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Fine particulate matter samples (PM 2.5) were collected from the three megacities of the Yangtze River Delta (i.e., Shanghai, Hangzhou, and Nanjing) during November 11 to December 6, 2016. Organic carbon (OC), elemental carbon (EC), and four types of non-polar organic compounds such as polycyclic aromatic hydrocarbons (PAHs), n -alkanes, hopanes and steranes were analyzed in the samples. The average PM 2.5 mass concentrations were 47.0, 64.2 and 45.5 μg m−3 in Shanghai, Hangzhou and Nanjing, respectively. OC and EC concentrations were 4.47 and 0.78 μg m−3, 15.38 and 2.97 μg m−3, 10.35 and 2.06 μg m−3, respectively. n -Alkanes accounted for the largest abundance of the identified non-polar organic compounds, followed by PAHs, hopanes and steranes. The positive matrix factorization (PMF) analysis demonstrated that vehicular emissions, coal burning and industrial emissions were the most important sources. Vehicle exhaust was the major source of emissions in Shanghai (61.0%) and Hangzhou (45.3%), whereas coal combustion emissions was dominant (48.8%) in Nanjing. In addition, the estimated lifetime cancer risk (ILCR) values exposed to PAHs for adults (6.54 × 10−5) and children (7.32 × 10−6) suggested that PAHs levels in this region pose potential health risks. Combining the health risk model with PMF further concluded that vehicular emissions (49.0%) and coal burning (35.7%) were the two major sources that contribute to the ILCR values in these three cities. • Characteristics and source apportionment of NPOCs from three megacities in the YRD were studied. • NPOCs with unique sources were used to track the source of the aerosols. • Vehicular emissions and coal burning were most important contributors of NPOCs. • Polycyclic aromatic hydrocarbons pose a greater health risk to adult than children. • Combining the health risk model and PMF to reveal the contribution of each source to health risk. [ABSTRACT FROM AUTHOR]

Analyzing the urban spatial structure of a city is a core topic within urban geographical information science that has the ability to assist urban planning, site selection, location recommendation, etc. Among previous studies, comprehending the functionality of places is a central topic and corresponds to understanding how people use places. With the help of big geospatial data which contain affluent information about human mobility and activity, we propose a novel multiple subspaces-based model to interpret the urban functional regions. This model is based on the assumption that the temporal activity patterns of places lie in a high-dimensional space and can be represented by a union of low-dimensional subspaces. These subspaces are obtained through finding sparse representations using the data science method known as sparse subspace clustering (SSC). The paper details how to use this method in the context of detecting functional regions. With these subspaces, we can detect the functionality of urban regions in a designated study area and further explore the characteristics of functional regions. We conducted experiments using real data in Shanghai. The experimental results and outperformance of our proposed model against the single subspace-based method prove the efficacy and feasibility of our model. [ABSTRACT FROM AUTHOR]