This study introduces a new chemistry option in the Weather Research and Forecasting model data assimilation (WRFDA) system, coupled with the WRF-Chem model (Version 4.4.1), to incorporate aqueous chemistry (AQCHEM) in the assimilation of ground-level chemical measurements. The new data assimilation capability includes the integration of aqueous-phase aerosols from the Regional Atmospheric Chemistry Mechanism (RACM) gas chemistry, the Modal Aerosol Dynamics Model for Europe (MADE) aerosol chemistry, and the Volatility Basis Set (VBS) for secondary organic aerosol (SOA) production. The RACM-MADE-VBS-AQCHEM scheme facilitates aerosol-cloud-precipitation interactions by activating aerosol particles in cloud water during the model simulation. With the goal of enhancing air quality forecasting in cloudy conditions, this new implementation is demonstrated in the weakly coupled three-dimensional variational data assimilation (3D-Var) system through regional air quality cycling over East Asia. Surface particulate matter (PM) concentrations and four gas species (SO$_2$, NO$_2$, O$_3$, and CO) are assimilated every 6 h for the month of March 2019. The results show that including aqueous-phase aerosols in both the analysis and forecast can represent aerosol wet removal processes associated with cloud development and rainfall production. During a pollution event with high cloud cover, simulations without aerosols defined in cloud water exhibit significantly higher values for liquid water path (LWP), and surface PM$_{10}$ (PM$_{2.5}$) concentrations are overestimated by a factor of 10 (3) when wet scavenging processes dominate. On the contrary, aqueous chemistry proves to be helpful in simulating the wet deposition of aerosols, accurately predicting the evolution of surface PM concentrations without such overestimation.

We present a very high-resolution (400 m) operational air quality forecasting system developed to alert citizens of Delhi and the National Capital Region (NCR) about acute air pollution episodes. Such a high-resolution system has been developed for the first time and is evaluated during October 2019-February 2020. The system assimilates near real time aerosol observations from in situ and space-borne observations in the WRF-Chem model to produce a 72-h forecast every day in a dynamical downscaling framework. The assimilation of aerosol optical depth and surface PM 2.5 observations improves the initial condition for surface PM 2.5 by about 45 µg/m 3 (about 50%). The accuracy of the forecast degrades slightly with time as mean bias increases from +2.5 µg/m 3 on the first day to-17 µg/m 3 on the third day of forecast. Our forecasts are found to be very capable both for PM 2.5 concentration and unhealthy/ very unhealthy air quality indices categories. 2

Progress in fundamental understanding of atmospheric chemistry in the past decades has enabled the development of advanced environmental predictive capability. Accurate chemical weather forecasts and source attribution information can play a decisive role in mitigating the exposure of the public to acute air pollution episodes in the short-term. Chemical climate projections will allow investigation of the long-term consequences of human choices and their impact on the future evolution of the Earth system. This commentary stresses the importance of integrating atmospheric chemistry in operational environmental prediction and projection systems.

While meteorology and aerosols are identified as key drivers of snow cover variability in High Mountain Asia (HMA), complex non-linear interactions between them are not adequately quantified. Here, we attempt to unravel these interactions through a simple relative importance (RI) analysis of meteorological and aerosol variables from ERA5/CAMS-EAC4 reanalysis against satellite-derived snow cover from MODIS across 2003-2018. Our results show a statistically significant 7% rise in the RI of aerosol-meteorology interactions (AMI) in modulating snow cover during late snowmelt season (June-July), notably over low snow-covered (LSC) regions. Sensitivity tests further reveal that the importance of meteorological interactions with individual aerosol species are more prominent than total aerosols over LSC regions. We find that the RI of AMI for LSC regions is clearly dominated by carbonaceous aerosols, on top of the expected importance of dynamic meteorology. These findings clearly highlight the need to consider AMI in hydrometeorological monitoring, modeling, and reanalyses.

This study investigates aerosol “sensitivity regimes” to explore the effectiveness of abating gaseous precursors to mitigate aerosols over the Indo-Gangetic Plain (IGP). A new mechanistic insight is proposed by integrating ISORROPIA-II thermodynamical model with high-resolution simultaneous measurements of precursor gases (HCl, HNO3, and NH3) and inorganic constituents of PM1 and PM2.5, monitored for the first time in India using MARGA-2S instrument. The estimated aerosol acidity (pH) of PM1 and PM2.5 was 4.49±0.53 and 4.58±0.48, respectively. The sensitivity of phase-partitioning (ε) of Cl-, NO3-, and NH4+ to pH, ALWC, HCl, HNO3, and NH3 showed that fine aerosols fall in the ”HCl and HNO3 sensitive regime”, emphasizing that HCl and HNO3 reductions would be the most effective pathway to reduce aerosols in NH3-rich IGP. Since existing mitigation strategies over IGP are random and ineffective, this novel insight is the first step in providing a thermodynamically consistent “roadmap” to mitigate aerosols effectively.

Wildland fires involve complicated processes that are challenging to represent in chemical transport models. Recent airborne measurements reveal remarkable chemical tomography in fresh wildland fire plumes, which remain yet to be fully explored using models. Here we present a high-resolution large eddy simulation (LES) model coupled to chemistry to study the chemical evolution in fresh wildland fire plume. The model is configured for a large fire heavily sampled during the Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) field campaign, and a variety of airborne measurements are used to evaluate the chemical heterogeneity revealed by the model. We show that the model captures the observed cross-transect variations of a number of compounds quite well, including ozone (O3), nitrous acid (HONO), and peroxyacetyl nitrate (PAN), etc. The combined observational and modeling results suggest that the top and edges of fresh plume drive the photochemistry, while dark chemistry is also present but in the lower part of the plume. The model spatial resolution is shown to be very important as it may shift the chemical regime, leading to biases in O3 and NOx chemistry. Based on findings in this work, we speculate that the impact of small fires on air quality may be largely underestimated in models with coarse spatial resolutions.