Research Papers

Potential Satellite Monitoring of Surface Organic Soil Properties in Artic Tundra From SMAP

By Mahta Moghaddam et al.

Water Resources Research

2022

Surface organic carbon content and soil moisture (SM) represent first-order controls on permafrost thaw and vulnerability, yet remain challenging to map accurately. Here we explored the links between surface organic soil properties and SM dynamics in the Alaska North Slope through data analysis and process-based modeling. Our analysis, based on in situ SM and brightness temperature data from the Soil Moisture Active Passive (SMAP) mission, indicated that the SM drydown process in Arctic tundra is closely related to surface soil organic carbon (SOC) properties. More rapid drydown was generally observed in areas with high SOC concentration (SOCC) or low bulk density. The drydown timescale derived from the SMAP polarization ratio (PR) was significantly correlated with SoilGrids surface (0–5 cm) SOCC data (R = −0.54 ∼ −0.68, p < 0.01) at regional scale. To understand the process, we used a coupled permafrost hydrology and microwave emission model to simulate changes in the L-band PR during the thaw season. The model accounts for the variations in organic soil hydraulic and dielectric properties with SOC content and decomposition state. Model sensitivity runs showed larger L-band PR decreases during the early thaw season in soils with higher SOCC consistent with the above analysis, whereby highly organic soils (SOCC > 34.8%) drain water more easily with a larger amount of water discharged or lost (through evapotranspiration) relative to soils with less carbon concentration (SOCC < 17.4%). Our findings indicate that satellite L-band observations are sensitive to tundra SM and carbon properties, and may provide critical constraints on predictions of Arctic permafrost thaw and vulnerability.


Supporting Climate Change Understanding with Novel Data Estimation Instruction, and Epistemic Prompts

By Gale M. Sinatra

Journal of Educational Psychology

2022

Texts presenting novel numerical data can shift learners’ attitudes and conceptions about controversial science topics. However, little is known about the mechanisms underlying this conceptual change. The purpose of this study was to investigate two potential mechanisms that underlie learning from novel data: numerical estimation skills and epistemic cognition. This research investigated combinations of two treatments—a numerical estimation and epistemic cognition intervention—that were designed to enhance people’s ability to make sense of key numbers about climate change when integrated into an existing intervention. Results indicated that undergraduate students (N = 516) who engaged with climate change data held fewer misconceptions compared with a group that read an expository text, though their judgments of climate change plausibility were similar. Results also showed that the two modifications to the central intervention did not have statistically significant effects on knowledge or plausibility when compared with the unmodified intervention. However, we found that individuals’ openness to reason with and integrate new evidence significantly moderated the knowledge effects of the intervention when the intervention was supplemented with both modifications. These findings provide emerging evidence that, among those who are open to reason with new evidence, supporting mathematical reasoning skills and reflection on discrepant information can enhance conceptual change in science.


Estimating Traffic Noise Over a Large Urban Area: An Evaluation of Methods

By Rob McConnell

Environmental International

2022

Unlike air pollution, traffic-related noise remains unregulated and has been under-studied despite evidence of its deleterious health impacts. To characterize population exposure to traffic noise, both acoustic-based numerical models and data-driven statistical approaches can generate estimates over large urban areas. The aim of this work is to formally compare the performances of the most common traffic noise models by evaluating their estimates for different categories of roads and validating them against a unique dataset of measured noise in Long Beach, California. Specifically, a statistical land use regression model, an extreme gradient boosting machine learning model (XGB), and three numerical/acoustic traffic noise models: the US Noise Model (FHWA-TNM2.5), a commercial noise model (CadnaA), and an open-source European model (Harmonoise) were optimized and compared. The results demonstrate that XGB and CadnaA were the most effective models for estimating traffic noise, and they are particularly adept at differentiating noise levels on different categories of road.


On the Differential Correlates of Climate Change Concerns and Severe Weather Concerns: Evidence from the World Risk Poll

By Wändi Bruine de Bruin, Andrew Dugan

Climatic Change

2022

Global climate action will likely be motivated by public concerns about climate change and severe weather, to the extent that they are different. Public perception researchers have been debating whether or not people conflate climate and weather. If climate change concerns and severe weather concerns are different, then they should be formed in different ways. Here, we compare how climate change concerns and severe weather concerns around the world are correlated with key predictors of risk concerns: (1) higher education, which facilitates risk understanding, and (2) experiences and perceptions of severe weather, which increase feelings of concern. We analyze data from the 2019 Lloyd’s Register Foundation World Risk Poll, which was conducted in 142 countries. We find that people who have a college or high-school degree (vs. at most completed elementary school) are more concerned about climate change, but education is unrelated to severe weather concerns. People with experiences and perceptions of severe weather events are more likely to report climate change concerns and severe weather concerns, but the relationships with severe weather concerns are stronger. Thus, climate change concerns and severe weather concerns seem to be formed differently. Findings hold when controlling for household income, other individual characteristics, and country characteristics. They also hold in separate analyses for each World Bank country-income category and continent. These findings suggest that climate change communications should aim to be understandable to audiences at all educational levels and facilitate connections to personal experiences and perceptions of severe weather to climate change.


