Abstract
Dynamic traffic and complex roadside environments always cause fine variations in traffic pollutants with many uncertainties in nonmotorized lanes located close to motorways; thus, reliable methods for identifying pollution risks are urgently needed so that measures can be taken to reduce these slow-moving risks. Focusing on the nonmotorized lanes along an expressway in Fuzhou, China, in this study, we established a cycling platform instrumented by portable detectors to collect fine particle (PM2.5), coarse particle (PM10), and black carbon (BC) concentrations at a high spatiotemporal resolution; then, wavelet transform (WT) and random forest (RF) methods were combined to reveal the fine-scale distribution of different particulate matter types. The results indicated that WT was able to accurately decompose the total measurement value (\({C}_{t}\)) of each particulate matter into immediate vehicle-emitted (\({C}_{v}\)) and background-contributed (\({C}_{b}\)) values, thereby successfully identifying the spatiotemporal variations in traffic-induced pollution hotspots rather than background-disguised hotspots. Furthermore, the RF results were substantially better than the land-use regression results with regards to the fine-scale prediction of each particle in nonmotorized lanes. Although the RF predictions of \({C}_{t}\) and \({C}_{v}\) particles differed, traffic pollution hotspots could still be captured by the results. Compared to the measurements, the spatial distributions of the PM2.5 and PM10 predictions presented R2 values larger than 0.96, higher than those of BC (R2 = 0.77); this was the result of the different impacts of the same predictors, especially their differentiated determinants such as barometric pressure, relative humidity and air temperature. This study highlights the potential of using WT and RF methods to reveal fine-scale variations in roadside traffic pollution, which is beneficial for preventing and controlling air pollution in road microenvironments.
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References
Abhijith KV, Kumar P, Gallagher J et al (2017) Air pollution abatement performances of green infrastructure in open road and built-up street canyon environments—a review. Atmos Environ 162:71–86. https://doi.org/10.1016/j.atmosenv.2017.05.014
Akyuz M, Cabuk H (2009) Meteorological variations of PM2.5/PM10 concentrations and particle-associated polycyclic aromatic hydrocarbons in the atmospheric environment of Zonguldak, Turkey. J Hazard Mater 170(1):13–21. https://doi.org/10.1016/j.jhazmat.2009.05.029
Baldwin N, Gilani O, Raja S et al (2015) Factors affecting pollutant concentrations in the near-road environment. Atmos Environ 115:223–235. https://doi.org/10.1016/j.atmosenv.2015.05.024
Barros N, Fontes T, Silva MP et al (2013) How wide should be the adjacent area to an urban motorway to prevent potential health impacts from traffic emissions? Transp Res Part A Policy Pract 50:113–128. https://doi.org/10.1016/j.tra.2013.01.021
Barwise Y, Kumar P (2020) Designing vegetation barriers for urban air pollution abatement: a practical review for appropriate plant species selection. NPJ Clim Atmos Sci. https://doi.org/10.1038/s41612-020-0115-3
Bellander T, Berglind N, Gustavsson P et al (2001) Using geographic information systems to assess individual historical exposure to air pollution from traffic and house heating in Stockholm. Environ Health Perspect 109(6):633–639. https://doi.org/10.1289/ehp.01109633
Belušić A, Herceg-Bulić I, Bencetić Klaić Z (2015) Using a generalized additive model to quantify the influence of local meteorology on air quality in Zagreb. Geofizika 32(1):47–77. https://doi.org/10.15233/gfz.2015.32.5
Brantley HL, Hagler GSW, Kimbrough ES et al (2014) Mobile air monitoring data-processing strategies and effects on spatial air pollution trends. Atmos Meas Tech 7(7):2169–2183. https://doi.org/10.5194/amt-7-2169-2014
Brauer M, Hoek G, van Vliet P et al (2003) Estimating long-term average particulate air pollution concentrations: application of traffic indicators and geographic information systems. Epidemiology 2003:228–239. https://doi.org/10.1097/00001648-200303000-00019
Briggs DJ, Collins S, Elliott P et al (1997) Mapping urban air pollution using GIS: a regression-based approach. Int J Geogr Inf Sci 11(7):699–718. https://doi.org/10.1080/136588197242158
Bukowiecki N, Dommen J, Prevot ASH et al (2002) A mobile pollutant measurement laboratory—measuring gas phase and aerosol ambient concentrations with high spatial and temporal resolution. Atmos Environ 36(36–37):5569–5579. https://doi.org/10.1016/S1352-2310(02)00694-5
