Influence of Different Rotations of Organic Formamidinium Molecule on Electronic and Optical Properties of FAPbBr3 Perovskite

Hybrid organic–inorganic halide perovskites (HOIPs) have recently represented a material breakthrough for optoelectronic applications. Obviously, studying the interactions between the central organic cation and the Pb-X inorganic octahedral could provide a better understanding of HOIPs. In this work, we used a first-principles theoretical study to investigate the effect of different orientations of central formamidinium cation (FA+) on the electronic and optical properties of FAPbBr3 hybrid perovskite. In order to do this, the band structure (with and without spin–orbit coupling (SOC)), density of states (DOS), partial density of states (PDOS), electron density, distortion index, bond angle variance, dielectric function, and absorption spectra were computed. The findings revealed that a change in the orientation of FA+ caused some disorders in the distribution of interactions, resulting in the formation of some specific energy levels in the structure. The interactions between the inorganic and organic parts in different directions create a distortion index in the bonds of the inorganic octahedral, thus leading to a change in the volume of PbBr6. This is the main reason for the variations observed in the electronic and optical properties of FAPbBr3. The obtained results can be helpful in solar-cell applications.

Prediction Potential of Remote Sensing-Related Variables in the Topsoil Organic Carbon Density of Liaohekou Coastal Wetlands, Northeast China

Wetland ecosystems contain large amounts of soil organic carbon. Their natural environment is often both at the junction of land and water with good conditions for carbon sequestration. Therefore, the study of accurate prediction of soil organic carbon (SOC) density in coastal wetland ecosystems of flat terrain areas is the key to understanding their carbon cycling. This study used remote sensing data to study SOC density potentials of coastal wetland ecosystems in Northeast China. Eleven environmental variables including normalized difference vegetation index (NDVI), difference vegetation index (DVI), soil adjusted vegetation index (SAVI), renormalization difference vegetation index (RDVI), ratio vegetation index (RVI), topographic wetness index (TWI), elevation, slope aspect (SA), slope gradient (SG), mean annual temperature (MAT), and mean annual precipitation (MAP) were selected to predict SOC density. A total of 193 soil samples (0–30 cm) were divided into two parts, 70% of the sampling sites data were used to construct the boosted regression tree (BRT) model containing three different combinations of environmental variables, and the remaining 30% were used to test the predictive performance of the model. The results show that the full variable model is better than the other two models. Adding remote sensing-related variables significantly improved the model prediction. This study revealed that SAVI, NDVI and DVI were the main environmental factors affecting the spatial variation of topsoil SOC density of coastal wetlands in flat terrain areas. The mean (±SD) SOC density of full variable models was 18.78 (±1.95) kg m−2, which gradually decreased from northeast to southwest. We suggest that remote sensing-related environmental variables should be selected as the main environmental variables when predicting topsoil SOC density of coastal wetland ecosystems in flat terrain areas. Accurate prediction of topsoil SOC density distribution will help to formulate soil management policies and enhance soil carbon sequestration.

Improved Wetland Soil Organic Carbon Stocks of the Conterminous U.S. Through Data Harmonization

Wetland soil stocks are important global repositories of carbon (C) but are difficult to quantify and model due to varying sampling protocols, and geomorphic/spatio-temporal discontinuity. Merging scales of soil-survey spatial extents with wetland-specific point-based data offers an explicit, empirical and updatable improvement for regional and continental scale soil C stock assessments. Agency-collected and community-contributed soil datasets were compared for representativeness and bias, with the goal of producing a harmonized national map of wetland soil C stocks with error quantification for wetland areas of the conterminous United States (CONUS) identified by the USGS National Landcover Change Dataset. This allowed an empirical predictive model of SOC density to be applied across the entire CONUS using relational %OC distribution alone. A broken-stick quantile-regression model identified %OC with its relatively high analytical confidence as a key predictor of SOC density in soil segments; soils <6% OC (hereafter, mineral wetland soils, 85% of the dataset) had a strong linear relationship of %OC to SOC density (RMSE = 0.0059, ~4% mean RMSE) and soils >6% OC (organic wetland soils, 15% of the dataset) had virtually no predictive relationship of %OC to SOC density (RMSE = 0.0348 g C cm−3, ~56% mean RMSE). Disaggregation by vegetation type or region did not alter the breakpoint significantly (6% OC) and did not improve model accuracies for inland and tidal wetlands. Similarly, SOC stocks in tidal wetlands were related to %OC, but without a mappable product for disaggregation to improve accuracy by soil class, region or depth. Our layered harmonized CONUS wetland soil maps revised wetland SOC stock estimates downward by 24% (9.5 vs. 12.5Pg C) with the overestimation being entirely an issue of inland organic wetland soils (35% lower than SSURGO-derived SOC stocks). Further, SSURGO underestimated soil carbon stocks at depth, as modeled wetland SOC stocks for organic-rich soils showed significant preservation downcore in the NWCA dataset (<3% loss between 0 and 30 cm and 30 and 100 cm depths) in contrast to mineral-rich soils (37% downcore stock loss). Future CONUS wetland soil C assessments will benefit from focused attention on improved organic wetland soil measurements, land history, and spatial representativeness.

