Yaochuang Yu  1,3,4, Junji Cao This email address is being protected from spambots. You need JavaScript enabled to view it.2,3 

1 Geography and Environment College, Baoji University of Arts and Sciences, Baoji, Shaanxi 721013, China
2 Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
3 Key Laboratory of Aerosol Chemistry and Physics, Institute of Earth Environment, Chinese Academy of Sciences, Xi’an, Shaanxi 710075, China
4 Key Laboratory of Disaster Monitoring and Mechanism Simulation of Shaanxi Province, Baoji University of Arts and Sciences, Baoji, Shaanxi 721013, China

Received: November 26, 2022
Revised: February 7, 2023
Accepted: March 22, 2023

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Download Citation: ||https://doi.org/10.4209/aaqr.220419  

Cite this article:

Yu, Y., Cao, J. (2023). Chemical Fingerprints and Source Profiles of PM10 and PM2.5 from Agricultural Soil in a Typical Polluted Region of Northwest China. Aerosol Air Qual. Res. 23, 220419. https://doi.org/10.4209/aaqr.220419


  • Crustal material contributed approximately 80% of agricultural dust PM10 and PM2.5 masses.
  • NH4+ was rich in agricultural soil dust profiles of Baoji (5.87–11.39%).
  • Agricultural soil dust was affected by Asian dust and local sources together.
  • Higher NH4+/Al, NO3/Ca2+, and NO3/SO42 can be used to trace agricultural dust.


Analysis of the chemical source profiles of agricultural soil dust (SD) can help accurately assess and apportion the contribution of agricultural sources to atmospheric particulate matter (PM). This study quantitatively analysed twenty-four elements, eight water-soluble ions, organic carbon (OC) and elemental carbon (EC) in PM10 and PM2.5 samples from agricultural resuspended SD to understand the chemical profiles of agricultural SD in Baoji city, Northwest China. The results showed that the elemental compositions in the PM10 and PM2.5 size fractions contributed 40.18% and 39.6%, respectively, followed by water-soluble ions (3.85% in PM10 and 6.62% in PM2.5) and carbonaceous fractions (3.46% in PM10 and 2.36% in PM2.5). The reconstructed crustal matter estimated from Al, Si, Ca, Ti, and Fe concentrations accounted for 79.58% and 78.8% of the total PM10 and PM2.5, respectively, indicating that crustal matter may be the most significant contributor to agricultural SD PM10 and PM2.5 mass. Agricultural SD was influenced not only by the long-range transport of Asian dust but also by local anthropogenic sources. Higher Sc, As, Ca2+, NO3, and NH4+ in PM2.5 indicated that agricultural SD was strongly influenced by anthropogenic industrial and agricultural activities. The ratios of Si/Al, Ca/Al, K/Al, Fe/Al, and Ti/Fe in Baoji samples are basically consistent with those of Asian dust, indicating that the long-range transport of Asian dust had an important impact on the elemental composition of agricultural SD. Source identification found that higher NH4+/Al, NO3/Ca2+, NO3/SO42ratios, and OC can be considered possible source indicators.

Keywords: PM2.5 emission, Chemical source profiles, Agricultural soil dust, Source tracing and apportionment


With the rapid development of China’s economy, urbanization and industrialization, as well as the continuous increase in population, ambient particulate matter has become increasingly severe in recent years (Hu et al., 2015). This increase has had a substantial impact on global and regional climate change (Bytnerowicz et al., 2007), led to reduced atmospheric visibility, and had negative impacts on human health, such as through cardiovascular disease and asthma (Dunea et al., 2016). Fugitive dust is one of the primary sources of particulate matter (PM) (Kim et al., 2013), and exposed surface soil dust particles have significantly impacted urban PM pollution (Cao et al., 2012; Tseng et al., 2022; Yang et al., 2022; Yuan et al., 2023), especially in regions with intense farming activity (Gu et al., 2021). The contributions of fugitive dust to PM10 and PM2.5 were 19%–65% and 3–46%, respectively (Bi et al., 2007; Cao et al., 2012; Cheng et al., 2015; Huang et al., 2014; Samara et al., 2003). The results of a recent analysis by Environment Canada indicated that fugitive dust was one of the largest emissions of PM10 and PM2.5 in Alberta. Furthermore, this work found that agricultural activities contributed 14.91–20.49% to PM10 and PM2.5 (Environment Canada, 2013–2021). Thus, it is necessary to understand the chemical profiles of dust from agricultural soils for improved source apportionment and control of PM2.5.

Most previous studies have mainly focused on road dust (RD) (Jiang et al., 2018; Shen et al., 2016), construction dust (CD) (Ho et al., 2003; Liu et al., 2016), cement (CE) (Ho et al., 2003; Vega et al., 2001) and a small amount of soil dust (SD) (Jiang et al., 2018; Liu et al., 2016). Unfortunately, studies of soil dust from agricultural land are very limited, and the chemical source profiles of dust from agricultural soil are also very scarce (Vega et al., 2001). The surface soil of agricultural land is typically exposed to a small amount of vegetation cover. Therefore, the finer soil fractions are easily released into the atmosphere, which can cause severe air pollution in dry and windy weather (Moreno et al., 2009). Previous studies have shown that farming methods (Kasumba et al., 2011), soil texture (Aimar et al., 2012), and wind speed (Kasumba et al., 2011) are the main factors affecting agricultural soil dust emissions.

