The hemispheric contrast in cloud microphysical properties constrains aerosol forcing

Solvent-Mediated Charge Transfer Dynamics of a Model Brown Carbon Aerosol Chromophore: Photophysics of 1-Phenylpyrrole Induced by Water Solvation

Nitrogen heterocycles are known to be important light-absorbing chromophores in a newly discovered class of aerosols, commonly referred to as "brown carbon" (BrC) aerosols. Due to their significant absorption and spectral overlap with the solar actinic flux, these BrC chromophores steer the physical and optical properties of aerosols. To model the local aqueous solvation environment surrounding BrC chromophores, we generated cold molecular complexes with water and a prototypical BrC chromophore, 1-phenylpyrrole (1PhPy), using supersonic jet-cooling and explored their intermolecular interactions using single-conformation spectroscopy. Herein, we utilized resonant two-photon ionization (R2PI) and UV holeburning (UV HB) double-resonance spectroscopies to obtain a molecular-level understanding of the role of water microsolvation in charge transfer upon photoexcitation of 1PhPy. Quantum chemical calculations and one-dimensional discrete variable representation simulations revealed insights into the charge transfer efficacy of 1PhPy with and without addition of a single water molecule. Taken together, our results indicate that the intermolecular interactions with water guide the geometry of 1PhPy to adopt a more twisted intramolecular charge transfer (TICT) configuration, thus facilitating charge transfer from the pyrrole donor to the phenyl ring acceptor. Furthermore, the water network surrounding 1PhPy reports on the charge transfer such that the H₂O solvent primarily interacts with the pyrrole ring donor in the ground state, whereas it preferentially interacts with the phenyl ring acceptor in the excited state. Large Franck-Condon activity is evident in the 1PhPy + 1H₂O excitation spectrum for the water-migration vibronic bands, supporting H₂O solvent reorganization upon excitation of the 1PhPy chromophore. Fluorescence measurements with increasing H₂O % volume corroborated our gas-phase studies by indicating that a polar water solvation environment stabilizes the TICT configuration of 1PhPy in the excited electronic state, from which emission is observed at a lower energy compared to the locally excited configuration.

Sensitivity of Air Pollution Exposure and Disease Burden to Emission Changes in China Using Machine Learning Emulation

Machine learning models can emulate chemical transport models, reducing computational costs and enabling more experimentation. We developed emulators to predict annual-mean fine particulate matter (PM_2.5) and ozone (O₃) concentrations and their associated chronic health impacts from changes in five major emission sectors (residential, industrial, land transport, agriculture, and power generation) in China. The emulators predicted 99.9% of the variance in PM_2.5 and O₃ concentrations. We used these emulators to estimate how emission reductions can attain air quality targets. In 2015, we estimate that PM_2.5 exposure was 47.4 μg m^-3 and O₃ exposure was 43.8 ppb, associated with 2,189,700 (95% uncertainty interval, 95UI: 1,948,000-2,427,300) premature deaths per year, primarily from PM_2.5 exposure (98%). PM_2.5 exposure and the associated disease burden were most sensitive to industry and residential emissions. We explore the sensitivity of exposure and health to different combinations of emission reductions. The National Air Quality Target (35 μg m^-3) for PM_2.5 concentrations can be attained nationally with emission reductions of 72% in industrial, 57% in residential, 36% in land transport, 35% in agricultural, and 33% in power generation emissions. We show that complete removal of emissions from these five sectors does not enable the attainment of the WHO Annual Guideline (5 μg m^-3) due to remaining air pollution from other sources. Our work provides the first assessment of how air pollution exposure and disease burden in China varies as emissions change across these five sectors and highlights the value of emulators in air quality research.

Opportunistic experiments to constrain aerosol effective radiative forcing

Aerosol-cloud interactions (ACIs) are considered to be the most uncertain driver of present-day radiative forcing due to human activities. The nonlinearity of cloud-state changes to aerosol perturbations make it challenging to attribute causality in observed relationships of aerosol radiative forcing. Using correlations to infer causality can be challenging when meteorological variability also drives both aerosol and cloud changes independently. Natural and anthropogenic aerosol perturbations from well-defined sources provide "opportunistic experiments" (also known as natural experiments) to investigate ACI in cases where causality may be more confidently inferred. These perturbations cover a wide range of locations and spatiotemporal scales, including point sources such as volcanic eruptions or industrial sources, plumes from biomass burning or forest fires, and tracks from individual ships or shipping corridors. We review the different experimental conditions and conduct a synthesis of the available satellite datasets and field campaigns to place these opportunistic experiments on a common footing, facilitating new insights and a clearer understanding of key uncertainties in aerosol radiative forcing. Cloud albedo perturbations are strongly sensitive to background meteorological conditions. Strong liquid water path increases due to aerosol perturbations are largely ruled out by averaging across experiments. Opportunistic experiments have significantly improved process-level understanding of ACI, but it remains unclear how reliably the relationships found can be scaled to the global level, thus demonstrating a need for deeper investigation in order to improve assessments of aerosol radiative forcing and climate change.