Abstract
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 PM2.5 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 PM2.5 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 PM2.5 in urban areas was higher than that in the suburbs, and the concentration of PM2.5 was lower on Longquan Mountain in Chengdu than in the surrounding areas.
Highlights
Land-use changes, including forest area reductions, increased desertification, the increase in urban areas, and the construction of fields around the sea, are the most direct impacts of human activities on nature
Upon using the land-use data from 2015, compared with the original data in Section 3.2.1.1, the coincidence index and the correlation coefficient improved, and the simulation results were close to the true values
When the simulation results of the 2015 data were compared with the original data in Section 3.2.1, it was found that the data in 2015 were improved in terms of the coincidence index and correlation coefficient, and the simulation results were close to the true values
Summary
Land-use changes, including forest area reductions, increased desertification, the increase in urban areas, and the construction of fields around the sea, are the most direct impacts of human activities on nature. Human activities have altered the natural vegetation, causing changes in heat, momentum, and water vapor exchange between the surface and the free atmosphere and affecting the weather and climate, affecting the migration and transformation of pollutants in the atmosphere [4,5,6]. Due to the increase in pollutant emissions caused by rapid industrialization and urbanization [7,8,9], the characteristics of urban regional air pollution have become prominent in the Sichuan Basin due to the synergistic effects of the unique topography and meteorological conditions that are unfavorable for pollution. The urban heat island effect accelerates secondary conversions of pollutants, while residents’
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