论文标题
使用印度不同的建模方法对锁定对空气污染的时空影响的统计评估
Statistical assessment of spatio-temporal impact of lockdown on air pollution using different modelling approaches in India
论文作者
论文摘要
空气污染的主要因素之一是颗粒物（PMXY），该物质引起了几种相关疾病，例如呼吸道问题和心血管疾病。因此，直径小于2.5 m（PM2.5）的所有气溶胶颗粒的空间和时间趋势分析对于控制患者合并症的风险因素至关重要。锁定在维持共同的19例病例和空气污染中起着重要作用，包括颗粒物。这项研究旨在通过各种统计建模方法分析封锁对印度大都市城市控制空气污染的影响。文献中的大多数研究文章都假设响应与协变量之间存在线性关系，并在模型中采用独立且相同分布的误差项，这可能不适合分析此类空气污染数据。在这项研究中，我们对2019年和2020年各个主要活动区域的每日PM2.5排放进行了模式分析。通过测量锁定效应，我们还考虑了季节性影响。
One of the main contributors to air pollution is particulate matter (PMxy), which causes several COVID-19 related diseases such as respiratory problems and cardiovascular disorders. Therefore, the spatial and temporal trend analysis of particulate matter and the mass concentration of all aerosol particles less than 2.5 m in diameter (PM2.5) has become critical to control the risk factors of co-morbidity of a patient. Lockdown plays a significant role in maintaining COVID-19 cases as well as air pollution, including particulate matter. This study aims to analyse the effect of the lockdown on controlling air pollution in metropolitan cities in India through various statistical modelling approaches. Most research articles in the literature assume a linear relationship between responses and covariates and take independent and identically distributed error terms in the model, which may not be appropriate for analysing such air pollution data. In this study, we performed a pattern analysis of daily PM2.5 emissions in various major activity zones during 2019 and 2020. By measuring the lockdown effect, we also considered seasonal influence.
