Biomass Values Research Articles

Effect of recycled polyethylene catalyst for Mediterranean pine seed shell biomass pyrolysis on hydrogen and methane production

ABSTRACT The present study exhibits the potential of hydrogen and methane gases production from the Mediterranean pine tree seed shell biomass over recycled polyethylene wastes as catalyst. In the study, firstly, the viscosity and density of polyethylene materials and degassing point were determined. The pyrolysis was carried out at a rate of 50 °C/min at 600°C temperature. The obtained gases were analyzed with gas chromatography device. At the last step of the experiments, solid residues were observed under an optical microscope, and their microstructures were also examined. The results showed that Mediterranean pine seed shell biomass and its mixture with low-density polyethylene may produce methane and hydrogen gases. M8 catalyst, which has one of the lowest viscosity values (0.45 gr/10 min.) and highest density values (0.950 gr/cm3) produced the highest methane and hydrogen gas yields. In the pyrolysis experiments, 38.44% CH4, 19.50% CO2 and 42.06% H2 gases were obtained from Mediterranean pine seed shell biomass. Moreover, CO2 emission values of biomass were calculated and compared with DEFRA values. The study demonstrated that, since hydrogen and methane can be obtained through the pyrolysis process, Mediterranean pine tree seed shell can be classified as a new biomass source and has potential to alternative fuel to fossil-based fuels for internal combustion engines.

Enhancing salt marshes monitoring: Estimating biomass with drone-derived habitat-specific models

Salt marshes play a vital role in biodiversity conservation, coastal protection, and carbon sequestration. Estimating aboveground biomass (AGB) is crucial for quantifying the carbon sequestration capacity of the system. However, traditional fieldwork methods are time-consuming, demanding, and detrimental to marsh ecosystems. In this study, we propose utilizing low-altitude remote sensing techniques to collect high-resolution LiDAR and multispectral (MS) data for biomass assessment. With these datasets, we characterized salt marsh vegetation habitats through the analysis of vegetation indices (VIs) variability. LiDAR high-resolution topographic information aids in evaluating habitat distribution. Moreover, the Anthocyanin Reflectance Index 2 (ARI2), combined with the Digital Surface Model, allows for the identification and separation of the two habitats with distinct dominant species (Sarcocornia spp. and Sporobolus maritimus). The analysis of the annual cycle of VIs reveals the presence of seasonality. However, the VIs for the two vegetation classes exhibit dissimilar seasonal changes, indicating distinct growth mechanisms for each. Biomass models are created for each season in the annual cycle. Habitat-specific models exhibit higher precision (up to 99%) than models that treat species uniformly. Depending on whether the marsh is considered as a whole or separated into dominant habitats, differences in biomass estimation trends are observed. This implies that the two dominant species exhibit varying behaviours throughout the year, contributing to diverse biomass production. The study reveals a seasonal pattern in the total AGB, with the highest biomass value in summer and the lowest peak in spring, with annual variation accounting for only 9% of total output. The reduced AGB value in spring may be due to increased soil salinity and stress. The use of LiDAR and MS data from an unmanned aerial vehicle (UAV) is crucial for accurately distinguishing primary marsh habitats and creating detailed biomass models, with unprecedented accuracy. This method is user-friendly, repeatable, and cost-effective, enabling the study of salt marshes, evolutionary trends, and climate change response requiring less fieldwork.

Increasing the planting density of Cryptolepis sanguinolenta (Lindl.) Schlt increased root biomass and cryptolepine yield

