Search

Urbanization has led to the rapid and large-scale changes in land use and land cover and has affected the spatial distribution of land surface temperature (LST) in urban areas. Studying the LST pattern and their spatial heterogeneity characteristics at different scales can help understand the dynamic mechanism of the thermal landscape and provide insights into urban ecological planning. We utilized transfer matrixes, landscape metrics, and spatial autocorrelation analyses to study the transfer of LST classes, changes in the LST pattern, and changes in LST clusters with varying grain sizes by taking the central urban districts of Hangzhou City in China as a case study. Results indicate that (1) the transfer proportion of the LST classes increased, except for high-temperature class, and each LST class shifted to the adjacent dominant LST class with the increase in grain size. (2) The landscape metrics remarkably changed as the grain size increased, indicating that the LST pattern was scale-dependent. As the grain size increased, the small patches gradually merged into large patches; the fragmentation, complexity, and ductility of the urban thermal landscapes decreased; and the shape of the patches became simple and regular. (3) The LST pattern exhibited a positive spatial autocorrelation. The area of low-low cluster decreased, whereas that of non-significant clusters substantially increased with the grain size. The area of high-high cluster remained steady when the grain size exceeded 90 m. (4) Patch density, mean patch fractal dimension, clumpiness index, and contagion index exhibited predictable responses to changing grain size, whereas Shannon's diversity and Shannon's evenness indexes showed erratic responses, indicating that the diversity and evenness of the LST pattern were not scale-dependent. (5) The suitable domain of scale for the analysis of LST pattern was (60, 120), and the optimal grain size was 120 m. The selection of domains of scale and optimal grain size need to be determined according to the changes in thermal landscape patterns at different grain sizes and regional environments., (© 2020. Springer-Verlag GmbH Germany, part of Springer Nature.)

In order to analyze and control air pollutant emissions effectively, on the basis of comprehensive consideration of three different pollution sources of industrial sulfur dioxide, industrial nitrogen oxides, and industrial smoke and dust, the Tapio decoupling model and LMDI decomposition model with six decomposition variables are constructed to compare the effects of socioeconomic factors on industrial air pollutant emissions in 11 cities in Zhejiang Province during 2006-2017. Then, a decoupling effort model is developed to analyze the effectiveness of the decoupling efforts taken at city level. This study found that (1) during the period of 2006-2017, the air pollutant emission reduction work in Zhejiang Province achieved remarkable results. More specifically, economic scale effect and population effect are the main factors for the increase of air pollutant emissions. And, the energy emission intensity effect and technological progress are the main driving forces for the reduction of three atmospheric pollutants, followed by the reduction effect of industrial structure and energy structure. (2) The environmental pollution problems of different air pollution sources in different cities are heterogeneous. (3) Eleven cities in Zhejiang Province have made significant decoupling efforts on the emission of three kinds of air pollutants, but there are some differences in the trend of the decoupling effort index of different pollution sources in different cities. In the future, illustrating by the example of Zhejiang, we should implement a "common but different" emission reduction strategy and emphasize pollutant emissions control during energy use in the efforts of further promoting the reduction of air pollutants.

As the second largest economy in the world, China experiences severe particulate matter (PM) pollution in many of its cities. Meteorological factors are critical in determining both areal and temporal variations in PM pollution levels; understanding these factors and their interactions is critical for accurate forecasting, comprehensive analysis, and effective reduction of this pollution. This study analyzed areal and temporal variations in concentrations of PM 2.5 , PM 10 , and PM coarse (PM 10 - PM 2.5 ) and PM 2.5 to PM 10 ratios (PM 2.5 /PM 10 ) and their relationships with meteorological conditions in 366 Chinese cities from January 1, 2015 to December 31, 2017. On the national scale, PM 2.5 and PM 10 decreased from 48 to 42 μg m - ³ and from 88 to 84 μg m - ³, respectively, and the annual mean concentrations were 45 μg m - ³ (PM 2.5 ) and 84 μg m - ³ (PM 10 ) during the time period (2015-2017). In most regions, largest PM concentrations occurred in winter. However, in northern China, in spring PM coarse concentrations were highest due to dust. The PM 2.5 /PM 10 ratio was higher in southern than in northern China. There were large regional disparities in PM diurnal variations. Generally, PM concentrations were negatively correlated with precipitation, relative humidity, air temperature, and wind speed, but were positively correlated with surface pressure. The sunshine duration showed negative and positive impacts on PM in northern and southern cities, respectively. Meteorological factors impacted particulates of different size differently in different regions and over different periods of time., (Copyright © 2018 Elsevier Ltd. All rights reserved.)