Concentration distribution and group disparity of traffic-derived NO₂ exposure in Baoshan District

Abstract

Motor vehicles are a major source of NO₂ emissions, making traffic-related pollution a key target for urban air pollution control management. However, research on traffic-related NO₂ exposure risks in China remains nascent, particularly regarding spatio-temporal variations and exposure inequities. To support evidence-based public health policies, it is essential to investigate group disparities in exposure across both spatial and temporal dimensions. This study utilizes the CALPUFF model and mobile phone signal data to examine the spatio-temporal patterns and population group disparities in NO₂ exposure within Baoshan District, Shanghai, China. The findings reveal a bimodal diurnal pattern, with higher NO₂ exposure levels on weekdays and lower levels on weekends. Areas with heavy traffic and high population density, such as port zones and the outer ring expressway, are identified as the most vulnerable. Furthermore, males and younger age groups experience greater exposure to traffic-related NO₂, whereas elderly individuals are comparatively less exposed.

Keywords

CALPUFF model / pollution modeling / spatio-temporal variation / NO₂ exposure risk / environmental justice

References

[1] Ministry of Ecology and Environment of China. State of the ecological environment bulletin. 2020. Available from: https://www.mee.gov.cn/hjzl/sthjzk/zghjzkgb/ [Last accessed on 19 May 2025].

[2] Anenberg SC,Goldberg DL.Long-term trends in urban NO₂ concentrations and associated paediatric asthma incidence: estimates from global datasets.Lancet Planet Health2022;6:e49-58

[3] Ott WR.Concepts of human exposure to air pollution.Environ Int1982;7:179-96

[4] Smith KR.Air pollution assessing total exposure in the United States.Environ Sci Policy Sustain Dev1988;30:10-38

[5] Afzali A,Afzali M.Prediction of air pollutants concentrations from multiple sources using AERMOD coupled with WRF prognostic model.J Clean Prod2017;166:1216-25

[6] Claggett M.Comparing predictions from the CAL3QHCR and AERMOD models for highway applications.Transport Res Rec2014;2428:18-26

[7] Carruthers D,Hunt J.UK-ADMS: a new approach to modelling dispersion in the earth's atmospheric boundary layer.J Wind Eng Ind Aerod1994;52:139-53

[8] Scire JS,Yamartino RJ. A user’s guide for the CALPUFF dispersion model. Concord, MA: Earth Tech Inc., 2000. Available from: https://www.eoas.ubc.ca/courses/atsc507/ADM/calpuff/documentation/CAL-v5/CALPUFF_UsersGuide.pdf [Last accessed on 19 May 2025]

[9] Ghannam K.Emissions characterization and regulatory compliance at an industrial complex: an integrated MM5/CALPUFF approach.Atmos Environ2013;69:156-69

[10] Ergun P,Bayram A,Altiok H.Application of an activity based approach to assess air quality from mobile sources in an urban center.Curr Environ Eng2014;1:64-72

[11] Joo S,Lee S.Assessing the impact of traffic crashes on near freeway air quality.Transp Res Part D2017;57:64-73

[12] Charabi Y,Al-Rawas G,Fadlallah S.Investigating the impact of monsoon season on the dispersion of pollutants emitted from vehicles: a case study of Salalah city, sultanate of Oman.Transp Res Part D2018;59:108-20

[13] Abdul-Wahab SA.A study of the effects of vehicle emissions on the atmosphere of Sultan Qaboos University in Oman.Atmos Environ2014;98:158-67

[14] Broomandi P.Modeling of air pollutants' dispersion by means of CALMET/CALPUFF (case study: district 7 in Tehran city). 2018.

[15] Farrell W,Goldberg M,Shekarrizfard M.Near roadway air pollution across a spatially extensive road and cycling network.Environ Pollut2016;212:498-507

[16] Zhang Y,Wang Z.Study on the distribution characteristics of PM_2.5 on both sides of the elevated road in Guangzhou.Transp Energy Conserv Environ Prot2019;15:76-81

[17] Shekarrizfard M,Hatzopoulou M.An examination of population exposure to traffic related air pollution: comparing spatially and temporally resolved estimates against long-term average exposures at the home location.Environ Res2016;147:435-44

[18] Chen Y,Greenstone M.Evidence on the impact of sustained exposure to air pollution on life expectancy from China's Huai River policy.Proc Natl Acad Sci USA2013;110:12936-41 PMCID:PMC3740827

[19] Fabisiak JP,Brink LL.A risk-based model to assess environmental justice and coronary heart disease burden from traffic-related air pollutants.Environ Health2020;19:34 PMCID:PMC7075037

[20] Levy I,Berman T,Barnett-Itzhaki Z.A hybrid model for evaluating exposure of the general population in Israel to air pollutants.Environ Monit Assess2019;192:4

[21] Guo H,He X,Li M.Study on the exposure intensity of air pollution in Zhengzhou city based on nighttime lighting.J Henan Polytech Univ2019;38:81-8

[22] Zhang X.Risk assessment of exposure to PM_2.5 in Beijing using multi-source data.J Peking Univ2018;54:1103-13

[23] Lim S,Kim T.Personal exposures to PM_2.5 and their relationships with microenvironmental concentrations.Atmos Environ2012;47:407-12

[24] Kousa A,Koistinen K,Jantunen M.Exposure chain of urban air PM_2.5 - associations between ambient fixed site, residential outdoor, indoor, workplace and personal exposures in four European cities in the EXPOLIS-study.Atmos Environ2002;36:3031-9

[25] Fu Q.Air pollution dispersion model and assessment of population weighted exposure.J Environ Health2004;414-6

[26] Hoek G,Goldbohm S,van den Brandt PA.Association between mortality and indicators of traffic-related air pollution in the Netherlands: a cohort study.Lancet2002;360:1203-9

