9. Vancouver classification of postoperative periprosthetic femur fractures around total hip arthroplasty

#### 9.1. Classification

Type A: Peritrochanteric.

AG: Greater trochanter.

AL: Lesser trochanter.

Type B: Around or just distal to the tip of the stem.

B1: Well-fixed femoral component.

B2: Loose femoral component.

B3: Loose femoral component and poor bone stock.

Type C: Well distal to the stem.

#### 9.2. Clinical application

This classification guides the surgeon with treatment decision.

AG and AL: Usually stable and can be treated non-operatively.

B1: ORIF if displaced.

B2: Revision to long stem.

B3: Revision with struct grafting.

C: ORIF.

9. Vancouver classification of postoperative periprosthetic femur fractures

around total hip arthroplasty

Figure 6. Intraoperative Vancouver classification.

B1: Well-fixed femoral component.

B2: Loose femoral component.

Type C: Well distal to the stem.

Type B: Around or just distal to the tip of the stem.

B3: Loose femoral component and poor bone stock.

9.1. Classification

Type A: Peritrochanteric. AG: Greater trochanter. AL: Lesser trochanter.

28 Total Hip Replacement - An Overview

#### 9.3. Reliability

A European validation for this classification was performed by Rayan et al. The study had included consultants, trainees and medical students. It was noted to have an inter observer reliability of substantial agreement among consultants (k value of 0.72–0.74) orthopedic trainees (k value of 0.68–0.70) and medical students (k value of 0.61). The validity within B type fractures revealed an agreement of 77% with a k value of 0.67 [9, 10] (Figure 7).

Figure 7. Postoperative Vancouver classification.


**3. Results and Discussion**

As a pre-interpretation step of the Raman spectra acquired from the Kerdijk paint-outs, production recipes collected at the Talens' archives were studied and evaluated and a list of expected SOPs was made [4]. Where possible, the commercial names used in the recipes were related to the Colour Index definitions, e.g., the pigment Hansa Geel 10 [Hansa Yellow 10] corresponds to PY3. Then, the acquired Raman spectra were compared with the reference spectra of the expected SOPs.

Table 2 gives a detailed overview of the analysed paint-outs and their interpretation. The expected SOPs according to the recipes are reported. The settings for acquisition refer to the averaged settings yielding the best result. It was found that the handheld Raman produces readable and interpretable spectra with defined peaks at almost constant settings of 150–200 ms exposure time and 100–200 acquisitions.

#### *3.1. Identified SOPs*

The Talens Rembrandt oil paints of the Kerdijk colour charts were revealed to contain the SOPs of eight different pigment groups. Furthermore, it was also possible to identify paints that contained a mixture of SOPs (Table 2).

Some of these SOPs belong to groups that are already well known to have been used in fine artists' materials and can be expected in this time period. This concerns SOPs belonging to the BONA, monoazo, and phthalocyanine group as well as the ß-Naphthol pigments PR3 and PO5, which have indeed been found in artworks from before 1950 [30]. In other cases, the Kraplak [madder], Karmijn [carmine], and Alizarine [alizarine] paintouts provided well defined spectra, but the SOPs have not yet been identified and are labeled as "unknowns".

Among the identified SOPs, there are also some pigments that were not yet known to have been used for the production of fine artists' paints dated before 1950. The occurrence of the mordant dyes and triarylcarbonium pigments are among the most surprising results of this study and emphasizes the importance of searching unexpected possible pigment groups in pre-1950 artists' materials. Three examples are discussed in more detail.

The paint-out Gamboge from 1932 was revealed to contain Mordant Yellow 1. This SOP was discovered by Nietzki in 1887 and is also known as Alizaringelb GG [Alizarine Yellow GG] or Beizengelb TG [Mordant Yellow TG] [45]. Wagner (1928) reports that this pigment was not very popular because of the elaborate preparation needed to obtain a pure product. It is therefore surprising that, according to the recipe records, Talens has used this SOP for fine artists' material production for almost 20 years [34,46]. Figure 2 displays the handheld Raman spectrum of the paint-out Gamboge and the microRaman spectrum of Mordant Yellow 1 from the in-house reference collection. The handheld Raman spectrum displays the peaks that allow an interpretation, but also contains artifacts.

Within the paint-out Talens Groen Licht [Talens Green Light] the pigment PG9, a SOP belonging to the group of Indamine, was detected. This pigment, a barium salt of an iron complex, was discovered in 1909 and labelled as Hansagrün GS [45]. It was recommended by the Colour Index to be used "particularly in mixture with CI PY 1 or 3" [45–47]. Schultz (1931) describes the pigment as a "very lively green when mixed with Hansa Yellow for all purposes (almost as fiery as Schweinfurter Green)" [48]. This suggests its use in artists' colours in mixture with monoazo yellows as a substitute of the toxic inorganic pigment and fits therefore to Talens' application, documented in the recipe of Talens Green Light.

Figure 3 shows the handheld Raman spectrum of the paint-out Talens Green Light and, for comparison, the microRaman spectrum of a Hansagrün GS reference (paint-out from Wagner, 1928). The peaks of the PY3 component in the reference spectrum are very faint, which can be related to different PY3 and PG9 ratios within the samples.

*Heritage* **2021**

 oil paint-outs dated 1932–1950.

**Table 2.**

Summary of the results obtained with the handheld Raman device on Kerdijk colour charts with Talens' Rembrandt

, *4*

*Heritage* **2021** , *4*

**Table 2.** *Cont.*

*Heritage* **2021**, *4*

**Table 2.** *Cont.*

**Table 2.** *Cont.*

are not yet interpreted.

**Figure 2.** Handheld Raman spectrum of the paint-out Gamboge (K32) and the microRaman spectrum of Mordant Yellow 1, labelled as Alizarine Yellow GG (reference collection of the Cultural Heritage Agency of the Netherlands, Amsterdam, The Netherlands).

**Figure 3.** Handheld Raman spectrum of paint-out Talens Green Light (K32) and the microRaman spectrum of reference Hansagreen GS (Wagner, 1927), mixed with barium sulphate (987 cm<sup>−</sup>1), both showing the characteristic peaks of PG9 in mixture with PY3. The peaks that clearly correspond to the KIK-IRPA reference for PY3 are highlighted with a star.

Two paints of the Talens in-house brand, labelled as "Talens paints", showed triarylcarbonium pigments. In the paint-out Talens Groen Donker [Talens Green Dark] of the 1941–1950 editions, the green pigment PG2 was identified. PG2, brought to the market under the product name Fanalgelbgrün [Fanal Yellow Green], is defined as either a mixture of Basic Green 1 and Basic Yellow 1, precipitated with phosphotungstomolybdic acid (PTMA), or Basic Green 1 with Auramin O, precipitated with PTMA [31,45,48].

The other triarylcarbonium pigment that was identified is PB8. It was detected in the paint-out Talens Green Dark (1932), contradicting the recipe (Table 2), and in the Talens Groen Blauw [Talens Green Blue] in the editions from 1932 and 1937. PB8, labelled in the Talens recipes as Fanalbremerblau G neu [Fanal Bremer Blue G new], is a mixture of Basic Blue 5 and Basic Green 1, precipitated with PTMA [17]. Figure 4 displays the handheld Raman spectrum of Talens Green Blue and the reference microRaman spectrum of a reference sample, labelled as a mixture of Basic Blue 5 and Basic Green 1 precipitated with PTMA, provided from the Historic dye collection of the technical university in Dresden.

**Figure 4.** Handheld Raman Spectrum of paint-out Talens Green Blue (K32) and the microRaman spectrum of a reference sample containing Basic Blue 5 and Basic Green 1, precipitated with PTMA (Historic Dye Collection, Dresden, Germany).

Both PB8 and PG2 belong to the Fanal brand, which was introduced by I.G. Farben in 1924 [31]. Lomax et al. [1] point out that "the major use of these pigments [triarylcarbonium] is in the printing ink industry" and it seems surprising that Talens chose these SOPs for their fine artists' oil paint brand. However, Steger et al. detected two pigments (PV2 and PG1), belonging to this group, in a reverse glass painting from 1945 [49].


**4. Discussion and Conclusions**

In this study, we reported the domain wall conductance by inclined domain walls in LNO thin films, which could be applied as a new multi-level non-volatile memory cell based on out-of-plane ferroelectric switching, by the precise tuning of the inclination and thus the conductance state. We investigated further the temperature dependence as well as the temporal dependence on erase voltage application and concluded that the main contribution to the current is space-charge-limited.

We obtained evidence the current to be dominated by a polaron gas formation at such inclined domain wall and confined by the band bending from the temperature dependence of the conductance. Upon local inversion, generally, charge carrier injection is discussed, even though it has been speculative for a long time. Assuming such charge carriers are injected at the DW, which are needed to compensate the inclined DW of the spike-domain-like nucleus, free charge carriers—electrons—are generated, which will condensate into similarly charged and, hence, screening electron polaronic, states inside the single crystal material. Such a transition has been observed in bulk material, e.g., by optical time-resolved spectroscopy, to happen within 200 fs [37]. Yet, at a CDW these values can be significantly larger due to the smaller energy gap between conductance band and Fermi energy. However, as the band-bending is not large enough to create a 2DEG, which would be apparent with much higher mobility conductivity, we assume a 2D polaron gas (2DPG) is obtained. Bound polarons will very

rapidly be trapped by iron impurities. Upon pure-diffusion transport, thus without an external electric field, the average mean free path of the bound polaron until trapping with FeLi is < tNbLi4+ ≥ 100 Å and for the free polaron is < tNbNb4+ ≥ 80 Å. These values were derived by interpolation from the data of Mhaouech and Guilbert [35] under consideration of the smaller iron impurity concentration cFe = 1017 cm−3 and a niobium antisite defect concentration of cNbLi = 1021 cm−3. Upon drift condition, these values can be larger. Hence, the bound polaron has a larger mean free path. However, the hopping rate of bound polarons is only wNbLi4+ = 107 s−1. Assuming the hopping rate to depend mainly on the nearest neighbor distance, like w~er[Å], we can calculate, that the hopping rate of free polarons is two orders of magnitude larger, hence wNbLi4+ = 109 s−1. Thus, the electric current with free polaron transport as in Mg:LNO can be significantly larger. This again explains why, for the case of bulk material only, congruent Mg:LNO revealed a conductance upon UV illumination. For the ultrathin LNO films in use in this study, even undoped material can give measureable conductance.

