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Article

Construction and Application of Ecological Remediation Technology for Sandy Soils in Northwest China

by
Jing Jiang
1,
Qian Wang
1,
Jinmei Zhao
2,*,
Jun Zhang
3,*,
Bo Dong
3,4 and
Xin Huang
1
1
School of Management, Gansu Agricultural University, Lanzhou 730070, China
2
College of Forestry and Environmental Sciences, Gansu Agricultural University, Lanzhou 730070, China
3
College of Resources and Environmental Sciences, Gansu Agricultural University, Lanzhou 730070, China
4
Institute of Dryland Agriculture, Gansu Academy of Agricultural Sciences, Lanzhou 730070, China
*
Authors to whom correspondence should be addressed.
Sustainability 2023, 15(20), 14730; https://doi.org/10.3390/su152014730
Submission received: 24 July 2023 / Revised: 22 September 2023 / Accepted: 24 September 2023 / Published: 11 October 2023
(This article belongs to the Special Issue Green Technology and Related Macro-Policy Regulation for Soil Remediation, Water Treatment, and CO2 Abatement)
Browse Figures
Versions Notes

Abstract

:
This study investigated the impact of three ecological restoration methods: PRS, OLRS, and NRM, on the soil quality and ecological environment of the cold plateau area in northwest Sichuan, China. Over a period of 3 years, these restoration modes were applied, and their effects on soil enzyme activity, organic carbon, and nitrogen were critically analyzed. The results demonstrated that PRS was the most effective restoration method, enhancing soil water content, organic activity, and enzyme content the most, followed by OLRS and NRM, respectively. In the 0~60 cm soil layer, each restoration model was able to improve the vegetation characteristics, and the modification effects were PRS > ORRS > NRM > UMD in descending order. After the restoration, the summer surface temperature decreased from 41.3 °C to 23.1 °C. The average moisture content of the soil increased from 3.11% to 5.86%. The average moisture content of the soil increased from 3.11% to 5.86%, and the bulk density decreased from 1.47 g/m3 to 1.40 g/m3, resulting in a more rational soil structure. This research offers invaluable insights into the nutrient restoration mechanism of sandy lands, emphasizing the significant role of appropriate vegetation restoration measures in ecological restoration. The findings further suggest the potential application of the PRS model for optimal soil repair effects in similar ecological settings.