China has approximately 1.784 million km2 of agricultural land (Huang et al., 2021). It is estimated that the annual emissions of PM10 and PM2.5 from agricultural soil dust into the atmosphere are approximately 7.15 and 4.69 million tons, respectively (Wang et al., 2019), which may seriously affect the air quality of the regional environment (Zhang et al., 2012). Previous studies on the contribution of soil dust to atmospheric particulate matter in China were limited to Beijing-Tianjin-Tangshan, Lanzhou, Hangzhou, and other densely populated developed areas (Bao et al., 2010; Wei et al., 2017; Wen et al., 2016). However, fewer studies have been conducted in the heavily polluted regions of China’s Fen-Wei Plain. Baoji, one of the most polluted cities in Northwest China, is located at the westernmost end of the heavily polluted Fen-Wei Plain and south of the Loess Plateau. Affected by the closed terrain, coal-based energy infrastructure, intense agricultural activities, and adverse meteorological conditions, the annual average PM2.5 concentration levels of Baoji in 2019 and 2020 were 52.5 µg m3 and 46.8 µg m3, respectively, which are approximately 11 times and 9 times, respectively, the recommended World Health Organization (WHO) limit of 5 µg m3 (WHO, 2021). At present, there is no report on the agricultural soil source profiles of PM10 and PM2.5 in Baoji, which severely limits source apportionment of atmospheric PM2.5 on the Fen-Wei Plain. Therefore, chemical profiles of agricultural soil dust are urgently needed to investigate its chemical composition and quantify its contribution to atmospheric particulate matter in Baoji city.

In this study, five different typical farmlands in Baoji were selected for agricultural soil dust analysis. The PM10 and PM2.5 samples from different agricultural soil dust sources were collected by the resuspension method (Wu et al., 2023), which is an effective method for obtaining fugitive dust source profiles (Zhao et al., 2006). The elemental composition, water-soluble ions and carbonaceous fractions in the PM10 and PM2.5 samples were analysed. This study aims to 1) establish a comprehensive chemical source profile of agricultural soil dust for the five different typical agricultural lands selected in Baoji and 2) identify the chemical characteristics and signatures of agricultural soil dust. In addition, this work provides a scientific basis for effectively reducing the uncertainty of the agricultural source profile and improving the accuracy of source apportionment analysis results in the future.


2.1 Study Area

Baoji lies between 106°18ʹ–108°03ʹ east longitude and 33°35ʹ–35°06ʹ north latitude. As of 2020, the city had a permanent population of 3.74 million and a land area of 18,116.93 square kilometres. It is a typical heavily polluted region in China’s Fen-Wei Plain, one of the critical regions of China’s national environmental protection and an important node city in China’s Silk Road Economic Belt. Baoji uses coal as its main energy source. The city is within a typical clayey loess zone, with most of the surface soil exposed, and has a dry climate, and severe fugitive dust emissions, which is conducive to the suspension of the topsoil and causes severe air pollution (Cao et al., 2008).

2.2 Sample Collection and Pretreatment

The soil sampling procedures for all five typical agricultural lands were the same. Each sample (approximately 500 g) was collected with a hard plastic shovel from the agricultural soil surface (0–5 cm) and packed into a plastic bag. Altogether, twenty samples were collected from seasonal farmland, corn fields, orchards (e.g., yellow peach, cherry, and grape fields), abandoned land, and vegetable fields in Baoji (Fig. S1 and Fig. S2). Then, they were delivered to the Key Laboratory of Aerosol Chemistry and Physics, Chinese Academy of Sciences, and air-dried for 7 days at room temperature (18°C–24°C) in a clean, closed laboratory.

After each sample was air-dried, it was resuspended in a homemade resuspension chamber and selected on quartz and Teflon filters with mini-vol samplers (Airmetrics, Eugene, OR, USA) at a flow rate of 5 L min1 for PM10 and PM2.5 particle size fractions (Wu et al., 2022, 2023). Detailed information on the resuspension collection of soil samples can be found in Wu et al. (2022, 2023).

2.3 Chemical Analysis

The chemical analytical methods for elements (Table S1), water-soluble ions (Table S2), and carbonaceous content are described in detail in the supplementary section.

2.4 Quality Control and Assurance

Quality control (QC) and quality assurance (QA) measures were strictly implemented and are detailed in the supplementary section.

2.5 Data Processing

Data processing is detailed in the supplementary section.


3.1 Chemical Compositions of Agricultural Fugitive Dust

3.1.1 Elemental profiles

The agricultural soil dust elements accounted for nearly 40% of the PM10 and PM2.5 composition profiles, among which crustal and trace elements accounted for 39.72%, 0.46%, 39.12%, and 0.48% of the soil dust, respectively.

The crustal elements Si, Al, Fe, Ca, and K are relatively abundant. Their contents exceeded 3% in both the PM10 and PM2.5 composition profiles (Table S3, Fig. 1). Si was the most abundant element, followed by Al, Fe, Ca, and K (Fig. 1). The abundance of Si varied between 12.19% and 19.21% with an average value of 15.77% in PM10 and 11.06–18.12% with an average value of 15.62% in PM2.5. The Al content in the two size fractions varied between 5.02–8.17% and 4.88–8.06%. This is consistent with previous findings on SD (Cao et al., 2008; Jiang et al., 2018), CE, RD, and CD samples (Ho et al., 2003). The abundance of Fe in PM10 (6.66%) was slightly higher than that in PM2.5 (6.55%). The abundance of Ca in PM10 and PM2.5 was 1.27–17.47% and 1.27–13.78%, respectively. The abundance of K in PM10 (3.52%) was slightly lower than that in PM2.5 (3.82%). Mg, Ti, and Mn are inertially chemically reactive elements whose abundances are relatively stable in different sizes and locations. Except for Al and K, the percentage of crustal elements in the soil dust samples in the PM10 samples was higher than that in the PM2.5 samples (Fig. 1). For trace elements, compared with other elements, the percentages of Co, Ni, Cu, Ga, Sr, Ba, and Pb were higher. In addition, the proportions of Mn, Ba, Sr, Cu, Ni, Pb, Co, and Ga in PM10 are higher than those in PM2.5. The contents of Sc, Zn, Cr, V, As and other trace elements in PM10 are slightly lower than those in PM2.5.

