城市空气质量感知方法综述

摘要/Abstract

摘要： 城市空气质量信息对于控制空气污染和保护大众健康都是尤为重要的。城市空气质量感知方法按传感器位置是否发生改变可划分为静态感知方法和动态感知方法两种。其中静态感知方法的数据是基于空气质量监测站、卫星遥感和固定位置的传感器进行采集的。再按成本高低可进一步划分为低成本静态感知和高成本静态感知。动态感知方法按是否以参与者为感知中心划分为参与式方法和非参与式方法。随着感知技术和计算能力的发展,将多源异构的城市数据,如气象数据、交通数据等进行融合,可进一步提高感知的准确性。文中首先对当前空气质量感知方法进行综述,然后分类介绍了各种方法的感知框架和数据处理方法,最后讨论了其面临的问题和挑战。

关键词: 城市感知, 机器学习, 空气污染, 数据采集

Abstract: Urban air quality information is especially important for controlling air pollution and protecting public health.According to whether the sensor position changes,urbanair quality sensing methods can be divided into two methods:static perception methods and dynamic perception methods.The data acquisition of the static sensing method is based on air quality monitoring stations,satellite remote sensing and fixed position sensors.Then,the static sensing method is further divided into low-cost static sensing method and high-cost static sensing method.The dynamic sensing method can be divided into participatory method and non-participating method according to whether the participant is the perceptual center.With the development of sensing technology and computing ability,the fusion of multi-source hete-rogeneous urban data,such as meteorological data and traffic data,can further improve the accuracy of perception.This paper firstly summarized current air quality sensing methods,then classified the sensing framework and data processing methods of various methods,and finally discussed the problems and challenges.

Key words: Air pollution, Data collection, Machine learning, Urban sensing

中图分类号:

TP181

王鹏跃, 郭茂祖, 赵玲玲, 张昱. 城市空气质量感知方法综述[J]. 计算机科学, 2019, 46(6A): 35-40. https://doi.org/

WANG Peng-yue, GUO Mao-zu, ZHAO Ling-ling, ZHANG Yu^1,4. Review on Urban Air Quality Perception Methods[J]. Computer Science, 2019, 46(6A): 35-40. https://doi.org/

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