Evaluation and application of site-specific data to revise the first-order decay model for estimating landfill gas generation and emissions at Danish landfills

<div><p>Methane (CH₄) generated from low-organic waste degradation at four Danish landfills was estimated by three first-order decay (FOD) landfill gas (LFG) generation models (LandGEM, IPCC, and Afvalzorg). Actual waste data from Danish landfills were applied to fit model (IPCC and Afvalzorg) required categories. In general, the single-phase model, LandGEM, significantly overestimated CH₄ generation, because it applied too high default values for key parameters to handle low-organic waste scenarios. The key parameters were biochemical CH₄ potential (BMP) and CH₄ generation rate constant (k-value). In comparison to the IPCC model, the Afvalzorg model was more suitable for estimating CH₄ generation at Danish landfills, because it defined more proper waste categories rather than traditional municipal solid waste (MSW) fractions. Moreover, the Afvalzorg model could better show the influence of not only the total disposed waste amount, but also various waste categories. By using laboratory-determined BMPs and k-values for shredder, sludge, mixed bulky waste, and street-cleaning waste, the Afvalzorg model was revised. The revised model estimated smaller cumulative CH₄ generation results at the four Danish landfills (from the start of disposal until 2020 and until 2100). Through a CH₄ mass balance approach, fugitive CH₄ emissions from whole sites and a specific cell for shredder waste were aggregated based on the revised Afvalzorg model outcomes. Aggregated results were in good agreement with field measurements, indicating that the revised Afvalzorg model could provide practical and accurate estimation for Danish LFG emissions. This study is valuable for both researchers and engineers aiming to predict, control, and mitigate fugitive CH₄ emissions from landfills receiving low-organic waste.</p><p>Implications: <i>Landfill operators use the first-order decay (FOD) models to estimate methane (CH₄) generation. A single-phase model (LandGEM) and a traditional model (IPCC) could result in overestimation when handling a low-organic waste scenario. Site-specific data were important and capable of calibrating key parameter values in FOD models. The comparison study of the revised Afvalzorg model outcomes and field measurements at four Danish landfills provided a guideline for revising the Pollutants Release and Transfer Registers (PRTR) model, as well as indicating noteworthy waste fractions that could emit CH₄ at modern landfills.</i></p></div

Rapid Sequestration of Ecosystem Carbon in 30-year Reforestation with Mixed Species in Dry Hot Valley of the Jinsha River

Reforestation plays an important role in the carbon cycle and climate change. However, knowledge of ecosystem carbon sequestration through reforestation with mixed species is limited. Especially in dry hot valley of the Jinsha River, no studies cover total ecosystem carbon sequestration level in mature mixed plantations for a limited area of mixed plantations and difficulty in the sampling of plant roots and deep soil. In this study, carbon sequestration of seven mixed plantations of different ages in dry hot valley of the Jinsha River was investigated with analogous sites method. The results are as follows: 1) Deep soil organic carbon (SOC) storage significantly increased with stand age (p = 0.025), possibly due to fine root exudates and dissolved organic carbon transportation into deep soil and retention. 2) Total biomass carbon storage in the 30-year-old mixed plantation was 77.78 t C ha&minus;1, 54 times reference wasteland and 9 times reference natural recovery shrub-grassland. However, total biomass carbon storage of 30-year-old mixed plantation was insignificantly lower than that of reference natural forest (p = 0.429). After 30 years of reforestation, plantation biomass carbon storage recovered to reference level, and its sequestration rate was 2.54 t C ha&minus;1 yr&minus;1. 3) The total ecosystem carbon storage of 30-year-old mixed plantation was 185.50 t C ha&minus;1, 2.38 times reference wasteland, 2.29 times reference natural recovery shrub grassland, and 29% lower than reference natural forest. It indicated that niche complementary, good stand structure, and characteristics of dominant species Leucaena leucocephala in mixed plantations facilitate more rapid carbon sequestration, especially biomass carbon in the dry hot valley

<i>Gcd</i> Gene Diversity of Quinoprotein Glucose Dehydrogenase in the Sediment of Sancha Lake and Its Response to the Environment

Quinoprotein glucose dehydrogenase (GDH) is the most important enzyme of inorganic phosphorus-dissolving metabolism, catalyzing the oxidation of glucose to gluconic acid. The insoluble phosphate in the sediment is converted into soluble phosphate, facilitating mass reproduction of algae. Therefore, studying the diversity of gcd genes which encode GDH is beneficial to reveal the microbial group that has a significant influence on the eutrophication of water. Taking the eutrophic Sancha Lake sediments as the research object, we acquired samples from six sites in the spring and autumn. A total of 219,778 high-quality sequences were obtained by DNA extraction of microbial groups in sediments, PCR amplification of the gcd gene, and high-throughput sequencing. Six phyla, nine classes, 15 orders, 29 families, 46 genera, and 610 operational taxonomic units (OTUs) were determined, suggesting the high genetic diversity of gcd. Gcd genes came mainly from the genera of Rhizobium (1.63&#8315;77.99%), Ensifer (0.13&#8315;56.95%), Shinella (0.32&#8315;25.49%), and Sinorhizobium (0.16&#8315;11.88%) in the phylum of Proteobacteria (25.10&#8315;98.85%). The abundance of these dominant gcd-harboring bacteria was higher in the spring than in autumn, suggesting that they have an important effect on the eutrophication of the Sancha Lake. The alpha and beta diversity of gcd genes presented spatial and temporal differences due to different sampling site types and sampling seasons. Pearson correlation analysis and canonical correlation analysis (CCA) showed that the diversity and abundance of gcd genes were significantly correlated with environmental factors such as dissolved oxygen (DO), phosphorus hydrochloride (HCl&#8315;P), and dissolved total phosphorus (DTP). OTU composition was significantly correlated with DO, total organic carbon (TOC), and DTP. GDH encoded by gcd genes transformed insoluble phosphate into dissolved phosphate, resulting in the eutrophication of Sancha Lake. The results suggest that gcd genes encoding GDH may play an important role in lake eutrophication