Cryptolepis sanguinolenta (Lindl.) Schlt. is an important multipurpose medicinal plant used for the treatment of ailments such as malaria. Despite the ongoing efforts in domesticating the herb, the ideal planting density and its benefits are unknown. A study was conducted to determine the influence of six C. sanguinolenta accessions and three planting densities (15, 30 and 45 plants/1.8 m2) on root biomass, cryptolepine concentration and cryptolepine yield. Also, benefit-cost ratios were determined for each plant density across the four cultivation periods (9, 12, 15 and 18 months). The cultivation of C. sanguinolenta at the highest planting density (45 plants/1.8 m2) increased root biomass (value), cryptolepine content (2.08 mg/100 mg dry root) and cryptolepine yield (23.31 mg mg/1.8 m2) compared to those cultivated at lower planting densities (15 and 30 plants/1.8 m2). The duration for growing C. sanguinolenta had a more significant influence on cryptolepine yield but not the cryptolepine content. Plants cultivated for 15 months gave the maximum cryptolepine yield (10.33 g/bed), indicating 15 months as the optimum time to harvest the roots. The benefit-cost analysis revealed that growing the plant at a density of 45 plants/1.8 m2 (25,920 plants/acre) for 18 months was a more profitable venture with a benefit-cost ratio of 3.45. Commercial cultivation of C. sanguinolenta at 45 plants per bed area of 1.8 m2 (25,920 plants/acre) for 15–18 months is recommended as the most profitable and promising cropping practice to ensure the sustainable supply of planting material.

A Determination Method for the Biomass Lower-Calorific-Value-Based Blending Ratio in Co-Firing Power Plants

The accurate measurement of the biomass lower-calorific-value-based blending ratio (BLBR) is the premise and key to scale development of the power generation technology from co-firing of biomass and coal. However, existing determination methods of BLBR are less accurate, which has impeded the scale utilization of the co-firing technology of biomass and coal. To accurately determine the BLBR, this research proposed a flue gas sampling system suitable for determining BLBR through 14C quantitative analysis and established a medium-scale experimental platform for BLBR determination. In addition, predictive models for the lower calorific values of coal and biomass fuels were built and the BLBR calculation model for co-firing power plants was deduced. Furthermore, experimental verification of the BLBR determination was also conducted. The results show that the lower calorific values of coal and biomass fuels are highly linearly positively correlated with the carbon content. The relative errors of the established prediction models for lower calorific values of biomass and coal are separately lower than 5% and 8%. The results indicate that the proposed BLBR determination method is accurate.

Biomass Higher Heating Value Estimation: A Comparative Analysis of Machine Learning Models

The research conducted focused on the capabilities of various non-linear and machine learning (ML) models in estimating the higher heating value (HHV) of biomass using proximate analysis data as inputs. The research was carried out to identify the most appropriate model for the estimation of HHV, which was determined by a statistical analysis of the modeling error. In this sense, artificial neural networks (ANNs), support vector machine (SVM), random forest regression (RFR), and higher-degree polynomial models were compared. After statistical analysis of the modeling error, the ANN model was found to be the most suitable for estimating the HHV biomass and showed the highest specific regression coefficient, with an R2 of 0.92. SVM (R2 = 0.81), RFR, and polynomial models (R2 = 0.84), on the other hand, also exhibit a high degree of estimation, albeit with somewhat larger modelling errors. The study conducted suggests that ANN models are best suited for the non-linear modeling of HHV of biomass, as they can generalize and search for links between input and output data that are more robust but also more complex in structure.

Sugarcane leaf litter biomass and its effects on increasing sugarcane drought stress tolerance and reducing CO₂ emissions

Burning biomass on sugarcane plantations can lead to pollution (CO₂ emissions) and degradation in soil properties. Adopting non-burning technology can increase the value of biomass by applying it directly to the soil, although this may result in nutrient immobilization, which can hinder optimal plant growth due to high lignin and cellulose content or high CN ratio. On the contrary, composting waste with a decomposer will boost plant nutrient availability and enhance other soil characteristics. This research was aimed to assess the effect of several types of sugarcane organic matter on improving soil characteristics, growth of sugarcane and the CO₂ emissions. The study was conducted using a Completely Randomized Factorial Design with two factors: type of organic matter (control, fresh litter and compost of sugarcane biomass) and level of drought stress (100, 75, 50 and 25%). Incubation occurred over 16 weeks, divided into two phases of 8 weeks each. The results showed that the application of organic materials in the form of compost could reduce water loss caused by environmental heat and sugarcane growing process. Furthermore, compost application improved soil chemical and biological properties by increasing soil pH, total nitrogen (N), total phosphate (P), total potassium (K), and the total microbial population, although differences were not significant compared to the control. Additionally, applying organic matter in the form of compost or litter helped suppress or reduce emissions, with compost treatment proving more effective than litter in reducing CO₂ emissions.

