metadata: authors: 0: author: Hoffmann,Bridget
1: author: Sveta Milusheva
count: Senegal
docna: 0: docna: Designing Air Quality Measurement Systems in
            Data-Scarce Settings
docty: Policy Research Working Paper
theme: Urban Development,Job Creation,Gender,Human Development and Gender,Data Development and Capacity Building,Public Transport,Private Sector Development,Public Sector Management,Urban and Rural Development,Jobs,Data production, accessibility and use
abstracts: cdata!: While populations in low- and
            middle-income countries are exposed to some of the highest
            levels of air pollution and its consequences, the majority
            of economics research on the topic is focused on high-income
            settings where there is greater data availability. This
            paper compares and evaluates the three principal sources of
            air pollution data (regulatory-grade monitors, satellites,
            and low-cost monitors) in a Sub-Saharan African context in
            terms of the accuracy of measurements of inhalable fine
            particulate matter across spatial and temporal frequencies
            and their performance when studying policy impacts.
            Satellite data is closely aligned with data from the
            regulatory-grade monitors at lower temporal frequencies. The
            low-cost monitors underestimate the amount of fine
            particulate matter relative to the other data sources.
            Calibration, especially context-specific calibration, of the
            low-cost monitors' data improves its alignment with
            other data sources. The paper uses each data source to
            evaluate the air pollution externality of mobility reduction
            policies using a difference-in-differences design and finds
            similar results, especially in terms of percent reduction.
            The paper considers policy makers' constraints to air
            pollution monitoring in low-income settings and demonstrates
            that co-locating one regulatory-grade monitor in a network
            of low-cost monitors can capture the spatial variation of
            pollution across an urban area and achieve better accuracy
            than either of these data sources alone. This provides a
            framework for policy makers to generate the data needed to
            evaluate environmental policies and externalities.
display_title: Designing Air Quality Measurement
            Systems in Data-Scarce Settings
listing_relative_url: /research/2024/10/34411077/designing-air-quality-measurement-systems-data-scarce-settings
topicv3: Impact Evaluation for Evidence-Based
            Policy m1326030 1157,Air Pollution m1327122 1791,Big Data m1327333 661,Coronavirus (COVID-19) m1939478 4828
publishtoextweb_dt: 2024-10-24T00:00:00Z
dois: 10.1596/1813-9450-10957
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
no_of_pages: 40
author_keys: 0: author_key: NA
1: author_key: NA
model_extractions: Policy Research Working Paper 10957 Designing Air Quality Measurement Systems in Data-Scarce Settings _Bridget Hoffmann Sveta Milusheva_ Development Economics Development Impact Group October 2024 A verified reproducibility package for this paper is available at http://reproducibility.worldbank.org, click **here** for direct access.
Policy Research Working Paper 10957

# **Abstract**

While populations in low- and middle-income countries are exposed to some of the highest levels of air pollution and its consequences, the majority of economics research on the topic is focused on high-income settings where there is greater data availability. This paper compares and evaluates the three principal sources of air pollution data (regulatory-grade monitors, satellites, and low-cost monitors) in a Sub-Saharan African context in terms of the accuracy of measurements of inhalable fine particulate matter across spatial and temporal frequencies and their performance when studying policy impacts. Satellite data is closely aligned with data from the regulatory-grade monitors at lower temporal frequencies. The low-cost monitors underestimate the amount of fine particulate matter relative to the other data sources. Calibration, especially context-specific calibration, of the low-cost monitors’ data improves its alignment with other data sources. The paper uses each data source to evaluate the air pollution externality of mobility reduction policies using a difference-in-differences design and finds similar results, especially in terms of percent reduction. The paper considers policy makers’ constraints to air pollution monitoring in low-income settings and demonstrates that co-locating one regulatory-grade monitor in a network of low-cost monitors can capture the spatial variation of pollution across an urban area and achieve better accuracy than either of these data sources alone. This provides a framework for policy makers to generate the data needed to evaluate environmental policies and externalities.

This paper is a product of the Development Impact Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at smilusheva@worldbank.org and bridgeth@IADB.org. A verified reproducibility package for this paper is available at http://reproducibility.worldbank.org, click **here** for direct access.

<!-- Start of picture text --> TRANSPARENT<br>ANALYSIS<br>G<br>R E<br>N<br>S<br>I P<br>Y E<br>K A<br>C A<br>R P<br>LI R<br>O E<br>O C<br>W R<br>H<br>S<br>P<br><!-- End of picture text --> _The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# Designing Air Quality Measurement Systems in Data-Scarce Settings

Bridget Hoffmann and Sveta Milusheva<sup>∗</sup> Originally published in the Policy Research Working Paper Series on _October 2024._ This version is updated on _November 2024._ To obtain the originally published version, please email prwp@worldbank.org.

**Keywords:** Air pollution, PM2.5 measurement, satellite, low-cost monitors, policy evaluation **JEL Codes:** Q53, Q58, Q52, C81

> ∗We would like to thank Niall Maher and Ruiwen Zhang for research assistance support and Aram Gassama, Marion Sagot, Mame Diarra Bousso Sarr, Tiloux Soundja, Abdallah Cissé and Alice Crolard for project and field coordination assistance. We would like to thank the Directorate for Environment and Classified Facilities (DEEC) and its Center for Air Quality Management (CGQA) at the Ministry of Environment, Sustainable Development and Ecological Transition, and the Executive Council for Sustainable Transport (CETUD). We also wish to thank all of the institutions that allowed us to install air quality monitors at their location and supported us in maintaining these monitors and ensuring our ability to collect data in these locations. Furthermore, we would like to thank the World Bank Senegal Country Management Unit and the BRT Transport team. We gratefully acknowledge funding from the UK Government through the ieConnect for Impact Program and from the European Union through the IntPact Program.
# **1 Introduction**

Air pollution has causal impacts on mortality, physical and mental health, labor supply and productivity, migration, cognitive performance, human capital accumulation, and decision-making (Hoffmann and Rud, 2024; Aguilar-Gomez _et al._ , 2022; Chen _et al._ , 2022; Guidetti _et al._ , 2021; Anderson, 2020; Deryugina _et al._ , 2019; Zhang _et al._ , 2017; Arceo _et al._ , 2016; Graff Zivin and Neidell, 2013). The majority of the evidence on the impacts of air pollution comes from high-income settings where there is high-quality data available to study pollution and its impacts.<sup>1</sup> However, individuals in low- and middle-income countries face greater exposure and negative impacts of air pollution than those in high-income countries. Approximately 80% of people exposed to unsafe levels of air pollution live in low- and middle-income countries (Rentschler and Leonova, 2022) and 89% of the mortality and morbidity attributed to air pollution globally occurs in developing countries (WHO, 2024).

