Ecological Informatics最新文献

{"title":"Multi-species insect recognition method based on computer visions: Sustainable agricultural development","authors":"Lijuan Zhang , Shanshan Sun , Hui Zhao , Zhiyi Li , Dongming Li","doi":"10.1016/j.ecoinf.2025.103125","DOIUrl":"10.1016/j.ecoinf.2025.103125","url":null,"abstract":"","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"88 ","pages":"Article 103125"},"PeriodicalIF":5.8,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143792389","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Innovative soil classification approach for achieving global biodiversity framework utilizing integrated data fusion of EMIT and multispectral satellite observations: Case study of Imam Turki bin Abdullah Royal Reserve, Kingdom of Saudi Arabia","authors":"Hesham Morgan ,&nbsp;Ali Elgendy ,&nbsp;Surendra Maharjan ,&nbsp;Wenzhao Li ,&nbsp;Tamer Ismail ,&nbsp;Yehya Kh. Shehadeh ,&nbsp;Ahmed ElGharib ,&nbsp;Ahmed Abdullah Al-Dughairi ,&nbsp;Ali El Mubarak ,&nbsp;Khaled Allam Harhash ,&nbsp;Hesham El-Askary","doi":"10.1016/j.ecoinf.2025.103123","DOIUrl":"10.1016/j.ecoinf.2025.103123","url":null,"abstract":"<div><div>Soil classification is essential for sustainable land management, ecological conservation, and combating desertification, particularly in arid and semi-arid regions. This study integrates hyperspectral data from the Earth Surface Mineral Dust Source Investigation (EMIT) and multispectral imagery from Sentinel-2 to achieve accurate soil classification for the Imam Turki bin Abdullah Royal Reserve (ITBA) in Saudi Arabia. Using advanced Machine Learning (ML) techniques, including Extreme Gradient Boosting (XGBoost), the study highlights the power of data fusion in addressing the limitations of standalone remote sensing methods. The integration of hyperspectral and multispectral data combines the spectral richness of hyperspectral imaging with the spatial resolution of multispectral data, providing detailed insights into the region's heterogeneous soil types. The Gram-Schmidt fusion technique enhanced spatial resolution, enabling precise identification of inter-dune soils, linear dunes, and rocky outcrops. The resulting soil classification map achieved an accuracy of 93 %, outperforming traditional methods and existing maps. Inter-dune soils, characterized by their loamy-skeletal texture and superior moisture retention, were identified as critical for supporting vegetation and afforestation efforts. This research also developed a suitability map for afforestation by incorporating weighted overlays of soil fertility, moisture retention, and vegetation indices. These findings directly contribute to global biodiversity priorities, supporting the Convention on Biological Diversity (CBD) and the associated Global Biodiversity Framework (GBF) targets such as reducing biodiversity loss (Target 1), restoring ecosystems effectively (Target 2), minimizing the impacts of climate change (Target 8), and enhancing sustainable agriculture (Target 10). Furthermore, the study utilizes these advancements in addressing land degradation and achieving the United Nations Sustainable Development Goals (SDGs), including Zero Hunger (SDG 2), Climate Action (SDG 13), and Life on Land (SDG 15). By integrating soil classification with afforestation strategies through remote sensing and advanced data sciences approaches, this research demonstrates a robust, scalable and precise solution to support biodiversity conservation, land management, and climate resilience in arid environments.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"87 ","pages":"Article 103123"},"PeriodicalIF":5.8,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143761023","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Detection transformer-based approach for mapping trees outside forests on high resolution satellite imagery","authors":"Tao Jiang ,&nbsp;Maximilian Freudenberg ,&nbsp;Christoph Kleinn ,&nbsp;Timo Lüddecke ,&nbsp;Alexander Ecker ,&nbsp;Nils Nölke","doi":"10.1016/j.ecoinf.2025.103114","DOIUrl":"10.1016/j.ecoinf.2025.103114","url":null,"abstract":"<div><div>High-resolution satellite imagery is a crucial data source for comprehensive trees outside forests (TOF) mapping at various spatial scales. Accurate identification of individual trees in satellite imagery remains challenging due to the heterogeneous nature of tree crowns, spectral similarities with other vegetation and the necessity to process large areas. The emergence of deep learning techniques, such as detection transformer models (DETR), offers new ways to analyse images more efficiently and accurately. In this study, we proposed an end-to-end approach for large-area TOF detection based on an established detection transformer architecture, specifically DETR with Improved deNoising anchOr boxes (DINO). We labelled 23,643 tree crowns with bounding boxes in 330 WorldView-3 image