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Shandong suffered from severe PM2.5 pollution in recent years as a result of its energy-intensive heavy industry. The initial step in implementing targeted control measures was to identify the main factors affecting PM2.5 concentrations. This paper explored the spatiotemporal characteristics of PM2.5 pollution in Shandong province during 2014-2020 based on observation data of 103 monitoring stations, thereafter, screened out the key natural and social-economical driving factors with the spatial Durbin model (SDM). The findings indicated that between 2014 and 2020, the annual average concentration of PM2.5 in Shandong decreased from 75.32 μg/m³ to 46.46 μg/m³. The monthly variation followed a clear U-shaped pattern and the daily variation followed an N-shaped distribution with peak concentrations at 09:00 and 23:00 respectively. Spatially, cities with high PM2.5 concentration tended to cluster and present high-west and low-east agglomeration characteristic at provincial scale. PM2.5 pollution showed considerable positive spatial autocorrelation and it took an apparent spatial agglomeration pattern in central and western cities. According to the estimates of the spatial Durbin model (SDM), PM2.5 pollution had significant spatial spillover effects in Shandong, and the indirect effects of the same factor were generally greater than the direct effects, indicating that the influence of neighboring cities could not be ignored. Of the multiple factors, socioeconomic factors had a considerably lesser influence on PM2.5 pollution than did natural ones like temperature, relative humidity and wind speed. [ABSTRACT FROM AUTHOR]

Since stating carbon peak goal, Shandong has garnered widespread concern over its large GHG emissions. Based on the latest emission factors and detailed activity data, a comprehensive highresolution GHG emission inventory of Shandong Province for 2020 was established by updating estimation methods and allocation profiles. The results indicated that the emissions of CO2, CH4, N2O, HFCs, PFCs and SF6 had reached 1233.46, 36.49, 45.79, 74.27, 6.21 and 0.56 Mt CO2eq, respectively. Specifically, CO2 emissions mainly originated from fossil fuel consumption (81.83%); the major sources of CH4 emissions were mining industries and agriculture, contributing 1.40% and 0.81% of the total GHG emissions, respectively; industry processes and product use discharged the largest amount of N2O (1.92%) and were responsible for all of the fluorinated greenhouse gas emissions. To obtain more accurate GHG emission information, GHG emissions were spatially allocated to grid cells with a resolution of 0.05°×0.05° according to appropriate surrogates and results showed that most of the emissions are concentrated over a small number of grid cells, specifically, 10% of grid cells accounts for 73% of GHG, 75% of CO2, 88% of CH4 and 40% of N2O emission. Spatially, the GHG emissions were mainly distributed in eastern and central cities of Shandong and decreased radially from urban centres to the surrounding areas. [ABSTRACT FROM AUTHOR]