ХАРАКТЕРИСТИКА ПРИЛИВНЫХ ОСУШЕК В МЕЗОПРИЛИВНЫХ ЭСТУАРИЯХ РЕК БЕЛОГО МОРЯ НА ПРИМЕРЕ УСТЬЯ РЕКИ ПИЛЬЕМА В ОНЕЖСКОМ ЗАЛИВЕ

The biogeochemical characteristics of tidal drying in the estuary of the small Pilema River in the Onega Bay of the White Sea are considered based on observations made in October 2023. Here, mesotidal conditions are observed with a mean tidal range of 2.4 m at the entrance to the river mouth. Along the eastern shore of the estuary, anomalously high suspended sediment loads (356–692 mg/L) were observed due to stormy weather, but the estuarine section of the river, which is closed to developed wind disturbance, had relatively low suspended sediment loads (6–21 mg/L). The upper tidal drying zone was characterized by low values of benthic abundance, biomass and biodiversity, low projective cover of the bottom by algae, and insignificant concentrations of heavy metals such as cadmium, copper, lead and zinc. This situation is mainly due to active lithodynamic processes observed in this zone.

Nexus between carbon stock, biomass, and CO2 emission of woody species composition: evidence from Ise-Ekiti Forest Reserve, Southwestern Nigeria

The carbon stock, biomass, and CO2 emissions in woody species play crucial roles in understanding and managing ecosystems. Understanding these aspects is crucial for sustainable forest management, conservation, and mitigating the impact of woody species on global carbon dynamics and climate change. This study examined the nexus between carbon stock, biomass, and CO2 emission of woody plant composition in disturbed and undisturbed areas in Southwestern Nigeria. The study involved the random establishment of plots in the disturbed and undisturbed areas and, in each plot, the woody plants were enumerated and identified to the species level. The results showed that total biomass (102.645 Mg ha-1), total carbon stock (51.323 Mg C ha-1), and total CO2 emission (188.354 Mg C ha-1) values of tree species in undisturbed plots were higher than the values of total biomass (70.768 Mg ha-1), total carbon stock (35.384 Mg C ha-1), and total CO2 emission (129.859 Mg C ha-1) recorded in disturbed plots. The results also revealed that total biomass (0.123 Mg ha-1), total carbon stock (0.061 Mg C ha-1), and total CO2 emission (0.225 Mg C ha-1 ) values of shrub species recorded in disturbed plots were higher than values of total biomass (0.067 Mg ha-1), total carbon stock (0.034 Mg C ha-1) and total CO2 content (0.124 Mg C ha-1) recorded in undisturbed plots, respectively. The findings showed that in undisturbed and disturbed plots of shrubs, biomass, carbon and CO2 emissions have a strong positive correlation of 1.000**. While biomass, carbon, and CO2 emission have a very strong positive correlation (0.999**) in undisturbed plots of trees, the biomass, carbon, and CO2 emission have moderate to strong positive correlations (0.458** to 0.974**) in disturbed plots of the tree. The study concluded that while biomass, carbon stock, and CO2 emission values of tree species were higher in undisturbed plots than in disturbed plots, the biomass, carbon stock, and CO2 emission values of shrub species were lower in undisturbed plots than in disturbed plots. It also concluded that the main purpose of establishing reserve forests is not totally achieved as human activities occurring in reserve forests still contribute to the increment of climate change.

