From Solid Waste to Technosols: Evaluation of Aggregate Stability, Microbial Community and Biotoxicity

Abstract

To meet the requirements for the efficient utilization of bulk solid wastes, technosols were cultivated using solid wastes as raw materials and their aggregate stability, bacterial community, mineralization process, and biological toxicity were investigated. A proportional mixture of four types of solid wastes (fly ash, sludge, straw, and earthworm manure) resulted in the formation of aggregates with excellent pore structure after two months of cultivation and four samples were obtained. Their soil organic matter (SOM) and total nitrogen (TN) contents were higher than those in Chinese surface soil. A total of 215 genera were common to all four samples. The high organic matter content in straw, along with its lignin content and the fine organic particles generated during the straw degradation process were conducive to the formation of highly stable aggregates, making the quality with added straw superior to that with added vermicompost. Furthermore, the addition of straw was more beneficial for increasing potential mineralized organic carbon. Amongst the four tested samples, sample 3# exhibited the best soil nutrient supply capacity along with strong mineralization but weak carbon sequestration. A seed germination test confirmed that four samples were all biologically safe. This study marked a shift from “pollution control” towards “resource utilization” in dealing with bulk solid wastes. Additionally, applying technosols for soil remediation could present an effective solution to ecological restoration challenges in soil degradation such as mining sites.

1. Introduction

Fly ash is a major industrial waste contributor to China’s high emissions, with an annual output maintained at 600–800 million tons [1]. The substantial volume of fly ash not only occupies significant land for storage but also causes pollution to the surrounding environment. Despite its increasing comprehensive utilization rate in recent years, it still cannot be fully consumed [2]. Furthermore, China faces significant pressure in treating and disposing of solid waste, with an annual increment of over 3 billion tons of bulk solid wastes such as sewage sludge and straw [3]. It is imperative to implement the green, efficient, high-quality, and large-scale utilization of these solid wastes.

Both fly ash and sewage sludge possess similar composition and properties with soil and can be utilized as a component in the cultivation of “technosols”. Technosols are a type of artificial soil cultivated from solid waste generated in human production and life, possessing structure and function analogous to natural soil. In 2006, the International Union of Soil Science officially included technosols as a new soil category in the World Reference Base of Soil Resources [4].

Currently, there is an urgent need for guest soil resources for soil restoration in ecologically fragile areas. As a kind of guest soil resource, technosols can effectively address issues related to soil degradation and pollution in ecologically fragile areas such as mining sites; thus serving as an alternative solution for ecosystem restoration [5]. Weiler et al. used conducted a mixture of fine coal waste and organic compost to cultivate technosols and suggested that the addition of compost allowed equivalent or even superior growth compared to agricultural soil when cultivating alfalfa [6]. Otremba et al. studied the technosols derived from the mixture of mine waste and soil in a coal mine for 40 years. The results indicated an augmentation of soil organic carbon (SOC) by 80–142%, a marginal decrease in bulk density, and an increase of more than 30% in N, P, and K. Furthermore, planting alfalfa and orchard grass could better facilitate the development of technosols [7]. Amaral et al. added residual sludge as nutrient sources into the desulfurization slurry of coal processing as soil substitutes for soil remediation in mining areas. The abovementioned findings indicates that these soil substitutes reduced the amount of topsoil utilized in mining restoration, minimizing the footprint of land use and the social and environmental impacts of mining activities. At the same time, it has also found a new direction for the resource utilization and large-scale utilization of bulk solid wastes like fly ash and sewage sludge [8].

Previous studies have primarily focused on the modification of macroscopic properties and the promotion of plant growth, with less attention given to the formation mechanism of aggregates. As a bridge connecting the macroscopic properties and microscopic structure of soil, its formation serves as one of the crucial indicators during the soil formation process. Aggregates are organized and arranged in three-dimensional space, constituting the macroscopic soil structure, and their stability represents an important metric for evaluating soil quality [9]. The establishment of stable aggregate structure is a key link for the survival and reproduction of microorganisms, the accumulation of organic carbon, and the transition from “sand” to “loam” [10]. Hence, it holds significant importance to study the cultivation process of technosols from a microscopic perspective on aggregates in order to facilitate their formation and enhance quality.

In this paper, technosols were cultivated using fly ash, sludge, straw, and earthworm castings as raw materials, and the following research was conducted: (1) analysis of physicochemical properties of technosols; (2) assessment of aggregate size distribution and stability; (3) identification dominant microorganisms and their mineralization in soil bacterial communities; (4) evaluation of the biological toxicity in technosols. The results could provide a theoretical basis for understanding the formation mechanism and stability regulation of aggregate structure during the technosol formation process.

2. Materials and Methods

2.1. Experimental Materials

The municipal sludge used in this study was air-dried sludge from Shandong Fuhang New Energy Environmental Technology Co. Ltd. (Dezhou, China). Fly ash, straw and earthworm manure were purchased from Jinan city and the surrounding areas. The mature soil at 0–20 cm from the ground on the campus of Qilu University of Technology in Jinan was selected as background soil.

