基于网格的华北平原PM2.5时空演变规律及影响因素分析

Analysis of temporal and spatial evolution law and influencing factors of PM2.5 in the North China Plain based on grid

关键词:  网格  华北平原  PM_2.5浓度  时空演变规律  影响因素

Key words:  grid  the North China Plain  concentration of PM2.5  temporal and spatial evolution law  influencing factors

摘要:

华北平原是我国PM_2.5污染较为严重地区之一。近年来，空气质量总体上有所好转，但仍未获得根本性改善。基于此，文章采用空间自相关分析和地理探测器，选取自然因素、社会经济因素和污染源排放作为评价因子，探讨2015—2020年华北平原PM_2.5时空演变规律及其影响因素。结果表明：（1）2015—2020年华北平原地区PM_2.5年均浓度波动下降，月均浓度总体呈U型变化趋势；（2）华北平原PM_2.5浓度分布总体表现为中部高，四周低，且有明显空间聚集特征；（3）地理探测显示社会经济是华北平原PM_2.5空间分布特征的主要影响因素，污染源排放因素次之；（4）交互作用结果表明自然因子内部的交互作用最明显，其次是人口密度与气象因子之间的交互作用。研究结果为华北平原PM_2.5的综合治理提供基础数据，为进一步降低 PM_2.5浓度水平，提高空气质量提供决策支撑。

Abstract:

Background, aim, and scope The North China Plain is one of the most severely PM_2.5 polluted regions in China. While overall air quality has improved in recent years, fundamental mitigation has yet to be fully achieved. An in-depth investigation of pollution in the North China Plain, revealing the primary drivers of local PM_2.5 formation, is critical for developing preventative strategies and accelerating mitigation efforts. To elucidate the pollution status and its driving forces, this study adopted a grid-based approach to investigate the spatiotemporal evolution of PM_2.5 in the North China Plain from 2015 to 2020. Materials and methods Utilizing spatial autocorrelation analysis and the geographic detector model, this study analyzed the spatiotemporal patterns of PM_2.5 and its influencing factors. Natural factors, socio-economic factors, and pollution emissions were selected as the primary explanatory variables. Results Firstly, from 2015 to 2020, the average annual PM_2.5 concentration in the North China Plain decreased by 23.69 μg/m³ , representing a decrease of 35.95%. Monthly average concentrations exhibited a U-shaped trend, decreasing from January to August and rebounding from August to December. Secondly, the spatial distribution of PM_2.5 concentration in the North China Plain is uneven, characterized by high concentrations in the central region and lower concentrations in the periphery. The average concentration in the central high-pollution area is 74.35 μg/m3 , compared to 38.91 μg/m³ in the northern lowpollution area. Finally, the geographical detector analysis reveals that socio-economy factors are the primary drivers affecting the spatial distribution characteristics of PM_2.5 in the North China Plain, with an average q value of 0.480, followed by pollution source emission factors, with an average q value of 0.479. The interaction results indicate that the interactions among natural factors are the most significant, followed by the interactions between population density and meteorological factors. Discussion In recent years, due to regulatory efforts by relevant departments, significant progress has been made in pollution control within the North China Plain; however, challenges remain. From 2015 to 2020, the average annual concentration of PM_2.5 showed a fluctuating downward trend, the average monthly concentration displayed a U-shaped pattern, and the duration of high pollution periods shortened. On a spatial scale, although the PM_2.5 concentration in the North China Plain decreased from 2015 to 2017, the scope of pollution showed an expanding trend. The spatial distribution of PM_2.5 in the North China Plain is mainly driven by socio-economic factors, while interactions among natural factors also play an important role. Conclusions While notable improvements have been achieved in the North China Plain, further actions are urgently needed. Future strategies should prioritize managing socio-economic drivers and strengthening cross-regional joint prevention and control mechanisms. Recommendations and perspectives The findings provide foundational data for the comprehensive management of PM_2.5 in the North China Plain and offer decision-making support for further improving air quality.