Estimates of spatially and temporally resolved constrained black carbon emission over the Indian region using a strategic integrated modelling approach

Verma, Shubha; Reddy, Dr.M.Anji; Ghosh, Souradip; Kumar, Dudam Bharath; Chowdhury, Arindrajit

doi:10.1016/j.atmosres.2017.05.007

Cited by 24 publications

(25 citation statements)

References 56 publications

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“…The normalised mean error (NME) in constrsimu is 16% (14)% for both the all-day (daytime) winter averaged BC concentration (refer to Table s1 as a supplement), which lies within the uncertainty range in BC measurements (10%-20%). Unlike, the previous modeling studies which showed a large discrepancy in the simulated BC distribution in global or regional chemical transport models, with the magnitude of simulated surface BC concentrations being 4 to 9 times lower than observations, especially during winter months over the IGP 7,[11][12][13] .…”

contrasting

confidence: 67%

“…A better agreement between model simulated and observed magnitude of atmospheric aerosol species was, however, delivered over the Indian region in a recent study using the constrained aerosol simulation approach 7,16 . Further, the constrained BC emissions were estimated over the Indian region constraining the simulated BC concentration in a general circulation model (Laboratoire de Météorologie Dynamique atmospheric General Circulation Model (LMDZT-GCM)) with the observed BC (observationally-constrained BC emissions) by combining forward and receptor modelling approaches 12 .…”

Section: Methodsmentioning

confidence: 99%

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Black carbon sanitary burden in the Indo-Gangetic plain: exposures, risks and mitigation

Verma

Ghosh

Boucher

et al. 2020

Preprint

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A large discrepancy between simulated and observed black carbon (BC) surface concentrations over the densely populated Indo-Gangetic plain (IGP) has so far limited our ability to assess the magnitude of BC sanitary impacts in terms of population exposure, morbidity, and mortality. We evaluate these impacts using an integrated modeling framework, including a successfully predicted BC surface concentration in a high-resolution chemical transport model CHIMERE with observationally-constrained BC emissions, combined with consistent health functions for BC. Population exposure to BC is noteworthy, with more than 60 million people identified living over hotspots of BC concentration (wintertime mean > 20 μg m−3). A fraction of 62% of the total cardiovascular diseases mortality (CVM) burden for the megacity is found attributable to wintertime BC exposure. The semi-urban area has 50% of the CVM burden attributable to BC exposure in the total population over the IGP. More than 400 thousand lives can potentially be saved from CVM annually, by implementing prioritized emission reduction from the combustion of domestic biofuel in the semi-urban area, and diesel oil in transportation and coal in thermal power plant and brick kiln industries in megacities.

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confidence: 67%

Section: Methodsmentioning

confidence: 99%

Black carbon sanitary burden in the Indo-Gangetic plain: exposures, risks and mitigation

Verma

Ghosh

Boucher

et al. 2020

Preprint

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“…The BC inventories include recently estimated India-based-(i) constrained and (ii) bottomup BC emissions (Smog-India), including the bottom-up BC emissions from global datasets extracted over India-(iii) EDGAR-V4 (EDGAR), (iv) CMIP6 and (v) PKU. Spatially and temporally resolved constrained BC emission over India is taken as per Verma et al (2017). The observationally-constrained BC emissions were estimated over the Indian region constraining the simulated BC concentration in a general circulation model (Laboratoire de Météorologie Dynamique atmospheric General Circulation Model (LMDZT-GCM)) with the observed BC by combining forward and receptor modelling approaches (Kumar et al, 2018;Verma et al, 2017).…”

Section: Implementation Of Bc Emissions and Multiple Chimere Simulationsmentioning

confidence: 99%

“…The systematic underestimation of BC aerosol absorption by the global climate model predictions relative to atmospheric observations as noticed specifically over south Asia and east Asia (Chung et al, 2012;Gustafsson and Ramanathan, 2016) is also in compliance with studies evaluating atmospheric BC concentration between model and observations. For example, recent evaluations of BC concentration from global and regional aerosol models over south Asia showed that the simulated BC concentration, though, exhibited a consistent correlation with, but was significantly lower (by a factor of about 2 to 11) than the measured concentration (Kumar et al, 2018;Verma et al, 2017;Kumar et al, 2015;Pan et al, 2015;Sanap et al, 2014;Moorthy et al, 2013;Nair et al, 2012). The factor of model underestimation was further noticed to be large specifically during wintertime over the Indo-Gangetic Plain (IGP) when the atmosphere is observed to be laden with a large BC burden.…”

Section: Introductionmentioning

confidence: 99%

Wintertime radiative effects of black carbon (BC) over Indo-Gangetic Plain as modelled with new BC emission inventoriesin CHIMERE