2.2. Analytical Methods

Moisture content was determined by weight method. pH and EC were determined by mixing soil and water at a ratio of 1:1 and 1:5 (w:v), and measured by pH meter (INESA Scientific Instrument CO.,LTD, Shanghai, China) and electrical conductivity meter ( Hach Water Analysis Instruments (Shanghai) Co., Ltd. Shanghai, China), respectively. Soil organic matter (SOM) was determined by heat capacity method with potassium dichromate and total nitrogen (TN) was determined by semi-micro Kjeldahl method [11]. Soil water holding capacity (WHC) referred to the method of Ravikumar et al. [12]. The physicochemical properties of the raw materials are shown in Table S1.

2.3. Experimental Design

Considering soil nutrients and other indicators, fly ash was selected as the inorganic ingredient and sludge, straw and earthworm manure as the organic ingredient. Various raw materials were air-dried, crushed, and sieved through 2 mm sieve, and mixed according to

Table 1. Raw material ratios (wt%) for different treatments.

Treatment	Sewage Sludge	Earthworm Manure	Straw	Fly Ash	Organic:Inorganic
1#	40	10	0	50	50:50
2#	36	24	0	40	60:40
3#	36	0	24	40	60:40
4#	40	0	10	50	50:50

, with a total weight of 2 kg. In total, 120 mL of bacterial suspension from background soil was added to each sample to introduce soil bacteria and accelerate the soil formation process. The bacterial suspension was prepared according to the method of Pronk et al. [13], shown in Text S1. Deionized water was added to maintain 60% of the maximum soil WHC, and incubated at room temperature. During the incubation period, the water consumed by evaporation was replenished periodically by weighing per 10 d. Also, the mixture was stirred and aerated every 10 d for a total of 60 days. Two replicates were set for each treatment, and two samples were taken from each replicate to determine the physicochemical properties and aggregate stability.

2.4. Aggregate Classification

The technosol aggregates were classified by dry sieving and wet sieving. A total of 100 g of air-dried technosol samples passing through 2 mm sieve were taken for sieving experiments. The samples were divided into 5 classes (<0.053 mm, 0.053–0.25 mm, 0.25–0.5 mm, 0.5–1 mm, and 1–2 mm) by dry sieving. In the wet sieving process, a set of sieves (1 mm, 0.5 mm, 0.25 mm, 0.053 mm) were gradually immersed into deionized water and oscillated for 30 min at a frequency of 30 cycles per minute. The fractions on each sieve were dried at 105 °C for 24 h and then weighed. The experiments were performed in 2 replicates. The percentage of >0.25 mm aggregates (R_0.25), mean weight diameter (MWD), mean geometric diameter (GMD), and proportion of aggregates destruction (PAD) were used to evaluated the aggregate stability using following Equations (1)–(4) [14,15].

$$R_{0.25}=({\sum }_{i=1}^5{m}_{i>0.25})/({\sum }_{i=1}^5{m}_i)$$

(1)

$$\mathrm{M}\mathrm{W}\mathrm{D}={\sum }_{\mathrm{i}=1}^5(\overline{{\mathrm{X}}_{\mathrm{i}}}\times {\mathrm{m}}_{\mathrm{i}})$$

(2)

$$\mathrm{G}\mathrm{M}\mathrm{D}=\mathrm{e}\mathrm{x}\mathrm{p}({\sum }_{\mathrm{i}=1}^5(\mathrm{l}\mathrm{n}\overline{{\mathrm{X}}_{\mathrm{i}}}\times {\mathrm{m}}_{\mathrm{i}}))$$

(3)

$$\mathrm{P}\mathrm{A}\mathrm{D}=({\mathrm{D}\mathrm{R}}_{0.25}-{\mathrm{W}\mathrm{R}}_{0.25})/{\mathrm{D}\mathrm{R}}_{0.25}\times 100$$

(4)

where m_i > _0.25 is the mass proportion of >0.25 mm size fractions; ${\overline{\mathrm{X}}}_{\mathrm{i}}$ is the mean diameter of each size fraction (mm); m_i is the mass proportion of the samples in the corresponding size fraction i; and n is the number of size fractions. DR_0.25 and WR_0.25 are the fraction of >0.25 mm soil aggregates obtained from dry sieving or wet sieving, respectively.

2.5. Calculation of Fractal Dimension

The fractal dimension (FD) was calculated using the soil particle fractal model proposed by Hou et al. [16], as shown in Equation (5).

$$({\displaystyle \frac{{\mathrm{W}}_{\mathrm{i}}}{{\mathrm{W}}_0}})={({\displaystyle \frac{{\mathrm{d}}_{\mathrm{i}}}{{\mathrm{d}}_{\mathrm{m}\mathrm{a}\mathrm{x}}}})}^{(3-\mathrm{F}\mathrm{D})}$$

(5)

where d_i is the average diameter (mm) of two adjacent fractions; d_max is the maximum aggregate diameter (mm); W_i is the accumulated mass (g) of aggregates with a diameter smaller than d_i; and W₀ is the sum mass of all aggregates (g).