Buonanno G, Fuoco FC, Stabile L (2011) Influential parameters on particle exposure of pedestrians in urban microenvironments. Atmos Environ 45(7):1434–1443. https://doi.org/10.1016/j.atmosenv.2010.12.015
Cheng S, Lang J, Zhou Y et al (2013) A new monitoring-simulation-source apportionment approach for investigating the vehicular emission contribution to the PM2.5 pollution in Beijing, China. Atmos Environ 79:308–316. https://doi.org/10.1016/j.atmosenv.2013.06.043
Choi Y-S, Ho C-H, Chen D et al (2008) Spectral analysis of weekly variation in PM10 mass concentration and meteorological conditions over China. Atmos Environ 42(4):655–666. https://doi.org/10.1016/j.atmosenv.2007.09.075
Cogliani E (2001) Air pollution forecast in cities by an air pollution index highly correlated with meteorological variables. Atmos Environ 35(16):2871–2877. https://doi.org/10.1016/S1352-2310(01)00071-1
Dallmann TR, Kirchstetter TW, Demartini SJ et al (2013) Quantifying on-road emissions from gasoline-powered motor vehicles: accounting for the presence of medium- and heavy-duty diesel trucks. Environ Sci Technol 47(23):13873–13881. https://doi.org/10.1021/es402875u
Deshmukh P, Isakov V, Venkatram A et al (2018) The effects of roadside vegetation characteristics on local, near-road air quality. Air Qual Atmos Health 12(3):259–270. https://doi.org/10.1007/s11869-018-0651-8
Dunea D, Pohoata A, Iordache S (2015) Using wavelet-feedforward neural networks to improve air pollution forecasting in urban environments. Environ Monit Assess 187(7):477. https://doi.org/10.1007/s10661-015-4697-x
Espinosa R, Palma J, Jiménez F et al (2021) A time series forecasting based multi-criteria methodology for air quality prediction. Appl Soft Comput 113:107850. https://doi.org/10.1016/j.asoc.2021.107850
Fuller CH, Brugge D, Williams P et al (2012) Estimation of ultrafine particle concentrations at near-highway residences using data from local and central monitors. Atmos Environ 57:257–265. https://doi.org/10.1016/j.atmosenv.2012.04.004
Goel A, Kumar P (2014) A review of fundamental drivers governing the emissions, dispersion and exposure to vehicle-emitted nanoparticles at signalised traffic intersections. Atmos Environ 97:316–331. https://doi.org/10.1016/j.atmosenv.2014.08.037
Goupillaud P, Grossmann A, Morlet J (1984) Cycle-octave and related transforms in seismic signal analysis. Geoexploration 23(1):85–102. https://doi.org/10.1016/0016-7142(84)90025-5
Gozzi F, Della Ventura G, Marcelli A (2016) Mobile monitoring of particulate matter: state of art and perspectives. Atmos Pollut Res 7(2):228–234. https://doi.org/10.1016/j.apr.2015.09.007
Hagler GSW, Yelverton TLB, Vedantham R et al (2011) Post-processing method to reduce noise while preserving high time resolution in aethalometer real-time black carbon data. Aerosol Air Qual Res 11(5):539–546. https://doi.org/10.4209/aaqr.2011.05.0055
Hagler GSW, Lin MY, Khlystov A et al (2012) Field investigation of roadside vegetative and structural barrier impact on near-road ultrafine particle concentrations under a variety of wind conditions. Sci Total Environ 419:7–15. https://doi.org/10.1016/j.scitotenv.2011.12.002
He HD, Gao HO (2021) Particulate matter exposure at a densely populated urban traffic intersection and crosswalk[J]. Environ Pollut 268:115931. https://doi.org/10.1016/j.envpol.2020.115931
HEI Panel on the Health Effects of Traffic-Related Air Pollution (2010) Traffic-related air pollution: a critical review of the literature on emissions, exposure, and health effects. HEI Special Report 17. Health Effects Institute, Boston
Hitchins J, Morawska L, Wolff R et al (2000) Concentrations of submicrometre particles from vehicle emissions near a major road. Atmos Environ 34(1):51–59. https://doi.org/10.1016/S1352-2310(99)00304-0
Hu S, Fruin S, Kozawa K et al (2009) A wide area of air pollutant impact downwind of a freeway during pre-sunrise hours. Atmos Environ 43(16):2541–2549. https://doi.org/10.1016/j.atmosenv.2009.02.033
Jazcilevich A, De La Cruz Zavala J, Erazo Arcos AM et al (2019) Sidewalk pollution flows caused by vehicular traffic place children at a higher acute exposure risk. J Expo Sci Environ Epidemiol 29(4):491–499. https://doi.org/10.1038/s41370-018-0083-4
Jeong C-H, Evans GJ, Healy RM et al (2015) Rapid physical and chemical transformation of traffic-related atmospheric particles near a highway. Atmos Pollut Res 6(4):662–672. https://doi.org/10.5094/APR.2015.075
