High seasonal variation of atmospheric C and particle concentrations in Delhi, India

Mandal, Papiya; Saud, T.; Sarkar, Raju; Mandal, Anubha; Sharma, S.K.; Mandal, T. K.; Bassin, J. K.

doi:10.1007/s10311-013-0438-y

Cited by 38 publications

(17 citation statements)

References 18 publications

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“…Whereas during summers, formation of secondary OC due to high photochemical activity is responsible for high OC, and EC could come from re-suspension of local materials. Similar seasonal changes in TC have also been observed by Mandal et al (2014). Both, OC and EC in Delhi's PM 10 aerosols were higher as compared to megacities like Chennai (9.1 and 6.5 µg m -3 ) (Pavuluri et al, 2011), Ahmedabad (12.8 and 2.1 µg m -3 ) (Rastogi and Sarin, 2009), Mumbai (25.3 and 12.6 µg m -3 ) (Venkataraman et al, 2002), and Beijing, China (21.2 and 8.9 µg m -3 ) .…”

Section: Oc and Ec Content In Pm 10supporting

confidence: 83%

Seasonal Variations in Water Soluble Inorganic Ions, OC and EC in PM10 and PM>10 Aerosols over Delhi: Influence of Sources and Meteorological Factors

Kumar

Yadav

2016

Aerosol Air Qual. Res.

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The PM 10 and particles of greater than 10 µm aerodynamic diameter (hence forth referred as PM >10 ) collected over Delhi exceeded the National Ambient Air Quality Standards (100 µg m -3 ) with an annual average of 215 µg m -3 and 495 µg m -3 , respectively. The water soluble inorganic ions (WSII) were higher throughout the year in PM 10 (13.5%) than in PM >10 (5%). SO 4 2-dominated over NO 3 -ions and contributed 54% to the total WSII in PM 10 . NO 3 -and Ca 2+ were dominant ions in PM >10 . NH 4 + during winter and autumn, and Ca 2+ in summer PM 10 samples were the major acid neutralizing species. The WSII showed monthly and seasonal changes. The total carbon (TC = EC + OC) constituted 8.8%-47.8% of total PM 10 mass, and OC and EC varied from 9.91 to 37.06 µg m -3 and 5.42 to 22.23 µg m -3 during the year long sampling period. The SOC contributed more to OC in summers and the char (EC1) dominated over soot (EC2 + EC3) in EC fractions throughout the year. In summer samples, secondary OC (SOC) contributed 81% to OC and the OC/EC ratio was 3.61 suggesting the possible role of mineral dust and high photochemical activity in SOC production. For the eight different fractions, three dominant sources were identified, coal combustion, biomass burning, and motor vehicle exhaust for OC1, OC2 and OC3; vehicle exhaust for OC4, OP, EC2 and EC3; and biomass and coal combustion for EC1. The seasonal changes in the WSII and carbon concentrations in PM 10 and PM >10 are attributed to both the sources and the meteorological conditions in and around the study area.

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Section: Oc and Ec Content In Pm 10supporting

confidence: 83%

Seasonal Variations in Water Soluble Inorganic Ions, OC and EC in PM10 and PM>10 Aerosols over Delhi: Influence of Sources and Meteorological Factors

Kumar

Yadav

2016

Aerosol Air Qual. Res.

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“…Table 1 summarizes with the lowest concentrations in the monsoon season (1.34± 0.90 µg m −3 ) and the highest concentrations in the postmonsoon season (OC: 2.57 ± 1.30 µg m −3 ). Similar seasonal differences were also found in other areas such as Lumbini, Nepal (Wan et al, 2017); Nepal Climate Observatory -Pyramid (NCO-P) (Bonasoni et al, 2010); Kanpur, India (Ram et al, 2012); and Delhi, India (Mandal et al, 2014). Moreover, monthly averaged EC and OC concentrations were analyzed for Mt.…”

