# Air Pollution: Sensitive Detection of PM2.5 and PM10 Concentration Using Hyperspectral Imaging
## 1 Introduction  
Hyperspectral remote sensing has been successfully used in various applications, including agriculture, forestry monitoring, food security, natural resources surveying, vegetation observation, geological mapping resource assessment, environmental monitoring, disaster warning, and other remote sensing domains [1; 2; 3; 4; 5; 6; 7; 8; 9]. Many contemporary studies on air pollution have revealed that air pollution is very harmful to the human body. Air pollution could increase the risk of respiratory and cardiovascular diseases and might even affect the proper functioning of the lungs and proliferate carcinogenesis, whereby normal cells are transformed into cancer cells [10; 11; 12; 13]. Many studies have inferred that suspended PM2.5 (particulate matter with a size of less than 2.5 \(\upmu\)m) has a substantial relationship with mortality. Suspended PM2.5 is one of the major causes of non-accidental deaths because of its small size, which enables the particles to penetrate the lung bubbles, directly enery into the blood vessels, and travel along the blood circulation to the entire body [14]. Knowing the concentration of air pollution is imperative for preventing its harmful effects. Most air pollution detection methods are on-the-spot testing methods that examine the extracted air from the atmosphere. [PERSON] et al. demonstrated a method to assess the potential of hyperspectral tree leaf reflectance in the monitoring of traffic-related air pollution [15]. [PERSON] et al. fused HSI-NIR and multivariate data analysis in a method validated by thermal-optical transmission analysis to quantify the carbon contentin the atmosphere [16]. [PERSON] et al. have developed a method to observe the slant column density of NO\({}_{2}\) present in the atmosphere using a concise HSI camera [17]. Although the accuracy of such techniques is often high as they are nearly unsusceptible to external errors and interference, remote air pollution concentration is practically impossible to detect. [PERSON] et al. used mid-infrared dual-comb spectroscopy technology to detect the quantitative concentration of volatile organic compounds, such as propanol and ethane, in an open path of up to 1 km and measured the released acetone and isoforms with a sensitivity of ppm-m [18]. [PERSON] et al. also used Open Path-Fourier Transform Infrared technology (OP/FTIR) to detect the influence of the inorganic compound ammonia (NH\({}_{3}\)) emitted by automobiles [19]. [PERSON] et al. deployed OP/FTIR technology onto an unmanned aerial vehicle for environmental monitoring using a spectrometer; this approach was able to provide rotational-vibrational spectroscopy over the entire molecular fingerprint area of 3-11 \(\upmu\)m [20]. However, the infrared light source used by OP/FTIR has a poor collimation ability, thereby resulting in a low signal-to-noise ratio on long paths. Therefore, [PERSON] et al. proposed the quantum cascade laser open-path system (QCLOPS) with spectroscopic ellipsometry. Because of the combination of the fast tunability of the QCL and the polarization of the phase modulation, a broadband between 900 and 1024 cm\({}^{-1}\) with a high resolution of 1 cm\({}^{-1}\) of elliptically polarized spectra can be obtained in less than 1 s [21]. [PERSON] used a continuous wave room temperature, a high-power QCL, and external diffraction grating cavity geometry to develop a sensitive photoelectron sensor system to detect sulfur dioxide (SO\({}_{2}\)) in the pseudo-units of parts per billion [22]. In addition, [PERSON] et al. proposed a promising technique for establishing the mass of emanated nitrogen oxide (NO) and other compositions of cigarette smoke [23]. [PERSON] et al. developed a piezoelectric effect-based detector based on a broadband tunable external cavity QCL [24]. [PERSON] et al. developed a detector based on quartz-enhanced photoacoustic spectroscopy, which is a compact, portable and rechargeable sensor platform for the sensitive detection of carbon monoxide (CO) [25].