School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing 100084, PR China.
School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing 100084, PR China.
Sci Total Environ. 2022 Jul 10;829:154478. doi: 10.1016/j.scitotenv.2022.154478. Epub 2022 Mar 10.
The spatial distribution of elevated particulate matter (PM) concentrations represents a public health concern due to its association with adverse health effects. In this study, a city-wide spatial variability of PM (PM and PM) concentrations in Jinan, China is evaluated using a combination of measurements from 1700 fixed sites and taxi-based mobile monitoring (300 taxis recruited). The taxi fleet provides high spatial resolution and minimizes temporal sampling uncertainties that a single mobile platform cannot address. A big dataset of PM concentrations covering three land-use domains (roadway, community and open-field) and pollution episodes is derived from the taxi-based mobile monitoring (~3 × 10 pairs of PM and PM). The ability of taxi-based mobile monitoring to characterize location-specific concentrations is assessed. We applied an "elevation ratio" to identify the elevated PM concentrations and quantified the ratios at 30-m road segments. Higher PM concentrations occurred during haze episode with lower elevation ratios in all land-use domains compares to non-haze episode. Different characteristics (distribution and range) of the elevation ratios are shown in different land-use domains which highlight the potential local emission hotspots and could have transformative implications for environmental management, thus, contribute to the effectiveness of pollution control strategy.
由于与不良健康影响有关，颗粒物（PM）浓度升高的空间分布代表了一个公共卫生关注点。在这项研究中，利用来自 1700 个固定站点和基于出租车的移动监测（招募了 300 辆出租车）的组合测量，评估了中国济南的 PM（PM 和 PM）浓度的全市范围空间变异性。出租车车队提供了高空间分辨率，并最大限度地减少了单个移动平台无法解决的时间采样不确定性。从基于出租车的移动监测中得出了一个涵盖三个土地利用领域（道路、社区和开阔地）和污染事件的大型 PM 浓度数据集（约 3×10 对 PM 和 PM）。评估了基于出租车的移动监测对特定位置浓度进行特征描述的能力。我们应用了“海拔比”来识别升高的 PM 浓度，并在 30 米的道路段量化了这些比值。与非雾霾事件相比，在所有土地利用领域，雾霾事件期间的 PM 浓度更高，海拔比更低。不同土地利用领域的海拔比表现出不同的特征（分布和范围），突出了潜在的局部排放热点，并可能对环境管理产生变革性影响，从而有助于污染控制策略的有效性。
