Precipitation as the Key Regulator of Acid Rain Inhibition on Forest Soil Organic Carbon Decomposition: A Global Meta-Analysis for Sustainable Ecosystem Management

Abstract

Acid rain poses a significant threat to forest ecosystems globally, with substantial impacts on soil organic carbon decomposition. This study employs a meta-analysis of 361 datasets from 63 published studies to investigate the response of SOC decomposition in forest ecosystems to acid rain. Our analysis reveals that acid rain has a significant inhibitory effect on SOC decomposition. Precipitation emerges as a crucial large-scale environmental factor that differentially modulates this effect; it alleviates acid rain’s suppressive impact on litter decomposition by diluting H⁺ ions but intensifies the inhibition of soil decomposition due to the soil’s strong adsorption capacity. Furthermore, our results indicate that acid rain exerts a more pronounced inhibitory effect on soil organic carbon decomposition than on litter decomposition. Compared to small-scale factors, precipitation plays a more significant role in regulating the inhibitory effects of acid rain on organic carbon decomposition. These findings underscore the need to integrate precipitation into carbon-cycle models and tailor management strategies to specific climates for sustainable forest carbon management. It also provides a theoretical foundation for predicting the response of forest carbon decomposition to environmental change and for balancing ecological protection with sustainable development in acid rain-impacted regions.

1. Introduction

Defined as precipitation with a pH value below 5.6, acid rain or acid deposition is a global environmental issue [1,2]. It encompasses both wet deposition (e.g., acid rain, acid snow) and dry deposition (e.g., SO₂, NO_X, and other acidic gases and aerosols) [3,4]. Since the industrial revolution, the excessive use of fossil fuels in industrial and transportation sectors has released large amounts of acidic precursors into the atmosphere, triggering regional acid deposition and causing widespread ecological damage [5,6,7,8]. For example, it accelerates the weathering of soil minerals; causes the leaching loss of nutrient elements in plants; affects the total amount of microorganisms and the content of organic matter in the soil [9]; and even leads to forest degradation in some regions [10], which is not conducive to the maintenance of carbon balance in regional ecosystems. With high energy consumption, North America, Europe, and China have become the world’s three major acid rain regions [11]. Taking China as an example, from 1989 to 2014, China’s GDP grew at an average annual rate of 9.1% [12]. However, this rapid economic growth was accompanied by severe acid rain problems. Environmental monitoring data show nearly half of the monitored counties and cities (210 out of 473) in China experienced acid rain, with 12 cities (2.5%) having a precipitation pH value of 4.70 in the 2010s [13,14,15]. Over the past few decades, Europe and the United States have implemented strong SO₂ and NO_X emission reduction policies [2,16]. In the past ten years, China has also taken a series of measures. As a result, the pH values of precipitation in many regions have increased, and the adverse effects of acid rain on forests, soils, and water bodies have been alleviated, with ecosystem service functions gradually recovering. However, under the current global economic slowdown, there is still a risk that some countries or regions may relax environmental protection in pursuit of short-term economic growth, potentially leading to a resurgence in acid rain problems.

As a global environmental challenge, acid rain not only damages the structure of forest ecosystems but also threatens the sustainability of their carbon storage function—a critical component of forest carbon sinks, which play a pivotal role in mitigating climate change and underpinning sustainable development. In forest ecosystems, soil organic carbon (SOC) decomposition is a key process linking biogeochemical cycles. It directly affects atmospheric CO₂ levels and thus plays a crucial role in global carbon balance and climate change [17]. Moreover, it is central to nutrient cycling (e.g., nitrogen and phosphorus) and provides essential nutrients for plant growth [18,19]. SOC decomposition is a complex biochemical process regulated by multiple factors [4,20], including the physical and chemical properties of organic matter [21,22,23], microbial community composition and activity [24], and environmental factors such as soil moisture, temperature, and pH [25,26,27,28]. Soil, as the main site of SOC decomposition, is an open and easily disturbed system, and changes in soil pH have been shown to significantly impact decomposition processes [29,30,31].

