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Evaluation of vertical and horizontal distribution of particulate matter near an urban roadway using an unmanned aerial vehicle.
Dubey, Ravish; Patra, Aditya Kumar; Joshi, Jayadev; Blankenberg, Daniel.
Afiliação
  • Dubey R; School of Environmental Science and Engineering, Indian Institute of Technology Kharagpur, Kharagpur, India.
  • Patra AK; School of Environmental Science and Engineering, Indian Institute of Technology Kharagpur, Kharagpur, India; Department of Mining Engineering, Indian Institute of Technology Kharagpur, Kharagpur, India. Electronic address: akpatra@mining.iitkgp.ac.in.
  • Joshi J; Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA.
  • Blankenberg D; Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA.
  • Nazneen; School of Environmental Science and Engineering, Indian Institute of Technology Kharagpur, Kharagpur, India.
Sci Total Environ ; 836: 155600, 2022 Aug 25.
Article em En | MEDLINE | ID: mdl-35504396
ABSTRACT
Measurement of traffic emissions has gained a lot of interest in recent times due to its contribution to urban pollution. This paper reports the outcome from an unmanned aerial vehicle (UAV) based measurement of PM concentration near an urban roadway at Kolkata, India. A total of 54 flights were carried out for simultaneous measurements of PM1, PM2.5 and PM10 mass concentration and meteorological parameters in vertical as well as in horizontal direction. Results for the vertical flight up to 100 m showed that the PM1, PM2.5 and PM10 concentrations at higher altitudes are less (mean; 24.6, 39.9 and 103.8 µg m-3) compared to the respective ground level concentrations (mean; 26.3, 50.4 and 201.9 µg m-3). For all the three particle sizes, the majority of the cases of higher PM concentration at higher altitudes happened during the evening flight. Low mixing height and low wind speed are suggested to be the reasons for the poor dispersion of pollutants in the evening. While there was a 7-10% fall of fine particles (PM1 and PM2.5) mass concentrations up to 90 m away from the road, no trend could be seen for PM10. The random forest model to predict the UAV/Ground concentration ratio showed high accuracy (R2 = 0.82-0.95) for all three particle sizes. This is an important finding from this study, which shows how UAV measurement data can be used to generate models that can predict the higher altitude concentrations from the ground based measurements.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Poluentes Atmosféricos / Material Particulado Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Poluentes Atmosféricos / Material Particulado Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2022 Tipo de documento: Article