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. 2024 Nov 11;58(47):20954–20967. doi: 10.1021/acs.est.4c04189

Photovoltaic Power Station Impacts on the Benthic Ecosystem and Sediment Carbon Storage in Tidal Flats in China

Lingxiang Jin †, Peisong Yu †, Chenggang Liu †, Qiang Liu †,‡, Qinghe Liu §, Rongliang Zhang †, Yanbin Tang †, Lu Shou †, Jiangning Zeng †,∥, Quanzhen Chen †,‡, Yibo Liao †,‡,*
PMCID: PMC11603782  PMID: 39527479

Abstract

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Photovoltaic power is a rapidly growing component of the renewable energy sector. Photovoltaic power stations (PVPSs) on coastal tidal flats offer benefits, but the lack of information on the effects of PVPSs on benthic ecosystems and sediment carbon storage can hamper the development of eco-friendly renewable energy. We sampled the macrobenthos and sediment cores at a PVPS on a coastal tidal flat in eastern China. The biodiversity indicators and benthic ecological quality based on macrobenthos were mostly higher under the photovoltaic panels than elsewhere. These variations were primarily driven by pH, sediment grain size, and chlorophyll-a content. However, the PVPS had exerted a considerable influence on the macrobenthic community structure. Furthermore, the carbon stocks in the sediment cores from under the photovoltaic panels were similar to those in the reference sites. These results suggest that this PVPS has not had discernible short-term adverse effects on the benthic ecosystems or sediment carbon storage of the tidal flat. Nevertheless, the potentially long-term and cascading risks throughout the ecosystem warrant caution. Therefore, we recommend that policymakers adopt a cautious development strategy and implement long-term, high-frequency monitoring to ensure the sustainability of renewable energy production on coastal tidal flats.

Keywords: photovoltaic power stations, macrobenthic communities, biodiversity, benthic ecological quality, sediment carbon storage

Short abstract

The first study of the first large-scale tidal flat photovoltaic power station in China showed that there were no discernible short-term adverse effects on local benthic ecosystems or sediment carbon storage.

Introduction

To sustain human production and livelihoods, maintaining the stability of the earth’s climate system is fundamental. Therefore, urgent action is required to control the increases in global temperatures to reduce the risk of triggering climate tipping points as the climate changes.1,2 Global energy-related CO2 emissions reached a new high of over 36.8 Gt in 2022,3 so the decarbonization of the energy sector becomes particularly crucial.4 Renewable energy serves as an effective measure to address climate change and mitigate associated challenges in that it produces significantly less greenhouse gas emissions than nonrenewable energy.5 Nevertheless, climate change is simultaneously threatening the renewable energy potential. Almost all forms of renewable energy production are threatened by the effects of climate change.6 For instance, under climate change, global wind energy resources are expected to decline,7 and forecasts for a specific dam indicate a reduction in hydropower generation of 15%.8 Under the worst climate change scenario, during the period of 2041–2100, the Earth is projected to experience a widespread decline in photovoltaic potential.9 Therefore, despite the unprecedented growth of renewable energy, the necessity for the accelerated development of renewable energy persists under climate change conditions to achieve decarbonization goals and address climate-related concerns.10,11

Solar energy constitutes one of the most dynamic sectors within the renewable energy field, with photovoltaic power representing its primary application.10 China is extensively and actively expanding photovoltaic power, with an installed capacity of 414 GW as of 2022, accounting for approximately 35% of the capacity worldwide.12 At the same, a shift from the land-rich west of China toward the east, where energy demand and markets are well developed, is occurring.13 There is still a need to deploy photovoltaic power stations (PVPSs) to achieve carbon neutrality in China and mitigate global climate change.14 However, PVPSs may compete with other land-use types due to high land-use intensity.13,15,16 Therefore, coastal tidal flats have been recognized as promising sites for PVPS installations,17 because (1) coastal tidal flats cover an area of 12,049 km218 and represent an abundant land resource, (2) the heat exchange induced by the tides helps to mitigate the adverse impact of temperature increases on the power generation efficiency,19,20 and (3) can be operated without disrupting existing agricultural functions.21

In light of the fact that coastal tidal flat ecosystems face a range of threats worldwide,22,23 it is especially relevant to understand how PVPSs on coastal tidal flats might impact threatened coastal tidal flat ecosystems, particularly on macrobenthic communities and sediment carbon storage. Macrobenthos, the primary biological group in benthic ecosystems in tidal flats, connect different trophic levels through feeding on lower trophic level organisms and detritus, and being consumed by higher trophic level organisms such as fish and birds.24−27 Thus, they are critical components in the energy flow and nutrient cycling in tidal flat ecosystems. Because macrobenthos fill vital ecological niches, they serve as robust indicators for assessing marine ecosystem health.28 Various indices are based on macrobenthos, such as the biodiversity index, the AZTI Marine Biotic Index (AMBI) or the abundance/biomass comparison curve (ABC curve), and have been used extensively to assess the health and stability of ecosystems.29−32 Given that PVPSs in terrestrial settings have effects on the microclimate and physical and chemical properties of the soil,33−37 when PVPSs are installed on coastal tidal flats, these effects may be sensed by macrobenthos and reflected in biodiversity and benthic ecological quality.

