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Struggling for Air offers the most comprehensive analysis to date of the Obama administration policies that have become known as the \"war on coal.\" Unconventionally, the authors trace the origins of this \"war\" to a fateful decision made by Congress almost half a century ago, when it passed the Clean Air Act of 1970. That Act imposed stringent requirements on new sources of pollution but, for the most part, gave existing sources a pass on controlling their emissions. This regulatory imbalance, known as grandfathering, left old, dirty, coal-fired power plants with a perverse incentive to continue operating for far longer than originally planned. The book argues that the Obama administration's environmental regulations are not the \"unprecedented regulatory assault\" on coal alleged by critics, but the culmination of 25 years of efforts, by administrations of both parties, to undo the negative consequences of Congress's 1970 error by cleaning up or closing down grandfathered power plants.

Pollution, Politics, and Power tells the story of the remarkable transformation of the electric power industry that has taken place over the last four decades. Electric power companies have morphed from polluting, regulated monopolies into cleaner deregulated generators, transmitters, and distributors of electrical power in a far more competitive economic environment. Companies are investing heavily in natural gas and utility-scale renewable resources, and they have quit building new coal-fired plants. The distribution side of the business has become a facilitator of end-use efficiency and a purchaser of excess electricity produced by rooftop solar panels and backyard wind turbines. Meanwhile, the once-powerful coal industry teeters on the edge of bankruptcy as electric power companies throughout the country shutter coal-fired power plants and reduced demand for coal causes mines to close throughout Appalachia and communities throughout the region suffer from high unemployment, reduced resources, and a spiraling opioid epidemic. Environmental regulation has played a major role in this transformation. The Environmental Protection Agency and its counterparts in the states have insisted that new power plants install the best available technology to control air pollution and that existing power plants limit their emissions and solid wastes. Market forces resulting from increased competition, increased supplies of natural gas and inexpensive wind and solar power have also played prominent roles. And the Trump administration's efforts to revive the coal industry by scaling back environmental controls and reregulating electricity prices have had little effect on the industry's decline.-- Provided by publisher

Exposure mixtures frequently occur in data across many domains, particularly in the fields of environmental and nutritional epidemiology. Various strategies have arisen to answer questions about exposure mixtures, including methods such as weighted quantile sum (WQS) regression that estimate a joint effect of the mixture components. We demonstrate a new approach to estimating the joint effects of a mixture: quantile g-computation. This approach combines the inferential simplicity of WQS regression with the flexibility of g-computation, a method of causal effect estimation. We use simulations to examine whether quantile g-computation and WQS regression can accurately and precisely estimate the effects of mixtures in a variety of common scenarios. We examine the bias, confidence interval (CI) coverage, and bias-variance tradeoff of quantile g-computation and WQS regression and how these quantities are impacted by the presence of noncausal exposures, exposure correlation, unmeasured confounding, and nonlinearity of exposure effects. Quantile g-computation, unlike WQS regression, allows inference on mixture effects that is unbiased with appropriate CI coverage at sample sizes typically encountered in epidemiologic studies and when the assumptions of WQS regression are not met. Further, WQS regression can magnify bias from unmeasured confounding that might occur if important components of the mixture are omitted from the analysis. Unlike inferential approaches that examine the effects of individual exposures while holding other exposures constant, methods like quantile g-computation that can estimate the effect of a mixture are essential for understanding the effects of potential public health actions that act on exposure sources. Our approach may serve to help bridge gaps between epidemiologic analysis and interventions such as regulations on industrial emissions or mining processes, dietary changes, or consumer behavioral changes that act on multiple exposures simultaneously. https://doi.org/10.1289/EHP5838.

This paper, which focuses on emissions from China's coal-fired power plants during 1990–2010, is the second in a series of papers that aims to develop a high-resolution emission inventory for China. This is the first time that emissions from China's coal-fired power plants were estimated at unit level for a 20-year period. This inventory is constructed from a unit-based database compiled in this study, named the China coal-fired Power plant Emissions Database (CPED), which includes detailed information on the technologies, activity data, operation situation, emission factors, and locations of individual units and supplements with aggregated data where unit-based information is not available. Between 1990 and 2010, compared to a 479 % growth in coal consumption, emissions from China's coal-fired power plants increased by 56, 335, and 442 % for SO2, NOx, and CO2, respectively, and decreased by 23 and 27 % for PM2.5 and PM10 respectively. Driven by the accelerated economic growth, large power plants were constructed throughout the country after 2000, resulting in a dramatic growth in emissions. The growth trend of emissions has been effectively curbed since 2005 due to strengthened emission control measures including the installation of flue gas desulfurization (FGD) systems and the optimization of the generation fleet mix by promoting large units and decommissioning small ones. Compared to previous emission inventories, CPED significantly improved the spatial resolution and temporal profile of the power plant emission inventory in China by extensive use of underlying data at unit level. The new inventory developed in this study will enable a close examination of temporal and spatial variations of power plant emissions in China and will help to improve the performances of chemical transport models by providing more accurate emission data.

