Air pollution has seriously endangered human health and the natural ecosystem during the last decades.Air quality monitoring stations(AQMS)have played a critical role in providing valuable data sets for recording regi...Air pollution has seriously endangered human health and the natural ecosystem during the last decades.Air quality monitoring stations(AQMS)have played a critical role in providing valuable data sets for recording regional air pollutants.The spatial representativeness of AQMS is a critical parameter when choosing the location of stations and assessing effects on the population to long-term exposure to air pollution.In this paper,we proposed a methodological framework for assessing the spatial representativeness of the regional air quality monitoring network and applied it to ground-based PM_(2.5)observation in the mainland of China.Weighted multidimensional Euclidean distance between each pixel and the stations was used to determine the representativeness of the existing monitoring network.In addition,the K-means clustering method was adopted to improve the spatial representativeness of the existing AQMS.The results showed that there were obvious differences among the representative area of 1820 stations in the mainland of China.The monitoring stations could well represent the PM_(2.5)spatial distribution of the entire region,and the effectively represented area(i.e.the area where the Euclidean distance between the pixels and the stations was lower than the average value)accounted for 67.32%of the total area and covered 93.12%of the population.Forty additional stations were identified in the Northwest,North China,and Northeast regions,which could improve the spatial representativeness by 14.31%.展开更多
Soil moisture is critical for climate prediction,ecological management,and disaster warning.However,multi-source datasets show spatiotemporal inconsistencies and uncertain regional applicability due to algorithmic and...Soil moisture is critical for climate prediction,ecological management,and disaster warning.However,multi-source datasets show spatiotemporal inconsistencies and uncertain regional applicability due to algorithmic and observational limitations.We assess the statistical performance and spatiotemporal variations of 23 global surface soil moisture datasets(1980-2023)from reanalysis,land surface models,and microwave remote sensing across global and regional scales(classified by K??ppen climates and IPCC land uses).Results show a slight long-term(1980-2023)global surface soil moisture decline(-4.30×10^(-4)m^(3)m^(-3)a^(-1)),with some datasets indicating short-term wetting(7.17×10^(-4)m^(3)m^(-3)a^(-1))post-2010(2010-2023).A dual-validation against 992 and a filtered subset of 483 highly representative in situ stations shows that most products perform moderately well(Pearson R≈0.5-0.7).Microwave remote sensing products,especially those based on SMAP,consistently demonstrate superior performance in capturing temporal dynamics(R≈0.7).Our analysis demonstrates that spatial representativeness error can mask true performance,with validation in the tropics improving dramatically after site filtering(mean R increase of 0.41).The findings highlight product-specific strengths and weaknesses,underscoring the necessity of a science-informed,application-specific approach to dataset selection for robust hydrological and climatic research.展开更多
基金funded by the National Natural Science Foundation of China (41977399)the National Key Research and Development Program (2017YFC0505800)
文摘Air pollution has seriously endangered human health and the natural ecosystem during the last decades.Air quality monitoring stations(AQMS)have played a critical role in providing valuable data sets for recording regional air pollutants.The spatial representativeness of AQMS is a critical parameter when choosing the location of stations and assessing effects on the population to long-term exposure to air pollution.In this paper,we proposed a methodological framework for assessing the spatial representativeness of the regional air quality monitoring network and applied it to ground-based PM_(2.5)observation in the mainland of China.Weighted multidimensional Euclidean distance between each pixel and the stations was used to determine the representativeness of the existing monitoring network.In addition,the K-means clustering method was adopted to improve the spatial representativeness of the existing AQMS.The results showed that there were obvious differences among the representative area of 1820 stations in the mainland of China.The monitoring stations could well represent the PM_(2.5)spatial distribution of the entire region,and the effectively represented area(i.e.the area where the Euclidean distance between the pixels and the stations was lower than the average value)accounted for 67.32%of the total area and covered 93.12%of the population.Forty additional stations were identified in the Northwest,North China,and Northeast regions,which could improve the spatial representativeness by 14.31%.
基金supported by the Strategic Priority Research Program of the Chinese Academy of Sciences(XDB0720400)the Third Xinjiang Scientific Expedition Program(2022xjkk0107)+2 种基金the West Light Foundation of the Chinese Academy of Sciences(xbzgzdsys-202208)the Program of China Scholarship Council(ICPIT-International Cooperative Program for Innovative Talents,202410630006)support from the European Research Council(ERC)Consolidator grant HEAT(101088405)。
文摘Soil moisture is critical for climate prediction,ecological management,and disaster warning.However,multi-source datasets show spatiotemporal inconsistencies and uncertain regional applicability due to algorithmic and observational limitations.We assess the statistical performance and spatiotemporal variations of 23 global surface soil moisture datasets(1980-2023)from reanalysis,land surface models,and microwave remote sensing across global and regional scales(classified by K??ppen climates and IPCC land uses).Results show a slight long-term(1980-2023)global surface soil moisture decline(-4.30×10^(-4)m^(3)m^(-3)a^(-1)),with some datasets indicating short-term wetting(7.17×10^(-4)m^(3)m^(-3)a^(-1))post-2010(2010-2023).A dual-validation against 992 and a filtered subset of 483 highly representative in situ stations shows that most products perform moderately well(Pearson R≈0.5-0.7).Microwave remote sensing products,especially those based on SMAP,consistently demonstrate superior performance in capturing temporal dynamics(R≈0.7).Our analysis demonstrates that spatial representativeness error can mask true performance,with validation in the tropics improving dramatically after site filtering(mean R increase of 0.41).The findings highlight product-specific strengths and weaknesses,underscoring the necessity of a science-informed,application-specific approach to dataset selection for robust hydrological and climatic research.
