改善颗粒物污染的道路绿地植被屏障研究进展

减少道路环境的颗粒物污染对通勤者的健康至关重要。道路绿地在交通排放和邻近区域之间形成屏障,但这种植被屏障是否可以有效消减局部颗粒物污染仍然需要深入研究和探讨。结合现有研究成果,阐述了植被对颗粒物污染的影响途径;分析了街道峡谷和开放道路两种典型城市道路环境中,植被组成及群落结构设计的关键特征对颗粒物分布和扩散的影响;总结了有利于消减颗粒物浓度的植被单株性状和叶片微形态;探讨了影响颗粒物浓度的其他因子的耦合作用;针对不同道路环境提出了有效的植被屏障设计建议,并指出了植被群落设计和叶片微形态方面的研究趋势,以期优化道路植被规划,改善路域空气质量。     

街谷绿化消减机动车排放污染物的研究方法与进展

随着街道两侧建筑群向高密度、高层化发展,传统“树越多越好”的绿化模式在应对城市街谷空气污染时是否适用受到广泛关注。明晰街谷绿化植物特征及其种植设计模式对机动车排放污染物消减的调控机理是发挥其提升街谷空气品质的重要前提。本文通过对近年国内外相关文献的梳理,比较分析了现场观测、风洞试验和数值模拟3种研究方法的适用性及局限性,详细剖析了行道树、绿篱两类绿化模式对街谷空气污染物扩散与沉降的影响机制,提出了一套面向空气质量提升的街谷绿化适应性设计工作框架。最后,分别从典型街谷绿化设计图示语言研制、街谷空气污染暴露评估技术指南编制、计算机流体力学模型中街谷绿化效应物理过程参数化方案优化等方面进行探讨,以期为后续研究提供思路和借鉴。     

Western Balkan societies' awareness of air pollution. Estimations using natural language processing techniques

Air pollution remains a severe concern in European countries, especially in Western Balkan, where the air monitoring data point to harmful ambient pollution. The public concern with this issue becomes particularly critical during the fall and winter months, when the contamination is more visible, provoking a series of reactions directed principally to the government authorities as the responsible entities for regulating air pollution levels. Since citizen-contributed data are generally considered valuable additional information for assessing the impacts of air pollution, the public contribution could act as a tool for increasing awareness and response about air pollution. Consequently, this study's objective focuses on researching public awareness of air pollution in Western Balkan. The study assumes that citizens' reactions will grow more intensely during the months with an increase in air pollution levels, principally due to winter heating. Therefore, Twitter activity and news articles related to air pollution have been investigated for the case of Macedonia, Serbia, Bosnia and Herzegovina and Montenegro, from November 2021 to March 2022. Natural Language Processing techniques such as sentiment analysis, topic modelling, and cross-correlations statistical analysis were employed to determine the relationship between Twitter discussions and news with actual PM₁₀ levels measured by official air monitoring stations. The aim was to observe whether tweets and news teasers reflect the realistic air pollution situation. The results affirm that social media discussions, mainly with a negative connotation, can serve as a measure of public awareness of temporal changes in the PM₁₀ concentration in the air and the negative consequences. The content of the resources reveals several topics of concern, contributing to better identification of public opinion and possibilities for tracking news trends. Nevertheless, attention should be paid to news interpretation, provided that sometimes they might offer a more neutral understanding of the situation, failing, in this way, to present the actual air conditions and possibly impacting society in forming an unrealistic opinion. Additionally, the public might not be able to obtain sufficient or accurate information about the primary sources of air pollution, emphasizing the need for more transparent communication and greater education regarding air pollution monitoring. Finally, the study provides deeper insights into the content of the data and helps detect the reasons for skepticism towards pro-environmental behavior occurring in social media discussions. Explicitly, personal disappointment with the air quality should be taken as an inflection point by responsible parties to intervene in improving citizens' quality of life.     

