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Basin ecosystems are vulnerable to natural resource depletion, ecological damage, and environmental pollution due to their fragile natural environment. Assessing the value of basin ecosystem services (ES) can facilitate informed decision making by policy makers and stakeholders in the context of competing resource use. The Konqi River Basin in China, an arid inland river basin, has suffered from degraded ecosystems due to overexploitation of soil and water resources. In response, the local government launched an ecological water conveyance (EWC) project in 2016 to enhance ecological restoration efforts. This paper analyzes and evaluates the value of ES in the Konqi River Basin based on land use and land cover (LULC) change characteristics before and after EWC in 2013 and 2020, respectively. Remote sensing data and related socioeconomic statistics data are used to assess a typical river basin from three unique locations in the Konqi River Basin, divided into upper and lower reaches. The results show that cropland and unused land are the most important land use types in the upper and lower reaches. The characteristics of ecosystem service value (ESV) changes in the study area are consistent with land use structure changes. The total ESV shows a decreasing trend in the upper reaches from 2013 to 2020, while the lower reaches show an increasing trend. The total ESV increases in the typical river reaches of the Konqi River Basin. Spatially, low-ESV areas are mainly located in ecologically fragile areas that are difficult to develop and use. The sensitivity indexes of the study area are all less than 1, making the results of this study credible. The Moran index shows a significant spatial correlation in the study area, indicating that the distribution characteristics of high-ESV areas are agglomerative. Hot spot areas in the upper reaches show an overall increasing trend, while in the lower reaches, former sub-hot spot areas transform into hot spot areas. Due to data limitations, this study is limited to demonstrating that the value of ES in the area changes due to a combination of EWC policies and other factors. Nevertheless, the analysis shows that EWC policies actively change the ESV of a typical river basin in Konqi. This study can provide a reference for evaluating ESV in inland river basins in the northwest arid region and a scientific basis for the rational development and utilization of water and soil resources in the study area, located in an arid and ecologically fragile area.

The ecological risks of six toxic metals (Zn, Cu, Cr, Pb, Hg, and As) in the industrial zone on the northern slope of the East Tianshan Mountains in Xinjiang, northwest (NW) China, were assessed. The results showed that the soil toxic metal contents of Zn, Pb, Hg, and As exceeded the regional background values, and concentrations of Zn, Cu, Pb, and As exceeded the national soil environmental quality standards of China (GB15618-1995). The Cu and Cr contamination levels were lower than the limits of both soil standards and mainly originated from the parent material, Pb mainly originated from agricultural activity and traffic emissions, and Hg mainly originated from the coal burning and chemical industries. As contamination originated from automobile exhaust emissions, and Zn contamination was influenced by a combination of natural factors and human activities. The mean geo-accumulation index (I geo) values of As, Hg, and Pb ranged from unpolluted to moderately polluted. There was an area not polluted by Zn, Cu, and Cr. The pollution index (PI) value of the six heavy metals showed that the mean PI values of Zn, Cr, and Cu showed no pollution, As and Pb presented medium pollution, and Hg presented heavy pollution. The results of the potential ecological risk analysis in this region showed that Zn, Cu, Cr, and Pb in all sample sites presented a low risk, while Hg presented a high ecological risk. Therefore, it is necessary to prevent further Hg contamination in this region.

The Xinjiang Zhundong Economic and Technological Development Zone, which contains the largest coalfield in China, is a mega energy base for west–east gas transmission and outbound electricity transmission in China; however, resource exploitation and the region’s arid climate have led to the region’s ecological environment being increasingly vulnerable. The morphological spatial pattern analysis (MSPA) method and landscape connectivity were used in this study to identify the ecological sources and extract the ecological corridors and ecological nodes based on the minimum cumulative resistance (MCR) model, used to construct the landscape ecological security pattern in the Zhundong region from 2016 to 2021. The results show that (a) from 2016 to 2021, the area of ecological sources increased by 117.86 ha and the distribution density of which decreased from the southern-central region to the northern and northwestern regions. (b) From 2016 to 2021, the number of ecological corridors and ecological nodes decreased, and the ecological corridors with dense distributions in the south gradually moved to the north and west. The length of the ecological corridors in the south gradually became longer, and the number of ecological corridors connecting the east and west in the north increased. (c) The landscape ecological security pattern of the Zhundong region was constructed by “a network and multiple points” using the model of ecological sources–ecological corridors–ecological nodes. The findings of this study provide a scientific foundation for the construction of an ecological security development plan and the ecologically protective development of coal resources in Zhundong.