As there are important differences in the effects of air pollution across higher- and lower-income settings (Arceo _et al._ , 2016), more research is needed in low-income countries to evaluate both the extent of the problem and the effectiveness of policies in tackling it. A necessary first step is to develop approaches that can accurately measure air pollution, taking into account the particular constraints faced by governments in low-income countries. Recent studies have expressed enthusiasm about new frontiers in monitoring air quality, such as the advent of low-cost monitoring devices and the availability of satellite data (Snyder _et al._ , 2013; Di _et al._ , 2016), but it is critical to understand how well they measure air pollution and how useful they are for policy evaluation. Additionally, identifying how to combine different data sources into a cost-efficient air quality monitoring system could help low-income countries improve their air pollution measurement.

In this paper, we compare and evaluate three sources of air pollution data in a lower middle-income context. We focus on particulate matter with a diameter of 2.5 micrometers or less (PM2.5), which is a mixture of fine inhalable particles of various chemical compositions including dust, dirt, soot, and smoke. PM2.5 is a widely regulated air pollutant and has stronger health effects across a broader range of health outcomes than coarser particles (Bell _et al._ , 2004; Pope and Dockery, 2006). First, we compare the data sources in terms of accuracy of PM2.5 measurement across different spatial and temporal frequencies. Second, we use each of the data sources to evaluate a specific policy that reduced air pollution and compare the estimated treatment effects on PM2.5. Third, we consider policy makers’ constraints to air quality measurement in low-income settings to make policy recommendations about designing systems of air pollution monitoring.

We compare and evaluate three principal sources of air pollution data. First, the gold standard is regulatory-grade monitors that are widely used by governments in high-income countries to create a network of ground monitoring stations that capture detailed, precise and accurate measurements of air pollutants. However, the high set-up and maintenance costs of regulatory-grade monitors preclude lowand middle-income countries (LMICs) from developing dense networks of regulatory-grade monitors and from replacing them at recommended intervals. Second, satellite images can be used to approximate

> 1One exception is China, which is an upper-middle income country, and is the focus of a large number of papers in the literature (Chang _et al._ , 2019; Fu _et al._ , 2021; He _et al._ , 2019; Zivin _et al._ , 2020).

2
the level of air pollution at ground level. Satellites produce images that can be used to create gridded data sets of aerosol optical depth (AOD) as well as individual aerosols that are comparable across the world and can be used to estimate levels of particulate matter. However, satellite data does not directly measure particulate matter at ground level and provides data at a lower temporal and spatial frequency. Third, low-cost monitors that citizens can use to track their own air pollution exposure can be deployed by governments to create a network of air pollution monitors at low-cost. However, low-cost monitors do not record pollution measurements at the same level of accuracy as regulatory-grade monitors. Because each of these data sources has advantages and disadvantages, evaluating their performance in measuring air pollution levels and in estimating the effects of policies to reduce air pollution is critical for researchers and policy makers. This is especially important for LMIC settings that can face additional challenges implementing these data sources, where there have been few studies comparing their performance, and where effective environmental policies are most needed.

Dakar, Senegal provides an ideal context for this study for three reasons. First, Dakar is a large and growing city in a lower middle-income country with large industrial plants and recurrent construction works. This implies that our results are informative for other fast-growing Sub-Saharan African cities that are similarly experiencing high levels of congestion and pollution. Second, in addition to the air pollution generated by congestion and economic activity, Dakar experiences Saharan desert storms, especially during the first few months of the year. These environmental conditions differ from those typical of the high-income contexts where air pollution data is usually calibrated and analyzed. Third, Dakar currently has seven regulatory-grade monitors, which allows for comparison and calibration. But in contrast to high-income settings, the density of the monitoring network is low, and the data available is sparse and of uncertain accuracy due to challenges in maintaining the monitors. While this amplifies the challenges in comparing data sources and calibrating the data from low-cost monitors, this reflects the realities of air quality monitoring in lower-income settings.<sup>2</sup> The dearth of high-quality data at fine intervals of time and space combined with high levels of air pollution highlight the importance of studying ways to utilize the existing data and increase the data available for rigorous policy analysis, which could then be applied to other settings in the region.

To collect the data for this study, we set up a network of low-cost air pollution monitors in 28 locations across Dakar, and we worked closely with the local government agency in charge of air quality data to access the data available from their regulatory-grade monitors. We test several different sources of satellite air pollution measures to identify the one that generates the values most aligned with the ground-based measures.

Armed with these three sets of air pollution data, we compare the level of PM2.5 at various temporal frequencies as measured by each data source. The satellite data is closely aligned with the data from the regulatory-grade monitors at the daily, weekly, and monthly levels but less so at the hourly level. There are substantial differences between the PM2.5 estimates from the regulatory-grade and the lowcost monitors in terms of level. Similarly, the low-cost monitors substantially underestimate PM2.5

> 2We use calibration to refer to the calibration of the data collected by low-cost monitors, not the calibration of the instrument (Jaffe _et al._ , 2023). In engineering or science literature, this is often called "data correction".

3
levels relative to the satellite measures. This relative underestimation is largely due to the low-cost monitors’ under-measurement of dust. Adjusting the data from the low-cost monitors, especially using a context-specific formula, improves the alignment of the data with both the data from the regulatory-grade monitors and the estimates from the satellite.

Next, we use the three data sets to evaluate pollution externalities of the set of mobility-reducing policies enacted during the COVID-19 pandemic. We use a difference-in-differences identification strategy to analyze the impact of the policies on the level of air pollution. Overall, we find the policies lead to a positive externality in the form of a reduction in air pollution, similar to research conducted in highincome settings (Brodeur _et al._ , 2021). Importantly, we find that all three data sources display similar patterns of PM2.5 reductions due to the policy. Using PM2.5 data at the hourly level, the impact of the policy in levels is smaller when using the data from the low-cost monitors than the other data sources because the low-cost monitors tend to underestimate PM2.5 levels. However, the impact of the policy in percent is similar across all data sources.

Finally, we demonstrate the value of co-locating at least one regulatory-grade monitor in a network of low-cost monitors. We demonstrate that for a given day, relying on only a regulatory-grade monitor or only the satellite data will provide no spatial variation. However, relying on only the network of low-cost monitors will provide spatial variation but severely underestimate the level of PM2.5. Using a co-located regulatory-grade monitor to develop a context-specific calibration formula to be applied to the data from the network of low-cost monitors improves the alignment in the level of PM2.5 while preserving spatial variation. Given the tight budget constraints that policy makers in LMICs face, investing in one well-maintained regulatory-grade monitor complemented with a dense network of low-cost monitors may strike the right balance. Barring any regulatory-grade monitors, calibration of PM2.5 measurements from low-cost monitors using satellite based estimates of PM2.5 can serve as an alternative. Given the better performance of satellite data at a lower temporal frequency, we also show that this alternative calibration should be applied at a daily level (rather than hourly), to achieve results that are better aligned with what would be expected from regulatory measurements.

