Metallic species in PM10 and source apportionment using PCA-MLR modeling over mid-Brahmaputra Valley

Deka, Pratibha; Bhuyan, Pranamika; Daimari, Rebecca; Sarma, Kamal; Hoque, Raza Rafiqul

doi:10.1007/s12517-016-2368-z

Cited by 19 publications

(5 citation statements)

References 68 publications

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“…( 2020 ) with slight modifications in arrival heights of 500, 1000, and 1500 m above ground level (agl) to accommodate the layers due to thermal inversions created by the Bay of Bengal as mentioned in a study by Deka et al. ( 2016 ).…”

Section: Methodsmentioning

confidence: 99%

“…Back-trajectory analysis for long-range air parcels was carried out using Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT, https://www.ready.noaa.gov/HYSPLIT.php) to find the possible paths of air mass transportation which may have influenced the pollution concentration of local source contributors (Guo et al, 2009;Khobragade & Ahirwar, 2019). The 72 h backward trajectories were considered for each sampling day computing the arrival of air parcels at the interval of 6 h (6:00, 12:00, 18:00, and 00:00 (consecutive day) UTC) at the sampling site following the method described by Bodor et al (2020) with slight modifications in arrival heights of 500, 1000, and 1500 m above ground level (agl) to accommodate the layers due to thermal inversions created by the Bay of Bengal as mentioned in a study by Deka et al (2016).…”

Section: Meteorological Data Analysismentioning

confidence: 99%

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Impact of COVID‐19 lockdown on the elemental profile of PM₁₀ present in the ambient aerosol of an educational institute in Kolkata, India

Tudu

Gaine

Mahanty

et al. 2022

Environmental Quality Mgmt

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Reduction in air pollution level was prime observation during COVID‐19 lockdown globally. Here, the study was conducted to assess the impact of lockdown on the elemental profile of PM 10 in ambient aerosol to quantify the elemental variation. To quantify the variation, phase‐wise sampling of air pollutants was carried out using the gravimetric method for PM 10 , while NO 2 and SO 2 were estimated through the chemiluminescence and fluorescent spectrometric method respectively. The elemental constituents of PM 10 were carried out using an Inductively Coupled Plasma Optical Emission Spectrometer and their source apportionment was carried out using the Positive Matrix Factorization model. The results showed that PM 10 , NO 2 and SO 2 reduced by 86.97%, 83.38%, and 88.60% respectively during the lockdown sampling phase. The highest mean elemental concentration reduction was found in Mn (97.47%) during the lockdown. The inter‐correlation among the pollutants exhibited a significant association indicating that they originate from the same source. The metals like Mn and Cu were found at a higher concentration during the lockdown phase corresponding to vehicular emissions. The comparative analysis of the elemental profile of PM 10 concluded that the lockdown effectuated in reduction of the majority of elements present in an aerosol enveloping metropolitan like Kolkata.

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Section: Methodsmentioning

confidence: 99%

Section: Meteorological Data Analysismentioning

confidence: 99%

Impact of COVID‐19 lockdown on the elemental profile of PM₁₀ present in the ambient aerosol of an educational institute in Kolkata, India

Tudu

Gaine

Mahanty

et al. 2022

Environmental Quality Mgmt

View full text Add to dashboard Cite

show abstract

“…The statistical methods used in this study include MLR, stepwise regression, Lasso regression, elastic-net regression, PCR and PLSR. These methods were selected because of their popularity in many air quality studies and other environmental studies [3][4][5][6][7][8]. Besides their popularity, the other four methods were considered based on their individual improvement over ordinary MLR.…”

Section: Statistical Modelling Methodsmentioning

confidence: 99%

Applying hybrid feature selection methods for statistical modelling of roadside particle concentrations (PM_2.5 and PNC

Suleiman¹,

Tight²,

Quinn³

2020

Int. J. EI

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The task of selecting a predictor variable to include in statistical models is enormous. A model built with fewer predictor variables can be more interpretable and less expensive than the one built with many input variables. In this study, the effects of hybrid feature selection methods (genetic algorithms [GA] and simulated annealing (SA) each combined with random forests [RF]) in improving the efficiency of five variants of multiple linear regression models in the prediction of roadside PM 2.5 and particle number count (PNC) concentrations are investigated. The GA-RF and SA-RF selected 9 and 16 variables, respectively, of the 27 predictor variables in the PM 2.5 training data. Thirteen variables were selected by the GA-RF of the 25 possible variables in the PNC training data, while the SA-RF selected 13 variables. The methods selected variables that are nearly the same especially for predicting PNC, while for the PM 2.5 models the SA-RF selected 16 variables and the GA-RF selected only 10 variables. The hybrid feature selection methods eliminated most of the correlated variables, especially the background pollutants and the traffic variables. Whereas the temporal variables and the meteorological variable have been selected in all the cases considered. The statistical performance of the linear models with the selected variables is similar to those developed using the entire predictor variables. The actual benefit derived from this study is the successful reduction in the number of predictor variables by more than half in most of the cases considered. The reduction in the number of variables will eventually result in the reduction of the operational and computational cost of the models without possibly compromising the predictive performance of the models. Also, the reduction in the number of variables will enhance interpretability.

