ABSTRACT:The Globeland30 dataset is the most highly spatial resolution global land cover mapping product, which developed by the National Geomatics Center of China (NGCC) in 2015. It plays a significant role in environmental monitoring, climate change, and ecosystem assessment, etc. In this study, Jiangxi province was selected as our study area, the 1:100000 land use data in 2010 was employed as the reference data. We aim to examine the accuracy of the Globeland30 from three methods, including area error analysis, shape consistency analysis and confusion matrix. The results show as follows: The land cover types in the study area are primarily occupied by the cultivated land and forest, and secondarily by grassland, water bodies and artificial surfaces. The area error of cultivated land, forest and water bodies are all less than 13%; The general conformance of the shape consistency reaches to 67%, but the shape consistency of every land type differs to a large degree, the best shape consistency of forests is up to 75%; The confusion matrix is obtained in two cases of different class boundary with buffer and no buffer area. It is found that the overall accuracy and kappa coefficient of GlobeLand30 are improved with buffer area. The value of overall accuracy is higher than 78%, the value of kappa coefficient is higher than 0.52.

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Currently, traffic-related sources are considered to be one of the major contributors to air pollutants in urban areas. As the number of motor vehicles increases, the impact of traffic-related air pollutants (TRAPs) on human health has also increased in recent years. People are easily exposed to TRAPs in their daily lives. However, long-term exposure to TRAPs can have adverse health effects. Mobile monitoring is more flexible compared to traditional urban monitoring stations and can effectively obtain the spatial variation characteristics of air pollutants. We mounted a sensor package on an electric bicycle and conducted mobile measurements of CO, NO2 and SO2 on a circular road in the center of Shaoxing, a city in the center of the Yangtze Delta, China. The CO, NO2 and SO2 concentrations were observed to be higher in the morning and evening rush hours, and the three pollutants show different seasonal and spatial variation characteristics. CO concentration was higher in urban arterial and crossroads. NO2 concentration was variable, alternating between high and low concentrations. SO2 concentration was relatively stable and aggregated. This study provides important information on the spatial and temporal variations of TRAPs, which helps commuters understand how to effectively reduce pollutant exposure during personal travel.