Additionally, a significant amount of carbon, sequestered from both upslope terrestrial regions and marine sources, has been stored in the sediments of coastal tidal flats for a long time.38 This carbon storage, however, has experienced significant losses over the past 70 years, which was caused mostly by sedimentary processes and anthropogenic activities.39 The constructed and operational PVPSs in terrestrial settings may exert effects on soil carbon that persist for several years, thereby influencing carbon cycling.40,41 Thus, it can be reasonably assumed that sediment carbon storage in tidal flats will respond in a sensitive manner to PVPSs, which merits close observation and analysis.

To date, there is little research on how PVPSs on coastal tidal flats impact the tidal flat ecosystems. Other renewable energy systems have been implemented on coastal or offshore ecosystems, and their environmental impacts have been analyzed. Offshore wind farms have been linked to increased heavy metal concentrations in sediments, alterations in water properties, and adverse effects on benthos.42,43 Tidal energy-based power plants slow down water flow, alter sediment distribution, and affect the behavior of aquatic organisms.44 In the water bodies beneath floating photovoltaic systems, reduced light penetration due to shading decreases algal metabolism and nutrient uptake, leading to lower levels of chlorophyll-a (Chl a), dissolved oxygen, and total organic carbon (TOC).45 Building on the observed effects of renewable energy systems and other artificial structures on coastal ecosystems, we propose hypotheses concerning the impact of PVPSs. First, the foundation piles of marine engineering structures can decrease the water flow velocity, leading to sediment accumulation and a reduction in sediment grain size near the piles.46,47 This can enhance macrobenthic biodiversity, as most macrobenthos prefer finer grain size.48 Also, the biomass of benthic microalgae, a significant food source for macrobenthos in tidal flats,49,50 may be adversely affected due to the reduced photosynthetically active radiation caused by the light shading.51 Overall, we hypothesize that (1) PVPSs decrease sediment grain size and benthic microalgae biomass while affecting other physical and chemical properties, (2) PVPSs decrease TOC and carbon storage in tidal flat sediments due to reduced primary production by microalgae, and (3) PVPSs influence macrobenthic biodiversity and benthic ecological quality through effects on the benthic microalgae and the physical and chemical properties of sediment. The aim of this study was to gain insights into how renewable energy developments on coastal tidal flats affect the ecosystems by analyzing data collected from the tidal flat ecosystem around the Xiangshan PVPS (Figure 1). This study provides scientific evidence that can be used to guide management recommendations about how to conserve coastal ecosystems in areas where PVPSs are planned.

Figure 1.

Figure 1

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Sampling sites in the PVPS and the control zone. (a) Map of Sanmen Bay. The area highlighted in red is the PVPS zone and area in cyan is the Control Zone. (b) The sampling sites in the PVPS zone. (c) The sampling sites in the control zone. (d) Schematic diagram of the sampling sites in areas covered or not covered by photovoltaic panels.

Methods

Study Site

This study was conducted at the Xiangshan Changdatu tidal flat photovoltaic power station, the first large-scale coastal tidal flat photovoltaic project in China, located at the mouth of Sanmen Bay in Zhejiang Province, China (Figure 1a). This 300-Megawatt PVPS is equipped with 685,216 units of 440-W monocrystalline silicon photovoltaic cells, set at a fixed tilt angle of 20°. The PVPS occupies an area of 301.29 ha of tidal flats, with approximately 46.45% of this area covered by photovoltaic panels. The PVPS consists of a permeable structure that allows tidal water to flow through, preserving tidal dynamics. Tidal waters are capable of periodically submerging the lower sections of the photovoltaic cell’s pilings, while the panels remain unaffected (Figure 1d). The PVPS allows the land-use type (coastal wetland) of the area to remain unchanged after its construction, enabling it to perform its original functions as a coastal wetland. The PVPS construction was initiated in September 2020, and power generation commenced in June 2021.

Sample Collection

Based on the local air temperature, with the highest temperature occurring in July and the lowest in January, and considering the lag effect of ocean temperature, August and February were designated as the summer and winter months, respectively, with other seasons set accordingly. Thus, we conducted sampling along transects in November 2022, February 2023, May 2023, and August 2023. Transects PV1 (4 sites), PV2 (5 sites), and PV3 (4 sites) were established on the unvegetated tidal flat within the PVPS (Figure 1b), and the number of sites depended on the length of the tidal flat. The sites closest to the seawall on each transect were without photovoltaic panels (abbreviated as n-PV), while the other sites were under the panels (abbreviated as PV) (Figure 1d). Considering that the tidal flat where we sampled was characterized by nonvegetated areas during the sampling period, yet was previously dominated by Spartina alterniflora marshes before the construction of the PVPS,52 a total of three control transects were established in both unvegetated and S. alterniflora zones which shared similar environmental conditions and human activities with the area before the PVPS was implemented. Two of the control transects, CZ1 (3 sites) and CZ2 (4 sites), were located on a unvegetated tidal flat (abbreviated as UNV), and the third, CZ3 (3 sites), was set up across a S. alterniflora marsh (abbreviated as SA) (Figure 1c). Since all the sites were in the midintertidal zone, the potential effects of cross-shore gradients were disregarded.