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“…Bhuyan et al , studied some of the chemical properties of PM 10 and source apportionment reporting the contributions from biomass burning, coal burning, petroleum refining, vehicular emission, and crustal dust in this region. Long-range transport and meteorology could also play a major role in the total load of aerosol in the region. , In the present study, the advanced-level physico-chemical analysis of PM 2.5 is performed in order to understand the main chemical components during winter high-loading in the Jorhat urban area of NER, and thereby determining the source contributions of these components and the impacts of meteorological conditions.…”

Section: Introductionmentioning

confidence: 99%

“…Longrange transport and meteorology could also play a major role in the total load of aerosol in the region. 15,16 In the present study, the advanced-level physico-chemical analysis of PM 2.5 is performed in order to understand the main chemical components during winter high-loading in the Jorhat urban area of NER, and thereby determining the source contributions of these components and the impacts of meteorological conditions.…”

Section: Introductionmentioning

confidence: 99%

Meteorological Influence and Chemical Compositions of Atmospheric Particulate Matters in an Indian Urban Area

Rabha

Saikia

Singh

et al. 2021

ACS Earth Space Chem.

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Meteorological conditions essentially impact the emission, distribution, formation, and characteristics of particulate matter (e.g., PM 2.5 ) in the atmosphere. In this study, sampling and chemical analysis of PM 2.5 were carried out for about two winter months during December 2018 and January 2019 to determine their chemical components and possible emission sources in a Northeast Indian urban area (Jorhat). PM 2.5 mass concentrations were observed to be varied from 81.65 to 193.34 μg m −3 with an average of 117.75 μg m −3 , exceeding the permissible limit (60 μg m −3 ) of the National Ambient Air Quality Standards (NAAQS) of India. The average total water-soluble ions account for 12.27% of total PM 2.5 mass, of which anions contributed up to 50.52% and cations contributed 49.48%. The correlations among the watersoluble ionic species indicate the formation of NH 4 NO 3 and (NH 4 ) 2 SO 4 as major ammonium products with high ammonium concentration (30.73% of the total water-soluble aerosol mass) making the PM 2.5 alkaline. A high concentration of acenaphthylene and naphthalene was also found out of the 16 US EPA poly-aromatic hydrocarbons (PAHs). The correlation study between the chemical components and the meteorological parameters pointed out coal and biomass burning as the main contributor to such high winter loading in Northeast India. Also, the formation of secondary organic carbon up to 21.84 μg m −3 was observed due to suitable meteorological conditions during winter.

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Metallic species in PM10 and source apportionment using PCA-MLR modeling over mid-Brahmaputra Valley

Cited by 19 publications

References 68 publications

Impact of COVID‐19 lockdown on the elemental profile of PM₁₀ present in the ambient aerosol of an educational institute in Kolkata, India

Impact of COVID‐19 lockdown on the elemental profile of PM₁₀ present in the ambient aerosol of an educational institute in Kolkata, India

Applying hybrid feature selection methods for statistical modelling of roadside particle concentrations (PM_2.5 and PNC

Meteorological Influence and Chemical Compositions of Atmospheric Particulate Matters in an Indian Urban Area

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Metallic species in PM10 and source apportionment using PCA-MLR modeling over mid-Brahmaputra Valley

Cited by 19 publications

References 68 publications

Impact of COVID‐19 lockdown on the elemental profile of PM10 present in the ambient aerosol of an educational institute in Kolkata, India

Impact of COVID‐19 lockdown on the elemental profile of PM10 present in the ambient aerosol of an educational institute in Kolkata, India

Applying hybrid feature selection methods for statistical modelling of roadside particle concentrations (PM2.5 and PNC

Meteorological Influence and Chemical Compositions of Atmospheric Particulate Matters in an Indian Urban Area

Contact Info

Product

Resources

About

Impact of COVID‐19 lockdown on the elemental profile of PM₁₀ present in the ambient aerosol of an educational institute in Kolkata, India

Impact of COVID‐19 lockdown on the elemental profile of PM₁₀ present in the ambient aerosol of an educational institute in Kolkata, India

Applying hybrid feature selection methods for statistical modelling of roadside particle concentrations (PM_2.5 and PNC