Selection Of Monitoring Sites Research Articles

Ecological and human health impact assessments based on long-term monitoring of soil PAHs near a coal-fired power plant.

The combustion of coal in power plants releases significant amounts of polycyclic aromatic hydrocarbons (PAHs), which are highly toxic and carcinogenic. This study assesses the ecological and human health impacts of PAHs contamination from a coal-fired power plant over 8years. The monitoring site selection considered the distance from the power plant and the prevailing wind direction in the investigated area. The results reveal that, during the monitoring period, PAH levels increased on average by 43%, 61%, and 37% in the zone of the prevailing wind direction, in the area proximate to the power plant, and the zone distant from it, respectively. The site, which has a radius of 4.5km in the prevailing wind direction, exhibited the highest ecological and human health impacts. Additionally, a strong correlation was observed between environmental and human health impacts, depending on the distance from the power plant, particularly in areas with the prevailing wind direction. These insights contribute to a comprehensive understanding of the intricate dynamics linking power plant emissions, PAHs contamination, and their far-reaching consequences on the environment and human health.

Assessing Air Quality Using Multivariate Statistical Approaches

The purpose of the current study was to evaluate air quality in Dong Thap province, Vietnam. The air quality data was collected during 2019–2020, representing the time of pre- and mid-COVID-19. Twenty-seven air quality samples (in the areas of urban, residential-administrative, hospital-schools, and industry-craft village areas) were used for the evaluation. Air quality was evaluated using national technical regulations on air quality, including QCVN 26:2010/BTNMT and QCVN 05:2013/BTNMT. The difference of mean air quality between the areas was examined using a one-way ANOVA followed by the Duncan test at a significant level of 5%. The relationship between air quality parameters and microclimate factors was tested using Pearson correlation. Principal component analysis (PCA) was utilized to identify critical variables and potential sources of air variation. Cluster analysis (CA) was applied to group similar air quality sites, thus recommending air monitoring site selection. The results show that the air quality in the study area is not polluted. The concentrations of noise, TSP, SO2, and NO2in the mid-COVID-19 pandemic were significantly lower than those in the pre-COVID-19 pandemic due to the social distancing policy. There was a close correlation among air quality parameters, except for air humidity. PCA identified two to four potential sources of air variation, explaining 84.3%, 100%, 100% and 89.7% of the total air quality variance at urban, residential–administrative, hospital-schools, and industry-craft villages, respectively. CA divided the 27 sampling sites into eight groups by the differences, mainly in humidity, wind speed noise, TSP, and CO. Eight sampling sites could be potentially reduced from the current monitoring program for representativeness and cost-effectiveness purposes. All air parameters in the current study are significant for monitoring, and the potential sources of air quality variation are traffic activities, industrial production, craft village activities, and daily life using fuels in residential areas. The results of the current study provide useful information for air quality monitoring and management. Future monitoring programs should include toxic air pollutants in air quality monitoring programs. Doi: 10.28991/CEJ-2024-010-02-012 Full Text: PDF

Optimisation method for atmospheric environment pollution monitoring site selection based on improved genetic algorithm

Optimisation method for atmospheric environment pollution monitoring site selection based on improved genetic algorithm

Optimisation method for atmospheric environment pollution monitoring site selection based on improved genetic algorithm

Optimisation method for atmospheric environment pollution monitoring site selection based on improved genetic algorithm

Establishing and Operating (Pilot Phase) a Telemetric Streamflow Monitoring Network in Greece

This paper describes HYDRONET, a telemetry-based prototype of a streamflow monitoring network in the Greek territory, where such data are sparse. HYDRONET provides free and near-real-time online access to data. Instead of commercially available stations, in-house-designed and -built telemetric stations were installed, which reduced the equipment cost by approximately 50%. The labour of hydrometric campaigns was reduced by applying a new maximum-entropy method to estimate the discharge from surface velocity observations. Here, we describe these novelty elements succinctly. The potential of HYDRONET to provide civil protection services is exemplified by a flood warning demonstrator for Kalamata’s City Centre. The network’s operation, including the hydraulic criteria for monitoring site selection, the characteristics of the telemetric equipment, the operational monitoring and hydrometric procedures, and the specifics of data transmission, quality control, and storage are described in detail, along with experiences with problems encountered during this pilot phase.

