Suitability of OMI aerosol index to reflect mineral dust surface conditions: Preliminary application for studying the link with meningitis epidemics in the Sahel

The aim of this study is to analyze the suitability of remotely-sensed aerosol retrievals to progress in the understanding of the influence of desert dust on health, and particularly on meningitis epidemics. In the Sahel, meningitis epidemics are a serious public health issue. Social factors are of prime importance in the dynamics of the epidemics, however climate and environmental factors are also suspected to play an important role.This study focuses on three Sahelian countries (Burkina Faso, Mali and Niger) which are among the most concerned in the “meningitis belt” and affected by strong dust events every year. It investigates the capability of the aerosol index (AI) derived from OMI (ozone monitoring instrument) to represent the aerosol optical thickness (AOT) and the aerosol surface concentration (particulate matter < 10 μm; PM10) at different time-steps. The comparison of the OMI-AI with ground-based measurements of AOT shows a good agreement at a daily time-step (R ≈ 0.7). The correlation between OMI-AI and PM10 measurements is lower (R ≈ 0.3) but it increases at a weekly time-step (R ≈ 0.5). The difference in the level of correlation between the AOT and the PM10 is partly related to changes in the altitude of the dust layers, especially from April to June, the period of transition from the dry to the wet season. A temporal shift is observed in the occurrence of the maximum of PM10 concentration (March), of AOT (April) and of OMI-AI (June). Nevertheless, during the core of the dry season (January to March) when dust is transported at low altitude, the OMI-AI is able to correctly detect the dust events and to reproduce the dust variability at the regional scale.For dust impact studies on health, only the surface level is relevant. Thus, we conclude that the OMI-AI is suitable especially at a weekly time-step from January to March. In particular for meningitis impact studies, it appears as suitable from the onset to the maximum of the epidemics. A preliminary investigation of the link between the OMI-AI and the WHO weekly national epidemiological reports reveals a 1-week time-lag between the occurrence of dust and meningitis during the increasing phase of the disease.

► We compare the aerosol index (OMI-AI) to ground based measurements.
► The comparison of the OMI-AI and AOT shows a good agreement at a daily time-step.
► The OMI-AI detects the dust events and reproduces the regional dust variability.
► The OMI-AI is suitable for health impact studies from January to March.
► An analysis between OMI-AI and meningitis epidemics reveals a 1-week time-lag.