| Air pollution caused by atmospheric particulate matter is a serious threat to natural ecology,socioeconomic activities and human life.Atmospheric particulate matter is a major component of air pollution,and PM2.5 and PM 10 have a strong impact on urban environment and human health.In recent years,China has become one of the areas that are experiencing a high level of air pollution,particularly in Beijing-Tianjin-Hebei(BTH)region,where PM2.5 and PM 10 concentrations are seriously exceeded.Human activities lead to the change of land use and land cover,directly or indirectly affecting the diffusion,dilution and pooling of air pollution.This condition in turn leads to deterioration of the atmospheric environment and triggers a series of environmental problems.Therefore,monitoring the temporal variation and spatial distribution of atmospheric particulate matter,analysing the spatio-temporal characteristics of land use and land cover and exploring their impact on atmospheric particulate matter are necessary.These tasks are important to effectively control the concentration of atmospheric particulate matter,improve urban air quality and further protect and enhance the environment.This study focuses its analysis on the BTH region based on air-quality measured data and satellite remote sensing data,and comprehensively uses geographic information system,remote sensing and spatial statistics to examine the impact of land use and land cover on atmospheric particulate matter.A series of studies have been conducted on the spatio-temporal characteristics of atmospheric particulate matter,impact of land use and vegetation cover on atmospheric particulate matter and reduction effect of vegetation on atmospheric particulate matter.Research results provide scientific theoretical support for regional sustainable development and air pollution prevention.The main work and conclusions of this study are summarised as follows:(1)Based on the hourly measured data gathered by the air-quality monitoring station from 2015 to 2018,the spatial distribution of PM2.5 and PM 10 concentrations were obtained and the spatio-temporal characteristics of the particle concentration were analysed at different time scales such as year,season,month,day and hour.The results showed that the annual average values of PM2.5 and PM10 and the ratio of PM2.5/PM10 in the study area exhibited a trend of decreasing year by year from 2015 to 2018.The results confirmed a series of recent measures taken in the prevention and control of pollution in the study area.The concentration in winter each year is substantially higher than that in summer,and the concentration in spring and autumn are in between.The PM2.5/PM10 ratio is highest in winter,lowest in spring and medium in summer and autumn.The concentrations of PM2.5 and PM10 showed a bimodal double valley pattern in one day,peak at night and morning and valley in the morning and afternoon.In the spatial distribution,the atmospheric particulate matter concentration shows the spatial distribution characteristics in the southeast and northwest.The concentration in the Haihe Plain is higher than that in the north Yanshan-Taihangshan Mountain and Bashang Plateau.(2)Based on satellite remote sensing data,the distribution and change of land use in the study area from 2015 to 2018 were analysed.The landscape ecological index was used to explore the impact of the spatial distribution of land use on the atmospheric particulate matter concentration.The results show that there are many types of land use in the study area.Urban development caused the conversion of agricultural land and grassland and other natural types into construction land,and the afforestation activities caused the conversion of agricultural land and grassland to forestland,which are the two main types of land use change.Among all land use types,construction land has the highest atmospheric particulate matter concentration due to high-intensity human activities,and agricultural land follows.Forestland and grassland,which are the types covered by vegetation,are substantially lower in concentration than other types due to fewer emission sources,suppression of dust and adsorption of atmospheric particulate matter.The land use landscape pattern has a considerable impact on the atmospheric particulate matter concentration.Generally,a low degree of plaque aggregation,high degree of fragmentation,greater complexity of shape and high abundance of landscape indicate a low atmospheric particulate matter concentration.The more concentrated the construction land is,the easier it is to concentrate the emission sources and increase the atmospheric particulate matter concentration.When the forestland is well formed and connected,it can play a crucial role in suppressing the increase of atmospheric particulate matter concentration.(3)This study improves on the basis of the traditional sinusoidal function model.The improved model has a larger increase in fitting accuracy and effect than the traditional model.The improved model can be used to fit the data with considerable periodic fluctuation characteristics such as particle concentration and vegetation index,and can find effective information in the variation laws.The fitting R2 of the monthly mean values of PM2.5 and PM 10 in the BTH region reached 0.74 and 0.58,respectively,and the overall fitting effect was good.The fitting results of each land use type indicate that the atmospheric particulate matter concentration is relatively high in construction and agricultural land,and the difference between high and low values is large.The concentration in spring and summer slowly decreases,and then rises rapidly in autumn and winter.Human activity is the main cause of concentration fluctuations.The difference between atmospheric particulate matter concentration in forestland and grassland is the lowest,the difference between high value and low value is small,the fluctuation law of concentration is relatively uniform,and the peak appearance time is slightly later than that of other types.Natural sources have a stronger impact on their concentration fluctuations.The higher concentration types generally showed a rapid downward trend,and the lower concentration types mostly showed a slow decline or remained stable.(4)Based on the normalized difference vegetation index(NDVI),the dynamic changes of vegetation cover in the study area were explored and the effects of these changes on the atmospheric particulate matter concentration were analysed.The results showed that the vegetation coverage in the study area exhibited a steady upward trend.Large-scale afforestation and rapid urban expansion are the main reasons for the increase and decline of vegetation coverage in different regions,respectively.Vegetation coverage shows stable cyclical changes throughout the year.The temporal variation of vegetation cover had a considerable effect on atmospheric particulate matter concentration,and both showed a considerable negative correlation.An exponential or a power function best reflects the relationship.The R2 of the overall NDVI of the study area and the monthly mean regression equation of the two concentrations reached 0.580 and 0.601,respectively.Vegetation coverage and the annual mean values of PM2.5 and PM10 concentrations were the most correlated in the range of 3 km and 2.5 km,respectively.The most relevant spatial range expands in summer and decreases in winter,indicating that the lush vegetation has a strong effect on the atmospheric particulate matter concentration and a larger range.The comparison of typical sites shows that vegetation coverage effectively inhibits the increase in atmospheric particulate matter concentration and substantially shortens the duration of concentration increase.This condition makes the atmospheric particulate matter concentration lower than that in non-vegetated areas and the trend is more gradual.(5)In view of the important role of vegetation in the process of reducing particulate matter concentration,a scientific model was used to quantity the dry deposition process of vegetation.The reduction effect of vegetation on PM2.5 and PM 10 was estimated on urban and built-up area scales.The results showed that the total amount of PM 10 in the BTH region was reduced by 505200,465500,477200 and 396500 tonnes during 2015-2018.Furthermore,the total PM2.5 was reduced by 19400,19200,16400 and 12700 tonnes during the same period.Urban green space reduced the total amount of PM 10 by 8616,8319,8648 and 7227 tonnes and decreased the total amount of PM2.5 by 360,388,277 and 237 tonnes.The decline in the amount of reduction each year is mainly due to the considerable decrease in particle size.Among the cities,Chengde has the highest overall atmospheric particulate matter reduction,while Beijing's urban green space has substantially more reductions than those in other cities.Over 80%of the annual reductions are concentrated from May to September,and the larger leaf area is the main reason for the largest reduction during the growing season.The reduction efficiency of the forestland is substantially higher than that of the grassland,and deciduous broad-leaved trees are the main force in each forest.The reduction effect of vegetation on PM 10 is more considerable than that of PM2.5,but because PM2.5 has a stronger correlation with human production and living activities,its reduction effect has greater value,and the reduction of vegetation to PM2.5 also deserves attention.By increasing the area and density of green land through afforestation and returning farmland to forests,cities can fully utilise the self-purification function of green land,which plays an important role in the reduction and control of atmospheric particulate matter concentration. |
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