The model explains why an asymmetric electrode condition can result in the efficient switching between an HRS and LRS. Upon the negative pulse application, the carrier density reduces, which destabilizes the inclined domain wall formation and finally reduces the conductance of the wall further. However, this process requires relatively high electric fields and times due to the low mobility of the charge carriers. Hence, the study suggests that Mg:LNO and thinner films can significantly improve the performance of the tunable non-volatile memory.

The thin-film lithium niobate resistive switching effect based on charged ferroelectric domain walls formation and erasure offers interesting advantages over conventional filament-based resistive switching, which can be interesting for neuromorphic computing applications.

**Author Contributions:** Investigation, T.K., B.W.; resources, A.H., L.M.E.; writing—review and editing, T.K.; supervision, L.-Q.C., L.M.E. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the Cluster of Excellence "Center of Advancing Electronics Dresden" and the DFG Research Grant HA 6982/1-1. We also acknowledge open access funding support by the publication fund of TU Dresden.

**Acknowledgments:** The authors would like to acknowledge the support of NanoLN for the provision of the lithium niobate thin film samples, H. Hui (Shandong University) for helpful discussions, and S. Johnston as well as Z.X. Shen (Stanford) for measurement support. B.W. acknowledges the support by the NSF-MRSEC grant number DMR-1420620. The effort of L.-Q.C. is supported by National Science Foundation (NSF) through Grant No. DMR-1744213.

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

## **References**

© 2020 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 (http://creativecommons.org/licenses/by/4.0/).


**ACCELERATOR PHYSICS - RADIATION SAFETY AND APPLICATIONS**

Edited by **Ishaq Ahmad** and **Maaza Malek**

#### **Accelerator Physics - Radiation Safety and Applications**

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*Article* **The Impact of Impervious Surface Expansion on Soil Organic Carbon: A Case Study of 0–300 cm Soil Layer in Guangzhou City**

**Jifeng Du 1,2,3, Mengxiao Yu 1 and Junhua Yan 1,\***

**Abstract:** Empirical evidence shows that the expansion of impervious surface threatens soil organic carbon (SOC) sequestration in urbanized areas. However, the understanding of deep soil excavation due to the vertical expansion of impervious surface remains limited. According to the average soil excavation depth, we divided impervious surface into pavement (IS20), low-rise building (IS100) and high-rise building (IS300). Based on remote-sensing images and published SOC density data, we estimated the SOC storage and its response to the impervious surface expansion in the 0–300 cm soil depth in Guangzhou city, China. The results showed that the total SOC storage of the study area was 8.31 Tg, of which the top 100 cm layer contributed 44%. The impervious surface expansion to date (539.87 km2 ) resulted in 4.16 Tg SOC loss, of which the IS20, IS100 and IS300 contributed 26%, 58% and 16%, respectively. The excavation-induced SOC loss (kg/m2 ) of IS300 was 1.8 times that of IS100. However, at the residential scale, renovating an IS100 plot into an IS300 plot can substantially reduce SOC loss compared with farmland urbanization. The gains of organic carbon accumulation in more greenspace coverage may be offset by the loss in deep soil excavation for the construction of underground parking lots, suggesting a need to control the exploitation intensity of underground space and promote residential greening.

**Keywords:** expansion of impervious surface; underground space development; deep soil excavation; SOC loss in deep soil; urban renovation; Guangzhou city

### **1. Introduction**

The share of urban people in the world has increased from 30% in 1950 to 55% in 2018 and this proportion is projected to reach 68% by 2050 [1]. Urbanized areas in 2030 will nearly triple from those in 2000 [2]. With the rapid expansion of urban areas, many agricultural or natural ecosystem areas have been converted into greenspace or impervious surface, which greatly changes the structure and function of the original ecosystems and has a far-reaching impact on soil organic carbon (SOC) storage [2–5]. Previous studies have shown that urban vegetation has high carbon sequestration capacity [6–11], and the SOC density observed in greenspace is higher than that of grassland or farmland in some cities [8,12,13]. In New York City, the SOC density of the greenspace is comparable to that of suburban and rural forest soil [14]. However, the vegetation and soil removal and soil sealing during the installation of impervious surface have seriously disturbed soil functions [15], which can counterbalance the positive effects from urban greenspace ecosystems [14]. Impervious surface has become the dominant land cover in urbanized areas. Impervious surface covers approximately 31% [13] and 65.91% [16] of the land within urban areas in the United States of America and China, respectively, and more

**Citation:** Du, J.; Yu, M.; Yan, J. The Impact of Impervious Surface Expansion on Soil Organic Carbon: A Case Study of 0–300 cm Soil Layer in Guangzhou City. *Sustainability* **2021**, *13*, 7901. https://doi.org/10.3390/ su13147901

Academic Editors: Kikuko Shoyama, Rajarshi Dasgupta and Ronald C. Estoque

Received: 9 April 2021 Accepted: 6 July 2021 Published: 15 July 2021

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

**Copyright:** © 2021 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/).

than 50% in many European cities [17]. Therefore, it is necessary to study the influence of impervious surface construction on SOC storage to comprehensively quantify the impact of urbanization on regional carbon budgets [18,19].

The effect of impervious surface expansion on soil carbon storage has been studied from two perspectives. Some studies revealed the differences in SOC storage between sealed soils and surrounding greenspace soil at the sample site scale [14,19–23], and others have used spatially explicit models driven by land cover change and/or other multiple environmental covariates to analyze the total SOC loss and its spatial and/or temporal variability at regional, national and even global scales [24–28]. These models provide a powerful and effective tool for presenting carbon changes in space and time. However, they usually treat urban area or impervious surface as a homogeneous space unit, and interpret land cover types from medium or low resolution remote sensing images, which cannot reflect the differences of soil carbon disturbance among impervious surface types. For urban carbon management, research based on high-resolution images and refined types of impervious surface could be more beneficial to policy making.

Topsoil removal is the main cause of depletion in SOC content under impervious surface [29]. Although the depth of soil removal varies in different types of impervious surface, few studies have explored in detail the difference of soil carbon disturbance between different impervious surfaces. In China, the depth of soil extraction for pavement is approximately 15–30 cm, and that for low-rise buildings without underground parking lots is approximately 80–100 cm. High-rise buildings usually include underground garages. According to the Design Code for Residential Buildings (GB 50096-2011) [30], the net height of underground garage driveways should not be less than 220 cm. Adding spaces for the construction of building foundations and for the installation of facilities, such as pipes, wires and ventilating ducts, the depth of soil extraction for buildings with underground parking requires a deeper depth than 300 cm. Large utility lines, subways, car park facilities and malls have been constructed under the urban surface in many big cities around the world [31,32], especially in China where high-rise buildings with underground parking lots are widely distributed in many medium-sized or big cities. Although the depth of soil excavation in many underground space utilizations is deeper than the top 100 cm soil layer, most published studies on urban SOC storage ignored the carbon below a depth of 100 cm. Soil at deeper depths (e.g., 100–300 cm) still has large carbon storage [33]; therefore, consideration of 0–20 cm or 0–100 cm soil depth cannot reveal the influence of the vertical expansion of impervious surface on SOC pools.

Soil is the largest contributor to the urban ecosystem carbon pool [13,34]. Although the SOC density beneath impervious surface is lower than that in adjacent greenspace areas [14,19,20,28,29], the contribution of soil under impervious surface to urban SOC pool is still large due to the large coverage area. For instance, in the Chicago and Boston metropolitan areas, the impervious surfaces contribute 28.76% and 22.46% to the total soil and plant carbon pools with aerial coverages of 60% and 53.9%, respectively [18]. In the China Urumqi urban area, the impervious surface covers 63% of the urban area and it contributes 57% of the SOC pool. Buildings account for a large proportion of impervious surface in cities; however, previous studies usually sample soils under pavement, such as roads, sidewalks, parking lots, paved backyards and paved squares [14,19,20,22,23,29], because it is hard to sample soil under buildings. The SOC stock in building-covered soil was assumed to be 0 kg/m2 [19,23], or was designated a proxy such as the SOC density of clean fill soil [13,18] or the provincial average SOC density [34], which greatly increased the uncertainty of urban SOC pool estimation and its response to the expansion of urban impervious surface. Thus, it is necessary to try other methods to estimate SOC storage in building-covered areas.