We make three contributions to the literature. First, we contribute unique evidence comparing the three principal sources of air pollution data in a LMIC context. Fowlie _et al._ (2019) contrasts air pollution levels from satellite data with those derived from regulatory-grade monitors in the U.S. and concludes that differences imply potential misclassifications of E.P.A. attainment status. In Greece, Stavroulas _et al._ (2020) find a moderate to strong correlation between PM2.5 levels measured with lowcost monitors and with regulatory-grade monitors. However, these studies do not compare all three data sources and the results of these studies may not translate directly to contexts in LMICs because the accuracy and precision of the air pollution data can vary across low- and high-income contexts. The precision and accuracy of air pollution data from low-cost monitors is affected by environmental conditions (e.g. relative humidity) and the type of particulate matter, such as dust events (Jaffe _et al._ , 2023). The availability, accuracy, and precision of air pollution data from regulatory-grade monitors depends on a dense network of monitors and regular investments in maintaining and replacing monitors.

Second, we extend this literature beyond a comparison of the air pollution levels captured by the three 4
principal data sources to provide a novel comparison of the data sources in terms of policy evaluation. In addition to accurate information on air pollution levels, which documents the extent of the problem, policy makers need accurate estimates of the impacts of policies on air pollution to design effective solutions. We evaluate the performance of these different data sources in a rigorous analysis of a set of policies that have an important environmental externality.

Third, we contribute policy recommendations for designing air quality monitoring systems in LMICs. We illustrate the value of operating at least one regulatory-grade monitor in a network of low-cost monitors. Co-locating a regulatory-grade monitor with a low-cost monitor allows for context specific calibration of the data from the low-cost monitor network that improves the accuracy of the data relative to using an off-the-shelf calibration formula. We also show that, in the absence of a regulatory-grade monitor, satellite PM2.5 estimates can be used to calibrate low-cost monitors to the local context. For a given budget, a combination of data sources including low-cost monitors can capture the spatial variation of pollution across an urban area and achieve better accuracy than a single data source alone.

The next section describes the context, and section 3 describes the three data sources in detail. Section 4 lays out the methods, section 5 provides the results, and section 6 concludes.

# **2 Context**

Dakar is the capital and largest city of Senegal, which is located on the coast of West Africa. Similar to many other cities in LMICs, Dakar has experienced rapid population growth and urbanization. The Dakar metro area has a population of 3.9 million and a population density of 14,000 per km<sup>2</sup> which is in line with cities such as New York City (Agence Nationale de la Statistique et de la Démographie, 2023).

Dakar currently has 7 regulatory-grade air quality monitors or approximately one monitor per 580,000 residents, which is on the higher side of the typical range (one monitor per 100,000 to 600,000 residents) for urban areas of Europe and North America (Pinder _et al._ , 2019).<sup>3</sup> However, for long stretches of time only one or two regulatory-grade monitors, and for a period of approximately a year no regulatory-grade monitors, were reporting PM2.5 data (see Figure 2 for a visualization of all available data from the regulatory monitors from 2012 to 2024). In practice, this puts Dakar on par with other African urban areas, which typically have one monitor per 4.5 million residents (Pinder _et al._ , 2019).

Dakar experiences high levels of ambient air pollution, typically exceeding World Health Organization standards. The main sources of particulate matter in Dakar are traffic (49%), mineral dust (16%–25%), sea salts (15%–20%), and industries (10%–11%) (Dou). While a large share of particulate matter is generated by anthropogenic sources such as traffic (vehicular, two-wheel two-strokes and resuspended road particles), biomass (wood, charcoal and fuel) combustion, waste burning, and industries, Saharan dust storms are another major source of particulate matter (Dou). Particularly during the first few months of the year, Saharan dust storms can lead to extremely high levels of particulate matter (Dou). Relative to other African cities, the air pollution in Senegal is reported to be moderate-to-severe. Of 24

> 3The 2023 population of the Dakar region was reported at 3,896,563 according to the Agence Nationale de la Statistique et de la Démographie (2023).

5
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by two types of monitors, the Met One BAM-1020 and the Envea MP101M, which both use a beta ray attenuation measurement technique.

We are able to access data at the hourly or daily level (depending on the station) from 2012 to 2024. The PM2.5 data availability is sparse (Figure 2), both because some air quality stations do not collect PM2.5 data and because of challenges maintaining the regulatory-grade monitors. Beginning in 2012, there is relatively consistent coverage with at least one monitor active, except for 2021 and 2022, which have almost no data. This is important because the data from a regulatory-grade monitor can be used to calibrate other data sources. Additionally, when more than one monitor is reporting data concurrently we can use readings from multiple monitors to validate the data. At the times when monitors overlap, the values are closely aligned. Importantly, on days when the values seem to be improbably high, readings from different active monitors both have very high values, which is reassuring that these represent days of extremely high pollution rather than an issue with the measurement instruments. The data was pre-processed by the CGQA for extreme values. In addition, we removed PM2.5 values greater than 500 _μm_ /m<sup>3</sup> .<sup>6</sup> To analyze the data, we take the values from whichever monitor is active, and in cases where there are multiple monitors, we use data from all active monitors.

# **Low-Cost Monitors**

In the last decade there has been significant advancement in the technical specifications of low-cost monitors and substantial growth in the number of low-cost monitors on the market, leading to an explosion in their use (Kumar _et al._ , 2015; Demanega _et al._ , 2021). In addition to their use by individuals and businesses, the recognition that air pollution in urban areas is hyperlocal led to initiatives in highincome settings to complement networks of regulatory-grade monitors with low-cost monitors to provide air pollution data at a fine spatial scale. For instance, London set up a dense network of low-cost monitors across the city and several cities in California mapped pollution street by street using sensors attached to Google Street View cars (Gil-Alana _et al._ , 2020; Mittal, 2020).

The ability to capture variation in pollution at a granular spatial level is crucial for low-income settings where the prevalence of pollution is high and where the technology for capturing the origin of the pollution is limited (Hajat _et al._ , 2015; Kamigauti _et al._ , 2024). Therefore, the team set up a network of low-cost monitors in Dakar to measure pollution at a higher spatial granularity. We selected to use the Purple Air PA-II-SD, which, at the time the study began in 2019, had one of the highest correlations with regulatory monitors (R-squared of 0.93 to 0.97) (South Coast Air Quality Management District, 2024).<sup>7</sup> Additionally, its accuracy has been tested under atmospheric conditions when exposed to a variety of pollutants and PM2.5 concentrations and, unlike monitors from other providers with a similar technology, PM2.5 measures captured by PA-II monitors are not found to be affected by changes in temperature and relative humidity, making them more reliable (Ardon-Dryer _et al._ , 2020). From February 2020 until 2023, the team progressively deployed PA-II monitors across 28 locations in the Dakar region (Figure 1). The team also used data from an additional PA-II monitor set up by the Ecole

> 6Only 0.17% of PM2.5 values were above 500.

> 7At the time when the decision was made, in 2019, only PA-II classic monitors were available on the market.

7
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While comparisons at the annual 10km level show a relatively high correlation between ground-based PM2.5 and AOD with an _R_<sup>2</sup> of 0.61 (Gendron-Carrier _et al._ , 2022), others find a weak correlation between AOD and PM2.5 at the daily 1km x 1km level (Handschuh _et al._ , 2023). In general, it is necessary to model the relationship between AOD and ground-based PM2.5 including other covariates to obtain good predictions.

