skip to main content


Title: Designing industrial landscapes for mitigating air pollution with spatially‐explicit techno‐ecological synergy
Abstract

Air pollution has posed health and environmental threats since the Industrial Revolution. Technological solutions present major expenses for industry, yet nature's ecosystems also provide pollution uptake. In the pursuit of techno‐ecological sustainable design, this work presents a framework for spatially‐explicit industrial site design that determines where and when ecological restoration should be considered. The framework considers land use changes and identifies the cheapest balance between technological and ecological uptake for industrial landscapes, including the impacts of long term ecological growth dynamics. This work presents the framework's construction along with a case study conducted for a coal‐fired power station in Ohio. The results provide spatial maps of proposed restoration areas, projected savings values, and spatial‐temporal maps that consider annual budget constraints. The results demonstrate a significant sensitivity to land use restoration costs and highlights ecological advantages, like simultaneous uptake of different chemical species.

 
more » « less
Award ID(s):
1804943
NSF-PAR ID:
10449191
Author(s) / Creator(s):
 ;  
Publisher / Repository:
Wiley Blackwell (John Wiley & Sons)
Date Published:
Journal Name:
AIChE Journal
Volume:
67
Issue:
10
ISSN:
0001-1541
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. Abstract

    Despite significant investments in watershed‐scale restoration projects, evaluation of their impacts on salmonids is often limited by inadequate experimental design. This project aimed to strengthen study designs by identifying and quantifying sources of temporal and spatial uncertainty while assessing population‐level salmonid responses in Before‐After‐Control‐Impact (BACI) restoration experiments. To evaluate sources of temporal uncertainty, meta‐analysis of 32 annual BACI experiments from the Pacific Northwest, USA was conducted. Experimental error was determined to be a function of the total temporal variation of both restoration and control salmonid population metrics and the degree of covariation, or synchrony, between these metrics (r2 = 1). However, synchrony was both weak ( = 0.18) and unrelated to experimental error (r = 0.01) while temporal variability was found to account for 91% of this error. Because synchrony did not reduce experimental error, we conclude that BACI designs will not normally exhibit greater power over uncontrolled Before‐After (BA) designs. To evaluate spatial uncertainty, hierarchical BACI designs were simulated. It was found that spatial variability of hypothetical steelhead (Oncorhynchus mykiss) growth values within watersheds can cause mis‐estimation of the restoration effect and reduce power. While hierarchical BACI designs can examine both reach and watershed‐scale restoration effects simultaneously, due to probable mis‐estimation of the restoration effect size, these scales should be examined separately. Paired‐reach designs such as Extensive Post‐Treatment (EPT) provide powerful replicated local‐scale restoration experiments, which can build understanding of restoration‐ecological mechanisms. Knowledge gained from reach‐scale experiments should then be implemented on watershed‐scales and monitored within a non‐hierarchical framework.

     
    more » « less
  2. null (Ed.)
    Sustainable provisioning of energy to society requires consideration of the nexus between food–energy–water (FEW) flows while meeting human needs and respecting nature's capacity to provide goods and services. In this work, we explore the FEW nexus of conventional and techno-ecologically synergistic (TES) systems by evaluating combinations of various technological, agricultural, and ecological strategies from the viewpoints of electricity generation, food production, life cycle water use, carbon footprint, nutrient runoff, corporate profitability, and societal well-being. We evaluate activities related to power generation (coal and gas extraction and use, transportation options, cooling technologies, solar panels, wind turbines), food production (farming with and without tillage), waste utilization (carbon dioxide capture and conversion to hydrocarbons, green hydrogen), and ecological restoration (forests and wetlands). Application of this framework to the Muskingum River watershed in Ohio, U.S.A. indicates that seeking synergies between human and natural systems can provide innovative solutions that improve the FEW nexus while making positive contributions to society with greater respect for nature's limits. We show that the conventional engineering approach of relying only on technological approaches for meeting sustainability objectives can have limited environmental and societal benefits while reducing profitability. In contrast, techno-ecologically synergistic design between agricultural systems and wetlands can reduce nutrient runoff with little compromise in other goals. Additional synergies between farming and photovoltaic systems along with the use of wetlands can further improve the FEW nexus while reducing CO 2 and nutrient emissions, with a relatively small compromise in corporate profitability. These results should motivate further work on innovative TES designs that can provide “win–win” solutions for meeting global energy needs in an environmentally and socially beneficial manner. 
    more » « less
  3. Purpose

    129Xe MRI and MRS signals from airspaces, membrane tissues (M), and red blood cells (RBCs) provide measurements of pulmonary gas exchange. However,129Xe MRI/MRS studies have yet to account for hemoglobin concentration (Hb), which is expected to affect the uptake of129Xe in the membrane and RBC compartments. We propose a framework to adjust the membrane and RBC signals for Hb and use this to assess sex‐specific differences in RBC/M and establish a Hb‐adjusted healthy reference range for the RBC/M ratio.