Traffic Volume Alters Elk Distribution and Highway Crossings in Arizona

ABSTRACT We used 38,709 fixes collected from December 2003 through June 2006 from 44 elk (Cervus elaphus) fitted with Global Positioning System collars and hourly traffic data recorded along 27 km of highway in central Arizona, USA, to determine how traffic volume affected elk distribution and highway crossings. The probability of elk occurring near the highway decreased with increasing traffic volume, indicating that elk used habitat near the highway primarily when traffic volumes were low (<100 vehicles/hr). We used multiple logistic regression followed by model selection using Akaike's Information Criterion to identify factors influencing probability of elk crossings. We found that increasing traffic rates reduced the overall probability of highway crossing, but this effect depended on both season and the proximity of riparian meadow habitat. Elk crossed highways at higher traffic volumes when accessing high quality foraging areas. Our results indicate that 1) managers assessing habitat quality for elk in areas with high traffic-volume highways should consider that habitat near highways may be utilized at low traffic volumes, 2) in areas where highways potentially act as barriers to elk movement, increasing traffic volume decreases the probability of highway crossings, but the magnitude of this effect depends on both season and proximity of important resources, and 3) because some highway crossings still occurred at the high traffic volumes we recorded, increasing traffic alone will not prevent elk-vehicle collisions. Managers concerned with elk-vehicle collisions could increase the effectiveness of wildlife crossing structures by placing them near important resources, such as riparian meadow habitat.     

Ambient Air Pollution and the Progression of Atherosclerosis in Adults

Background

Cross-sectional studies suggest an association between exposure to ambient air pollution and atherosclerosis. We investigated the association between outdoor air quality and progression of subclinical atherosclerosis (common carotid artery intima-media thickness, CIMT).

Methodology/Principal Findings

We examined data from five double-blind randomized trials that assessed effects of various treatments on the change in CIMT. The trials were conducted in the Los Angeles area. Spatial models and land-use data were used to estimate the home outdoor mean concentration of particulate matter up to 2.5 micrometer in diameter (PM2.5), and to classify residence by proximity to traffic-related pollution (within 100 m of highways). PM2.5 and traffic proximity were positively associated with CIMT progression. Adjusted coefficients were larger than crude associations, not sensitive to modelling specifications, and statistically significant for highway proximity while of borderline significance for PM2.5 (P = 0.08). Annual CIMT progression among those living within 100 m of a highway was accelerated (5.5 micrometers/yr [95%CI: 0.13–10.79; p = 0.04]) or more than twice the population mean progression. For PM2.5, coefficients were positive as well, reaching statistical significance in the socially disadvantaged; in subjects reporting lipid lowering treatment at baseline; among participants receiving on-trial treatments; and among the pool of four out of the five trials.

Conclusion

Consistent with cross-sectional findings and animal studies, this is the first study to report an association between exposure to air pollution and the progression of atherosclerosis – indicated with CIMT change – in humans. Ostensibly, our results suggest that air pollution may contribute to the acceleration of cardiovascular disease development – the main causes of morbidity and mortality in many countries. However, the heterogeneity of the volunteering populations across the five trials, the limited sample size within trials and other relevant subgroups, and the fact that some key findings reached statistical significance in subgroups rather than the sample precludes generalizations to the general population.     

Risk perception of air pollution: An exploration of self-relevancy

Air pollution is a serious environmental issue that has been long recognized. Whereas government policies attempt to reduce air pollution by controlling various pollution sources, research on risk perception of air pollution tends to treat air pollution as a single unified risk and neglects the complication of the polluting sources. Previous research consistently demonstrates that lay people have an inaccurate understanding of air pollution and are unwilling to change their behaviors to decrease pollution. However, these findings can hardly be employed in policy-making because researchers and policy-makers treat air pollution differently. The aim of the present study is to obtain a nuanced understanding of people's risk perception of air pollution from different sources. Moreover, we propose a new risk dimension, Self-Relevancy, and try to understand people's reluctance to change their behaviors to reduce pollution. Three main findings emerged. First, we demonstrated that people perceived each air pollution source differently. Second, factor analysis of risk characteristics showed that ratings of Self-Relevancy questions mainly loaded on the same factor, which indicates the robustness of the Self-Relevancy risk dimension. Finally, we found that people's ratings of Self-Relevancy positively predicted their perceived risk for some of the pollution sources. These findings highlight the importance of investigating sub-categories of risks and exploring the risk dimension of Self-Relevancy in future risk perception research to provide detailed and informative data for policy-makers.     

