Fe–P separation and the behaviors of Fe and P in separation process of high-phosphorus oolitic hematite ore from western Hubei province in China were studied. Various parameters, including reducing temperature and time, dosage of the pulverized coal, grinding fineness of magnetic separation feed, magnetic field intensity and grinding fineness of flotation feed were investigated. Experimental results show that the iron concentrate with total iron grade of 61.78% and phosphorus content of 0.23% was obtained from an oolitic hematite ore containing 47.71% iron and 0.874% phosphorus by magnetization roasting-magnetic separation-reverse flotation separation process under the proper conditions. In the magnetization roasting process, the Fe and P grade was rising by 1.30% and 11.49%, respectively; the forms of Fe and P are oolite hematite (magnetite) and collophanite which mainly distributed inside oolite structure and a small portion being distributed in other shapes. In the magnetic separation process, the Fe is from 48.33% to 58.52%, and P grade was slipped to 0.70% from 0.97%, however, the forms of Fe and P have no change compared with the roasted ore. In the reverse flotation separation process, Fe is from 58.52% to 61.78%, phosphorus is from 0.70% to 0.23%, the flotation concentrate mainly contained fine iron mineral and phosphorus is finely disseminated inside the iron mineral.

Evaluating the carbon storage of forests and identifying the factors that influence it are essential in working towards the “dual carbon” goal. This assessment will facilitate research on carbon neutrality and promote regional ecological protection and development. This study utilized the “One Map” data of forest resources (2020) and the first year (2017) of the establishment of the national park in Wuyi Mountain National Park (WMNP). The continuous biomass expansion factor method, in conjunction with the vegetation carbon content coefficient, was employed to estimate the forest carbon storage within the park’s forested areas. Subsequently, the distribution of forest carbon storage was analyzed using remote sensing estimation methods, and a comparison was made between the forest carbon storage of these two years. Finally, correlation analysis and path analysis were conducted to identify the primary factors influencing forest carbon storage. The study findings reveal that in 2020, the total carbon storage in forests reached 4.851 × 10⁶ t C, with an average carbon density of 49.55 t C·hm⁻². Furthermore, the study identified positive correlations between dominant tree species, age groups, and elevation with carbon storage, whereas slope length and aspect were found to have negative correlations. Dominant tree species were observed to have the greatest impact on forest carbon storage in both 2017 and 2020, followed by age groups. These findings offer valuable scientific insights for the implementation of forest carbon storage pilot projects in WMNP.

Monitoring mangrove phenology requires frequent, high-resolution remote sensing data, yet satellite imagery often suffers from coarse resolution and cloud interference. Traditional methods, such as denoising and spatiotemporal fusion, faced limitations: denoising algorithms usually enhance temporal resolution without improving spatial quality, while spatiotemporal fusion models struggle with prolonged data gaps and heavy noise. This study proposes an optimized mangrove phenology extraction approach (OMPEA), which integrates Landsat and MODIS data with a denoising algorithm (e.g., Gap Filling and Savitzky–Golay filtering, GF–SG) and a spatiotemporal fusion model (e.g., Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model, ESTARFM). The key of OMPEA is that GF–SG algorithm filled data gaps from cloud cover and satellite transit gaps, providing high-quality input to ESTARFM and improving its accuracy of NDVI imagery reconstruction in mangrove phenology extraction. By conducting experiments on the GEE platform, OMPEA generates 1-day, 30 m NDVI imagery, from which phenological parameters (i.e., the start (SoS), end (EoS), length (LoS), and peak (PoS) of the growing season) are derived using the maximum separation (MS) method. Validation in four mangrove areas along the coastal China shows that OMPEA significantly improves the potential to capture mangrove phenology in the presence of incomplete data. The OMPEA significantly increased usable data, adding 7–33 Landsat images and 318–415 MODIS images per region. The generated NDVI series exhibits strong spatiotemporal consistency with original data (R²: 0.788–0.998, RMSE: 0.007–0.253) and revealed earlier SoS and longer LoS at lower latitudes. Cross-correlation analysis showed a 2–3 month lagged effects of temperature on mangroves’ growth, with precipitation having minimal impact. The proposed OMPEA improves the possibility of capturing mangrove phenology under non-continuous and low-resolution data, providing valuable insights for large-scale and long-term mangrove conservation and management.

