江西农业学报2024，Vol.36Issue(1)：138-145,8.DOI:10.19386/j.cnki.jxnyxb.2024.01.020
基于地理时空加权回归分析长江经济带区域氨气排放量变化与主要影响因素
Variation Characteristics and Main Influencing Factors of Ammonia Emissions in Yangtze River Economic Belt Based on Geographically and Temporally Weighted Regression
摘要
Abstract
This article takes the Yangtze River Economic Belt as the research area,analyzes the spatial and temporal evolution characteristics of ammonia emissions by constructing an index system of influencing factors for ammonia emissions changes based on the geographically and temporally weighted regression model,and explores the relationship between socio-economic factors,natural environmental factors and changes in ammonia emissions.The research findings are as follows:(1)The overall distribution pattern of ammonia emissions in the Yangtze River Economic Belt region is characterized by"high on both ends and low in the middle",with the most significant changes in economically developed areas.(2)The perennial moisture and mild climatic conditions brought by the Yangtze River Basin facilitate the volatilization of ammonia from the soil,making natural environment become a significant contributor to ammonia emissions.With the increase of global temperature,the impact of temperature on ammonia emissions is also slowly increasing.(3)Agricultural ammonia sources remain the primary source of ammonia emissions in China.In major agricultural provinces such as Sichuan,Hunan and Jiangxi,the use of nitrogen fertilizer for agricultural irrigation has a significant impact on ammonia emissions.However,the expansion of urbanization and the enhancement of socio-economic development reflect the growing influence of urban life and production on ammonia emissions in economically developed regions and cities.关键词
氨气排放/地理时空加权回归/影响因素/时空特征Key words
Ammonia emission/Geographically and temporally weighted regression(GTWR)/Influence factor/Spatial-temporal feature分类
管理科学引用本文复制引用
孔望,吴静..基于地理时空加权回归分析长江经济带区域氨气排放量变化与主要影响因素[J].江西农业学报,2024,36(1):138-145,8.基金项目
国家自然科学基金"基于廉价传感器的多尺度PM2.5和O3模型优化研究"(42107113). （42107113）
