Environmental Monitoring Data Research Articles

Temporal trend evaluation in monitoring programs with high spatial resolution and low temporal resolution using geographically weighted regression models

Data from monitoring programs with high spatial resolution but low temporal resolution are often overlooked when assessing temporal trends, as the data structure does not permit the use of established trend analysis methods. However, the data include uniquely detailed information about geographically differentiated temporal trends driven by large-scale influences, such as climate or airborne deposition. In this study, we used geographically weighted regression models, extended with a temporal component, to evaluate linear and nonlinear trends in environmental monitoring data. To improve the results, we tested approaches for station-wise pre-processing of data and for validation of the resulting models. To illustrate the method, we used data on changes in total organic carbon (TOC) obtained in a monitoring program of around 4800 Swedish lakes observed once every 6 years between 2008 and 2021. On applying the methods developed here, we identified nonlinear changes in TOC from consistent negative trends over most of Sweden around 2010 to positive trends during later years in parts of the country.

A Multi-Species Simulation of Mosquito Disease Vector Development in Temperate Australian Tidal Wetlands Using Publicly Available Data.

Worldwide, mosquito monitoring and control programs consume large amounts of resources in the effort to minimise mosquito-borne disease incidence. On-site larval monitoring is highly effective but time consuming. A number of mechanistic models of mosquito development have been developed to reduce the reliance on larval monitoring, but none for Ross River virus, the most commonly occurring mosquito-borne disease in Australia. This research modifies existing mechanistic models for malaria vectors and applies it to a wetland field site in Southwest, Western Australia. Environmental monitoring data were applied to an enzyme kinetic model of larval mosquito development to simulate timing of adult emergence and relative population abundance of three mosquito vectors of the Ross River virus for the period of 2018-2020. The model results were compared with field measured adult mosquitoes trapped using carbon dioxide light traps. The model showed different patterns of emergence for the three mosquito species, capturing inter-seasonal and inter-year variation, and correlated well with field adult trapping data. The model provides a useful tool to investigate the effects of different weather and environmental variables on larval and adult mosquito development and can be used to investigate the possible effects of changes to short-term and long-term sea level and climate changes.

Progress in understanding the vulnerability of freshwater ecosystems.

The ability to collect and synthesize long-term environmental monitoring data is essential for the effective management of freshwater ecosystems. Progress has been made in assessment and monitoring approaches that have integrated routine monitoring programs into more holistic watershed-scale vulnerability assessments. While the concept of vulnerability assessment is well-defined for ecosystems, complementary and sometimes competing concepts of adaptive management, ecological integrity, and ecological condition complicate the communication of results to a broader audience. Here, we identify progress in freshwater assessments that can contribute to the identification and communication of freshwater vulnerability. We review novel methods that address common challenges associated with: 1) a lack of baseline information, 2) variability associated with a spatial context, and 3) the taxonomic sufficiency of biological indicators used to make inferences about ecological conditions. Innovation in methods and communication are discussed as a means to highlight meaningful cost-effective results that target policy towards heuristic ecosystem-management.

PREDICTION OF AQI USING HYBRID APPROACH IN MACHINE LEARNING

Forecast of urban air pollutant concentrations must deal with a growth in the volume of environmental monitoring data as well as complex changes in the air pollutants. This initiates the need of efficient prediction techniques to increase prediction accuracy and stop pollution causing issues. In this paper, the combination of Random Forest Regression (RFR) and Support Vector Regression (SVR) based machine learning model is proposed to predict the AQI values. Daily meteorological and air pollution data between March 2020 and June 2022 from Jind city in Haryana were used for model training and test the model’s results. First, the important factors affecting air quality are extracted and null values are replaced by their mean values to handle the irregularities in the dataset. Second, the Random Forest regression machine is used for model training and prediction of the value, the SVR model is used in correction of residual items, and finally the predicted values of AQI are obtained. The experimental results showed that the proposed prediction model of RFR-SVR had a better prediction result than the standard Random Forest Regression, support vector regression machine learning. RMSE, R2, MAPE are used as evaluation indicators to evaluate the performance of the proposed model.

Assessment of the bioaccumulation potential of four commonly used phenolic benzotriazoles based on in silico and experimental in vivo data.