The microbiota characterizing huge carbonatic moonmilk structures and its correlation with preserved organic matter

BackgroundMoonmilk represents complex secondary structures and model systems to investigate the interaction between microorganisms and carbonatic rocks. Grotta Nera is characterized by numerous moonmilk speleothems of exceptional size hanging from the ceiling, reaching over two meters in length. In this work we combined microbiological analyses with analytical pyrolysis and carbon stable isotope data to determine the molecular composition of these complex moonmilk structures as well as the composition of the associated microbiota.ResultsThree moonmilk structures were dissected into the apical, lateral, and core parts, which shared similar values of microbial abundance, richness, and carbon isotopes but different water content, microbiota composition, and organic matter. Moonmilk parts/niches showed higher values of microbial biomass and biodiversity compared to the bedrock (not showing moonmilk development signs) and the waters (collected below dripping moonmilk), indicating the presence of more complex microbial communities linked to carbonate rock interactions and biomineralization processes. Although each moonmilk niche was characterized by a specific microbiota as well as a distinct organic carbon profile, statistical analyses clustered the samples in two main groups, one including the moonmilk lateral part and the bedrock and the other including the core and apical parts of the speleothem. The organic matter profile of both these groups showed two well-differentiated organic carbon groups, one from cave microbial activity and the other from the leaching of vascular plant litter above the cave. Correlation between organic matter composition and microbial taxa in the different moonmilk niches were found, linking the presence of condensed organic compounds in the apical part with the orders Nitrospirales and Nitrosopumilales, while different taxa were correlated with aromatic, lignin, and polysaccharides in the moonmilk core. These findings are in line with the metabolic potential of these microbial taxa suggesting how the molecular composition of the preserved organic matter drives the microbiota colonizing the different moonmilk niches. Furthermore, distinct bacterial and archaeal taxa known to be involved in the metabolism of inorganic nitrogen and C1 gases (CO2 and CH4) (Nitrospira, Nitrosopumilaceae, Nitrosomonadaceae, Nitrosococcaceae, and novel taxa of Methylomirabilota and Methanomassiliicoccales) were enriched in the core and apical parts of the moonmilk, probably in association with their contribution to biogeochemical cycles in Grotta Nera ecosystem and moonmilk development.ConclusionsThe moonmilk deposits can be divided into diverse niches following oxygen and water gradients, which are characterized by specific microbial taxa and organic matter composition originating from microbial activities or deriving from soil and vegetation above the cave. The metabolic capacities allowing the biodegradation of complex polymers from the vegetation above the cave and the use of inorganic nitrogen and atmospheric gases might have fueled the development of complex microbial communities that, by interacting with the carbonatic rock, led to the formation of these massive moonmilk speleothems in Grotta Nera.

Growth and cadmium and nickel uptake of maize (Zea mays L.) in a cadmium and nickel co‑contaminated soil and phytoremediation efficiency using ethylenediamine tetraacetic acid

Background: Anthropogenic activities release cadmium (Cd), nickel (Ni), and other heavy metals into soil. Zea mays can clean up contaminated soils, but little is known about how Cd and Ni co-contamination stress affects ethylenediamine tetraacetic acid (EDTA)-based phytoextraction and phytoremediation, hence this study was conducted. Methods: The experiment involved nine treatment levels (0, 5, 10, 15, and 20 mg kg-1 Cd and 0, 15, 30, 45, and 60 mg kg-1 Ni), grouped into three categories: CT as the control group, P as Cd + Ni only, and CAP as Cd + Ni + EDTA (n=3). The treatments used (CH3COO)2Cd•2H2O and NiSO4 as the source of Cd and Ni, respectively, and EDTA was applied at two rates (0 and 0.5 g/kg). After the experimental period, measurements were taken for shoot length, biomass, and metal concentrations in both the roots and shoots using established procedures. Results: The concentrations of metals in plants' roots and shoots increased as the concentrations in soil increased, but shoot length, biomass, bioconcentration factor (BCF), and translocation factor (TF) values decreased with increasing soil metal content. The application of EDTA increased metal uptake but led to greater root and shoot biomass loss. Generally, TF values for Cd and Ni were less than 1 but most of the BCF values were greater than 1. Conclusions: The study found that phytostabilization is the main mechanism for phytoremediation of Cd-Ni-co-contaminated soils with Zea mays, with EDTA addition enhancing metal accumulation and reducing biomass yield.

