Air Pollution Exposure Based on Nighttime Light Remote Sensing and Multi-source Geographic Data in Beijing

Air pollution is a problem that directly affects human health,the global environment and the climate.The air quality index(AQI)indicates the degree of air pollution and effect on human health;however,when assessing air pollution only based on AQI monitoring data the fact that the same degree of air pollution is more harmful in more densely populated areas is ignored.In the present study,multi-source data were combined to map the distribution of the AQI and population data,and the analyze their pollution population exposure of Beijing in 2018 was analyzed.Machine learning based on the random forest algorithm was adopted to calculate the monthly average AQI of Beijing in 2018.Using Luojia-1 nighttime light remote sensing data,population statistics data,the population of Beijing in 2018 and point of interest data,the distribution of the permanent population in Beijing was estimated with a high precision of 200 m×200 m.Based on the spatialization results of the AQI and population of Beijing,the air pollution exposure levels in various parts of Beijing were calculated using the population-weighted pollution exposure level(PWEL)formula.The results show that the southern region of Beijing had a more serious level of air pollution,while the northern region was less polluted.At the same time,the population was found to agglomerate mainly in the central city and the peripheric areas thereof.In the present study,the exposure of different districts and towns in Beijing to pollution was analyzed,based on high resolution population spatialization data,it could take the pollution exposure issue down to each individual town.And we found that towns with higher exposure such as Yongshun Town,Shahe Town and Liyuan Town were all found to have a population of over 200000 which was much higher than the median population of townships of51741 in Beijing.Additionally,the change trend of air pollution exposure levels in various regions of Beijing in 2018 was almost the same,with the peak value being in winter and the lowest value being in summer.The exposure intensity in population clusters was relatively high.To reduce the level and intensity of pollution exposure,relevant departments should strengthen the governance of areas with high AQI,and pay particular attention to population clusters.

基于机器感知与学习的空气颗粒物智能检测、识别与预警方法研究综述

随着空气污染问题的不断加剧,准确检测和及时预警空气颗粒物(particulate matter,PM)的重要性日益突出。传统方法依赖专业设备,不适用于实时检测。与传统方法相比,基于机器感知与学习的方法体现出技术优势,具有可实时检测、准确性高等优点。因此,对近几年的基于机器感知与学习的PM智能检测、识别与预警方法进行详细综述。首先,对PM的标准和来源进行介绍;然后,从检测、识别和预警这3个方面详细总结了各类方法,并对比各方法的特点和性能,其中,基于机器学习和深度学习的方法在各研究中取得了较大进展;最后,总结全文主要内容,并提出当前领域面临的挑战以及未来的重点研究方向。未来的研究应该继续关注技术创新和数据质量,以实现更好的空气质量监测和管理。

Characteristics and Cause Analysis of Heavy Haze in Changchun City in Northeast China

Northeast China has been reported as having serious air pollution in China with increasing occurrences of severe haze epi- sodes. Changchun City, as the center of Northeast China, has longstanding industry and is an important agricultural base. Additionally, Changchun City has a long winter requiring heating of buildings emitting pollution into the air. These factors contribute to the complex- ity of haze pollution in this area. In order to analyze the causes of heavy haze, surface air quality has been monitored from 2013 to 2015. By using satellite and meteorological data, atmospheric pollution status, spatio-temporal variations and formation have been analyzed. Results indicated that the air quality in 88.9% of days exceeding air quality index （AQI） level-1 standard （AQI 〉50） according to the National Ambient Air Quality Standard （NAAQS） of China. Conversely, 33.7% of the days showed a higher level with AQI 〉 100. Ex- treme haze events （AQI 〉 300） occurred frequently during agricultural harvesting period （from October 10 to November 10）, intensive winter heating period （from Late-December to February） and period of spring windblown dust （April and May）. Most daily concentra- tions of gaseous pollutants, i.e., NO2 （43.8 gg/m3）, CO （0.9 mg/m3）, SO2 （37.9 gg/m3）, and 03 （74.9 gg/m3） were evaluated within level-1 concentration limits of NAAQS standards. However, particulate matter （PM2.5 and PMI0） concentrations （67.3 ~tg/m3and 115.2 ~g/m3, respectively） were significantly higher than their level-1 limits. Severe haze in spring was caused by offsite transported dust and windblown surface soil. Heavy haze periods during fall and winter were mainly formed by intensive emissions of atmospheric pollutants and steady weather conditions （i.e., low wind speed and inversion layer）. The overlay emissions of widespread straw burning and coal combustion for heating were the dominant factors contributing to haze in autumn, while intensive coal burning during the coldest time was the primary component of total emissions. In addition, general emissions including automobile exhaust, road and construction dust, residential and industrial activities, have significantly increased in recent years, making heavy haze a more frequent occurrence. There- fore, both improved technological strategies and optimized pollution management on a regional scale are necessary to minimize emis- sions in specified seasons in Changchun City, as well as comprehensive control measures in Northeast China.

Mortality weighting-based method for aggregate urban air risk assessment

This paper deals with a mortality-weighted synthetic evaluation （MWSE） method for evaluating urban air risk. Sulphur dioxide （SO2）, nitrogen oxide （NOx）, and particulate matter （PMl0） were used as pollution indices. The urban area of Hangzhou, China is divided into 756 grid cells, with a resolution of 1 km× 1 km, and is evaluated using the MWSE and the air quality index （AQI）, a widely-used method to evaluate ambient air quality and air risk. In an evaluation of one day in April 2004, the surface areas categorized as levels Ⅰ and Ⅲ, as defined by the integrated air risk evaluation, were 27.3% and 3.3% lower, respectively, than grades Ⅰ and Ⅲ defined by the AQI evaluation. Meanwhile, the areas classified as level Ⅱ or above level Ⅲ by the integrated air risk evaluation were 55.1% and 101. 1% higher, respectively, than grade Ⅱ or above grade Ⅲ when using the AQI evaluation. From this comparison, we find that the MWSE method is more sensitive than the AQI method. The AQI method uses a single index to assess integrated air quality and is therefore unable to evaluate integrated air risks due to multiple pollutants. The MWSE method overcomes this problem, providing improved accuracy in air risk assessment.