Andr ́ee To investigate this further, the current paper looks at confirmed cases and COVID-19 related hospital admissions in 355 municipalities in the Netherlands and uses regression techniques to investigate correlations between COVID-19 case data and particulate matter concentrations, controlling for a variety of demographic characteristics and data on health related pre-conditions. The analysis finds that PM2 _._ 5 is a highly significant predictor of both the number of confirmed COVID-19 cases and the number of related hospital admissions per 100,000 inhabitants.

The analysis suggests that the association between air pollution and case incidence is robust to proxies for worse respiratory health and symptom severity. The findings are also robust to other important control variables and different regression specifications that allow for spatial dependence, nonlinearity, alternative error distributions and outlier treatment. Results are obtained with ground-measurements and satellite-derived PM2 _._ 5. Analyzing COVID-19 data from alternative dates resulted in similar conclusions.

The remainder of this paper is organized as follows. Section 2 visually inspects several available confirmed case maps and discusses the spatial distribution. Section 3 introduces the data used for analysis. Section 4 presents regression results and discusses several of the estimates. Section 5 concludes.

# **2. Spatial Distribution of COVID-19: Country Examples**

Suggestive evidence that the spatial distribution of COVID-19 cases is not purely random and might be related to environmental factors can be found by exploring several maps of confirmed cases. A few easily accessible fine resolution maps are presented below, in particular for the Netherlands, Germany, Spain and Italy. The data for the Netherlands is taken from the Dutch National Institute for Public Health and Environment (RIVM).<sup>2</sup> The data for Germany is from the Robert Koch Institute.<sup>3</sup> The data for Italy can be viewed via a live dashboard,<sup>4</sup> and the raw data is well organized and available on a github page.<sup>5</sup> The Spanish data was taken from this link.<sup>6</sup> A number of features of the spatial distributions are striking. First, there is a strong spatial correlation visible in all four countries, which is to be expected for a virus that spreads by human contact. It is intriguing, however, that the highest case density in the Netherlands is in Brabant, the southeastern part of the country, while major cities like Amsterdam and Rotterdam are in the west part of the country where the case density is lower. While Brabant is not the most populous province, it accounts for the highest contribution to nation-wide industrial GDP. Within the province, the sub-region Zuidoost-Noord-Brabant produces the highest contribution to industrial GDP.<sup>7</sup> This area approximately spans the COVID-19 case cluster that can be seen on the map.

> 2. https://www.rivm.nl/coronavirus-kaart-van-nederland-per-gemeente.

> 3. https://experience.arcgis.com/experience/478220a4c454480e823b17327b2bf1d4.

> 4. http://opendatadpc.maps.arcgis.com/apps/opsdashboard/index.html#/b0c68bce2cce478eaac82fe38d4138b1 5. https://github.com/pcm-dpc/COVID-19/tree/master/dati-province

> 6. https://www.rtve.es/noticias/20200323/mapa-del-coronavirus-espana/2004681.shtml

> 7. https://www.cbs.nl/nl-nl/nieuws/2018/31/belang-industrie-voor-de-regio

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