Examining and predicting the influence of climatic and terrestrial factors on the seasonal distribution of ozone column depth over Tehran province using satellite observations

Acta Geophysica - Trang 1-36 - 2023
Faezeh Borhani1, Amir Houshang Ehsani2, Savannah L. McGuirk3, Majid Shafiepour Motlagh1, Seyed Mohsen Mousavi4, Yousef Rashidi5, Seyed Mohammad Mirmazloumi6
1Department of Civil & Environmental Engineering, Faculty of Environment, University of Tehran, Tehran, Iran
2Department of Environmental Design, Faculty of Environment, University of Tehran, Tehran, Iran
3ARC Training Centre for CubeSat’s UAV’s and their Applications (CUAVA), School of Physics, University of Sydney, Sydney, Australia
4Department of Environmental Planning and Design, Shahid Beheshti University, Tehran, Iran
5Environmental Sciences Research Institute, Shahid Beheshti University, Tehran, Iran
6Leibniz Centre for Agricultural Landscape Research (ZALF), Müncheberg, Germany

Tóm tắt

When combined with conducive atmospheric conditions, air pollution caused by fossil fuel consumption associated with transportation, industry and electricity production for households, create and sustain continuous pollution over the megalopolis of Tehran, Iran. In conjunction with daily meteorological forecasts, remote sensing can be used to identify and predict days with hazardous levels of air pollution, providing an opportunity for air quality alert systems to be triggered and warnings circulated to reduce health risks to citizens of Tehran province. Combining remotely sensed ozone column density (OCD) data from the Sentinel-5 TROPOMI sensor with NASA Giovanni data concerning meteorological parameters (temperature (T), wind speed (WS) and specific humidity (SH)), geographical parameters and population data, this study considers the drivers and effects of ozone pollution on the urban climate and vegetation condition (normalized difference vegetation index (NDVI)) of 16 counties in Tehran province, Iran during 12 months (i.e., January 2021 to December 2021). Future monthly forecasts of the OCD, climatic and terrestrial factors in 2022 are also presented. Google Earth Engine and the NASA Giovanni platforms were employed for the processing and analysis of data using an interpolation technique. Additionally, a Box–Jenkins ARIMA and Exponential Smoothing (ETS) models were compared and tailored to generate monthly forecasts of OCD, T, WS, SH and NDVI. The highest and lowest OCD was obtained in June and December 2021, with a concentration of 0.14277 mol/m2 and 0.12383 mol/m2, respectively. However, the annual average OCD was higher in the cities of Shahriar and Pakdasht in March, with values of 0.13237 mol/m2 and 0.13244 mol/m2, respectively. The lowest OCD recorded was 0.13105 mol/m2, in Shemiranat city, in the north of Tehran. The results indicate a positive correlation between OCD and NDVI, and a negative correlation between OCD, SH, WS and T. A strong seasonal trend in OCD was identified for all cities, but across the entire province, altitude and population size were the most significant explanatory variables for spatial variations in OCD. This research demonstrates that an effective OCD monitoring and forecasting model may be generated from remote sensing and meteorological variables. The implementation and utilization of these models are of paramount importance as they offer vital information to authorities for continuous air quality monitoring and strategic planning, particularly for days with hazardous air pollution. By effectively implementing the OCD model, it has the potential to directly contribute to improved health outcomes in major cities across Iran.

