Air Quality Multi-sensors Systems (AQMS) are IoT devices based on low cost chemical microsensors array that recently have showed capable to provide relatively accurate air pollutant quantitative estimations. Their availability permits to deploy pervasive Air Quality Monitoring (AQM) networks that will solve the geographical sparseness issue that affect the current network of AQ Regulatory Monitoring Systems (AQRMS). Unfortunately their accuracy have shown limited in long term field deployments due to negative influence of several technological issues including sensors poisoning or ageing, non target gas interference, lack of fabrication repeatability, etc. Seasonal changes in probability distribution of priors, observables and hidden context variables (i.e. non observable interferents) challenge field data driven calibration models which short to mid term performances recently rose to the attention of Urban authorithies and monitoring agencies. In this work, we address this non stationary framework with adaptive learning strategies in order to prolong the validity of multisensors calibration models enabling continuous learning. Relevant parameters influence in different network and node-to-node recalibration scenario is analyzed. Results are hence useful for pervasive deployment aimed to permanent high resolution AQ mapping in urban scenarios as well as for the use of AQMS as AQRMS backup systems providing data when AQRMS data are unavailable due to faults or scheduled mainteinance.

空气质量多传感器系统（AQMS）是基于低成本化学微传感器阵列的IoT设备，最近已证明能够提供相对准确的空气污染物定量估计。它们的可用性允许部署普遍的空气质量监测（AQM）网络，这将解决影响当前AQ监管监测系统（AQRMS）网络的地理稀疏问题。不幸的是，由于一些技术问题（包括传感器中毒或老化，非目标气体干扰，缺乏制造重复性等）的负面影响，它们的准确性在长期的现场部署中显示出局限性。先验概率分布，观测值和隐含上下文变量的季节性变化（即 不可观测的干扰物）挑战了现场数据驱动的校准模型，该模型最近在短期和中期的表现受到了市区当局和监视机构的注意。在这项工作中，我们通过自适应学习策略解决了这种非平稳框架，以延长支持连续学习的多传感器校准模型的有效性。相关参数在不同网络中的影响，并分析了节点到节点的重新校准场景。因此，结果对于在城市场景中进行永久性高分辨率AQ映射的广泛部署以及将AQMS用作AQRMS备份系统（当由于故障或计划维护而无法使用AQRMS数据时提供数据）有用。我们使用自适应学习策略来解决这种非平稳框架，以延长支持连续学习的多传感器校准模型的有效性。相关参数在不同网络中的影响，并分析了节点到节点的重新校准场景。因此，结果对于在城市场景中进行永久性高分辨率AQ映射的广泛部署以及将AQMS用作AQRMS备份系统（当由于故障或计划维护而无法使用AQRMS数据时提供数据）有用。我们使用自适应学习策略来解决这种非平稳框架，以延长启用连续学习的多传感器校准模型的有效性。相关参数在不同网络中的影响，并分析了节点到节点的重新校准场景。因此，结果对于在城市场景中进行永久性高分辨率AQ映射的广泛部署以及将AQMS用作AQRMS备份系统（当由于故障或计划维护而无法使用AQRMS数据时提供数据）有用。