在印度正在进行的与空气污染的斗争中,全面的方法至关重要,结合技术进步,加强监管,和广泛的社会参与。弥合技术差距涉及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。该评论发现,将人工智能和机器学习(AI&ML)集成在空气质量预测中显示出可喜的结果,并具有显着的模型效率。在这项研究中,最初,我们使用5种关键气溶胶的表面质量浓度,如黑碳(BC),计算印度的PM2.5浓度,灰尘(DU),有机碳(OC),海盐(SS)和硫酸盐(SU),分别。该研究确定了几个由于特定来源而极易受到PM2.5污染的地区。印度-恒河平原受到高浓度BC的显著影响,OC,和人为活动产生的SU。由于靠近撒哈拉沙漠,印度西部的DU浓度较高。此外,印度东北部的某些地区显示出生物活动对OC的重大贡献。此外,基于卷积自动编码器架构的AI和ML模型经过了严格的训练,测试,并验证预测整个印度的PM2.5浓度。结果揭示了其在PM2.5预测中的卓越精度,正如模型评估指标所证明的那样,包括结构相似性指数超过0.60,峰值信噪比范围为28-30dB,均方误差低于10μg/m3。然而,监管挑战依然存在,需要健全的框架和一致的执行机制,预测PM2.5浓度的复杂性证明了这一点。实施量身定制的区域污染控制策略,集成AI和ML技术,加强监管框架,促进可持续做法,鼓励国际合作是减轻印度空气污染的重要政策措施。
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 PM2.5 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 PM2.5 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 PM2.5 concentrations across India. The results reveal its exceptional precision in PM2.5 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/m3. However, regulatory challenges persist, necessitating robust frameworks and consistent enforcement mechanisms, as evidenced by the complexities in predicting PM2.5 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.