2.6. SOC Mineralization

SOC mineralization was determined by the alkali absorption method proposed by Lin et al. [17], which was shown in Supplemental Materials (Text S2). The calculations were as given in Equations (6) and (7).

$$\mathrm{S}\mathrm{O}\mathrm{C} \mathrm{m}\mathrm{i}\mathrm{n}\mathrm{e}\mathrm{r}\mathrm{a}\mathrm{l}\mathrm{i}\mathrm{z}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n} ({\mathrm{m}\mathrm{g} \mathrm{C}\mathrm{O}}_2/\mathrm{k}\mathrm{g} \mathrm{s}\mathrm{o}\mathrm{i}\mathrm{l})={\displaystyle \frac{{\mathrm{C}}_{\mathrm{H}\mathrm{C}\mathrm{l}}\times ({\mathrm{V}}_0-{\mathrm{V}}_1)\times 22}{\mathrm{m}}}$$

(6)

$$\mathrm{S}\mathrm{O}\mathrm{C} \mathrm{m}\mathrm{i}\mathrm{n}\mathrm{e}\mathrm{r}\mathrm{a}\mathrm{l}\mathrm{i}\mathrm{z}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n} \mathrm{r}\mathrm{a}\mathrm{t}\mathrm{e}[\mathrm{m}\mathrm{g} {\mathrm{C}\mathrm{O}}_2/(\mathrm{k}\mathrm{g} \mathrm{s}\mathrm{o}\mathrm{i}\mathrm{l}.\mathrm{d})]={\displaystyle \frac{\mathrm{S}\mathrm{O}\mathrm{C} \mathrm{m}\mathrm{i}\mathrm{n}\mathrm{e}\mathrm{r}\mathrm{a}\mathrm{l}\mathrm{i}\mathrm{z}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n} (\mathrm{m}\mathrm{g}\frac{\mathrm{C}{\mathrm{O}}_2}{\mathrm{k}\mathrm{g}}\mathrm{s}\mathrm{o}\mathrm{i}\mathrm{l})}{\mathrm{i}\mathrm{n}\mathrm{c}\mathrm{u}\mathrm{b}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n} \left(\mathrm{d}\right)}}$$

(7)

where C_HCl is the HCl concentration; V₀ is the consumed HCl volume of the control; V₁ is the volume of the consumed HCl; and m is the quality of soil.

The first order kinetic Equation (8) was used to fit the cumulative mineralization amount of organic carbon with the incubation days.

$$C_t=C_o(1-exp(-kt\left)\right)$$

(8)

where C_t is the accumulative mineralization amount of organic carbon after t time (g/kg), t is the incubation days (d), C_o is the potential mineralizable organic carbon (g/kg), and k is the turnover constant of the organic carbon pool (d⁻¹).

2.7. Seed Germination Assay

Germination experiments were conducted to evaluate the biotoxicological impact of the prepared technosols on plant growth. Ten grams of air-dried samples (technosols or background soil) were mixed with deionized water at a solid–liquid ratio of 1:10, and then extracted by oscillation at 25 °C for 24 h. Five milliliters of the filtered extract was placed in a 9 cm Petri dish. Twenty ryegrass seeds of uniform size and full grain were evenly placed in each Petri dish and incubated at room temperature, protected from light, for 7 d. The background soil treatment was designated as the control group. During this period, water was replenished regularly by weighing. Three replications were made for each treatment. The number of germinated seeds and the length of the primary root were recorded. Relative seed germination (RSG), relative root elongation (RRG), and germination index (GI) were calculated according to Equations (9)–(11) [18].

$$\mathrm{RSG} (\%)={\displaystyle \frac{\mathrm{N}\mathrm{u}\mathrm{m}\mathrm{b}\mathrm{e}\mathrm{r} \mathrm{o}\mathrm{f} \mathrm{s}\mathrm{e}\mathrm{e}\mathrm{d} \mathrm{g}\mathrm{e}\mathrm{r}\mathrm{m}\mathrm{i}\mathrm{n}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n} \mathrm{i}\mathrm{n} \mathrm{e}\mathrm{x}\mathrm{p}\mathrm{e}\mathrm{r}\mathrm{i}\mathrm{m}\mathrm{e}\mathrm{n}\mathrm{t}\mathrm{a}\mathrm{l} \mathrm{g}\mathrm{r}\mathrm{o}\mathrm{u}\mathrm{p}}{\mathrm{N}\mathrm{u}\mathrm{m}\mathrm{b}\mathrm{e}\mathrm{r} \mathrm{o}\mathrm{f} \mathrm{s}\mathrm{e}\mathrm{e}\mathrm{d} \mathrm{g}\mathrm{e}\mathrm{r}\mathrm{m}\mathrm{i}\mathrm{n}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n} \mathrm{i}\mathrm{n} \mathrm{c}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{o}\mathrm{l} \mathrm{g}\mathrm{r}\mathrm{o}\mathrm{u}\mathrm{p}}}\times 100\%$$

