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The Study On The Spatial And Temporal Characteristics Of PM2.5 In Jiangsu Province

Posted on:2018-06-04Degree:MasterType:Thesis
Country:ChinaCandidate:T ChenFull Text:PDF
GTID:2311330512998567Subject:Cartography and Geographic Information System
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Chinese economy and urbanization has been developing in high speed.However,the environment pollution problems brought by the development of economy also have been becoming more and more serious.The sharp contradiction between economy and environment has threatened Chinese sustainable development.Since spring 2014,many areas in the South and the East have suffered serious hazes frequently.Several studies have proved that the high concentration PM2.5 played an important part for hazes.Because of aerodynamic diameter is less than or equal to 2.5 ?m,PM2.5 could stay in air for a long time,which could further lead to diseases like lung cancers,respiratory disease and cardiovascular disease,even the increase of death rates and severe deterioration of weather conditions.As a result it poses a threat to personal health and living environment.The economic gross of Jiangsu province current lead the nation,whereas environment problems are obvious under the rapid economic expansion especially the highly-attentioned PM2.5 pollution.Based on 13 cities in Jiangsu province air monitoring data from January 2013 to December 2016 and relative social economy data from 2014 to 2015 with several cities' meteorological data from 2013 to 2015,this paper studied PM2.5 spatial distribution characteristics,temporal variation characteristics?annual variation,seasonal variation,monthly variation and daily variation?and affected factors?meteorological factor,social economic factor?in Jiangsu province.The main studies contents and the conclusions are followed:?1?An analysis on temporal variation characteristics of PM2.5 concentration.Using various statistical methods,it studies 13 cities'variation characteristics of PM2.5 concentration in different times and makes the conclusion.From 2013 to 2016 the average concentration in Jiangsu province has been declining.The PM2.5 concentration in different seasons changes obviously,which is decreasing in line of winter,spring,autumn and summer.The average month PM2.5 concentration variations could be divided into "U"and "W" trends.The months of the lowest and the highest concentration are different in different cities,which are mainly distributed from July to September and December to January.The daily PM2.5 concentration variation is bimodal distribution,and the two maxim figures are two rush hours.The PM2.5 concentration is slightly different between weekdays and weekends in each city of Jiangsu province.?2?An analysis on spatial distribution characteristics of PM2.5 concentration.Using the average PM2.5 concentration to represent the whole city's PM2.5 spatial distribution characteristics,it designs the spatial distribution characteristics of annual and seasonal average PM2.5 concentrations.The serious polluted areas are mainly located in west inland of Jiangsu province,while the coastal areas are lightly polluted.Moreover the PM2.5 pollution among cities have significant difference in diverse seasons.?3?An analysis on affected factors of PM2.5 concentration.Using the correlation analysis methods of Pearson,it separately studies that how the other air pollutants,meteorological factors and social economic factors effect upon PM2.5 concentration.Particulate matters have remarkable relation.PM2.5 exhibits obviously positive correlation with pollutant gas like CO?NO2 and SO2;while it has no siginificant connection with O3,temperature and atmospheric pressure.The relative humidity have different effect upon PM2.5 concentration in cities.Wind speed and rainfall could weaken the PM2.5 concentration evidently.The analysis of social economy factors is not found a significant association between them and it will be analyzed and verified by expanding study area in the future study.
Keywords/Search Tags:Jiangsu province, PM2.5, Spatial and Temporal distribution, Meteorological conditions, Socioeconomics factors, Pearson correlation analysis
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