PM10 source identification using the trajectory based potential source apportionment (TraPSA) toolkit at Kochi, India

Shanavas, Afifa Kombanezhathu; Zhou, Chuanlong; Menon, Ratish; Hopke, Philip K.

doi:10.1016/j.apr.2020.06.019

Cited by 15 publications

(8 citation statements)

References 21 publications

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“…In the southwest coastal region, the Arabian Sea could be a substantial source of pollutants in the coastal region. The increase in SO 2 levels during the LD period particularly at TM could be attributed to the marine sources as Shanavas et al ( 2020 ) observed that the emissions from marine traffic contribute to the concentration of SO 2 along the coastal regions of south India. According to Madineni et al ( 2021 ), the relative increase in the concentration of some trace gases in the southern part of India suggests a contribution from other sources including emissions from natural sources (e.g., forest fires, biomass burning) and long-range transport.…”

Section: Discussionmentioning

confidence: 99%

Impact of COVID-19 Lockdown on Ambient Air Quality in the Southwest Coastal Urban Regions of India

Thomas¹,

Thomas

Devika³

et al. 2023

Aerosol Sci Eng

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The rapid growth of urban areas and population as well as associated development over recent decades have been a major factor controlling ambient air quality of the urban environment in Kerala (India). Being located at the southwestern fringe of the Indian peninsula, Kerala is one of the regions that has been significantly influenced by the activities in the Indian Ocean. The present study focuses on the effect of the COVID-19 lockdown (in 2021) on ambient air quality in the selected coastal metropolitan areas of Kerala. Although previous research studies reported improvement in ambient air quality in Kerala during the lockdown period, this study demonstrates the potential of onshore transport of air pollutants in controlling the air quality of coastal urban regions during the lockdown period. Data from the ambient air quality monitoring stations of the Kerala State Pollution Control Board in the urban areas of Thiruvananthapuram (TM), Kollam (KL), Kozhikode (KZ), and Kannur (KN) are used for the analysis. Temporal variation in the concentration of air pollutants during the pre-lockdown (PRLD), lockdown (LD), and post-lockdown (PTLD) periods (i.e., 1 March to 31 July) of 2021 is examined to assess the effect of lockdown measures on the National Air Quality Index (AQI). Results indicate a significant decline in the levels of air pollutants and subsequent improvement in air quality in the coastal urban areas. All the effect of lockdown measures has been evident in the AQI, an increase in the concentration of different pollutants including CO, SO 2 , and NH 3 during the LD period suggests contributions from multiple sources including onshore transport due to marine traffic and transboundary transport.

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Section: Discussionmentioning

confidence: 99%

Impact of COVID-19 Lockdown on Ambient Air Quality in the Southwest Coastal Urban Regions of India

Thomas¹,

Thomas

Devika³

et al. 2023

Aerosol Sci Eng

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“…Piver 1983; Jang et al, 2007;Anastasopolos et al 2023). This source can be attributed to marine diesel emissions from ships in the ports, or in nearby shipping lanes (Agrawahl et al, 2008a,b;Shanavas et al, 2020) as well as emissions from petrochemical plants, refineries, or oil-fired industrial boilers in Ulsan and Dalian (Choi et al, 2021;Zhao et al, 2021). The higher contributions during the non-heating period suggest that this source is more influenced by ship emissions than point sources.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

Apportioning and Locating PM2.5 Sources Affecting Coastal Cities: Ulsan in South Korea and Dalian in China

Choi,

Jeon,

Lee

et al. 2024

Aerosol Air Qual. Res.

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PM2.5 mass and its constituent species were analyzed in two coastal cities (Ulsan, South Korea, and Dalian, China) between July 13, 2018, and September 20, 2019. Ten and nine sources were identified in Ulsan and Dalian, respectively, using positive matrix factorization (PMF). In Ulsan, three sources (secondary nitrate [SN], secondary sulfate [SS], and traffic) contributed ~83.0% of the PM2.5 mass concentration (23.7 µg m -3 ) during the heating period. In Dalian, four sources (SN, SS, traffic, and residential burning) accounted for ~84.3% of the total PM2.5 mass concentration (47.8 µg m -3 ). Higher contributions of residential burning in Dalian (11.7 µg m -3 ) than biomass burning in Ulsan (0.22 µg m -3 ) were resolved during the heating period as was a higher proportion of SS contributions in Ulsan (6.28 µg m -3 , 41.6%) than in Dalian (6.42 µg m -3 , 21.2%) during nonheating period. Squared correlation coefficients (r 2 ) of sources common to the two cities were examined for lag times from -2 days to +4 days from Dalian to Ulsan. The largest r 2 of PM2.5 mass concentrations during the heating period was 0.34 on Lag day 1. The same day, largest r 2 during the non-heating period was 0.14 indicating, stronger, lagged PM 2.5 correlations during the heating period. The SN, SS, soil, and oil combustion sources, with r 2 values of 0.25, 0.20, 0.41, and 0.25, respectively, show fair correlations between the cities for these sources during the heating period.Probable source locations were identified by simplified quantitative transport bias analysis (SQTBA) and potential source contribution function (PSCF) as a multiple site approach and a single site approach, respectively. Weaker correlations of SN (r 2 = 0.15) and SS (r 2 < 0.1) during the non-heating period were supported by the different probable source locations. This study identified the sources requiring individual national and/or joint international efforts to reduce ambient PM 2.5 in these neighboring countries.

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“…The Air Resource Laboratory's HYSPLIT model was widely used to determine the source of air masses and to establish the relationship between source and receptor. [25,61] Twenty-four hours backtrajectories were calculated during days of maximum concentrations to identify the trend analysis of winds and identification of potential sources in the region. Winds during different seasons carried away the particulates to different regions, hundreds of kilometers to away from their actual sources.…”

Section: Environmental Conditions and Back Trajectories Of Air Massesmentioning

confidence: 99%

“…[16,19,20] Identification of pollution sources is a crucial stage in air quality management since it allows for better understanding of pollution control techniques and estimation of their respective contributions. [21][22][23][24][25][26][27] Modeling strategies involving source-oriented or receptor models to associate emissions from pollution sources are referred to as source apportionment of particles. [28][29][30][31][32][33][34] Using robust mathematical tools, such data give essential knowledge for identifying and quantifying PM sources.…”

Section: Introductionmentioning

confidence: 99%

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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PM10 source identification using the trajectory based potential source apportionment (TraPSA) toolkit at Kochi, India

Cited by 15 publications

References 21 publications

Impact of COVID-19 Lockdown on Ambient Air Quality in the Southwest Coastal Urban Regions of India

Impact of COVID-19 Lockdown on Ambient Air Quality in the Southwest Coastal Urban Regions of India

Apportioning and Locating PM2.5 Sources Affecting Coastal Cities: Ulsan in South Korea and Dalian in China

Seasonal Variation and Source Identification of PM₁₀ in an Industrialized City

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PM10 source identification using the trajectory based potential source apportionment (TraPSA) toolkit at Kochi, India

Cited by 15 publications

References 21 publications

Impact of COVID-19 Lockdown on Ambient Air Quality in the Southwest Coastal Urban Regions of India

Impact of COVID-19 Lockdown on Ambient Air Quality in the Southwest Coastal Urban Regions of India

Apportioning and Locating PM2.5 Sources Affecting Coastal Cities: Ulsan in South Korea and Dalian in China

Seasonal Variation and Source Identification of PM10 in an Industrialized City

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Product

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Seasonal Variation and Source Identification of PM₁₀ in an Industrialized City