Harmful Algal Blooms as Emerging Marine Pollutants: A Review of Monitoring, Risk Assessment, and Management with a Mexican Case Study

Abstract

Harmful algal blooms (HABs) represent an escalating threat in marine and coastal ecosystems, posing increasing risks to ecological balance, public health, and blue economy industries including fisheries, aquaculture, and tourism. This review examines the impact of climate change and anthropogenic pressures on the escalation of HAB occurrences, focusing especially on vulnerable regions in Mexico, which are the primary case study for this investigation. The methodological framework integrates HAB risk assessment (RA) methods found in the literature. Progress in detection and monitoring technologies—such as sensing, in situ sensor networks, and prediction tools based on machine learning—are reviewed for their roles in enhancing early-warning systems and aiding decision support. The key findings emphasize four linked aspects: (i) patterns of HAB risk in coastal zones, (ii) deficiencies and prospects in HAB-related policy development, (iii) how governance structures facilitate or hinder effective actions, and (iv) the growing usefulness of online monitoring and evaluation tools for real-time environmental observation. The results emphasize the need for coupled technological and governance solutions to reduce HAB impacts, protect marine biodiversity, and enhance the resilience of coastal communities confronting increasingly frequent and severe bloom events.

1. Introduction

The rapid expansion of urbanization, industrialization, and climate-driven ecological change has intensified the frequency and severity of environmental disturbances such as eutrophication, thermal stratification, and algal blooms across global aquatic systems [1,2]. Harmful algal blooms (HABs)—whether in marine, brackish, or freshwater environments—pose escalating threats to ecological stability, human health, and the blue economy, producing toxins that contaminate water, impact fisheries, and degrade tourism assets [3,4,5]. These blooms often manifest as dense accumulations of algae, typically visible as green or discolored surface waters, and are associated with acute and chronic toxicological risks affecting both humans and wildlife [6].

Recent global analyses demonstrate a marked rise in HAB occurrences since the 1980s, including a 44% increase from the 2000s to 2010s, driven by nutrient loading, wastewater discharges, agricultural expansion, and climate warming [3]. Although mitigation measures in North America, Europe, and Oceania have stabilized bloom occurrences, legacy nutrients and rising temperatures continue to trigger resurgences in both freshwater and marine systems. Similar trends are reported in rapidly developing coastal regions, where escalating eutrophication and hypoxic events contribute to mass fish mortality, biodiversity loss, and broader ecosystem degradation [7,8,9]. From a human health perspective, the effects of HAB-derived toxins extend beyond ingestion risks and include dermatological reactions, respiratory irritation, and potential long-term carcinogenic associations [4,10]. Increasing evidence highlights the need for integrated One-Health frameworks linking human, animal, and environmental health to ensure rapid detection and a coordinated response to bloom-associated illnesses.

Given these wide-ranging consequences, accurate detection, risk assessment, and early warning remain foundational components of HAB management. Various methods have been developed for evaluating HAB-related risks across anthropogenic, esthetic, and environmental dimensions, including satellite image processing integrated with clustering techniques, Adverse Outcome Pathway (AOP) models, Cyanobacterial Cell Density (CCD) estimation via Sentinel-3 OLCI, and phytoplankton community monitoring [11,12,13]. Risk assessment (RA) frameworks typically rely on five stages—hazard identification, exposure analysis, risk evaluation, documentation, and continuous monitoring—each informed by physicochemical, biological, and environmental indicators [14,15]. The main advantage of RA frameworks is that they provide a systematic and comprehensive approach to assessing HAB risks across multiple dimensions. A potential limitation is that their effectiveness depends on the availability and quality of input data, which may vary across regions and monitoring programs. Figure 1, Figure 2 and Figure 3 outline the cross-sectoral impacts of HABs, experimental parameters, and major recent RA studies, while Table 1 summarizes key advancements in monitoring and predictive modeling.

Table 1. Key advances in monitoring, detection, and predictive modeling of harmful algal blooms.

