Application and evaluation of a low-cost PM sensor and data fusion with CMAQ simulations to quantify the impacts of prescribed burning on air quality in Southwestern Georgia, USA
Application and evaluation of a low-cost PM sensor and data fusion with CMAQ simulations to quantify the impacts of prescribed burning on air quality in Southwestern Georgia, USA
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DOI：
10.1080/10962247.2021.1924311
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发表时间：
2021-04
影响因子：
2.7
通讯作者：
R. Huang;Raj M. Lal;Momei Qin;Yongtao Hu;A. Russell;M. Odman;S. Afrin;F. Garcia‐Menendez;
中科院分区：
文献类型：
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作者：
R. Huang;Raj M. Lal;Momei Qin;Yongtao Hu;A. Russell;M. Odman;S. Afrin;F. Garcia‐Menendez;
在美国东南部，规定燃烧（PB）是PM2. 5的主要来源，暴露于PB烟雾是一种健康风险。随着燃烧需求的增加和对其他人为来源实施更严格的控制，PB排放往往是PM2.5浓度增加的原因。在这里，为了量化PB对空气质量的影响，低成本的传感器被用来测量格鲁吉亚西南部的PM2.5浓度。使用低成本的传感器作为补充测量工具的可行性进行评估，通过比较它们与参考仪器。化学传输模型，CMAQ，也被用来模拟PB的PM2.5浓度的贡献。模拟的PM2.5浓度与低成本传感器和参考监测器的观测结果进行了比较。最后，数据融合方法被应用于产生小时的时空暴露场融合PM2.5浓度从CMAQ模型和所有的观测。结果表明，由于缺乏监管监测站点，可能会错过PB对当地空气质量和公众健康的严重影响。在西南部的格鲁吉亚PM2.5浓度是高度非均匀的，空间变化是没有捕捉到，即使在模型模拟的水平分辨率为4公里。低成本的PM传感器可以改善PB影响的检测，并提供有用的空间和时间信息与空气质量模型集成。当数据融合应用于模型模拟时，在评估地点保留观测值，回归与观测值的R2从平均0.09增加到0.40。增加低成本传感器的观测数据减少了对夜间PM2.5浓度的低估，并重现了模拟错过的峰值。未来，来自低成本传感器密集网络的观测结果可以与模型模拟的PM2.5场融合，以更好地估计每小时暴露于PB烟雾的情况。影响：规定的燃烧排放是PM2.5浓度升高的主要原因，对人类健康构成风险。然而，由于美国某些地区（如格鲁吉亚西南部）缺乏监管监测点，无法适当量化其影响。低成本PM传感器可用作补充测量工具，并提供有用的空间和时间信息，用于与空气质量模型模拟集成。在未来，来自低成本传感器密集网络的数据可以与模型模拟的PM2.5场融合，以提供对规定燃烧产生的烟雾每小时暴露量的改进估计。
ABSTRACT Prescribed burning (PB) is a prominent source of PM2.5 in the southeastern US and exposure to PB smoke is a health risk. As demand for burning increases and stricter controls are implemented for other anthropogenic sources, PB emissions tend to be responsible for an increasing fraction of PM2.5 concentrations. Here, to quantify the effect of PB on air quality, low-cost sensors are used to measure PM2.5 concentrations in Southwestern Georgia. The feasibility of using low-cost sensors as a supplemental measurement tool is evaluated by comparing them with reference instruments. A chemical transport model, CMAQ, is also used to simulate the contribution of PB to PM2.5 concentrations. Simulated PM2.5 concentrations are compared to observations from both low-cost sensors and reference monitors. Finally, a data fusion method is applied to generate hourly spatiotemporal exposure fields by fusing PM2.5 concentrations from the CMAQ model and all observations. The results show that the severe impact of PB on local air quality and public health may be missed due to the dearth of regulatory monitoring sites. In Southwestern Georgia PM2.5 concentrations are highly non-homogeneous and the spatial variation is not captured even with a 4-km horizontal resolution in model simulations. Low-cost PM sensors can improve the detection of PB impacts and provide useful spatial and temporal information for integration with air quality models. R2 of regression with observations increases from an average of 0.09 to 0.40 when data fusion is applied to model simulation withholding the observations at the evaluation site. Adding observations from low-cost sensors reduces the underestimation of nighttime PM2.5 concentrations and reproduces the peaks that are missed by the simulations. In the future, observations from a dense network of low-cost sensors could be fused with the model simulated PM2.5 fields to provide better estimates of hourly exposures to smoke from PB. Implications: Prescribed burning emissions are a major cause of elevated PM2.5 concentrations, posing a risk to human health. However, their impact cannot be quantified properly due to a dearth of regulatory monitoring sites in certain regions of the United States such as Southwestern Georgia. Low-cost PM sensors can be used as a supplemental measurement tool and provide useful spatial and temporal information for integration with air quality model simulations. In the future, data from a dense network of low-cost sensors could be fused with model simulated PM2.5 fields to provide improved estimates of hourly exposures to smoke from prescribed burning.