Skip to main content

Advertisement

SpringerLink
Log in

Evaluation of vehicular pollution levels using line source model for hot spots in Muscat, Oman

  • Research Article
  • Published:
Environmental Science and Pollution Research Aims and scope Submit manuscript

Abstract

A detailed investigation was carried out to assess the concentration of near-road traffic-related air pollution (TRAP) using a dispersion model in Muscat. Two ambient air quality monitoring (AQM) stations were utilized separately at six locations near major roadways (each location for 2 months) to monitor carbon monoxide (CO) and nitrogen oxides (NOx). The study aimed to measure the concentration of near-road TRAP in a city hot spots and develop a validated dispersion model via performance measures. The US Environmental Protection Agency (US EPA) Line Source Model was implemented in which the pollutant emission factors were obtained through Comprehensive Modal Emission Model (CMEM) and COmputer Programme to calculate Emissions from Road Transport (COPERT) model. Traffic data of all vehicle categories under normal driving conditions including average vehicle speed limits and local meteorological conditions were included in the modeling study. The analysis of monitoring data showed that hourly (00:00 to 23:00) concentrations of CO were within the US EPA limits, while NOx concentration was exceeded in most locations. Also, the measured pollutant levels were consistent with hourly peak and off-peak traffic volumes. The overall primary statistical performance measures showed that COPERT model was better than CMEM due to the high sensitivity of CMEM to the local meteorological factors. The best fractional bias (0.47 and 0.39), normalized mean square error (0.44 and 0.50), correlation coefficient (0.64 and 0.70), geometric mean bias (1.07 and 1.57), and geometric variance (2.00 and 2.32) were obtained for CO and NOx, respectively. However, the bootstrap 95% CI estimates over normalized mean square error, fractional bias, and correlation coefficient for COPERT and CMEM were found to be statistically significant from 0 in the case of combined model comparison across all the traffic locations for both CO and NOx. In overall, certain roads showed weak performance mainly due to the terrain features and the lack of reliable background concentrations, which need to be considered in the future study.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  • Abdul-Wahab SA, Fadlallah SO (2014) A study of the effects of vehicle emissions on the atmosphere of Sultan Qaboos University in Oman. Atmos Environ 98:158–167

    CAS  Google Scholar 

  • Abou-Senna H, Radwan E, Westerlund K, Cooper CD (2013) Using a traffic simulation model (VISSIM) with an emissions model (MOVES) to predict emissions from vehicles on a limited-access highway. J Air Waste Manage Assoc 63:819–831

    CAS  Google Scholar 

  • Agudelo-Castaneda DM, Calesso Teixeira E, Norte Pereira F (2014) Time-series analysis of surface ozone and nitrogen oxides concentrations in an urban area at Brazil. Atmos Pollut Res 5:411–420

    CAS  Google Scholar 

  • Amoatey P, Sulaiman H (2017) Options for greenhouse gas mitigation strategies for road transportation in Oman. Am J Clim Chang 06:217–229

    Google Scholar 

  • Amoatey P, Omidvarborna H, Affum HA, Baawain M (2018a) Performance of AERMOD and CALPUFF models on SO2 and NO2 emissions for future health risk assessment in Tema Metropolis. Hum Ecol Risk Assess 25:772–786

    Google Scholar 

  • Amoatey P, Sulaiman H, Kwarteng A, Al-Reasi HA (2018b) Above-ground carbon dynamics in different arid urban green spaces. Environ Earth Sci 77(12):1–10

    CAS  Google Scholar 

  • Amoatey P, Omidvarborna H, Baawain MS, Al-Mamun A (2019) Emissions and exposure assessments of SOX, NOX, PM10/2.5 and trace metals from oil industries: a review study (2000–2018). Process Saf Environ 123:215–228

    CAS  Google Scholar 

  • Baawain MS, Al-Serihi AS (2014) Systematic approach for the prediction of ground-level air pollution (around an industrial port) using an artificial neural network. Aerosol Air Qual Res 14:124–134

    CAS  Google Scholar 

  • Bodisco TA, Rahman SMA, Hossain FM, Brown RJ (2019) On-road NOx emissions of a modern commercial light-duty diesel vehicle using a blend of tyre oil and diesel. Energy Rep 5:349–356