Year	Contribution/Advancement	Reference
1981	Early conceptual framework for HAB prediction; foundational ecological indicators.	[18]
1996	Dynamic HAB soft sensor using hyperspectral image data mining for accessory pigment analysis.	[19]
2009	Integration of morphological and molecular data (rbcL, ITS) to enhance taxonomic detection of HAB species.	[20]
2011	Machine learning-based spatio-temporal data mining for HAB detection in the Gulf of Mexico; introduction of cubical neighborhood spectral retrieval.	[21]
2017a	Neural-network processors for detecting HABs in optically complex waters using MERIS 10-year archive.	[22]
2017b	Forty-three algorithmic combinations for HAB prediction using multispectral and hyperspectral bands.	[23]
2017c	Semi-analytical spectral matching and novel algorithms for chlorophyll-a retrieval supporting eutrophication control.	[24]
2018	Time-series LSTM model for HAB prediction in four major South Korean rivers.	[25]
2020	Evaluation of band-ratio vs. machine learning models for Chl-a prediction as a proxy for cyanobacterial blooms.	[26]
2020	Real-time blue tide prediction model using sulfur concentration derived from GOCI observations.	[27]
2021	Synoptic review of ML approaches for HAB and biotoxin prediction (ANN, RF, SVM, and PGMs).	[28]
2021	ML-based short-term HAB prediction using optimized environmental feature selection.	[29]
2022	CNN-LSTM model combining spatio-temporal features for Chl-a prediction; improved accuracy and training speed.	[30]
2022	Integrated CNN-LSTM model for predicting CyanoHAB spatial extent.	[11]
2022	Hybrid XG-LSTM (XGBoost-Long Short-Term Memory) architecture predicting algal cell density and microcystins in Three Gorges Reservoir.	[31]
2022	Spatial prediction of Cochlodinium polykrikoides blooms using CNN-based domain-specific configurations.	[32]
2022	Review of monitoring, modeling, and long-term projection of HABs in China, including early-warning remote sensing.	[33]
2023	Review of freshwater HAB increases; assessment of hydrodynamic reservoir management strategies affecting bloom occurrence.	[34]
2024	Global HAB trend review showing 44% increase from 2000s to 2010s; emphasizes integrated monitoring networks and data fusion for improved forecasts.	[3]
2024	One-Health review on HAB toxins in marine and freshwater systems; integration of human, animal, and environmental surveillance.	[4]
2025	Review of eutrophication-driven coastal HAB expansion; assessment of physical, chemical, and biological treatment strategies.	[9]
2025	Review of links between climate change-driven CyanoHAB proliferation and dermatologic impacts; calls for improved exposure monitoring.	[10]

Table 1. Key advances in monitoring, detection, and predictive modeling of harmful algal blooms.

Year	Contribution/Advancement	Reference
1981	Early conceptual framework for HAB prediction; foundational ecological indicators.	[18]
1996	Dynamic HAB soft sensor using hyperspectral image data mining for accessory pigment analysis.	[19]
2009	Integration of morphological and molecular data (rbcL, ITS) to enhance taxonomic detection of HAB species.	[20]
2011	Machine learning-based spatio-temporal data mining for HAB detection in the Gulf of Mexico; introduction of cubical neighborhood spectral retrieval.	[21]
2017a	Neural-network processors for detecting HABs in optically complex waters using MERIS 10-year archive.	[22]
2017b	Forty-three algorithmic combinations for HAB prediction using multispectral and hyperspectral bands.	[23]
2017c	Semi-analytical spectral matching and novel algorithms for chlorophyll-a retrieval supporting eutrophication control.	[24]
2018	Time-series LSTM model for HAB prediction in four major South Korean rivers.	[25]
2020	Evaluation of band-ratio vs. machine learning models for Chl-a prediction as a proxy for cyanobacterial blooms.	[26]
2020	Real-time blue tide prediction model using sulfur concentration derived from GOCI observations.	[27]
2021	Synoptic review of ML approaches for HAB and biotoxin prediction (ANN, RF, SVM, and PGMs).	[28]
2021	ML-based short-term HAB prediction using optimized environmental feature selection.	[29]
2022	CNN-LSTM model combining spatio-temporal features for Chl-a prediction; improved accuracy and training speed.	[30]
2022	Integrated CNN-LSTM model for predicting CyanoHAB spatial extent.	[11]
2022	Hybrid XG-LSTM (XGBoost-Long Short-Term Memory) architecture predicting algal cell density and microcystins in Three Gorges Reservoir.	[31]
2022	Spatial prediction of Cochlodinium polykrikoides blooms using CNN-based domain-specific configurations.	[32]
2022	Review of monitoring, modeling, and long-term projection of HABs in China, including early-warning remote sensing.	[33]
2023	Review of freshwater HAB increases; assessment of hydrodynamic reservoir management strategies affecting bloom occurrence.	[34]
2024	Global HAB trend review showing 44% increase from 2000s to 2010s; emphasizes integrated monitoring networks and data fusion for improved forecasts.	[3]
2024	One-Health review on HAB toxins in marine and freshwater systems; integration of human, animal, and environmental surveillance.	[4]
2025	Review of eutrophication-driven coastal HAB expansion; assessment of physical, chemical, and biological treatment strategies.	[9]
2025	Review of links between climate change-driven CyanoHAB proliferation and dermatologic impacts; calls for improved exposure monitoring.	[10]

As shown in Table 1, foundational conceptual work in the 1980s gradually evolved into the integration of molecular tools, multispectral and hyperspectral remote sensing, and eventually advanced machine learning and deep learning architectures such as CNN, LSTM, hybrid XG-LSTM, and spatio-temporal fusion models. Also, the background knowledge of HAB prediction and detection is illustrated in Figure 4. As shown in Figure 4, HAB prediction systems have evolved through three key phases over time. The initial method was introduced by Steidinger and Haddad (1981) [18], followed by an improved system developed by Blondeau-Patissier et al. (2014) [35]. The most recent advancement is the operational application implemented by the National Oceanic and Atmospheric Administration (NOAA) [36].

