Rural production–living–ecological (PLE) space is the essential carrier of China's rural land resource planning and management. However, the pattern identification, optimal prediction, and multi–scale integration of rural PLE space lack sufficient scientific evidence and reliable quantitative analysis. To fill this gap, this paper proposes a multi-scale land use optimization method based on benefit coupling evaluation, BP–ANN and CLUE–S models. The typical hilly area of the upper reaches of the Yangtze River in southwest China were used as a case for empirical study. The results showed that there was a high correlation between land use patterns and benefits. The key to land use optimization in hilly areas is to increase the area of ecological land and improve the capacity of regional ecosystem services. Through the evaluation of the degree of comprehensive benefit coupling, 44 sample towns with an optimal land use pattern were effectively identified to construct the optimization model. At the regional scale, the BP-ANN model predicted the optimal ratio of land use structure for each township unit based on natural, social, and economic influencing factors. The optimization results reduced production land by 8.94% on average, increased ecological land by 9.2% on average, and kept living land relatively stable. The proportions of production, living, and ecological land were adjusted to 59.85%, 8.34%, and 31.81%, respectively, which can better meet the land space demand for food security and ecological protection in the future. Regarding the smaller scale, the CLUE-S model took the regional-scale optimization results as the goal to simulate the spatial distribution of land use. The proportion of production, living, and ecological land in the town of Taiping was optimized from the previous 88.17%, 6.25%, and 5.58% to 62.92%, 7.83%, and 29.25%, respectively. The optimization results not only ensure the stability of high-quality cultivated land, but also effectively improve ecological functions such as soil conservation and water purification. This novel method was proven to be effective for quantitative optimization of land use and multi-scale functional space cohesion and integration, providing scientific support for the sustainable use of rural land resources in China.
Abstract. Water-soluble and water-insoluble organic aerosol (WSOA and WIOA) constitute a large fraction of fine particles in winter in northern China, yet our understanding of their sources and processes are still limited. Here we have a comprehensive characterization of WSOA in cold season in Beijing. Particularly, we present the first mass spectral characterization of WIOA by integrating online and offline organic aerosol measurements from high-resolution aerosol mass spectrometer. Our results showed that WSOA on average accounted for 59 % of the total OA and comprised dominantly secondary OA (SOA, 69 %). The WSOA composition showed significant changes during the transition season from autumn to winter. While the photochemical-related SOA dominated WSOA (51 %) in early November, the oxidized SOA from biomass burning increased substantially from 8 % to 29 % during the heating season. Comparatively, local primary OA dominantly from cooking aerosol contributed the major fraction of WSOA during clean periods. WIOA showed largely different spectral patterns from WSOA which were characterized by prominent hydrocarbon ions series and low oxygen-to-carbon (O/C = 0.19) and organic mass-to-organic carbon (OM/OC = 1.39) ratios. The nighttime WIOA showed less oxidized properties (O/C = 0.16 vs. 0.24) with more pronounced polycyclic aromatic hydrocarbons (PAHs) signals than daytime, indicating the impacts of enhanced coal combustion emissions on WIOA. The evolution process of WSOA and WIOA was further demonstrated by the triangle plot of f44 (fraction of m/z 44 in OA) vs. f43, f44 vs. f60, and the Van Krevelen diagram (H/C vs. O/C). We also found more oxidized WSOA and an increased contribution of SOA in WSOA compared with previous winter studies in Beijing, indicating that the changes in OA composition due to clean air act have affected the sources and properties of WSOA.
Abstract Climate warming may induce substantial changes in ecosystem carbon cycle, particularly for those climate-sensitive regions, such as alpine grasslands on the Tibetan Plateau. By synthesizing findings from in-situ warming experiments, this review elucidates the mechanisms underlying the impacts of experimental warming on carbon cycle dynamics within these ecosystems. Generally, alterations in vegetation structure and prolonged growing season favor strategies for enhanced ecosystem carbon sequestration under warming conditions. Whilst warming modifies soil microbial communities and their carbon-related functions, its effects on soil carbon release fall behind the increased vegetation carbon uptake. Despite no significant accumulation of soil carbon stock has been detected upon warming, notable changes in its fractions indicate potential shifts in carbon stability. Future studies should prioritize deep soil carbon dynamics, the interactions of carbon, nitrogen, and phosphorus cycles under warming scenarios, and the underlying biological mechanisms behind these responses. Furthermore, the integration of long-term warming experiments with Earth system models is essential for reducing the uncertainties of model predictions regarding future carbon-climate feedback in these climate-sensitive ecosystems.
