How can we monitor air pollution cost-effectively to measure the impacts of policies? Lessons from Senegal

Ninety-nine percent of the global population lives in areas where air quality does not meet WHO guidelines. The World Health Organization estimates that ambient air pollution caused 4.2 million premature deaths globally in 2019, with approximately 89% of these deaths occurring in low- and middle-income countries.

To understand the extent of air pollution and the effectiveness of policies to tackle it, governments in low- and middle-income countries (LMICs) need accurate air pollution monitoring systems. High-income countries use networks of ground monitoring stations equipped with regulatory-grade monitors to accurately and precisely measure air pollution levels. However, these monitors are costly to purchase and maintain, making it challenging for LMICs to develop dense networks to capture air pollution levels across cities. For instance, urban areas of Africa have only one monitor per 4.5 million residents on average, compared to one monitor per 100,000 to 600,000 residents in Europe and North America. The high costs can also deter LMICs from maintaining and replacing regulatory-grade monitors at recommended intervals, reducing the quality of the air pollution measurements.

New sources of air pollution data, such as low-cost consumer-grade monitors and estimates based on satellite images, hold promise for improving air quality monitoring in LMICs at a lower cost. How effective are they in measuring the impact of policies that can reduce air pollution?

A recent study in Dakar, Senegal, compared these three data sources in terms of measuring air pollution levels and evaluating the impacts of policies to reduce air pollution. The study set up a network of low-cost air pollution monitors in 28 locations across Dakar, considered numerous satellite estimates, and worked closely with the local government agency in charge of air quality data to access the data available from their seven regulatory-grade monitors.  

^{Caption: Location of regulatory and low-cost monitors in Dakar, Senegal}

We found that each data source has advantages and disadvantages. Low-cost monitors can capture differences in air pollution across neighborhoods because they can be deployed widely at low cost. In contrast, satellite data provides only one value for the entire city, and there are many days with only one regulatory-grade monitor in Dakar recording data. Significant differences in pollution levels across the city, captured by low-cost monitors, are crucial for policymakers to target interventions towards those most affected.

Although satellite data provides only one value for the city, it aligns closely with regulatory-grade monitor data at daily, weekly, and monthly levels, but not at the hourly level. This makes satellite data suitable for understanding trends in air pollution but less effective at capturing daily pollution peaks. It is also better suited for measuring pollution across a country rather than within an urban setting. In contrast, low-cost monitors substantially underestimate PM2.5 levels relative to the satellite measures and the regulatory-grade monitors because they do not capture dust pollution accurately.

Combining a network of low-cost monitors with another source of air pollution data can provide a cost-effective solution. For example, the data from one regulatory-grade monitor can correct the overall level of air pollution recorded by a network of low-cost monitors. This combination can better match the levels of pollution reported by regulatory-grade monitors while capturing differences in pollution across neighborhoods at relatively low cost. When there are no regulatory-grade monitors, satellite data can correct the overall pollution levels recorded by low-cost monitors.  

^{Caption: Example of PM2.5 on one day across Dakar measured using different data sources}

The study also evaluated whether these alternative data sources can measure policy impacts. By comparing air pollution levels before and after mobility restriction policies implemented in 2020 to the same period in other years, the study found that the policies reduced air pollution. The patterns of PM2.5 reductions at the city level were similar across all three data sources. Although low-cost monitors' under-measurement of dust led to a smaller estimated impact in levels, the estimated impact in percent was similar across all data sources.

^{Caption: PM2.5 Measures before and after Covid mobility restrictions in 2020}

While the study was conducted in Dakar, the results can guide policymakers in other LMICs. Similar to Dakar, many sub-Saharan African cities are experiencing fast growth, leading to high levels of congestion and pollution. The study illustrates that monitoring the effects of policies to improve air quality can be done cost-effectively.