Enhanced Deep-Learning Model for Carbon Footprints of Chemicals.

Millions of chemicals have been designed; however, their product carbon footprints (PCFs) are largely unknown, leaving questions about their sustainability. This general lack of PCF data is because the data needed for comprehensive environmental analyses are typically not available in the early molecular design stages. Several predictive tools have been developed to estimate the PCF of chemicals, which are applicable to only a narrow range of common chemicals and have limited predictive ability.

Global site-specific health impacts of fossil energy, steel mills, oil refineries and cement plants.

Climate change and particulate matter air pollution present major threats to human well-being by causing impacts on human health. Both are connected to key air pollutants such as carbon dioxide (CO[Formula: see text]), primary fine particulate matter (PM[Formula: see text]), sulfur dioxide (SO[Formula: see text]), nitrogen oxides (NO[Formula: see text]) and ammonia (NH[Formula: see text]), which are primarily emitted from energy-intensive industrial sectors. We present the first study to consistently link a broad range of emission measurements for these substances with site-specific technical data, emission models, and atmospheric fate and effect models to quantify health impacts caused by nearly all global fossil power plants, steel mills, oil refineries and cement plants.

Net-zero transition of the global chemical industry with CO-feedstock by 2050: feasible yet challenging.

Carbon capture, utilization and storage (CCUS) have been projected by the power and industrial sectors to play a vital role towards net-zero greenhouse gas emissions. In this study, we aim to explore the feasibility of a global chemical industry that fully relies on CO as its carbon source in 2050. We project the global annual CO demand as chemical feedstock to be 2.

Globally Regionalized Monthly Life Cycle Impact Assessment of Particulate Matter.

This work provides a globally regionalized approach for quantifying particulate matter (PM) health impacts. Atmospheric transport and pollutant chemistry of primary particulate matter, sulfur dioxide (SO), nitrogen oxide (NO), and ammonia (NH) from stack emissions were modeled and used to calculate monthly high-resolution maps of global characterization factors that can be used for life cycle impact assessment (LCIA) and risk assessment. These characterization factors are applied to a global data set of coal power emissions.