It is worth mentioning that despite all sampling sites except SA being characterized by unvegetated tidal flats, a mowing + plowing control method for S. alterniflora was implemented twice: first in the PVPS zone before its construction, and again during the PVPS operation in SA from May to August 2023. The initial control was required for the PVPS construction, while the latter followed the government’s directive outlined in the “Special Action Plan for the Prevention and Control of S. Alterniflora (2022–2025)″. The plant fragments as a result of the control were buried in the soil to a depth of 20–30 cm.53

Four replicate macrobenthos samples were collected at each site using a 0.25 × 0.25 m2 sampling frame and then preserved in 5% formalin. Macrobenthos specimens were separated from the sediment through gentle sieving with a 0.5 mm mesh sieve and then were identified to the lowest taxonomic level possible in the lab. All the taxon names were checked against the World Register of Marine Species (WoRMS, https://www.marinespecies.org). Specimens of each taxon were counted and wet-weighed to an accuracy of 0.001 g.

In addition, three surface-sediment samples were collected at each site for in situ pH measurement, for laboratory analysis of surface Chl a (a proxy for microalgal biomass), and for laboratory analysis of other sediment variables, respectively.

The pH was measured using a pH meter (Orion 898) with a 1:1 sediment sample to water ratio.

To determine the Chl a content, the sediment samples were frozen, stored, and thawed in the dark. Well-mixed sediment of each sample (1 g) was taken and mixed with 100% cold acetone (5 mL). Subsequently, cell disruption was performed under ice bath conditions. Then, after extraction at −20 °C for 20–30 h, the refrigerated sample was mixed and centrifuged for 15 min using a high-speed centrifuge. The supernatant was collected and the Chl a concentration in the extract was measured using a chlorophyll fluorometer (Trilogy, Turner Designs). The Chl a content in the sediment sample was then calculated based on the Chl a concentration in the extract.

We also measured water content (WC), median grain size (D50), TOC, and heavy metals of the sediment samples. The WC was determined by drying the sediment samples in an oven at 105 °C for 24 h until the mass was constant. Prior to analysis of D50, TOC, and heavy metals, the samples were freeze-dried using a freeze-dryer (Christ Delta1-24LSC). One part of the freeze-dried sample was analyzed for D50 using a laser diffraction granulometer (Mastersizer 3000, Malvern Panalytical, UK), while the remaining portion was ground using a ball mill (TH-0.4 L, Tuohe, China) and sieved through a 100-mesh sieve before determining the TOC and heavy metal concentrations. The TOC concentrations of the pretreated samples were determined using an elemental analyzer (Elementar Vario MICRO cube, Germany).54 We pretreated the samples and quantified the concentrations of a range of heavy metals, namely chromium (Cr), manganese (Mn), cobalt (Co), nickel (Ni), copper (Cu), zinc (Zn), arsenic (As), cadmium (Cd), lead (Pb), and mercury (Hg), using an inductively coupled plasma mass spectrometer (iCAP-Q, Thermo Fisher Scientific).54

To assess the variability in the sediment carbon storage, we collected 9 sediment cores in PV, 7 cores in UNV, and 2 cores in SA using sediment corers designed to minimize disturbance. The sediment cores in SA were collected before the S. alterniflora control activity. All sediment cores were sectioned at 10 cm intervals, resulting in samples that represented the 0–10, 10–20, 20–30, 30–40, 40–50, 50–60, 60–70, 70–80, 80–90, and 90–100 cm layers. Bulk density (BD) and TOC of sediment samples that had no visible organisms were maintained at −20 °C before measurement in the laboratory. The BD is the ratio of the dry weight of the sediment to the volume. The TOC concentrations in the sediment sections were determined using the same method that was used for analyzing the TOC in the surface sediment, as described earlier.

Overall, the specific dates, sample sizes and test indicators for each sampling event across the different habitats are showed in Table S1.

Data Analyses

Given the variability in sample size of the different areas, nonparametric tests, namely Mann–Whitney test (M–W test) and Kruskal–Wallis test (K–W test), were chosen to assess the differences in the sediment variables, macrobenthic biodiversity, benthic ecological quality, and sediment carbon stocks between PV and the other habitats. These tests examine the ranks of the data rather than the raw data, thus mitigating the effects of sample size, extreme values, and outliers on the results, and were conducted using the scipy library of the Python 3.12 software. The data were analyzed exclusively with two-tailed tests.

