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Urban Air Pollution Mapping Using Fleet Vehicles as Mobile Monitors and Machine Learning

机译：城市空气污染用舰队车辆用作移动监控器和机器学习

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摘要

Spatially explicit urban air quality information is important for developing effective air quality control measures. Traditionally, urban air quality is measured by networks of stationary monitors that are not universally available and sparsely sited. Mobile air quality monitoring using equipped vehicles is a promising alternative but has focused on vehicle-level experiments and lacks fleet-level demonstration. Here, we equipped 260 electric vehicles in a ride-hailing fleet in Beijing, China with low-cost sensors to collect real-time, spatial-resolved data on fine particulate matter (PM_(25)) concentrations. Using this data, we developed a decision tree model to infer the distribution of PM_(25) concentrations in Beijing at 1 km by 1 km and 1 h resolution. Our results are able to show both short- and long-term variations of urban PM_(25) concentrations and identify local air pollution hotspots. Compared with a benchmark model that only uses data from stationary monitoring sits, our model has shown significant improvement with the coefficient of determination increased from 0.56 to 0.80 and root mean square error decreased from 12.6 to 8.1μg/m~3. To the best of our knowledge, this study collects the largest mobile sensor data for urban air quality monitoring, which are augmented by state-of-the-art machine learning techniques to derive high-quality urban air pollution mapping. Our results demonstrate the potential and necessity of using fleet vehicles as routine mobile sensors combined with advanced data science methods to provide high-resolution urban air quality monitoring.
机译：空间明确的城市空气质量信息对于开发有效的空气质量控制措施非常重要。传统上，城市空气质量由静止监视器网络衡量，这些内部监测器网络不普遍可用，稀疏地突出。使用装备车辆的移动空气质量监控是一个有前途的替代方案，但专注于车辆级实验，缺乏舰队级演示。在这里，我们在北京的乘车船队中装备了260辆电动车，以低成本的传感器收集实时，在细颗粒物质上进行实时的空间分辨数据（PM_（25））浓度。使用此数据，我们开发了一个决策树模型，以推断北京的PM_（25）浓度的分布1公里，1公里，1小时分辨率。我们的业绩能够展示城市PM_（25）浓度的短期和长期变化，并确定当地的空气污染热点。与仅使用静止监测的数据的基准模型相比，我们的模型表现出显着的改善，测定系数增加到0.56至0.80，螺根均方误差从12.6降至8.1μg/ m〜3。据我们所知，本研究采集了城市空气质量监测最大的移动传感器数据，这是由最先进的机器学习技术增强，以推导出高质量的城市空气污染测绘。我们的结果展示了使用舰队车辆作为日常移动传感器的潜在和必要性，结合先进的数据科学方法，提供高分辨率的城市空气质量监测。

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  • 来源
    《Environmental Science & Technology》 |2021年第8期|5579-5588|共10页
  • 作者

    Bu Zhao; Long Yu; Chunyan Wang; Chenyang Shuai; Ji Zhu; Shen Qu; Morteza Taiebat; Ming Xu;

  • 作者单位

    School for Environment and Sustainability and Michigan Institute for Computational Discovery &Engineering University of Michigan Ann Arbor Michigan 48109-1382 United States;

    Department of Statistics Fudan University Shanghai 200433 China;

    School of Environment Tsinghua University Beijing 100084 China;

    School for Environment and Sustainability and Michigan Institute for Computational Discovery &Engineering University of Michigan Ann Arbor Michigan 48109-1382 United States;

    Department of Statistics University of Michigan Ann Arbor Michigan 48109-1107 United States;

    School of Management and Economics and Center for Energy & Environmental Policy Research Beijing Institute of Technology Beijing 10081 China;

    School for Environment and Sustainability and Department of Civil and Environmental Engineering University of Michigan Ann Arbor Michigan 48109-1382 United States;

    School for Environment and Sustainability and Department of Civil and Environmental Engineering University of Michigan Ann Arbor Michigan 48109-1382 United States;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);美国《生物学医学文摘》(MEDLINE);美国《化学文摘》(CA);
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