Regional Biomass Research Articles

Biomass-burning sources control ambient particulate matter, but traffic and industrial sources control volatile organic compound (VOC) emissions and secondary-pollutant formation during extreme pollution events in Delhi

Abstract. Volatile organic compounds (VOCs) and particulate matter (PM) are major constituents of smog. Delhi experiences severe smog during the post-monsoon season, but a quantitative understanding of VOCs and PM sources is still lacking. Here, we conduct a source apportionment study for VOCs and PM using a recent (2022), high-quality dataset of 111 VOCs, PM2.5, and PM10 in a positive matrix factorization (PMF) model. Contrasts between clean monsoon air and polluted post-monsoon air, VOC source fingerprints, and molecular tracers enabled us to differentiate paddy residue burning from other biomass-burning sources, which had previously been impossible. Burning of fresh paddy residue, as well as residential heating and waste burning, contributed the most to observed PM10 levels (25 % and 23 %, respectively) and PM2.5 levels (23 % and 24 %, respectively), followed by heavy-duty vehicles fuelled by compressed natural gas (CNG), with a PM10 contribution of 15 % and a PM2.5 contribution of 11 %. For ambient VOCs, ozone formation potential, and secondary-organic-aerosol (SOA) formation potential, the top sources were petrol four-wheelers (20 %, 25 %, and 30 %, respectively), petrol two-wheelers (14 %, 12 %, and 20 %, respectively), industrial emissions (12 %, 14 %, and 15 %, respectively), solid-fuel-based cooking (10 %, 10 %, and 8 %, respectively), and road construction (8 %, 6 %, and 9 %, respectively). Emission inventories tended to overestimate residential biofuel emissions at least by a factor of 2 relative to the PMF output. The major source of PM pollution was regional biomass burning, while traffic and industries governed VOC emissions and secondary-pollutant formation. Our novel source apportionment method even quantitatively resolved similar biomass and fossil fuel sources, offering insights into both VOC and PM sources affecting extreme pollution events. This approach represents a notable advancement compared to current source apportionment approaches, and it could be of great relevance for future studies in other polluted cities and regions of the world with complex source mixtures.

Mapping phenoregions and phytoplankton seasonality in Northeast Pacific marine coastal ecosystems via a satellite-based approach

Phytoplankton phenology describes yearly algal growth cycles and characterises the timing, duration, and magnitude of bloom occurrences. This study used satellite chlorophyll-a data from 1998 to 2020 and the Hierarchical Agglomerative Clustering method to define phenoregions based on phytoplankton phenology spatial patterns over the British Columbia and Southeast Alaska coastal oceans. The defined phenoregions were used to simplify the spatial complexity of the heterogenous study region and thus better describe phytoplankton seasonality across the target area. The cluster analysis allowed the delineation of four coherent regions: two coastal regions and northern and southern shelf/offshore regions. Results showed that each phenoregion had distinguishable phytoplankton phenological characteristics, likely due to different physical forcings acting in these areas. Moreover, the interannual variability of the spring bloom initiation was evaluated considering interactions between sea surface temperature (SST) anomalies and the El Niño Southern Oscillation Index (ENSO). Early spring blooms were associated with positive SST anomalies and El Niño conditions; conversely, average or late spring blooms occurred in years with negative SST anomalies and La Niña conditions, with the strongest relationship occurring in the southern shelf/offshore phenoregion. This study provided new insights into the regionalisation of the British Columbia and Southeast Alaska coastal oceans based on phytoplankton phenology patterns. Given the critical role of phytoplankton as the base of the marine food web, such phenoregions have implications for regional zooplankton biomass and fish production. The link between phytoplankton phenology and climate drivers points to the importance of environmental change in phytoplankton bloom dynamics. Further research into the connection between phytoplankton bloom indices and zooplankton community structure and production would be an important step towards using these indices for ecosystem monitoring and fisheries management.

