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Sunlight can oxidize dissolved organic carbon (DOC) to dissolved inorganic carbon (DIC) in freshwaters. The importance of complete photooxidation, or photomineralization, as a sink for DOC remains unclear in temperate rivers, as most estimates are restricted to lakes, high latitude rivers, and coastal river plumes. In this study, we construct a model representing over 75,000 river reaches in the Connecticut River Watershed (CRW), USA, to calculate spectrally resolved photomineralization. We test the hypothesis that photomineralization is a negligible DOC sink across all reaches and flow conditions relative to DOC fluxes. Our model quantifies reaction rates and transport drivers within the river reaches for the ranges of flow conditions, incoming solar irradiance, and canopy cover shading observed throughout the year. Our model predicts average daily areal photomineralization rates ranging from 1.16 mg‐C m−2 day−1 in low flow river reaches in the winter, to 18.33 mg‐C m−2 day−1 in high flow river reaches during the summer. Even for high photomineralization fluxes, corresponding photomineralization uptake velocities are typically at least an order of magnitude smaller than those reported for other instream processes. We calculate DOC elimination by photomineralization relative to DOC fluxes through individual stream reaches as well as the entire riverine portion of the CRW. We find that relative photomineralization fluxes are highest in summer drought conditions in low order streams. In median flows and mean light intensities, for an average watershed travel distance, 3%–5% of the DOC fluxes are eliminated, indicating that photomineralization is a minor DOC sink in temperate rivers. Plain Language Summary: Rivers are an important part of the carbon cycle, moving carbon compounds from land to the ocean. Within rivers, dissolved organic carbon molecules can be broken down into inorganic carbon molecules, including the greenhouse gas carbon dioxide. Sunlight shining into rivers can cause these organic molecules to break down in a process called photomineralization, but it is not clear if this process is important compared to the total amount of organic carbon that travels through rivers every day. In this paper, we build a model for the river sections of temperate Connecticut River Watershed, which calculates photomineralization for possible river flow conditions, dissolved organic carbon concentrations, and seasons, and compares the size of the sunlight‐driven breakdown of dissolved organic carbon to the amount of dissolved organic carbon in the river. This is the first model that puts photomineralization rates in the context of a flowing temperate river network. We show that compared with the dissolved organic carbon amounts present in the river at any time of the year or any flow conditions, photomineralization is essentially an unimportant process, removing on average 3%–5% of the dissolved organic carbon through an average watershed river route. Key Points: We build a model calculating reaction and transport drivers for photomineralization in 75,000 temperate river reachesPhotomineralization is a negligible dissolved organic carbon (DOC) sink relative to DOC fluxesDOC elimination by photomineralization in temperate rivers is limited by short water residence times and canopy cover [ABSTRACT FROM AUTHOR]

Streams and rivers are major sources of greenhouse gases (GHGs) to the atmosphere, as carbon and nitrogen are converted and outgassed during transport. Although our understanding of drivers of individual GHG fluxes has improved with numerous site‐specific studies and global‐scale compilations, our ability to parse out interrelated physical and biogeochemical drivers of gas concentrations is limited by a lack of consistently collected, temporally continuous samples of GHGs and their associated drivers. We present a first analysis of such a dataset collected by the National Ecological Observatory Network across 27 streams and rivers across ecoclimatic domains of the United States. Average concentrations of CO2 ranged from 36.9 ± 0.88 to 404 ± 33 μmol L−1, CH4 from 0.003 ± 0.0003 to 4.99 ± 0.72 μmol L−1, and N2O from 0.015 to 0.04 μmol L−1 and spanned ranges of previous global compilations. Both CO2 and CH4 were strongly affected by physical drivers including mean air temperature and stream slope, as well as by dissolved oxygen and total nitrogen concentrations. N2O was exclusively correlated with total nitrogen concentrations. Results suggested that potential for gas exchange dominated patterns in gas concentrations at the site level, but contributions of in‐stream aerobic and anaerobic metabolism, and groundwater also likely varied across sites. The highest gas concentrations as well as highest variability occurred in low‐gradient, warmer, and nonperennial systems. These results are a first step in providing unprecedented, continuous estimates of GHG flux constrained by temporally variable physical and biogeochemical drivers of GHG production. [ABSTRACT FROM AUTHOR]

