Optical satellite observations have been recently introduced as the backbone of several harmful algal bloom monitoring frameworks for regional or continental-scale decision-making. Documented in prior peer-reviewed publications, these satellite-based decision support systems are not directly comparable, making a synthesis effort inevitable for future improvements. This review highlights select, widely used harmul cyanobacteria bloom (cyanoHABs) monitoring services, including the Cyanobacteria Assessment Network (CyAN), Cyanobacterial Bloom Indicator (CyaBI), CyanoTRACKER, EOLakeWatch, and CyanoKhoj, by focusing on their effectiveness in freshwater and inland waters. We selected these systems for their widespread use, documented effectiveness, and diverse approaches to cyanoHABs monitoring. These services provide early warnings and actionable insights, enabling effective responses to protect water quality, ecosystem health, and public safety. It considers the broader remote-sensing-based monitoring landscape, noting the capabilities and impacts of these services. Our assessments underscore the transformative impact of services like CyAN, which provide robust early warnings using the Cyanobacteria Index (CI). CyanoTRACKER and EOLakeWatch improve community engagement and data collection, increasing monitoring effectiveness. CyanoKhoj leverages high-resolution monitoring through GEE, offering valuable insights. The quality of cyanoHABs products depends on satellite imagery and processing level, noting that most processors leverage Top of Atmosphere or Rayleigh-corrected reflectance products to arrive at cyanoHABs products. Challenges in cyanoHABs monitoring also include variability in ecosystems and accurate biomass estimations. Despite challenges, services like CyAN, CyanoTRACKER, EOLakeWatch, and CyanoKhoj have made significant strides in communicating and managing cyanoHABs risks. This review identifies key future research directions: (1) improving algorithmic approaches and accuracy, (2) defining a universal threshold for bloom formation, (3) utilizing emerging technologies and democratizing data and information, and (4) addressing satellite technique trade-offs in cyanoHABs analysis. By focusing on these areas and leveraging machine learning, future advancements promise more accurate and comprehensive monitoring to protect aquatic ecosystems and public health.

With the exponential growth of data generated from enterprise systems, social networks, and the Internet of Things, traditional data mining techniques face major challenges in terms of scalability and efficiency. As a foundational unsupervised learning method for detecting patterns in transactional datasets, Association Rule Mining (ARM) is commonly encountered in distributed environments with performance bottlenecks due to excessive memory consumption, static resource provisioning, and costly data shuffle. The present paper presents DatApollo, a novel serverless orchestration framework that enables the execution of distributed ARM workflows in a scalable and efficient manner. DatApollo, based on the Apollo orchestration engine, offers stateless cloud functions, dynamic scheduling, intermediate state persistence, and fault-tolerant coordination in order to address the limitations of both traditional cluster-based architectures and existing Function-as-a-Service models. By decomposing ARM pipelines into orchestrated microfunctions, the framework enables elastic, cloud-native execution with minimal idle time. Using real-world healthcare and meteorological datasets, we describe the architectural design, algorithmic components, and computational complexity of DatApollo and perform a comprehensive experimental evaluation. DatApollo provides up to five times faster execution time compared to Apache Spark and lowers infrastructure costs by utilizing elastic scaling and event-driven function invocations. The results demonstrate that DatApollo is a robust, cost-effective and high-performance alternative to ARM in dynamic, large-scale data environments.

The Middle East frontal sand and dust storms (SDS) occur in non-summer seasons, and represent an important phenomenon of this region’s climate. Among the mentioned type, spring SDS are the most common. Trend analysis was used in the current study to investigate the spatial-temporal variability of springtime dust events in the Middle East using synoptic station observation from 2011 to 2022. The plausible changes in some controlling factors of dust activity at selected important dust sources in the Middle East were also studied during this time period. Our results showed a statistically significant spike in springtime dust events across the Middle East, particularly in May 2022. To evaluate the relative importance of controlling factors, the applied feature of importance analysis using random forest (RF) showed the higher relative importance of topsoil layer wetness, surface soil temperature, and surface wind speed in dust activity over the Middle East between 2011 and 2022. Long-term trend analysis of topsoil moisture and temperature, using the Mann-Kendall trend test, showed a decrease in soil moisture and an increase in soil temperature in some selected important dust sources in the Middle East. Moreover, our predictions using ARIMA models showed a high tendency to dust activities in selected major dust origins (domain 2 and domain 5) with a statistically significant increase (p-value < 0.05) between 2023 and 2029. Observed spatial and temporal changes within SDS hotspots can act as the first step to build up for the first time an SDS precise intensity scale, as well as establishing an SDS early warning system in future.