Interacting Sentinel-2A, Sentinel 1A, and GF-2 Imagery to Improve the Accuracy of Forest Aboveground Biomass Estimation in a Dry-Hot Valley

Carbon absorption and storage in forests is one of the important ways to mitigate climate change. Therefore, it is essential to use a variety of remote-sensing resources to accurately estimate forest aboveground biomass (AGB) in dry-hot valley regions. In this study, satellite images from the Sentinel-1A, Sentinel-2A, and Gaofen-2 satellites were utilized to estimate the forest AGB in Yuanmou County, Yunnan Province, China. Different combinations of image data, based on selected variables of stepwise regression and their performance in constructing linear stepwise regression (LSR) and random forest (RF) models, were explored. The results showed that: (1) after adding the polarized values of the synthetic aperture radar backscatter coefficients, the combination fitting effect was significantly improved; (2) the fitting effect of the Sentinel-1A + Sentinel-2A + Gaofen-2 data combination was superior to the other combinations, indicating that the effective extraction of forest horizon and vertical information can improve the estimation effect of the forest AGB; and (3) the RF model exhibited superior fitting performance compared to the LSR model across all permutations of remotely sensed image datasets, with R2 values of 0.71 and 0.65, and RMSE values of 30.67 and 33.79 Mg/ha, respectively. These findings lay the groundwork for enhancing the precision of AGB estimation in dry-hot valley areas by integrating Sentinel-2A, Sentinel-1A, and GF-2 imagery, providing valuable insights for future research and applications.

Ethanol, acetaldehyde, and methanol in the gas phase and rainwater in different biomes and urban regions of Brazil

In the context of the increasing global use of ethanol biofuel, this work investigates the concentrations of ethanol, methanol, and acetaldehyde, in both the gaseous phase and rainwater, across six diverse urban regions and biomes in Brazil, a country where ethanol accounts for nearly half the light-duty vehicular fuel consumption. Atmospheric ethanol median concentrations in São Paulo (SP) (12.3 ± 12.1 ppbv) and Ribeirão Preto (RP) (12.1 ± 10.9 ppbv) were remarkably close, despite the SP vehicular fleet being ∼13 times larger. Likewise, the rainwater VWM ethanol concentration in SP (4.64 ± 0.38 μmol L−1) was only 26 % higher than in RP (3.42 ± 0.13 μmol L−1). This work demonstrated the importance of evaporative emissions, together with biomass burning, as sources of the compounds studied. The importance of biogenic emissions of methanol during forest flooding was identified in campaigns in the Amazon and Atlantic forests. Marine air masses arriving at a coastal site led to the lowest concentrations of ethanol measured in this work. Besides vehicular and biomass burning emissions, secondary formation of acetaldehyde by photochemical reactions may be relevant in urban and non-urban regions. The combined deposition flux of ethanol and methanol was 6.2 kg ha−1 year−1, avoiding oxidation to the corresponding and more toxic aldehydes. Considering the species determined here, the ozone formation potential (OFP) in RP was around two-fold higher than in SP, further evidencing the importance of emissions from regional distilleries and biomass burning, in addition to vehicles. At the forest and coastal sites, the OFP was approximately 5 times lower than at the urban sites. Our work evidenced that transition from gasoline to ethanol or ethanol blends brings the associated risk of increasing the concentrations of highly toxic aldehydes and ozone, potentially impacting the atmosphere and threatening air quality and human health in urban areas.

Frost matters: incorporating late-spring frost into a dynamic vegetation model regulates regional productivity dynamics in European beech forests

Abstract. Late-spring frost (LSF) is a critical factor influencing the functioning of temperate forest ecosystems. Frost damage in the form of canopy defoliation impedes the ability of trees to effectively photosynthesize, thereby reducing tree productivity. In recent decades, LSF frequency has increased across Europe, likely intensified by the effects of climate change. With increasing warming, many deciduous tree species have shifted towards earlier budburst and leaf development. The earlier start of the growing season not only facilitates forest productivity but also lengthens the period during which trees are most susceptible to LSF. Moreover, recent forest transformation efforts in Europe intended to increase forest resilience to climate change have focused on increasing the share of deciduous species in forests. To assess the ability of forests to remain productive under climate change, dynamic vegetation models (DVMs) have proven to be useful tools. Currently, however, most state-of-the-art DVMs do not model processes related to LSF and the associated impacts. Here, we present a novel LSF module for integration with the dynamic vegetation model Lund–Potsdam–Jena General Ecosystem Simulator (LPJ-GUESS). This new model implementation, termed LPJ-GUESS-FROST, provides the ability to directly attribute simulated impacts on forest productivity dynamics to LSF. We use the example of European beech, one of the dominant deciduous species in central Europe, to demonstrate the functioning of our novel LSF module. Using a network of tree-ring observations from past frost events, we show that LPJ-GUESS-FROST can reproduce productivity reductions caused by LSF. Further, to exemplify the effects of including LSF dynamics in DVMs, we run LPJ-GUESS-FROST for a study region in southern Germany for which high-resolution climate observations are available. Here, we show that modeled LSF plays a substantial role in regulating regional net primary production (NPP) and biomass dynamics, emphasizing the need for LSF to be more widely accounted for in DVMs.