patches from the megacity of Bengaluru, India. Using this dataset, we trained and tested DINO for individual tree detection. In addition, we adopted a two-level tiling scheme and developed an R-tree-based Box Merging method to adapt to large images and remove redundant predictions more efficiently. Comparative analyses underscore the superior detection performance of DINO with a SWIN transformer as backbone, exhibiting an F1 score of 74% and an AP of 76%, surpassing other models such as Faster RCNN, YOLO, RetinaNet, DETR, Deformable-DETR, and DINO-Res50. For further validation we evaluated the proposed detection approach in two additional locations, Delhi and Shanghai, with varying image quality, achieving F1 scores of 87% and 73%, respectively. Our work advances remote sensing applications by providing a robust solution for large-scale TOF mapping and management.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"87 ","pages":"Article 103114"},"PeriodicalIF":5.8,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143777257","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing coastal bathymetric mapping with physics-informed recurrent neural networks synergizing Gaofen satellite imagery and ICESat-2 lidar data: A case in the South China Sea","authors":"Congshuang Xie ,&nbsp;Siqi Zhang ,&nbsp;Zhenhua Zhang ,&nbsp;Peng Chen ,&nbsp;Delu Pan","doi":"10.1016/j.ecoinf.2025.103121","DOIUrl":"10.1016/j.ecoinf.2025.103121","url":null,"abstract":"&lt;div&gt;&lt;div&gt;Satellite-derived bathymetry (SDB) has emerged as a critical technique in response to the growing demand for large-scale coastal bathymetric mapping. However, high-resolution multispectral imagery from Gaofen satellites presents significant challenges owing to low signal-to-noise ratios (SNRs). This study aimed to enhance coastal bathymetric mapping by integrating high-resolution Gaofen satellite imagery with ICESat-2 lidar-derived bathymetry. The specific goals are to develop a novel physics-informed recurrent neural network (PI-RNN) for SDB that does not rely on prior information and assess its performance in terms of accuracy and robustness. We propose a physics-based RNN model that combines spectral and radiative transfer information from Gaofen satellite imagery with reference bathymetric data from ICESat-2. This methodology includes an adaptive ellipse density-based spatial clustering of applications with noise (AE-DBSCAN) algorithm for ICESat-2 data extraction, which surpasses standard DBSCAN in terms of accuracy. The RNN model was trained using various band combinations of Gaofen satellite data, and its performance was evaluated against in-situ measurements from Ganquan Island in the South China Sea. The physics-based RNN model achieved good bathymetric accuracy, with a coefficient of determination (R&lt;sup&gt;2&lt;/sup&gt;) value &gt;0.93 and a root mean square error (RMSE) &lt; 0.83 m when compared to in-situ measurements on Ganquan Island, indicating the model demonstrates high robustness even under low-SNR satellite imagery conditions. In addition, the inclusion of radiative transfer information in the band combination significantly improved the training accuracy of the model, with the average RMSE being 0.2 m lower and the average R&lt;sup&gt;2&lt;/sup&gt; improving by 3 % compared to results without physical information. On Huaguang Reef, the model performance further improved with R&lt;sup&gt;2&lt;/sup&gt; values ranging from 0.93 to 0.97 and RMSE between 0.55 and 0.66 m after applying atmospheric correction when compared to the ICESat-2 reference bathymetric data. Without atmospheric correction, the R&lt;sup&gt;2&lt;/sup&gt; of the estimated depth was in the range of 0.85 to 0.94 and RMSE in the range of 0.62 to 0.71 m, indicating that although the model mitigated some atmospheric interference effects, atmospheric correction was still necessary to achieve higher accuracy under strong atmospheric conditions. This study demonstrates that a PI-RNN can significantly enhance SDB accuracy, even under challenging conditions. The integration of active and passive remote-sensing data provides a reliable and efficient tool for large-scale coastal bathymetric mapping. The unique contribution of this study lies in the development of a novel RNN model that leverages both spectral and physical information, offering a more accurate and generalised approach to SDB.