No COVID-19 climate silver lining in the US power sector.

Recent studies conclude that the global coronavirus (COVID-19) pandemic decreased power sector CO2 emissions globally and in the United States. In this paper, we analyze the statistical significance of CO2 emissions reductions in the U.S. power sector from March through December 2020. We use Gaussian process (GP) regression to assess whether CO2 emissions reductions would have occurred with reasonable probability in the absence of COVID-19 considering uncertainty due to factors unrelated to the pandemic and adjusting for weather, seasonality, and recent emissions trends. We find that monthly CO2 emissions reductions are only statistically significant in April and May 2020 considering hypothesis tests at 5% significance levels. Separately, we consider the potential impact of COVID-19 on coal-fired power plant retirements through 2022. We find that only a small percentage of U.S. coal power plants are at risk of retirement due to a possible COVID-19-related sustained reduction in electricity demand and prices. We observe and anticipate a return to pre-COVID-19 CO2 emissions in the U.S. power sector.

The Effect of Coal-Fired Power Plant Closures on Emergency Department Visits for Asthma-Related Conditions Among 0- to 4-Year-Old Children in Chicago, 2009-2017.

Objectives. To investigate the effects of coal-fired power plant closures on zip code-level rates of emergency department visits for asthma-related conditions among 0- to 4-year-old children in Chicago, Illinois.Methods. We used data on wind, population, PM2.5 (particulates measuring ≤ 2.5 µm in diameter), and zip code-level rates of emergency department visits for asthma-related conditions among 0- to 4-year-old children between 2009 and 2017 in Chicago. The difference-in-differences research design compared rates of emergency department visits in zip codes near 3 coal-fired power plants before and after their closures to rates in zip codes farther away during the same time period.Results. We found that emergency department visits for asthma-related conditions among 0- to 4-year-old children decreased by 12% in zip codes near the 3 coal-fired power plants following their closures relative to rates in zip codes farther away during the same period. The crude and age-specific rates of emergency department visits decreased by 2.41 visits per ten thousand inhabitants and 35.63 visits per ten thousand children aged 0 to 4 years, respectively.Conclusions. Our findings demonstrate that closing coal-fired power plants can lead to improvements in the respiratory health of young children.

Did an Ultra-Low Emissions Policy on Coal-Fueled Thermal Power Reduce the Harmful Emissions? Evidence from Three Typical Air Pollutants Abatement in China.

Thermal power generation based on coal-fired power plants has the advantages of stability and controllability and has been the largest source of electricity supply in China. Coal-fired power plants, however, are also accompanied by high carbon emissions and the release of harmful substances (mainly including sulfur dioxide, nitrogen oxides, and smoke dust), and are even regarded as the "chief criminal" in terms of air pollution. However, thermal power is also a pioneering industry involved in several environmental regulations and cleaner production techniques before other industries. Evidence of this is China's ultra-low emissions (ULE) policy on coal-fired power plants, implemented in 2015. To verify this policy's effect, this study treats ULE as an exogenous impact variable, examining its emissions reduction effect on SO2, NOx, and smoke dust in Eastern and Central China using the difference-in-difference method (DID). The results show that the total emissions of the three pollutants were abated by 0.133%, 0.057% and 0.036% in Eastern, and by 0.120%, 0.035% and 0.043% in Central China at every 1% rise of thermal power generated after ULE. In addition, several other factors can also argue for the promotion of thermal power. Other industries, such as steel or chemical, have proven that they can contribute significant SO2 and NOx emissions. Based on these results, we provide suggestions on synergistic emissions reduction among multiple industries, as well as a discussion on the necessity of implementing ULE in Western China.

The data dimensionality reduction and bad data detection in the process of smart grid reconstruction through machine learning.