In typical semi-arid areas, the timely and effective monitoring and evaluation of soil heavy metal pollution are of critical importance to prevent soil deterioration and achieve the sustainable use of soil resources. To further understand the degree of soil heavy metal pollution in different functional areas, we studied the soil heavy metal pollution on the northern slope of the eastern Tianshan Mountains in Xinjiang. We collected 104 surface soil samples from typical commercial (A), industrial (B), and agricultural (C) areas with different land-use patterns. The contents of Zn, Cu, Cr, Pb, As, and Hg in the soil of different functional areas were evaluated using the geo-accumulation index, the single-factor pollution index, and potential ecological risk. The results showed that the contents of Pb, As, and Hg in soils of different functional areas exceeded 4.47, 8.03, and 1.5 times the background values of Xinjiang soil, respectively. The average contents of Zn, Cu, and Cr were lower than the background values of Xinjiang soil. Except for As in different functional areas, the contents of the other elements in the different functional areas reached the level of soil environmental quality standards in China (GB15618-2018). The geo-accumulation index of heavy metals in different functional areas was in the order of Area C > Area A > Area B, indicating that Area C was the most polluted. The results of the single-factor pollution index showed that the pollution levels of Pb, As, and Hg were higher, and the pollution levels of Cr, Cu, and Zn were lower. The results of the potential ecological risk index showed that the northwest of Area A was higher, the southeast of Area B was more polluted, and the central and eastern parts of Area C were more polluted. From the perspective of spatial distribution, the spatial distribution characteristics of Zn and Cr elements in different functional areas are consistent, but the spatial distribution characteristics of Cu, Pb, As, and Hg in different functional areas are quite different. The high values of these four elements are mainly distributed in residential areas, factories, and metal smelters. It is necessary to divide the functional areas based on different land-use patterns, and the prevention of soil single-element and heavy metal pollution in different functional areas is reasonable for land resources and provides a scientific basis for the effective planning of quality assurance.

In recent years, soil heavy metal pollution has become an important issue of general concern because it is an important factor that threatens the soil environment. To assess the risk of the human health of the people living in the economic belt on the northern slope of the eastern Tianshan Mountains, and provide guidance for pollution control and risk prevention, the northern slope of the eastern Tianshan Mountains was selected as the study area, and six heavy metals (i.e., Zn, Cu, Cr, Pb, and Hg) were measured. The results revealed that the Cu, Pb, Hg, and As contents of the soil exceeded the soil background value of Xinjiang by 1.02, 4.10, 1.76, and 7.98 times, respectively. The Zn and Cr contents were lower than the limits of regional soil standards. Based on the pollution assessment using the pollution index (PI), values indicate that the levels of Zn and Cr pollution were low, those of Cu and Hg pollution were moderate, and those of Pb and As pollution were high. The health risk assessment results revealed that there are non-carcinogenic risks to children. The carcinogenic health risks posed by the heavy metals in this region are higher than the threshold (10−4), indicating that they pose a hazard to human health. The results of this research provide a theoretical basis and reference for soil heavy metal pollution control and human health risk management in this area.

Soil salinity is one of the major factors causing land degradation and desertification on earth, especially its important damage to farming activities and land-use management in arid and semiarid regions. The salt-affected land is predominant in the Keriya River area of Northwestern China. Then, there is an urgent need for rapid, accurate, and economical monitoring in the salt-affected land. In this study, we used the electrical conductivity (EC) of 353 ground-truth measurements and predictive capability parameters of WorldView-2 (WV-2), such as satellite band reflectance and newly optimum spectral indices (OSI) based on two dimensional and three-dimensional data. The features of spectral bands were extracted and tested, and different new OSI and soil salinity indices using reflectance of wavebands were built, in which spectral data was pre-processed (based on First Derivative (R-FD), Second Derivative (R-SD), Square data (R-SQ), Reciprocal inverse (1/R), and Reciprocal First Derivative (1/R-FD)), utilizing the partial least-squares regression (PLSR) method to construct estimation models and mapping the regional soil-affected land. The results of this study are the following: (a) the new OSI had a higher relevance to EC than one-dimensional data, and (b) the cross-validation of established PLSR models indicated that the &beta, PLSR model based on the optimal three-band index with different process algorithm performed the best result with R2V = 0.79, Root Mean Square Errors (RMSEV) = 1.51 dS&middot, m&minus, 1, and Relative Percent Deviation (RPD) = 2.01 and was used to map the soil salinity over the study site. The results of the study will be helpful for the study of salt-affected land monitoring and evaluation in similar environmental conditions.

