Soil salinity and nutrient availability are major constraints affecting crop productivity, soil quality, and agroecosystem sustainability, particularly in coastal regions vulnerable to seawater intrusion. This study provides a comprehensive spatial and temporal assessment of soil properties and quality dynamics on Hainan Island by integrating field observations and multi-temporal remote sensing (RS) datasets. In 2024, a total of 152 sampling sites were surveyed, with three topsoil soil samples collected at each location. Multi-year RS data (2024–2021), including soil salinity reflectance indices (SRSI and SI), the Normalized Difference Vegetation Index (NDVI), and land use and land cover (LULC), were analyzed to evaluate temporal and spatial variability. The soil fertility index was calculated using alkali-hydrolyzed nitrogen (AN), available phosphorus (AP), available potassium (AK), soil pH, and soil organic matter (SOM). The soil quality index was calculated using the same parameters with the addition of chromium (Cr) to account for potential heavy metal contamination. Furthermore, in this study the Inverse Distance Weighting (IDW) method was used for spatial distribution maps of soil properties and other indices. The results indicated that soils were predominantly acidic (pH < 6.0) with generally low electrical conductivity (0.01–0.53 mS cm⁻¹) across inland areas, whereas higher salinity levels (2.28–5.31 mS cm⁻¹) were observed in southern and eastern coastal zones, suggesting potential seawater intrusion. Nutrient concentrations ranged from 60.1 to 150 mg kg⁻¹ (AN), 4 to 332 mg kg⁻¹ (AP), and 50.1 to 100 mg kg⁻¹ (AK). NDVI values (0.70–0.94) indicated high vegetation density over most agricultural landscapes. Significant positive correlations were observed between soil EC and the SRSI (r = 0.781) and SI (r = 0.663; p < 0.01), demonstrating the reliability of RS-derived indices for salinity assessment. The integrated indicator-based framework developed in this study provides a scientific basis for precision agriculture, soil health monitoring, and sustainable land management in coastal agroecosystems.

Substantial amounts of nitrogenous (N) compounds, as well as bisphenol A (BPA) and bisphenol S (BPS), contribute to the impurities of pharmaceutical contamination (PC) in wastewater, which have detrimental effects on the environment, humans, and aquaculture. The anammox processes is primarily used to treat wastewater contamination, in which certain microbial communities play a crucial role. In this regard, the present study focuses on microbial communities and the functional genes involved in the anammox process. Further, the current study highlights the secondary (biological) and tertiary (advanced) methods; these techniques are more effective solutions for PC treatment. Anammox bacteria are the primary drivers of the wastewater’s ammonium and nitrite removal process. However, overall, 25 anammox species have been recognized between five important genera, including Anammoxoglobus, Anammoximicrobium, Brocadia, Kuenenia, and Jettenia, which are mainly found in activated sludge and marine environments. The group of bacteria called anammox has genes that encode enzymes such as hydrazine synthase (HZS), hydrazine dehydrogenase (HDH), nitrite oxidoreductase reductase (NIR), hydroxylamine oxidoreductase (HAO), and ammonium monooxygenase (AMO). The anammox process is responsible for developing about 30% to 70% N gases worldwide, making it a critical component of the nitrogen cycle as well. Therefore, this review paper also investigates the pathways of hydrazine, an intermediate in the anammox process, and discusses the potential way to significantly decrease the N-compound contamination from wastewater systems and the environmental effects of determined organic contaminants of BPA and BPS.

Climate change and growing populations are major challenges for food security. Understanding single-season rice (SSR) growth patterns and how much area changes over time is essential for sustaining rice distribution patterns and ensuring food security. This study utilized ground trothing data with the remote sensing (RS) technique for estimation of the SSR pattern in Jiangsu Province. A total of 1700 rice and 470 non-rice points were collected during the field visit in April–September 2023 across Jiangsu Province. The current study employed advanced machine learning (ML) and the random forest (RF) model using Google Earth Engine (GEE). This study evaluates the SSR cropping area, including the Normalized Difference Vegetation Index (NDVI), land surface temperature (LST), and land use–land cover (LULC) variation from 2018 to 2023 with different satellites. The results of NDVI show an increasing trend with mean values rising from 0.30 in 2018 to 0.42 in 2023. Additionally, higher mean values of LST were noticed in 2020 by 14.4 °C and in 2022 by 14.1 °C. Furthermore, the SSR area has significantly changed, mostly in the eastern and southern regions of Jiangsu Province, from 2018 to 2023. The higher rice cropping area decreased by 1.42% in 2019 compared to 2018, while the total reduction over the 2018–2023 period was 0.92%. Total cultivated crop areas continued to decline because most of the crop areas changed into built-up areas, resulting in a total variation of 2.75% from 2020 to 2023. The overall accuracy of RF model range was 77.33% to 93.55% with a Kappa coefficient of 0.55 and 0.87, indicating moderate to substantial classification agreement across the study period. The results of LULC indicate that the crop area decreased by 4.13% from 2018 to 2023, and major areas were converted into water bodies and built areas. In conclusion, the single-season cropping pattern decreased during the study period, accompanied by a reduction in total cropland area in Jiangsu Province. Therefore, these findings highlight the influence of urbanization and climate change on agricultural land and emphasize adaptive strategies in Jiangsu Province to ensure food security in the face of environmental challenges.

Salinity is a major environmental constraint that influences nutrient acquisition and internal transport in coastal plant species. However, the electrophysiological mechanisms underlying nutrient flow regulation in mangroves remain poorly understood. This study investigates the active transport flow of nutrients (NAF) and nutrient plunder capacity (NPC) in two ecologically significant mangrove species, Aegiceras corniculatum (L.) Blanco (A. corniculatum) and Kandelia obovata Sheue, H.Y. Liu & J.W.H. Yong (K. obovata), using intrinsic electrophysiological leaf traits including inherent impedance (IZ), inherent capacitive reactance (IXC), inherent inductive reactance (IXL), and inherent capacitance (IC). A randomized block design was employed using three different saline treatments with control, such as control (0 mM), low (T1,100 mM), medium (T2, 250 mM), and high (T3, 450 mM). The results of the fitting equations show a positive correlation between resistance (Re), capacitive reactance (XC), and inductive reactance (XL) with clamping force (CF); all values of R² are ≥0.98, and p-values are <0.0001. Nutrient transport capacity (NTC) was non-significant in control and low-salt treatment for both mangrove species, indicating resistance to low levels of salt stress. NAF results of A. corniculatum showed a slight reduction of 7.9% under low salinity, while K. obovata displayed strong positive responses NAF increasing by 63.7% compared to the control. Additionally, the NPC of A. corniculatum species was not significantly affected at low and medium salt levels but declined at high salt levels. In contrast, K. obovata exhibited a higher growth rate and better photosynthetic performance than A. corniculatum. Our findings provide novel mechanistic insights into how electrophysiological regulation governs nutrient transport under salinity stress and highlight interspecies differences in adaptive strategies, with implications for understanding mangrove resilience in saline environments.