CAOA is excited to announce its first-ever financial support from CAMS-Net in a sum of $10,000 to explore the robustness of low-cost sensors for understanding the impacts of location-specific agricultural practices on local air quality in Ghana. The project is in collaboration with the University of Ghana, OpenAQ, Clarity Movement and AfriqAir. The project will be executed with Gnana’s Council for Scientific and Industrial Research (CSIR).

Background of Project:

A significant number of studies have demonstrated the link between agriculture and air pollution. For example, Bauer et al., 2019 showed that in most parts of Africa, the slash and burn approaches used for farming are responsible for local and regional air pollution as well as reduced food production. In these environments, however, understanding and quantifying the degree of damage is hindered by sporadic air quality monitoring. The lack of monitoring is firstly linked to the cost associated with procuring and operating conventional air quality monitors which is on the order of 100,000’s of dollars but is also tied to limited local expertise and logistics. Additionally, identifying the impacts of agricultural practices on air pollution requires knowledge of baseline pollution. Specifically for rural Africa, baseline levels of air pollution are not known. In Ghana, regulatory air quality monitoring is centred at Accra with an output of ~6 data points per month from each station. This type of monitoring does not provide the needed information to understand agricultural emissions and consequently develop the needed mitigation policies. LCS can mitigate some of the drawbacks of traditional monitors. LCS are low-cost, offer high-spatiotemporal monitoring, handy, require minimal human operation and logistics, are user friendly, and are capable of monitoring key species in near-real-time and reporting data to internet-based platforms (which can be accessed remotely) to mention but a few of their benefits (see for example Hodoli et al., 2020).

Scope of Project:

This work will evaluate the resulting data following similarly previous works (e.g. Mead et al., 2013). R — a programming language and environment will be used to manage, visualise, and analyse the reported data using the “openair” package for air pollution data analysis. These will include performing time series analysis, including diurnal trends, and background air pollution level determination along with events (agricultural burning, tillage, and fertilizer application — NPK, Urea and Ammonia) impact quantification.

Timeline:

This is a year project commencing in May 2022.

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