Month: October 2019

Bi et al.: Contribution of low-cost sensor measurements to the prediction of PM2.5 levels

Contribution of low-cost sensor measurements to the prediction of PM2.5 levels: A case study in Imperial County, California, USA

Regulatory monitoring networks are often too sparse to support community-scale PM2.5 exposure assessment, while emerging low-cost sensors have the potential to fill in the gaps. In this study, the contribution of low-cost sensor measurements to high-resolution PM2.5 prediction was evaluated in an exemplary region – Imperial County in California – with a community-operated low-cost network entitled Identifying Violations Affecting Neighborhoods (IVAN). This study shows that the integration of low-cost sensor measurements is an effective way to significantly improve the quality of PM2.5 modeling, while the remaining uncertainty in calibrated sensor measurements still caused apparent outliers in the prediction results. This study highlights the need for more effective calibration or integration methods to relieve the negative impact of remaining sensor uncertainty.

Bi, J., Stowell, J., Seto, E.Y., English, P.B., Al-Hamdan, M.Z., Kinney, P.L., Freedman, F.R. & Liu, Y. (2020). Contribution of low-cost sensor measurements to the prediction of PM2.5 levels: A case study in Imperial County, California, USA. Environmental Research, p.108810. 10.1016/j.envres.2019.108810

 

She et al.: Hourly PM2.5 levels during heavy winter episodes in the Yangtze River Delta

In this publication, we quantitatively investigated the feasibility of using the aerosol optical depth (AOD) data retrieved by the Geostationary Ocean Color Imager (GOCI) to estimate hourly PM2.5 concentrations during winter haze episodes in the Yangtze River Delta (YRD). We developed a three-stage spatial statistical model, using GOCI AOD and fine mode fraction, as well as corresponding monitoring PM2.5 concentrations, meteorological and land use data on a 6-km modeling grid with complete coverage in time and space. The 10-fold cross-validation R2 was 0.72 with a regression slope of 1.01 between observed and predicted hourly PM2.5 concentrations. We further analyzed two representative large regional episodes, i.e., a “multi-process diffusion episode” during December 21–26, 2015 and a “Chinese New Year episode” during February 7–8, 2016. We concluded that AOD retrieved by geostationary satellites could serve as a new valuable data source for analyzing the heavy air pollution episodes.

 

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