Simulating Grassland Carbon Dynamics in Gansu for the Past Fifty (50) Years (1968–2018) Using the Century Model

China is one of the countries most impacted by desertification, with Gansu Province in the northwest being one of the most affected areas. Efforts have been made in recent decades to restore the natural vegetation, while also producing food. This has implications for the soil carbon sequestration and, as a result, the country’s carbon budget. Studies of carbon (C) dynamics in this region would help to understand the effect of management practices on soil organic carbon (SOC) as well as aboveground biomass (ABVG), and to aid informed decision-making and policy implementation to alleviate the rate of global warming. It would also help to understand the region’s contribution to the national C inventory of China. The CENTURY model, a process-based model that is capable of simulating C dynamics over a long period, has not been calibrated to suit Gansu Province, despite being an effective model for soil C estimation. Using the soil and grassland maps of Gansu, together with weather, soil, and reliable historical data on management practices in the province, we calibrated the CENTURY model for the province’s grasslands. The calibrated model was then used to simulate the C dynamics between 1968 and 2018. The results show that the model is capable of simulating C with significant accuracy. Our measured and observed SOC density (SOCD) and ABVG had correlation coefficients of 0.76 and 0.50, respectively, at p < 0.01. Precipitation correlated with SOCD and ABVG with correlation coefficients of 0.57 and 0.89, respectively, at p < 0.01. The total SOC storage (SOCS) was 436.098 × 106 t C (approximately 0.4356% of the national average) and the average SOCD was 15.75 t C/ha. There was a high ABVG in the southeast and it decreased towards the northwest. The same phenomenon was observed in the spatial distribution of SOCD. Among the soils studied, Hostosols had the highest SOC sequestration rate (25.6 t C/ha) with Gypsisols having the least (7.8 t C/ha). Between 1968 and 2018, the soil carbon stock gradually increased, with the southeast experiencing the greatest increase.

Estimation and spatio-temporal analysis of soil organic carbon stock in China using machine learning algorithms from 2000 to 2014

&lt;p&gt;Soil is the largest carbon pool in terrestrial ecosystems, storing up to 2 or 3 times the amount of carbon present in the atmosphere, and a small change in soil carbon stock could have profound effects on atmospheric CO&lt;sub&gt;2&lt;/sub&gt; and climate change. However, an accurate estimate of soil organic carbon (SOC) stock is still challenging. Previous studies on SOC stock prediction across China were mainly from biogeochemical models and national soil inventories, and large uncertainties still remained. In this study, we predicted SOC stock at 0 &amp;#8211; 20 cm and 0 &amp;#8211; 100 cm with 3419 and 2479field observations using artificial neural network (ANN), extreme gradient boosting (XGBoost), random forest (RF), and gradient boosting regression trees (GBRT) across China with the linkage of climate, vegetation and soil variables. Results showed that RF performed best among the four machine learning approaches with model efficiency of 0.61 for 0 &amp;#8211; 20 cm and 0.52 for 0 &amp;#8211; 100 cm. The trained RF model was used to predicted the temporal and spatial patterns of SOC stock at a spatial resolution of 1 km from 2000 to 2014 across China. Temporally, SOC stock at 0 &amp;#8211; 20 cm (p = 0.07) and 0 &amp;#8211; 100 cm (p = 0.3) did not change significantly. However, SOC density showed strong spatial patterns, the mean value of SOC density at 0-20 cm and 0-100 cm increased firstly, then decreased and then increased with the increase of latitude, and the minimum density was 39.83&amp;#176; and 41.59&amp;#176;, respectively. The total SOC stocks across China were 33.68 and 95.01 Pg C for 0 &amp;#8211; 20 cm and 0 &amp;#8211; 100 cm, respectively. The developed SOC stock could serve as an independent dataset that could be used for decision-making and help with baseline assessments for inventory and monitoring SOC stocks for global biogeochemical models in China.&lt;/p&gt;