Section: Characteristics Of the Carbonaceous Aerosolssupporting

confidence: 62%

Seasonal variation and light absorption property of carbonaceous aerosol in a typical glacier region of the southeastern Tibetan Plateau

et al. 2018

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Abstract. Deposition and accumulation of light-absorbing carbonaceous aerosol on glacier surfaces can alter the energy balance of glaciers. In this study, 2 years (December 2014 to December 2016) of continuous observations of carbonaceous aerosols in the glacierized region of the Mt. Yulong and Ganhaizi (GHZ) basin are analyzed. The average elemental carbon (EC) and organic carbon (OC) concentrations were 1.51 ± 0.93 and 2.57 ± 1.32 µg m −3 , respectively. Although the annual mean OC / EC ratio was 2.45 ± 1.96, monthly mean EC concentrations during the post-monsoon season were even higher than OC in the high altitudes (approximately 5000 m a.s.l.) of Mt. Yulong. Strong photochemical reactions and local tourism activities were likely the main factors inducing high OC / EC ratios in the Mt. Yulong region during the monsoon season. The mean mass absorption efficiency (MAE) of EC, measured for the first time in Mt. Yulong, at 632 nm with a thermal-optical carbon analyzer using the filter-based method, was 6.82 ± 0.73 m 2 g −1 , comparable with the results from other studies. Strong seasonal and spatial variations of EC MAE were largely related to the OC abundance. Source attribution analysis using a global aerosol-climate model, equipped with a black carbon (BC) source tagging technique, suggests that East Asia emissions, including local sources, have the dominant contribution (over 50 %) to annual mean near-surface BC in the Mt. Yulong area. There is also a strong seasonal variation in the regional source apportionment. South Asia has the largest contribution to near-surface BC during the premonsoon season, while East Asia dominates the monsoon season and post-monsoon season. Results in this study have great implications for accurately evaluating the influences of carbonaceous matter on glacial melting and water resource supply in glacierization areas.

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“…Additionally, seasonal effect was estimated by sin(day) and cos(day), respectively. A strong seasonal variability of major APs was reported in previous studies (Cheng et al, 2007;Castell-Balaguer et al, 2012;Mandal et al, 2014), reflecting the variability of emissions and atmospheric transport processes. The basic descriptive statistics of the variables for individual localities DU, RO and HK are presented in Table 2.…”

Section: Methodsmentioning

confidence: 56%

Predicting Common Air Quality Index - The Case of Czech Microregions

Hájek

Olej

2015

Aerosol Air Qual. Res.

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This paper presents a design of models for common air quality index prediction using computational intelligence methods. In addition, the sets of input variables were optimized for each air pollutant prediction by genetic algorithms. Based on data measured by the three monitoring stations of Dukla, Rosice and Brnenska in the Czech Republic, the models were designed to predict air quality indices for each air pollutant separately and, consequently, to predict the common air quality index. Considering the root mean squared error, the results showed that the compositions of individual prediction models significantly outperform single prediction models of the common air quality index. The feature selection procedure indicates that the determinants of air quality indices were strongly locality specific. Therefore, the models can be applied to obtain more accurate one day ahead predictions of air quality indices. Here we show that the composition models achieve high prediction accuracy for maximum air quality indices (between 50.69 and 63.36%). The goal of the prediction by various methods was to compare the results of the prediction with the aim of various recommendations to micro-regional public administration management.

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High seasonal variation of atmospheric C and particle concentrations in Delhi, India

Cited by 38 publications

References 18 publications

Seasonal Variations in Water Soluble Inorganic Ions, OC and EC in PM10 and PM>10 Aerosols over Delhi: Influence of Sources and Meteorological Factors

Seasonal Variations in Water Soluble Inorganic Ions, OC and EC in PM10 and PM>10 Aerosols over Delhi: Influence of Sources and Meteorological Factors

Seasonal variation and light absorption property of carbonaceous aerosol in a typical glacier region of the southeastern Tibetan Plateau

Predicting Common Air Quality Index - The Case of Czech Microregions

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