Features of phytoplankton changes in the Sevastopol and Karadag coastal areas (the Black sea) in spring

Based on field-monitoring and satellite dataset obtained during spring season, we studied the features of phytoplankton changes under effect of hydrometeorological factors in the coastal areas of Sevastopol in 2000–2021 and Karadag in 1989–2021. For these areas, significant positive correlations were observed between phytoplankton biomass and winds causing coastal upwellings which lift nutrients from deep layers to the surface. On the basis of Principal Component Analysis (PCA) and ANOVA, a positive effect of the north wind speed on phytoplankton biomass in the Sevastopol and Karadag coastal areas was established. Different mechanisms of the north wind effect on the nutrient-rich waters rise for two investigated areas were revealed and it was due to peculiarities of these coastal areas. The north wind also contributes to nutrient supply from the river runoff of the northwestern shelf to the Sevastopol coastal area. High values of phytoplankton biomass and chlorophyll a concentration were mainly observed in cold years.

Toward a More Robust Estimation of Forest Biomass Carbon Stock and Carbon Sink in Mountainous Region: A Case Study in Tibet, China

Mountainous forests are pivotal in the global carbon cycle, serving as substantial reservoirs and sinks of carbon. However, generating a reliable estimate remains a considerable challenge, primarily due to the lack of representative in situ measurements and proper methods capable of addressing their complex spatial variation. Here, we proposed a deep learning-based method that combines Residual convolutional neural networks (ResNet) with in situ measurements, microwave (Sentinel-1 and VOD), and optical data (Sentinel-2 and Landsat) to estimate forest biomass and track its change over the mountainous regions. Our approach, integrating in situ measurements across representative elevations with multi-source remote sensing images, significantly improves the accuracy of biomass estimation in Tibet’s complex mountainous forests (R2 = 0.80, root mean squared error = 15.8 MgC ha−1). Moreover, ResNet, which addresses the vanishing gradient problem in deep neural networks by introducing skip connections, enables the extraction of complex spatial patterns from limited datasets, outperforming traditional optical-based or pixel-based methods. The mean value of forest biomass was estimated as 162.8 ± 21.3 MgC ha−1, notably higher than that of forests at comparable latitudes or flat regions in China. Additionally, our findings revealed a substantial forest biomass carbon sink of 3.35 TgC year−1 during 2015–2020, which is largely underestimated by previous estimates, mainly due to the underestimation of mountainous carbon stock. The significant carbon density, combined with the underestimated carbon sink in mountainous regions, emphasizes the urgent need to reassess mountain forests to better approximate the global carbon budget.

Floristic composition and biomass estimate of Rubiaceae in Bangi Permanent Forest Reserve, Selangor, Peninsular Malaysia

A study on floristic composition, community structure and biomass of Rubiaceae was carried out in Bangi Permanent Forest Reserve, Selangor. In the 0.45 ha of ecological plots, a total of 17 species from three genera of Rubiaceae was recorded for trees with a diameter ≥ 1.0 cm DBH. Three genera viz. Canthium, Ixora and Urophyllum were represented by two species each, while remaining genera comprised of a single species each. Shrub group represented the largest total number of species with seven species followed by the understorey with four species. The range of tree DBH in the study plot was between 1.0 and 31.3 cm. The genus with highest density was Aidia, viz. 244 trees/ha, represented by Aidia densiflora. Total basal area for Rubiaceae was 1.69 m2/ha, with the genus Aidia, specifically the species Aidia densiflora, contributing the highest basal area of 0.57 m2/ha (33.91%). Total biomass estimate (above ground and below ground) for this family was 16.71 t/ha. At genus level, Timonius contributed the highest total biomass value, viz. 5.82 t/ha (34.85%), represented by the species Timonius wallichianus.

