Disproportionality in Power Plants' Carbon Emissions: A Cross-National Study

Improving the Oxygen Evolution Reaction Performance of Ternary Layered Double Hydroxides by Tuning All Three Cations' Electronic Structures

The pursuit of efficient and sustainable hydrogen production is essential in the fight against climate change. One important method for achieving this is the electrolysis of water, particularly through the oxygen evolution reaction (OER). Recent studies indicate that trimetallic layered double hydroxides (LDHs) can enhance OER performance compared to bimetallic LDHs. This improvement occurs because the third cation alters the electronic structures of the other two cations, thereby increasing the intermediates' binding energies and enhancing electrical conductivity. This study proposes an approach enabling the modulation of the electronic structures of all three cations involved in the synthesis of the trimetallic LDHs. It suggested intercalating sodium dodecyl sulfate (SDS) into the interlayer of the trimetallic NiFe-La-LDH. A successful intercalation of SDS has been confirmed through the XRD, FT-IR, EDS, and XPS. This has expanded the interlayer distance which was beneficial for the electrical conductivity. Furthermore, SDS generated sulphur, which modulated the electronic structures of all three cations enriching the active sites and improving electrical conductivity and OER performance compared to its counterparts. This approach is beneficial: 1. The interlayer can be further enlarged by using different doping ratios of SDS. 2. Sulphur can enrich the active sites and improve the OER performance.

Exploratory Weather Data Analysis for Electricity Load Forecasting Using SVM and GRNN, Case Study in Bali, Indonesia

Accurate forecasting of electricity load is essential for electricity companies, primarily for planning electricity generators. Overestimated or underestimated forecasting value may lead to inefficiency of electricity generator or electricity deficiency in the electricity grid system. Parameters that may affect electricity demand are the weather conditions at the location of the electricity system. In this paper, we investigate possible weather parameters that affect electricity load. As a case study, we choose an area with an isolated electricity system, i.e., Bali Island, in Indonesia. We calculate correlations of various weather parameters with electricity load in Bali during the period 2018–2019. We use two machine learning models to design an electricity load forecasting system, i.e., the Generalized Regression Neural Network (GRNN) and Support Vector Machine (SVM), using features from various weather parameters. We design scenarios that add one-by-one weather parameters to investigate which weather parameters affect the electricity load. The results show that the weather parameter with the highest correlation value with the electricity load in Bali is the temperature, which is then followed by sun radiation and wind speed parameter. We obtain the best prediction with GRNN and SVR with a correlation coefficient value of 0.95 and 0.965, respectively.

Comparison of PM2.5 and CO2 Concentrations in Large Cities of China during the COVID-19 Lockdown

Estimating the impacts on PM2.5 pollution and CO2 emissions by human activities in different urban regions is important for developing efficient policies. In early 2020, China implemented a lockdown policy to contain the spread of COVID-19, resulting in a significant reduction of human activities. This event presents a convenient opportunity to study the impact of human activities in the transportation and industrial sectors on air pollution. Here, we investigate the variations in air quality attributed to the COVID-19 lockdown policy in the megacities of China by combining in-situ environmental and meteorological datasets, the Suomi-NPP/VIIRS and the CO2 emissions from the Carbon Monitor project. Our study shows that PM2.5 concentrations in the spring of 2020 decreased by 41.87% in the Yangtze River Delta (YRD) and 43.30% in the Pearl River Delta (PRD), respectively, owing to the significant shutdown of traffic and manufacturing industries. However, PM2.5 concentrations in the Beijing-Tianjin-Hebei (BTH) region only decreased by 2.01% because the energy and steel industries were not fully paused. In addition, unfavorable weather conditions contributed to further increases in the PM2.5 concentration. Furthermore, CO2 concentrations were not significantly affected in China during the short-term emission reduction, despite a 19.52% reduction in CO2 emissions compared to the same period in 2019. Our results suggest that concerted efforts from different emission sectors and effective long-term emission reduction strategies are necessary to control air pollution and CO2 emissions.

Satellite-Based Detection and Characterization of Industrial Heat Sources in China

Despite their important contribution to the economic domain, active heat-releasing industrial plants have significant implications for human health and climate change. However, a spatially detailed dataset of various heat-releasing industrial sectors and large-scale characterization of heat emissions from industrial sources have not been reported yet. In this study, a dataset of heat-releasing industries was established using a national detection map of thermal anomalies produced by a novel and more accurate method employing daily nighttime visible infrared imaging radiometer suite thermal infrared images corresponding to 1 year. Subsequently, we quantified the dimensional features of heat radiation fluxes of China's industrial plants. A total of 12 114 industrial objects were structured in a two-level hierarchical dataset of heat-releasing industries, representing a magnitude of at least 1 order higher than the number enumerated in the state-of-the-art inventory of industrial heat sources across China. The satellite observations helped more completely characterize industrial heat plumes, which represent the industrial heat radiation fluxes with higher levels of densities that prevail in the central-eastern part of China having spatial clustering islands. Our results could be used to inform policy and environmental management in relation to meaningful dynamic industrial supervision, targeting extreme polluters and differentiated emission mitigation measurements.

