PurpleAir sensors, which measure particulate matter (PM), are widely used by individuals, community groups, and other organizations including state and local air monitoring agencies. PurpleAir sensors comprise a massive global network of more than 10 000 sensors. Previous performance evaluations have typically studied a limited number of PurpleAir sensors in small geographic areas or laboratory environments. While useful for determining sensor behavior and data normalization for these geographic areas, little work has been done to understand the broad applicability of these results outside these regions and conditions. Here, PurpleAir sensors operated by air quality monitoring agencies are evaluated in comparison to collocated ambient air quality regulatory instruments. In total, almost 12 000 24 h averaged PM_2.5 measurements from collocated PurpleAir sensors and Federal Reference Method (FRM) or Federal Equivalent Method (FEM) PM_2.5 measurements were collected across diverse regions of the United States (US), including 16 states. Consistent with previous evaluations, under typical ambient and smoke-impacted conditions, the raw data from PurpleAir sensors overestimate PM_2.5 concentrations by about 40 % in most parts of the US. A simple linear regression reduces much of this bias across most US regions, but adding a relative humidity term further reduces the bias and improves consistency in the biases between different regions. More complex multiplicative models did not substantially improve results when tested on an independent dataset. The final PurpleAir correction reduces the root mean square error (RMSE) of the raw data from 8 to 3 µg m⁻³, with an average FRM or FEM concentration of 9 µg m⁻³. This correction equation, along with proposed data cleaning criteria, has been applied to PurpleAir PM_2.5 measurements across the US on the AirNow Fire and Smoke Map (https://fire.airnow.gov/, last access: 14 May 2021) and has the potential to be successfully used in other air quality and public health applications.

中文翻译：

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对使用 PurpleAir 传感器收集的 PM2.5 数据进行美国范围内校正的开发和应用

PurpleAir 传感器可测量颗粒物 (PM)，被个人、社区团体和其他组织（包括州和地方空气监测机构）广泛使用。PurpleAir 传感器由超过 10,000 个传感器组成的庞大全球网络组成。以前的性能评估通常是在较小的地理区域或实验室环境中研究有限数量的 PurpleAir 传感器。虽然对于确定这些地理区域的传感器行为和数据标准化很有用，但很少有工作来了解这些结果在这些区域和条件之外的广泛适用性。在这里，将空气质量监测机构操作的 PurpleAir 传感器与并置的环境空气质量监管仪器进行比较进行评估。总共，通过并置 PurpleAir 传感器和联邦参考方法 (FRM) 或联邦等效方法 (FEM) PM _2.5测量值，在美国 (US) 不同地区（包括 16 个州）收集了近 12 000 个 24 小时平均 PM _{2.5测量值。}与之前的评估一致，在典型的环境和烟雾影响条件下，PurpleAir 传感器的原始数据将美国大部分地区的PM _{2.5浓度高估了约 40%。}简单的线性回归可以在很大程度上减少美国大多数地区的这种偏差，但添加相对湿度项可以进一步减少偏差并提高不同地区之间偏差的一致性。在独立数据集上进行测试时，更复杂的乘法模型并没有显着改善结果。最终的 PurpleAir 校正将原始数据的均方根误差 (RMSE) 从 8  µ g m ⁻³降低至 3 µ g m −3 ，平均 FRM 或 FEM 浓度为 9  µ g m ⁻³。该修正方程以及拟议的数据清理标准已应用于 AirNow 火灾和烟雾地图上的美国PurpleAir PM _2.5测量（ https://fire.airnow.gov/，最后访问时间：2021 年 5 月 14 日），并已具有成功用于其他空气质量和公共卫生应用的潜力。