1. Introduction

Soil organic carbon (SOC) influences the physical and chemical characteristics of soil and biological material utilization. SOC is a source of microbial life and a key indicator for evaluating soil quality [1]. Changes in SOC content have a significant impact on land productivity, and the setting concerns the entire grassland ecosystem. Active organic carbon is the most active SOC component, which can reflect the micro-change in the soil ecological environment, providing an evaluation basis for the balance of the soil carbon reservoir [2]. Nitrogen is indispensable for vegetation growth and development, which provides nutrients for vegetation growth. Nitrogen can effectively regulate land ecology as well as improve the productivity of vegetation communities [3]. Soil enzymes are biologically active substances with bio-catalytic capacity, which are mainly formed by plant and animal residues, plant root secretions, and microbial activities. It is beneficial to the transformation of organic soil, promoting the circulation of matter and energy in the soil cycle [4].
The research area is at the alternation between the edge of Sichuan and the Tibetan Plateau, which belongs to Hong Yuan County, Aba Prefecture, Sichuan Province. The grassland area is an area with better quality and higher grass production among alpine meadows. With the development of the agricultural economy, the number of livestock has been increasing. Over-cultivation has continued to deteriorate the ecology of grasslands. With the influence of global warming and the intensification of rodent and insect pests, the desertification area of alpine meadows in northwest Sichuan has been expanding [5]. According to statistics, from 1975 to 2005, the desertification land in northwest Sichuan has expanded by about 2105 km2 with an average annual expansion of about 70 km2 [6]. The severe desertification of the alpine grassland in northwest Sichuan has caused a large area of bare grassland. With the influence of natural factors, the coarse particles increase on the surface. From the soil, the loss of organic matter and nutrients is serious, becoming the most serious environmental problem in this area.
Ecological restoration has begun to attract the attention of most scholars, which can effectively control grassland desertification and rebuild surface vegetation. It has been pointed out that different ecological restoration measures can rebuild surface vegetation and diversify biodiversity. Meanwhile, the water retention capacity of soil can be enhanced to reduce soil erosion, improving the ecological structure of sandy grasslands [7]. After vegetation restoration and reconstruction, drifting sand is fixed by plant roots, promoting the activity and reproduction of organisms, enhancing the organic content, strengthening the wind resistance, and preventing desertification of the grassland. During the ecological restoration of sandy grassland, soil fertility also influences the interaction between soil and plants. During the restoration process, changes in soil structure and nutrient content can evaluate the effect of land restoration and select the optimal ecological restoration model based on the results. Therefore, achieving surface vegetation community restoration and improving soil quality has become one of the main goals of the ecological restoration of alpine deserts currently.
In recent decades, countries around the world have attached great importance to the prevention and control of grassland desertification and have tried a lot of methods. On the whole, this improves the desertification degree of the land, windbreak, and sand fixation ability of the land to a certain extent. Yan et al. conducted a community phylogenetic study in the Hulunbeier sandy area and found the bacterial community of different plant communities. The research revealed the structure and potential functions of bacterial communities in different plant communities. The relative abundance of soil dominant groups varied significantly under different vegetation types. Among them, the abundance of the assimilative nitrate-reducing gene in camphor pine was the highest, and the abundance of nitrate-reducing gene and nitrate reductase in salt shrub was the highest [4]. Fu et al. addressed the ecological restoration of vegetation in sandy grasslands using unsealed natural restoration and sealed natural restoration. The vegetation ecological restoration was investigated using different restoration models: unsealed natural, sealed natural, unsealed artificial, and sealed artificial. After 15 years of vegetation restoration, it was found that the combination of fencing and sealing and artificial restoration management could achieve the best effect of vegetation ecological restoration. Moreover, the cover and biomass were significantly and positively correlated with the organic and cation exchange [8]. Kooijman et al. analyzed the soil properties of agricultural fields, three in situ reference sites, and five restored agricultural fields for changes in the soil microstructure, chemical properties, nitrogen and phosphorus effectiveness, and vegetation composition aimed at restoring soil life. The results showed that the better methods for agricultural soil restoration were afforestation and switching to a semi-natural model [9]. Yu et al. sought to reveal the relationship between re-vegetation and ecology in Maowusu Sandy Land by measuring the soil water content at 10 min intervals from April 2012 to October 2013. The results showed that with the increase in rainfall, the infiltration coefficient reached a stable value. In herbaceous communities, only rainfall greater than 35.0 mm could replenish soil moisture. The results showed that the soil water of the herbaceous dominant community was mainly retained in the shallow layer, and the fine roots were mainly distributed in the deep layer [10]. Zhao et al. used statistical methods of linear sampling and classical statistics to analyze the soil moisture in the coal mining collapse area of Maowusu Sandy Land and changes in the soil moisture in the 0–100 cm dune area in the sinkhole and non-sinkhole areas. The study showed that the changes in the soil moisture along the vertical and horizontal directions were different at different levels 2 years after the collapse. Soil water loss was nearly 10% to 30% more severe than the control dune, and the standard deviation increased by 52 from 0.54 to 1.05. The differences were significant when compared to the non-subsidence regions. The probability of a positive greater than 1 was more than 50%, 80% higher than the non-subsidence area. After ecological restoration, the dispersion of soil water in the directions was greatly increased, and the spatial variability of the soil water was increased [11]. In order to study the corresponding configuration of mine site types and ecological restoration techniques, Ruipeng L. et al. selected an open-pit coal mine dump that had been ecologically restored for many years in the grassland mining area of western Inner Mongolia for their study. By comprehensively evaluating the slope protection effect, vegetation restoration effect, soil improvement effect, and engineering technology cost, they categorized the site types of open pit coal mine dumps and determined their corresponding ecological restoration technologies. The results of the study show that soil thickness, slope gradient, and slope direction are the main factors affecting the stockpile site. According to different site types, the study gives specific ecological restoration recommendations. For example, for gentle slopes with semi-shaded and thick soil, methods such as plant grid slope protection + planting + seeding + spray irrigation are preferred [12]. Soil organic carbon (SOC) mineralization has an important effect on carbon loss and accumulation. However, it is not clear how the quality of vegetation apoplastic matter regulates SOC mineralization and inventory. Li Y. et al. conducted an indoor incubation experiment in the Tengger Desert in northern China considering the effects of different temperatures (10 °C, 20 °C, 30 °C, and 40 °C), humidity (5%, 10%, and 20%), and the quality of vegetative apoplastic matter (Artemisia oleifera, Lemonade, and Triticum aestivum) on the mineralization of SOC. The results showed that the addition of vegetative apoplast increased cumulative CO2-C emission, the maximum rate, the potential mineralization C, and the moisture sensitivity but decreased the temperature sensitivity. Different apoplastic quality had significant differences on the above indexes. The rate of SOC mineralization increased with increasing temperature and humidity, while total carbon and lignin content in the apoplastic were the key factors regulating SOC mineralization. Input of low-quality apoplastic litter reduced the loss of SOC mineralization caused by stimulation effects and decreased SOC accumulation in the replanted area. The above study provides a rational model for apoplastic litter management to stabilize soil carbon pools in arid regions [13].
Recent research on the re-ecological effects of sandy soils primarily delves into the characteristics of surface vegetation communities and the quantification of soil’s physical and chemical indicators. However, a notable research gap exists: There are limited investigations into active organic carbon and nitrogen fractions. Distinguishing our work from preceding studies, we innovatively employed three distinct ecological restoration models aimed at rehabilitating the environment in the cold region of the Northwest Sichuan Plateau. This study meticulously analyzes variations in soil organic carbon, nitrogen, and enzyme activities in this unique locale, further shedding light on the ecological and environmental repercussions of sandy grassland restoration. Our exploration includes the PRS, OLRS, and NRM models, characterized by their distinct vegetation seeding methods. Emphasizing enzyme activity, organic carbon, and ammonia as crucial parameters, our research dissects the influence of different restoration modes on these indicators. Our findings aim to unveil the most efficacious ecological restoration model tailored for the specific conditions of the Northwest Sichuan Plateau region, thus filling a critical knowledge void and providing invaluable insights for future restoration endeavors.

2. Theoretical Hypothesis

2.1. Regional Overview

The specific experimental site of this study was the Hongyuan Moon Bay Natural Scenic Area in Hongyuan County, Aba Tibetan and Qiang Autonomous Prefecture, Sichuan Province, China. The longitude of Moon Bay is 120.155972, and the latitude is 35.932988. The Sichuan–Tibet Plateau’s alternating zone is where Hong Yuan County is situated. The average yearly temperature is 1.2 °C with average lows of −9.4 °C in January and highs of 11.3 °C in July. It has a cool, moderate monsoon climate on the continental plateau; the annual precipitation is 754 mm, and most of it falls during the third quarter. Local precipitation also outweighs local evaporation. The average elevation is over 3800 m, and the terrain slopes from southeast to northwest. The region’s soil is mostly composed of Holocene Quaternary mounded sediment, alluvium, meadow soil, and bog soil, as well as Triassic mounded remnants of sandstone, schist, and slate. Legumes, Asteraceae, and Salicaceae make up the region’s forage, which is mostly composed of alpine meadows, alpine scrub grasses, and subalpine coniferous woods. Herders split the land into winter and summer pastures, grazing in the winter pastures in the winter and spring while reserving a certain area of mowed land for storing food. The area is mostly dominated by breeding pastures for Tibetan sheep and yak sheep [14]. The trial location map distribution is shown in Figure 1.

