Low-cost particulate matter sensors (LCS) are vital for improving the spatial and temporal resolution of air quality data, supplementing sparsely placed official monitoring stations. Despite their benefits, LCS readings can be biased due to the physical properties of aerosol particles and device limitations. An optimization model is essential to enhance LCS data accuracy. This paper presents a calibration study of the LCS network of Timișoara, Romania. The calibration began by selecting LCS devices near National Air Quality Monitoring Network (NAQMN) stations and developing parametric models, choosing the best for broader application. Plantower, Sensirion, and Honeywell sensors showed comparable accuracy. Calibration involved clusters within a 750 m radius around NAQMN stations. Models incorporating RH corrections and multiple linear regression (MLR) were fitted. The best model was validated against data from unseen sensors, leading to mean bias errors (MBE) within 9-17% and RMSEs of 33-35%, within sensor uncertainty margins. Applied to the city-wide LCS network, the model identified several stations regularly exceeding the EU daily PM10 threshold, unnoticed by NAQMN stations due to their limited coverage. The study highlights the necessity of granular monitoring to accurately capture urban air quality variations.

低成本颗粒物传感器 （LCS） 对于提高空气质量数据的空间和时间分辨率至关重要，是对稀疏的官方监测站的补充。尽管有其优点，但由于气溶胶颗粒的物理特性和设备限制，LCS 读数可能会有偏差。优化模型对于提高 LCS 数据准确性至关重要。本文介绍了罗马尼亚蒂米什瓦拉 LCS 网络的校准研究。校准首先选择美国国家空气质量监测网络 （NAQMN） 站附近的 LCS 设备，并开发参数模型，为更广泛的应用选择最佳模型。Plantower、Sensirion 和 Honeywell 传感器显示出相当的准确性。校准涉及 NAQMN 站周围 750 m 半径内的集群。拟合了包含 RH 校正和多元线性回归 （MLR） 的模型。最佳模型根据来自看不见的传感器的数据进行了验证，导致平均偏差误差 （MBE） 在 9-17% 范围内，RMSE 在 33-35% 之间，在传感器不确定性范围内。该模型应用于全市范围的 LCS 网络，确定了几个经常超过欧盟每日 PM10 阈值的站点，由于覆盖范围有限，NAQMN 站点没有注意到这些站点。该研究强调了精细监测以准确捕捉城市空气质量变化的必要性。