Monitoring Stations Research Articles

Assessing and monitoring air quality in cities and urban areas with a portable, modular and low-cost sensor station: calibration challenges

ABSTRACT Air pollution affects not only the air in cities but also extends to all indoor environments (homes, offices, schools, public places, transportation, etc.), where we spend between 80% and 90% of our time. Both indoor and outdoor air quality have emerged as significant health concerns and are integral to national strategies implemented by health and environmental institutes in each country. Recently, complaints regarding outdoor air quality have risen in cities, primarily due to automobile traffic and industrial activities in urban areas, and also indoors within homes, offices, and schools. The following paper presents a methodology for the calibration of low-cost monitoring stations based on measurements in a couple of cities in Colombia as part of the development of a project to reduce the environmental awareness gap in urban areas for the estimation of the air quality through low-cost, flexible, modular, and mobile air quality monitoring station design that could be used to assess air pollution in different indoor and outdoor environments. With the implementation of the low-cost stations, we have calibrated and evaluated the performance of the stations using usual linear regression methods, but we have also explored the use of unsupervised estimation with the help of machine learning algorithms, specifically with Random Forest estimators. We have found a significant improvement with using Random Forest for station calibration compared with those found using simple linear regressions for calibration effects. We have found that all the models offer a significant improvement in terms of RMSE. The regression model improves RMSE by up to 70%, while the multiple regression model does so by up to 73%. However, it is the Random Forest that shows the most remarkable improvement, with a reduction in RMSE of up to 86%.

Environmental Monitoring of Changes in the Flooded Areas of the Marshes and Their Relationship to Water Quality Using GIS Techniques

This study aims to examine the relationship between the flooded areas of the marshes in southern Iraq and their water quality, as well as their relationship with climatic factors such as temperatures and rainfall amounts and their impact on the water discharge into the marshes of southern Iraq in the provinces (Basra, Maysan, and Dhi Qar) through the interpretation of maps with remote sensing tools of the wet cover (water and vegetation), descriptive and quantitative analysis of the flooded areas comparing them with the values of salt concentrations and clarifying the monitoring sites of the water quality and water discharge into the marshes through GIS, as well as the relationship of the wet cover with temperatures, rainfall amounts, and evaporation. The data of this study were obtained from environmental field observation data at climate monitoring stations, incoming water discharge stations, and water quality measurement sites in the three southern governorates. As for the areas of vegetation, the data was obtained through the interpretation of the satellites’ visuals (Sentinel-2 satellite) with a resolution of 15 m using GIS programs through a temporal and spatial comparison of the marshland for April -June-October for ten years (2010-2015-2020) respectively, The results showed that there is a direct relationship between the water quality and the wet cover (vegetation and water) through classification as well as the effect of climatic elements in the marshes.

ConvFormer-KDE: A Long-Term Point-Interval Prediction Framework for PM2.5 Based on Multi-Source Spatial and Temporal Data.

Accurate long-term PM2.5 prediction is crucial for environmental management and public health. However, previous studies have mainly focused on short-term air quality point predictions, neglecting the importance of accurately predicting the long-term trends of PM2.5 and studying the uncertainty of PM2.5 concentration changes. The traditional approaches have limitations in capturing nonlinear relationships and complex dynamic patterns in time series, and they often overlook the credibility of prediction results in practical applications. Therefore, there is still much room for improvement in long-term prediction of PM2.5. This study proposes a novel long-term point and interval prediction framework for urban air quality based on multi-source spatial and temporal data, which further quantifies the uncertainty and volatility of the prediction based on the accurate PM2.5 point prediction. In this model, firstly, multi-source datasets from multiple monitoring stations are preprocessed. Subsequently, spatial clustering of stations based on POI data is performed to filter out strongly correlated stations, and feature selection is performed to eliminate redundant features. In this paper, the ConvFormer-KDE model is presented, whereby local patterns and short-term dependencies among multivariate variables are mined through a convolutional neural network (CNN), long-term dependencies among time-series data are extracted using the Transformer model, and a direct multi-output strategy is employed to realize the long-term point prediction of PM2.5 concentration. KDE is utilized to derive prediction intervals for PM2.5 concentration at confidence levels of 85%, 90%, and 95%, respectively, reflecting the uncertainty inherent in long-term trends of PM2.5. The performance of ConvFormer-KDE was compared with a list of advanced models. Experimental results showed that ConvFormer-KDE outperformed baseline models in long-term point- and interval-prediction tasks for PM2.5. The ConvFormer-KDE can provide a valuable early warning basis for future PM2.5 changes from the aspects of point and interval prediction.

A framework for validating watershed ecosystem service models in the United States using long-term water quality data: Applications with the InVEST Nutrient Delivery (NDR) model in Puerto Rico

Modeling of watershed Ecosystem Services (ES) processes has increased greatly in recent years, potentially improving environmental management and decision-making by describing the value of nature. ES models may be sensitive to different conditions and, therefore, should ideally be validated against observed data for their use as a decision-support instrument. However, outcomes from such ES modeling are barely validated, making it difficult to assess uncertainties associated with the modeling and justify their actual usefulness to develop generalizable management recommendations. This study proposes a framework for the systematic validation of one of such tools, the InVEST Nutrient Delivery Model (NDR) for nutrient retention estimates. The framework is divided into three stages: 1) running the NDR model inputs, processes, and outputs; 2) building a long-term reference dataset from open access water quality observations; and 3) using the reference data for model calibration and validation. We applied this framework to twenty watersheds in the Commonwealth of Puerto Rico, where data availability resembles thar of watersheds across the United States. Long-term water quality data from monitoring stations facilitated model calibration and validation. Our framework provided a reproducible method to linking the vast monitoring network in the U.S. and its territories for evaluating the InVEST's NDR model performance. Beyond the framework development, this study found that the InVEST NDR model explained 62.7 % and 79.3 % of the variance in the total nitrogen and total phosphorus between 2000 and 2022, respectively, supporting the suitability of the model for watershed scale ecosystem services assessment. The findings can also serve as a reference to support the use of InVEST for other locations in the tropics without publically available monitoring data.

