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On the varied emission fingerprints of particulate matter over typical locations of NCR (Delhi) - A perspective for mitigation plans.
Latha, R; Mukherjee, A; Dahiya, K; Bano, S; Pawar, P; Kalbande, R; Maji, S; Beig, G; Murthy, B S.
Afiliação
  • Latha R; Indian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pune, India. Electronic address: latha@tropmet.res.in.
  • Mukherjee A; Indian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pune, India.
  • Dahiya K; Indian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pune, India.
  • Bano S; Indian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pune, India.
  • Pawar P; Indian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pune, India.
  • Kalbande R; Indian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pune, India.
  • Maji S; Indian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pune, India.
  • Beig G; Indian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pune, India.
  • Murthy BS; Indian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pune, India.
J Environ Manage ; 311: 114834, 2022 Mar 11.
Article em En | MEDLINE | ID: mdl-35287076
ABSTRACT
Source apportionment study of PM2.5 using positive matrix factorization was performed to identify the emission characteristic from different sectors (sub-urban residential, industrial and rapidly urbanizing) of Delhi during winter. Chemical characterization of PM2.5 included metals (Ca, Cd, Cr, Cu, Fe, K, Mg, Mn, Na, Ni, Pb and Zn), water soluble ionic compounds (WSICs) (Cl-, NO3-, SO42- and NH4+) and Carbon partitions (OC, EC). Particulates (PM2.5) were collected on filter twice daily for stable and unstable atmospheric conditions, at the locations with specific characteristics, viz. Ayanagar, Noida and Okhla. Ions solely occupied 50% of the total PM2.5 concentration. Irrespective of location, high correlation between OC and EC (0.871-0.891) at p ≤ 0.1 is observed. Relatively lower ratio of NO3/SO4 at Ayanagar (0.696) and Okhla (0.84) denotes predominance of emission from stationary sources rather than mobile sources like that observed at Noida (1.038). Using EPA PMF5.0, optimum factors for each location are fixed based on error estimation (EE). Crustal dust, vehicular emission, biomass burning and secondary aerosol are the major contributing sources in all the three locations. Incineration contributes about 19% at Ayanagar and 18% at Okhla. Metal industries in Okhla contribute about 19% to PM2.5. These specific local emissions with considerable potency are to be targeted for long-term policymaking. Considerable secondary aerosol contribution (15%-24%) indicates that gaseous emissions also need to be reduced to improve air quality.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Revista: J Environ Manage Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Revista: J Environ Manage Ano de publicação: 2022 Tipo de documento: Article
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