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The influences of source intensity and meteorological factors on sulfur dioxide and nitrogen oxides based on the path analysis mode

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
With rapid economic development and industrialization, air pollution is becoming a critical global issue affecting health. Sulfur dioxide and nitrogen oxides are the major contributors to acid rain and the key indicators for evaluating atmospheric pollution. And source intensity and meteorological factors are the main ways to influence the concentrations of sulfur dioxide and nitrogen oxides. Thus, to investigate the specific effects of source intensity, temperature, humidity, wind speed and atmospheric pressure on SO2 and NOx, the path analysis method was used for the model. The results showed that Source intensity significantly affects the concentrations of SO2and NO2. For both NO2 and SO2, the source intensity accounted for around 40%. Meteorological factors have very limited effects on the concentrations of SO2and NO2. The effects of the meteorological factors on air pollutants are specific as differences in material properties. Humidity significantly affects the concentration of SO2while temperature, humidity and wind speed have significantly affected the concentration of NO2.
Rocznik
Strony
51--65
Opis fizyczny
Bibliogr. 37 poz., rys., tab.
Twórcy
autor
  • College of Hehai, Chong Qing Jiao Tong University, Chong Qing, Nanan, China
autor
  • College of Hehai, Chong Qing Jiao Tong University, Chong Qing, Nanan, China
  • College of Hehai, Chong Qing Jiao Tong University, Chong Qing, Nanan, China
autor
  • College of Hehai, Chong Qing Jiao Tong University, Chong Qing, Nanan, China
Bibliografia
  • [1] ZHANG Y., Dynamic effect analysis of meteorological conditions on air pollution: A case study from Beijing, Sci. Total Environ., 2019, 684, 178–185. DOI: 10.1016/j.scitotenv.2019.05.360.
  • [2] PAN L., YAO E., Impact analysis of traffic-related air pollution based on real-time traffic and basic meteorological information, J. Environ. Manage., 2016, 183 (Pt. 3), 510–520. DOI: 10.1016/j.jenvman.2016.09.010.
  • [3] ZHANG H., WANG Y., Relationships between meteorological parameters and criteria air pollutants in three megacities in China, Environ. Res., 2015, 140, 242–254. DOI: 10.1016/j.envres.2015.04.004.
  • [4] ATKINSON R., BARREGÅRD L., Review of evidence on health aspects of air pollution – REVIHAAP Project, WHO Regional Office for Europe, Copenhagen 2013.
  • [5] ZHAO Y., ZHANG K., Substantial changes in nitrate oxide and ozone after excluding meteorological impacts during the COVID-19 outbreak in mainland China, Environ. Sci. Technol. Lett., 2020, 7 (6). DOI: 10.1021/acs.estlett.0c00304.
  • [6] CHEN Z., WANG J.-N., China tackles the health effects of air pollution, The Lancet, 2013, 382 (9909), 1959–1960. DOI: 10.1016/s0140-6736(13)62064-4.
  • [7] ZAIB S., LU J., Spatiotemporal characteristics of air quality index (AQI) over Northwest China, Atmos., 2022, 13 (3), 375. DOI: 10.3390/atmos13030375.
  • [8] ZHAO H., GUI K., Effects of different aerosols on the air pollution and their relationship with meteorological parameters in North China Plain, Front. Environ. Sci., 2022, 10. DOI: 10.3389/fenvs.2022.814736.
  • [9] CALKINS C., GE C., Effects of meteorological conditions on sulfur dioxide air pollution in the North China Plain during winters of 2006–2015, Atmos. Environ., 2016, 147, 296–309. DOI: 10.1016/j.atmosenv. 2016.10.005.
  • [10] XUE D., YIN J., Meteorological influence on predicting surface SO2 concentration from satellite remote sensing in Shanghai, China, Environ. Monit. Assess., 2014, 186 (5), 2895–2906. DOI: 10.1007/s10661-013-3588-2.
  • [11] PARASCHIV S., BARBUTA-MISU N., Influence of NO2, NO and meteorological conditions on the tropospheric O3 concentration at an industrial station, En. Rep., 2020, 6, 231–236. DOI: 10.1016/j.egyr.2020.11.263.
  • [12] SULAYMON I.D., Influence of transboundary air pollution and meteorology on air quality in three major cities of Anhui Province, China, J. Clean. Prod., 2021, 329, 129641. DOI: 10.1016/j.jclepro.2021.129641.
  • [13] HE J., GONG S., Air pollution characteristics and their relation to meteorological conditions during 2014–2015 in major Chinese cities, Environ. Poll., 2017, 223, 484–496. DOI: 10.1016/j.envpol.2017.01.050.
  • [14] HE J., YU Y., Numerical model-based artificial neural network model and its application for quantifying impact factors of urban air quality, Water, Air, Soil Poll., 2016, 227 (7), 235. DOI: 10.1007/s11270-016-2930-z.
  • [15] CHEN X., LI X., Effects of human activities and climate change on the reduction of visibility in Beijing over the past 36 years, Environ. Int., 2018, 116, 92–100. DOI: 10.1016/j.envint.2018.04.009.
  • [16] YANG J., SHAO M., Impacts of extreme air pollution meteorology on air quality in China, J. Geophys. Res. Atm., 2021, 126 (7). DOI: 10.1029/2020jd033210.