2.2. Research Methodology

The test materials used include four species of crops: oats, ryegrass, pendulous phyllanthus, and sclerotinia, all of which are cold and drought-tolerant and have strong root tillering ability. All four plants selected for the experiment can survive in the harsh conditions of alpine sandy land and can restore sandy grassland [15].
The restoration demonstration area of alpine sandy grassland in Hong Yuan County was selected as the experimental area, and the area with severe sandy grassland was selected. The altitude and slope were taken into account to ensure that all conditions of the experimental sample points remained consistent. The sample area is about 2400 m2. Table 1 shows the basic soil properties before the test. In Table 1, TN is total nitrogen, AN is alkaline solution nitrogen, AP is fast-acting phosphorus, and AK is fast-acting potassium.
The ecological restoration model used in this experiment consisted of three models: PRS, OLRS, and NRM, and the Unrestored Mobile Dune (UMD) mode was applied as the control. The PRS mode is a mixture of phyllanthus, ryegrass, and sclerotium in equal proportions with 500 seeds/m2 at a depth of 4 cm [16]; the OLRS mode is a mixture of overhanging leaves, ryegrass, and sclerotium in equal proportions with 500 seeds/m at a depth of 4 cm [17]; the NRM mode indicates Natural Recovery Mode; the UMD mode is a large area of sandy grassland with few sandy plant species and less than 5% cover and bare surface soil [18].

2.3. Sample Data Collection

The data for this study were collected in July 2018 when vegetation growth was at its peak. The entire experiment took three years to complete, and the final comparison of soil changes over the three-year period from 2018 to 2020 determined the best ecological restoration model. A sample area of 1 × 1 m was set up in the test plot to facilitate the calculation, and a soil sample sampling point was set up in the sample area. A radius of 3.75 was used to drill the soil samples and remove the debris in the soil five times. After mixing the soil, two mixed soil samples were obtained, and the drilling depth was 0~20 cm, 20~40 cm, and 40–60 cm. One sample was placed in a box and refrigerated at 4 °C to measure the soil enzyme activity, Soil Microbial Carbon (SMC), Microbial Nitrogen (MN), ammonia nitrogen (NH4+-N), and nitrate nitrogen (NO3−-N) in the test; the other was placed in a numbered bag and dried naturally for measuring the conventional and other carbon and nitrogen components in the test. Air-dried soil samples were used to measure conventional indicators and the other carbon and nitrogen components in the test.

2.4. Soil Sample Measurement Methods and Data Processing Methods

In this experiment, samples were measured for basic properties, organic carbon and active fractions, the nitrogen fraction, and enzyme activity. (1) Determination of basic physical and chemical properties [19]: Soil water content and temperature at noon in the summer were measured with moisture meter, pH value was measured by potential method, and volume weight was measured by ring knife method [20]; AP and AK were determined by NaHCO3 leaching with molybdenum antimony anti-colorimetric method and NH4OAc leaching with flame photometric method, respectively [21]. (2) Determination of organic carbon and active fractions [22]: SOC, Easily Oxidizable Organic Carbon (EOC), Particulate Organic Carbon (POC), SMC, and Mineralizable Carbon (MC) were determined by external heating with potassium dichromate, oxidation with potassium permanganate, dispersion with sodium hexametaphosphate, fumigation extraction, and aerobic incubation, respectively [23]. (3) TN, AN, NH4+-N, NO3−-N, MBN, and Soil Mineralizable Nitrogen (SMN) were determined by the semi-micro Kjeldahl method, alkaline diffusion method, KCL leaching indophenol blue colorimetric method, dual-wavelength spectrophotometric method, chloroform fumigation method, and aerobic incubation method, respectively [24]. (4) Soil protease (PRO), nitrate reductase (NR), urease (URE), and Polyphenol Oxidase (PPO) were determined by the Gallus River method, chromogenic colorimetric method, sodium phenol–sodium hypochlorite colorimetric method, and o-phenotriol colorimetric method, respectively [25]. Cellulase (CEL), amylase (AMY), and sucrase (SUC) were determined by the 3,5-dinitrosalicylic acid colorimetric method [26].
SPSS 20.0 software (IBM, Armonk, NY, USA) was applied to conduct a one-way analysis of variance for the data, and all the charts were drawn with Excel 2007 (Microsoft, Redmond, WA, USA).