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Plain language summary&lt;/h3&gt;&lt;div&gt;Traditional methods for creating these maps are limited because of ","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"87 ","pages":"Article 103121"},"PeriodicalIF":5.8,"publicationDate":"2025-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143747404","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"HawkEars: A regional, high-performance avian acoustic classifier","authors":"Jan Huus ,&nbsp;Kevin G. Kelly ,&nbsp;Erin M. Bayne ,&nbsp;Elly C. Knight","doi":"10.1016/j.ecoinf.2025.103122","DOIUrl":"10.1016/j.ecoinf.2025.103122","url":null,"abstract":"<div><div>Passive acoustic monitoring is rapidly emerging as a dominant approach for studying acoustic wildlife, with neural networks used as an increasingly common and promising approach for extracting detections of particular species from acoustic recordings. Existing options for avian classifiers include small custom models for focal species or large models that attempt to classify the entire global avian community, which suggests a possible tradeoff between classifier performance and species coverage. We argue that building domain-specific classifiers for particular geographic regions provides improved performance in exchange for reduced species coverage and present HawkEars, a regional avian classifier for Canada that includes 314 bird and 13 amphibian species. A major challenge in classifier development is the weak labeling of open access datasets. We developed a novel solution, using embedding-based search to efficiently generate strong labels. We evaluated HawkEars performance for bird species relative to two prominent avian community classifiers: BirdNET, and Perch for two datasets representing two applications: bird community surveys and studies of vocal activity rate. We found HawkEars had substantially higher performance across all metrics, detected on average two more species per recording minute in our community evaluation dataset, and had a recall of nearly twice Perch and four times BirdNET, given a precision of 0.9, for our vocal activity evaluation dataset. We suggest HawkEars provides better classification performance because a smaller species pool allows for more resources allocated per species to training and tuning and reduces the risk of class overlap, and our strong labeling method ensures high-quality training data. While our classifier, HawkEars, is a substantial improvement for practitioners studying acoustic wildlife in Canada and the northern United States, practitioners in other regions can use the HawkEars open-source code to build classifiers for other geographic regions. By continuing to improve deep-learning classification performance, HawkEars has the potential to substantially improve the efficiency and utility of passive acoustic monitoring studies.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"87 ","pages":"Article 103122"},"PeriodicalIF":5.8,"publicationDate":"2025-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143747403","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Temporal dynamics of biodiversity in benthic macroinvertebrate communities from a 140-year sedimentary DNA record and their driving mechanisms","authors":"Siwei Yu ,&nbsp;Zitong Sun ,&nbsp;Rui Bian ,&nbsp;Yajun Wang ,&nbsp;Hongwei Yu ,&nbsp;Gaoqi Duan ,&nbsp;Xiaofeng Cao ,&nbsp;Weixiao Qi ,&nbsp;Jianfeng Peng ,&nbsp;Huijuan Liu ,&nbsp;Jiuhui Qu","doi":"10.1016/j.ecoinf.2025.103119","DOIUrl":"10.1016/j.ecoinf.2025.103119","url":null,"abstract":"<div><div>The ecological processes that influence the temporal components of β diversity and the interplay between taxonomic and functional β diversity are poorly understood. Therefore, the mechanisms that drive these processes and their ecological significance require further investigation. In this study, we utilized sedimentary DNA (<em>seda</em>DNA) metabarcoding to analyze an approximately 140-year-long record of the benthic macroinvertebrate communities found in Lake Chenghai, southwestern China. Our findings revealed a decrease in taxonomic and functional dissimilarity within the β diversities of these communities from 1886 to 2017, with a pronounced homogenization trend observed between 1987 and 2017. This homogenization was primarily driven by taxonomic and functional turnover, which was caused by increased nutrient levels, especially increased total nitrogen content. In addition, autogenic organic matter inputs and increased evaporation also play a significant role in this phase. Predictive models indicate that to maintain optimal water quality and ecological health, total nitrogen and total phosphorus should be controlled to within approximate ranges of 0.565 mg/L ± 0.441 mg/L and 0.026 ± 0.001 mg/L, respectively. Our study highlights the role of temporal species turnover in shaping community structures and provides valuable insights for managing lake ecosystems and preserving biodiversity within benthic macroinvertebrate communities.