Impacts of atmospheric fine particulate matter (PM2.5) on urban heat island with multi-source data: a case study of Beijing
： 2019 - 03 - 18
： 2019 - 09 - 02
： 2019 - 09 - 05
129 0 0

Abstract & Keywords
Abstract: Background, aim, and scope Different sources of PM2.5 influence the chemical composition of airborne particles and ultimately, human health. Studies on the characteristics of water-soluble ions in PM2.5 have largely been conducted in inland areas, particularly for large and medium-sized cities. There are limited studies on water-soluble ions in PM2.5 for coastal cities. This study investigated the characteristics and sources of water-soluble ions in PM2.5 for Zhuhai, a typical coastal city in China. The study collected PM2.5 between June to December, 2016 to gain useful information and insights into pollution control strategies for local governments. Materials and methods PM2.5 samples were collected using a high-volume air sampler with a PM2.5 cutting head. Each sample was run for 48 h with quartz filter, where all filters were pre-combusted at 500℃. Filters were cut at 4 cm×4 cm to determine nine water-soluble ions by ion chromatography. Online data on meteorological parameters in Zhuhai were collected from the National Oceanic and Atmospheric Administration (NOAA). Results The concentrations of water-soluble ions in PM2.5 exhibited a significant seasonal variation, with levels in winter being higher than those in summer. The main components of PM2.5 were , , and , accounting for 85.07% of the total ion composition. From summer to winter, the proportion of and in ions decreased, while the proportion of increased. Discussion Based on cluster analysis of back trajectory, the seasonal variations in ion concentrations were associated with different air masses. The concentrations associated with terrigenous air masses in winter were higher than the ocean air masses in summer and autumn. Based on the charge balance between total anion and cation equivalents, PM2.5 is alkaline in summer and autumn and acidic in winter. The high concentration of , and in PM2.5 indicate that the level of secondary ions was relatively high. The mean ratio of / was 0.2, demonstrating that the main source of water soluble ions were stationary source emissions in Zhuhai. Principal factor analysis demonstrated that the mix of man-made sources with oceanic sources contributed 80.2% of the PM2.5 while agricultural sources contributed 16%. Ion and enrichment factor correlations implied the form and sources of ions. Conclusions Anthropogenic activities play an important role in PM2.5 ion concentrations in Zhuhai. PM2.5 is likely to be acidic in winter. Air mass transport plays a major role in PM2.5 ions concentration, particularly in winter. Recommendations and perspectives The government needs be attentive to the possibility of stationary source emissions from the air mass in Zhuhai in winter.
Keywords: urban heat island intensity; atmospheric fine particles; correlation analysis

1   研究区域、方法与数据来源
1.1   研究区域与地面监测站点

Fig.1 Study area and meteorological monitoring station
1.2   MODIS地表温度数据与预处理
MODIS是美国宇航局对地观测系统EOS系列卫星Aqua和Terra上的重要仪器。Aqua分别在下午13点30（当地太阳时）和夜间1点30左右过境，Terra分别在上午10点30和晚上22点30左右过境。对北京地区来讲，每天可提供四次观测数据，因此MODIS数据比较适于研究北京市城市热岛效应。MODIS地表温度LST数据是MODIS数据业务化反演的产品之一。大量研究表明，MODIS分裂窗算法反演得到的地表温度达到了1 K的精度（白杨等，2013）。

Fig.2 Surface temperature sampling point and annual average surface temperature in Beijing
1.3   研究方法及技术路线
1.3.1   冠层城市热岛强度计算

$$\mathrm{ }\mathrm{C}\mathrm{U}\mathrm{H}\mathrm{I}={T}_{urban}-{T}_{suburban}=\frac{1}{13}\sum _{i=1}^{13}{T}_{i}-\frac{1}{7}\sum _{j=1}^{7}{T}_{j}$$ 式中：$${T}_{urban}$$为城区气温站点日平均温度，$${T}_{suburban}$$为郊区气温站点日平均温度，$${T}_{i}$$$${T}_{j}$$分别为城区和郊区某一站点的日平均温度。
1.3.2   地表城市热岛强度计算

$\mathrm{S}\mathrm{U}\mathrm{H}\mathrm{I}={T}_{urban}-{T}_{suburban}=\frac{1}{n}\sum _{i=1}^{n}{T}_{i}-\frac{1}{n}\sum _{j=1}^{n}{T}_{j}$

1.3.3   地表城市热岛强度指数计算

${M}_{i}={P}_{i}-\frac{1}{n}\sum {P}_{roi}$

1.3.4   相关系数法

${r}_{xy}=\frac{{\sum }_{i=1}^{n}\left({x}_{i}-\stackrel{-}{x}\right)\left({y}_{i}-\stackrel{-}{y}\right)}{\sqrt{{\sum }_{i=1}^{n}{\left({x}_{i}-\stackrel{-}{x}\right)}^{2}}\sqrt{{\sum }_{i=1}^{n}{\left({y}_{i}-\stackrel{-}{y}\right)}^{2}}}$

2   结果与分析
2.1   城市热岛强度

Fig.3 Monthly variation of CUHI and SUHI in Beijing
2.2   城市热岛强度与PM2.5质量浓度相关性分析
2.2.1   冠层城市热岛强度与PM2.5质量浓度

Fig.4 Relationships between PM2.5 and CUHI in Beijing

Fig.5 Monthly variation and seasonal variation of CUHI under different pollution levels
2.2.2   地表城市热岛强度与PM2.5质量浓度

Fig.6 Relationships between PM2.5 and SUHII

Fig.7 Relationships between daytime SUHII and PM2.5 (a), nighttime SUHII and PM2.5 (b)
2.3   城市热岛强度空间分布与PM2.5质量浓度相关性分析

Fig.8 Spatial distribution of SUHII under different pollution levels
Fig.8 Spatial distribution of SUHII under different pollution levels
3   结论
（1）北京市地表城市热岛强度的月、季间变化明显，主要受土地覆盖类型影响，夏季高于冬季，冠层城市热岛强度的月、季间变化较小。
（2）PM2.5质量浓度与冠层城市热岛强度、地表城市热岛强度均呈显著负相关，相关系数分别为-0.5199和-0.6115。随着PM2.5质量浓度的增加，冠层城市热岛强度与地表城市热岛强度均随之下降。
（3）昼间地表城市热岛强度与PM2.5质量浓度的相关性高于夜间，证明了城市热岛强度与PM2.5之间存在相关性的原因在于地面吸收太阳辐射的强弱变化。
（4）PM2.5质量浓度变化对地表城市热岛的空间分布有显著影响。随着PM2.5质量浓度的增加，强热岛空间范围向城区缩减。

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CHEN Chen

LI Cuilin

SUN Jixing

LI Hui
RSLiHui@cug.edu.cn

National Natural Science Foundation of China (41772352); Open Fund of Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Land and Resources (KF-2018-03-055)

Journal of Earth Environment