    Google Scholar 

  • Borrego C, Amorim JH, Tchepel O, Dias D, Rafael S, Sá E, Pimentel C, Fontes T, Fernandes P, Pereira SR, Bandeira JM, Coelho MC (2016) Urban scale air quality modelling using detailed traffic emissions estimates. Atmos Environ 131:341–351

    CAS  Google Scholar 

  • Bowatte G, Lodge CJ, Knibbs LD, Erbas B, Perret JL, Jalaludin B, Morgan GG, Bui DS, Giles GG, Hamilton GS, Wood-Baker R, Thomas P, Thompson BR, Matheson MC, Abramson MJ, Walters EH, Dharmage SC (2018) Traffic related air pollution and development and persistence of asthma and low lung function. Environ Int 113:170–176

    CAS  Google Scholar 

  • Breeze (2019) Meteorological analysis. Breeze AERMOD. https://www.breeze-software.com/data/meteorological-analysis/, Accessed 6/5/2019

  • Cai H, Xie S (2011) Traffic-related air pollution modeling during the 2008 Beijing Olympic Games: the effects of an odd-even day traffic restriction scheme. Sci Total Environ 409:1935–1948

    CAS  Google Scholar 

  • Cai Y, Hodgson S, Blangiardo M, Gulliver J, Morley D, Fecht D, Vienneau D, de Hoogh K, Key T, Hveem K, Elliott P, Hansell AL (2018) Road traffic noise, air pollution and incident cardiovascular disease: a joint analysis of the HUNT, EPIC-Oxford and UK Biobank cohorts. Environ Int 114:191–201

    CAS  Google Scholar 

  • Chang JC, Hanna SR (2004) Air quality model performance evaluation. Meteorog Atmos Phys 87:167–196

    Google Scholar 

  • Charabi Y, Abdul-Wahab S, Al-Rawas G, Al-Wardy M, Fadlallah S (2018) Investigating the impact of monsoon season on the dispersion of pollutants emitted from vehicles: a case study of Salalah City, Sultanate of Oman. Transport Res D - TR E 59:108–120

    Google Scholar 

  • Checkley W, Epstein LD, Gilman RH, Figueroa D, Cama RI, Patz JA, Black RE (2000) Effects of EI Niño and ambient temperature on hospital admissions for diarrhoeal diseases in Peruvian children. Lancet 355:442–450

    CAS  Google Scholar 

  • Directorate General of Meteorology (2019) Current local weather. Directorate General of Meteorology.http://www.met.gov.om/opencms/export/sites/default/dgman/en/home/index.html. Accessed 4/5/2019 2019

  • Gokhale S, Pandian S (2007) A semi-empirical box modeling approach for predicting the carbon monoxide concentrations at an urban traffic intersection. Atmos Environ 41:7940–7950

    CAS  Google Scholar 

  • Halonen JI, Blangiardo M, Toledano MB, Fecht D, Gulliver J, Ghosh R, Anderson HR, Beevers SD, Dajnak D, Kelly FJ, Wilkinson P, Tonne C (2016) Is long-term exposure to traffic pollution associated with mortality? A small-area study in London. Environ Pollut 208:25–32

    CAS  Google Scholar 

  • Heusinger J, Sailor DJ (2019) Heat and cold roses of U.S. cities: a new tool for optimizing urban climate. Sustain Cities Soc 51:101777

    Google Scholar 

  • Kadiyala A, Kumar A (2012) Guidelines for operational evaluation of air quality models. Lambert Academic Publishing GmbH & Co, Saarbrücken, p 123

    Google Scholar 

  • Khreis H, de Hoogh K, Nieuwenhuijsen MJ (2018) Full-chain health impact assessment of traffic-related air pollution and childhood asthma. Environ Int 114:365–375

    CAS  Google Scholar 

  • Ko J, Myung C-L, Park S (2019) Impacts of ambient temperature, DPF regeneration, and traffic congestion on NOx emissions from a Euro 6-compliant diesel vehicle equipped with an LNT under real-world driving conditions. Atmos Environ 200:1–14

    CAS  Google Scholar 

  • Korfant M, Gogola M (2019) Traffic generation by various types of urban facilities within Slovak Republic. Transp Res Proc 40:310–316

    Google Scholar 

  • Kumar A, Bellam NK, Sud A (2012) Performance of an industrial source complex model: predicting long-term concentrations in an urban area. Environ Prog 18:93–100

    Google Scholar 

  • Kwarteng AY, Dorvlo AS, Vijaya Kumar GT (2009) Analysis of a 27-year rainfall data (1977-2003) in the Sultanate of Oman. Int J Climatol 29:605–617