3. Results and Analysis

3.1. Analysis of Changes in Regional Vegetation Characteristics under Multiple Scenarios

After 3 years of restoration, the characteristics of the changes in the surface vegetation cover, height, and biomass under the various restoration modes were observed, as shown in Figure 2. Figure 2a shows the comparison results of the vegetation cover under the four restoration modes, showing that there are significant differences between the two comparisons of the four modes (p < 0.05). Among them, the cover of PRS is 74.08%, which is significantly higher than the other three restoration modes, followed by OLRS, NRM, and UMD, which are 48.48%, 21.23%, and 5.35%, respectively. This reflects that the vegetation cover can be improved to different degrees under various restoration modes, and all of them can improve the growth of vegetation, among which PRS has the best effect. Figure 2b shows the comparison results of the vegetation height under the four restoration modes, showing that there are significant differences between the two comparisons of the four modes (p < 0.05). The vegetation height under PRS is 29.89 cm, which is significantly better than that of OLRS, NRM, and UMD (22.63 cm, 12.85 cm, and 10.53 cm, respectively). Figure 2c shows the performance of the above- and below-ground biomass under the four restoration modes. The above- and below-ground biomass is significantly different (p < 0.05) in the two comparisons under all four modes. The highest above-ground and below-ground biomass is found in PRS, followed by OLRS, NRM, and UMD, respectively. Compared to UMD, the above-ground biomass of PRS, OLRS, and NRM increases by 9.86, 6.33, and 1.95 times, respectively, and the underground biomass increases by 13.87, 9.59, and 2.05 times, respectively.
Since the whole area has been restored for three years, this study introduced the indicator of time and used two-factor ANOVA to further compare the characteristics of changes in the surface vegetation cover and height under different restoration modes as well as under different times of the year, as shown in Figure 3. Figure 3a shows the vegetation cover of the four restoration modes over three years of restoration time. Through Figure 3a, it can be seen that the vegetation cover of the four restoration modes did not change significantly with the increase in years, but the vegetation cover of PRS was still the highest value, followed by OLRS, NRM, and UMD, respectively. Figure 3b shows the vegetation height of the four restoration modes over three years of restoration time. Figure 3b shows that the vegetation heights under different restoration modes did not change significantly in three years, among which, the vegetation heights under PRS were much higher than those under the other three restoration modes. In summary, the vegetation characteristics did not change significantly after the introduction of time indicators. Combined with Figure 2 and Figure 3, it can be seen that different restoration modes had the greatest influence on vegetation characteristics. Therefore, the subsequent study will use one-way analysis to investigate the changes in vegetation properties under different ecological restoration modes.
Vegetation characteristics are important factors and a theoretical basis for analyzing the ecosystem restoration process, and the vegetation restoration reflects the ecosystem restoration effect [27]. In this experiment, after 3 years of restoration, all restoration modes in soil layers 0–60 cm were able to improve the vegetation characteristics. The modification effects are PRS > OLRS > NRM > UMD from high to low. The results indicate that seeding grass seed can increase the vegetation coverage, vegetation height, and biomass of sandy grasslands and improve the vegetation community structure. After the completion of ecological restoration, the plant roots are widely distributed and deep underground, the soil has strong soil nutrient activation ability, and the vegetation characteristics show a rapid growth trend. PRS is the best model for vegetation characteristic restoration, mainly because oats have the characteristics of cold resistance, infertile tolerance, and strong root tillering power, which can grow in harsh environments and facilitate the soil restoration process. The water content of the soil after restoration is especially enhanced, and the surface temperature at noon is reduced, optimizing the soil environment for other plant growth and accelerating the recovery of surface vegetation [28]. In contrast, although the OLRS model can improve soil characteristics to a certain extent, its effect is not as good as PRS in extremely harsh environments where soil desertification is serious and vegetation survival probability is low after artificial sowing. Although the NRM model reduces animal foraging and trampling, the growth of grass species is very slow in harsh environments. If this model is used for the natural recovery of sanded land, it will take longer.

3.2. Regional Soil Physicochemical Characteristics Analysis under Multiple Scenarios

Soil physical and chemical characteristics include mainly the changes in surface midday temperature, soil water content, bulk weight, and pH [29]. After 3 years of restoration, the characteristics of the changes in summer noon surface temperature under different restoration modes are shown in Figure 4. The PRS temperature is 23.1 °C, significantly lower than OLRS and UMD modes (p < 0.05), followed by OLRS at 28.4 °C, significantly lower than NRM and UMD modes (p < 0.05); comparing NRM (39.5 °C) and UMD (41.3 °C), there is no significant difference. It indicates that the three modes can effectively improve the extremely high-temperature condition of sandy grasslands, and then regulate the local temperature and ecological environment of the soil surface.
Table 2 compares the physical and chemical properties of the soil after restoration in the three models with the UMD model. The soil moisture content of the four models in the three soil layers is PRS > OLRS > NRM > UMD. In the 40–60 cm layer, PRS is higher than UMD (p < 0.05), while OLRS and NRM are not significant in comparison with UMD. For different soil layers, the UMD model is significantly different in both comparisons (p < 0.05), while the other three restoration modes only show significant differences between 0–20 cm, 20–40 cm, and 40–60 cm (p < 0.05). For the soil capacity, from small to large, PRS < OLRS < NRM < UMD. Compared with UMD, the capacity of PRS and OLRS decreases to a certain extent at each depth, showing a trend of gradually increasing capacity from shallow to deep, but no statistical significance was found in the comparison between them (p > 0.05). It indicates that the re-ecological area has improved the soil pore space and texture in the area, and the capacitance continues to decrease in the process of benign restoration of vegetation communities. For the pH condition of the soil, from small to large, PRS < OLRS < NRM < UMD, where PRS and OLRS all show a trend of gradually increasing the pH from shallow to deep, but both comparisons are not statistically significant; while under UMD, the comparison is significant between them (p < 0.05).
Destructive human development has caused the lowering of the water table. Therefore, the soil water cannot be replenished, and sand dunes cannot easily form, thus accelerating the erosion of sand in the grassland [30]. As a result, the physical and chemical properties of surface soils in the area are low, vegetation does not grow easily, and soil productivity is severely lacking. After 3 years of restoration, the surface temperature decreased from 41.3 °C to 23.1 °C in the summer. Therefore, the soil water could not be replenished, and sand dunes could not easily form, thus accelerating the erosion of sand in the grassland. The average soil water content ranges from 3.11% to 5.86%, and the water content is more than three times that of the dry sandy grassland, mainly due to the strong water fixation effect of the vegetation root system. The water content of the topsoil is lower than that of the bottom layer because the water is volatile, which is caused by strong solar radiation. When vegetation is degraded, it reduces the ability of roots to absorb water, allowing water to penetrate to the bottom. As a result, most of the water in the region is kept in the subsurface soil [31]. For soil capacitance, after restoration, the capacitance decreases from 1.47 g/m3 to 1.40 g/m3. Due to the improvement of vegetation roots and coverage in the process of ecological restoration, soil water content increases and unit mass decreases, resulting in a further reduction in capacitance. The soil moisture and pore space of the unrestored grassland gradually decrease due to damage by humans and animals, resulting in a lower capacity of the bottom layer than the surface layer. The pH of the soil after ecological restoration is reduced to some extent because the restored vegetation improves microbial activity and increases biological secretions. The increase in root exudates and organic acids in the soil causes the soil pH to be higher than that of unrecovered sandy land. The above analysis shows that re-ecology has a significant improvement in soil physicochemical properties, enhancing the water fixation capacity and allowing the structure to be adjusted in a benign direction [32].

