showed that in each city, the secondary decomposition algorithm improved the accuracy and prediction stability of the prediction models. When compared with other prediction models, LSTM effectively extracted featured information and achieved relatively accurate time-series prediction. The hybrid model of CEEMDAN-VMD-LSTM achieved a better prediction in the five cities (R2 = 0.9803 ± 0.01) compared with the benchmark models (R2 = 0.7537 ± 0.03). The results indicate that the proposed approach can identify the inherent correlations and patterns among complex datasets, particularly in time-series analysis.

Simulation of regional groundwater levels in arid regions using interpretable machine learning models.

Regional groundwater level forecasting is critical to water resource management, especially for arid regions which require effective management of groundwater resources to meet human and ecosystem needs. In this study Machine Learning and Deep Learning approaches - Support Vector Machine, Generalized Regression Neural Network, Decision Tree, Random Forest (RF), Convolutional Neural Network, Long Short Term Memory and Gated Recurrent Network- have been used to simulate the groundwater levels in the lower Tarim River basin (LTRB) which is an extreme dryland. The results showed that models developed here with easily available input data such as relative humidity, flow volume and distance to the riverbank can fully utilize spatiotemporally inconsistent groundwater monitoring data to predict the spatiotemporal variation of the groundwater system in arid regions where exist intermittent flow. The shapely additive explanations method was used to interpret the RF model and discover the effect of meteorological, hydrological and environmental variables on the regional groundwater. These explanations showed that the flow volume, the distance to the river channel and reservoir have a critical impact on groundwater changes. Within 300 m distance to the riverbank, groundwater would mainly be influenced by the flow volume and the distance to the reservoir. While far from the riverbank, these effects decreased gradually further away from the river course. The RF prediction results showed that in the next three years (2021-2023), the groundwater level on average may decline to -6.4 m, and the suitable areas for natural vegetation growth would be limited to 39% if no water conveyance in the LTRB. To guarantee the stability of ecosystems in the LTRB, it is necessary to convey the water annually. These results can support spatiotemporal predictions of groundwater levels predominantly recharged by intermittent flow, and form a scientific basis for sustainable water resources management in arid regions.

Occurrences, transport drivers, and risk assessments of antibiotics in typical oasis surface and groundwater.

Intensive use of antibiotics affects biogeochemical cycles and stimulates the evolution of antibiotic resistance, thus threatening global health and social development. The spatiotemporal distributions of antibiotics in single aqueous matrices have been widely documented; however, their occurrence in surface-groundwater systems has received less attention, especially in arid regions that usually have fragile ecosystems. Therefore, we investigated the occurrence of thirty-one antibiotics in the surface water and adjacent groundwater in the Xinjiang Uygur Autonomous Region, China. The results showed that the total concentrations of detected antibiotics varied from 17.37 to 84.09 ng L-1 and from 16.38 to 277.41 ng L-1 in surface and groundwater, respectively. The median concentration of antibiotics showed the pattern of norfloxacin (4.86 ng L-1) > ciprofloxacin (3.93 ng L-1) > pefloxacin (3.39 ng L-1) in surface water; whereas in groundwater, this was in the order of pefloxacin (6.30 ng L-1) > norfloxacin (4.33 ng L-1) > ciprofloxacin (2.68 ng L-1). Heatmap analysis indicated that vertical infiltration had limited effects on antibiotic exchange in surface-ground water systems because of the high potential evaporation and low water storage. Redundancy analysis suggested that the oxidation-reduction potential (p < 0.01) and dissolved oxygen (p < 0.05) jointly affected the distribution of antibiotics in surface water. Ecological risk assessment showed that antibiotics in 98.9% of surface water and 99.1% of groundwater did not pose significant risks to aquatic species. The findings of this study will help develop effective mitigation strategies for antibiotics in aquatic environments.

Analysis of desertification combating needs based on potential vegetation NDVI-A case in the Hotan Oasis.

