Introduction
In recent years, with a gradual increase in health awareness, society has become more concerned about the frequent occurrence of air pollution. As is known to all, fine particulate matter (diameter ≤ 2.5 µm, PM2.5) is the main cause of haze, which is suspended in the air and generated through a series of physical and chemical changes.1 The sources of PM2.5 include factories, power plants, motor vehicles, and construction activities, and its composition is subject to a wide variety of natural and human activities.2
Many studies have provided hard evidence that PM2.5 has an adverse influence on human health. On one hand, some large-scale epidemiological studies have shown that PM2.5 is closely related to the morbidity and mortality of patients with respiratory, cardiovascular, and cerebrovascular diseases, especially among the elderly, infants, and high-risk groups suffering from basic chronic diseases. The study from Shah et al.3 concerning the global association of air pollution and heart failure suggested that heart failure hospitalization or death was associated with increases in PM2.5 (2.12% per 10 µg/m3). Another survey indicated that exposure to PM2.5 was associated with a notable proportion of mortalities due to numerous diseases, including lung cancer (23.9%), chronic obstructive pulmonary disease (COPD; 18.7%), stroke (40.3%) and ischemic heart disease (IHD; 26.8%).4 Research from Hayes et al. that involved 565,477 men and women in America showed that each increase of 10 µg/m3 PM2.5 was associated with a 16% increase in mortality from IHD and a 14% increase in mortality from stroke.5
On the other hand, many in vivo animal experiments have indicated that PM2.5 can contribute to the acute exacerbation of asthma, heart failure, and COPD through the activation of various signaling pathways, such as adenosine monophosphate (AMP)-activated protein kinase (AMPK) catalytic subunit alpha 1 and signal transducer and activator of transcription (STAT)-1.6 In addition to the adverse effects of exposure to particulate matter on respiratory and cardio-cerebrovascular systems, studies have shown that it can also damage other organs and DNA. According to Gao et al.,7 exposure to PM2.5 at a concentration between 20 µg/mL and 200 µg/mL decreased cell viability and increased lactate dehydrogenase release.7 Treatment in human corneal epithelial cells with PM2.5 remarkably increased DNA double-stand breaks, increased expression of a DNA repair-related protein (phosphorylated H2A histone family member X [γH2AX]), elevated formation of reactive oxygen species, and altered cellular ultrastructure.7 It was observed that exposure to PM2.5 could increase the number of leukocytes in the lung and the liver of PM2.5-treated mice. A recent study based on data analysis from 3,080 counties revealed that each 1 µg/m3 increase in PM2.5 causes approximately an 8% increase in the coronavirus disease (COVID)-19 death rate (95% confidence interval [CI]: 2%, 15%).8 Enkhjargal et al.9 summarized that each 10 µg/m3 increase of PM2.5 led to a 0.65% increase in the hospitalization for cardiovascular disease (CVD) on the day of exposure, and on the second day of exposure, a 10 µg/m3 growth of the pollutant contributed to 0.66% increase.9
PM2.5 also imposes a burden on the economy and society. It was estimated that a mean reduction in PM2.5 of 3.9 µg/m3 would prevent 7,978 heart failure hospitalizations and save one-third of a billion US dollars per year.3 A study based on PM2.5 concentration of 338 Chinese cities showed that the national PM2.5-attributable mortality was 0.964 (95% CI: 0.447, 1.355), which accounts for approximately 9.98% of total reported deaths in China, and PM2.5 exposure led to an economic loss of $101.39 billion, which was 0.91% of the national gross domestic product (GDP) in 2016.10 According to a report by World Bank,11 lost income for countries in South Asia due to air pollution totaled more than $66 billion in 2013, the equivalent of nearly 1% of GDP. The magnitude of losses was greatest in East Asia and the Pacific, where premature mortality costs reached the equivalent of 7.5 percent of GDP in 2013, closely followed by South Asia where costs were on the order of 7.4 percent of GDP equivalent.
