Development of lake macroinvertebrate-based multimetric index for monitoring ecological health in North Central Nigeria.

The use of organisms like macroinvertebrates in developing bioassessment tools, such as multimetric indices (MMIs), is gaining global recognition in monitoring the health status of lakes. The transition from traditional methods of physico-chemical parameters is due to the financial and time costs involved in their analysis while failing to provide accurate early warning signals on ecosystem conditions. Currently, there is scanty information on the use of MMIs in the conservation and management of lakes in Nigeria. This study aimed at developing a macroinvertebrate-based MMI to assess the ecological status of lakes in North Central Nigeria. The study was conducted on Tagwai and Old Gawu Lakes, from April to October 2022. Sampling sites were clustered based on organic pollution and categorized into reference (four) and impaired (four) sites. Out of 54 macroinvertebrate-based candidate metrics, only five were selected after discriminatory, stability, and redundancy tests (performed using R software). The final metrics were abundance of Ephemeroptera + Trichoptera; abundance of Coleoptera + Ephemeroptera; Gastropoda richness; Shannon Wiener index; and percentages of shredders + predators + scrapers, hereafter referred to as North Central Nigeria-Lakes Multimetric Index (NCN-LMMI). The NCN-LMMI values ranged as follows: 21-25, 16-20, 11-15, and 5-10 corresponding to categories I, II, III, and IV for water quality, as indications of good, fair, poor, and very poor ecological status of the lake, respectively. The developed NCN-LMMI will be a useful tool for aquatic resource managers and environmentalists to assess the ecological condition of lakes, mainly the North Central Nigeria municipal lakes.

A Large-Scale, Multiagency Approach to Defining a Reference Network for Pacific Northwest Streams.

Aquatic monitoring programs vary widely in objectives and design. However, each program faces the unifying challenge of assessing conditions and quantifying reasonable expectations for measured indicators. A common approach for setting resource expectations is to define reference conditions that represent areas of least human disturbance or most natural state of a resource characterized by the range of natural variability across a region of interest. Identification of reference sites often relies heavily on professional judgment, resulting in varying and unrepeatable methods. Standardized methods for data collection, site characterization, and reference site selection facilitate greater cooperation among assessment programs and development of assessment tools that are readily shareable and comparable. We illustrate an example that can serve the broader global monitoring community on how to create a consistent and transparent reference network for multiple stream resource agencies. We provide a case study that offers a simple example of how reference sites can be used, at the landscape level, to link upslope management practices to a specific in-channel response. We found management practices, particularly areas with high road densities, have more fine sediments than areas with fewer roads. While this example uses data from only one of the partner agencies, if data were collected in a similar manner they can be combined and create a larger, more robust dataset. We hope that this starts a dialog regarding more standardized ways through inter-agency collaborations to evaluate data. Creating more consistency in physical and biological field protocols will increase the ability to share data.

Behaviour of 137Cs and 210Pb inventory at three candidate reference sites for erosion study in the upstream Citarum watershed area, West Java, Indonesia.

The fallout radionuclide (FRN) analysis needs a reference site (RS) inventory to determine erosion and sedimentation in the study area. The investigated area is in the upstream Citarum watershed, West Java, Indonesia. Twenty-seven corings and 22 scrap samples have been prepared well and measured using HPGe gamma spectroscopy. The data below the minimum detectable activity (MDA) was found for 137Cs in RS6 cor 4 and 7 (<0.16 ± 0.08 Bq kg-1). MDA quantification implies that the inventory below MDA eroded greater than its maximum value (76.02 tons ha-1 a-1). The comparison 137Cs inventory in this study is lower than the three estimation models; however, the inventory of Mt. Papandayan is closer to the model. This study found the depth percentage of 20-30 cm using the proportion of 0-20 cm/0-30 cm ratio and predicted the portion of the existence of 137Cs and 210Pbex in the 20-30 cm in the bulk sample. The highest H0 (142.04 kg m-2), the relaxation length λ, and proportion of 20% of 137Cs in 20-30 cm depth imply that 137Cs inventory activity is possibly deeper than 30 cm. This study recommends that Mt. Papandayan could be the alternative RS for the upstream Citarum watershed.

