The expansion of urban areas and the increase in the number of buildings and urbanization characteristics, such as roads, affect the meteorological environment in urban areas, resulting in weakened pollutant dispersion. First, this paper uses GIS (geographic information system) spatial analysis technology and landscape ecology analysis methods to analyze the dynamic changes in land cover and landscape patterns in Chengdu as a result of urban development. Second, the most appropriate WRF (Weather Research and Forecasting) model parameterization scheme is selected and screened. Land-use data from different development stages in the city are included in the model, and the wind speed and temperature results simulated using new and old land-use data (1980 and 2015) are evaluated and compared. Finally, the results of the numerical simulations by the WRF-Chem air quality model using new and old land-use data are coupled with 0.25 • × 0.25 • -resolution MEIC (Multi-resolution Emission Inventory for China) emission source data from Tsinghua University. The results of the sensitivity experiments using the WRF-Chem model for the city under different development conditions and during different periods are discussed. The meteorological conditions and pollution sources remained unchanged as the land-use data changed, which revealed the impact of urban land-use changes on the simulation results of PM 2.5 atmospheric pollutants. The results show the following. (1) From 1980 to 2015, the land-use changes in Chengdu were obvious, and cultivated land exhibited the greatest changes, followed by forestland. Under the influence of urban land-use dynamics and human activities, both the richness and evenness of the landscape in Chengdu increased.(2) The microphysical scheme WSM3 (WRF Single-Moment 3 class) and land-surface scheme SLAB (5-layer diffusion scheme) were the most suitable for simulating temperatures and wind speeds in the WRF model. The wind speed and temperature simulation results using the 2015 land-use data were better than those using the 1980 land-use data when assessed according to the coincidence index and correlation coefficient. (3) The WRF-Chem simulation results obtained for PM 2.5 using the 2015 land-use data were better than those obtained using the 1980 land-use data in terms of the correlation coefficient and standard deviation. The concentration of PM 2.5 in urban areas was higher than that in the suburbs, and the concentration of PM 2.5 was lower on Longquan Mountain in Chengdu than in the surrounding areas.

Abstract:To perform a high-resolution aerosol optical depth (AOD) inversion from the HJ-1 satellites, a dark pixel algorithm utilizing the HJ-1 satellite data was developed based on the Moderate-Resolution Imaging Spectroradiometer (MODIS) algorithm. By analyzing the relationship between the apparent reflectance from the 1.65 μm and 2.1 μm channels of MODIS, a method for estimating albedo using the 1.65 μm channel data of the HJ-1 satellites was established, and a high-resolution AOD inversion in the Chengdu region based on the HJ-1 satellite was completed. A comparison of the inversion results with CE318 measured data produced a correlation of 0.957, respectively, with an absolute error of 0.106. An analysis of the AOD inversion results from different aerosol models showed that the rural aerosol model was suitable as a general model for establishing an aerosol inversion look-up table for the Chengdu region.

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The vertical distribution of the tropospheric ozone column concentration (OCC) in China from 2005 to 2020 was analysed based on the ozone profile product of the ozone monitoring instrument (OMI). The annual average OCC in the lower troposphere (OCCLT) showed an increasing trend, with an average annual increase of 0.143 DU. The OCC in the middle troposphere showed a downward trend, with an average annual decrease of 0.091 DU. There was a significant negative correlation between the ozone changes in the two layers. The monthly average results show that the peak values of OCCLT occur in May or June, the middle troposphere is significantly influenced by topographic conditions, and the upper troposphere is mainly affected by latitude. Analysis based on multi-source data shows that the reduction in nitrogen oxides (NOx) and the increase in volatile organic compounds (VOCs) weakened the titration of ozone generation, resulting in the increase in OCCLT. The increase in vegetation is closely related to the increase in OCCLT, with a correlation coefficient of up to 0.875. The near-surface temperature increased significantly, which strengthened the photochemical reaction of ozone. In addition, the increase in boundary layer height also plays a positive role in the increase in OCCLT.

Abstract. The Modular Emission Inventory Allocation Tool for Community Multiscale Air Quality Model (MEIAT-CMAQ) v1.0 is a resource that enables the refinement of coarse emission inventories by delivering complete temporal, species, and vertical allocations. This tool generates model-ready emission files for CMAQ, and it effectively addresses the challenges concerning the pinpointing of grid information and the spatial allocation for spatial surrogates with specific shapes. These features significantly enhance the accuracy of the allocation of emissions from transportation sectors. Additionally, MEIAT-CMAQv1.0 features an efficient operational algorithm and a modular design, thus conferring greater flexibility and making it suitable for both gridded and tabulated emissions inventories. By inputting pre-assessment and post-assessment emissions separately into the CMAQ model, we observe that post-allocation inventory has a significant positive effect on both O3 and PM2.5 simulations. The development of MEIAT-CMAQv1.0 provides valuable insights into the automated operation of air quality models and the development of emission inventory allocation tools.