Air pollution is a leading cause of global premature mortality and is especially prevalent in many low- and middle-income countries (LMICs). In sub-Saharan Africa, preliminary monitoring networks, satellite retrievals of air-quality-relevant species, and air quality models show ambient fine particulate matter (PM 2.5 ) concentrations that far exceed the World Health Organization guidelines, yet many areas remain largely unmonitored and understudied. Deploying a network of five low-cost PurpleAir PM 2.5 monitors over 2 years (2019–2021), we present the first multiyear ambient air pollution monitoring data results from Lomé, Togo, a major West African coastal city with a population of about 1.4 million people. The full-study time period network-wide mean measured daily PM 2.5 concentration is 23.5 μg m –3 m –3 . The strong regional influence of the dry and dusty Harmattan wind increases the local average PM 2.5 concentration by up to 58% during December through February, but the diurnal and weekly trends in PM 2.5 are largely controlled by local influences. At all sites, more than 87% of measured days exceeded the new WHO Daily PM 2.5 guidelines; these first measurements highlight the need for air quality improvement in a rapidly growing urban metropolis.

In this study, we demonstrate the feasibility of using miniaturised optical particle counters (OPCs) for understanding AQ in Sub-Saharan Africa. Specifically, the potential use of OPCs for high-density ground-based air pollution networks and the use of derived data for quantification of atmospheric emissions were investigated. Correlation and trend analysis for particulate matter (PM), including PM 10 , PM 2.5 and PM 1 were undertaken on hourly basis alongside modelled meteorological parameters. Hourly averaged PM values were 500 μg/m 3 , 90 μg/ m 3 and 60 μg/m 3 for PM 10 , PM 2.5 and PM 1 , respectively and Pearson's correlation coefficient ranged between 0.97 and 0.98. These levels are in the agreement with range of PM emission reported for these types of environmental settings. PM was locally associated with low wind speeds (<¼ 2 ms À1 ) and was closely linked to anthropogenic activities. This study provides a benchmark for future AQ and demonstrates the feasibility of the current generation of OPCs for AQ monitoring in environments typical of large parts of West and Sub Saharan Africa.

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where she is the principal investigator of the Air Quality Modeling and Exposure Lab. She earned her Ph.D. in environmental engineering from the Georgia Institute of Technology in 2016. Her research centers on atmospheric modeling, source apportionment, data assimilation, exposure monitoring, and environmental justice applications.

&lt;p&gt;Air pollution is one of the leading risk factors for poor health in Africa, resulting in millions of premature deaths and economic losses. Of particular interest is exposure to fine particulate matter (PM&lt;sub&gt;2.5&lt;/sub&gt;) which is the driver for a majority of deaths across the continent. However, PM monitoring, and by extension, ground-level data on PM&lt;sub&gt;2.5&lt;/sub&gt; is very limited; this limits our understanding of the widespread societal and health impacts linked to PM pollution. The robustness of low-cost PM sensors and their ability to report in situ data in tropical environments via internet-based platforms as well as relative affordability has created the opportunity to employ low-cost sensors (LCS) for air quality monitoring but calibration methodologies and the usefulness of the high-temporal resolution data for source identification remain a challenge. Increasingly, local governments in African countries are also turning to low-cost sensors to monitor air quality. In this study, two Airnote PM monitors were colocated with reference-grade Teledyne PM mass monitor T640 for ~4 weeks at the University of Ghana, Accra to establish their performance using a simplified data correction methodology - multiple linear regression (MLR) model. A split ratio of 80% and 20% was used to train and test the populated Airnote PM&lt;sub&gt;2.5&lt;/sub&gt; data respectively based on measurements from Teledyne T640 with temperature and relative humidity values from the Airnote monitor. Sectoral and calendar analysis with wind component data were used to triangulate the sources of PM&lt;sub&gt;2.5&lt;/sub&gt;. We observed a high consistency between the two Airnote monitors. Hourly and 24-hour average PM&lt;sub&gt;2.5&lt;/sub&gt; values ranged from 25 to 95 &amp;#956;g/m3, and 29 to 54 &amp;#956;g/m3 respectively, and in most cases, were significantly higher than the WHO Air Quality Guideline. MLR using Pearson&amp;#8217;s correlation analysis improved the out-of-the-box quality of low-cost Airnote PM&lt;sub&gt;2.5&lt;/sub&gt; data; the R&lt;sup&gt;2&lt;/sup&gt; improved from 0.69 to 0.84 and the mean absolute error from 11.75 to 4.20 &amp;#956;g/m3 respectively. Also, the MLR correction model was found to improve the Airnote PM&lt;sub&gt;2.5&lt;/sub&gt; data quality for higher relative humidity (between 50 and 90%) but not lower. PM&lt;sub&gt;2.5&lt;/sub&gt; pollution was local and from N, NE and SW winds for the raw, corrected and Teledyne PM mass monitor T640 measurements. Together, these results indicate that with appropriate corrections, low-cost PM sensors can generate the much needed data for air pollution research and mitigation in areas with limited air quality monitoring and data.&lt;/p&gt;