Fig. 1. Comparison of chemical components for composite profiles of PM10 and PM2.5 in Baoji.Fig. 1. Comparison of chemical components for composite profiles of PM10 and PM2.5 in Baoji.

By estimating the mass of crustal material, the effectiveness of resuspension and collection processes can be accurately assessed, and the contribution of soil dust to atmospheric aerosols can be accurately predicted. According to Eq. (S1), the reconstructed crustal mass from the agricultural soil dust samples estimated from those of Al, Si, Ca, Ti, and Fe was 79.58% for PM10 and 78.8% for PM2.5, implying that crustal material may be the most significant contributor to agricultural soil dust PM10 and PM2.5 masses. These findings are relatively consistent with those reported by Sun et al. (2019), who found that urban fugitive PM2.5 dust comprised approximately 70% of the dust from twelve northern cities.

The EFs were calculated by Eq. (S2) to determine whether element components are of natural or anthropogenic origin (Fig. 2). The EFs of Mg, Al, Si, P, Ba, Sr, and Se for both PM10 and PM2.5 were generally low and close to 1 in the agricultural soil source profile, suggesting that the dominant source of these elements is weathered crustal material (Cao et al., 2008) (Fig. 2). The EFs of Fe, K, Ca, Ti, Mn, Zn, Cr, V, Ni, Co, Cu, and Pb for both PM10 and PM2.5 (including Ga in PM10) ranged from 1 to 10, indicating that the sources of these elements are mainly affected by the combined influence of anthropogenic and natural sources (Hama et al., 2021). Ni, Cu, Zn, Mn, and Co may mainly originate from the deposition of industrial emissions such as metal smelting; Cr was mainly contributed by industrial emissions and biomass burning, and the contribution of motor vehicles cannot be ignored (Han et al., 2021). Additionally, motor vehicles are also one of the major sources of Pb (Liu et al., 2018). The EFs of Sc and As for both PM10 and PM2.5 varied between 10 and 40, indicating that agricultural soil dust in Baoji was affected by anthropogenic sources. These enriched elements, Sc and As, may come from using scandium sulphate, pesticides, herbicides, insecticides, and inorganic fertilizers (Cao et al., 2008). Additionally, the large amounts of coal fuel used in Baoji may be another source of As. This result is supported by Mitra et al. (2002). Therefore, Sc and As may be useful for identifying agricultural soil sources.

Fig. 2. Elemental enrichment factors relative to UCC for PM10 and PM2.5 of composite profiles.Fig. 2. Elemental enrichment factors relative to UCC for PM10 and PM2.5 of composite profiles.

3.1.2 Water soluble ion compositions

The average mass fractions of Cl, NO3, SO42, Na+, NH4+, K+, Mg2+, and Ca2+ were significantly different between PM10 and PM2.5 (Table S3, Fig. 1). Overall, the total amount of water-soluble ions in PM10 accounted for only 3.85% of the total mass and 6.62% of the total mass of PM2.5, indicating that most of the substances in soil dust are insoluble and are enriched in PM2.5. This result is similar to that of total water-soluble ions in urban fugitive soil samples from Xi’an, which were less than 5% (Zhang et al., 2014). For Baoji, Ca2+, NH4+, SO42, Na+, NO3, Mg2+, and Cl in resuspended soil dust samples were the most abundant components, accounting for 77.93%, 5.87%, 3.58%, 3.56%, 2.72%, 3.37%, and 1.88% of the total WSIs in PM10 and 44.43%, 11.39%, 1.92%, 8.91%, 9.33%, 2.01%, and 4.59% of the total WSIs in PM2.5, respectively. The abundance of NH4+, Na+, NO3, Cl and K+ in PM10 was relatively lower than that in PM2.5 (Fig. 1). The high NO3 and NH4+ levels in PM2.5 may be related to the large amount of fertilization in agricultural production, which may mean that optimizing and reducing the use of nitrogen fertilizer will help control PM2.5 (Gu et al., 2021). Cl and Na+ abundances were relatively higher in Baoji than in other areas. The lower content of K+ in PM10 and PM2.5 may be correlated with the control of straw burning and less potassium application in recent years.

3.1.3 Carbon fractions

OC and EC are the main chemical components of PM10, PM2.5, and PM1 in the fugitive dust that pollutes the atmosphere, especially PM2.5 and PM1 (Cao et al., 2007). In this study, OC accounted for 2.59% and 2.23%, and EC accounted for 0.87% and 0.13% in PM10 and PM2.5, respectively (Table S3, Fig. 1). OC accounted for 75% and 96% of the total carbon in the PM10 and PM2.5 samples, respectively, indicating that the content of OC in PM2.5 was significantly higher than that in PM10. The abundance of surface soil organic carbon in Baoji may be related to wheat straw burning, soil rich in organic matter (Hama et al., 2021), and agricultural and biological activities (Cao et al., 2008).

3.2 Comparison of Chemical Profiles of Agricultural Soil Dust in Different Cities and Regions

There are significant regional differences in the contents of crustal elements, water-soluble ions and carbon fractions of the PM10 and PM2.5 dust profiles from different regions (Fig. 3). The detailed comparison is described in the supplementary section.

