Wu Jian-Sheng, Wang Xi, Li Jia-Cheng, Tu Yuan-Jie
Key Laboratory for Urban Habitat Environmental Science and Technology, School of Urban Planning and Design, Peking University, Shenzhen 518055, China.
Laboratory of Earth Surface Processes of Ministry of Education, College of Urban and Environment Science, Peking University, Beijing 100871, China.
Huan Jing Ke Xue. 2017 Jun 8;38(6):2191-2201. doi: 10.13227/j.hjkx.201611114.
Due to the rapid urbanization and increasing energy consumption, air pollution, especially some fine particulates like PM rise in the context of fast urbanization. PM pollution has been given considerable attention recent years. High PM concentration is the main reason for the atmospheric haze in Beijing-Tianjin-Hebei region. Air pollution has become the key issue restricting the sustainable development of Beijing-Tianjin-Hebei region and even the whole country. Long-term exposure to PM is likely to cause adverse effects on human health. The spatial-temporal variation of air pollution can be characterized by the land use regression model. It is significant to have a good knowledge of spatial characteristics of PM concentration, which could assist air pollution management and the epidemiological research. This manuscript used air quality data of 104 monitoring sites of Beijing-Tianjin-Hebei region from 1st January 2014 to 31st December, 2014, combined with VⅡRS (visible infrared imaging radiometer) AOD(aerosol optical depth), land use, meteorological factors, road network, population, and pollutant sources distribution to establish the land use regression model by least square method and geographically weighted method respectively. The four models established were least square land use regression model with VⅡRS AOD data, geographically weighted land use regression model with VⅡRS AOD data, least square land use regression model without VⅡRS AOD data and geographically weighted land use regression model without VⅡRS AOD data. And the adjusted values for these four models were 82.13%, 84.87%, 80.45% and 81.99%, respectively. Research results demonstrated that the geographically weighted method performed better than the least square method and improved the land use regression model to a certain extent.
由于快速的城市化进程和不断增长的能源消耗，空气污染问题日益严重，尤其是一些细颗粒物，如在快速城市化背景下上升的PM。近年来，PM污染受到了广泛关注。高PM浓度是京津冀地区大气雾霾的主要原因。空气污染已成为制约京津冀地区乃至全国可持续发展的关键问题。长期暴露于PM可能会对人体健康造成不利影响。空气污染的时空变化可以通过土地利用回归模型来表征。了解PM浓度的空间特征具有重要意义，这有助于空气污染管理和流行病学研究。本研究使用了京津冀地区104个监测站点2014年1月1日至2014年12月31日的空气质量数据，结合VIIRS（可见红外成像辐射计）AOD（气溶胶光学厚度）、土地利用、气象因素、道路网络、人口和污染源分布，分别采用最小二乘法和地理加权法建立土地利用回归模型。建立的四个模型分别是含VIIRS AOD数据的最小二乘土地利用回归模型、含VIIRS AOD数据的地理加权土地利用回归模型、不含VIIRS AOD数据的最小二乘土地利用回归模型和不含VIIRS AOD数据的地理加权土地利用回归模型。这四个模型的调整值分别为82.13%、84.87%、80.45%和81.99%。研究结果表明，地理加权法比最小二乘法表现更好，在一定程度上改进了土地利用回归模型。
