Characterization and morphological analysis of airborne PM2.5 and PM10 in Agra located in north central India

Pipal, Atar Singh; Kulshrestha, Aditi; Taneja, Ajay

doi:10.1016/j.atmosenv.2011.03.062

Cited by 150 publications

(56 citation statements)

References 42 publications

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“…The composition of tarball particles were observed dominated by carbon, oxygen with traces of S and K, which is similar as observed in other studies also in fine mode (Posfai et al, 2004). The individual elemental percentage of these particles is different from our previous study (Pipal et al, 2011) and having cluster, spherical, irregular shape at RD where these particles originated from fuel oil combustion.…”

Section: Mineral Particlessupporting

confidence: 56%

“…The atmospheric pollution load is high because of downwind sources like, oil refinery at Mathura (50 km far from the centre of the city) from where pollutants may be transported to different areas (Kumar et al, 2007). Agra has about 13,11,000 total inhabitants and the population density is about 21,148 persons per sq km with 386,635 vehicles registered and 32,030 generator sets (Pipal et al, 2011(Pipal et al, , 2014. In Agra, 60% pollution is due to vehicles and three highways (NH-2, NH-3 and NH-11) cross the city.…”

Section: Study Areamentioning

confidence: 99%

“…It also come from incomplete combustion sources and is mainly associated with carbon and transition metals. The possible sources of K are soil due to the preparing of farmland because SR site is moving towards urbanization and holds construction and agriculture activities which enhance the use of different building materials (cement and sand) (Pipal et al, 2011). The elemental compositions of PM were observed with the help of energy dispersive spectrometer (EDS) which are semi quantitative analyses considering the bulk or individual particles.…”

Section: Statistical and Trajectory Analysis For Source Identificatiomentioning

confidence: 99%

“…The application of SEM-EDS can provide additional information concerning the composition, source, formation, transport, reactivity, transformation reactions and the number and volume-size distribution of atmospheric aerosols (Ma et al, 2001). During the last few decades, SEM-EDS has been successfully applied to the chemical and physical characterization of individual particles (Xie et al, 2005;Slezakova et al, 2008;Pipal et al, 2011). In order to improve air quality, efforts must be made to understand the physical and chemical characteristics of airborne PM (PM 2.5 and PM 10 ) and to identify their origins.…”

Section: Introductionmentioning

confidence: 99%

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Study of Surface Morphology, Elemental Composition and Origin of Atmospheric Aerosols (PM2.5 and PM10) over Agra, India

Pipal

Jan

Satsangi

et al. 2014

Aerosol Air Qual. Res.

Self Cite

110

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In situ measurements of PM (PM 2.5 and PM 10 ) particles were carried out using a medium volume air sampler (offline) and particle number concentrations of PM were measured by a Grimm aerosol spectrophotometer (online) during the study period of 2010-2011. The morphology and elemental composition analyses of PM were performed by Scanning Electron Microscopy (SEM) and Energy Dispersive Spectrometry (EDS), respectively. The average mass concentrations of PM 2.5 and PM 10 were 97.2 and 242.6 µg/m 3 at roadside (RD) and 121.2 and 230.5 µg/m 3 at a semirural (SR) site, respectively. These concentrations were substantially higher than the NAAQS, WHO and USEPA standards. The highest mass and number concentrations of PM 2.5 and PM 10 were observed during winter, followed by those during the post-monsoon period and summer, with the lowest in the monsoon period. SEM and EDS analysis of PM indicated the presence of soot, mineral, tarballs, fly ash, aluminosilicates/silica, fluorine, carbon rich, and Cl-Na rich particles. Of these particles, soot, tarballs, and F-C rich particles dominate in PM 2.5 , whereas mineral, aluminosilicates, and Cl-Na rich particles dominate in PM 10 . The morphology and elemental composition of the particles varied over the seasons due to atmospheric processing. The highest carbon concentration (56%) was observed in PM 2.5 during summer at the RD, while in the monsoon, postmonsoon period and winter the carbon concentration was ~9% lower at the RD as compared to the SR. However, the concentration of carbon in PM 10 was ~38% higher at the RD as compared to SR during both summer and winter. Air mass backward trajectory cluster analysis was performed, and the results indicate that the aerosol loadings over Agra are mainly transported from the Middle East and Arabian Sea during the summer and monsoon period, while during the pre-monsoon period and winter the aerosol loadings came from the northern region, and were due to the burning of biomass and coal, as well as other local activities.

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Section: Mineral Particlessupporting

confidence: 56%

Section: Study Areamentioning

confidence: 99%

Section: Statistical and Trajectory Analysis For Source Identificatiomentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Study of Surface Morphology, Elemental Composition and Origin of Atmospheric Aerosols (PM2.5 and PM10) over Agra, India

Pipal

Jan

Satsangi

et al. 2014

Aerosol Air Qual. Res.