Microscale spatial variability of Beryllium-7 at a reference site in southwest China.

Quantitative assessment of soil erosion and deposition rates using fallout radionuclides (FRNs), including Beryllium-7 (7Be), requires establishment of a reliable reference inventory i.e. the inventory of a non-eroding point. Little information, however, is currently available on the microscale spatial variability of 7Be inventory within reference sites. This is important information to inform sample design and replication, and in addition, to evaluate the uncertainty of derived soil redistribution data. In this study, soil samples were taken systematically at grid points on a 5 m × 12 m experimental reference plot with a bare soil surface, at two sampling occasions (2019 and 2021) in southwest China. 7Be activities were measured to explore the potential variability of 7Be inventory at the microscale. To determine possible causes of 7Be inventory variation, physicochemical characteristics including organic matter content (OM), pH, cation exchange capacity (CEC) and grain size compositions were analyzed at each sample location. 7Be inventories for the two periods were estimated at 211.1 ± 20.0 and 456.1 ± 43.8 Bq m-2 (mean ± 2 SEM, n = 44), with coefficients of variation of 31.4 and 31.9% for the 2019 and 2021 sampling cases, respectively. No significant correlations were observed between 7Be activity and the measured soil compositional properties, suggesting observed spatial variability is primarily a result of random variation due to rainsplash and other processes, although sampling and measuring processes may contribute some uncertainties. Using the traditional method, ca. 40 independent reference samples are required to estimate the mean 7Be inventory, i.e. to represent input across the site, with an allowable error of 10% at 95% confidence, while application of a bootstrap approach suggests that ca. 28 would be adequate under similar accuracy. Overall, results of this study emphasize that the simple assumption of uniform distribution of 7Be across the reference area needs detailed examination on a case-by-case basis, if this radionuclide is to be used effectively to assess patterns and rates of soil redistribution from field to hillslope scale.

Adaptive baseline model for autonomous marine equipment and systems.

With the rapid development of the Internet of Things (IoT) and the Fourth Industrial Revolution, marine equipment and systems are becoming increasingly automated and autonomous. Judging the status of equipment and systems for autonomous shipping assumes that the benchmark of status evaluation is not easily obtained, and the performance baseline for the benchmark is usually static and cannot be accurately adapted under dynamic operating conditions. This paper deals with the issue of establishing a baseline for marine equipment and systems by using a data-driven method. Considering the working conditions of marine equipment and systems, a reference-site (R-S) model was first proposed to determine the initial baseline. This method could solve the problem of inadequate parameters in the initial state very well. Then, a dynamic kernel (D-K) model was used to increase the number of reference sites and update the reference points. This method reduced the amount of data calculation in the process of a dynamic update of the baseline. Continuously fitting the reference points enabled the dynamically updated performance baseline to cope with the working conditions. To implement the proposed method, the index parameters in the R-S and D-K models were processed, and the sliding window capacity was determined using the Kolmogorov-Smirnov method. Finally, the proposed baseline model was applied to a practical case of the exhaust temperature of a marine diesel engine. The result revealed that the proposed method yielded a more accurate baseline and better adaptive performance.

How pollution effects the immune systems of invertebrate organisms (Mytilus galloprovincialis Lamark, 1819).

