TY - JOUR
T1 - Hyperlocal environmental data with a mobile platform in urban environments
AU - Wang, An
AU - Mora, Simone
AU - Machida, Yuki
AU - deSouza, Priyanka
AU - Paul, Sanjana
AU - Oyinlola, Oluwatobi
AU - Duarte, Fábio
AU - Ratti, Carlo
N1 - Funding Information:
The study was funded by the MIT Senseable City Lab Consortium (LandWey, Consiglio per la Ricerca in Agricoltura e l’Analisi dell’Economia Agraria, Volkswagen Group America, FAE Technology, Samoo Architects & Engineers, Shell, GoAigua, ENEL Foundation, University of Tokyo, Weizmann Institute of Science, Universidad Autónoma de Occidente, Instituto Politecnico Nacional, Imperial College London, Università di Pisa, KTH Royal Institute of Technology, AMS Institute, Helsingborg, Laval, Stockholm, Amsterdam). We thank Sarah Johnson at New York City Department of Health, Prof. Issam Lakkis and Prof. Nareg Karaoghlanian at American University Beirut for providing complementary data sources, facilitating data collection campaigns, and their local knowledge inputs, without which this work cannot be finished.
Publisher Copyright:
© 2023, Springer Nature Limited.
PY - 2023/12
Y1 - 2023/12
N2 - Environmental data with a high spatio-temporal resolution is vital in informing actions toward tackling urban sustainability challenges. Yet, access to hyperlocal environmental data sources is limited due to the lack of monitoring infrastructure, consistent data quality, and data availability to the public. This paper reports environmental data (PM, NO2, temperature, and relative humidity) collected from 2020 to 2022 and calibrated in four deployments in three global cities. Each data collection campaign targeted a specific urban environmental problem related to air quality, such as tree diversity, community exposure disparities, and excess fossil fuel usage. Firstly, we introduce the mobile platform design and its deployment in Boston (US), NYC (US), and Beirut (Lebanon). Secondly, we present the data cleaning and validation process, for the air quality data. Lastly, we explain the data format and how hyperlocal environmental datasets can be used standalone and with other data to assist evidence-based decision-making. Our mobile environmental sensing datasets include cities of varying scales, aiming to address data scarcity in developing regions and support evidence-based environmental policymaking.
AB - Environmental data with a high spatio-temporal resolution is vital in informing actions toward tackling urban sustainability challenges. Yet, access to hyperlocal environmental data sources is limited due to the lack of monitoring infrastructure, consistent data quality, and data availability to the public. This paper reports environmental data (PM, NO2, temperature, and relative humidity) collected from 2020 to 2022 and calibrated in four deployments in three global cities. Each data collection campaign targeted a specific urban environmental problem related to air quality, such as tree diversity, community exposure disparities, and excess fossil fuel usage. Firstly, we introduce the mobile platform design and its deployment in Boston (US), NYC (US), and Beirut (Lebanon). Secondly, we present the data cleaning and validation process, for the air quality data. Lastly, we explain the data format and how hyperlocal environmental datasets can be used standalone and with other data to assist evidence-based decision-making. Our mobile environmental sensing datasets include cities of varying scales, aiming to address data scarcity in developing regions and support evidence-based environmental policymaking.
UR - http://www.scopus.com/inward/record.url?scp=85166598155&partnerID=8YFLogxK
U2 - 10.1038/s41597-023-02425-3
DO - 10.1038/s41597-023-02425-3
M3 - Journal article
C2 - 37543703
AN - SCOPUS:85166598155
SN - 2052-4463
VL - 10
JO - Scientific data
JF - Scientific data
IS - 1
M1 - 524
ER -