Establishing an efficient PM_2.5 prediction model and in-depth knowledge of the relationship between the predictors and PM_2.5 in the model are of great significance for preventing and controlling PM_2.5 pollution and policy formulation in the Yangtze River Delta (YRD) where there is serious air pollution. In this study, the spatial pattern of PM_2.5 concentration in the YRD during 2003–2019 was analyzed by Hot Spot Analysis. We employed five algorithms to train, verify, and test 17 years of data in the YRD, and we explored the drivers of PM_2.5 exposure. Our key results demonstrated: (1) High PM_2.5 pollution in the YRD was concentrated in the western and northwestern regions and remained stable for 17 years. Compared to 2003, PM_2.5 increased by 10–20% in the southeast, southwest, and western regions in 2019. The hot spot for percentage change of PM_2.5 was mostly located in the southwest and southeast regions in 2019, while the interannual change showed a changeable spatial distribution pattern. (2) Geographically Weighted Random Forest (GWRF) has great advantages in predicting the presence of PM_2.5 in comparison with other models. GWRF not only improves the performance of RF, but also spatializes the interpretation of variables. (3) Climate and human activities are the most important drivers of PM_2.5 concentration. Drought, temperature, and temperature difference are the most critical and potentially threatening climatic factors for the increase and expansion of PM_2.5 in the YRD. With the warming and drying trend worldwide, this finding can help policymakers better consider these factors for PM_2.5 prediction. Moreover, the effect of interference from humans on ecosystems will increase again after COVID-19, leading to a rise in PM_2.5 concentration. The strong explanatory power of comprehensive ecological indicators for the distribution of PM_2.5 will be a crucial indicator worthy of consideration by decision-making departments.

It is of great significance to understand the drivers of PM_2.5 and fire carbon emission (FCE) and the relationship between them for the prevention, control, and policy formulation of severe PM_2.5 exposure in areas where biomass burning is a major source. In this study, we considered northern Laos as the area of research, and we utilized space cluster analysis to present the spatial pattern of PM_2.5 and FCE from 2003–2019. With the use of a random forest and structural equation model, we explored the relationship between PM_2.5 and FCE and their drivers. The key results during the target period of the study were as follows: (1) the HH (high/high) clusters of PM_2.5 concentration and FCE were very similar and distributed in the west of the study area; (2) compared with the contribution of climate variables, the contribution of FCE to PM_2.5 was weak but statistically significant. The standardized coefficients were 0.5 for drought index, 0.32 for diurnal temperature range, and 0.22 for FCE; (3) climate factors are the main drivers of PM_2.5 and FCE in northern Laos, among which drought and diurnal temperature range are the most influential factors. We believe that, as the heat intensifies driven by climate in tropical rainforests, this exploration and discovery can help regulators and researchers better integrate drought and diurnal temperature range into FCE and PM_2.5 predictive models in order to develop effective measures to prevent and control air pollution in areas affected by biomass combustion.

The genetic effects of habitat fragmentation are complex and are influenced by both species traits and landscape features. For plants with strong seed or pollen dispersal capabilities, the question of whether the genetic erosion of an isolated population becomes stronger or is counterbalanced by sufficient gene flow across landscapes as the timescales of fragmentation increase has been less studied. In this study, we compared the population structure and genetic diversity of a distylous herb, Hedyotis chyrsotricha (Rubiaceae), in two contrasting island systems of southeast China. Based on RAD-Seq data, our results showed that populations from the artificially created Thousand-Island Lake (TIL) harbored significantly higher levels of genetic diversity than those from the Holocene-dated Zhoushan Archipelago (ZA) (π = 0.247 vs. 0.208, H_O = 0.307 vs. 0.256, H_E = 0.228 vs. 0.190), while genetic differences between island and mainland populations were significant in neither the TIL region nor the ZA region. A certain level of population substructure was found in TIL populations, and the level of gene flow among TIL populations was also lower than in ZA populations (m = 0.019 vs. 0.027). Overall, our comparative study revealed that genetic erosion has not become much stronger for the island populations of either the TIL or ZA regions. Our results emphasized that the matrix of water in the island system may facilitate the seed (fruit) dispersal of H. chrysotricha, thus maintaining population connectivity and providing ongoing resilience to the effects of habitat fragmentation over thousands of years.