Due to the rapid land use/cover changes in China, ecosystem services intensity experienced a continuously decreasing trend from 1995 to 2015, especially in large megacities [35], and ecosystem service values for provision, regulation, support and culture also decreased during the period of 1988–2008 [36]. Guangzhou is a representative megacity with rapid

urbanization and increasing impervious surface in China. Urban area in Guangzhou has increased from 187.40 km2 in 1990 to 1324.17 km2 in 2019 with an annual expansion area of 39 km2 [37]. Urban underground space use has become an essential part of the urban master plan in China. According to the urban subsurface development target of "The master plan of Guangzhou city (2011–2020)", the city-wide area of developed underground space will reach 90 million square meters, of which 8 million square meters will be used as commercial space. This large-scale soil excavation will cause large disturbances to SOC storage, which should not be ignored in the environmental assessment of underground space exploitation. The objectives of this study were: (1) to estimate the SOC stocks of pavement, low-rise buildings and high-rise buildings based on their average soil extraction depth and the SOC density of pre-urbanization pervious surfaces; (2) to estimate the SOC storage in the 0–300 cm soil layer and then quantify the impacts of urban impervious surface expansion on the SOC stock at the residential and regional scale. To our knowledge, this is the first study in which the urban SOC and its response to the expansion of impervious surface in the 0–300 cm soil layer have been quantified. This research provides implications for policy making on how to reduce disturbances to the deep soil carbon pool during urban underground space development.

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

### *2.1. Study Area*

Guangzhou city (112◦570~114◦030 E, 22◦260~23◦560 N) is located in the lower reaches of the Pearl River Delta near the South China Sea. This city is the economic and transportation center of South China, with a built-up area of 1263.34 km2 and a large population of 14.49 million in 2018 [37], making it a key area to study the effects of impervious surface expansion on soil carbon sequestration. Guangzhou city, which is dominated by lateritic red soil and paddy soil, has a subtropical monsoon climate with an average annual temperature of 21.4–22.6 ◦C and an average annual precipitation of 1600–2300 mm [37]. The northeastern part of this city is dominated by middle and low mountains, the central part contains a hilly basin and the southern part contains a coastal alluvial plain. We delineated the urban core area (735.66 km2 ) as our study area on Google Earth images (0.27 m resolution) using geographic information system software (ArcGIS version 10.4, ESRI) (Figure 1). This area is separated from the surrounding regions by rivers, woodlands, farmland and rural settlements.

**Figure 1.** Location of Guangzhou city and its land use/cover in the urban core area in 2019.

### *2.2. Mapping Land Use/Cover in 2019*

We first classified land use/cover into nine types: urban village plot, high-rise building plot (>28 m), farmland, conservatory, residual forest, tree orchard, water, greenspace with area larger than 1500 m2 and low-rise building (<28 m) plot. Then, using manual digitization techniques, we digitized the first eight land use/cover types from Google Earth images (0.27 m resolution) using the software of ArcGIS (version 10.4, ESRI). The area of the low-rise building plot was the total size of this study area minus the summed area of the other eight land cover types. Second, we grouped the above nine land use/cover types into six categories: greenspace, woodland, farmland, pavement (excavated soil thickness ≤ 20 cm, IS20), low-rise building (excavated soil thickness ≤ 100 cm, IS100) and high-rise building (excavated soil thickness ≥ 300 cm, IS300). According to codes for road and building foundation construction, consultation with engineers and our field investigation, we assigned the evacuation thickness of IS20, IS100 and IS300 as 0–20, 0–100 and 0–300 cm, respectively, and detailed information was presented in the Section 1 of the Supplementary Materials [30,38]. The area of green space was the difference between the total area of this study and the summed area of the other five land cover types. Tree orchard land was grouped with forestland. Conservatory land was classified as farmland. Buildings located in urban village plots and low-rise building plots were grouped as IS100. IS300 included buildings located in high-rise building plots and low-rise building plots or villas equipped with underground parking or other kinds of underground space development. To calculate the area covered by buildings, we multiplied the area of each type of building plot by its building density. The building densities of low-rise building plots, high–rise building plots and urban village plots were randomly sampled based on Google Earth images (0.27 m resolution) using the software of ArcGIS. To calculate the area of pavement, we first calculated the sum area of the impervious surface and bare soil and then subtracted

the area of the buildings and conservatory areas (according to Google Earth images, most of the bare soil plots are land parcels under construction, and they were digitized as high-rise building plots; impervious surface interpreted from Landsat images included conservatories that were grouped with farmland). We used the method developed by Fan et al. [39] to estimate the area of imperious surface (Figure S1) and bare soil from Landsat8 OLI images (30 m resolution) at the subpixel scale. The detailed framework for the land use/cover type interpretation was presented in Figure S1 and Table S1. The accuracy assessment of impervious surface map and land use/cover data was presented in the Section 2 of the Supplementary Materials [39–42].

### *2.3. Compiled SOC Densities for Pervious Surfaces*

By assuming that the 100–200 cm soil layer had a constant SOC density, we calculated the SOC density of the 100–200 cm layer according to the difference in SOC density between the 0–180 cm and 0–100 cm soil layers. The SOC density of woodland was an area-weighted value of the SOC density in residual forest and tree orchard soil. The SOC density of the 0–20 cm, 0–100 cm and 0–180 cm soil layers in farmland, woodland, orchard and greenspace were derived from Zhu [39,40].

For the SOC density of the 200–300 cm layers in woodland and farmland soil, we used the globally averaged SOC density of 200–300 cm soil in tropical evergreen forest and farmland [33] as a proxy, respectively. Urban land in this study area was mainly converted from farmland; therefore, the SOC density for greenspace in the 200–300 cm layer was assigned the value of the corresponding layer in the farmland soil.

### *2.4. Estimates of SOC Density for Impervious Surfaces*

Previous studies have suggested that there is no significant difference between SOC density at the equivalent depth and soil sampled under impervious surface and greenspace [21,23]. The pattern of vertical decline of the SOC density in imperviouscovered soil is similar to the trend of rural soils, and the disturbance of pavement installation to the subsoil layers may not be serious; therefore, the vertical distribution of the SOC density before soil sealing is largely maintained [19]. Due to the inaccessibility of soil sampling beneath impervious surface, especially soil under buildings, we estimated the SOC density of sealed soil according to the SOC density of the residual soil layer of the land use/cover before urbanization and the average depth of soil excavation for an impervious surface installation. We hypothesize that: (1) the mineralization rate of residual original soil layers was zero and that all excavated soil was removed from this study area; (2) for regional SOC loss estimation, soil excavation for the construction of buildings and underground facilities was limited to the areas where the buildings would be located. The specific assignment method was as follows.

The average excavation depth was included in the profile of impervious-covered soil; however, its SOC density was assigned as 0 kg/m2 . The SOC density of the soil layer beneath impervious surface was equal to the SOC density of the residual soil layer of the land use/cover before urbanization. The average soil excavation depths were 0–20 cm, 0–100 cm and 0–300 cm for IS20, IS100 and IS300, respectively. By subtracting the SOC0–20 density from the SOC0–100 density of the pre-urbanization land use/cover, we obtained the SOC0–100 density of IS20. By assuming that the SOC density between 100 cm and 200 cm soils was a constant value, we evaluated the SOC100–200 density for IS20 or IS100 according to the averaged SOC density of the 0–180 cm layer in the original land use/cover soil before urbanization. We assigned the SOC200–300 density of farmland soil from Jobbágy and Jackson [33] as the SOC200–300 density of IS20 and IS0–100.

Field investigations [19,21,23] and estimation based on the measured SOC density of interval soil layers [14,22] are the main methods by which to obtain the SOC density of deep soil under pavement. SOC density of building-capped soil was assigned as 0 kg/m2 [14,19] or the density of clean-fill soil [13,18]. For comparison with the other methods and their values (Tables S2 and S3 within which references [19,21,23,42–46] were cited). we sampled

soil (0–20 cm) beneath impervious surface from 11 new road reconstruction projects and clean-fill soil (a kind of backfill soil excavated from natural sedimentary soil layer, and into which organic waste, stone or white lime should not be mixed) from three road greening projects. We also collected SOC content data from granitic residual soil and oceaniccontinental sedimentary clay soil (the widely distributed parent material in Guangzhou), as well as SOC content data from the bottom soil layers of paddy soil and lateritic red soil (the widely distributed soils in Guangzhou). Based on these literature-derived SOC data and the code of soil compaction used for road construction (to evaluate the bulk density of soil under impervious surface), we calculated the SOC density of the deep-soil layer beneath buildings and pavement (Tables S2 and S3). Methods of soil sample and SOC density calculation for the sealed soil and clean-fill soil were present in the Section 3 of the Supplementary Materials [38,46–61]. The SOC density estimated according to the SOC density of the remaining soil layer in the original pervious soil profile was used to assess the total SOC pool and its loss due to the expansion of impervious surface, while the SOC densities calculated by the other methods were mainly used for comparison.

### *2.5. Estimate of SOC Storage Loss Caused by Impervious Surface Expansion*

Topsoil removal largely contributed to the depletion in SOC content under impervious surface [21,23,29]. We used the SOC storage in the removal soil layer as a representation of the carbon loss caused by impervious surface expansion since it is almost impossible to survey soil beneath buildings. SOC0–300 density changes and the total SOC0–300 pool loss were estimated to reveal the impact of impervious surface expansion at the residential and regional scale, respectively. For farmland urbanization, the loss of SOC0–300 storage at the residential scale was estimated by the area-weighted average SOC0–300 loss in pavement, building and greenspace. For impervious surface and green space, we assumed that the SOC density loss for each impervious surface was the difference between the SOC density of the original pervious surface before urbanization and the estimated SOC density of each surface type. It is very difficult to conduct a spatially explicit study on the conversion between each impervious surface and original pervious surfaces; therefore, we took the average SOC0–300 density of woodland (including tree orchard) and farmland as a proxy for the SOC density of original pervious surfaces (farmland, tree orchard and woodland were the main land use/cover types before urbanization in this region, and the differences in SOC0–180 density between these land covers were small [40]). For urban renewal plots, the loss of SOC storage was the difference of SOC0–300 density before and after urban renewal. The detailed method of this section is present in the Table S5 [62–64].