Veterinary antibiotics and metals impact the mass, composition and hydrolysis of biomass cultivated in piggery wastewater treatment photobioreactors

Photobioreactors are a promising alternative for piggery wastewater treatment which allows to produce valuable biomass, but the presence of antibiotics or heavy metals in the pig manure can influence the biomass composition and the posterior valorization processes. This study evaluates the effect of these pollutants on mass, composition and hydrolysis yields of the consortia of microalgae and bacteria obtained in a 1280 L photobioreactor treating pig manure. The photobioreactor feed was doped with 1) veterinary antibiotics; 2) copper, zinc, and arsenic; and 3) combination of both pollutants. The pollutants presence decreased the mass of biomass grown in the photobioreactor by up to 35% while glucose content of the biomass by up to 42% and increased protein and xylose by 30% and 16% due to oxidative stress. Likewise, they increased the protein solubilization yield by acid hydrolysis at 120 ºC by 32% while reduced glucose solubilization by 49% after alkaline hydrolysis at 120 ºC. Applying enzymatic hydrolysis and ultrasound assisted enzymatic extractions, glucose and xylose recoveries were drastically reduced in presence of heavy metals (∼100%). Antibiotics increased xylose solubilization by ultrasonication (74%), but also its degradation, decreasing xylose recovery. Thus, the presence of all these pollutants in pig manure affected biomass production and valorization and these results must be considered for biorefinery processes.

Generalizability of empirical correlations for predicting higher heating values of biomass

ABSTRACT Designing efficient biomass energy systems requires a thorough understanding of the physicochemical, thermodynamic, and physical properties of biomass. One crucial parameter in assessing biomass energy potential is the higher heating value (HHV), which quantifies its energy content. Conventionally, HHV is determined through bomb calorimetry, but this method is limited by factors such as time, accessibility, and cost. To overcome these limitations, researchers have proposed a diverse range of empirical correlations and machine-learning approaches to predict the HHV of biomass based on proximate and ultimate analysis results. The novelty of this research is to explore the universal applicability of the developed empirical correlations for predicting the Higher Heating Value (HHV) of biomass. To identify the best empirical correlations, nearly 400 different biomass feedstocks were comprehensively tested with 45 different empirical correlations developed to use ultimate analysis (21 different empirical correlations), proximate analysis (16 different empirical correlations) and combined ultimate-proximate analysis (8 different empirical correlations) data of these biomass feedstocks. A quantitative and statistical analysis was conducted to assess the performance of these empirical correlations and their applicability to diverse biomass types. The results demonstrated that the empirical correlations utilizing ultimate analysis data provided more accurate predictions of HHV compared to those based on proximate analysis or combined data. Two specific empirical correlations including coefficients for each element (C, H, N) and their interactions (C*H) demonstrate the best HHV prediction with the lowest MAE (~0.49), RMSE (~0.64), and MAPE (~2.70%). Furthermore, some other empirical correlations with carbon content being the major determinant also provide good HHV prediction from a statistical point of view; MAE (~0.5–0.8), RMSE (~0.6–0.9), and MAPE (~2.8–3.8%).

Valorisation of cotton post-industrial textile waste into lactic acid: chemo-mechanical pretreatment, separate hydrolysis and fermentation using engineered yeast

