Study on Land-use Changes and Their Impacts on Air Pollution in Chengdu

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 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.

Characteristics of LDAPS-Predicted Surface Wind Speed and Temperature at Automated Weather Stations with Different Surrounding Land Cover and Topography in Korea

“…Figure 4 shows WE and TE for Uf AWSs. Numerical weather prediction models are well known to overestimate the roughness lengths and surface wind speeds [34][35][36] in urban areas because most of them did not sufficiently reflect the effects of roughness such as buildings. LDAPS overestimated wind speeds measured at a height of 10 m and underestimated temperatures measured at a height of 1.5 m at most Uf AWSs (Figure 4).…”

Retrieval of Land-Use/Land Cover Change (LUCC) Maps and Urban Expansion Dynamics of Hyderabad, Pakistan via Landsat Datasets and Support Vector Machine Framework

“…Thus, corresponding assessments of temperature change caused by urbanization processes are important, because associated climatic variations could easily reduce thermal comfort, and trap pollutants within street canyons, especially in metropolitan cities, as a result causing devastating health impacts to residents. Therefore, on top of monitoring temperature changes within urbanized areas, one should also focus on evaluating how changing land-use patterns, local traffic and mobility conditions, and meteorological conditions could affect pollutant distributions at fine spatial resolutions, via satellite remote sensing techniques and modelling approaches [130][131][132].…”

Impacts of Projected Urban Expansion on Rainfall and Temperature during Rainy Season in the Middle-Eastern Region in Tanzania

“…The LULC was modified using the methodology developed by the previous studies [31,60,61]. External tool such as Geographic Information System Software (GIS) was used to extrapolate the data [62]. Due to the difference in format between Seto and MODIS, the data were manipulated using the windows PowerShell, ArcGIS and Java (see the supplement material S1).…”