Tropical tropospheric aerosol sources and chemical composition observed at high altitude in the Bolivian Andes

Abstract. The chemical composition of PM10 and non-overlapping PM2.5 was studied at the summit of Mt. Chacaltaya (5380 m a.s.l., lat. −16.346950°, long. −68.128250°) providing a unique long-term record spanning from December 2011 to March 2020. The chemical composition of aerosol at the Chacaltaya Global Atmosphere Watch (GAW) site is representative of the regional background, seasonally affected by biomass burning practices and by nearby anthropogenic emissions from the metropolitan area of La Paz–El Alto. Concentration levels are clearly influenced by seasons with minima occurring during the wet season (December to March) and maxima occurring during the dry and transition seasons (April to November). Ions, total carbon (EC + OC), and saccharide interquartile ranges for concentrations are 558–1785, 384–1120, and 4.3–25.5 ng m−3 for bulk PM10 and 917–2308, 519–1175, and 3.9–24.1 ng m−3 for PM2.5, respectively, with most of the aerosol seemingly present in the PM2.5 fraction. Such concentrations are overall lower compared to other high-altitude stations around the globe but higher than Amazonian remote sites (except for OC). For PM10, there is dominance of insoluble mineral matter (33 %–56 % of the mass), organic matter (7 %–34 %), and secondary inorganic aerosol (15 %–26 %). Chemical composition profiles were identified for different origins: EC, NO3-, NH4+, glucose, and C2O42- for the nearby urban and rural areas; OC, EC, NO3-, K+, acetate, formate, levoglucosan, and some F− and Br− for biomass burning; MeSO3-, Na+, Mg2+, K+, and Ca2+ for aged marine emissions from the Pacific Ocean; arabitol, mannitol, and glucose for biogenic emissions; Na+, Ca2+, Mg2+, and K+ for soil dust; and SO42-, F−, and some Cl− for volcanism. Regional biomass burning practices influence the soluble fraction of the aerosol between June and November. The organic fraction is present all year round and has both anthropogenic (biomass burning and other combustion sources) and natural (primary and secondary biogenic emissions) origins, with the OC/EC mass ratio being practically constant all year round (10.5 ± 5.7, IQR 8.1–13.3). Peruvian volcanism has dominated the SO42- concentration since 2014, though it presents strong temporal variability due to the intermittence of the sources and seasonal changes in the transport patterns. These measurements represent some of the first long-term observations of aerosol chemical composition at a continental high-altitude site in the tropical Southern Hemisphere.

Aggregating taxa and the influence of scale: Potential concerns for analysis of stability using functional measures

AbstractA functional group approach to analyzing ecological processes calls for aggregating species populations' data (e.g., biomass, abundance) into larger functional units. This method offers a more direct insight into the coarse community and ecosystem dynamics than a species level. Since aggregation changes the scale of analysis, it is reasonable to expect that the results and inferences will also change. To gauge the nature and size of this effect, we examined two sets of communities using the same methodology at taxonomic and functional levels. We asked how asynchrony among constituent populations stabilizes the regional biomass or abundance in two systems: zooplankton in North American temperate lakes and rock pool invertebrates in Jamaica. We aggregated species into pairs by organism size for the functional level analysis because it correlates well with life history traits. We hypothesized that taxonomical and functional level analyses would yield different pictures and inferences. We analyzed asynchrony among local populations, metapopulations, and populations of different species at different locations. We found that in lakes, different asynchrony classes contributed to the stabilization of the regional metric than in the rock pools. We further found that aggregation of taxa into functional units changed the pattern of differences between lakes and rock pools revealed by taxonomical analysis and that each set of communities responded differently to species lumping. While aggregating taxa leads to different results, the taxonomic and functional lenses add new insights to the biological interpretation of mechanisms stabilizing communities when interpreted jointly. Specifically, population asynchrony—a known stabilizing factor—can provide complementary stabilizing mechanisms when seen through taxonomic and functional lenses. Without exposing these modes, inferences about abundance/biomass/richness stabilizing contributions arising from asynchrony will remain tenuous. Exposing these modes offers a path for significant breakthroughs in linking local, spatial, and functional structures to multispecies system dynamics.

