UNIVERSITY PARK, Pa. — In smart cities of the future, sensors distributed throughout buildings and bridges could monitor infrastructure health. Cloud-based computing could decrease traffic with real-time analysis available to commuters. Windows could tint themselves darker on sunny days or lighten to brighten a room on cloudy ones. 

None of these innovations, however, can materialize without managing the enormous amounts of data generated by robust sensing networks, according to an interdisciplinary team of Penn State researchers. They published a perspective article on March 19 in Science, highlighting smart materials that can sense environmental changes and respond accordingly — without externally transferring data — as one avenue to avoid this data overload.

Science also released a podcast featuring the work on March 18, outlining the benefits of implementing smart materials in tomorrow’s cities, ranging from self-healing concrete structures to building materials that can solve complex equations. 

“Since this problem sits at the intersection of materials science, structural health monitoring and computation, collaboration was important from the get-go,” said Rebecca Napolitano, assistant professor of architectural engineering, who co-wrote the article. 

Napolitano collaborated with Wesley Reinhart, assistant professor of materials science and engineering in the College of Earth and Mineral Sciences, and Juan Pablo Gevaudan, affiliate professor of architectural engineering and Marie Sklodowska-Curie Research Fellow at the University of Leeds. 

Napolitano’s interest in smart cities began during her graduate studies, when she wondered how historic buildings would be accommodated in cities of the future. Her current research, focused on supporting merging infrastructures via computational methods, led her to collaborate with Gevaudan. He investigates how modern concrete materials degrade to better engineer their response and ability to adapt to certain conditions in new and existing buildings. The two researchers lead the adaptive architecture research area in the Convergence Center for Living Multifunctional Material Systems at Penn State.

Reinhart and Napolitano previously collaborated to investigate building damage at the micro- and macroscales through computation. Reinhart works on metamaterials, which are materials engineered to gain unique properties from their structure, and their potential applications in infrastructure. For example, such a metamaterial could sense the path of the sun and perform a calculation to adjust a solar panel accordingly and optimize the energy stored. 

The researchers plan to continue exploring avenues for developing these materials and robust computational methods to optimize them. With further research, according to the researchers, the implementation of smart materials could increase the lifetime of buildings and civil structures, reduce energy consumption and reduce waste from production and use of electronic sensors — all on a citywide scale.

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Lower-income urban communities bear the brunt of environmental burdens, even in wealthy green cities around the world.

The trouble with comparing cities, researchers have found, is you end up comparing apples and oranges—coasts and interiors, seasonal freezes and yearlong tropical humidity, strictly planned communities and suburban sprawl. It’s even more problematic than that because a city is not defined by a single uniform identity. Each city comprises a unique blend of neighborhoods where social and environmental conditions can change from street to street.

Given this complexity, how can we possibly assess global progress toward Sustainable Development Goal 11 (SDG 11), making cities inclusive, safe, resilient and sustainable? Can we measure these concepts universally?

Now an international research team has developed an approach using publicly available big data. The tool—the Urban Environment and Social Inclusion Index (UESI)—can assess environmental conditions at the scale of individual neighborhoods.

Applying UESI to 164 cities spread across all continents (excluding Antarctica), the researchers found that most cities leave lower-income communities with higher shares of environmental burdens and lower shares of environmental benefits. Interestingly, stark inequalities are also seen in many cities with high overall environmental performance—wealthy cities that regularly receive plaudits for their green credentials.

“Copenhagen, Paris, and London, even if they’re doing really well overall on environmental indicators—so their air pollution levels are low, they have green space—they’re not providing amenities and environment benefits equally amongst all their citizens,” said Angel Hsu of the University of North Carolina at Chapel Hill, who led the research.

Targets Are Cheap, Data Are Expensive

By 2050, two thirds of the global population—6.5 billion people—will reside in urban areas. With that trend comes a number of challenges with consequences for the planet and urban dwellers.

SDG 11 was developed to address our increasingly urban population and has 10 specific targets for 2030. Many of these have an environmental component. Targets include all city residents having access to affordable public transport (11.2), adequate waste management systems (11.6), and green spaces (11.7). There is also a crosscutting focus on protecting vulnerable communities exposed to urban hazards such as air pollution.

Few would disagree with these inclusive targets, but it is a challenge to quantify progress in the real world. “The word ‘inclusive’ in itself is very nebulous; what do we really mean by inclusivity? And then you also have to think about how we actually go about measuring it,” said Hsu.

Cost is another challenge. It is expensive to collect granular data at local levels, especially for cities in developing nations. As a result, data reported are inconsistent, and it can be precarious to start comparing urban areas in different parts of the world.

“Rankings can lead to perverse incentives to hide data to avoid negative publicity,” said Jacqueline Klopp, codirector of the Center for Sustainable Urban Development at Columbia University, who was not involved in the UESI project. Klopp recalled how Dakar, Senegal, was labeled as having some of the worst air pollution in Africa, purely because local officials had made the progressive move of making air pollution data public.

Big Data Solution

To tackle this information deficit, Hsu’s team at the Data-Driven Lab turned to the skies. The group’s UESI tool tracks surface conditions using satellite data, mainly from the Landsat program and the MODIS (Moderate Resolution Imaging Spectroradiometer) instrument aboard NASA’s Terra satellite. Crowdsourced transport data are collected from Open Street Map. By integrating these data with demographic information, UESI can evaluate global neighborhoods in a consistent fashion.

In the study, published in Frontiers in Sustainable Cities, participating cities are placed within a four-quadrant plot. This plot indicates each city’s overall environmental performance as well as its environmental inclusivity.

Only a handful of cities scored highly on both environmental performance and environmental inclusivity, including Stockholm, Sweden; Darwin, Australia; Quito, Equator; and Freetown, Sierra Leone. No cities with a population greater than 10 million people made it into this category.

More than half of the 164 cities fell into the category of good overall performance but with lower-income neighborhoods bearing a disproportionate share of environmental burdens. This group includes Seattle, Copenhagen, and Melbourne—just three among several that regularly appear on “livable cities” lists.

Reasons for these inequalities are linked with local contexts. In inner-city communities, air pollution is often worse, and a lack of tree cover and green space can result in dangerously high summer temperatures through the urban heat island effect. There are exceptions to this pattern in emerging economies like China and India, however, where inner-city neighborhoods are occupied by wealthier communities.

Full results can be seen on the Data-Driven Lab website, and Hsu said that other locations can be added if city representatives provide basic demographic data.

“The UESI has some important advantages in helping to meet the challenges of measuring progress towards urban elements of the SDGs,” said David Simon, a development geography researcher at Royal Holloway, University of London, who was not involved in the new research. Simon stresses, however, that ground truthing will remain essential, given that many phenomena are invisible to remotely sensed imagery.

To develop the research, Hsu’s team plans to add higher-resolution pollution data and to develop more nuanced metrics such as recognizing informal transport networks in developing countries.

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