LocationsThis is the second article in the Blog Article Series on Air Pollution.
Applications of Low-Cost Air Quality Sensors for Citizen Engagement and Air Pollution Mitigation
November 25, 2024
This image was created by Author with the assistance of DALL·E 3
The previous article introduced the advantages of leveraging low-cost air quality sensors (LCS) for air quality monitoring. In essence, the use of LCS offers a viable solution complementary to traditional reference sensors in providing a general indicator of air quality levels. LCS also has the potential of wider spatial coverage compared to traditional reference sensors in monitoring hyperlocal air pollutant levels.
While a significant number of publications on LCS networks are on their testing, calibration, and validation, fewer studies have been found where the data from the LCS networks was used for the development of local air pollution control interventions. These case studies are crucial for demonstrating the value of the LCS network beyond air quality monitoring, specifically in using the data to raise citizen awareness, foster community engagement, advocate for the development of local air pollution plans, support air pollution control policies, and support relevant stakeholders in making informed decisions on reducing their exposure to air pollution.
The next section presents a summary of several applications of LCS led by governments, academia, and citizens.
1) Empower Citizens with Real-Time Access to Air Quality Data: By providing local communities with real-time air quality information, they will be more aware of local air pollution levels for individual decision-making and communicate with relevant government authorities. For example, if the ambient air quality levels are high or in the unhealthy range, individuals can choose to postpone or cancel their outdoor activities, thus reducing their exposure to air pollution. Awareness of the general air quality levels within the city can also empower communities to develop or advocate for local air pollution interventions.
- An example of this is seen in Cork City, Ireland, where real-time air quality (PM2.5) data from a network of LCS is made available on an interactive map
Screenshot of real-time air quality map from low-cost sensor data.
Cork Air Quality Dashboard
- Similarly, UNDP, UNICEF, and People in Need (PIN) launched a real-time air quality platform for air quality data visualisation from LCS in Ulaanbaatar City in Mongolia
Snapshot of air quality data from http://www.hazegazer.mn
2) Understand Pollution Hotspots within a City: Understanding the local variations of air quality within a city and identifying air pollution hotspots is the first step before targeted air pollution interventions can be developed to mitigate or reduce air pollution from a source. For example, if a traffic intersection is found to be a pollution hotspot, traffic pollution control plans can be developed, ranging from simple interventions such as changing an individual’s walking route to avoid the traffic intersection, to a more complex intervention at a government level by implementing vehicular emission control policies.
- UNDP Argentina’s Accelerator Lab developed a mobile network of LCS based on citizen science to collect air quality data around Buenos Aires over a period of 7 weeks. The air quality data was aggregated to produce a spatial map of air pollution and shared with the Ministry of Environment and Sustainable Development, as well as the city government, to understand localised hotspots (e.g. traffic junctions) around the city that exceeded acceptable levels of air pollution.
Data were collected from volunteers who rode bicycles with sensors around the city.
UNDP Accelerator Lab, Argentina
3) Promote Community Engagement and Education: Having access to air quality data can help empower local communities with air quality information and promote awareness to facilitate community-led initiatives to improve air quality. For example, members of a community in the example below were the driving force in helping to reduce air pollution at a primary school.
- Data from the LCS network Breathe London was used by CleanAir4Schools, a community-led movement to reduce air pollution at William Patten Primary School in London. The school falls within 98% of London’s primary and secondary state schools located in areas that exceed the WHO Ambient Air Quality Guidelines. An air pollution framework for local interventions was developed based on the air pollution data collected by the LCS, including plans for the removal of a bus stop and idling buses outside the playground at the school to reduce air pollution exposure for school-going children.
Bus stops can be a pollution hotspot due to the increase in vehicular exhaust emissions when the buses stop, start, and move slowly.
Pexels/Mehmet Turgut Kirkgoz
4) Quantify Effects of Air Pollution on Health: The risks and effects of air pollution exposure and health can be determined by using statistical models to correlate air quality levels from the LCS network with health outcomes. Local health statistics related to air pollution such as stroke, lung cancer, and pneumonia are used to estimate the number of deaths and diseases related to air pollution. Such health data is typically used as the basis for setting Air Quality Standards for a particular city or country and can also be used as scientific evidence to support the planning and implementation of air pollution control policies.
- Six months of monitoring data from LCS sites in Kibera and Viwandani in Nairobi, Kenya was used to carry out an air pollution health impact assessment. The air quality data was used to quantify the impacts of short and long-term exposure to ambient air pollution. The study found that around 1,200 premature deaths in Nairobi County could be attributed to long-term exposure to PM2.5 guidelines exceeding the WHO Ambient Air Quality Guidelines of 10 micrograms per cubic meter.
- Statistical analysis was carried out to analyse relationships between the air pollution data from LCS in Poland, Belarus, Ukraine, Serbia, Macedonia, Bosnia and Herzegovina, Norway, and Denmark, with multiple other factors such as population density, number of COVID-19 cases, number of deaths, etc. Positive correlations were found between air pollution and the number of COVID-19 cases, as well as air pollution and the number of deaths in 2019 and 2020. Results show that poor air quality is likely the most important factor in facilitating the spread of the COVID-19 pandemic in Central Europe, Eastern Europe, and the Balkans, on top of being responsible for other adverse health outcomes.
5) Provide Early Warning for Smoke Haze Pollution: The air quality data from the LCS network can be linked to messaging, alert, or alarm systems, to provide early warning for relevant parties to be prepared for an air pollution event. This would allow enough time for authorities to mitigate the air pollution event and protect the public against exposure to high levels of air pollution.
- The low-cost and real-time monitoring of haze air quality disasters in rural communities in Thailand and Southeast Asia, or SEA-HAZEMON consists of an LCS network deployed in more than 100 sites in urban and rural areas in Thailand, Laos, Philippines, and Indonesia. It also includes a forest fire detection model for carbon monoxide and particulate matter (PM). Real-time air quality data from the LCS are made available online. In addition, messages are sent to local forest fire authorities via short messaging applications for early warning and preparation for impending smoke haze episodes.
Real-time air quality monitoring data from SEA-HAZEMON (https://www.hazemon.in.th/)
In short, LCS networks have been proven to be useful and applied in many different situations by governments and stakeholders in managing air quality. An LCS network in a city can empower local communities with the tools and air quality data to develop community-led plans for air pollution interventions, and support governments in planning and decision-making.
In the next articles, examples and case studies of more advanced applications of LCS will be shared. These include LCS data assimilation with other sensor data, satellite data, dispersion modelling, and other statistical methods and machine learning for air quality research.