A typical heavy pollution weather in Beijing-Tianjin-Hebei region from October 5 to 12, 2014 was selected for numerical simulation based on WRF-Chem model, using environmental monitoring data, meteorological observation data and reanalysis data to analyze the meteorological impact factors of the pollution process and the spatial and temporal variation characteristics of pollutants.

Vegetation parameter, especially the leaf area index (LAI), is a key variable that take part in the land-atmosphere interaction. Many studies have found that by updating LAI in land surface models can improve the simulation ability for land surface variables. This study used the coupled Community Land Model with explicit carbon and nitrogen components (CLM4CN) and Community Atmosphere Model (CAM) as well as Data Assimilation Research Testbed (DART) to update assimilated LAI, to improve the simulation ability for air temperature and precipitation. The Global Land Surface Data Assimilation System (GLDAS) reanalysis data were used to estimate the coupled CLM4CN/CAM/DART performance.

The air quality model can reproduce the processes of atmospheric transport, chemical reaction and removal, and analyze the spatio-temporal evolution law, internal mechanism and origin of air pollution. WRF-Chem model is the third generation of air quality model. Its biggest advantage is that the meteorological model and chemical transmission model are fully coupled in time and spatial resolution to achieve true online feedback, which is the main development direction of the future model. Therefore, this paper selected Xuzhou City, a heavy industry city with serious air pollution, as the research area, and took December when air quality was poor as the research time. WRF-Chem model was used to simulate and analyze meteorological parameters such as wind speed, temperature, relative humidity and concentration of air pollutants including PM2.5and O3in Xuzhou in December 2020. The simulation results were compared with the site data, and the simulation results under different parameter Settings of WRF-Chem mode were studied and compared.

As an important parameter to characterize land surface vegetation, leaf area index (LAI) is not only an important vegetation biophysical parameter that affects surface radiation transfer and material and energy balance, but also an important parameter to connect micro biogeochemical processes such as photosynthesis and respiration of vegetation. Because of the strong inhomogeneity of the lower land surface, correct description of the canopy structure as well as canopy physical and biogeochemical processes is the basis and prerequisite for the correct description of surface radiation characteristics and land-air interaction. In this paper, based on the Community Land Model (CLM4) and Data Assimilation Research Testbed (DART) developed by the National Center for Atmospheric Research (NCAR), the Global LAnd Surface Satellite (GLASS) LAI data are assimilated into the carbon and nitrogen component of CLM4 (CLM4-CN), and the impact of LAI change on climate change on a regional scale is further analyzed in the case of land-atmosphere coupling.

Studying evapotranspiration is of great importance for understanding the hydrological cycle and terrestrial ecosystem both at global and regional scales. In this study, the area of Qinghai lake from 1986 to 2022 were obtained by remote sensing technology, and relative hydrological variables including evapotranspiration (ET), transpiration, canopy water evaporation, direct evaporation from bare soil, as well as potential evaporation rate, were obtained from the Global Land Data Assimilation System (GLDAS) products during the same period to quantitatively analyze the impact of lake on the hydrological variables.

Methane is the world's second largest greenhouse gas after carbon dioxide, accounting for about one-sixth of the total greenhouse gas emissions. Due to the development of animal husbandry, carbon dioxide and methane produced in the process of animal husbandry breeding have gradually become one of the important sources of the greenhouse effect. The research of methane gas emissions from animal husbandry is of great significance to sorting out the sources of greenhouse gases. This paper is aimed at Hebei Province, using TROPOMI sensors onboard Sentinel-5P, TANSO-FTS onboard GOSAT observation data, and the related ground station data. The main research content: (1) The temporal and spatial distribution characteristics of methane in Hebei Province from 2009 to 2020; (2) The proportion of methane emissions from ruminants in Hebei Province Estimate using ruminant methane emission formula. The average proportion is 6.10% from 2009 to 2018.

This paper calculates the Air Quality Index (AQI) in Xuzhou from 2018 to 2020 and analyzes its annual and monthly changes in air quality to determine the month of severe pollution. Through AQI calculation and interpolation analysis of different locations in Xuzhou, the air quality conditions in different regions are determined. Through the HYSPLIT backward trajectory model, cluster analysis of the air masses in December 2020 is carried out. Combined with the PM2.5 concentration data released every hour by the Xuzhou State Control Station, PSCF and CWT methods are used to determine potential sources of PM2.5 pollution in different regions. This paper also uses the hourly PM2.5 data in Xuzhou area obtained from the retrieval of the Himawari-8/AHI AOD through the IGTWR model for the traceability analysis of pollutants. The results show that there are large potential pollution sources in Henan, northern Anhui, northern Hubei and other places. At the same time, pollutant emissions in parts of northern Jiangsu, southern Shanxi and Shandong province also contribute to PM2.5 in Xuzhou. This paper also traced the source of the PM2.5 heavy pollution days in Xuzhou in December 2020 and determined the location of the specific pollution source based on the actual situation.

Dust as a kind of atmospheric pollutants, its harm are gradually being paid attention to. However, most of the existing supervision of dust relies on ground monitoring sites, which has strong spatial limitations. In this paper, high-resolution remote sensing images are used to identify regional dust sources, which can identify dust at a more macroscopic level from “plane”. At the same time, regional particulate emission inventory was established based on multi-source data, and then the dust emission is quantified. This paper takes Quanshan District, Xuzhou City, Jiangsu Province as an example, and the experimental results show that the method can reflect the actual dust situation well, and provide scientific data support for the supervision of urban dust.