Jian L, Zhao Y, Zhu YP et al (2012) An application of ARIMA model to predict submicron particle concentrations from meteorological factors at a busy roadside in Hangzhou, China. Sci Total Environ 426:336–345. https://doi.org/10.1016/j.scitotenv.2012.03.025
Kaminska JA (2019) A random forest partition model for predicting NO2 concentrations from traffic flow and meteorological conditions. Sci Total Environ 651(Pt 1):475–483. https://doi.org/10.1016/j.scitotenv.2018.09.196
Kaur S, Nieuwenhuijsen MJ, Colvile RN (2007) Fine particulate matter and carbon monoxide exposure concentrations in urban street transport microenvironments. Atmos Environ 41(23):4781–4810. https://doi.org/10.1016/j.atmosenv.2007.02.002
Kendrick CM, Koonce P, George LA (2015) Diurnal and seasonal variations of NO, NO2 and PM2.5 mass as a function of traffic volumes alongside an urban arterial. Atmos Environ 122:133–141. https://doi.org/10.1016/j.atmosenv.2015.09.019
Kimoanh N, Chutimon P, Ekbordin W et al (2005) Meteorological pattern classification and application for forecasting air pollution episode potential in a mountain-valley area. Atmos Environ 39(7):1211–1225. https://doi.org/10.1016/j.atmosenv.2004.10.015
Kumar P, Patton AP, Durant JL et al (2018) A review of factors impacting exposure to PM2.5, ultrafine particles and black carbon in Asian transport microenvironments. Atmos Environ 187:301–316. https://doi.org/10.1016/j.atmosenv.2018.05.046
Laulainen NS (1993) Summary of conclusions and recommendations from a visibility science workshop. Pacific Northwest Lab, Richland. https://doi.org/10.2172/10149541
Li Y, Ye C, Liu J et al (2016) Observation of regional air pollutant transport between the megacity Beijing and the North China Plain. Atmos Chem Phys 16(22):14265–14283. https://doi.org/10.5194/acp-16-14265-2016
Li Y, Tan Z, Ye C et al (2019) Using wavelet transform to analyse on-road mobile measurements of air pollutants: a case study to evaluate vehicle emission control policies during the 2014 APEC summit. Atmos Chem Phys 19(22):13841–13857. https://doi.org/10.5194/acp-19-13841-2019
Liu Y, Park RJ, Jacob DJ et al (2004) Mapping annual mean ground-level PM2.5 concentrations using multiangle imaging spectroradiometer aerosol optical thickness over the contiguous United States. J Geophys Res Atmos. https://doi.org/10.1029/2004JD005025
Mamtimin B, Meixner FX (2011) Air pollution and meteorological processes in the growing dryland city of Urumqi (Xinjiang, China). Sci Total Environ 409(7):1277–1290. https://doi.org/10.1016/j.scitotenv.2010.12.010
Morlet J, Arens G, Fourgeau E et al (1982a) Wave propagation and sampling theory—Part I: complex signal and scattering in multilayered media. Geophysics 47(2):203–221. https://doi.org/10.1190/1.1441328
Morlet J, Arens G, Fourgeau E et al (1982b) Wave propagation and sampling theory—Part II: sampling theory and complex waves. Geophysics 47(2):222–236. https://doi.org/10.1190/1.1441329
Nicole AHJ, Hoek G, Simic-Lawson M et al (2011) Black carbon as an additional indicator of the adverse health effects of airborne particles compared with PM10 and PM2.5. Environ Health Perspect 119(12):1691–1699. https://doi.org/10.1289/ehp.1003369
Peng Z-R, Wang D, Wang Z et al (2015) A study of vertical distribution patterns of PM2.5 concentrations based on ambient monitoring with unmanned aerial vehicles: a case in Hangzhou, China. Atmos Environ 123:357–369. https://doi.org/10.1016/j.atmosenv.2015.10.074
Riley EA, Banks L, Fintzi J et al (2014) Multi-pollutant mobile platform measurements of air pollutants adjacent to a major roadway. Atmos Environ 98:492–499
Roorda-Knape MC, Janssen NAH, De Harthog JJ et al (1998) Air pollution from traffic in city districts near major motorways. Atmos Environ 32(11):1921–1930. https://doi.org/10.1016/S1352-2310(97)00496-2
Saarikoski S, Niemi JV, Aurela M et al (2021) Sources of black carbon at residential and traffic environments obtained by two source apportionment methods. Atmos Chem Phys 21(19):14851–14869. https://doi.org/10.5194/acp-21-14851-2021
Samset BH, Myhre G, Herber A et al (2014) Modelled black carbon radiative forcing and atmospheric lifetime in AeroCom Phase II constrained by aircraft observations. Atmos Chem Phys 14(22):12465–12477. https://doi.org/10.5194/acp-14-12465-2014
Sfetsos A, Vlachogiannis D (2010) A new approach to discovering the causal relationship between meteorological patterns and PM10 exceedances. Atmos Res 98(2–4):500–511. https://doi.org/10.1016/j.atmosres.2010.08.021
