Air pollution nowadays is a serious threat to life. In terms of the global air quality, nitrogen dioxide is one of the prominent pollutants as per the reports of the World Health Organization. Nitrogen dioxide is the cause of about 92% of the asthma cases. Epidemiological studies have unfolded nitrogen dioxide contribution to mortality. Apart from the significant health effects, it also plays a role in the formation of other major pollutants ozone and particulate matter. The monitoring and assessment of pollutants is a complex and expensive procedure, simultaneously very important for the country’s wealth and health. The problem is dealt with before using various statistic and deterministic models considering the dependence of nitrogen dioxide on different pollutants and meteorological parameters. The present study contributes to the prediction of nitrogen dioxide for good policy making. The proposed model is less resource-intensive and more effective compared to the existing models.

中文翻译：

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使用神经元网络开发可持续的二氧化氮预测模型

如今的空气污染是对生命的严重威胁。就全球空气质量而言，根据世界卫生组织的报告，二氧化氮是主要的污染物之一。二氧化氮约占哮喘病例的92％。流行病学研究表明二氧化氮对死亡率的贡献。除了对健康有重大影响外，它还在其他主要污染物臭氧和颗粒物的形成中发挥作用。污染物的监测和评估是一个复杂而昂贵的过程，同时对于该国的财富和健康也非常重要。在使用各种统计和确定性模型之前，考虑到二氧化氮对不同污染物和气象参数的依赖性，要解决该问题。本研究有助于预测二氧化氮的良好决策。与现有模型相比，该模型耗费资源较少，效率更高。