BackgroundThe textile industry has several negative impacts, mainly because it is based on a linear business model that depletes natural resources and produces excessive amounts of waste. Globally, about 75% of textile waste is disposed of in landfills and only 25% is reused or recycled, while less than 1% is recycled back into new garments. In this study, we explored the valorisation of cotton fabric waste from an apparel textile manufacturing company as valuable biomass to produce lactic acid, a versatile chemical building block.ResultsPost-industrial cotton patches were pre-treated with the aim of developing a methodology applicable to the industrial site involved. First, a mechanical shredding machine reduced the fabric into individual fibres of maximum 35 mm in length. Afterwards, an alkaline treatment was performed, using NaOH at different concentrations, including a 16% (w/v) NaOH enriched waste stream from the mercerisation of cotton fabrics. The combination of chemo-mechanical pre-treatment and enzymatic hydrolysis led to the maximum recovery yield of 90.46 ± 3.46%, corresponding to 74.96 ± 2.76 g/L of glucose released, which represents a novel valorisation of two different side products (NaOH enriched wastewater and cotton textile waste) of the textile industry. The Saccharomyces cerevisiae strain CEN.PK m850, engineered for redirecting the natural alcoholic fermentation towards a homolactic fermentation, was then used to valorise the glucose-enriched hydrolysate into lactic acid. Overall, the process produced 53.04 g/L ± 0.34 of l-lactic acid, with a yield of 82.7%, being the first example of second-generation biomass valorised with this yeast strain, to the best of our knowledge. Remarkably, the fermentation performances were comparable with the ones obtained in the control medium.ConclusionThis study validates the exploitation of cotton post–industrial waste as a possible feedstock for the production of commodity chemicals in microbial cell-based biorefineries. The presented strategy demonstrates the possibility of implementing a circular bioeconomy approach in manufacturing textile industries.Graphical

Analysis of light recipe, seeding density, and fertilization effects on secondary metabolite accumulation and growth-defense responses in Brassicaceae microgreens

Although there have been large improvements to crop yield over time, this has not been accompanied by an increase in human nutritional wellbeing. In fact, there are worsening health crises associated with over- and under-consumption of particular food groups, resulting in negative human health outcomes. One solution to this is to utilize controlled environment agriculture to produce microgreens that have a high density of valuable Secondary Metabolites (SMs) such as antioxidants, phenolics, or pigment molecules that are associated with positive human health outcomes. However, optimal growth recipes to produce microgreens and their valuable nutritional compounds are not well known due to much species-specific variation, as well as biological tradeoffs between biomass and SM production. This is known as the growth defense hypothesis which describes how plants have a finite carbon budget from which to allocate to growth, or defensive, processes. To further research in this regard, this project used climate chambers to grow three species of Brassicaceae microgreens (kohlrabi, mustard, and radish) in highly controlled environmental conditions, where we used five Light Recipes, two fertilization levels, and two seeding density treatments. Our results showed that there were significant differences in SM production of these microgreens due to changes in incident light, as well as significant interactions between Fertilizer and Light Recipe for all SMs except Anthocyanins. For example, for all three species, the High Far-Red Light Recipe had the significantly highest Phenolic concentration, but with lower values of the other four SMs. The low-intensity 24-V high efficiency LEDs had the significantly highest Trolox Equivalent Antioxidant Capacity (TEAC) concentrations, while the High Red recipe had the highest Ferric Reducing Antioxidant Power (FRAP) and Flavonoid concentrations. For Anthocyanins, there were less clear patterns, with the No Green or High Red recipes having generally higher concentrations, but not always significantly. We did find some evidence supporting the growth-defense hypothesis, where our higher biomass values were negatively correlated with SM concentrations. We also found significant differences between the concentrations of SMs in leaves and stems for kohlrabi and mustard microgreens. Finally, we found some significant relationships between increasing Fertilizer dosage and SMs, for instance that Flavonoids and FRAP concentrations increased with Fertilizer application, while Anthocyanin decreased, and Phenolic and TEAC had little effect. In conclusion, we found that there were significant relationships for Light Recipe and Fertilizer, and oftentimes their interaction, for the accumulation of SMs in Brassicaceae microgreens, which can inform microgreen production environments.

Are native microalgae consortia able to remove microplastics from wastewater effluents?

