Using a Network of Locally Developed Low Cost Particulate Matter Sensors for Land Use Regression Modeling of PM2.5 in Urban Uganda
Using a Network of Locally Developed Low Cost Particulate Matter Sensors for Land Use Regression Modeling of PM2.5 in Urban Uganda
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DOI：
10.20944/preprints202006.0158.v1
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发表时间：
2020-06
期刊：
影响因子：
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通讯作者：
E. Coker;Ssematimba Joel
中科院分区：
文献类型：
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作者：
E. Coker;Ssematimba Joel
背景：撒哈拉以南非洲的空气污染监测存在重大差距。发展本区域进行空气监测的能力有助于为流行病学研究估计空气污染暴露情况。我们的研究的目的是开发一个土地利用回归（LUR）模型，使用低成本的空气质量传感器开发的一个研究小组在乌干达（AirQo）。研究方法：使用这些低成本传感器，我们在2019年5月1日至2020年2月29日期间在乌干达城市的22个监测点收集了细颗粒物（PM2.5）的连续测量值。我们将AirQo传感器的月平均PM2.5浓度与美国驻坎帕拉大使馆的BAM-1020参考监测器的测量值进行了比较。每月PM2.5浓度用于LUR建模。我们使用了八种机器学习（ML）算法和集成建模;使用10倍交叉验证和均方根误差（RMSE）来评估模型性能。结果：每月PM2.5浓度为60.2 µg/m3（IQR：45.4-73.0 µg/m3;中位数= 57.5 µg/m3）。对于ML LUR模型，RMSE值范围在5.43 µg/m3 - 15.43 µg/m3之间，解释了每月PM2.5变化的28%至92%。广义加性模型解释了最大量的PM2.5变异性（R2=0.92），并在保持的测试集中产生了最低的RMSE（5.43 µg/m3）。月PM2.5浓度最重要的预测因素包括月降水量、主要道路密度、人口密度、纬度、绿化程度和使用固体燃料的家庭百分比。结论：据我们所知，我们的研究是第一个使用从该区域本身开发的空气监测器来模拟撒哈拉以南非洲城市空气污染空间分布的研究。LUR建模的非参数ML对每月PM2.5水平的预测具有高精度。我们的分析表明，当地生产的低成本空气质量传感器可以帮助建立在该地区进行空气污染流行病学研究的能力。
Background: There are major air pollution monitoring gaps in sub-Saharan Africa. Developing capacity in the region to conduct air monitoring in the region can help estimate exposure to air pollution for epidemiology research. The purpose of our study is to develop a land use regression (LUR) model using low-cost air quality sensors developed by a research group in Uganda (AirQo). Methods: Using these low-cost sensors, we collected continuous measurements of fine particulate matter (PM2.5) between May 1, 2019 and February 29, 2020 at 22 monitoring sites across urban municipalities of Uganda. We compared average monthly PM2.5 concentrations from the AirQo sensors with measurements from a BAM-1020 reference monitor operated at the US Embassy in Kampala. Monthly PM2.5 concentrations were used for LUR modeling. We used eight Machine Learning (ML) algorithms and ensemble modeling; using 10-fold cross validation and root mean squared error (RMSE) to evaluate model performance. Results: Monthly PM2.5 concentration was 60.2 µg/m3 (IQR: 45.4-73.0 µg/m3; median= 57.5 µg/m3). For the ML LUR models, RMSE values ranged between 5.43 µg/m3 - 15.43 µg/m3 and explained between 28% and 92% of monthly PM2.5 variability. Generalized additive models explained the largest amount of PM2.5 variability (R2=0.92) and produced the lowest RMSE (5.43 µg/m3) in the held-out test set. The most important predictors of monthly PM2.5 concentrations included monthly precipitation, major roadway density, population density, latitude, greenness, and percentage of households using solid fuels. Conclusion: To our knowledge, ours is the first study to model the spatial distribution of urban air pollution in sub-Saharan Africa using air monitors developed from the region itself. Non-parametric ML for LUR modeling performed with high accuracy for prediction of monthly PM2.5 levels. Our analysis suggests that locally produced low-cost air quality sensors can help build capacity to conduct air pollution epidemiology research in the region.