Urban Particulate Matter Hazard Mapping and Monitoring Site Selection in Nablus, Palestine

Few air pollution studies have been applied in the State of Palestine and all showed an increase in particulate matter concentrations above WHO guidelines. However, there is no clear methodology for selecting monitoring locations. In this study, a methodology based on GIS and locally calibrated low-cost sensors was tested. A GIS-based weighted overlay summation process for the potential sources of air pollution (factories, quarries, and traffic), taking into account the influence of altitude and climate, was used to obtain an air pollution hazard map for Nablus, Palestine. To test the methodology, eight locally calibrated PM sensors (AirUs) were deployed to measure PM2.5 concentrations for 55 days from 7 January to 2 March 2022. The results of the hazard map showed that 82% of Nablus is exposed to a high and medium risk of PM pollution. Sensors’ readings showed a good match between the hazard intensity and PM concentrations. It also shows an elevated PM2.5 concentrations above WHO guidelines in all areas. In summary, the overall average for PM2.5 in the Nablus was 48 µg/m3. This may indicate the effectiveness of mapping methodology and the use of low-cost, locally calibrated sensors in characterizing air quality status to identify the potential remediation options.

Optimal selection of monitoring sites in cities for SARS-CoV-2 surveillance in sewage networks.

Selecting sampling points to monitor traces of SARS-CoV-2 in sewage at the intra-urban scale is no trivial task given the complexity of the networks and the multiple technical, economic and socio-environmental constraints involved. This paper proposes two algorithms for the automatic selection of sampling locations in sewage networks. The first algorithm, is for the optimal selection of a predefined number of sampling locations ensuring maximum coverage of inhabitants and minimum overlapping amongst selected sites (static approach). The second is for establishing a strategy of iterations of sample&analysis to identify patient zero and hot spots of COVID-19 infected inhabitants in cities (dynamic approach). The algorithms are based on graph-theory and are coupled to a greedy optimization algorithm. The usefulness of the algorithms is illustrated in the case study of Girona (NE Iberian Peninsula, 148,504 inhabitants). The results show that the algorithms are able to automatically propose locations for a given number of stations. In the case of Girona, always covering more than 60% of the manholes and with less than 3% of them overlapping amongst stations. Deploying 5, 6 or 7 stations results in more than 80% coverage in manholes and more than 85% of the inhabitants. For the dynamic sensor placement, we demonstrate that assigning infection probabilities to each manhole as a function of the number of inhabitants connected reduces the number of iterations required to detect the zero patient and the hot spot areas.

Monitoring and evaluating the contribution of the rural development program to high nature value farmland dominated by traditional mosaic landscape in Slovakia

This paper focuses on designing an approach to monitor the effects of the Rural Development Program (RDP) on High Nature Value Farmland type 2 – farmland dominated by traditional mosaics of cultivated land and small-scale features (HNV2) – and its application to farmland in Slovakia. Based on the identification of HNV2 on the national level and selection of monitoring sites, we carried out monitoring for two periods: 1st monitoring of RDP 2007–2013 and 2nd monitoring of RDP 2014–2020. We developed a set of indicators for monitoring of HNV2, which were mapped in the field: management intensity, categories of land cover and type of management, share of semi-natural elements, management activities. The most significant changes were observed in the proportion of semi-natural vegetation and management intensity. We defined five categories of changes in management intensity: either positive – 1) improved, or 2) persistent regular management – or negative – 3) continued abandonment, 4) decrease in management intensity, or 5) intensification causing decrease of landscape diversity. The evaluation of land cover changes on the basis of landscape indices shows higher landscape diversity on sites without subsidies. This shows that subsidies are linked to farmland with less diverse landscape structure. Statistical analyses showed semi-natural vegetation to be dominant on both subsidised and unsubsidised sites. The total area of HNV2, which is one of the basic indicators evaluated under the RDP, is decreasing: partly due to updates and refinements of the Land Parcel Identification System (LPIS), and exclusion of HNV2 sites from LPIS due to a presence of woody elements, but also partly due to abandonment. The results of our study showed a slightly positive contribution of RDP to maintenance and sustainable management of HNV2.