At the same time, significant progress has been made in smart detection systems and predictive analytics. Machine learning approaches [37,38], deep learning architectures (e.g., CNN–LSTM and XG-LSTM), and remote sensing advancements [39,40] have enabled increasingly accurate detection, forecasting, and spatial mapping of HAB events [25,31,41]. Integrated monitoring networks and data-sharing platforms are now recognized as essential for improving forecasting accuracy, especially under accelerating climate variability [3,28]. In parallel, comprehensive mitigation strategies continue to evolve. Traditional physical, chemical, and biological control measures—while useful—are insufficient when applied in isolation, prompting a shift toward interdisciplinary and integrated management frameworks [9]. Reservoir management practices, hydrodynamic manipulation, nutrient control, and ecosystem-based interventions offer additional pathways for reducing bloom frequency and severity [34]. Most mentioned studies prioritize algorithmic performance or sensor capability but do not fully address the downstream requirements for decision-making, contingency planning, or coastal ecosystem management. The integration of advanced detection tools with governance mechanisms, policy frameworks, and ecological management strategies remains critical for protecting coastal communities and safeguarding environmental resilience [42]. Yet, this integration is precisely where a significant gap persists: few reviews provide a unified synthesis that connects technological progress with practical regulatory applications, cross-sector coordination, and sustainable mitigation strategies. Our review fills this gap by bridging scientific, technological, and policy-oriented perspectives, contributing a framework that links state-of-the-art monitoring and predictive modeling tools with real-world management needs and strategic planning for marine HAB resilience.

From a larger perspective, the outcomes of the scientometric analysis of HAB RA, conducted using the Scopus database and the Bibliometrix toolbox in R software v.4.4, are illustrated in Figure 5, Figure 6, Figure 7 and Figure 8. As shown in Figure 5, it is evident that for the effective risk assessment of HABs as a critical factor in water quality, the cyanobacteria population must be detected and monitored with high accuracy. Additionally, the analysis indicates that previous studies have primarily focused on the human effects of HABs as a significant risk factor.

As shown in Figure 6, research collaborations on the topic of HABs have expanded globally, with the majority of studies conducted in the USA, often in collaboration with Norway and the UK. However, Mexico, which serves as the case study for the present systematic meta-analysis, is not among the primary collaborators in this extensive research field.

The Sankey diagram of the bibliometric assessment (Figure 7) illustrates that in studies on the anthropogenic effects of HABs, the concentration of cytotoxins is evaluated, with a focus on epidemiological influences. Additionally, from an environmental protection perspective, the potential for eutrophication is assessed as a primary concern.

Based on Figure 8, it is evident that research on HAB risk assessment in relation to eutrophication has been a focus of study from 2016 to the present. Currently, quality control of drinking water, with particular attention to cyanobacteria, is regarded as a critical issue. The present study aims to develop an executional framework for implementing HAB risk assessment in Mexico’s water resources to mitigate anthropogenic, environmental, and esthetic impacts associated with this issue.

Figure 9 illustrates the high-risk HAB locations in Mexico, serving as the case study for this research. The figure highlights that there are over 200 high-risk cases in the country’s water resources, emphasizing the significance of the present study. Mexico was selected because its coastal and inland waters experience recurrent HAB events driven by a combination of climatic variability, nutrient enrichment, and intensive coastal development. The country also maintains relatively comprehensive monitoring records, allowing for clear examination of detection challenges, ecological impacts, and policy responses. Highlighting Mexico therefore provides a concrete example of how technological tools and governance frameworks operate in a region facing sustained HAB pressure, while offering insights applicable to other coastal nations confronting similar conditions.

2. Methodology

This review takes a systematic method to identifying, screening, and synthesizing scientific information on the detection, prediction, and management of HABs. The process includes the following steps:

2.1. Scope and Focus

The review aims to (i) examine major drivers influencing HAB proliferation, (ii) summarize methodological advances in monitoring and predictive modeling, (iii) evaluate risk assessment frameworks applied to HABs, and (iv) analyze case studies—particularly from Mexico—highlighting the integration of scientific, technological, and policy-based interventions. The collected evidence is also used to assess guidance for marine governance and mitigation strategies.

2.2. Literature Search Strategy

A thorough literature search was performed using Scopus, Web of Science, ScienceDirect, and Google Scholar. The keywords were “harmful algal blooms,” “marine pollution,” “risk assessment,” “remote sensing,” “machine learning,” “predictive modeling,” “monitoring”, “policymaking,” and even “case studies.” The search covered works from 1990 to 2024, including both fundamental ecological studies and modern technological breakthroughs.

2.3. Screening and Eligibility Criteria

The initial search returned a broad set of publications related to HAB risk assessment, detection methods, environmental drivers, and management strategies. Articles were screened according to the following criteria:

o

Direct relevance to HAB monitoring, prediction, risk analysis, or management;

o

Publication in peer-reviewed journals;

o

Availability of full text;

o

Studies providing conceptual, methodological, or applied insights.

Using the PRISMA framework, the dataset was refined to the 36 core studies most relevant to this investigation. The screening process is summarized in Figure 10.

2.4. Data Extraction and Categorization

Selected papers were reviewed in detail, and information was extracted regarding

o

Monitoring and detection techniques (remote sensing, in situ sensors, machine learning, and molecular tools);

o

Risk assessment approaches, including anthropogenic, esthetic, and environmental dimensions;

o

Modeling frameworks for HAB prediction;

o

Governance, policy responses, and mitigation strategies;

o

Documented case studies, with emphasis on Mexican coastal systems.

