بررسی اثرات آلاینده‌‌های جوی معیار و پارامترهای هواشناسی بر تغییر غلظت کربن سیاه در تهران و تبریز

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری آب و هواشناسی، گروه جغرافیای طبیعی، دانشگاه شهید بهشتی

2 استاد آب و هواشناسی، گروه جغرافیای طبیعی، دانشگاه شهید بهشتی

3 استادیار آب و هواشناسی، گروه جغرافیای طبیعی، دانشگاه شهید بهشتی

4 دانشیار جنگلداری، گروه جنگلداری، دانشگاه تربیت مدرس

چکیده

کربن سیاه (BC) یکی از اجزای مهم ذرات ریز معلق در هواست که تأثیر قابل توجهی بر آب و هوا و سلامت انسان دارد و فعالیت­های انسانی همراه با شرایط آب و هوایی بر تغییرپذیری آن در طولانی مدت تأثیر می گذارد. از این رو، مطالعه حاضر به بررسی روابط آماری بین پارامترهای هواشناسی (دما، بارش، سرعت باد، رطوبت نسبی، فشار هوا، ساعات آفتابی، تابش خورشیدی و ابرناکی)، آلاینده­های معیار هوا (CO، NO2، SO2، O3، PM10 و PM2.5) و آلاینده کربن سیاه و همچنین ارزیابی و مقایسه کارایی پنج الگوریتم یادگیری ماشین (رگرسیون خطی چندگانه (MLR)، مدل جمعی تعمیم یافته (GAM)، درخت طبقه بندی و رگرسیون (CART)، جنگل تصادفی (RF) و تقویت گرادیان (GBM)) در مدلسازی آلاینده‌ها و عوامل آب و هوایی مؤثر در تغییرات سطح غلظت آلاینده کربن سیاه در تبریز و تهران (2021 -2004) با استفاده از نرم‌افزار R 4.3.2 پرداخته است. نتایج مطالعه­ی حاضر بیانگر تفاوت آشکار تأثیر پارامترهای هواشناسی و آلاینده­های جوی معیار بر سطح غلظت آلاینده کربن سیاه در تبریز و تهران به دلیل موقعیت جغرافیایی، شرایط آب و هوایی و ساختار منطقه­ای متفاوت این شهرها است. ذرات کربن سیاه روند صعودی معناداری را با سرعت نسبتاً برابر در طول دوره آماری مورد مطالعه در شهرهای تبریز و تهران تجربه کرده­اند. بر اساس یافته­های حاصل از تحلیل همبستگی اسپیرمن، ذرات کربن سیاه دارای همبستگی مثبت با آلاینده­های PM2.5، NO2، CO و SO2 و همبستگی منفی با O3 است. آلاینده کربن سیاه دارای بیشترین همبستگی با پارامترهای سرعت باد (منفی) و رطوبت نسبی (مثبت) در تبریز و پارامترهای دما (منفی) و فشار هوا (مثبت) در تهران است. بر اساس ارزیابی عملکرد مدل‌های پیشگو و با توجه به اصل صرفه‌جویی، در تبریز مدل GAM و در تهران مدل مبنای MLR از عملکرد بهتری در پیش­بینی مقادیر کربن سیاه نسبت به سایر مدل­ها برخوردار بودند.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Investigating the effects of criteria air pollutants and meteorological parameters on the change of black carbon concentration in Tehran and Tabriz

نویسندگان [English]

  • Parisa Kahrari 1
  • Shahriar Khaledi 2
  • Ghasem Keikhosravi 3
  • Seyed Jalil Alavi 4
1 PhD Student of Climatology, Department of Natural Geography, University of Shahid Beheshti, Tehran, Iran
2 Professor of Climatology, Department of Natural Geography, University of Shahid Beheshti, Tehran, Iran
3 Assistant Professor of Climatology, Department of Natural Geography, University of Shahid Beheshti, Tehran, Iran
4 Associate Professor of Forestry, Department of Forestry, University of Tarbiat Modarres, Mazandaran, Iran
چکیده [English]