A New Route for Indirect Mineralization of Carbon Dioxide-Sodium Oxalate as a Detergent Builder

Here, we bridge the gap between carbon mineralization and laundry detergent builder with sodium oxalate. Daily laundry can help mineralize carbon dioxide. First, we screen an environment-friendly process to produce sodium oxalate, using CO₂ as a raw material. Then, we evaluate the properties of sodium oxalate as a detergent builder and prove the formation of calcium oxalate under laundry conditions. Our data suggest that sodium oxalate has excellent calcium-removing properties. Detergents based on sodium oxalate have good detergency. Furthermore, solid calcium oxalates (calcium oxalate monohydrate or calcium oxalate dihydrate, which has good stability in water and thermal stability), is obtained from washing waters. These results demonstrate the possibility of using sodium oxalate as detergent builder. The whole process can transform the greenhouse gas CO₂ into commodity chemicals and can mineralize carbon.

Carbon Dioxide Capture from Flue Gas Using Tri-Sodium Phosphate as an Effective Sorbent

Fossil fuels are dominant as an energy source, typically producing carbon dioxide (CO2) and enhancing global climate change. The present work reports the application of low-cost tri-sodium phosphate (TSP) to capture CO2 from model flue gas (CO2 + N2) mixture, in a batch mode and fixed-bed setup. It is observed that TSP has a high CO2 capture capacity as well as high CO2 selectivity. At ambient temperature, TSP shows a maximum CO2 capture capacity of 198 mg CO2/g of TSP. Furthermore, the CO2 capture efficiency of TSP over a flue gas mixture was found to be more than 90%. Fresh and spent materials were characterized using powder X-ray diffraction (PXRD), scanning electron microscopy (SEM), and Fourier transformed infrared spectroscopy (FTIR). Preliminary experiments were also conducted to evaluate the performance of regenerated TSP. The spent TSP was regenerated using sodium hydroxide (NaOH) and its recyclability was tested for three consecutive cycles. A conceptual prototype for post-combustion CO2 capture based on TSP material has also been discussed.

Lifetime cancer risks from hazardous air pollutants in US public school districts

Background

Children are sensitive to the health impacts of environmental contaminants, but research assessing outdoor environmental exposures for children and schools is underdeveloped. There are no national-level studies examining geographical and social disparities in air pollution exposure for children in school districts. Focusing on school districts is important because they are meaningful decision-making entities for schools.

Methods

Using data from the National Air Toxics Assessment, we spatially reallocated lifetime cancer risk (LCR) from hazardous air pollutants (HAPs) within US school district boundaries, and paired those estimates with school district level sociodemographic measures obtained through the Integrated Public Use Microdata Series National Historic Geographic Information System. We employed local Moran’s I to identify district-level hotpots and generalised estimating equations (GEEs) to quantify risk disparities.

Results

We identified hotspots of elevated LCR from all sources of HAPs (called ‘total’). A regional hotspot extends throughout the southeastern USA and smaller regional hotspots are present in southern Arizona, southern California and in California’s central valley. School districts with higher proportions of children, children with disabilities, foreign-born children, black children and multiracial/other race children, and lower proportions of Native American children, had greater total LCR (p<0.001). The effect of poverty on total LCR (p<0.001) was nonlinear; the lowest and highest poverty districts had lower total LCR.

Conclusions

Geographical and social disparities in LCR across US school districts may be affecting children’s health and future potential. This new knowledge can inform policy changes, as school districts can advocate for the environmental health of children.

Social science perspectives on drivers of and responses to global climate change

This article provides a review of recent anthropological, archeological, geographical, and sociological research on anthropogenic drivers of climate change, with a particular focus on drivers of carbon emissions, mitigation and adaptation. The four disciplines emphasize cultural, economic, geographic, historical, political, and social-structural factors to be important drivers of and responses to climate change. Each of these disciplines has unique perspectives and makes noteworthy contributions to our shared understanding of anthropogenic drivers, but they also complement one another and contribute to integrated, multidisciplinary frameworks. The article begins with discussions of research on temporal dimensions of human drivers of carbon emissions, highlighting interactions between long-term and near-term drivers. Next, descriptions of the disciplines' contributions to the understanding of mitigation and adaptation are provided. It concludes with a summary of key lessons offered by the four disciplines as well as suggestions for future research. This article is categorized under: Climate Economics > Economics and Climate Change.