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Automated, High-resolution Mobile Collection System for the Nitrogen Isotopic Analysis of NOx
Published on: December 20, 2016
The impact of urban agglomeration on ozone precursor conditions: A systematic investigation across global
Jiayi Li1, Yuan Gao1, Xin Huang2
1School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China.
Abstract:
Urbanization significantly influences ozone via two conditions of its formation: 1) precursor concentration; and 2) chemical regime. Recently, there has been raised concern about the influence of urban agglomerations on these two conditions. Although valuable efforts have been made, some contrary viewpoints exist. Meanwhile, urban agglomerations in developed and developing regions are experiencing different urbanization processes, so a systematic comparison between these two regions is warranted. In this context, by leveraging multi-source geospatial datasets, this paper systematically gauges the influence of urban agglomerations on ozone precursor conditions and further investigates the spatiotemporal variations. Based on the analysis of 71 global agglomerations during 2005-2016, it is found that: 1) not all urban agglomerations have a positive effect on ozone precursor conditions; 2) the negative effects of urban agglomerations can be attributed to the low latitudes and the ecological areas (p < 0.05); 3) the agglomeration influence intensifies with the increase of built-up area, population, and latitude (p < 0.05); 4) the anthropogenic nitrogen oxide (NOx) emission from all sectors can aggravate the magnitude of the urban agglomeration influence (p < 0.05), while for volatile organic compounds (VOCs), only the contribution of industrial emissions is significant (p < 0.05); and 5) in view of the temporal dynamics, the influence of urban agglomeration on ozone precursor condition is opposite in developed and developing regions. This study will provide important insights for future urban agglomeration studies and ozone pollution monitoring with geospatial datasets.
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