[Retrieving Dustfall Distribution in Beijing City Based on Ground Spectral Data and Remote Sensing]

Area of Science:

• Environmental Science
• Remote Sensing
• Air Quality Monitoring

Background:

• Vegetation dust accumulation serves as an indicator of urban air pollution.
• Analyzing spatial dust-fall patterns on urban vegetation is crucial for effective pollution control policies.
• Existing methods for dust-fall assessment can be costly and labor-intensive.

Purpose of the Study:

• To develop and validate a remote sensing-based method for mapping urban vegetation dust-fall distribution.
• To investigate the relationship between leaf spectral reflectance and dust-fall weight.
• To provide data support for environmental protection agencies in analyzing pollution sources.

Main Methods:

• Collected dust-fall weight, spectral reflectance, and leaf area data for four plant species in Beijing.
• Compared spectral reflectance curves of 'dusty' versus 'clean' leaves, focusing on near-infrared bands.
• Developed regression models using Landsat 8 green band and Normalized Difference Vegetation Index (NDVI) to estimate dust-fall weight, and interpolated results across Beijing.

Main Results:

• Dusty leaves showed significantly lower reflectance than clean leaves in the 780–1300 nm near-infrared range.
• High correlations (up to -0.626) were found between narrow band reflectance (520–620 nm and 1390–1600 nm) and dust-fall weight.
• The NDVI-based model (R²=0.465) accurately retrieved dust-fall distribution, showing high concentrations in the north, east, and downtown areas, and low concentrations in the south, west, and suburbs.

Conclusions:

• Leaf spectral analysis, particularly in near-infrared bands, is effective for assessing dust accumulation.
• Remote sensing, especially using NDVI, offers a low-cost and efficient method for mapping urban dust-fall distribution.
• The study provides valuable data for understanding urban air pollution dynamics and informing environmental management strategies.