Black carbon (BC) is a primary component of fine particulate matter which has a significant effect on climate and human health, and anthropogenic activity along with weather conditions affects its long-term variability. This study aimed to investigate the statistical relationships between meteorological elements (temperature, rainfall, wind speed, relative humidity, air pressure, sunshine hours, solar radiation, and cloudiness), criteria air pollutants (CO, NO2, SO2, O3, PM10, and PM2.5) and black carbon particles (BC), as well as assess and compare the efficacy of five different machine learning algorithms (multiple linear regression (MLR), generalized additive model (GAM), classification and regression trees (CART), random forest (RF) and gradient boosting machine (GBM)) in modeling pollutants and climatic factors responsible for variations in black carbon concentration levels in Tabriz and Tehran from 2004 to 2021 using R 4.3.2 software. The results of the present study showed a significant variation in the influence of meteorological parameters and criteria air pollutants on the level of black carbon pollutant concentration in Tabriz and Tehran depending on the different geographical locations, weather conditions, and regional structure. Black carbon particles have experienced a significant upward trend with a relatively equal speed during the statistical period studied in the cities of Tabriz and Tehran. Based on the results of Spearman's correlation analysis, black carbon particles have a positive correlation with PM2.5, NO2, CO, and SO2 and a negative correlation with O3. Black carbon was highly correlated with parameters of wind speed (negatively) and relative humidity (positively) in Tabriz and temperature (negatively) and air pressure (positively) in Tehran. Based on the performance evaluation of predictive models and concerning the parsimony principle, the GAM model in Tabriz and the MLR model in Tehran had better performance in predicting black carbon values than other models.

کلیدواژه‌ها [English]

