Fine particulate matter (PM_2.5) pollution’s passive effects on public health have attracted a great deal of attention. Extensive studies have tried to uncover the impacts of external drivers on PM_2.5 pollution variation; however, the topography’s effects on PM_2.5 pollution remain poorly understood. Using annual high-resolution concentration estimates for PM_2.5, this paper quantitatively disentangled the geographical impacts of topography on the PM_2.5 pollution in China and quantified the mountain blocking effects on the PM_2.5 pollution dispersion on a macro scale. The results show that, in China, the plains and surrounding platforms and hills tend to suffer from long-term severe PM_2.5 pollution. The most polluted topography type is the plains. In comparison, regions such as high-altitude mountains and plateaus are less affected by PM_2.5 pollution. Mountains have significant and evident blocking effects on the cross-regional spread of PM_2.5 pollution. Generally, Level I mountains (high elevation, density and coverage mountains) provide better blocking effects than Level II (middle elevation, density and coverage mountains) mountains and Level III mountains (low elevation, density and coverage mountains). The mountains’ blocking effects begin to play an efficient role when their altitudes reach a certain value; however, the exact altitude values vary by different mountains with a value of 163 m for all typical mountains with absolute PM_2.5 concentration differences between their two sides greater than 10 μg/m³. In heavily polluted areas, PM_2.5 pollution may overflow where the surrounding mountains are not high enough or the mountains’ stretch breaks. This study can provide key theoretical support for air pollution modelling and regional air pollution joint prevention and control.

Accurate prediction of wetland soil organic carbon concentration and an understanding of its controlling factors are important for studying regional climate change and wetland carbon cycles; with that knowledge mechanisms can be put in place that are conducive to sustainable ecosystem management for environmental health. In this study, a hybrid approach combining an artificial neural network and ordinary kriging and 103 soil samples at three soil depth ranges (0–30, 30–60, and 60–100 cm) were used to predict wetland soil organic carbon concentration in China’s Liao River Basin. The model evaluation indicated that a combination of artificial neural network and ordinary kriging and limited soil samples achieved good performance in predicting wetland soil organic carbon concentration. Wetland soil organic carbon concentration in the Liao River Basin has apparent spatial and vertical heterogeneities with values decreasing from southeast to northwest and concentrates present mainly in the topsoil (0–30 cm). Mean wetland soil organic carbon concentration values at the three soil depths were 10.43 ± 0.38, 7.93 ± 0.25, and 7.61 ± 0.22 g/kg, respectively, which are smaller than those over other wetland regions in Northeast China. Terrain aspect contributed the most in predicting wetland soil organic carbon concentration at each of the three soil depths, followed by normalized difference vegetation index at 0–30 cm and mean annual precipitation at 30–60 and 60–100 cm. This study provides a framework method and baseline to quantify the soil organic carbon concentration dynamics in response to climatic and anthropogenic drivers.

Total column ozone (TCO) describes the amount of ozone in the entire atmosphere. Many scholars have used the lower resolution data to study TCO in different regions, but new phenomena can be discovered using high-precision and high-resolution TCO data. This paper used the long time, high accuracy, and high-resolution MSR2 dataset (2000–2019) to analyze the spatial and temporal variation characteristics of TCO over the Yangtze River Delta Urban Agglomeration to explore the relationship between the TCO and meteorological and socio-economic factors. The correlations between the TCO and climatic factors and the driving forces of meteorological and socio-economic factors on the spatial and temporal variability of TCO were also analyzed, and different mathematical models were constructed to fit the TCO for the past 20 years and predict the future trend of the TCO. The results show the following. (1) The TCO over the study area exhibited a quasi-latitudinal distribution, following a slight downtrend during 2000–2019 (0.01 ± 0.18 DU per year) and achieved its maximum in 2010 and minimum in 2019; throughout the year, an inverted V-shaped cycle characterizes the monthly variability of TCO; TCO was significantly higher in spring than in summer and autumn than winter. (2) Precipitation and the absorbed aerosol index (AAI) had critical effects on the spatial distribution of TCO, but meteorological factors were weakly correlated with the annual variation of TCO subject to the game interactions between different external driving factors. The monthly changes in the TCO were not in synergy with that of other meteorological factors, but with a significant hysteresis effect by 3–5 months. Socio-economic factors had a significant influence on TCO over the study area. (3) The Fourier function model can well describe the history and future trend of the annual TCO over the study area. The TCO over the study area shows a fluctuating upward trend (0.27 ± 1.35 DU per year) over the next 11 years. This study enriches the theoretical and technical system of ozone research, and its results can provide the necessary theoretical basis for ozone simulation and forecasting.