**5.2 Carry forward of unspent CSR funds**

There have been instances wherein a company might have found that in course of a given financial year, it has not succeeded in spending all the funds earmarked for an ongoing CSR project. While the 2019 Act does not clarify all the possible contours of such ongoing projects, there has been a subsequent attempt on the legislature's part to define the same in the draft Companies (CSR) Amendment Rules, 2020, as a multi-year project that has been undertaken by a company in order to fulfil its CSR obligations, with the project being supposed to span a maximum range of four years including the year on which it has commenced [22]. Questions can and have been raised as to whether projects that might be started in the month of February of a financial year and intended to be continued up to say, June of the next financial year can be included in this category; further, in the light of the COVID-19 pandemic, the gestation period of four years might also need to be reexamined [23]. There may also be additional arguments made about whether ongoing projects might include those for which the company has already disbursed funds, but not commenced the project within the same financial years, or those for which the company has made budgetary provisions but not disbursed the funds within the same financial year. According to the 2019 Act, if there is any unspent fund originally dedicated to such a project by the end of any financial year, then the same needs to be transferred to a specific escrow account called the Unspent CSR Account that the company would have to open with a scheduled commercial bank. In case the company fails to spend the proceeds from such account towards its CSR projects within 3 years from the date of such transfer, then the leftover proceeds from that account would have to be transferred to a fund specified under Schedule VII of the Companies Act, 2013 within 30 days from the end of the third financial year [21]. Some of the funds mentioned under Schedule VII are the Prime Minister's National Relief Fund, the Prime Minister's Citizen Assistance and Relief in Emergency Situations Fund, the Clean Ganga Fund, as well as other funds established by the Central or State governments for the socio-economic development or relief or welfare of the scheduled castes and tribes, other backward classes, minority, and women. In the light of this development, it seems quite clear that existing and future CSR policies of companies would have to make year-wise budget allocation for all

CSR projects and develop strategic plans accordingly. However, the present legal position about projects which would otherwise have gone on for more than four years, seems uncertain, as is the position about whether the companies are required to form separate escrow accounts for each ongoing project, or a consolidated one for all projects combined.

Another important concern regarding this escrow fund is one that may arise involving companies that route their CSR expenditure through trusts or specific agencies that professionally engage in project implementation. Especially in case of group companies, lump-sum CSR funds may be diverted as a practice by each company to such trusts or agencies, and given that such diversion might not be considered as actual CSR expenditure in itself, unspent amounts remaining with the trusts might also be required to be transferred to the escrow account. Moreover, the companies themselves might not have control over the day-today spending by the agencies, some of which might be established and reputed non-governmental organisations –this practice may also potentially get affected because of such legislative amendments.

In the event of the company retaining any surplus fund from a project that does not qualify as an ongoing project, the company even earlier could not have claimed such funds as part of its profits; however, the 2019 Act clearly requires such surplus fund to either be used for the same project within the same financial year, or otherwise transfer the entirety of the surplus to one of the funds mentioned above under Schedule VII within six months from the end of the financial year concerned. Therefore, the government would in effect gain control of such surplus corporate fund to use in a centralised manner. As has been discussed in subsequent parts of this paper, such a stance on the part of the legislature has effectively rendered the CSR spending by the companies in every financial year mandatory, unlike the earlier position, wherein the companies were still left with an option of providing reasonable justification for their inability to spend such amount in any given financial year, thus providing new rigour to the CSR regime.

#### **5.3 Compliance monitoring**

The 2019 amendment has given the Central Government the power to 'give such general or special directions to a company or class of companies as it considers necessary to ensure compliance of provisions of this section and such company or class of companies shall comply with such directions' [21]. This provision is vague as it does not specify when such directions will be given to companies and in what context. It also has the power to be potentially dangerous since it could open up a pandora's box whereby, it could end up allowing the Central Government 'to issue general or special directions to a company or class of companies (PSUs for example) to contribute towards a specific government programme or project' [24]. Such a move would be disastrous not only from the view point of the voluntariness that CSR is supposed to uphold but also may open up significant abuse to amongst other things, democracy.

#### **5.4 Penalty**

One of the biggest frailties of Section 135 has been that in its original form it was largely unenforceable. The 2019 amendment of the Companies Act altered that by creating both a criminal and a civil liability for breaching the CSR requirements. Such liability is to be imposed on both the company, which is subject to a 'fine which shall not be less than fifty thousand rupees, but which may extend to twenty-five lakh rupees' [21] and on every officer of the company who is in default [25], who

'shall be punishable with imprisonment for a term which may extend to three years or with fine which shall not be less than fifty thousand rupees but which may extend to five lakh rupees, or with both' [21].