Tài liệu tham khảo

Akinyemi ML (2010) Total ozone as a stratospheric indicator of climate variability over West Africa. Int J Phys Sci 5(5):447–451. https://doi.org/10.5897/IJPS.9000599 Amiraslani F (2022) Climate change and urban citizens: the role of media in publicising the conservation of green spaces and mitigation of air pollution. Conservation 2(2):219–232. https://doi.org/10.3390/conservation2020014 Azeem SMI, Palo SE, Wu DL, Froidevaux L (2001) Observations of the 2-day wave in UARS MLS temperature and ozone measurements. Geophys Res Lett 28(16):3147–3150. https://doi.org/10.1029/2001GL013119 Bais AF, Bernhard G, McKenzie RL, Aucamp PJ, Young PJ, Ilyas M, Deushi M (2019) Ozone–climate interactions and effects on solar ultraviolet radiation. Photochem Photobiol Sci 18(3):602–640. https://doi.org/10.1039/C8PP90059K Blackstock JJ, Allen MR (2012) The science and policy of short-lived climate pollutants Borhani F, Noorpoor A (2017) Cancer risk assessment Benzene, Toluene, Ethylbenzene and Xylene (BTEX) in the production of insulation bituminous. Environ Energy Econ Res 1(3):311–320. https://doi.org/10.22097/eeer.2017.90292.1010 Borhani F, Noorpoor A (2020) Measurement of air pollution emissions from chimneys of production units moisture insulation (Isogam) Delijan. J Environ Sci Technol 21(12):57–71. https://doi.org/10.22034/JEST.2020.25934.3488 Borhani F, Noorpoor A, Khalili K (2017a) Measuring and evaluation of non-hydrocarbon air pollutants emitted in the production of insulation bituminous (Isogam) exhaust flue gas. In: International conference on advances in science and arts, March 2017a, Istanbul, Turkey, p. 335–343 Borhani F, Mirmohammadi M, Aslemand A (2017b) Experimental study of benzene, toluene, ethylbenzene and xylene (BTEX) concentrations in the air pollution of Tehran, Iran. J Res Environ Health 3(2):105–115. https://doi.org/10.22038/jreh.2017.23688.1151 Borhani F, Zahed F, Noorpoor A (2019) Modeling and evaluating the contribution of NOX and CO pollutants emitted in the insulation Bituminous units (Isogam) exhaust flue gas on the around area (Case study: Delijan City). New Sci Technol 1(2):91–100 Borhani F, Shafiepour Motlagh M, Stohl A, Rashidi Y, Ehsani AH (2021) Changes in short-lived climate pollutants during the COVID-19 pandemic in Tehran, Iran. Environ Monitor Assess 193(6):1–12. https://doi.org/10.1007/s10661-021-09096-w Borhani F, Shafiepour Motlagh M, Stohl A, Rashidi Y, Ehsani AH (2022a) Tropospheric ozone in Tehran, Iran, during the last 20 years. Environ Geochem Health 44:3615–3637. https://doi.org/10.1007/s10653-021-01117-4 Borhani F, Shafiepour Motlagh M, Rashidi Y, Ehsani AH (2022b) Estimation of short-lived climate forced sulfur dioxide in Tehran, Iran using machine learning analysis. Stochast Environ Res Risk Assess. https://doi.org/10.1007/s00477-021-02167-x Borhani F, Shafiepour Motlagh M, Ehsani AH, Rashidi Y, Maddah S, Mousavi SM (2022c) On the predictability of short-lived particulate matter around a cement plant in Kerman, Iran: machine learning analysis. Int J Environ Sci Technol. https://doi.org/10.1007/s13762-022-04645-3 Borhani F, Shafiepour Motlagh M, Ehsani AH, Rashidi Y (2022d) Evaluation of short-lived atmospheric fine particles in Tehran, Iran. Arabian J Geosci 15(16):1–10. https://doi.org/10.1007/s12517-022-10667-5 Borhani F, Ehsani AH, Shafiepour Motlagh M, Rashidi Y (2023a) Estimate ground-based PM2.5 concentrations with Merra-2 aerosol components in Tehran, Iran: Merra-2 PM2.5 concentrations verification and meteorological dependence. Environ Develop Sustain. https://doi.org/10.1007/s10668-023-02937-3 Borhani F, Shafiepour Motlagh M, Ehsani AH, Rashidi Y, Ghahremanloo M, Amani M, Moghimi A (2023b) Current status and future forecast of short-lived climate-forced ozone in Tehran, Iran, derived from ground-based and satellite observations. Water Air Soil Pollut. https://doi.org/10.1007/s11270-023-06138-6 Borhani F, Ehsani AH, Hosseini Shekarabi HS (2023c) Prediction and spatiotemporal analysis of atmospheric fine particles and their effect on temperature and vegetation cover in