Heat stress due to external heat sources such as fire is an ecological problem for plants. When forest plants suffer from fire, high temperatures cause an array of morphological, physiological, and biochemical changes, which affect growth and development. Michelia macclurei Dandy is an evergreen broad-leaved tree species with the characteristics of fast growth, strong adaptability, and good fire-resistance. Some studies have improved the understanding of how fire behavior affects physiology, function and mortality, but the extreme heat response genes and mechanisms need improved understanding. In this study, we conducted a fire experiment (slight and severe) and RNA-Seq in M. macclure. The de novo assembly obtained 104,052 unigenes, and 48.46% were annotated in at least one public database. Specifically, 4458 and 4810 differentially expressed genes (DEGs) were identified in slight and severe fire treatment groups, respectively. In two treatment groups, 612 unigenes were differentially expressed, which were enriched in ‘oxidoreductase activity’ in the molecular function (MF) category of Gene Ontology (GO) enrichment analysis, suggesting the core role of oxidoreductase activity in response to extremely high temperatures in M. macclurei. In KEGG enrichment analysis of DEGs, the ‘plant hormone signal transduction’ is overrepresented, suggesting that this process plays an important role during heat response in M. macclurei. In the pathways of cytokinine and salicylic acid, some vital DEGs were enriched, which were related to cell division, shoot initiation, and disease resistance, and the potential interactions during heat stress were discussed. Moreover, the DEGs linked to heat stress response were identified, including heat shock factors, stress enhanced protein, signal transduction, photosystem, and major transcription factors. The qRT-PCR examination of various tissues, expression dynamics, and treatments revealed that the genes coding for the heat shock protein HSF30, stress enhanced protein, and photosystem I reaction center subunit II exhibited particularities in leaf tissue. Genes coding for heat shock proteins displayed a distinct expression pattern between fire treatment and conventional heat stress, which could signify the distinctive function of HSPs and the mechanism of heat responses. Altogether, these may interact to respond to fire stress through alterations in cellular processes, signaling transduction, and the synthesis and degradation of response proteins in M. macclurei. The results of this study provide a crucial transcriptional profile influenced by heat stress in M. macclurei, and could be of great use to explore the fire prevention mechanisms of fire-resistant tree species.

Given the increasing importance of effectively identifying synergistic changes between PM_2.5 and O₃ and comprehensively analyzing their impact on air quality management in China, we employ the Sen+Mann–Kendall (Sen+M-K) trend test in this study to examine the temporal and spatial variation trends of PM_2.5 and O₃ in the Yangtze River Delta (YRD), from 2003 to 2020. We identified the regions where these pollutants exhibited synergistic changes and established the pathways between the pollutants and their potential drivers, using geographically weighted random forest algorithms and structural equation modeling. The study results revealed as follows: (1) Overall, the PM_2.5 concentrations show a decreasing trend, while the O₃ concentrations exhibit an increasing trend, in the YRD. Analysis of the combined trends indicates that approximately 95% of the area displays opposing trends for PM_2.5 and O₃, with only about 4% in the southern region showing synergistic trends for both pollutants. (2) Drought and the average temperature are the main drivers of the changes in PM_2.5 and O₃ concentrations in areas experiencing synergistic changes. Their combined effects alleviate the aggregation of PM_2.5 and reduce the formation of VOCs, indirectly reducing the generation of pollutants. The negative effect of the average temperature on the O₃ concentration may indicate the existence of nonlinear effects and complex interaction effects between the drivers. NO_x and VOCs play important dual roles in the generation and conversion of pollutants, although their overall impact is smaller than meteorological factors. They produce significant indirect effects through their interaction with meteorological and other human factors, further affecting the concentrations of PM_2.5 and O₃. In areas without coordinated changes, the main impact of meteorological factors remains unchanged, and the relationship between the two anthropogenic emission sources and their effects on PM_2.5 and O₃ are complex, with different directions and levels involved. This study provides detailed insights into the drivers of air quality changes in the YRD and offers a scientific basis for environmental management authorities to develop more comprehensive and targeted strategies for balancing the control of PM_2.5 and O₃ pollution.

Forest fires significantly impact the global climate through carbon emissions, yet the multi-scale coupling mechanisms among meteorological factors, fire behavior, and emissions remain uncertain. Focusing on tropical Asia, this study integrated satellite-based fire behavior products, meteorological datasets, and emission factors, and employed machine learning together with structural equation modeling (SEM) to explore the mediating role of fire behavior in the meteorological regulation of carbon emissions. The results revealed significant differences among vegetation types in both carbon emission intensity and sensitivity to meteorological drivers. For example, average gas emissions (GEs) and particle emissions (PEs) in mixed forests (MF, 323.68 g/m²/year for GE and 0.73 g/m²/year for PE) were approximately 172% and 151% higher, respectively, than those in evergreen broadleaf forests (EBF, 118.92 g/m²/year for GE and 0.29 g/m²/year for PE), which exhibited the lowest emission intensity. Mixed forests and deciduous broadleaf forests exhibited stronger meteorological regulation effects, whereas evergreen broadleaf forests were comparatively stable. Temperature and vapor pressure deficit emerged as the core drivers of fire behavior and carbon emissions, exerting indirect control through fire behavior. Overall, the findings highlight fire behavior as a critical link between meteorological conditions and carbon emissions, with ecosystem-specific differences determining the responsiveness of carbon emissions to meteorological drivers. These insights provide theoretical support for improving the accuracy of wildfire emission simulations in climate models and for developing vegetation-specific fire management and climate adaptation strategies.