Spatiotemporal variations of dissolved organic carbon (DOC) and inorganic carbon (DIC) in 26 waters across the semi-humid/semi-arid Songnen Plain, China, were examined with data collected during 2008–2011. Fresh (n = 14) and brackish (n = 12) waters were grouped according to electrical conductivity (threshold = 1000 μS cm⁻¹) Significant differences in the average DOC and DIC concentrations were observed between the fresh (5.63 mg L⁻¹, 37.39 mg L⁻¹) and the brackish waters (15.33 mg L⁻¹, 142.93 mg L⁻¹). Colored dissolved organic matter (CDOM) and DOC concentrations were mainly controlled by climatic–hydrologic conditions. The investigation indicated that the outflow conditions in the semi-arid region had condensed effects on the dissolved carbon, resulting in close relationships between salinity vs. DOC (R² = 0.66), and salinity vs. DIC (R² = 0.94). An independent data set collected in May 2012 also confirmed this finding (DOC: R² = 0.79, DIC: R² = 0.91), highlighting the potential of quantifying DOC and DIC via salinity measurements for waters dispersed in the plain. Indices based on the CDOM absorption spectra (e.g., the DOC-specific CDOM absorption (SUVA₂₅₄), absorption ratio a₂₅₀ : a₃₆₅ (E₂₅₀ : E₃₆₅) and the spectral slope ratio (Sr, S_275−295/S_350−400) were applied to characterize CDOM composition and quality. Our results indicate that high molecular weight CDOM fractions are more abundant in the fresh waters than the brackish waters.

The Songnen Plain of the Northeast China is one of the three largest soda saline-alkali regions worldwide. To better understand soil alkalinization and salinization in this important agricultural region, it is vital to explore the distribution and variation of soil alkalinity and salinity in space and time. This study examined soil properties and identified the variables to extract soil alkalinity and salinity via physico-chemical, statistical, spectral, and image analysis. The physico-chemical and statistical results suggested that alkaline soils, coming from the main solute Na₂CO₃ and NaHCO₃ in parent rocks, characterized the study area. The pH and electric conductivity (EC ) were correlated with both narrow band and broad band reflectance. For soil pH, the sensitive bands were in short wavelength (VIS) and the band with the highest correlation was 475 nm (r = 0.84). For soil EC, the sensitive bands were also in VIS and the band with the highest correlation was 354 nm (r = 0.84). With the stepwise regression, it was found that the pH was sensitive to reflectance of OLI band 2 and band 6, while the EC was only sensitive to band 1. The R²_Adj (0.73 and 0.72) and root mean square error (RMSE) (0.98 and 1.07 dS/m) indicated that, the two stepwise regression models could estimate soil alkalinity and salinity with a considerable accuracy. Spatial distributions of soil alkalinity and salinity were mapped from the OLI image with the RMSE of 1.01 and 0.64 dS/m, respectively. Soil alkalinity was related to salinity but most soils in the study area were non-saline soils. The area of alkaline soils was 44.46% of the basin. Highly alkaline soils were close to the Zhalong wetland and downstream of rivers, which could become a severe concern for crop productivity in this area.

Peatland is a key component of terrestrial ecosystems in permafrost regions and have important effects on climate warming. Soil enzymes are involved in biogeochemical cycle of soil carbon (C), nitrogen (N) and phosphorus (P), which can be used as early sensitive indicators of soil nutrient changes caused by climate change. To predict the possible effects of permafrost degradation on soil enzymes in peatlands, ten peatlands from three types of permafrost regions along the permafrost degradation sequence (predominantly continuous permafrost region-predominantly continuous and island permafrost region-sparsely island permafrost region) in northeast China were selected to examine the activities of soil invertase, β-glucosidase, urease and acid phosphatase and their relationships with soil physicochemical properties. The results demonstrated that permafrost type had significant effect on soil enzyme activities. Soil enzyme activities in predominantly continuous and island permafrost region were significantly higher than those in sparsely island permafrost region and predominantly continuous permafrost region. The activities of four soil enzymes were higher in 0–15 cm than 15–30 cm soil layer. Soil enzymes activities were positively correlated with soil ammonia nitrogen (NH₄⁺-N), soil moisture content (SMC), total phosphorus (TP) and total nitrogen (TN), but negatively correlated with soil nitrate nitrogen (NO₃⁻-N). Soil inorganic nitrogen and moisture contents were the main factors affecting soil enzyme activities, with NH₄⁺-N accounted for 41.6% of the variance, SMC 29.6%, and NO₃⁻-N 11.0%. These results suggested that permafrost degradation may change soil enzyme activities by changing soil physicochemical properties. In this study, only 0–30 cm peat soil in permafrost regions was collected during the complete thawing period of permafrost active layer, further studies should be placed on the change of soil enzyme activities in active layer and permafrost layer during freezing and thawing process in the southernmost location of northeast China in the Eurasia permafrost body and boreal forest belt.