Spectroscopy is regarded as a quick and nondestructive method to classify and quantitatively analyze many elements of the soil. Visible and Near-infrared reflectance spectroscopy offers a conductive tool for investigating soil heavy metal pollution. The main goal of this work is to obtain spectral optimized indices (RSI, NPDI and NDSI) related to soil heavy metal Arsenic (As), to estimate the As contents in soil based on geographically weighted regression model (GWR), and to investigate the plausibility of using these spectral optimized indices to map the distribution of heavy metal Arsenic in the soil of coal mining areas. The spectral optimized indices (RSI, NPDI and NDSI) derived from the original and transformed reflectance (the reciprocal (1/R), logarithm (lg R ), logarithm-reciprocal (1/lg R ) and root mean square method ( R ) were used to construct the GWR models. Then, the variables (RSIs, NPDIs and NDIs) were applied in estimating the Arsenic (As) concentration and in the mapping of the As distribution in this study region. The NPDIs calculated by the original and transformed reflectance ( R , 1/ R , lg R , 1/lg R , and R ) indicated higher correlation coefficient values than NDSI and RSI. The highest correlation coefficient and lowest p -values ( r ≥0.73 and p =0.001) were found in thenear-infrared (NIR, 780–1100 nm) and shortwave infrared (SWIR, 1100–1935 nm). From the 4 prediction models (GWR) performances, it can be seen that Model-a ( R ) showed superior performance to the other three models (Model-b (1/ R ), Model-c ( R ) and Model-d (lg R )), and it has the highest validation coefficients ( R 2 = 0.831, RMSE =4.912 μg/g, RPD=2.321) and lowest AIC (Akaike Information Criterion) value (AIC=179.96). NPDI 1417 nm, 1246 nm is more sensitive and potential hyperspectral index for As in the study area. Thus, the two band optimized index (NPDI 1417 nm, 1246 nm ) might be recommended as an indicator for estimating soil As content. The hyperspectral optimized indices may help to quickly and accurately evaluate Arsenic contents in soil, and furthermore, the results provide theoretical and data support to access the distribution of heavy metal pollution in surface soil, promoting fast and efficient investigation of mining environment pollution and sustainable development of ecology.

Soil salinity is one of the common factors leading to land degradation problems on earth, especially in arid and semiarid regions. There is an urgent need for rapid, accurate and cost-effective monitoring and assessment of soil salinization. Remote Sensing (RS) and Geographical Information Systems (GIS) are employed as viable technologies for detecting, monitoring, and predicting spatial-temporal patterns of soil salinization. The purpose of this study is to establish partial least squares regression (PLSR) models that are based on remotely sensed data and field measured electrical conductivity (ECa) and to retrieve soil salinity estimates by constructing an optimal model. First, the soil adjusted vegetation index (SAVI) was calculated based on WorldView-2 images. Second, a statistical regression method was applied to analyze the correlation between ECa and SAVI under different parameters. The SAVI that was measured as the most stable parameter was an optimum index. Finally, a PLSR prediction model of soil salinity was established based on the sensitivity bands, the optimum index and ECa. The results of this study are the following: (a) According to the adjusted parameter (L = 100), the SAVI index illustrated the best correlation with ECa, and ECa was also significantly related to the bands ((Red Edge) Band6, (Near-IR1) Band7 and (Near-IR2) Band8) derived from a World-view-2 image. (b) The results of the PLSR predictive model calibration showed that the model-D performed best through the sensitivity bands and optimal index, with the highest coefficient of determination (R2 = 0.67) and the smallest root mean square error (RMSE) of 1.19 dS·m-1. The results indicated that the model-D that is constructed and applied in this paper could provide quantitative information for detecting and monitoring soil salinization in the Keriya Oasis and could also supply examples for the study of soil salinization in arid and semiarid regions with similar environmental conditions.