Observer retention, site selection and population dynamics interact to bias abundance trends in bats

Abstract Many long‐term wildlife population monitoring programmes rely on citizen scientists for data collection. This can offer several benefits over traditional monitoring practices as it is a cost‐effective, large‐scale approach capable of providing long time series data and raising public environmental awareness. Whilst there is a debate about the quality of citizen science data, a standardised sampling design can allow citizen science data to be of a similar quality to those collected by professionals. However, many programmes use subjective, opportunistic selection of monitoring sites and this introduces several types of bias, which are not well understood. Using bat roost counts as a case study, we took a ‘virtual ecologist’ approach to simulate the effect of opportunistic site selection and uneven observer retention on our ability to accurately detect abundance trends. We simulated populations with different levels of temporal variability and site fidelity. Our simulations reveal that opportunistic site selection and low observer retention can result in biased trends and that these biases are magnified when monitored populations exhibit high levels of inter‐annual variation and low site fidelity. These results show that the synergistic effects of observer behaviour, site selection, and population dynamics lead to biased abundance trends in monitoring programmes. This study highlights the value of engaging and retaining citizen science observers, a standardised sampling design, and the collection of metadata. We conclude that monitoring programmes need to be aware of their focal species' temporal variability and site fidelity to adequately assess the potential bias caused by opportunistic site selection and low observer retention. Synthesis and applications. Accurate data on population changes are key for conservation success. Therefore, it is important that citizen science monitoring programmes assess and potentially quantify the biases present in their data. We demonstrate the applicability of an established simulation framework to assess the effect of biases on our ability to correctly detect abundance trends. Our findings highlight that monitoring programmes need to be aware of their study species’ temporal variability and site fidelity to assess and account for the effects of biased site selection and observer retention.

A general framework for prediction in penalized regression

There are two main approaches to carrying out prediction in the context of penalized regression: with low-rank basis and penalties or through the smooth mixed models. In this article, we give further insight in the case of P-splines showing the influence of the penalty on the prediction. In the context of mixed models, we can connect the new predicted values to the observed values through a joint normal distribution, which allows us to compute prediction intervals. In this work, we propose an alternative approach, called the extended mixed model approach, that allows us to fit and predict data simultaneously. The methodology is illustrated with two real datasets, one of them on aboveground biomass and the other on monthly sulphur dioxide ([Formula: see text]) levels in a selection of monitoring sites in Europe.

Research on Monitoring Location Model Based on Geo-Video Semantics

Abstract. With the flourishing and advancement of Smart City, security concerns arise, and the use of video surveillance system is regarded as a means of resolving security problems. The public environment of smart city shows the characteristics of multiscale and spatiotemporal, while Geo-video system has gradually become a trend solution in the monitoring field, which supports the correlation analysis of geo-video data, video content and geographical environment. But it has such a problem of the mass geographic video data: inefficient store, retrieval and linkage analysis. Therefore, Geo-video system has put forward an urgent demand for geographic video data with higher quality and lower scrap rate.The collection of geographic video data still depends on the monitoring equipment, and the picture content depends on the geographical location, frame size, frame rate, resolution, elevation and other parameters of the monitoring equipment. Therefore, the geo-video semantics are taken into account in the monitoring site selection model, and the semantic information such as the characteristics, behaviors and events of the monitoring objects are integrated into the construction of the monitoring screen, which is conducive to the event inference, information mining and correlation analysis of geo-video, and responds to the monitoring target and demand of geo-video system. In this way, it can start from the site selection and serve the overall construction of geo-video system.Considering the research hotspot and trend in the field of geo-video, we comprehensively analysed the location requirements, monitoring equipment parameters, location semantics and feature semantics of monitoring objects from the perspective of GIS, and a monitoring location model based on the semantics of geo-video has been proposed, which realized the fusion of monitoring picture and 2D map. Firstly, four typical geographic video semantic models (geographic entity, geographic scene, monitoring equipment and location principle) are analyzed to extract a set of extensible geographic video semantic classification model for monitoring location. Secondly, the geographic video semantic model is studied in combination with the research area, including monitoring requirement analysis, classification of geographic scenes and geographic entities, and description of the research area in terms of feature semantics and location semantics. Finally, generate the overlapping facets according to the monitoring candidate group and the visual field of the monitoring equipment, which are input as parameters into the set coverage model based on monitoring location. Linear programming method is used to solve the set coverage problem of regional monitoring scope, and the results are visualized in 2D or 3D.By comparing the two cases of the same method with different data and the same method with different data, it can be found that the location method based on geo-video semantics can scientifically and reasonably select the location of monitoring equipment in the research area, and changes across data and actual situation do not negatively impact the experimental results. The monitoring location method based on geo-video semantics is prospective and stable, and can overcome the problems of uncertain location results, uneven distribution and irregularity in general methods. Monitoring location model based on geo-video semantics can provide high quality geo-video data for video surveillance system and lay a foundation for security construction of smart city.