Iran using exponential smoothing approach in python. J Nat Environ 76(2):325–344. https://doi.org/10.22059/jne.2023.354696.2521 Borhani F, Shafiepour Motlagh M, Ehsani AH, Rashidi Y, Noorpoor A, Maddah S (2023d) Optimization models for reducing the air pollutants emission in the production of insulation bituminous. Environ Energy Econ Res 7(2):1–14. https://doi.org/10.22097/eeer.2023.364295.1266 Bowerman NH, Frame DJ, Huntingford C, Lowe JA, Smith SM, Allen MR (2013) The role of short-lived climate pollutants in meeting temperature goals. Nat Clim Chang 3(12):1021–1024. https://doi.org/10.1038/nclimate2034 Statistical Center of Iran, SCI (2021) https://www.amar.org.ir/ Chen X, Li BL (2010) Global scale assessment of the relative contribution of climate and non-climate factors on annual vegetation change. Geofizika 27(1):37–43 Cheraghi A, Borhani F (2016a) Assessing the effects of air pollution on four methods of pavement by using four methods of multi-criteria decision in Iran. J Environ Sci Stud 1(1):59–71 Cheraghi A, Borhani F (2016b) Evaluation of environmental and sustainable development of four pavements in Iran by four method of multi-criteria analysis. J Environ Sci Stud 1(2):51–62 Cofano A, Cigna F, Santamaria Amato L, Siciliani de Cumis M, Tapete D (2021) Exploiting Sentinel-5P TROPOMI and ground sensor data for the detection of volcanic SO2 plumes and activity in 2018–2021 at Stromboli, Italy. Sensors 21(21):6991. https://doi.org/10.3390/s21216991 Coldewey-Egbers M, Loyola DG, Lerot C, Roozendael V (2022) Global, regional and seasonal analysis of total ozone trends derived from the 1995–2020 GTO-ECV climate data record. Atmos Chem Phys 22(10):6861–6878. https://doi.org/10.5194/acp-22-6861-2022 Cui Y, Jiang L, Zhang W, Bao H, Geng B, He Q, Streets DG (2019) Evaluation of China’s environmental pressures based on satellite NO2 observation and the extended STIRPAT model. Int J Environ Res Public Health 16(9):1487. https://doi.org/10.3390/ijerph16091487 Danielsen EF (1968) Stratospheric-tropospheric exchange based on radioactivity, ozone and potential vorticity. J Atmos Sci 25(3):502–518. https://doi.org/10.1175/1520-0469(1968)025%3C0502:STEBOR%3E2.0.CO;2 de Oliveira EM, Oliveira FLC (2018) Forecasting mid-long term electric energy consumption through bagging ARIMA and exponential smoothing methods. Energy 144:776–788. https://doi.org/10.1016/j.energy.2017.12.049 Dehghan A, Khanjani N, Bahrampour A, Goudarzi G, Yunesian M (2018) The relation between air pollution and respiratory deaths in Tehran, Iran-using generalized additive models. BMC Pulm Med 18(1):1–9. https://doi.org/10.1186/s12890-018-0613-9 EEA, European Environment Agency. (2018). https://www.eea.europa.eu/ Fishman J, Wozniak AE, Creilson JK (2003) Global distribution of tropospheric ozone from satellite measurements using the empirically corrected tropospheric ozone residual technique: identification of the regional aspects of air pollution. Atmos Chem Phys 3(4):893–907. https://doi.org/10.5194/acp-3-893-2003 Fowler D, Amann M, Anderson R, Ashmore M, Cox P, Depledge M et al (2008) Ground-level ozone in the 21st century: future trends, impacts and policy implications (Vol. 15, No. 08) Garane K, Koukouli ME, Verhoelst T, Lerot C, Heue KP, Fioletov V, Zimmer W (2019) TROPOMI/S5P total ozone column data: global ground-based validation and consistency with other satellite missions. Meteorol Measur Tech 12(10):5263–5287. https://doi.org/10.5194/amt-12-5263-2019 Ge W, Deng L, Wang F, Han J (2021) Quantifying the contributions of human activities and climate change to vegetation net primary productivity dynamics in China from 2001 to 2016. Sci Total Environ 773:145648. https://doi.org/10.1016/j.scitotenv.2021.145648 Giovanni, NASA’s Goddard Earth Sciences Data and Information Services Center (2021) https://giovanni.gsfc.nasa.gov/giovanni/ Golkar F, Mousavi SM (2022) Variation of XCO2 anomaly patterns in the Middle East from OCO-2 satellite data. Int J Digit Earth 15(1):1219–1235. https://doi.org/10.1080/17538947.2022.2096936 Gorelick N, Hancher M, Dixon M, Ilyushchenko