(9)

$$\mathrm{R}\mathrm{R}\mathrm{G}(\%)={\displaystyle \frac{\mathrm{A}\mathrm{v}\mathrm{e}\mathrm{r}\mathrm{a}\mathrm{g}\mathrm{e} \mathrm{r}\mathrm{o}\mathrm{o}\mathrm{t} \mathrm{l}\mathrm{e}\mathrm{n}\mathrm{g}\mathrm{t}\mathrm{h} \mathrm{i}\mathrm{n} \mathrm{e}\mathrm{x}\mathrm{p}\mathrm{e}\mathrm{r}\mathrm{i}\mathrm{m}\mathrm{e}\mathrm{n}\mathrm{t}\mathrm{a}\mathrm{l} \mathrm{g}\mathrm{r}\mathrm{o}\mathrm{u}\mathrm{p}}{\mathrm{A}\mathrm{v}\mathrm{e}\mathrm{r}\mathrm{a}\mathrm{g}\mathrm{e} \mathrm{r}\mathrm{o}\mathrm{o}\mathrm{t} \mathrm{l}\mathrm{e}\mathrm{n}\mathrm{g}\mathrm{t}\mathrm{h} \mathrm{i}\mathrm{n} \mathrm{c}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{o}\mathrm{l} \mathrm{g}\mathrm{r}\mathrm{o}\mathrm{u}\mathrm{p}}}\times 100\%$$

(10)

$$\mathrm{G}\mathrm{I}={\displaystyle \frac{\mathrm{R}\mathrm{S}\mathrm{G}\times \mathrm{R}\mathrm{R}\mathrm{G}}{100}}$$

(11)

2.8. Bacterial Community Analysis

High-throughput sequencing technology was used to analyze the bacterial community structure in the technosol samples. DNA was extracted using the FastDNA^®SPIN Kit (Beijing Bitab Biotech Co., Ltd, Beijing, China), and the bacterial 16rRNA V4-V5 region was amplified after gene quality detection. PCR amplification, library construction and testing, and on-machine sequencing based on Illumina MiSeq platform were all performed by Genesky Biotechnologies In. (Shanghai, China). Detailed steps are shown in the Supplemental Materials Text S3.

2.9. Statistical Analysis

The significance of the differences in the physicochemical properties of the samples and the stability of aggregates was analyzed using one-way ANOVA conducted in Excel 2010. Error bars were calculated based on standard deviation within Excel 2010. Correlation analysis and Venn diagrams were generated using correlation plot APP and Venn diagram APP, respectively, in Origin 2022. OTU abundance and alpha diversity analyses were conducted via the Genesky biological platform (http://cloud.geneskybiotech.com/#/tools/all (accessed on 14 August 2024). All figures (including the abundance of dominant bacteria and Venn plots, SOC mineralization) were analyzed and plotted using Origin 2022.

3. Results and Discussion

3.1. Physical and Chemical Properties of Technosols

The physical and chemical properties of four technosol samples are presented in

Table 2. The physical and chemical properties of technosols.

Treatment	pH	EC (ms/cm)	TN (g/kg)	SOM (g/kg)	Soil WHC (g Water/g Soil)
1#	7.60 ± 0.03 a	3.48 ± 0.46 a	5.02 ± 0.35 b	64.79 ± 6.68 c	0.668 ± 0.041 c
2#	7.38 ± 0.04 b	3.32 ± 0.42 a	6.61 ± 0.17 ab	79.02 ± 5.47 c	0.697 ± 0.007 c
3#	7.02 ± 0.01 c	3.51 ± 0.16 a	7.19 ± 0.97 a	179.00 ± 1.16 a	1.197 ± 0.014 a
4#	7.30 ± 0.05 b	2.84 ± 0.36 a	5.06 ± 0.34 b	136.46 ± 5.06 b	0.859 ± 0.091 b

Note: Different letters indicate significant differences between different technosol samples through one-way ANOVA (p < 0.05).

. pH served as an indicator of soil acidity or alkalinity, playing an important role in the growth and development of crops. Its fluctuations have varying degrees of impact on organic matter decomposition, mineral content, and microbial activity [19]. Following 60 days of cultivation, the pH values for each treatment fell within the range of 7.02–7.60, indicating a weak alkaline state. As the quantity of organic raw materials increased and resulted in more acidic formed technosols, it suggested that the alkalinity of fly ash could be neutralized through its combination with organic materials. The pH level of organic raw materials was lower, and its nitrogen content was higher. During cultivation, some nitrogen underwent nitrification reaction to convert to NO₃⁻ and H⁺ generated during the nitrification process led to a decrease in pH [20].

The EC values for the four samples ranged from 2.84 to 3.51 ms/cm, with no significant difference between them.

SOM is a critical factor influencing the stability of soil structure. As an adhesive to promote the formation of soil aggregates, SOM helps to promote the formation and stability of soil aggregates [21]. The contents of SOM in the four samples ranged from 74.79–129.00 g/kg, surpassing China’s farmland topsoil average SOM content (24.65 g/kg) [22]. The SOM contents in samples 3# and 4# reached 179.00 g/kg and 136.46 g/kg, respectively, which were significantly higher than those of samples 1# and 2#.

Nitrogen is essential for plant growth, making TN content an important indicator for measuring soil fertility. Sample 3# had the highest TN content, followed by sample 2#. The proportion of organic ingredients in these two samples was high, and the organic ingredients contained nitrogen, while the inorganic ingredients fly ash did not contain nitrogen, leading to elevated TN content within sample 2# and 3#. The TN content of the four samples ranged from 5.02–7.19 g/kg—exceeding China’s surface soil average TN content (1.54 g/kg) [23].

