Environmental, Economic, and Energy Assessment of the Ultimate Analysis and Moisture Content of Municipal Solid Waste in a Parallel Co-combustion Process

Use of municipal solid waste (MSW) as fuel for electricity generation reduces landfill disposal and can mitigate air quality degradation associated with combustion of conventional fossil fuels. Co-combustion is a waste-to-energy technology that can use MSW and coal as co-fuels, offering potential energy recovery and reduced air emissions. This research discerns how MSW composition influences the heating value and air pollution for the co-combustion of coal with MSW using five MSW composition scenarios, four of which were derived by a reduction of plastics, organics, paper, or a combination thereof, as compared to the national average MSW composition. Numerous combustion products could be evaluated; this study focused on five high impact air combustion products: SO₂, CO, CO₂, NO, and NO₂. The moisture content was varied from ∼10% (considered dry) to 40% (average MSW moisture). AspenPlus software was used for the deterministic simulation modeling of incineration (MSW only) and parallel co-firing (co-combustion of coal and MSW) to determine theoretical heating values and pollutant effluent concentrations. The United States Environmental Protection Agency (U.S. EPA) models WAR and WARM were used to determine the potential environmental impacts (PEIs) and greenhouse gas emission equivalencies, respectively, for each MSW scenario. For the WAR model, values for each impact category parameter can vary, but each parameter is weighed equally. Of the MSW scenarios studied, the national average held the highest heating value with 8519 MBtu/lb and the lowest occurred for the MSW scenario with recycled paper and composted organics, with 8251 MBtu/lb. Results show that SO₂, CO, CO₂, NO, and NO₂ flue gas concentrations (and therefore PEIs) depend upon the composition and moisture of the MSW, in addition to the MSW/coal ratio. Approximate ranges for the WAR results (PEI/h) are 7410–7663 for NO, 4–8 for NO₂, 18–105 for CO, 30–46 for CO₂, and 89–2152 for SO₂. WARM results show lower net CO₂ emission equivalents to landfill MSW with reduced paper and organics, while combustion is preferred for MSW with paper reduction, organics reduction, and plastics reduction. The results for the national average MSW were independent of the disposal processing method. Reduction in pollutant concentrations did not yield overall cost savings for the electricity producer, as profit was reduced by ∼20–30%. There are savings associated with emission costs using MSW in lieu of coal: up to ∼3.3% for NO, ∼20–47% for NO₂, and ∼95% for SO₂. A hypothetical carbon dioxide tax was also imposed to realize the potential cost savings by reducing CO₂ emissions. In summary, the measurable impact MSW composition and moisture had on pollutant concentration, heating value, and economic parameters was important

RawSexCombFitnessData

Selection on sex combs across several episodes of selection

RawQGSexCombData

Half sib breeding design to estimate the genetic variances and covariance for sex comb traits

Inverse scatterplot of ecosystem risk and the levelized cost of energy for leased (black) and unleased (gray) locations throughout our study areas.

<p>The outer envelope of these points represents the <i>production possibility frontier</i> for siting a windfarm of 80 5-megawatt turbines in fewer than 60m water depth. Currently leased areas cluster near the minimum risk and minimum cost areas, but not around the joint minimum of both (upper right hand corner).</p

Ecosystem risk and overlapping stressors for the Northeast and Mid-Atlantic at different scales.

<p>In all maps, warmer colors represent greater risk or a greater number of overlapping human activities. (A) Ecosystem risk for the entire study area. Median risk is 15.8. (B) Number of overlapping stressors for the entire study area. The number of stressors is broken down by decile to allow for comparison with risk deciles. (C) Ecosystem risk map for Cape Cod, Massachusetts. The northern star is Boston Bay and the southern star is Plymouth Bay. (D) Ecosystem risk map for Delaware and Chesapeake Bay. (E) Ecosystem risk map for the Maine coastline. (F) Number of overlapping stressors for Cape Cod, Massachusetts. (G) Number of overlapping stressors for Delaware and Chesapeake Bay. (H) Number of overlapping stressors for the Maine coastline.</p

Distribution of cumulative risk for each habitat in each region.

<p>Boxes represent 25^th to 75^th percentiles, the bar represents the median, and dashed lines represent minimum and maximum values, having excluded outliers. Gray boxes represent habitats in the Mid-Atlantic and white boxes represent habitats in the Northeast. Letters represent groups of habitats that have statistically similar means from a Tukey honest significant difference test with a 95% family-wise confidence level.</p

Causes of risk for nearshore and offshore habitats.

