Structuring an integrated air quality monitoring network in large urban areas – Discussing the purpose, criteria and deployment strategy

Abstract

Air pollution in large urban areas has become a serious issue due to its negative impacts on human health, building materials, biodiversity and urban ecosystems in both developed and less-wealthy nations. In most large urban areas, especially in developed countries air quality monitoring networks (AQMN) have been established that provide air quality (AQ) data for various purposes, e.g., to monitor regulatory compliance and to assess the effectiveness of control strategies. However, the criteria of structuring the network are currently defined by single questions rather than attempting to create a network to serve multiple functions. Here we propose a methodology supported by numerical, conceptual and GIS frameworks for structuring AQMN using social, environmental and economic indicators as a case study in Sheffield, UK. The main factors used for air quality monitoring station (AQMS) selection are population-weighted pollution concentration (PWPC) and weighted spatial variability (WSV) incorporating population density (social indicator), pollution levels and spatial variability of air pollutant concentrations (environmental indicator). Total number of sensors is decided on the basis of budget (economic indicator), whereas the number of sensors deployed in each output area is proportional to WSV. The purpose of AQ monitoring and its role in determining the location of AQMS is analysed. Furthermore, the existing AQMN is analysed and an alternative proposed following a formal procedure. In contrast to traditional networks, which are structured based on a single AQ monitoring approach, the proposed AQMN has several layers of sensors: Reference sensors recommended by EU and DEFRA, low-cost sensors (LCS) (AQMesh and Envirowatch E-MOTEs) and IoT (Internet of Things) sensors. The core aim is to structure an integrated AQMN in urban areas, which will lead to the collection of AQ data with high spatiotemporal resolution. The use of LCS in the proposed network provides a cheaper option for setting up a purpose-designed network for greater spatial coverage, especially in low- and middle-income countries.