S, Thau D, Moore R (2017) Google earth engine: planetary-scale geospatial analysis for everyone. Remote Sens Environ 202:18–27. https://doi.org/10.1016/j.rse.2017.06.031 Guan X, Wu H (2008) Parallel optimization of IDW interpolation algorithm on multicore platform. In: Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Advanced Spatial Data Models and Analyses (Vol. 7146, p. 71461Y). International Society for Optics and Photonics. https://doi.org/10.1117/12.813163 Helland IS (1987) On the interpretation and use of R2 in regression analysis. Biometrics. https://doi.org/10.2307/2531949 Hoveidi H, Aslemand A, Borhani F, Naghadeh SF (2017) Emission and health costs estimation for air pollutants from municipal solid waste management scenarios, case study: NOX and SOX pollutants, Urmia, Iran. J Environ Treat Tech 5(1):59–64 Isaksen ISA, Zerefos C, Kourtidis K, Meleti C, Dalsøren SB, Sundet JK, Balis D (2005) Tropospheric ozone changes at unpolluted and semipolluted regions induced by stratospheric ozone changes. J Geophys Res Atmos. https://doi.org/10.1029/2004JD004618 Javid M, Bahramifar N, Younesi H, Taghavi SM, Givehchi R (2015) Dry deposition, seasonal variation and source interpretation of ionic species at Abali, Firouzkouh and Varamin, Tehran Province, Iran. Atmos Res 157:74–90. https://doi.org/10.1016/j.atmosres.2015.01.018 Ji H, Chen S, Zhang Y, Chen H, Guo P, Zhao P (2018) Comparison of air quality at different altitudes from multi-platform measurements in Beijing. Atmos Chem Phys 18(14):10645–10653. https://doi.org/10.5194/acp-18-10645-2018 Jumaah HJ, Ameen MH, Kalantar B, Rizeei HM, Jumaah SJ (2019) Air quality index prediction using IDW geostatistical technique and OLS-based GIS technique in Kuala Lumpur, Malaysia. Geomat Nat Haz Risk 10(1):2185–2199. https://doi.org/10.1080/19475705.2019.1683084 Junge CE (1962) Global ozone budget and exchange between stratosphere and troposphere. Tellus 14(4):363–377. https://doi.org/10.1111/j.2153-3490.1962.tb01349.x Kaplan G, Avdan ZY (2020) Space-borne air pollution observation from sentinel-5p tropomi: relationship between pollutants, geographical and demographic data. Int J Eng Geosci 5(3):130–137. https://doi.org/10.26833/ijeg.644089 Khalaf I, Taweek YQ, Naïf SS, Al-Taai OT (2021) Total ozone column variability of selected stations over Iraq. In: IOP conference series: earth and environmental science (Vol. 722, No. 1, p. 012025). IOP Publishing. https://doi.org/10.1088/1755-1315/722/1/012025 Maddah S, Bidhendi GN, Borhani F, Taleizadeh AA (2022) Resilient-sustainable supplier selection considering health-safety-environment performance indices: a case study in automobile industry. https://doi.org/10.21203/rs.3.rs-2046543/v1 Mohammadi KA, Talebi AA (2013) A study of the genus Orthocentrus (Hymenoptera: Ichneumonidae, Orthocentrinae) in Gilan and Tehran provinces of Iran, with first records of seven species and one subspecies. Appl Entomol Phytopathol 80(2):29–39. https://doi.org/10.22092/jaep.2013.100582 Monks PS, Archibald AT, Colette A, Cooper O, Coyle M, Derwent R, Williams ML (2015) Tropospheric ozone and its precursors from the urban to the global scale from air quality to short-lived climate forcer. Meteorol Chem Phys 15(15):8889–8973 Mousavi SM, Dinan NM, Ansarifard S, Borhani F, Ezimand K, Naghibi A (2023) Examining the role of the main terrestrial factors won the seasonal distribution of atmospheric carbon dioxide concentration over Iran. J Indian Soc Remote Sens. https://doi.org/10.1007/s12524-022-01650-4 Muniraj K, Panneerselvam B, Devaraj S, Jesudhas CJ, Sudalaimuthu K (2021) Evaluating the effectiveness of emissions reduction measures and ambient air quality variability through ground-based and Sentinel-5P observations under the auspices of COVID pandemic lockdown in Tamil Nadu, India. Int J Environ Anal Chem. https://doi.org/10.1080/03067319.2021.1902997 Nyoni T, Mutongi C (2019) Modeling and forecasting carbon dioxide emissions in China using Autoregressive Integrated Moving Average (ARIMA) models. EPRA Int J Multidiscip Res 5:215–224 Özbay B (2012) Modeling the effects of