粤港澳大湾区PM2.5时空分布特征及其与气象要素的关系

Abstract

细颗粒物（PM_2.5）污染不仅是现代社会城市化进程中的痛点，也是城市大气环境研究不可忽略的重要焦点。粤港澳大湾区作为世界级城市群，既是城市区域经济社会文化发展的重要体现，更是国家区域发展战略的重要构成与政策实施落脚点，其生态环境的优劣尤其受瞩目。对1999-2016年大湾区地表PM_2.5浓度栅格数据集进行了时空分布特征分析，其中空间自相关分析选取莫兰指数（Moran'I指数）作为度量；并利用多元线性回归模型探讨研究区内PM_2.5与气象要素之间关系。结果表明：粤港澳大湾区1999-2016年历年PM_2.5浓度呈先增加后减小的趋势，2008年为时间拐点，该时间节点之后空气质量显著提高，且1999、2009、2016三年，年平均PM_2.5浓度相似值趋于聚集分布。冷热点分析结果表明：热点区域集中于湾区行政核心区域范围内；冷点集中于核心边缘区域，空气质量较优。利用皮尔森相关分析最终筛选出实际蒸散量（aet）、太阳辐射（srad）、最低温度（tmmn）、蒸汽压（vap）、饱和水汽压差（vpd）、风速（ws）等6个气象因子，利用回归分析判断影响PM_2.5浓度时空分布的显著因子。结果表明：本研究区太阳辐射与PM_2.5浓度关系呈负相关，该结果与其他城市相关研究有较大差异，最小温度与PM_2.5浓度呈正相关，风速与PM_2.5浓度呈负相关，饱和水气压差与PM_2.5浓度呈正相关。;Fine particulate matter (PM_2.5) pollution is a serious environmental problem in the process of urbanization in modern society, and it is one of the most important research hotspots in the study of urban atmospheric environment. As a world-class urban agglomeration, Guangdong-Hong Kong-Macao Greater Bay Area is not only an important embodiment of the economic, social and cultural development of urban areas, but also an important component of the national regional development strategy and the foothold of policy implementation. Therefore, the ecological environment of this region is particularly important. The temporal and spatial distribution characteristics of PM_2.5 concentration data in Guangdong-Hong Kong-Macao Greater Bay Area from 1999 to 2016 were analyzed. Moran'I index was selected as the measurement in spatial autocorrelation analysis. The relationship between PM_2.5 concentration and meteorological factors in the study area was discussed by multiple linear regression model. The results indicated that the PM_2.5 concentration in Guangdong-Hong Kong-Macao Greater Bay Area increased firstly and then decreased from 1999 to 2016. The air quality improved significantly after the highest concentration in 2008, and the similar values of annual average PM_2.5 concentration tended to aggregate distribution in 1999, 2009 and 2016. The hot spots were concentrated in the administrative core area of the bay area; the cold spots were concentrated in the core edge area, where the air quality was better. Pearson correlation analysis (actual evapotranspiration (aet), solar radiation (srad), minimum temperature (tmmn), vapor pressure (vap), saturated vapor pressure difference (vpd), wind speed (ws)) was used to select six meteorological factors for regression analysis to evaluate the significant factors affecting the temporal and spatial distribution of PM_2.5 concentration. The results showed that there was a negative correlation between srad and PM_2.5 in this study area, which is quite different from other related studies in other cities. The tmmn and vpd were positively correlated with PM_2.5 concentration, while ws was negatively correlated with PM_2.5 concentration.