Journals / Atmospheric Pollution Research / 2016 / Cilt: 7 - Sayı: 1

Spatial variation of near-surface CO2 concentration during spring in Shanghai

Pages
31–39
DOI
—
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AI Summary & Key Takeaways

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

The results indicated that CO2 concentration for the region was 409.4 ± 20.8 parts per million (ppm), showing significant spatial heterogeneity.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Urban area plays an important role in global carbon cycle, yet few efforts have been made to investigate CO2 concentration and its spatial heterogeneity in cities.
📊
Key Findings & Data:Using Shanghai, the commercial and financial center of mainland China, as a case study, this paper explored the spatial pattern and determinants of near-surface urban CO2 concentration during spring.
💡
Conclusion & Impact:The results indicated that CO2 concentration for the region was 409.4 ± 20.8 parts per million (ppm), showing significant spatial heterogeneity.
🔬Design:Research Article
📍Scope:2014 · 173 sample
⚙️Variables:Domain Parameters

Özet

Urban area plays an important role in global carbon cycle, yet few efforts have been made to investigate CO2 concentration and its spatial heterogeneity in cities. Using Shanghai, the commercial and financial center of mainland China, as a case study, this paper explored the spatial pattern and determinants of near-surface urban CO2 concentration during spring. A total number of 173 sample points were collected covering the whole urban area, to examine the atmospheric CO2 concentration across various land use types (transportation, commercial, agricultural, recreational, institutional, residential and industrial land use) and urbanization gradients (heavy, moderately heavy, medium, moderately low, and low urbanization). CO2 concentrations were measured at a height of 2 m above the ground during 9:00e11:00 am in April and May 2014. The results indicated that CO2 concentration for the region was 409.4 ± 20.8 parts per million (ppm), showing significant spatial heterogeneity. The daytime ambient CO2 concentration varied across land use types (p < 0.05), with the highest and lowest value appeared in transportation area (420.5 ± 33.3 ppm) and agricultural land (398.9 ± 16.2 ppm). Across the urban gradients of Shanghai, a significant decrease of CO2 concentration was observed as the distance from urban core increased. With the influence of both high residential density and activities in production, the highest CO2 concentration occurred in moderate urbanization area (423.9 ± 29.3 ppm), rather than the high urbanization area (417.2 ± 17.5 ppm). Further analysis indicated that the percent of impervious surface cover (ISA) was an important indicator to describe near surface CO2 concentration with a positive correlation, and the percent of vegetation coverage (Veg) had lower explanatory power with a negative correlation. When buffer distance of spatial scale was 2000 m, atmospheric CO2 concentration showed the highest correlation with impervious surface and vegetation coverage.

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Cite This Article

LİU, M., ZHU, X., PAN, C., CHEN, L., ZHANG, H., JİA, W., XİANG, W. (2016). Spatial variation of near-surface CO2 concentration during spring in Shanghai. Atmospheric Pollution Research, Cilt: 7 - Sayı: 1, 31–39. https://ajindex.com/articles/351525

References (1)

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