3.3. Regional Soil Organic Carbon Characterization under Multiple Scenarios

The soil organic carbon characteristics mainly include the changes in SOC, EOC, Soil Soluble Organic Carbon (SSOC), POC, MC, and SMC [33]. The SOC contents in the 0–60 cm layer under various remediation modes are shown in Figure 5a. From the result, the SOC values of PRS are significantly higher than those of other modes in the 0–40 cm soil layer (p < 0.05); in the 40–60 cm layer, the SOC values of PRS, OLRS, and NRM soils all increase to some extent relative to UMD, but the differences are not significant (p > 0.05). The mean SOC value of PRS increases the most. The mean SOC values of PRS, OLRS, and NRM are about 1.83, 1.50, and 1.28 times higher than UMD, and the overall SOC values show a decrease with the deepening of the layer. Figure 5b shows the accumulation of EOC under various restoration modes. Compared with UMD, the comparisons of PRS, OLRS, and NRM are significantly different (p < 0.05) in the 0–20 cm layer, and the comparisons of PRS and OLRS are significantly different (p < 0.05) in the 20–60 cm layer. From the profile as a whole, the soil EOC content of all restoration modes shows a decrease with increasing depth. The EOC values of all three remediation modes are significantly higher in the 0–20 cm layer than in the 40–60 cm layer, indicating that the greatest increase in EOC is found in the 0–20 cm layer. Figure 5c shows the DOC contents under various remediation modes. Compared with UMD, PRS and OLRS both have significant differences (p < 0.05) in the 0–60 cm layer, while NRM has no significant differences. From the profile as a whole, the soil DOC content of all restoration modes shows a decrease with increasing depth. Figure 5d shows the POC content in various remediation modes. From the results, the soil POC content shows a trend of decreasing with increasing depth, especially in the 0–20 cm layer, and there is a significant difference in POC content among the different modes (p < 0.05); in the 20–60 cm layer, although the trend of POC change is the same as that in the 0–20 cm layer, the change is smaller, and only PRS and OLRS have significant differences (p < 0.05) in comparison to UMD in the 20–60 cm layer, and NRM has no significant difference (p > 0.05) in comparison to UMD. Figure 5e shows that the MC contents in the various restoration modes in the overall order from high to low are PRS, OLRS, NRM, and UMD, while showing a decrease with increasing depth. Compared to UMD, the MC content of PRS is significantly different in the 0–60 cm layer (p < 0.05), OLRS is significantly different in the 0–40 cm layer (p < 0.05), and NRM is not statistically significant in the 0–60 cm layer (p > 0.05). Figure 5f shows that the SMC contents in various restoration modes in the overall order from high to low are PRS, OLRS, NRM, and UMD. The SMC comparison between different modes is very different and decreases with increasing depth. The SMC of each model in the 0–20 cm layer is significantly higher than that of other layers (p < 0.05); the SMC content of PRS and OLRS in the 0–40 cm layer is higher than that of UMD (p < 0.05), and the SMC content of NRM compared with that of UMD is not significant (p > 0.05); in the 40–60 cm layer, only the SMC content of PRS is higher than that of UMD (p < 0.05). The SMC content in the 40–60 cm is higher than that in UMD (p < 0.05).
Soil fertility determines the regrowth of vegetation and can effectively prevent desertification of grasslands [34]. Organic carbon is precisely an important component of fertility and one of the indicators to evaluate the effectiveness of re-sand [35]. From the results, the SOC and active organic carbon content of the 0–60 cm layer are enhanced with each restoration mode. The significant differences between modes are mainly due to the different growth conditions in the vegetation caused by artificial grassland sowing. It causes different degrees of impact on each grassland, so the SOC content and distribution are different, further indicating that ecological restoration can effectively promote organic carbon content and improve the proportion of active organic carbon in the soil. After 3 years of ecological restoration, the enhancement in EOC, DOC, POC, MC, and SMC in the test area is significantly higher than that in SOC, mainly because active organic carbon is the most active part of the soil, which can reflect environmental changes on a very small scale, indicating that the active organic carbon fraction is more suitable as an indicator for evaluating soil quality [36]. From the results, the SOC and reactive organic carbon contents of PRS are significantly higher than those of OLRS and NRM, mainly because the vegetation in the PRS model is more luxuriant and has a higher number of roots and decomposition rate, thus the SOC content is effectively enhanced. In addition, the vegetation recovered best in PRS mode, which enhances the water content of the soil and makes the surface temperature drop significantly, preventing the vegetation seedlings from being scalded to death. Therefore, there is more litter later on, which increases the organic carbon content. However, after 3 years of ecological restoration, the organic carbon content in the experimental area is still low, and a longer ecological restoration time may be needed for the accumulation of organic carbon in the sandy grassland in the alpine semi-humid zone.