Hemocytes are the main immunologic parameters for invertebrate organisms as a response to diseases and pollutions. This study was aimed to reveal the relations between pollution and the total and differential hemocytes numbers. The gulf of Izmir was selected as location for sampling due to its contaminated by different anthropogenic pollutants mainly industrial effects. The effects of the pollutants were investigated on the mussels that have been collected from eight stations in the Izmir Bay (1- Inciralti, 2- Göztepe 3-Konak-4- Pasaport 5-Alsancak, 6-Karsiyaka, 7-Bostanli), which are known as the most polluted part of inner Bay of Izmir (Western Coast of Turkey) and 8-Foça, (an anthropogenically industrialized and shipping -impacted area). The pollution effects in the bay were determined on hemolymphs by biomarkers such as total hemocyte counts (THCs) and differential hemocyte counts (DHCs). Microscopic examinations have shown the statistical differences at THCs and DHCs. The highest total hemocyte number was determined in the station 7 (Bostanli) and the lowest in the station 4 (Pasaport) (P < 0.05). The number of total hemocyte counts were not differed significantly in the Stations among 2 (Göztepe), 5 (Alsancak) and 6 (Karsiyaka), and between Stations 8 (Foça) and 1 (Inciralti) (P > 0.05). In addition, there have been significant differences in subpopulation according to differential hemocyte numbers. The results indicate that the station 8 (reference site-Foça) has the highest agranulocyte number while station 7 (Bostanli) and the station 1 (Inciralti) have the lowest agranulocyte numbers (P < 0.05). As for the basophilic hemocyte numbers, this situation was reversed highest number in station 1 (Inciralti) and 7 (Bostanli) and lowest in the station 8 (Foça) (p < 0.05). The station 4 (Pasaport) has the highest eosinophilic hemocyte number while station 8 (Foça) has the lowest. We concluded that differential hemocytes counts are more sensitive as biomarker with the highest numbers of agranulocytes and lowest numbers of eosinophilic and basophilic hemocytes in reference site.

Identifying the minimum amplicon sequence depth to adequately predict classes in eDNA-based marine biomonitoring using supervised machine learning.

Environmental DNA metabarcoding is a powerful approach for use in biomonitoring and impact assessments. Amplicon-based eDNA sequence data are characteristically highly divergent in sequencing depth (total reads per sample) as influenced inter alia by the number of samples simultaneously analyzed per sequencing run. The random forest (RF) machine learning algorithm has been successfully employed to accurately classify unknown samples into monitoring categories. To employ RF to eDNA data, and avoid sequencing-depth artifacts, sequence data across samples are normalized using rarefaction, a process that inherently loses information. The aim of this study was to inform future sampling designs in terms of the relationship between sampling depth and RF accuracy. We analyzed three published and one new bacterial amplicon datasets, using a RF, based initially on the maximal rarefied data available (minimum mean of > 30,000 reads across all datasets) to give our baseline performance. We then evaluated the RF classification success based on increasingly rarefied datasets. We found that extreme to moderate rarefaction (50-5000 sequences per sample) was sufficient to achieve prediction performance commensurate to the full data, depending on the classification task. We did not find that the number of classification classes, data balance across classes, or the total number of sequences or samples, were associated with predictive accuracy. We identified the ability of the training data to adequately characterize the classes being mapped as the most important criterion and discuss how this finding can inform future sampling design for eDNA based biomonitoring to reduce costs and computation time.

Strategy for Deployment of Integrated Healthy Aging Regions Based Upon an Evidence-Based Regional Ecosystem-The Styria Model.

In 2013, the European Commission founded the platform European Innovation Partnership on Active and Healthy Aging as a communication and innovation network in this domain. The goal of the current study was the development of an integrated regional ecosystem for active and healthy aging for the region of Styria via a step-by-step co-creation process. A mixed model approach was used to establish an ecosystem for active and healthy aging, which includes macro-, meso- and micro-level stakeholders in the province of Styria, Austria. Based on the results, eight recommendations for the deployment of a healthy aging region were developed. The visibility and accessibility of healthy aging products and services were evaluated as key factors for innovation in active and healthy aging in the region. Health professionals were identified as major drivers of innovation related to active and healthy aging in Styria. The study presented in this article assessed the capacities for healthy aging in the Styria region and identified the need to improve communication pathways between all levels of the public health system and market.

Baseline study of heavy metal contamination in the Sangu River estuary, Chattogram, Bangladesh.

