气候变化领域集成服务门户(CCPortal): Responsive high-resolution air quality index mapping using model, regulatory monitor, and sensor data in real-time

CCPortal

DOI	10.1088/1748-9326/abb62b
	Responsive high-resolution air quality index mapping using model, regulatory monitor, and sensor data in real-time
	Schulte N.; Li X.; Ghosh J.K.; Fine P.M.; Epstein S.A.
发表日期	2020
ISSN	17489318
卷号	15 期号:10
英文摘要	Interpolated regulatory monitor measurements that are used to calculate the Air Quality Index (AQI) have low spatial resolution and are less accurate where regulatory monitors are sparsely distributed, especially during episodic events such as wildfires and dust storms. In this paper, an AQI map that is more responsive and accurate than current formulations with 5 km spatial and hourly temporal resolution is constructed for Southern California using interpolated concentration fields. The fine particle mass (PM2.5) fields are calculated by combining regulatory-grade monitored concentrations, low-cost sensor measurements, and Community Multiscale Air Quality Model forecasts, using Residual Kriging (RK) interpolation to account for the uncertainty of each input component. The Ozone fields are calculated by combining regulatory monitor and model forecasted concentrations using RK. The interpolated concentration fields have root mean square interpolation errors (RMSE) of 5.59 µg m-3 (PM2.5) and 6.66 ppb (Ozone), smaller than corresponding RMSE of current inverse distance weighting methods used by the U.S. Environmental Protection Agency AirNOW system, and a previous interpolation method used by the South Coast Air Quality Management District. The inclusion of low-cost sensor measurements makes the AQI more responsive to wildfire events, with a PM2.5 RMSE of 7.73 µg m-3, a significant improvement from the other interpolation methods. The mass of particles with an aerodynamic diameter smaller than 10 micrometers (PM10) calculated using natural neighbor interpolation of the difference, PM10 - PM2.5, and carbon monoxide and nitrogen dioxide fields calculated using natural neighbor interpolation, have small but usually not statistically significant reductions of RMSE relative to the other interpolation methods. Quality control and correction procedures applied to the low-cost sensor measurements are described. Display of the blended AQI data on an interactive map in real time provides an important tool for the public to minimize their exposure to poor air quality in one of the most polluted regions in the United States. © 2020 The Author(s). Published by IOP Publishing Ltd.
英文关键词	air quality communication; air quality index; AQI; Kriging; low-cost air quality sensor; purpleair; wildfire
语种	英语
scopus关键词	Air quality; Carbon monoxide; Costs; Environmental Protection Agency; Fires; Inverse problems; Nitrogen oxides; Ozone; Quality assurance; Storms; Uncertainty analysis; Aerodynamic diameters; Community multi-scale air quality models; Concentration fields; Correction procedure; Inverse distance weighting method; Natural neighbor interpolation; South Coast Air Quality Management District; U.S. Environmental Protection Agency; Interpolation; air quality; anthropogenic source; atmospheric pollution; concentration (composition); index method; mapping; real time; regulatory framework; sensor
来源期刊	Environmental Research Letters
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/153553
作者单位	South Coast Air Quality Management District, Diamond Bar, CA, United States
推荐引用方式 GB/T 7714	Schulte N.,Li X.,Ghosh J.K.,et al. Responsive high-resolution air quality index mapping using model, regulatory monitor, and sensor data in real-time[J],2020,15(10).
APA	Schulte N.,Li X.,Ghosh J.K.,Fine P.M.,&Epstein S.A..(2020).Responsive high-resolution air quality index mapping using model, regulatory monitor, and sensor data in real-time.Environmental Research Letters,15(10).
MLA	Schulte N.,et al."Responsive high-resolution air quality index mapping using model, regulatory monitor, and sensor data in real-time".Environmental Research Letters 15.10(2020).