{"title":"用于估算城市地区街道级高空间分辨率近地面 PM2.5 和 O3 浓度的新型预测框架","authors":"","doi":"10.1016/j.buildenv.2024.112141","DOIUrl":null,"url":null,"abstract":"<div><div>National monitoring stations lack spatial coverage for reflecting microscopic changes in air pollution. Several studies have attempted to use locally measured data to develop prediction models to complement national stations. However, the lack of meteorological stations in urban areas makes it challenging to obtain temperature (TEM) and relative humidity (RH) with high spatial resolution; since these are important air pollution predictors, these models cannot be applied to entire urban areas. Here, we propose a new prediction framework that estimates near-ground high spatial resolution PM<sub>2.5</sub> and O<sub>3</sub> concentrations based on short-time and large-scale monitoring and multisource urban data. We conducted a mobile monitoring experiment in Wuhan using electric bicycles to collect PM<sub>2.5</sub> and O<sub>3</sub> concentrations and TEM and RH to train our models. First, we predicted the near-surface TEM and RH via mobile monitoring of the TEM, RH and other built environment data. Second, we used near-surface TEM and RH with other urban big data to predict the PM<sub>2.5</sub> and O<sub>3</sub> concentrations. The results revealed that the estimation performance of the proposed two-stage machine learning prediction models is high, with R<sup>2</sup> values above 0.95. Satellite top of atmosphere reflectance (TOA), land surface reflectance (LSR) and street view data were incorporated into the new framework to obtain higher-spatial-resolution (50 m) air pollution maps. Our results revealed that TEM and RH varied considerably between the near-surface and meteorological stations. Accurate near-ground TEM and RH are important for predicting near-ground PM<sub>2.5</sub> and O<sub>3</sub> concentrations. Furthermore, TOA and LSR are promising for predicting near-ground PM<sub>2.5</sub> concentrations.</div></div>","PeriodicalId":9273,"journal":{"name":"Building and Environment","volume":null,"pages":null},"PeriodicalIF":7.1000,"publicationDate":"2024-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A novel prediction framework for estimating high spatial resolution near-ground PM2.5 and O3 concentrations at street-level in urban areas\",\"authors\":\"\",\"doi\":\"10.1016/j.buildenv.2024.112141\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>National monitoring stations lack spatial coverage for reflecting microscopic changes in air pollution. Several studies have attempted to use locally measured data to develop prediction models to complement national stations. However, the lack of meteorological stations in urban areas makes it challenging to obtain temperature (TEM) and relative humidity (RH) with high spatial resolution; since these are important air pollution predictors, these models cannot be applied to entire urban areas. Here, we propose a new prediction framework that estimates near-ground high spatial resolution PM<sub>2.5</sub> and O<sub>3</sub> concentrations based on short-time and large-scale monitoring and multisource urban data. We conducted a mobile monitoring experiment in Wuhan using electric bicycles to collect PM<sub>2.5</sub> and O<sub>3</sub> concentrations and TEM and RH to train our models. First, we predicted the near-surface TEM and RH via mobile monitoring of the TEM, RH and other built environment data. Second, we used near-surface TEM and RH with other urban big data to predict the PM<sub>2.5</sub> and O<sub>3</sub> concentrations. The results revealed that the estimation performance of the proposed two-stage machine learning prediction models is high, with R<sup>2</sup> values above 0.95. Satellite top of atmosphere reflectance (TOA), land surface reflectance (LSR) and street view data were incorporated into the new framework to obtain higher-spatial-resolution (50 m) air pollution maps. Our results revealed that TEM and RH varied considerably between the near-surface and meteorological stations. Accurate near-ground TEM and RH are important for predicting near-ground PM<sub>2.5</sub> and O<sub>3</sub> concentrations. Furthermore, TOA and LSR are promising for predicting near-ground PM<sub>2.5</sub> concentrations.