Abstract
Under rapid urbanization, many cities in China suffer from serious fine particulate matter (PM2.5) pollution. As the emission sources or adsorption sinks, land use and the corresponding landscape pattern unavoidably affect the concentration. However, the correlation varies with different regions and scales, leaving a significant gap for urban planning. This study clarifies the correlation with the aid of in situ and satellite-based spatial datasets over six urban agglomerations in China. Two coverage and four landscape indices are adopted to represent land use and landscape pattern. Specifically, the coverage indices include the area ratios of forest (F_PLAND) and built-up areas (C_PLAND). The landscape indices refer to the perimeter-area fractal dimension index (PAFRAC), interspersion and juxtaposition index (IJI), aggregation index (AI), Shannon’s diversity index (SHDI). Then, the correlation between PM2.5 concentration with the selected indices are evaluated from supporting the potential urban planning. Results show that the correlations are weak with the in situ PM2.5 concentration, which are significant with the regional value. It means that land use coverage and landscape pattern affect PM2.5 at a relatively large scale. Furthermore, regional PM2.5 concentration negatively correlate to F_PLAND and positively to C_PLAND (significance at p < 0.05), indicating that forest helps to improve air quality, while built-up areas worsen the pollution. Finally, the heterogeneous landscape presents positive correlation to the regional PM2.5 concentration in most regions, except for the urban agglomeration with highly-developed urban (i.e., the Jing-Jin-Ji and Chengdu-Chongqing urban agglomerations). It suggests that centralized urbanization would be helpful for PM2.5 pollution controlling by reducing the emission sources in most regions. Based on the results, the potential urban planning is proposed for controlling PM2.5 pollution for each urban agglomeration.
Highlights
Introduction2.5 μm, causes seriously adverse human health (i.e., respiratory infection, heart disease, and lung cancer) [1,2,3,4]
Fine particulate matter (PM2.5 ), defined as the particles with aerodynamic diameter less than2.5 μm, causes seriously adverse human health [1,2,3,4]
F_PLAND shows the most significant correlation, followed by C2_PLAND and C1_PLAND, and interspersion and juxtaposition index (IJI) shows the weakest correlation. This indicates that the areas of land use would have strong effects on the regional PM2.5 concentration
Summary
2.5 μm, causes seriously adverse human health (i.e., respiratory infection, heart disease, and lung cancer) [1,2,3,4]. PM2.5 pollution has become an extreme environmental and social problem in many developing countries ( of China), causing millions of premature mortalities [5,6,7]. The direct explanation is that water-soluble species of PM2.5 could be dissolved in water, resulting in a significant reduction in concentration in rainfall conditions [11,12]. Wind is another important climate factor, which helps to disperse and dilute PM2.5 concentration [13]. Observation experiments demonstrated that, PM2.5 would decrease by Remote Sens. 2017, 9, 918; doi:10.3390/rs9090918 www.mdpi.com/journal/remotesensing
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