TY - JOUR
T1 - Satellite-informed smart sensor placement framework for near-optimal PM2.5 monitoring in urban areas
AU - Chang-Silva, Roberto
AU - Tariq, Shahzeb
AU - Kim, Sang Youn
AU - Moosazadeh, Mohammad
AU - Park, Seonyoung
AU - Yoo, Chang Kyoo
N1 - Publisher Copyright:
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.
PY - 2024
Y1 - 2024
N2 - Air pollution is a global public health concern, particularly due to PM2.5, which can cause respiratory and cardiovascular diseases. Accurate placement of monitoring sensors is essential to effectively monitor and mitigate PM2.5 effects. However, the complex nature of air pollution, including factors like traffic density, population density, and weather conditions, poses challenges for sensor placement. Additionally, cost and resource constraints further complicate the process. In this study, we propose a novel algorithm that utilizes a multi-criteria optimization approach to identify optimal locations and distribution of PM2.5 monitoring sensors. The algorithm integrates various geographical covariates, such as roads, population density, terrain elevation, and satellite observations of surface PM2.5. By applying the Non-dominated Sorting Genetic Algorithm II (NSGA-II), we optimize sensor placement. Our algorithm is validated through a case study in a metropolitan area, demonstrating its ability to identify optimal sensor locations while reducing their number and maintaining high accuracy. Furthermore, we highlight the value of satellite observations for initial PM2.5 estimates and aiding sensor placement. Our comprehensive algorithm optimizes air quality monitoring, enabling the identification of pollution hotspots, assessment of health risks, and informing policy and mitigation strategies.
AB - Air pollution is a global public health concern, particularly due to PM2.5, which can cause respiratory and cardiovascular diseases. Accurate placement of monitoring sensors is essential to effectively monitor and mitigate PM2.5 effects. However, the complex nature of air pollution, including factors like traffic density, population density, and weather conditions, poses challenges for sensor placement. Additionally, cost and resource constraints further complicate the process. In this study, we propose a novel algorithm that utilizes a multi-criteria optimization approach to identify optimal locations and distribution of PM2.5 monitoring sensors. The algorithm integrates various geographical covariates, such as roads, population density, terrain elevation, and satellite observations of surface PM2.5. By applying the Non-dominated Sorting Genetic Algorithm II (NSGA-II), we optimize sensor placement. Our algorithm is validated through a case study in a metropolitan area, demonstrating its ability to identify optimal sensor locations while reducing their number and maintaining high accuracy. Furthermore, we highlight the value of satellite observations for initial PM2.5 estimates and aiding sensor placement. Our comprehensive algorithm optimizes air quality monitoring, enabling the identification of pollution hotspots, assessment of health risks, and informing policy and mitigation strategies.
KW - Air quality monitoring
KW - Earth observation
KW - Geographical covariates
KW - Non-dominated Sorting Genetic Algorithm II
KW - PM
KW - Sensor placement
UR - http://www.scopus.com/inward/record.url?scp=85210040854&partnerID=8YFLogxK
U2 - 10.1007/s11356-024-35568-w
DO - 10.1007/s11356-024-35568-w
M3 - Article
AN - SCOPUS:85210040854
SN - 0944-1344
JO - Environmental Science and Pollution Research
JF - Environmental Science and Pollution Research
ER -