Acid rain is a major external driver of soil acidification and significantly inhibits SOC decomposition in forest ecosystems through multiple mechanisms. It directly acidifies soil, reducing pH levels and inhibiting microbial activity, biomass, and community composition [32,33,34,35,36,37]. It also interferes with nutrient cycling by affecting soil enzyme activity and altering nutrient availability and leaching, disrupting the nutrient balance required by microorganisms [38,39,40]. Furthermore, acid rain can alter the chemical composition of litterfall, potentially affecting subsequent decomposition stages [41,42,43].

However, the existing research has limitations. Most studies have been conducted at specific locations or on regional scales, with a lack of global scale integration, most notably in Europe [44,45], the eastern United States [46,47], Sichuan and Guangdong provinces in China, and the Yangtze and Pearl River deltas [48,49,50,51]. Global synthesis is required to discern universal patterns. The heterogeneity of responses to acid rain across different forest types, climatic conditions, and substrate properties remains unclear. Additionally, there is a lack of quantitative assessment of the average inhibitory effect of acid rain and its variation across different contexts on a global scale. To address these gaps, this study uses meta-analysis to integrate global data and explore the following key questions: What is the overall impact of acid rain on SOC decomposition in forest ecosystems across the globe? What is the average level of its inhibitory effect? How do factors such as substrate type, forest type, and climate conditions (particularly annual rainfall) influence this process?

This study aims to unravel the mechanisms by which acid rain affects forest carbon cycling, providing scientific support for achieving the goals of sustainable development. Thus, we hypothesize that acid rain significantly inhibits organic carbon decomposition in forest ecosystems at the global scale, and that this inhibitory effect is modulated by key environmental factors such as substrate type, forest type, and climate conditions. To test this hypothesis, our study provides the first global-scale quantitative assessment of acid-rain impacts on forest SOC decomposition, identifies the dominant patterns and regulatory mechanisms of the response, and offers insights into the ecological consequences of these effects. The results will enhance our understanding of the role of acid rain in forest carbon cycling, improve the accuracy of forest carbon cycle models under acid deposition, and support environmental decision-making by clarifying the impact of acid rain on forest carbon sinks and their key drivers.

2. Materials and Methods

2.1. Data Collection and Selection

The study uses “acid rain, acid deposition, acidic precipitation” and “litter decomposition, carbon cycle, soil organic matter decomposition, organic carbon, litter degradation, litter decay, recalcitrant carbon, labile, carbon mineralization, and carbon” as keywords to search published articles using China National Knowledge Infrastructure (CNKI, https://www.cnki.net/), Web of Science (https://www.webofscience.com/), Elsevier ScienDirect (https://service.elsevier.com/), Springer Link (https://www.springer.com/), Wiley (https://onlinelibrary.wiley.com/), Google Scholar (https://scholar.google.com), EBSCO (https://search.ebscohost.com/), and other Chinese or English databases. The searching strategy was used as follows: TI (in title) = (“acid rain” OR “acid deposition” OR “acidic precipitation”) AND TS (in topics) = (“litter decomposition” OR “carbon cycle” OR “soil organic matter decomposition” OR “organic carbon” OR “litter degradation” OR “litter decay” OR “recalcitrant carbon” OR “labile carbon” OR “carbon mineralization” OR “carbon”). The literature search was conducted up to 15 March 2025. The study compiled 363 related papers. This meta-analysis strictly followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to ensure transparency and reproducibility of the data collection process [52]. To avoid publication bias, it applied three screening criteria: (1) papers must include one of the variables (forest type, rainfall, or organic matter category); (2) acid rain treatment and control groups must have the same initial climatic, soil, and vegetation conditions; and (3) target variables must have directly reported mean, standard deviation, and repetition numbers, or these can be calculated from the text data or extracted from images using WebPlotDigitizer 4.3. After screening, 63 valid papers were retained, yielding 361 cases for analysis. The database includes explanatory variables like forest type, organic substrate type, and rainfall, along with a target variable of organic carbon decomposition and experimental background information (see Table S1 in Supplementary Materials). In the same experiment, measurements with different acid rain application levels are considered independent observations.

2.2. Data Grouping and Analysis Methods

In line with the research objectives, we categorized the data as follows: Forest types: broad-leaved forests (L), mixed forests (M), coniferous forests (N); Organic substrate types: litter organic carbon decomposition and soil organic carbon decomposition [53]. We conducted the meta-analysis using the Metafor package in R version 4.3.3.

