The impact of COVID-19 public health restrictions on particulate matter pollution measured by a validated low-cost sensor network in Oxford, UK.
The impact of COVID-19 public health restrictions on particulate matter pollution measured by a validated low-cost sensor network in Oxford, UK.
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DOI：
10.1016/j.buildenv.2023.110330
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发表时间：
2023-06-01
影响因子：
7.4
通讯作者：
Leach, Felix C. P.
中科院分区：
文献类型：
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作者：
Bush, Tony;Bartington, Suzanne;Pope, Francis D.;Singh, Ajit;Thomas, G. Neil;Stacey, Brian;Economides, George;Anderson, Ruth;Cole, Stuart;Abreu, Pedro;Leach, Felix C. P.
新型冠状病毒疫情的紧急应对措施导致出行行为和经济活动发生重大变化，对城市空气质量产生影响。到目前为止，这些与封锁措施相关的空气质量变化通常使用有限的城市级监管监测数据进行评估，然而，低成本的空气质量传感器提供了以更高的时空分辨率评估多个位置变化的能力，从而产生与未来空气质量干预相关的见解。这项研究的目的是利用高空间分辨率的空气质量信息，利用来自英国牛津15个低成本空气质量传感器的经验证（使用随机森林现场校准）网络的数据，以监测2020年1月至2021年9月期间多项COVID-19公共卫生限制对颗粒物浓度（PM10，PM2. 5）的影响。将监测点内和监测点之间的PM10和PM2.5颗粒尺寸分数的测量值与大流行前相关的公共卫生限制基线进行比较。虽然PM10和PM2.5的平均峰值浓度比近年来经历的典型峰值水平降低了9-10 μg/m3，但PM10和PM2.5的平均日浓度仅低0.11 μg/m3，并且这些观测结果存在显著的时间（随着限制的增加和取消）和空间变异性（在15个传感器网络中）。在整个15传感器网络，我们观察到一个小的本地影响，从交通相关的排放源后，颗粒浓度附近的交通导向传感器具有较高的平均和峰值浓度以及更大的动态范围，相比更多的中间和背景导向传感器的位置。浓度的动态范围越大，表明暴露于更多可变排放源，如公路运输排放。我们的研究结果强调了低成本传感器技术的巨大潜力，可以识别由于行为变化（在这种情况下受COVID-19限制的影响）而导致的污染物浓度的高度局部化变化，从而深入了解这种情况下非交通对PM排放的贡献。很明显，牛津需要采取额外的非交通相关措施，将PM10和PM2.5水平降低到世卫组织基于健康的指导方针范围内，并达到根据《2021年环境法》制定的PM2.5目标。
Emergency responses to the COVID-19 pandemic led to major changes in travel behaviours and economic activities with arising impacts upon urban air quality. To date, these air quality changes associated with lockdown measures have typically been assessed using limited city-level regulatory monitoring data, however, low-cost air quality sensors provide capabilities to assess changes across multiple locations at higher spatial-temporal resolution, thereby generating insights relevant for future air quality interventions. The aim of this study was to utilise high-spatial resolution air quality information utilising data arising from a validated (using a random forest field calibration) network of 15 low-cost air quality sensors within Oxford, UK to monitor the impacts of multiple COVID-19 public heath restrictions upon particulate matter concentrations (PM10, PM2.5) from January 2020 to September 2021. Measurements of PM10 and PM2.5 particle size fractions both within and between site locations are compared to a pre-pandemic related public health restrictions baseline. While average peak concentrations of PM10 and PM2.5 were reduced by 9–10 μg/m3 below typical peak levels experienced in recent years, mean daily PM10 and PM2.5 concentrations were only ∼1 μg/m3 lower and there was marked temporal (as restrictions were added and removed) and spatial variability (across the 15-sensor network) in these observations. Across the 15-sensor network we observed a small local impact from traffic related emission sources upon particle concentrations near traffic-oriented sensors with higher average and peak concentrations as well as greater dynamic range, compared to more intermediate and background orientated sensor locations. The greater dynamic range in concentrations is indicative of exposure to more variable emission sources, such as road transport emissions. Our findings highlight the great potential for low-cost sensor technology to identify highly localised changes in pollutant concentrations as a consequence of changes in behaviour (in this case influenced by COVID-19 restrictions), generating insights into non-traffic contributions to PM emissions in this setting. It is evident that additional non-traffic related measures would be required in Oxford to reduce the PM10 and PM2.5 levels to within WHO health-based guidelines and to achieve compliance with PM2.5 targets developed under the Environment Act 2021.
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影响因子：
3.8
作者：
Hagan DH;Kroll JH
通讯作者：
Kroll JH
影响因子：
4.5
作者：
Chadwick E;Le K;Pei Z;Sayahi T;Rapp C;Butterfield AE;Kelly KE
通讯作者：
Kelly KE
DOI：
10.1016/j.envpol.2020.116011
发表时间：
2021-03-01
期刊：
Environmental pollution (Barking, Essex : 1987)
影响因子：
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作者：
Jephcote C;Hansell AL;Adams K;Gulliver J
通讯作者：
Gulliver J
影响因子：
2.9
作者：
Leach, Felix C. P.;Peckham, Mark S.;Hammond, Matthew J.
通讯作者：
Hammond, Matthew J.
DOI：
10.3390/s22134841
发表时间：
2022-06-27
期刊：
Sensors (Basel, Switzerland)
影响因子：
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作者：
通讯作者：
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