The unbalanced growth of employment among regions is ultimately attributed to the difference in industrial structure and industrial competitiveness. At the regional level, both shift-share EM1 and EM2 models can realize the purpose of the shift-share model, that is, thoroughly separate the effects of the industrial structure and industrial competitiveness on employment growth deviation. While the expectation is to further achieve a complete separation on employment growth deviation at the industrial level, the EM1 model is not competent, and this is precisely the role of the EM2 model—to separate the employment growth deviation caused by the differences in industrial structure and industrial competitiveness at the industrial level. Therefore, the shift-share EM2 model can realize the separation of the structural component and competitive component at the industrial level. In the process of industrialization, the manufacturing industry presents a significant trend of unbalanced growth in both spatial and industrial dimensions. The setting of the share component in the EM2 model covers the balanced growth of employment in both spatial and industrial dimensions. The allocation component is reinterpreted as the magnification or inhibition effect of industrial competitiveness on the initial industrial structure. In order to exclude the influence of the regional employment scale difference, the concept and calculation method of the regional deviation degree is put forward. Finally, the spatial pattern of unbalanced employment growth in China’s manufacturing industry from 1999 to 2019 was analyzed using the shift-share EM2 model at the regional level. The results show that the spatial imbalance trend of employment growth in China’s manufacturing industry is significant, the regional deviation scale of employment growth forms a north–south difference pattern, and the regional deviation degree forms a southeast to northwest difference pattern. Taking Sichuan Province as an example, the application of the shift-share EM2 model at the industrial level is demonstrated.

Thallium (Tl) contamination of soils in lead-zinc mining areas poses potential ecological risks, and the impact of microplastics on Tl accumulation by hyperaccumulator plants remains unclear. This study examined soils collected from the Daliangzi lead-zinc mining area to investigate the characteristics of Tl contamination. These soil samples were used in plant cultivation experiments. The thallium contents in both the soil and plant samples were determined using acid digestion followed by inductively coupled plasma mass spectrometry (ICP-MS). The contamination level, plant enrichment capacity, and ecological risk were then comprehensively evaluated through the Geo-accumulation index (I_geo), Bioconcentration factor (BCF), and potential ecological risk index. The results indicated that the I_geo of Tl in the soil was 2.413, corresponding to a moderately to heavily polluted level, which necessitates focused attention. Polyethylene exhibited markedly opposing effects on Pteris vittata and Solanum nigrum: it significantly promoted Tl accumulation in the former, while distinctly inhibiting it in the latter. Microplastics could modify the pH value of soil, as well as the contents of nitrogen (N) and phosphorus (P). Risk assessment indices indicated that Tl pollution in this region reaches a very high contamination level with moderate potential ecological risk. Polyethylene and polypropylene demonstrated a species-specific promoting effect on Tl adsorption by the two hyperaccumulator plants, with polyethylene significantly enhancing the Tl accumulation capacity of P. vittata and polypropylene distinctly promoting Tl adsorption in S. nigrum.

The Anning River, traversing the mineral-rich Panxi region, is highly susceptible to contamination by potentially toxic elements (PTEs). This study systematically investigated the contamination profiles, source apportionment, and probabilistic human health risks of eight PTEs in the surface sediments of the basin. Index-based evaluations revealed that Cd acts as the dominant ecological threat, exhibiting extreme enrichment, whereas V, Cr, and Ni reflect natural background signatures. Receptor modeling via Positive Matrix Factorization (PMF) successfully decoupled four distinct sources: mining and smelting emissions (Cd, Zn), natural lithogenic weathering (V, Cr, Ni), mixed traffic/urban inputs (Pb, Cu), and a Tl-specific mixed source. Crucially, while deterministic approaches suggested safe exposure levels, probabilistic Monte Carlo simulations uncovered hidden vulnerabilities: children face a striking 60.51% probability of exceeding the acceptable total carcinogenic risk (TCR) threshold of 1.0 × 10⁻⁴, primarily governed by Cr and Ni. These findings underscore the urgent need for differentiated environmental management in similar mining-impacted basins. Specifically, stringent source controls for Cd must be implemented alongside exposure pathway interruptions to safeguard vulnerable demographics from Cr and Ni.