3.4. Regional Soil Nitrogen Characterization under Multiple Scenarios

The nitrogen characteristics of the sandy soil mainly include the contents of TN, AN, NO3−-N, NH4+-N, SMN, and MBN [37]. The variation in soil nitrogen characteristics of each ecological restoration model are PRS, OLRS, NRM, and UMD from high to low, showing a trend of decreasing in value with an increasing stratum depth in general. Figure 6a shows the TN content in various remediation modes, and the variation range is 0.17–0.55 g/kg. The TN content in all modes is significantly higher in the 0–20 cm layer than the 20~60 cm layer (p < 0.05), and the TN content of PRS and OLRS in the 0–60 cm layer is higher than that of UMD (p < 0.05), and the TN content of NRM is only significantly higher in the 0~20 cm layer (p < 0.05). The TN content in the 0~20 cm layer is significantly higher than that in UMD (p < 0.05). Figure 6b shows the AN content in various restoration modes with a variation range of 8.22–24.76 mg/kg. The AN content in all modes is significantly higher in the 0–20 cm layer than in the 20–60 cm layer (p < 0.05), and it is higher in the 0–40 cm layer than in the UMD mode; only PRS is significantly higher than in the UMD mode in the 40–60 cm layer (p < 0.05). Figure 6c shows the NO3−-N content in various restoration modes with the variation range from 2.50 to 9.18 mg/kg. The NO3−-N contents of PRS and OLRS in the 0–60 cm layer are significantly higher than that of UMD (p < 0.05), while the comparison between NRM and UMD is not statistically significant. Figure 6d shows the NH4+-N contents in various restoration modes in which the greatest increase is observed in the 0–20 cm layer, which is significantly higher than that in the 20–60 cm layer (p < 0.05); in the 20–40 cm layer, the NH4+-N contents in all modes are significantly higher than that in UMD (p < 0.05), while there is no significant difference in the 40–60 cm layer. The variance results indicate that the NH4+-N content of PRS is significantly higher than that of UMD in the 0–60 cm layer (p < 0.05), and OLRS is significantly higher than that of UMD in the 0–40 cm layer (p < 0.05). Figure 6e shows the SMN content in various restoration modes with a variation range of 3.61–7.21 g/kg, and all modes have significantly higher SMN content in the 0–20 cm layer than in the 20–60 cm layer (p < 0.05). In the 0–20 cm layer, the SMN contents of all models are higher than those of UMD (p < 0.05); in the 20–60 cm layer, the SMN contents of PRS and OLRS are significantly higher than those of UMD (p < 0.05). Figure 6f shows the MBN content under various restoration modes with a variation range of 4.13–15.75 mg/kg. In the 0–60 cm layer, the MBN content of PRS is significantly higher than that of UMD (p < 0.05), and the comparison of OLRS and NRM with UMD is not significant (p > 0.05). Under the same restoration mode, the MBN contents of PRS are significantly different (p < 0.05) in comparison with each soil depth; the MBN contents of OLRS, NRM, and UMD are significantly different (p < 0.05) in comparison with the 20–60 cm and 0–20 cm layers.
One of the key indicators of soil nutrients is nitrogen, which is also an important element to promote vegetation growth [38]. The experiment shows that the nitrogen content in the 0–60 cm layer is higher in all three ecological restoration modes than in UMD, which reflects that the artificial restoration mode can significantly improve the accumulation of nitrogen in the soil. The reason for this result is that artificial restoration promotes the growth of vegetation and increases the number of microorganisms and organic matter. The two interact to enhance the nitrogen source. In addition, the sand barrier deployment in the artificial restoration measures effectively avoids the loss of nitrogen and further promotes the accumulation of nitrogen in the soil. The root distribution and extracellular substances of plants affect the input and output of soil nitrogen, so different vegetation types influence the nitrogen content. This experiment shows that the variation of soil nitrogen characteristics of each ecological restoration model from high to low are PRS, OLRS, NRM, and UMD. The reason for obtaining this result is that oats can better enhance vegetation cover and soil biomass, and more zooplankton increases nitrogen input, which is significantly better than other ecological restoration models. There are some differences in the comparison of soil physical and chemical properties, which results in the differences in soil nitrogen and fraction content. First, the higher soil moisture in PRS improves the vegetation and microbial population and enhances the input and output of soil nitrogen, thus promoting nitrogen production; second, different modes have different soil physical and chemical properties, which have different effects on the number of microorganisms and decomposition activities, making the soil nitrogen content in different modes differ greatly. The effect of soil nitrogen accumulation in PRS is the best in this experiment, and the effect of OLRS and NRM ranks second, mainly due to the severe climate and sandy grassland in the experimental area, making it difficult for the grass species to survive and grow slowly in OLRS mode. The survival rate of native grass species in NRM mode is even lower, and the effect is not as good as PRS. Therefore, more time is required for the natural recovery of the experimental area by setting fences and prohibiting grazing.

3.5. Characterization of Regional Soil Enzyme Activity under Multiple Scenarios

The characteristics of the enzyme activities of sandy soil mainly include the activities of SUC, AMY, CEL, PPO, URE, PRO, and NR. The changes in soil enzyme activity characteristics of each ecological restoration mode from high to low are PRS, OLRS, NRM, and UMD. Figure 7a shows the changes in SUC activity in each remediation mode. The results indicate that the SUC activities of PRS and OLRS are significantly higher than those of UMD in the 0–40 cm layer (p < 0.05); the SUC activities of NRM are significantly higher than those of UMD in the 0–20 cm layer (p < 0.05). Figure 7b shows the changes in AMY activity under each restoration mode. In the 0–20 cm layer, the AMY activity of the three modes is significantly higher than that of UMD (p < 0.05); In the 20–40 cm layer, the AMY activity of PRS and OLRS is significantly higher than that of UMD (p < 0.05). Figure 7c shows the changes in CEL activity under each restoration mode; in the 0–20 cm layer, the CEL activity of the three modes is significantly higher than that of UMD (p < 0.05); in the 20–40 cm layer, the CEL activity of PRS and OLRS is significantly higher than that of UMD (p < 0.05). Figure 7d shows the variation of PPO activity in each remediation mode with the variation interval ranging from 4.24 to 8.27 mg/g−2 h. In the 0–20 cm layer, the PPO activity in all modes is significantly higher than that in the 20–60 cm layer. The PPO activity of PRS in the 0–60 cm layer is higher than that of UMD (p < 0.05); the PPO activity of OLRS is higher than that of UMD in the 0–40 cm layer (p < 0.05); NRM is higher than that of UMD in the 0–20 cm soil layer only (p < 0.05).
Figure 8a shows the changes in URE activity in each remediation mode with a variation range of 7.18–11.94 mg/g−2 h. In the 0–20 cm layer, the URE activity is enhanced optimally, and the URE activity in all modes is significantly higher than that of UMD (p < 0.05); the increase in URE activity in the 20–60 cm layer is also significant, and in the 20–40 cm layer, the URE activity of PRS and OLRS increases by 32.04% and 27.71%, respectively; the growth is significant (p < 0.05); in 40–60 cm layer, the URE activity of PRS increases by 17.60%, and the growth is significant (p < 0.05). Figure 8b shows the changes in PRO activity in each restoration mode. In the 0–40 cm layer, the PRO activity of PRS and OLRS is significantly higher than UMD (p < 0.05); in the 40–60 cm layer, the PRO activity of PRS is significantly higher than UMD (p < 0.05). Longitudinally, only OLRS and NRM have significant differences (p < 0.05) in the comparison of PRO activity in the 0–40 cm and 40–60 cm layers. Figure 8c shows the changes in NR activity in each restoration mode. In the 0–20 cm layer, the NR activity of all three modes is significantly higher than that of UMD (p < 0.05); in the 20–40 cm layer, the NR activity of PRS is significantly higher than that of UMD (p < 0.05). Longitudinally, only PRS and OLRS have significant differences (p < 0.05) in the comparison of NR activity in the 0–20 cm and 20–60 cm layers.
Soil enzymes are one of the important organic components in the soil micro-environment and one of the key indicators for evaluating soil biological activity. Soil enzymes provide an important guarantee for soil nutrient cycling and the supply of nutrients required by vegetation, reflecting the intensity of soil biological activity and biochemical reactions and influencing the soil carbon and carbon cycling [39]. Th results show that all three models significantly enhance the activity of soil enzymes. The main reason is that after ecological restoration in the experimental area, the soil organic matter has been effectively enhanced. During this process, plant root system changes become more developed, and soil microbial habitats are improved. In addition, soil biological metabolism is enhanced, and the activities of oxidase and hydrolase in the soil are also enhanced. In contrast, the soil in the UMD model is trampled by livestock, and the soil ecology is seriously affected, which interferes with the growth of vegetation and microbial metabolism, thus reducing the number of soil microorganisms and vegetation. The results of this experiment show that soil enzymes under PRS restoration mode have the highest activity, followed by OLRS, and the weakest is NRM. When the vegetation is luxuriant and the roots are developed, it can provide more energy for the soil microorganisms, thus providing the basis for the production of enzymes. Various types of vegetation secretions and dead branches also contribute to enzyme activity. Soils under PRS are improved due to organic matter and nutrients, which enhances soil microbial activity. The soil under PRS mode is significantly superior to other modes; as organic matter and nutrients increase, the number of soil microorganisms increases, and their metabolic processes are improved. The increase in soil enzymes secreted by microorganisms enhances the intensity of soil biochemical reactions.