In addition to AOD, other instruments on satellites can measure the levels of specific gases and aerosols. For example, the TROPOMI (TROPOspheric Monitoring Instrument) instrument on the Sentinel-5P satellite is capable of measuring levels of ozone, sulfur dioxide, nitrogen dioxide, carbon monoxide, formaldehyde and methane (Zeng _et al._ , 2020). These raw data from different satellites and instruments have also been processed and integrated to generate assimilations, where data is integrated into atmospheric models. Examples of assimilations include the Copernicus Atmosphere Monitoring Service (CAMS) and NASA’s MERRA-2 (Modern-Era Retrospective analysis for Research and Applications, version 2), which provide usable atmospheric information in one system (Buchard _et al._ , 2017; Flemming _et al._ , 2017).<sup>10</sup> In preliminary comparisons, AOD captured from MODIS does not correlate well with the data from regulatory-grade monitors in Dakar at the daily level, which aligns with findings in the literature for other contexts (Handschuh _et al._ , 2023). This is likely due to several important limitations of the AOD measure. AOD has trouble distinguishing water vapor from other particles, while the ground-based monitors capture only dry particles. In a context like Dakar, which is surrounded by water, this can have important consequences (Gendron-Carrier _et al._ , 2022). Additionally, particles that are present in the atmosphere, such as smoke or dust from long-range transport, can lead to no (or poor) correlation between satellite measures and PM from ground-monitors, which measure particles only at the surface (Kumar _et al._ , 2007).

Given this, we focus on an estimate of PM2.5 that we generate using data from the MERRA-2 assimilation. This assimilation provides processed data on individual aerosols at the surface rather than column level, helping to address the issue of water vapor and additional particles in the atmosphere. The MERRA-2 data is accessed via Google Earth Engine, and it is available hourly at the 55 x 69km2 spatial level (Gelaro _et al._ , 2017). MERRA-2 provides assimilation data on individual aerosols (dust, organic carbon, black carbon, sulfate, and sea salt). To generate a measure of PM2.5, we combine these aerosols using a formula provided by NASA (2023): The MERRA-2 data has numerous benefits. It is freely available. It provides a measure of pollution that can be compared across the world, and there is high temporal granularity, so we are able to produce PM2.5 estimates at the hourly level. It has data from the time period of 1980 to the present day. However, an important limitation is that due to the low level of spatial granularity, the MERRA-2 data in effect provides only a single value of pollution for Dakar at any given time.

> 10See Appendix Table A1 for a description of different satellite products.

10
# **4 Methods**

## **4.1 Calibration**

The raw Purple Air sensor data can have significant biases. Therefore, the data needs to be calibrated. Different calibration equations have been developed for this purpose, including one from the U.S. EPA (Barkjohn _et al._ , 2023) and one from Barkjohn _et al._ (2021). More recently, Jaffe _et al._ (2023) find that while these calibrations perform well under certain conditions, such as urban aerosols and smoke aerosols, they largely underestimate PM under high dust pollution. They present an alternative calibration that involves identifying potential dust events and further adjusting the estimate by a factor of 5.6. We test both the Barkjohn _et al._ (2021) and Jaffe _et al._ (2023) calibrations.

The Barkjohn _et al._ (2021) calibration applies the following equation: The Jaffe 2023 calibration uses a two-step method that applies either the Barkjohn _et al._ (2021) calibration or a variation of it depending on the value of the 0 _._ 3 _μm/_ 5 _μm_ ratio. This is the ratio of the concentration of particles with a diameter of 0.3 micrometers to those with a diameter of 5 micrometers.

- If 0 _._ 3 _μm/_ 5 _μm >_ 190, use the original Barkjohn _et al._ (2021) calibration

- If 0 _._ 3 _μm/_ 5 _μm <_ 190, use the Barkjohn _et al._ (2021) calibration _×_ 5 _._ 6

As one of our low-cost monitors lacks data on levels of particles of size 0 _._ 3 _μ_ m and 5 _μ_ m, a substitution is made. An alternative criterion for identifying dust events is when the PM1/10 ratio < 0.25 (Jaffe _et al._ , 2023). Comparing the two Jaffe specifications, we find that approximately 86% of hourly values are the same. Since the monitor that lacks the 0 _._ 3 _μ_ m and 5 _μ_ m values is one of the only ones functioning in 2020, we use this alternative method for generating the Jaffe calibration in order to be able to conduct the analyses of the COVID-19 mobility policies implemented in 2020.

These calibrations both use data from the U.S. where conditions may differ substantially from Dakar, leading to a different relationship between the data from low-cost monitors and regulatory-grade monitors. For example, the lack of paved roads may affect the amount and type of dust particles in the air, along with different fuel standards, high levels of construction, and the proximity to the Sahara Desert. Therefore, we also produce a correction based on calibrating the Purple Air data using data from local regulatory-grade monitors. We co-located two of the PA monitors with two regulatory-grade monitors in the city. One was located in Bel Air and provided overlapping data from July 21, 2020, to April 17, 2021.<sup>11</sup> The second was located in Pikine and provided overlapping data from April 17, 2023, to December 15, 2023.<sup>12</sup> To generate the calibration formula, we use a lasso method with a linear model and a five-fold crossvalidation. We include the Purple Air PM2.5 value along with humidity and temperature averaged across

> 11Some additional dates are missing within this period, most notably October 26-December 31, 2020.

> 12Again, there are some missing dates within this period including all of August and October.

11
Dakar as predictors.<sup>13</sup> The humidity and temperature data for Dakar are collected by Visual Crossing (2024). We run the model separately for each monitor as well as combining the data from both monitors. We also run the model separately for hourly observations and for daily observations. We implement the calibrations at both the hourly and daily levels.

Table 1: Calibration Coefficients comparing Regulatory and nearby Purple Air monitors (Hourly)

||Monitor a|t Bel-Air|Monitor a|t Pikine|Bel-air &|Pikine|
|---|---|---|---|---|---|---|
||(1)<br>Mass<br>PM2.5<br>(_μg/m_<sup>3</sup>)|(2)<br>Count<br>>2.5|(3)<br>Mass<br>PM2.5<br>(_μg/m_<sup>3</sup>)|(4)<br>Count<br>>2.5|(5)<br>Mass<br>PM2.5<br>(_μg/m_<sup>3</sup>)|(6)<br>Count<br>>2.5|
|Mass Concentration PM2.5|2.378||0.762||1.588||
|Count concentration >2.5||1.486||1.780||1.715|
|Humidity|-0.606|-0.674|-1.766|-1.564|-1.178|-0.860|
|Temperature|-1.072|-0.504|-2.423|-1.228|-1.583|0.819|
|Constant|66.63|72.85|232.1|171.2|142.5|57.96|
|Out-of-Sample _R_<sup>2</sup>|0.615|0.799|0.365|0.515|0.449|0.565|

**Note** : Lasso linear model with 5-fold cross-validation. Mass PM2.5 ( _μg/m_<sup>3</sup> ) refers to the mass concentration of fine particulates with a diameter of fewer than 2.5 microns. Count (particles/100ml) refers to the count of all particles greater than or equal to 2.5 micrometers in diameter. Data is aggregated hourly before calibration.