Aerosol characterization and peculiarities of source apportionment in Moscow, the largest and northernmost European megacity

High population and a wide range of activities in a megacity lead to large-scale ecological consequences which require the assessment with respect to distinct characteristics of climate, location, fuel consumption, and emission sources. In-depth study of aerosol characteristics was carried out in Moscow, the largest megacity in Europe, during the cold period (autumn and winter) and in spring. PM10 chemical speciation based on carbonaceous matter, water-soluble ions, and elements was carried out to reconstruct the PM mass and evaluate the primary and secondary aerosol contribution. For the whole study period organic matter, mineral dust, and secondary inorganic/organic accounted for 34, 24, and 16 % of PM10 mass, respectively. PM10, OC, and EC approached a maximum in spring and decreased in winter. Mineral dust seasonal fraction increased from spring (17 %) to autumn (32 %), and then decreased in winter (22 %). Secondary inorganic aerosols (SIA) in opposite showed the maximum 27 % in winter. K+ marked the residential biomass burning in the region surrounding a megacity in spring and autumn, agriculture fires in spring. In winter primary aerosol contribution dropped down 56 % while secondary approached practically equal 44 %. Source factors with the relative contributions are quantified, namely city dust (26 %), traffic (23 %), industrial (20 %), biomass burning (12 %), secondary (12 %), and de-icing salt (7 %); they were significantly varying between the cold heating period and springtime. The relevance of sources to meteorological parameters and mass transportation is investigated by using both bivariate polar plots and Lagrangian integrated trajectory (HYSPLIT) model. Trajectory clustering demonstrates regional sources being crucial contributors to PM10 pollution. Aerosol speciation and source apportion factors identify the differences of the Moscow urban background among large European and Asian cities due to northern climate conditions, fast construction, long-range transport from industrial-developing area surrounding a city, regional biomass burning preferably in spring and autumn, and winter road management.

A regional-level spatiotemporal perspective of land use and land cover change impact on forest aboveground biomass in three gorges reservoir region, China

This study used multi-platform remote sensing datasets to assess the impacts of land use and land cover (LULC) change on forest biomass density across the Three Gorges Reservoir (TGR) region along the Yangtze River. This study maps land use and land cover (LULC) change and forest aboveground biomass estimation (AGB) using Sentinel-2A and GEDI-L4B data based on observations between 2019 and 2022. The data analysis results of monitoring revealed a strong association frequency between land-use change and forest land cover since the significant relationship with forest aboveground biomass density has also been explored. The total deforestation decreased significantly in 2022. The accuracy assessment results show a classification accuracy of between 90.4% and 94.1%, with a kappa coefficient of 82.4%–92.1%. For the TGR region forest, the minimum to maximum mean above-ground biomass values are from 15.81 to 373.66 Mgha-1, respectively. Moreover, the fitting performance of the statistical analysis of aboveground biomass density of forest mean for the forest was quite decent with training (R 2: 0.92, RMSE: 12.30 Mg/ha) and testing (R 2: 0.79, RMSE: 18.07 Mg/ha) datasets. The results highlight LULC change impacts, particularly forest land class over forest AGB stands, and the effectiveness of the GEDI-L4B product in regional biomass estimation in the TGR region.

Biomass and bioenergy potentials of bioresidues: assessment methodology development and application to the region of Lafões

AbstractBioenergy research aims to uncover the potentials of biological residues. Regional-specific characterization of such potentials is needed to improve the use of local resources, decisions on bioenergy conversion routes, and services within global efforts against climate change. The definition and calculation of the theoretical and technical biomass and bioenergy potentials are keys for developing sustainable use pathways at a regional level. The present work develops a methodology where theoretical framework, quantification methods, and values for the necessary parameters, to assess regional biomass and bioenergy potentials, are considered. The region of Lafões (Portugal) is the case study to illustrate the application of the methods, resulting in three bioresidue categories (agricultural by-products, forestry residues, and municipal waste) and two bioenergy conversion routes (biochemical and thermochemical). The biochemical conversion route revealed a technical energy potential of 765 TJ yr−1, comparing favourably with the 543 TJ yr−1 achieved by the thermochemical route. Also, the environmental and economic performances, associated with the implementation of bioenergy technologies, are possibly better achieved through the biochemical route, to be assessed through life cycle analyses and life cycle costing. Regardless of action priorities, the two conversion routes combined can potentially cover the entire current electrical energy demand of the region. This should also be appraised with expectations in mind for both flexible bioenergy services (with other renewables) and for bioenergy usage in applications which are difficult to defossilize.