  • Air pollution
  • Particulate matter
  • Machine learning
  • Nonparametric algorithms
  • R software
  • Spearman’s correlation
رئیس پور، کوهزاد؛ خسروی، یونس. (1400). پایش بلندمدت غلظت آلاینده‌ کربن سیاه (BC) در ایران با استفاده از داده‌های مدل مبنای NASA/MERRA-2. فصلنامه علوم محیطی، 19(3)، 99-122. https://doi.org/10.52547/envs.2021.33941
سامانه درخواست داده­های هواشناسی. سازمان هواشناسی کشور. https://data.irimo.ir/
سرور، هوشنگ؛ اسمعیل پور، مرضیه؛ خیری­زاده، منصور؛ امرایی، مهتاب. (1399). تحلیل فضایی مؤلفه­های تاثیرگذار بر آلودگی هوای شهر تبریز. مجله مخاطرات محیط طبیعی، 9(24)، 172-151. https://doi.org/10.22111/JNEH.2020.31469.1558
شرکت کنترل کیفیت هوا. شهرداری تهران. https://air.tehran.ir/
مرکز پایش و کنترل آلودگی هوای شهر تبریز. اداره کل حفاظت محیط زیست استان آذربایجان شرقی. https://as.doe.ir/
Ahmed, T., Dutkiewicz, V. A., Khan, A. J., & Husain, L. (2014). Long term trends in black carbon concentrations in the Northeastern United States. Atmospheric research, 137, pp 49-57. https://doi.org/1 0.1016/j.atmosres.2013.10.003
Alas, H.D., Müller, T., Birmili, W., Kecorius, S., Cambaliza, M.O., Simpas, J.B.B., Cayetano, M., Weinhold, K., Vallar, E., Galvez, M.C. & Wiedensohler, A. (2018). Spatial characterization of black carbon mass concentration in the atmosphere of a Southeast Asian megacity: an air quality case study for Metro Manila, Philippines. Aerosol and Air Quality Research, 18(9), pp 2301-2317. https://doi.org/10.4209/aaqr.2017.08.0281  
Babu, S. S., & Moorthy, K. K. (2002). Aerosol black carbon over a tropical coastal station in India. Geophysical Research Letters, 29(23), pp 13-1.  https://doi.org/10.1029/2002GL015662
Barman, N. & Gokhale, S. (2019). Urban black carbon-source apportionment, emissions, and long-range transport over the Brahmaputra River Valley. Science of the Total Environment, 693, p.133577. https://doi.org/10.1016/j.scitotenv.2019.07.383
Barton, K. (2023). MuMIn: multi-model inference. R package version 1.47.5. https://CRAN.R-project.org/package=MuMIn.
Beck, H. E., Zimmermann, N. E., McVicar, T. R., Vergopolan, N., Berg, A., & Wood, E. F. (2018). Present and future Köppen-Geiger climate classification maps at 1-km resolution. Scientific data, 5(1), pp 1-12. https://doi.org/10.1038/sdata.2018.214
Bhat, M. A., Romshoo, S. A., & Beig, G. (2017). Aerosol black carbon at an urban site-Srinagar, Northwestern Himalaya, India: Seasonality, sources, meteorology and radiative forcing. Atmospheric Environment, 165, pp 336-348. https://doi.org/10.1016/j.atmosenv.2017.07.004
Bian, H., Colarco, P. R., Chin, M., Chen, G., Rodriguez, J. M., Liang, Q., ... & Wisthaler, A. (2013). Source attributions of pollution to the Western Arctic during the NASA ARCTAS field campaign. Atmospheric Chemistry and Physics, 13(9), pp 4707-4721. https://doi.org/10.5194/acp-13-4707-2013
Bibi, S., Alam, K., Chishtie, F., Bibi, H., & Rahman, S. (2017). Temporal variation of Black Carbon concentration using Aethalometer observations and its relationships with meteorological variables in Karachi, Pakistan. Journal of Atmospheric and Solar-Terrestrial Physics, 157, pp 67-77. https://doi.org/10.1016/j.jastp.2017.03.017
Boehmke, B. & Greenwell, B.M. (2019). Hands-on machine learning with R. Chapman and Hall/CRC., 1st Eds, 514p.
Bond, T.C., Doherty, S.J., Fahey, D.W., Forster, P.M., Berntsen, T., DeAngelo, B.J., Flanner, M.G., Ghan, S., Kärcher, B., Koch, D. & Zender, C. S. (2013). Bounding the role of black carbon in the climate system: A scientific assessment. Journal of geophysical research: Atmospheres, 118(11), pp 5380-5552. https://doi.org/10.1002/jgrd.50171
Botsa, S. M., Tara, D. L. L. M., Magesh, N. S., & Tiwari, A. K. (2021). Characterization of black carbon aerosols over Indian Antarctic station, Maitri, and identification of potential source areas. Environmental Science: Atmospheres, 1(6), pp 416-422. https://doi.org/10.1039/D1EA00024A
Bounakhla, Y., Benchrif, A., Costabile, F., Tahri, M., El Gourch, B., El Hassan, E.K., Zahry, F. & Bounakhla, M. (2023). Overview of PM10, PM2. 5 and BC and Their Dependent Relationships with Meteorological Variables in an Urban Area in Northwestern Morocco. Atmosphere, 14(1), p 162. https://doi.org/10.3390/atmos14010162
