The use of random forests in modelling short-term air pollution effects based on traffic and meteorological conditions: A case study in Wrocław

Journal of Environmental Management

Volumn 217, Issue , 2018, Pages 164-174

  Kamińska, Joanna A ^a  

^a WROC AW UNIVERSITY OF ENVIRONMENTAL AND LIFE SCIENCES   (Poland)

Author keywords

Data subsets; Meteorological conditions; Random forest; Traffic flow; Urban air pollution

Indexed keywords

NITROGEN OXIDE;

ATMOSPHERIC POLLUTION; CONCENTRATION (COMPOSITION); ENVIRONMENTAL MODELING; METEOROLOGY; PARTICULATE MATTER; RELATIVE HUMIDITY; TRAFFIC EMISSION; URBAN POLLUTION; WIND DIRECTION; WIND VELOCITY;

AIR POLLUTION; AIR PRESSURE; AIR QUALITY; ARTICLE; CLIMATE; CONCENTRATION (PARAMETERS); ENVIRONMENTAL TEMPERATURE; EXHAUST GAS; EXPLANATORY VARIABLE; HEAT; HUMIDITY; METEOROLOGY; POLAND; RANDOM FOREST; SUMMER; TRAFFIC; WIND; AIR POLLUTANT; ENVIRONMENTAL MONITORING; PARTICULATE MATTER; SEASON; TEMPERATURE; THEORETICAL MODEL; WEATHER;

AIR POLLUTANTS; AIR POLLUTION; ENVIRONMENTAL MONITORING; MODELS, THEORETICAL; NITROGEN OXIDES; PARTICULATE MATTER; SEASONS; TEMPERATURE; WEATHER;

EID: 85044522746     PISSN: 03014797     EISSN: 10958630     Source Type: Journal    
DOI: 10.1016/j.jenvman.2018.03.094     Document Type: Article

References (28)