2.2.1. Meta-Analysis Using Fixed and Random Effects Models

The data were analyzed using the meta-analysis method described by Hedges et al. [54]. Both fixed and random effects models were applied to test the effect size, which was calculated as the response ratio (RR) to evaluate the impact of acid rain on organic carbon decomposition in forest ecosystems, as follows:

$$\mathrm{RR}=\ln \left({\displaystyle \frac{X_t}{X_c}}\right)=\ln \left(X_t\right)-\ln \left(X_c\right)$$

(1)

where $X_t$ and $X_c$ are the mean values of the target variable for the treatment and control groups, respectively. Due to its lesser bias and a sampling distribution that is approximately normal [4], the natural logarithm of RR was used as the effect size for meta-analysis.

The variance of RR ($v$) was calculated as:

$$v={\displaystyle \frac{{S_t}^2}{n_t{X_t}^2}}+{\displaystyle \frac{{S_c}^2}{n_c{X_c}^2}}$$

(2)

where $n_t$ and $n_c$ are the sample sizes, and $S_t$ and $S_c$ are the standard deviations of the treatment and control groups, respectively.

To test whether environmental conditions affect the effect size, three explanatory variables—organic matter category, forest type, and rainfall—were used. The data were subgrouped, and the mean (${\mathrm{RR}}_{++}$) and standard error ($S\left({\mathrm{RR}}_{++}\right)$) of the response ratio calculated as:

$${\mathrm{RR}}_{++}={\displaystyle \frac{{\displaystyle {\sum }_{i=1}^m{\displaystyle {\sum }_{j=1}^{k_i}{W}_{ij}R{R}_{ij}}}}{{\displaystyle {\sum }_{i=1}^m{\displaystyle {\sum }_{j=1}^{k_i}{W}_{ij}}}}}$$

(3)

$$S\left({\mathrm{RR}}_{++}\right)=\sqrt{{\displaystyle \frac{1}{{\displaystyle {\sum }_{i=1}^m{\displaystyle {\sum }_{j=1}^{k_i}{W}_{ij}}}}}}$$

(4)

where $m$ is the number of groups (e.g., forest types, organic carbon types, and rainfall levels), ${{\displaystyle k}}_i$ is the comparison number of the ith group, and ${{\displaystyle W}}_{ij}$ is the weight factor, calculated as:

$$W_{ij}={\displaystyle \frac{1}{v}}$$

(5)

The 95% confidence interval (CI) for the effect size is:

$$95\%CI={\mathrm{RR}}_{++}\pm 1.96S\left({\mathrm{RR}}_{++}\right)$$

(6)

2.2.2. Assessing the Impact of Environmental Factors (Explanatory Variables)

A positive mean effect size indicates a positive effect, while a negative value indicates a negative effect. If the 95% confidence interval (CI) of RR does not include zero, the effect of the environmental factor (explanatory variable) on the effect size is significant; otherwise, it is not.

To determine whether to include an explanatory variable, assess the significance of the overall data heterogeneity (Q_t). If significant, a mixed-effects model is used. After introducing the variable, analyze the significance of the heterogeneity explained by the variable (Q_m). If p < 0.05, the variable significantly affects the effect size; otherwise, focus on the model’s effect size. Next, evaluate the residual heterogeneity (Q_e). If p < 0.05, residual heterogeneity remains, suggesting the need to introduce additional explanatory variables [55]. For the categorical variables, forest type was taken into account as a discrete moderator. For the continuous variables, precipitation was introduced into the analyses as a continuous moderator. For precipitation, we fitted a simple linear regression with the log response ratio as the dependent variable. The model was estimated by using restricted maximum likelihood (REML), and the slope was deemed significant at α = 0.05. Building on the single-moderator models described above, we extended the analysis to a two-factor framework to account for the high residual heterogeneity observed when each factor was considered in isolation. We therefore constructed mixed-effects meta-regression models that simultaneously incorporated both forest type (categorical) and mean annual precipitation (continuous) as fixed-effect moderators, along with their interaction term. As before, study identity was retained as a random effect, and parameters were estimated via restricted maximum likelihood (REML) using the rma function in the Metafor package. The joint contribution of the moderators was evaluated with the omnibus Q_m test, and residual heterogeneity (Q_e) was examined to determine whether additional explanatory variables were still required.