Buchard, V., Da Silva, A. M., Colarco, P. R., Darmenov, A., Randles, C. A., Govindaraju, R., ... & Spurr, R. (2015). Using the OMI aerosol index and absorption aerosol optical depth to evaluate the NASA MERRA Aerosol Reanalysis. Atmospheric Chemistry and Physics, 15(10), pp 5743-5760. https://doi.org/10.5194/acp-15-5743-2015
Chen, C., McCabe, D. C., Fleischman, L. E., & Cohan, D. S. (2022). Black carbon emissions and associated health impacts of gas flaring in the United States. Atmosphere, 13(3), p 385. https://doi.org/10.3390/atmos13030385
Chen, W., Tian, H., & Qin, K. (2019). Black carbon aerosol in the industrial city of Xuzhou, China: Temporal characteristics and source appointment. Aerosol and Air Quality Research, 19(4), pp 794-811. https://doi.org/10.4209/aaqr.2018.07.0245
Cheng, Y. H., & Yang, L. S. (2016). Characteristics of ambient black carbon mass and size-resolved particle number concentrations during corn straw open-field burning episode observations at a rural site in southern Taiwan. International journal of environmental research and public health, 13(7), p 688. https://doi.org/10.3390/ijerph13070688
Colarco, P., da Silva, A., Chin, M., & Diehl, T. (2010). Online simulations of global aerosol distributions in the NASA GEOS‐4 model and comparisons to satellite and ground‐based aerosol optical depth. Journal of Geophysical Research: Atmospheres, 115(D14).  https://doi.org/10.1029/2009JD012820
Ding, A.J., Huang, X., Nie, W., Sun, J.N., Kerminen, V.M., Petäjä, T., Su, H., Cheng, Y.F., Yang, X.Q., Wang, M.H. & Fu, C. B. (2016). Enhanced haze pollution by black carbon in megacities in China. Geophysical Research Letters, 43(6), pp 2873-2879. https://doi.org/10.1002/2016GL067745
Falk, C. F., & Muthukrishna, M. (2023). Parsimony in model selection: Tools for assessing fit propensity. Psychological Methods, 28(1), p 123. https://doi.org/10.1037/met0000422
Fox, J. & Weisberg, S. (2018). An R companion to applied regression, third ed. Sage, Thousand Oaks CA.
Greenwell, B. M., Boehmke, B. C., & Gray, B. (2020). Variable Importance Plots Introduction to the VIP Package. R J., 12(1), p 343. https://doi.org/10.32614/RJ-2020-013
Grieshop, A. P., Reynolds, C. C., Kandlikar, M., & Dowlatabadi, H. (2009). A black-carbon mitigation wedge. Nature Geoscience, 2(8), pp 533-534. https://doi.org/ 10.1038/ngeo595
Harrell, F.E. & Dupont, C. (2023). Hmisc: Harrell miscellaneous. R package version 5.1-1. 
Healy, R.M., Sofowote, U., Su, Y., Debosz, J., Noble, M., Jeong, C.H., Wang, J.M., Hilker, N., Evans, G.J., Doerksen, G. & Munoz, A. (2017). Ambient measurements and source apportionment of fossil fuel and biomass burning black carbon in Ontario. Atmospheric Environment, 161, pp 34-47. https://doi.org/10.1016/j.atmosenv.2017.04.034
https://earthengine.google.com/
https://gmao.gsfc.nasa.gov/reanalysis/MERRA-2/
Janssen, N.A., Gerlofs-Nijland, M.E., Lanki, T., Salonen, R.O., Cassee, F., Hoek, G., Fischer, P., Brunekreef, B. & Krzyzanowski, M. (2012). Health effects of black carbon. World Health Organization. Regional Office for Europe.
Jing, A., Zhu, B., Wang, H., Yu, X., An, J., & Kang, H. (2019). Source apportionment of black carbon in different seasons in the northern suburb of Nanjing, China. Atmospheric Environment, 201, pp 190-200. https://doi.org/10.1016/j.atmosenv.2018.12.060
Kim, S., Yu, S., & Yun, D. (2017). Spatiotemporal association of real-time concentrations of black carbon (BC) with fine particulate matters (PM2. 5) in urban hotspots of South Korea. International Journal of Environmental Research and Public Health, 14(11), p 1350. https://doi.org/10.3390/ijerph14111350
Koch, D., & Del Genio, A. D. (2010). Black carbon semi-direct effects on cloud cover: review and synthesis. Atmospheric Chemistry and Physics, 10(16), pp 7685-7696. https://doi.org/10.5194/acp-10-7685-2010
Korkmaz, S., Göksülük, D., & Zararsiz, G. Ö. K. M. E. N. (2014). MVN: An R package for assessing multivariate normality. R JOURNAL, 6(2).
Kouassi, A., Doumbia, M., Silue, S., Yao, EM., Dajuma, A., Adon, M., Touré, T. & Yoboue, V. (2021). Measurement of atmospheric black carbon concentration in rural and urban environments: cases of Lamto and Abidjan.  Journal of Environmental Protection,12, pp 855-872. https://doi.org/ 10.4236/jep.2021.1211050
Kuhn, M. & Wickham, H. (2020). Tidymodels: A Collection of Packages for Modeling and Machine Learning using Tidyverse Principles. https:/www.tidymodels.org
