Hyperlocal air quality monitoring and we need to make sure our operational systems represent that accurately.” Other projects and solutions It’s important to note that TEMPO isn’t alone in its work to advance hyperlocal air quality. Academic research is underway at universities around the world, including Complutense University in Madrid (see How Planetwatch’s data can improve air quality monitoring , below), and a pilot study called Air Inequality in New York was a part of this summer’s coordinated research campaign in the US. This entailed researchers walking and biking around the city wearing an instrument-laden backpack to gather data at a truly hyperlocal scale. “We’re talking a scale of hundreds of meters,” says Schwantes. “At the same time as they traveled around, we were flying above them. We hope that with this data we can bridge the gap and understand what information we’re missing.” Much of the research into hyperlocal air quality forecasts is being done by the private sector, and solutions such as BreezoMeter, recently acquired by Google, and Vaisala’s Xweather are already available. “Our enhanced hyperlocal air quality forecasting has a 13m resolution and provides a nowcast with one-hour updates and a long-term forecast up to 4.5 days ahead,” says Laura Alku, air quality product manager at Vaisala. “You can monitor the air quality index, the level of pollutants and the impact of forest fires on your city.” ABOVE: Vaisala’s Xweather solution showing nitrogen dioxide levels in Salt Lake City, Utah The future of hyperlocal air quality monitoring There’s still much work to be done to improve hyperlocal air quality monitoring and Judd notes that there will always be a desire for higher-resolution data. However, that comes with its own challenges. “That’s much more data to deal with and interpret, especially in a city, where air quality is dynamic and variable,” she says. Vladimir Kuzmanovski, data scientist at Vaisala, believes that striving for ever higher resolution may not bring much extra value because, “We’re already at a point where the resolution is good enough for all practical needs.” Instead, he believes the focus should be on improving the quality of the data. “Reliable and precise forecasts require access to high-quality data from various sources – better sensor observations, satellite images, geographical data and dynamic emission data. “We also need to improve the observation network by analyzing the existing air quality sensor network and recommending optimal locations for new sensors. In practice, this will allow the conversion of data into information that can be used in near real time, in a personalized manner, and offer insights for proactive air quality management and policies.” T HOW PLANETWATCH’S DATA CAN IMPROVE AIR QUALITY MONITORING he Transport, Infrastructure and Territory Research Group (tGIS) at the Complutense University of Madrid has increasingly become drawn to the use of big data in the fields of mobility, transportation and urban dynamics. In June 2023, during his work on local-scale spatial analysis of urban air pollution concentrations, group member Richard Hewitt came across PlanetWatch, which uses blockchain, advanced algorithms and sensors to deploy hyperlocal air quality monitoring networks to empower organizations with accurate data to fight air pollution. He was looking at how to use existing air pollution data from regional monitoring stations for air quality models. “The main importance of data provided by PlanetWatch is that it potentially enables the problem of interpolation based on very few points to be solved by providing sensor data at a higher spatial resolution and higher temporal resolution than existing public monitoring stations,” he explains. “Potentially this means that much more accurate forecasting becomes possible.” PlanetWatch sensors record humidity, particle matter at 10µm and 2.5µm or less in diameter, and temperature. “The coverage is complementary to existing stations and the network is denser,” notes Hewitt. “In this sense the PlanetWatch sensors clearly add value to existing public data sets for the monitoring of these variables.” The group is now exploring how PlanetWatch’s data can improve on existing approaches to air quality monitoring. By reviewing data on London, Munich, Madrid and Budapest, it aims to develop better-quality interpolations showing how air pollution varies over time and space, and compare PlanetWatch data with existing outputs from air quality simulation models. “My personal interest relates to the use of air quality data to explore issues of social justice,” says Hewitt. “Lower-income and disadvantaged neighborhoods tend to be in more contaminated areas, but it’s very hard to demonstrate this at neighborhood level. “The PlanetWatch data offers an opportunity to examine this. If statistically significant patterns do emerge, showing that marginalized communities are systematically more affected by air pollution, it becomes easier to persuade city authorities to enact measures to improve the situation. Without this evidence it’s harder to enact policy, and without good-quality data we cannot collect the evidence,” he concludes. Mean household income (2015-18) by census tract 42 • www.meteorologicaltechnologyinternational.com • January 2024