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Monitoring the spatiotemporal distribution of formaldehyde (HCHO) is crucial for reducing volatile organic compounds (VOCs) emissions, and the long-term evolution of socio-natural sources contributions to tropospheric HCHO over China is still unclear. We propose an oversampling algorithm for quantitatively tracking the evolution of uncertainty, which lowers the uncertainty of the original Level 2 OMI HCHO data (50 % -105 %) to 0-50 %, and then we examine the evolution of contributions from various emissions sources applying multi-scale correlation. We found that the high formaldehyde vertical column densities (VCD) caused by human activities in eastern China are crossing the Hu Huanyong Line, which was formerly used to demarcate the population distribution. National-scale analysis indicate that HCHO VCD are significantly correlated with per capita Gross Domestic Product (per capita GDP) (r = 0.948) and Normalized Difference Vegetation Index (r = 0.864), while no substantial correlation with land surface temperature (LST) (r = 0.233). A valuable finding at city-scale is that the vast majority of cities exhibits clear latitude zoning characteristics in the correlation between HCHO VCD and per capita GDP. Diagnosis at pixel scale reveals that anthropogenic emissions continue to weaken the contributions of emissions caused by the increase in vegetation proportion. NDVI = 0.8 is the critical characteristic point where the contribution of natural source exceeds that of anthropogenic sources, while the point presents a decreasing trend in recent years due to the enhancement of human activities levels. Rise in LST over vegetation areas show positive driving effect on formaldehyde emissions, but continuous urbanization is diminishing this contribution. NDVI = 0.8 is a characteristic point to determine whether the contribution proportion of regional surface temperature to formaldehyde emissions from vegetation begun to rise. Our research identifies the evolutionary process and characteristics of the spatiotemporal distribution and socio-nature sources contributions of tropospheric formaldehyde of China from 2005 to 2022.

RESUMEN

In early 2020, two unique events perturbed ship emissions of pollutants around Southern China, proffering insights into the impacts of ship emissions on regional air quality: the decline of ship activities due to COVID-19 and the global enforcement of low-sulfur (<0.5%) fuel oil for ships. In January and February 2020, estimated ship emissions of NOx, SO2, and primary PM2.5 over Southern China dropped by 19, 71, and 58%, respectively, relative to the same period in 2019. The decline of ship NOx emissions was mostly over the coastal waters and inland waterways of Southern China due to reduced ship activities. The decline of ship SO2 and primary PM2.5 emissions was most pronounced outside the Chinese Domestic Emission Control Area due to the switch to low-sulfur fuel oil there. Ship emission reductions in early 2020 drove 16 to 18% decreases in surface NO2 levels but 3.8 to 4.9% increases in surface ozone over Southern China. We estimated that ship emissions contributed 40% of surface NO2 concentrations over Guangdong in winter. Our results indicated that future abatements of ship emissions should be implemented synergistically with reductions of land-borne anthropogenic emissions of nonmethane volatile organic compounds to effectively alleviate regional ozone pollution.

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Soil moisture content (SMC) plays an essential role in geoscience research. The SMC can be retrieved using an artificial neural network (ANN) based on remote sensing data. The quantity and quality of samples for ANN training and testing are two critical factors that affect the SMC retrieving results. This study focused on sample optimization in both quantity and quality. On the one hand, a sparse sample exploitation (SSE) method was developed to solve the problem of sample scarcity, resultant from cloud obstruction in optical images and the malfunction of in situ SMC-measuring instruments. With this method, data typically excluded in conventional approaches can be adequately employed. On the other hand, apart from the basic input parameters commonly discussed in previous studies, a couple of new parameters were optimized to improve the feature description. The Sentinel-1 SAR and Landsat-8 images were adopted to retrieve SMC in the study area in eastern Austria. By the SSE method, the number of available samples increased from 264 to 635 for ANN training and testing, and the retrieval accuracy could be markedly improved. Furthermore, the optimized parameters also improve the inversion effect, and the elevation was the most influential input parameter.

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RESUMEN

Satellite HCHO data are widely used as a reliable proxy of non-methane volatile organic compounds (NMVOCs) to constrain underlying emissions and chemistry. Here, we examine global significant changes in HCHO columns at the early stage of the COVID-19 pandemic (January-April 2020) compared with the same period in 2019 with observations from the TROPOspheric Monitoring Instrument (TROPOMI). HCHO columns decline (11.0%) in the Northern China Plain (NCP) because of a combination of meteorological impacts, lower HCHO yields as NO x emission plunges (by 36.0%), and reduced NMVOC emissions (by 15.0%) resulting from the lockdown. HCHO columns change near Beijing (+8.4%) due mainly to elevated hydroxyl radical as NO x emission decreases in a NO x -saturated regime. HCHO columns change in Australia (+17.5%), Northeastern Myanmar of Southeast Asia (+14.9%), Central Africa (+7.8%), and Central America (+18.9%), consistent with fire activities. Our work also points to other changes related to temperature and meteorological variations.