Fig. 3. Comparison of chemical components for composite profiles of PM10 and PM2.5 in different regions.Fig. 3. Comparison of chemical components for composite profiles of PM10 and PM2.5 in different regions.

3.3 Source Identification of Agricultural Soil Dust

A source signature, or fingerprint, is a physical or chemical signature of a specific emission source that is unique to the source (Mitra et al., 2002). To further understand the source and identify agricultural soil source profiles, the chemical diagnostic ratios of different chemical substances (Cao et al., 2012) and the relative source signatures (Table S4) in the supplementary section on agricultural soil dust were calculated. For comparison, the diagnostic ratios of desert dust (Asian dust), Chinese loess and soil dust reported in the literature are shown in Table 1 and Table 2, respectively.

Table 1. Comparison of PM10 and PM2.5 elemental ratios in different cities and regions.

Table 2. Comparison of PM10 and PM2.5 chemical diagnostic ratios in different cities and regions.

Elemental ratios are widely used as fingerprints for dust particles (Chatoutsidou and Lazaridis, 2022; Song et al., 2022; Zhang et al., 2018). The highest Si/Al ratio was found in the soil dust samples, followed by Fe/Al, Ca/Al, and K/Al. The Si/Al ratio in this study was approximately three times lower than that reported in Desert and Gobi soil (< 100 µm) (Ta et al., 2003), which may be related to the enrichment of Si content in large particles with 10–100 mm diameters. The ratios of Si, K, Fe and Al were similar to those reported in the Loess Plateau of China (Cao et al., 2008), Xi’an (Cao et al., 2008), Korea (Kim et al., 2003), and Japan (Ohta et al., 2003). This suggests that these crustal elements retain the elemental characteristics of Asian dust during long-range transport (Cao et al., 2008; Zhang et al., 2014). The Fe/Al ratios in Baoji were nearly two times those in the Mu Us Desert (Arimoto et al., 2004), Asian dust (CJ-2) (Nishikawa et al., 2000), and China Loess (CJ-1) (Nishikawa et al., 2000) for both PM2.5 and PM10. This may be due to the use of fertilizers such as ferrous sulphate, ferric sulphate, and ammonium ferrous sulphate in agricultural activities, industrial sedimentation, and soil cohesion. In terms of the composition profile of PM10 and PM2.5, the Ca/Al ratio in this study was lower than that in the Loess Plateau (Cao et al., 2008) and Xifeng (Wu et al., 2011) in the upwind regions but higher than that in South Korea (Kim et al., 2003) and Japan (Ohta et al., 2003). This may mean that the Ca/Al ratio of Asian dust gradually decreases from upwind to downwind, possibly due to the reaction between dust and contaminated aerosols, as well as calcium consumption during long-distance migration under a low dust layer (Arimoto et al., 2006). This result is supported by Cao et al. (2008). The Ti/Fe ratio for SD in this study was comparable to that reported for the Tengger Desert (Wu et al., 2023), Mu Us Desert (Arimoto et al., 2004), Asian dust (CJ-2) (Nishikawa et al., 2000), Chinese Loess (CJ-1) (Nishikawa et al., 2000), Chinese Loess Plateau (Cao et al., 2008), Xifeng (Wu et al., 2011), and Xi’an (Cao et al., 2008). Therefore, the Si/Al, K/Al, Ca/Al, Fe/Al, and Ti/Fe ratios of the agricultural soil dust in this study were comparable to those of Asian dust, indicating that the agricultural soil dust in Baoji has the characteristics of Asian dust.

The ratio between water-soluble ions is also commonly used to trace the source of soil dust in different desert regions (Arimoto et al., 2004; Shen et al., 2007). As shown in Table 2, this study lists the relative ratios of the selected water-soluble ions compared with previous reports. For example, the NH4+/Al and NO3/Ca2+ ratios for SD in Baoji were significantly higher than those for RD in Xi’an, Yinchuan, and Lanzhou (Sun et al., 2019). Nevertheless, the SO42/Al and SO42/Ca2+ ratios were much lower than those for RD in these cities. This may be due to the use of quicklime and ammonium salt-based fertilizers in agricultural activities. In addition, SO42− is mainly emitted from coal combustion, industry, and power plant emissions, and it may be relatively lower in agricultural sources. Therefore, the higher NH4+/Al and NO3/Ca2+ ratios are potentially helpful for identifying agricultural soil sources.

The SO42/OC, SO42/EC, and NO3/SO42 ratios are also used as chemical diagnostic ratios of source indicators (Cao et al., 2012). For example, the NO3/SO42 ratio is an excellent marker for distinguishing stationary and mobile sources (Arimoto et al., 2006). In this study, the ratios of NO3/SO42− in PM10 and PM2.5 are 0.75 and 5.33, respectively, which are much higher than those in the China Loess Plateau (Cao et al., 2008), the RD of Xi’an, Yinchuan, and Lanzhou (Sun et al., 2019) and the SD of Mexico (Vega et al., 2001), especially in PM2.5 (Table 2), which may be due to the higher NO3 and lower SO42− levels in agricultural soil dust. As mentioned above, the use of fertilizers such as NH4NO3 and (NH4)2SO4 would lead to an increase in NO3 in surface soil. The ratio of NO3/SO42− in PM2.5 (5.33) was significantly higher than that in aerosol samples from Beijing (0.52) and Tongliao (0.27–0.52) (Shen et al., 2007; Wang et al., 2005). This may indicate that the agricultural soil dust in this study not only came from stationary sources but also had the characteristics of long-range transport. It also indicated that the higher NO3/SO42− content in PM2.5 is a potential indicator to identify agricultural soil sources.

