Your browser doesn't support javascript.
loading
Mostrar: 20 | 50 | 100
Resultados 1 - 2 de 2
Filtrar
Más filtros











Base de datos
Intervalo de año de publicación
1.
Environ Pollut ; 276: 116707, 2021 May 01.
Artículo en Inglés | MEDLINE | ID: mdl-33609902

RESUMEN

The space-borne measured fine-mode aerosol optical depth (fAOD) is a gross index of column-integrated anthropogenic particulate pollutants, especially over the populated land. The fAOD is the product of the AOD and the fine-mode fraction (FMF). While there exist numerous global AOD products derived from many different satellite sensors, there have been much fewer, if any, global FMF products with a quality good enough to understand their spatiotemporal variations. This is key to understanding the global distribution and spatiotemporal variations of air pollutants, as well as their impacts on global environmental and climate changes. Modifying our newly developed retrieval algorithm to the latest global-scale Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol product (Collection 6.1), a global 10-year FMF product is generated and analyzed here. We first validate the product through comparisons with the FMF derived from Aerosol Robotic Network (AERONET) measurements. Among our 169,313 samples, the satellite-derived FMFs agreed with the AERONET spectral deconvolution algorithm (SDA)-retrieved FMFs with a root-mean-square error (RMSE) of 0.22. Analyzed using this new product are the global patterns and interannual and seasonal variations of the FMF over land. In general, the FMF is large (>0.80) over Mexico, Myanmar, Laos, southern China, and Africa and less than 0.5 in the Sahelian and Sudanian zones of northern Africa. Seasonally, higher FMF values occur in summer and autumn. The linear trend in the satellite-derived and AERONET FMFs for different countries was explored. The upward trend in the FMFs was particularly strong over Australia since 2008. This study provides a new global view of changes in FMFs using a new satellite product that could help improve our understanding of air pollution around the world.


Asunto(s)
Contaminantes Atmosféricos , Imágenes Satelitales , Aerosoles/análisis , África , Contaminantes Atmosféricos/análisis , Australia , China , Monitoreo del Ambiente , México , Material Particulado/análisis
2.
Environ Int ; 149: 106392, 2021 04.
Artículo en Inglés | MEDLINE | ID: mdl-33516989

RESUMEN

Despite their extremely small size, fine-mode aerosols have significant impacts on the environment, climate, and human health. However, current understandings of global changes in fine-mode aerosols are limited. In this study, we employed newly developed satellite retrieval data and an attentive interpretable deep learning model to explore the status, changes, and association factors of the global fine-mode aerosol optical depth (fAOD) and aerosol fine-mode fraction (FMF) from 2008 to 2017. At the global scale, the results show a significant increasing trend in land FMF (2.34 × 10-3/year); however, the FMF over the ocean and the fAOD over land and ocean did not reveal significant trends. Between 2008 and 2017, high levels of both fAOD (>0.30) and FMF (>0.75) were identified over China, southeastern Asia, India, and Africa. Seasonally, global land FMF showed high values in summer (>0.70) and low values in spring (<0.65), while land fAOD was high in summer (>0.15) but low in winter (<0.13). Importantly, Australia and Mexico experienced significant increasing trends in FMF during all four seasons. At the regional scale, a significant decline in fAOD was identified in China, which indicates that government emission controls and reductions have been effective in recent decades. The deep learning model was used to interpret the result and showed that O3 was significantly associated with changes in both the FMF and fAOD. This finding suggests the importance of synergizing the regulations for both O3 and fine particles. Our work comprehensively examined global spatial and seasonal fAOD and FMF changes and provides a holistic understanding of global anthropogenic impacts.


Asunto(s)
Contaminantes Atmosféricos , Aprendizaje Profundo , Aerosoles/análisis , Contaminantes Atmosféricos/análisis , Australia , China , Monitoreo del Ambiente , Humanos , India , México , Estaciones del Año
SELECCIÓN DE REFERENCIAS
DETALLE DE LA BÚSQUEDA