# Air Pollution: Sensitive Detection of PM2.5 and PM10 Concentration Using Hyperspectral Imaging
## 4 Results
### Regression Analysis  
PM2.5 and PM10 primarily exhibit Mie scattering in the visible, near-infrared, and far-infrared bands. Therefore, the spectrum data ware affected by the scattering and exhibit an energy drop. However, the degree of reduction does not change with the wavelength. Thus, the spectrum data are integrated to obtain the variation range and the relationship between the variation range and the concentration of suspended particles according to the Beer-Lambert law and the regression analysis. By integrating the normalized spectrum data of brightness and performing a regression analysis on PM2.5 and PM10 using Equations (7) and (8) the extinction coefficients of PM2.5 and PM10 can be obtained. Subse  
Figure 3: (**a**) The 20 locations selected for spectrum analysis; (**b**,**d**,**f**) the source reflectance at different wavelengths at 9:00, 12:00, and 15:00 GMT+8; (**c**,**e**,**g**) represents same picture after brightness normalization correction. The different colors represent the 20 different locations selected for spectrum analysis.  
quently, the concentrations of PM2.5 and PM10 can be obtained using Equations (9) and (10), where \(RW/OPM\) is the reflectance that is unaffected by suspended particles.  
\[f = \frac{RW\times e^{-E\times PM2.5}}{OPM2.5} \tag{7}\] \[f = \frac{RW\times e^{-E\times PM10}}{OPM10}\] (8) \[PM2.5 = \frac{log\frac{RW}{OPM2.5}-log(f)}{E}\] (9) \[PM10 = \frac{log\frac{RW}{OPM10}-log(f)}{E} \tag{10}\]  
The result of the regression analysis on the spectrum data in the visible light band for PM2.5 and PM10 is shown in Figure 4. The correlation coefficients for PM2.5 and PM10 were 0.9789 and 0.9738, and the extinction coefficients were 0.005135 and 0.001837, respectively. The correlation coefficient in this band was quite high; that is, the data in this band were highly correlated with the regression equation and had a very close relationship with the suspended particles. The near-infrared HSI (NIR-HSI) used in this study was based on the image of a near-infrared camera and combined with the NIR hyperspectral algorithm (NIR-HSA) (see Supplementary Materials for the regression analysis results of the far-infrared spectrum). The correlation coefficients for PM2.5 and PM10 were 0.6919 and 0.7803, while the extinction coefficients were 0.005765 and 0.003024, respectively. The correlation coefficient in this waveband was lower than that of the visible light waveband; that is, the relationship between the data in this waveband and the suspended particles was less prominent than that in the visible light waveband.  
The far-infrared HSI (FIR-HSI) used in this study is based on the image of the thermal imaging camera combined with the FIR hyperspectral algorithm (FIR-HSA; see Supplementary Materials Figure S3 for a detailed description of the FIR-HSI). The correlation coefficients for PM2.5 and PM10 were 1 and \(1.902\times 10^{-8}\), respectively, while the extinction coefficients were 1 and \(7.482\times 10^{-9}\), respectively. Although the correlation coefficient was 1 because the extinction coefficient was quite low, the transmittance was near 1 and could thus be regarded as hardly being affected by the suspended particles (see Supplementary Materials for the estimated results of PM2.5 and PM10 in the FIR and NIR spectra).