Phenolic benzotriazoles (BTZs) are used globally as light stabilizers in various plastic products to protect them from photooxidative degradation. The same physical-chemical properties that confer their functionality, like a sufficient photostability and a high octanol-water partition coefficient, also raise concerns on their potential for environmental persistence and bioaccumulation based on in silico predictive tools. To evaluate their bioaccumulation potential in aquatic organisms, standardized fish bioaccumulation studies according to OECD TG 305 were conducted with four of the most commonly used BTZs: UV 234, UV 329, UV P, and UV 326. The resulting growth- and lipid-corrected BCF values revealed that UV 234, UV 329, and UV P were below the bioaccumulation threshold (BCF ≤ 2000), but UV 326 is considered very bioaccumulative (BCF ≥ 5000) with respect to the bioaccumulation criteria under REACH. Comparing these experimentally derived data with quantitative structure activity related or other calculated values using a logarithmic partitioning coefficient octanol-water (log Pow ) driven mathematical formula revealed significant discrepancies demonstrating the weakness of current in silico approaches for this group of substances. Furthermore, available environmental monitoring data demonstrate that these rudimentary in silico approaches can lead to unreliable bioaccumulation estimates for this chemical class due to considerable uncertainties in underlying assumptions (e.g., concentration and route of exposure). However, using more sophisticated in silico methods (i.e., CATALOGIC base-line model), the derived BCF values were better aligned with the experimentally derived ones.

Nutrients and Saltwater Exchange as Drivers of Environmental Change in a Danish Brackish Coastal Lake over the Past 100 Years

Many northwest European lake systems are suffering from the effects of eutrophication due to continued loading and/or poor, ineffective management strategies. Coastal brackish lakes are particularly difficult to manage due to complex nitrogen, phosphorus, and salinity dynamics that may exert varying influence on lake biological communities, but long-term data on how these important and often biodiverse systems respond to change are rare. In this study, palaeolimnological data (including sedimentary parameters, diatoms, and plant macrofossils) and environmental monitoring data (for the last ~40 years) have been used to assess environmental change over the last 100 years in Kilen, a brackish lake in northwest Jutland, Denmark. Kilen has been regularly monitored for salinity (since 1972), TP (from 1975), TN (from 1976), and since 1989 for biological data (phytoplankton, zooplankton, and macrophytes), which allows a robust comparison of contemporary and paleolimnological data at high temporal resolution. The palaeolimnological data indicate that the lake has been nutrient rich for the last 100 years, with eutrophication peaking from the mid-1980s to the late 1990s. Reduced nutrient concentrations have occurred since the late 1990s, though this is not reflected in the sediment core diatom assemblage, highlighting that caution must be taken when using quantitative data from biological transfer functions in paleolimnology. Lake recovery over the last 20 years has been driven by a reduction in TN and TP loading from the catchment and shows improvements in the lake water clarity and, recently, in macrophyte cover. Reduced salinity after 2004 has also changed the composition of the dominant macrophyte community within the lake. The low N:P ratio indicates that in summer, the lake is predominately N-limited, likely explaining why previous management, mainly focusing on TP reduction measures, had a modest effect on the water quality of the lake. Despite a slight recovery, the lake is still nutrient-rich, and future management of this system must continue to reduce the nutrient loads of both TN and TP to ensure sustained recovery. This study provides an exceptional opportunity to validate the palaeolimnological record with monitoring data and demonstrates the power of using this combined approach in understanding environmental change in these key aquatic ecosystems.

Research on safety early warning of uranium tailings dam based on abnormal radioactive indexes of water leachate

The radioactive index value of the leachate of the uranium tailings dam is affected by the internal damage of the dam. Therefore, a way of using the deviation of the radioactive index concentration in the leachate to warn the instability of the dam is innovatively proposed in this paper. Firstly, the SSA-BP algorithm is used to predict and analyze the five groups of parameters U, Ra, ∑ α, ∑ β and Rn. Then, the deviation between the actual value and the predicted value is computed. Finally, an early warning is given based on the entropy weight extension decision-making model. The model is verified by the leachate environment monitoring data of a uranium tailings dam in southern China from 2016 to 2020, which shows that the model can effectively caution of the instability of the uranium tailings dam and provides a reference for the subsequent decommissioning management.

Prediction and early warning model of mixed exposure to air pollution and meteorological factors on death of respiratory diseases based on machine learning.