Additionally, the correlation between water-soluble ions can reflect the potential source of ions. For example, SO42 showed significant correlations with NH4+ in PM2.5 (r = 0.688, p < 0.01), which may be caused by the use of chemical fertilizer (NH4)2SO4 and NH4HSO4 in agricultural activities (Alvi et al., 2019). The significant positive correlation between Cl, K+ and Ca2+ (r = 0.549, p < 0.05; r = 0.469, p < 0.05) indicated that it may be caused by human activities. There was a significant correlation between Cl and Na+ in PM10 and PM2.5 (r = 0.706, p < 0.01; r = 0.807, p < 0.01), indicating that they had similar sources (Yu et al., 2020). Ca2+ was strongly correlated with Mg2+ (r = 0.801, p < 0.01; r = 0.790, p < 0.01) in PM10 and PM2.5, implying that they have same origin.


The chemical source profiles of agricultural soil dust in Baoji were developed in detail for source tracing and apportionment in atmospheric environmental studies. The results showed that the chemical source profiles of PM10 and PM2.5 from agricultural soil sources were affected not only by Asian dust but also by local anthropogenic activities. The contents of Si, Al, Fe, and Ca elements in agricultural soil dust are relatively rich, and the ratios of Si, K, Ti, Mn, and Fe to Al were well compared with those of Asian dust, indicating that these elements in this region are mainly sourced from Asian dust and less related to agricultural activities. The significant enrichment of Sc and As and higher Ca2+, NH4+, SO42, Na+, NO3, Mg2+, and Cl in PM10 and PM2.5 in agricultural soil dust were mainly associated with the use of scandium sulphate, quicklime, pesticides, herbicides, insecticides, and inorganic fertilizers in farming activities. The total carbon component in the agricultural soil dust was mainly dominated by OC.

Additionally, depositions related to industrial and coal-burning activities may also lead to increases in As and Fe content, SO42, Ca2+, Na+, and Cl. Agricultural activities may have significant effects on water-soluble ions in PM10 and PM2.5. The chemical diagnostic ratios of higher NO3/SO42, NH4+/Al, and NO3/Ca2+ may be beneficial for tracing agricultural soil dust.


The authors declare that our manuscript came from our team without any conflict of financial interest or personal relationship.


This work was supported by the Chinese National Natural Science Foundation (grant number NSFC42030511); and the State Key Laboratory of Loess and Quaternary Geology in Institute of Earth Environment, Chinese Academy of Sciences (grant number SKLLQG1934).


  1. Aimar, S.B., Mendez, M.J., Funk, R., Buschiazzo, D.E. (2012). Soil properties related to potential particulate matter emissions (PM10) of sandy soils. Aeolian Res. 3, 43–443. https://doi.org/​10.1016/j.aeolia.2010.12.001

  2. Alvi, M.U., Kistler, M., Mahmud, T., Shahid, I., Alam, K., Chishtie, F., Hussain, R., Kasper-Giebl, A. (2019). The composition and sources of water-soluble ions in PM10 at an urban site in the Indo-Gangetic Plain. J. Atmos. Sol. Terr. Phys. 196, 105142. https://doi.org/10.1016/j.jastp.2019.105142

  3. Arimoto, R., Zhang, X.Y., Huebert, B.J., Kang, C.H. Savoie, D.L., Prospero, J.M., Sage, S.K., Schloesslin, C.A., Khaing, H.M., Oh, S.N. (2004). Chemical composition of atmospheric aerosols from Zhenbeitai, China, and Gosan, South Korea, during ACE-Asia. J. Geophys. Res. 109, D19S04. https://doi.org/10.1029/2003JD004323

  4. Arimoto, R., Kim, Y.J., Kim, Y.P., Quinn, P.K., Bates, T.S., Anderson, T.L., Gong, S., Uno, I., Chin, M., Huebert, B.J., Clarke, A.D., Shinozuka, Y., Weber, R.J., Anderson, J.R., Guazzotti, S.A., Sullivan, R.C., Sodeman, D.A., Prather, K.A., Sokolik, I.N. (2006). Characterization of Asian dust during ACE-Asia. Global Planet. Change 52, 23–56. https://doi.org/10.1016/j.gloplacha.2006.02.013

  5. Bao, Z., Feng, Y.C., Jiao, L., Hong, S.M., Liu, W.G. (2010). Characterization and source apportionment of PM2.5 and PM10 in Hangzhou. China Environ. Monit. 26, 44–48. https://doi.org/10.19316/​j.issn.1002-6002.2010.02.012

  6. Bi, X.H., Feng, Y.C., Wu, J.H., Wang, Y.Q., Zhu, T. (2007). Source apportionment of PM10 in six cities of northern China. Atmos. Environ. 41, 903–912. https://doi.org/10.1016/j.atmosenv.2006.​09.033

  7. Bytnerowicz, A., Omasa, K., Paoletti, E. (2007). Integrated effects of air pollution and climate change on forests: A northern hemisphere perspective. Environ. Pollut. 147, 438–445. https://doi.org/10.1016/j.envpol.2006.08.028

  8. Cao, J.J., Lee, S.C., Chow, J.C., Watson, J.G., Ho, K.F., Zhang, R.J., Jin, Z.D., Shen, Z.X., Chen, G.C., Kang, Y.M., Zou, S.C., Zhang, L.Z., Qi, S.H., Dai, M.H., Cheng, Y., Hu, K. (2007). Spatial and seasonal distributions of carbonaceous aerosols over China. J. Geophys. Res. 112, D22S11. https://doi.org/10.1029/2006JD008205