The rapid urbanization of Guangzhou city began around 1990; however, the earliest fine resolution images that we could get were from 2000. Therefore, we did not consider the specific time period of impervious surface expansion, but rather assessed the total SOC loss according to the current distributions and areas of these impervious surface types. To estimate the SOC pool loss caused by the expansion of each impervious surface, we multiplied the area of each impervious surface by its loss of SOC0–300 density. Summing the SOC pool losses caused by each impervious surface, we obtained the total SOC pool loss due to the expansion of impervious surface to date. A summary of the method and characteristics of the study area are shown in Figure 2.

**Figure 2.** The summary of the methodology and land use/cover characteristics of the study area. Numbers in the round brackets represent the coverage proportion of green space, building and pavement in each typical residential land parcel. The GS, PA, FS, UPL, BF and RS represent green space, pavement, fill soils, underground parking lots, building foundations and residual soils, respectively. IS20, IS100 and IS300 represent pavement with a soil evacuation thickness of around 20 cm, buildings with soil evacuation thickness ≤ 100 cm and buildings with evacuation thickness ≥ 300 cm, respectively. (1) and (2) represent renovating urban village plots into high-rise building plots I and Π, respectively; (3) and (4) represent renovating low-rise building plots into high-rise building plots I and Π, respectively. Areas of high-rise building land parcels, low-rise building land parcels and urban village plots in 2019 were 121.41, 364.97 and 89.53 km2 , respectively, and IS20, IS100 and IS300 totally covered 73% of the study area (Table S1).

### **3. Results**

### *3.1. Area Estimation by Land Cover in 2019*

IS20 had the largest area (35.54%), followed by IS100 (32.88%), greenspace (9.13%), woodland (8.02%), IS300 (4.96%) and farmland (3.39%) (Figure 1). The areas of urban village plots, low-rise building plots and high-rise building plots were 89.53, 364.97 and 121.41 km2 , respectively (Table S1). Large areas of urban roads were subsumed in the low-rise building plots. However, these roads were not considered in the building density sample, which may result in an overestimation of 16% of the low-rise building's area (Section 2 of the Supplementary Materials). We used the root mean square error (RMSE) to assess the accuracy of the impervious surface map, and the RMSE citywide was 0.20 (Section 2, Figure S1). The total area of IS20, IS100 and IS300 (539.87 km2 ) was much higher than the impervious surface area interpreted from Landsat 8 OLI images (Figures S1 and S2), because the bare soil areas were regarded as high-rise building plots under construction.

### *3.2. SOC Stock and Its Profile Distribution by Land Cover*

When the thickness of the excavated soil layer was included in the profile of imperviouscovered soil, the SOC density at the 0–300 cm layer of farmland, woodland, greenspace, IS20, IS100 and IS300 was 19.04 kg/m2 , 17.32 kg/m2 , 17.15 kg/m2 , 4.04 kg/m2 , 8.22 kg/m2 and 0 kg/m2 , respectively (Table 1). Relative to the top 100 cm, the percentage of SOC density in the 0–20 cm layer was 26%, 28% and 26% for farmland, woodland and greenspace, respectively; the proportion of SOC density in the 100–200 cm layer was 0.73%, 0.67%, and 0.75%, respectively (Figure 3). The SOC0–100 density and SOC100–200 density for IS20 accounted for 41% and 39% of SOC0–300 density, respectively. The SOC density of IS100 in the 100–200 cm layer was 67% of that in the 0–300 cm layer (Table 1).

**Figure 3.** Proportional distributions of SOC density by land cover.

The total SOC pool of 300 cm was 8.31 Tg (the thickness of the excavated soil layer was included in the profile of impervious-covered soil), and the largest pool by land cover was IS20 (3.67 Tg), followed by IS100 (1.99 Tg), greenspace (1.15 Tg), woodland (1.02 Tg), farmland (0.48 Tg) and IS300 (0 Tg) (Figure 4). By soil horizon, the contributions of the 0–20 cm, 20–100 cm, 100–200 cm and 200–300 cm layers to the total SOC pool were 4%, 30%, 44% and 22%, respectively (Figure 4, Table S4). Pervious surface carbon pools contributed 32% of the total SOC pool, of which 48% was stored in the 0–100 cm soil layer; impervious surface carbon pools contributed 69% of the total SOC pool, of which 49% was stored in the 100–200 cm layer (Table S4).

### *3.3. SOC Loss Caused by Soil Removal for the Installation of Impervious Surface*

All residential districts converted from farmland had high SOC0–300 loss (6.16–14.18 kg/m2 ), particularly the high-rise building plots for which underground parking lots covered the whole residential land (14.18 kg/m2 ) (Figure 5, Table S5). The SOC0–300 losses of renovating urban village into high-rise building plot were lower than that of renovating low-rise building plot. If underground parking lot covers 30% of the residential area, urban village renovation may even slightly increase SOC storage (0.85 kg/m2 ) (Figure 5, Table S5).

The total SOC loss caused by the expansion of impervious surface (539.87 km2 ) was 4.16 Tg (0–300 cm), of which the IS100, IS20 and IS300 contributed 58%, 26% and 16%, respectively (Table 2). The SOC density loss by impervious surface type was 18.18 kg/m2 , 9.96 kg/m2 and 4.14 kg/m2 for IS300, IS100 and IS20, respectively (Table 2).

Note: \* The area-weighted loss of SOC density due to the construction of the IS20, IS100 and IS300.

**Figure 4.** Profile distributions of SOC pools by land cover.

**Figure 5.** SOC storage (black columns) and SOC loss of farmland urbanization and urban renovation (white columns) at the residential scale; I and II represent the coverage fraction of underground parking lots (30% and 100%, respectively). The detailed estimation method is presented in Table S5.

### **4. Discussion**

### *4.1. Comparison Analysis of SOC Density for Pervious Surfaces*

Our results showed that the SOC0–20 density and SOC0–100 density in farmland were much lower than the global mean values of the corresponding soil layers [33]. The average proportion of SOC0–20 density to SOC0–100 density in farmland was 26% (Figure 3), which was close to the sampled value of 29% in the Ten-Thousand-Mu Orchard in the Haizhu District of Guangzhou city [65], but slightly lower than the citywide average value of paddy soils (33%) in Guangzhou [66]. All of the above-mentioned ratios in the farmland of Guangzhou were much lower than the global mean value of 40% [33], and farmland management practices may be the main reason for this difference. Compared with the utilization of chemical fertilizer, less organic fertilizer was applied in the farmland of Guangzhou, which was disadvantageous to the SOC accumulation, especially in the topsoil. Sulman et al. [67] reported that tilled soil had low organic carbon concentrations in the top mineral soil. In Guangzhou city, farmers rarely let their land lie fallow, instead tilling their farmland more frequently because of the farming system of the rice-rice-vegetable rotation or the rotation of various vegetables. Farmers plow paddy fields into deep furrows (to a depth of 20–30 cm covered with water) and then heap up soil into rows of broad ridges to grow vegetables on them. Therefore, soils layers deeper than 20 cm are often

flooded, which is beneficial to the accumulation of organic carbon in the deep soil. The SOC0–200 density in farmland (Table 1) was slightly higher than the global averaged value of 15.0 kg/m2 [33]. This may contribute to landform and soil parent material in this region. Buried soil layers in the alluvial plain of a river can increase the organic carbon storage of the deep soil layer [68,69]. Our study area is located in the lower reaches of the Pearl River Delta and near the South China Sea. Organic-rich, oceanic-continental sedimentary clay soils are widely distributed in this area. Furthermore, large areas of this clay soil are buried no deeper than 300 cm [70], and its average organic carbon concentration is as high as 2% [46].

The average SOC0–100 density of woodland was only 43% of the global average value [33]; the low stand age and anthropogenic disturbance may be the main reasons for these lower carbon stocks. Guo and Gifford [71] noted that the conversion of natural forests into plantations reduced soil carbon storage. Many forests in the study area are secondary forests or young forests planted in the middle of the 1980s. The average SOC density of the young, middle-aged and mature forests in the Pearl River Delta (where Guangzhou city is located) was only 6.67 kg/m2 , 8.41 kg/m2 and 10.26 kg/m2 , respectively [72], all of which are much lower than the values reported by Jobbágy and Jackson [33]. The SOC100–200 density in woodland (Table 1) was comparable to the average value in tropical evergreen forest soil [33], which may be due to the deeply buried organic-rich clay soil under the orchard land (tree orchards were grouped with woodland).

We assigned the SOC200–300 density of farmland reported by Jobbágy and Jackson [33] as a proxy to the SOC200–300 density of farmland soil in our study area. Turnover profiles, which were removed from the dataset of Jobbágy and Jackson, were widely distributed in our study area because of the organic-rich sedimentary clay soil that developed in the deep layer of paddy soil [43]; therefore, this proxy maybe an underestimated value.