Not unsurprisingly, the penalty provision, especially the criminal liability, met strong resistance from Indian Companies [26]. It was labelled by industry specialists and academics as harsh [26], retrograde [27], reminiscent of socialism [28] and forcing the hand [29] of companies, similar to taxation. It was also criticised heavily in the general context of the problems attached to criminalising economic offences [30]. In 2018, a High Level Committee on Corporate Social Responsibility had been set up by the Ministry of Corporate Affairs under the chairmanship of Injeti Srinivas. This Committee presented its report in August 2019. Amidst a series of recommendations, the Committee also recommended that the offence within Section 135 be de-criminalised and made a civil offence, although it recommended that penalties be imposed, it highlighted that there should not be any imprisonment [18]. It did so stating that 'CSR is a means to partner corporates for social development and such penal provisions are not in harmony with the spirit of CSR' [18].

On the basis of this report, the finance minister, Nirmala Sitharaman made an announcement at a meeting of the Confederation of Indian Industries that the provision would be reviewed once again [31]. On 23rd August 2019, she announced in a press conference that that the government had no intention to go through the prosecution route, making Section 135 only a civil offence and that the Ministry of Corporate Affairs would once again review the section which would remain unnotified for the time being [32].


3.5 Real-time results

QPEV o ið Þ ;t � �2

Research Trends and Challenges in Smart Grids

≤

SOCF

where the final SOC reached in Stage (I), that is, SOCR

V2 ð Þ i;t Xð Þi

!2

QPEV o ið Þ;t þ

achieved SOC, that is, SOCF

chð Þi ð Þ;t :

3.3.2 Problem formulation of stage (II)

except for (47), which is replaced by

3.3.3 Problem formulation of stage (III)

3.4 Coordination between V2GQ and COC

Stage (II), that is, PR

66

SOCF

Thus, the objective function of Stage (II) is

where SPEV

owners SOCD

≤ SPEV o ið Þ ;t � �2

Vmax c ið Þ Vð Þ i;t Xð Þi � �2

chð Þi ð Þ;t ≤ SOCD

chð Þi ð Þ;t ¼ SOCR

maxγ ∑ i∈IDG

restore a feasible solution for the COC and relax the tap operation. Thus,

minγ ∑ i∈Ib

o ið Þ ;t , should be maintained.

Po ið Þ ;t ¼ PR

� PPEV o ið Þ;t � �2

> � PPEV o ið Þ ;t � �2

o ið Þ ;t is the rated power of the PEV converter. In addition, the final

In Stage (II), the objective is to minimize the DG active power curtailment,

ensure maximum customer satisfaction, which is the highest priority of the V2GQ technology. Therefore, this stage is subject to all of the constraints in Stage (I)

Stage (III) aims at minimizing the voltage deviation using the DGs and PEVs to

1 � Vð Þ i;t � �2

Besides all the constraints in Stage (II), this problem is subject to the constraint defined in (50), in which the maximum injected powers from the DGs reached in

The charging decisions and active/reactive dispatch signals produced in Stage (III) are sent to all PEV parking lots and DGs, as shown in Figure 9. To ensure that the PEV and DG converters settle at the desired active and reactive power references, a time delay Δtconv is introduced. The converter settling time may vary from 50 to 100 ms, depending on the primary controllers of the DC/AC converter [7]. For slow automatic interactions, such as voltage regulation, the maximum communication time delay is 100 ms as per the IEC 61850 [20]. Thus, Δtconv is assumed to be

chð Þi ð Þ;t , should be limited by the SOC desired by the PEV

chð Þi ð Þ;t , ∀i ∈IPEV, chð Þi , t (47)

chð Þi ð Þ;t , ∀i ∈I PEV, chð Þi , t (48)

Po ið Þ ;t , ∀t (49)

, ∀i, t (50)

o ið Þ ;t , ∀i∈I DG, t (51)

, ∀i∈I PEV, t (45)

, ∀i ∈I PEV, t (46)

chð Þi ð Þ;t , must be attained to

In this section, various case studies are presented to validate the robustness and effectiveness of the optimal coordinated voltage regulation algorithm. The 38-bus 12.66-kV distribution system is used as a test system, as shown in Figure 10. The system data can be found in [21]. The system is modified to accommodate two PEV parking lots and four PV-based DGs, with power ratings as given in Figure 10. The power demands of the two parking lots are extracted from data provided by the TPA for a weekday in 2013. Both parking lots are commercial, where P1 represents a lot in the vicinity of a train station and P2 is a lot located near downtown Toronto. The total number of PEVs during a day is displayed in Figure 11. Due to confidentiality, the addresses of the real parking lots are not mentioned. The central control unit receives the desired SOCs and sends the charging decisions to all vehicles in the parking lots. An OPAL real-time simulator (RTS) is used to model the visual test network using the SimPowerSystems blockset, which is available in Simulink/ Matlab, and an ARTEMiS plug-in [22]. The network, PEV, and DG models are

Figure 10. Test network with an HiL realization.

impacts of the OLTC. Figure 12(a) and (b) illustrate the response of the conventional controller for the OLTC over 24 hours. Although the OLTC does not suffer from an excessive tap operation (13 taps/day), undervoltage and overvoltage prob-

As expected, the overvoltage happens during the peak power generation from DGs, while the undervoltage coincides with the peak demand of PEV. The COC is enabled to mitigate these voltage violations. Figure 12(c) and (d) demonstrate that the COC can limit the voltage violations without the PEV/DG voltage support, but it suffers from a hunting problem. This problem happens when the overvoltage and undervoltage occur concurrently. In this situation, the COC should be deactivated.