Use of Topographic Models for Mapping Soil Properties and Processes

Landscape topography is an important driver of landscape distributions of soil properties and processes due to its impacts on gravity-driven overland and intrasoil lateral transport of water and nutrients. Rapid advancements in aerial, space, and geographic technologies have led to large scale availability of digital elevation models (DEMs), which have proven beneficial in a wide range of applications by providing detailed topographic information. In this report, we presented a summary of recent topography-based soil studies and reviewed five main groups of topographic models in geospatial analyses widely used for soil sciences. We then compared performances of two types of topography-based models—topographic principal component regression (TPCR) and TPCR-kriging (TPCR-Kr)—to ordinary kriging (OKr) models in mapping spatial patterns of soil organic carbon (SOC) density and redistribution (SR) rate. The TPCR and OKr models were calibrated at an agricultural field site that has been intensively sampled, and the TPCR and TPCR-Kr models were evaluated at another field of interest with two sampling transects. High-resolution topographic variables generated from light detection and ranging (LiDAR)-derived DEMs were used as inputs for the TPCR model building. Both TPCR and OKr models provided satisfactory results on SOC density and SR rate estimations during model calibration. The TPCR models successfully extrapolated soil parameters outside of the area in which the model was developed but tended to underestimate the range of observations. The TPCR-Kr models increased the accuracies of estimations due to the inclusion of residual kriging calculated from observations of transects for local correction. The results suggest that even with low sample intensives, the TPCR-Kr models can reduce estimation variances and provide higher accuracy than the TPCR models. The case study demonstrated the feasibility of using a combination of linear regression and spatial correlation analysis to localize a topographic model and to improve the accuracy of soil property predictions in different regions.

Soil Organic Carbon Distribution and Isotope Composition Response to Erosion in Cropland under Soybean/Maize Production

&lt;p&gt;Soil erosion and deposition patterns can affect the fate of soil organic carbon (SOC) in agroecosystems. Topographic constraints affect soil redistribution processes and create spatial structure in SOC density. We combined isoscape (isotopic landscape) analyses for &amp;#948;&lt;sup&gt;13&lt;/sup&gt;C and cesium-137 (&lt;sup&gt;137&lt;/sup&gt;Cs) inventory via digital terrain analysis quantifying SOC dynamics and soil redistribution patterns to gain insight on their responses to topographic constraints in an Iowa cropland field under soybean/maize (C3/C4) production. Additionally, historic bare soil orthophotos were used to determine soil carbon distribution before the 1960s (prior to global &lt;sup&gt;137&lt;/sup&gt;CS fallout). Topography&amp;#8208;based models were developed to estimate &lt;sup&gt;137&lt;/sup&gt;Cs inventory, SOC density, and &amp;#948;&lt;sup&gt;13&lt;/sup&gt;C distributions using stepwise principal component regression. Findings showed that spatial patterns of SOC were similar to soil erosion/deposition patterns with high SOC density in depositional areas and low SOC density in eroded areas. Soil redistribution, SOC density, and &amp;#948;&lt;sup&gt;13&lt;/sup&gt;C signature of SOC were all highly correlated with topographic metrics indicating that topographic constraints determined the spatial variability in erosion and SOC dynamics. The &amp;#948;&lt;sup&gt;13&lt;/sup&gt;C isoscape indicated that C3&amp;#8208;derived SOC density was strongly controlled by topographic metrics whereas C4&amp;#8208;derived SOC density showed much weaker expression of spatial pattern and poor correlation to topographic metrics. The resulting topography&amp;#8208;based models captured more than 60% of the variability in total SOC density and C3&amp;#8208;derived SOC density but could not reliably predict C4&amp;#8208;derived SOC density. This study demonstrated the utility of exploring relationships between &amp;#948;&lt;sup&gt;13&lt;/sup&gt;C and &lt;sup&gt;137&lt;/sup&gt;Cs isoscapes to gain insight on fate of SOC within eroding agricultural landscapes.&lt;/p&gt;

