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Journals / Atmospheric Pollution Research / 2016 / Cilt: 7 - Sayı: 3

Synergy of receptor and dispersion modelling: Quantification of PM10 emissions from road and soil dust not included in the inventory

Swetha Priya DARSHİNİ
Mukesh SHARMA
Dhirendra SİNGH
Pages
403–411
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Therevised EI showed a significant improvement in AERMOD performance examined through statisticaltests.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Dispersion modelling-based apportionment of emitting sources impacting the air quality depends onquality of Emission Inventory (EI) and meteorological data.
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Key Findings & Data:The CMB (Chemical Mass Balance) receptormodel and AERMOD dispersion model have been combined to identify and account for un-quantifiedsources of PM10 in EI.
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Conclusion & Impact:Therevised EI showed a significant improvement in AERMOD performance examined through statisticaltests.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Özet

Dispersion modelling-based apportionment of emitting sources impacting the air quality depends onquality of Emission Inventory (EI) and meteorological data. The CMB (Chemical Mass Balance) receptormodel and AERMOD dispersion model have been combined to identify and account for un-quantifiedsources of PM10 in EI. The speciated PM10 data from Baddi-Nalagarh (30.9412 N latitude, 76.78 Elongitude), India was used in CMB model. The CMB analyses identified that fugitive sources, soil and roaddust, contribute significantly to PM10 concentration; these sources were not considered in the existing EI.As a result, AERMOD significantly underestimated the PM10 concentration at most locations. The existingEI in each grid was improved by adjusting emission as per road lengths and deficit in measured andcomputed concentrations. The estimated PM10 road dust emission from all grids was 653 kg/d. Therevised EI showed a significant improvement in AERMOD performance examined through statisticaltests.

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Cite This Article

DARSHİNİ, S. P., SHARMA, M., SİNGH, D. (2016). Synergy of receptor and dispersion modelling: Quantification of PM10 emissions from road and soil dust not included in the inventory. Atmospheric Pollution Research, Cilt: 7 - Sayı: 3, 403–411. https://ajindex.com/articles/351506

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