Finally, piecewise structural equation modeling (piecewiseSEM) was used to explore direct and indirect relationships between variables by evaluating the relative effects of forest type and precipitation on soil organic carbon decomposition (RR-Soil) and litter organic carbon decomposition (RR-Litter). Initially, a prior model that included all hypothesized pathways was constructed. The model was then iteratively simplified by removing non-significant pathways until the final model was achieved. The suitability of the final model was evaluated using Fisher’s C statistic, as implemented in the piecewiseSEM package for R version 4.3.3 [56].

2.2.3. Evaluating Publication Bias

The fail-safe number (Rosenthal fail-safe number, N) indicates the number of missing studies needed to nullify the observed effect. The critical value is calculated as:

$$N=5k+10$$

(7)

where k is the number of studies.

Funnel plot and symmetry tests: Funnel plots graphically display publication bias. Asymmetry in the funnel plot suggests potential bias. Egger’s regression test quantifies this asymmetry; a significant result (typically p < 0.05) indicates potential publication bias.

These methods collectively assess the reliability of the meta-analysis results and the potential impact of publication bias [57].

3. Results

3.1. Data Description

The database summarized 63 literature sources and comprised 361 cases, with all sampling sites located within forest ecosystems, as illustrated in Figure 1.

The constructed database classifies the types of decomposing organic carbon into litter and soil and encompasses three categories of forest. The proportion of cases for litter and soil organic carbon across the three forest categories is as follows: For broad-leaved forests (L), the proportions are 50.19% and 44.33% respectively; for mixed forests (M), the proportions are 7.60% and 23.71% respectively; and for coniferous forests (N), the proportions are 42.21% and 31.96% respectively (

Table 1. Distribution of all data cases on organic carbon decomposition of litter and soil.

Organic Carbon Type	Forest Type	Number of Cases	Percentage (%)
Litter	L	132	50.19
	M	20	7.60
	N	111	42.21
Soil	L	43	44.33
	M	23	23.71
	N	31	31.96

).

3.2. Fixed and Random Effects Models

The random effects and fixed effects models for litter organic carbon decomposition showed average effect sizes of −0.02 and −0.01, respectively, both indicating that acid rain suppresses litter organic carbon decomposition (Figure 2a). Similarly, for soil organic carbon decomposition, the average effect sizes were −0.13 and −0.07, respectively, aligning with the response of litter organic carbon decomposition (Figure 2b). Despite the more significant effects shown by the fixed effects model, the random effects model was chosen for subsequent analyses due to its consideration of between-study variability. The random effects model analysis for organic carbon types showed an insignificant Q_m value (p > 0.05) and overlapping confidence intervals, indicating no significant difference in effect sizes between organic substrate types (Figure 3). However, acid rain significantly inhibited soil organic carbon decomposition. In the effect model analysis, the significant Q_t values (p < 0.05) suggest strong heterogeneity in effect sizes, necessitating the introduction of explanatory variables (Figure 2). Consequently, the random effects model evolved into a mixed effects model. Subsequent analyses will incorporate forest type (a categorical variable) and rainfall (a continuous variable) as explanatory variables to explore the heterogeneity further.

3.3. Impact of Individual Environmental Factors on Effect Sizes

3.3.1. Forest Type

For litter organic carbon decomposition (Figure 4a), the Q_m values for between-group heterogeneity were non-significant (p > 0.05), indicating no significant differences in effect sizes among forest types. For soil organic carbon decomposition (Figure 4b), although the overall test was significant (p < 0.05), the overlapping confidence intervals in most pairwise comparisons indicate no significant differences between most forest types. In litter organic carbon decomposition, the effect sizes for broad-leaved forests (L), mixed forests (M), and coniferous forests (N) were −0.01, −0.03, and −0.04, respectively, all negative, indicating a negative effect of acid rain across all forest types (Figure 4a). Similarly, for soil organic carbon decomposition, the effect sizes were −0.01, −0.36, and −0.09 for L, M, and N, respectively, also negative, consistent with the response of litter organic carbon decomposition (Figure 4b).