To assess the macrobenthic biodiversity, Margalef richness index (d, the formula is expressed in Table S2a), Shannon–Wiener diversity index (H’, the formula is expressed in Table S2b), and Pielou’s evenness index (J, the formula is expressed in Table S2c) based on abundance were employed. A higher d value and H’ value indicate greater biodiversity. The value of J typically ranges from 0 to 1, with values closer to 1 indicating a more even distribution of species within the community.

The dissimilarities in the macrobenthic communities from the different habitats were assessed by permutational multivariate analysis of variance (PERMANOVA), based on the Bray–Curtis algorithm.55 Macrobenthos abundance was utilized for these tests, and the values were forth-root transformed before calculation to mitigate the impact of numerous zero values on the analysis results.55 These processes were conducted using the ade4 and Vegan packages of the R 4.3.2 software. Additionally, the dominant species, defined as those with a dominance (the formula is expressed in Table S2d) exceeding 0.02, were used as indicators for the community structure.

We employed W statistic, AMBI index, and M-AMBI index based on the macrobenthos data to classify benthic ecological quality into different levels (Table S3). The W-statistic (the formula is expressed in Table S2e) has values in the range (−1, 1), with negative values indicating the predominance of small organisms (disturbed or stressed community), and positive values indicating the dominance of larger organisms (undisturbed state).56 The AMBI and its multivariate extension, M-AMBI, are based on known models of ecological succession under stressful environmental conditions. All macrobenthos, except vertebrates, were assigned to five ecological groups (EGI–EGV), that ranged from sensitive species to opportunistic species. When that of a species could not be determined, their ecological group was replaced with that of a species in the same genus with similar ecological habits. The AMBI was assigned a maximum value of 6, and the M-AMBI, which combines the AMBI with the species richness and H’, was calculated by increasing the highest H’ and the species richness by 15%.57 The AMBI and M-AMBI values were calculated with the AMBI v6.0 software.

Generalized linear models (GLMs) have been employed to analyze the correlation between sediment variables and macrobenthos.58 GLMs are used to reveal the effects of sediment variables on bioindices, as ranking their relative importance. Variance inflation factors (VIFs) are the widely used method to reduce variable collinearity.59 Collinearity between sediment variables was assessed, and variables with the highest VIF were sequentially removed until all remaining variables had a VIF of less than 10.59 With the exception of the pH data and the values of the biodiversity and ecological quality indices, the data were log (X + 1) transformed to mitigate the influence of zero values and extreme values on the data distribution.60 All metrics were modeled with Gaussian distributed GLMs and the identity link.58,59 A model selection procedure using backward elimination was applied (‘step()’ of R 4.3.2 software), which, by pruning the models and minimizing the Akaike information criterion (AIC), identified the most parsimonious model that adequately described the data. A lower AIC indicates a more superior model, because AIC offers a method for achieving an optimal balance between the goodness of fit of the model and its complexity. The GLM modeling process was conducted using the stats package of R 4.3.2 software.

The sediment carbon stock per unit area was represented as the TOC density (TOCD, the formula is expressed in Table S2f). Three of the sediment cores were only 90 cm long. The values of TOCD for the 90–100 cm layer in these three sediment cores were estimated using the method described by Xia et al.61 The measured TOCD in the 80–90 cm layer was used to predict the unknown TOCD in the 90–100 cm layer based on a linear prediction model (y = 0.522x + 1.925, R2 = 0.326, p < 0.001) which is illustrated in detail in Figure S1.

Results

Sediment Characteristics

The Chl a, D50, and TOC did not decrease noticeably in PV, and the pH was significantly lower in SA (Figure 2). The patterns in WC, TOC, and heavy metals were highly consistent, with the highest values observed in n-PV and the lowest in UNV, while values in PV and SA were often similar (Figure 2). The measured values of physical and chemical properties of the sediments from the different habitats are presented in Table S4.

Figure 2.

Figure 2

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Relative values of sediment variables of the different habitats. The habitats were the photovoltaic panel-covered area (PV) and the uncovered area (n-PV) in the PVPS zone, the unvegetated areas (UNV), and the S. alterniflora areas (SA) in the control zone. Bars represent values relative to averages in UNV, with whiskers as standard errors. Sediment variables: chlorophyll-a (Chl a), median grain size (D50), water content (WC), total organic carbon (TOC). Asterisks (*) indicate the significance level with * for p < 0.05, ** for p < 0.01, and *** for p < 0.001. Asterisks next to the x-axis indicate the significance level when compared with those in UNV.

Macrobenthic Communities

Biodiversity

A total of 102 species of macrobenthos were collected. Polychaetes (33), mollusks (31), and crustaceans (28) made similar contributions to the total species richness. There were the highest numbers of macrobenthic species and endemic species in PV (Figure S2).

