The dynamics of interactions between the environmental and the meteorological variables in an urban region is extremely complex due to continuously evolving coupled human-natural processes in an urban setting We attempt to understand the same with the networks of variables using information theory, known as process network We monitored local meteorological variables at half-hourly scale using an eddy covariance observation system combined with available concentration of pollutants from other sources Both the datasets are for Powai, Mumbai, India, from January to April 2020 that includes pre-lockdown and lockdown periods associated with interventions in response to COVID-19 Analysis of the weekly process networks developed with the same data shows that they are more dominated by memory during the lockdown period We find that a high concentration of pollutants under no-lockdown scenarios, during specific work commute hours, interrupts the memory of the network A seasonal transition in temperature during the pre-lockdown period failed to make any major changes Our analysis shows that the dynamics of pollutant concentration drives the interaction between the variables of urban environmental meteorological system © 2020 The Author(s) Published by IOP Publishing Ltd

中文翻译：

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污染物控制城市环境气象的过程网络

由于城市环境中不断发展的耦合人与自然过程，城市区域中环境变量和气象变量之间的相互作用的动态极其复杂我们试图使用信息理论（称为过程网络）通过变量网络来理解这一点我们使用涡流协方差观测系统结合来自其他来源的可用污染物浓度，以半小时为单位监测当地气象变量这两个数据集均针对印度孟买的 Powai，从 2020 年 1 月到 4 月，其中包括与响应 COVID-19 的干预措施相关的锁定前和锁定期 对使用相同数据开发的每周流程网络的分析表明，在锁定期间，它们更多地由记忆主导我们发现高非封锁情景下的污染物浓度，在特定的工作通勤时间，中断了网络的记忆 封锁前期间温度的季节性转变没有产生任何重大变化 我们的分析表明，污染物浓度的动态驱动了相互作用© 2020 作者：IOP Publishing Ltd. Published by IOP Publishing Ltd网络记忆中断 封城前温度的季节性转变 未发生重大变化 我们的分析表明，污染物浓度的动态驱动了城市环境气象系统变量之间的相互作用 © 2020 The Author(s)由 IOP Publishing Ltd 出版网络记忆中断 封城前温度的季节性转变 未发生重大变化 我们的分析表明，污染物浓度的动态驱动了城市环境气象系统变量之间的相互作用 © 2020 The Author(s)由 IOP Publishing Ltd 出版