Daily estimation of NO2 concentrations using digital tachograph data

被引:0
作者
Joo, Yoohyung [1 ]
Joo, Minsoo [1 ]
Nguyen, Minh Hieu [1 ]
Hong, Jiwan [1 ]
Kim, Changsoo [2 ]
Wong, Man Sing [3 ]
Heo, Joon [1 ]
机构
[1] Yonsei Univ, Dept Civil & Environm Engn, 50 Yonsei Ro, Seoul 03722, South Korea
[2] Yonsei Univ, Coll Med, Dept Prevent Med, Seoul, South Korea
[3] Hong Kong Polytech Univ, Dept Land Surveying & Geoinfomat, Hong Kong, Peoples R China
基金
新加坡国家研究基金会;
关键词
NO2; concentrations; DTG data; Daily estimation; Land use regression (LUR); Spatial-temporal variation; LAND-USE REGRESSION; AIR-POLLUTION CONCENTRATIONS; AMBIENT NITROGEN-DIOXIDE; INTRAURBAN VARIABILITY; SPATIAL VARIABILITY; TRAFFIC POLLUTION; SULFUR-DIOXIDE; MODELS; EMISSIONS; EXPOSURE;
D O I
10.1007/s10661-024-13190-0
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Traffic information is crucial for estimating NO2 concentrations, but it is static and limited in predicting constantly changing NO2 levels. To overcome these challenges, this study utilized real-time spatial big data to capture both the spatial and temporal fluctuations in traffic. Digital tachograph (DTG) data, sourced from digital devices in all commercial vehicles, are employed to construct a DTG land use regression (LUR) model, and its performance is compared with that of a non-DTG-LUR model. The DTG-LUR model exhibits superior performance, with an explanatory power of 0.46, in contrast to the 0.36 of the non-DTG model. This significant improvement stems from the spatially and temporally dynamic DTG variables such as cargo traffic. This study introduces a novel approach for incorporating DTG data in correlating with NO2 concentrations. It underscores the advantage of DTG data in predicting daily NO2 fluctuations at a precise 200-m grid, which is not feasible with conventional data. The findings of the study highlight the immense potential of spatial big data for fine-grained analyses, which could enable hourly predictions of air pollution.
引用
收藏
页数:25
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