    Google Scholar 

  • Liang D et al (2018) Errors associated with the use of roadside monitoring in the estimation of acute traffic pollutant-related health effects. Environ Res 165:210–219

    CAS  Google Scholar 

  • Liu H, Rodgers MO, Guensler R (2019) Impact of road grade on vehicle speed-acceleration distribution, emissions and dispersion modeling on freeways. Transport Res D - TR E 69:107–122

    Google Scholar 

  • López-Pérez E, Hermosilla T, Carot-Sierra J-M, Palau-Salvador G (2019) Spatial determination of traffic CO emissions within street canyons using inverse modelling. Atmos Pollut Res 1(0):1140–1147

    Google Scholar 

  • Mannucci PM, Franchini M (2017) Health effects of ambient air pollution in developing countries. Int J Environ Res Public Health 14:1048

    Google Scholar 

  • Milando CW, Batterman SA (2018) Operational evaluation of the RLINE dispersion model for studies of traffic-related air pollutants. Atmos Environ 182:213–224

    CAS  Google Scholar 

  • Mohan M, Bhati S, Sreenivas A, Marrapu P (2011) Performance evaluation of AERMOD and ADMS-urban for total suspended particulate matter concentrations in megacity Delhi. Aerosol Air Qual Res 11:883–894

    Google Scholar 

  • Munir S, Habeebullah TM (2018) Vehicular emissions on main roads in Makkah, Saudi Arabia—a dispersion modelling study. Arab J Geosci 11:543

    Google Scholar 

  • Muscat Municipality (2019) Open data. https://www.mm.gov.om/Page.aspx?PAID=122#OpenData&MID=128&Slide=True&MoID=72, Accessed 07/05/2019

  • NAEI (2019) Emission factors for transport National Atmospheric Emission Inventory http://naei.beis.gov.uk/data/ef-transport. 08/05/2019

  • National Centre for Statistics and Information (2018) Statistical yearbook 2018: issue 46. National Centre for Statistics and Information (NCSI). https://www.ncsi.gov.om/Elibrary/Pages/LibraryContentDetails.aspx?ItemID=GxJuqSZUD0v4K7T%2FPJp13A%3D%3D. Accessed 02/05/2019

  • Omidvarborna H, Baawain M, Al-Mamun A (2018) Ambient air quality and exposure assessment study of the Gulf Cooperation Council countries: a critical review. Sci Total Environ 636:437–448

    CAS  Google Scholar 

  • Pedersen M, Olsen SF, Halldorsson TI, Zhang C, Hjortebjerg D, Ketzel M, Grandström C, Sørensen M, Damm P, Langhoff-Roos J, Raaschou-Nielsen O (2017) Gestational diabetes mellitus and exposure to ambient air pollution and road traffic noise: a cohort study. Environ Int 108:253–260

    CAS  Google Scholar 

  • Pelletier G, Rigden M, Kauri LM, Shutt R, Mahmud M, Cakmak S, Kumarathasan P, Thomson EM, Vincent R, Broad G, Liu L, Dales R (2017) Associations between urinary biomarkers of oxidative stress and air pollutants observed in a randomized crossover exposure to steel mill emissions. Int J Hyg Environ Health 220:387–394

    CAS  Google Scholar 

  • Ribeiro AG, Downward GS, Freitas CU, Chiaravalloti Neto F, Cardoso MRA, Latorre MRDO, Hystad P, Vermeulen R, Nardocci AC (2019) Incidence and mortality for respiratory cancer and traffic-related air pollution in Sao Paulo, Brazil. Environ Res 170:243–251

    CAS  Google Scholar 

  • Righi S, Lucialli P, Pollini E (2009) Statistical and diagnostic evaluation of the ADMS-Urban model compared with an urban air quality monitoring network. Atmos Environ 43:3850–3857

    CAS  Google Scholar 

  • Rosenlieb EG, McAndrews C, Marshall WE, Troy A (2018) Urban development patterns and exposure to hazardous and protective traffic environments. J Transp Geogr 66:125–134

    Google Scholar 

  • Song J, Wang Z-H, Myint SW, Wang C (2017) The hysteresis effect on surface-air temperature relationship and its implications to urban planning: an examination in Phoenix, Arizona, USA. Landscape Urban Plan 167:198–211