Behavior of Moose Relative to a Road Network

Abstract Roads often negatively affect terrestrial wildlife, via habitat loss or fragmentation, noise, and direct mortality. We studied moose (Alces alces) behavior relative to a road network, in an area with a history of moose-vehicle accidents, to determine when moose were crossing roadways or using areas near roads and to investigate if environmental factors were involved in this behavior. We tracked 47 adult moose with Global Positioning System collars in a study area crossed by highways and forest roads. We hypothesized that moose would avoid crossing roads but would make occasional visits to roadsides to feed on sodium-rich vegetation and avoid biting insects. Further, we expected moose avoidance to be greater for highways than forest roads. We recorded 196,710 movement segments but only observed 328 highway and 1,172 forest-road crossings (16 and 10 times lower than expected by chance). Moose usually avoided road proximity up to ≥500 m on each side but 20% of collared moose made visits to areas within 50 m of highways, which might have resulted from moose searching for sodium in vegetation and roadside salt pools. In fact, vegetation along highways had higher sodium concentrations and was browsed in similar proportions to vegetation in adjacent forest, despite moose avoidance of these zones. Moose, however, did not use areas near roads more during periods of biting insect abundance. Our results supported the hypothesis of scale-dependent selection by moose; avoidance of highways at a coarse scale may confer long-term benefits, whereas selection of highway corridors at finer scales may be part of a strategy to overcome short-term limiting factors such as sodium deficiency. We found a positive relationship between home-range size and the proportion of road axes they contained, suggesting that moose either compensated for habitat loss or made specific movements along highways to gather sodium. The presence of sodium along highways likely increases moose-vehicle accident risks. Removal of salt pools or use of a de-icing salt other than sodium chloride should render highway surroundings less attractive to moose.     

Using Social Media to Detect Outdoor Air Pollution and Monitor Air Quality Index (AQI): A Geo-Targeted Spatiotemporal Analysis Framework with Sina Weibo (Chinese Twitter)

Outdoor air pollution is a serious problem in many developing countries today. This study focuses on monitoring the dynamic changes of air quality effectively in large cities by analyzing the spatiotemporal trends in geo-targeted social media messages with comprehensive big data filtering procedures. We introduce a new social media analytic framework to (1) investigate the relationship between air pollution topics posted in Sina Weibo (Chinese Twitter) and the daily Air Quality Index (AQI) published by China’s Ministry of Environmental Protection; and (2) monitor the dynamics of air quality index by using social media messages. Correlation analysis was used to compare the connections between discussion trends in social media messages and the temporal changes in the AQI during 2012. We categorized relevant messages into three types, retweets, mobile app messages, and original individual messages finding that original individual messages had the highest correlation to the Air Quality Index. Based on this correlation analysis, individual messages were used to monitor the AQI in 2013. Our study indicates that the filtered social media messages are strongly correlated to the AQI and can be used to monitor the air quality dynamics to some extent.     

Stochastic Modelling of Air Pollution Impacts on Respiratory Infection Risk

The impact of air pollution on people’s health and daily activities in China has recently aroused much attention. By using stochastic differential equations, variation in a 6 year long time series of air quality index (AQI) data, gathered from air quality monitoring sites in Xi’an from 15 November 2010 to 14 November 2016 was studied. Every year the extent of air pollution shifts from being serious to not so serious due to alterations in heat production systems. The distribution of such changes can be predicted by a Bayesian approach and the Gibbs sampler algorithm. The intervals between changes in a sequence indicate when the air pollution becomes increasingly serious. Also, the inflow rate of pollutants during the main pollution periods each year has an increasing trend. This study used a stochastic SEIS model associated with the AQI to explore the impact of air pollution on respiratory infections. Good fits to both the AQI data and the numbers of influenza-like illness cases were obtained by stochastic numerical simulation of the model. Based on the model’s dynamics, the AQI time series and the daily number of respiratory infection cases under various government intervention measures and human protection strategies were forecasted. The AQI data in the last 15 months verified that government interventions on vehicles are effective in controlling air pollution, thus providing numerical support for policy formulation to address the haze crisis.     

Sustainable forest management reporting in Australia

Criteria and indicators are used in a number of sectors to assess progress towards specified goals or targets. The adoption by the Australian Government of a modified set of Montreal Process criteria and indicators to report Australia's progress towards sustainable forest management, at national and sub-national levels, has improved the ability to report comprehensively and consistently, on economic, environmental and social values. The establishment of Australia's Montreal Process Implementation Group, with members from all States and Territories representing forest conservation, production, public and private forest management, provides a strong regional ownership and guidance of the framework. The adoption of the framework by State government agencies, involved in both production and conservation forests, for reporting sustainable forest management demonstrates the framework's relevance at national and sub-national levels. A major development was the implementation, for the first time, of Australia's sustainable forest management reporting framework in Australia's State of the Forest Report 2003. The implementation process revealed issues of relevance to indicators at national and regional levels, data availability, duplication, ambiguity and gaps between some indicators. A national review of the framework is underway to improve the reporting of progress towards sustainable forest management reporting in Australia.     