To address the hunting problem, presented in the previous case, the V2GQ is coordinated with the COC. Two case studies dealing with PEV/DG voltage support

1. DG active power curtailment, without PEV and DG reactive power supports,

Figure 13 demonstrates the performance of the coordination algorithm when the voltage support is merely achieved via the DG active power curtailment (i.e., the

o ið Þ ;t ¼ 0, ∀i ∈IPEV and Qo ið Þ;t ¼ 0, ∀i ∈I DG

o ið Þ ;t ; Po ið Þ ;t ; Qo ið Þ ;t

h i

3.5.2 OLTC control with PEV/DG voltage support

h i, where QPEV

Response of coordination algorithm, assuming DG active power curtailment.

2. PEV and DG reactive power dispatch, that is, γ ¼ X chð Þi ð Þ;t ; QPEV

are carried out, as follows:

Voltage Regulation in Smart Grids

DOI: http://dx.doi.org/10.5772/intechopen.85108

that is, γ ¼ X chð Þi ð Þ;t ; Po ið Þ ;t

lems occur.

Figure 13.

69

Figure 11. Number of vehicles in the parking lots.

distributed between the RTS cores for performing parallel computations. The RTS is used to perform a hardware-in-the-loop (HiL) realization, where a central control unit, emulated by a host computer running GAMs, exchanges real-time data with the test network modeled in the RTS. The sampling time used to realize the HiL application is 100 μs.

### 3.5.1 OLTC control without PEV/DG voltage support

This section compares the responses of the conventional and COC controllers for the OLTC. The voltage support from PEVs and DGs is disabled to study their

Figure 12. OLTC response: (a & b) conventional control and (c & d) COC.


*Article* **Soil Organic Carbon Mineralization and Its Temperature Sensitivity under Different Substrate Levels in the Mollisols of Northeast China**

**He Yu 1 , Yueyu Sui 2,3, Yimin Chen 2,3, Tianli Bao 1,\* and Xiaoguang Jiao 1,\***

**Abstract:** Soil organic carbon (SOC) mineralization plays an important role in global climate change. Temperature affects SOC mineralization, and its effect can be limited by the substrate available. However, knowledge of the effects of temperature and substrate quality on SOC mineralization in the Mollisols of Northeast China is still lacking. In this study, based on a spatial transplant experiment, we conducted a 73-day incubation to examine the effects of temperature on SOC mineralization and its temperature sensitivity under different carbon levels. We found that the SOC content, incubation temperature and their interaction had significant effects on SOC mineralization. A higher SOC content and higher incubation temperature resulted in higher SOC mineralization. The temperature sensitivity of SOC mineralization was affected by the substrate quality. The temperature sensitivity of SOC mineralization, showed a downward trend during the incubation period, and the range of variation in the Q10 declined with the increment in the SOC content. The study suggested that there was a higher SOC mineralization in high levels of substrate carbon when the temperature increased. Further, SOC mineralization under higher SOC contents was more sensitive to temperature changes. Our study provides vital information for SOC turnover and the CO2 sequestration capacity under global warming in the Mollisols of Northeast China and other black soil regions of the world.

**Keywords:** substrate quality; carbon dioxide; Kinetic theory; Mollisols; temperature sensitivity

#### **1. Introduction**

Soil organic carbon (SOC) is one of the most important carbon pools in terrestrial ecosystems, accounting for 60~80% of the global terrestrial carbon [1]. SOC decomposition in soils affects atmospheric CO2 concentrations [2] and subsequently influences global climate change [3]. The study of SOC decomposition has been the focus of attention worldwide. Additionally, terrestrial ecosystems and their carbon dynamics significantly impact the global carbon budget [4]. Most studies have suggested that the changes in soil carbon content are associated with different types of land use [5,6] and different soil moisture and temperature conditions [7,8]. Better understanding of the dynamics of terrestrial SOC in different ecosystems is essential to determine SOC decomposition and turnover. The quantitative dynamics of SOC are important for predicting ecological processes and assessing soil fertility.