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"87 ","pages":"Article 103119"},"PeriodicalIF":5.8,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143715226","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A photogrammetric approach to the estimation of distance to animals in camera trap images","authors":"B. Mirka ,&nbsp;C.D. Lippitt ,&nbsp;G.M. Harris ,&nbsp;R. Converse ,&nbsp;M. Gurule ,&nbsp;S.E. Sesnie ,&nbsp;M.J. Butler ,&nbsp;D.R. Stewart ,&nbsp;Z. Rossman","doi":"10.1016/j.ecoinf.2025.103120","DOIUrl":"10.1016/j.ecoinf.2025.103120","url":null,"abstract":"<div><div>The ability to assess accurate distance measurements, meaning the distance from an animal to the observer, is critical for wildlife monitoring methods such as distance sampling. This process has typically been done through manual measurements or using reference points, making fieldwork and analysis labor intensive and error prone. Using Structure from Motion (SfM) photogrammetry and geotagged imagery captured from smartphones offers a simple and cost-effective solution to distance estimation from camera traps. This research evaluates the potential of SfM to create 3D models of the areas within a camera trap field-of-view to produce depth maps that can be used to automate the distance estimation process from bounding box-labeled imagery. These methods have the potential, when paired with deep learning algorithms or crowdsourcing, to automate the process of wildlife identification and distance estimation, reducing the need for manual fieldwork or analysis while increasing the precision and replicability of population estimates. To test the accuracy of these methods and evaluate best practices, three types of GNSS integration into the SfM model were assessed at three camera trap sites placed within the Sevilleta National Wildlife Refuge in New Mexico, USA. The evaluation focused on the geometric accuracy of the resulting model, quantified using Root Mean Square Error (RMSE) against known ground control points, the accuracy of distance estimates relative to field-measured distances, and the comparison of modeled distances of detected animals to ground control points. The first method used only the GNSS data embedded (geotagged) cell phone images (ImageGNSS), the second used ground control points (GCPGNSS), and the third used both GNSS sources (AllGNSS). Models created using ImageGNSS had the highest accuracy, with an overall RMSE of 1.25 pixels and a mean absolute error (MAE) of 1.76 m for distance estimation from GCPs. A comparison of the modeled distance for 10 animals to known distance points produced minimal error (MAE of 3.31 m), demonstrating the potential of photogrammetric approaches to make accurate distance estimations from camera trap imagery. Unlike other digital distance estimation techniques, error rates do not increase significantly (<em>p</em> = 0.36) at distances greater than 15 m to at least a distance of 35 m. The biggest driver of distance error was scene complexity (complex topography, dense vegetation, etc.)</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"89 ","pages":"Article 103120"},"PeriodicalIF":5.8,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143892204","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Detection of anthropogenic noise pollution as a possible chronic stressor in Antarctic Specially Protected Area N°150, Ardley Island","authors":"Maximiliano Anzibar Fialho ,&nbsp;Martín Rocamora ,&nbsp;Lucía Ziegler","doi":"10.1016/j.ecoinf.2025.103117","DOIUrl":"10.1016/j.ecoinf.2025.103117","url":null,"abstract":"<div><div>Anthropogenic noise pollution is emerging as an important environmental stressor with the potential effect of disrupting natural ecosystems, since many taxa rely on acoustic signals for social interaction and communication. Antarctic wildlife is increasingly experiencing the impact of growing human presence on the continent, especially near populated areas such as research stations. Until now, most studies on the sound impact in Antarctica have focused on marine ecosystems, with a clear paucity of studies at the level of terrestrial environments. In this study, we analyze the presence of a specific anthropogenic sound source, a power generator, in the soundscape of the Antarctic Specially Protected Area (ASPA) N°150, Ardley Island. We used Audiomoth recorders to hourly monitor the soundscape in Ardley Island and create a simple yet effective detection method based on spectral features of the source. We cross-validate the detection algorithm with human perception classification of the source presence in the recordings, obtaining a Pearson correlation coefficient of 0.61 between the two methods. Further, we relate the detection with wind velocity and direction, concluding that under certain meteorological conditions, the source can be clearly heard from Ardley. Our results suggest that the soundscape of Ardley Island is altered by the near presence of an anthropogenic noise source which could represent an impact on animal life in the ASPA. We consider this kind of study to be relevant in bringing awareness of noise pollution in Antarctic ecosystems and improving management plans in the ASPAs.