  • [17] WANG G., ZHANG R., Persistent sulfate formation from London fog to Chinese haze, Proc. Natl. Acad. Sci., 2016, 113 (48), 13630–13635. DOI: 10.1073/pnas.1616540113
  • [18] CHANTARA S., SILLAPAPIROMSUK S., Atmospheric pollutants in Chiang Mai (Thailand) over a five- -year period (2005–2009), their possible sources and relation to air mass movement, Atmos. Environ. 2012, 60, 88–98. DOI: 10.1016/j.atmosenv.2012.06.044.
  • [19] DUAN W., WANG X., Influencing factors of PM 2.5 and O3 from 2016 to 2020 based on DLNM and WRF-CMAQ, Environ. Poll., 2021, 285, 117512. DOI: 10.1016/j.envpol.2021.117512.
  • [20] CHEN C., LI W., The effect of meteorological factors, seasonal factors and air pollutions on the formation of particulate matter, IOP Conference Series: Earth and Environmental Science, 2020, 450 (1), 012012. DOI: 10.1088/1755-1315/450/1/012012.
  • [21] FREDERICKSON L.B., SIDARAVICIUTE R., Are dense networks of low-cost nodes better at monitoring air pollution? A case study in Staffordshire, EGUsphere 2022, 2022, 1–28. DOI: 10.5194/egusphere-2022-407.
  • [22] MCARDLE J.J., Structural factor analysis experiments with incomplete data, Multivar. Behav. Res., 1994, 29 (4), 409–454. DOI: 10.1207/s15327906mbr2904_5.
  • [23] HUANG X., ZHANG J., The influence of GDP, population, and net export value on energy consumption, En. Sour., Part B: Econ. Plan. Pol., 2017, 12 (9), 1–7. DOI: 10.1080/15567249.2017.1300958.
  • [24] WRIGHT S., The method of path coefficients, Ann. Math. Stat., 1934, 5 (3), 161–215. DOI: 10.1214/aoms/1177732676.
  • [25] ZHOU W.-Y., YANG W., The influences of industrial gross domestic product, urbanization rate, environmental investment, and coal consumption on industrial air pollutant emission in China, Environ. Ecol. Stat., 2018, 25 (4), 429–442. DOI: 10.1007/s10651-018-0412-8.
  • [26] QIN Y.-G., YI C., Investigating the influence of meteorological factors on particulate matters: A case study based on path analysis, En. Environ., 2020, 31 (3), 479–491. DOI: 10.1177/0958305x19876696.
  • [27] LIU X.-C., ZHANG M.-D., A path analysis for chemical oxygen demand and ammonia nitrogen discharge from industrial sewage in China, Water Res., 2020, 47 (6), 1012–1019. DOI: 10.1134/s0097807820060172.
  • [28] WANG W.Y., KE B.J., Study on the countermeasures of SO2 emission reduction in Chengdu Economic Circle, Adv. Mat. Res., 2013, 807–809, 1388–1396. DOI: 10.4028/www.scientific.net/amr.807-809.1388.
  • [29] CHENG Y., ZHENG G., Reactive nitrogen chemistry in aerosol water as a source of sulfate during haze events in China, Sci. Adv., 2016, 2 (12), e1601530. DOI: 10.1126/sciadv.1601530.
  • [30] LI R., WANG Z., Air pollution characteristics in China during 2015–2016: Spatiotemporal variations and key meteorological factors, Sci. Total Environ., 2019, 648, 902–915. DOI: 10.1016/j.scitotenv.2018.08.181.
  • [31] LI R., FU H., The spatiotemporal variation and key factors of SO2 in 336 cities across China, J. Clean. Prod., 2019, 210, 602–611. DOI: 10.1016/j.jclepro.2018.11.062.
  • [32] KLIENGCHUAY W., MEEYAI A.C., Relationships between meteorological parameters and particulate matter in Mae Hong Son Province, Thailand, Int. J. Environ. Res. Public Health, 2018, 15 (12), 2801. DOI: 10.3390/ijerph15122801.
  • [33] BORHANI F., MOTLAGH M.S., Estimation of short-lived climate forced sulfur dioxide in Tehran, Iran, using machine learning analysis, Stoch. Environ. Res. Risk Assess., 2022, 1–14. DOI: 10.1007/s00477-021-02167-x.
  • [34] YANG Y., ZHOU R., Seasonal variations and size distributions of water-soluble ions of atmospheric particulate matter at Shigatse, Tibetan Plateau, Chemosphere, 2016, 145, 560–567. DOI: 10.1016/j.chemosphere.2015.11.065.
  • [35] ZHANG J., ZHANG L., Identifying the major air pollutants base on factor and cluster analysis, a case study in 74 Chinese cities, Atmos. Environ., 2016, 144, 37–46. DOI: 10.1016/j.atmosenv.2016.08.066.
  • [36] LIU W., LI X., Land use regression models coupled with meteorology to model spatial and temporal variability of NO2 and PM10 in Changsha, China, Atmos. Environ., 2015, 116, 272–280. DOI: 10.1016/j.atmosenv.2015.06.056.
  • [37] ZHOU W.-Y., XIE Y.-X., Estimating the remaining atmospheric environmental capacity using a single-box model in a high pollution risk suburb of Chengdu, China, J. Environ. Manage., 2020, 258, 272–280. DOI: 10.1016/j.atmosenv.2015.06.056.
Uwagi
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-7c080bba-031b-4ea2-a5c9-881357ee707a
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