Multiple lines of evidence highlight the dire straits of yellowfin tuna in the Indian Ocean.

Yellowfin tuna (Thunnus albacares) are highly valued pelagic fisheries target species. Regional fisheries management organizations (RFMOs) are the principal mechanism that manage yellowfin tuna fisheries. Determining changes in population abundances is crucial for effective conservation and management. We use multiple methods for monitoring biomass trends and evaluating the status of yellowfin tuna in each ocean basin and show how additional, multiple lines of evidence can enhance our understanding of the conservation and exploitation status of this species. Our analysis of regional biomass trajectories and Catch-MSY++ assessments corroborate the findings of the most recent RFMO stock assessments suggesting yellowfin tuna in the Indian Ocean are in critical condition, while the Eastern Pacific yellowfin tuna population shows the lowest levels of exploitation. These results are supported by fisheries-independent data from baited remote underwater video systems (BRUVS), showing that the Indian Ocean yellowfin tuna population is the least common, least abundant, and smallest across all oceans. Our findings support previous claims of systematic and widespread overfishing of yellowfin tuna in the Indian Ocean and thus confirm calls to reduce current fishing levels to ensure the long-term viability of the species.

Dynamic savanna burning emission factors based on satellite data using a machine learning approach

Abstract. Landscape fires, predominantly found in the frequently burning global savannas, are a substantial source of greenhouse gases and aerosols. The impact of these fires on atmospheric composition is partially determined by the chemical breakup of the constituents of the fuel into individual emitted chemical species, which is described by emission factors (EFs). These EFs are known to be dependent on, amongst other things, the type of fuel consumed, the moisture content of the fuel, and the meteorological conditions during the fire, indicating that savanna EFs are temporally and spatially dynamic. Global emission inventories, however, rely on static biome-averaged EFs, which makes them ill-suited for the estimation of regional biomass burning (BB) emissions and for capturing the effects of shifts in fire regimes. In this study we explore the main drivers of EF variability within the savanna biome and assess which geospatial proxies can be used to estimate dynamic EFs for global emission inventories. We made over 4500 bag measurements of CO2, CO, CH4, and N2O EFs using a UAS and also measured fuel parameters and fire-severity proxies during 129 individual fires. The measurements cover a variety of savanna ecosystems under different seasonal conditions sampled over the course of six fire seasons between 2017 and 2022. We complemented our own data with EFs from 85 fires with locations and dates provided in the literature. Based on the locations, dates, and times of the fires we retrieved a variety of fuel, weather, and fire-severity proxies (i.e. possible predictors) using globally available satellite and reanalysis data. We then trained random forest (RF) regressors to estimate EFs for CO2, CO, CH4, and N2O at a spatial resolution of 0.25∘ and a monthly time step. Using these modelled EFs, we calculated their spatiotemporal impact on BB emission estimates over the 2002–2016 period using the Global Fire Emissions Database version 4 with small fires (GFED4s). We found that the most important field indicators for the EFs of CO2, CO, and CH4 were tree cover density, fuel moisture content, and the grass-to-litter ratio. The grass-to-litter ratio and the nitrogen-to-carbon ratio were important indicators for N2O EFs. RF models using satellite observations performed well for the prediction of EF variability in the measured fires with out-of-sample correlation coefficients between 0.80 and 0.99, reducing the error between measured and modelled EFs by 60 %–85 % compared to using the static biome average. Using dynamic EFs, total global savanna emission estimates for 2002–2016 were 1.8 % higher for CO, while CO2, CH4, and N2O emissions were, respectively, 0.2 %, 5 %, and 18 % lower compared to GFED4s. On a regional scale we found a spatial redistribution compared to GFED4s with higher CO, CH4, and N2O EFs in mesic regions and lower ones in xeric regions. Over the course of the fire season, drying resulted in gradually lower EFs of these species. Relatively speaking, the trend was stronger in open savannas than in woodlands, where towards the end of the fire season they increased again. Contrary to the minor impact on annual average savanna fire emissions, the model predicts localized deviations from static averages of the EFs of CO, CH4, and N2O exceeding 60 % under seasonal conditions.