Studies were categorized thematically to support structured analysis.

3. Risk Assessment Methods in HABs

Risk assessment (RA) of HABs encompasses multiple dimensions, including anthropogenic pressures, environmental drivers, and socio-esthetic impacts. The approach reflects how HAB-related risks manifest in practice—with overlapping ecological, human health, and societal components.

3.1. Overview of HAB Risk Assessment Approaches

Across the reviewed literature, RA methodologies fall into three principal categories:

(1)

Human health and anthropogenic risk assessment;

(2)

Socio-esthetic and cultural ecosystem impacts;

(3)

Environmental and ecological risk assessment.

Most studies focus on the During HAB (DHAB) phase, with substantially fewer addressing early-warning (BHAB) or post-event (AHAB) risk estimation. This imbalance highlights a critical gap in predictive and long-term RA strategies.

3.2. Anthropogenic and Human Health Risk Assessment

The anthropogenic effects of HABs are discussed in six studies, as presented in Table 2. Furthermore, based on the information in the table, it can be observed that most of the reviewed studies focus on three key stages: experimental practices, threshold assessment based on human health risks, and mapping (Figure 11).

Additionally, a limited number of previous studies have considered both the causes (N (Nitrogen), P (Phosphorus), and COD (Chemical Oxygen Demand)) and effects (toxic release) as part of a novel risk assessment system. When categorizing the HAB process into three stages—Before HABs (BHABs), During HABs (DHABs), and After HABs (AHABs)—the literature review indicates that most investigations focus primarily on DHABs, assessing the risk of the event itself. However, both BHABs and AHABs have received comparatively less attention from the scientific community. Furthermore, no numerical methods based on N, P, or COD/BOD₅ (BOD: Biochemical Oxygen Demand) have been developed for predicting and controlling HABs and their effects on surface water resources. In the final section of this part, it is important to highlight the toxic outputs of HABs, which include various compounds and toxins produced by organisms such as Karenia brevis [43] and Pseudo-nitzschia sp. [44], as well as specific toxins including domoic acid [45,46]. Additionally, waterborne pathogens such as Vibrio cholerae [42] may be associated with HAB events.

Table 2. The anthropogenic effect assessment based on RA methods.

Authors	Pollution	Method	Main Outcome
[43]	Karenia brevis	Provide a risk assessment framework for the recognition and prioritization of the required information to determine human health risks	Use of obtained framework to focus and solve the data gaps
[44]	Pseudo-nitzschia and domoic acid (DA)	Implementation of California HAB risk mapping (C-HARM) by using a blend of numerical models, ecological forecast models, and satellite ocean color imagery	Best correlation between DA measured with solid phase adsorption toxin tracking (SPATT) and marine mammal strandings from DA toxicosis
[47]	HABs	Performing laboratory simulation experiments to investigate the effect of algal blooms on the cycling of arsenic by conceptual modeling	Direct connection between release of acute arsenic in water and the atmosphere and dosage of harmful algal bloom
[42]	Escherichia coli, cyanobacterial harmful algae bloom (cHAB), and V. cholerae	Microbial risk analysis based on measuring the concentration of pollution in different sites of bathing water	Observing exceedances of moderate- and high-risk thresholds for cHAB
[45]	Domoic acid	Risk assessment by policymaking and checking with federal laws	Fundamental reasons for HAB controlling with increasing trust-building in society
[46]	Domoic acid (DA) and saxitoxin (PST)	Detection of toxic concentration by experimental methods such as ELISA analyses and LC-FLD confirmation analyses	DA and PST have a high potential for storage in body of fish that are eaten by larger predators and finally enter the human chain food

Table 2. The anthropogenic effect assessment based on RA methods.

Authors	Pollution	Method	Main Outcome
[43]	Karenia brevis	Provide a risk assessment framework for the recognition and prioritization of the required information to determine human health risks	Use of obtained framework to focus and solve the data gaps
[44]	Pseudo-nitzschia and domoic acid (DA)	Implementation of California HAB risk mapping (C-HARM) by using a blend of numerical models, ecological forecast models, and satellite ocean color imagery	Best correlation between DA measured with solid phase adsorption toxin tracking (SPATT) and marine mammal strandings from DA toxicosis
[47]	HABs	Performing laboratory simulation experiments to investigate the effect of algal blooms on the cycling of arsenic by conceptual modeling	Direct connection between release of acute arsenic in water and the atmosphere and dosage of harmful algal bloom
[42]	Escherichia coli, cyanobacterial harmful algae bloom (cHAB), and V. cholerae	Microbial risk analysis based on measuring the concentration of pollution in different sites of bathing water	Observing exceedances of moderate- and high-risk thresholds for cHAB
[45]	Domoic acid	Risk assessment by policymaking and checking with federal laws	Fundamental reasons for HAB controlling with increasing trust-building in society
[46]	Domoic acid (DA) and saxitoxin (PST)	Detection of toxic concentration by experimental methods such as ELISA analyses and LC-FLD confirmation analyses	DA and PST have a high potential for storage in body of fish that are eaten by larger predators and finally enter the human chain food