Spatio-temporal air quality analysis and PM2.5 prediction over Hyderabad City,India using artificial intelligence techniques

Air pollution is one of the most serious environmental issues faced by humans, and it affects the quality of life in cities. PM_2.5 forecasting models can be used to create strategies for assessing and warning the public about anticipated harmful levels of air pollution. Accurate pollutant concentration measurements and forecasting are critical criteria for assessing air quality and are the foundation for making the right strategic decisions. Data-driven machine learning models for PM_2.5 forecasting have gained attention in the recent past. In this study, PM_2.5 prediction for Hyderabad city was carried out using various machine learning models viz. Multi-Linear Regression (MLR), decision tree (DT), K-Nearest Neighbors (KNN), Random Forest (RF), and XGBoost. A deep learning model, the Long Short-Term Memory (LSTM) model, was also used in this study. The results obtained were finally compared based on error and R² value. The best model was selected based on its maximum R² value and minimal error. The model's performance was further improved using the randomized search CV hyperparameter optimization technique. Spatio-temporal air quality analysis was initially conducted, and it was found that the average winter PM_2.5 concentrations were 68% higher than the concentrations in summer. The analysis revealed that XGBoost regression was the best-performing machine learning model with an R² value of 0.82 and a Mean Absolute Error (MAE) of 7.01 μg/ m³, whereas the LSTM deep learning model performed better than XGBoost regression for PM_2.5 modeling with an R² value of 0.89 and an MAE of 5.78 μg/ m³.     

Disentangle the Causes of the Road Barrier Effect in Small Mammals through Genetic Patterns

Road barrier effect is among the foremost negative impacts of roads on wildlife. Knowledge of the factors responsible for the road barrier effect is crucial to understand and predict species’ responses to roads, and to improve mitigation measures in the context of management and conservation. We built a set of hypothesis aiming to infer the most probable cause of road barrier effect (traffic effect or road surface avoidance), while controlling for the potentially confounding effects road width, traffic volume and road age. The wood mouse Apodemus sylvaticus was used as a model species of small and forest-dwelling mammals, which are more likely to be affected by gaps in cover such as those resulting from road construction. We confront genetic patterns from opposite and same roadsides from samples of three highways and used computer simulations to infer migration rates between opposite roadsides. Genetic patterns from 302 samples (ca. 100 per highway) suggest that the highway barrier effect for wood mouse is due to road surface avoidance. However, from the simulations we estimated a migration rate of about 5% between opposite roadsides, indicating that some limited gene flow across highways does occur. To reduce highway impact on population genetic diversity and structure, possible mitigation measures could include retrofitting of culverts and underpasses to increase their attractiveness and facilitate their use by wood mice and other species, and setting aside roadside strips without vegetation removal to facilitate establishment and dispersal of small mammals.     

基于景感生态学的生态环境物联网框架构建

更高的城市化率造成新的城市生态环境问题和变化趋势,新理论与新技术也为生态环境监测与管理提供了新方法。景感生态学是以可持续发展为目标,基于生态学的基本原理,从自然要素、物理感知、心理感知、社会经济、过程与风险等相关方面,研究土地利用规划、建设与管理的科学。基于景感生态学理论,开展生态环境物联网的监测网络设计与监测平台构建。首先,从自然要素中的光、热、水、土壤、综合气象等,以及物理感知的视觉、嗅觉、听觉、触觉等作为监测要素并进行特征分析;其次,结合多目标约束和聚类分析约束条件,提出非规则网格最优法进行生态环境监测网络的布设。再其次,重点探讨了联合地面固定监测站、无人船与无人机构建的"陆海空一体化"的生态环境数据采集与监测;其中,在地面构建综合气象与土壤传感器,可监测自然要素的光照、热量、雨量、气压,以及物理感知要素的风速、风向温度、湿度及噪声等;在海洋中,基于无人船搭载的水环境与水质传感器,可监测水体温度、水浑浊度与水污染状况,以及水体总磷与水体质量等;在空中,基于无人机搭载的气体监测仪、多/高光谱传感器与数码相机,可获取不同区域与不同高度的CO₂、SO_2<...     

