Mobile air quality monitoring systems may experience operational interruptions caused by urban traffic conditions such as congestion, road works, accidents, or me-chanical failures. These disruptions can interrupt monitoring routes and create spatial gaps in the collected data, potentially reducing the representativeness of the monitoring campaign. To mitigate these effects, operational strategies such as network redundancy and adaptive route planning can be implemented. Maintaining a small proportion of mon-itoring vehicles on standby allows rapid replacement of unavailable units and helps maintain monitoring continuity. In addition, dynamic route adjustment based on traffic conditions can improve the efficiency of spatial coverage. When short interruptions occur, spatial interpolation techniques such as inverse distance weighting (IDW) or kriging may be used to estimate pollutant concentrations in unobserved locations using nearby measurements. These approaches help maintain the spatial continuity of the monitoring dataset and reduce the impact of temporary operational disruptions (Apte et al., 2017; Snyder et al., 2013). 4.2.4 Management of communication network dependence: Mobile air quality monitoring systems rely heavily on wireless communication networks to transmit data in real time to centralized databases. In areas with limited network coverage, temporary disconnections may occur, potentially delaying data transmission and affecting the real-time availability of monitoring information. To reduce this dependence, mobile monitoring platforms can incorporate local data storage systems capable of retaining measurements during communication interrup-tions. Once the connection is restored, the stored data can be automatically synchro-nized with the central database. In addition, using multi-network communication technologies (e.g., 4G, 5G, or Wi-Fi) improves transmission reliability and reduces the risk of data loss. These strategies ensure continuous data acquisition while maintaining the operational flexibility of mobile monitoring systems.

Mobile air quality monitoring systems can face operational disruptions due to various urban traffic situations, including traffic jams, construction work, accidents, or equipment breakdowns. Such interruptions may disrupt planned monitoring routes and result in spatial data gaps, which could compromise how well the monitoring campaign represents actual conditions. Several operational approaches can help address these challenges. Implementing network redundancy and flexible route planning serves as effective mitigation strategies. Keeping a small number of monitoring vehicles in reserve enables quick replacement when units become unavailable, helping preserve continuous monitoring operations. Additionally, adjusting routes dynamically according to current traffic situations can enhance the effectiveness of spatial data collection. For brief interruptions, researchers can apply spatial interpolation methods like inverse distance weighting (IDW) or kriging to estimate pollutant levels at locations where direct measurements weren't possible, using data from nearby monitoring points. These techniques help preserve spatial consistency in the monitoring dataset and minimize the effects of temporary operational problems (Apte et al., 2017; Snyder et al., 2013). 4.2.4 Managing communication network reliance: Mobile air quality monitoring systems depend significantly on wireless communication networks for transmitting real-time data to central databases. In locations where network coverage is poor, temporary connection losses may happen, potentially causing delays in data transmission and impacting the immediate availability of monitoring data. To minimize this reliance, mobile monitoring platforms can include local data storage capabilities that preserve measurements when communication is interrupted. After connectivity returns, the saved data can automatically sync with the central database. Furthermore, employing multiple communication network technologies (such as 4G, 5G, or Wi-Fi) enhances transmission dependability and lowers the chance of losing data. These approaches guarantee uninterrupted data collection while preserving the operational adaptability that mobile monitoring systems require.