To detect false data injection attacks (FDIAs) in power grid reconstruction and solve the problem of high data dimension and bad abnormal data processing in the power system, thereby achieving safe and stable operation of the power grid system, this study introduces machine learning methods to explore the detection of FDIAs. First, through the utilization of the standard IEEE node system and the simulation of FDIAs under the condition of non-complete topology information, the construction of the attack data set is completed, and the MatPower tool is applied to simulate and analyze the data set. Second, based on the isolated Forest (iForest) abnormal score data processing algorithm combined with the Local Linear Embedding (LLE) data dimensionality reduction method, an algorithm for data feature extraction is constructed. Finally, based on the combination of the Convolutional Neural Network (CNN) and the Gated Recurrent Unit (GRU) network, an algorithm model for FDIAs detection is constructed. The results show that in the IEEE14-bus node and IEEE118-bus node systems, the overall distribution of the state estimated before and after the attack vector injection is consistent with the initial value. In the iFores algorithm, the number of iTree and the number of samples affect the extraction of abnormal score data. When the number of iTree n is determined to be 100, and the corresponding number of samples w is determined to be 10, the algorithm has the best detection effect. The FDIAs detection algorithm model based on CNN-GRU shows good detection effects under high attack intensity, with an accuracy rate of more than 95%, and its performance is better than other traditional detection algorithms. In this study, the bad data detection model based on deep learning has an active role in the realization of the safe and stable operation of the smart grid.

GAN-SAE based fault diagnosis method for electrically driven feed pumps.

The running of high-speed electrically driven feed pump has a direct impact on the safety of personnel equipment and economic benefits of power plant, as the result, intelligent condition monitoring and fault diagnosis of electrically driven feed pump becomes an urgent need. In the practical process of electrically driven feed pump fault diagnosis, the running of the equipment is in normal state for a long time, occasionally, with faults, which makes the fault data very rare in a large number of monitoring data, and makes it difficult to extract the internal fault features behind the original time series data, When the deep learning theory is used in practice, the imbalance between the fault data and the normal data occurs in the operation data set. In order to solve the problem of data imbalance, this paper proposes a fault diagnosis method of GAN-SAE. This method first makes compensation for the imbalance of sample data based on the Generative Adversarial Network (GAN), and then uses the Stacked Auto Encoder (SAE) method to extract the signal features. By designing the fault diagnosis program, compared with only using SAE, back propagation neural networks (BP) and multi-hidden layer neural networks(MNN) method, the GAN-SAE method can offer better capability of extracting features, and the accuracy of fault diagnosis of electrically driven feed pump could be improved to 98.89%.

Incidence of spontaneous abortions and congenital anomalies in the vicinity of a uranium processing plant.

OBJECTIVES: The exposure of embryos or foetuses to ionising radiation can cause serious detriments to health. Thus, an enhanced incidence of spontaneous abortions and congenital anomalies might be expected in the vicinity of a uranium processing plant. We analysed the situation in the vicinity of MAPE Mydlovary, a facility about 20 km from Ceské Budejovice, South Bohemia, Czech Republic, which was in operation from 1963 to 1992. METHODS: No relevant data are available for the period of operation of the uranium processing plant. Statistical data have only been collected since 1994. As sanitation work in the area was initiated at around that time and has yet to be completed, we considered a study of possible prenatal effects in the vicinity of MAPE Mydlovary to still be of interest. Data were provided by the Institute of Health Information and Statistics of the Czech Republic for the years 1994-2013. RESULTS: We tested whether there are demonstrable, statistically significant differences between the microregions of the four closest villages (Mydlovary, Olesník, Zahájí, and Zliv), the District of Ceské Budejovice, the South-Bohemian Region, and the Czech Republic. CONCLUSIONS: No increase was found in the incidence of spontaneous abortions and congenital anomalies in the vicinity of this former uranium processing plant compared to the surrounding District of Ceské Budejovice, the South Bohemian Region, or the Czech Republic as a whole.

Disparities in Distribution of Particulate Matter Emissions from US Coal-Fired Power Plants by Race and Poverty Status After Accounting for Reductions in Operations Between 2015 and 2017.

