Source apportionment and health risk assessment of airborne particulates over central Indo-Gangetic Plain

Murari, Vishnu; Singh, N. P.; Rai, Ravi Ranjan; Singh, Ram Sharan; Banerjee, Tirthankar

doi:10.1016/j.chemosphere.2020.127145

Cited by 51 publications

(31 citation statements)

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“…However, these cities also pose potential source field over central highlands and lower Gangetic plain, but their overall contribution are relatively low (CWT < 0.2). This concludes that during postmonsoon to winter, continental westerlies originating from upper IGP and western semiarid regions predominately contribute high to very high columnar aerosol loading in cities across the IGP (Kumar et al., 2017, 2018; Murari et al., 2020). Potential aerosol source field for Dhaka, however, slightly differs with moderate to high CWTs (>0.4) originating mainly from central Bangladesh with added contribution by westerly blowing across IGP.…”

Section: Resultsmentioning

confidence: 94%

“…CWTs, Concentration weighted trajectories; IGP, Indo-Gangetic plain; SEA, Southeast Asia. Murari et al, 2020). Potential aerosol source field for Dhaka, however, slightly differs with moderate to high CWTs (>0.4) originating mainly from central Bangladesh with added contribution by westerly blowing across IGP.…”

Section: Aerosol Potential Source Fieldsmentioning

confidence: 95%

“…Kanpur (26.51°N, 80.23°E), an industrial city in central IGP experiences seasonal variations in aerosol types linked to biomass and refuse burning emissions, with dominating proportions of carbonaceous and sulfate aerosols (Chakraborty et al., 2015). Varanasi (25.31°N, 82.97°E), reported to have the highest columnar aerosol loading among the cities in IGP (Kumar et al., 2018); have aerosols mostly of secondary in nature with contribution from biomass/waste burning, crustal resuspensions, and residential emission sources (Murari et al., 2020; Singh et al., 2021b). Fine absorbing aerosols mainly prevail in Dhaka (23.72°N, 90.39°E), with sources like vehicular emissions, household energy practices and emissions from brick kilns (Begum et al., 2004).…”

Section: Data and Experimental Methodsmentioning

confidence: 99%

“…Varanasi (25.31°N,82.97°E), reported to have the highest columnar aerosol loading among the cities in IGP (Kumar et al, 2018); have aerosols mostly of BANERJEE ET AL. secondary in nature with contribution from biomass/waste burning, crustal resuspensions, and residential emission sources (Murari et al, 2020;Singh et al, 2021b). Fine absorbing aerosols mainly prevail in Dhaka (23.72°N, 90.39°E), with sources like vehicular emissions, household energy practices and emissions from brick kilns (Begum et al, 2004).…”

Section: Site Descriptionmentioning

confidence: 99%

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Aerosol Climatology Over South and Southeast Asia: Aerosol Types, Vertical Profile, and Source Fields

et al. 2021

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Aerosol climatology during typical haze dominating period over South and Southeast Asia was explored using several Earth-Observing A-Train satellite products retrieved in between 2010 and 2020. Comparatively high aerosol optical depth (AOD) with dominance of fine and UV-absorbing aerosols are noted across the Indo-Gangetic plain, South Asia (IGP; AOD: 0.58; UVAI: 0.74) against weak UV-absorbing fine aerosols over Southeast Asia (SEA; AOD: 0.26; UVAI: 0.07). Among inland IGP sites, decadal mean AOD resembles in Lahore (0.72 ± 0.45), Delhi (0.81 ± 0.46), Kanpur (0.84 ± 0.42), and Varanasi (0.78 ± 0.45); all exhibiting bimodal AOD distribution with a first peak in early November followed by a second in early January. In contrast, except mainland site Chiang Mai, all SEA maritime cities resemble in having typical September-October AOD peak, with the presence of fine and UV-neutral aerosols. Urban hotspots across IGP and SEA (except Dhaka, Chiang Mai) denote a spatially consistent minor increasing trend in AOD (0.2-1.8 × 10 −2 year −1) while increase in UVAI is more prominent over upper IGP. Dust aerosols dominate only in Karachi (46%) against strong UV-absorbing smoke aerosols over rest of the IGP (71-91%), and UV-neutral smoke aerosols across SEA (84-92%). Vertical stratification of aerosol types is noted across IGP as in lower atmosphere (<4 km) polluted dust/urban aerosols remain abundant, with gradual decrease in dust aerosols from upper to lower IGP and consequent increase in smoke aerosols. At upper atmosphere (>4 km), however, dust aerosols clearly dominate. Over SEA, smoke are the most abundant aerosols across the atmospheric column followed by polluted dust. No evidence of intracontinental transport of aerosols from IGP to Southeast Asia or vice versa is, however, noted. Plain Language Summary Aerosol climatology during typical haze dominating period over two global aerosol hotspots: South and Southeast Asia was explored using several satellite-based aerosol optical properties retrieved using Earth Observing A-Train satellites (Aqua, Aura, CALIPSO). Comparatively high aerosol optical depth with dominance of fine and absorbing aerosol are noted across the Indo-Gangetic plain, South Asia against weak absorbing fine particles over Southeast Asia. Presence of strong absorbing aerosol over Indo-Gangetic plain may have greater implications on regional climate. We note absorbing dust aerosols only over Karachi against strong absorbing smoke aerosols over Lahore, Delhi, Kanpur, Varanasi, and Dhaka, and UV-neutral smoke aerosols over Singapore, Penang, and Kuala Lumpur. Vertical stratification of aerosol types is also noted across IGP as in lower atmosphere (<4km) polluted dust/ polluted urban aerosol remains abundant, with gradual decrease in dust aerosols from upper to lower IGP and consequent increase in smoke aerosols. No evidence in intracontinental transport of smoke aerosols is found, as biomass burning emissions over upper IGP, Kalimantan, and Sumatra primarily modulating climatology of regional aerosols. BAN...