As scientists deepen their understanding of PM2.5, the hazards of particulate matter have become widely known to the public. This article summarizes the progress of source analysis, pathogenic mechanisms, component analysis, and a diffusion deposition model. Relevant studies and publications have been identified using the search terms PM2.5, source analysis, component analysis, and indoor PM2.5 in the literature databases MEDLINE, Web of Science, PubMed, and Google Scholar.
Composition analysis
Currently, methods to determine the components in PM2.5 primarily include inductively coupled plasma mass spectrometry (ICP-MS),12,13 high resolution aerosol mass spectrometry (HR-AMS),14 inductively coupled plasma atomic emission spectrometry (ICP-AES),15 and wavelength dispersive X-ray fluorescence (WDXRF) spectrometry.16
ICP-MS has become one of the main approaches to determine the composition of PM2.5, which was developed in the 1980s for the measurement of inorganic elements and isotopes.17 ICP-MS adopted a unique interface technology that combines the high-temperature ionization characteristics of inductively coupled plasma with the sensitivity and high scanning speed of the mass spectrometer to form a highly sensitive analytical technique with the advantages of high sensitivity and rapidity.18 HR-AMS14 conducts a quantitative chemical analysis of aerosols by means of thermal evaporation and electron bombardment ionization mass spectrometry, which can also be applied to quantify the compositions of organic matters. Similarly, liquid chromatography mass spectrometry (LC-MS),19 gas chromatography mass spectrometry (GC-MS)20 and the other methods can be used to determine the compositions of organic matters in PM2.5 as well. Compared to the other approaches, sample derivatization is a necessary procedure in GC-MS, suggesting higher complexity. LC-MS technology, on the other hand, has the most substantial advantage among all methods for its capability to detect unstable thermal organic compounds and polar organic compounds (Table 1).12–16,19,20
Table 1Composition analysis methods for fine particulate matter (particles less than 2.5 micrometers in diameter; PM2.5)
| Author | Method |
|---|
| Palomo-Marín et al.12 and Mitra et al.13 | Inductively coupled plasma mass spectrometry (ICP-MS) |
| Zhang et al.14 | High resolution aerosol mass spectrometry (HR-AMS) |
| Chen et al.15 | Inductively coupled plasma atomic emission spectrometry (ICP-AES) |
| Wang et al.16 | Wavelength dispersive X-ray fluorescence (WDXRF) spectrometry |
| Warnke et al.19 | Liquid chromatography mass spectrometry (LC-MS) |
| Kim et al.20 | Gas chromatography mass spectrometry (GC-MS) |
The particle mass concentration online monitoring technology mainly utilizes the tapered element oscillating micro-balance (TEOM) method,21 β-ray method22 and light scattering method,23 of which TEOM is the most commonly used. Its mechanism is as follows: under the action of an electric field, a hollow conical tube is in a state of reciprocating oscillation, and the oscillation frequency is determined by the characteristics and mass of the conical tube. A change in the mass of the filter leads to an alteration in the oscillation frequency that is inversely proportional to the square root of the mass of the thin head. The mass of the PM2.5 particles collected is then calculated from the oscillation frequency. Next, the ambient temperature, air pressure, and the mass concentration of particulate matter during this period can be calculated based on the flow rate.
Prior to composition analysis, the identity of the collected samples needs to be verified as PM2.5. In general, dynamic light scattering is used to determine whether the collected particles are PM2.5 samples.24 This technique measures the size of the particle in the suspension sample according to the change in the scattering light intensity. Due to the positive correlation between the change in the fluctuation of the light intensity signal and the speed of the small particle in Brownian motion, the correlation between the light intensity after a slightly longer time and the initial light intensity is lower. However, the behavior of large particles is opposite to that of small particles. Therefore, this method used to distinguish the size of the particulate suspensions. To fully understand the morphology of PM2.5, scanning electron microscope (SEM) is usually used for direct observation.