The macrobenthic biodiversity varied significantly in response to PVPS. Only the abundance and species richness in PV were markedly lower than those in UNV, while the values of the diversity indices (d, H’, and J) were often significantly higher in PV than in the other habitats (Figure 3). Comprehensively, the detrimental impact of the PVPS on biodiversity was found to be limited, whereas a certain degree of positive effects have been substantiated.

Figure 3.

Figure 3

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Biodiversity of macrobenthos across habitats. (a) Abundance, p < 0.001. To better illustrate the main data, outliers have been hidden. (b) Biomass, p = 0.003. (c) Species richness, p < 0.001. (d) Margalef species richness, p < 0.001. (e) Shannon-Weiner diversity, p < 0.001. (f) Pielou’s evenness, p < 0.001. Boxes represent the interquartile range, with the median as the middle bar and the mean as the triangle. Whiskers extend to 1.5 times the interquartile range, and points outside this range are considered outliers. The habitats were the photovoltaic panel-covered area (PV) and the uncovered area (n-PV) in the PVPS zone, the unvegetated areas (UNV), and the S. alterniflora areas (SA) in the control zone. Numbers next to the x-axis indicate sample sizes. Asterisks (*) indicate the significance level when compared with those in PV, with * for p < 0.05, ** for p < 0.01, and *** for p < 0.001.

Community Structure

The results of PERMANOVA (R2 = 0.139, p = 0.001) indicated that the macrobenthic community structures from the different habitats can be significantly distinguished (Table 1). And even though the biotic composition of abundance remained consistent across the different habitats, with polychaetes and mollusks as the main contributors, the biomass composition varied stochastically between the habitats (Figure S3). Meanwhile, most of the dominant species varied significantly across the different habitats (Figure S4). These findings suggested that the PVPS had exerted a considerable influence on the community structure.

Table 1. Results of PERMANOVA for Fully Paired Comparisons between the Different Habitatsa.
paired group sum of squares R2 F p value
UNV/SA 1.080 0.163 7.418 0.001
UNV/n-PV 0.622 0.106 4.526 0.001
UNV/PV 0.679 0.073 5.207 0.001
SA/n-PV 0.442 0.109 2.677 0.004
SA/PV 0.741 0.096 5.283 0.001
n-PV/PV 0.364 0.051 2.712 0.001
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a

The habitats were the photovoltaic panel-covered area (PV) and the uncovered area (n-PV) in the PVPS zone, the unvegetated areas (UNV), and the Spartina. alterniflora areas (SA) in the control zone.

Ecological Quality

These indices can be used to classify ecological quality into different levels. Most sites had ecological quality ranging from moderate status to high status (Figure 4). The ecological quality of PV was occasionally observed to be superior to that of the other habitats, while at other times, it showed no significant differences compared to them. Overall, no negative impact of the PVPS on benthic ecological quality was observed.

Figure 4.

Figure 4

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Ecological quality based on macrobenthos across habitats. (a) W-statistic, p < 0.001. (b) AMBI, p = 0.142. (c) M-AMBI, p < 0.001. Boxes represent the distribution of data, with the median depicted as the middle bar and the mean represented by a triangle. Whiskers extend to 1.5 times the interquartile range. The habitats were the photovoltaic panel-covered area (PV) and the uncovered area (n-PV) in the PVPS zone, the unvegetated areas (UNV), and the S.alterniflora areas (SA) in the control zone. Numbers next to the x-axis indicate sample sizes. Asterisks (*) denote significance at * for p < 0.05, ** for p < 0.01, and *** for p < 0.001.

Environmental Relations

The pH, Cd, Chl a were frequently selected by all the GLMs as the positive variables (Figure 5). D50 typically acted as a negative variable when explaining abundance, but served as a positive variable when explaining the J value and the W statistic. The pH, Cd, Chl a, and D50 were identified as the important environmental variables for explaining the biological data.

Figure 5.

Figure 5

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Best generalized linear models (GLMs) for bioindices. Sediment variables explained the variation in the dominant species abundance ((a) - (h)), total abundance (i) and biomass (j), biodiversity indices ((k) - (m)), and ecological quality indices ((n) - (p)). Sediment variables: chlorophyll-a (Chla), median grain size (D50), water content (WC), and total organic carbon (TOC). Samples with missing values for environmental variables were excluded, resulting in a final sample size of 75 for analysis. Whiskers represented the standard errors of the coefficients. Coefficients reflect the expected change in the dependent variable with a one-unit change in an independent variable. ’Negative’ or ’Positive’ variables indicate a significant negative or positive impact on the response variable, with ’Nonsignificant variables’ showing no significant effect. ’Negative variables’ suggest an inverse relationship with the response variable, while ’Positive variables’ indicate a direct relationship.