To calibrate the low-cost monitor data to the local regulatory-grade monitors, we use the model that combines the data from the two locations and use the PA PM2.5 measure as the main predictor. Table 1 displays the coefficients and out of sample R<sup>2</sup> for each calibration conducted at the hourly level.<sup>14</sup> We apply the following calibration equation corresponding to the fifth column of Table 1 to generate a local calibration: Local Calibration PM2 _._ 5 = (Unadjusted PM2 _._ 5 _×_ 1 _._ 59) _−_ (Humidity _×_ 1 _._ 18) _−_ (Temperature _×_ 1 _._ 58)+142 _._ 2 (3) In the absence of data from regulatory-grade monitors, satellite data may provide an alternative for calibrating the data from low-cost monitors. Therefore, we also calibrate the Purple Air data with the satellite data using a similar calibration procedure.Using the MERRA-2 satellite data for the calibration, there is only a single value for Dakar. Therefore, we use a Dakar-wide average of the Purple Air monitors to generate the satellite calibration model: Satellite Calibration PM2 _._ 5 = (Unadjusted PM2 _._ 5 _×_ 1 _._ 66) _−_ (Humidity _×_ 1 _._ 3) _−_ (Temperature _×_ 4 _._ 84)+249 _._ 6

> 13Additionally, we test including the number of particles larger than 2.5 micrometers as a predictor instead of the PM2.5 value, because the number of larger particles has a very high correlation with the regulatory-grade monitor values.

> 14In a similar manner, we generate a daily local calibration using the same data aggregated daily (Table A2).

12
# **4.2 Comparison of Different Measures and Policy Analysis**

We begin by comparing the three data sources visually and exploring the correlations between them. We then demonstrate how the data sources compare when used to measure the impact of a particular policy. The focus is on the set of policies that were implemented in 2020 as a response to the COVID19 pandemic. As in many other countries, the Government of Senegal instituted a number of policies starting on March 14, 2020, with the goal of limiting mobility and thus curbing the spread of COVID19.<sup>15</sup> Literature from other contexts has documented important externalities from these types of policies, including large decreases in pollution (Venter _et al._ , 2020). Therefore, it is possible to use the timing of these policies in Senegal to study how the different sources of data measure this externality in Dakar. Additionally, this is a time for which we have data from all three data sources, which makes it possible to compare measured impact across all three.

To study the policy impact, a difference-in-differences style design is used. Air pollution data right before and right after the policy is compared to air pollution data during those same pre- and post-policy periods in a different year. This strategy makes it possible to control for seasonality, which plays an important role in PM2.5 levels. The specification estimated is: where _PMyt_ is PM2.5 at hour _t_ of year _y_ . _I_ 2020 _y_ is an indicator equal to one if the year is 2020 and zero otherwise. _Postt_ is an indicator equal to one for all hours after the policies were put in place (March 14 to May 12) and zero before the COVID-19 policies (January 23 to March 13). _Xyt_<sup>_′_isavectorofcontrols</sup> that includes weather variables (temperature and humidity) and indicator variables for hour of the day, the day of the week and week of the year (i.e., an indicator of the week within a calendar year, starting from week 1), and _εyt_ is the error term.<sup>16</sup> We conduct the analysis comparing 2020, when we expect reduced mobility and levels of PM2.5, to 2023, when we expect mobility to have normalized in the city.<sup>17</sup> There was consistent data (more than 80 days during the analysis period) available from two regulatory-grade monitors in 2020, Guediawaye and Republique. We include data from both monitors and control for monitor fixed effects. For 2023, there is only data from a third regulatory monitor that was recently installed in Pikine. We use the hourly MERRA-2 data for 2020 and 2023 for the city. There is only one Purple Air monitor for which data is available for several months before the policy in 2020 and also after the policy, but this monitor does not have data beyond 2020. Instead, for 2023 we include data from all monitors, which includes data on at least 80 different days within the period of 110 days of the analysis. We control for monitor fixed effects. In addition to using the unadjusted Purple Air data, we also conduct the analysis using two off-the-shelf calibrations of the data from Barkjohn and Jaffe, as well as our local and satellite calibrations.

We conduct several robustness checks. We conduct the analysis at the daily level, using both the

> 15These included banning public and private gatherings, a curfew from 20:00 to 6:00, and closing schools.

> 16Precipitation is not included as a weather control because the period of analysis includes only the dry period in Dakar and precipitation is consistently zero.

> 17There were still some COVID-19 policies in place in 2021 that could affect mobility, and therefore pollution, and we do not have data from any regulatory monitors in 2022 to allow for comparison of the policy results across data sources.

13
daily average across hours as well as the maximum hourly value of the day as outcomes. We use data from 2020 and 2023, and we include controls for weather variables and indicator variables for day of the week and month of the year. We also study a longer time period, allowing us to better control for seasonality and overall high variance in the outcome variable. We use the satellite data and the data from regulatory-grade monitors for 2017-2020 and use the Purple Air data for 2020, 2022 and 2023.<sup>18</sup> We include results for these analyses using the longer time period at both the hourly and daily levels. For these robustness checks, we analyze both the unadjusted low-cost monitor data and the locally calibrated data, using the hourly calibration values for regressions at the hourly level and the daily calibration values for regressions at the daily level.

# **5 Results**

## **5.1 Comparison of PM2.5 Levels**

First, we compare the PM2.5 estimates generated using satellite data from MERRA-2 with the data from the regulatory-grade monitors. Figure 4 demonstrates that there is a strong alignment of values at the monthly level (Figure 4A), weekly level (Figure 4B), and daily level (Figures 4C and 4D), and that aggregating the data temporally improves alignment. The satellite data is able to capture the peaks in pollution that occur in the first two months of each calendar year (January - February), but the satellite measures also record increases in pollution in May-June for 2013-2015 that are not reflected in the data from the regulatory-grade monitors (Figure 4A). Particularly in 2019-2020, the PM2.5 estimates from the satellite data at the monthly-level are very closely aligned with the PM2.5 data from the regulatory-grade monitors (Panel 4A). This reflects the fact that in 2019-2020, the regulatory data comes primarily from a new monitor installed in 2018 in Guediawaye, which measures much higher levels of PM2.5 compared to the other regulatory monitors in the city, and thus aligns more closely with the higher estimates of pollution generated with the satellite data.<sup>19</sup> When aggregated to the weekly level, the two data sources still track each other closely for 2019-2020 but there is greater disparity in the levels than when aggregated to the monthly level. There are some discrepancies between the two measures at the daily level, with the satellite data recording greater levels of PM2.5 than the regulatory-grade data at some times and lower levels at other times (Figures 4C and 4D).

