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The accumulation of cadmium (Cd) in agricultural soils presents a significant threat to crop safety, emphasizing the critical necessity for effective monitoring and management of soil Cd levels. Despite technological advancements, accurately monitoring soil Cd concentrations using satellite hyperspectral technology remains challenging, particularly in efficiently extracting spectral information. In this study, a total of 304 soil samples were collected from agricultural soils surrounding a tungsten mine located in the Xiancha River basin, Jiangxi Province, Southern China. Leveraging hyperspectral data from the ZY1-02D satellite, this research developed a comprehensive framework that evaluates the predictive accuracy of nine spectral transformations across four modeling approaches to estimate soil Cd concentrations. The spectral transformation methods included four logarithmic and reciprocal transformations, two derivative transformations, and three baseline correction and normalization transformations. The four models utilized for predicting soil Cd were partial least squares regression (PLSR), support vector machine (SVM), bidirectional recurrent neural networks (BRNN), and random forest (RF). The results indicated that these spectral transformations markedly enhanced the absorption and reflection features of the spectral curves, accentuating key peaks and troughs. Compared to the original spectral curves, the correlation analysis between the transformed spectra and soil Cd content showed a notable improvement, particularly with derivative transformations. The combination of the first derivative (FD) transformation with the RF model yielded the highest accuracy (R2 = 0.61, RMSE = 0.37 mg/kg, MAE = 0.21 mg/kg). Furthermore, the RF model in multiple spectral transformations exhibited higher suitability for modeling soil Cd content compared to other models. Overall, this research highlights the substantial applicative potential of the ZY1-02D satellite hyperspectral data for detecting soil heavy metals and provides a framework that integrates optimal spectral transformations and modeling techniques to estimate soil Cd contents.

Rapid and accurate mapping of soil properties in farmlands is crucial for guiding agricultural production and maintaining food security. Traditional methods using spectral features from remote sensing prove valuable for estimating soil properties, but are restricted to short periods of bare soil occurrence within agricultural settings. Addressing the challenge of predicting soil properties under crop cover, this study proposed an improved soil modeling framework that integrates dynamic crop growth information with machine learning techniques. The methodology’s robustness was tested on six key soil properties in an agricultural region of China, including soil organic carbon (SOC), total nitrogen (TN), total phosphorus (TP), dissolved organic carbon (DOC), dissolved organic nitrogen (DON), and pH. Four experimental scenarios were established to assess the impact of crop growth information, represented by the normalized difference vegetation index (NDVI) and phenological parameters. Specifically, Scenario I utilized only natural factors (terrain and climate data); Scenario II added phenological parameters based on Scenario I; Scenario III incorporated time-series NDVI based on Scenario I; and Scenario IV combined all variables (traditional natural factors and crop growth information). These were evaluated using three advanced machine learning models: random forest (RF), Cubist, and Extreme Gradient Boosting (XGBoost). Results demonstrated that incorporating phenological parameters and time-series NDVI significantly improved model accuracy, enhancing predictions by up to 36% over models using only natural factors. Moreover, although both are crop growth factors, the contribution of the time-series NDVI variable to model accuracy surpassed that of the phenological variable for most soil properties. Relative importance analysis suggested that the crop growth information, derived from time-series NDVI and phenology data, collectively explained 14–45% of the spatial variation in soil properties. This study highlights the significant benefits of integrating remote sensing-based crop growth factors into soil property inversion under crop-covered conditions, providing valuable insights for digital soil mapping.

