Source apportionment and health risks assessment of black carbon Aerosols in an urban atmosphere in East India

Ambade, Balram; Sankar, Tapan Kumar

doi:10.1007/s10874-021-09418-9

Cited by 31 publications

(8 citation statements)

References 63 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Black carbon (BC) is produced by incomplete combustion of fossil fuels or biomass in environment and plays an important role in the carbon biogeochemical process of ecosystem (Hammes et al, 2007;Ambade and Sankar, 2021;Ambade et al, 2021a). Total global BC can be broken down into residues of vegetation fires of 50-270 Tg C year −1 (Kuhlbusch, 1995) and from the consumption of fossil fuel of 4.4 Tg C a −1 , showing a linear increasing trend (Bond et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

Centennial records of Polycyclic aromatic hydrocarbons and black carbon in Altay Mountains peatlands, Xinjiang, China

et al. 2022

View full text Add to dashboard Cite

Black carbon (BC) is an important inert carbon component in the cycling process of surface carbon. Polycyclic aromatic hydrocarbons (PAHs) are a kind of thick cyclic organic compounds with carcinogenic, teratogenic, and mutagenic effects. The incomplete burning process of biomass and carbolic fuel is the important source of their co-occurrence. This study collected a 60-cm peat core from the Jiadengyu (JDY), Altay Mountain. The core was dated using the 210Pb and 137Cs methods. The results showed BC, total organic carbon, and PAHs of the JDY peat core to be 1.14–72.6 mg g−1, 17.09–47.2%, and 260.58–1,610.77 ng·g−1, respectively. δ13CBC was between −31.5 and − 29.4‰ (mean of −30.56‰). The results of scanning electron microscopy (SEM) indicated irregular or lumpy peat BC particles, retaining a plant fiber structure. δ13CBC, ratios of PAHs, and the SEM revealed that the BC to be the dominant source of biomass combustion in the peatland. BC showed an increasing trend between 1950 and 1980, after which it decreased. The discrepancy between the change in BC and δ13CBC from the national pattern of BC emission likely reflects the effect of local agricultural exploration, and thus an increase in crop burning.

show abstract

Section: Introductionmentioning

confidence: 99%

Centennial records of Polycyclic aromatic hydrocarbons and black carbon in Altay Mountains peatlands, Xinjiang, China

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Peak levels in the data for rainfall (2.48 mm/day) in the year 2021 coincided with the post‐monsoon season, when the AOD 550 nm (0.87) and PM2.5 (0.07 g/m 3 ) increases. The backward trajectories indicated that air masses came from various heights and locations, according to BC (Ambade et al, 2022) research's study, particulate matter sources from north, northwest and east also the PM2.5 level is higher in post‐monsoon season due to Industrialization and traffic load (Ambade & Sankar, 2021). During the pre‐monsoon season, the precipitation is observed to be 3.1 mm/day in the year 2021 and the PM2.5 (0.13 μg/m 3 ) and AOD (0.77) value is higher.…”

Section: Resultsmentioning

confidence: 99%

Insight into seasonal aerosol concentrations and meteorological influence over a flood affected area using satellite data and statistical model simulation

Divya,

Moses,

Jasmin Shany

et al. 2023

Geological Journal

View full text Add to dashboard Cite

Aerosol particles in the atmosphere are one of the many factors that might affect floods. One of India's most flood‐prone regions, Muzaffarpur district in Bihar, has been chosen as the study area. The time period considered for the satellite data on meteorological influence is from the year 2000 to 2021. The correlation coefficient between past forecasts and its confirming data is often used to assess the effectiveness of weather and climate forecasting systems. Therefore, changes in correlation can be utilized to gauge advancements in forecasting ability. In this study, the relationship between aerosol optical depth (AOD) 550 nm, temperature, relative humidity, wind speed, PM2.5 and precipitation are carried out with correlation co‐efficient. The average rainfall throughout the monsoon was found to be 162.5 mm, during the period 2000–2021. The descriptive statistical data for seasonal data were determined for AOD (550 nm), PM2.5, wind speed, relative humidity, and temperature values. It is inferred that there is a positive correlation between precipitation and AOD for the monsoon season. Hence the fitting of meteorological parameter AOD for the monsoon season are done with six different probability distributions Gamma (3P), Logistics, Lognormal (3P), Normal, Rayleigh (2P) and Weibull (3P). The parameters of these distributions are evaluated using the Maximum Likelihood Estimation and the goodness of fit is tested using the test statistics Kolmogorov Smirnov and Anderson Darling test. Among the six discussed distributions, Weibull (3P) distribution provides the best fit to model the meteorological data from the satellite, the flood prediction would be obtained from the model proposed.