Next, we look at alignment between the two data sources at higher frequency. One of the advantages of the data from MERRA-2 over other sources of satellite data is that it can generate estimates of PM2.5 at the hourly level. The estimates from MERRA-2 and the data from the regulatory-grade monitor are not as closely aligned at this higher temporal frequency. Figure 5 uses data from March 2020 to illustrate that the measurements of PM2.5 at the hourly level from these two data sources frequently diverge by

> 50 _μ_ g _/m_<sup>3</sup> or more and are often moving in the opposite direction (i.e. one data series is not a level shift

> 18Because of the inconsistent availability of data from the regulatory-grade monitors across years, we combined data from different monitors, using data from Bel Air for 2017, data from Republique in 2018, from Guediewaye in 2019 and data for Republique and Guediewaye in 2020.

> 19The higher levels of pollution measured by the monitor at Guediawaye may reflect higher levels of pollution in this part of the city or may be due to the calibration of the instrument.

14
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the low-cost values and the regulatory monitor (Table 2 Panel A), the figure shows that there remains a large underestimation when using the low-cost monitor data adjusted using the Jaffe calibration, which is confirmed by a larger RMSE when comparing the regulatory and Jaffe-adjusted data (Table 2 Panel B).

We find that aggregating the data temporally improves alignment, as it did with the satellite and regulatory data comparison. This can be seen both visually in Figure 6 Panel D, where the data is aggregated weekly, and also in the higher correlation of 0.89 at the weekly level (Table 2 Panel A). Comparing at a higher temporal frequency at the hourly level leads to lower alignment (Figure 5). Nevertheless, the locally calibrated data performs much better at the hourly level (correlation of 0.74) as compared to the satellite data (correlation of 0.54) (Table 2 Panel A).

Third, we compare the PM2.5 values from the low-cost monitors to the PM2.5 estimates produced using the satellite MERRA-2 data. In a similar result to the comparison between the regulatory-grade and low-cost monitors, we find that the low-cost monitors record much lower levels of pollution compared to the satellite data (Figure 7). This underestimation was particularly severe in early 2020 and early 2021, when dust storms were frequent, where the unadjusted Purple Air PM2.5 values do not increase substantially despite peaks in PM2.5 in the satellite data (Figure 7A).

To confirm that the substantial divergence between the PM2.5 measurements from the two data sources is due to an underestimation of dust by the Purple Air monitors in our context, we generate a modified PM2.5 estimate using all of the MERRA-2 components except dust.<sup>20</sup> Comparing the MERRA2 estimates of PM2.5 with and without dust suggests that much of the PM2.5 pollution in Dakar is due to dust (Figure A2). The PM2.5 estimate excluding dust from MERRA-2 and the unadjusted PM2.5 values from the low-cost monitors are more closely aligned (Figure 7B). However, the Purple Air values tend to be higher, especially during peaks in PM2.5, than the satellite-derived estimates that exclude dust since some dust pollution is captured by the Purple Air monitors. Furthermore, while the closer alignment between these two pollution series provides evidence that the low-cost monitors underestimate PM2.5 in Dakar due to incomplete measurement of dust pollution, the closer alignment does not reflect a better measurement of PM2.5 in Dakar.

As with the comparison between the data from the regulatory-grade monitors and the low-cost monitors, we compare the satellite estimates of PM2.5 to two adjustments of the PM2.5 data from the low-cost monitors. Again, the adjustment using the Jaffe calibration does not improve the underestimation of values (Figure 7C), but there is an increase in the correlation between the adjusted and satellite data (Table 2). The local calibration that we develop improves the alignment of the data from the low-cost monitor with the satellite estimates, both in terms of levels and the correlation (Figure 7D and Table 2).

Considering alignment with both the PM2.5 measurements from the regulatory-grade monitors and the satellite estimates, calibrating the data from the low-cost monitor appears to partially compensate for its underestimation of PM2.5 due to dust. Furthermore, the results suggest that local calibration improves alignment relative to off-the-shelf adjustment of the data from the low-cost monitors.

> 20To generate the MERRA-2 PM2.5 estimate without dust, we use the same equation to combine the different components except dust is not included.

17
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20 Table 2: Comparison of PM2.5 Measures

||||**Panel A: Co**|**rrelations**||
|---|---|---|---|---|---|
|||Regulatory Monit|ors|Sa|tellite Measures|
||Hourly<br>(1)|Daily<br>(2)|Weekly<br>(3)|Hourly<br>(4)|Daily<br>Weekly<br>(5)<br>(6)|
|Satellite Measures|0.54|0.75|0.87|.|.<br>.|
|Low-cost (unadjusted)|0.60|0.81|0.83|0.53|0.72<br>0.84|
|Jafe Calibration|0.62|0.90|0.96|0.54|0.76<br>0.89|
|Local Calibration|0.74|0.87|0.89|0.63|0.80<br>0.91|
|Satellite Calibration|0.68|0.87|0.90|0.64|0.82<br>0.92|

|||**Panel B: **|**Root Me**|**an Squar**|**e Error**||
|---|---|---|---|---|---|---|
|||Regulatory Monitors|||Satellite Measures||
||Hourly<br>(1)|Daily<br>(2)|Weekly<br>(3)|Hourly<br>(4)|Daily<br>(5)|Weekly<br>(6)|
|Satellite Measures|55.4|34.8|20.6|.|.|.|
|Low-cost (unadjusted)|59.9|52.1|46.9|60.2|53.5|46.6|
|Jafe Calibration|61.8|60|56.4|66.4|61.8|56.6|
|Local Calibration|39.5|26.9|21.3|42.7|29.8|18.8|
|Satellite Calibration|44.9|32.1|26.5|42.2|28.5|18|

**Note** : The date range used is for the full year 2023. The regulatory monitor values come from the average of CGQA regulatory-grade monitors. Satellite measures are generated from the MERRA-2 assimilation. The satellite value is the MERRA-2 PM2.5 estimate closest to the Bel-air regulatory-grade monitor station. The regulatory monitor values come from the average of CGQA regulatory-grade monitors. The low-cost unadjusted and calibrated values use averages across all low-cost monitors active for a given time interval. Jaffe calibration uses PM1/10 ratio to identify dust events. The local calibration is the unadjusted Purple Air values calibrated to the local context using data from two local regulatory monitors. The satellite calibration uses the MERRA-2 measure to calibrate the unadjusted low-cost monitors.
one regulatory-grade monitor functioning at any given time. Therefore, this data implies a uniform level of pollution across the city (Figure 8 Panel A). The PM 2.5 level from the regulatory-grade monitor aligns closely with that from the satellite data (Figure 8 Panel B), but both sources miss variation across space. A network of low-cost monitors captures significant spatial variation in pollution across the city, but the levels are substantially lower than those from the regulatory or satellite data due to the limitations in capturing dust (Figure 8 Panel C). The presence of at least one regulatory-grade monitor makes it possible to conduct a local calibration, which can then be used to adjust the PM2.5 data from the low-cost monitors to better align with the levels from the regulatory monitor, while preserving spatial variation across the city (Figure 8 Panel D). We can also calibrate the low-cost data using the satellite data, since there may not be high-quality regulatory data available for calibration in LMIC contexts. Calibrating the low-cost monitors using the satellite data shows potentially an overestimate of air pollution across the city, with the average value for the city higher than the regulatory one.

