School of Statistics and Data Science, Jiangxi University of Finance and Economics, Nanchang, 330013, China.
Department of Data Science and Information Technology, Taiz University, 9674, Taiz, Yemen.
Environ Sci Pollut Res Int. 2023 Nov;30(51):110931-110955. doi: 10.1007/s11356-023-30048-z. Epub 2023 Oct 6.
The rapid development of the Belt and Road Initiative (BRI) has led to severe air pollution dominated by PM2.5 concentrations which can cause a profound negative impact on human health and economic activity. This problem poses a critical environmental challenge to efficiently handling large-scale spatial-temporal PM2.5 data in this extended region. Functional data analysis (FDA) technique offers powerful tools that have the potential to enhance the analysis of spatial distributions and temporal dynamic changes in high-dimensional pollution data. However, modeling the spatial-temporal variability of PM2.5 concentrations by FDA remains unrevealed in the BRI region. To address this research gap, our study aimed to achieve two main objectives: first, to model the spatial-temporal dynamic variability of PM2.5 in 125 BRI nations (1998-2021), and second, to identify the underlying clusters behind the variations. We employed the recently developed functional adaptive density peak (FADP) clustering approach to solve the current problem. The proposed method is based on the joint use of functional principal components (FPCs) and functional cluster analyses. The main results are as follows: (i) The first three FPCs almost captured 99% of the total variations involving all valuable information on PM2.5 concentrations. (ii) PM2.5 pollution was highly concentrated in the developing countries (Pakistan, Bangladesh, and Nigeria) and the developed countries (Arabian Gulf countries: Qatar, United Arab Emirates, Bahrain, Saudi Arabia, Oman), and the least developed countries (Yemen and Chad). (iii) Three optimal clusters were identified and thus classified the PM2.5 into three distinct degrees of pollution: severe, moderate, and light. (iv) Cluster 1 had a severe pollution effect degree with a high rate of change, and it covered the Arabian Peninsula countries, African countries (Cameroon, Egypt, Gambia, Mali, Mauritania, Nigeria, Sudan, Senegal, Chad), Bangladesh, and Pakistan. (v) About 62 BRI countries belonged to cluster 2 showing a light pollution degree with annul average of less than 20 [Formula: see text]; this pointed out that the PM2.5 concentration remains stable in the cluster 2-related countries. The findings of this research would benefit governments and policymakers in preventing and controlling PM2.5 pollution exposure in BRI. Furthermore, this research could pay attention to sustainable development goals and the vision of the Green BRI policy.
“一带一路”倡议（BRI）的快速发展导致了以 PM2.5 浓度为主导的严重空气污染，这对人类健康和经济活动造成了深远的负面影响。这个问题对高效处理这个扩展区域的大规模时空 PM2.5 数据构成了一个关键的环境挑战。功能数据分析（FDA）技术提供了强大的工具，有可能增强对高维污染数据的空间分布和时间动态变化的分析。然而，FDA 对 PM2.5 浓度的时空可变性进行建模在 BRI 地区仍未得到揭示。为了解决这一研究空白，我们的研究旨在实现两个主要目标：首先，对 125 个 BRI 国家（1998-2021 年）的 PM2.5 时空动态变化进行建模，其次，识别变化背后的潜在聚类。我们采用了最近开发的功能自适应密度峰值（FADP）聚类方法来解决当前的问题。该方法基于功能主成分（FPC）和功能聚类分析的联合使用。主要结果如下：（i）前三个 FPC 几乎捕获了涉及 PM2.5 浓度所有有价值信息的总变化的 99%。（ii）PM2.5 污染高度集中在发展中国家（巴基斯坦、孟加拉国和尼日利亚）和发达国家（阿拉伯海湾国家：卡塔尔、阿拉伯联合酋长国、巴林、沙特阿拉伯、阿曼），以及最不发达国家（也门和乍得）。（iii）确定了三个最佳聚类，并据此将 PM2.5 分为三个不同的污染程度：严重、中度和轻度。（iv）聚类 1 具有严重的污染影响程度和较高的变化率，它覆盖了阿拉伯半岛国家、非洲国家（喀麦隆、埃及、冈比亚、马里、毛里塔尼亚、尼日利亚、苏丹、塞内加尔、乍得）、孟加拉国和巴基斯坦。（v）大约 62 个 BRI 国家属于聚类 2，污染程度较轻，年均值低于 20 [公式：见正文]；这表明聚类 2 相关国家的 PM2.5 浓度保持稳定。这项研究的结果将有利于 BRI 政府和决策者预防和控制 PM2.5 污染暴露。此外，这项研究可以关注可持续发展目标和绿色 BRI 政策的愿景。