Sediment Carbon Stock

The average TOC concentration in the sediment layers was highest in PV (5.921 g/kg), followed by SA (5.177 g/kg), and UNV (4.281 g/kg). The average TOC in PV was only significantly different from that in UNV (p < 0.001). BD exhibited the opposite trend, and was highest for UNV (828 kg/m3), followed by SA (688 kg/m3), and PV (668 kg/m3), and the BD value in PV was significantly different from in UNV (p = 0.012). The TOCD was highest in PV (39.345 Mg/ha), followed by SA (36.138 Mg/ha), and UNV (35.512 Mg/ha), demonstrating that the PVPS did not cause significant variability in the sediment carbon stocks (p = 0.299). Regardless of whether the overall values of TOC, BD, and TOCD were affected, the vertical distributions in the different habitats were similar (Figure 6).

Figure 6.

Figure 6

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Vertical distributions across the different habitats. (a) Total organic carbon (TOC). (b) Bulk density (BD). (c) Sediment TOC density (TOCD). The habitats were the photovoltaic panel-covered area (PV) in the PVPS zone, the unvegetated areas (UNV), and the S. alterniflora areas (SA) in the control zone. Whiskers represented the standard errors.

Discussion

Sediment Dynamics and Macrobenthic Community Responses to the PVPS

Unexpectedly, the D50 increased noticeably in either PV or n-PV compared to that in UNV (Figure 2, Table S4). Yet, the D50 continuously decreased in both PV and n-PV, while it fluctuated in both UNV and SA (Figure S5). Water flow velocity decreases around marine engineering piles, consequently leading to a reduction in sediment grain size in vicinity of the piles, as fine sediments accumulate when the flow is reduced, whereas sediments become coarser as the flow increases or when wave energy increases scour.46,47,62 The decreased D50 suggests that the foundation piles of the PVPS facilitate sediment accumulation. Despite varying preferences for sediment grain size among disparate macrobenthic species,63 higher biodiversity is frequently associated with finer sediment grain sizes.48,64 Nevertheless, the results of the GLMs indicated that lower D50 values were associated with lower J and W statistic values, likely due to the rapid increase in the number of small-sized individuals as D50 decreased.

Contrary to our hypothesis, the benthic microalgal biomass was not lower in PV than in the other habitats. Shading caused by photovoltaic panels in terrestrial settings generally has a direct effect on total above-ground plant biomass due to a reduction of photosynthetically active radiation,33 and a similar reduction in benthic microalgae biomass is observed in a microtidal coastal ecosystem when the microalgae face shading.51 Yet, evidence suggests that the impact of shading on benthic microalgal biomass on coastal tidal flats is not significant.65 Microalgae biomass can be replenished by tidal action, through resuspension and deposition of microalgae,66,67 which explains this phenomenon in this study. Benthic microalgae serve as the principal food source for intertidal macrobenthos,49,68 and the similarity in Chl a concentrations indicated that food availability for macrobenthos was not significantly affected.

The heavy metals concentrations in intertidal sediments of Sanmen Bay have remained relatively stable between this study and June 2021.69 The interactions between heavy metals and organisms extra- and intracellularly induce adverse effects on organisms.70 Based on the concentration levels of heavy metals (Figure S6), Ni was the most likely to pose a biological hazard because it exceeded the probable effects level (42.8 mg/kg71) at some sites (Figure S6c), which encompassed most of the sites in n-PV, half of the sites in PV and SA, and only a few of the sites in UNV. Bioavailability and chronic toxicity of Ni to macrobenthos varies based on sediment characteristics. Elevated TOC can diminish the bioavailability of Ni due to the formation of complexes between organic carbon and Ni.72 The high positive correlation between TOC and Ni (Figure S7) contributed to the absence of Ni biotoxicity. Additionally, Cd, whose concentrations were well below the threshold effects level (0.68 mg/kg,71Figure S6h), frequently acted as a positive variable in GLMs, demonstrating minimal biotoxicity in macrobenthos. It is worth noting that despite the potential of photovoltaic modules to release heavy metals such as As and Cd,44 similarly higher concentrations of heavy metals were observed in both SA and PV (Figure 2, Table S4, Figure S6). S. alterniflora provides temporary storage and should be considered a source of metal contamination.73 Thus, the higher concentrations may be attributed to the presence or past existence of plants rather than to the PVPS.

Moreover, the positive influence of pH on macrobenthos suggests that within the current range of variation, macrobenthos are adversely affected by a decrease in pH. The decrease in pH adversely affects the breeding, development, metabolism, genetic expression, and community stability of macrobenthos.74 The lower pH levels were observed in SA (Figure 2, Table S4) since the presence of S. alterniflora reduces the sediment pH,75 probably through exudation of acetate from roots.76 Nevertheless, the sediments with dead plants will consume these acids through a high acetate oxidation rate,76 which may explain the higher pH levels observed in PV than those in SA.

Macrobenthos on coastal tidal flats has been adversely impacted by S. alterniflora invasion,77 and S. alterniflora control methods that disturb the sediment have not enabled the macrobenthos to recover to levels comparable to those in natural unvegetated tidal flats in the short term.78 The higher biodiversity in PV suggested that the macrobenthos may have recovered well alongside the PVPS operation. Nevertheless, there was a high similarity between macrobenthic communities subjected to control methods and those in natural unvegetated tidal flats.78 Conversely, the macrobenthic community structure in PV exhibited considerable variation compared to that in UNV, indicating that the PVPS has significantly influenced macrobenthic community structure.

