High-Resolution Urban Air Quality Mapping for Multiple Pollutants Based on Dense Monitoring Data and Machine Learning

Abstract

Spatially explicit urban air quality information is important for urban fine-management and public life. However, existing air quality measurement methods still have some limitations on spatial coverage and system stability. A micro station is an emerging monitoring system with multiple sensors, which can be deployed to provide dense air quality monitoring data. Here, we proposed a method for urban air quality mapping at high-resolution for multiple pollutants. By using the dense air quality monitoring data from 448 micro stations in Lanzhou city, we developed a decision tree model to infer the distribution of citywide air quality at a 500 m × 500 m × 1 h resolution, with a coefficient of determination (R2) value of 0.740 for PM2.5, 0.754 for CO and 0.716 for SO2. Meanwhile, we also show that the deployment density of the monitoring stations can have a significant impact on the air quality inference results. Our method is able to show both short-term and long-term distribution of multiple important pollutants in the city, which demonstrates the potential and feasibility of dense monitoring data combined with advanced data science methods to support urban atmospheric environment fine-management, policy making, and public health studies.

Indoor Air Sources of Outdoor Air Pollution: Health Consequences, Policy, and Recommendations: An Official American Thoracic Society Workshop Report

“…PM 2.5 from commercial cooking and food processing has been estimated to result in 2,730 to 5,300 deaths per year ( 45 ). High-resolution air pollution networks can likely play a role in identifying super-emitters and may also offer a potential way to quantify the impact of cooking in commercial cooking in buildings such as restaurants ( 50 ).…”

How opportunistic mobile monitoring can enhance air quality assessment?

“…Guo et al proposed a high-resolution air quality mapping approach for multiple pollutants in [9]. The method uses a dense monitoring network and combines dense networks and machine learning techniques.…”

“…Researchers have utilized fixed stations and mobile sensor data to estimate pollution maps. Some studies have relied exclusively on fixed stations [4,10,27], while others have applied air pollution estimation methods used in fixed stations to low-cost mobile sensor data [9,20]. However, recent research proposes combining data from fixed and mobile sensors [11,26].…”

Synergistic control of air pollution and heat waves in the urban built environment: Challenges and opportunities

“…24,25 For instance, recent advancements have enabled the use of smart phone signalling to obtain dynamic population distribution data, 26 while high-density urban monitoring networks can predict the spatial distribution of atmospheric pollutants and temperature. 27 Therefore, there is a need to improve the precision and extent of data involved in pollution exposure assessments to better understand their multi-faceted impact on human health and the environment.…”