3.3. Socio-Esthetic and Cultural Risk Dimensions

The esthetic aspects of HAB risk assessment (RA) have been addressed in only a few studies, primarily focusing on experimental evaluation of cyanobacteria abundance [48] and the concentrations of associated toxins, including microcystin [49] and domoic acid [45]. However, the social and psychological dimensions of this category have not been thoroughly evaluated in previous investigations (Table 3). Notably, only Ekström et al. (2020) [45] assessed the sociological aspects of HABs and their implications for policymaking in their case study. According to Sadeghi et al. (2014) [50], in any dynamic ecosystem, various features, such as visual quality and the enrichment of views and vistas, should be assessed. Furthermore, Tribot et al. (2018) [51] highlighted the interconnectedness of environmental esthetics, neuro-esthetics, psychology, and sociology. Despite these insights, the psychological and sociological impacts of HABs have largely been overlooked in the literature (Figure 12).

Table 3. The esthetic effect assessment based on RA methods.

Authors	Pollution	Method	Main Outcome
[48]	Cyanobacteria	Risk assessment by chlorophyll-a threshold assignment for eutrophication control	Implementation of alert management system in eutrophication event
[49]	Microcystin	Creating a framework using a statistical model (Bernoulli model) for evaluation of water quality specification	Nutrient management is the best option to reduce the frequency of high-microcystin events
[45]	Domoic acid	Risk assessment by policymaking and checking with federal laws	Fundamental reasons for HAB controlling with increasing trust-building in society

Table 3. The esthetic effect assessment based on RA methods.

Authors	Pollution	Method	Main Outcome
[48]	Cyanobacteria	Risk assessment by chlorophyll-a threshold assignment for eutrophication control	Implementation of alert management system in eutrophication event
[49]	Microcystin	Creating a framework using a statistical model (Bernoulli model) for evaluation of water quality specification	Nutrient management is the best option to reduce the frequency of high-microcystin events
[45]	Domoic acid	Risk assessment by policymaking and checking with federal laws	Fundamental reasons for HAB controlling with increasing trust-building in society

As can be found in Figure 12, the study by Del Rossi (2018) [52] explores how psychological distance and knowledge mediate the impact of harmful algal blooms (HABs) on cultural ecosystem services (CESs) in the Lake Champlain Basin. The research finds that individuals’ psychological distance from HABs influences their perception of the ecological issue, impacting their connection to CESs such as heritage and bequest values. While knowledge of HABs contributes to awareness, it does not significantly mediate the primary relationship between psychological distance and CESs, except for these two dimensions. The study suggests that reducing psychological distance by enhancing local engagement and awareness could strengthen public support for environmental management strategies.

The sociological effects of HABs, as examined by Goodrich and Tong (2025) [53], highlight the complex interplay between public awareness, perception, and behavior in the context of environmental hazards. HABs, which have increased in frequency and intensity, pose significant risks to aquatic organisms, livestock, and human health. However, the study finds that there is limited research on the psychological, behavioral, and social dimensions of HAB exposure.

One of the key sociological impacts of HABs is the disparity in public knowledge and risk perception. Many recreators at Lake Harsha, Ohio [53,54], where HAB occurrences have been documented, lacked fundamental awareness of the blooms and their associated health risks. This knowledge gap creates vulnerability, as uninformed individuals may inadvertently expose themselves, their children, and their pets to toxic water conditions. The study categorizes recreators into different groups based on their awareness and behavior, identifying a particularly at-risk segment—those who are unaware or only mildly cautious. This social divide in risk perception can influence public health outcomes and highlights the need for targeted educational interventions. Moreover, the study underscores the role of effective communication in environmental risk management. Given that HABs can significantly alter recreational behaviors, economic activities, and local community interactions, the lack of comprehensive public outreach exacerbates the issue. Misinformation or the absence of clear warnings may lead to unnecessary exposure, while overly cautious responses could deter recreational activities, affecting local economies dependent on tourism and outdoor recreation. Additionally, the study suggests that individuals who frequently engage with affected water bodies may develop a sense of familiarity or normalization of the risk, leading to behavioral complacency. This phenomenon reflects broader sociological patterns where communities exposed to recurring environmental hazards may either heighten their risk aversion or, conversely, downplay the perceived threat over time. In addressing these sociological effects, the research advocates for more effective and strategic dissemination of HAB-related knowledge. Water managers and policymakers must bridge communication gaps by employing accessible, engaging, and localized information campaigns tailored to different demographic groups. Such interventions can not only enhance public understanding but also promote responsible environmental behaviors, ultimately contributing to improved HAB mitigation and management efforts.

3.4. Environmental and Ecological Risk Assessment

According to Table 4, environmental impact analysis based on RA methods is essential for understanding and mitigating the effects of HABs and associated pollutants. Various studies have employed diverse RA techniques to assess pollution sources, analyze environmental risks, and propose management strategies. Key pollutants include HABs, cyanobacteria, ciguatoxins (CTXs), domoic acid (DA), microcystin, and arsenic emissions, all of which pose significant ecological and public health concerns.