Effects of the PVPS on Sediment Carbon Storage

When photovoltaic panels are installed in terrestrial ecosystems, the topsoil and existing vegetation need to be removed, which disrupts the soil structure and affects the carbon in the soil.19,79 The negative impacts persist for several years, even when the vegetation is restored through artificial planting, which means that the soil under the panels has a reduced capacity to sequester carbon.41 However, the TOC values, whether from surface-sediment samples (Figure 2e) or sediment cores, were considerably higher in PV compared to UNV and consistent with those in SA. TOC in tidal flats mainly originates from suspended particles, microalgae, and plants.80,81 Despite benthic microalgae possessing substantial carbon sequestration capabilities,82,83 the primary production of benthic microalgae in PV is likely inhibited, even though the Chl a levels were similar to those in other areas. The suspended particles in PV may be affected due to alterations in hydrodynamic conditions caused by the piles, which still requires further investigation. Additionally, the mowing + plowing method used to control S. alterniflora triggers an increase in sediment carbon,53,84 since the degradation of the dead plants releases a large amount of carbon into sediment.84 Therefore, plant-related factors were considered the key drivers influencing the variation in TOC across the different habitats.

Sediment properties,85 biological disturbances,86 tidal cycles,87 and human activities88 can all affect regional carbon fluxes and thus carbon storage. Comparison of the sediment carbon stocks at this site with other coastal tidal flats at similar latitudes in China showed that there appeared to be less sediment carbon stocks at this site than elsewhere (Figure S8), which suggests that the baseline value in this area is lower. Sediment carbon stock in S. alterniflora marshes in China varies widely, depending on local biogeochemical conditions, with the values ranging from 14.4 to 327.7 Mg C ha–1.39 The carbon stocks in SA are at the lower end of this range, which may be attributed to the younger saltmarsh that had a shorter development time.89 Moreover, sediment carbon stocks are often higher in S. alterniflora marshes than in unvegetated tidal flats,90 because suspended particles can be captured more easily in vegetated areas,91 and the organic matter from the plants is a source of carbon storage.80 Nevertheless, the carbon stocks in PV and UNV were similar to those in SA. The high TOC resulting from the control of S. alterniflora in PV and the high BD in UNV contributed to the similar carbon stocks in this study.

Additionally, there was a similarity across the different habitats in the vertical distributions of TOC with a slight increase in TOC with depth. Although the surface layer of sediment receives a higher input of TOC, there may be more carbon removal from tidal erosion and biological processes than in deeper layers.92,93 In the deeper layers, anaerobic conditions usually impede the decomposition of TOC,94 which may result in a gradual accumulation of TOC. Surface sediments tend to respond directly and rapidly to disturbances, while relatively deeper sediments require a longer response time, as these processes occur indirectly through various biogeochemical cycling mechanisms. The similarity in the vertical distributions of TOC suggests that the effects of both the S. alterniflora control method and the PVPS construction have permeated throughout the depth of 1 m and extended even deeper.

The vertical variations in BD are generally a result of compaction. In addition to the weaker effect of compaction experienced by shallow sediments, the presence of plant roots,95 sediment organic carbon content,95,96 and human activities,97 such as the control of S. alterniflora and subsistence fishing, contribute to the lower BD values in the top 20 cm of sediment. Although the overall values of TOC and BD were affected, the trends in vertical distribution remained consistent, indicating a limited impact of the PVPS on the sediment.

Potential Long-Term Ecological Effects of the PVPS

The PVPS did not have discernible adverse effects on the benthic ecosystems or the sediment carbon storage in the tidal flats. In fact, the short-term monitoring shows that the macrobenthic biodiversity and benthic ecological quality were slightly higher in the PVPS. These findings suggest that, under certain conditions, renewable energy infrastructure may coexist with and even benefit local ecosystems. This case of the PVPS aligns with the broader trend in sustainable energy production, where innovations, including low-energy catalytic advancements,98 are driving a synergy between environmental stewardship and energy efficiency. This synergy represents a significant stride toward holistic sustainability.

Nevertheless, studies on PVPS applications on coastal tidal flats are relatively limited. PVPSs in terrestrial settings lead to heterogeneity in soil moisture distribution99 and reduced soil TOC,41,79 and water-based floating photovoltaic systems result in lower Chl a and TOC levels in water bodies.45 Conversely, PVPSs on coastal tidal flats may benefit from the characteristics of the intertidal zone. Regular seawater inundations caused by tidal action facilitate the exchange of materials with other ecosystems, replenishing potentially depleted Chl a and TOC levels.66,67,80,81 Moreover, the observed decrease in D50 in both PV and n-PV areas (Figure S5) supports the hypothesis that the PVPS may be continuously reducing sediment grain size.