Soil WHC plays a vital role in characterizing cultivated water sources; it is closely linked to soil structure and influenced by physical factors such as bulk density, porosity etc., as well as organic matter content [24]. The soil WHC of sample 3# was obviously higher than that of the other three samples, which might attribute to higher organic matter content in sample 3# compared to the other three samples. During the cultivation process, the high organic matter content facilitated the formation of aggregates, improved soil pores, and enhanced soil WHC [25].

3.2. Microscopic Morphology of Technosols

The samples comprised multi-level particles with diverse shapes, including spherical fly ash particles and agglomerates formed by the binding of fly ash particles and organic matter. These characteristics were observed in the low-magnification (300 times) SEM images. Samples 3# and 4# exhibited a higher presence of macro-agglomerates compared to samples 1# and 2#. The majority of fly ash particles in samples 3# and 4# had rough surfaces with more attachments, believed to be formed by micro-aggregates through binding action. In contrast, samples 1# and 2# contained more fly ash particles that had not undergone cementation, resulting in relatively smooth surfaces. High-magnification SEM images revealed the presence of macro-aggregates in all four samples, formed by the cementation of several fly ash particles through organic matter. These aggregates displayed extensive organic matter cementation structures and numerous pores, mainly consisting of small pores and micropores (Figure 1). As a result of these pores, the soil WHC of the technosols obtained increased by 22.3–36.0% compared to untreated solid waste mixtures (Table S2). It could be concluded that after two months’ cultivation, technosols developed aggregates with a well-defined pore structure.

3.3. Formation and Influencing Factors of Aggregates in Technosols

3.3.1. Distribution and Stability of Aggregates

As the fundamental unit of soil structure, aggregates control the retention and migration of water, air, heat, and nutrients in soil. They play a crucial role in soil stability, erosion resistance, compaction ability, and nutrient retention ability, directly impacting plant root growth [9,26].

The proportions of 2–1 mm and 1–0.5 mm aggregates in samples 3# and 4# were higher compared to those in samples 1# and 2#. Conversely, the proportions of <0.053 mm aggregates in samples 3# and 4# were lower than those in samples 1# and 2# (Figure 2a). After two months of cultivation, no significant difference was observed in MWD and GMD between samples containing earthworm manure (samples 1# and 2#), as well as between samples containing straw (samples 3# and 4#). However, the MWD and GMD of the samples containing straw (samples 3# and 4#) were significantly higher than for those containing earthworm manure (samples 1# and 2#) (p < 0.05) (Figure 2b,c). It was believed that the aggregates in samples 3# and 4# exhibited higher mechanical and water stability.

In general, 0.25 mm is used as the limit to classify macro-aggregates and micro-aggregates. Water-stable macro-aggregates with a particle size exceeding 0.25 mm have been shown to positively impact soil erosion resistance and water retention. The parameter R_0.25 is commonly used to quantify aggregate stability [27]. Analysis revealed that the water-stable aggregates in samples 1# and 2# mainly fell within the <0.053 mm range, accounting for approximately 54.16% and 52.81%, respectively, with R_0.25 values at only 25.5% and 25.6%. In contrast, samples 3# and 4# had significantly higher R_0.25 values of 44.2% and 45.6%, respectively (Figure 2d). The R_0.25 of all four samples either exceeded or approached the normal level of soil in China (12.92% for brown soil, 16.88% for black soil, 29.08% for red soil) [28], indicating that various combinations of sludge, earthworm castings, and straw possessed the potential to cultivate good aggregation structures. The addition of sludge, earthworm manure, and straw provided substantial amount of organic matter, serving as a source of carbon and nitrogen essential for microbial growth during cultivation process. This promoted microbial reproduction along with the production of organic binding substances such as extracellular polymeric substances (EPS) and spheroplast enzymes, which enhanced agglutination and aggregation among soil micro-aggregates [29,30]. Sandhya et al. suggested that inoculating the soil with Pseudomonas putida GAP-P45—which was known for its propensity to secrete EPS—could enhance the aggregate stability by more than 50% [31]. Additionally, fungi and actinomycetes were able to form aggregates by enveloping their mycelia around particles, significantly improving the stability of soil aggregate structure [32]. Thus, reasonable control over the proportion of organic matter relative to inorganic matter in the ingredients could effectively stimulate the activity of soil microorganisms, so as to build microbial system in the technosols as soon as possible and speed up the soil formation process.

The PAD values in yjr treatments containing earthworm manure were higher than those containing straw (Figure 2e). A comparison of the particle size distribution and stability of the aggregates from the four samples revealed that the addition of straw resulted in better particle size and stability compared to the samples treated with earthworm castings. This finding is consistent with the results from of Zhou et al. [20]. According to Table 2, the SOM content of straw was 582.06 g/kg while that in earthworm castings was 115.39 g/kg. The higher organic matter content in straw provided more exogenous organic matter for aggregate formation during technosol cultivation, stimulating soil biological activity and facilitating aggregate formation [33]. Furthermore, fine organic particles produced through straw decomposition could serve as carriers for adsorbing soil mineral particles and bonding to form micro-aggregates according to Tisdell’s aggregate level development model. These micro-aggregates would then bond into larger macro-aggregates [34]. The high lignin content in straw contributed to increasing aggregate stability while also reducing PAD [35].