Objectives. To investigate potential changes in burdens from coal-fired electricity-generating units (EGUcfs) that emit fine particulate matter (PM2.5, defined as matter with a nominal mean aerodynamic diameter of ≤ 2.5 µm) among racial/ethnic and economic groups after reduction of operations in 92 US EGUcfs.Methods. PM2.5 burdens calculated for EGUs listed in the 2008, 2011, and 2014 National Emissions Inventory were recalculated for 2017 after omitting emissions from 92 EGUcfs. The combined influence of race/ethnicity and poverty on burden estimates was characterized.Results. Omission of 92 EGUcfs decreased PM2.5 burdens attributable to EGUs by 8.6% for the entire population and to varying degrees for every population subgroup. Although the burden decreased across all subgroups, the decline was not equitable. After omission of the 92 EGUcfs, burdens were highest for the below-poverty and non-White subgroups. Proportional disparities between White and non-White subgroups increased. In our combined analysis, the burden was highest for the non-White-high-poverty subgroup.Conclusions. Our results indicate that subgroups living in poverty experience the greatest absolute burdens from EGUcfs. Changes as a result of EGUcf closures suggest a shift in burden from White to non-White subgroups. Policymakers could use burden analyses to jointly promote equity and reduce emissions.

Internalization of External Benefits Brought by Hydropower Development.

Hydropower development brings a very large number of external benefits which are enjoyed by the beneficiaries for free. These external benefits are defined and the beneficiaries are identified. Models to measure the external benefits are established to reflect their dynamic changes at different periods. To improve the benefit sharing mechanism, a model to internalize these external benefits is established to further compensate those adversely affected. The Z hydropower project in China is taken as the example to calculate its external benefits and their internalization. The external benefits enjoyed by beneficiaries in the surrounding and downstream areas gradually increase from 18 million US dollars in 2006 to 114 million US dollars in 2065, and their compensation standards increase from 4 million US dollars in 2006 to 97 million US dollars in 2065. The external benefits enjoyed by beneficiaries in the power receiving areas increase from 125 million US dollars in 2015 to the maximum of 133 million in 2026, and their compensation standards increase from 38 million US dollars in 2015 to the maximum of 133 million US dollars in 2033. Sharing of external benefits can improve the benefit-sharing mechanism, and properly redistribute the external benefits of hydropower development.

Health symptoms among adults living near a coal-burning power plant.

Coal ash is a waste product generated when coal is burned for energy. The purpose of this study was to assess health symptoms in adults living near a coal-burning power plant and compare the symptoms to a non-exposed population. A community-based mixed methods study was conducted with four neighborhoods adjacent to a coal-burning power plant. The comparison population was not exposed to coal ash and did not live near a coal-burning power plant. Adults who lived near the coal-burning power plant were significantly more likely to suffer from respiratory (AOR = 5.27, 95% CI = 2.16-12.0), gingiva (AOR = 2.46, 95% CI = 1.46-4.15), and skin symptoms (AOR = 3.37, 95% CI = 2.09-5.43). Results suggest that health symptoms may develop in people living near coal-burning power plants.

Occupational exposure to respirable dust from the coal-fired power generation process: sources, concentration, and health risk assessment.

To investigate the contamination levels of respirable dust released in the work environment and the induced workers' health risk at a coal-fired power plant, we collected 405 dust samples from different dusty workstations by personal sampling during the coal-fired power generation process. Then, an inhalation risk assessment model from the USEPA was combined with the Monte Carlo simulation method to quantitatively evaluate the health risk caused by dust inhalation. Of 10 workstations researched, the dust concentration in the most workstations exceeded the prescribed occupational exposure limit. Workers engaged in ash removal suffered the highest health risk at 4.08 × 10-6 ± 2.85 × 10-6 (95% CI), closely followed by those involved in other job categories. The results can contribute to the formulation of targeted dust prevention measures and implementation of risk management for the coal-fired power sector.