Forest Carbon Storage and Carbon Sequestration Potential in Shaanxi Province, China

The carbon storage of forest vegetation plays a crucial role in the terrestrial carbon budget. The objective of this study is to elucidate the current biomass carbon storage and sequestration capacity, as well as the future carbon sequestration potential of forest ecosystems in Shaanxi Province of China, thus providing data support and policy references for sustainable forest management and the response of carbon sequestration to climate change. Based on the data obtained from the seventh and ninth forest resource inventories, the regional biomass conversion factors, and carbon measurement parameters, the biomass conversion factor method is employed to estimate the biomass storage and carbon sequestration capacity of forest ecosystems. (1) The total carbon storage of forest lands in Shaanxi Province was 285.20 Tg. The carbon storage of arbor forests, sparse woodlands, scattered forests, four-side trees, shrub woodland, and bamboo forests were 237.09 Tg, 2.93 Tg, 12.30 Tg, 5.98 Tg, 26.35 Tg, and 0.56 Tg, respectively. (2) Over the 10 years from the seventh (2005) to the ninth (2015) forest resource inventories, the carbon storage of forests increased from 207 Tg to 285 Tg, with a total increase of 78.01 Tg (37.65%), demonstrating a significant carbon sink function. (3) From 2015 to 2060, the carbon density of arbor forests will increase from 33.53 Mg/ha to 46.90 Mg/ha, and the carbon storage will increase from 237 Tg to 432 Tg. These results indicate that forests have significant net carbon sequestration capacity and can play an important role in achieving China’s carbon peak and carbon neutrality goals. Aiming for carbon neutrality, improving forest management, along with protecting and utilizing forest resources through technological innovation, will become the driving force for increasing carbon storage in Shaanxi Province in the future.

Comparing Algorithms for Estimation of Aboveground Biomass in Pinus yunnanensis

Comparing algorithms are crucial for enhancing the accuracy of remote sensing estimations of forest biomass in regions with high heterogeneity. Herein, Sentinel 2A, Sentinel 1A, Landsat 8 OLI, and Digital Elevation Model (DEM) were selected as data sources. A total of 12 algorithms, including 7 types of learners, were utilized for estimating the aboveground biomass (AGB) of Pinus yunnanensis forest. The results showed that: (1) The optimal algorithm (Extreme Gradient Boosting, XGBoost) was selected as the meta-model (referred to as XGBoost-stacking) of the stacking ensemble algorithm, which integrated 11 other algorithms. The R2 value was improved by 0.12 up to 0.61, and RMSE was decreased by 4.53 Mg/ha down to 39.34 Mg/ha compared to the XGBoost. All algorithms consistently showed severe underestimation of AGB in the Pinus yunnanensis forest of Yunnan Province when AGB exceeded 100 Mg/ha. (2) XGBoost-Stacking, XGBoost, BRNN (Bayesian Regularized Neural Network), RF (Random Forest), and QRF (Quantile Random Forest) have good sensitivity to forest AGB. QRNN (Quantile Regression Neural Network), GP (Gaussian Process), and EN (Elastic Network) have more outlier data and their robustness was poor. SVM-RBF (Radial Basis Function Kernel Support Vector Machine), k-NN (K Nearest Neighbors), and SGB (Stochastic Gradient Boosting) algorithms have good robustness, but their sensitivity was poor, and QRF algorithms and BRNN algorithm can estimate low values with higher accuracy. In conclusion, the XGBoost-stacking, XGBoost, and BRNN algorithms have shown promising application prospects in remote sensing estimation of forest biomass. This study could provide a reference for selecting the suitable algorithm for forest AGB estimation.

