## 3 Results and Discussion
### Formatting of Mathematical Components  
#### 3.4.1 Keyword Network and Research Topics  
Similar to the keyword analysis performed on subsets for influential authors, an analysis of all keywords of all authors provides a macroscopic understanding of the research domains within the BCL dataset. VOSviewer's clustering technique is based on the concept of association strength and a modularity-based clustering variant. The clustering process aims to group items, such as keywords, that have strong relationships with each other based on co-occurrence, co-citation, or bibliographic coupling [31; 57]. Figure 6a presents the overall keyword network, where each node represents a keyword. It is worth noting that to achieve more accurate keyword statistics and greater clarity in the keyword network, synonymous terms with different expressions are merged before calculating the network with VOSviewer, for example, \"aerosol\" and \"aerosols\" or \"CO\({}_{2}\)\" and \"carbon dioxide\". Additionally, only the top 200 most frequently occurring non-isolated keywords were selected. In Figure 6, nodes of different colors represent different clusters, numbered sequentially from Clusters 1 to 6 in the order of navy blue, red, green, blue, purple, and yellow. Furthermore, in the following sections, the research characteristics of each cluster will be outlined based on the main keywords. The largest node in Cluster 1 is \"black carbon\", which also stands as the largest node in the whole keyword network, encompassing the core of the entire network and maintaining close associations with all other clusters. This cluster features keywords such as \"biomass burning\", \"Tibetan plateau\", \"Indo-Gangetic plain\", \"brown carbon\", \"light absorption\", \"levoglucosan\", and \"aethalometer\". This research theme focuses on investigating the optical properties of black carbon and brown carbon, with a primary emphasis on regions such as the Tibetan Plateau and the Indo-Gangetic Plain. The primary emphasis lies on phenomena related to biomass burning, with a particular focus on levoglucosan as a significant marker [16; 20; 58; 59]. In Cluster 2, \"aerosols\" emerges as the largest node, closely linked with Clusters 1, 3, 4, and 5. Other prominent keywords within Cluster 2 include \"radiative forcing\", \"climate models\", \"climate effect\", \"precipitation\", \"snow\", \"clouds\", \"monsoon\", \"Arctic\", \"haze\", \"anthropogenic aerosols\", and \"WRF-Chem\". This research theme investigates the relationships between aerosols, including black carbon, and various climate variables, such as rainfall, snowfall, cloud formation, and monsoon patterns, focusing on their pivotal interactions through radiative forcing and employing climate modeling approaches like WRF-Chem [7; 60; 61]. Cluster 3 is anchored by the central theme of \"climate change\", complemented by significant keywords including \"air pollution\", \"air quality\", \"emissions\", \"human health\", \"particulate matter\", \"greenhouse gases\", \"global warming\", \"soot\", \"PAHs\", \"carbon dioxide\", \"nitro oxide\", and \"combustion\". This theme encompasses both atmospheric environment and climate issues, investigating the effects of atmospheric pollutants such as soot, PAHs, carbon dioxide, nitrous oxide, and greenhouse gases on air quality, human health, and climate change [54; 62; 63; 64]. Cluster 4 focuses on the keyword \"AOD\", alongside related terms such as \"MODIS\", \"MERRA-2\", \"aerosol radiative forcing\", \"Himalayas\", \"glaciers\", \"remote sensing\", \"snow albedo\", and \"CALIPSO\". This research theme primarily utilizes satellite remote sensing techniques to investigate the impacts of aerosols, such as black carbon, on the Himalayan region and glaciers [48; 65]. \"AOD\" (Aerosol Optical Depth) is crucial for assessing the impacts of aerosols on light transmission. \"MODIS\" (Moderate Resolution Imaging Spectroradiometer) is the main satellite sensor for data collection [66].