Irrational use and management of water and land are associated with poor hydro-geological conditions causing water logging and salinization problems, possibly leading to farmland abandonment and economic loss. This poses a great challenge to the sustainability of oasis&rsquo, and requires desalinization through reasonable landscape design by multiple crossing studies so we collected traditional knowledge by field interviews and literature schemes, except for the modern desalinization approaches by literature, and we found that the salinization problem has been solved by traditional land reclamation, traditional drainage, natural drainage and flood irrigation, locally. It is worth mentioning that the traditional reclamation in salinized areas requires flood water, sand dunes and a salinized pit area, the sand dunes are used to elevate the pit surface, and water is used to leach salt from the soil. Natural drainage (the depth and width are 4&ndash, 10 m and 50&ndash, 100 m, respectively) caused by flash flooding has significant benefits to some salinized villages in the range of 3000&ndash, 5000 m and ancient groundwater drainage systems, such as Karez are supporting the oasis with drainage water for centuries. In addition landscape characteristics, salinization and hydro-geological conditions of the oasis were studied from Landsat image, DEM, literature and field photos. Then based on the gathered information above, a desalinization model was developed to decrease the groundwater table and salt leaching in the water logging landscape. Then according to landscape characteristics, different desalinization approaches were recommended for different landscapes. To address environmental uncertainties, an adaptive landscape management and refinement approach was developed, and acceptance of the model was validated by stakeholder opinion. The results provide guidelines for sustainable desalinization design and highlight the importance of combining traditional knowledge and modern ecological principles in sustainable landscape design.

Comprehensive studies on toxic metal pollution in the soils of arid coal-mining areas have not drawn the attention they deserve. In the present study, we investigated the concentrations of 6 pollutants (Zn, Cu, Cr, Pb, Hg, and As) in soils of an arid mining area, the Eastern Junggar Basin (EJB) of Xinjiang, Northwestern China. Noticeable concentrations were found for Cr, Hg, and As, whereas Zn, Cu, and Pb were lower than their provincial (Xinjiang) guideline values. The average enrichment of As exceeded both Quality Standards (I and II) for soil recommended by the Chinese Environmental Protection Administration, whereas other metals were found within the allowable values of Soil Quality Standard I. There were different degrees of comprehensive ecological risks in the area, and Hg was the major contributor to the PER. In health risk assessments, children seem to have a higher health risk hazards than adults, with As as the primary contributor, followed by Cr. The positive matrix factorization (PMF) model explains five possible sources, including coal combustion, vehicle emissions, atmospheric deposition, industrial discharge, and natural factors, which accounted for 20.79%, 16.83%, 16.83%, 27.72%, and 17.82% of the total contribution, respectively. The results will be informative for local authorities to establish more effective targeting policies to control the spread of contaminants and provide reference materials for soil quality protection and improvement and other research programs in this area.

Soil toxic metal pollution is one of the most prominent environmental problems in the rapid industrialization of societies because of the considerable harm caused to human existence and the surrounding environments. Soil samples from 80 sampling sites around the coal-mining region of northwestern China were collected, and the geo-accumulation index (Igeo), pollution index (PI), and potential ecological risk index (PRI) were calculated, with the objective of assessing the soil toxic metal pollution level. The results showed that the average concentrations of Cr, Hg, and As exceeded the regional background values and the national soil environmental quality standards of China, while those of Zn, Cu, and Pb were below both soil-quality standards. The Igeo of toxic metals was ranked as Hg > As > Cr > Pb > Cu > Zn. The Igeo of Zn, Cu, and Pb indicated low pollution; the soils were moderately polluted by Hg and slightly moderately polluted by As, while other elements presented low pollution levels. The PI values of both As and Hg were higher than 3, indicating heavy pollution of these two metals. Zn and Cu originated from parent material, while Cr, As, and Hg originated from human activities such as coal burning, chemical industry, and traffic. Pb was influenced by both natural factors and human activities. The results of ecological risk assessment in the region showed that Zn, Cu, Cr, and Pb in all sample sites presented a low ecological risk, while Hg presented a high ecological risk. Therefore, Hg is the most hazardous toxic metals in the region. The spatial distribution trends revealed that the high-risk regions were found to be the industrial region of the study area. The research results provide a scientific basis and technical support for monitoring and early warning of soil pollution in arid regions.