SOC mineralization, which mediates critical ecosystem processes important for the **decomposition and turnover** of organic matter, is an important monitor of the soil carbon cycle and can provide an estimate of soil carbon decomposition [9]. Carbon mineralization

**Citation:** Yu, H.; Sui, Y.; Chen, Y.; Bao, T.; Jiao, X. Soil Organic Carbon Mineralization and Its Temperature Sensitivity under Different Substrate Levels in the Mollisols of Northeast China. *Life* **2022**, *12*, 712. https:// doi.org/10.3390/life12050712

Academic Editor: Dmitry L. Musolin and Ling Zhang

Received: 8 April 2022 Accepted: 5 May 2022 Published: 10 May 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

can be affected by many factors, such as **soil organic matter (SOM) quality and quantity, oxygen (O2)**, water availability, soil biota and soil temperature [10]. One of the most important drivers affecting SOC mineralization is soil temperature, which might be considered a driver of organic matter turnover in soils [1]. The temperature dependence of SOC mineralization, which is the key source of soil heterotrophic respiration, has been the subject of intense scientific debate [8]. Liu et al. [11] suggested that elevated temperature resulted in an exponential reduction in dissolved organic carbon (DOC). Xiao et al. [12] found that rising temperatures tended to result in a higher portion of stable C in soils. The observed contradictory views indicate that the effects of soil temperature on SOC are complex. The determination of SOC mineralization under different soil temperatures may provide more insights into the causal processes of global warming.

Kinetic theory indicates that the temperature sensitivity of SOM decomposition should increase with substrate recalcitrance [13]. Carney et al. [14] have suggested that carbon inputs stimulate microbial activity and result in higher SOC turnover. Giardina and Ryan [15] found that SOC mineralization in mineral soils was controlled more by substrate quality than temperature. A study on Mollisols demonstrated that when incubated at 25 ◦C, soils with a higher SOC content had higher CO2 production, but when incubated at 15 ◦C, no link between the SOC content and CO2 production was detected [16]. Substrate quality and availability affect the decomposition of SOM [17], and soil temperature influences SOC mineralization by its effects on microbial metabolic activity. The combination of the above two drivers has a interactive effect on SOC turnover rates compared with either factor alone [18]. Thus, the warming effect may be limited by the amount of substrate available and water for decomposition [19]. A better understanding of SOC mineralization is needed to assess the effects of soil temperature and substrate inputs on soil carbon storage.

Northeast China is one of the major regions of Mollisols worldwide. Mollisols, which have a large C pool, are characterized by high SOC and nutrient contents [20]. High amounts of CO2 can be released from the carbon in black soil. Differences in the SOC density and structure among different soil types result in the characteristics of SOC mineralization and its temperature sensitivity [21]. Therefore, increased recognition of regional SOC mineralization and its temperature sensitivity are important for understanding the C cycle as well as for improving C management strategies in the Mollisols of Northeast China [6,7].

Based on a spatial transplant experiment, we conducted an incubation experiment to examine how substrate C contents affect SOC mineralization under different temperatures and investigate its temperature sensitivity (Q10). We hypothesized that higher SOC content would induce larger CO2 production than those low levels of SOC content with the increment of temperature. In order to test the hypothesis, our objectives were to (1) examine how temperature affects SOC mineralization; (2) evaluate the effect of different SOC contents on SOC stability; and (3) determine the temperature sensitivity of SOC mineralization under substrates with different C contents. This study is necessary for determining SOC stocks and the CO2 sequestration potential in response to global warming in Mollisols of Northeast China and other black soil regions of the world.

#### **2. Materials and Methods**

#### *2.1. Study Region*

In October 2004, five sampling sites in Northeast China were selected for this study based on the SOC content. These sites were located in Lishu County and Dehui city in Jilin Province and in Hailun city, Bei'an city, and Nenjiang County in Heilongjiang Province in China. These regions have a typical temperate continental monsoon climate. The soil is classified as typical black soil, i.e., a Mollisol according to USDA soil taxonomy. Soil inorganic C could be ignored due to the lack of carbonates in these soils. Maize (*Zea mays*) is a common cultivated crop in these regions.

#### *2.2. Experimental Design*

We collected soil samples from a size of 1.4 m (length) × 1.2 m (width) × 1.0 m (depth) by wooden boxes from Lishu County, Dehui city, Hailun city, Bei'an city and Nenjiang County. These samples were transported to the Hailun Agroecology Experiment Station of the Chinese Academy of Sciences. The mean annual temperature is 1.5 ◦C in this region, and the mean annual precipitation ranges from 500 mm to 600 mm. With the use of a spatial transplant method (The crops were planted in a unified mode and management that eliminated the complexity of different climate conditions and land management ways), same standard (1.4 m × 1.2 m × 1.0 m) plots were established. The plots were separated by a 20-cm-thick brick wall, which was covered with cement and pasted with tarpaulin inside to minimize the risk of sampling nonindependent areas. In the study, treatments were divided into SOC10, SOC19, SOC29, SOC34, and SOC63 according to the C contents in the soils from the above sampling regions. Each treatment consisted of three replicates. Site descriptions are shown in Table 1.