基于环境库兹涅茨曲线的我国大气污染防治重点区域环境空气质量与经济增长关系研究

改革开放以来,中国经济迅猛发展,但大气污染等环境问题日益突出。进入21世纪,我国通过颁布实施多项大气污染防治政策,将京津冀及周边地区、长三角地区、珠三角地区等大气污染较严重的区域划定为重点区域,针对性制定治污措施和实施减排工程,努力推动区域环境空气质量改善。基于2000-2019年我国31个省（自治区、直辖市）（以下简称31个省份）GDP,以及SO₂、PM₁₀、NO₂三项大气污染物浓度数据,利用环境库兹涅茨曲线（EKC,Environmental Kuznets Curve）模型,对31个省份和京津冀及周边地区、长三角地区、珠三角地区的经济增长情况、大气污染物浓度演变以及二者之间的关系进行了系统全面的分析评估。研究结果显示：（1）近年来实施的各项大气污染防治政策,特别是2013年以来颁布实施的《大气污染防治行动计划》《打赢蓝天保卫战三年行动计划》,推动环境空气质量改善的同时,促进了经济发展与环境保护长期关系协调性逐步增强,除NO₂浓度呈U型外,31个省份SO₂浓度、PM₁₀浓度与人均GDP的EKC曲线呈倒U型和倒N型,并处于快速下降阶段。（2）京津冀及周边地区SO₂浓度与人均GDP呈倒U型,且处于快速下降阶段;PM₁₀和NO₂浓度均呈现U型关系,且均处于上升期。（3）长三角地区SO₂、PM₁₀浓度与人均GDP呈现倒U型和U型,但均处于下降阶段;NO₂浓度与人均GDP无相关关系。（4）珠三角地区SO₂、PM₁₀和NO₂浓度与人均GDP均呈现倒U型关系,且均处于下降阶段。为此,建议"十四五"期间我国政府要继续实施新一轮的大气污染防治行动计划,聚焦机动车NO_x污染管控,大力推动NO₂浓度稳步下降,以实现我国环境空气质量持续改善,为统筹经济高质量发展和生态环境高水平保护奠定坚实基础。     

Development of pollution indices for the middle section of the Lower Seyhan Basin (Turkey)

Pollution indices aggregate concentrations of several water or air quality parameters into a single quantity to indicate the general status of pollution in a region. Several pollution index models are present in literature. However, their application for different cases may require modifications based on implementation goals and available data. In this study, modified pollution indices were used to evaluate the pollution status in the middle section of the Lower Seyhan River Basin by employing a geographical information system (GIS) software (ArcGIS 9.3) for data processing, estimations and evaluations. Air quality index (AQI) and water quality index (WQI) were utilized to evaluate air and water pollution levels, respectively. Moreover, a composite air–water quality index (AWQI) was developed to perform a general assessment about the overall pollution status. The WQI and AQI were calculated for 2004–2010 and 2007–2010, respectively. The AWQI was developed for the period of 2007–2010. Results indicated that for the available data and time frame considered in the study, air and water qualities were in good conditions (low pollution), in general. Yet, precautions could still be taken for improvement. Results also indicated the need for improvement of monitoring network for better assessment of the environmental quality in the whole basin. In general, GIS tools were very helpful in the development of the indices.     

UWMAIS: ubiquitous water monitoring platform

Water quality is vital to human life and economy. However, one sixth of the world’s population suffers from lack of safe drinking and domestic water. Aiming to improve the capability of predicting and responding to river pollution disasters, this project collaborated with local offices of Chinese National Bureau of Water Resource to explore new solutions to coping with the ever-growing threat of river water pollution. We presented a distributed data analysis algorithm, Infinitesimal Dividing and Analysis, to efficiently locate pollution sources with data gathered from a ubiquitous wired/wireless sensor network. We elaborate on a π-calculus based paradigm to enhance collaboration and interaction among individual monitoring stations. Based on these two enabling technologies, we applied our framework to water quality monitoring at two carefully chosen sites in China.     