In an environment with economic policy uncertainty, the policies of energy efficiency are probably compromised due to weak regulation measures. Inconsistent economic policies may lead to higher energy consumption, which damages the environment. However, works of literature are silent on the role of economic policy uncertainty in carbon emission and energy nexus. Hence, the study is an attempt to investigate the role of economic policy uncertainty in the relationship between energy intensity and carbon dioxide (CO2) emissions in the United States by using annual data spanning from 1985 to 2017. A recent time series method proposed by Jordan and Philips (2018) is employed to get robust and consistent estimation results in the study. Empirical results reveal that (a) higher energy intensity contributes to pollution, (b) economic policy uncertainty adversely affects environmental quality, (c) economic policy uncertainty strengthens the detrimental effect of energy intensity on CO2 emissions. [ABSTRACT FROM AUTHOR]

Production and consumption of nitrous oxide (N2O), methane (CH4), and carbon dioxide (CO2) are affected by complex interactions of temperature, moisture, and substrate supply, which are further complicated by spatial heterogeneity of the soil matrix. This microsite heterogeneity is often invoked to explain non‐normal distributions of greenhouse gas (GHG) fluxes, also known as hot spots and hot moments. To advance numerical simulation of these belowground processes, we expanded the Dual Arrhenius and Michaelis–Menten model, to apply it consistently for all three GHGs with respect to the biophysical processes of production, consumption, and diffusion within the soil, including the contrasting effects of oxygen (O2) as substrate or inhibitor for each process. High‐frequency chamber‐based measurements of all three GHGs at the Howland Forest (ME, USA) were used to parameterize the model using a multiple constraint approach. The area under a soil chamber is partitioned according to a bivariate log‐normal probability distribution function (PDF) of carbon and water content across a range of microsites, which leads to a PDF of heterotrophic respiration and O2 consumption among microsites. Linking microsite consumption of O2 with a diffusion model generates a broad range of microsite concentrations of O2, which then determines the PDF of microsites that produce or consume CH4 and N2O, such that a range of microsites occurs with both positive and negative signs for net CH4 and N2O flux. Results demonstrate that it is numerically feasible for microsites of N2O reduction and CH4 oxidation to co‐occur under a single chamber, thus explaining occasional measurement of simultaneous uptake of both gases. Simultaneous simulation of all three GHGs in a parsimonious modeling framework is challenging, but it increases confidence that agreement between simulations and measurements is based on skillful numerical representation of processes across a heterogeneous environment. [ABSTRACT FROM AUTHOR]

In this article, we propose a stochastic formulation of mass balances that impose physical constraints on production technologies. The estimation of the model involves a composed error term structure that is commonly applied in the literature on stochastic frontier analysis of productive efficiency. Moreover, we discuss how ordinary least squares and maximum likelihood methods can be used to estimate emission coefficients based on the proposed model and compare the results to estimates based on linear programming techniques from input–output analysis. In contrast to previous approaches, our model allows one to estimate the physical limitations to production possibilities in the presence of statistical noise and depends on substantially weaker data requirements. We apply our approach to estimate emission coefficients for sulfur dioxide and carbon dioxide using a sample of fossil‐fueled power plants in the United States. [ABSTRACT FROM AUTHOR]

The ice‐covered period on lakes in the northern hemisphere has often been neglected or assumed to have less importance relative to the open water season. However, recent studies challenge this convention, suggesting that the winter period is more dynamic than previously thought. In this review, we synthesize the current understanding of under‐ice carbon dioxide (CO2) and methane (CH4) dynamics, highlighting the annual importance of CO2 and CH4 emissions from lakes at ice‐melt. We compiled data from 25 studies that showed that the ice‐melt period represents 17% and 27% of the annual CO2 and CH4 emissions, respectively. We also found evidence that the magnitude and type of emission (i.e., CO2 and CH4) varies with characteristics of lakes including geographic location, lake morphometry, and physicochemical conditions. The scarcity of winter and spring carbon data from northern lakes represents a major gap in our understanding of annual budgets in these lakes and calls for future research during this key period. [ABSTRACT FROM AUTHOR]