Ghosh

Verma

Kuttippurath

et al. 2020

Preprint

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Abstract. To reduce the uncertainty in the black carbon (BC) induced climatic impacts from the global and regional aerosol-climate model simulations, it is a foremost requirement to improve the prediction of modelled BC distribution. And that specifically, over the regions where the atmosphere is loaded with a large amount of BC, e.g., the Indo-Gangetic plain (IGP) in the Indian subcontinent. Here we present the wintertime radiative perturbation due to BC with an efficiently modelled BC distribution over the IGP in a high-resolution (0.1° × 0.1°) chemical transport model, CHIMERE, implementing new BC emission inventories. The model efficiency in simulating the observed BC distribution was examined executing five simulations: Constrained and bottomup (Smog, Cmip, Edgar, Pku) implementing respectively, the recently estimated India-based constrained BC emission and the latest bottom-up BC emissions (India-based: Smog-India, and global: Coupled Model Intercomparison Project phase 6 (CMIP6), Emission Database for Global Atmospheric Research-V4 (EDGAR-V4) and Peking University BC Inventory (PKU)). A low estimated value of the normalised mean bias (NMB) and root mean square error (RMSE) from Constrained estimated BC concentration (NMB: < 17 %) and aerosol optical depth due to BC (BC-AOD) (NMB: 11 %) indicated that simulation with constrained BC emissions in CHIMERE could simulate the distribution of BC pollution over the IGP more efficiently than with the bottom-up. The large BC pollution covering the IGP region comprised of wintertime all-day (daytime) monthly mean BC concentration and BC-AOD from the Constrained, respectively, in the range 14–25 (6–8) µg m−3 and 0.04–0.08, with a strong correlation between the variance in BC emission and simulated BC mass concentration or BC-AOD. Five main hotspot locations were identified in and around Delhi (northern-IGP), Prayagraj (or Allahabad)-Varanasi (central-IGP), Patna-Palamu (upper/lower mideastern-IGP), and Kolkata (eastern-IGP). The wintertime radiative perturbation due to BC aerosols from the Constrained included a wide-spread enhancement in atmospheric radiative warming by 2–3 times and a reduction in surface cooling by 10 %–20 %, with net warming at the top of atmosphere (TOA) of 10–15 W m−2, compared to the atmosphere without BC, for which, a net cooling at the TOA was, although, exhibited. These perturbations were spotted being the strongest around megacities (Kolkata and Delhi), and were inferred as 30 %–50 % lower from the bottomup than the Constrained.

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“…As mentioned in Section 2.2, the assumption of near linearity between emissions and concentrations is a principle of the multiple regression model, given the weak chemistry reactivity of BC. The principle has been applied in previous studies to constrain BC emissions (Fu et al, 2012;Kondo et al, 2011;Wang et al, 2013;Park et al, 2003;Verma et al, 2017). In the actual fact, however, processes other than chemical reaction, e.g., precipitation or wet deposition, impact the linearity.…”

Section: Uncertainty Analysis Of the Multiple Regression Modelmentioning

confidence: 99%

Top-down estimate of black carbon emissions for city cluster using ground observations: A case study in southern Jiangsu, China

Zhao¹,

Zhao²,

Dong³

et al. 2018

Preprint

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Abstract. We combined a chemistry transport model (CTM), a multiple regression model and available ground observations, to derive top-down estimate of black carbon (BC) emissions and to reduce deviations between simulations and observations for southern Jiangsu city cluster, a typical developed region of eastern China. Scaled from a high-resolution inventory for 2012 based on changes in activity levels, the BC emissions in southern Jiangsu were calculated at 27.0&#8201;Gg/yr for 2015 (JS-prior). The annual mean concentration of BC at Xianlin Campus of Nanjing University (NJU, a suburban site) was simulated at 3.4&#8201;&#956;g/m3, 11&#8201;% lower than the observed 3.8&#8201;&#956;g/m3. In contrast, it was simulated at 3.4&#8201;&#956;g/m3 at Jiangsu Provincial Academy of Environmental Science (PAES, an urban site), 36&#8201;% higher than the observed 2.5&#8201;&#956;g/m3. The discrepancies at the two sites implied the uncertainty of the bottom-up inventory of BC emissions. Assuming a near-linear response of BC concentrations to emission changes, we applied a multiple regression model to fit the hourly surface concentrations of BC at the two sites, based on the detailed source contributions to ambient BC levels from brute-force simulation. Constrained with this top-down method, BC emissions were estimated at 13.4&#8201;Gg/yr (JS-posterior), 50&#8201;% smaller than the bottom-up estimate, and stronger seasonal variations were found. Biases between simulations and observations were reduced for most months at the two sites when JS-posterior was applied. At PAES, in particular, the simulated annual mean was elevated to 2.6&#8201;&#956;g/m3 and the annual normalized mean error (NME) decreased from 72.0&#8201;% to 57.6&#8201;%. However, application of JS-posterior slightly enhanced NMEs in July and October at NJU where simulated concentrations with JS-prior were lower than observations, implying that reduction in total emissions could not correct CTM underestimation. The effects of numbers and spatial representativeness of observation sites on top-down estimate were further quantified. The best CTM performance was obtained when observations of both sites were used with their difference in spatial functions considered in emission constraining. Given the limited BC observation data in the area, therefore, more measurements with better spatiotemporal coverage were recommended for constraining BC emissions effectively. Top-down estimates derived from JS-prior and the Multi-resolution Emission Inventory for China (MEIC) were compared to test the sensitivity of the method to initial emission input. The differences in emission levels, spatial distributions and CTM performances were largely reduced after constraining, implying that the impact of initial inventory was limited on top-down estimate. Sensitivity analysis proved the rationality of near linearity assumption between emissions and concentrations, and the impact of wet deposition on the multiple regression model was demonstrated moderate through data screening based on simulated wet deposition and satellite-derived precipitation.

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Estimates of spatially and temporally resolved constrained black carbon emission over the Indian region using a strategic integrated modelling approach

Cited by 24 publications

References 56 publications

Black carbon sanitary burden in the Indo-Gangetic plain: exposures, risks and mitigation

Black carbon sanitary burden in the Indo-Gangetic plain: exposures, risks and mitigation

Wintertime radiative effects of black carbon (BC) over Indo-Gangetic Plain as modelled with new BC emission inventoriesin CHIMERE

Top-down estimate of black carbon emissions for city cluster using ground observations: A case study in southern Jiangsu, China

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