4. Discussion

In this project, three ecological restoration strategies were used to recover the chilly portion of the Northwest Sichuan Plateau. This study looked at the effects of several restoration modes on soil enzyme activity, organic carbon, and ammonia nitrogen in order to establish the most efficient ecological restoration technique for the plateau area of northwest Sichuan, China. After three years of repair, conclusions for the debate that follows can be reached.
Firstly, all restoration techniques in the 0~60 cm soil layer could enhance vegetation traits, and in descending order, their enhancement impacts were PRS > ORRS > NRM > UMD. The results might be connected to the PRS model’s method of planting grass seeds after creating artificial grassland. Sowing grass seeds after artificial grassland construction could increase the vegetation cover, height, and biomass of sandy grassland and improve the vegetation community structure because the sown plots had better vegetation growth environments and therefore played a crucial role in improving the vegetation community structure.
Secondly, the summer surface temperature decreased from 41.3 °C to 23.1 °C. The average water content of the soil went from 3.11% to 5.86%, which was more than three times that of the arid sandy grassland, which was mainly due to the strong water fixation effect of the vegetation root system. After the restoration, the bulk density decreased from 1.47 g/m3 to 1.40 g/m3, and the soil structure was rationalized. The results of this study showed that the significant reduction in surface temperature and the increase in soil water content in the summer reflected the positive effects of ecological restoration on both microclimate and soil water status. The water-fixing effect of the vegetation root system and the decrease in soil water content suggest the interaction between vegetation growth and soil structure. These changes imply that ecological restoration can impose a reversal of the harsh environment, bringing it closer to its natural, undisturbed state.
Thirdly, with notable variations across models, each restoration strategy raised the SOC and active organic carbon content from 0 to 60 cm. The fundamental cause was that each grassland was influenced differently by the artificial grass planting, which led to various vegetation-growing circumstances. Due to variations in SOC content and distribution, ecological restoration may be able to successfully encourage an increase in organic carbon content and boost the fraction of active organic carbon in the soil. It should be emphasized, nevertheless, that even after three years of restoration, the level of regional organic carbon was still low, suggesting that ecological restoration was a long-term, complicated process that took time to complete.
Fourth, the nitrogen content in the 0–60 cm soil layer of all three ecological restoration modes was higher than that of UMD, reflecting that the artificial restoration modes could significantly increase the accumulation of nitrogen in the soil. The reason for this result is that artificial restoration promotes the growth of vegetation and increases microorganisms and organic matter in the soil. The interaction between the two enhanced the nitrogen source in the soil. In addition, the sand barriers set up in the artificial restoration measures effectively avoided the loss of nitrogen, which further promoted the accumulation of nitrogen in the soil. The root distribution and apoplastic material of vegetation will affect the input and output of soil nitrogen, so different vegetation types will affect the nitrogen content.
Fifth, all three modes significantly increased soil enzyme activities, mainly because the ecological restoration in the experimental area effectively improved the soil organic matter, plant root system, soil microbial habitat, and soil biological metabolism capacity. The soil under the PRS mode showed an increase in soil microorganisms and an improvement in metabolic processes due to the increase in organic matter and nutrients. Soil enzymes secreted by microorganisms increased, and their activities were enhanced, which led to an increase in the intensity of soil biochemical reactions so that the enzyme activities under the PRS model were significantly better than those of the other models.
In summary, this study offers useful experimental data and a theoretical framework for ecological restoration in the chilly Northwest Sichuan Plateau, as well as fresh insights and practical strategies for the long-term ecological restoration in this area.

5. Conclusions

This study evaluated three ecological restoration models in the cold region of the Northwest Sichuan Plateau. The results showed that the PRS model was more effective than the other models in restoring vegetation, improving soil physical and chemical properties, and increasing soil organic carbon and nitrogen content in all soil layers. In addition, the PRS model had the highest soil enzyme activities, indicating better results. Overall, all models improved soil quality in the study area, but the PRS model was the most successful. In future ecological restoration studies, the PRS model should be prioritized for practical soil restoration in similar places. More future research directions may examine the scalability and long-term effects of the PRS model for longer and more diverse ecological studies.