Kutzner, R. D., von Schneidemesser, E., Kuik, F., Quedenau, J., Weatherhead, E. C., & Schmale, J. (2018). Long-term monitoring of black carbon across Germany. Atmospheric Environment, 185, pp 41-52. https://doi.org/10.1016/j.atmosenv.2018.04.039
Liakakou, E., Stavroulas, I., Kaskaoutis, D.G., Grivas, G., Paraskevopoulou, D., Dumka, U.C., Tsagkaraki, M., Bougiatioti, A., Oikonomou, K., Sciare, J. & Mihalopoulos, N. (2020). Long-term variability, source apportionment and spectral properties of black carbon at an urban background site in Athens, Greece. Atmospheric Environment, 222, p 117137. https://doi.org/10.1016/j.atmosenv.2019.117137
Liu, B., Ma, Y., Gong, W., Zhang, M., & Shi, Y. (2019). The relationship between black carbon and atmospheric boundary layer height. Atmospheric Pollution Research, 10(1), pp 65-72. https://doi.org/10.1016/j.apr.2018.06.007
Liu, X., Wei, Y., Liu, X., Zu, L., Wang, B., Wang, S., Zhang, R. & Zhu, R. (2022). Effects of Winter Heating on Urban Black Carbon: Characteristics, Sources and Its Correlation with Meteorological Factors. Atmosphere, 13(7), p 1071. https://doi.org/10.3390/atmos13071071
Lund, M. T., Berntsen, T. K., & Samset, B. H. (2017). Sensitivity of black carbon concentrations and climate impact to aging and scavenging in OsloCTM2–M7. Atmospheric Chemistry and Physics, 17(9), pp 6003-6022. https://doi.org/10.5194/acp-17-6003-2017 
Mann, H. B. (1945). Nonparametric tests against trend. Econometrica: Journal of the econometric society, pp 245-259.
Miller, A. (2020). leaps: regression subset selection. R package version 3.1. https://CRAN.R-project.org/package=leaps.
Mousavi, A., Sowlat, M.H., Lovett, C., Rauber, M., Szidat, S., Boffi, R., Borgini, A., De Marco, C., Ruprecht, A.A. & Sioutas, C. (2019). Source apportionment of black carbon (BC) from fossil fuel and biomass burning in metropolitan Milan, Italy. Atmospheric environment, 203, pp 252-261. https://doi.org/10.1016/j.atmosenv.2019.02.009
Mukaka, M. M. (2012). A guide to appropriate use of correlation coefficient in medical research. Malawi Medical Journal, 24(3), pp 69-71. PMID: 23638278; PMCID: PMC3576830.
Navinya, C. D., Vinoj, V., & Pandey, S. K. (2020). Evaluation of PM2.5 surface concentrations simulated by NASA’s MERRA version 2 aerosol reanalysis over India and its relation to the air quality index. Aerosol and Air Quality Research, 20(6), pp 1329-1339. https://doi.org/10.4209/aaqr.2019.12.0615
Pena, E.A. & Slate, E.H. (2014). gvlma: global validation of linear models assumptions. R package version 1.0. 0.3.
Pohlert, T. (2023). Trend: non-parametric trend tests and change-point detection. R package version, 1.1.6.
Rad, A.K., Shamshiri, R.R., Naghipour, A., Razmi, S.O., Shariati, M., Golkar, F. & Balasundram, S.K. (2022). Machine learning for determining interactions between air pollutants and environmental parameters in three cities of Iran. Sustainability, 14(13), p.8027. https://doi.org/10.3390/su14138027
Rienecker, M. M., Suarez, M. J., Gelaro, R., Todling, R., Bacmeister, J., Liu, E., ... & Woollen, J. (2011). MERRA: NASA’s modern-era retrospective analysis for research and applications. Journal of Climate, 24(14), pp 3624-3648. https://doi.org/10.1175/JCLI-D-11-00015.1
Ryu, C. (2022). dlookr: Tools for data diagnosis, exploration, and transformation. R package version 0.6.2.9001.  
Şahin, Ü.A., Onat, B., Akın, Ö., Ayvaz, C., Uzun, B., Mangır, N., Doğan, M. & Harrison, R.M. (2020). Temporal variations of atmospheric black carbon and its relation to other pollutants and meteorological factors at an urban traffic site in Istanbul. Atmospheric Pollution Research, 11(7), pp 1051-1062. https://doi.org/10.1016/j.apr.2020.03.009
Samset, B.H., Myhre, G., Schulz, M., Balkanski, Y., Bauer, S., Berntsen, T.K., Bian, H., Bellouin, N., Diehl, T., Easter, R.C. & Zhang, K. (2013). Black carbon vertical profiles strongly affect its radiative forcing uncertainty. Atmospheric Chemistry and Physics, 13(5), pp 2423-2434. http://dx.doi.org/10.5194/acp-13-2423-2013
Sand, M., Samset, B. H., Tsigaridis, K., Bauer, S. E., & Myhre, G. (2020). Black carbon and precipitation: An energetics perspective. Journal of Geophysical Research: Atmospheres, 125(13), e2019JD032239. https://doi.org/10.1029/2019JD032239
