Changes in Human Activity Intensity on the Spatial and Temporal Distribution of PM2.5 in Chengdu-Chongqing Economic Circle

doi:10.16258/j.cnki.1674-5906.2025.07.011

Ecology and Environmental Sciences ›› 2025, Vol. 34 ›› Issue (7): 1111-1120.DOI: 10.16258/j.cnki.1674-5906.2025.07.011

• Research Article [Environmental Science] • Previous Articles Next Articles

Changes in Human Activity Intensity on the Spatial and Temporal Distribution of PM_2.5 in Chengdu-Chongqing Economic Circle

HUANG Yi¹^,²(), LAN Ting¹, SHENG Jiliang²^,^*()

1. College of Mathematics and Statistics, Jishou University, Jishou 416000, P. R. China
2. School of Statistics and Data Science, Jiangxi University of Finance and Economics, Nanchang 330013, P. R. China

Received:2025-01-19 Online:2025-07-18 Published:2025-07-11

人类活动强度变化对成渝地区双城经济圈PM_2.5时空分布的影响研究

黄毅¹^,²(), 兰婷¹, 盛积良²^,^*()

1.吉首大学数学与统计学院，湖南吉首 416000
2.江西财经大学统计与数据科学学院，江西南昌 330013

通讯作者: *E-mail: shengjiliang@163.com
作者简介:黄毅（1990年生），男（土家族），讲师，博士，研究方向为复杂数据研究等。E-mail: huangy0014@163.com
基金资助:
国家自然科学基金项目(52160024);湖南省教育厅青年基金项目(22B0527);湖南省教育厅一般项目(22C0275);湖南省大学生创新训练项目(S202410531026)

Abstract

Abstract:

Control measures during the COVID-19 pandemic reduced the intensity of human activities and provided an opportunity to study the extent to which air pollution is affected by these activities. This study takes the PM_2.5 of the Chengdu-Chongqing Economic Circle as the research object and constructs a fluctuation spillover network through GARCH-BEKK to investigate the influence of human activity intensity change on the spatial and temporal distribution of PM_2.5. The results showed that there was an evident external spillover effect of PM_2.5 across all regions, and the PM_2.5 spillovers were concentrated in several cities in the Chengdu-Chongqing Economic Circle during the lockdown period, whereas they were more dispersed during the recovery period. The ranges of PM_2.5, pollution output, and input centers shrink as the intensity of human activity diminishes, and the intensity of PM_2.5, out-spillover (in-spillover), decreases as the distance from the centers increases. The PM_2.5 input modes were more fragile, with greater variability in response to changes in human activity intensity, whereas the PM_2.5 output modes showed higher resilience to change. Additionally, the spillover relationship of PM_2.5 pollution varied between different blocks, with relatively simple spillover relationships observed during the lockdown period and more complex dynamics observed during the recovery period. The increased intensity of human activity is an important factor in PM_2.5 transport complexity.

Key words: spillover network, Chengdu-Chongqing economic Circle, PM_2.5, GARCH-BEKK model, human activity intensity

摘要：

COVID-19期间管控措施减少了人类活动强度，为研究大气污染受人类活动影响的程度提供了机会。以成渝双城经济圈PM_2.5为研究对象，通过GARCH-BEKK构建波动溢出网络，探究人类活动强度变化对成渝地区PM_2.5时空分布的影响。结果表明各城市PM_2.5存在显著溢出效应，成渝地区封锁时期PM_2.5溢出主要集中在几个城市，而恢复期则较为分散。PM_2.5污染输出和输入中心范围随人类活动强度的减弱而有所缩小，且随着与中心的距离的增加，PM_2.5溢出（溢入）强度逐渐减小。PM_2.5输入模式更脆弱，对人类活动强度的响应更敏感，而PM_2.5输出模式则表现出更高的恢复弹性。此外，不同模块的PM_2.5输出关系存在差异，封锁期PM_2.5输出关系相对简单，恢复期PM_2.5输出关系较为复杂，人类活动强度加剧是PM_2.5输送复杂的重要因素。

关键词: 波动溢出网络, 成渝地区双城经济圈, PM_2.5, GARCH-BEKK模型, 人类活动强度

CLC Number:

HUANG Yi, LAN Ting, SHENG Jiliang. Changes in Human Activity Intensity on the Spatial and Temporal Distribution of PM_2.5 in Chengdu-Chongqing Economic Circle[J]. Ecology and Environmental Sciences, 2025, 34(7): 1111-1120.

黄毅, 兰婷, 盛积良. 人类活动强度变化对成渝地区双城经济圈PM_2.5时空分布的影响研究[J]. 生态环境学报, 2025, 34(7): 1111-1120.

Figures/Tables 11

Table 1 Four block types about network

内部关系比	接收关系比
内部关系比	≈0	>0
≥(g_k−1)/(g−1)	双边溢出	向内溢出
<(g_k−1)/(g−1)	向外溢出	经纪人

Figure 1 Average correlation coefficients of different cities in Chengdu-Chongqing Economic Circle

Figure 2 The PM2.5 fluctuation spillover network in Chengdu-Chongqing Economic Circle in different periods

Table 2 Structural characteristics of PM2.5 fluctuation spillover networks in different time periods

网络结构	直径¹⁾	平均最短路径²⁾	网络密度³⁾	关联度⁴⁾	效率⁵⁾	等级⁶⁾
封锁时期	3	1.48	0.53	1	0.76	0
恢复时期	4	1.87	0.34	1	0.64	0

Figure 3 Accumulative distribution of the weighted edges in different periods

Figure 4 Spatial distribution of local out-spillover index in different periods

Figure 5 Spatial distribution of local in-spillover index in different periods

Figure 6 Variation of PM2.5 output pattern during different periods

Figure 7 Variation of PM2.5 input pattern during different periods

Table 3 The relevance and role of blocks in different periods

时期	模块编号	城市个数	接受关系		发送关系		内部期望连边率/ %	内部实际连边率/ %	模块类型
时期	模块编号	城市个数	模块内	模块外	模块内	模块外	内部期望连边率/ %	内部实际连边率/ %	模块类型
封锁时期	模块1	5	17	31	17	22	33.33	43.59	向内溢出
	模块2	3	3	13	3	22	13.33	12	向外溢出
	模块3	3	6	23	6	21	13.33	22.22	双向溢出
	模块4	5	8	25	8	27	33.33	22.85	经纪人
恢复时期	模块1	3	5	20	5	9	13.33	35.71	向内溢出
	模块2	5	2	14	2	27	33.33	6.89	向外溢出
	模块3	3	5	18	5	15	13.33	25	双向溢出
	模块4	5	0	18	0	19	33.33	0	经纪人

Figure 8 The transport pathways of PM2.5 between modules in different time periods

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