Background .Urban forests help in mitigating carbon emissions; however, their associations with landscape patterns are unclear.Understanding the associations would help us to evaluate urban forest ecological services and favor urban forest management via landscape regulations.We used Harbin, capital city of the northernmost province in China, as an example and hypothesized that the urban forests had different landscape metrics among different forest types, administrative districts, and urban-rural gradients, and these differences were closely associated with forest carbon sequestration in the biomass and soils.Methods.We extracted the urban forest tree coverage area on the basis of 2 GF-1 remote sensing images and object-oriented based classification method.The analysis of forest landscape patterns and estimation of carbon storage were based on tree coverage data and 199 plots .We also examined the relationships between forest landscape metrics and carbon storage on the basis of forest types, administrative districts, ring roads, and history of urban settlements by using statistical methods.Results.The small patches covering an area of less than 0.5 ha accounted for 72.6% of all patches (average patch size, 0.31 ha).The mean patch size (AREA_MN) and largest patch index (LPI) were the highest in the landscape and relaxation forest and Songbei District.The landscape shape index (LSI) and number of patches linearly decreased along rural-urban gradients (p < 0.05).The tree biomass carbon storage varied from less than 10 thousand tons in the urban center (first ring road region and 100-year regions) to more than 100 thousand tons in the rural regions (fourth ring road and newly urbanized regions).In the same urban-rural gradients, soil carbon storage varied from less than 5 thousand tons in the urban centers to 73-103 thousand tons in the rural regions.The association analysis indicated that the total forest area was the key factor that regulates total carbon storage in trees and soils.However, in the case of carbon density (ton ha -1 ), AREA_MN was strongly associated with tree biomass carbon, and soil carbon density was negatively related to LSI (p < 0.01) and AREA_MN (p < 0.05), but positively related to LPI (p < 0.05).Discussion.The urban forests were more fragmented in Harbin than in other provincial cities in Northeastern China, as shown by the smaller patch size, more complex patch shape, and larger patch density.The decrease in LSI along the rural-urban gradients may contribute to the forest carbon sequestrations in downtown regions, particularly underground soil carbon accumulation, and the increasing patch size may benefit tree carbon sequestration.Our findings help us to understand how forest landscape metrics are associated with carbon storage function.These findings related to urban forest design may maximize forest carbon sequestration services and facilitate in precisely estimating
Urban forests help in mitigating carbon emissions; however, their associations with landscape patterns are unclear. Understanding the associations would help us to evaluate urban forest ecological services and favor urban forest management via landscape regulations. We used Harbin, capital city of the northernmost province in China, as an example and hypothesized that the urban forests had different landscape metrics among different forest types, administrative districts, and urban-rural gradients, and these differences were closely associated with forest carbon sequestration in the biomass and soils.We extracted the urban forest tree coverage area on the basis of 2 GF-1 remote sensing images and object-oriented based classification method. The analysis of forest landscape patterns and estimation of carbon storage were based on tree coverage data and 199 plots. We also examined the relationships between forest landscape metrics and carbon storage on the basis of forest types, administrative districts, ring roads, and history of urban settlements by using statistical methods.The small patches covering an area of less than 0.5 ha accounted for 72.6% of all patches (average patch size, 0.31 ha). The mean patch size (AREA_MN) and largest patch index (LPI) were the highest in the landscape and relaxation forest and Songbei District. The landscape shape index (LSI) and number of patches linearly decreased along rural-urban gradients (p < 0.05). The tree biomass carbon storage varied from less than 10 thousand tons in the urban center (first ring road region and 100-year regions) to more than 100 thousand tons in the rural regions (fourth ring road and newly urbanized regions). In the same urban-rural gradients, soil carbon storage varied from less than five thousand tons in the urban centers to 73-103 thousand tons in the rural regions. The association analysis indicated that the total forest area was the key factor that regulates total carbon storage in trees and soils. However, in the case of carbon density (ton ha-1), AREA_MN was strongly associated with tree biomass carbon, and soil carbon density was negatively related to LSI (p < 0.01) and AREA_MN (p < 0.05), but positively related to LPI (p < 0.05).The urban forests were more fragmented in Harbin than in other provincial cities in Northeastern China, as shown by the smaller patch size, more complex patch shape, and larger patch density. The decrease in LSI along the rural-urban gradients may contribute to the forest carbon sequestrations in downtown regions, particularly underground soil carbon accumulation, and the increasing patch size may benefit tree carbon sequestration. Our findings help us to understand how forest landscape metrics are associated with carbon storage function. These findings related to urban forest design may maximize forest carbon sequestration services and facilitate in precisely estimating the forest carbon sink.
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