Sediment samples were collected from twelve selected sites of the Sangu River estuary and seven metals (As, Cr, Cu, Cd, Pb, Ni, Zn) were analyzed with Inductively Coupled Plasma-mass Spectrometry (ICP-MS) to assess the contamination degree of heavy metals in the Sangu River estuary and to represent it as a reference site. This study revealed the descending order of studied metals (mg/kg) observed in sediment as Zn (88.97 ±â€¯58.97) > Ni (32.75 ±â€¯16.09) > Cu (29.2 ±â€¯10.78) > Cr (25.14 ±â€¯5.20) > Pb (19.57 ±â€¯7.01) > As (2.58 ±â€¯2.55). Cadmium was observed below the detection level. Various indices like geo-accumulation index (Igeo), contamination factor (CF), pollution load index (PLI) suggested that the Sangu River estuary is not contaminated by studied metals excepting Pb. PCA and correlation matrix analysis indicates that Pb and Zn may have anthropogenic sources and As, Ni, Zn, Cu, Cr may come from natural sources.

Are we closer to the vision? A proposed framework for incorporating omics into environmental assessments.

Environmental science has benefited a great deal from omics-based technologies. High-throughput toxicology has defined adverse outcome pathways (AOPs), prioritized chemicals of concern, and identified novel actions of environmental chemicals. While many of these approaches are conducted under rigorous laboratory conditions, a significant challenge has been the interpretation of omics data in "real-world" exposure scenarios. Clarity in the interpretation of these data limits their use in environmental monitoring programs. In recent years, one overarching objective of many has been to address fundamental questions concerning experimental design and the robustness of data collected under the broad umbrella of environmental genomics. These questions include: (1) the likelihood that molecular profiles return to a predefined baseline level following remediation efforts, (2) how reference site selection in an urban environment influences interpretation of omics data and (3) what is the most appropriate species to monitor in the environment from an omics point of view. In addition, inter-genomics studies have been conducted to assess transcriptome reproducibility in toxicology studies. One lesson learned from inter-genomics studies is that there are core molecular networks that can be identified by multiple laboratories using the same platform. This supports the idea that "omics-networks" defined a priori may be a viable approach moving forward for evaluating environmental impacts over time. Both spatial and temporal variability in ecosystem structure is expected to influence molecular responses to environmental stressors, and it is important to recognize how these variables, as well as individual factor (i.e. sex, age, maturation), may confound interpretation of network responses to chemicals. This mini-review synthesizes the progress made towards adopting these tools into environmental monitoring and identifies future challenges to be addressed, as we move into the next era of high throughput sequencing. A conceptual framework for validating and incorporating molecular networks into environmental monitoring programs is proposed. As AOPs become more defined and their potential in environmental monitoring assessments becomes more recognized, the AOP framework may prove to be the conduit between omics and penultimate ecological responses for environmental risk assessments.

Trace metal bioavailability in sediments from a reference site, Ribeira Bay, Brazil.

Surface sediments were collected near potential contamination sources impacting Ribeira Bay (Brazil), a system considered as a 'reference site' for trace metals. Physicochemical properties (pH and Eh), grain size and concentrations of total organic carbon (TOC), total phosphorus (TP), acid-volatile sulfides (AVS) and simultaneously-extracted metals (Fe, Mn, Cd, Cu, Ni, Pb and Zn) were analyzed. Although relatively low metal concentrations were found, correlations of Zn and Ni with high TP levels suggested an association with sewage inputs, while other metals presented associations with specific geochemical carriers (TOC, Fe and Mn compounds). AVS levels exceeding those of the sums of Cd, Cu, Ni, Pb and Zn (ΣSEM) by at least one order of magnitude and TOC-normalized differences between ΣSEM and AVS ((ΣSEM-AVS)/fOC) near to or below than -200µmolgOC(-1) indicated that there were sufficient AVS and TOC levels to control trace metal bioavailability in sediment pore water.