Pixel-Level Projection of PM_2.5 Using Landsat Images and Cellular Automata Models in the Yangtze River Delta, China

Abstract

In this study, we proposed a pixel-level projection method for fine particulate matter (PM2.5) over a long term and across a large area using a combination of Landsat images, PM2.5 data from monitoring stations, and historical gridded PM2.5 data. We considered the spatial dependence effects of the particulate matter using a spatial lag model to quantify the relationship between PM2.5 concentration and land coverage indices, where the latter were calculated by the built-up, vegetation, and water indices. The future land coverage indices for the pixel-level projection of PM2.5 were derived from the future land-use scenario predicted by the Futureland model. We applied the method to analyze the spatial patterns of PM2.5 in the Yangtze River Delta (YRD), China, from 2000 to 2020, and then projected its pixel-level scenario in 2030. The projected PM2.5 shows high concentrations in the north and low in the south and temporally decreases compared to 2010. The projection of the fine-grained PM2.5 scenario can help adjust YRDs environmental and industrial policies, as well as implement its management strategies for sustainable urban development. Our method can be used to predict future patterns not only for long-term and large-scale pixel-level PM2.5 concentrations but also for other environmental parameters.

Keywords

• Cellular automata (CA)
• $_2.5$ )%22%2C%22default_operator%22%3A%22AND%22%7D%7D%7D"> fine particulate matter (PM<named-content xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" content-type="math" xlink:type="simple"> <inline-formula> <tex-math notation="LaTeX">$_2.5$</tex-math> </inline-formula> </named-content>)
• land-use change
• pixel-level projection
• spatial lag model (SLM)
• Yangtze River Delta (YRD)