#### *2.3. Soil Sampling and Preparation*

The selected samples from different regions were used to determine the effect of carbon levels on SOC mineralization. Clearly, under the premise of satisfying different C contents, the other physical-chemical properties of the soil samples were not consistent. In fact, the situation in the field was complex and changed. The samples, obtained from different regions, can better reflect the actual soil conditions in the field. A single-variable experiment is necessary to explore the complicated effects of the soil C level on SOC mineralization in the future.

After maize harvest in October 2016, twenty 10 cm × 5 cm × 20 cm (length × width × depth) samples were randomly collected to a depth of 0–20 cm at each sampling site. The twenty subsamples were thoroughly mixed to generate one composite sample, and fine roots and other residues were removed from the samples. The composite sample was the total amount of the twenty samples. Soon after collection, the samples were transported to the Key Laboratory of Ecological Restoration and Resource Utilization for Cold Region in Harbin, Heilongjiang Province. All samples were air-dried and sieved through a 1 mm mesh for the incubation experiment, the measurement of soil physical and chemical properties. The rest of the samples were sieved through a 0.25 mm mesh to determine SOC, total nitrogen (TN), total phosphorus (TP) and total potassium (TK). Soil pH was determined with an automatic acid-base titrator using a 1:2.5 soil:water suspension. The SOC content was measured by the Walkley Black method [22]. TN was determined by the kjeldahl method. TP was determined via the H2SO4-HClO4 digestion method. TK was analyzed according to NaOH melting with flame photometry. Available nitrogen (AN) was measured by alkali hydrolysis diffusion method. Available phosphorus (AP) was determined using the NaHCO3 leaching molybdenum antimony colorimetric technique, and available potassium (AK) was tested by NH4OAc extraction with flame photometry [23]. Soil properties are presented in Table 2.

 Soil properties of selected sites.

Note: Different lowercase letters indicate significant differences among sampled sites. Unmarked means that there are no significant differences between treatments. C/N = SOC: TN. Values are means ± standard error (*n* = 9).

#### *2.4. Laboratory Incubation Experiment*

We incubated soils in the laboratory using a factorial design of substrate (5 levels: SOC10, SOC19, SOC29, SOC34, and SOC63) and temperature (4 levels: 5 ◦C, 15 ◦C, 25 ◦C, and 35 ◦C) with three replicates. Air-dried soils (100 g) were placed in a 1000 mL jar and preincubated at 60% water-holding capacity (WHC) at 25 ◦C in the dark for 7 days. The WHC was determined by the method described by Alef and Nannipieri (1995) [24]. After preincubation, the samples were incubated at selected temperatures (5, 15, 25 or 35 ◦C) in the dark for 73 days. It is worth noting that the changes in the indices in the study tended to be stable after 73 days of incubation. During the incubation, samples were sealed in the incubator, and soil moisture was adjusted to 60% of WHC with deionized water.

## *2.5. CO2 Production*

In this study, the accumulation of CO2 production was used to represent SOC mineralization in the soils. A beaker containing 25 mL 1.0 mol L−1 NaOH solution was placed in each jar to capture evolved CO2. The NaOH solution was exchanged on days 1, 3, 5, 7, 14, 21, 28, 35, 42, 49, 56, 63, and 73 of the incubation. Then, the CO2 was titrated with 0.5 mol L−1 HCl in excessive amounts of BaCl2 [25]. We calculated the cumulative CO2 production, which characterized SOC mineralization, during two successive sampling intervals. The cumulative CO2 production was expressed as mg CO2 kg−1 soil, representing the amount of CO2 released from the soil.

#### *2.6. Evaluations and Calculations*

In our study, the SOC mineralization rate and the temperature sensitivity of SOC mineralization were expressed as *Rs* and *Q*10 [26], respectively. The following equations were used.

$$R\_{\rm s} = a \cdot e^{bT} \tag{1}$$

$$Q\_{10} = e^{10b} \tag{2}$$

where *Rs* is the SOC mineralization rate (mg SOC kg−1 soil d−1 ), *T* represents the incubation temperature, *a* is the SOC mineralization rate at a temperature of 0 ◦C, and *b* is the temperature coefficient, which is related to the *Q*10 (increase in the rate of respiration over a 10 ◦C increase in temperature).

