Estimation of on-road NO2 concentrations, NO2/NOX ratios, and related roadway gradients from near-road monitoring data

被引:60
|
作者
Richmond-Bryant, Jennifer [1 ]
Owen, R. Chris [2 ]
Graham, Stephen [2 ]
Snyder, Michelle [3 ]
McDow, Stephen [1 ]
Oakes, Michelle [4 ]
Kimbrough, Sue [5 ]
机构
[1] US EPA, Natl Ctr Environm Assessment, Durham, NC 27711 USA
[2] US EPA, Off Air Qual Planning & Stand, Durham, NC 27711 USA
[3] Univ N Carolina, Inst Environm, Chapel Hill, NC 27517 USA
[4] Oak Ridge Inst Sci Educ, Durham, NC 27711 USA
[5] US EPA, Natl Risk Management Res Lab, Durham, NC 27711 USA
来源
AIR QUALITY ATMOSPHERE AND HEALTH | 2017年 / 10卷 / 05期
关键词
Near road; NO2; Oxides of nitrogen; Nitrogen dioxide; Dispersion; NITROGEN-DIOXIDE; DISPERSION MODEL; AIR-POLLUTION; PARTICULATE MATTER; LAS-VEGAS; IN-USE; EMISSIONS; VARIABILITY; POLLUTANTS; EXPOSURE;
D O I
10.1007/s11869-016-0455-7
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
This paper describes a new regression modeling approach to estimate on-road nitrogen dioxide (NO2) and oxides of nitrogen (NOX) concentrations and near-road spatial gradients using data from a near-road monitoring network. Field data were collected in Las Vegas, NV, at three monitors sited 20, 100, and 300 m from Interstate-15 between December 2008 and January 2010. Measurements of NO2 and NOX were integrated over 1-h intervals and matched with meteorological data. Several mathematical transformations were tested for regressing pollutant concentrations against distance from the roadway. A logit-ln model was found to have the best fit (R-2 = 94.7 %) and also provided a physically realistic profile. The mathematical model used data from the near-road monitors to estimate on-road concentrations and the near-road gradient over which mobile source pollutants have concentrations elevated above background levels. Average and maximum on-road NO2 concentration estimates were 33 and 105 ppb, respectively. Concentration gradients were steeper in the morning and late afternoon compared with overnight when stable conditions preclude mixing. Estimated on-road concentrations were also highest in the late afternoon. Median estimated on-road and gradient NO2 concentrations were lower during summer compared with winter, with a steeper gradient during the summer, when convective mixing occurs during a longer portion of the day. On-road concentration estimates were higher for winds perpendicular to the road compared with parallel winds and for atmospheric stability with neutral-to-unstable atmospheric conditions. The concentration gradient with increasing distance from the road was estimated to be sharper for neutral-to-unstable conditions when compared with stable conditions and for parallel wind conditions compared with perpendicular winds. A regression of the NO2/NOX ratios yielded on-road ratios ranging from 0.25 to 0.35, substantially higher than the anticipated tailpipe emissions ratios. The results from the ratios also showed that the diurnal cycle of the background NO2/NOX ratios were a driving factor in the on-road and downwind NO2/NOX ratios.
引用
收藏
页码:611 / 625
页数:15
相关论文
共 50 条
  • [41] LUR modeling of long-term average hourly concentrations of NO2 using hyperlocal mobile monitoring data
    Yuan, Zhendong
    Shen, Youchen
    Hoek, Gerard
    Vermeulen, Roel
    Kerckhoffs, Jules
    SCIENCE OF THE TOTAL ENVIRONMENT, 2024, 922
  • [42] LUR modeling of long-term average hourly concentrations of NO2 using hyperlocal mobile monitoring data
    Yuan, Zhendong
    Shen, Youchen
    Hoek, Gerard
    Vermeulen, Roel
    Kerckhoffs, Jules
    Science of the Total Environment, 2024, 922
  • [43] Estimate of near-surface NO2 concentrations in Fenwei Plain, China, based on TROPOMI data and random forest model
    Yarui Wu
    Honglei Liu
    Shuangyue Liu
    Chunhui Lou
    Environmental Monitoring and Assessment, 2023, 195
  • [44] On-road and laboratory emissions of NO, NO2, NH3, N2O and CH4 from late-model EU light utility vehicles: Comparison of diesel and CNG
    Vojtisek-Lom, Michal
    Beranek, Vit
    Klir, Vojtech
    Jindra, Petr
    Pechout, Martin
    Vorisek, Tomas
    SCIENCE OF THE TOTAL ENVIRONMENT, 2018, 616 : 774 - 784
  • [45] Estimate of near-surface NO2 concentrations in Fenwei Plain, China, based on TROPOMI data and random forest model
    Wu, Yarui
    Liu, Honglei
    Liu, Shuangyue
    Lou, Chunhui
    ENVIRONMENTAL MONITORING AND ASSESSMENT, 2023, 195 (11)
  • [46] Forecast of NO2 concentrations based on coupled air quality model simulations and monitoring data using machine learning method
    Huang, Yong-Xi
    Zhu, Yun
    Xie, Yang-Hong
    Li, Hai-Xian
    Zhang, Zhi-Cheng
    Li, Jie
    Li, Jin-Ying
    Yuan, Ying-Zhi
    Zhongguo Huanjing Kexue/China Environmental Science, 2023, 43 (12): : 6225 - 6234
  • [47] Estimation of Surface NO2 Concentrations over Germany from TROPOMI Satellite Observations Using a Machine Learning Method
    Chan, Ka Lok
    Khorsandi, Ehsan
    Liu, Song
    Baier, Frank
    Valks, Pieter
    REMOTE SENSING, 2021, 13 (05) : 1 - 24
  • [48] Near-Surface NO2 Concentration Estimation by Random Forest Modeling and Sentinel-5P and Ancillary Data
    Li, Meixin
    Wu, Ying
    Bao, Yansong
    Liu, Bofan
    Petropoulos, George P.
    REMOTE SENSING, 2022, 14 (15)
  • [49] Estimation of near-surface O3 concentration based on TROPOMI NO2,CO and HCHO reconstruction data
    Chen, Xiaojuan
    Qin, Kai
    Cohen, Jason
    He, Qin
    National Remote Sensing Bulletin, 2024, 28 (09) : 2348 - 2361
  • [50] Potential Ozone Impacts of Excess NO2 Emissions from Diesel Particulate Filters for On- and Off-Road Diesel Engines
    Bar-Ilan, Amnon
    Johnson, Jeremiah R.
    DenBleyker, Allison
    Chan, Lit-Mian
    Yarwood, Gregory
    Hitchcock, David
    Pinto, Joseph P.
    JOURNAL OF THE AIR & WASTE MANAGEMENT ASSOCIATION, 2010, 60 (08) : 977 - 992