Community-based Monitoring of Urban Environmental Data

Looking forward: citizen science is changing the research landscape

Environmental data measured by the general public on their immediate local surroundings are providing new sources of fine-grained data in cities. Jonathon Taylor, Anna-Kaisa Viitanen & Alonso Espinosa (Tampere University) explain how this recent phenomenon can lead to a richer understanding of urban form, microclimates and environmental exposures.

The 21st century has seen rapid urbanisation, with cities now accounting for around 57% of the global population. This has led to increased density and sprawl of built-up areas (buildings and infrastructure), leading to increasing challenges to the environment and to health. For instance, worsening air pollution is causing more physical and mental health problems (Zang et al. 2022) and  the Urban Heat Island (UHI) is accelerating rising temperatures in cities across the world (Heaviside et al. 2017).

The health risks from these environmental hazards are well documented and have been a focus of significant research, monitoring and tracking, and public awareness. In order to monitor environmental conditions, governments and scientists have typically deployed various static high precision weather and air pollution measurement stations. Measurement campaigns using vehicle transects have also been used to capture data via high quality instrumentation and conditions in urban areas are also routinely evaluated by computationally expensive simulation models and remotely-sensing via satellite imagery.

However, the past 10 years have seen a rapid growth in the amount of data available from Community-Based Monitoring (CBM) i.e. environmental data measured by the community. Several factors have contributed to its rise. Firstly, the prices and availability of instruments which can measure environmental conditions have significantly reduced. Secondly, the emergence of Internet-of-Things (IoT) connectivity means that many of these devices allow remote access and data to be uploaded to a server. And thirdly, growing public awareness of the harmful effects of these exposures may be leading to increasing uptake. This development has made it accessible for the general public to measure their immediate local environment, and often share this data publicly, leading to new and as-of-yet under-utilised sources of environmental data in cities.

Growth of CBM data in cities

To illustrate the rapid growth of publicly available CBM environmental monitoring data in cities, two examples are provided: Personal Weather Stations (PWS) and Air Quality Sensors (AQS).  PWS provide opportunities to evaluate temperature exposures in urban environments and their surrounding rural areas, with platforms such as Weather Underground and Netatmo providing crowd-sourced climate data from PWS devices. AQS devices are increasingly being used to measure outdoor and indoor air pollution levels, with online platforms such as sensor.community and OpenAQ providing access to monitored data from low-cost devices such as those from Air Gradient, Megasense, as well as instructions such to assemble do-it-yourself (DIY) sensor systems.

The rapid growth of CBM is illustrated in a recent study that utilises PWS from Netatmo in London (Taylor et al. 2024) and AQS that measure PM_2.5 (particulate pollution less 2.5 micrometers in diameter) from sensor.community across Europe. Focusing on London (PWS) and Berlin (AQS), (Figure 1) and now in many cities worldwide, AQS outnumber official monitoring stations.

This increased density of sensors in cities provides an opportunity to evaluate environmental exposures in urban environments and for examining conditions during extreme events.

This growth of CBM data provides significant research opportunities which are beginning to be realised. PWS have, for example, been used to examine urban climates (Brousse et al. 2022; Fenner et al. 2017; Potgieter et al. 2021; Varentsov et al. 2021), the effect of land cover on temperatures (Du et al. 2024; Taylor et al. 2024), act as an input to building physics models (Benjamin et al. 2021), and to validate and correct urban climate simulations (Brousse et al. 2023; Hammerberg et al. 2018). AQS have been used to study the potential to deliver real time air pollution data to local populations, to improve air quality predictions, to study the infiltration of air pollution (Bi et al. 2020; English et al. 2020; Wallace et al. 2022), and to supplement official data (Duvall et al. 2021; Gabrys 2017, 2017).

Challenges

Sensor reliability and comparability

The sensors themselves are less reliable and accurate than higher cost official instruments. PWS, for example, may lack sufficient radiation shields, while AQS have a range of limitations including an accuracy that depends on humidity and concentrations. This results in much greater uncertainty in measurements and lower reliability, and fall short of the necessary standards needed by instruments certified for regulatory monitoring. Sensors require regular calibration but information whether or not the calibration has been conducted, not to mention calibration specifications are not typically available. There are also a range of different types of sensors, raising issues of comparability and interoperability. While guidelines are available on the placement and use of these devices, it is not possible to know whether they have been followed. For example, PWS may be positioned close to radiative sources of heat, while AQS can be placed near an emission source, or in a way that restricts air intake.

Metadata

Engaging the public in data collection through low-cost sensors can expand data coverage, increase public awareness and involvement in environmental health issues, and empower communities to act for change. However, these devices are unlikely to be deployed by citizens in a standardised manner, and there is a lack of metadata about how these sensors are deployed.  AQS data can have a variable indicating whether the sensors are indoors or outdoors, however there can be user configuration errors indicating placement and sensors assumed to be outdoors may in fact be located indoors, and vice versa.  Location information is often available, but users can restrict the locational accuracy of the public information they share. For AQS located indoors, the types of buildings, floor-plans, and emission sources are not available from these CBM platforms for privacy reasons.

Location and biases

There may also be biases in geographical location. These devices are predominantly used by those with higher incomes and presumable with technical preparedness, leading to an under-representation of lower income areas. Conclusions drawn without accounting for these biases can lead to misinformed policies.  CBM sensors are also largely located in residential areas. While this is useful to determine, for example, air pollution concentrations where people live and increasingly where they work, it can miss out on the roadside concentrations and industrial background concentrations that can help inform an individual’s wider exposure.

Future directions

CBM has traditionally been seen as a means of public engagement, involving communities in planning and regulatory processes, or in academic research settings with specific or narrow focus. However, the pace at which the public has acquired sensors, and the huge amounts of data being constantly collected  suggests an as-yet unrealised potential for research and policy development in urban areas. If this is to happen, then there is a need to overcome the challenges related to data quality and biases.

One way is to use advanced quality-control and data cleaning methods. For PWS,  methods have been developed to clean and remove weather data (Fenner et al. 2021), while researchers using AQS use a variety of non-standard approaches to clean and check the data. Development and standardisation of data quality control algorithms can help mitigate some of the challenges posed by citizen data. There are opportunities for researchers and the community to work together, with multiple agencies producing public guidance on AQS, for example (Clements et al. 2022; EEA, European Environment Agency 2019; US EPA 2016; Yatkin et al. 2022).

Conclusion

CBM data represent an emerging source of data on the environmental conditions in cities, allowing for real-time identification of exposures and areas of increased risk. The increasing number and density of low-cost sensors has the potential to yield better understanding of relationships between urban form, microclimates and environmental exposures.  However, significant challenges remain, and data needs to be treated with due care and nuanced understanding of the limitations inherent to these low-cost sensors.

References

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