Abstract
An accurate forecast of fine particulate matter (PM2.5) concentration in the forthcoming days is crucial since it can be used as an early warning for the prevention of general public from hazardous PM2.5 pollution events. Though the European Copernicus Atmosphere Monitoring Service (CAMS) provides global PM2.5 forecasts up to the next 120 h at a 3 h time interval, the data accuracy of this product had not been well evaluated. By using hourly PM2.5 concentration data that were sampled in China and United States (US) between 2017 and 2018, the data accuracy and bias levels of CAMS PM2.5 concentration forecast over these two countries were examined. Ground-based validation results indicate a relatively low accuracy of raw PM2.5 forecasts given the presence of large and spatially varied modeling biases, especially in northwest China and the western United States. Specifically, the PM2.5 forecasts in China showed a mean correlation value ranging 0.31–0.45 (0.24–0.42 in US) and RMSE of 38–83 (8.30–16.76 in US) μg/m3, as the forecasting time horizons increased from 3 h to 120 h. Additionally, the data accuracy was found to not only decrease with the increase of forecasting time horizons but also exhibit an evident diurnal cycle. This implies the current CAMS forecasting model failed to resolve the local processes that modulate the diurnal variability of PM2.5. Moreover, the data accuracy varied between seasons, as accurate PM2.5 forecasts were more likely to be derived in the autumn in China, whereas these were more likely in spring in the US. To improve the data accuracy of the raw PM2.5 forecasts, a statistical bias correction model was then established using the random forest method to account for large modeling biases. The cross-validation results clearly demonstrated the effectiveness and benefits of the proposed bias correction model, as the diurnal varied and temporally increasing modeling biases were substantially reduced after the calibration. Overall, the calibrated CAMS PM2.5 forecasts could be used as a promising data source to prevent general public from severe PM2.5 pollution events given the improved data accuracy.
Highlights
With the fast pace of urbanization and economic growth, the deteriorated air quality, the increasing concentrations of fine particulate matter like PM2.5, has raised great concern given the negative impacts on public health, environment, and even climate [1,2]
These spatially varied large modeling biases indicate that the current Copernicus Atmosphere Monitoring Service (CAMS) forecasting model failed to accurately resemble PM2.5 concentration levels across China, and this could be attributable to the lack of accurate emission inventories and limited access to observational data when simulating aerosols over China
The data accuracy of CAMS PM2.5 forecasts was evaluated using two-year hourly in situ PM2.5 concentration measurements that were sampled from the national air quality monitoring network in China and contiguous US (CONUS)
Summary
With the fast pace of urbanization and economic growth, the deteriorated air quality, the increasing concentrations of fine particulate matter like PM2.5 (particles with an aerodynamic diameter no more than 2.5 μm), has raised great concern given the negative impacts on public health, environment, and even climate [1,2]. As fine particles suspend in the atmosphere, PM2.5 can influence the environment and climate by reducing atmospheric visibility and changing the earth energy balance through the aerosol-cloud effect [5,8,9,10,11,12]. In such a context, an accurate PM2.5 concentration dataset is of critical importance for the investigation of mechanisms behind severe haze events and the assessment of PM2.5 related negative impacts [13,14,15]. The data accuracy of these numerical simulations is relatively low due to possible modeling defects and the lack of accurate emission inventories [31,32,33]
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