Human health risk due to variations in PM10-PM2.5 and associated PAHs levels

WHO (2012) reports that chronic exposure to air pollutants, including particulate matter (PM), causes the death of 7 million people, constituting the most important environmental risk for health in the world. IARC classifies contaminated outdoor air as carcinogenic, Group 1 category. However, in our countries there are few studies regarding air pollution levels and possible associated effects on public health.The current study determined PM and associated polycyclic aromatic hydrocarbons (PAHs) levels in outdoor air, identified their possible emission sources and analysed health risks in the city of Tandil (Argentina). PM10 and PM2.5 samples were collected using a low volume sampler (MiniVol TAS) in three areas: city centre, industrial and residential. Concentrations were determined by gravimetric methods and the content of the US EPA 16 priority PAHs was found by high performance liquid chromatography (HPLC). Description of the main emission sources and selection of monitoring sites resulted from spatial analysis and the IVE (International Vehicle Emissions) model was used in the characterisation of the traffic flow. Median values of 35.7 μgm-3 and 9.6 μgm-3 in PM10 and PM2.5 respectively and characteristic profiles were found for each area. Local values PAHs associated to PM10 and PM2.5, in general, were lower than 10ngm-3. The estimated Unit Risk for the three areas exceeds US EPA standards (9 × 10−5). The number of deaths attributable to short term exposure to outdoor PM10 was 4 cases in children under 5 years of age, and 21 cases in total population, for a relative risk of 1.037.

Strategies for Monitoring Multiuse Trail Networks

Many municipalities, park districts, and nonprofit organizations have begun monitoring nonmotorized traffic on multiuse trails as the need for information about the use of facilities has grown and relatively low-cost sensors for automated monitoring have become available. As they have gained experience, they have begun to move from site-specific monitoring on individual trails to a more comprehensive monitoring of trail networks. This case study review compares strategies developed by 10 organizations for monitoring traffic on multiuse trails, including local, multicounty, statewide, and multistate trail networks. The focus is on approaches to the design of monitoring networks, particularly the rationales or objectives for monitoring and the selection of monitoring sites. It is shown that jurisdictions are following principles of monitoring established by FHWA and that the design of monitoring networks is evolving to meet new challenges, including monitoring large-scale networks. Relevant outcomes and implications for practice are summarized. The researchers concluded that FHWA guidelines can be adapted to many circumstances and can increase information for decision making. Trail monitoring is informing decisions related to facility planning, investment, and safety.

A framework for habitat monitoring and climate change modelling: construction and validation of the Environmental Stratification of Estonia

Environmental stratifications provide the framework for efficient surveillance and monitoring of biodiversity and ecological resources, as well as modelling exercises. An obstacle for agricultural landscape monitoring in Estonia has been the lack of a framework for the objective selection of monitoring sites. This paper describes the construction and testing of the Environmental Stratification of Estonia (ESE). Principal components analysis was used to select the variables that capture the most amount of variation. Seven climate variables and topography were selected and subsequently subjected to the ISODATA clustering routine in order to produce relatively homogeneous environmental strata. The ESE contains eight strata, which have been described in terms of soil, land cover and climatic parameters. In order to assess the reliability of the stratification procedure for the selection of monitoring sites, the ESE was compared with the previous map of Landscape Regions of Estonia and correlated with five environmental data sets. All correlations were significant. The stratification has therefore already been used to extend the current series of samples in agricultural landscapes into a more statistically robust series of monitoring sites. The potential for applying climate change scenarios to assess the shifts in the strata and associated ecological impacts is also examined.