Soil, a complex porous medium with irregular shapes, exhibits specific fractal characteristics [36]. The FD, a metric used to characterize soil structure, effectively reflects the intrinsic and irregular distribution of soil particles. A smaller value indicates a coarser texture of the soil particles [37]. It was noteworthy that the FDs of samples 3# and 4# were significantly lower than those of samples 1# and 2# (Figure 2f).

In summary, samples 3# and 4# had higher MWD and GMD, larger R_0.25, smaller PAD, and FD. Therefore, the quality and stability of the aggregates formed in these two samples were better than in samples 1# and 2#. The addition of straw resulted in the superior quality and stability of aggregates compared to adding earthworm manure. In this study, due to the limited cultivation period (only 60 days), the proportion of large macro-aggregates (>2 mm) formed in the four samples was quite low (ranging from 1 to 2%). Extending the cultivation time to increase the proportion of large macro-aggregates is the subsequent work to be done.

3.3.2. Distribution Characteristics of SOM and TN in Aggregates

For samples 1# and 2#, the SOM content decreased as the aggregate particle size decreased. The SOM content of micro-aggregates with particle size 0.25–0.053 mm was the lowest, primarily due to the different formation mechanism of macro-aggregates and micro-aggregates. Macro-aggregate formation heavily relied on organic matter cementation and microbial mycelium, while micro-aggregates were mainly connected by cation bridges between various inorganic colloids [38], resulting in a higher organic matter presence in the macro-aggregates. In samples 3# and 4#, the SOM content of the aggregates with a particle size of 0.25–0.053 mm was actually higher than those of the macro-aggregates with a particle size larger than 0.25 mm (Figure 3a). This could be attributed to the addition of straw as an organic material in samples 3# and 4#, which promoted the formation of micro-aggregates [34]. Furthermore, the organic matter content of straw was much higher than that of earthworm castings (Table 2), leading to aggregates with a particle size of 0.25–0.053 mm having the highest SOM content in samples 3# and 4#.

Similarly, the TN content in the micro-aggregates (0.25–0.053 mm) were lower than those in the macro-aggregates across all four technosol samples (Figure 3b), indicating that, during macro-aggregate formation, micro-aggregate bonding by organic matter led to increased TN content and stability within aggregates. Therefore, it was evident that organic matter played a crucial role in macro-aggregate formation, aligning with research findings from Qi et al. [39].

3.3.3. Correlation Analysis Between Physicochemical Properties of Technosols and Their Aggregate Stability

The FD showed a significant negative correlation with 2–1 mm aggregates and a positive correlation with <0.053 mm aggregates, suggesting that soil fractal characteristics could reflect the composition of soil particles to some extent. Additionally, the FD exhibited a significant positive correlation with PAD, and an extremely negative correlation with MWD, GMD and R_0.25 (Figure 4). These indicators were important indicators for evaluating the stability of soil aggregates, indicating that as the FD of soil particles decreased the stability of soil aggregates increased. This finding was consistent with previous research by Wang et al. [36]. Furthermore, there was a significant negative correlation between the FD and SOM, indicating that the FD of soil particles could characterize soil nutrient status and reflect soil fertility quality to some extent. SOM exhibited an extremely significant positive correlation with MWD and R_0.25, as well as a significant positive correlation with GMD. Moreover, SOM displayed an extremely significant negative correlation with particle sizes of 0.25–0.053 mm and <0.053 mm, further indicating that SOM served as a binding substance between micro-aggregates, and increasing the SOM content could help micro-aggregates bind to each other and form macro-aggregates. The stability of aggregates was also closely related to the increase in SOM content. As the SOM content increased, aggregate stability correspondingly enhanced [29], resulting in a significant negative correlation between SOM and PAD. The formation of macro-aggregates improved soil porosity and enhanced soil WHC, leading to a significant positive correlation between SOM and soil WHC, which aligns with the research findings of He et al. [24].

3.4. Bacteria Community and Their Mineralization Characteristics in Technosols

3.4.1. Bacteria Community Characteristics

The coverage of each sample library was above 99.7% (Table S3), and the sample rarefaction curves for four samples tended to be flat (Figure S1), indicating that the sequencing data were reasonable and could accurately reflect the true information of soil bacterial communities.

After two months of cultivation, the Chao1 and Shannon indices of samples 1# and 2# were significantly higher than those of samples 3# and 4# (Table S3), suggesting that the biodiversity of samples 1# and 2# exceeded that of samples 3# and 4#. Meanwhile, the relative abundance of Actinomycetes in samples 3# and 4# surpassed that of samples 1# and 2#, as shown in Figure 5a. This difference might be attributed to the addition of straw as an organic solid waste component during the cultivation process for samples 3# and 4#. Some Actinomycetes possessed a high proportion of carbohydrate-degrading enzymes, which could degrade the recalcitrant organic matter such as cellulose, thereby playing a crucial role in the decomposition of plant residues [40]. The addition of straw led to an increase in the relative abundance of Actinomycetes. They exhibited strong competition with other bacterial groups, consequently inhibiting other bacteria and reducing their bacterial diversity, which was consistent with previous research results [41,42].