3.3.2. Rainfall

For litter organic carbon decomposition, a significant positive correlation was found between rainfall and the effect size (Figure 5a), indicating that higher rainfall reduces the inhibitory effect of acid rain on litter organic carbon decomposition. Conversely, for soil organic carbon decomposition, a significant negative correlation was observed (Figure 5b), meaning that increased rainfall strengthens the inhibitory effect of acid rain on soil organic carbon decomposition.

3.4. Impact of Multiple Environmental Factors on Organic Carbon Decomposition

The single-factor heterogeneity analysis shows high residual heterogeneity when only one factor is considered. Thus, it is essential to examine the combined impact of multiple factors and their interactions on effect sizes. Results indicate that forest type doesn’t significantly affect the effect sizes for either organic carbon type, while rainfall significantly impacts the effect size only for litter organic carbon decomposition, aligning with the single-factor analysis findings. Moreover, the interaction between forest type and rainfall significantly affects the effect sizes only for soil organic carbon types (

Table 2. Effect of forest type and rainfall on effect size for litter organic carbon decomposition.

Factors	Test of Moderators
	Qm	p-Val
Forest type	3.3911	0.1835
Precipitation	25.9919	<0.0001 ***
Interaction between forest type and precipitation	4.8513	0.0884

Note: *** p < 0.001 represents extremely significant.

and

Table 3. Effect of forest type and rainfall on effect size for soil organic carbon decomposition.

Factors	Test of Moderators
	Qm	p-Val
Forest type	1.1698	0.5572
Precipitation	1.3049	0.3090
Interaction between forest type and precipitation	8.0101	<0.05 *

Note: * p < 0.05 represents significant.

).

3.5. Piecewise Structural Equation Model (piecewiseSEM)

Piecewise structural equation models for the effect sizes of litter and soil organic carbon decomposition showed a good fit, with χ² = 0 and p > 0.05 (Figure 6). These models explained 20% (RR-Litter) and 10% (RR-Soil) of the total data variation (Figure 6). In both models, forest type had a non-significant negative effect on the effect sizes, consistent with the above analyses. Rainfall had a significant positive effect on the effect size for litter organic carbon decomposition and a significant negative effect on that for soil organic carbon decomposition, aligning with the single-variable correlation analysis. Additionally, rainfall had a greater impact than forest type in both SEMs. Based on the structural equation modeling, which accounts for the intercorrelations among predictor variables, its conclusions are prioritized for interpreting the overall statistical results.

3.6. Assessing Publication Bias

The fail-safe number (N) was 6178, exceeding the critical value of 1715, indicating that the results are unaffected by publication bias and are reliable. For the two types of organic carbon, the funnel plot tests for publication bias showed p-values of 0.7133 (litter) and 0.3192 (soil). For forest types, the p-values were 0.5380 (broad-leaved, L), 0.5163 (mixed, M), and 0.1542 (coniferous, N). The rainfall publication bias test p-value was 0.3624 (Figure 7). All these p-values exceeded 0.05, confirming the funnel plots’ symmetry and indicating no publication bias.

