Enhanced validation and application of satellite-derived formaldehyde data for assessing photochemical pollution in the Chinese Greater Bay Area.

Formaldehyde (HCHO) is a key player in photochemical processes and serves as a crucial precursor in the formation of hydroxyl radicals and ozone (O). While satellite observations can offer extensive spatiotemporal distributions of HCHO at both global and regional scales, the reliability of these satellite-derived HCHO measurements remains uncertain. In this study, we generated a five-year (June 2018-May 2023) Level 3 HCHO dataset, by applying spatial averaging technique to the TROPOspheric Monitoring Instrument (TROPOMI) Level 2 data.

Multi-scale correlation reveals the evolution of socio-natural contributions to tropospheric HCHO over China from 2005 to 2022.

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.

Impact of climate change on SARS-CoV-2 epidemic in China.

The outbreak and prevalence of SARS-CoV-2 have severely affected social security. Physical isolation is an effective control that affects the short-term human-to-human transmission of the epidemic, although weather presents a long-term effect. Understanding the effect of weather on the outbreak allow it to be contained at the earliest possible.

Effects of hyperspectral data with different spectral resolutions on the estimation of soil heavy metal content: From ground-based and airborne data to satellite-simulated data.

Soil heavy metal distribution maps can provide decision-making information for pollution control and agricultural management. However, the estimation of heavy metals is sensitive to the resolution of the soil spectra due to their sparse content in soils. The purposes of this study were to test the sensitivity of Ni, Zn and Pb prediction results to variations in spectral resolution, then to map their spatial distributions over a large area.

[Monitor of Cyanobacteria Bloom in Lake Taihu from 2001 to 2013 Based on MODIS Temporal Spectral Data].

Algal bloom highly impacts the ecological balance of inland lakes. Remote sensing provides real-time and large-scale observations, which plays an increasingly significant role in the monitoring of algal bloom. Various Vegetation Indices (VIs) derived from satellite images have been used to monitor algae.

[Study on hyperspectral response to surface alteration by oil and gas microseepage under loess area].

The present takes the Qingyang, Gansu as an example, based on the mechanism of oil-gas microseepage, the spectra of the loess samples were measured, and the contents of carbonate minerals, clay minerals, Fe2+ and Fe3+, were analyzed. From their contents, it was shown that the carbonate mineralization and the red fading phenomenon are obvious for the known oil-gas field. Meanwhile, the parameters of absorption spectra of the loess samples were identified after the continuum was removed.

[Vegetation stress spectra and their relations with the contents of metal elements within the plant leaves in metal mines in Heilongjiang].

In this paper, Duobao Mt. and Tong Mt. were taken as examples.

[Spatiotemporal differentiation of construction land expansion in a typical town of south Jiangsu Province].

Choosing Xinzhuang Town in south Jiangsu Province as study area, and by using 1980, 1991, 2001, and 2009 high-resolution remote sensing images and GIS spatial analysis technology, an integrated expansion degree index model was established based on the existing indicators of construction land expansion, and the general and spatiotemporal differentiation characteristics of construction land expansion in the Town in three time periods of 1980-2009 were quantitatively analyzed. In 1980-2009, with the acceleration of rural urbanization and industrialization, the area of construction land in the Town increased significantly by 19.24 km2, and especially in 2001-2009, the expanded area, expanded contribution rate, and expansion intensity reached the maximum.

[Spatiotemporal pattern of urban growth and its driving forces in urban agglomeration of central Liaoning Province, China].

Based on the five temporal Landsat TM remote sensing data of 1988, 1992, 1997, 2000, and 2004, and by using GIS spatial analysis and landscape pattern analysis, this paper analyzed the spatiotemporal pattern of urban growth and its driving forces in the urban agglomeration of central Liaoning Province (UACLP). From 1988 to 2004, the urban area in the UACLP had being increased from 812.55 km2 to 1345.

[Spatiotemporal expansion of urban and rural built-up areas in Shenyang City: an analysis based on remote sensing and GIS technology].

By using 1985, 1995, 1997, 2000 and 2004 satellite images and GIS technology, three indices including built-up area density, expansion intensity index, and fractal dimension were chosen to analyze the spatiotemporal characteristics, spatial differentiation, and morphological changes of urban and rural built-up areas in Shenyang City in 1985-2004, with the main driving factors discussed. In the study period, the high-density area of urban built-up area in the City increased year by year, and that of rural built-up area changed slightly before 1997 but increased gradually thereafter. The increased area, expansion speed, and expansion intensity of built-up area were evidently greater in urban than in rural area.

[Simulation and prediction of urban and rural settlement growth and land use change in Yingkou City].

Based on the 1988, 1992, 1997, 2000, and 2004 Landsat TM remote sensing data of Yingkou City, Liaoning Province, the urban and rural settlement growth and land use change in the city from 2005 to 2030 were simulated and predicted by using the SLEUTH urban growth and land use change model with six scenarios (current trend scenario, no protection scenario, moderate protection scenario, managed growth scenario, ecologically sustainable growth scenario, and regional and urban comprehensive planning scenario). The results showed that in the city, the increased area of urban and rural settlement growth from 1988 to 2004 was 14.93 km2, and the areas of water area, orchard, mine, and agricultural land changed greatly from 1997 to 2004.