Influencing factors of coexistence PreDM and PreHTN in occupational population of state grid corporation of Chinese.

BACKGROUND: Early intervention of coexisting prediabetes (PreDM) and prehypertension (PreHTN) has great significance for the prevention and treatment of cardiovascular diseases. Therefore, the influencing factors of the coexisting PreDM and PreHTN has been widely concerned by human beings. The State Grid Corporation occupational population as a special group, who are often exposed to a certain amount of voltage. Earlier studies have shown that exposure to a certain level of voltage can cause cardiovascular disease. The aim of the present study was to explore the risk factors of coexisting PreDM and PreHTN, and to provide theoretical basis for early intervention. METHODS: A stratified random sampling method was used to randomly select Occupational population from the five power supply regions of China in 2012 for questionnaire surveys and clinical examinations. Respondents were divided into Normal blood glucose group, PreDM group, Diabetes group, Normal blood pressure group, PreHTN group, Hypertension group. RESULTS: The prevalence of coexisting PreDM and PreHTN in the study population was 1.9%. The binary Logistic regression results showed that region, gender, age, BMI, triglyceride (TG), and low density lipoprotein cholesterol (LDL-C) were the effects of factor coexisting PreDM and PreHTN. CONCLUSION: It is important to pay attention to the early stage of hypertension and diabetes, control the transition from PreHTN and PreDM to hypertension and diabetes, and improve the health of Power Supply Enterprise Population.

Geographical mapping and modelling of noise pollution from industrial motors: a case study of the Mbalmayo Thermal Power Plant in Cameroon.

To be able to geographically map, model and evaluate noisy sound emissions from industrial motors, emphases have been laid on various phenomena linked to the propagation of sound waves and their effects on the environment. The failure to respect factory-servicing norms coupled with the depreciation of parts of an industrial motor lead to additional sound production, which due to an accumulation in acoustic power and pressure levels contributes to environmental noise pollution. In this work, a study has been carried out on environmental noise pollution from a thermal power plant in Cameroon, using empirical, diagrammatic, analytical and noise map elaboration methods, with the aim of proposing an optimal protection of the surroundings of the thermal plant from the noise pollution. The results obtained show a similarity in propagation of acoustic pressure and power levels for the different types of frequencies considered. Besides these, the study has revealed that the inhabitants are exposed to sound levels higher than the upper limit of 50 dB and above the alert threshold level of 80 dB. At the geo-localized motors of the plant, the primary sources of acoustic power levels were found to fall within the range from 60 to 98 dB, which is very close to the acoustic pressure levels of between 60 and 95 dB. Due to dispersion of sound, the acoustic power levels are also felt at different points around the plant, considered here as secondary sources. From a general point of view, the observed distribution of the iso-sound contours from the collected data and their general NE-SW orientations show the development of new sources due to cumulative effects and superposition of sound waves at regular intervals. At the same time, the acoustic power and pressure levels have been found to be higher than 80 dB, which is the threshold value for human hearing. This therefore is considered detrimental to human health and wellbeing, provoking the need for a more profound investigation on the existing correlation between levels of sound due to stationary sources and frequency in a high sound medium and elaborating a strategic noise map for the town of Mbalmayo and its environs.

Impact of Coal-fired Power Plant Emissions on Children's Health: A Systematic Review of the Epidemiological Literature.

Coal-based energy production is the most utilized method of electricity production worldwide and releases the highest concentration of gaseous, particulate, and metallic pollutants. This article aims to systematically review the public health impact of coal-fired power plant emissions on children's health. PubMed, Web of Science, and Toxline databases were queried for the past 20 years. Inclusion criteria included original scientific articles with (a) coal-fired power plant exposure assessment, (b) at least one primary pediatric health outcome, and (c) assessment of potential sources of confounding and bias. Only morbidity and mortality studies were included; economic analysis and risk assessment studies without a primary health outcome were not included. Of 513 articles initially retrieved, 17 epidemiological articles were included in the final systematic review after screening and eligibility. The articles reviewed showed a statistically significant adverse effect on pediatric neurodevelopment; birth weight and pediatric respiratory morbidity was associated with exposure to coal-fired power plant emissions, primarily particulate matter and polyaromatic hydrocarbon exposure. There is a lack of consistency of exposure assessment and inadequate control of significant potential confounders such as social economic status. Future research should focus on improving exposure assessment models with an emphasis on source-apportionment and geographic information system methods to model power plant-specific emissions.