Different RA methods have been utilized to evaluate these environmental threats. Numerical modeling and satellite imaging, as implemented by Anderson et al. (2015) [44], have proven effective in correlating DA presence with marine mammal strandings, demonstrating the role of remote sensing in early-warning systems. Statistical models, such as the Bernoulli model applied by Kelly et al. (2019) [49], highlight the importance of nutrient management in controlling microcystin outbreaks. The SPATT technique, used by Roué et al. (2020) [55], improves toxin detection, aiding in the identification of ciguatera risks in marine environments. Additionally, policy-based risk assessment, as examined by Ekstrom et al. (2020) [45], emphasizes the role of regulatory frameworks and societal trust in managing HAB-related hazards.

The environmental impacts of these pollutants are far-reaching. Laboratory simulations, such as those conducted by Tang et al. (2019) [47], reveal the direct link between HABs and arsenic release, emphasizing the need for pollution control measures. Lin et al. (2020) [56] demonstrated that eutrophication indexes are valuable tools for detecting and managing HABs, reinforcing their significance in water quality monitoring. Similarly, Yang et al. (2020) [57] established a strong correlation between rising temperatures and increased cyanobacterial abundance, underscoring the role of climate change in exacerbating HAB events. Seasonal variations also play a critical role, as shown by Theodorou et al. (2020) [58], who identified peak HAB intensities in May and June, aiding in predictive management approaches.

Mapping and field surveys, such as those conducted by Hartman et al. (2021) [54], highlight the risk of recurring HABs due to Prymnesium parvum emissions, stressing the long-term ecological risks associated with such pollutants. The combined insights from these studies reinforce the necessity of integrated RA methodologies to mitigate environmental damage. Effective strategies must incorporate advanced monitoring techniques, policy regulations, and climate-related assessments to reduce the ecological and human health risks posed by HABs and associated toxins.

Table 4. The environmental impact analysis based on RA methods.

Study	Pollution	Method	Main Outcome
[44]	Pseudo-nitzschia and domoic acid (DA)	Implementation of California HAB risk mapping (C-HARM) by using a blend of numerical models, ecological forecast models, and satellite ocean color imagery	Best correlation between DA measured with solid phase adsorption toxin tracking (SPATT) and marine mammal strandings from DA toxicosis
[49]	Microcystin	Creating a framework using a statistical model (Bernoulli model) for evaluation of water quality specification	Nutrient management is the best option to reduce the frequency of high-microcystin events
[47]	HABs	Performing laboratory simulation experiments to investigate the effect of algal blooms on the cycling of arsenic by conceptual modeling	Direct connection between release of acute arsenic in water and the atmosphere and dosage of harmful algal bloom
[56]	HABs	Risk assessment by mathematical methods	Eutrophication indexes are valid tools due to HAB detection and controlling
[57]	Filamentous Cyanobacteria, Planktothrix sp., Limnothrix sp., and Cylindrospermopsis Raciborskii	Experimental measurement of water quality indicators	High relevance between filamentous cyanobacteria abundance and increasing water temperature
[55]	Ciguatoxins (CTXs)	Using the solid phase adsorption toxin tracking (SPATT) method	In areas where the ciguatera risk is unknown or considered low-to-moderate, increasing the amount of resin and time of deployment could help improve toxin detection.
[45]	Domoic acid	Risk assessment by policymaking and checking with federal laws	Fundamental reasons for HAB controlling with increasing trust-building in society
[58]	HABs	Risk assessment by methodological approaches based on expert view information elicitation using questionnaire and data analyses by two-way analysis of variance (two-way ANOVA)	Intensity of HABs is increased in May-June
[54]	Prymnesium parvum harmful algae bloom	Survey on river and mapping risk assessment based on Prymnesium parvum emission	Prymnesium parvum can cause repetition of HABs in rivers and increasing ecological risks

Table 4. The environmental impact analysis based on RA methods.

Study	Pollution	Method	Main Outcome
[44]	Pseudo-nitzschia and domoic acid (DA)	Implementation of California HAB risk mapping (C-HARM) by using a blend of numerical models, ecological forecast models, and satellite ocean color imagery	Best correlation between DA measured with solid phase adsorption toxin tracking (SPATT) and marine mammal strandings from DA toxicosis
[49]	Microcystin	Creating a framework using a statistical model (Bernoulli model) for evaluation of water quality specification	Nutrient management is the best option to reduce the frequency of high-microcystin events
[47]	HABs	Performing laboratory simulation experiments to investigate the effect of algal blooms on the cycling of arsenic by conceptual modeling	Direct connection between release of acute arsenic in water and the atmosphere and dosage of harmful algal bloom
[56]	HABs	Risk assessment by mathematical methods	Eutrophication indexes are valid tools due to HAB detection and controlling
[57]	Filamentous Cyanobacteria, Planktothrix sp., Limnothrix sp., and Cylindrospermopsis Raciborskii	Experimental measurement of water quality indicators	High relevance between filamentous cyanobacteria abundance and increasing water temperature
[55]	Ciguatoxins (CTXs)	Using the solid phase adsorption toxin tracking (SPATT) method	In areas where the ciguatera risk is unknown or considered low-to-moderate, increasing the amount of resin and time of deployment could help improve toxin detection.
[45]	Domoic acid	Risk assessment by policymaking and checking with federal laws	Fundamental reasons for HAB controlling with increasing trust-building in society
[58]	HABs	Risk assessment by methodological approaches based on expert view information elicitation using questionnaire and data analyses by two-way analysis of variance (two-way ANOVA)	Intensity of HABs is increased in May-June
[54]	Prymnesium parvum harmful algae bloom	Survey on river and mapping risk assessment based on Prymnesium parvum emission	Prymnesium parvum can cause repetition of HABs in rivers and increasing ecological risks

