Sensors, Mobile Monitoring & Citizen Involvement: Complementary Tools for More Accurate Air Quality Exposure Assessments
Authors: Hofman Jelle , Van Poppel Martine , Hoek Gerard , Viana Mar , Theunis Jan , Peters Jan , Kerckhoffs Jules , Moreno Teresa , Rivas Ioar , Basagaña Xavier , Nogueira dos Santos Vanessa , Harrison Roy , Bousiotis Dimitrios , Green David , Petäja Tuukka , Zaidan Martha , Hossein Motlagh Naser , Bougiatioti Aikaterini 
Published: February 2023

&lt;p&gt;Traditional fixed air quality monitoring networks fulfill requirements as set in the European Air Quality Directive (2008/50/EC) and provide valuable information on ambient concentrations and temporal trends of air quality at the international, national, regional and urban level. Some short-lived pollutants or constituents, like ultrafine particle (UFPs), black carbon (BC) and nitrogen oxides (NOx), exhibit a high spatial (street-level) variability, requiring a higher monitoring resolution for more accurate exposure assessments in health or epidemiological studies. Advances in sensing and Internet of Things (IoT) technologies have resulted in smaller and more affordable stationary and mobile monitoring solutions, enabling data collection at unprecedented &amp;#160;scales. Moreover, citizens can contribute in data collection resulting in more wide-scale data collection, dissemination and resulting impact. The collected data, however, needs adequate processing and validation in order to obtain representative exposure maps (i.e., long-term averaged concentration maps) for epidemiological studies and policy assessment.&lt;/p&gt;&lt;p&gt;RI-URBANS aims to develop and test innovative and complementary air quality monitoring approaches in different European pilot cities. This methodological work focusses on the potential of mobile and stationary sensor applications as complementary tools for traditional (low-density) monitoring networks (Figure 1). Complementary measurements can contribute to understand spatial variability of short-lived constituents of air pollution from a diversity of pollution sources.&lt;/p&gt;&lt;p&gt;&lt;img src=&quot;https://contentmanager.copernicus.org/fileStorageProxy.php?f=gnp.cc5e037d6db367078533761/sdaolpUECMynit/32UGE&amp;app=m&amp;a=0&amp;c=cca09f8aca7104a58996763145844438&amp;ct=x&amp;pn=gnp.elif&amp;d=1&quot; alt=&quot;&quot;&gt;&lt;/p&gt;&lt;p&gt;&lt;em&gt;Figure 1: Mobile and fixed sensor applications, resulting data resolution and associated requirements in terms of device (devices) and monitoring strategy (setup).&lt;/em&gt;&lt;/p&gt;&lt;p&gt;We identify different data users and use cases for mobile, stationary (or combined) sensor applications and their resulting implications regarding device specifications, monitoring strategy and data processing needs. By reflecting on past studies and projects, we summarize common methodological approaches and best practices to increase the spatial resolution of air quality data. Moreover, the role of citizen engagement is evaluated, both in generating more data and air quality impact (awareness raising).&lt;/p&gt;&lt;p&gt;This work serves as methodological input for the RI-URBANS service tools that will be tested in the pilot cities and is openly available at https://riurbans.eu/wp-content/uploads/2022/10/RI-URBANS_D13_D2.5.pdf&amp;#160;&lt;/p&gt;