Forecasting uncertainties among meteorological fields have long been recognized as the main limitation on the accuracy and predictability of air quality forecasts.However,the particular impact of meteorological foreca...Forecasting uncertainties among meteorological fields have long been recognized as the main limitation on the accuracy and predictability of air quality forecasts.However,the particular impact of meteorological forecasting uncertainties on air quality forecasts specific to different seasons is still not well known.In this study,a series of forecasts with different forecast lead times for January,April,July,and October of 2018 are conducted over the Beijing-Tianjin-Hebei(BTH)region and the impacts of meteorological forecasting uncertainties on surface PM_(2.5)concentration forecasts with each lead time are investigated.With increased lead time,the forecasted PM_(2.5)concentrations significantly change and demonstrate obvious seasonal variations.In general,the forecasting uncertainties in monthly mean surface PM_(2.5)concentrations in the BTH region due to lead time are the largest(80%)in spring,followed by autumn(~50%),summer(~40%),and winter(20%).In winter,the forecasting uncertainties in total surface PM_(2.5)mass due to lead time are mainly due to the uncertainties in PBL heights and hence the PBL mixing of anthropogenic primary particles.In spring,the forecasting uncertainties are mainly from the impacts of lead time on lower-tropospheric northwesterly winds,thereby further enhancing the condensation production of anthropogenic secondary particles by the long-range transport of natural dust.In summer,the forecasting uncertainties result mainly from the decrease in dry and wet deposition rates,which are associated with the reduction of near-surface wind speed and precipitation rate.In autumn,the forecasting uncertainties arise mainly from the change in the transport of remote natural dust and anthropogenic particles,which is associated with changes in the large-scale circulation.展开更多
考虑在函数型解释变量部分观测的情况下,用函数线性模型刻画与标量响应变量的关系.基于函数型主成分分析(Functional Principal Component Analysis,简称FPCA)实现了对缺失部分样本的重构,并通过实证分析,对一组北京市2010-2014年间统...考虑在函数型解释变量部分观测的情况下,用函数线性模型刻画与标量响应变量的关系.基于函数型主成分分析(Functional Principal Component Analysis,简称FPCA)实现了对缺失部分样本的重构,并通过实证分析,对一组北京市2010-2014年间统计的包括部分观测PM2.5数值的气象数据,分析了PM2.5作为部分观测函数型解释变量对标量响应变量平均气温的影响,结果表明了该方法具有处理缺失函数数据的现实意义.展开更多
Due to the transboundary nature of air pollutants,a province's efforts to improve air quality can reduce PM2.5 concentration in the surrounding area.The inter-provincial PM2.5 pollution transport could bring great...Due to the transboundary nature of air pollutants,a province's efforts to improve air quality can reduce PM2.5 concentration in the surrounding area.The inter-provincial PM2.5 pollution transport could bring great challenges to related environmental management work,such as financial fund allocation and subsidy policy formulation.Herein,we examined the transport characteristics of PM2.5 pollution across provinces in 2013 and 2020 via chemical transport modeling and then monetized inter-provincial contributions of PM2.5 improvement based on pollutant emission control costs.We found that approximately 60%of the PM2.5 pollution was from local sources,while the remaining 40%originated from outside provinces.Furthermore,about 1011 billion RMB of provincial air pollutant abatement costs contributed to the PM2.5 concentration decline in other provinces during 2013-2020,accounting for 41.2%of the total abatement costs.Provinces with lower unit improvement costs for PM2.5,such as Jiangsu,Hebei,and Shandong,were major contributors,while Guangdong,Guangxi,and Fujian,bearing higher unit costs,were among the main beneficiaries.Our study identifies provinces that contribute to air quality improvement in other provinces,have high economic efficiency,and provide a quantitative framework for determining inter-provincial compensations.This study also reveals the uneven distribution of pollution abatement costs(PM2.5 improvement/abatement costs)due to transboundary PM2.5 transport,calling for adopting inter-provincial economic compensation policies.Such mechanisms ensure equitable cost-sharing and effective regional air quality management.展开更多
基金supported by the National Key Research and Development Program of China(No.2022YFC3700701)National Natural Science Foundation of China(Grant Nos.41775146,42061134009)+1 种基金USTC Research Funds of the Double First-Class Initiative(YD2080002007)Strategic Priority Research Program of Chinese Academy of Sciences(XDB41000000).