At the phylum level, Proteobacteria (40.7–42.4%), Actinobacteria (12.2–18.4%), Chloroflexi (8.3–11.4%), and Gemmatimonadetes (6.9–13.1%) were identified as dominant bacteria in all the four technosol samples, collectively accounting for 74.2–82.2% of the bacterial community composition (Figure 5a). Among these phyla, Proteobacteria demonstrate rapid reproduction rates and strong adaptability to unstable carbon sources. They are widely distributed in soils across various environments worldwide, making them an advantageous group within the soil [43]. Actinobacteria and Chloroflexi are closely associated with disease inhibition functions within the soil environment. They not only decompose organic matter but also have biocontrol effects by metabolizing various compounds such as antibiotics and fungal antagonists—thus finding widespread application in managing the occurrence of soil-borne diseases [44].

At the class level, predominant bacteria included γ-Proteobacteria (21.4–24.8%), α-Proteobacteria (11.5–16.4%), Actinobacteria (6.0–14.8%), and Thermomicrobia (7.3–8.3%), Bacilli (2.6–4.9%), Acidimicrobiia (3.2–5.4%) (Figure 5b). In samples 3# and 4#, the relative abundances of α-Proteobacteria and Actinobacteria were significantly higher than in samples 1# and 2#. This might be attributed to straw addition as a carbon source in samples 3# and 4#, where Actinobacteria played a key role in plant residue decomposition, leading to an increase in their relative abundance. In an acidic environment, α-Proteobacteria can convert hard-to-degrade carbon sources into intermediate small molecules to supply nutrients for other microorganisms [45]. The pH value of samples 1# and 2# was higher than that of samples 3# and 4#, which has restrained α-Proteobacteria propagation, resulting in decreased relative abundance.

The predominant bacteria at the genus level were Lysobacter (4.57–15.5%), JG30-KF-CM45_unidentified (5.6–7.0%), Xanthomonadaceae_unidentified-1 (1.0–11.4%), Gemm-3_unidentified (1.6–5.9%), Gemm-5_unidentified (2.5–4.1%), Bacillus (2.1–3.7%), Acidimicrobiales_unidentified (1.9–3.3%), Gemmatimonadetes_unidentified (1.0–3.0%), Devosia (0.3–3.4%), and AKYG1722_unidentified (0.9–2.3%) (Figure 5c). The relative abundance of Lysobacter was higher in samples 1# and 2#, while Xanthomonadaceae_unidentified-1 had a higher relative abundance in samples 3# and 4#. Both genera belong to the Xanthomonadaceae family. Xanthomonadaceae microorganisms, which are widely existing beneficial bacteria in the soil and can restrict the reproduction of pathogens, making them useful as biocontrol agents against several pathogens [46]. An increase in their relative abundance is beneficial to soil health. Bacillus is capable of secreting over 60 types of antibacterial substances, such as antibiotics, bactericins, and cell wall degrading enzymes, etc., which can establish a growth environment to inhibit pathogens or directly kill them [47,48].

The Venn diagram analysis revealed that 215 genera of common microorganisms were present among the four samples (Figure 5d), constituting 89.72%, 86.27%, 92.06%, and 91.72% of the total abundance, respectively. The top 10 most abundant genera in all the four samples were Lysobater, JG30-KF-CM45_unidentified, Xanthomonadaceae_unidentified-1, Gemm-3_unidentified, Gemm-5_unidentified, Bacillus, Acidimicrobiales_unidentified, Gemmatimonadetes_unidentified, Devosia, and AKYG1722_unidentified. The number of unique genera in samples 1#, 2#, 3#, and 4# was 51, 74, 38, and 33, respectively, indicating that the bacterial community structures of the samples were overlapping in species distribution but varied in specific distribution. Nevertheless, the abundance of these unique microbial genera in each sample was low, suggesting that aggregate formation was mainly driven by the common bacteria shared by the four samples.

3.4.2. Mineralization Characteristics of Organic Carbon in Technosols

Soil mineralization is the process by which organic matter in the soil is decomposed and transformed into inorganic forms (such as ammonium salts, nitrates, phosphates, sulfates, etc.) that can be utilized by plants. Mineralization process can be represented by the following Equation (12).

$$\mathrm{Organic}\mathrm{matter}\to \mathrm{Minerals} ({{\mathrm{PO}}_4}^{3-}+{{\mathrm{NH}}_4}^{+}+{{\mathrm{SO}}_4}^{2-})+C{O}_2+H_{2}O$$

(12)

The mineralization of soil organic carbon is affected by soil physicochemical properties, microbial activity, environmental conditions, etc. Mineralization has a significant impact on the migration and transformation of soil nutrients [17,49]. Through the mineralization of SOC, the inorganic nutrients released by the decomposition of soil organic matter can be absorbed and utilized by plants. High SOC mineralization is regarded as a sign of healthy soil mainly due to the enhanced microbial activity. Consequently, the intensity of SOC mineralization can mirror the decomposition degree of soil organic materials, the supply of soil nutrients, and the activity of soil microorganisms, and serve as a crucial index for evaluating soil fertility.

