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Air pollution is one of the most pressing environmental issues in the Kathmandu Valley, where the capital city of Nepal is located. We estimated emissions from two of the major source types in the valley (vehicles and brick kilns) and analyzed the corresponding impacts on regional air quality. First, we estimated the on-road vehicle emissions in the valley using the International Vehicle Emission (IVE) model with local emission factors and the latest available data for vehicle registration. We also identified the locations of the brick kilns in the Kathmandu Valley and developed an emissions inventory for these kilns using emission factors measured during the Nepal Ambient Monitoring and Source Testing Experiment (NAMaSTE) field campaign in April 2015. Our results indicate that the commonly-used global emissions inventory, the Hemispheric Transport of Air Pollution (HTAP_v2.2), underestimates particulate matter emissions from vehicles in the Kathmandu Valley by a factor greater than 100. In addition, brick kilns account for nearly 70% of total sulfur dioxide (SO2) emissions from all sectors considered in HTAP_v2.2. Next, we simulated air quality using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) for April 2015 based on three different emission scenarios: HTAP only, HTAP with updated vehicle emissions, and HTAP with both updated vehicle and brick kilns emissions. Comparisons between simulated results and observations indicate that the model underestimates observed surface elemental carbon (EC) and SO2 concentrations under all emissions scenarios. However, our updated estimates of vehicle emissions significantly reduced model bias for EC, while updated emissions from brick kilns improved model performance in simulating SO2. These results highlight the importance of improving local emissions estimates for air quality modeling. We further find that model overestimation of surface wind leads to underestimated air pollutant concentrations in the Kathmandu Valley. Future work should focus on improving local emissions estimates for other major and underrepresented sources (e.g., crop residue burning and garbage burning) with a high spatial resolution, as well as the model's boundary-layer representation, to capture strong spatial gradients of air pollutant concentrations. [ABSTRACT FROM AUTHOR]

International shipping emissions (ISE), particularly sulfur dioxide, can influence the global radiation budget by interacting with clouds and radiation after being oxidized into sulfate aerosols. A better understanding of the uncertainties in estimating the cloud radiative effect (CRE) of ISE is of great importance in climate science. Many international shipping tracks cover oceans with substantial natural dimethyl sulfide (DMS) emissions. The interplay between these two major aerosol sources on cloud radiative effects over vast oceanic regions with relatively low aerosol concentration is an intriguing yet poorly addressed issue confounding estimation of the cloud radiative effects of ISE. Using an Earth system model including two aerosol modules with different aerosol mixing configurations, we derive a significant global net CRE of ISE (-0.153 W m-2 with p" = 0.01 and standard error of 0.004 W m-2) when using emissions consistent with current ship emission regulations. This global net CRE would become much weaker and actually insignificant (-0.001 W m-2 with p" = 0.98 and standard error of 0.007 W m-2) if a more stringent regulation were adopted. We then reveal that the ISE-induced CRE would achieve a significant enhancement when lower DMS emission is prescribed in the simulations, owing to the sub-linear relationship between aerosol concentration and cloud response. In addition, this study also demonstrates that the representation of certain aerosol processes, such as mixing states, can influence the magnitude and pattern of the ISE-induced CRE. These findings suggest a re-evaluation of the ISE-induced CRE with consideration of DMS variability. [ABSTRACT FROM AUTHOR]

Interactions between aerosol particles and clouds contribute a great deal of uncertainty to the scientific community's understanding of anthropogenic climate forcing. Aerosol particles serve as the nucleation sites for cloud droplets, establishing a direct linkage between anthropogenic particulate emissions and clouds in the climate system. To resolve this linkage, the community has developed parameterizations of aerosol activation which can be used in global climate models to interactively predict cloud droplet number concentrations (CDNC). However, different activation schemes can exhibit different sensitivities to aerosol perturbations in different meteorological or pollution regimes. To assess the impact these different sensitivities have on climate forcing, we have coupled three different core activation schemes and variants with the CESM-MARC. Although the model produces a reasonable present day CDNC climatology when compared with observations regardless of the scheme used, ΔCDNC between the present and pre-industrial era regionally increase by over 100 % in zonal mean when using the most sensitive parameterization. These differences in activation sensitivity lead to a spread of over 0.8 W m-2 in global average shortwave indirect effect (AIE) diagnosed from the model, a range which is as large as the inter-model spread from the AeroCom inter-comparison. Model-derived AIE strongly scales with the simulated pre-industrial CDNC burden, and those models with the greatest pre-industrial CDNC tend to have the smallest AIE, regardless of their ΔCDNC. This suggests that present day evaluations of aerosol-climate models may not provide useful constraints on the magnitude of AIE, which will arise from differences in model estimates of the pre-industrial aerosol and cloud climatology. [ABSTRACT FROM AUTHOR]