Literature DB >> 25192649

Using an improved Source Directional Apportionment method to quantify the PM(2.5) source contributions from various directions in a megacity in China.

Ying-Ze Tian1, Guo-Liang Shi2, Bo Han3, Jian-Hui Wu1, Xiao-Yu Zhou1, Lai-Dong Zhou4, Pu Zhang4, Yin-Chang Feng1.   

Abstract

The transport of particulate matter (PM) and chemical species is an essential mechanism for determining the fate of PM pollutants and their effects. To determine source transport quantitatively, an ambient PM2.5 dataset from a megacity in China was analysed using a novel method called "Source Directional Apportionment" (SDA). The SDA method is developed in this work to quantify contributions of each source category from various directions. The three steps of SDA are (1) to estimate source categories and time series of source contributions to PM with a factor analysis model, (2) to identify directions by trajectory cluster analysis and (3) to quantify source directional contributions for each source category by combining the time series of source contributions to the back trajectories in each direction. For PM2.5 in Chengdu, crustal dust, vehicular exhaust, coal combustion and secondary sulphate are all important contributors to PM; secondary nitrate and cement dust are relatively less influential. Four potential source directions were identified in Chengdu during the sampling period from 2009 to 2011. The percentages of source directional contributions from Directions 1-4 (northeast, southwest to south, southwest and west) were estimated as follows: crustal dust (7.9%, 9.1%, 6.4% and 6.2%, respectively), cement dust (1.0%, 1.2%, 1.3% and 1.1%, respectively), vehicular exhaust (6.4%, 6.0%, 5.6% and 7.0%, respectively), secondary sulphate (5.1%, 5.2%, 5.6% and 8.6%, respectively) and secondary nitrate (2.0%, 2.4%, 2.5% and 2.3%, respectively). Finally, the source directional contributions to important chemical species were quantified to determine their transport from sources to receptor.
Copyright © 2014 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Chemical species; PM; PMF; SDA; Source apportionment; Trajectories cluster analysis

Mesh:

Substances:

Year:  2014        PMID: 25192649     DOI: 10.1016/j.chemosphere.2014.08.015

Source DB:  PubMed          Journal:  Chemosphere        ISSN: 0045-6535            Impact factor:   7.086


  4 in total

Review 1.  A review of AirQ Models and their applications for forecasting the air pollution health outcomes.

Authors:  Gea Oliveri Conti; Behzad Heibati; Itai Kloog; Maria Fiore; Margherita Ferrante
Journal:  Environ Sci Pollut Res Int       Date:  2017-01-04       Impact factor: 4.223

2.  Development and application of three-dimensional potential source contribution function (3D-PSCF).

Authors:  In Sun Kim; Daehyun Wee; Yong Pyo Kim; Ji Yi Lee
Journal:  Environ Sci Pollut Res Int       Date:  2016-05-06       Impact factor: 4.223

3.  The performance of a modified EWMA control chart for monitoring autocorrelated PM2.5 and carbon monoxide air pollution data.

Authors:  Yadpirun Supharakonsakun; Yupaporn Areepong; Saowanit Sukparungsee
Journal:  PeerJ       Date:  2020-12-15       Impact factor: 2.984

4.  Potential Risks of PM2.5-Bound Polycyclic Aromatic Hydrocarbons and Heavy Metals from Inland and Marine Directions for a Marine Background Site in North China.

Authors:  Qianqian Xue; Yingze Tian; Xinyi Liu; Xiaojun Wang; Bo Huang; Hongxia Zhu; Yinchang Feng
Journal:  Toxics       Date:  2022-01-11
  4 in total

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