In recent years, with the repeated occurrence of extreme weather and the continuous increase of air pollution, the incidence of weather-related diseases has increased yearly. Air pollution and extreme temperature threaten sensitive groups' lives, among which air pollution is most closely related to respiratory diseases. Owing to the skewed attention, timely intervention is necessary to better predict and warn the occurrence of death from respiratory diseases. In this paper, according to the existing research, based on a number of environmental monitoring data, the regression model is established by integrating the machine learning methods XGBoost, support vector machine (SVM), and generalized additive model (GAM) model. The distributed lag nonlinear model (DLNM) is used to set the warning threshold to transform the data and establish the warning model. According to the DLNM model, the cumulative lag effect of meteorological factors is explored. There is a cumulative lag effect between air temperature and PM2.5, which reaches the maximum when the lag is 3days and 5days, respectively. If the low temperature and high environmental pollutants (PM2.5) continue to influence for a long time, the death risk of respiratory diseases will continue to rise, and the early warning model based on DLNM has better performance.

Use of Remote Sensing Data for Environmental Monitoring of Desertification

Use of Remote Sensing Data for Environmental Monitoring of Desertification

Analysis of the Salinity of the Vistula River Based on Patrol Monitoring and State Environmental Monitoring

Background: Secondary salinity of river water reduces the value of ecosystem services, negatively impacting the entire aquatic ecosystem and reducing the possibility of water use. In Poland, significant anthropogenic salinity of rivers and water reservoirs is usually associated with mining activity consisting of pumping salty mine water into settling ponds or often directly into rivers. However, to assess the reasons for the salinity of the Vistula waters, it is necessary to identify all sources of salt in surface waters, enabling the assessment of the salt load in the waters. Methods: The paper presents four sources of data which have been compiled to propose a valuable method for analyzing the threat of the river. Patrol monitoring was one method of data acquisition, and State Environmental Monitoring data ware also used. Clustering and correlation statistical techniques were used for analysis. Results: Of the 20 physical and chemical parameters analyzed, chloride, calcium sulphate and magnesium ions are important for salinity. Measurements with multi-parameter probes allowed for the identification of increased salinity pressure sites, while flow analyses were required to calculate the load. Conclusions: The Vistula River had the highest concentrations of the analyzed ions in the Silesia Region. The use of patrol monitoring can be highly useful in determining the causes of emerging problems with water quality and supporting State Environmental Monitoring.

Recovery of Outliers in Water Environment Monitoring Data

Recovery of Outliers in Water Environment Monitoring Data

Dependency of the source term estimation method for radionuclides released into the atmosphere on the available environmental monitoring data and its applicability to real-time source term estimation

ABSTRACT It is essential to establish a method for reconstructing the source term and spatiotemporal distribution of radionuclides released into the atmosphere due to a nuclear accident for emergency countermeasures. We examined the dependency of a source term estimation method based on Bayesian inference using atmospheric dispersion simulation and environmental monitoring data on the availability of various monitoring data. Additionally, we examined the applicability of this method to a real-time estimation conducted immediately after an accident. A sensitivity analysis of the estimated source term during the Fukushima Daiichi Nuclear Power Station (FDNPS) accident for combinations of various monitoring data indicated that using monitoring data with a high temporal and spatial resolution and the concurrent use of air concentration and surface deposition data is effective for accurate estimation. A real-time source term estimation experiment assuming the situation of monitoring data acquisition during the FDNPS accident revealed that this method could provide the necessary source term for grasping the overview of surface contamination in the early phase and evaluating the approximate accident scale. If the immediate online acquisition of monitoring data and regular operation of an atmospheric dispersion simulation are established, this method can provide the source term in near-real time.

Assessing the radiological risks associated with primarily natural radioactivities of coastal seawater in northen Vietnam using the Erica software