Soil dissolved organic matter (DOM) plays a vital role in biogeochemical processes. Global warming leads to increased freeze–thaw cycles (FTCs) in boreal forest soils, which can change DOM production and consumption. However, the interactions between the chemical composition of DOM molecules and the microbial communities that drive C decomposition in the context of freeze–thaw are poorly understood. Here, a FTCs incubation experiment was conducted. Combined with pyrolysis gas chromatography–mass spectrometry and high-throughput sequencing techniques, the relationships between DOM chemodiversity and microbial community structure were assessed. Results indicated that both low-frequency (2FTCs) and high-frequency freeze–thaw cycles (6FTCs) significantly increased soil dissolved organic carbon (DOC) contents in the surface (0–10 cm) and subsurface (50–60 cm) soil layers. In the topsoil, FTCs significantly reduced the relative abundance of aromatic compounds, but increased the relative proportions of alkanes, phenols, fatty acid methyl esters (Me) and polysaccharides in the DOM. In the subsuface soil layer, only the relative abundance of Me in the 6FTCs treatment increased significantly. The response of bacterial communities to FTCs was more sensitive than that of fungi, among which only the relative abundance of Gammaproteobacteria increased by FTCs. Moreover, the relative abundance of these taxa was positively correlated with the increment of DOC. Co-occurrence networks confirmed DOM-bacterial interactions, implying that specific microorganisms degrade specific substrates. At class level, Gammaproteobacteria were significantly positively correlated with labile C (polysaccharides and alkanes), whereas other bacterial classes such as Actinobacteria, Alphaproteobacteria, and Thermoleophilia were significantly positively correlated with aromatic compounds in the topsoil. Collectively, FTCs tended to activate DOM and enhance its biodegradability of DOM, potentially hampering DOC accumulation and C sequestration. These findings highlight the potential of DOM molecular mechanisms to regulate the functional states of soil bacterial communities under increased FTCs.

Plant growth and its changes over space and time are effective indicators for signifying ecosystem health. However, large uncertainties remain in characterizing and attributing vegetation changes in the ecologically fragile South China Karst region, since most existing studies were conducted at a coarse spatial resolution or covered limited time spans. Considering the highly fragmented landscapes in the region, this hinders their capability in detecting fine information of vegetation dynamics taking place at local scales and comprehending the influence of climate change usually over relatively long temporal ranges. Here, we explored the spatiotemporal variations in vegetation greenness for the entire South China Karst region (1.9 million km2) at a resolution of 30m for the notably increased time span (1987-2018) using three decadal Landsat images and the cloud-based Google Earth Engine. Moreover, we spatially attributed the vegetation changes and quantified the relative contribution of driving factors. Our results revealed a widespread vegetation recovery in the South China Karst (74.80%) during the past three decades. Notably, the area of vegetation recovery tripled following the implementation of ecological engineering compared with the reference period (1987-1999). Meanwhile, the vegetation restoration trend was strongly sustainable beyond 2018 as demonstrated by the Hurst exponent. Furthermore, climate change contributed only one-fifth to vegetation restoration, whereas major vegetation recovery was highly attributable to afforestation projects, implying that anthropogenic influences accelerated vegetation greenness gains in karst areas since the start of the new millennium during which ecological engineering was continually established. Our study provides additional insights into ecological restoration and conservation in the highly heterogeneous karst landscapes and other similar ecologically fragile areas worldwide.

Grassland is the primary land use in China but has experienced severe degradation in recent decades due to overgrazing and conversion to agricultural production. Here, we conducted a field experiment in northeastern Inner Mongolia to test the effectiveness of sown pastures in lowering the grazing pressure on grasslands and raising the quality of marginal soils. Alfalfa and smooth bromegrass monocultures and mixture were sown in a marginal cropland field in Hulunber in June 2016. Biomass productivity, soil physicochemical, and biological properties were monitored annually from 2016 to 2020. The results showed that the marginal cropland soil responded consistently positively to sown pastures for major soil properties. Soil organic carbon (SOC) and total nitrogen (TN) increased by 48 and 21%, respectively, from 2016 to 2020 over the 0-60 cm soil depth range. Soil microbes responded proactively too. The soil microbial biomass C (SMBC) and N (SMBN) increased by 117 and 39%, respectively, during the period of 2016-2020. However, by the end of the experiment, the soil of a natural grassland field, which was included in the experiment as a control, led the sown pasture soil by 28% for SOC, 35% for TN, 66% for SMBC, and 96% for SMBN. Nevertheless, the natural grassland soil’s productive capacity was inferior to that of the sown pasture soil. The average aboveground biomass productivity of sown pastures was measured at 8.4 Mg ha-1 in 2020, compared to 5.0 Mg ha-1 for natural grassland, while the root biomass of sown pastures was averaged at 7.5 Mg ha-1, leading the natural grassland by 15%. Our analyses also showed that the sown pastures’ biomass productivity advantage had a much-neglected potential in natural grassland protection. If 50% of the available marginal cropland resources in Hulunber under the current environmental protection law were used for sown pastures, the livestock grazing pressure on the natural grasslands would decrease by a big margin of 38%. Overall, these results represent systematic empirical and analytical evidence of marginal cropland soil’s positive responses to sown pastures, which shows clearly that sown pasture is a valid measure both for soil rehabilitation and biomass production.

