Journal of Environmental Engineering Technology•2026•DOI: 10.13205/j.hjgc.202604017
The Shule River Basin, a typical arid inland river basin, faces critical water scarcity that threatens ecological security and sustainable development. This study integrated the FLUS and InVEST models to simulate water yield in 2030 and 2050 under three climate scenarios (SSP119, SSP245, SSP585). Geographic detectors quantified the driving mechanisms of natural and human factors. Results showed: (1) Desert dominates land use (78.6% in 2020). Under SSP119, desert area decreases by 0.69% by 2050, while under SSP585 it expands by 5.7%, with grassland loss of 23.0%, indicating severe ecological degradation. (2) Water yield exhibits a south-high, north-low spatial pattern, with high values in glacier-covered and high-altitude areas. SSP119 yields the most significant increase (147.6×10^8 t by 2050), whereas SSP585 shows minimal increase (43.9×10^8 t) due to extreme climate. (3) Precipitation and DEM are core driving factors; the interaction between land use type and precipitation has the strongest influence, implying that artificial land use changes can significantly regulate water yield. This multi-scenario framework provides decision support for water resource management and ecological governance in arid inland river basins.
Chinese Journal of Environmental Engineering•2026•DOI: 10.12030/j.cjee.202509072
Accurate identification of pollutant emission source parameters is critical for effective pollution response. This study evaluates the performance of genetic algorithm (GA), Nelder-Mead simplex (NM), particle swarm optimization (PSO), and their coupled variants on multi-dimensional, multi-extremum benchmark functions, and develops a source parameter inversion technique integrating PSO-NM with a Gaussian dispersion model. Validation via sulfur hexafluoride (SF6) single-point and multi-point release experiments demonstrates that PSO-NM achieves mean values closest to theoretical optima on Shubert, Hartmann, and Shekel functions, with superior stability and precision. In single-point source experiments, the relative deviation of source strength (Q) inversion ranges from -27.1% to 38.5%, with positional errors below 10 m, indicating robust convergence and repeatability. Multi-point source inversion exhibits stability across two scenarios but with reduced accuracy compared to single-point cases. When source strength is unknown, inversion accuracy for low-release sources (relative deviation 37.3%-70.4%) surpasses that for high-release sources; when position is unknown, positional deviations generally remain below 50 m, with low-release sources yielding better x0 deviations (-1.6 to 8.2 m) but slightly worse y0, z0, and distance parameters. Inversion errors primarily stem from meteorological non-stationarity, inter-source interference, algorithmic local optima, low-concentration measurement noise, and model assumptions. Future improvements may incorporate real-time meteorological correction and source-specific constraints to enhance accuracy and robustness in complex scenarios. The findings provide technical support for precise source tracing, monitoring, and refined management of pollutant emissions at microscale in industrial parks and enterprises.