However, the higher TOC values from surface-sediment samples or sediment cores in PV may just be a temporary phenomenon. The sediment TOC content in areas that underwent S. alterniflora control might first increase and then decrease over time due to the diminishing benefits from the decomposition of dead plants.53,100 This suggests that the potential risk of PVPS negatively impacting sediment TOC and carbon stocks still persists.

In coastal regions of China, concerns about Ni and Cd levels in seafood tissues are growing due to increasing health risks to humans.101 Despite no evidence of heavy metal biotoxicity, research in Sanmen Bay indicates that the accumulation of heavy metals in seafood poses a potential health risk to humans.102 In the long term, PVPSs could pose a risk of heavy metal pollution due to potential damage to photovoltaic panels caused by climate-related disasters.44 Thus, we are calling for more attention to be paid to heavy metals, both in the environment and in seafood in PVPSs.

The community structure of macrobenthos has undergone significant changes in response to the PVPS. And the highest species richness was observed in PV, where 13 species occurred exclusively but with low abundance and occurrence frequencies (Figure S9). These occasional species were originally distributed across different temperature or tidal zones of Chinese seas,103 possibly due to ocean currents or seasonal factors leading to their temporary presence. It is also worth noting that we observed barnacles attached to the pilings during the field surveys. Thus, while PVPSs may enhance local biodiversity, they could pose a long-term threat to native communities, potentially fostering developing biofouling communities and even introducing the invasive species.104

Tidal flat ecosystems not only preserve biodiversity and provide carbon sequestration, but also facilitate food resources that benefit humanity.105 The vast coastal tidal flats in China are commonly used for fishing and aquaculture. It was reported that PVPSs on fish ponds have a moderately negative impact on fish production.21 In this study, economic mollusk species of macrobenthos were still collected in the PVPS. The similarity in average weight of the most valuable mollusks across the different habitats indicated that the PVPS did not significantly affect the quality of the food resources (Figure S10).

Additionally, approximately 230 species of shorebirds utilize Chinese coastal wetlands as breeding grounds, stopover sites during migration, and wintering habitats,106 and macrobenthos on coastal tidal flats provide food for shorebirds.26,27 And the increase in macrobenthic biodiversity benefits shorebirds by greater food availability.107 However, PVPSs in terrestrial settings may represent an avian mortality source, including direct fatality as a result of collision with infrastructure.108 Changes in the macrobenthic community structure could impact the food sources for shorebirds, and those less adaptable may struggle to meet this challenging situation.109 In the long term, the application of PVPSs on coastal tidal flats may exert certain adverse effects on shorebirds.

These findings in this study suggest that renewable energy development, like PVPSs, can coexist with and even support coastal biodiversity and ecological quality. Given the current circumstances, the positive findings regarding the impacts of PVPS on benthic ecosystem and carbon storage on coastal tidal flats, highlight the potential for integrating renewable energy with coastal ecosystem management, promoting both sustainable energy and environmental conservation. Nevertheless, in the long term, potential risks concerning heavy metal contamination, changes in carbon storage, macrobenthic community dynamics, and global climate change may undermine these benefits. Additionally, aspects not specifically examined in this study, such as ecosystem services like food resources and other biological groups like shorebirds, may currently face or could potentially face significant threats. Therefore, we recommend that policymakers adopt a cautious development strategy, considering optimizing engineering designs with a focus on environmental sustainability and implementing long-term, high-frequency, and multidisciplinary monitoring.

Acknowledgments

The authors acknowledge the financial support by the Key R&D Program of Zhejiang Province, China (2023C02003, 2023C03120), the Scientific Research Fund of the Second Institute of Oceanography, MNR, China (SZ2302), the National Natural Science Foundation of China (42306170).

Supporting Information Available

The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.est.4c04189.

  • The predicted carbon density of missing values for the 90–100 cm layer in three sediment cores (Figure S1); species richness of macrobenthos in the different habitats (Figure S2); taxonomic composition of macrobenthic abundance and biomass across habitats (Figure S3); dominant species patterns across the different habitats (Figure S4); variations of median grain size in the different habitats over time (Figure S5); concentrations of heavy metals compared with background values, threshold effects levels, and probable effects levels (Figure S6); scatter diagram between Ni and TOC with the Spearman Correlation Coefficient (Figure S7); sediment carbon density in the study area compared to adjacent areas (Figure S8); abundance and frequency of the endemic species in the photovoltaic panel-covered area (Figure S9); individual average weight of valuable mollusk species (Figure S10); specific dates, sample sizes and test indicators for each sampling event across the different habitats (Table S1); formulas mentioned in this study (Table S2); threshold levels of indices for benthic ecological quality status assessment (Table S3); Sediment variables of the different habitats (Table S4) (PDF)

The authors declare no competing financial interest.

Supplementary Material

es4c04189_si_001.pdf (909.8KB, pdf)

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