    Methods

    We combined the 1D model of xenon gas exchange (MOXE) with the principle of TR‐flip angle equivalence to establish scaling factors that normalize the dissolved‐phase signals with respect to a standard (14 g/dL).129Xe MRI/MRS data from a healthy, young cohort (n = 18, age = 25.0 3.4 years) were used to validate this model and assess the impact of Hb adjustment on M/gas and RBC/gas images and RBC/M.

    Results

    Adjusting for Hb caused RBC/M to change by up to 20% in healthy individuals with normal Hb and had marked impacts on M/gas and RBC/gas distributions in 3D gas‐exchange maps. RBC/M was higher in males than females both before and after Hb adjustment (p < 0.001). After Hb adjustment, the healthy reference value for RBC/M for a consortium‐recommended acquisition of TR = 15 ms and flip = 20° was 0.589 0.083 (mean SD).

    Conclusion

    MOXE provides a useful framework for evaluating the Hb dependence of the membrane and RBC signals. This work indicates that adjusting for Hb is essential for accurately assessing129Xe gas‐exchange MRI/MRS metrics.

     
    more » « less
  4. Abstract

    Forecasting rates of forest succession at landscape scales will aid global efforts to restore tree cover to millions of hectares of degraded land. While optical satellite remote sensing can detect regional land cover change, quantifying forest structural change is challenging. We developed a state‐space modeling framework that applies Landsat satellite data to estimate variability in rates of natural regeneration between sites in a tropical landscape. Our models work by disentangling measurement error in Landsat‐derived spectral reflectance from process error related to successional variability. We applied our modeling framework to rank rates of forest succession between 10 naturally regenerating sites in Southwestern Panama from about 2001 to 2015 and tested how different models for measurement error impacted forecast accuracy, ecological inference, and rankings of successional rates between sites. We achieved the greatest increase in forecasting accuracy by adding intra‐annual phenological variation to a model based on Landsat‐derived normalized difference vegetation index (NDVI). The best‐performing model accounted for inter‐ and intra‐annual noise in spectral reflectance and translated NDVI to canopy height via Landsat–lidar fusion. Modeling forest succession as a function of canopy height rather than NDVI also resulted in more realistic estimates of forest state during early succession, including greater confidence in rank order of successional rates between sites. These results establish the viability of state‐space models to quantify ecological dynamics from time series of space‐borne imagery. State‐space models also provide a statistical approach well‐suited to fusing high‐resolution data, such as airborne lidar, with lower‐resolution data that provides better temporal and spatial coverage, such as the Landsat satellite record. Monitoring forest succession using satellite imagery could play a key role in achieving global restoration targets, including identifying sites that will regain tree cover with minimal intervention.

     
    more » « less
  5. Abstract

    Brain networks extracted by independent component analysis (ICA) from magnitude‐only fMRI data are usually denoised using various amplitude‐based thresholds. By contrast, spatial source phase (SSP) or the phase information of ICA brain networks extracted from complex‐valued fMRI data, has provided a simple yet effective way to perform the denoising using a fixed phase change. In this work, we extend the approach to magnitude‐only fMRI data to avoid testing various amplitude thresholds for denoising magnitude maps extracted by ICA, as most studies do not save the complex‐valued data. The main idea is to generate a mathematical SSP map for a magnitude map using a mapping framework, and the mapping framework is built using complex‐valued fMRI data with a known SSP map. Here we leverage the fact that the phase map derived from phase fMRI data has similar phase information to the SSP map. After verifying the use of the magnitude data of complex‐valued fMRI, this framework is generalized to work with magnitude‐only data, allowing use of our approach even without the availability of the corresponding phase fMRI datasets. We test the proposed method using both simulated and experimental fMRI data including complex‐valued data from University of New Mexico and magnitude‐only data from Human Connectome Project. The results provide evidence that the mathematical SSP denoising with a fixed phase change is effective for denoising spatial maps from magnitude‐only fMRI data in terms of retaining more BOLD‐related activity and fewer unwanted voxels, compared with amplitude‐based thresholding. The proposed method provides a unified and efficient SSP approach to denoise ICA brain networks in fMRI data.

     
    more » « less