The objective of this study was to investigate heavy metal contamination in four major vegetable bases and determine the health risks of residents in the vicinity of the highly urbanized city Urumqi in Xinjiang, China. In this paper, we determined the contents of six heavy metals (i.e., As, Zn, Cd, Cr, Hg, and Pb) in surface soil and groundwater to evaluate the levels of heavy metal pollution and human health risks using the pollution index (PI), the Nemerow integrated pollution index (NIPI), the ecological risk factor (Eir), risk index (RI) and the health risk assessment model. The results showed that (1) The PI, NIPI, the ecological risk factor and risk index indicated that Cd and Hg were the primary pollutants in Sishihu village. These indices suggested moderate to slightly heavy potential ecological risks. In Anningqu town, Hg and Cd led to high levels of pollution and posed slightly heavy potential ecological risks. In Qinggedahu village, it was concluded that the metals Zn, Cr, Cd, Hg, and Pb caused moderate to heavy pollution. In Liushihu village, the pollution trends in the area were low. The results of the pollution level of the irrigation well water (i.e., groundwater) indicated that the well water was considerably safer than the soil, but Cr posed a slight pollution risk. (2) The non-carcinogenic risks for adults based on the HI values of these four vegetable bases were Sishihu village > Anningqu town. For children, the carcinogenic risks posed by As through trough inhalation and ingestion were the main exposure pathways. From the TCR results, it can be seen that in Sishihu village, Anningqu town, and Qinggedahu village, the TCR values for adults and children were >1 × 10−4 (unitless), and this degree of carcinogenic risk is unacceptable. (3) The identification of risk sources determined the main pollution sources affecting the vegetable bases were human activities and natural sources. Anthropogenic activities were most often related to traffic pollution sources and agricultural pollution sources, such as the irrational use of pesticides and fertilizers and stock farming. The results are important for designing remediation scenarios to control the spread of contamination as well as for serving as a reference point for soil environmental protection efforts in this region.

Significant anthropogenic and biophysical changes have caused fluctuations in the soil salinization area of the Keriya Oasis in China. The Driver-Pressure-State-Impact-Response (DPSIR) sustainability framework and Bayesian networks (BNs) were used to integrate information from anthropogenic and natural systems to model the trend of secondary soil salinization. The developed model predicted that light salinization (vegetation coverage of around 15–20%, soil salt 5–10 g/kg) of the ecotone will increase in the near term but decelerate slightly in the future, and that farmland salinization will decrease in the near term. This trend is expected to accelerate in the future. Both trends are attributed to decreased water logging, increased groundwater exploitation, and decreased ratio of evaporation/precipitation. In contrast, severe salinization (vegetation coverage of around 2%, soil salt ≥20 g/kg) of the ecotone will increase in the near term. This trend will accelerate in the future because decreased river flow will reduce the flushing of severely salinized soil crust. Anthropogenic factors have negative impacts and natural causes have positive impacts on light salinization of ecotones. In situations involving severe farmland salinization, anthropogenic factors have persistent negative impacts.

This study aims at identifying multi-source heavy metal pollution from natural and anthropogenic sources using a regression model, principal component analysis, and five different indices (geo-accumulation index (I geo), the modified degree of contamination, pollution load index (PLI), enrichment factor, and ecological risk factor. Results revealed that: (1) although the average concentrations of soil heavy metals (Cu, Cr, Pb, Hg, As, Zn) were generally low, Hg, As, and Cr concentrations exceeded national standard values by approximately 0.91, 1.84, and 0.91 times with maximum concentrations up to 0.41, 78.6, and 175.2 μg/g, respectively; (2) PLI results showed that the industrial park and Wucaiwan open coal mining area were the most polluted (PLI of 1.98, 1.71). The potential ecological hazards index indicated that the E i r of three heavy metals (Cu, Hg, As) in the soil were relatively high, presenting potential ecological risk factors of 74.89, 16.71, 4.15%, respectively; (3) stepwise regression model and principal component analysis suggest that Cu and Zn were primarily effected by the natural geological condition and atmospheric dust fall. Cr, Hg, Pb are mainly derived from anthropogenic sources, particularly coal mining activities and industrial sources. Results of this research have some significant implications for heavy metal pollution prevention and the sustainable development of the economy and ecology of arid regions in China.