The Northeast Farming-pastoral ecotone (NFPE) in China is crucial for farming and pastoralism. However, sustainable development of land resources faces several challenges from human activities and environmental degradation. Thus, assessing land resource-carrying capacity (LRCC) is crucial for effective land-use planning and management. This study proposes a comprehensive indicator system to evaluate LRCC in the NFPE. We integrate remote sensing, model simulation, and statistical data, employing Geographic Information System analysis and statistical techniques to assess spatiotemporal evolution characteristics of LRCC. Findings indicate increasing LRCC with spatial-temporal heterogeneity. The northern region exhibits relatively high and growing LRCC, while some eastern regions have low LRCC but a high growth trend. The southern region displays both low LRCC and growth. Balancing regional development and ecological protection requires considering spatial heterogeneity of LRCC in land-use planning and management decisions, facilitating differentiated land-use strategies for sustainable development.

As an important means of production and habitation, land is the core natural resource for people’s life and production. However, the escalating demand for land resources, along with population growth, has created a pressing challenge at the intersection of land, population, and grain. In this study, we used a combination of temporal and spatial perspectives, along with spatial analysis methods, to analyze the supply and demand of land resources in the Northeast Farming–Pastoral Ecotone (NFPE) based on the population–grain relationship. The results show that the carrying capacity of land resources in this region increased gradually and the number of people it can carry increased from 34.23 million (2000) to 127.96 million (2018). From 2000 to 2018, the land resource carrying index (LRCI) was greater than 1.125, showing a state of surplus in grain, and the LRCI increased by 0.23 per year. Most areas in this region are in a state of grain surplus, with 13 counties showing a population and grain balance, and 2 counties being overpopulated. However, the increasing trend of land resource carrying capacity of the overpopulated areas and the balanced population–grain areas are notable. The carrying capacity of land resources in the NFPE generally exhibits a positive trend with a surplus of grain; however, there are spatial variations. Areas in the south and northeast demonstrate lower change trends, while the north and southeast exhibit higher change trends. This research provides critical insights into the dynamic interplay between land, population, and grain, enabling informed decision making for sustainable development in the NFPE and offering valuable lessons for similar regions globally.

Surface ozone (O3) pollution is an emerging environmental abiotic stress that poses substantial risks to crop yield losses and food security worldwide, and especially in China. However, the O3-induced detrimental effects on double-season rice have rarely been investigated at large scales and over relatively long temporal spans. In this study, we estimated the crop production reductions and associated economic losses for double-season rice across southern China during 2013–2019, using a high spatial resolution surface ozone reanalysis dataset and rice distribution maps, and county-level production data, in combination with a locally derived exposure-response function for rice. Results show that AOT40 (cumulative hourly O3 exposure above 40 ppb) presented generally increasing trends over growing seasons in 2013–2019, spanning from 4.0 to 7.1 ppm h and 6.1 to 10.5 ppm h for double-early rice and double-late rice, respectively. Moreover, O3-induced relative yield losses ranged from 4.0% to 6.6% for double-early rice and 6.3% to 11.1% for double-late rice. Over the seven years, ambient O3 exposure resulted in crop production losses of 1951.5 × 104 tons and economic losses of 8,081.03 million USD in total. To combat the O3-induced agricultural risks, measures such as stringent precursors emission reductions and breeding O3-resistant cultivars should be continuously implemented in the future.

Abstract Forests and grasslands play a significant role in global nitrous oxide (N2O) emission, while a sophisticated model is missing to interpret the microbial processes involved in N2O production and consumption in these ecosystems. A new N2O emission model, MicN model, was developed for natural forest and grassland ecosystems by integrating with Integrated BIosphere Simulator (IBIS). This new model includes four major N2O correlated processes: autotrophic nitrification, heterotrophic nitrification, nitrifier denitrification and denitrifier denitrification. Thirty‐nine observation sites, including various forest and grassland ecosystem types, were selected to calibrate and validate IBIS‐MicN model. The IBIS‐MicN model explained 89% and 85% of observed daily variability in N2O emission for all calibration and validation sites, respectively. In addition, the model successfully predicted the concentrations of two important intermediates (i.e., ammonium and nitrate) for N2O production at nine validation sites. Compared with Forest‐DNDC model, IBIS‐MicN model performed better in simulating N2O emission, soil NH4+ and NO3−concentrations. At site scale, the simulated N2O emissions by IBIS‐MicN model positively correlated with annual mean soil temperature and annual precipitation, but negatively correlated with soil pH. This study provides an alternative model to estimate global N2O emission from natural forest and grassland ecosystems and to investigate corresponding environmental regulations.

