Abstract
Abstract. In many cities around the world people are exposed to elevated levels of air pollution. Often local air quality is not well known due to the sparseness of official monitoring networks or unrealistic assumptions being made in urban-air-quality models. Low-cost sensor technology, which has become available in recent years, has the potential to provide complementary information. Unfortunately, an integrated interpretation of urban air pollution based on different sources is not straightforward because of the localized nature of air pollution and the large uncertainties associated with measurements of low-cost sensors. This study presents a practical approach to producing high-spatiotemporal-resolution maps of urban air pollution capable of assimilating air quality data from heterogeneous data streams. It offers a two-step solution: (1) building a versatile air quality model, driven by an open-source atmospheric-dispersion model and emission proxies from open-data sources, and (2) a practical spatial-interpolation scheme, capable of assimilating observations with different accuracies. The methodology, called Retina, has been applied and evaluated for nitrogen dioxide (NO2) in Amsterdam, the Netherlands, during the summer of 2016. The assimilation of reference measurements results in hourly maps with a typical accuracy (defined as the ratio between the root mean square error and the mean of the observations) of 39 % within 2 km of an observation location and 53 % at larger distances. When low-cost measurements of the Urban AirQ campaign are included, the maps reveal more detailed concentration patterns in areas which are undersampled by the official network. It is shown that during the summer holiday period, NO2 concentrations drop about 10 %. The reduction is less in the historic city centre, while strongest reductions are found around the access ways to the tunnel connecting the northern and the southern part of the city, which was closed for maintenance. The changing concentration patterns indicate how traffic flow is redirected to other main roads. Overall, it is shown that Retina can be applied for an enhanced understanding of reference measurements and as a framework to integrate low-cost measurements next to reference measurements in order to get better localized information in urban areas.
Highlights
Due to growing urbanization in the last decades, more than half of the world’s population lives in cities nowadays
This study presents a practical approach to producing high-spatiotemporal-resolution maps of urban air pollution capable of assimilating air quality data from heterogeneous data streams
As air pollution gradients can be strong in the urban environment, it is essential to combine measurements with an air quality model when aiming at street-level resolution
Summary
Due to growing urbanization in the last decades, more than half of the world’s population lives in cities nowadays. Over 80 % of the urban dwellers are forced to breathe air which does not meet the standards of the World Health Organization (WHO, 2016). Good monitoring is important to better understand the local dynamics of air pollution, to identify hot spots, and to improve the ability to anticipate events. This is especially relevant for nitrogen dioxide (NO2) concentrations, which can vary considerably from street to street. NO2 is, apart from being a toxic gas on its own, an important precursor of particulate matter, ozone, and other regional air pollutants. Urban-air-quality reference networks are usually sparse or even absent due to their high installation and maintenance costs.
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