Self Cite

110

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“…The highest average PM 10 concentration was obtained in urban areas like Agra, India (ranged from 270-290 μg/m 3 (Pipal et al, 2011) and Guangzhou, China (ranged from 120-130 μg/m 3 ) (Cao et al, 2004). The highest average PM 2.5 concentration was also obtained in urban areas like Agra, India (ranged from 80-90 μg/m 3 ) (Pipal et al, 2011) and Guangzhou, China (78 μg/m 3 ) (Cao et al 2004) and Ordos, China (51 μg/m 3 ) (Wang et al, 2012). The PM 10 , PM 2.5 and PM 0.1 concentrations obtained in this study were found to be the lowest in the Asian region, with mean values of about 39.45 ± 11.58, 21.82 ± 7.50 and 1.42 ± 0.56 μg/m 3 , respectively.…”

Section: Comparison Of Particle Concentrations and Source Contributiomentioning

confidence: 99%

Source Characterization and Apportionment of PM10, PM2.5 and PM0.1 by Using Positive Matrix Factorization

Gugamsetty

Han

Liu

et al. 2012

Aerosol Air Qual. Res.

209

117

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Ambient Particulate Matters (PM 10 , PM 2.5 and PM 0.1 ) were investigated at Shinjung station in New Taipei City, Taiwan. Samples were collected simultaneously using a dichotomous sampler (Andersen Model SA-241) and a MOUDI (MSP Model 110) over a 24-h period from May 2011 to November 2011 at Shinjung station. Samples were analyzed for metallic trace elements using ion coupled plasma mass spectroscopy (ICP-MS) and ionic compounds by ion chromatography (IC). The average concentrations of PM 10 , PM 2.5 and PM 0.1 were found to be 39.45 ± 11.58, 21.82 ± 7.50 and 1.42 ± 0.56 μg/m 3 , respectively. Based on the chemical information, positive matrix factorization (PMF) was used to identify PM sources. A total of five source types were identified, including soil dust, vehicle emissions, sea salt, industrial emissions and secondary aerosols, and their contributions were estimated using PMF. The crustal enrichment factors (EF) were calculated using Al as a reference for the trace metal species to identify the sources. Conditional probability functions (CPF) were computed using wind profiles and factor contributions. The results of CPF analysis were used to identify local point sources. The results suggest a competitive relationship between anthropogenic and natural source processes over the monitoring station.

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Micro‐ to macro‐scaling analysis of PM_2.5 in sensitive environment of Himalaya, India

Kimothi,

Chilkoti,

Rawat

et al. 2023

Geological Journal

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Fine particulate matter (PM) in the atmosphere has become a significant air contaminant with substantial health consequences. Although airborne remote sensing and ground sensor monitoring can offer air quality datasets containing PM2.5, there are limitations to effectively analysing large long‐term datasets. The research aims to evaluate air quality over the Himalayan region using the multifractal approach for the PM2.5 data time series. Fractal dimension (FD), Hurtz exponent (H), and predictability (PI) are estimated using the rescaled range. PM2.5 is found to have high concentration and frequency throughout the day. The same is found in the night hours during peak tourism months. The hourly PM2.5 time series datasets have shown multifractality. The primary reason for this is emissions produced by vehicles and anthropocentric activities in the region. The H is used to assess the dynamic features of the PM2.5 time series in terms of persistence and self‐correlation. In the context of climate change studies, it is crucial to monitor the spatial distribution and dynamic behaviour of PM2.5 in the Himalayan foothills. This study aims to provide prediction analyses and air quality index (AQI) estimates and demonstrate how PM2.5 concentrations alter the sensitive environment throughout the micro to macro scale. This will help us to build a long‐term strategy for reducing the harmful effect of increasing pollution levels on the ecosystem and human health.

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Characterization and morphological analysis of airborne PM2.5 and PM10 in Agra located in north central India

Cited by 150 publications

References 42 publications

Study of Surface Morphology, Elemental Composition and Origin of Atmospheric Aerosols (PM2.5 and PM10) over Agra, India

Study of Surface Morphology, Elemental Composition and Origin of Atmospheric Aerosols (PM2.5 and PM10) over Agra, India

Source Characterization and Apportionment of PM10, PM2.5 and PM0.1 by Using Positive Matrix Factorization

Micro‐ to macro‐scaling analysis of PM_2.5 in sensitive environment of Himalaya, India

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Characterization and morphological analysis of airborne PM2.5 and PM10 in Agra located in north central India

Cited by 150 publications

References 42 publications

Study of Surface Morphology, Elemental Composition and Origin of Atmospheric Aerosols (PM2.5 and PM10) over Agra, India

Study of Surface Morphology, Elemental Composition and Origin of Atmospheric Aerosols (PM2.5 and PM10) over Agra, India

Source Characterization and Apportionment of PM10, PM2.5 and PM0.1 by Using Positive Matrix Factorization

Micro‐ to macro‐scaling analysis of PM2.5 in sensitive environment of Himalaya, India

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Micro‐ to macro‐scaling analysis of PM_2.5 in sensitive environment of Himalaya, India