3.5. Synthesis of Integrated RA Needs

While each RA domain—anthropogenic, socio-esthetic, and environmental—provides valuable insight, the literature reveals a lack of integrated RA frameworks capable of linking ecological, health, and societal risks. Predictive analytics based on nutrient loading, climate patterns, and toxin dispersion remain underdeveloped for early-warning purposes. Moreover, social and behavioral factors are rarely incorporated, limiting the capacity of RA systems to support community-level decision-making.

Strengthening integrated RA models will require the following:

• Improved coupling of ecological and socio-economic indicators;
• Quantitative tools for BHAB and AHAB phases;
• Incorporation of climate-projection scenarios;
• Enhanced public-communication and risk-perception frameworks;
• Harmonization of remote sensing, machine learning, and policy-based RA approaches.

4. HAB Risk Analysis in Mexican Cases

According to Figure 13a, the study by Peters et al. (2024) [59] conducted a risk analysis for HAB in Mexico by assessing the vulnerability of abalone species to environmental stressors. The risk analysis followed a structured methodology based on the IUCN Red List framework, incorporating multiple criteria to evaluate population declines, habitat degradation, and exposure to climate-driven stressors. The study considered past and projected trends in overfishing, illegal harvesting, and climate-induced changes, such as marine heatwaves and algal bloom outbreaks, which have caused mass mortalities in the region. Specific to Mexico, the analysis highlighted the role of extreme environmental events and the loss of kelp forests, exacerbated by sea urchin population expansion, in reducing abalone populations. By integrating fishery-dependent and independent data, conservation experts assessed population density changes, habitat loss, and mortality rates, allowing them to categorize the extinction risk of different species. The study concluded that HABs, alongside warming seas and illegal exploitation, have significantly increased the risk of local extinction for several abalone species in Mexico, necessitating urgent conservation interventions, including habitat restoration, enforcement of fishing regulations, and potential stock enhancement programs.

The study by Kibler et al. (2015) [60] conducted a risk analysis for harmful algal blooms (HABs) in the Gulf of Mexico and the Caribbean Sea, specifically focusing on the effects of climate change on ciguatera fish poisoning (CFP). The analysis began with data collection and site selection, where six oceanographic buoy stations were identified across the region to represent diverse environmental conditions. Long-term sea surface temperature data were retrieved from the National Data Buoy Center (NDBC) and were supplemented with hindcast data from NOAA’s Extended Reconstructed Sea Surface Temperature (ERSST) dataset to ensure comprehensive historical coverage. To predict future temperature changes, the study utilized climate model projections from eleven global climate models (GCMs) under the Coupled Model Intercomparison Project Phase 5 (CMIP5). The researchers employed the RCP6.0 emissions scenario, which assumes a moderate increase in greenhouse gases, to forecast warming trends through to the end of the 21st century. These projections provided a basis for estimating the potential shifts in environmental conditions that would influence the distribution and abundance of CFP-associated dinoflagellates. A key component of the risk analysis involved evaluating the temperature-growth relationships of five species of Gambierdiscus and Fukuyoa. Using experimental growth data, the study developed polynomial models to describe how these species respond to different temperature conditions. These models were then combined with projected sea surface temperatures to predict changes in species abundance and distribution. The findings indicated that warming seas would lead to a northward expansion of certain Gambierdiscus species into the Gulf of Mexico and the U.S. southeast Atlantic coast, while species adapted to cooler conditions would decline in the Caribbean Sea as temperatures approach their upper thermal limits. The study also assessed seasonal variations in temperature and their influence on bloom dynamics. By examining periods of optimal and suboptimal growth, researchers determined that rising temperatures would extend the growing season of CFP-associated dinoflagellates in some regions while suppressing growth during peak summer months in others. In the Gulf of Mexico, for instance, higher temperatures would make blooms more likely to happen, while in some sections of the Caribbean, severe heat could slow growth for some species. Finally, the study looked at what these changes meant for the risk of CFP. The analysis found that as ocean temperatures keep going up, the danger of CFP would climb in the Gulf of Mexico and the U.S. southeast Atlantic coast. This is because dinoflagellates will have more time to thrive in good conditions. On the other hand, CFP dangers in the Caribbean may stay the same or even go down a little in some places since excessive warming kills some species. The results show that we need to monitor and regulate CFP in a warmer climate to lessen its possible health and environmental effects.