  9. Cao, J.J., Chow, J.C., Watson, J.G., Wu, F., Han, Y.M., Jin, Z.D., Shen, Z.X., An, Z.S. (2008). Size-differentiated source profiles for fugitive dust in the Chinese Loess Plateau. Atmos. Environ. 42, 2261–2275. https://doi.org/10.1016/j.atmosenv.2007.12.041

  10. Cao, J.J., Shen, Z.X., Chow, J.C., Watson, J.G., Lee, S.C., Tie, X.X., Ho, K.F., Wang, G.H., Han, Y.M. (2012). Winter and summer PM2.5 chemical compositions in fourteen Chinese Cities. J. Air Waste Manage. Assoc. 62, 1214–1226. https://doi.org/10. 1080/10962247.2012.701193

  11. Chatoutsidou, S.E., Lazaridis, M. (2022). Mass concentrations and elemental analysis of PM2.5 and PM10 in a coastal Mediterranean site: A holistic approach to identify contributing sources and varying factors. Sci. Total Environ. 838, 155980. https://doi.org/10.1016/j.scitotenv.2022.​155980

  12. Cheng, Y., Lee, S.C., Gu, Z.L., Ho, K.F., Zhang, Y.W., Huang, Y., Chow, J.C., Watson, J.G., Cao, J.J., Zhang, R.J. (2015). PM2.5 and PM10-2.5 chemical composition and source apportionment near a Hong Kong roadway. Particuology 18, 96–104. https://doi.org/10.1016/j.partic.2013.10.003

  13. Dunea, D., Iordache, S., Liu, H.Y., Bohler, T., Pohoata, A., Radulescu, C. (2016). Quantifying the impact of PM2.5 and associated heavy metals on respiratory health of children near metallurgical facilities. Environ. Sci. Pollut. Res. 23, 15395–15406. https://doi.org/10.1007/s11356-016-6734-x

  14. Environment Canada (2013–2021). Air pollutant emissions for Alberta. Air Pollutant Emission Inventory Search, National Pollutant Release Inventory. Environment Canada, Ottawa, Ontario, Canada. 

  15. Gu, B.J., Zhang, L., Dingenen, R.V., Vieno, M., Van Grinsven, H.J.M., Zhang, X.M., Zhang, S.H., Chen, Y.F., Wang, S.T., Ren, C.C., Rao, S., Holland, M., Winiwarter, W., Chen, D.L., Xu, J.M., Sutton, M.A. (2021). Abating ammonia is more cost-effective than nitrogen oxides for mitigating PM2.5 air pollution. Science 374, 758–762. https://doi.org/10.1126/science.abf8623

  16. Hama, S., Kumar, P., Alam, M.S., Rooney, D.J., Bloss, W.J., Shi, Z.B., Harrison, R.M., Crilley, L.R., Khare, M., Gupta, S.K. (2021). Chemical source profiles of fine particles for five different sources in Delhi. Chemosphere 274, 129913. https://doi.org/10.1016/j.chemosphere. 2021.129913

  17. Han, Y., Wang, Z.C., Zhou, J.W., Che, H.X., Tian, M., Wang, H.B., Shi, G.M., Yang, F.M., Zhang, S.M., Chen, Y. (2021). PM2.5-bound heavy metals in Southwestern China: Characterization, sources, and health risks. Atmosphere 12, 929. https://doi.org/10.3390/atmos12070929

  18. Ho, K.F., Lee, S.C., Chow, J.C., Watson, J.G. (2003). Characterization of PM10 and PM2.5 source profiles for fugitive dust in Hong Kong. Atmos. Environ. 37, 1023–1032. https://doi.org/​10.1016/S1352-2310(02)01028–2

  19. Hu, G.P., Zhong, N., Ran, P. (2015). Air pollution and COPD in China. J. Thorac. Dis. 7, 59–66. https://doi.org/10.3978/j.issn.2072-1439.2014.12.47

  20. Huang, H.C., Wen, L.Y., Kong, X.B., Chen, W.G., Sun, X.B. (2021). The impact of spatial pattern evolution of cultivated land on cultivated land suitability in China and its policy implication. China Land Sci. 35, 61–70. https://doi.org/10.11994/zgtdkx.20210309.151912 (in Chinese)

  21. Huang, R.J., Zhang, Y.L., Bozzetti, C., Ho, K.F., Cao, J.J., Han, Y.M., Daellenbach, K.R., Slowik, J.G., Platt, S.M., Canonaco, F., Zotter, P., Wolf, R., Pieber, S.M., Bruns, E.A., Crippa, M., Ciarelli, G., Piazzalunga, A., Schwikowski, M., Abbaszade, G., Schnelle-Kreis, J., Zimmermann, R., An, Z.S., Szidat, S., Baltensperger, U., El Haddad, I., Prevot, A.S.H. (2014). High secondary aerosol contribution to particulate pollution during haze events in China. Nature 514, 218–222. https://doi.org/10.1038/nature13774

  22. Jiang, N., Dong, Z., Xu, Y.Q., Yu, F., Yin, S.S., Zhang, R.Q., Tang, X.Y. (2018). Characterization of PM10 and PM2.5 source profiles of fugitive dust in Zhengzhou, China. Aerosol Air Qual. Res. 18, 314–329. https://doi.org/10.4209/aaqr.2017.04.0132