show abstract

“…Coal is still the most important energy source in China, contributing 67.5% and 56.9% of total primary energy production and energy consumption, respectively (NBS, 2022 ). Combustion of coal can emit CO 2 as well as air pollutants such as SO 2 , black carbon (BC), and polycyclic aromatic hydrocarbons (PAHs), especially in rural areas under incomplete combustion conditions, and they can thereafter be transported to other compartments such as soil, rivers, and road dust (Ambade & Sankar, 2021 ; Ambade et al, 2021a , b and 2022a , b ; Kurwadkar et al, 2022 ; Maharjan et al, 2021 ; Wei et al, 2015 , 2020a , b , 2021 ). The Chinese government initiated the Air Pollution Prevention and Control Action Plan (APPCAP) in 2013 and the Three-Year Action Plan for Winning the Blue Sky Defence Battle in 2018 to address serious air pollution episodes.…”

Section: Introductionmentioning

confidence: 99%

Historical trend and drivers of China’s CO2 emissions from 2000 to 2020

Wei

2022

Environ Dev Sustain

View full text Add to dashboard Cite

China is the largest CO 2 emitter in the world and announced that carbon peak and neutral targets will be achieved before 2030 and 2060, respectively. A retrospective analysis of past CO 2 emissions and their drivers is important for the actions of peaking CO 2 emissions before 2030 in China. CO 2 emissions from energy use (coal, oil, and natural gas) and cement production from 2000 to 2020 were calculated first, and their drivers were decomposed into economic and population growth, energy intensity, and emission coefficient by logarithmic mean Divisa index (LMDI) analysis in this study. China’s CO 2 emissions increased nearly threefold from 3385 in 2000 to 10,788 million tonnes (Mt) in 2020, with a decline from 2013 to 2016. Coal was the major emission sector contributing more than 70% in most years, while natural gas emissions increased nearly 13 times from 53 to 723 Mt in the two decades, although its contribution only accounted for 6.7% in 2020. Economic growth was the major positive driver, while energy intensity reduction was the major negative driver of the emission increments by year and by the Five Year Plan (FYP). Emission coefficient reduction gradually became important due to its negative effect, especially in the 13th FYP, which offset ~ 30% of the emissions induced by economic growth. The projections of CO 2 emissions in 2025, 2030, and 2035 could be 11,596 ± 582, 11,774 ± 621, and 11,401 ± 672 Mt, respectively, suggesting that China’s carbon emissions could peak around 2030 with an increment of ~ 1000 Mt on the 2020 levels. Under the sustainable growth of the economy and population, it is possible to reduce the carbon peak value or achieve peak time earlier through the additional reduction of energy intensity and emission coefficient by technological progress and energy alternatives such as non-fossil fuels. Graphical Abstract Supplementary Information The online version contains supplementary material available at 10.1007/s10668-022-02811-8.

show abstract

Source apportionment and health risks assessment of black carbon Aerosols in an urban atmosphere in East India

Cited by 31 publications

References 63 publications

Centennial records of Polycyclic aromatic hydrocarbons and black carbon in Altay Mountains peatlands, Xinjiang, China

Centennial records of Polycyclic aromatic hydrocarbons and black carbon in Altay Mountains peatlands, Xinjiang, China

Insight into seasonal aerosol concentrations and meteorological influence over a flood affected area using satellite data and statistical model simulation

Historical trend and drivers of China’s CO2 emissions from 2000 to 2020

Contact Info

Product

Resources

About