Source analysis for PM2.5
Ambient PM2.5 mainly comes from nature and human activities. Natural sources include primarily salt water evaporation, natural dust, and volcanic eruption.25 Human activities principally include stationary sources (such as industrial production and fossil fuel) and mobile sources (such as vehicle emission).25,26 According to previous research, the proportion of urban ambient PM2.5 sources from transportation, civil fuel combustion, industrial activities, and other human activities were 25%, 20%, 15%, and 22%, respectively, whereas Salt water evaporation and natural dust contributed 18%.27 The components of PM2.5 consisted of soluble components, inorganic elements and carbon composition.28 Soluble components accounted for 20% of the total PM2.5 mass, which mainly consisted of SO42+, NO3−, K+, NH+, Na+, Ca2+, Mg2+ and Cl−. SO42+ and NH+ were from motor vehicle exhaust and the secondary conversion process of gas produced by fuel combustion. The ratio of NO3−/SO42+ was usually applied to identify if the pollution source was from a stationery or mobile source. Inorganic elements found in PM2.5 included Al, Si, Ca, P, K, Fe, Mn, Cu, Cd, Co, Cs, Au, Hg, Cr, and others.29 Carbon composition include organic carbon, elemental carbon, and carbonate. Organic carbon is a mixture of hundreds of organic compounds, including polycyclic aromatic hydrocarbons, n-alkanes, phthalates, aldehydes, ketones, and other toxic and harmful substances. Organic carbon can be divided into water-soluble and water-insoluble.
Source analysis models can be divided into diffusion or receptor models.30 The diffusion model can quantitatively identify a fixed pollution source and pollutant source at a time to calculate the rate of contribution of the pollution source in the fixed location at a certain time.31 Meteorological conditions and pollution sources can be introduced into the diffusion model, but are not considered in receptor models. The comparison of diffusion and receptor models is presented in Table 2. A weakness of receptor models is that they cannot be used to predict the contribution rate of each pollution source.
Table 2The comparison of the diffusion and receptor models
| Diffusion model | Receptor model |
|---|
| Basic conditions | Emission factor, geographical factors, meteorological data, transportation and diffusion of PM2.5 | Size distribution, chemical composition |
| Result | Emission forecast | Contribution rate of pollution sources |
| Default | Dynamic temporal variation of pollution sources | Cannot be used for prediction |
Receptor models included enrichment factor (EF), positive matrix factorization (PMF) analysis, principal component analysis (PCA), and chemical mass balance (CMB).31,32 Source component spectrum is required for CMB and it is sensitive to collinearity issues. Additionally, the complex usage of PMF is a weakness (Table 3).
Table 3The comparison of receptor models
| CMB | PMF | PCA | EF |
|---|
| Sample size | Less | More | More | Less |
| Source component spectrum | Yes | No | No | No |
| Feature identification element | No | Yes | Yes | Yes |
Diffusion deposition model of indoor PM2.5
The concentration and composition of indoor PM2.5 are mainly determined by both outdoor infiltration and indoor source emission. A study by Gilbert et al. in Australia showed that smoking, frying, and grilling caused PM2.5 levels to be three, 30, and 90 times higher than the background value, respectively.33 It was found that tobacco smoke and cooking are major sources of indoor PM2.5 in both residential and non-industrial environments.34 Nitta et al. investigated indoor air pollution in downtown Tokyo and found that smoking increases the concentration of indoor PM2.5 by approximately 50–80%.35 Thus, mass balance model has been proposed to predict the settlement of PM2.5. According to Figure 1, the source of indoor PM2.5 consists of outdoor air input, re-suspension, and infiltration. Based on this model, three typical PM2.5 deposition diffusion models are summarized in this paper.