Developing machine learning models with multiple environmental data to predict stand biomass in natural coniferous-broad leaved mixed forests in Jilin Province of China

Forest biomass is influenced by multiple environmental factors. Multi-source data on site-specific soil, climate, and stand factors are essential for stand biomass prediction. While previous studies concerning planted forest biomass at stand level highlighted the importance of environmental factors, few have been conducted on the effects of climate and soil on stand biomass estimation, especially for natural mixed forest with complicated stand structure. Machine learning (ML) algorithms provide new tools for examining the effects, but the applications are still limited. Therefore, the objectives of this paper were to develop ML models for stand biomass estimation with stand and multiple environmental predictors, and quantify the relative importance of environmental variables. Tree above- and below-ground biomass at stand level was set as output variable with the random forest (RF) and boosted regression tree (BRT) regression methods. The data were from 286 sample plots from the National Forest Inventory (NFI) in Jilin Province, northeast China. The results showed that models with the inclusion of stand, soil, and climate variables explained > 89% of forest biomass variations. BA (stand basal area) and H (stand average height) were the most important stand variables, DD18 (degree-days above 18 °C) was the most important climate variable, and bdod (bulk density) and pH were the most important soil variables. Relative importance of climate and soil factors was 0.5% to 5.1% and 1.2% to 4.1%, respectively, which were weak but important predictors of stand biomass. In the RF model, climate variables were more important than soil variables, but the opposite in the BRT models. We concluded that above forest stand, climate and soil factors were significant input variables for regional forest biomass mapping using ML.

Ocean iron fertilization may amplify climate change pressures on marine animal biomass for limited climate benefit.

Climate change scenarios suggest that large-scale carbon dioxide removal (CDR) will be required to maintain global warming below 2°C, leading to renewed attention on ocean iron fertilization (OIF). Previous OIF modelling has found that while carbon export increases, nutrient transport to lower latitude ecosystems declines, resulting in a modest impact on atmospheric CO2 . However, the interaction of these CDR responses with ongoing climate change is unknown. Here, we combine global ocean biogeochemistry and ecosystem models to show that, while stimulating carbon sequestration, OIF may amplify climate-induced declines in tropical ocean productivity and ecosystem biomass under a high-emission scenario, with very limited potential atmospheric CO2 drawdown. The 'biogeochemical fingerprint' of climate change, that leads to depletion of upper ocean major nutrients due to upper ocean stratification, is reinforced by OIF due to greater major nutrient consumption. Our simulations show that reductions in upper trophic level animal biomass in tropical regions due to climate change would be exacerbated by OIF within ~20 years, especially in coastal exclusive economic zones (EEZs), with potential implications for fisheries that underpin the livelihoods and economies of coastal communities. Any fertilization-based CDR should therefore consider its interaction with ongoing climate-driven changes and the ensuing ecosystem impacts in national EEZs.

Fish species richness is a key predictor of community biomass and productivity in littoral regions of Severn Sound, an embayment of southern Georgian Bay, Lake Huron

Catches from standardized littoral electrofishing (EF) surveys (1990–2016) at various locales in Severn Sound, Georgian Bay, were analyzed. Catches were adjusted for size-selectivity using published results of laboratory immobilization experiments. Adjusted abundance-mean size relationships were consistent with the metabolic theory of ecology where body size has a central role in structuring community features. Relationships among species richness and areal estimates of biomass and annual production of the adjusted catches were assessed. Richness was the key variable driving other community metrics and was, in turn, largely driven by habitat fetch metrics and sampling time of day. Richness, biomass, and production decreased with increasing maximum effective fetch. Richness increased with time after sunset. Year and locale had smaller roles in variation of community metrics with more vegetated/ less urbanized areas having higher values. Higher fish production is expected in sheltered areas where the contributions of allochthonous and benthic primary production are expected to be greater. Further analyses using additional EF datasets from Great Lakes’ littoral zones should increase understanding of the differences between littoral and offshore fish productivity and help guide management of littoral habitats to ensure healthy fish communities.