The proportion of SOC0–20 density to SOC0–100 density in greenspace (Figure 3) was equivalent to the value of 0.25 in a greenspace in the Ten-Thousand-Mu Orchard in the Haizhu District of Guangzhou city [65]. Many topsoil layers in the greenspace originated from clean fill soil, plus the younger age of the vegetation and the carbon storage in the upper 20 cm soil was low. The proportion of SOC100–200 density to SOC0–100 density in the greenspace was as high as 0.75, which was slightly higher than the proportion in the woodland. Most of the greenspaces were converted from farmland. Perhaps because the high carbon pool of 100–200 cm in farmland had not been greatly disturbed during the conversion, the greenspace soil inherited the high carbon density at a depth of 100–200 cm (Table 1 and Figure 3). In addition, areas with thick organic-rich clay soil are usually developed as greenspaces rather than buildings because of their low bearing capacity.

### *4.2. Comparison Analysis of SOC Density of Impervious-Covered Soil*

For soils under pavement, the SOC0–100 density estimated from the residual original pervious surface was similar to the value calculated by the measured SOC density of 0–20 cm soil beneath pavement (Table S2 and Section 3). Our estimated SOC0–100 density in soils sealed by pavement was comparable to that in Urumqi, China [19] and in New York City, USA as reported by Raciti et al. [14]; however, it was much lower than that in Leicester, UK [23] and in New York City, USA as reported by Cambou et al. [21] (Table S2). Clay content and climatic differences may account for the carbon density differences among cities [23]. For clean-fill soil or constructional layers (mainly carbon-free materials) under pavement, SOC0–100 density estimation based on the SOC density of residual pervious surface may lead to a large overestimation. For instance, the SOC density estimated based on the measured organic carbon content in clean-fill soil (100 cm thick) in this study was 3.94 kg/m2 for artificially tampered soil and 3.30 kg/m2 for untampered soil (Table S2 and Section 3), which was similar to the estimation by Pouyat et al. in USA [18]. The sampled SOC density of filled soil under pavement of the Seoul residential area in South Korea was as low as 2.7 kg/m2 [42], and the sampled value for constructional layers (mainly

gravel, crushed rock and sand) below the road surface in Lahti, Finland, was as low as 1.20 kg/m2 [22].

For the SOC density of the 0–100 cm soil layer under buildings, Pouyat et al. [18] took the estimated SOC density of clean fill soil as a proxy, which was equivalent to the value calculated in this study (Table S2). If we are using the SOC density of the residual original soil layer before urbanization as a proxy, assuming that the decomposition rate of the residual soil was zero, the estimated values of 5.52 kg/m2 (an estimation based on the SOC density of the residual original soil layer) and 5.43 kg/m2 (an estimation based on the averaged SOC content of the bottom soil in original pervious soil layer) were both higher than the SOC density of the clean fill soil (Table S2 and Section 3). Fill soil mainly comes from excavated deep soil, which has low organic matter content; therefore, the SOC density of clean fill soil may be used as an underestimated proxy for the 0–100 cm soil under IS100, especially for buildings without a clean fill soil layer.

For the SOC200–300 density of pavement and IS100 covered soil, we took the SOC200–300 density (Table 1) in farmland as a proxy, which was close to the estimated SOC density of the granitic residual soil (100 cm thickness) (Table S2). The proxy coincided with the measured SOC density of the 0–100 cm fill soil under impervious surface in a Seoul residential area in South Korea [42] (Table S2). Weathered granitic residual soils excavated during the construction of building foundations is commonly used as a kind of high-quality fill soil. This coincidence may support the rationality of the above-mentioned proxy, since most fill soil came from excavated deep soil layers. However, if the organic-rich, oceanic-continental sedimentary clay soil (Table S2) developed beneath pavement and IS100, this proxy may underestimate the SOC200–300 density of pavement and IS100 covered soil.

Topsoil removal, decomposition and leaching of the residual soil layers beneath impervious surface may attribute to the carbon loss from sealed soil [14]. A controlled field study reported that the removal of 0–10 cm soil contributed 57% to the total carbon loss (0–30 cm) [29]. We assumed that the SOC mineralization of the residual soil was zero, which would underestimate the carbon loss. However, SOC decomposition in imperviouscovered areas will be slowed because of anoxic conditions [20,29]. The SOC turnover rate varies with the type, degree and extent of sealing [20,23,29]. Evidence has shown that the SOC density decreases from the edge to center in imperviously covered areas [19]. For small patches of impervious surface surrounded by greenspace, soil could still be colonized by vegetation roots and retain the organic carbon turnover rate to some extent, while organic carbon could be more stable in areas with impervious surface largely distributed [20,23]. Large areas of impervious surface continuously distributed in the study area, and the soil was excavated deeper than the organic-rich topsoil for the construction of building foundations. Therefore, the carbon mineralization in residual soil may contribute less to the carbon loss. Thus, the underestimation of carbon loss due to the above-mentioned assumption may be small.

### *4.3. Impacts of Impervious Surface Expansion on SOC Storage*

Our result showed that there was a large amount of organic carbon in imperviouscovered areas. SOC stored in sealed soil in the 0–300 cm layer and 100–300 cm layer in our study area was equivalent to 9% and 6% of the 0–100 cm SOC pool in Guangzhou city [66], respectively. If SOC density in building-covered soil were assigned 0 kg/m2 (Table S6, scenario 3), the total SOC pool in the 0–300 cm soil layer would decrease 34% (Table S6, scenario 1) or 42% (Table S6, scenario 2), and the loss of the SOC pool due to impervious surface expansion would increase by 48% or 82%, respectively (Table S6). Therefore, ignoring the SOC pool under IS100 will greatly underestimate the urban carbon pool and then greatly overestimate the negative impact of impervious surface expansion on urban soil storage.

At the residential scale, carbon loss per unit area varied with the coverage of greenspace and pavement, building density and development intensity of underground space. Foundation-excavation-induced SOC loss of IS300 was 1.8 times that of IS100 (Table 2);

however, at the residential scale, if the intensity of underground space development is limited within a reasonable range, the SOC loss of IS300 plots can by roughly equivalent to that of IS100 plots (Figure 5, Table S5). Underground parking lots in many high-rise residential districts had extended to the underground spaces covered by greenspace and pavement. According to the urban construction code of the Planting Soil for Greening (CJ/T 340-2011) [64], the thickness of an effective soil layer for tree planting should not be less than 100 cm. Appropriately raising this thickness criterion may increase SOC stock in greenspace. Compared with farmland urbanization, renovating urban village or low-rise building plots into high-rise building plots has potential to substantially reduce SOC loss (Figure 5, Table S5). Adding the increment of vegetation carbon due to the increased greenspace coverage, urban renewal will be more profitable to urban carbon management. Analysis of the total organic carbon loss per unit area in vegetation and soil at the residential scale can further our understanding.

In areas with deep soil layers and intensive utilization of underground space, studies at conventional depths of 0–20 cm or 0–100 cm cannot fully reveal the negative impact of the vertical expansion of impervious surface on SOC storage. The SOC pool estimated in the 0–100 cm soil layer was 73% (scenario 1) or 54% (scenario 2) lower than that in the 0–300 cm soil layer, and the SOC loss of the 0–100 cm layer was 23% (scenario 1) or 47% (scenario 2) lower than that in the 0–300 cm soil layer (Table S6). We used SOC storage in the excavated soil layer as a representation of carbon loss caused by the expansion of impervious surface. However, these removed SOC cannot simply be considered as a carbon source because they were not necessarily mineralized rapidly [29].

The difficulty of sealed soil sampling, the higher heterogeneity and the lack of standardized methods for urban soil sampling are the main barriers to a robust estimation of the urban soil pool and its response to urbanization [21]. If the thickness of the excavated soil layer is included in the soil profile of the building-covered soil (0 kg/m2 is assigned as the SOC density of the excavated soil layer), and the SOC density of the residual pervious soil layer is taken as a proxy for soil under buildings, the great difficulty of deep soil sampling conducted beneath buildings could be avoided. Indeed, soil surveys are the basic method to accurately assess the urban soil carbon pool; however, the method presented here may be used as a last resort.

### *4.4. Uncertainties of SOC Pool Evaluation*

Buried cultural layers may greatly contribute to the urban deep soil carbon pool [42,73]. We neglected carbon pools stored in the buried A horizon and buried sediment from ponds, which underestimated the regional carbon pool. The possibility of buried A horizons in greenspaces and paved ground is relatively high. If there are buried A horizons under all the greenspaces and pavement in the study area, and by assigning the SOC density of the 0–20 cm layer in farmland (the main land use before urbanization) as a proxy for these horizons, then the total SOC pool of these buried layers is 0.82 Tg. Rapid urbanization in Guangzhou city began in the 1990s. From 1990 to 2018, a total of 7.74 km2 of water area (mainly fish ponds and streams) was converted into construction land, according to the dataset provided by the Data Center for Resources and Environmental Sciences, Chinese Academy of Sciences [74]. The SOC density of this sludge in the 0–100 cm layer can reach 19.64 kg/m2 [65]. If we assume that all these converted water areas contain a buried sludge layer (100 cm depth with a SOC density of 19.64 kg/m2 ), then the SOC pool of this buried sediment is 0.15 Tg. Taken together, the underestimation of SOC stored in buried soil layers may not be larger than 12% of the total carbon pool.

We ignored the SOC losses in deep soil under pavement and greenspace. If we assume that all of the 100–300 cm soil layers under greenspaces and pavement in high-rise building plots have also been removed during the construction of underground parking lots, then the SOC pool loss of IS300 will increase by 0.75 Tg (the SOC density of greenspace and pavement at the 0–100 cm layer is derived from Table 1), which is 9% and 18% of the total SOC pool and SOC pool loss, respectively. In addition, a small number of low-rise building plots also have small underground parking lots. The construction of subways and underground shopping malls also results in a larger amount of soil excavation. However, carbon loss due to these kinds of underground space developments is difficult to estimate due to the inaccessibility of data on the volume of the soil cuts and fills.