Shairsingh KK, Jeong C-H, Wang JM et al (2018) Characterizing the spatial variability of local and background concentration signals for air pollution at the neighbourhood scale. Atmos Environ 183:57–68
Tian G, Qiao Z, Xu X (2014) Characteristics of particulate matter (PM10) and its relationship with meteorological factors during 2001–2012 in Beijing. Environ Pollut 192:266–274. https://doi.org/10.1016/j.atmosenv.2018.04.010
Tiwary A, Robins A, Namdeo A et al (2011) Air flow and concentration fields at urban road intersections for improved understanding of personal exposure. Environ Int 37(5):1005–1018. https://doi.org/10.1016/j.envint.2011.02.006
Van Poppel M, Peters J, Bleux N (2013) Methodology for setup and data processing of mobile air quality measurements to assess the spatial variability of concentrations in urban environments. Environ Pollut 183:224–233. https://doi.org/10.1016/j.envpol.2013.02.020
Wang Z, Wang D, Peng ZR, et al (2018) Performance assessment of a portable nephelometer for outdoor particle mass measurement[J]. Environ Sci: Processes & Impacts 20(2):370–383. https://doi.org/10.1039/c7em00336F
Wang Z, Zhao H, Peng Z (2021) Spatiotemporal analysis of pedestrian exposure to submicron and coarse particulate matter on crosswalk at urban intersection[J]. Build Environ 204:108149. https://doi.org/10.1016/j.buildenv.2021.108149
Wang Z, Zhong S, He H-D et al (2018) Fine-scale variations in PM2.5 and black carbon concentrations and corresponding influential factors at an urban road intersection. Build Environ 141:215–225. https://doi.org/10.1016/j.buildenv.2018.04.042
Wei P, Brimblecombe P, Yang F et al (2021) Determination of local traffic emission and non-local background source contribution to on-road air pollution using fixed-route mobile air sensor network. Environ Pollut 290:118055. https://doi.org/10.1016/j.envpol.2021.118055
Westerdahl D, Wang X, Pan X et al (2009) Characterization of on-road vehicle emission factors and microenvironmental air quality in Beijing, China. Atmos Environ 43(3):697–705. https://doi.org/10.1016/j.atmosenv.2008.09.042
Zheng X, He L, He X et al (2022) Real-time black carbon emissions from light-duty passenger vehicles using a portable emissions measurement system. Engineering 16:73–81. https://doi.org/10.1016/j.eng.2020.11.009
Zhu Y, Kuhn T, Mayo P et al (2006) Comparison of daytime and nighttime concentration profiles and size distributions of ultrafine particles near a major highway. Environ Sci Technol 40(8):2531–2536. https://doi.org/10.1021/es0516514
Zhu X, Lu K, Peng Z, et al (2022) Characterizing carbon emissions from China V and China VI gasoline vehicles based on portable emission measurement systems[J]. J Cleaner Prod 378:134458. https://doi.org/10.1016/j.jclepro.2022.134458
Acknowledgements
We also express appreciation to Xin Chen, Shuting Chen, Fan Ma and Jie Wu from the Traffic Pollution Research Group at the Fujian Agriculture and Forestry University as well as others not mentioned here who provide assistance in this study. Comments and suggestions from the reviewers and editor are highly appreciated.
Funding
This work is supported by the Natural Science Foundation of Fujian Province, China (No. 2021J01105), the National Natural Science Foundation of China (No. 4170155), the Science and Technology Innovation Foundation by Fujian Agriculture and Forestry University (No. KFb22101XA), and the Social Science Foundation of Fujian Province, China (No. FJ2022B065).
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BL: Investigation, methodology, software, validation, visualization, writing—original draft, writing—review and editing; RC: Investigation, software, visualization, writing—original draft, writing—review and editing; WY: Investigation, writing—review and editing; ZW: Conceptualization, data curation, formal analysis, funding acquisition, project administration, resources, supervision, investigation, methodology, writing—original draft, writing—review and editing; XH: Formal analysis, resources, supervision, writing—review and editing; QX, ZF: Formal analysis, resources, writing—review and editing; LZ: Funding acquisition, writing—review and editing.
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Luo, B., Cao, R., Yang, W. et al. Analysing and predicting the fine-scale distribution of traffic particulate matter in urban nonmotorized lanes by using wavelet transform and random forest methods. Stoch Environ Res Risk Assess 37, 2657–2676 (2023). https://doi.org/10.1007/s00477-023-02411-6
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DOI: https://doi.org/10.1007/s00477-023-02411-6