  23. Kasumba, J., Holmén, B.A., Hiscox, A., Wang, J.M., Miller, D. (2011). Agricultural PM10 emissions from cotton field disking in Las Cruces, NM. Atmos. Environ. 45, 1668–1674. https://doi.org/​10.1016/j.atmosenv.2011.01.004

  24. Kim, D., Chin, M.A., Bian, H.S., Tan, Q., Brown, M.E., Zheng, T., You, R.J., Diehl, T., Ginoux, P., Kucsera, T. (2013). The effect of the dynamic surface bareness on dust source function, emission, and distribution. J. Geophys. Res. 118, 871–886. https://doi.org/10.1029/2012JD017907

  25. Kim, K.H., Choi, G.H., Kang, C.H., Lee, J.H., Kim, J.Y., Youn, Y.H., Lee, S.R. (2003). The chemical composition of fine and coarse particles in relation with the Asian Dust events. Atmos. Environ. 37, 753–765. https://doi.org/10.1016/S1352-2310(02)00954-8

  26. Liu, J.W., Chen, Y.J., Chao, S.H., Cao, H.B., Zhang, A.C., Yang, Y. (2018). Emission control priority of PM2.5-bound heavy metals in different seasons: A comprehensive analysis from health risk perspective. Sci. Total Environ. 644, 20–30. https://doi.org/10.1016/j.scitotenv. 2018.06.226

  27. Liu, Y.Y., Zhang, W.J., Bai, Z.P., Yang, W., Zhao, X.Y., Han, B., Wang, X.H. (2016). Characteristics of PM10 chemical source profiles for geological dust from the South-West Region of China. Atmosphere 7, 146. https://doi.org/10.3390/atmos7110146

  28. Mitra, A., Morawska, L., Sharma, C., Zhang, J. (2002). Chapter two: methodologies for characterisation of combustion sources and for quantification of their emissions. Chemosphere 49, 903–922. https://doi.org/10.1016/S0045-6535(02)00236-9

  29. Moreno, T., Amato, F., Querol, X., Alastuey, A., Elvira, J., Gibbons, W. (2009). Bedrock controls on the mineralogy and chemistry of PM10 extracted from Australian desert sediments. Environ. Geol. 57, 411–420. https://doi.org/10.1007/s00254-008-1312-2

  30. Nishikawa, M., Hao, Q., Morita, M. (2000). Preparation and evaluation of certified reference materials for Asian mineral dust. Global Environ. Res. 4, 103–113.

  31. Ohta, A., Terashima, S., Kanai, Y., Kamioka, H., Imai, N., Matsuhisa, Y., Shimizu, H., Takahashi, Y., Kai, K., Hayashi, M. (2003). Grain-size distribution and chemical composition of water-insoluble components in aeolian dust collected in Japan in spring 2002. Bull. Geol. Surv. Jpn. 54, 303–322. https://doi.org/10.9795/bullgsj.54.303

  32. Samara, C., Kouimtzis, T., Tsitouridou, R., Kanias, G., Simeonov, V. (2003). Chemical mass balance source apportionment of PM10 in an industrialized urban area of Northern Greece. Atmos. Environ. 37, 41–54. https://doi.org/10.1016/S1352-2310(02)00772-0

  33. Shen, Z.X., Cao, J.J., Arimoto, R., Zhang, R.J., Jie, D.M., Liu, S.X., Zhu, C.S. (2007). Chemical composition and source characterization of spring aerosol over Horqin sand land in northeastern China. J. Geophys. Res. 112, D14315. https://doi.org/10.1029/2006JD007991

  34. Shen, Z.X., Sun, J., Cao, J.J., Zhang, L.M., Zhang, Q., Lei, Y.L., Gao, J.J., Huang, R.J., Liu, S.X., Huang, Y., Zhu, C.S., Xu, H.M., Zheng, C.L., Liu, P.P., Xue, Z.G. (2016). Chemical profiles of urban fugitive dust PM2.5 samples in Northern Chinese cities. Sci. Total Environ. 569, 619–626. https://doi.org/​10.1016/j.scitotenv.2016.06.156

  35. Song, L.L., Bi, X.H., Zhang, Z.Y., Li, L.X., Dai, Q.L., Zhang, W.H., Li, H., Wang, X.H., Liang, D.N., Feng, Y.C. (2022). Impact of sand and dust storms on the atmospheric environment and its source in Tianjin-China. Sci. Total Environ. 825, 153980. https://doi.org/10.1016/j.scitotenv.2022.153980

  36. Sun, J., Shen, Z.X., Zhang, L.M., Lei, Y.L., Gong, X.S., Zhang, Q., Zhang, T., Xu, H.M., Cui, S., Wang, Q.Y., Cao, J.J., Tao, J., Zhang, N.N., Zhang, R.J. (2019). Chemical source profiles of urban fugitive dust PM2.5 samples from 21 cities across China. Sci. Total Environ. 649, 1045–1053. https://doi.org/10.1016/j.scitotenv.2018.08.374

  37. Ta, W.Q., Xiao, Z., Qu, J.J., Yang, G.S., Wang, T. (2003). Characteristics of dust particles from the desert/Gobi area of northwestern China during dust-storm periods. Environ. Geol. 43, 667–679. https://doi.org/10.1007/s00254-002-0673-1

  38. Taylor, S.R., Malennan, S.M. (1995). The geochemical evolution of the continental crust. Rev. Geophys. 33,241–265. https://doi.org/10.1029/95RG00262

  39. Tseng, Y.L., Wong, K.W., Yuan, C.S., Lin, C. (2022). Diurnal variation of chemical characteristics and source identification of fine particles in the Kaohsiung Harbor. Aerosol Air Qual. Res. 22, 220100. https://doi.org/10.4209/aaqr.220100

  40. Vega, E., Mugica, V., Reyes, E., Sanchez, G., Chow, J.C., Watson, J.G. (2001). Chemical composition of fugitive dust emitters in Mexico City. Atmos. Environ. 35, 4033–4039. https://doi.org/​10.1016/S1352-2310(01)00164-9

  41. Wang, Y.Q. (2019). Study on blown dust characteristics of PM10 and PM2.5 from bare agricultural soils based on wind tunnel experiment. Master's Thesis, School of Geographic and Environmental Sciences, Tianjin Normal University, China.