Throughout the entire culture cycle, the mineralization rate followed the order of 3# > 4# > 1# ≈ 2#, with samples 3# and 4# maintaining a high CO₂ releasing rate within the first 6 d, which then decreased significantly after the sixth day. The mineralization rates of samples 1# and 2# started to decline significantly from the third day onward. After a mineralization reaction period of 21 d, the mineralization rates of the four samples tended to stabilize (Figure 6a). The SOM in technosols served as a substrate for microbial activity, with the SOM content ranking was as follows: 3# > 4# > 1# ≈ 2#. It was observed that a higher SOM content could sustain a high mineralization rate for an extended period by ensuring that microorganisms had sufficient resources. As the culture time increased, the availability of organic matter for microbial carbon metabolism gradually decreased, leading to inhibited microbial activity and a subsequent decline in organic carbon mineralization rate. The relationship between mineralization rate (y) and incubation time (x) for each sample followed a logarithmic function relationship y = a − bln(x), consistent with the previous findings on the SOC mineralization process [50].

The cumulative amount of mineralization was fitted against the incubation days Figure 6b, yielding an R² exceeding 0.920, indicating a good fitting. This equation model could accurately reflect the dynamic process of cumulative organic carbon mineralization. The parameters in the fitting equation for mineralization kinetics were summarized in

Table 3. Fitting parameters for mineralization process.

Treatment	Co (mg CO2/kg Soil)	K (d−1)	Co/SOC (%)	Initial Potential Mineralization Rate Co × k (mg CO2/(kg Soil·d))
1#	1328.2	0.039	0.547	51.8
2#	847.7	0.096	0.466	81.4
3#	6505.1	0.022	0.781	143.6
4#	1509.2	0.078	0.471	117.7

. C_o is the potential organic carbon in soil available for decomposition by microorganisms, and is an important indicator for evaluating SOM quality and the soil nutrient supply capacity. The C_o values for each sample were ranked as follows: 3# > 4# > 1# > 2#, indicating that sample 3# had a superior soil nutrient supply capacity compared to others. Additionally, it was noted that adding straw had a more pronounced impact on C_o than adding earthworm manure.

The k value serves as a sensitivity index influenced by factors such as soil texture, nutrient content, and soil structure [51]. The k value of sample 3# was lower than those of the other three samples, indicating a more intense organic carbon mineralization in sample 3#. The C_o/SOC value, which represents the proportion of potential mineralized organic carbon in soil organic carbon, reflects the soil’s carbon sequestration capacity. A higher value indicates stronger soil organic carbon mineralization ability and weaker carbon sequestration ability. Sample 3# has the highest ratio, suggesting a high content of readily degradable organic matter and highly active soil microorganisms. This led to its strong organic carbon mineralization ability, weak carbon sequestration ability, and the highest initial potential mineralization rate (C_ok) (Table 3).

3.5. Biological Toxicity of Technosols

The suitability of cultivated technosols for plant cultivation depends not only on their structure and nutrients but also on whether they contain substances detrimental to plant growth. Due to the complexity and diversity of raw materials used in the technosol cultivation, as well as the difficulty in predicting the type and content of hazardous substances, seed germination experiments are conducted to assess its biosafety.

RSG and RRG are used to evaluate the effects of the planting medium on seed germination and root growth, respectively. GI is a comprehensive index to examine the effects of pollutants on seed germination and root elongation [52]. As depicted in Figure 7, the RSG of each treatment exceeded 100% when the four technosol samples were used as the substrate, signifying that the extract of technosols exerted a promoting effect on seed germination. Additionally, the toxicity level of inhibiting radicle elongation is lower than that of inhibiting seed germination, meaning that the RRG is a more sensitive indicator of toxicity compared to the RSG [53]. All samples exhibited an RRG surpassing 95%, and there was no significant negative impact on radicle growth. All four samples exceeded a GI of 100%, indicating no obvious phytotoxic effect on seed germination and radicle growth. Therefore, it was concluded that these technosols were biosafe and could be utilized as an alternative soil for mine remediation.

4. Conclusions

Following a two-month cultivation period, four types of solid waste (fly ash, sludge, straw and earthworm castings) were mixed in proportion and cultivated to obtain four technosol samples which had formed aggregates with a perfect pore structure. Their pH ranged from 7.02 to 7.60, EC fell within 2.84–3.51 ms/cm, and the soil SOM content varied from 74.79 to 129.00 g/kg with a TN content ranging from 5.02 to 7.19 g/kg.

The R_0.25 values in all four samples exceeded the normal level for soil in China. The samples cultivated with the addition of straw exhibited improved aggregate quality and stability, lower Chao1 and Shannon indices, and higher soil C_o. There were 215 genera of microorganisms present in the four samples that played a crucial role in aggregate formation. All four samples demonstrated biological safety, indicating their potential as alternative soils for mine restoration.

In the subsequent research, the incubation time will be extended to enhance the content of the large macro-aggregates. The environmental safety of the formed technosols will be comprehensively evaluated through a combination of seed germination assay and plant pot trials. These studies aim to improve technosol quality and provide a theoretical basis for its application in soil remediation.