The Co-Benefits of California Offshore Wind Electricity

By Adam Rose, Dan Wei, Adam Einbinder

The Electricity Journal

2022

California has set forth an ambitious goal of generating all its electricity from carbon-free technologies by 2045. Offshore wind (OSW) presents several attractive system, economic, and environmental attributes to help the state achieve these goals. Inclusion of OSW into the clean electricity generation portfolio could contribute significantly to total resource cost savings. In addition, OSW offers several major co-benefits. Its high and consistent capacity factor and generation time profile complements that of solar and helps enhance renewable electricity generation reliability. OSW could also be instrumental in early retirement of costly and pollution-heavy natural gas plants and lead to substantial job creations. Moreover, California could reap additional economic co-benefits from the development of a local wind energy industry. Additionally, OSW has the potential to advance environmental justice through reduction of ordinary air pollutants in urban areas and by bringing economic opportunities to lagging areas. At the same time, there are multiple challenges that must be addressed for OSW to reach its full potential. Our analysis is intended also to serve as a template for studies elsewhere by providing a comprehensive
framework for estimating co-benefits, taking account of important local conditions, and identification of challenges and how they might be overcome.


Near-Roadway Air Pollution, Immune Cells and Adipokines Among Obese Young Adults

By Rob McConnell

Environmental Health

2022

Air pollution has been associated with metabolic disease and obesity. Adipokines are potential mediators of these effects, but studies of air pollution-adipokine relationships are inconclusive. Macrophage and T cells in adipose tissue (AT) and blood modulate inflammation; however, the role of immune cells in air pollution-induced dysregulation of adipokines has not been studied. We examined the association between air pollution exposure and circulating and AT adipokine concentrations, and whether these relationships were modified by macrophage and T cell numbers in the blood and AT.


Benefit-Cost Analysis of Low-Cost Floor Inundation Sensors

By Adam Rose, Dan Wei, Juan Machado, Kyle Spencer

American Society of Civil Engineers

2022

The demand for inexpensive and reliable warning systems has increased in recent years as a result of the increase in the number and severity of flood disasters. A new generation of low-cost sensors for flood monitoring and warning is being developed by the federal government and private sectors, in some cases collaboratively. We perform a benefit-cost analysis of this new product category, (i.e., low-cost flood inundation sensors), which can readily be deployed in a wireless or internet of things network. The use of these sensors can improve the coverage and lengthen the lead time of flood warning systems. The production costs of this new technology are only a fraction of those of
existing sensors with similar capability and reliability, and operating costs are modest. Benefits depend on such factors as the ability to improve lead times of warnings to reduce property damage, deaths, and injuries from floods as well as the extent of adoption of the new sensors. Our analysis indicates a benefit–cost ratio of 1.4 to 1. However, our results are based on several assumptions. Hence, we
have undertaken extensive sensitivity analyses to determine that our results are robust.


Confidence Levels and Likelihood Terms in IPCC Reports: A Survey of Experts from Different Scientific Disciplines

By Wändi Bruine de Bruin et al.

Climatic Change

2022

Scientific assessments, such as those by the Intergovernmental Panel on Climate Change (IPCC), inform policymakers and the public about the state of scientific evidence and related uncertainties. We studied how experts from different scientific disciplines who were authors of IPCC reports, interpret the uncertainty language recommended in the Guidance Note for Lead Authors of the IPCC Fifth Assessment Report on Consistent Treatment of Uncertainties. This IPCC guidance note discusses how to use confidence levels to describe the quality of evidence and scientific agreement, as well likelihood terms to describe the probability intervals associated with climate variables. We find that (1) physical science experts were more familiar with the IPCC guidance note than other experts, and they followed it more often; (2) experts’ confidence levels increased more with perceptions of evidence than with agreement; (3) experts’ estimated probability intervals for climate variables were wider when likelihood terms were presented with “medium confidence” rather than with “high confidence” and when seen in context of IPCC sentences rather than out of context, and were only partly in agreement with the IPCC guidance note. Our findings inform recommendations for communications about scientific evidence, assessments, and related uncertainties.


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