Indoor PM2.5 deposition diffusion model under different ventilation conditions
Based on the mass balance principle, the deposition and diffusion model of indoor PM2.5 under different ventilation conditions is proposed.36 The following conclusions can be drawn: different ventilation modes in residential buildings can lead to different levels of indoor fine particulate matter mass concentration and indoor exposure. The model is as follows:
VdCdt=Qvη1Co−QvC+QnPpCo−QnCo−Qcη2C−vdAC
where V is the residential volume, m3; C, indoor particle mass concentration, g/m3; T, time, h; Qv, mechanical ventilation volume, m3/h; η1, the filter efficiency for fine particles in mechanical ventilation system; Co, the concentration of particulate matter in the atmosphere, g/m3; Qn, natural ventilation rate, m3/h; Pp, penetration coefficient of fine particles-Pp = 0.8 (closed window)/Pp = 1 (open window); Qc, air purification air volume, m3/h; η2, primary filtration efficiency of air purifier for the fine particles; and Vd, particle deposition rate. For fine particles, the general indoor particle deposition rate K is as follows:K=vdA/V=0.09h−1
where A is the residential surface area.Evaluation model of outdoor PM2.5 infiltration and settlement characteristics according to the gap ventilation of building exterior windows
Utilizing the concepts of the law of the conservation of mass, mathematical statistics and controlled variables, Wang et al. proposed and verified a model of the relationship between the gap ventilation of building exterior windows and indoor PM2.5 settlement.37 The model is as follows:
VdCi,tdt=aPVCo,t+vi+RLfAf−aVCi,t−kVCi,t
where V is room volume, m3; Ci,t, indoor PM2.5 concentration at time T, g/m3; a, the number of air changes, h−1; P, penetration coefficient; Co,t, outdoor PM2.5 concentration at time T, g/m3; vi, indoor PM2.5 concentration per unit time, µg/h; k, settling rate, h−1; R, secondary suspension rate of PM2.5, h−1; Lf, PM2.5 mass per unit area, h−1; and Af, surface area of the room, m2.Evaluation model on indoor PM2.5 concentration level of building structure based on the influence of infiltration ventilation conditions
Under the condition of ignoring chemical reactions such as coagulation and phase transition of particulate matter without an indoor pollution source, the mass concentration balance equation of indoor particulate matter under permeable ventilation is established based on the principle of mass conservation, specifically as follows:38
dCindt=aPCo−kCin−aCin
where Co is outdoor PM2.5 concentration, µg/m3; Cin, indoor PM2.5 concentration, µg/m3; a, ventilation times, h−1; P, PM2.5 penetration coefficient; and k, PM2.5 natural sedimentation rate, h−1.Another study demonstrates that, the number of time-related parameters, such as the ventilation frequency of infiltration ventilation as well as indoor and outdoor PM2.5 concentration, remain stable with small fluctuation39 over a relatively short period of time. In formula (4), the left side can be regarded as zero to obtain formula (5).
aPCo−kCin−aCin=0
Rewriting the above equation, formula (6) can be obtained:
I/O=CinCo=aPa+k
Then, the concentration ratio of indoor and outdoor PM2.5 particles can be calculated using formula (6).The pathogenic mechanisms of PM2.5
Patients with chronic respiratory diseases are susceptible to damage by PM2.5. The mechanisms of lung injury and aggravation caused by PM2.5 include oxidative stress, inflammatory reaction, and gene toxicity, as shown in Figure 2.