    Google Scholar 

  • Triantafyllopoulos G, Dimaratos A, Ntziachristos L, Bernard Y, Dornoff J, Samaras Z (2019) A study on the CO2 and NOx emissions performance of Euro 6 diesel vehicles under various chassis dynamometer and on-road conditions including latest regulatory provisions. Sci Total Environ 666:337–346

    CAS  Google Scholar 

  • US EPA (2019) Smog, soot, and other air pollution from transportation. United States Environmetal Protection Agency. https://www.epa.gov/transportation-air-pollution-and-climate-change/smog-soot-and-local-air-pollution. Accessed May 3 2019

  • US EPA (2018a) User’s guide for the AERMOD meteorological preprocessor (AERMET). US Environmental Protection Agency. https://www3.epa.gov/ttn/scram/7thconf/aermod/aermet_userguide.pdf. Accessed May 2 2019

  • US EPA (2018b) User’s guide for the AMS/EPA regulatory model (AERMOD). US Environmental Protection Agency, Washington, D.C

  • US EPA (2018c) Primary National Ambient Air Quality Standards (NAAQS) for nitrogen dioxide. US Environmental Protection Agency. https://www.epa.gov/no2-pollution/primary-national-ambient-air-quality-standards-naaqs-nitrogen-dioxide. Accessed on 17/04/2020

  • Wallace HW, Jobson BT, Erickson MH, McCoskey JK, VanReken TM, Lamb BK, Vaughan JK, Hardy RJ, Cole JL, Strachan SM, Zhang W (2012) Comparison of wintertime CO to NOx ratios to MOVES and MOBILE6.2 on-road emissions inventories. Atmos Environ 63:289–297

    CAS  Google Scholar 

  • Weichenthal S, van Rijswijk D, Kulka R, You H, van Ryswyk K, Willey J, Dugandzic R, Sutcliffe R, Moulton J, Baike M, White L, Charland JP, Jessiman B (2015) The impact of a landfill fire on ambient air quality in the north: a case study in Iqaluit, Canada. Environ Res 142:46–50

    CAS  Google Scholar 

  • Wen D, Zhai W, Xiang S, Hu Z, Wei T, Noll KE (2017) Near-roadway monitoring of vehicle emissions as a function of mode of operation for light-duty vehicles. J Air Waste Manage Assoc 67:1229–1239

    CAS  Google Scholar 

  • WHO (2019) Air pollution. World Health Organisation. https://www.who.int/sustainable-development/transport/health-risks/air-pollution/en/. Accessed 03/05/2019

  • Zhang Y, Ioannou PA (2016) Environmental impact of combined variable speed limit and lane change control: a comparison of MOVES and CMEM model. IFAC-PapersOnLine 49:323–328

    CAS  Google Scholar 

  • Zhang L, Hu X, Qiu R, Lin J (2019) Comparison of real-world emissions of LDGVs of different vehicle emission standards on both mountainous and level roads in China. Transport Res D - TR E 69:24–39

    Google Scholar 

  • Zhou H, Gao H (2018) The impact of urban morphology on urban transportation mode: a case study of Tokyo. Case Stud Transp Policy 8:197–2015

    CAS  Google Scholar 

Download references

Funding

The authors received the support provided by the Ministry of Environment and Climatic Affairs (MECA), Oman, under the Grant No. CR/DVC/CESAR/16/05.

Author information

Authors and Affiliations

  1. Department of Civil and Architectural Engineering, College of Engineering, Sultan Qaboos University, P.O. Box 33, Al-Khoudh, 123, Muscat, Sultanate of Oman

    Patrick Amoatey, Hamid Omidvarborna, Mahad Said Baawain & Abdullah Al-Mamun

  2. Global Centre for Clean Air Research, Department of Civil and Environmental Engineering, Faculty of Engineering and Physical Sciences, University of Surrey, Guildford, Surrey, GU2 7XH, UK

    Hamid Omidvarborna

Authors
  1. Patrick Amoatey
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hamid Omidvarborna
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mahad Said Baawain
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Abdullah Al-Mamun
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mahad Said Baawain.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Responsible Editor: Gerhard Lammel

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Amoatey, P., Omidvarborna, H., Baawain, M.S. et al. Evaluation of vehicular pollution levels using line source model for hot spots in Muscat, Oman. Environ Sci Pollut Res 27, 31184–31201 (2020). https://doi.org/10.1007/s11356-020-09215-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11356-020-09215-z

Keywords

Search

Navigation