Estimating lightning NOx production over South Africa

Abstract

Nitrogen oxides (NOx = NO + NO2) are toxic air pollutants and play a significant role in tropospheric chemistry. Global NOx hotspots are the industrialised regions of the USA, Europe, Middle East, East Asia and eastern parts of South Africa. Lightning is one of the many natural and anthropogenic sources of NOx to the troposphere. It plays a role in the formation of particulate matter and tropospheric ozone, which are both linked to harmful health and climate effects. The discourse on NOx over the southern African continent has mainly focused on anthropogenic sources. However, lightning is known to be a main source of tropospheric NOx globally. It is therefore important to understand its contribution to the national and global NOx budget. Data from the South African Lightning Detection Network were used to approximate the influence of lightning on the NOx load over the country, and to develop a gridded data set of lightning-produced NOx (LNOx) emissions for the period 2008 2015. The Network monitors cloud-toground lightning strikes; and theoretically has a detection efficiency of 90% and a location accuracy of 0.5 km. An emission factor of 11.5 kg NO2/flash was employed to calculate the LNOx budget of ~270 kt NO2/year. The calculated LNOx was 14% of the total NOx emission estimates published in the EDGAR v4.2 data set for the year 2008. The LNOx emission inventory will improve model performance and prediction, and enhance the understanding of the contribution of lightning to ambient NO2.

Application of the Multi-Scale Infrastructure for Chemistry and Aerosols version 0 (MUSICAv0) for air quality research in Africa

“…S5 in the Supplement) compared to a standard CAM-chem simulation (not shown); therefore, the high ozone in the upper troposphere may be due to an overestimate of lightning NO. We also compared our modeled lightning NO emissions with a multi-year average climatology (2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015) from Maseko et al (2021) over Southern Africa and found that the seasonal cycles from MU-SICAv0 and standard CAM-chem are consistent with the climatology. The magnitude of MUSICAv0 lightning NO emissions agree better overall with the climatology compared to that from standard CAM-chem simulation.…”

Deep Learning Prediction of Thunderstorm Severity Using Remote Sensing Weather Data

“…Additionally, the IC/CG flash ratio may vary during a thunderstorm [16]. In South Africa, the estimated IC/CG ratio value is 3:1 -the most commonly used ratio for the mid-latitude region [17].…”