Oxidative stress
Aerobic metabolism of cells can produce reactive oxygen species (ROS), which participates in the signal transduction and regulation of gene expression. Under normal conditions, the concentration of ROS in the body is low and in a state of dynamic balance. Once the equilibrium state is disrupted, a high concentration of ROS can lead to oxidative damage of large molecules such as DNA, and the degeneration or even necrosis of cells, ultimately resulting in oxidative stress. As PM2.5 enters the human body, the copper ions (Cu2+) contained in the PM2.5 can lead to an increase in ROS,40,41 resulting in an imbalance between the oxidation and anti-oxidation,3 which can cause further damage to the respiratory system. Oxidative stress pathways related to PM2.5 include the Kelch epoxy chloropropane related proteins 1-nuclear factor E2 related factor 2-oxidation reaction signaling (Keapl-Nrf2-ARE) pathway, phosphatidyl inositol-3-kinase/protein kinase B (PI3K/Akt) pathway, mitogen-activated protein kinase (MAPK) signaling pathway, tyrosine protein kinase/STAT signaling, and nuclear factor (NF)-kB signaling pathways.42,43
Inflammatory response
PM2.5 exposure can also lead to inflammatory cell infiltration by releasing inflammatory factors such as interleukins, which causes damage to the trachea and lungs. Relevant studies have demonstrated that PM2.5 turbidity increases the concentration of interleukin (IL) 6, tumor necrosis factor-alpha (TNF-α), and the activation level of inflammatory responses in the body.41 It has been illustrated that PM2.5 has the potential to cause lung fiber hyperplasia and proliferative inflammation.44 For COPD patients who have alveolar macrophage phagocytosis defect, exposure to PM2.5 exacerbates the situation since macrophages are one of the most important cells involved in the inflammatory response. Moreover, it was also shown that PM2.5 exposure can exacerbate the phagocytic dysfunction of alveolar macrophages in a mouse model of COPD.45
Gene toxicity
One study has shown that, compared with a control group, the expression level of repair genes (such as apurinic/apyrimidinic endonuclease 1 [APE1]) in cells exposed to PM2.5 is significantly higher, which indirectly proves DNA damage caused by PM2.5.46 When exposed to 300 µg/m3 of PM2.5 over a short term, approximately 2,800 sites in the DNA of human peripheral blood mononuclear cells may be hypomethylated, thus inhibiting the normal expression of around 400 genes.47
Future directions
Research focusing on PM2.5 has made considerable progress as detailed above, but it is believed that there is still room for improvement.
Components and biological effect
Due to different regions, levels of economic development and economic pillars, the concentration and composition of PM2.5 are greatly varied, which leads to different results among scholars. The human exposure level to air pollution is a low dose exposure, and it is not a simple linear cumulative effect, so it is difficult to determine the effective relationship between exposure dose and health in the short term. In addition, the mobility of the population makes it even more difficult to monitor human exposure quantitatively. The complexity of the factors affecting human health status make it difficult to identify and monitor the exposure level in a certain target or a certain class of objects, and while some researchers investigate the effect of PM2.5 based on certain components, this might lead to the neglect of some critical adverse components. Differences between individual populations make it difficult to apply specific research results to other populations effectively.
Prediction for PM2.5
PM2.5 concentration prediction is meaningful and important to guide the travel of high-risk and sensitive groups. Factors affecting the variations in PM2.5 concentration include the source and diffusion factors. Sudden events often lead to a sharp change in PM2.5 concentration and warning with a delayed effect. Current studies regarding PM2.5 are limited to a certain city, and should be coordinated between regions, taking into account monsoons and other factors that accelerate PM2.5 diffusion between different regions.
Conclusion
The present review aims to provide a brief overview of new insights into the composition analysis, source analysis, indoor diffusion and deposition model, and the pathogenic mechanism of PM2.5, which have been explored with new technologies in recent years. This review will help to provide reference for PM2.5 related policy formulation.
Abbreviations
- PM2.5:
fine particulate matter (diameter ≤ 2.5 µm)
- MS:
mass spectrometry
- EF:
enrichment factor
- PMF:
positive matrix factorization
- PCA:
principal component analysis
- CMB:
chemical mass balance
Declarations
Funding
This study was supported by the project of Jinan 20 Universities (#2019GXRC040), 5150 Talent Attraction and Talent Multiplication Program, and the Shandong Institute of Advanced Technology, Chinese Academy of Sciences (#YJZX003).
Conflict of interest
The authors declare no conflict of interest.
Authors’ contributions
All authors have participated in this study, and consent to publish this article. Guarantor of integrity of entire study (CC), study concepts (CC, YS), study design (CC, YS), literature research (YS, JA, ZM), manuscript preparation (YS, XM), manuscript definition of intellectual content (CC, YS), manuscript editing (CC, YS, QL), manuscript revision/review (XL, QL, CC, YS).