AI Article Synopsis

  • Modeling is an efficient way to estimate ultrafine particle (UFP) levels using advanced techniques rather than traditional land-use regression.
  • Researchers conducted in-situ measurements in central Taiwan and created a model that taps into satellite data, weather conditions, and land-use types for daily UFP estimations at a 1-km scale.
  • The study utilized machine learning models, particularly XGBoost, which demonstrated superior performance, revealing that traffic emissions significantly influence UFP levels, especially near main roads across different seasons.

Article Abstract

Modeling is a cost-effective measure to estimate ultrafine particle (UFP) levels. Previous UFP estimates generally relied on land-use regression with insufficient temporal resolution. We carried out in-situ measurements for UFP in central Taiwan and developed a model incorporating satellite-based measurements, meteorological variables, and land-use data to estimate daily UFP levels at a 1-km resolution. Two sampling campaigns were conducted for measuring hourly UFP concentrations at six sites between 2008-2010 and 2017-2021, respectively, using scanning mobility particle sizers. Three machine learning algorithms, namely random forest, eXtreme gradient boosting (XGBoost), and deep neural network, were used to develop UFP estimation models. The performances were evaluated with a 10-fold cross-validation, temporal, and spatial validation. A total of 1,022 effective sampling days were conducted. The XGBoost model had the best performance with a training coefficient of determination (R) of 0.99 [normalized root mean square error (nRMSE): 6.52%] and a cross-validation R of 0.78 (nRMSE: 31.0%). The ten most important variables were surface pressure, distance to the nearest road, temperature, calendar year, day of the year, NO, meridional wind, the total length of roads, PM, and zonal wind. The UFP levels were elevated along the main roads across different seasons, suggesting that traffic emission is an important contributor to UFP. This hybrid model outperformed prior land use regression models and thus can provide more accurate estimates of UFP for epidemiological studies.

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http://dx.doi.org/10.1016/j.envint.2023.107937DOI Listing

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