Sharma Divyansh, Thapar Sapan, Sachdeva Kamna
Department of Sustainable Engineering, TERI School of Advanced Studies, New Delhi, India.
Department of Sustainability Sciences, Delhi Skill and Entrepreneurship University, Dwarka, New Delhi, India.
Environ Monit Assess. 2025 Jun 3;197(7):723. doi: 10.1007/s10661-025-14121-3.
This study advances our approach to modeling particulate matter levels-specifically, PM and PM-in Delhi's dynamic urban environment through an extensive evaluation of traditional time series models (ARIMAX, SARIMAX) and machine learning models (RF, SVM) across air quality monitoring stations utilizing data from the period 2019 to 2023. We established a clear baseline of air quality variations using seasonal decomposition, highlighting critical seasonal peaks in PM and PM concentrations influenced by localized emissions and adverse weather conditions. Subsequent trend analysis revealed increasing PM levels at several key monitoring stations, underscoring the impact of urban activities and seasonal variations. In contrast, reduction was observed in PM levels at most monitoring stations. We utilized a wide range of exogenous variables, including other pollutants and meteorological parameters in our time series models to enhance the accuracy of predicting particulate matter. The SVM model proved to be more accurate in predicting particulate matter levels. It achieved testing RMSE values between 12.48 and 67.22 µg/m for PM and 8.38 and 48.95 µg/m for PM, with testing R-squared values between 0.30 and 0.95 for PM and 0.41 and 0.96 for PM. This research pioneers a methodologically enriched approach by systematically incorporating these exogenous factors, enhancing predictive capabilities, and deepening the understanding of complex environmental dynamics specific to urban cities like Delhi. The extensive spatial coverage and robust integration of diverse exogenous factors can significantly enhance environmental modeling, providing actionable insights for policymakers and advancing air quality forecasting in urban megacities.
本研究推进了我们对颗粒物水平建模的方法——具体而言，是通过对2019年至2023年期间空气质量监测站的数据，广泛评估传统时间序列模型（ARIMAX、SARIMAX）和机器学习模型（随机森林、支持向量机），来对德里动态城市环境中的细颗粒物（PM）和可吸入颗粒物（PM）水平进行建模。我们利用季节性分解建立了空气质量变化的清晰基线，突出了受局部排放和不利天气条件影响的PM和PM浓度的关键季节性峰值。随后的趋势分析显示，几个关键监测站的PM水平有所上升，凸显了城市活动和季节变化的影响。相比之下，大多数监测站的PM水平有所下降。我们在时间序列模型中使用了广泛的外生变量，包括其他污染物和气象参数，以提高颗粒物预测的准确性。支持向量机模型在预测颗粒物水平方面被证明更准确。其在测试中，PM的均方根误差（RMSE）值在12.48至67.22微克/立方米之间，PM的RMSE值在8.38至48.95微克/立方米之间，PM的决定系数（R平方）测试值在0.30至0.95之间，PM的R平方测试值在0.41至0.96之间。本研究通过系统纳入这些外生因素，开创了一种方法丰富的途径，增强了预测能力，并加深了对德里等城市特有的复杂环境动态的理解。广泛的空间覆盖和多种外生因素的稳健整合可以显著增强环境建模，为政策制定者提供可操作的见解，并推进特大城市的空气质量预测。