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Section: Resultsmentioning

confidence: 94%

Section: Aerosol Potential Source Fieldsmentioning

confidence: 95%

Section: Data and Experimental Methodsmentioning

confidence: 99%

Section: Site Descriptionmentioning

confidence: 99%

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Aerosol Climatology Over South and Southeast Asia: Aerosol Types, Vertical Profile, and Source Fields

et al. 2021

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“…The role of geography in public health has become prominent in recent years by highlighting its spatial perspective, and revealing that the spatio-temporal distribution of diseases exhibits significant regional characteristics. Research in the area has focused on laws of the spatio-temporal distribution of diseases and the relevant health conditions (Artois et al, 2018;Guinat et al, 2018;Gong et al, 2020), models for the spatial transmission of infectious diseases (Wang et al, 2020), balancing and regulating the relationships among the environment, development, and health (Zhang et al, 2007), medico-geographical and health-related risk assessments (Shkurinskii and Chibilev, 2012;Murari et al, 2020), regional allocation of healthcare systems and facilities (Holley, 1998;Trujillo and Plough, 2016), and the relation between the urban microenvironment and health (Rosenberg, 2017;Qi and Zhou, 2018;Yang et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Spatio-temporal Differentiation in the Incidence of Influenza and Its Relationship with Air Pollution in China from 2004 to 2017

et al. 2021

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The Healthy China Initiative is a major health strategy being pursued by the country. To prevent and control different types of diseases as well as their complex variants, research on the spatio-temporal differentiation among and mechanisms of influence of epidemic diseases is growing worldwide. This study analyzed monthly data on the incidence of influenza by using different methods, including Moran’s I , the hotspot analysis model, concentration analysis, and correlation analysis, to determine the characteristics of spatio-temporal differentiation in the incidence of influenza across prefecture-level cities in China from 2004 to 2017, and to examine its relationship with air pollution. According to the results, the overall incidence of influenza in China exhibited a trend of increase from 2004 to 2017, with small peaks in 2009 and 2014. More cases of influenza were recorded in the first and fourth quarters of each year. Regions with higher incidences of influenza were concentrated in northwestern and northern China, and in the coastal areas of southeastern China. Over time, the distribution of regions with a higher incidence of influenza has shifted from the west to the east of the country. A significant relationship was observed between the incidence of influenza and factors related to air pollution. The contents of five air pollutants (PM 2.5 , PM 10 , SO 2 , NO 2 , and CO) were significantly positively correlated with the incidence of influenza, with a decreasing order of contribution to it of SO 2 > CO > NO 2 > PM 2.5 > PM 10 . The content of O 3 in the air was negatively correlated with the incidence of influenza. The influence of air pollution-related factors on the incidence of influenza in different regions and seasons showed minor differences. The large-scale empirical results provided here can supply a scientific basis for governmental disease control authorities to formulate strategies for regional prevention and control.

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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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Source apportionment and health risk assessment of airborne particulates over central Indo-Gangetic Plain

Cited by 51 publications

References 53 publications

Aerosol Climatology Over South and Southeast Asia: Aerosol Types, Vertical Profile, and Source Fields

Aerosol Climatology Over South and Southeast Asia: Aerosol Types, Vertical Profile, and Source Fields

Spatio-temporal Differentiation in the Incidence of Influenza and Its Relationship with Air Pollution in China from 2004 to 2017

Seasonal Variation and Source Identification of PM₁₀ in an Industrialized City

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Source apportionment and health risk assessment of airborne particulates over central Indo-Gangetic Plain

Cited by 51 publications

References 53 publications

Aerosol Climatology Over South and Southeast Asia: Aerosol Types, Vertical Profile, and Source Fields

Aerosol Climatology Over South and Southeast Asia: Aerosol Types, Vertical Profile, and Source Fields

Spatio-temporal Differentiation in the Incidence of Influenza and Its Relationship with Air Pollution in China from 2004 to 2017

Seasonal Variation and Source Identification of PM10 in an Industrialized City

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Seasonal Variation and Source Identification of PM₁₀ in an Industrialized City