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AI to Shape Air Quality Management

Image credits: psu.edu
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This summer, Northeastern Americans faced heightened air quality concerns due to thick wildfire smoke containing health-risky fine particulate matter (PM 2.5). A Penn State-led team used AI and mobility data to develop improved PM 2.5 exposure models for public health strategies.

In light of this, the researchers analysed PM 2.5 measurements in eight US metropolitan areas, using data from EPA monitoring stations and local community-distributed low-cost sensors to calculate hourly PM 2.5 averages. They then fed this data into a land use regression model that considered geographical factors like aerosol optical depth, proximity to roads or waterways, elevation, vegetation, and meteorological conditions.

These factors are examined to understand their impact on air quality. It’s noteworthy that prior models took a linear approach to assess air pollution, assigning a fixed level of importance to each geographical factor and its influence on air quality. However, as Yu clarified, certain factors like vegetation and meteorological conditions exhibit hourly or seasonal variations. They may interact with other elements that impact air quality, rendering a linear approach inadequate.

Yu and her research team employed an innovative nonlinear strategy to tackle the intricate and ever-changing factors that influence air pollution exposure. They integrated automated machine learning, a type of artificial intelligence capable of independently managing labour-intensive tasks like data preparation, parameter selection, and model deployment, into their land use regression model.

This approach leveraged an ensemble method, enabling the machine to run and consolidate multiple models to determine the most effective one for each region. Additionally, they analysed anonymised cell phone mobility data to identify areas with poor air quality and high visitor volumes.

The process began with collecting diverse datasets, including information from low-cost sensors, EPA monitoring stations, and anonymised cell phone mobility data. These datasets provided essential insights into air quality, weather conditions, and human movement patterns.

AutoML then took charge of model selection, identifying the most suitable algorithms for predicting air pollution levels. Feature selection followed, with the system pinpointing critical variables like aerosol optical depth and meteorological factors. Machine learning models were trained on the data, learning the intricate relationships between input features and air pollution levels.

Moreover, integrating mobility data into the models allowed for identifying high PM 2.5 exposure areas and creating an alert system to notify individuals about unhealthy air quality, empowering them to take necessary precautions. This comprehensive approach demonstrates how machine learning can substantially enhance the accuracy and practicality of air quality models, offering valuable insights for public health strategies.

Yu elaborated, “Numerous regions may consistently grapple with elevated air pollution levels, particularly those near factories and major transportation hubs. Yet, this information alone does not suffice to prioritise locations requiring additional monitoring or health alerts.”

Their exposure maps, grounded in mobility data, provide a more comprehensive perspective by spotlighting areas characterised by both subpar air quality and significant visitor traffic. This dataset can then be harnessed to transmit alerts to individuals’ mobile devices as they enter regions with exceptionally high PM 2.5 levels, allowing them to curtail their exposure to unhealthy air quality and ultimately bolstering public health efforts.

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Qlik’s vision is a data-literate world, where everyone can use data and analytics to improve decision-making and solve their most challenging problems. A private company, Qlik offers real-time data integration and analytics solutions, powered by Qlik Cloud, to close the gaps between data, insights and action. By transforming data into Active Intelligence, businesses can drive better decisions, improve revenue and profitability, and optimize customer relationships. Qlik serves more than 38,000 active customers in over 100 countries.

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CTC Global Singapore, a premier end-to-end IT solutions provider, is a fully owned subsidiary of ITOCHU Techno-Solutions Corporation (CTC) and ITOCHU Corporation.

Since 1972, CTC has established itself as one of the country’s top IT solutions providers. With 50 years of experience, headed by an experienced management team and staffed by over 200 qualified IT professionals, we support organizations with integrated IT solutions expertise in Autonomous IT, Cyber Security, Digital Transformation, Enterprise Cloud Infrastructure, Workplace Modernization and Professional Services.

Well-known for our strengths in system integration and consultation, CTC Global proves to be the preferred IT outsourcing destination for organizations all over Singapore today.

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Planview has one mission: to build the future of connected work. Our solutions enable organizations to connect the business from ideas to impact, empowering companies to accelerate the achievement of what matters most. Planview’s full spectrum of Portfolio Management and Work Management solutions creates an organizational focus on the strategic outcomes that matter and empowers teams to deliver their best work, no matter how they work. The comprehensive Planview platform and enterprise success model enables customers to deliver innovative, competitive products, services, and customer experiences. Headquartered in Austin, Texas, with locations around the world, Planview has more than 1,300 employees supporting 4,500 customers and 2.6 million users worldwide. For more information, visit www.planview.com.

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SIRIM is a premier industrial research and technology organisation in Malaysia, wholly-owned by the Minister​ of Finance Incorporated. With over forty years of experience and expertise, SIRIM is mandated as the machinery for research and technology development, and the national champion of quality. SIRIM has always played a major role in the development of the country’s private sector. By tapping into our expertise and knowledge base, we focus on developing new technologies and improvements in the manufacturing, technology and services sectors. We nurture Small Medium Enterprises (SME) growth with solutions for technology penetration and upgrading, making it an ideal technology partner for SMEs.

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HashiCorp provides infrastructure automation software for multi-cloud environments, enabling enterprises to unlock a common cloud operating model to provision, secure, connect, and run any application on any infrastructure. HashiCorp tools allow organizations to deliver applications faster by helping enterprises transition from manual processes and ITIL practices to self-service automation and DevOps practices. 

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IBM is a leading global hybrid cloud and AI, and business services provider. We help clients in more than 175 countries capitalize on insights from their data, streamline business processes, reduce costs and gain the competitive edge in their industries. Nearly 3,000 government and corporate entities in critical infrastructure areas such as financial services, telecommunications and healthcare rely on IBM’s hybrid cloud platform and Red Hat OpenShift to affect their digital transformations quickly, efficiently and securely. IBM’s breakthrough innovations in AI, quantum computing, industry-specific cloud solutions and business services deliver open and flexible options to our clients. All of this is backed by IBM’s legendary commitment to trust, transparency, responsibility, inclusivity and service.