#### *2.7. Data Analysis*

The Kolmogorov-Smirnov test and Levene's test were used to determine the normality and equality of the data, respectively. One-way analysis of variance (ANOVA) and least significant difference (LSD) multiple comparisons (*p* < 0.05) were used to assess the soil physical-chemical properties, soil cumulative CO2 production and *Q*10. Two-way ANOVA was performed to evaluate the effects of SOC level and soil temperature on cumulative CO2 production. The relationship between cumulative CO2 production and selected soils was evaluated with Pearson correlation analysis. The effects of the interaction of SOC contents and incubation temperatures on cumulative CO2 production were determined by a univariate general linear model with Duncan's test. Curve estimation with an exponential model was used to evaluate parameter *b* in Equation (1). All statistical analyses were

performed using SPSS statistical software ver. 20 (SPSS Inc., Chicago, IL, USA). Graphs were generated using SigmaPlot 12.5.

#### **3. Results**

## *3.1. Changes in Cumulative CO2 Production under Different SOC Contents at 5, 15, 25 and 35* ◦*C*

The cumulative CO2 production from soils with different SOC contents under different incubation temperatures can be seen as Figure 1, and the cumulative CO2 production ranged from 567.10 to 1003.91 mg CO2 kg−1 soil at the end of the incubation. During the incubation period, we found the cumulative CO2 production increased with the increase in the SOC content. After 7 days of incubation, the cumulative CO2 production under different SOC treatments began to show obvious differences as follows: SOC63 > SOC34 > SOC29 > SOC19 > SOC10. The cumulative CO2 production stabilized across the treatments at different temperatures after 49 days of incubation. We also found that cumulative CO2 production increased as the incubation temperature increased, with significantly higher cumulative CO2 production at higher temperatures with increasing SOC content (Supplementary Tables S1–S9).

**Figure 1.** Changes in cumulative CO2 production throughout the incubation at different soil temperatures (5 ◦C, 15 ◦C, 25 ◦C and 35 ◦C). Data scatters are means ± standard error (*n* = 9).

## *3.2. Characteristics of the Q10 Value under Different SOC Contents*

We averaged the *Q*10 values from the different incubation temperatures to reflect the temperature sensitivity of SOC mineralization in the study, and we found that the *Q*10 value showed a descending trend with the extension of the incubation time (Figure 2). During the incubation, the *Q*10 value varied in response to different SOC contents, ranging from 1.07 to 1.53, 1.08~1.40, 1.07~1.32, 1.08~1.32, and 1.11~1.26 in SOC10, SOC19, SOC29, SOC34 and SOC63, respectively.

**Figure 2.** Effects of the soil carbon content on the *Q*10 value. Data scatters are means ± standard error (*n* = 9).

Both the highest and lowest *Q*10 values appeared in SOC10 on day 1 and day 35, respectively. The *Q*10 value showed a downward trend with little fluctuation and roughly leveled off by the end of the incubation in SOC10, SOC19 and SOC34. In SOC29 and SOC63, the *Q*10 value continued to decrease until day 21 and showed a modest upward trend with fluctuation thereafter. During the first five days of the incubation, soils with lower SOC contents had higher *Q*10 values. At the end of the incubation, the *Q*10 value in descending order was SOC63, SOC29, SOC34, SOC10 and SOC19 (Supplementary Table S10). Additionally, we also examined the changes in the *Q*10 value between the first day and the end of the incubation (the 73rd day of the incubation), and we found that the range of variation in the *Q*10 value decreased with the increase in the SOC content. There were significant differences of the *Q*10 between SOC63 and other treatments (Figure 3 and Supplementary Table S10).

**Figure 3.** Changes in the *Q*10 value under different carbon levels during the incubation. Vertical bars of (**A**) = (Value of *Q*10 on the first day of incubation−value of *Q*10 on the 73rd day of incubation)/value of *Q*10 on the first day of incubation. (**B**) Day 1 = Value of *Q*10 on the first day of incubation; Day 73 = Value of *Q*10 on the 73rd day of incubation. Different lowercase letters indicate significant differences between SOC levels.

#### *3.3. Key Factors That Drive Changes in SOC Mineralization*

According to the two-way ANOVA, we found that the SOC contents, incubation temperature and their interactions significantly affected SOC mineralization, and the temperature had a larger effects on SOC mineralization than the SOC contents (Table 3). Additionally, Pearson correlation analysis showed that soil organic carbon, total N, total P, total K, available N and available K were significantly positively correlated with cumulative CO2 production, indicating that soil nutrients were the main factor that determined the changes in SOC mineralization (Table 4).

**Table 3.** Effects of SOC level and soil temperature on cumulative CO2 production based on analysis of variance.

Note: Data for statistical analysis are based on a *n* = 12 samples per treatment.

**Table 4.** Correlations between soil physical-chemical properties and cumulative CO2 production.

Note: Data for statistical analysis are based on a *n* = 12 samples per treatment. \* and \*\* represent significance differences at *p* < 0.05 and *p* < 0.01, respectively.