The activity concentrations of naturally occurring radionuclides 226Ra, 232Th, and 238U were determined in marine sediments, seawaters and seafood along the Gulf of Vietnam to establish baseline data for future environmental monitoring at a surface water depth of 0–3 cm. The concentration of uranium, thorium and radium were determined using a low background gamma spectrum as well as activity ofy of 238U(214Bi), 232Th(228Ac) and 226Ra. The mean radioactivity concentrations of 226Ra, 232Th, and 238U were found to be 8.59 ± 0.54, 1.31 ± 0.15, and 6.91 ± 0.64 Bq m−3, respectively, in seawater samples and 32.96 ± 1.90, 37.64 ± 1.91, and 39.28 ± 1.96 Bq kg−1, respectively, in marine sediments, 0.21 ± 0.03, 0.69 ± 0.11, and 0.19 ± 0.03 Bq kg−1, respectively, in fish samples and 0.23 ± 0.041, 0.41 ± 0.06, and 0.31 ± 0.06 Bq kg−1, respectively, in clam samples. The radioactivity concentrations in seawater are higher than those in sediment and compared with those reported in other countries. The mean values of distribution coefficient (L/kg) is 0.53, 0.13, and 0.23, respectively, in fish samples and 0.19, 0.16 and 0.13, respectively, in clam samples at Hai Phong, Quang Ninh and Ha Tinh. Moreover, the ecological dose at Hai Phong, Quang Ninh and Ha Tinh are 0.03, 0.02 and 0.02 µGy h−1, respectively, in fish and 0.02, 0.03, and 0.03 µGy h−1, respectively, in clams and the mean human’s seafood consumers dose rate is 1.13×10-6 Sv/yrs. Results were discussed and compared with those reported in similar studies and with internationally recommended values within limits recommended by UNSCEAR.

Inversion Method for Multiple Nuclide Source Terms in Nuclear Accidents Based on Deep Learning Fusion Model

During severe nuclear accidents, radioactive materials are expected to be released into the atmosphere. Estimating the source term plays a significant role in assessing the consequences of an accident to assist in actioning a proper emergency response. However, it is difficult to obtain information on the source term directly through the instruments in the reactor because of the unpredictable conditions induced by the accident. In this study, a deep learning-based method to estimate the source term with field environmental monitoring data, which utilizes the bagging method to fuse models based on the temporal convolutional network (TCN) and two-dimensional convolutional neural network (2D-CNN), was developed. To reduce the complexity of the model, the particle swarm optimization algorithm was used to optimize the parameters in the fusion model. Seven typical radionuclides (Kr-88, I-131, Te-132, Xe-133, Cs-137, Ba-140, and Ce-144) were set as mixed source terms, and the International Radiological Assessment System was used to generate model training data. The results indicated that the average prediction error of the fusion model for the seven nuclides in the test set was less than 10%, which significantly improved the estimation accuracy compared with the results obtained by TCN or 2D-CNN. Noise analysis revealed the fusion model to be robust, having potential applicability toward more complex nuclear accident scenarios.

Use of Open, Spatial and Satellite Data for the Purpose of Researching Landfills for Municipal

The present work traces the development of an illegal landfill near the town of Svoge in Bulgaria. After 2016, a waste management plan is prepared on the territory of the municipality and a decision is made to dispose of it by depositing it in a regional landfill for municipal waste in the town of Kostinbrod. The aim of the study is to trace the development of biochemical processes that took place on the territory of the former landfill and whether their impact continues to this day. Data from the Open Data Portal (https://data.egov.bg/), the National Spatial Data Portal INSPIRE (https://inspire.egov.bg/bg), satellite data and GIS, several spectral profiles and indices such as Normalized difference vegetation index (NDVI), Tasseled Cap Transformation (TCT) were used. Composite images from the multispectral instrument (MSI) of the Sentinel 2 platform and radar (SAR) from the Sentinel 1 platform of the Copernicus program of the European Space Agency were used. The surface temperature of the landfill was calculated using the heat channels from the Landsat 5-7 (ETM) and Landsat 8 and Landsat 9 (OLI / TIRS) sensors. A combination of radar and optical data was made. Data from different seasons and years were used in order to monitor the dynamics of thermal pollution in the study area. Quantitative and qualitative assessment of the territory on which the landfill is located has been made. Last but not least, the role of high-value open data in environmental monitoring has been demonstrated, which will reduce the administrative burden of making responsible decisions for each smaller municipality and will be successfully implemented in future methodologies for improving Digital twins through technical work in Destination Earth (DestinE) and information about any point, area or globally significant territories.

