Rautela Kuldeep Singh, Goyal Manish Kumar
Department of Civil Engineering, Indian Institute of Technology Indore, Simrol, Indore, 453552, Madhya Pradesh, India.
Sci Rep. 2024 Sep 2;14(1):20412. doi: 10.1038/s41598-024-71269-7.
A comprehensive approach is essential in India's ongoing battle against air pollution, combining technological advancements, regulatory reinforcement, and widespread societal engagement. Bridging technological gaps involves deploying sophisticated pollution control technologies and addressing the rural-urban disparity through innovative solutions. The review found that integrating Artificial Intelligence and Machine Learning (AI&ML) in air quality forecasting demonstrates promising results with a remarkable model efficiency. In this study, initially, we compute the PM concentration over India using a surface mass concentration of 5 key aerosols such as black carbon (BC), dust (DU), organic carbon (OC), sea salt (SS) and sulphates (SU), respectively. The study identifies several regions highly vulnerable to PM pollution due to specific sources. The Indo-Gangetic Plains are notably impacted by high concentrations of BC, OC, and SU resulting from anthropogenic activities. Western India experiences higher DU concentrations due to its proximity to the Sahara Desert. Additionally, certain areas in northeast India show significant contributions of OC from biogenic activities. Moreover, an AI&ML model based on convolutional autoencoder architecture underwent rigorous training, testing, and validation to forecast PM concentrations across India. The results reveal its exceptional precision in PM prediction, as demonstrated by model evaluation metrics, including a Structural Similarity Index exceeding 0.60, Peak Signal-to-Noise Ratio ranging from 28-30 dB and Mean Square Error below 10 μg/m. However, regulatory challenges persist, necessitating robust frameworks and consistent enforcement mechanisms, as evidenced by the complexities in predicting PM concentrations. Implementing tailored regional pollution control strategies, integrating AI&ML technologies, strengthening regulatory frameworks, promoting sustainable practices, and encouraging international collaboration are essential policy measures to mitigate air pollution in India.
在印度持续对抗空气污染的斗争中，综合方法至关重要，它需要结合技术进步、加强监管以及广泛的社会参与。弥合技术差距包括部署先进的污染控制技术，并通过创新解决方案解决城乡差距问题。该综述发现，将人工智能和机器学习（AI&ML）整合到空气质量预测中显示出了有前景的结果，模型效率显著。在本研究中，最初，我们分别使用黑碳（BC）、沙尘（DU）、有机碳（OC）、海盐（SS）和硫酸盐（SU）这5种关键气溶胶的地表质量浓度来计算印度上空的颗粒物浓度。该研究确定了几个因特定来源而极易受到颗粒物污染影响的地区。印度恒河平原受到人为活动导致的高浓度黑碳、有机碳和硫酸盐的显著影响。印度西部由于靠近撒哈拉沙漠，沙尘浓度较高。此外，印度东北部的某些地区显示出生物源活动对有机碳有显著贡献。此外，一个基于卷积自动编码器架构的AI&ML模型经过了严格的训练、测试和验证，以预测印度各地的颗粒物浓度。结果显示其在颗粒物预测方面具有卓越的精度，模型评估指标证明了这一点，包括结构相似性指数超过0.60、峰值信噪比在28 - 30分贝之间以及均方误差低于10微克/立方米。然而，监管挑战依然存在，需要强大的框架和一致的执行机制，预测颗粒物浓度的复杂性就证明了这一点。实施量身定制的区域污染控制策略、整合AI&ML技术、加强监管框架、推广可持续做法以及鼓励国际合作是减轻印度空气污染的重要政策措施。
