TL;DR — Although collecting data is an essential first step in effective air quality monitoring, data collection alone is not enough to bring about meaningful change. Instead, taking a multi-stakeholder approach under the concept of Air Quality Management 2.0 — and leveraging the data analysis capacities of researchers and academic institutions — extends the positive impact that air quality action can have on improving public and environmental health. Open data sharing with researchers also helps to support their work to uncover trends, expose inequities, and make policy-relevant conclusions that support cleaner air.
This blog is part of a series focusing on the concept of Air Quality Management 2.0, which aims to link together key stakeholders, from communities to industries, in the work to create sustainable air quality management programs and improve public health when it comes to the negative impacts of air pollution. Read our white paper on Air Quality Management 2.0 here.
The importance of data analysis
Air quality data collection alone is not enough — the data must also be effectively analyzed to bring about meaningful behavioral and policy insights that lead to air quality improvements.
Data analysts can employ cloud analytics platforms to connect air quality data to emissions inventories, health records, and a wide variety of other sources to gain insight and inform regulations, planning, and communications.
This data analysis helps researchers to draw the links between emission levels and corresponding health risks and pollution sources, helping to drive policy change and supporting both regulators and communities in air quality action.
Spatial analysis is also an important piece of the puzzle, as it can help shed light on what areas or neighborhoods are experiencing the highest pollution burden.
Ultimately, data-driven insights help accelerate the policy changes we know are needed to secure clean air. Our cities’ health depends on it.”
— Sean Wihera
Turning air quality data into actionable insights through Air Quality Management 2.0
Academic institutions and other bodies with similar expertise play a vital role in bringing greater data analysis capacities and helping to extract more insight from the data that has been collected.
Data can be shared with researchers to help shed light on where the greatest public health improvements can be realized.
Air Quality Management 2.0 emphasizes the importance of collaborative partnerships between various stakeholders, including sharing open data with researchers.
This means that researchers can benefit from the air quality data that is already being collected and add significant value to it by uncovering trends and policy-relevant conclusions, helping to expose inequities in air quality exposure that can be mitigated.
Findings can feed directly into policy consultations, public communications, and accountability reporting — regardless of the sector.”
— Sean Wihera
Effective air quality data analysis applications
Let’s take a look at a few successful examples of air quality data analysis in partnership with research.
Researchers at the University College Cork wanted to monitor air pollution at the neighborhood level to calibrate the Life Emerald model, which would serve as the basis for Ireland’s first countrywide air quality model and forecast system.
Because the researchers needed more localized air quality data measurements, using reference-grade monitors would not be effective, as they are too expensive to site at the density that is needed for this type of data collection.
Thus, UCC deployed a network of over 20 low-cost sensors, providing an unprecedented density of hyperlocal air quality data.
In Helsinki, Finland, the Institute for Atmospheric and Earth System Research (INAR) deployed a network of low-cost sensors in order to support their virtual sensor network.
The researchers needed their low-cost sensors to capture continuous data with little interruption as well as measure relevant meteorological variables.
The researchers used a combination of the low-cost sensor data and AI calibration models to develop an air quality model that would be suitable for Helsinki, a region that tends to have low ambient PM2.5 levels, and found that the combination of low-cost and virtual sensors was a successful one — presenting an interesting potential when it comes to innovative air quality monitoring technology use.
Located in central Africa, Angola tends to experience issues with air pollution, especially during biomass burning season when PM2.5 levels spike across the country and threaten public health.
A team from the Higher Polytechnic Institute of Tundavala in Angola installed low-cost sensors in eight locations across Angola, especially focusing their siting at schools and universities in both urban and rural locations.
This work helped to establish a comprehensive, real-time air quality monitoring network in the country, support air quality education and awareness, and advocate for policy change. Additionally, the project helps to gather data to support future research initiatives that can bring about a positive impact.
A multi-stakeholder approach to air quality data analysis
As with the other stakeholders under Air Quality Management 2.0, air quality data analysts benefit from collaboration with researchers, academic institutions, and technologies to more comprehensively understand air pollution, its impacts, and potential initiatives that can drive positive change.
Download the Air Quality Management 2.0 white paper here to learn more about the importance of engaging key stakeholders in partnership for meaningful air quality improvement, including communities, regulators, analysts, and technologies.
Interested in measuring air quality for cleaner air and a healthier climate? Get in touch with our team to learn more about our Sensing-as-a-Service solution for governments, businesses, and community organizations, using our Clarity Node-S monitors and Modules.