Optimal path for controlling pollution emissions in the Chinese electric power industry considering technological heterogeneity.

The electric power industry is not only an important part in the Chinese economic system but also the key industry with the highest emissions of air pollutants in China. This paper aims to control the pollution emissions of the Chinese electric power industry and enhance its electric-generation capacity though pollution-emission allocation patterns and inefficiency elimination. The data envelopment analysis centralized allocation model (DEA-CA) under metafrontier framework is adopted to distribute pollution emissions and electric-generation capacity considering technological heterogeneity at regional and national levels. The empirical result shows that the emission reduction responsibility is directly proportional to regional power generation performance. The metafrontier framework allocates emission permits to combine the national and regional, which makes the adjustment of each province more reasonable. At last, the relationship between the aggregate optimal electricity capacity and the pollution emission control coefficient is shown to follow an inverted U-shape curve, which implies that a modest emission control policy might be more appropriate for the electric power industry to achieve the joint optimizing goal of electricity generation enhancement and pollution emission control.

Have wind turbines in Germany generated electricity as would be expected from the prevailing wind conditions in 2000-2014?

The planning of the energy transition from fossil fuels to renewables requires estimates for how much electricity wind turbines can generate from the prevailing atmospheric conditions. Here, we estimate monthly ideal wind energy generation from datasets of wind speeds, air density and installed wind turbines in Germany and compare these to reported actual yields. Both yields were used in a statistical model to identify and quantify factors that reduced actual compared to ideal yields. The installed capacity within the region had no significant influence. Turbine age and park size resulted in significant yield reductions. Predicted yields increased from 9.1 TWh/a in 2000 to 58.9 TWh/a in 2014 resulting from an increase in installed capacity from 5.7 GW to 37.6 GW, which agrees very well with reported estimates for Germany. The age effect, which includes turbine aging and possibly other external effects, lowered yields from 3.6 to 6.7% from 2000 to 2014. The effect of park size decreased annual yields by 1.9% throughout this period. However, actual monthly yields represent on average only 73.7% of the ideal yields, with unknown causes. We conclude that the combination of ideal yields predicted from wind conditions with observed yields is suitable to derive realistic estimates of wind energy generation as well as realistic resource potentials.

A decoupled circuital model methodology for calculating DC currents in AC grids induced by HVDC grounding current.

Large currents are injected into the earth from grounding poles of HVDC systems under monopole ground return mode. The currents change the earth surface potential and result in DC currents in AC systems. This paper proposes a computationally efficient decoupled circuital calculation method for assessing the unwanted DC currents in AC grids. Firstly, the earth resistive network is acquired by simulating the DC grounding current distribution using Finite Element Method (FEM). Secondly, the earth resistive network and AC grid are combined to develop a decoupled circuital model of the overall system. The acquired model is used to calculate the DC currents in AC grids by solving a set of linear equations. The proposed method is computationally more efficient as compared to field-circuit coupled methods. In addition, its accuracy is proved by showing a close agreement between our results and field-circuit coupled model as well as the actual measurements. Finally, in Shanghai area power grid the DC currents are calculated using the proposed technique. Based on these calculations, remedial measures for reducing the DC currents in AC grid are suggested. Our research results indicate that DC currents in AC systems can be reduced by operating the two HVDC projects with opposite polarities.

Air quality accountability: Developing long-term daily time series of pollutant changes and uncertainties in Atlanta, Georgia resulting from the 1990 Clean Air Act Amendments.

