PDF(Pubmed)
Abstract：
Low-cost optical sensors are used in many countries to monitor fine particulate (PM2.5) air pollution, especially in cities and towns with large spatial and temporal variation due to woodsmoke pollution. Previous peer-reviewed research derived calibration equations for PurpleAir (PA) sensors by co-locating PA units at a government regulatory air pollution monitoring site in Armidale, NSW, Australia, a town where woodsmoke is the main source of PM2.5 pollution. The calibrations enabled the PA sensors to provide accurate estimates of PM2.5 that were almost identical to those from the NSW Government reference equipment and allowed the high levels of wintertime PM2.5 pollution and the substantial spatial and temporal variation from wood heaters to be quantified, as well as the estimated costs of premature mortality exceeding $10,000 per wood heater per year. This follow-up study evaluates eight PA sensors co-located at the same government site to check their accuracy over the following four years, using either the original calibrations, the default woodsmoke equation on the PA website for uncalibrated sensors, or the ALT-34 conversion equation (see text). Minimal calibration drift was observed, with year-round correlations, r = 0.98 ± 0.01, and root mean square error (RMSE) = 2.0 μg/m3 for daily average PA PM2.5 vs. reference equipment. The utitilty of the PA sensors without prior calibration at locations affected by woodsmoke was also demonstrated by the year-round correlations of 0.94 and low RMSE between PA (woodsmoke and ALT-34 conversions) and reference PM2.5 at the NSW Government monitoring sites in Orange and Gunnedah. To ensure the reliability of the PA data, basic quality checks are recommended, including the agreement of the two laser sensors in each PA unit and removing any transient spikes affecting only one sensor. In Armidale, from 2019 to 2022, the continuing high spatial variation in the PM2.5 levels observed during the colder months was many times higher than any discrepancies between the PA and reference measurements. Particularly unhealthy PM2.5 levels were noted in southern and eastern central Armidale. The measurements inside two older weatherboard houses in Armidale showed that high outdoor pollution resulted in high pollution inside the houses within 1-2 h. Daily average PM2.5 concentrations available on the PA website allow air pollution at different sites across regions (and countries) to be compared. Such comparisons revealed major elevations in PA PM2.5 at Gunnedah, Orange, Monash (Australian Capital Territory), and Christchurch (New Zealand) during the wood heating season. The data for Gunnedah and Muswellbrook suggest a slight underestimation of PM2.5 at other times of the year when there are proportionately more dust and other larger particles. A network of appropriately calibrated PA sensors can provide valuable information on the spatial and temporal variation in the air pollution that can be used to identify pollution hotspots, improve estimates of population exposure and health costs, and inform public policy.
摘要：
许多国家使用低成本光学传感器来监测细颗粒物（PM2.5）空气污染，特别是在因木烟污染而时空变化较大的城镇。先前的同行评审研究通过在阿米代尔的政府监管空气污染监测站共同安置PA单元，得出了PurpleAir（PA）传感器的校准方程，新南威尔士州,澳大利亚,一个以木烟为PM2.5主要污染源的城镇。校准使PA传感器能够提供与新南威尔士州政府参考设备几乎相同的PM2.5准确估算值，并允许对冬季PM2.5的高水平污染以及木材加热器的巨大时空变化进行量化，以及每年每个木材加热器过早死亡的估计成本超过10,000美元。这项后续研究评估了八个位于同一政府站点的PA传感器，以检查其在接下来的四年中的准确性，使用原始校准，PA网站上未校准传感器的默认woodsmoke方程，或ALT-34转换方程(见正文)。观察到最小的校准漂移，与全年相关，r=0.98±0.01，均方根误差（RMSE）=2.0μg/m3，日平均PAPM2.5与参考设备。在新南威尔士州政府监测点Orange和Gunnedah，PA（木烟和ALT-34转换）与参考PM2.5之间的全年相关性为0.94和较低的RMSE，也证明了PA传感器在受木烟影响的位置未经事先校准的实用性。为了确保PA数据的可靠性，建议进行基本质量检查，包括在每个PA单元的两个激光传感器的协议和消除任何瞬态尖峰只影响一个传感器。在阿米代尔,从2019年到2022年，在较冷月份观察到的PM2.5水平的持续高空间变化比PA和参考测量值之间的任何差异高出许多倍。在阿米代尔中部南部和东部，PM2.5水平尤其不健康。在Armidale的两个较旧的挡风板房屋中进行的测量显示，高室外污染导致房屋内部在1-2小时内产生高污染。PA网站上提供的每日平均PM2.5浓度允许跨地区（和国家）的不同地点的空气污染进行比较。这样的比较揭示了Gunnedah的PAPM2.5的主要升高，橙色,莫纳什（澳大利亚首都地区），和克赖斯特彻奇（新西兰）在木材供暖季节。Gunnedah和Muswellbrook的数据表明，在一年中的其他时间，当灰尘和其他较大的颗粒按比例增加时，PM2.5的估计值略有低估。适当校准的PA传感器网络可以提供有关空气污染的空间和时间变化的有价值的信息，可用于识别污染热点，改善对人口暴露和健康成本的估计，并告知公共政策。
