Optimising Citizen-Driven Air Quality Monitoring Networks for Cities

Abstract

Air quality has had a significant impact on public health, the environment and eventually on the economy of countries for decades. Effectively mitigating air pollution in urban areas necessitates accurate air quality exposure information. Recent advancements in sensor technology and the increasing popularity of volunteered geographic information (VGI) open up new possibilities for air quality exposure assessment in cities. However, citizens and their sensors are put in areas deemed to be subjectively of interest (e.g., where citizens live, school of their kids or working spaces), and this leads to missed opportunities when it comes to optimal air quality exposure assessment. In addition, while the current literature on VGI has extensively discussed data quality and citizen engagement issues, few works, if any, offer techniques to fine-tune VGI contributions for an optimal air quality exposure assessment. This article presents and tests an approach to minimise land use regression prediction errors on citizen-contributed data. The approach was evaluated using a dataset (N = 116 sensors) from the city of Stuttgart, Germany. The comparison between the existing network design and the combination of locations selected by the optimisation method has shown a drop in spatial mean prediction error by 52%. The ideas presented in this article are useful for the systematic deployment of VGI air quality sensors, and can aid in the creation of higher resolution, more realistic maps for air quality monitoring in cities.

Air Quality Monitoring and Analysis for Sustainable Development of Solid Waste Dump Yards Using Smart Drones and Geospatial Technology

“…For example, ref. [34] created an air quality monitoring system based on citizen science that utilizes GIS mapping and crowdsourcing to gather and analyze air pollution data. Another research undertaking was carried out to study the meteorological parameters using IoT and drones for the polar regions [35].…”

Mindful Application of Digitalization for Sustainable Development: The Digitainability Assessment Framework

“…However, to better understand the high-resolution spatial variability in air pollution in cities, data accuracy profoundly depends on the location where the data is collected. Considering the importance of the topic to public health globally, we use the work conducted by Gupta et al [88] as a test case to understand the potential implication of the intervention for the achievement of SDGs. The method defined in this study supports identifying the "optimal" location in the city of Münster (Germany) to place the optimal number of IoT devices for air quality monitoring in a systematic manner for sustainable use of citizen's effort and IoT devices for air quality monitoring at the city level.…”

IoT and Satellite Sensor Data Integration for Assessment of Environmental Variables: A Case Study on NO2

“…Air pollution, global warming and other pollutants have a great impact on the environment, and have become a major global concern [ 1 ]. NO , which is used as the case study in this paper, is one of the greenhouse gases, and an important indicator of air pollution.…”