1. Adam, M., Schikowski, T., Carsin, A.E., Cai, Y., Jacquemin, B., Sanchez, M., Vierkötter, A., Marcon, A., Keidel, D., Sugiri, D., Kanani, Z.A., Nadif, R., Siroux, V., Hardy, R., Kuh, D., Rochat, T., Bridevaux, P.-O., Eeftens, M., Tsai, M.-Y., Villani, S., Phuleria, H.C., Birk, M., Cyrys, J., Cirach, M., Nazelle, A., Nieuwenhuijsen, M.J., Forsberg, B., Hoogh, K., Declerq, K., Bono, R., Piccioni, P., Quass, U., Heinrich, J., Jarvis, D., Pin, I., Beelen, R., Hoek, G., Brunekreef, B., Schindler, C., Sunyer, J., Krämer, U., Kauffmann, F., Hansell, A.L., Künzli, N., Probst-Hensch, N., Adult lung function and long-term air pollution exposure. ESCAPE: a multicentre cohort study and meta-analysis. Eur. Respir. J. 45 (2015), 38–50, 10.1183/09031936.00130014.
2. Aldrin, M., Haff, I.H., Generalized additive modelling of air pollution, traffic volume and meteorology. Atmos. Environ. 39:11 (2005), 2145–2155 https://doi.org/10.1016/j.atmosenv.2004.12.020.
3. Archer, K.J., Kimes, R.V., Empirical characterization of random forest variable importance measures. Comput. Stat. Data Anal. 52:4 (2008), 2249–2260 https://doi.org/10.1016/j.csda.2007.08.015.
4. Barratt, B., Atkinson, R., Ross Anderson, H., Beevers, S., Kelly, F., Mudway, L., Wilkinson, P., Investigation into the use of the CUSUM technique in identifying changes in mean air pollution levels following introduction for a traffic management scheme. Atmos. Environ. 41:8 (2007), 1784–1791 https://doi.org/10.1016/j.atmosenv.2006.09.052.
5. Bertaccini, P., Dukic, V., Ignaccolo, R., Modeling the short-term effect of traffic and meteorology on air pollution in Turin with generalized additive models. Adv. Meteorol. 2012 (2012), 1–16, 10.2139/ssrn.1422567.
6. Boylan, J.W., Russell, A.G., PM and light extinction model performance metrics, goals, and criteria for three-dimensional air quality models. Atmos. Environ. 40 (2006), 4946–4959 https://doi.org/10.1016/j.atmosenv.2005.09.087.
7. Breiman, L., Random forests. Mach. Learn. 1 (2001), 5–32 45 https://doi.org/10.1023/A:1010933404324.
8. Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J., Classification and Regression Trees. 1984, Wadsworth & Brooks/Cole Advanced Books & Software, Monterey, CA.
9. Flores-Pajot, M.-C., Ofner, M., Do, M.T., Lavigne, E., Villeneuve, P.J., Childhood autism spectrum disorders and exposure to nitrogen dioxide, and particulate matter air pollution: a review and meta-analysis. Environ. Res. 151 (2016), 763–776 https://doi.org/10.1016/j.envres.2016.07.030.
10. González-Aparicio, I., Hidalgo, J., Baklanov, A., Padró A., Santa-Coloma, O., An hourly PM10 diagnosis model for the Bilbao metropolitan area using a linear regression methodology. Environ. Sci. Pollut. Res. 20:7 (2013), 4469–4483, 10.1007/s11356-012-1353-7.
11. Hien, T.T., Linh, H.N., Luong, L.M.T., Thai, P.K., Air pollution and risk of respiratory and cardiovascular hospitalizations in the most populous city in Vietnam. Sci. Total Environ. 557–558 (2016), 322–330 https://doi.org/10.1016/j.scitotenv.2016.03.070.
12. Hoek, G., Krishnan, R.M., Beelen, R., Peters, A., Ostro, B., Brunekreef, B., Kaufman, J.D., Long-term air pollution exposure and cardio-respiratory mortality: a review. Environ. Health, 12(1), 2013, 43 https://doi.org/10.1186/1476-069X-12-43.
13. Information on air quality in Wrocław [Informacja o jakości powietrza na terenie miasta Wrocławia], Provincial Environment Protection Inspectorate [WIOS], Wrocław. 2016.
14. 2.5 levels in three European cites: analysis of intra and inter urban variations. Atmos. Environ. 87 (2014), 153–163 https://doi.org/10.1016/j.atmosenv.2014.01.004.
15. Kazak, J., Chalfen, M., Kamińska, J., Szewrański, S., Świąder, M., Geo-dynamic decision support system for urban traffic management. Ivan, I., Horak, J., Inspektor, T., (eds.) Dynamics in GIScience. GIS Ostrava 2017. Lecture Notes in Geoinformation and Cartography, 2018, Springer, Cham 195-207 https://doi.org/10.1007/978-3-319-61297-3_14.
16. Keeler, R.H., A Machine Learning Model of Manhattan Air Pollution at High Spatial Resolution. PhD thesis, 2014 http://hdl.handle.net/1721.1/90659.
17. Laña, I., Del Ser, J., Pedró A., Vélez, M., Casanova-Mateo, C., The role of local urban traffic and meteorological conditions in air pollution: a data-based study in Madrid, Spain. Atmos. Environ. 145 (2016), 424–438 https://doi.org/10.1016/j.atmosenv.2016.09.052.
18. Malytska, L., Balabukh, V., Atmospheric self-cleaning coefficients as indicators of the atmospheric ability to dissipate pollutants in Ukraine. Meteorol. Hydrol. Water Manage 6:1 (2018), 59–65 https://doi.org/10.26491/mhwm/79450.
19. Mlakar, P., Boinar, M., Perception neutral network-based model predicts air pollution. Intelligent Information Systems, 1997, 345–349, 10.1109/IIS.1997.645288.
20. Pei-Chen, Lee, Liu, Li-Ling, Yu Sun, M.D., Yu-An, Chen, Chih-Ching, Liu, Chung-Yi, Li, Hwa-Lung, Yu, Beate, Ritz, Traffic-related air pollution increased the risk of Parkinson's disease in Taiwan: a nationwide study. Environ. Int. 96 (2016), 75–81 https://doi.org/10.1016/j.envint.2016.08.017.
21. Rao, C.R., Lovric, M.M., Testing point null hypothesis of a normal mean and the truth: 21st century perspective. J. Mod. Appl. Stat. Methods, 15(2), 2016, 10.22237/jmasm/1478001660 Article 3.
22. Regulation of the Minister of Environment of 14 August 2012 on Levels of Certain Substances in the Air [Rozporządzenie Ministra Środowiska z dnia 24 Sierpnia 2012 R. w Sprawie Poziomów niektórych Substancji w Powietrzu] (Dz.U.2012.1031).
23. Rethinking the Ozone Problem in Urban and Regional Air Pollution, 1991, The National Academies Press, Washington.
24. x are influenced by the background levels, traffic density, and meteorological conditions using boosted regression trees. Atmos. Environ. 127 (2016), 163–175 https://doi.org/10.1016/j.atmosenv.2015.12.024.
25. Tang, G., Zhao, P., Wang, Y., Gao, W., Cheng, M., Xin, Y., Li, X., Wang, Y., Mortality and air pollution in Beijing: the long-term relationship. Atmos. Environ. 150 (2017), 1238–1243 https://doi.org/10.1016/j.atmosenv.2016.11.045.
26. Tribby, C.P., Miller, H.J., Song, Y., Smith, K.R., Do air quality alerts reduce traffic? An analysis of traffic data from the Salt Lake City metropolitan area, Utah, USA. Transp. Pol. 30 (2013), 173–185 https://doi.org/10.1016/j.tranpol.2013.09.012.
27. Zhang, K., Batterman, S., Air pollution and health risks due to vehicle traffic. Sci. Total Environ. 450–451 (2013), 307–316, 10.1016/j.scitotenv.2013.01.074.
28. 2.5 concentrations and their relationships with meteorological conditions over the last decade in Beijing. Atmos. Environ. 108 (2015), 67–75 https://doi.org/10.1016/j.atmosenv.2015.02.071.