Fine particulate matter (PM_2.5) was simultaneously collected from the indoor and outdoor environments in urban area of Jinan in North China from November to December 2018 to evaluate the characteristics and sources of indoor PM_2.5 pollution. The concentrations of indoor and outdoor PM_2.5 were 69.0 ± 50.5 µg m⁻³ and 128.7 ± 67.9 µg m⁻³, respectively, much higher than the WHO-established 24-h standards for PM_2.5, indicating serious PM_2.5 pollution of indoor and outdoor environments in urban Jinan. SO₄²⁻, NO₃⁻, NH₄⁺, and organic carbon (OC) were the predominant components, which accounted for more than 60% of the PM_2.5 concentration. The total elemental risk values in urban Jinan for the three highly vulnerable groups of population (children (aged 2–6 years and 6–12 years) and older adults (≥70 years)) were nearly 1, indicating that exposure to all of the elements in PM_2.5 had potential non-carcinogenic risks to human health. Further analyses of the indoor/outdoor concentration ratios, infiltration rates (F_INF), and indoor-generated concentration (C_ig) indicated that indoor PM_2.5 and its major chemical components (SO₄²⁻, NO₃⁻, NH₄⁺, OC, and elemental carbon) were primarily determined by outdoor pollution. The lower indoor NO₃⁻/SO₄²⁻ ratio and F_INF of NO₃⁻ relative to the outdoor values were due to the volatility of NO₃⁻. Positive matrix factorization (PMF) was performed to estimate the sources of PM_2.5 using the combined datasets of indoor and outdoor environments and revealed that secondary aerosols, dust, cement production, and coal combustion/metal smelting were the major sources during the sampling period.

As the largest energy-consuming province in China, Shandong faces the dual task of greenhouse gas (GHG) reduction and atmospheric pollution control. Based on the latest activity data and updated emission factors, this study establishes a high-resolution emission inventory (5 km × 5 km) for GHGs and main atmospheric pollutants from the energy consumption sectors of Shandong Province from 2010 to 2021, quantifies the relationship between social economic factors and GHGs and atmospheric pollutants emissions using the expanded stochastic environmental impact assessment (STIRPAT) model, and forecasts the future emission trend with the help of the scenario analysis method. Results indicate that the electricity and transportation sectors are the main contributors to all pollutants. Spatially, the high value of pollutants is mainly concentrated in the urban agglomerations of central and eastern Shandong. Up to 72% of GHGs and 50% of air pollution emissions are attributed to the top 10% of emission grids. Emission peaks occur mainly in summer and winter due to straw burning, increased utilization of temperature-controlled facilities, and expansion of plant capacity. Population, energy consumption, the proportion of secondary industry, and energy consumption intensity are the most significant influencing factors for pollutant emissions. Scenario analysis results indicate Shandong province can reach its carbon peak in 2027 without sacrificing population growth or economic progress.

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.

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.