In this paper, the spatial and temporal variability of groundwater depth and groundwater salinity associated with agricultural activities and land cover and land use (LCLU) in the Keriya Oasis, Xinjiang Uyghur Autonomous Region of China, was investigated. A decision tree classification was developed for LCLU mapping by using Landsat 8 imagery. Radial basis function, Pearson’s correlation coefficient, Moran’s I statistics and geographically weighted regression were employed to identify the spatial and temporal relationship between topsoil salinity and LCLU types. The results showed that groundwater depth in about 75 % of the study area was less than 3.0 m. Groundwater salinity showed an increasing trend with decreasing groundwater depth. A relatively higher positive correlation (r = 0.87) was found between groundwater salinity and topsoil salinity. Topsoil salinity was exacerbated by agricultural activities at the upstream of seasonal rivers, causing severe soil salinization and soil degradation in the downstream. Both groundwater depth and soil salinity showed seasonal fluctuations with the shallowest groundwater level occurring in spring, while during summer and autumn, groundwater depth increased due to the extensive of agricultural water consumption and the regional evapotranspiration, which was followed by a winter recharge; non-saline groundwater region was 21 % of the research area in spring, which dropped to 1.1, 1.0 and 1.2 %, respectively, during the summer, autumn and winter. The results also demonstrated that shallow groundwater depth of 2.5 m is the critical depth value for soil salinization in the Keriya Oasis.

Irrigation salinity is a common environmental threat for sustainable development in the Keriya Oasis, arid Northwest China. It is mainly caused by unreasonable land management and excessive irrigation. The aim of this study was to assess and map the salinity risk distribution by developing a composite risk index (CRI) for seventeen risk parameters from traditional and scientific fields, based on maximizing deviation method and analytic hierarchy process, the grey relational analysis and the Pressure-State-Response (PSR) sustainability framework. The results demonstrated that the northern part of the Shewol and Yeghebagh village has a very high salinity risk, which might be caused by flat and low terrain, high subsoil total soluble salt, high groundwater salinity and shallow groundwater depth. In contrast, the southern part of the Oasis has a low risk of salinity because of high elevation, proper drainage conditions and a suitable groundwater table. This achievement has shown that southern parts of the Oasis are suitable for irrigation agriculture, for the northern area, there is no economically feasible solution but other areas at higher risk can be restored by artificial measures. Therefore, this study provides policy makers with baseline data for restoring the soil salinity within the Oasis.

Soil salinization is one of the most widespread soil degradation processes on Earth, especially in arid and semi-arid areas. The salinized soil in arid to semi-arid Xinjiang Uyghur Autonomous Region in China accounts for 31% of the area of cultivated land, and thus it is pivotal for the sustainable agricultural development of the area to identify reliable and cost-effective methodologies to monitor the spatial and temporal variations in soil salinity. This objective was accomplished over the study area (Keriya River Basin, northwestern China) by adopting technologies that heavily rely on, and integrate information contained in, a readily available suite of remote sensing datasets. The following procedures were conducted: (1) a selective principle component analysis (S-PCA) fusion image was generated using Phased Array Type L-band SAR (PALSAR) backscattering coefficient (σ°) and Landsat Enhanced Thematic Mapper Plus (ETM+) multispectral image of Keriya River Basin; and (2) a support vector machines (SVM) classification method was employed to classify land cover types with a focus on mapping salinized soils; (3) a cross-validation method was adopted to identify the optimum classification parameters, and obtain an optimal SVM classification model; (4) Radarsat-2 (C band) and PALSAR polarimetric images were used to analyze polarimetric backscattering behaviors in relation to the variation in soil salinization; (5) a decision tree (DT) scheme for multi-source optical and polarimetric SAR data integration was proposed to improve the estimation and monitoring accuracies of soil salinization; and (6) detailed field observations and ground truthing were used for validation of the adopted methodology, and quantity and allocation disagreement measures were applied to assess classification outcome. Results showed that the fusion of passive reflective and active microwave remote sensing data provided an effective tool in detecting soil salinization. Overall accuracy of the adopted SVM classifier with optimal parameters for fused image of ETM+ and PALSAR data was 91.25% with a Kappa coefficient of 0.89, which was further improved by the DT data integration and classification method yielding an accuracy of 93.01% with a Kappa coefficient of 0.92 and lower disagreement of quantity and allocation.