### **5. Conclusions**

We estimated the total SOC pool and its response to impervious surface expansion in the 0–300 cm layer in the core area of Guangzhou city. Our results suggest that there was a large amount of SOC in low-rise building- and pavement-covered soils. Deep soil excavation during the vertical expansion of impervious surface can seriously disturb soil carbon sequestration, and the conventional 0–100 cm soil profile cannot fully reveal the negative effect of this vertical expansion on the regional soil carbon storage. For high-rise residential areas, the gains of organic carbon accumulation in more greenspace coverage may be offset by the loss in deep soil excavation for the construction of underground parking lots. However, compared with farmland urbanization, renovating urban village or low-greenspace-coverage low-rise building plots into high-rise building plots can generate lower SOC0–300 loss. Large areas of deep soil excavation and sealing could greatly disturb the capacity of soil to provide supporting, provisioning, regulatory and cultural services. Possible disposals of these excavated soils include discarding, dumping into designated landfill sites and using as backfill soil to foundation ditch, greenspace, ponds, etc. For regional sustainable development, it is necessary for policy-makers to manage the intensity of underground space development and to promote urban greening. It is also very necessary to investigate the disposal method of excavated soil and its corresponding effects on SOC so as to find the proper solution to meet both the requirements of urban construction and soil protection.

There are some uncertainties in SOC estimations because they are based on data compiled from the literature and a series of assumptions. For the inaccessibility of fine resolution images circa 1990 and the impossibility of deep soil sampling in impervious surface area, we used the statistical summary method to evaluate the possible excavationinduced SOC losses caused by existing impervious surface. Soil profiles formed during the construction of building foundation or underground parking lots can be used to investigate the SOC content in deep soil. Models driven by space-time explicit land cover map (fine resolution) and other natural or human-mediated covariates (such as sediment characteristics, disposal method of excavated soil and its corresponding SOC mineralization characteristics, evacuation thickness, etc.) can provide more useful information for urban construction and soil protection at site at the regional scale.

**Supplementary Materials:** The following are available online at https://www.mdpi.com/article/ 10.3390/su13147901/s1, Section 1 The basis of assuming evacuation thickness as IS20, IS100 and IS300, Section 2 Accuracy assessment of impervious surface map and land use/cover data, Section 3 Calculation method of SOC density in clean fill soil and sealed soil, Figure S1: Framework for land use/cover type interpretation based on Google Earth and Landsat 8 OLI images, Figure S2: Impervious surface fraction of the urban core area of Guangzhou city in 2018, Table S1: Land use/cover classification and its area, Table S2: Comparison of SOC densities and their calculation methods by impervious surface type, Table S3: Parameters for SOC density calculation for imperviouscovered soil, Table S4: Profiles of SOC pool distribution by land cover in the urban core area of Guangzhou city, Table S5: SOC storage and SOC losses due to farmland urbanization & urban renovation at the residential scale (0–300 cm), Table S6: Losses of SOC stock due to the imperious surface expansion under different scenarios.

**Author Contributions:** Conceptualization, J.D. and J.Y.; methodology, J.D. and M.Y.; software, J.D. and M.Y.; validation, J.Y. and M.Y.; investigation, J.D. and M.Y.; writing—original draft preparation, J.D.; writing—review and editing, J.Y. and M.Y.; funding acquisition, J.Y. and M.Y. All authors have read and agreed to the published version of the manuscript.

**Funding:** This study was supported by the National Science Fund for Distinguished Young Scholars (41825020), the China Postdoctoral Science Foundation (2020M672863) and Guangdong Basic and Applied Research Foundation (2021A1515012147).

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

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** The data that support the findings of this study are available from the corresponding author upon request.

**Acknowledgments:** We thank H. Li for her help with laboratory soil carbon measurements, Z. Wu for his help in the impervious surface extraction and S. Kong, W. Liang and Z. Liang for their help with soil sample.

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

### **References**


**4. Various treatment modalities and tumour down-staging**

According to Wright et al [10], marked difference in pathologic response based on the induction therapy is favouring CT/RT. Surgical resection of Pancoast tumours after neoadju‐ vant high-dose CT/RT was carried out in 40.5% of patients according to Kwong et al [18].

Pathological downstaging although it does not correlate with the radiological appearance [16] is reported to be impressively above 30% in various series. As per Pourel et al [15], pathological complete response was observed in 39.5% of the patients, necrosis of tumoural tissues between 50% and 95% in 22.5% and less than 50% in 38% of the patients. Along the same lines JCOG reported [17] pathologic downstaging of the tumour in 40% of the patients; No residual viable tumour cells in the resected specimens, was achieved in 16% of the treated patients. Finally SWOG [16] summarised that pathologic no residual microscopic tumour was seen in one third of the resected specimens and minimal microscopic residual (few scattered tumour foci within a mostly necrotic or fibrotic mass), was observed in one third of the resected specimens.


*2.1. Mathematical Model Structure*

Locations of raw material trajectories, shape of the burden profile, and ratio of ore to coke on the top of the blast furnace are gained by calculating the charging process. This is important for predicting the reducing gas distribution and chemical reactions between layers and the gas in the shaft of the blast furnace. Therefore, combining the mathematical model of the charging with the experience of the production and radar data, the operation of the blast furnace can be optimized to become more stable and efficient.

From the raw material in the hopper to the formation of burden layers in the throat of the furnace, the charging process is decomposed into the trajectory of the burden flow, burden profile, burden layer structure, and burden distribution evaluation. The four steps are calculated by four models: Burden flow trajectory model, burden profile model, burden distribution model, and burden evaluation model, and are shown in Figure 1.

**Figure 1.** Mathematical model structure.

In Figure 1, after the calculation of the material movement by Newton's second law, the velocity of the burden at the hopper exit, velocity of the burden into the chute, and velocity of the burden leaving the chute tip are obtained successively. Then, based on the inclination angle and rotation speed of the chute, the trajectories of the raw material after leaving the chute tip are calculated. From the trajectories and burden profile model, the coordinates of a new burden profile can be calculated based on the original one. Then, the descent speed of the burden is used to modify the new burden profile and the modified one is saved in the database. In the next calculation, the modified one is considered as an original profile and the procedure is repeated. Finally, the ratio of ore to coke in the last two profiles is obtained and is used as a criterion of evaluation.


**4. Discussion**

#### *4.1. Mechanism of Polycyclic Compounds Fillers Inhibiting Electrical Treeing Growth*

The growth process of the electrical tree with the DC-impulse voltage is closely related to the hot electrons motion and trap distribution [40]. After the addition of the polycyclic compound, the trap depth and density of samples increase significantly. The traps enhance the charge trapping ability of the needle tip and easily forms the same polarity charge accumulation to weaken the external electric field, resulting in a decrease in the injected charge amount [41]. At the same time, with the deep trap level and the trap density increasing, the internal charge trapping of the sample enhances, resulting in a decrease in the internal free charge of the sample. For the growth of the electrical tree, the charge injection process is suppressed and the free charge inside the sample is reduced. Therefore, the molecular chain breaking process is suppressed, and the electrical tree deterioration resistance of the XLPE/ polycyclic compounds composites is improved. In addition, in the process of electrical tree growth, some of the electrical tree channels are carbonized, and the carbonization channel delivers charges as a conductive current to the end of the channel, enhancing the electric field strength [42]. The deeper trap captures the transport charges in the carbonized electrical tree channels, reducing the electric field strength at the end of the channel, thereby decreasing the growth of the electrical tree [29]. The carrier migration process is closely related to the trap distribution of the composite material. The free charge may be captured by the local state (trap) during the extended state migration process. The charge transfer and exchange process between the extended state and the local state affects the migration characteristics of carriers [41]. After the addition of the polycyclic compound, the carrier mobility is reduced, and the carrier mobility of XLPE-A is the smallest. The deep trap energy level increases, leading to the increase of trap barrier to overcome for charge trapping. The deep trap density increases, leading to a decrease of the charge average free travel and energy obtained from migration, increasing the probability of charge trapping, and leading to a decrease in mobility. Therefore, the internal collision ionization probability of the composite material is reduced, leading to the improvement in the resistance to electrical tree.

#### *4.2. Electrical Tree Dependence on Polycyclic Compounds with DC-Impulse Voltage*

It can be seen from the results in Section 3.1 that the e ffect of the polycyclic compound is related to the polarity of the DC-impulse voltage, and the suppression e ffect of the polycyclic compound with the opposite polarity is better than with the same polarity. When −35 kV impulse is superimposed on −25 kV DC voltage, after the DC voltage is applied, electrons are accumulated at the needle tip. After the impulse voltage is applied, a large number of electrons are injected into polymer over a short period of time to push accumulated charges to move. During the process of charge trapping, as the state of charge changes from high energy to low energy state, excess energy is transferred to other charges, making them hot electrons, destroying the XLPE molecular chain, and causing growth of the electrical tree. When +35 kV impulse is superimposed on −25 kV DC voltage, and after the polarity of the voltage changes, part of the positive charge is injected and the positive and negative charges neutralize to generate energy, which accelerates the growth of the electrical tree [43]. In addition, as the polarity of the voltage changes from negative to positive, the electrons change from the same-polar space charge to the hetero-polar space charge, causing electric field distortion, aggravating partial discharge, causing molecular chain breakage, and accelerating electrical tree growth [44]. The mechanism of electrical tree growth with di fferent polarities is di fferent, which results in di fferent e ffects of polycyclic compounds with di fferent polarities. With the opposite DC-impulse polarity voltage, and after the polycyclic compound is added, on the one hand, the electric field distortion caused by the polarity change is reduced due to the decrease of the injected charges; on the other hand, since free charges inside the sample are reduced, the energy generated by the positive and negative charges is reduced. Therefore, the polycyclic compound e ffect with the opposite polarity is better.