<p>(A) Relative contribution of different human activities and stressors to total risk for nearshore and offshore habitats types. The relative contribution is averaged across all nearshore or offshore habitats. Nearshore habitats include beach, salt marsh, tidal flats, rocky intertidal, algal, seagrass, soft-bottom nearshore, and hard-bottom nearshore habitats. Offshore habitats include soft-bottom shelf, hard-bottom shelf, bathyal shelf, shallow pelagic, and deep pelagic zones. (B) Risk plot for tidal flats and (C) hard shelf habitat (combined across Northeast and Mid-Atlantic); each point represents a unique human activity or stressor. For each habitat, included human activities (points) are cumulatively responsible for 80% of risk. (D) Risk plot for nearshore (green) and offshore (blue) habitats. Points represent the unique exposure and consequence from a particular stressor. Points shown for each habitat category cumulatively contribute 80% of risk. Individual human activities or stressors contributing more than 5% to risk are labeled (SST = increasing sea surface temperatures, Fish DD = demersal destructive fishing, Fish NDD = non-destructive demersal fishing, Fish A = Artisanal fishing, Tramp = human trampling, C Eng = coastal engineering).</p

Habitat risk map for tidal flats and shelf habitats.

<p>Tidal flats occur along the coastline and shelf habitats are distant from the coastline. Note that risk deciles are scaled for each habitat independently. Shelf habitats include both soft and hard-bottom shelf habitat categories.</p

DataSheet1_A GEE toolkit for water quality monitoring from 2002 to 2022 in support of SDG 14 and coral health in marine protected areas in Belize.pdf

Coral reefs are highly diverse ecosystems that provide many goods and ecosystem services globally. Coral reef ecosystems are also threatened by environmental stressors from anthropogenic sources and shifting climates. The United Nations Sustainable Development Goal 14 (“Life Below Water”) addresses the need to conserve and sustainably use the ocean, seas, and marine ecosystems, including reef systems. Belize’s coral reef system is the second largest in the world, providing sources of income to Belizeans through tourism and fisheries as well as coastline protection. In order to conserve their marine ecosystems, Belize has a network of Marine Protected Areas (MPAs) throughout their coastal waters. Using Aqua MODIS satellite imagery from 2002 to 2022, Google Earth Engine, and RStudio, we present a workflow to calculate stress days on MPAs and a coral vulnerability index based on sea surface temperature (SST) and Kd (490), a proxy of water clarity. The Corozal Bay, Swallow Caye, Port Honduras, and South Water Caye MPAs had the highest percentages of stress days and coral vulnerability stress index score based on these two parameters among the 24 MPAs analyzed. Additionally, SST in the warmest month of the year in Belize were seen to increase across all MPAs from 2002 to 2022 (p < 0.01). This GEE toolkit provides a straightforward and accessible tool to help governments monitor both water quality and risks to coral reefs in accordance with SDG 14.</p

Insight into small molecule binding to the neonatal Fc receptor by X-ray crystallography and 100 kHz magic-angle-spinning NMR.

Aiming at the design of an allosteric modulator of the neonatal Fc receptor (FcRn)-Immunoglobulin G (IgG) interaction, we developed a new methodology including NMR fragment screening, X-ray crystallography, and magic-angle-spinning (MAS) NMR at 100 kHz after sedimentation, exploiting very fast spinning of the nondeuterated soluble 42 kDa receptor construct to obtain resolved proton-detected 2D and 3D NMR spectra. FcRn plays a crucial role in regulation of IgG and serum albumin catabolism. It is a clinically validated drug target for the treatment of autoimmune diseases caused by pathogenic antibodies via the inhibition of its interaction with IgG. We herein present the discovery of a small molecule that binds into a conserved cavity of the heterodimeric, extracellular domain composed of an α-chain and β2-microglobulin (β2m) (FcRnECD, 373 residues). X-ray crystallography was used alongside NMR at 100 kHz MAS with sedimented soluble protein to explore possibilities for refining the compound as an allosteric modulator. Proton-detected MAS NMR experiments on fully protonated [13C,15N]-labeled FcRnECD yielded ligand-induced chemical-shift perturbations (CSPs) for residues in the binding pocket and allosteric changes close to the interface of the two receptor heterodimers present in the asymmetric unit as well as potentially in the albumin interaction site. X-ray structures with and without ligand suggest the need for an optimized ligand to displace the α-chain with respect to β2m, both of which participate in the FcRnECD-IgG interaction site. Our investigation establishes a method to characterize structurally small molecule binding to nondeuterated large proteins by NMR, even in their glycosylated form, which may prove highly valuable for structure-based drug discovery campaigns