5. HABs and Policymaking

HABs pose significant socioecological and economic challenges, particularly in regions where aquaculture, fisheries, and tourism depend on marine ecosystem services. As climate change accelerates ocean warming and eutrophication, the frequency and intensity of HABs are expected to rise, necessitating improved predictive capabilities and management strategies. In response to this challenge, Gajardo et al. (2023) [61] propose a novel ecosystem-based approach to policymaking for HABs by incorporating the concept of the holobiome, which considers the co-evolutionary interactions between algae and bacteria (Figure 14a). Their study highlights the role of phycosphere-associated bacteria as potential bioindicators of bloom onset and decline, offering predictive capabilities for HAB monitoring. The research emphasizes the importance of integrating scientific findings with policy decisions, advocating for a transdisciplinary approach that involves multiple stakeholders, including policymakers, aquaculture industries, and local communities. The authors discuss how the Monitoring Algae in Chile (MACH) project provides a framework for translating holobiome-based predictive models into policy, using lessons learned from the catastrophic 2016 HAB event in Chile. The study suggests that an improved early-warning system, informed by the dynamics of the HAB holobiome, could enhance adaptive management strategies and mitigate the socioecological impacts of HABs on aquaculture, fisheries, and public health.

As per Figure 14b, O’Leary et al. (2024) [62] explored the role of social media discourse in shaping public perception and policy responses to harmful algal blooms (HABs), specifically focusing on the prolonged 2017–2019 Karenia brevis “red tide” event along Florida’s southwest coast. Using a mixed-method approach that combines machine learning topic modeling with qualitative coding, the study analyzes over 5000 tweets to understand the politicization of HABs and its impact on marine policy. The findings reveal that public discussions on Twitter (now X) exhibited significant polarization, with discourse varying across place-based concerns (e.g., “Florida,” and “beach”), actors (individuals or organizations), and epistemic values (scientific versus media-driven narratives). The study highlights how social media not only amplifies partisan debates but also influences stakeholder engagement, shaping policy narratives and public responses to marine hazards. The authors argue that understanding these dynamics is crucial for environmental managers and policymakers, as social media discourse can either reinforce misinformation or serve as a strategic tool for public engagement and crisis management.

6. Online Monitoring and Assessment Systems

Figure 15 illustrates a set of online dashboards and monitoring systems designed for detecting and controlling harmful algal blooms (HABs), primarily implemented in the United States. These dashboards integrate Geographic Information System (GIS) techniques to visualize real-time and historical data regarding algal bloom occurrences. The NJDEP Algal Bloom Sampling Status (Figure 15a) ArcGIS v.3.5 [63] provides an interactive platform for tracking sample locations, status updates, and risk assessments in New Jersey. Similarly, the Harmful Algal Bloom Observing System (Figure 15b) (www.ncei.noaa.gov (accessed on 1 November 2025)) [64] serves as a nationwide surveillance tool, offering extensive query options and mapping capabilities to analyze HAB events in coastal and inland waters. The Pennsylvania HABs Dashboard (Figure 15c) (https://www.pa.gov/agencies/health/programs/environmental-health/habs-dashboard (accessed on 1 November 2025)) [65] is another example, allowing users to explore HAB reports and response levels through a structured query-based system.

These GIS-based tools demonstrate a robust framework for HAB detection and management, relying on spatial analysis and real-time monitoring. The integration of diverse datasets, including water quality parameters and satellite observations, enhances the accuracy of bloom prediction and risk assessment. By offering interactive maps, historical trends, and categorization of HAB severity, these dashboards facilitate decision-making for environmental agencies, researchers, and the public. The user-friendly design and extensive dataset incorporation make them effective tools for assessing bloom distribution and severity, enabling proactive mitigation strategies.

These GIS-based HAB monitoring systems work well in the United States. If they were used in Mexico, they may greatly improve the management of water quality in aquatic environments. Climate change and human activity make Mexico’s coastal and inland waterways more likely to have HABs. This makes it a good place to research how this technology might be used more widely. This method can make it easier to find, monitor, and suppress HABs by adapting the system to fit the local environment, adding local water quality statistics, and using machine learning to anticipate blooms. So, using this method in Mexico could be a useful addition to GIS-based algal bloom control, leading to more environmentally friendly and data-driven policy.

7. Conclusions

Harmful algal blooms (HABs) continue to pose a growing threat to coastal oversight, safeguarding public health, and the durability of marine ecosystems. This analysis has reviewed identification, surveillance and hazard evaluations illustrating how combined predictive models can greatly improve early-warning systems. The three main HAB risk assessment (RA) areas— health and human-caused risks, socio-esthetic and cultural ecosystem effects, and environmental and ecological threats—offer an organized basis for understanding bloom intensity and guiding appropriate management responses. Examples from Mexico’s risk coastal areas highlight the necessity for location-specific approaches that consider local environmental factors, governance limitations, and the susceptibilities of communities. The case study emphasized that relying on technological progress is inadequate without supportive policy measures and collaborative institutional systems. Successful HAB mitigation demands the integration of methods with regulatory frameworks, enforcement abilities, and sustainable coastal management. The study also emphasizes the increasing importance of GIS-based monitoring systems, which provide scalable real-time tools for identifying bloom events, directing resource distribution, and aiding multi-agency decision processes. As shown by the examples, these platforms can greatly enhance operational preparedness and diminish the socio-economic consequences of significant bloom outbreaks. Overall, addressing HABs demands a genuinely multidisciplinary approach that integrates scientific innovation, robust governance, community engagement, and adaptive management. Future work should prioritize expanding predictive model accuracy, improving data-sharing infrastructures, strengthening policy coherence, and fostering regional and international collaborations.