Smart Cultivation System using IoT

Agriculture plays an essential role in this world. In addition to other nations, India makes up the majority of the world's contribution to agriculture and its linked industries. Eighty-one percent of its farmers are tiny and marginal, providing a source of income for seventy-two percent of its rural households. However, modern agriculture faces many complex challenges, including how to cope with climatic conditions, soil erosion, poor irrigation techniques & poor quality, etc. To overcome these problems, IoT sensors like Soil moisture sensors, Ultrasonic sensors & Bosch’s BMP280 environment monitor sensors are used. These sensors allow us to predict temperature, altitude, humidity, soil moisture, atmospheric pressure, etc. Using mobile applications and statistics of the most-in-demand crop at a specific time of year can be analysed. The primary seasonal plants are split into two broad groups, for example, crops sown at the start of the winter season are known as Rabi crops, and crops sown at the start of the monsoon season, are known as Kharif crops. Each crop needs a particular set of climatic conditions to grow well. The goal of the project is to present a Smart Cultivation System that is based on the Internet of Things (IoT). This system will help farmers get real-time data for effective environmental monitoring, which will help them increase production and product quality.

Tungsten toxicity and carcinogenesis.

Tungsten is an emerging contaminant in the environment. Research has demonstrated that humans are exposed to high levels of tungsten in certain settings, primarily due to increased use of tungsten in industrial applications. However, our understanding of the potential human health risks of tungsten exposure is still limited. An important point we have learned about the toxicity profile of tungsten is that it is complex because tungsten can often augment the effects of other co-exposures or co-stressors, which could result in greater toxicity or more severe disease. This has shaped the tungsten toxicology field and the types of research questions being investigated. This has particularly been true when evaluating the toxicity profile of tungsten metal alloys in combination with cobalt. In this chapter, the current state of the tungsten toxicology field will be discussed focusing on data investigating tungsten carcinogenicity and other major toxicities including pulmonary, cardiometabolic, bone, and immune endpoints, either alone or in combination with other metals. Environmental and human monitoring data will also be discussed to highlight human populations most at risk of exposure to high concentrations of tungsten, the forms of tungsten present in each setting, and exposure levels in each population.

Research on Power IoT System Based City Block Air Pollutant Emission Prediction

In order to constantly improve city environmental air quality, it is necessary to accurately control the major pollutants emissions such as air fine particulate matter. By adopting the proposed iterative update framework of air pollutant emission inventory, combined with block-level real-time electricity consumption data acquired by the smart city power IoT, and utilizing station-level and hourly environmental air quality monitoring data in specific areas of Yuxi and Dali in Yunnan Province from 2020 to 2021, the iterative update of emission inventory and prediction of air pollutant emission are studied. The experimental results shows that the prediction of the monthly average major air pollutants emissions such as NO2/PM10/PM2.5 in specific neighbourhoods of the two cities mentioned above reaches the same accuracy level as using numerical simulation prediction methods, but the prediction computational power requirements are greatly reduced, making it more suitable for the application requirements of the power IoT. This study provides a new idea for improving the regulatory capacity of intelligent environment and achieving higher urban air quality based on the smart city power IoT.

LSTM-Based Anomaly Detection in Manufacturing Environmental Monitoring Data

LSTM-Based Anomaly Detection in Manufacturing Environmental Monitoring Data

Integrating Logis Regression and XGBoost to Construct Indoor Air Quality Improvement Research

In the face of the severe global epidemic, indoor architectural space has become one of the critical issues, and the construction of a new type of “built environment” while solving “health and epidemic prevention” has become the goal of active development in countries around the world (SDGs & Pandemic Response); Pollutant concentration, optimization of indoor heat and humidity environment, and release of indoor environmental monitoring data, etc. It can not only protect the short-term needs of building users but also provide long-term health protection for building users and ultimately achieve the purpose of physical and mental health of building users. This study uses GIA-K007-12 Air Box to collect “environmental characteristics” variables; IAQ, PM1, PM2.5, PM10, CO2, TVOC, HCHO, Fungi index, TEMP, and HUMD are input variables for XGBOOST, using IBM SPSS Statistics 20.0 performs statistical analysis, modelling and using PYTHON to simulate the accuracy of the building fresh air system model and the decision ranking of essential factors. The test results are based on the XGBOOST decision tree. The accuracy value reaches 94.24%, and the order of critical environmental factors for the indoor fresh air system is PM1, HCHO, IAQ, Fungi index, TVOC, etc. The research results can provide the basis for constructing a teaching space for epidemic prevention and demonstrate that the establishment of an “air quality control platform that can be calculated in real-time” can improve the environmental health awareness (EHL) of stakeholders and provide for future development of epidemic prevention space planning and design in the post-epidemic era Reference and application of operation management.