Rapid and accurate mapping of soil organic carbon (SOC) is of great significance to understanding the spatial patterns of soil fertility and conducting soil carbon cycle research. Previous studies have dedicated considerable efforts to the spatial prediction of SOC content, but few have systematically quantified the effects of environmental covariates selection, the spatial scales and the model types on SOC prediction accuracy. Here, we spatially predicted SOC content through digital soil mapping (DSM) based on 186 topsoil (0–20 cm) samples in a typical hilly red soil region of southern China. Specifically, we first determined an optimal covariate set from different combinations of multiple environmental variables, including multi-sensor remote sensing images (Sentinel-1 and Sentinel-2), climate variables and DEM derivatives. Furthermore, we evaluated the impacts of spatial resolution (10 m, 30 m, 90 m, 250 m and 1000 m) of covariates and the model types (three linear and three non-linear machine learning techniques) on the SOC prediction. The results of the performance analysis showed that a combination of Sentinel-1/2-derived variables, climate and topographic predictors generated the best predictive performance. Among all variables, remote sensing covariates, especially Sentinel-2-derived predictors, were identified as the most important explanatory variables controlling the variability of SOC content. Moreover, the prediction accuracy declined significantly with the increased spatial scales and achieved the highest using the XGBoost model at 10 m resolution. Notably, non-linear machine learners yielded superior predictive capability in contrast with linear models in predicting SOC. Overall, our findings revealed that the optimal combination of predictor variables, spatial resolution and modeling techniques could considerably improve the prediction accuracy of the SOC content. Particularly, freely accessible Sentinel series satellites showed great potential in high-resolution digital mapping of soil properties.

Abstract Background Although soil erosion plays a key role in the carbon cycle, a holistic and mechanistic understanding of the soil erosion process within the cycle is still lacking. The aim of this study was therefore to improve our mechanistic understanding of soil organic carbon (SOC) and soil respiration dynamics through an experiment conducted in an eroding black soil farmland landscape in Northeast China. Results The depositional profiles store 5.9 times more SOC than the eroding profiles and 3.3 times more SOC than the non-eroding profiles. A linear correlation between the SOC and 137Cs (Caesium-137) was observed in our study, suggesting that the SOC decreased with increased soil erosion. Furthermore, the fractions of intermediate C and the microaggregate C were lowest at the eroding position and highest at the depositional position. In the depositional topsoil, the input of labile materials plays a promotional role in soil respiration. Conversely, in the subsoil (i.e., below 10 cm), the potential mineralization rates were lowest at the depositional position—due to effective stabilization by physical protection within soil microaggregates. The field results of soil surface respiration also suggest that the depositional topsoil SOC is prone to be mineralized and that SOC at this depositional context is stabilized at subsoil depth. In addition, the high water contents at the depositional position can limit the decomposition rates and stabilize the SOC at the same time. Conclusions The findings from this study support that a majority of the SOC at footslope is stored within most of the soil profile (i.e., below 10 cm) and submitted to long-term stabilization, and meanwhile support that the depositional profile emits more CO2 than the summit due to its high amount and quality of SOC.

Accurate and reliable land cover information is vital for ecosystem management and regional sustainable development, especially for ecologically vulnerable areas. The South China Karst, one of the largest and most concentrated karst distribution areas globally, has been undergoing large-scale afforestation projects to combat accelerating land degradation since the turn of the new millennium. Here, we assess five recent and widely used global land cover datasets (i.e., CCI-LC, MCD12Q1, GlobeLand30, GlobCover, and CGLS-LC) for their comparative performances in land dynamics monitoring in the South China Karst during 2000–2020 based on the reference China Land Use/Cover Database. The assessment proceeded from three aspects: areal comparison, spatial agreement, and accuracy metrics. Moreover, divergent responses of overall accuracy with regard to varying terrain and geomorphic conditions have also been quantified. The results reveal that obvious discrepancies exist amongst land cover maps in both area and spatial patterns. The spatial agreement remains low in the Yunnan–Guizhou Plateau and heterogeneous mountainous karst areas. Furthermore, the overall accuracy of the five datasets ranges from 40.3% to 52.0%. The CGLS-LC dataset, with the highest accuracy, is the most accurate dataset for mountainous southern China, followed by GlobeLand30 (51.4%), CCI-LC (50.0%), MCD12Q1 (41.4%), and GlobCover (40.3%). Despite the low overall accuracy, MCD12Q1 has the best accuracy in areas with an elevation above 1200 m or a slope greater than 25°. With regard to geomorphic types, accuracy in non-karst areas is evidently higher than in karst areas. Additionally, dataset accuracy declines significantly (p < 0.05) with an increase in landscape heterogeneity in the region. These findings provide useful guidelines for future land cover mapping and dataset fusion.