Sankar, T. K., Ambade, B., Mahato, D. K., Kumar, A., & Jangde, R. (2023). Anthropogenic fine aerosol and black carbon distribution over the urban environment. Journal of Umm Al-Qura University for Applied Sciences, pp 1-10. https://doi.org/10.1007/s43994-023-00055-4
Sarle, WS. (1990). The VARCLUS Procedure, in SAS/STAT® 9.3 User’s Guide, 4th eds. SAS Institute Inc., Cary, NC, pp. 8065-8097.
Singh, V., Ravindra, K., Sahu, L., & Sokhi, R. (2018). Trends of atmospheric black carbon concentration over the United Kingdom. Atmospheric environment, 178, pp 148-157. https://doi.org/10.1016/j.atmosenv.2018.01.030
Song, S., Wu, Y., Xu, J., Ohara, T., Hasegawa, S., Li, J., Yang, L. & Hao, J. (2013). Black carbon at a roadside site in Beijing: Temporal variations and relationships with carbon monoxide and particle number size distribution. Atmospheric Environment, 77, pp 213-221. https://doi.org/10.1016/j.atmosenv.2013.04.055
Spearman, C. (1961). The proof and measurement of association between two things.
Stekhoven, D. J., & Bühlmann, P. (2012). MissForest—non-parametric missing value imputation for mixed-type data. Bioinformatics, 28(1), pp 112-118. https://doi.org/10.1093/bioinformatics/btr597
Swader, A. (2022). Black Carbon and Meteorological Parameters at Two Locations in Northern Mississippi. Mississippi" (2022). Honors Theses. 2718.https://egrove.olemiss.edu/hon_thesis/2718
Taheri, A., Aliasghari, P., & Hosseini, V. (2019). Black carbon and PM2.5 monitoring campaign on the roadside and residential urban background sites in the city of Tehran. Atmospheric environment, 218, p 116928. https://doi.org/10.1016/j.atmosenv.2019.116928
Vaishya, A., Singh, P., Rastogi, S., & Babu, S. S. (2017). Aerosol black carbon quantification in the central Indo-Gangetic Plain: Seasonal heterogeneity and source apportionment. Atmospheric Research, 185, pp 13-21. https://doi.org/10.1016/j.atmosres.2016.10.001
Wahab, B. I., Hassan, B. J., Al-Timimi, Y. K., & Al-Ataby, I. K. (2023, August). Relationship Between the Concentrations of Black Carbon and some Meteorological Parameters Over Iraq using GIS Techniques. In IOP Conference Series: Earth and Environmental Science (Vol. 1223, No. 1, p. 012015). IOP Publishing. https://doi.org/10.1088/17551315/1223/1/012015
Wang, X., Smith, K., & Hyndman, R. (2006). Characteristic-based clustering for time series data. Data mining and knowledge Discovery, 13, pp 335-364. https://doi.org/10.1007/s10618-005-0039-x
Wang, Z., Zhong, S., Peng, Z. R., & Cai, M. (2018). Fine-scale variations in PM2. 5 and black carbon concentrations and corresponding influential factors at an urban road intersection. Building and Environment, 141, pp 215-225. https://doi.org/10.1016/j.buildenv.2018.04.042
Wei, T. & Simko, V.R. (2021). Package “Corrplot”: Visualization of a Correlation Matrix (Version 0.92). Package Corrplot for R Software.
Winiger, P., Andersson, A., Eckhardt, S., Stohl, A., Semiletov, I.P., Dudarev, O.V., Charkin, A., Shakhova, N., Klimont, Z., Heyes, C. & Gustafsson, Ö. (2017). Siberian Arctic black carbon sources are constrained by model and observation. Proceedings of the National Academy of Sciences, 114(7), pp E1054-E1061. https://doi.org/10.1073/pnas.1613401114
Yeganeh, B., Khuzestani, R. B., Taheri, A., & Schauer, J. J. (2021). Temporal trends in the spatial-scale contributions to black carbon in a Middle Eastern megacity. Science of the Total Environment, 792, p 148364. https://doi.org/10.1016/j.scitotenv.2021.148364
Yu, N., Zhu, Y., Xie, X., Yan, C., Zhu, T. and Zheng, M. (2015). Characterization of Ultrafine Particles and Other Traffic-Related Pollutants Near Roadways in Beijing. Aerosol Air Qual. Res. 15, pp 1261-1269. https://doi.org/10.4209/aaqr.2014.11.0295  
Zambrano-Bigiarini, M. (2024). Goodness-of-fit functions for comparison of simulated and observed hydrological time series, R package version 0.5-4.
Zhang, Y., Li, Y., Guo, J., Wang, Y., Chen, D., & Chen, H. (2019). The climatology and trend of black carbon in China from 12-year ground observations. Climate Dynamics, 53, pp 5881-5892. https://doi.org/10.1007/s00382-019-04903-0
Zhao, P., Dong, F., Yang, Y., He, D., Zhao, X., Zhang, W., Yao, Q. & Liu, H. (2013). Characteristics of carbonaceous aerosol in the region of Beijing, Tianjin, and Hebei, China. Atmospheric Environment, 71, pp 389-398. https://doi.org/10.1016/j.atmosenv.2013.02.010.