Wastewater Treatment Plants (WWTPs) are potential sources of microplastics (MPs) in the aquatic environment. This study aimed to investigate the potential of wastewater-native microalgae consortia to remove MPs from the effluent of two different types of WWTPs as a dual-purpose solution for MPs mitigation and biomass production. For that purpose, the occurrence of MPs from two types of WWTP effluents was analysed over one year. MPs were characterized in terms of morphology (microbead, foam, granule, irregular, filament and film), colour and size. The wastewater characterisation was followed by the removal of MP loads, using native microalgae consortia, pre-adapted to the wastewater effluent. Microalgae consortia evolved naturally through four mitigation assays, adapted to seasonal conditions, such as temperature, photoperiod, and wastewater composition. MPs were present in all the effluent samples, ranging from 52 to 233 MP L−1. The characterisation of MPs indicated a predominance of white and transparent particles, with irregular and filament shapes, mainly under 500 μm in size. The μFTIR analysis revealed that 43% of the selected particles were plastic, with a prevalence of polypropylene (PP) (34%) and polyethylene terephthalate (PET) (30 %). In the mitigation experiments, substantial biomass production was achieved (maximum of 2.6 g L−1 (d.w.)), with successful removal of MPs, ranging from 31 ± 25% to 82 ± 13%. These results show that microalgae growth in wastewater effluents efficiently promotes the removal of MPs, reducing this source of contamination in the aquatic environment, while generating valuable biomass. Additionally, the strategy employed, requires minimal control of culture conditions, simplifying the integration of these systems in real-world WWTP facilities for improved wastewater management.

MODEL ALOMETRIK PENDUGA ABOVEGROUND BIOMASS Gigantochloa atter DI DESA ALU, KABUPATEN POLEWALI MANDAR

Biomass allometric models are needed to determine the carbon content absorbed by bamboo plants. There are still many bamboo plants that do not have a local allometric model to estimate their biomass, one of which is Gigantochloa atter bamboo. This research aims to produce an allometric model for estimating the biomass of Gigantochloa atter bamboo found in Alu Village, Polewali Mandar Regency, West Sulawesi Province. Dustructive sampling method of 30 bamboo plants, to obtain wet weight and water content, starting with measuring the diameter and total height. Water content to determine biomass. Biomass, diameter and height values were analyzed by regression to develop allometric models according to predetermined equations. Substitution of measurement values into the allometric equation and data analysis using the coefficient of determination (R2), bias, error index and Mean Square Error Prediction variable scoring method. The scoring results of six models for estimating the biomass of Gigantochloa atter bamboo plants in Alu Village, Polewali Mandar Regency were selected as the best model Y = 0.785+0.011(D2H) with the highest score values including R2 value (0.982), Bias value (0.124), IE value (25.086) and MSEP (1.435).

Effects of Humic Acids on Calorific Value and Chemical Composition of Maize Biomass in Iron-Contaminated Soil Phytostabilisation

An interesting feedstock for energy purposes is plant biomass due to its renewability, widespread availability and relatively low cost. One plant with a high and versatile use potential is maize. Plants used for energy production can be grown in polluted areas, e.g., with iron. The aim of the study was to determine the effect of humic acids (HAs) on the yield, calorific value and other energy parameters and chemical composition of maize biomass applied as a phytostabiliser on iron-contaminated soil. The soil was contaminated with iron at 0, 250, 500 and 750 mg kg−1. The HAs were added to the soil in the following amounts: 0, 0.3, 0.6 and 0.9 g kg−1 of soil. Soil contamination with iron had relatively little effect on the heat of combustion and calorific value of biomass and very strongly reduced plant height (42%), dry matter yield (95%) and energy production of maize biomass (90%), the SPAD index at the fifth leaf unfolded stage (44%) (as opposed to the stem elongation stage), sodium, magnesium and phosphorus contents, and increased calcium, potassium and nitrogen contents of maize. The application of HAs to the soil had a positive and very large effect on both the height and biomass parameters studied, resulting in an increase in plant height (22%), dry matter yield (67%) and energy production from maize biomass (62%). Changes in the heat of combustion and calorific value of the biomass were minimal but positive. HAs contributed to a decrease in the value of the SPAD index during the stem elongation phase of maize and in the content of all macronutrients in maize biomass as a result of a reduction in the effect of iron on macronutrient content and to a significant increase in maize dry matter yield in plots with their application. The application of HAs appears to be an effective adjunct in the phytostabilisation of iron-contaminated soils by growing crops for energy purposes.