Ground deformation at the natural resource extraction area results from the interplay of multiple anthropogenic and hydrogeological factors. Identifying the main factors and their relationship with deformation is crucial for mitigating disasters when extreme events happen. Here, we show how multimodal artificial intelligence models can disentangle the effects of dynamic and static features related to deformation in the Rhineland coalfield in Germany. The public dataset was applied in this study, as narrated above. Among them, InSAR and groundwater are time-series datasets; the rest are static datasets. These datasets are original and were used to generate the experimental dataset in this study. Detailed information can be obtained from the paper.

Ground deformation at the natural resource extraction area results from the interplay of multiple anthropogenic and hydrogeological factors. Identifying the main factors and their relationship with deformation is crucial for mitigating disasters when extreme events happen. Here, we show how multimodal artificial intelligence models can disentangle the effects of dynamic and static features related to deformation in the Rhineland coalfield in Germany. The public dataset was applied in this study, as narrated above. Among them, InSAR and groundwater are time-series datasets; the rest are static datasets. These datasets are original and were used to generate the experimental dataset in this study. Detailed information can be obtained from the paper.

RSTB-2023-0240 Tree shrew data.zip

RSTB-2023-0240 Tree shrew data.zip

We have compiled high-resolution emission inventory of full-volatility organic from cooking souce in mainland China from 2015 to 2021. This dataset provides multi-dimensional, multi-resolution emissions for anaylsis and application. First, the dataset provide provincial-level emissions by year, province, subsectors and volatility bin in the xlsx file. - The emissions are all in kt/y. - The years include every year from 2015-2021. - The provinces cover all 31 provinces of mainland China. - The subsectors include cuisine-specific commercial cooking (The nine cuisines are home-style cuisine, Chinese fast food and snacks, hotpot, barbecue, Sichuan-Hunan cuisine, Guangdong-Fujian cuisine, Jiangsu-Zhejiang cuisine, other Chinese cuisines and non-Chinese cuisines.), home cooking, and canteen cooking. -The volatility range is expressed as log10C* (μg/m3), with values of ≤-2, -1,0,1,2,3,4,5,6,≥7. For the commercial cooking emissions with point-source resolution, the dataset report the emission amount by volatility bin, location (longitude and latitude), cuisine type and province of every commercial restaurant in China in 2021 in the csv file. To meet the requirements of the atmospheric chemical transport model, we also provide gridded emissions in China in 2021, at a resolution of 27 km × 27 km, by four volatility ranges and three types of cooking sources, in the txt files. Each column represents one of the cooking sources, and each row represents a grid. The grids are arranged in a row-base order starting at the left-bottom corner of the mesh (i.e. the first row in the file represents the grid emissions of the first row and first column in the south-west corner, the second row in the file represents the grid emissions of the first row and second column, and so on). The emissions are all in t/km2/yr. The file GRIDCRO2D_cn27 provide the geographic information of the grid. This file is generated by MCIP based on WRF simulation.

We have compiled high-resolution emission inventory of full-volatility organic from cooking souce in mainland China from 2015 to 2021. This dataset provides multi-dimensional, multi-resolution emissions for anaylsis and application. First, the dataset provide provincial-level emissions by year, province, subsectors and volatility bin in the xlsx file. - The emissions are all in kt/y. - The years include every year from 2015-2021. - The provinces cover all 31 provinces of mainland China. - The subsectors include cuisine-specific commercial cooking (The nine cuisines are home-style cuisine, Chinese fast food and snacks, hotpot, barbecue, Sichuan-Hunan cuisine, Guangdong-Fujian cuisine, Jiangsu-Zhejiang cuisine, other Chinese cuisines and non-Chinese cuisines.), home cooking, and canteen cooking. -The volatility range is expressed as log10C* (μg/m3), with values of ≤-2, -1,0,1,2,3,4,5,6,≥7. For the commercial cooking emissions with point-source resolution, the dataset report the emission amount by volatility bin, location (longitude and latitude), cuisine type and province of every commercial restaurant in China in 2021 in the csv file. To meet the requirements of the atmospheric chemical transport model, we also provide gridded emissions in China in 2021, at a resolution of 27 km × 27 km, by four volatility ranges and three types of cooking sources, in the txt files. Each column represents one of the cooking sources, and each row represents a grid. The grids are arranged in a row-base order starting at the left-bottom corner of the mesh (i.e. the first row in the file represents the grid emissions of the first row and first column in the south-west corner, the second row in the file represents the grid emissions of the first row and second column, and so on). The emissions are all in t/km2/yr. The file GRIDCRO2D_cn27 provide the geographic information of the grid. This file is generated by MCIP based on WRF simulation.