| PM2.5 refers to solid particles suspended in the air,whose aerodynamic diameter is less than or equal to 2.5?m.It can enter the human body through the respiratory tract with adsorbed toxic compounds and stay in the bronchi and alveoli,thereby endangering human health.A large number of epidemiological studies have shown that long-term exposure to PM2.5 will increase the morbidity and mortality of respiratory and cardiovascular diseases.In 2017,there were nearly 3 million premature deaths that were associated with ambient PM2.5 pollution,of which more than half came from China and India.The Guangdong-Hong Kong-Macao Greater Bay Area(GBA)is the most developed,potential and dynamic region in China,with a large economic aggregate and rapid economic growth.According to the statistics data in 2017,the GDP of the GBA has reached 10 trillion yuan,surpassing the San FranciscoBay Area(6 trillion yuan),closely following the New York Bay Area(11.2 trillion yuan),and becoming Bay Area in the world in terms of economic volume.However,in terms of ecological environment,and air quality the GBA still faces huge challenges,with PM2.5 annual concentration in the GBA more than twice that of the other three Bay Areas.The ground-based monitoring stations can provide PM2.5 monitoring data,but only for the location itself.It is more urgent and practical for public and government to timely understand the spatial distribution of PM2.5 in the entire area.In this study,hourly and monthly PM2.5 data from Jun,2014 to May,2019 were collected in 9 mainland cities,and Hong Kong and Macaw,respectively.The main purpose of this paper is to analyze the temporal and spatial distribution of PM2.5 concentration in the GBA over the past five years.It will include exploratory data analysis of PM2.5 concentration,identifying significant factors associated with PM2.5 concentration,and applying LUR regression model to reveal spatial distribution of PM2.5 at different time periods in the study area.The main conclusions of this paper are as follows:1)Temporal pattern of PM2.5: during the four natural years of 2015-2018,the daily average value of PM2.5 concentration all showed a consistent parabola shape with an upward opening;overall,the number of PM2.5 moderate pollution days from January to April and October to December is higher than that from May to September,with two peaks in January and December;PM2.5 concentration in winter is the highest among the four quarters,while summer has the lowest;PM2.5 concentration in the Greater Bay Area has decreased within 5 years.2)LUR model and validation: the results of five LUR models can explain more than 79% variance of PM2.5;the mean RMSE of the leave-one-out and 10-fold crossvalidation model are between 1.87 and 2.90,respectively,which imply the results of the five LUR models established are reliable.3)Spatial pattern of PM2.5: the spatial distribution of PM2.5 concentration varies greatly in different seasons,and the highly polluted concentration areas vary according to the season;in summer,it is less polluted by PM2.5,and the entire GBA is within the national average annual secondary index;winter is the season with the highest degree of PM2.5 pollution in a year,and over 3/4 of the areas have not reached the average annual secondary index;the PM2.5 pollution level of coastal cities is lower than that of inland cities... |
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