4. Discussion

The findings of this study, particularly those derived from the fixed-effects model, demonstrate that acid rain significantly suppresses organic carbon decomposition in forest ecosystems—a result consistent with previous research [58,59,60]. As emphasized by Porre et al. [61], environmental drivers play a fundamental role in regulating large-scale litter decomposition and associated biogeochemical processes [62,63]. Among these drivers, acid rain exerts a particularly strong regulatory influence [64,65]. Prolonged acid deposition increases H⁺ concentrations in the organic layer, exacerbating soil acidity. This process induces multiple adverse effects, including microbial toxicity, enzyme denaturation, shifts in microbial community structure, and the suppression of key ecosystem functions such as organic carbon mineralization [40,66,67,68]. The suppression of organic carbon decomposition by acid rain has profound implications for forest carbon storage dynamics globally [69]. Reduced decomposition rates lead to the accumulation of carbon in forest soils and litter layers. While this may enhance short-term carbon sequestration, the long-term consequences are considerably more complex. Accumulated carbon can modify soil physicochemical properties, potentially affecting plant productivity and ecosystem functioning [70,71,72,73,74]. Furthermore, the stability of this stored carbon remains uncertain. If organic carbon becomes more recalcitrant due to altered decomposition pathways, it may persist in soils over extended timescales. Conversely, if decomposition inhibition is reversed—or if the ecosystem experiences disturbances such as wildfires, pest outbreaks, or climate-related stressors—the sequestered carbon could be rapidly mineralized and released into the atmosphere [75,76]. Such a scenario could trigger a positive feedback loop, amplifying climate change. The potential ramifications of altered carbon storage for atmospheric CO₂ levels and climate change mitigation strategies are substantial. While suppressed decomposition enhances carbon retention in forests—potentially offsetting some anthropogenic CO₂ emissions—this effect is likely marginal compared to the scale of fossil fuel emissions. Moreover, if stored carbon becomes vulnerable to release due to environmental shifts or disturbances, it could elevate atmospheric CO₂ concentrations, exacerbating global warming [77]. Additionally, the overall capacity of forests to act as carbon sinks may be compromised when considering the concurrent negative effects of acid rain on tree health and ecosystem stability. These complexities highlight the challenges in evaluating the net impact of acid rain on climate mitigation efforts. When compared to other environmental stressors, acid rain exhibits distinct mechanisms of influence on forest carbon cycling. For instance, drought primarily disrupts carbon uptake by impairing photosynthesis and water-use efficiency, with effects that are often immediate and contingent on drought severity [78,79]. In arid and semi-arid regions or calcareous soils, acid rain may accelerate soil acidification, promoting carbonate dissolution and thereby increasing CO₂ emissions from inorganic carbon sources. However, most of the current studies have focused on organic carbon, potentially neglecting the interference of inorganic carbon [80]. Therefore, it is necessary to distinguish between acid-driven organic carbon decomposition and inorganic carbon dissolution in future research. In contrast, acid rain exerts a more gradual and indirect impact by altering soil biogeochemistry and microbial activity [81]. Similarly, nutrient deposition, particularly nitrogen, can have dual effects: while it may stimulate plant growth and carbon sequestration in some ecosystems, it can also induce soil acidification and nutrient imbalances, counteracting potential benefits [82,83]. The relative importance of acid rain in comparison to these stressors is context-dependent, varying across ecosystems and environmental conditions [84,85,86]. In certain regions, acid rain may dominate carbon cycling dynamics, whereas in others, synergistic interactions among multiple stressors may be more influential. A comprehensive understanding of these interactions is critical for developing effective strategies to safeguard forest ecosystems amid global environmental change.

The forest floor’s organic layer, comprising chemically distinct litter and soil components, exhibits differential responses to acid rain-induced decomposition. While previous studies have reported varying sensitivities between these substrates—with Wei et al. [87] demonstrating substrate quality-dependent effects and Liang et al. [88] showing greater acid rain sensitivity in litter respiration—our results reveal a more pronounced inhibitory effect on soil organic carbon decomposition. This divergence likely stems from two interrelated mechanisms: substrate composition and microbial community dynamics. Litter’s abundant lignocellulose undergoes acid hydrolysis under acid rain exposure [89,90], partially offsetting decomposition inhibition, whereas SOC’s mineral-associated and humified fractions lack this compensatory mechanism. Concurrently, soil’s microbial communities exhibit lower diversity and acid tolerance compared to litter-associated microbiota [91], further exacerbating the suppression of SOC decomposition. Although statistical analyses indicate no significant global divergence in acid rain effects between substrates, their distinct biogeochemical properties drive differential responses. Litter’s labile carbon pools (e.g., sugars, starches) and porous structure sustain microbial activity and buffer microenvironmental fluctuations, while SOC’s chemically recalcitrant, mineral-protected fractions are more vulnerable to acid rain-induced pH shifts that disrupt protective mechanisms and mobilize stabilized carbon [2,92,93]. These substrate-specific dynamics are further elucidated by molecular evidence showing how lignocellulose acidolysis modifies organic structures in litter to enhance microbial degradability [94], and the way acid rain alters soil microbial metabolic pathways [2,95]. The interplay of these chemical and biological factors ultimately determines the net acid rain effect on forest carbon cycling, with soil processes exhibiting greater sensitivity due to their more direct exposure to acidification-induced stressors like aluminum toxicity and base cation depletion [96,97].