#### **4. Discussion**

#### *4.1. Effects of SOC Contents and Temperature on SOC Mineralization*

The temperature effect may be limited by the substrate available for decomposition [19]. In this study, we found that the temperature and substrate C contents had significant effects on cumulative CO2 production (Figure 1; Table 3).

A temperature increase resulted in a higher cumulative CO2 production (Figure 1). Many incubation experiments have shown that increasing temperature can promote SOC mineralization [17,27,28]. Temperature affects SOC mineralization by its effects on microbial metabolic activity. An increase in temperature is favorable to microbial activity and therefore increased the turnover rate of SOC [29,30]. Leifeld and Fuhrer [28] also suggested that temperature is a major controlling factor for SOC turnover. The responses of SOC to temperature are important for the evaluation of possible atmospheric feedbacks from the SOM reservoir [31]. In the current study, the incubation environment was not suitable for the collection of microbial data. Additional research needs to be conducted to explore how changes in the microbial community drive variations in SOC mineralization.

As expected, we found that the cumulative CO2 production increased with increasing substrate carbon contents (Figure 1), and the alterations in cumulative CO2 production were significantly correlated with soil nutrients (Table 4). Substrate quality affected cumulative CO2 production, with the magnitudes varying across soils and substrate SOC [16]. There are higher C mineralization rates in the soils have been found in high contents of SOC [32]. Additionally, these results may primarily be attributed to microbial effects. Many studies have indicated that changes in soil nutrients modify microbial growth [33–35]. Greater C content lead to an increase in the abundance of microorganisms. A larger microbial community may be more efficient at SOC decomposition [14,15]. Competition is less crucial to limiting soil microbes because more niches may appear on account of improvements in soil nutritional resources [36]. Thus, the high levels of carbon may stimulate microbial activity and result in higher SOC turnover. As a result, the increase in substrate carbon might enhance SOC mineralization.

We found the increased SOC contents and warming had strong effects on SOC mineralization, and these effects were pronounced when warming and substrate treatments were applied together (Figure 1, Table 3). These two drivers has a different effect on SOC mineralization has a different effect on than either treatment alone [37]. Increased rates

of SOC cycling caused by increased C inputs were exacerbated by warming [17]. It may be that warming affected the SOC turnover rate, the amount of CO2 production is determined by the amount of substrate C in soils [38]. Steinweg et al. [39] demonstrated that there are distinct mechanisms by which the temperature and substrate quality affect microbial respiration. The increased temperature promoted microorganisms to take up and metabolize substrates more quickly, and higher substrate C made greater amounts of C available to soil microorganisms in general. Furthermore, many other factors can influence the SOC mineralization, such as oxygen (O2), soil moisture and the fraction of substrate C and so on. Further research is necessary to explore the mechanism of these factors on SOC mineralization.

#### *4.2. Influences of SOC Contents on the Temperature Sensitivity of SOC Mineralization*

Temperature sensitivity of SOC mineralization (*Q*10) in all carbon treatments showed a downward trend as incubation times increased, and a higher carbon content resulted in a smaller drop in *Q*10 in this study (Figure 2). The reason for the decline in *Q*10 may be the reduction in the SOC mineralization rate over time. Reichstein et al. [40] suggested that *Q*10 changed with incubation time, which may be caused by the alteration of labile C and recalcitrant C during incubation. Generally, the stability of SOC play a key role on the temperature sensitivity of the SOC decomposition, additional research needs to be conducted to explore whether changes in labile C and recalcitrant C drive changes in the SOC decomposition.

To further determine the changes in *Q*10, we examined the variation amplitude of *Q*10 between the first day and the end of the incubation (the 73rd day of the incubation), and we found that *Q*10 was higher under a high carbon content than under low levels of carbon at the end of the incubation (Figure 3). The availability of SOC affects the response of soil heterotrophic respiration to temperature change, and *Q*10 decreases with the reduction in labile SOC content in soils [41]. The availability of soil nutrients was relatively high under the high soil carbon content compared with the low levels of soil carbon when the external temperature increased.

We also demonstrated that SOC mineralization under higher SOC contents was more sensitive to temperature changes in the middle and later period of SOC mineralization, and SOC mineralization under lower SOC contents was more sensitive to temperature changes at the early stage of SOC mineralization. To be degree, high levels of substrate carbon can stimulate SOC mineralization and result in greater SOC turnover. These findings can provide theoretical support for the impacts of different substrate gradients on SOC mineralization. Determining the temperature sensitivity of the decomposition of the different levels of SOC pools is critical for predicting the long-term impacts of climate change on SOC storage of Mollisols in Northeast China in the context of global warming.