Author Contributions

Formal analysis, J.Z. (Jinmei Zhao), B.D. and X.H.; Investigation, J.J., Q.W., J.Z. (Jinmei Zhao) and J.Z. (Jun Zhang); Writing—original draft, J.J.; Writing—review & editing, J.Z. (Jun Zhang) and B.D. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by key R&D Project in Gansu Province (No. 23YFNA0036; No. 21YF5NA007); Gansu Province Higher Education Institutions Industrial Support Program Project (No. 2023CYZC-45); The National Key Technology Research &Development Program of China (No. 2021YFD190070406).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

All data generated or analysed during this study are included in this published article.

Conflicts of Interest

The authors declare no conflict of interest.

Abbreviations

AbbreviationFull Name
PRSA mixture of phyllanthus, ryegrass, and sclerotium
OLRSA mixture of overhanging leaves, ryegrass, and sclerotium
NRMNatural Recovery Mode
UMDUnrestored Mobile Dune
SOCSoil organic carbon
SMCSoil Microbial Carbon
MNMicrobial Nitrogen
NH4+-NAmmonia nitrogen
NO3−-NNitrate nitrogen
EOCEasily Oxidizable Organic Carbon
POCParticulate Organic Carbon
MCMineralizable Carbon
SMNSoil Mineralizable Nitrogen
PROSoil protease
NRNitrate reductase
UREUrease
PPOPolyphenol Oxidase
CELCellulase
AMYAmylase
SUCSucrase
SSOCSoil Soluble Organic Carbon

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Figure 1. Map of the test location.
Figure 1. Map of the test location.
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Figure 2. Characteristics of vegetation communities in different ecological restoration models.
Figure 2. Characteristics of vegetation communities in different ecological restoration models.
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Figure 3. Surface vegetation cover and height characteristics of different ecological restoration models under different years.
Figure 3. Surface vegetation cover and height characteristics of different ecological restoration models under different years.
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Figure 4. Surface temperature characteristics of different ecological restoration models.
Figure 4. Surface temperature characteristics of different ecological restoration models.
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Figure 5. Characteristics of soil total organic carbon and active components under ecological restoration model.
Figure 5. Characteristics of soil total organic carbon and active components under ecological restoration model.
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Figure 6. Characterization of soil nitrogen under different ecological restoration models.
Figure 6. Characterization of soil nitrogen under different ecological restoration models.
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Figure 7. Characteristics of soil carbon-related enzyme activities under different ecological restoration models.
Figure 7. Characteristics of soil carbon-related enzyme activities under different ecological restoration models.
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Figure 8. Characteristics of soil nitrogen-related enzyme activities under different ecological restoration models.
Figure 8. Characteristics of soil nitrogen-related enzyme activities under different ecological restoration models.
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Table 1. Basic properties of tested soil.
Table 1. Basic properties of tested soil.
Item0–20 cm20–40 cm40–60 cm
Water (%)2.68 ± 0.943.55 ± 1.304.42 ± 1.11 *#
Bulk density (g/cm3)1.47 ± 0.151.48 ± 0.081.48 ± 0.17
TN (g/kg)0.129 ± 0.090.124 ± 0.070.117 ± 0.05
AN (mg/kg)7.03 ± 1.444.80 ± 1.173.26 ± 1.20 *
AP (mg/kg)5.76 ± 1.054.43 ± 0.803.90 ± 0.71 *
AK (mg/kg)28.74 ± 4.8217.99 ± 4.22 *15.10 ± 2.98 *
SOC (g/kg)1.72 ± 0.091.65 ± 0.131.55 ± 0.07 *
Note: Compared to 0–20 cm, * p < 0.05; compared to 20–40 cm, # p < 0.05.
Table 2. Effects of different models on basic physicochemical properties of soil.
Table 2. Effects of different models on basic physicochemical properties of soil.
PatternLayer (cm)Water (%)Bulk Density (g/m3)pH
PRS0–204.42 ± 0.441.38 ± 0.126.60 ± 0.22
20–404.93 ± 0.46 *1.39 ± 0.106.84 ± 0.13
40–605.86 ± 0.58 *1.44 ± 0.117.15 ± 0.12
OLRS0–204.13 ± 0.321.41 ± 0.136.78 ± 0.28
20–404.53 ± 0.42 *1.42 ± 0.117.03 ± 0.24
40–605.22 ± 0.52 *#1.45 ± 0.127.22 ± 0.22
NRM0–203.11 ± 0.53 ab1.42 ± 0.106.90 ± 0.24
20–403.85 ± 0.39 *1.44 ± 0.117.05 ± 0.22
40–604.64 ± 0.33 *#a1.46 ± 0.097.33 ± 0.27
UMD0–202.32 ± 0.11 ab1.48 ± 0.097.14 ± 0.20
20–403.54 ± 0.22 *ab1.48 ± 0.086.88 ± 0.17 *
40–604.31 ± 0.14 *#1.46 ± 0.117.37 ± 0.22 #
Note: Under the same repair mode, compared to 0–20 cm, * p < 0.05, compared to 20–40 cm, # p < 0.05; compared to PRS, a p < 0.05; compared to PRS, b p < 0.05.
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MDPI and ACS Style

Jiang, J.; Wang, Q.; Zhao, J.; Zhang, J.; Dong, B.; Huang, X. Construction and Application of Ecological Remediation Technology for Sandy Soils in Northwest China. Sustainability 2023, 15, 14730. https://doi.org/10.3390/su152014730

AMA Style

Jiang J, Wang Q, Zhao J, Zhang J, Dong B, Huang X. Construction and Application of Ecological Remediation Technology for Sandy Soils in Northwest China. Sustainability. 2023; 15(20):14730. https://doi.org/10.3390/su152014730

Chicago/Turabian Style

Jiang, Jing, Qian Wang, Jinmei Zhao, Jun Zhang, Bo Dong, and Xin Huang. 2023. "Construction and Application of Ecological Remediation Technology for Sandy Soils in Northwest China" Sustainability 15, no. 20: 14730. https://doi.org/10.3390/su152014730

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