</div></div>\",\"PeriodicalId\":9273,\"journal\":{\"name\":\"Building and Environment\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":7.1000,\"publicationDate\":\"2024-10-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Building and Environment\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0360132324009831\",\"RegionNum\":1,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"CONSTRUCTION & BUILDING TECHNOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Building and Environment","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0360132324009831","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CONSTRUCTION & BUILDING TECHNOLOGY","Score":null,"Total":0}
引用次数: 0
摘要
国家监测站缺乏反映空气污染微观变化的空间覆盖范围。一些研究试图利用当地测量的数据来开发预测模型,以补充国家监测站的不足。然而,由于城市地区缺乏气象站,要获得高空间分辨率的温度(TEM)和相对湿度(RH)具有挑战性;由于这些数据是重要的空气污染预测指标,这些模型无法应用于整个城市地区。在此,我们提出了一个新的预测框架,该框架可根据短时和大规模监测以及多源城市数据估算近地面高空间分辨率 PM2.5 和 O3 浓度。我们在武汉进行了一次移动监测实验,使用电动自行车收集 PM2.5 和 O3 浓度以及 TEM 和 RH,以训练我们的模型。首先,我们通过移动监测 TEM、RH 和其他建筑环境数据来预测近地表 TEM 和 RH。其次,我们利用近地表 TEM 和 RH 以及其他城市大数据来预测 PM2.5 和 O3 浓度。结果表明,所提出的两阶段机器学习预测模型的估计性能很高,R2 值在 0.95 以上。卫星大气顶部反射率(TOA)、陆地表面反射率(LSR)和街景数据被纳入新框架,以获得更高空间分辨率(50 米)的空气污染地图。我们的研究结果表明,近地面和气象站之间的 TEM 和相对湿度差异很大。准确的近地面 TEM 和 RH 对于预测近地面 PM2.5 和 O3 浓度非常重要。此外,TOA 和 LSR 对预测近地面 PM2.5 浓度也很有帮助。
A novel prediction framework for estimating high spatial resolution near-ground PM2.5 and O3 concentrations at street-level in urban areas
National monitoring stations lack spatial coverage for reflecting microscopic changes in air pollution. Several studies have attempted to use locally measured data to develop prediction models to complement national stations. However, the lack of meteorological stations in urban areas makes it challenging to obtain temperature (TEM) and relative humidity (RH) with high spatial resolution; since these are important air pollution predictors, these models cannot be applied to entire urban areas. Here, we propose a new prediction framework that estimates near-ground high spatial resolution PM2.5 and O3 concentrations based on short-time and large-scale monitoring and multisource urban data. We conducted a mobile monitoring experiment in Wuhan using electric bicycles to collect PM2.5 and O3 concentrations and TEM and RH to train our models. First, we predicted the near-surface TEM and RH via mobile monitoring of the TEM, RH and other built environment data. Second, we used near-surface TEM and RH with other urban big data to predict the PM2.5 and O3 concentrations. The results revealed that the estimation performance of the proposed two-stage machine learning prediction models is high, with R2 values above 0.95. Satellite top of atmosphere reflectance (TOA), land surface reflectance (LSR) and street view data were incorporated into the new framework to obtain higher-spatial-resolution (50 m) air pollution maps. Our results revealed that TEM and RH varied considerably between the near-surface and meteorological stations. Accurate near-ground TEM and RH are important for predicting near-ground PM2.5 and O3 concentrations. Furthermore, TOA and LSR are promising for predicting near-ground PM2.5 concentrations.
期刊介绍:
Building and Environment, an international journal, is dedicated to publishing original research papers, comprehensive review articles, editorials, and short communications in the fields of building science, urban physics, and human interaction with the indoor and outdoor built environment. The journal emphasizes innovative technologies and knowledge verified through measurement and analysis. It covers environmental performance across various spatial scales, from cities and communities to buildings and systems, fostering collaborative, multi-disciplinary research with broader significance.