Contrast Between Automated and Manual Measurements of Atmospheric PM2.5: Influences of Environmental Factors and the Improving Correction Method

In this work, we tested the performance of automated atmospheric PM2.5 monitoring instruments and contrasted the data from automated measurements with those from filter-based reference measurements. The tested instruments include four brands of beta attenuation instruments (two were made in China, D1 and D2; the other two were imported from other countries, I1 and I2) and one brand of a light scattering instrument (also imported from another country, I3). The automated monitoring data were corrected based on the reference tests. The total testing period lasted 18 months. The objective of this work is to evaluate the influences of environmental factors on the performance of different automated instruments, and to improve the accuracy of the automated instruments by using a correction method. The results showed that contrasted with the reference tests, the absolute errors (MAE, mean absolute error; SD, standard deviation; and RMSE, root mean square error) of the automated monitoring instruments werehigher for temperature (T ≤ 10 °C), humidity (60% ≤ RH < 80%), and PM2.5 concentrations (PM2.5 ≥ 75 μg/m3). Meanwhile, the relative errors (CV, coefficient of variation; and NRMSE, normalized root mean square error) of the automated monitoring instruments were higher for humidity (RH > 80%) and PM2.5 concentrations (PM2.5 < 15 μg/m3). For winter data, it proved challenging to pass the reference test, which was based on a linear regression between 24-h average automated monitoring data and the integrated filter-based PM2.5 data (aka the KBR test). Before corrections, the pass rates of D1, D2, I1, I2, and I3 in the rolling KBR tests are 57.7%, 51.3%, 41.1%, 21%, and 90.2%, respectively. After corrections, the rates increase to 79.6%, 86.6%, 81.8%, 58.9%, and 91.8%, respectively. The coefficient corrections (corrections of system errors) have made the most prominent contribution to improving the pass rates of the winter samples. The quarterly correction method can significantly improve the data accuracy of automated monitoring instruments.