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
Accurately identifying and quantifying the factors influencing PM_(2.5) pollution is of great significance for the prevention and control of pollution. However, the redundancy among potential factors of PM_(2.5) may b...Accurately identifying and quantifying the factors influencing PM_(2.5) pollution is of great significance for the prevention and control of pollution. However, the redundancy among potential factors of PM_(2.5) may be overlooked. Meanwhile, the inconsistent spatial distribution of the natural and socioeconomic conditions brings unique implications for the cities within a region, which may lead to an uncertain understanding of the relationship between pollution and environmental factors. This study focused on the Beijing-TianjinHebei(BTH) Region, China, which presents complex and varied background conditions. Potential impact factors on PM_(2.5) were firstly screened by combining systematic cluster analysis with a random forest recursive feature elimination algorithm. Then, the representative multi-factor responsible for PM_(2.5) pollution in the region during the key period of 2014–2018(when the strict national air pollution control policy was implemented). The results showed that the key driving factors of PM_(2.5) pollution in the BTH cities are different, indicating that the uniqueness of a city will have an impact on the leading causes of pollution. Further discussion shows that air control policy provides an effective way to improve air quality. This study aims to deepen the understanding of the risk drivers of air pollution within the BTH Region. In the future, it is recommended that more attention should be paid to the specific differences between the cities when formulating PM_(2.5) concentration control measures.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.
基金Under the auspices of National Natural Science Foundation of China (No. 42171094)Natural Science Foundation of Shandong Province (No. ZR2021MD095, ZR2021QD093)Humanities and Social Science Foundation of Ministry of Education of China (No. 20YJCZH198)。
文摘Accurately identifying and quantifying the factors influencing PM_(2.5) pollution is of great significance for the prevention and control of pollution. However, the redundancy among potential factors of PM_(2.5) may be overlooked. Meanwhile, the inconsistent spatial distribution of the natural and socioeconomic conditions brings unique implications for the cities within a region, which may lead to an uncertain understanding of the relationship between pollution and environmental factors. This study focused on the Beijing-TianjinHebei(BTH) Region, China, which presents complex and varied background conditions. Potential impact factors on PM_(2.5) were firstly screened by combining systematic cluster analysis with a random forest recursive feature elimination algorithm. Then, the representative multi-factor responsible for PM_(2.5) pollution in the region during the key period of 2014–2018(when the strict national air pollution control policy was implemented). The results showed that the key driving factors of PM_(2.5) pollution in the BTH cities are different, indicating that the uniqueness of a city will have an impact on the leading causes of pollution. Further discussion shows that air control policy provides an effective way to improve air quality. This study aims to deepen the understanding of the risk drivers of air pollution within the BTH Region. In the future, it is recommended that more attention should be paid to the specific differences between the cities when formulating PM_(2.5) concentration control measures.