Over the past few decades, concentrations of carbon dioxide (CO₂), a key greenhouse gas, have risen at a global rate of approximately 2 ppm/a. China is the largest CO₂ emitter and is the principle contributor to the increase in global CO₂ levels. Based on a satellite-retrieved atmospheric carbon dioxide column average dry air mixing ratio (XCO₂) dataset, derived from the greenhouse gas observation satellite (GOSAT), this paper evaluates the spatial and temporal variations of XCO₂ characteristics in China during 2009–2016. Moreover, the factors influencing changes in XCO₂ were investigated. Results showed XCO₂ concentrations in China increased at an average rate of 2.28 ppm/a, with significant annual seasonal variations of 6.78 ppm. The rate of change of XCO₂ was greater in south China compared to other regions across China, with clear differences in seasonality. Seasonal variations in XCO₂ concentrations across China were generally controlled by vegetation dynamics, characterized by the Normalized Difference Vegetation Index (NDVI). However, driving factors exhibited spatial variations. In particular, a distinct belt (northeast–southwest) with a significant negative correlation (r < −0.75) between XCO₂ and NDVI was observed. Furthermore, in north China, human emissions were identified as the dominant influencing factor of total XCO₂ variations (r > 0.65), with forest fires taking first place in southwest China (r > 0.47). Our results in this study can provide us with a potential way to better understand the spatiotemporal changes of CO₂ concentration in China with NDVI, human activity and biomass burning, and could have an enlightening effect on slowing the growth of CO₂ concentration in China.

With global warming, the large amount of greenhouse gas emissions released by permafrost degradation is important in the global carbon and nitrogen cycle. To study the feedback effect of greenhouse gases on climate change in permafrost regions, emissions of CO₂, CH₄, and N₂O were continuously measured by using the static chamber-gas chromatograph method, in three forest soil ecosystems (Larix gmelinii, Pinus sylvestris var. mongolica, and Betula platyphylla) of the Daxing’an Mountains, northeast China, from May 2016 to April 2018. Their dynamic characteristics, as well as the key environmental affecting factors, were also analyzed. The results showed that the flux variation ranges of CO₂, CH₄, and N₂O were 7.92 ± 1.30~650.93 ± 28.12 mg·m⁻²·h⁻¹, −57.71 ± 4.65~32.51 ± 13.03 ug·m⁻²·h⁻¹, and −3.87 ± 1.35~31.1 ± 2.92 ug·m⁻²·h⁻¹, respectively. The three greenhouse gas fluxes showed significant seasonal variations, and differences in soil CO₂ and CH₄ fluxes between different forest types were significant. The calculation fluxes indicated that the permafrost soil of the Daxing’an Mountains may be a potential source of CO₂ and N₂O, and a sink of CH₄. Each greenhouse gas was controlled using different key environmental factors. Based on the analysis of Q₁₀ values and global warming potential, the obtained results demonstrated that greenhouse gas emissions from forest soil ecosystems in the permafrost region of the Daxing’an Mountains, northeast China, promote the global greenhouse effect.

Urban heat islands (UHIs) have a significant and negative impact on the urban ecological environment and on human health, and it is imperative to examine factors that lead to UHIs. Although numerous studies have been conducted in this field, little research has considered seasonal variations in UHIs in coastal cities. Moreover, parametric statistical analyses, such as regression and correlation analyses, have been typically applied to examine the influential factors. Such analyses are flawed because they cannot uncover the complicated relationships between UHIs and their factors. Taking Dalian, a coastal city in China, as an example, this paper reveals the dynamic mechanism of the UHI effect for different seasons using the cubist regression tree algorithm. Analyses suggest that the UHI effect only exists in spring and summer, and no obvious UHIs can be found in autumn and winter. The adjacency to the sea leads to moderate UHI effects in spring and summer and no UHI or urban cooling island (UCI) effects in autumn and winter. The distance to the coastline, however, does not play a role in the UHI effect. Furthermore, as one of the most important factors, the vegetation coverage plays a significant role in the UHI effect in spring and summer and significantly mediates the UHI in autumn and winter. Comparatively, the elevation (e.g., digital elevation models (DEMs)) is consistently negatively associated with the land surface temperature in all seasons, although a stronger relationship was found in spring and summer. In addition, the surface slope is also a significant factor in spring and winter, and the population density impacts the UHI distribution in summer as well.