Quantification of carbon stock under different land use regimes of Chitwan district, Nepal

Carbon sequestration in terrestrial ecosystems is gaining a global attention, including Nepal, to address the issues of climate change. Since, the quantification of carbon stock under different land use systems with focus on both biomass and soil profile is lacking, objective of this paper is to quantify carbon stock in biomass and in soil profile under different land use regimes, namely community forest, leasehold forest and agricultural land of Chitwan district. The carbon stock in biomass was calculated using the standard allometric equations, and Dry Combustion Method was used to determine the Soil Organic Carbon (SOC). The carbon content in above ground tree biomass (AGTB) was found to be higher (81.25 t/ha) in community forest than in leasehold forest (80.09 t/ha). The carbon stock in above ground sapling biomass (AGSB) was calculated only for the community forest, and was found to be 3. 67 t/ha. Similarly, the density of leaf litter, herbs and grasses (LHG) was also found to be higher (9. 25 t/ha) in the community forest in comparison to leasehold forest (6.45 t/ha). Further, the root carbon stock density was also higher (16.25 t/ha) in the community forest than in the leasehold forest (16.02 t/ha). However, the SOC density was highest in the agricultural land (73.42t/ha) followed by the community forest (66.38 t/ha)and the leasehold forest (52. 62 t/ha). Overall, the carbon stock was highest in the community forest (176.8 t/ha) then in leasehold forest (155.18 t/ha) followed by the agricultural land (73.42 t/ha). Hence, this study shows that well managed community forest can contribute significantly in offsetting global carbon emission.

Global soil–climate–biome diagram: linking surface soil properties to climate and biota

Surface soils interact strongly with both climate and biota and provide fundamental ecosystem services that maintain food, climate and human security. However, the quantitative linkages between soil properties, climate and biota remain unclear at the global scale. By compiling a comprehensive global soil database, we mapped eight major soil properties (bulk density; clay, silt, and sand fractions; soil pH; soil organic carbon, SOC, density; soil total nitrogen, STN, density; and soil C:N mass ratios) in the surface soil layer (0–30 cm), based on machine learning algorithms, and demonstrated the quantitative linkages between surface soil properties, climate and biota at the global scale, which we call the global soil–climate–biome diagram. In the diagram, bulk density increased significantly with higher mean annual temperature (MAT) and lower mean annual precipitation (MAP); soil clay fraction increased significantly with higher MAT and MAP; soil pH decreased with higher MAP and lower MAT and the “critical MAP”, which means the corresponding MAP at a soil pH of =7.0 (a shift from alkaline to acidic soil), decreased with lower MAT. SOC density and STN density were both jointly affected by MAT and MAP, showing an increase at lower MAT and a saturation towards higher MAP. Surface soil physical and chemical properties also showed remarkable variation across biomes. The soil–climate–biome diagram suggests shifts in soil properties under global climate and land cover change.

Global soil-climate-biome diagram: linking surface soil properties to climate and biota

Surface soils interact strongly with both climate and biota and provide fundamental ecosystem services that maintain food, climate, and human security. However, the quantitative linkages between soil properties, climate, and biota at the global scale remain unclear. By compiling a comprehensive global soil database, we mapped eight major soil properties (bulk density; clay, silt, and sand fractions; soil pH; soil organic carbon [SOC] density; soil total nitrogen [STN] density; and soil C : N mass ratios) in the surface (0–30 cm) soil layer based on machine learning algorithms, and demonstrated the quantitative linkages between surface soil properties, climate, and biota at the global scale (i.e., global soil-climate-biome diagram). On the diagram, bulk density increased significantly with higher mean annual temperature (MAT) and lower mean annual precipitation (MAP); soil clay fraction increased significantly with higher MAT and MAP; Soil pH decreased with higher MAP and lower MAT, and the critical MAP for the transition from alkaline to acidic soil decreased with decreasing MAT; SOC density and STN density both were jointly affected by MAT and MAP, showing an increase at lower MAT and a saturation tendency towards higher MAP. Surface soil physical and chemical properties also showed remarkable variations across biomes. The soil-climate-biome diagram suggests the co-evolution of the soil, climate, and biota under global environmental change.