Exposure assessment of PM2.5 using smart spatial interpolation on regulatory air quality stations with clustering of densely-deployed microsensors. (1st January 2022)

• Record Type: Journal Article
• Authors: Chen, Pi-Cheng; Lin, Yu-Ting
• Is Part Of: Environmental pollution. Volume 292:Part B(2022)
• Publication Date: 2022-01-01
• Subjects: Spatial clustering -- Exposure estimation model -- Big data -- Inverse distance weighting; Pollution -- Periodicals; Pollution -- Environmental aspects -- Periodicals; Environmental Pollution -- Periodicals; Pollution -- Périodiques; Pollution -- Aspect de l'environnement -- Périodiques; Pollution -- Effets physiologiques -- Périodiques; Pollution; Pollution -- Environmental aspects; Periodicals; Electronic journals; 363.73
• DOI: 10.1016/j.envpol.2021.118401
• Languages: English
• ISSNs: 0269-7491

Abstract

Abstract: Accurate mapping of air pollutants is essential for epidemiological studies and environmental risk assessments. Concentrations measured by air quality monitoring stations (AQMS) have primarily been used to assess the exposure of PM2.5 . However, the low coverage and amount of monitoring stations affect the errors of spatial interpolation or geostatistical estimates. In contrast to other integrated approaches developed for improved air pollution estimates, this study utilizes data from low-cost microsensors densely deployed in Taiwan to improve the popular spatial interpolation approach called inverse distance weighting (IDW). A large dataset from thousands of low-cost sensors could improve spatial interpolation by describing the distribution of PM2.5 in detail. Therefore, this study presents a clustering-based method to assess the distribution of PM2.5 . Then, a smarter IDW is performed based on correlated observations from the selected air quality stations. The publicly available data chosen for this investigation pertained to Taiwan, which has deployed 74 monitoring stations and more than 11, 000 low-cost sensors since December 2020. The results of leave-one-out cross-validation indicate that there are fewer PM2.5 estimation errors in the developed approach than in estimations that use kriging across almost all of the months and sampled dates of 2019 and 2020, particularly those with higher PM2.5 spatial heterogeneities. Spatial heterogeneities could result in more significant estimation errors in mainstream approaches. The root mean square error of the monthly average estimate for PM2.5 ranged from 1.17 to 3.86 μg/m 3 . We also found that the clustering of one month characterizing the pattern of PM2.5 distribution could perform well in spatial interpolations based on historical data from monitoring stations. According to the information on the openaq platform, low-cost sensors are in demand in cities and areas. This trend might pave the way for the application of the proposed approach in other areas for superior exposure assessments. Graphical abstract: Image 1 Highlights: Spatial clustering of thousands of microsensors improve PM2.5 spatial interpolation. Our approach is more accurate than kriging and IDW for almost all the tested months. Improvement is more significant in the months of higher PM2.5 spatial heterogeneity. In the study area, 5.6 microsensors/100 km 2 is enough for accurate PM2.5 estimation. A clustering capturing the PM2.5 spatial patterns applies to other months' mapping.