Exogenous nitrogen (N) inputs greatly change the emission and uptake of carbon dioxide (CO2) and methane (CH4) from temperate grassland soils, thereby affecting the carbon (C) budget of regional terrestrial ecosystems. Relevant research focused on natural grassland, but the effects of N fertilization on C exchange fluxes from different forage soils and the driving mechanisms were poorly understood. Here, a three-year N addition experiment was conducted on cultivated grassland planted with alfalfa (Medicago sativa) and bromegrass (Bromus inermis) in Inner Mongolia. The fluxes of soil-atmospheric CO2 and CH4; the content of the total dissolved N (TDN); the dissolved organic N (DON); the dissolved organic C (DOC); NH4+–N and NO3−–N in soil; enzyme activity; and auxiliary variables (soil temperature and moisture) were simultaneously measured. The results showed that N fertilization (>75 kg N ha−1 year−1) caused more serious soil acidification for alfalfa planting than for brome planting. N fertilization stimulated P-acquiring hydrolase (AP) in soil for growing Bromus inermis but did not affect C- and N-acquiring hydrolases (AG, BG, CBH, BX, LAP, and NAG). The oxidase activities (PHO and PER) of soil for planting Bromus inermis were higher than soil for planting Medicago sativa, regardless of N, whether fertilization was applied or not. Forage species and N fertilization did not affect soil CO2 flux, whereas a high rate of N fertilization (150 kg N ha−1 year−1) significantly inhibited CH4 uptake in soil for planting Medicago sativa. A synergistic effect between CO2 emission and CH4 uptake in soil was found over the short term. Our findings highlight that forage species affect soil enzyme activity in response to N fertilization. Soil enzyme activity may be an important regulatory factor for C exchange from temperate artificial grassland soil in response to N fertilization.

Key Findings: Combining physical fractionation and pyrolysis−gas chromatography/mass spectrometry (py-GC/MS) technique can help better understand the dynamics of soil organic matter (SOM). Background and Objectives: SOM plays a critical role in the global carbon (C) cycle. However, its complexity remains a challenge in characterizing chemical molecular composition within SOM and under nitrogen (N) deposition. Materials and Methods: Three particulate organic matter (POM) fractions within SOM and under N treatments were studied from perspectives of distributions, C contents and chemical signatures in a subtropical forest. N addition experiment was conducted with two inorganic N forms (NH4Cl and NaNO3) applied at three rates of 0, 40, 120 kg N ha−1 yr−1. Three particle-size fractions (>250 μm, 53−250 μm and Results: A progressive proportion transfer of mineral-associated organic matter (MAOM) to fine POM under N treatment was found. Only C content in fine POM was sensitive to N addition. Principal component analyses (PCA) showed that the coarse POM had the largest plant-derived markers (lignins, phenols, long-chain n-alkanes, and n-alkenes). Short-chain n-alkanes and n-alkenes, benzofurans, aromatics and polycyclic aromatic hydrocarbons mainly from black carbon prevailed in the fine POM. N compounds and polysaccharides from microbial products dominated in the MAOM. Factor analysis revealed that the degradation extent of three fractions was largely distinct. The difference in chemical structure among three particulate fractions within SOM was larger than treatments between control and N addition. In terms of N treatment impact, the MAOM fraction had fewer benzofurans compounds and was enriched in polysaccharides, indicating comparatively weaker mineralization and stronger stabilization of these substances. Conclusions: Our findings highlight the importance of chemical structure in SOM pools and help to understand the influence of N deposition on SOM transformation.

