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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