Combating desertification is vital for arresting land degradation and ensuring sustainable development of the global ecological environment. This study has analyzed the current desertification status and determined its control needs based on the difference between potential normalized difference vegetation index (PNDVI) and actual normalized difference vegetation index (ANDVI) in the Hotan desertoasis. The MaxEnt model, combined with the distribution point data of natural vegetation with long-term stable normalized difference vegetation index (NDVI) and 24 environmental factors was used to predict the PNDVI spatial distribution of different vegetation coverage grades and compared it with ANDVI. Excluding the areas of intense human activity such as arable land, the simulation results show that PNDVI with high, medium, and low vegetation cover was mainly distributed in the southwest and southeast of Hotan Oasis, in the midstream and downstream of Kalakash River and Yulong Kashi River, and the desert or Gobi area outside the oasis, respectively. The distribution of PNDVI with high, medium, and low vegetation cover accounted for 6.80%, 7.26%, and 9.17% of Hotan oasis, respectively. The comparison between ANDVI and PNDVI shows that 18.04% (ANDVI < PNDVI, about 3900 km2) of the study area is still suffering from desertification, which is mainly distributed in the desert-oasis ecotone in Hotan. The findings of this study implied that PNDVI could be used to assess the desertification status and endorsement of desertification control measures in vulnerable ecosystems. Hence, PNDVI can strengthen the desertification combating efforts at regional and global scales and may serve as a reference point for the policymakers and scientific community towards sustainable land development.

Distribution pattern of plant species diversity in the mountainous region of Ili River Valley, Xinjiang.

In this paper, detrended canonical correspondence analysis was performed to analyze the relationships between diversity indices and environmental gradients, generalized additive model was employed to modal the response curves of diversity indices to the elevation, based on data from field investigation in the mountainous region of the Ili River Valley and a survey of 94 sample plots. Two hundred fifty-nine plant species were recorded in the 94 sample plots investigated, up to 235 species all appeared in the herb layer, and the species of woody plants were very limited. The communities with a complicated vertical structure presented higher values of indices. The distribution pattern of plant species diversity on the northern slope was affected by such factors as elevation, slope aspect, slope gradient, total nitrogen, total potassium, soil water content, organic matter, and that on the southern slope was mainly affected by such factors as slope gradient, elevation, available phosphorus, and soil water content. On the northern slope, Patrick index and Shannon-Wiener index of the plant communities presented a bimodality pattern along altitude; Simpson index and Pielou index showed a partially unimodal pattern. On the southern slope all the distribution pattern of species diversity indices showed two peaks, though Patrick index's bimodality pattern was not an obvious one. These altitudinal patterns were formed by the synthetic action of a variety of environmental factors with elevation playing an important role.

Linking soil profile N2O concentration with surface flux in a cotton field under drip fertigation.

It remains unclear how the source and rate of nitrogen (N) fertilizers affect N2O concentration and effluxes along the soil profile under the drip-fertigated agricultural system. A plot-based field study was performed in 2017 and 2018 in a cotton field in arid northwestern China, with an objective to elucidate the impact of the applications of conventional urea (Urea), polymer-coated urea (ESN) and stabilized urea (SuperU) at rates of 120 and 240 kg N ha-1 on concentration and efflux of N2O in the soil profile and its relationship with N2O surface emissions. The in-situ N2O concentrations at soil depths of 5, 15, 30 and 60 cm were measured and used to estimate soil profile N2O effluxes. Estimates of surface N2O flux using the concentration gradient-based (GM) were compared with those measured using the chamber-based (CM) method. In both years, soil N2O concentrations at all depths increased in response to basal N application at planting or in-season fertigation events. However, N rate or source did not affect soil N2O concentrations or effluxes at each depth. Surface emissions of N2O were mostly associated with that presented in the top layer of 0-15 cm. Surface N2O efflux determined by GM was poorly or not associated with those of chamber measurements, which was attributed to the low N2O production restricted by soil moisture condition under the drip-fertigated condition. These results highlight the challenge of applying the enhanced efficiency N fertilizer products in the drip-fertigated agricultural system.

A comparative analysis of artificial neural networks and wavelet hybrid approaches to long-term toxic heavy metal prediction.

The occurrence of toxic metals in the aquatic environment is as caused by a variety of contaminations which makes difficulty in the concentration prediction. In this study, conventional methods of back-propagation neural network (BPNN) and nonlinear autoregressive network with exogenous inputs (NARX) were applied as benchmark models. Explanatory variables of Fe, pH, electrical conductivity, water temperature, river flow, nitrate nitrogen, and dissolved oxygen were used as different input combinations to forecast the long-term concentrations of As, Pb, and Zn. The wavelet transformation was applied to decompose the time series data, and then was integrated with conventional methods (as WNN and WNARX). The modelling performances of the hybrid models of WNN and WNARX were compared with the conventional models. All the given models were trained, validated, and tested by an 18-year data set and demonstrated based on the simulation results of a 2-year data set. Results revealed that the given models showed general good performances for the long-term prediction of the toxic metals of As, Pb, and Zn. The wavelet transform could enhance the long-term concentration predictions. However, it is not necessarily useful for each metal prediction. Therefore, different models with different inputs should be used for different metals predictions to achieve the best predictions.