Academic contributions in environmental science and engineering (ESE) research are needed to ensure a cleaner, productive, and environmentally conscious society. Hence, an understanding of the critical trends, topics, and research developments in the field is crucial towards facilitating the identification, communication, and improved research collaboration of nascent and increasingly complex environmental problems. As a deeper analysis of the broader trend evolution of ESE research is lacking in the literature, we employ a more robust content analysis approach in the form of a topic modeling computational text analysis method to unearth key temporal and regional insights in the field. As such, we apply a latent Dirichlet allocation (LDA) model on abstract metadata based on 3572 articles procured from reputable journals that published subject matter related to ESE research from 2005 to 2019 inclusive. We analyze the statistical composition of each inferred topic in the form of word clouds and uncover general research trends, such as topics related to environmental impact assessments, improved clean cookstoves, solid waste management, and environmental lead pollution. We also perform temporal analysis experiments across each respective journal and observe a high degree of consistency and variance among topic focuses. Moreover, whilst quantifying trends at the regional level, we detect that certain countries display clearly discernible patterns, suggesting that research communities in ESE from various countries tend to focus on different sub-fields. The main contribution of our work is the application of a more refined computer-assisted content analysis method in ESE trend analysis research that can serve as the foundation for future exploratory trend analysis investigations in ESE and other related fields.

需要在环境科学与工程 (ESE) 研究方面做出学术贡献，以确保一个更清洁、多产且具有环保意识的社会。因此，了解该领域的关键趋势、主题和研究发展对于促进新生和日益复杂的环境问题的识别、交流和改进研究合作至关重要。由于文献中缺乏对 ESE 研究更广泛趋势演变的更深入分析，我们采用了主题建模计算文本分析方法形式的更强大的内容分析方法，以发掘该领域的关键时间和区域见解。因此，我们基于从 2005 年到 2019 年发表与 ESE 研究相关主题的知名期刊的 3572 篇文章，对抽象元数据应用了潜在狄利克雷分配 (LDA) 模型。我们以词云的形式分析每个推断主题的统计组成，并揭示一般研究趋势，例如与环境影响评估、改进的清洁炉灶、固体废物管理和环境铅污染相关的主题。我们还对每个相应的期刊进行了时间分析实验，并观察到主题重点之间的高度一致性和差异。此外，在量化区域一级趋势的同时，我们发现某些国家显示出清晰可辨的模式，表明来自不同国家的 ESE 研究社区倾向于关注不同的子领域。我们工作的主要贡献是在 ESE 趋势分析研究中应用更精细的计算机辅助内容分析方法，可以作为未来 ESE 和其他相关领域的探索性趋势分析调查的基础。