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"87 ","pages":"Article 103117"},"PeriodicalIF":5.8,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143715225","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessment of the green development level and the identification of obstacles to grass-based livestock husbandry in the farming–pastoral ecotone of northern China","authors":"Dan Liu ,&nbsp;Jianjun Jin ,&nbsp;Xuan Zhang ,&nbsp;Xin Qiu ,&nbsp;Rui He ,&nbsp;Jie Yang","doi":"10.1016/j.ecoinf.2025.103112","DOIUrl":"10.1016/j.ecoinf.2025.103112","url":null,"abstract":"<div><div>The green development of livestock husbandry represents the balance between livestock production and environmental protection. In this study, a comprehensive framework and evaluation indices were developed for assessing the green development level of grass-based livestock husbandry (GDL-GLiH), including the green growth degree (GGD), green carrying capacity (GCC) and green guarantee capability (GGC). On the basis of the combined weight Technique Order Preference by Similarity to an Ideal Solution (TOPSIS) model and an analysis of obstacles, the GDL-GLiH in the farming–pastoral ecotone of northern China (FPEN) was evaluated, and the main obstacles were identified. The results indicated a general upward trend in the GDL-GLiH across the FPEN, increasing from 0.343 in 2010 to 0.416 in 2020, reflecting a growth rate of 21.138 %. Among the three dimensions, the GGC showed the most substantial increase of 95.937 %, whereas the GCC exhibited minimal growth of 1.006 %. Spatial variations were observed, with livestock-dominated systems exhibiting higher average levels (0.398) but lower growth rates (19.740 %) than crop-dominated systems (0.356; 22.252 %). Additionally, the production of milk (average obstacle degree: 12.970 %), the proportion of forage cultivation in crop cultivation (11.312 %), the total mechanical power per unit agricultural sown area (10.081 %) and the availability of purebred bovine and ovine breeding stock (9.034 %) were identified as the key obstacles. This study provides a holistic assessment framework for green livestock development and serves as a reference for formulating green development strategies in the FPEN, as well as in similar agricultural systems.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"87 ","pages":"Article 103112"},"PeriodicalIF":5.8,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143715227","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Toward causal artificial intelligence approach for PM2.5 interpretation: A discovery of structural causal models","authors":"Mallika Kliangkhlao ,&nbsp;Apaporn Tipsavak ,&nbsp;Thanathip Limna ,&nbsp;Racha Dejchanchaiwong ,&nbsp;Perapong Tekasakul ,&nbsp;Kirttayoth Yeranee ,&nbsp;Thanyabun Phutson ,&nbsp;Bukhoree Sahoh","doi":"10.1016/j.ecoinf.2025.103115","DOIUrl":"10.1016/j.ecoinf.2025.103115","url":null,"abstract":"<div><div>Understanding the causal mechanisms underlying PM_2.5 generation is critical for developing effective prevention strategies, necessitating an approach that goes beyond prediction and seeks deeper causal explanations to support decision-making. This study addresses these concerns through a novel causal artificial intelligence framework employing structural causal models (SCMs) to interpret PM_2.5 dynamics. The research uncovers hidden cause-and-effect relationships between meteorological factors and PM_2.5 exposure by leveraging a data-driven causal structure discovery approach, effectively representing complex data-generating processes. The proposed SCMs undergo systematic validation across two critical dimensions: demonstrating human-like intelligence understanding and achieving significant alignment with real-world observations. The PC-based SCM is particularly outstanding when compared to other algorithms like GES- and Chow-Lui-based SCMs, delivering a remarkable performance in discovering cause-and-effect relationships with an F-measure of approximately 80 % compared to the gold-standard SCM. Statistical validation provided robust evidence of the model's reliability, with fit indices—including <em>NFI</em>, <em>TLI</em>, <em>CFI</em>, <em>GFI</em>, and <em>AGFI</em>—reaching approximately 0.98 and <em>RMSEA</em> approximating 0.05. These findings demonstrate that SCM can encode human-like reasoning and naturally align with real-world meteorological systems. This method is especially effective for urban air quality monitoring, where accessible meteorological data and transparent causal relationships are essential. Its capacity to inform evidence-based policy decisions makes it a powerful tool for creating intelligent decision-support systems in PM2.5 analysis and environmental mitigation strategies.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"87 ","pages":"Article 103115"},"PeriodicalIF":5.8,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143679825","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}