Divergent responses of grassland productivity and plant diversity to intra‐annual precipitation variability across climate regions: A global synthesis

Abstract Global warming intensifies the hydrological cycle and may result in changes in the frequency and intensity of precipitation events. However, a knowledge gap still exists about the general influences of intra‐annual precipitation variability on grassland plant diversity and ecosystem function at the global scale. Here, we synthesized field manipulative experiments from 66 publications to quantify the effects of intra‐annual precipitation variability increases (IPVI) on community biomass and plant diversity in grasslands worldwide. At the global scale, we found that IPVI generally increased grassland community above‐ground biomass (AGB) by 6.90%, and decreased grass biomass and soil ammonium content by 10.38% and 25.35% respectively. IPVI increased and decreased total AGB in arid and humid regions, respectively, and IPVI enhanced grassland below‐ground biomass and plant species richness in arid regions, but showed no effect in humid regions. Furthermore, moderate IPVI stimulated plant species richness and community biomass in arid grasslands, while extreme IPVI impaired them in mesic grasslands. Changes in total AGB under IPVI were related to changes in the biomass of plant functional groups, species richness and soil moisture. Structural equation modelling demonstrated that climate conditions (mean annual temperature and mean annual precipitation) and background soil properties (soil sand content and soil organic carbon content) jointly regulated grassland total AGB responses to IPVI across climate regions. Synthesis. Overall, our study shows divergent responses of grassland productivity and diversity to IPVI in arid and humid regions. Accounting for climate, edaphic properties and precipitation variability can help improve our ability to predict the responses of grassland ecosystems to future changes in precipitation at global scales.

Hydrothermal treatment (HTT) improves the combustion properties of regional biomass waste to face the increasing sustainable energy demand in Africa

The increasing worldwide energy demand in parallel to the increasing climate change effects require the utilization of sustainable energy sources. Biomass residues are a feasible energy source, but their chemical composition often compromises their utilization due to slag formation or high emissions. It is investigated in this study whether hydrothermal treatment (HTT) can improve the combustion properties of biomass residues from Ghana. Six biomass residues (Prekese pods, ground nut husks, coconut husks, cassava peels, shea cake, deoiled shea cake) treated at three temperatures (150 °C, 170 °C, 185 °C) were analyzed for combustion properties and emission precursors according to European Standards. The results were evaluated considering consistencies of combustion related properties and especially of ash element reduction behavior. There was a clear trend to increasing mass loss and increasing heating value with increasing treatment temperature that was reflected in the carbon, hydrogen and oxygen amounts, while the ash content and its composition varied. Consistencies in the ash element behavior were highlighted and discussed in order to identify chemical key parameters that led to or inhibited specific ash element reductions. HTT was identified as a feasible biomass upgrade method with advantages over water leaching at room temperature and high temperature hydrothermal carbonization in case of the availability of off-heat from e.g. biomass electricity production.

Response of soil microbial biomass and enzyme activity in coffee-based agroforestry systems in a high-altitude tropical climate region of Brazil

Soil microbial communities are key indicators of changes in soil quality caused by cropping systems. Although there are studies that prove that coffee agroforestry systems increase or maintain soil organic matter levels, the effect of microbial biomass and enzymatic activity in tropical regions, especially in places with high altitude and mild temperatures, is still poorly understood. Thus, the present study aimed to evaluate the influence of two agroforestry systems (AFS-G: coffee intercropped with Grevillea robusta, AFS-C: coffee intercropped with Toona ciliata) and Coffea arabica monoculture on soil microbial biomass and activity and on the arylsulfatase and β-glucosidase enzyme activities, taking a native forest (Semi-deciduous tropical forest, with a predominance of the genera Parapiptadenia and Anadenanthera) as the control system. This study was performed in Oxisol soil areas located in the municipality of Barra do Choça, Bahia, Brazil. Litter (L, F and H layers) and soil topsoil (0–10 cm depth) samples were collected. Both were collected with four repetitions (four composite samples) in each system, litter with a 0.0625 m2 template and soil using a Dutch auger. The carbon microbial biomass, microbial activity (CO₂ released), arylsulfatase and β-glucosidase enzyme activities, organic carbon (SOC) and total nitrogen were also analyzed in the soil samples. The coffee with Grevillea robusta agroforestry system (AFS-G) did not reduce SOC compared to native forest, but the coffee with cedar agroforestry system had SOC losses at a rate of 0.56 Mg ha−1 year−1. The AFS-C cultivation system presented more MB-C (468.43 µg g−1) when compared to the other coffee systems. The AFS-G maintained the microbial activity levels and β-glucosidase and arylsulfatase enzyme activities in relation to the native forest. Coffee agroforestry systems (both AFS-G and AFS-C) in a tropical high-altitude climate region in Brazil are more favorable to maintain microorganisms and their activity in the soil, and therefore should be encouraged in order to more sustainably manage coffee growing.