Negative air ions (NAIs) are crucial for assessing the impact of forests on wellbeing and enhancing the physical and mental health of individuals. They serve as pivotal indicators for assessing air quality. Comprehensive research into the distribution patterns of NAI concentrations, especially the correlation between NAI concentrations and meteorological elements in tourist environments, necessitates the accumulation of additional long-term monitoring data. In this paper, long-term on-site monitoring of NAI concentrations, air temperature, relative humidity, and other factors was conducted in real time over 24 h, from April 2020 to May 2022, to explore the temporal dynamic patterns of NAIs and their influencing factors. The results showed that (1) the daily dynamics of NAI concentrations followed a U-shaped curve. The peak concentrations usually occurred in the early morning (4:30–8:00) and evening (19:10–22:00), and the lowest concentrations usually occurred at noon (12:50–14:45). (2) At the monthly scale, NAI concentrations were relatively high in February, August, and September and low in January, June, and December. At the seasonal scale, NAI concentrations were significantly higher in winter than in other seasons, with higher concentrations occurring in the summer and autumn. (3) Relative humidity, air temperature, and air quality index (AQI) were the primary factors that influenced NAI concentrations. Relative humidity showed a significant positive correlation with NAI concentrations, while air temperature and AQI both exhibited a significant negative correlation with NAI concentrations. Higher air quality corresponds to higher NAI concentrations. Our research provides new insights into NAI temporal dynamics patterns and their driving factors, and it will aid in scheduling outdoor recreation and forest health activities.

Guangdong, Fujian, and Zhejiang (GFZ), located on China’s southeast coast, have long been economically active and rapidly growing provinces in China. However, the rising energy consumption in these provinces poses a major challenge to their carbon emissions reduction. Due to the spatial variation in the natural environment and socio-economic activities, energy carbon emissions (ECEs) and their reduction may vary among counties. The matter of scientifically formulating localized carbon reduction paths has therefore become a critical issue. This study proposed a novel path analysis framework based on exploring spatiotemporal heterogeneity using a spatiotemporal statistic model (i.e., spatiotemporal weighted regression). The path’s learning procedure was based on linking the changes in the amount of ECEs to the shifts in dominant factors, which were detected through local significance tests on the coefficients of STWR. To verify its effectiveness, we conducted a county-level empirical study considering four drivers (i.e., population (P), impervious surfaces (I), the proportion of secondary industry (manufacturing, M), and the proportion of tertiary industry (services, S)) in GFZ from 2014 to 2021. The ECEs show two different trends that may be affected by the COVID-19 pandemic and economic recession; hence, we divided them into two periods: an active period (2014–2018) and a stable period (2018–2021). Many interpretable paths and their occurrences were derived from our results, including the following: (1) P and S showed higher sensitivity to the changes in ECEs compared with I and M. Most counties (more than 50%) were dominated by P, but the dominator P may shift to I, M, and S during the active period. Many S-dominated counties reverted to being P-dominated ones during the stable period. (2) For the active period, the two most significant paths, M⁺ → S⁻ and M⁺ → P⁺ (+/− denotes positive or negative impacts of dominated driver), reduced ECEs by about 7.747 × 10⁵ tons and 3.145 × 10⁵ tons, respectively. Meanwhile, the worst path, S⁺ → P⁺, increased ECEs by nearly 1.186 × 10⁶ tons. (3) For the stable period, the best path (S⁺ → I⁺) significantly reduced ECEs by 1.122 × 10⁶ tons, while the worst two paths, M⁻ → P⁺ and I⁺ → P⁺, increased ECEs by 1.978 × 10⁶ tons and 4.107 ×10⁵ tons, respectively. These findings verify the effectiveness of our framework and further highlight the need for tailored, region-specific policies to achieve carbon reduction goals.