Spatio-Temporal Variation and Its Driving Forces of Soil Organic Carbon along an Urban–Rural Gradient: A Case Study of Beijing

Abstract

Rapid urbanization has reshaped land cover and the ecological environment, potentially improving or deteriorating soil organic carbon (SOC). However, the response of SOC to urbanization has not yet been fully exploited. Herein, by using the land-use transfer matrix, the Sen & Mann–Kendall tests, the Hurst index, and a geographical and temporal weighted regression (GTWR) model, as well as an urban–rural gradient perspective, we assessed the dynamic response of SOC to Beijing’s urbanization from 2001 to2015 and identified the main drivers. The results found that SOC stock decreased by 7651.50 t C during the study period. SOC density varied significantly along an urban–rural gradient, with high value areas mainly being located in remote mountainous rural areas and low value areas mainly being located in urban areas on the plains. There was an uneven variation in SOC density across the urban–rural gradient, with suburban areas (25–40 km away from urban cores) losing the most SOC density while urban areas and rural areas remained relatively unchanged. GTWR model revealed the spatio-temporal non-flat stability of various driving forces. Precipitation, the proportion of forest, the proportion of grassland, the population, distance to the urban center, the slope, and the silt content are the main factors related to SOC stock change. As a result, we suggest policy makers reconceptualize the uneven variation in the SOC between urban and rural areas, emphasize suburban areas as a target for controlling SOC loss, and take into consideration the spatial and temporal heterogeneity of the factors influencing SOC stock when evaluating policies.

Mapping Soil Organic Carbon in Floodplain Farmland: Implications of Effective Range of Environmental Variables

“…In addition, the closer the dry land is to the irrigation canal, the better the soil water and heat conditions are, which is conducive to the accumulation of SOC. Thus, the negative relation of Dis_RS and Dis_IC to SOC is proven [59][60][61], which is consistent with the results of this article.…”

A Method for Assessing Urban Ecological Resilience and Identifying Its Critical Distance Belt Based on the “Source-Sink” Theory: A Case Study of Beijing

“…The encroachment of new buildings on a large area of cropland was the main feature of land use change in Beijing, and it mainly occurred in the plains. This feature was also confirmed by Liu et al [57] and Wang et al [55] in their studies on land use change in Beijing. As early as 2014, Zhao et al [58], by studying the spatial and temporal characteristics of cropland change in China over the past 30 years, found that the resultant decrease in cropland in China's peri-urban areas was mainly associated with an increase in building.…”

Impact of Land Use Change on Carbon Storage Based on FLUS-InVEST Model: A Case Study of Chengdu–Chongqing Urban Agglomeration, China

“…Regarding the assessment scale, the study of carbon storage has been expanded from single ecosystem types such as forest, grassland, and farmland to river basins, provinces, typical ecological areas, and urban agglomerations [16,[20][21][22]. Many studies have investigated the carbon emissions related to land use in large developed cities in East China, such as Beijing and Guangdong, and economically developed regions such as the Beijing-Tianjin-Hebei urban agglomeration, the Pearl River Delta urban agglomeration, and the Yangtze River Delta urban agglomeration [13,[23][24][25][26][27]. The results show that the optimization of the land use structure is an effective means of reducing carbon emissions [28,29].…”