We report a novel material testing system (MTS) that uses hierarchical designs for in-situ mechanical characterization of multiscale materials. This MTS is adaptable for use in optical microscopes (OMs) and scanning electron microscopes (SEMs). The system consists of a microscale material testing module (m-MTM) and a nanoscale material testing module (n-MTM). The MTS can measure mechanical properties of materials with characteristic lengths ranging from millimeters to tens of nanometers, while load capacity can vary from several hundred micronewtons to several nanonewtons. The m-MTM is integrated using piezoelectric motors and piezoelectric stacks/tubes to form coarse and fine testing modules, with specimen length from millimeters to several micrometers, and displacement distances of 12 mm with 0.2 µm resolution for coarse level and 8 µm with 1 nm resolution for fine level. The n-MTM is fabricated using microelectromechanical system technology to form active and passive components and realizes material testing for specimen lengths ranging from several hundred micrometers to tens of nanometers. The system’s capabilities are demonstrated by in-situ OM and SEM testing of the system’s performance and mechanical properties measurements of carbon fibers and metallic microwires. In-situ multiscale deformation tests of Bacillus subtilis filaments are also presented.

To date, few studies are conducted to quantify the effects of reduced ammonium (NH4+) and oxidized nitrate (NO3-) on soil CH4 uptake and N2O emission in the subtropical forests. In this study, NH4Cl and NaNO3 fertilizers were applied at three rates: 0, 40 and 120 kg N ha(-1) yr(-1). Soil CH4 and N2O fluxes were determined twice a week using the static chamber technique and gas chromatography. Soil temperature and moisture were simultaneously measured. Soil dissolved N concentration in 0-20 cm depth was measured weekly to examine the regulation to soil CH4 and N2O fluxes. Our results showed that one year of N addition did not affect soil temperature, soil moisture, soil total dissolved N (TDN) and NH4+-N concentrations, but high levels of applied NH4Cl and NaNO3 fertilizers significantly increased soil NO3(-)-N concentration by 124% and 157%, respectively. Nitrogen addition tended to inhibit soil CH4 uptake, but significantly promoted soil N2O emission by 403% to 762%. Furthermore, NH4+-N fertilizer application had a stronger inhibition to soil CH4 uptake and a stronger promotion to soil N2O emission than NO3(-)-N application. Also, both soil CH4 and N2O fluxes were driven by soil temperature and moisture, but soil inorganic N availability was a key integrator of soil CH4 uptake and N2O emission. These results suggest that the subtropical plantation soil sensitively responses to atmospheric N deposition, and inorganic N rather than organic N is the regulator to soil CH4 uptake and N2O emission.

We added the stable isotope (15)N in the form of ((15)NH4)2SO4 and K(15)NO3 to forest ecosystems in eastern China under two different N deposition levels to study the fate of the different forms of deposited N. Prior to the addition of the (15)N tracers, the natural (15)N abundance ranging from -3.4‰ to +10.9‰ in the forest under heavy N deposition at Dinghushan (DHS), and from -3.92‰ to +7.25‰ in the forest under light N deposition at Daxinganling (DXAL). Four months after the tracer application, the total (15)N recovery from the major ecosystem compartments ranged from 55.3% to 90.5%. The total (15)N recoveries were similar under the ((15)NH4)2SO4 tracer treatment in both two forest ecosystems, whereas the total (15)N recovery was significantly lower in the subtropical forest ecosystem at DHS than in the boreal forest ecosystem at DXAL under the K(15)NO3 tracer treatment. The (15)N assimilated into the tree biomass represented only 8.8% to 33.7% of the (15)N added to the forest ecosystems. In both of the tracer application treatments, more (15)N was recovered from the tree biomass in the subtropical forest ecosystem at DHS than the boreal forest ecosystem at DXAL. The amount of (15)N assimilated into tree biomass was greater under the K(15)NO3 tracer treatment than that of the ((15)NH4)2SO4 treatment in both forest ecosystems. This study suggests that, although less N was immobilized in the forest ecosystems under more intensive N deposition conditions, forest ecosystems in China strongly retain N deposition, even in areas under heavy N deposition intensity or in ecosystems undergoing spring freezing and thawing melts. Compared to ammonium deposition, deposited nitrate is released from the forest ecosystem more easily. However, nitrate deposition could be retained mostly in the plant N pool, which might lead to more C sequestration in these ecosystems.

A novel, large-deflection piezoelectric folded cantilever microactuator (PFCM) is presented. A multimorph model for the large-deflection PFCM is derived, in which unified formulas for deflection angle and vertical displacement of N-level PFCM are obtained.