Junjie Tian
Biographic Data
| ID | 7927559 |
|---|---|
| NAME | Junjie Tian |
| GIVEN NAMES | Junjie |
| FAMILY NAME | Tian |
| SIGNATURE | TIAN J |
| AFFILIATIONS | College of Geography and Remote Sensing Science Xinjiang University Urumqi China |
| VERIFIED | No |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 2 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2022 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Unveiling the Advantages of UV −Vis/ NIR − pXRF Data Fusion for Precise Estimation of Soil Heavy Metals in Farmland
Heavy metal contamination in agricultural soils threatens ecosystem stability and food safety. Rapid and accurate estimation of arsenic (As), cadmium (Cd), and lead (Pb) is therefore essential for environmental protection and soil remediation. Near‐field sensing technologies provide a fast and cost‐efficient alternative to laboratory analysis, yet single‐spectrum approaches often suffer from limited information coverage and reduced prediction acc…
Quantifying the Carbon Reduction Potential of Recycling Construction Waste Based on Life Cycle Assessment
The recycling of construction waste is key to reducing waste generation and CO 2 emissions. This study aimed to develop a quantitative model for analyzing the carbon reduction potential of recycling construction, demolition, and renovation waste (CDRW) in Jiangsu province. The waste generation rate calculation method and nonlinear autoregressive artificial neural network model were used to estimate and predict CDRW generation. The life cycle asse…
No prominent works on this page.
Quantifying the Carbon Reduction Potential of Recycling Construction Waste Based on Life Cycle Assessment
The recycling of construction waste is key to reducing waste generation and CO 2 emissions. This study aimed to develop a quantitative model for analyzing the carbon reduction potential of recycling construction, demolition, and renovation waste (CDRW) in Jiangsu province. The waste generation rate calculation method and nonlinear autoregressive artificial neural network model were used to estimate and predict CDRW generation. The life cycle asse…
Unveiling the Advantages of UV −Vis/ NIR − pXRF Data Fusion for Precise Estimation of Soil Heavy Metals in Farmland
Heavy metal contamination in agricultural soils threatens ecosystem stability and food safety. Rapid and accurate estimation of arsenic (As), cadmium (Cd), and lead (Pb) is therefore essential for environmental protection and soil remediation. Near‐field sensing technologies provide a fast and cost‐efficient alternative to laboratory analysis, yet single‐spectrum approaches often suffer from limited information coverage and reduced prediction acc…
Carbon fibers (1 work) · Civil engineering (1 work) · Computer Science (1 work) · Data pre-processing (1 work) · Demolition (1 work) · Demolition waste (1 work) · Ecology (1 work) · Engineering (1 work) · Environmental Engineering (1 work) · Environmental Science (1 work)