The 1990 Clean Air Act Amendments codified major institutional changes relating to the management of air pollutants in the United States. Recent research years has attributed reduced emissions over the past two decades to regulations enacted under these Amendments, but none have separated long-term daily impacts of individual regulatory programs on multiple source categories under a consistent framework. Using daily emissions and air quality measurements along with a detailed review of national and local regulations promulgated after the Amendments, we quantify daily changes in emissions and air quality attributable to regulations on electricity generating units and on-road mobile sources. To quantify daily changes, we develop nine sets of counterfactual emissions and ambient air pollution concentration time series for 10 pollutants that assume individual regulatory programs and combinations thereof were not implemented. In addition to daily impacts, we estimate uncertainties in these results. These counterfactual daily ambient concentrations reveal high seasonality and increasing effectiveness of most regulations between 1999 and 2013. Monthly average counterfactual concentrations in scenarios that assume no new regulations on electricity generating units and mobile sources are greater than observed concentrations for all pollutants except ozone, which has seen increased wintertime concentrations accompany summertime decreases. By the end of the period, electricity generating unit emissions reductions under the Acid Rain Program and Clean Air Interstate Rule and their respective related local programs led to similar PM2.5 concentration decreases. Of the mobile source regulations, rules on gasoline and diesel vehicles led to similar reductions in annual PM2.5, and gasoline programs led to double the summertime ozone reductions as diesel programs. The nine sets of daily time series and their uncertainties were designed for use in air pollution accountability health studies.

Bioavailability of mercury in power plant wastewater and ambient river samples: Evidence that the regulation of total mercury is not appropriate.

Mercury (Hg) is a neurotoxin that can cause debilitating effects to human and environmental receptors under high exposure conditions. For industrial and municipal point sources that discharge Hg, wastewater limitations on total Hg (THg) concentrations or loads are typical. While this regulatory practice provides simplicity for regulated industry and water resource agencies (i.e., for analytical detection and reporting purposes), it ignores the important considerations of speciation and bioavailability. In this study, water samples were collected from multiple power plant wastewater, simulated mixing zone, and ambient river locations (N = 10 to 20) and were analyzed for bioavailable Hg forms (methylmercury and acid-labile Hg, or BHg), THg, and dissolved Hg. The median concentration of THg in wastewater, mixing zone, and ambient river samples was 7.1, 5.3, and 2.3 ng/L, respectively. The percentages of THg as BHg (median values) were 18.7%, 29.3%, and 8.5% for wastewater, mixing zone, and ambient river samples. The percentages of methylmercury (MeHg) as THg were not statistically different between paired ambient and mixing zone samples (P > 0.05); this result indicates that wastewater did not increase the MeHg fraction when mixed with ambient water. Multiple regression analysis indicated that variation in THg for combined wastewater and mixing zone samples could be adequately explained by pooled water quality parameters (total suspended solids, total dissolved solids, sulfate, total organic carbon, pH, specific conductivity; r2 = 0.51; P < 0.05); however, no significant regression relationships were apparent for the percentage of BHg. These results, at least for the wastewater samples evaluated, indicate that regulating THg is likely overly conservative, and mechanisms to regulate the bioavailable forms of Hg are needed. If Hg fish tissue monitoring data indicate that concentrations are less than consumption thresholds, metal translator methodologies or bioavailability-based criterion techniques (as currently used for non-Hg trace elements) should be allowed for Hg. Integr Environ Assess Manag 2019;15:142-147. © 2018 SETAC.

Mercury Pollution from Coal-Fired Power Plants: A Critical Analysis of the Australian Regulatory Response to Public Health Risks.

This article explores the benefits likely to arise from Australia's ratification of the Minamata Convention on Mercury with regard to reducing public health risks from mercury emissions from coal-fired power plants. The current legislative frameworks regulating mercury pollution are critiqued, an exploration of the international approaches is undertaken, and recommendations are made aiming to produce a stronger, more stringent and long-term mercury protection policy for Australian communities.