Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, Gansu, China.
School of Economics and Management, City University of Hefei, Hefei, Anhui, China.
Sci Rep. 2024 Aug 2;14(1):17923. doi: 10.1038/s41598-024-68972-w.
With the ongoing challenge of air pollution posing serious health and environmental threats, particularly in rapidly industrializing regions, accurate forecasting and effective pollutant identification are crucial for enhancing public health and ecological stability. This study aimed to optimize air quality management through the prediction of the Air Quality Index (AQI) and identification of air pollutants. Our study spans nine representative cities (Hohhot, Yinchuan, Lanzhou, Beijing, Taiyuan, Xi'an, Shanghai, Nanjing, Wuhan) in China, with data collected from January 1, 2015, to November 30, 2021. We proposed a new model for daily AQI prediction, termed VMD-CSA-CNN-LSTM, which employed advanced machine learning techniques, including convolutional neural networks (CNN) and long short-term memory (LSTM) networks, and leveraged the chameleon swarm algorithm (CSA) for hyperparameter optimization, integrated through a variational mode decomposition approach. The model was developed using data from Lanzhou, with a split ratio of 8:1:1 into training, validation, and test sets, achieving an RMSE of 2.25, MAPE of 0.02, adjusted R-squared of 98.91%, and training efficiency of 5.31%. The model was further externally validated in the other eight cities, yielding comparable results, with an adjusted R-squared above 96%, MAPE below 0.1, and RMSE below 7.5. Additionally, we employed a random forest algorithm to identify the primary pollutants contributing to AQI levels. Our results indicated that PM was the most significant pollutant in Beijing, Taiyuan, and Xi'an, while PM was dominant in Hohhot, Yinchuan, and Lanzhou. In Shanghai, Nanjing, and Wuhan, both PM and PM were critical, with ozone also identified as a major air pollutant. This study not only advances the predictive accuracy of AQI models but also aids policymakers by providing a reliable tool for air quality management and strategic planning aimed at pollution reduction. The integration of these advanced computational techniques into environmental monitoring practices offers a promising avenue for enhancing air quality and mitigating pollution-related risks.
随着空气污染持续构成严重的健康和环境威胁，特别是在快速工业化地区，准确预测和有效识别污染物对于提高公众健康和生态稳定性至关重要。本研究旨在通过预测空气质量指数（AQI）和识别空气污染物来优化空气质量管理。我们的研究涵盖了中国的九个有代表性的城市（呼和浩特、银川、兰州、北京、太原、西安、上海、南京、武汉），数据采集时间为 2015 年 1 月 1 日至 2021 年 11 月 30 日。我们提出了一种新的每日 AQI 预测模型，称为 VMD-CSA-CNN-LSTM，它采用了先进的机器学习技术，包括卷积神经网络（CNN）和长短期记忆（LSTM）网络，并利用变色龙群算法（CSA）进行超参数优化，通过变分模态分解方法集成。该模型是使用兰州的数据开发的，采用 8:1:1 的比例分为训练集、验证集和测试集，RMSE 为 2.25，MAPE 为 0.02，调整后的 R-squared 为 98.91%，训练效率为 5.31%。该模型在其他八个城市进行了外部验证，结果相当，调整后的 R-squared 均在 96%以上，MAPE 均低于 0.1，RMSE 均低于 7.5。此外，我们还采用随机森林算法识别了导致 AQI 水平的主要污染物。我们的结果表明，在北京、太原和西安，PM 是最重要的污染物，而在呼和浩特、银川和兰州，PM 则占主导地位。在上海、南京和武汉，PM 和 PM 都是关键污染物，臭氧也被确定为主要空气污染物。本研究不仅提高了 AQI 模型的预测精度，还为决策者提供了一个可靠的空气质量管理和旨在减少污染的战略规划工具。将这些先进的计算技术集成到环境监测实践中，为提高空气质量和减轻与污染相关的风险提供了一个有前途的途径。
