Using Kriging incorporated with wind direction to investigate ground-level PM2.5 concentration

Conventional interpolation methods, such as spatial averaging, nearest neighbor, inverse distance weigh and ordinary Kriging (OK); for estimating the spatial distribution of ground-level particulate mailer (PM) data, do not account for the wind direction for estimating the spatial dislribution of PM2.5. In this work, an interpolation algorithm, Win-OK accounting for the wind direction, is developed. In contrast to ordinary Kriging where all locations (irrespective of the wind direction) in the vicinity of a site is considered, the new algorithm (Win-OK)predicts the value at a certain location based on the measured values at locations upwind as determined by the wind direction. This new methodology, Win-OK is validated by applying it to analyze the hourly spatial distribution of ground-level PM2.5 concentrations during Chinese New Year and Chinese National Day in 2017 in Xinxiang city, China. The performance of OK and Win-OK are compared by using them to build PM2.5 concentration heat-maps. A leave-one-out cross validation methodology is used to calculate the root-mean-square error (RMSE) and standard deviation for evaluating both algorithms. The results show that OK sometimes gives an extremely high RMSE value using a Gaussian semi-variance model, and the standard deviation significantly deviates from the measured values. Win-OK was found to more accurately predict the PM2.5 spatial distribution in a specific sector. The performance of Win-OK is more stable than OK as established by comparing the calculated RMSE and standard deviation from predictions of both algorithms. Win-OK with a spherical semi-variance model is the most accurate method investigated here for deriving the spatial distribution of ground-level PM2.5. The new algorithm developed here could improve the prediction accuracy of PM2.5 spatial distribution by considering the effect of wind direction. (C) 2020 Elsevier BM. All rights reserved.

Automated summary

A new interpolation algorithm, Win-OK, was developed to predict the spatial distribution of ground-level PM2.5 by accounting for wind direction. The performance of Win-OK was found to be more stable and accurate compared to the traditional method OK when analyzing PM2.5 concentration heat-maps.