meteorological factors on SO2 and PM10 concentrations with statistical approaches. Clean-Soil Air Water 40(6):571–577. https://doi.org/10.1002/clen.201100356 Pan S, Zhao X, Yue Y (2019) Spatiotemporal changes of NDVI and correlation with meteorological factors in northern china from 1985–2015. In: E3S web of conferences (Vol. 131, p. 01040). EDP Sciences. https://doi.org/10.1051/e3sconf/201913101040 Park JH, Lee DK, Gan J, Park C, Kim S, Sung S, Hong SC (2018) Effects of climate change and ozone concentration on the net primary productivity of forests in South Korea. Forests 9(3):112. https://doi.org/10.3390/f9030112 Pierrehumbert RT (2014) Short-lived climate pollution. Annu Rev Earth Planet Sci 42:341–379 Pujiharta P, Darma YD, Wiyanti NR, Gunawan A (2022) Comparative analysis of arima model and exponential smoothing in predicting inventory in automotive companies. Budapest Int Res Critics Inst J (BIRCI-J), 5(1): 1056–1065. https://doi.org/10.33258/birci.v5i1.3707 Raissi V, Saber V, Bahadory S, Akhlaghi E, Raiesi O, Aslani R, Ibrahim A et al (2020) Comparison of the prevalence of Toxocara spp. eggs in public parks soils in different seasons, from 2017 to 2018, Tehran Province, Iran. Clin Epidemiol Global Health 8(2):450–454. https://doi.org/10.1016/j.cegh.2019.10.007 Rezaei M, Farajzadeh M, Mielonen T, Ghavidel Y (2019) Analysis of spatio-temporal dust aerosol frequency over Iran based on satellite data. Atmos Pollut Res 10(2):508–519. https://doi.org/10.1016/j.apr.2018.10.002 Rouse Jr JW, Haas RH, Deering DW, Schell JA, Harlan JC (1974) Monitoring the vernal advancement and retrogradation (green wave effect) of natural vegetation (No. E75–10354) Safarianzengir V, Sobhani B, Yazdani MH, Kianian M (2020) Monitoring, analysis and spatial and temporal zoning of air pollution (carbon monoxide) using Sentinel-5 satellite data for health management in Iran, located in the Middle East. Air Qual Atmos Health 13(6):709–719. https://doi.org/10.1007/s11869-020-00827-5 Saradjian MR, Sherafati S (2015) Trend assessment of spatio-temporal change of Tehran Heat Island using satellite images. Int Arch Photogrammet Remote Sens Spatial Inf Sci 40(1):657 Scheiter S, Kumar D, Corlett RT, Gaillard C, Langan L, Lapuz RS, Tomlinson KW et al (2020) Climate change promotes transitions to tall evergreen vegetation in tropical Asia. Global Change Biol 26(9):5106–5124. https://doi.org/10.1111/gcb.15217 Shi Z, Huang L, Li J, Ying Q, Zhang H, Hu J (2020) Sensitivity analysis of the surface ozone and fine particulate matter to meteorological parameters in China. Meteorol Chem Phys 20(21):13455–13466. https://doi.org/10.5194/acp-20-13455-2020 Stevenson DS, Dentener FJ, Schultz MG, Ellingsen K, Van Noije TPC, Wild O et al (2006) Multimodel ensemble simulations of present-day and near-future tropospheric ozone. J Geophys Res Atmos 111(D8):1–23. https://doi.org/10.1029/2005JD006338 Tucker CJ, Pinzon JE, Brown ME, Slayback DA, Pak EW, Mahoney R, El Saleous N et al (2005) An extended AVHRR 8-km NDVI dataset compatible with MODIS and SPOT vegetation NDVI data. Int J Remote sens 26(20):4485–4498. https://doi.org/10.1080/01431160500168686 Wang Z (2021) Satellite-observed effects from ozone pollution and climate change on growing-season vegetation activity over China during 1982–2020. Atmosphere 12(11):1390. https://doi.org/10.3390/atmos12111390 Zhang Y, Choo WC, Ho JS, Wan CK (2022) Single or combine? Tourism demand volatility forecasting with exponential weighting and smooth transition combining methods. Computation 10(8):137. https://doi.org/10.3390/computation10080137 Zhou M, Huang Y, Li G (2021) Changes in the concentration of air pollutants before and after the COVID-19 blockade period and their correlation with vegetation coverage. Environ Sci Pollut Res 28(18):23405–23419. https://doi.org/10.1007/s11356-020-12164-2 Zoran MA, Savastru RS, Savastru DM, Tautan MN (2020) Assessing the relationship between ground levels of ozone (O3) and nitrogen dioxide (NO2) with coronavirus (COVID-19) in Milan, Italy. Sci Total Environ. https://doi.org/10.1016/j.scitotenv.2020.140005