Incidence of COVID-19 and Connections with Air Pollution Exposure in accordance with country-level legislation on public health. The 355 municipalities are grouped into 25 GGD areas, each covering a population of approximately 600,000 inhabitants. The GGD borders are visible in figure 5 which visualizes the hospital admissions.

The data is combined with demographic statistics (2019) obtained from the Dutch Central Bureau of Statistics.<sup>10</sup> The data contains the official population headcount at district level, as well as a number of relevant household characteristics. A number of surveyed health statistics (2016) have been obtained as well from the RIVM (maps can be viewed in the source link).<sup>11</sup> . The data is based on a survey of 457,000 people and includes the share of population in each district with a documented long-term illness (illnesses over 6 months), the prevalence of overweight and obesity, alcohol abuse, smoking and noise due to traffic. Hence the data controls for the presence of possible pre-conditions that make certain populations more vulnerable.

A variety of air pollution data sets exist. For the main analysis, annual average particulate matter concentrations from the RIVM are used to capture long-term exposure (2017, published September 2019).<sup>12</sup> The data is used by the government for official monitoring in accordance with EU guidelines on air quality monitoring and has a resolution of 25 meter grids. These high-resolution grids are produced by spatial interpolation of groundmeasurements. For this analysis, the grids have been averaged to the municipality level. The spatial distribution of pollution has remained relatively stable in recent years. The intensity of air pollution has gradually gone down since 2013, though the difference between the 2017 and 2015 data is relatively small. This suggests that the spatial variation of the 2017 data is still relevant to analyze the role of long-term pollution exposure in the current situation. The temporal lag in the pollution data also ensures that there is no endogeneity due to feedback between case incidence and changes in pollution levels that follow lock-down policies.

To test whether the main findings of the analysis generalize to other pollution data sets, a second analysis presented in the appendix uses the coarser grids from the global PM2 _._ 5 data set of van Donkelaar et al. (2016). The main conclusions of the analysis do not change when this alternative pollution data set is used, and since this data is mainly satellite-derived, it may be used in other countries where detailed PM2 _._ 5 measurements are not easily available. Figure 6 visualizes the spatial distribution of the main PM2 _._ 5 and PM10 statistics. Table 3 summarizes the full set of covariates used in the analysis.

# **4. Results**

The analysis is organized into two main investigations and a set of robustness analyses. First, section 4.1 analyzes the confirmed cases per 100,000 inhabitants using linear models that account for possible spatial autocorrelation and residual dependence. Section 4.2 analyzes the data nonlinearly, allowing parameter estimates to vary across locations and levels in the data. Additional results are included in the appendix, section 6.2. In particular, the robustness of the results is diagnosed by using alternative measures of incidence, a different source of pollution data, and alternative distributional assumptions.

> 10. https://www.cbs.nl/nl-nl/dossier/nederland-regionaal/geografische-data/wijk-en-buurtkaart-2019

> 11. https://www.rivm.nl/media/smap/langdurigeziekte.html

> 12. https://www.atlasleefomgeving.nl/kaarten

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