  42. Wang, Y., Zhuang, G.S., Tang, A.H., Yuan, H., Sun, Y.L., Chen, S.A., Zheng, A.H. (2005). The ion chemistry and the source of PM2.5 aerosol in Beijing. Atmos. Environ. 39, 3771–3784. https://doi.org/10.1016/j.atmosenv.2005.03.013

  43. Wei, Q.Z., Wang, Y.H., Li, S., Li, S., Luo, B., Jia, Q., Yu, J.L., Zhao, H., Yuan, X.R., Niu, J.P. (2017). The source apportionment of PM, based on PCA-MLR Model in Lanzhou City. J. Environ. Hyg. 7, 267–273. https://doi.org/10.13421/j.cnki.hjwsxzz.2017.04.003

  44. Wen, W., Cheng, S.Y., Liu, L., Wang, G., Wang, X.Q. (2016). Source apportionment of PM2.5 in Tangshan, China–Hybrid approaches for primary and secondary species apportionment. Front. Env. Sci. Eng. 10,83–96. https://doi.org/10.1007/s11783-016-0839-9

  45. World Health Organization (WHO) (2021). WHO global air quality guidelines: particulate matter (‎PM2.5 and PM10)‎, ozone, nitrogen dioxide, sulfur dioxide and carbon monoxide. World Health Organization. 

  46. Wu, F., Chow, J.C., An, Z.S., Watson, J.G., Cao, J.J. (2011). Size-differentiated chemical characteristics of Asian paleo dust: Records from aeolian deposition on Chinese loess plateau. J. Air Waste Manage. Assoc. 61, 180–189. https://doi.org/10.3155/1047-3289.61.2.180

  47. Wu, F., Cheng, Y., Hu, T.F., Song, N., Zhang, F., Shi, Z.B., Ho, S.S.H., Cao, J.J., Zhang, D.Z. (2022). Saltation−sandblasting processes driving enrichment of water-soluble salts in mineral dust. Environ. Sci. Technol. Lett. 9, 921–928. https://doi.org/10.1021/acs.estlett.2c00652

  48. Wu, F., Song, N., Hu, T.F., Ho, S.S.H., Cao, J.J., Zhang, D.Z. (2023). Surrogate atmospheric dust particles generated from dune soils in laboratory: Comparison with field measurement. Particuology 72, 29–36. https://doi.org/10.1016/j.partic.2022.02.007

  49. Yang, D.Y., Li, Z.Y., Yue, Z.Y., Liu, J.X., Zhai, Z., Li, Z.L., Gao, M.L., Hu, A., Zhu, W.J., Ding, N., Li, Z.X., Guo, S.T., Wang, X.X., Wang, L., Wei, J.H. (2022). Variations in sources, composition, and exposure risks of PM2.5 in both pre-heating and heating seasons. Aerosol Air Qual. Res. 22, 210333. https://doi.org/10.4209/aaqr.210333

  50. Yuan, C.S., Ceng, J.H., Yen, P.H., Chiang, K.C., Tseng, Y.L., Wong, K.W., Jeng, M.S. (2023). Temporospatial variation, chemical composition, and source resolution of PM2.5 in the Southeastern Taiwan Island. Aerosol Air Qual. Res. 23, 220350. https://doi.org/10.4209/aaqr.220350

  51. Yu, H., Zhao, X.Y., Wang, J., Yin, B.H., Geng, C.M., Wang, X.H., Gu, C., Huang, L.H., Yang, W., Bai, Z.P. (2020). Chemical characteristics of road dust PM2.5 fraction in oasis cities at the margin of Tarim Basin. J. Environ. Sci. 95, 217–224. https://doi.org/101016/j.jes.2020.03.030

  52. Zhang, J., Wu, L., Fang, X.Z., Li, F.H., Yang, Z.W., Wang, T., Mao, H.J., Wei, E.Q. (2018). Elemental composition and health risk assessment of PM10 and PM2.5 in the roadside microenvironment in Tianjin, China. Aerosol Air Qual. Res. 18, 1817–1827. http://doi.org/10.4209/aaqr.2017.10.​0383

  53. Zhang, Q., He, K.B., Huo, H. (2012). Cleaning China’s air. Nature 484, 161–162. https://doi.org/​10.1038/484161a

  54. Zhang, Q., Shen, Z.X., Cao, J.J., Ho, K.F., Zhang, R.J., Bie, Z.J., Chang, H.R., Liu, S.X. (2014). Chemical profiles of urban fugitive dust over Xi’an in the south margin of the Loess Plateau, China. Atmos. Pollut. Res. 5, 421–430. https://doi.org/10.5094/APR.2014.049

  55. Zhao, P.S., Feng, Y.C., Zhu, T., Wu, J.H. (2006). Characterizations of resuspended dust in six cities of North China. Atmos. Environ. 40, 5807–5814. https://doi.org/10.1016/j.atmosenv.2006.​05.026

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