#### *4.3. Electrical Tree Dependence on Polycyclic Compounds at Di*ff*erent Temperatures*

Electrical treeing is an electro-thermal aging phenomenon, which is a comprehensive process including charge motion, partial discharge, local high pressure, and local high temperature. The hot electrons accelerate in the free volume, which impacts the molecular chain of the polymer and accelerates the formation of low-density regions. Charges collide with ionization in low-density regions, releasing energy to destroy more molecular chains and forming micropores. Subsequent discharges then occur in the micropores. Partial discharge produces local temperature rise in a short time. When the ambient temperature plus local temperature rise is greater than the local softening temperature of the insulating material ( Δ *T ambient temperature* + Δ *T discharge temperature*<sup>&</sup>gt; *T softening temperature*), local thermal breakdown will occur, and the air gaps form cracks along the applied field direction [45]. Under the action of charged particles, the XLPE molecular chains are broken rapidly and proceed to decompose and gasify. With these gases generated in a short time, the gas pressure in the micropores and cracks increases rapidly, resulting in the material around the micropores or cracks to undergo an expansion stress. Under the action of this stress, the micropores and cracks rapidly expand toward the amorphous region where the mechanical strength is weak, resulting in the growth of the electrical tree. As the temperature increases, on the one hand, the carrier mobility increases, the ionization probability increases, and the polycyclic compound's ability to capture hot electrons is relatively reduced; on the other hand, the local thermal breakdown increases, and the influence of hot electrons on the growth characteristics of the electrical tree is relatively reduced. Therefore, the e ffect of the polycyclic compound decreases with temperature increase.


*Article* **The Mediating Effect of Cytokines on the Association between Fungal Sensitization and Poor Clinical Outcome in Asthma**

**Ching-Hsiung Lin 1,2,3,4,\*, Yi-Rong Li 5, Chew-Teng Kor 6,7, Sheng-Hao Lin 1,4,5, Bin-Chuan Ji 1, Ming-Tai Lin 1 and Woei-Horng Chai 1**

**Abstract:** Sensitization to fungal allergens is one of the proposed phenotypes in asthma. An association between fungal sensitization and worse clinical outcomes is apparent. Moreover, fungal sensitization in asthma that is associated with different type of immunological mechanism has been reported. How the role of cytokines mediates the association between fungal sensitization and poorer asthmatic outcomes remains unclear. We aimed to determine role of cytokines in the relationship between fungal sensitization and worse clinical outcomes in asthma. Method: We conducted a prospective study to recruit adult patients with asthma. Data including age, sex, height, weight, smoking history, medication, emergency visit and admission, pulmonary function testing result, and Asthma Control Test (ACT) scores were collected. We used the automated BioIC method to measure fungal allergen sIgE in sera. Serum levels of Interleukin (IL) -4, IL-13, IL-6, IL-9, IL-10, IL-17 A, IL-22, Interferon (IFN) -γ, Immunoglobulin E (IgE), Tumor necrosis factor-α (TNF-α), and Transforming growth factor-β (TGF-β) were measured using ELISA. Result: IL-6 and IL-17A had a significant positive correlation between sensitization and most fungi species compared to IgE. Sensitization to *Candida albicans* had strongly positive association both with IL-6 and IL-17A. However, only IL-17A had significant relationship with ED visit times. The mediation analysis result indicates that IL-17A had a significant positively mediating effect (ME) on the association between *Candida albicans* and ED visit times. Conclusion: IL-17A is a potential mediator to link *Candida albicans* sensitization and ED visits for asthma. We suggest that patients with fungal sensitization, such as *Candida albicans*, have poorer outcomes associated with Th17-mediated immune response rather than Th2.

**Keywords:** asthma; fungal sensitization; IL-17A; *Candida albicans*; mediation effect


*3.6.4 Pulmonary drug delivery*

The treatment of lower respiratory infections is difficult due to the presence of microbes deep in the respiratory system. Nanoparticles as a targeted drug delivery system to the site of infection have been actively applied to enhance antimicrobial drug resistance, sustain drug release, and increase bioavailability. Importantly, nanoparticle systems that contain multiple antimicrobial substances can be used [51, 52].


**1. Introduction**

The onboard antennas of the returned spacecraft are subjected to intensive aerodynamic heating when the spacecraft passes through the dense layers of the atmosphere [1]. In these conditions, radio-transparent heat-resistant thermal protection is used to protect the antennas from external influences. The open ends of the transmission lines are used as the emitter to obtain a wide directional pattern. The most offer used radiation from the open end of the round waveguide. In the first approximation, we consider a flat uniform thermal protection. Under the conditions of aerodynamic heating, the electrical parameters of thermal protection significantly change (relative permittivity ε and tangent of the dielectric loss angle tgδ). These changes lead to a noticeable increase in absorption losses in the heat shield, reflection from its boundaries, as well as to the appearance of surface and

side waves. Together, all this leads to a change in the directional pattern and a decrease in the efficiency of the onboard antenna.

Evaluation of these changes is absolutely necessary to determine the radio technical characteristics of the onboard radio equipment. The paper solves the problem of determining the characteristics of the radiation of a circular waveguide through a heat shield subjected to aerodynamic heating.

that is, in fact, the slow-moving processes of heating of the dielectric thermal protection are considered. This approach and methods of solution were used in a

*Characteristics of Radiation of a round Waveguide through a Flat Homogeneous Heat Shield*

The problem can be formulated as a boundary with respect to the tangent magnetic field in the material, and with respect to the tangent electric field. The second method is more convenient because of the simple form of boundary condi-

The magnetic component of the electromagnetic field *Hy* at z ≥ 0 must satisfy

*∂*2 *Hy ∂z*2 þ *k*2

We apply the Fourier transform for x and y coordinates to equation (Eq. (1)).

*x* � *k*2 *y*

*Hy*ð Þ *x*, *y*, 0 exp �*j kxx* þ *kyy* � � � � *dxdy:*

Solution of equation (Eq. (2)) satisfying the radiation conditions (for z ≥*d*) has the form for area 1, that is, the area occupied by the dielectric plate (0 < z < d),

> *z* � � þ *L* exp *jkz*1

*y* ¼ *M* exp �*jkzz* � �,

Reasoning in a similar way, with respect to the tangent magnetic component of

*z* � � þ *B* exp *jkz*1

*x* ¼ *C*exp �*jkzz* � �*:*

Satisfying the equations arising from the Maxwell equation, we obtain expres-

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi *k*2 � *k*2

*x* � *k*2 *y*

*:*

� �*H*^ *y* ¼ 0, (2)

*z* � �*:*

*z* � �,

where *ε*= *ε*1 for 0 ≤ z ≤ d, *ε* = 1 for z > d, and k is the wave number.

*ε* � *k*2

*εHy* ¼ 0, (1)

the following wave equation in a Cartesian coordinate system x, y, z:

*∂*2 *Hy ∂y*2 þ

*∂*2 *Hy ∂x*2 þ

*∂*2 *H*^ *y ∂z*2 þ *k*2

ð ð∞

�∞

*H*^ ð Þ1

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi

*H*^ ð Þ1

sions for the spectral components of the electric field

*x* � *k*2 *y*

*k*2 ε � *k*2

q

*y* ¼ *D* exp �*jkz*1

*H*^ ð Þ2

; *kz* ¼

*x* ¼ *A* exp �*jkz*1

*H*^ ð Þ2

the field, we obtain the following equations for the spectral component:

q

For region 2, that is, the region behind the plate (z ≥ d), we get

where is the direct Fourier transform,

*H*^ *y* ¼

number of works, for example [7].

*DOI: http://dx.doi.org/10.5772/intechopen.92036*

**2. Main part**

tions for z = 0 [8].

where *kz*1 ¼

**169**

We get

The problems of calculating the interaction of the onboard antenna with a heatshielding dielectric insert are very difficult and poorly developed. The development of mathematical models of antenna windows is reduced to solving an external problem of electrodynamics—electromagnetic excitation of bodies or diffraction of radio waves. At the same time, we will use well-known analytical methods of solution. The radio technical characteristics of the antenna window, for which we obtain an analytical description, include a radiation pattern, radiation conductivity, antenna temperature, and a number of other characteristics that describe more subtle electrodynamic effects, as well as energy characteristics.

In theoretical terms, the electrodynamic problem in general can be formulated as follows. There is a radiating, open antenna a, located on an infinite screen, in front of which is a dielectric layer of thickness d with a complex permittivity-bridge εα(x, y, z, t) (see **Figure 1**).

In this general formulation, the solution of the electrodynamic problem is associated with significant mathematical difficulties, the main problem being the need to solve the Maxwell equations for an arbitrary law of change in the parameters of media in space and time. With some simplifying assumptions, the problem was solved in the ray approximation. In [2], the wave front method is used to analyze the radiation diagram of an antenna covered by a dielectric layer. In [3–5], the method proposed in [6] is used to find the radiation diagram, according to which the antenna radiation diagram in the presence of an infinite flat dielectric layer is simply multiplied by the diagram in the free space by the flat wave transmission coefficient for the flat layer, taking into account the corresponding angle of arrival and the plane of polarization of the wave.