Back-extrapolated and year-specific NO2 land use regression models for Great Britain - Do they yield different exposure assessment?

Robust methods to estimate historic population air pollution exposures are important tools for epidemiological studies evaluating long-term health effects. We developed land use regression (LUR) models for NO2 exposure in Great Britain for 1991 and explored whether the choice of year-specific or back-extrapolated LUR yields 1) similar LUR variables and model performance, and 2) similar national and regional address-level and small-area concentrations. We constructed two LUR models for 1991using NO2 concentrations from the diffusion tube monitoring network, one using 75% of all available measurement sites (that over-represent industrial areas), and the other using 75% of a subset of sites proportionate to population by region to study the effects of monitoring site selection bias. We compared, using the remaining (hold-out) 25% of monitoring sites, the performance of the two 1991 models with back-extrapolation of a previously published 2009 model, developed using NO2 concentrations from automatic chemiluminescence monitoring sites and predictor variables from 2006/2007. The 2009 model was back-extrapolated to 1991 using the same predictors (1990 & 1995) used to develop 1991 models. The 1991 models included industrial land use variables, not present for 2009. The hold-out performance of 1991 models (mean-squared-error-based-R2: 0.62–0.64) was up to 8% higher and ~1μg/m3 lower in root mean squared error than the back-extrapolated 2009 model, with best performance from the subset of sites representing population exposures. Year-specific and back-extrapolated exposures for residential addresses (n=1.338,399) and small areas (n=10.518) were very highly linearly correlated for Great Britain (r>0.83). This study suggests that year-specific model for 1991 and back-extrapolation of the 2009 LUR yield similar exposure assessment.

Implementing Effective Monitoring Sites for Emerging Contaminants from the Assessment of Their Critical Areas: Comparative Studies of Multiple Watersheds in the United States

AbstractEmerging contaminants traditionally have not been measured in the environment, but they recently have been detected in various water systems as a result of advancements in analytical methods. These contaminants pose potential threats to ecosystems and public health, but are generally not regulated, nor are they required to be routinely monitored. This paper presents the implementation of watershed-scale monitoring-site selection as an effective monitoring design for emerging contaminants. The study aimed to identify critical areas of emerging contaminants for both point and nonpoint sources in five selected watersheds in the United States with different land-use compositions and climate conditions. Spatial association analysis using the Getis-Ord’s Gi* statistic was employed to identify clusters of point and nonpoint emerging contaminants considering the impact of neighboring subwatersheds. This analysis identified subbasins containing major wastewater treatment systems as point-source hotspots an...

Design of optimal ecosystem monitoring networks: hotspot detection and biodiversity patterns

Effective monitoring of ecosystems is crucial for assessing and possibly anticipating shifts, quantifying ecosystem services, and optimal decision making based on shifts and services. The selection of monitoring sites is typically suboptimal following local stakeholder or research interests that do not allow to capture ecosystem patterns and dynamics as a whole. The design of optimal monitoring network is crucial for the accurate determination of biodiversity patterns of ecosystems. A novel model for the design of optimal monitoring networks for biodiversity based on the concept of the value of information (VoI) is proposed. The VoI is assigned to species richness that is the economically and ecologically valuable metric. As a case study the trinational frontier ecosystem among Brazil, Peru, and Bolivia is considered for the model. A multiresolution texture-based model estimates species richness and turnover on satellite imagery calibrated on different sets of information coming from forest plot data organized in network topologies. The optimal monitoring network is the network that minimizes the integrated VoI defined as the variation of the VoI in the 28 years considered. This is equivalent to minimize the sum of the species turnover of the ecosystem. The small world network is identified as the optimal and most resilient monitoring network whose nodes are the hotspots of species richness. The hotspots are identified as the sites whose VoI is the highest for the whole period considered. Hence, the hotspots are the most valuable communities for inferring biodiversity patterns and the most ecologically/economically valuable according to the richness—resilience hypothesis. Most hotspots are honored by the small world network that can be thought as the ”backbone” ecological network of the ecosystem. The small world monitoring network has an accuracy ~50 % higher than other network topologies in predicting biodiversity patterns. This network has the highest VoI at any time step and scale considered; thus, it guarantees to track changes of ecosystems in space and time. The network that results from the optimal trade-off between data value with their uncertainty and relevance, has deep implications for understanding ecosystem function and for management decisions. The model allows to include preferences for ecosystem communities by using differential weights on the VoI of these communities, and economic constraints that limit the extension of the network. Because of the optimal integration of environmental, social, and economical factors the model allows a sustainable monitoring and planning of biodiversity for the future.

