Statistical methods, particularly machine learning models, have gained significant popularity in air quality predictions. These prediction models are commonly trained using the historical measurement datasets independently collected at the environmental monitoring stations and their operational forecasts in advance using inputs of the real-time ambient pollutant observations. Therefore, these high-quality machine learning models only provide site-available predictions and cannot solely be used as the operational forecast. In contrast, deterministic chemical transport models (CTMs), which simulate the full life cycles of air pollutants, provide predictions that are continuous in the 3D field. Despite their benefits, CTM predictions are typically biased, particularly on a fine scale, owing to the complex error sources due to the emission, transport, and removal of pollutants. In this study, we proposed a fusion of site-available machine learning prediction, which is from our regional feature selection-based machine learning model (RFSML v1.0), and a CTM prediction. Compared to the normal pure machine learning model, the fusion system provides a gridded prediction with relatively high accuracy. The prediction fusion was conducted using the Bayesian-theory-based ensemble Kalman filter (EnKF). Background error covariance was an essential part in the assimilation process. Ensemble CTM predictions driven by the perturbed emission inventories were initially used for representing their spatial covariance statistics, which could resolve the main part of the CTM error. In addition, a covariance inflation algorithm was designed to amplify the ensemble perturbations to account for other model errors next to the uncertainty in emission inputs. Model evaluation tests were conducted based on independent measurements. Our EnKF-based prediction fusion presented superior performance compared to the pure CTM. Moreover, covariance inflation further enhanced the fused prediction, particularly in cases of severe underestimation.@en

Atmospheric ammonia has been hazardous to the environment and human health for decades. Current inventories are usually constructed in a bottom-up manner and subject to uncertainties and incapable of reproducing the spatiotemporal characteristics of ammonia emission. Satellite measurements, for example, Infrared Atmospheric Sounder Interferometer (IASI) and Cross-Track Infrared Sounder, which provide global coverage of ammonia distribution, have gained popularity in ammonia emission estimation through data assimilation methods. However, satellite-based emission inversion studies on China are limited. In this study, we propose a four-dimensional ensemble variational-based ammonia emission inversion system to optimize ammonia emissions in China. It was developed by assimilating the IASI ammonia retrievals onboard Meteorological Operational satellite A and B into a chemical transport model Goddard Earth Observing System Chemical model (GEOS-Chem). Monthly inversion experiments were conducted in April, July, and October 2016 to test the performance. The inversion result indicated that the prior inventory from the MEIC model captured ammonia spreads in general; however, it heterogeneously underrated the emission intensity. The increments obtained in the assimilation were as high as 50% in North, East, and Northwest China. The posterior emission inventory presented a regional emission flux consistent with relevant studies. Driven by the optimized source estimate, GEOS-Chem provides superior results than using the prior in the evaluation of the assimilated IASI retrievals and the surface ammonia concentration measured by the ground-based Ammonia Monitoring Network in China.

@en

Achieving pseudocapacitive intercalation in MXenes with neutral aqueous electrolytes and driving reversible redox reactions is scientifically appealing and practically useful. Here, we report that the partial oxidation of MXene intensifies pseudocapacitive Li+ intercalation into Ti3C2Tx MXene from neutral water-in-salt electrolytes. An in situ X-ray absorption near-edge structure analysis shows that the Ti oxidation state changes during the Li+ intercalation, indicating the presence of a surface redox reaction. The Ti oxidation/reduction is further confirmed by an in situ extended X-ray absorption fine structure analysis, which shows a reversible contraction/expansion of the Ti-C interatomic distance. The intensified Li+ pseudocapacitive intercalation can be explained by the higher oxidation state of Ti at the open circuit potential. This work demonstrates the possibility of tuning the pseudocapacitive intercalation by adjusting the initial oxidation state of the transition metal on the MXene and offers a facile way to enhance the pseudocapacitance of various MXenes.

@en

In this paper, we report a modified three step anisotropic wet etching (TSWE) method to fabricate solid-state silicon nanoslits. The slit-opening process is performed by <111> crystal plane etching. The etching rate of the <111> crystal plane is reasonably slow as it is only 1/45 of the <100> etching rate, thus allowing and therefore good slits-opening controllability. By slowly etching the <111> crystal plane, the over-etching was effectively reduced. Perfectly rectangular nanoslits with different dimensions were successfully obtained. The smallest achieved feature size of the nanoslit is 8.3 nm.@en