The targets of limiting global warming levels below 2°C or even 1.5°C set by Paris Agreement heavily rely on bioenergy with carbon capture and storage (BECCS), which can remove carbon dioxide in the atmosphere and achieve net zero greenhouse gas (GHG) emission. Biomass and coal co‐firing with CCS is one of BECCS technologies, as well as a pathway to achieve low carbon transformation and upgrading through retrofitting coal power plants. However, few studies have considered co‐firing ratio of biomass to coal based on each specific coal power plant's characteristic information such as location, installed capacity, resources allocation, and logistic transportation. Therefore, there is a need to understand whether it is worth retrofitting any individual coal power plant for the benefit of GHG emission reduction. It is also important to understand which power plant is suitable for retrofit and the associated co‐firing ratio. In order to fulfill this gap, this paper develops a framework to solve these questions, which mainly include three steps. First, biomass resources are assessed at 1 km spatial resolution with the help of the Geography Information Science method. Second, by setting biomass collection points and linear program model, resource allocation and supply chain for each power plants are complete. Third, is by assessing the life‐cycle emission for each power plant. In this study, Hubei Province in China is taken as the research area and study case. The main conclusions are as follows: (a) biomass co‐firing ratio for each CCS coal power plant to achieve carbon neutral is between 40% and 50%; (b) lower co‐firing ratio sometimes may obtain better carbon emission reduction benefits; (c) even the same installed capacity power plants should consider differentiated retrofit strategy according to their own characteristic. Based on the results and analysis above, retrofit suggestions for each power plant are made in the discussion. To achieve net zero carbon emission target set out in the Paris Agreement, this study explores how to retrofit coal‐fired power plants with biomass co‐firing and carbon capture and storage, taking into consideration of the detailed parameters of each plant, road network map, biomass collection points, and spatial distribution of biomass, based on Geography Information System and Life Cycle Assessment framework. Hubei Province of China has been chosen as the case region. This study can provide targeted retrofitting suggestion for each coal power plant and provide a common methodological framework for other power plants to develop such retrofitting plans.

Coal-fired power plants are widely used to achieve a power balance in grids with renewable energy, which leads to new requirements for speediness in load dispatch. This paper presents a nondominated-sorting grey wolf optimizer algorithm (NSGWO) for the multiobjective load dispatch of coal-fired power plants that employed efficient nondominated sorting, a reference-point selection strategy, and a simulated binary crossover operator. The optimization results of the benchmark functions indicated that the NSGWO algorithm had a better accuracy and a better distribution than the traditional multiobjective grey wolf optimizer algorithm. Regarding the load dispatch of economy, environmental protection, and speediness strategies, the NSGWO had the best performance of all the simulated algorithms. The optimal-compromise solutions of the economy and speediness strategies of the NSGWO algorithm had a good distribution, which elucidated that this novel algorithm was favorable to allowing coal-fired power plants to accommodate renewable energy.

The conversion of coal-fired power plants to nuclear power stations is a potential method for decarbonizing coal power and offers a pathway for low-carbon development in China’s power industry. This paper focuses on retrofitting China’s coastal coal-fired power stations and compares the potential nuclear reactor technologies for the retrofit: China’s mainstream pressurized water reactor and the commercially operated fourth-generation high-temperature gas-cooled reactor (HTGR). The analysis compares the degree of matching between the two technologies and coal-fired power stations in terms of unit capacity, thermal system parameters, unit speed, structural dimensions, and weight, which significantly impact the retrofit scheme. The results indicate that HTGR is more compatible with coal-fired power plants and is recommended as the type of nuclear reactor technology to be retrofitted. The study selected the 210 MWe High-Temperature Gas-Cooled Reactor Pebble-Bed Module (HTR-PM) as the reactor technology for retrofitting a typical 300 MW class subcritical coal-fired unit. Based on the concept of subcritical parameters upgrading, the potential analysis and strategy study of retrofit is carried out in terms of the turbine, the main heat exchange equipment, the main pumps, and the main thermal system pipelines in the conventional island. The results indicate that the conventional island of the HTR-PM nuclear power plant has significant potential for retrofitting, which can be a crucial research direction for nuclear retrofitting of coal-fired power plants.