For the two calibrations, we use the values generated based on the hourly calibration and average the calibrated hours across the day. Alternatively, we can average the hours across the day and apply the daily calibration. The daily calibration, both with the regulatory and the satellite data, has a much higher out-of-sample R2, which may suggest that it could lead to better estimates (Tables A2). Applying the daily calibration for the same day, the daily local calibration looks quite similar to the one using the hourly calibration, but importantly the satellite calibration is now much more closely aligned with the local calibration and closer to the average regulatory value. These results suggest that investing in a combination of regulatory and low-cost monitors allows for a much richer dataset that can capture both magnitudes and variation in space. In the case where regulatory data is not available, though, it may be possible to calibrate low-cost monitors using satellite data at a lower temporal frequency (daily or lower).

21
<!-- Start of picture text --> PM2.5 (ug/m3)<br>0 38 7 115<br>De bow cost Monitor Location<br><!-- End of picture text -->
<!-- Start of picture text --> 120 i — Low-costSatellite 2020 2020Data Data 200<br>H +++ Initial COVID-19 Mobility Restrictions<br>: 175<br>100 :<br>= i 150 _<br>Es H ><br>re5 2 ii 125 6=<br>z Hi =Ni<br>’ i 100 ~<br>38 0 :: 2=<br>FS3 75 7)<br>40<br>; 50<br>20 i 25<br>02-01 03-01 04-01 05-01<br>Date<br><!-- End of picture text --> <!-- Start of picture text --> 225 i: — ReguSatel l iteatory2020 2020Data Data 200<br>200 : +++ Initial COVID-19 Mobility Restrictions<br>i 175<br>175 i<br>€ : 150<br>= 150 H ><br>rea | iH 125 =o<br>© 126 q: =Ni<br>' ' 100 ~<br>5 100 | £3<br>FE)& 4¥ |) 75 isra)<br>75 F<br>H 50<br>H sp 7 j a}<br>25 i v g<br>02-01 03-01 04-01 05-01<br>Date<br><!-- End of picture text -->
<!-- Start of picture text --> “ i — 2020 Data<br>E — 2023 Data<br>H s+ Initial COVID-19 Mobility Restrictions<br>120 i<br>100 :<br>ro ><br>a 6 :<br>40 i<br>2 i<br>0 i<br>sn sN sn N<br><!-- End of picture text --> <!-- Start of picture text --> i — 2020 Data<br>“0 E — 2023 Data<br>H +++ Initial COVID-19 Mobility Restrictions<br>120 i<br>100 i<br>ry i<br>= 0 :<br>40 i<br>20<br>° i<br>sn sN sn N<br><!-- End of picture text --> <!-- Start of picture text --> “0 — 2020 Data<br>i — 2023 Data<br>i ssss++ Initial COVID-19 Mobility Restrictions<br>120 i<br>100 :<br>P p<br>3<br>Ps<br>$a 60<br>0 i<br>vsn 8sN &sn €N<br><!-- End of picture text -->
Table 3: Impact of Covid Mobility Restrictions on Air Pollution Measures (Hourly Averages, 2020 & 2023)

||(1)<br>Regulatory|(2)<br>Satellite|(3)<br>Low-cost|(4)<br>PA Local Calibration|
|---|---|---|---|---|
|Policy Period (Mar 14-May 12)|-37.58<sup>_∗∗∗_</sup>|-29.41<sup>_∗∗∗_</sup>|-4.18<sup>_∗∗∗_</sup>|-6.32<sup>_∗∗∗_</sup>|
||(2.91)|(7.92)|(0.66)|(1.04)|
|2020|20.54<sup>_∗∗∗_</sup>|31.90<sup>_∗∗∗_</sup>|2.61<sup>_∗∗∗_</sup>|4.21<sup>_∗∗∗_</sup>|
||(1.46)|(2.95)|(0.62)|(0.98)|
|Policy Period (March 14-May 12)*2020|-24.98<sup>_∗∗∗_</sup><br>(1.71)|-30.51<sup>_∗∗∗_</sup><br>(3.67)|-8.50<sup>_∗∗∗_</sup><br>(0.50)|-13.61<sup>_∗∗∗_</sup><br>(0.78)|
|Average|100.07|129.38|23.08|58.72|
|% of Average|-24.97|-23.58|-36.85|-23.18|

**Note** : Controlling for temperature and humidity, with hourly, weekly, monthly, day-of-week and monitor fixed effects. Contains data from 22nd January to 12th May for 2020 and 2023. The ’Average’ is based off the pre-Covid average in 2020. Regulatory data comes from monitors operated by the CGQA. Satellite estimates are generated from the MERRA-2 assimilation. The satellite value is the MERRA-2 PM2.5 estimate closest to the Bel-air regulatory-grade monitor station. The Low-cost measurements in Column 3 are the unadjusted values from all Purple Air monitors active in 2020 or 2023. The Local calibration is based on calibrating the unadjusted Purple Air data using co-located regulatory data from two local monitors. Winsorizing outliers at 99th percentile.<sup>_∗_</sup> _p <_ 0 _._ 05,<sup>_∗∗_</sup> _p <_ 0 _._ 01,<sup>_∗∗∗_</sup> _p <_ 0 _._ 001.

the coefficient is larger (though still smaller than the coefficient in the regression using regulatory data), and the percent decrease becomes perfectly in line with the regulatory and satellite data.

These findings are encouraging because they demonstrate that it is possible to measure similar impacts of a policy on PM2.5 pollution at the city level using three different data sources. Thus, if there is data available from only one source, it is still possible to measure the impacts of a policy that affects pollution at a larger geographic level. Nevertheless, the ability to accurately measure the impact of a policy with any data source will depend on the type of policy and the spatial and temporal frequency of interest. For instance, if there is interest in evaluating the impact of an intervention that is spatially concentrated and is unlikely to affect air pollution globally in a city, the satellite data would not be adequate since it provides only one value for the city. Similarly, if policy makers are interested in the impact of a policy to reduce short-term peaks in air pollution, the satellite data would not be a good choice since it is not well aligned with data from regulatory-grade monitors at the hourly level. In these cases, it would be necessary to use data from air pollution monitors on the ground that collect data across space (whether low-cost or regulatory-grade), and the accuracy would depend on the density of the network.

