A good understanding of how meteorological conditions exacerbate or mitigate air pollution is critical for developing robust emission reduction policies. Thus, based on a multiple linear regression (MLR) model in this study, the quantified impacts of six meteorological variables on PM2.5 (i.e., particle matter with diameter of 2.5 µm or less) and its major components were estimated over the Yangtze River Basin (YRB). The 38-year (1980–2017) daily PM2.5 and meteorological data were derived from the newly-released Modern-Era Retrospective Analysis and Research and Application, version 2 (MERRA-2) products. The MERRA-2 PM2.5 was underestimated compared with ground measurements, partly due to the bias in the MERRA-2 Aerosol Optical Depth (AOD) assimilation. An over-increasing trend in each PM2.5 component occurred for the whole study period; however, this has been curbed since 2007. The MLR model suggested that meteorological variability could explain up to 67% of the PM2.5 changes. PM2.5 was robustly anti-correlated with surface wind speed, precipitation and boundary layer height (BLH), but was positively correlated with temperature throughout the YRB. The relationship of relative humidity (RH) and total cloud cover with PM2.5 showed regional dependencies, with negative correlation in the Yangtze River Delta (YRD) and positive correlation in the other areas. In particular, PM2.5 was most sensitive to surface wind speed, and the sensitivity was approximately −2.42 µg m−3 m−1 s. This study highlighted the impact of meteorological conditions on PM2.5 growth, although it was much smaller than the anthropogenic emissions impact.

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It is important to analyze the expansion of an urban area and the factors that drive its expansion. Therefore, this study is based on Defense Meteorological Satellite Program Operational Linescan System (DMSP/OLS) night lighting data, using the landscape index, spatial expansion strength index, compactness index, urban land fractal index, elasticity coefficient, the standard deviation ellipse, spatial correlation analysis, and partial least squares regression to analyze the spatial and temporal evolution of urban land expansion and its driving factors in the Yangtze River Economic Belt (YREB) over a long period of time. The results show the following: Through the calculation of the eight landscape pattern indicators, we found that during the study period, the number of cities and towns and the area of urban built-up areas in the YREB are generally increasing. Furthermore, the variations in these landscape pattern indicators not only show more frequent exchanges and interactions between the cities and towns of the YREB, but also reflect significant instability and irregularity of the urbanization development in the YREB. The spatial expansion intensity indices of 1992–1999, 1999–2006, and 2006–2013 were 0.03, 0.16, and 0.34, respectively. On the whole, the urban compactness of the YREB decreased with time, and the fractal dimension increased slowly with time. Moreover, the long axis and the short axis of the standard deviation ellipse of the YREB underwent a small change during the inspection period. The spatial distribution generally showed the pattern of “southwest-north”. In terms of gravity shift, during the study period, the center of gravity moved from northeast to southwest. In addition, the Moran's I values for the four years of 1992, 1999, 2006, and 2013 were 0.451, 0.495, 0.506, and 0.424, respectively. Furthermore, by using correlation analysis, we find that the correlation coefficients between these four driving indicators and the urban expansion of the YREB were: 0.963, 0.998, 0.990 and 0.994, respectively. Through the use of partial least squares regression, we found that in 1992-2013, the four drivers of urban land expansion in the YREB were ranked as follows: gross domestic product (GDP), total fixed asset investment, urban population, total retail sales of consumer goods.

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To grasp the evolutionary characteristics and regularity of urban land expansion patterns in the Poyang Lake Ecological Economic Zone, this study, based on nighttime lighting data, uses the Landsat series satellite simultaneous data and cluster analysis to correct the Defense Meteorological Satellite Program–Operational Linescan System (DMSP-OLS) nighttime lighting data and then uses the auxiliary data-based comparison method to determine the threshold for extracting the urban built-up area. Based on this threshold, a total of eight typical landscape pattern indicators, including landscape total area, total patches number, patches density, maximum patches index, and agglomeration index, etc., are selected. Next, the landscape spatial pattern analysis method and standard deviation ellipse method are used. The results show the following: (1) In 1992–2013, urbanization in the Poyang Lake Ecological Economic Zone expanded rapidly. The urban built-up area increased by 8.13 times, the number of plaques increased by 1.5 times, and the shape complexity of landscape plaques gradually increased. There is a large correlation between the changes in the total boundary length, and the average boundary density, the average annual growth rate of the two is 21.33% and 17.45%. (2) The two indicators of maximum plaque index and aggregation index show a downward trend year by year. However, there are some fluctuations and irregularities in the evolution of the total landscape area, total plaque number and plaque density. (3) The long axis and the short axis of the standard deviation ellipse of the Poyang Lake Ecological Economic Zone show small variation during the inspection period and generally have an elliptical shape. The movement of the center of gravity is mainly from the southwest to the northeast, but the migration of the center of gravity is relatively small. Based on this, this paper proposes three countermeasures and suggestions as a guide to promote the optimization and development of the spatial expansion pattern of the Poyang Lake eco-economic zone.