Estimation of Parameters in the Two-Compartment Model for Exhaled Nitric Oxide

The fractional concentration of exhaled nitric oxide (FeNO) is a biomarker of airway inflammation that is being increasingly considered in clinical, occupational, and epidemiological applications ranging from asthma management to the detection of air pollution health effects. FeNO depends strongly on exhalation flow rate. This dependency has allowed for the development of mathematical models whose parameters quantify airway and alveolar compartment contributions to FeNO. Numerous methods have been proposed to estimate these parameters using FeNO measured at multiple flow rates. These methods—which allow for non-invasive assessment of localized airway inflammation—have the potential to provide important insights on inflammatory mechanisms. However, different estimation methods produce different results and a serious barrier to progress in this field is the lack of a single recommended method. With the goal of resolving this methodological problem, we have developed a unifying framework in which to present a comprehensive set of existing and novel statistical methods for estimating parameters in the simple two-compartment model. We compared statistical properties of the estimators in simulation studies and investigated model fit and parameter estimate sensitivity across methods using data from 1507 schoolchildren from the Southern California Children''s Health Study, one of the largest multiple flow FeNO studies to date. We recommend a novel nonlinear least squares model with natural log transformation on both sides that produced estimators with good properties, satisfied model assumptions, and fit the Children''s Health Study data well.     

Quantifying personal exposure to air pollution from smartphone‐based location data

Personal exposure assessment is a challenging task that requires both measurements of the state of the environment as well as the individual's movements. In this paper, we show how location data collected by smartphone applications can be exploited to quantify the personal exposure of a large group of people to air pollution. A Bayesian approach that blends air quality monitoring data with individual location data is proposed to assess the individual exposure over time, under uncertainty of both the pollutant level and the individual location. A comparison with personal exposure obtained assuming fixed locations for the individuals is also provided. Location data collected by the Earthquake Network research project are employed to quantify the dynamic personal exposure to fine particulate matter of around 2500 people living in Santiago (Chile) over a 4‐month period. For around 30% of individuals, the personal exposure based on people movements emerges significantly different over the static exposure. On the basis of this result and thanks to a simulation study, we claim that even when the individual location is known with nonnegligible error, this helps to better assess personal exposure to air pollution. The approach is flexible and can be adopted to quantify the personal exposure based on any location‐aware smartphone application.     

Edge Effects of Linear Canopy Openings on Tropical Rain Forest Understory Microclimate

We investigated microclimatic edge gradients associated with grassy powerlines, paved highways and perennial creeks in wet tropical forest in northeastern Australia during wet and dry seasons. Photosynthetically active radiation, air temperature and vapor pressure deficit, soil temperature, canopy temperature, soil moisture, and air speed in the rain forest understory were measured during traverses perpendicular to the forest edge. Light intensity was elevated near the edges of powerlines, highways, and creeks, but this effect was strongest for creek edges. Air temperature and vapor pressure deficit were elevated near powerline edges in the dry season and highway edges in both wet and dry seasons but were not elevated near creek edges in either season. In contrast, soil moisture was lowered near creek edges but not near either powerline or highway edges. No edge gradients were detected for air speed. Canopy temperature was elevated near highway edges and lowered near powerline edges in the wet season but no edge gradients in canopy temperature were detected near creek edges in either the wet or the dry season. We suggest that these different edge gradients may be largely the result of differences in the fluxes of latent and sensible heat within each type of linear canopy opening, with periodic flood disturbance assisting by maintaining a more open canopy near creek edges. Our data indicate that the nature of the linear canopy opening is at least as important as the width in determining the nature and severity of microclimatic edge effects, analogous to the "matrix effect" of traditional fragmentation studies.     

Cyberinfrastructure for sourcing and processing ecological data

Cyberinfrastructure is a product of the information age that provides a framework for informing adaptive management of ecological entities under the impact of regional and global change. It supports proximity monitoring, user-friendly data management, knowledge discovery by data synthesis, and decision making by forecasting.A workflow is proposed that suits the iterative nature of adaptive management. It takes advantage of novel sensor, genomics, and communication technology for ecological monitoring, of ontologies, semantic webs and blockchain for data management, of hybrid, machine and deep learning concepts for data synthesis and forecasting. Forecasting at different time horizons is guiding decision making for adjusting management and continuing monitoring.This review aims to make researchers, decision makers and stakeholders aware of currently existing technology to make better use of ecological data and models for timely and evidence-based decisions.