On the effect of preferential sampling in spatial prediction

The choice of the sampling locations in a spatial network is often guided by practical demands. In particular, many locations are preferentially chosen to capture high values of a response, for example, air pollution levels in environmental monitoring. Then, model estimation and prediction of the exposure surface become biased due to the selective sampling. Since prediction is often the main utility of the modeling, we suggest that the effect of preferential sampling lies more importantly in the resulting predictive surface than in parameter estimation. Our contribution is to offer a direct simulation-based approach to assessing the effects of preferential sampling. We compare two predictive surfaces over the study region, one originating from the notion of an 'operating' intensity driving the selection of monitoring sites, the other under complete spatial randomness. We can consider a range of response models. They may reflect the operating intensity, introduce alternative informative covariates, or just propose a flexible spatial model. Then, we can generate data under the given model. Upon fitting the model and interpolating (kriging), we will obtain two predictive surfaces to compare. It is important to note that we need suitable metrics to compare the surfaces and that the predictive surfaces are random, so we need to make expected comparisons.

Development of Land Use Regression Models for PM2.5, PM2.5 Absorbance, PM10 and PMcoarse in 20 European Study Areas; Results of the ESCAPE Project

Land Use Regression (LUR) models have been used increasingly for modeling small-scale spatial variation in air pollution concentrations and estimating individual exposure for participants of cohort studies. Within the ESCAPE project, concentrations of PM(2.5), PM(2.5) absorbance, PM(10), and PM(coarse) were measured in 20 European study areas at 20 sites per area. GIS-derived predictor variables (e.g., traffic intensity, population, and land-use) were evaluated to model spatial variation of annual average concentrations for each study area. The median model explained variance (R(2)) was 71% for PM(2.5) (range across study areas 35-94%). Model R(2) was higher for PM(2.5) absorbance (median 89%, range 56-97%) and lower for PM(coarse) (median 68%, range 32- 81%). Models included between two and five predictor variables, with various traffic indicators as the most common predictors. Lower R(2) was related to small concentration variability or limited availability of predictor variables, especially traffic intensity. Cross validation R(2) results were on average 8-11% lower than model R(2). Careful selection of monitoring sites, examination of influential observations and skewed variable distributions were essential for developing stable LUR models. The final LUR models are used to estimate air pollution concentrations at the home addresses of participants in the health studies involved in ESCAPE.

A Node Deployment Strategy Considering Environmental Factors and the Number of Nodes in Surveillance and Reconnaissance Sensor Networks

In wireless sensor networks, sensor coverage and network connectivity problems are caused by the restricted sensing and communication range of the nodes. To solve these problems, many studies have been undertaken but most of this work is too limited to be applied to real environments because they do not consider various environmental factors that affect wireless sensor network deployment. In this paper, we propose a node deployment strategy that considers environmental factors and the number of nodes for surveillance and reconnaissance sensor networks (SRSNs). Our strategy consists of four steps for the installation of a surveillance and reconnaissance sensor network: the analysis of environmental factors, sensor node deployment, selection of monitoring sites and relay node deployment. It deploys the sensor nodes, and relay nodes while considering features of the surveillance and reconnaissance sensor networks as well as environmental factors. Simulation results show that our strategy improves sensor coverage and network connectivity.