Analysis Of The PM_2.5 Pollution Spatiotemporal Distribution,Driving Mechanism,and Health Risks In China

Due to the economic development and accelerated urbanization process in China,the rapid growth of anthropogenic emissions has led to air pollution becoming an environmental risk that cannot be ignored.PM_2.5is one of the most prominent of the main atmospheric pollutants,which poses great harm to human health.Therefore,understanding the spatiotemporal evolution pattern of PM_2.5pollution from 2015 to2020,revealing the impact of ENSO and ENSO Modoki on PM_2.5pollution,and assessing the health risks of PM_2.5pollution are of great significance for PM_2.5pollution prevention in China Mainland.Based on the hourly monitoring PM_2.5concentration data from nearly 1500 ground monitoring stations across the country,this study took Chinese Mainland as the research area,and used spatial autocorrelation analysis,anomaly analysis,clustering analysis and wavelet analysis to analyze the temporal and spatial evolution pattern of PM_2.5pollution in China from2015 to 2020.Based on regression analysis,the impact of ENSO on PM_2.5pollution and its meteorological driving mechanism during two ENSO（El Ni（?）o-Southern Oscillation）events（2015-2017,2018-2020）were explored,and the impact of ENSO and ENSO Modoki on PM_2.5pollution and their meteorological driving mechanisms during cold and warm seasons were compared.Finally,based on the outdoor activity time and respiratory frequency affected exposure coefficient weighted integrated-exposure-response（IER）model,selecting ischemic heart disease（IHD）,chronic obstructive pulmonary disease（COPD）,and lung cancer（LC）as health endpoints,dividing population into young and middle-aged population（15-49 years old）,middle and elderly-aged population（50-69 years old）,and elderly-aged population（over 70 years old）,the spatiotemporal evolution of the health benefits and economic losses of PM_2.5pollution in China from 2015 to 2020 was systematically evaluated,and the impact of ENSO on the health benefits of PM_2.5pollution was analyzed.The conclusion was as follows:（1）PM_2.5pollution in China had significant spatial autocorrelation,with significant spatial agglomeration characteristics.However,with the overall downward trend of PM_2.5pollution in China Mainland,the spatial autocorrelation of PM_2.5pollution was decreasing year by year.Higher PM_2.5concentration stations were usually found in North China,followed by Central China and northern East China,while lower PM_2.5concentration stations were in Qinghai-Tibet Plateau,southern South China and Southwest China,especially in the coastal regions of South China.The PM_2.5concentration at most stations presented significant seasonal fluctuations:the PM_2.5concentration gradually decreased from winter to summer and reached to minimum in summer,then gradually rose from summer to winter,and reached to maximum in winter,presenting a"U"shaped trend as a whole.The PM_2.5concentration gradually decreased in spring and summer from March to August,and the range of high values gradually shrank;The PM_2.5concentration gradually increased from September to November in autumn,and the range of high values gradually expanded;from November to February of the next year,PM_2.5concentration first increased and then decreased.（2）The majority of the PM_2.5concentration anomalies in North China,East China,Northeast China and northern parts of Central China were positively correlated with the positive phase of ENSO（El Ni（?）o）;while the majority of the anomalies in South China,Southwest China,southern parts of Central China and East China were negatively correlated with the negative phase of ENSO（La Ni（?）a）.These PM_2.5anomalies could be partially explained by the effects of natural factor anomalies associated with ENSO.（3）When comparing the impact of ENSO and ENSO Modoki on PM_2.5pollution in cold and warm seasons,it was found that during the warm season,ENSO positive phase El Ni（?）o（negative phase La Ni（?）a）was likely to lead to an increase（decrease）in PM_2.5concentrations at most regions in China,while ENSO Modoki positive phase El Ni（?）o Modoki（negative phase La Ni（?）a Modoki）was likely to lead to a decrease（increase）in PM_2.5concentrations at most regions in China.During the cold season,ENSO positive phase El Ni（?）o（negative phase La Ni（?）a）easily leaded to an increase（decrease）in PM_2.5concentrations in eastern,northern,and western regions of China,and also leaded to a decrease（increase）in PM_2.5concentrations in South,Southwest,China and southern parts of Central and East China.PM_2.5concentration anomaly regression patterns of ENSO and ENSO Modoki were somewhat similar during the cold season.The impact of ENSO and ENSO Modoki on atmospheric circulation,especially on precipitation and 850-h Pa wind anomalies,could partially explain their impact on PM_2.5concentrations.（4）In terms of interannual variation,the total number of premature deaths from the three diseases in 2015,2017,and 2020 showed a downward trend,and the decline in 2020 was greater than that in 2017.Compared to 2015,the total number of premature deaths in 2017 decreased by 10%,and compared to 2017,the total number of premature deaths in 2020 decreased by 19.9%.The proportion of premature deaths from the three diseases was relatively stable,with little change over time.The proportion of premature deaths from ischemic heart disease（IHD）,chronic obstructive pulmonary disease（COPD）,and lung cancer（LC）was almost stable at around 6:2:2 per year.For populations of different age groups,whether young and middle-aged（15-49 years old）,middle and elderly-aged（50-69 years old）,or elderly aged population（over 70 years old）,the proportion of premature deaths from ischemic heart disease（IHD）was the largest and decreased first and then increased with age growing;the proportion of premature deaths from chronic obstructive pulmonary disease（COPD）was the smallest among young and middle-aged,middle and elderly-aged populations,but the proportion increased with age growing,leading to a higher proportion of premature deaths from COPD than from lung cancer（LC）in the elderly-aged population;the proportion of LC premature deaths increased first and then decreased with age growing,with the lowest proportion among the elderly-aged population.The population with the largest proportion of premature deaths from the three diseases is the elderly-aged population,followed by the middle and elderly-aged population,and the smallest is the young and middle-aged population,indicating that population aging would significantly increase the health risk of PM_2.5pollution.In terms of spatial distribution,the cities with the highest total number of premature deaths were usually found in North China,northern Central China,central Northeast China,and northern East China,followed by southern Central China,southern East China,South China,and Southwest China.Qinghai-Tibet Plateau and Inner Mongolia had the lowest total number of premature deaths,while except for the southwest of Xinjiang,the Northwest China also had the lowest total number of premature deaths.Due to the extremely high PM_2.5pollution,the total number of premature deaths in the southwest of Xinjiang was higher than that in other regions in Northwest China.