[27] Zou B,Luo Y,Zhang W.Research on multi-indicator spatial zoning for urban PM_2.5 pollution prevention and control.J Saf Environ2016;16:337-42

[28] Saraswat A,Brauer M.PM_2.5 population exposure in New Delhi using a probabilistic simulation framework.Environ Sci Technol2016;50:3174-83

[29] Tang R,Thach TQ.Integrating travel behavior with land use regression to estimate dynamic air pollution exposure in Hong Kong.Environ Int2018;113:100-8

[30] Brusselaers N,Mommens K.The health impact of freight transport-related air pollution on vulnerable population groups.Environ Pollut2023;329:121555

[31] Xu Y,Cabison J.The impact of GPS-derived activity spaces on personal PM_2.5 exposures in the MADRES cohort.Environ Res2022;214:114029 PMCID:PMC11905758

[32] Guo H,Yao F.Who are more exposed to PM_2.5 pollution: a mobile phone data approach.Environ Int2020;143:105821

[33] Lu Y.Impacts of distinct travel behaviors on potential air pollution exposure measurement error.Atmos Environ2023;306:119820

[34] Cutter SL.Race, class and environmental justice.Prog Human Geogr1995;19:111-22

[35] Martuzzi M,Forastiere F.Inequalities, inequities, environmental justice in waste management and health.Eur J Public Health2010;20:21-6

[36] Mitchell G,Mullin K.Who benefits from environmental policy? An environmental justice analysis of air quality change in Britain, 2001-2011.Environ Res Lett2015;10:105009

[37] Ministry of Ecology and Environment of China. Technical guidelines for environmental impact assessment - atmospheric environment. 2018. Available from: https://www.mee.gov.cn/ywgz/fgbz/bz/bzwb/other/pjjsdz/201808/t20180814_451386.shtml [Last accessed on 19 May 2025]

[38] Luo M,Chen Q.Characteristics and simulation methods for photochemical transformation of nitrogen oxides.Environ Eng2017;35:106-12

[39] Wang H,Liu C,Yi M.Spatio-temporal variation-induced group disparity of intra-urban NO₂ exposure.Int J Environ Res Public Health2022;19:5872 PMCID:PMC9141847

[40] Ta N,Hu L.Gender difference in commuting travel: a comparative study of suburban residents in Beijing and Shanghai.Transp Policy2022;28:196-203

[41] Ministry of Ecology and Environment of China. Shanghai’s air pollution control efforts found lacking. Available from: https://www.mee.gov.cn/ywgz/zysthjbhdc/dcjl/202406/t20240613_1075708.shtml [Last accessed on 19 May 2025]

[42] Feng J.Factors influencing travel behavior of urban elderly people in Nanjing.J Transp Geogr2015;34:1598-608

[43] Pommier M.Estimations of NO_x emissions, NO₂ lifetime and their temporal variation over three British urbanised regions in 2019 using TROPOMI NO₂ observations.Environ Sci Atmos2023;3:408-21

[44] Demetillo MAG,Mcdonald BC,Sun K.Space-based observational constraints on NO₂ air pollution inequality from diesel traffic in major US cities.Geophys Res Lett2021;48:e2021GL094333

[45] Mo S,Peng M.Sex disparity in cognitive aging related to later-life exposure to ambient air pollution.Sci Total Environ2023;886:163980

[46] Hu Y,Liu Y.Personal exposure to ambient PM_2.5, PM₁₀, O), NO₂, and SO₂ for different populations in 31 Chinese provinces.Environ Int2020;144:106018

[47] Kim H,Noh Y,Koh SB.Gender difference in the effects of outdoor air pollution on cognitive function among elderly in Korea.Front Public Health2019;7:375 PMCID:PMC6915851

[48] Chau CK,Chan DWT.Estimating the total exposure to air pollutants for different population age groups in Hong Kong.Environ Int2002;27:617-30

[49] Setton E,Cloutier-Fisher D.Gender differences in chronic exposure to traffic-related air pollution - A simulation study of working females and males.Prof Geogr2010;62:66-83

[50] Sillman S.The relation between ozone, NO_x and hydrocarbons in urban and polluted rural environments.Atmos Environ1999;33:1821-45

[51] Atkinson R.Atmospheric chemistry of VOCs and NOx.Atmos Environ2000;34:2063-101

[52] Beidi D.Temporal-spatial distribution characteristics of provincial industrial NOx emissions and driving factors in China from 2006 to 2013.Resour Sci2016;38:93-8

[53] Chen XP,Li QP.Research on social differentiation of urban road network in Guangzhou: Gender differences of travel distribution based on trajectory data.Geogr Res2021;40:1652-66

[54] Shen Y.Segregation through space: a scope of the flow-based spatial interaction model.J Transp Geogr2019;76:10-23

[55] Kwan MP.Gender differences in space-time constraints.Area2000;32:145-56

[56] Tong X.A gender comparative study of travel modes of urban residents.J Shanxi Univ2018;45:64-69

[57] Shanghai Statistics Bureau. Analysis on the development status and characteristics of female population in Shanghai. 2011. Available from: https://tjj.sh.gov.cn/tjfx/20111123/0014-236073.html [Last accessed on 19 May 2025]

[58] Institute of Urban and Rural Construction and Transportation Development. Main results of the fifth comprehensive traffic survey in Shanghai.Traffic Transp2015;31:15-18

[59] Huang J,Hu G.Exploring the daily life circle of the elderly based on spatio-temporal behavior - Spatial recognition and feature analysis.Urban Plan Forum2019;87-95

[60] Yan H,Li T.Research advancements in elderly travel behavior characteristics utilizing knowledge graphs.J Chang'an Univ2021;41:101-14