Finally, the results up to this point suggest that the unadjusted PM2.5 measurements from the lowcost monitors give much lower magnitudes (and a lower coefficient) relative to the regulatory and satellite data, even as the effects of a policy measured in percent change is largely aligned across data sources. Therefore, we also evaluate the impact of the policy at the hourly level using the four different calibrations for the Purple Air Data: the two off-the-shelf calibrations from the literature (Barkjohn and Jaffe), and our two co-located calibrations based on PM2.5 measurements from regulatory-grade monitors and satellite estimates (Table 4). Both off-the-shelf calibrations estimate much smaller coefficients (treatment effect in levels), even as the treatment effect as a percent change stays around the same value for the Barkjohn calibration. The co-located calibrations result in a larger coefficient than the unadjusted data and are more in line with the coefficient estimated with the data from the regulatory-grade monitors.

25
Table 4: Comparison of Low-cost Calibration Regression Coefficients (Hourly, 2020 & 2023)

|||**Type**|**of Calib**|**ration**||
|---|---|---|---|---|---|
||(1)<br>Unadjusted|(2)<br>Barkjohn|(3)<br>Jafe|(4)<br>Local|(5)<br>Satellite|
|Policy Period (Mar 14-May 12)|-4.18<sup>_∗∗∗_</sup>|-2.57<sup>_∗∗∗_</sup>|-2.88<sup>_∗∗∗_</sup>|-6.32<sup>_∗∗∗_</sup>|-6.80<sup>_∗∗∗_</sup>|
||(0.66)|(0.35)|(0.61)|(1.04)|(1.10)|
|2020|2.61<sup>_∗∗∗_</sup>|1.91<sup>_∗∗∗_</sup>|-6.36<sup>_∗∗∗_</sup>|4.21<sup>_∗∗∗_</sup>|4.35<sup>_∗∗∗_</sup>|
||(0.62)|(0.32)|(0.81)|(0.98)|(1.03)|
|Policy Period (March 14-May 12)*2020|-8.50<sup>_∗∗∗_</sup>|-4.93<sup>_∗∗∗_</sup>|-1.61<sup>_∗∗_</sup>|-13.61<sup>_∗∗∗_</sup>|-14.18<sup>_∗∗∗_</sup>|
||(0.50)|(0.26)|(0.59)|(0.78)|(0.82)|
|Average|23.08|14.64|18.68|58.72|79.74|
|% of Average|-36.85|-33.66|-8.64|-23.18|-17.78|

**Note** : Data is aggregated hourly. Controlling for temperature and humidity, with hourly, weekly, monthly, day-of-week and monitor fixed effects. The ’Average’ is based off the pre-Covid average in 2020. Barkjohn and Jaffe are Purple Air corrections based on previous literature. The Jaffe calibration uses the PM1/10 ratio to identify dust events. Local and Satellite corrections are based on calibrating the Purple Air data on co-located regulatory and satellite data, respectively. Winsorizing outliers at 99th percentile.<sup>_∗_</sup> _p <_ 0 _._ 05,<sup>_∗∗_</sup> _p <_ 0 _._ 01,<sup>_∗∗∗_</sup> _p <_ 0 _._ 001.

For the local calibration, the impact of the policy as a percent change is estimated as 23%, which is very similar to the 25% estimated with the data from the regulatory-grade monitors and 24% with the satellite data. The coefficient (treatment effect in levels) estimated using the satellite calibration is very similar to the one using the regulatory monitor calibration, though the percent change is smaller at only 18%.

We conduct several robustness checks. Using data from only 2020 and 2023, we compare the results across the three data sources at the daily level using both the maximum hourly PM2.5 value of the day and the average of the hourly values for the day (Table A3 Panels A and B). Since there is only one year of comparison and therefore a small number of observations when conducting the analysis at the daily level, the results are much less stable. For both outcome variables, the coefficient remains negative when using data from the regulatory-grade monitor, but it is no longer significant and the percent change is much smaller. Using the estimates from satellite data, the coefficient is positive and not significant. Using the low-cost data, the results remain negative and significant when the outcome variable is the daily average, though the percent change is smaller. We conduct an additional robustness check, adding more years to the analysis.<sup>23</sup> The analysis at the hourly level shows very similar results to what we find when only comparing 2020 and 2023 at the hourly level, increasing our confidence in those results (Table A3 Panel C). At the daily level, we now find the results are fully aligned with the results when using the hourly data, in contrast to what we find when using only one year for comparison. Additionally, the local calibration coefficient at the daily level is much closer aligned to the coefficient using the regulatory data. This is in line with our findings that the calibration at the daily level provides a better fit (Table A2). This highlights two points. First, it demonstrates the benefit of having higher resolution hourly data, especially in cases where there is a shorter time-series available. It also shows that in cases where there is a longer time-series, using a lower temporal aggregation (daily or higher) for calibrating the low-cost

> 23For the satellite and regulatory data we analyze 2017-2020 due to data availability and for the low-cost data we analyze 2020, 2022 and 2023, removing 2021 since some policies were still in place at that time.

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monitors can help provide policy results that are better aligned with the regulatory data.

Data from a spatially dense network of low-cost monitors can be calibrated using at least one regulatory-grade monitor to improve the measurement of PM2.5 levels and of policy impacts. Investing in a combination of low-cost and regulatory-grade monitors can improve the air quality data used for policy evaluation. In the absence of data from regulatory-grade monitors, the results suggest that satellite data can be used to calibrate the PM2.5 measurements from a network of low-cost monitors, although the results may not be as accurate in terms of measurement of PM2.5 levels or policy impacts as when calibrated with regulatory-grade data.

# **6 Conclusion**

In this paper, we compare and evaluate the three principal sources of PM2.5 data in Dakar, Senegal. We find that the low-cost monitors report substantially lower PM2.5 levels due to incomplete measurement of dust and that, at higher levels of temporal aggregation, PM2.5 levels derived from satellite data are closely aligned with PM2.5 levels from regulatory-grade monitors. Using mobility restrictions during COVID-19, we show that all three data sources provide similar estimates of the impact of a policy on PM2.5 at the city-level, particularly when measured in percent changes instead of levels. Finally, we show the value of investing in at least one regulatory-grade monitor. Developing a context-specific calibration formula provides substantial improvement over off-the-shelf calibration formulas in aligning the data from low-cost monitors with data from regulatory-grade monitors. Faced with budget constraints, an air pollution monitoring system based on one well-maintained regulatory-grade monitor complemented with a dense network of low-cost monitors can provide PM2.5 data with high spatial variation that can also be used to accurately estimate the impact of policies. Barring any data from regulatory-grade monitors, calibration of PM2.5 measurements from low-cost monitors using satellite-based estimates of PM2.5 can improve the measurement of PM2.5 levels and policy impacts as long as the calibration is conducted at a lower temporal frequency.

Our results have important implications for policy makers in rapidly growing urban areas of LMICs. Our results also speak to rural or suburban areas of higher-income countries where the coverage of regulatory-grade monitors is also sparse. Overall, the results are encouraging and point to the potential that low-cost monitors have as part of a system of air pollution monitoring. Increasing the data availability in these settings could help lead to an important expansion of the literature focused on air pollution, providing a better understanding of context-specific challenges and potential environmental policy solutions to address these.

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