Real-time measurement and source apportionment of elements in Delhi's atmosphere

Rai, Pragati; Furger, Markus; Haddad, Imad El; Kumar, Varun; Wang, Liwei; Singh, Atinderpal; Dixit, Kuldeep Kumar; Bhattu, Deepika; Petit, Jean-Eudes; Ganguly, Dilip; Rastogi, Neeraj; Baltensperger, Urs; Tripathi, Sachchida Nand; Slowik, Jay G.; Prévôt, A. S. H.

doi:10.1016/j.scitotenv.2020.140332

Cited by 101 publications

(107 citation statements)

References 96 publications

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“…Evaluating the backward trajectory is a common method to determine the source region and movement of pollutants within the atmosphere [37,38]. The long-distance pollutants transmission from other regions are generally considered to play a vital role in local haze episodes [39].…”

Section: Air-mass Backward Trajectoriesmentioning

confidence: 99%

Temporal Variation and Source Analysis of Carbonaceous Aerosol in Industrial Cities of Northeast China during the Spring Festival: The Case of Changchun

Zhang

Bao

et al. 2020

Atmosphere

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Carbonaceous aerosol, one of the major components of atmospheric aerosols, significantly affects haze episodes, climate change, and human health. Northeastern China suffers severe air pollution, especially in some periods (e.g., the Spring Festival). However, studies on carbonaceous aerosols in typical northeast industrial cities (i.e., Changchun) are rare, limiting further comprehension of the atmospheric haze formation. In this study, we monitored the concentrations of carbonaceous aerosols (i.e., OC and EC) in Changchun during the Lunar New Year of 2018 (i.e., from Lunar 20 December to Lunar 20 January), and analyzed the temporal variation and source contributions via the HYbrid-Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model with the potential source contribution factor weights (PSCF) method. The daily concentrations of OC and EC were 9.00 ± 2.81 and 1.57 ± 0.46 µg m−3, respectively, and were significantly lower at nighttime than at the day during the Spring Festival. The concentrations during the major period (i.e., OC: 8.13 ± 2.93 µg m−3; EC: 1.47 ± 0.47 µg m−3 in festival days), including the Lunar Little New Year; the Lunar New Year’s Eve; New Year’s Day; Lunar 5 January, and the Spring Lantern Festival, were mainly from the northwestward with the wind speed of 4–6 m/s being lower than that of normal period (OC: 9.87 ± 2.46 µg m−3; EC: 1.67 ± 0.44 µg m−3) from the southeastward with a wind speed of 6–7 m/s. The direction of the airflow trajectory was mainly in local, northwestward, and northward, carrying particulate matter and gaseous pollutants. In major period, the daily concentration of atmospheric pollutants presented a bimodal trend, with peaks appearing regularly from 11:00 a.m. to 12:00 p.m. and 5:00 p.m. 10:00 p.m., which might be related to traffic, cooking, and firecrackers. The OC/EC was greater than 2 during the whole period, indicating the generation of secondary organic aerosols (i.e., SOC). This study was essential to understand the formation mechanisms of severe pollution episodes and develop control measures for the industrial cities of Northeast China during the Spring Festival.

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Section: Air-mass Backward Trajectoriesmentioning

confidence: 99%

Temporal Variation and Source Analysis of Carbonaceous Aerosol in Industrial Cities of Northeast China during the Spring Festival: The Case of Changchun

Zhang

Bao

et al. 2020

Atmosphere

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“…In Beijing, Delhi, and Krakow, sampling and analysis was conducted with an Xact 625i ® Ambient Metals Monitor (Cooper 90 Environmental, Tigard, OR, USA) with an alternating PM10 and PM2.5 inlet switching system (Furger et al, 2020). Details of the Xact can be found in previous studies (Cooper et al, 2010;Furger et al, 2017;Rai et al, 2020;Tremper et al, 2018). The field measurements with the Xact were performed with 1 h time resolution in Beijing and 0.5 h time resolution in Krakow and Delhi.…”

Section: Instrumentationmentioning

confidence: 99%

“…In the Asian cities, high concentrations are also observed overnight. This is likely related to heavy-duty vehicular activities, which in these cities occur 200 predominantly at night due to their ban during peak traffic hours (07:30 -11:00 LT and 17:00 -22:00 LT and less dominant during daytime) in Delhi (Rai et al, 2020) and the entire day in Beijing (Zheng et al, 2015). As the two non-exhaust traffic emissions (i.e., brake wear and dust resuspension) are related to traffic activity, the time series of most elements in Groups 1 and 2 are relatively well correlated, although not as tightly as the Group 1 elements are among themselves due to their common https://doi.org/10.5194/acp-2020-618 Preprint.…”

Section: Characteristic Element Groupsmentioning

confidence: 99%

“…Further, Delhi, Beijing and Krakow are far from the ocean and de-icing salt was not used on the roads during the measurement periods. In London and Delhi, K was attributed to solid fuel combustion via SA studies (Rai et al, 2020;Visser et al, 2015a). The diurnals in Delhi and Krakow 215…”

