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首页> 外文期刊>Environmental Pollution >Identifying low-PM_(2.5) exposure commuting routes for cyclists through modeling with the random forest algorithm based on low-cost sensor measurements in three Asian cities
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Identifying low-PM_(2.5) exposure commuting routes for cyclists through modeling with the random forest algorithm based on low-cost sensor measurements in three Asian cities

机译:Identifying low-PM_(2.5) exposure commuting routes for cyclists through modeling with the random forest algorithm based on low-cost sensor measurements in three Asian cities

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摘要

Cyclists can be easily exposed to traffic-related pollutants due to riding on or close to the road during commuting in cities. PM2.5 has been identified as one of the major pollutants emitted by vehicles and associated with cardiopulmonary and respiratory diseases. As routing has been suggested to reduce the exposures for cyclists, in this study, PM2.5 was monitored with low-cost sensors during commuting periods to develop models for identifying low exposure routes in three Asian cities: Taipei, Osaka, and Seoul. The models for mapping the PM2.5 in the cities were developed by employing the random forest algorithm in a two-stage modeling approach. The land use features to explain spatial variation of PM2.5 were obtained from the open-source land use database, Open-StreetMap. The total length of the monitoring routes ranged from 101.36 to 148.22 km and the average PM2.5 ranged from 13.51 to 15.40 mu g/m(3) among the cities. The two-stage models had the standard k-fold cross-validation (CV) R-2 of 0.93, 0.74, and 0.84 in Taipei, Osaka, and Seoul, respectively. To address spatial autocorrelation, a spatial cross-validation approach applying a distance restriction of 100 m between the model training and testing data was employed. The over-optimistic estimates on the predictions were thus prevented, showing model CV-R-2 of 0.91, 0.67, and 0.78 respectively in Taipei, Osaka, and Seoul. The comparisons between the shortest-distance and lowest-exposure routes showed that the largest percentage of reduced averaged PM2.5 exposure could reach 32.1 with the distance increases by 37.8. Given the findings in this study, routing behavior should be encouraged. With the daily commuting trips expanded, the cumulative effect may become significant on the chronic exposures over time. Therefore, a route planning tool for reducing the exposures shall be developed and promoted to the public.

著录项

  • 来源
    《Environmental Pollution》 |2022年第2期|118597.1-118597.11|共11页
  • 作者单位

    Natl Taiwan Univ, Coll Publ Hlth, Inst Environm & Occupat Hlth Sci, 17 Xuzhou Rd,Room 717, Taipei 10055, Taiwan|Natl Taiwan Univ, Coll Publ Hlth, Innovat & Policy Ctr Populat Hlth & Sustainable E, 17 Xuzhou Rd, Taipei 10055, Taiwan;

    Natl Taiwan Univ, Coll Publ Hlth, Inst Environm & Occupat Hlth Sci, 17 Xuzhou Rd,Room 717, Taipei 10055, Taiwan|Kyoto Univ, Grad Sch Med, Dept Hlth & Environm Sci, Sakyo Ku, Yoshida Konoe Cho, Kyoto 6068501, Japan;

    Natl Taiwan Univ, Coll Publ Hlth, Inst Occupat Med & Ind Hyg, 17 Xuzhou Rd, Taipei 10055, TaiwanKyoto Univ, Grad Sch Med, Dept Hlth & Environm Sci, Sakyo Ku, Yoshida Konoe Cho, Kyoto 6068501, JapanSeoul Natl Univ, Grad Sch Publ Hlth, Dept Environm Hlth Sci, 1 Gwanak Ro, Seoul 08826, South KoreaPeking Univ, Sch Publ Hlth, Dept Occupat & Environm Hlth Sci, 38 Xueyuan Rd, Beijing 100191, Peoples R ChinaUniv Illinois, Dept Civil & Environm Engn, 205 N Mathews Ave, Urbana, IL 61801 USANatl Hlth Res Inst, Inst Populat Hlth Sci, Div Biostat & Bioinformat, 35 Keyan Rd, Zhunan Town 35053, Miaoli County, TaiwanVietnam Natl Univ Ho Chi Minh City, Univ Sci, 227 Nguyen Van Cu St,Dist 5, Ho Chi Minh City, Vietnam|Natl Taiwan Univ, Inst Epidemiol & Prevent Med, 17 Xuzhou Rd, Taipei 10055, TaiwanNatl Taiwan Univ, Inst Epidemiol & Prevent Med, 17 Xuzhou Rd, Taipei 10055, TaiwanNatl Taiwan Univ, Dept Geog, 1,Sec 4,Roosevelt Rd, Taipei 10617, Taiwan;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);美国《生物学医学文摘》(MEDLINE);美国《化学文摘》(CA);
  • 原文格式 PDF
  • 正文语种 英语
  • 中图分类
  • 关键词

    PM2.5; Cyclist; Routing; Asian city; Random forest; Spatial autocorrelation;

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