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Journals / Atmospheric Pollution Research / 2020 / Cilt: 11 - Sayı: 8

Spatial distribution and source contributions of PM2.5 concentrations in Jincheng, China

Dongpeng GUO
Ran WANG
Zhao PENG HONG
Pages
1281–1289
DOI
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AI Summary & Key Takeaways

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

The results show that the CALPUFF simulation resultsunderestimate the daily average concentration of PM2.5, and the statistical analysis also reveals an underestimationtrend.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This study investigates the spatial distribution and source contributions of fine particles (PM2.5) in complexterrain in Jincheng, China, using the Weather Research and Forecast (WRF) Model/California Puff Model(CALPUFF) modeling system.
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Key Findings & Data:To evaluate the CALPUFF performance, the simulated results were compared withthe observational data at the three monitoring stations.
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Conclusion & Impact:The results show that the CALPUFF simulation resultsunderestimate the daily average concentration of PM2.5, and the statistical analysis also reveals an underestimationtrend.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Özet

This study investigates the spatial distribution and source contributions of fine particles (PM2.5) in complexterrain in Jincheng, China, using the Weather Research and Forecast (WRF) Model/California Puff Model(CALPUFF) modeling system. To evaluate the CALPUFF performance, the simulated results were compared withthe observational data at the three monitoring stations. The results show that the CALPUFF simulation resultsunderestimate the daily average concentration of PM2.5, and the statistical analysis also reveals an underestimationtrend. The spatial distribution shows that PM2.5 concentrations decrease gradually outward from theJincheng city center. Primary PM2.5 concentrations in December are significantly higher than those in July.However, the concentrations of secondary sulfate ions (SO42−) and nitrate ions (NO3−) in July are higher thanthose in December. In December, residential heating was the main contributor to the PM2.5 concentration, accountingfor 50%, and industrial processes and dust (an anthropogenic dust from construction processes, openyards and roads) accounted for 18% and 14%, respectively.

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Cite This Article

GUO, D., WANG, R., HONG, Z. P. (2020). Spatial distribution and source contributions of PM2.5 concentrations in Jincheng, China. Atmospheric Pollution Research, Cilt: 11 - Sayı: 8, 1281–1289. https://ajindex.com/articles/351256

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