Optimizing NO2 monitoring network using a background map for spatial heterogeneity stratification

Abstract

Nitrogen dioxide (NO2) is a critical air pollutant affecting health and the environment. However, existing monitoring station networks often fail to adequately capture the regional distribution of NO2, indicating a need for enhanced sampling strategies. This study focuses on Southwest Fujian in China and introduces a high-precision NO2 background map to delineate spatial stratified heterogeneity. Subsequently, the Mean of Surface with Non-homogeneity (MSN) method was employed to optimize the design of the NO2 monitoring network, proposing the addition of 125 new stations. The Bayesian Kriging analysis, utilized to evaluate the optimized network, resulted in a coefficient of determination (R2) of 0.87, a mean absolute percentage error (MAPE) of 9.61%, a mean absolute error (MAE) of 2.75 μg/m3 and a root mean square error (RMSE) of 2.47 μg/m3. The improved accuracy and efficiency of the NO2 monitoring network were validated against the background map. This research underscores the effectiveness of integrating precise mapping techniques with strategic network optimization for superior environmental monitoring outcomes.