TY - JOUR

T1 - Development of Air Quality Monitoring Systems: Balancing Infrastructure Investment and User Satisfaction Policies

AU - Sokolova, Olga

AU - Yurgenson, Anastasia

AU - Shakhov, Vladimir

N1 - This work was supported by a grant for research centers, provided by the Analytical Center for the Government of the Russian Federation in accordance with the subsidy agreement (agreement identifier 000000D730324P540002) and the agreement with the Novosibirsk State University dated 27 December 2023 No. 70-2023-001318.

PY - 2025/1/31

Y1 - 2025/1/31

N2 - Air quality monitoring is a critical aspect of urban management. While poor air quality negatively impacts public health and well-being, implementing effective monitoring systems often involves significant costs. This paper addresses the optimization of air quality monitoring systems by balancing cost-effectiveness with citizen satisfaction. The core objective is to identify an optimal trade-off between user satisfaction and budgetary constraints. To achieve this, we optimize the number of clusters, where each cluster represents a group of users served by the nearest air quality sensor. Additionally, we present a penalty function that emphasizes prompt air pollution warnings, facilitating preventive actions to reduce exposure to polluted areas while ensuring a cost-effective solution. This approach enables the formulation of well-founded performance requirements for AI-driven algorithms tasked with analyzing air quality data. The findings contribute to the development of efficient, user-centric air quality monitoring systems, highlighting the delicate balance between infrastructure investment, AI algorithm efficiency, and user satisfaction.

AB - Air quality monitoring is a critical aspect of urban management. While poor air quality negatively impacts public health and well-being, implementing effective monitoring systems often involves significant costs. This paper addresses the optimization of air quality monitoring systems by balancing cost-effectiveness with citizen satisfaction. The core objective is to identify an optimal trade-off between user satisfaction and budgetary constraints. To achieve this, we optimize the number of clusters, where each cluster represents a group of users served by the nearest air quality sensor. Additionally, we present a penalty function that emphasizes prompt air pollution warnings, facilitating preventive actions to reduce exposure to polluted areas while ensuring a cost-effective solution. This approach enables the formulation of well-founded performance requirements for AI-driven algorithms tasked with analyzing air quality data. The findings contribute to the development of efficient, user-centric air quality monitoring systems, highlighting the delicate balance between infrastructure investment, AI algorithm efficiency, and user satisfaction.

KW - Lambert W function

KW - air pollution detection

KW - air pollution monitoring

KW - air quality

KW - artificial intelligence

KW - cluster number optimization

KW - environmental monitoring

KW - sensors

KW - smart city

UR - https://www.mendeley.com/catalogue/88e0a181-ae92-3218-aff3-3851f3a4bc48/

UR - https://www.scopus.com/record/display.uri?eid=2-s2.0-85217766351&origin=inward&txGid=498f243e8ceda3639c0b1f4651ac1c95

UR - https://pubmed.ncbi.nlm.nih.gov/39943512/

UR - https://pmc.ncbi.nlm.nih.gov/articles/PMC11819879/

U2 - 10.3390/s25030875

DO - 10.3390/s25030875

M3 - Article

C2 - 39943512

VL - 25

JO - Sensors

JF - Sensors

SN - 1424-3210

IS - 3

M1 - 875

ER -