Spatiotemporal Changes in PM2.5 and Their Relationships with Land-Use and People in Hangzhou

Abstract

Increases in the extent and level of air pollution in Chinese cities have become a major concern of the public and burden on the government. While ample literature has focused on the status, changes and causes of air pollution (particularly on PM2.5 and PM10), significantly less is known on their effects on people. In this study we used Hangzhou, China, as our testbed to assess the direct impact of PM2.5 on youth populations that are more vulnerable to pollution. We used the ground monitoring data of air quality and Aerosol optical thickness (AOT) product from the Moderate Resolution Imaging Spectroradiometer (MODIS) for the spatiotemporal changes of PM2.5 by season in 2015. We further explored these distributions with land cover, population density and schools (kindergarten, primary school and middle school) to explore the potential impacts in seeking potential mitigation solutions. We found that the seasonal variation of PM2.5 concentration was winter > spring > autumn > summer. In Hangzhou, the percentage of land area exposed to PM2.5 > 50 µg m−3 accounted for 59.86% in winter, 56.62% in spring, 40.44% in autumn and 0% in summer, whereas these figures for PM2.5 of <35 µg m−3 were 70.01%, 5.28%, 5.17%, 4.16% in summer, winter, autumn and spring, respectively. As for land cover, forest experienced PM2.5 of 35–50 µg m−3 (i.e., lower than those of other cover types), likely due to the potential filtering and absorption function of the forests. More importantly, a quantitative index based on population-weighted exposure level (pwel) indicated that only 9.06% of the population lived in areas that met the national air quality standards. Only 1.66% (14,055) of infants and juveniles lived in areas with PM2.5 of <35 µg m−3. Considering the legacy effects of PM2.5 over the long-term, we highly recommend improving the monitoring systems for both air quality and people (i.e., their health conditions), with special attention paid to infants and juveniles.

Integration of Remote Sensing and Social Sensing Data in a Deep Learning Framework for Hourly Urban PM2.5 Mapping

“…Thus, both the RTCI and TID have great impacts on the model accuracy. As for NDVI, likely due to the potential filtering and absorption function of the vegetation [36], NDVI affected the model accuracy to a certain extent. The results demonstrated that with the integration of the remote sensing data NDVI, the dynamic social sensing data and other auxiliary data, the modeling result can be effectively promoted.…”

“…It is worth noting that there are many factors that are related to PM 2.5 , including meteorological influences, atmospheric boundary layer height, land use types, urban form, traffic conditions, human activities, and so on [34,35,36,37,38,39]. To mine the complex relationships between the various influencing factors and PM 2.5 , machine learning methods [11,13,14,15,16,17,18,19] have been widely used, especially the deep learning methods [27,40].…”

Spatial regression modelling of particulate pollution in Calgary, Canada

“…Particulate pollution is associated not only with reduced visibility, environmental degradation, and climate change (Ramanathan and Carmichael 2008 ), but also with adverse health effects, not limited to respiratory and cardiovascular morbidity and mortality (Ruckerl et al 2011 ; Janssen et al 2012 ). Particle mixture and chemical composition—as well as health impacts—vary by size fraction, and health impacts vary across individuals, e.g., by age and susceptibility (Kelly and Fussell 2012 ; Tian et al 2018 ). Scientific evidence of the environmental and health effects of BC pollution has grown in recent years, along with increasing attention to its spatial variability.…”

“…The LUR literature presents relatively few analyses of particulate matter, the majority of which, despite some exceptions (e.g., Bertazzon et al 2016;Henderson et al 2007;Xu et al 2018;Zhang et al 2015), are concerned with PM 2.5 . Studies focusing on black carbon have only begun to emerge in recent years (Clougherty et al 2013;Dons et al 2013;Saraswat et al 2013;Hankey and Marshall 2015;van Nunen et al 2017;Weichenthal et al 2016;Lee et al 2017). The main contribution of this paper to the LUR fine particle literature is the development of explicitly spatial LUR models, that is, spatial lag autoregressive models.…”

Influencing Factors of PM2.5 Pollution: Disaster Points of Meteorological Factors

“…On the other hand, according to the Chinese Environmental Status Bulletin, from 2013 to 2016, the annual average concentration of PM 2.5 in China is 57.75 μg/m 3 (8.47 μg/m 3 in the USA, 10.00 recommended by the World Health Organization (WHO) [2]). Long-term exposure to PM 2.5 pollution has a significant impact on the health of human beings, especially infants and juveniles [3]. Therefore, it is necessary to study the influencing factors of PM 2.5 and effectively control PM 2.5 pollution.…”