Section: Characteristic Element Groupsmentioning

confidence: 99%

“…A simple conceptual framework allows characterization of major sources, site-to-site similarities and local differences, and identification of key information required for efficient policy development. Moreover, this method does not requiring a full source apportionment (SA) analysis (presented elsewhere for London and Delhi (Visser et al, 2015a;Rai et al, 2020)), which is complex and time-consuming, and which can be challenging 60 to compare across sites due to differences in source definitions.…”

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confidence: 99%

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Highly time-resolved measurements of element concentrations in PM<sub>10</sub> and PM<sub>2.5</sub>: Comparison of Delhi, Beijing, London, and Krakow

Rai¹,

Slowik²,

Furger³

et al. 2020

Preprint

Self Cite

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Abstract. We present highly time-resolved (30 to 120 min) measurements of size-fractionated (PM10 and PM2.5) elements in two cities in Asia (Delhi and Beijing) and Europe (Krakow and London). For most elements, the mean concentrations in PM10 and PM2.5 are higher in Asian cities (up to 24 and 28 times, respectively) than in Krakow, and often higher in Delhi than in Beijing. Among European cities, Krakow shows higher elemental concentrations (up to 20 and 27 times, respectively) than London. The enrichment factor of an element together with the size distribution allows for a rough classification of elements by major sources. We define five groups: (1) dust-related, (2) non-exhaust traffic emissions, (3) solid fuel combustion, (4) mixed traffic/industrial emissions, and (5) industrial/coal/waste burning emissions, with the last group exhibiting the most site-to-site variability. Hourly maximum concentrations of Pb and Zn reach up to 1 µg m−3 in Delhi, substantially higher than at the other sites. We demonstrate that the high time resolution and size-segregated elemental dataset can be a powerful tool to assess aerosol composition and sources in urban environments. Our results highlight the need to consider the size distributions of toxic elements, diurnal patterns of targeted emissions, and local vs. regional effects in formulating effective environmental policies to protect public health.

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Seasonal Variation and Source Identification of PM₁₀ in an Industrialized City

Khobragade

Ahirwar

2023

Macromolecular Symposia

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A 1‐year PM10 sampling campaign from January 2019 to December 2019 is conducted in an urban industrial area Raipur, India. For the development of effective pollution control strategies, quantitative assessment of potentially toxic metals and accurate identification of pollutant sources are essential. The annual average PM10 aerosol mass concentration during the study period is recorded as 187.89 ± 91 µg m−3. PM10 concentrations are found to vary with meteorological parameters such as wind speed, temperature, rainfall, and humidity. Heavy metals including Fe, Mn, Ni, Pb, Zn, Al, Ba, Cr, Sr, Ga, Mg, and V are studied using atomic absorption spectroscopy (AAS). Seasonally, average winter time PM10 concentration (234.86 ± 104.3 µg m−3) is accounted higher as compared to summer (169.02 ± 27.1 µg m−3) and rainy season (106.28 ± 32.6 µg m−3) during the study period. The aerosol sources identified by positive matrix factorization (PMF) model are: iron and steel production (34.7%), vehicular emissions (29.3%), road dust (14.6%), heavy oil/petroleum (14.6%), and coal combustion (6.9%). Among these sources, iron and steel production contributes maximum in PM10 concentrations. The probable source locations are recognized through concentration weighted trajectory (CWT) analysis. The AirQ+ model is used to assess the health effects of regional PM10 concentrations.

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Real-time measurement and source apportionment of elements in Delhi's atmosphere

Cited by 101 publications

References 96 publications

Temporal Variation and Source Analysis of Carbonaceous Aerosol in Industrial Cities of Northeast China during the Spring Festival: The Case of Changchun

Temporal Variation and Source Analysis of Carbonaceous Aerosol in Industrial Cities of Northeast China during the Spring Festival: The Case of Changchun

Highly time-resolved measurements of element concentrations in PM<sub>10</sub> and PM<sub>2.5</sub>: Comparison of Delhi, Beijing, London, and Krakow

Seasonal Variation and Source Identification of PM₁₀ in an Industrialized City

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Real-time measurement and source apportionment of elements in Delhi's atmosphere

Cited by 101 publications

References 96 publications

Temporal Variation and Source Analysis of Carbonaceous Aerosol in Industrial Cities of Northeast China during the Spring Festival: The Case of Changchun

Temporal Variation and Source Analysis of Carbonaceous Aerosol in Industrial Cities of Northeast China during the Spring Festival: The Case of Changchun

Highly time-resolved measurements of element concentrations in PM&lt;sub&gt;10&lt;/sub&gt; and PM&lt;sub&gt;2.5&lt;/sub&gt;: Comparison of Delhi, Beijing, London, and Krakow

Seasonal Variation and Source Identification of PM10 in an Industrialized City

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Highly time-resolved measurements of element concentrations in PM<sub>10</sub> and PM<sub>2.5</sub>: Comparison of Delhi, Beijing, London, and Krakow

Seasonal Variation and Source Identification of PM₁₀ in an Industrialized City