文摘Forecasting uncertainties among meteorological fields have long been recognized as the main limitation on the accuracy and predictability of air quality forecasts.However,the particular impact of meteorological forecasting uncertainties on air quality forecasts specific to different seasons is still not well known.In this study,a series of forecasts with different forecast lead times for January,April,July,and October of 2018 are conducted over the Beijing-Tianjin-Hebei(BTH)region and the impacts of meteorological forecasting uncertainties on surface PM_(2.5)concentration forecasts with each lead time are investigated.With increased lead time,the forecasted PM_(2.5)concentrations significantly change and demonstrate obvious seasonal variations.In general,the forecasting uncertainties in monthly mean surface PM_(2.5)concentrations in the BTH region due to lead time are the largest(80%)in spring,followed by autumn(~50%),summer(~40%),and winter(20%).In winter,the forecasting uncertainties in total surface PM_(2.5)mass due to lead time are mainly due to the uncertainties in PBL heights and hence the PBL mixing of anthropogenic primary particles.In spring,the forecasting uncertainties are mainly from the impacts of lead time on lower-tropospheric northwesterly winds,thereby further enhancing the condensation production of anthropogenic secondary particles by the long-range transport of natural dust.In summer,the forecasting uncertainties result mainly from the decrease in dry and wet deposition rates,which are associated with the reduction of near-surface wind speed and precipitation rate.In autumn,the forecasting uncertainties arise mainly from the change in the transport of remote natural dust and anthropogenic particles,which is associated with changes in the large-scale circulation.
文摘考虑在函数型解释变量部分观测的情况下,用函数线性模型刻画与标量响应变量的关系.基于函数型主成分分析(Functional Principal Component Analysis,简称FPCA)实现了对缺失部分样本的重构,并通过实证分析,对一组北京市2010-2014年间统计的包括部分观测PM2.5数值的气象数据,分析了PM2.5作为部分观测函数型解释变量对标量响应变量平均气温的影响,结果表明了该方法具有处理缺失函数数据的现实意义.
基金National Natural Science Foundation of China under Grant No.72171157 and 72140005.
文摘Due to the transboundary nature of air pollutants,a province's efforts to improve air quality can reduce PM2.5 concentration in the surrounding area.The inter-provincial PM2.5 pollution transport could bring great challenges to related environmental management work,such as financial fund allocation and subsidy policy formulation.Herein,we examined the transport characteristics of PM2.5 pollution across provinces in 2013 and 2020 via chemical transport modeling and then monetized inter-provincial contributions of PM2.5 improvement based on pollutant emission control costs.We found that approximately 60%of the PM2.5 pollution was from local sources,while the remaining 40%originated from outside provinces.Furthermore,about 1011 billion RMB of provincial air pollutant abatement costs contributed to the PM2.5 concentration decline in other provinces during 2013-2020,accounting for 41.2%of the total abatement costs.Provinces with lower unit improvement costs for PM2.5,such as Jiangsu,Hebei,and Shandong,were major contributors,while Guangdong,Guangxi,and Fujian,bearing higher unit costs,were among the main beneficiaries.Our study identifies provinces that contribute to air quality improvement in other provinces,have high economic efficiency,and provide a quantitative framework for determining inter-provincial compensations.This study also reveals the uneven distribution of pollution abatement costs(PM2.5 improvement/abatement costs)due to transboundary PM2.5 transport,calling for adopting inter-provincial economic compensation policies.Such mechanisms ensure equitable cost-sharing and effective regional air quality management.