Forest type represents a fundamental ecological determinant of decomposition processes through its influence on litter chemical composition and physical properties. However, our analysis reveals that acid rain’s inhibitory effect on organic carbon decomposition remains consistent across broad-leaved, coniferous, and mixed forest ecosystems. This finding corroborates previous research demonstrating acid rain’s capacity to override substrate-specific decomposition patterns [39,90,98,99], suggesting a universal suppression mechanism that transcends forest-type variations. The observed homogeneity in decomposition response may be explained by three principal mechanisms. First, acid rain’s profound impact on soil biogeochemistry, particularly through pH reduction and base cation depletion, creates a uniformly stressful environment for decomposer communities across all forest types, effectively masking potential differences in decomposition dynamics [34,100]. Second, while litter chemistry varies among forest types, the universal acid hydrolysis of lignocellulosic compounds may generate comparable compensatory effects on decomposition rates under acid deposition [89,90]. Third, unaccounted variables such as microbial functional redundancy or enzymatic adaptation may mediate decomposition responses in ways that diminish forest-type effects [101,102]. These results challenge conventional ecological paradigms that emphasize vegetation type as a primary controller of decomposition dynamics, instead highlighting how anthropogenic acidification can become the dominant regulator of carbon cycling processes in forest ecosystems. The findings underscore the need to reconsider traditional decomposition models in polluted environments, where anthropogenic stressors may supersede natural ecosystem properties in governing biogeochemical cycles.

Precipitation serves as a critical large-scale environmental factor that significantly modulates the impact of acid rain on organic carbon decomposition processes in forest ecosystems [2]. Unlike smaller-scale factors such as substrate type and forest type, precipitation exhibits differential effects on litter and soil decomposition through distinct hydrological and biogeochemical mechanisms. For litter decomposition, precipitation results in a concentration-dilution dynamic where initial rainfall events are absorbed by the litter layer, temporarily concentrating H⁺ ions and suppressing microbial activity, while subsequent precipitation exceeding the litter’s water-holding capacity facilitates H⁺ ion leaching and consequent acid stress alleviation [90]. In contrast, soil systems exhibit a unidirectional response, whereby increased precipitation enhances H⁺ ion infiltration and accumulation due to soil’s superior adsorption capacity, leading to progressive acidification through pH depression and elevated concentrations of accompanying anions such as SO₄²⁻ and NO³⁻, thereby exacerbating acid rain’s inhibitory effect on soil organic carbon decomposition [103,104,105]. These contrasting responses between litter and soil matrices underscore the fundamental importance of system-specific properties in mediating acid rain impacts. The mechanistic understanding of precipitation–acid rain interactions enables more accurate regional predictions of decomposition dynamics, suggesting that high-precipitation zones may experience accelerated litter decomposition coupled with retarded soil carbon turnover, while arid regions likely exhibit the opposite pattern. These differential responses necessitate the development of climate-specific models that incorporate precipitation regimes and their interactions with acid deposition to improve the accuracy of decomposition rate predictions across varying hydroclimatic conditions. From a practical carbon management perspective, these findings advocate for interventions tailored to local precipitation regimes. These include implementing soil acidification mitigation measures, such as liming or planting base cation-enhancing vegetation, in high-rainfall regions. This strategy not only stabilizes the soil carbon pool but also maintains forest productivity, thereby directly supporting sustainable livelihoods that rely on forest resources like timber and non-timber products. In arid zones, litter protection practices such as mulching are recommended to preserve decomposition processes essential for nutrient cycling and soil fertility, which underpin sustainable forestry and desertification control. Furthermore, adaptive strategies, including real-time impact monitoring and dynamic carbon accounting systems, should be adopted in regions with highly variable precipitation patterns. From a policy standpoint, these region-specific interventions provide a scientific basis for revising environmental policies, such as acid rain emission standards and forest management plans, to balance short-term economic needs with long-term ecological sustainability. This comprehensive understanding of precipitation-mediated acid rain effects provides a robust scientific foundation for refining decomposition models and designing targeted carbon management strategies that account for regional hydrochemical dynamics, ultimately contributing to more effective climate change mitigation efforts in forest ecosystems under increasing environmental pressures.

