Literature DB >> 21671645

Spatiotemporal aspects of real-time PM(2.5): low- and middle-income neighborhoods in Bangalore, India.

Adam F Both1, Arun Balakrishnan, Bobby Joseph, Julian D Marshall.   

Abstract

We measured outdoor fine particulate matter (PM(2.5)) concentrations in a low- and a nearby middle-income neighborhood in Bangalore, India. Each neighborhood included sampling locations near and not near a major road. One-minute average concentrations were recorded for 168 days during September 2008 to May 2009 using a gravimetric-corrected nephelometer. We also measured wind speed and direction, and PM(2.5) concentration as a function of distance from road. Average concentrations are 21-46% higher in the low- than in the middle-income neighborhood, and exhibit differing spatiotemporal patterns. For example, in the middle-income neighborhood, median concentrations are higher near-road than not near-road (56 versus 50 μg m(-3)); in the low-income neighborhood, the reverse holds (68 μg m(-3) near-road, 74 μg m(-3) not near-road), likely because of within-neighborhood residential emissions (e.g., cooking; trash combustion). A moving-average subtraction method used to infer local- versus urban-scale emissions confirms that local emissions are greater in the low-income neighborhood than in the middle-income neighborhood; however, relative contributions from local sources vary by time-of-day. Real-time relative humidity correction factors are important for accurately interpreting real-time nephelometer data.

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Year:  2011        PMID: 21671645     DOI: 10.1021/es104331w

Source DB:  PubMed          Journal:  Environ Sci Technol        ISSN: 0013-936X            Impact factor:   9.028


  5 in total

1.  The mobile monitoring of black carbon and its association with roadside data in the Chinese megacity of Shanghai.

Authors:  Xiao-Ning Lei; Ji-Wei Bian; Guang-Li Xiu; Xiao-Feng Hu; Xin-Sheng Gu; Qing-Gen Bian
Journal:  Environ Sci Pollut Res Int       Date:  2017-01-22       Impact factor: 4.223

2.  Spatio-Temporal Characteristics of PM2.5 Concentrations in China Based on Multiple Sources of Data and LUR-GBM during 2016-2021.

Authors:  Hongbin Dai; Guangqiu Huang; Jingjing Wang; Huibin Zeng; Fangyu Zhou
Journal:  Int J Environ Res Public Health       Date:  2022-05-22       Impact factor: 4.614

3.  Humidity and gravimetric equivalency adjustments for nephelometer-based particulate matter measurements of emissions from solid biomass fuel use in cookstoves.

Authors:  Sutyajeet Soneja; Chen Chen; James M Tielsch; Joanne Katz; Scott L Zeger; William Checkley; Frank C Curriero; Patrick N Breysse
Journal:  Int J Environ Res Public Health       Date:  2014-06-19       Impact factor: 3.390

4.  Characteristics and health risk assessment of fine particulate matter and surface ozone: results from Bengaluru, India.

Authors:  Vignesh Prabhu; Pratima Singh; Padmavati Kulkarni; V Sreekanth
Journal:  Environ Monit Assess       Date:  2022-02-23       Impact factor: 3.307

5.  Use of spatiotemporal characteristics of ambient PM2.5 in rural South India to infer local versus regional contributions.

Authors:  M Kishore Kumar; V Sreekanth; Maëlle Salmon; Cathryn Tonne; Julian D Marshall
Journal:  Environ Pollut       Date:  2018-05-08       Impact factor: 8.071

  5 in total

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