颗粒物空气污染是全球死亡的主要原因,特别是在亚洲和非洲。解决高范围和广泛的空气污染水平需要环境监测,但许多低收入和中等收入国家(LMICs)几乎没有受到监测。为了解决这些数据差距,最近的研究已经利用低成本的传感器。这些传感器具有不同的性能,在非洲,关于传感器相互比较的文献很少。通过共定位2QuantAQModulair-PM,2PurpleAirPA-IISD,和16个ClarityNode-SGenerationII监视器,在阿克拉使用参考级Teledyne监视器,加纳,我们首次对非洲不同品牌的低成本传感器进行比较,证明每种类型的低成本传感器PM2.5与参考PM2.5密切相关,但对于在阿克拉发现的环境混合源而言偏高。当与参考监视器比较时,QuantAQModulair-PM的平均绝对误差最低,为3.04μg/m3,其次是PurpleAirPA-II(4.54μg/m3)和ClarityNode-S(13.68μg/m3)。我们还比较了4种统计或机器学习模型的使用情况(多元线性回归,随机森林,高斯混合回归,和XGBoost)来纠正低成本传感器数据,发现XGBoost在测试中表现最好(R2:0.97,0.94,0.96;PurpleAirPA-II的平均绝对误差:0.56,0.80和0.68μg/m3,ClarityNode-S,和Modulair-PM,分别),但是基于树的模型在校正超出托管训练范围的数据时表现不佳。因此,我们使用高斯混合回归来纠正来自阿克拉周围部署的17个ClarityNode-S监视器网络的数据,加纳,从2018年到2021年。我们发现,阿克拉的网络日平均PM2.5浓度为23.4μg/m3,是世界卫生组织每日PM2.5指南15μg/m3的1.6倍。虽然这一水平低于一些非洲大城市(如金沙萨,刚果民主共和国),应尽快制定缓解战略,以防止空气质量进一步受损,如阿克拉,和整个加纳,迅速成长。
Particulate matter air pollution is a leading cause of global mortality, particularly in Asia and Africa. Addressing the high and wide-ranging air pollution levels requires ambient monitoring, but many low- and middle-income countries (LMICs) remain scarcely monitored. To address these data gaps, recent studies have utilized low-cost sensors. These sensors have varied performance, and little literature exists about sensor intercomparison in Africa. By colocating 2 QuantAQ Modulair-PM, 2 PurpleAir PA-II SD, and 16
Clarity Node-S Generation II monitors with a reference-grade Teledyne monitor in Accra, Ghana, we present the first intercomparisons of different brands of low-cost sensors in Africa, demonstrating that each type of low-cost sensor PM2.5 is strongly correlated with reference PM2.5, but biased high for ambient mixture of sources found in Accra. When compared to a reference monitor, the QuantAQ Modulair-PM has the lowest mean absolute error at 3.04 μg/m3, followed by PurpleAir PA-II (4.54 μg/m3) and
Clarity Node-S (13.68 μg/m3). We also compare the usage of 4 statistical or machine learning models (Multiple Linear Regression, Random Forest, Gaussian Mixture Regression, and XGBoost) to correct low-cost sensors data, and find that XGBoost performs the best in testing (R2: 0.97, 0.94, 0.96; mean absolute error: 0.56, 0.80, and 0.68 μg/m3 for PurpleAir PA-II,
Clarity Node-S, and Modulair-PM, respectively), but tree-based models do not perform well when correcting data outside the range of the colocation training. Therefore, we used Gaussian Mixture Regression to correct data from the network of 17
Clarity Node-S monitors deployed around Accra, Ghana, from 2018 to 2021. We find that the network daily average PM2.5 concentration in Accra is 23.4 μg/m3, which is 1.6 times the World Health Organization Daily PM2.5 guideline of 15 μg/m3. While this level is lower than those seen in some larger African cities (such as Kinshasa, Democratic Republic of the Congo), mitigation strategies should be developed soon to prevent further impairment to air quality as Accra, and Ghana as a whole, rapidly grow.