This study, by integrating 361 datasets from 63 global studies, overcomes the limitations of small sample sizes and restricted spatial scales inherent in other studies of single-site or regional scope [39,64,84]. It quantifies the overall inhibitory effect of acid rain on forest organic carbon decomposition and identifies consistent regulatory patterns across regions. Its key strength lies in revealing precipitation as a large-scale environmental factor exerting substrate-dependent regulation on acid rain effects (alleviating inhibition of litter decomposition while intensifying inhibition of soil decomposition). Crucially, precipitation’s regulatory role outweighs that of smaller-scale factors like forest type, and the study elucidates the underlying mechanistic differences, offering a novel perspective for understanding global acid rain impacts. Regarding practical applications, the findings can guide regionally differentiated carbon management strategies (e.g., soil acidification mitigation via liming in high-precipitation zones, enhanced litter protection for carbon sequestration in arid regions) [106,107], optimize forest carbon cycle models by incorporating precipitation–acid rain interactions to improve predictive accuracy [108], and inform the dynamic adjustment of acid rain control and forest conservation policies (e.g., through establishing monitoring systems) [109]. The multifaceted environmental impacts on forests include: for carbon sequestration services, while acid rain inhibition temporarily increases carbon storage, its long-term effects and stability are modulated by precipitation (potentially promoting long-term accumulation in high-rainfall areas but increasing short-term release risks in arid zones) [110]; for nutrient cycling, inhibited decomposition alters nutrient release and storage, with precipitation adding complexity [111]; for soil health and hydrology, slowed decomposition alters soil structure, and precipitation-intensified acidification may mobilize toxic ions (e.g., aluminum), threatening soil health and water quality [112]; for biodiversity, acid rain (regulated by precipitation) may reduce decomposer community diversity and functional redundancy, impacting ecosystem stability [113]. These interconnections significantly deepen understanding of organic carbon decomposition response mechanisms and substantially enhance the ecological relevance of our findings.

5. Conclusions

This study delves into the intricate dynamics of acid rain’s impact on organic carbon decomposition within forest ecosystems, unearthing pivotal insights with broad implications. Our findings confirm that acid rain significantly inhibits organic carbon decomposition, a conclusion echoed by prior studies and reinforced by our analysis. This inhibition stems from acid rain-induced soil acidification, which elevates H⁺ concentrations, disrupts microbial communities, and curtails key ecosystem functions like carbon mineralization. While this suppression can temporarily boost carbon sequestration in forests, the long-term outlook is more complex. Accumulated carbon may alter soil properties, and its potential remobilization due to environmental disturbances or shifting decomposition pathways could exacerbate climate change. Crucially, our study highlights the differential effects of acid rain on soil and litter decomposition. Unlike forest type, precipitation emerges as a significant large-scale moderator. For litter, adequate precipitation can mitigate acid rain’s inhibition, whereas for soil, it can enhance the suppression of organic carbon decomposition. These insights underscore the need for climate-specific models to predict decomposition dynamics accurately. From a policy perspective, these findings advocate for targeted carbon management strategies. In regions with high rainfall, measures should focus on mitigating soil acidification, such as liming practices or promoting vegetation that enhances soil base cation status. Conversely, in arid regions, efforts should prioritize protecting litter decomposition processes. These strategies, grounded in our comprehensive understanding of acid rain’s impacts, offer a roadmap for refining decomposition models and improving forest carbon sequestration management in the face of environmental pressures. However, due to the lack of standardized reporting in the original studies, this research was unable to incorporate variables such as specific microbial community composition, which limits the in-depth exploration of underlying mechanisms. Additionally, as the meta-analysis focuses on average effects, further verification with more refined data is required in future studies regarding the potential nonlinear responses of organic carbon decomposition to acid rain intensity and precipitation gradients.

This research advances sustainability science by deepening understanding of acid rain’s role in forest carbon cycling, a core component of terrestrial ecosystem sustainability. By identifying precipitation as a key regulatory factor, we provide methodological support for optimizing sustainability assessment tools (e.g., forest carbon life cycle assessments) and formulating adaptive management strategies. These findings contribute to global efforts to quantify, monitor, and enhance forest sustainability, ultimately laying a scientific foundation for low-carbon, resilient pathways to sustainable development.