#### **5. Conclusions**

In the study, the cumulative CO2 production was relatively high under the high levels of soil carbon content. It suggested that high contents of SOC in soils resulted in a higher C mineralization. Changes in temperature can also affect the SOC mineralization. Larger cumulative CO2 production were found with the increasing of temperature. We also found the effects of increased SOC contents and warming on SOC mineralization were significant when these two drivers were applied together. High levels of substrate carbon can stimulate SOC mineralization and result in greater SOC turnover when the temperature increased. Q10, which represents the temperature sensitivity of SOC mineralization, varied between different SOC contents. The results suggested that the SOC mineralization under higher SOC contents was more sensitive to temperature changes. These fndings are important for achieving a better understanding of SOC turnover and the CO2 sequestration capacity under global warming in the Mollisols of Northeast China and other black soil regions of the world.

**Supplementary Materials:** The following supporting information can be downloaded at: https:// www.mdpi.com/article/10.3390/life12050712/s1. Table S1. Changes in cumulative CO2 production with varied SOC contents when incubated at 5 ◦C. Table S2. Variations of cumulative CO2 production among different SOC contents when incubated at 15 ◦C. Table S3. Alterations in cumulative CO2 production between SOC contents when incubated at 25 ◦C. Table S4. Changes in cumulative CO2 production with different SOC contents when incubated at 35 ◦C. Table S5. Alterations of cumulative CO2 production under SOC10 among incubation temperatures. Table S6. Changes in cumulative CO2 production in the SOC19 with different incubation temperatures. Table S7. Variations of cumulative CO2 production under SOC29 between incubation temperatures. Table S8. Changes in cumulative CO2 production under SOC34 with incubation temperatures. Table S9. Variations of cumulative CO2 production in the SOC63 among incubation temperatures. Table S10. Changes in the *Q*10 values with SOC contents in soils.

**Author Contributions:** Conceptualization, X.J.; methodology, Y.S.; formal analysis, T.B. and Y.C.; data curation, T.B. and Y.C.; writing-original draft preparation, H.Y. and T.B.; writing-review and editing, T.B.; visualization, T.B.; supervision, X.J.; project administration, X.J.; funding acquisition, X.J.; All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by National Nature Science Foundation Program of China (grant NOs. 42077081) and National Science and Technology Basic Resources Investigation Special Project (grant NOs. 2021FY100400).

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** The datasets generated and analyzed during the current study are available from the corresponding author on reasonable request.

**Conflicts of Interest:** The authors declare no conflict of interest.

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**4. Receptor protein for selective autophagy in** *C. elegans*

Germ granules are restricted to the germ cells of many higher eukaryotes and are believed to carry germ cell determinants (Strome and Lehmann, 2007). Germ granules in *Caenorhabditis elegans,* also known as P granules, are maternally contributed and dispersed throughout the cytoplasm of a newly fertilized embryo. During *C. elegans* embryogenesis, some P granules are left in the cytoplasm destined for the somatic daughter cell and these P granules are quickly disassembled and/or degraded (Hird et al., 1996). Recently, Zhang *et al.* provided evidence that the P granule components PGL-1 and PGL-3 that remain in the cytoplasm destined for somatic daughters are selectively removed by autophagy through the receptor protein SEPA-1 (Zhang et al., 2009). In autophagy-deficient somatic cells, PGL-1 and PGL-3 extensively accumulate in the P granules, and SEPA-1 mediates the accumulation of these P granules into aggregates, termed PGL granules, through its self-oligomerization and direct interaction with PGL-3. SEPA-1 can also directly interact with LGG-1, an Atg8 homolog. Thus, PGL granules associated with SEPA-1 could be incorporated into autophagosomes through the interaction of SEPA-1 with LGG-1. Because the expression of SEPA-1 is restricted to somatic cells, the selective exclusion of P granules is ensured only in these cells. An *in vitro* pull-down assay showed that the SEPA-1 fragment containing amino acids 39 to 160 is required for both self-oligomerization and interaction with PGL-3 and that the SEPA-1 fragment containing amino acids 289 to 575 is required for interaction with LGG-1. The SEPA-1 fragment that interacts with LGG-1 contains a canonical AIM sequence, 469-YQEL-472 (Noda et al., 2010). Thus, the YQEL sequence in SEPA-1 is a potential candidate for a functional AIM.


**Abstract**

The gold standard in the diagnosis and treatment of proctological diseases is the exploration of the anal canal and distal rectum under anaesthesia (EUA), routinely performed as day case surgery. In selected cases it can be conducted as an outpatient exploration (OE) during a specialist surgical consultation. In the outpatient setting it is possible and safe to perform rubber band ligation, sclerotherapy and infrared coagulation for the treatment of haemorrhoidal disease, excision and incision of thrombosed external haemorrhoids, abscess drainage, setonage and fistulotomy also in case of perianal Crohn's disease, anal warts and skin tags removal. In terms of patients' satisfaction and success rate OE is comparable to EUA. All procedures can be performed under local anaesthesia. Pain control after the procedure is provided by oral pain killers.