At their core, all these methods are close to each other and are essentially based on the approximation of geometric optics, which is true in the quasi-optical domain. In relation to the problem under consideration (the resonance region), a strict solution of the Maxwell equations is required. From analytical methods of the solution, it is possible to apply the method of integral transformations and the method of eigenfunctions. Both of these methods will be used in the future. In this case, we assume that the parameters of thermal protection do not depend on time,

*Characteristics of Radiation of a round Waveguide through a Flat Homogeneous Heat Shield DOI: http://dx.doi.org/10.5772/intechopen.92036*

that is, in fact, the slow-moving processes of heating of the dielectric thermal protection are considered. This approach and methods of solution were used in a number of works, for example [7].


**6. Regulatory considerations**

(mutTGFβ3) was generated by ablating its binding site for the latency‐associated TGFβ‐ binding protein (LTBP‐1) [78]. A localized intradermal transduction using a lentiviral vector expressing the mutTGFβ3 in a mouse skin wounding model was demonstrated to reduce reepithelialization density and fibroblast/myofibroblast trans‐differentiation within the wound area. Both of which reduced scar tissue formation (**Figure 2**). Using a noninvasive imaging system, the kinetics of luciferase gene expression was studied when delivered in an adenoviral vector (replication‐deficient adenovirus, Ad5). A peak of gene expression occurred at 7 days after delivery [79]. The esophageal cancer‐related gene‐4 (Ecrg4) delivering a viral‐ mediated gene was evaluated in a cutaneous wound healing model [80]. Both Ecrg4 mRNA and its protein product were localized to the epidermis, dermis, and hair follicles of healthy

Gene delivery using adenoviral vectors in tissue regeneration is hindered by a short duration of transgene expression. A fibrin scaffold was used to enhance delivery of the adenovirus to a wound site, precluding the need for high repeated doses [81]. An anti‐fibrotic interfering RNA (RNAi) delivery system using exogenous microRNA (miR)‐29B was proposed to modulate ECM remodeling following cutaneous injury. A collagen scaffold was used as the carrier of (miR)‐29B. The mRNA expressions of collagen type I and collagen type III were reduced up to 2 weeks after fibroblasts culture. *In vivo* evaluation in full‐thickness wounds treated with miR‐29B delivery revealed that collagen type III/I ratio and matrix metalloproteinase (MMP)‐ 8 to TIMP‐1 ratio were improved [82]. Porous (100 and 60 μm) and nonporous (n‐pore) hyaluronic acid‐MMP hydrogels with encapsulated reporter (pGFPluc) or proangiogenic (pVEGF) plasmids are used as a scaffold‐mediated gene delivery [83]. Alginate‐DNA gels were used to treat diabetic wounds, which provided sustained release of bioactive factors, such as neuropeptides and VEGF [13]. Silver nanoparticles (AgNPs) can be further augmented for gene delivery applications. The biofunctionalized stable AgNPs with good DNA‐binding ability for efficient transfection and minimal toxicity were developed [84]. Polyethylene glycol (PEG)‐ stabilized chitosan‐g‐polyacrylamide was used to modify AgNPs. To enhance the efficiency of gene transfection, the Arg‐Gly‐Asp‐Ser (RGDS) peptide was immobilized on the surface of AgNPs. The transfection efficiency of AgNPs increased significantly after immobilization of the RGDS peptide reaching up to 42 ± 4% and 30 ± 3% in HeLa and A549 cells, respectively. The transfection efficiency was significantly higher than 34 ± 3% and 23 ± 2%, respectively, with the

For treating diabetic patients with a threat of limb amputations, genes of various growth factors have been proposed in delivery systems. A simple nonviral gene delivery using minicircle plasmid DNA encoding VEGF was combined with an arginine‐grafted cationic dendrimer PAM‐RG4 [85]. Mouse ASCs were transfected with DNA plasmid encoding VEGF or green fluorescent protein (GFP) using biodegradable poly (β‐amino) esters (PBAE). Cells transfected with Lipofectamine™ 2000, a commercially available transfection reagent, were included as controls. ASCs transfected using PBAEs showed an enhanced transfection efficiency and 12– 15‐folds higher VEGF production compared with the controls (\**P* < 0.05) [86]. Keratinocyte

mouse skin.

**5.2. Nonviral vectors in gene delivery**

84 Wound Healing - New insights into Ancient Challenges

use of polyethylenimine (PEI, 25 kDa).

The major concerns of commercialization of drug/protein/cell/gene delivery wound dressings are the complicated registration process, specifically regulatory approval, protocol consider‐ ation, and clinical trial process. Among all the parameters of delivery wound dressings, the type and source of the materials (e.g., human and animal origin) are critical to the regulatory approval process. A product composed of two or more regulated components, that is, drug/ device, biologic/device, drug/biologic, or drug/device/biologic, that are physically, chemically, or otherwise combined or mixed and produced as a single entity is defined as a combination product [90]. The FDA (Food and Drug Administration, United States) regulation of a combination product (e.g., delivery system for wound healing) is mainly determined by the component with the primary mode of action. According to the classification of the product, the clinical trials (for premarket approval, PMA) must provide valid scientific evidence of safety and efficacy to support the indicated use of the wound healing delivery systems. Generally, preclinical studies contain toxicity studies and animal model evaluations. Delivery systems of drugs, bioactive proteins, cells, and genes in wound healing and nanomedicine should test their biocompatibility according to ISO 10993, including dermal irritation, dermal sensitization, cytotoxicity, acute systemic toxicity, hemocompatibility/hemolysis, pyrogenicity, mutagenicity studies, subchronic toxicity, chronic toxicity, and immunogenic potential [91]. Good clinical practices (GCPs) are the standards for designing, conducting, recording, and reporting clinical trials required for Class III medical devices.

For example, autologous stem cells are under clinical trial and are effective in ulcer healing and angiogenesis. However, translating delivery of stem cell application in *in vitro* and *in vivo* experiments from animal models to human clinical trials is still in its infancy. Preclinical studies suggest that cell delivery systems represent an effective and safe therapeutic strategy in the treatment of nonhealing wounds. More clinical studies on human subjects, including better data management of the patients and long‐term follow‐up of the patients' conditions, are necessary. Improved stem cell delivery vehicles in large‐scale human clinical trials may be promising for diabetics with foot ulcers. There are no serious complications or side effects, but its therapeutic mechanisms, effects, and standardization still require further research [92]. While delivery system‐based products offer increasingly important strategies for managing complex wounds, potential drawbacks include the risks of infectious agent transfer and immunological rejection. The manufacturing process, transport, and storage of delivery systems in wound healing are major cost implications; thus, their current clinical use remains limited [93]. Many current clinical trials are placing a high emphasis on addressing safety issues in all stem cell therapies, including stem cell delivery in wound healing [94]. The serious adverse effects of stem cell delivery are mainly immune response and tumorigenic potential. Delivery systems used in cell therapy encompass four main approaches, which are systemic administration, injection, topical, and local deliveries. Localized delivery of cells in wound healing is an optimal delivery approach for wound treatments [95]. Nonimmunogenic, nontoxic, biodegradable, and biocompatible biomaterials have been developed as carriers of stem cells that can protect cells and improve wound healing. However, clinical use of stem cells, for example, allogeneic EPCs, is currently inhibited by the risk of immunogenicity and tumorigenicity. To modulate the immune response, mesenchymal stromal cells or umbilical cord blood is already used in clinical trials, but definitive results are still pending. MSCs are known to be hypoimmunogenic [96]. Current challenges are standardized and quality‐ controlled cell therapy, the differentiation of MSCs to unwanted tissue, and potential tumori‐ genicity [94]. MSCs have been applied clinically for the treatment of diabetic wounds. Long *in vitro* expansion time and multiple handling procedures are barriers for its clinical application and increase the chances of infection [97]. Autologous induced pluripotent stem cells are nonimmunogenic and can be a promising cell source used in wound healing [98]. By compar‐ ison, clinical use of allogeneic cells is more complex and requires additional regulatory, legal, and safety hurdles to be overcome [99]. All things considered, the future prospects for the utilization of both autologous and allogeneic cells in cell delivery systems are bright. In the United States, there are three regulatory processes for the registration of wound healing delivery systems [100]. Only wound dressing with lower complexity and risk that is substan‐ tially equivalent to a marketed "predicate" device may be cleared through the 510(k) premarket notification process. In another words, those types of wound dressings are classified as Class I medical device. Clinical data are typically not needed for 510(k) clearance of Class II medical devices. Higher‐risk Class III medical devices typically require premarket approval (PMA). In summary, the regulatory processes are depending on multiple factors including the device's classification, the availability of a substantially equivalent predicate, and the level of risk. Before commercialization, investigational devices maybe clinically investigated within the USA through the investigational device exemption (IDE) process, which is a request to conduct clinical research on an investigational device with "significant" risk in the **United States**.

User fees are required with the submissions of 510(k) premarket notifications and PMA application in the **United States**. Recently, Health Canada released a consultation document that discusses the cost recovery (user fee) framework which shows the basis for accountability at Health Canada for the review process [101]. Essentially, the fees "guarantee" a certain level of service from Health Canada—for instance, specifying the target number of days in which Health Canada will process different types of applications. If the targets are not met, that is, if "performance" does not meet the established standard, the entity being charged the user fee will have their future fees reduced by a corresponding amount. Providing a framework for registration approval globally of delivery wound dressings would translate those delivery systems studied from the laboratory investigation stage to clinical use, which will benefit patients' quality of life.