# **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).

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