Assessing the impact of aerosol radiative effects on Air quality over India

Aerosol–radiation interaction (ARI) not only affects the climate of our earth, but it also provides feedback to air quality and PM_2.5 concentrations near the surface by influencing the stability of the planetary boundary layer and modulating the actinic flux required for several photochemical reactions which are part of the lifecycle of various secondary air pollutants. Additionally, modification of photolysis due to scattering or absorbing solar radiation by aerosols (aerosol–photolysis interaction (API) can alter the atmospheric oxidizing capacity and impact PM_2.5  pollution levels by affecting secondary aerosol formation. Most studies in the past have focussed on understanding the effects of ARI on climate but, the consequence of combined and separate impact of ARI and API on regional air quality and its feedback on the climate remains largely unknown.

Here we used the WRF-Chem model to understand and quantify the contributions of API on the regional air quality of north India.  We performed three simulations of the month of May 2018 over the Indian region: (1) BASE, the base simulation coupled with the aerosol and radiation interactions; (2) NOARI, same as the BASE case but without the effect of ARI. and (3) NOAPI, same as the BASE case but without the effect of API. The impacts of API and ARI are investigated by analyzing the differences in model outputs.

Firstly, we evaluated model performance over the study region. We validated the simulated variables, namely, 2m temperature, PBL height, wind, AOD, surface O₃, and PM_2.5, using available ground-based observations, satellite data, and reanalysis datasets. The validation results show that the modelled 2m temperature showed better agreement with observations among all the variables. The model also captures the spatial distribution in AOD over our study region reasonably well and is comparable with various observations showing the highest values over the Indo-Gangetic Plain (IGP). We further find that although the model overestimates the simulation of O₃ and PM_2.5 concentrations, it can accurately replicate the distinctive diurnal patterns in both these variables as noted in ground-based observations. Our results show that due to ARI, the concentration of surface O₃, showed enhancements, while PM_2.5 concentration mostly showed a reduction. The decrease in PM_2.5 concentration was highly related to stabilization induced by meteorological variables and primary aerosol concentration increases. On the other hand, due to API, the photolysis rate of NO₂ increased at the surface. The spatial distribution of O₃ and OH is consistent with that of the photolysis rate. Pronounced enhancement in photolysis rates due to API inevitably increase the abundance of the atmospheric oxidants. Thus, we observed an opposite effect of ARI and API on PM_2.5, shows a regional decrease and increase respectively. Our study emphasizes the need to avoid any unintended consequences of emission reduction strategies for climate change mitigation and clean air goals on air quality. More results with greater details will be presented.