Non-linear association between air pollutants and secondary sensitive skin in acne patients.

There is a growing number of patients suffering from sensitive skin secondary to acne, but its prevalence and influencing factors are not yet well-understood. The aim of this study is to investigate the nonlinear relationship between air pollutants and secondary sensitive skin in acne patients. A cross-sectional study comprising 4325 acne outpatients in China was carried out between September 2021 and December 2022, employing a simple random sampling approach. Air pollutants data was derived from the nearest air quality monitoring station corresponding to the subjects' residential locations. Furthermore, socio-economic characteristics, biological attributes, and lifestyle data of patients were acquired via questionnaire surveys. The data were subsequently analyzed utilizing the XGBoost machine learning model. A nonlinear relationship has been observed between secondary sensitive skin in acne patients and various factors, including particulate matter (PM2.5), inhalable particulate matter (PM10), ozone (O3), sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), the severity of depression, different levels of exercise intensity, acne grading, frequency of sunscreen application, gender, and age. The occurrence of secondary sensitive skin in acne patients be mitigated through the implementation of measures such as the control of air pollutant emissions, regulation of negative emotions, and improvement of personal lifestyle.

Ozone exceedance forecasting with enhanced extreme instance augmentation: A case study in Germany

Accurately forecasting ozone levels that exceed specific thresholds is pivotal for mitigating adverse effects on both the environment and public health. However, predicting such ozone exceedances remains challenging due to the infrequent occurrence of high-concentration ozone data. This research, leveraging data from 57 German monitoring stations from 1999 to 2018, introduces an Enhanced Extreme Instance Augmentation Random Forest (EEIA-RF) approach that significantly improves the prediction of days when the maximum daily 8-hour average ozone concentrations exceed 120μg/m3. A pre-trained machine learning model is used to generate additional high-concentration data, which, combined with selectively reduced low-concentration data, forms a new dataset for training a refined model. This method achieved an improvement of at least 8% in the accuracy of predicting days with ozone exceedances across Germany. Our experiment underscores the approach’s value in enhancing atmospheric modeling and supporting public health advisories and environmental policy-making related to ozone pollution.

Recurrent neural network using LSTM for prediction of atmospheric pollutants in the State of Veracruz, Mexico

This article shows the use of a recurrent neural network LSTM whose objective was to make a 24-hour ozone forecast with data obtained from the INECC (National Institute of Ecology and Climate Change) and the validated database of the National Information System of Air Quality (SINAICA spanish acronym). The problem of air quality in the world is essential due to health problems. Currently, the techniques used to generate forecasts in monitoring stations are not accurate and do not allow generating alerts to avoid exposure to poor air quality. The state of Veracruz in Mexico has 7 air quality monitoring stations, showing that it is feasible to alternatively have a betterquality pollutant concentration forecasting system. The results obtained on the correlations of variables, although weak, positive or negative, allowed us to recognize their usefulness in the development of training in the RNN. The use of neural networks was demonstrated for the forecast of ozone concentration values in the metropolitan area of Poza Rica, using the LSTM using Python, Tensor Flow and Keras, with a good fit to the normalized pattern with an average RMSE of 0.0053 ppm of the days chosen for testing between the period from June 2019 to July 2020. Future work will consider PM10 and PM2.5.

Implementation of a slope stability method in the CRITERIA-1D agro-hydrological modeling scheme

AbstractThis paper presents the implementation of a slope stability method for rainfall-induced shallow landslides in CRITERIA-1D, which is an agro-hydrological model based on Richards’ equation for transient infiltration and redistribution processes. CRITERIA-1D can simulate the presence and development of roots and canopies over space and time, the regulation of transpiration activity based on real meteorological data, and the evaporation reduction caused by canopies. The slope can be considered composed of a multi-layered soil, leading to the possibility of simulating the bedrock and of setting an initial water table level. CRITERIA-1D can consider different soil horizons characterized by different hydraulic conductivities and soil water retention curves, thus allowing the simulation of capillarity barriers. The validation of the proposed physically based slope stability model was conducted through the simulation of the collected water content and water potential data of an experimental slope. The monitored slope is located close to Montuè, in the north-eastern sector of Oltrepò Pavese (northern Apennines—Italy). Just close to the monitoring station, a shallow landslide occurred in 2014 at a depth of around 100 cm. The results show the utility of agro-hydrological modeling schemes in modeling the antecedent soil moisture condition and in reducing the overestimation of landslides events detection, which is an issue for early warning systems and slope management related to rainfall-induced shallow landslides. The presented model can be used also to test different bioengineering solutions for slope stabilization, especially when data about rooting systems and plant physiology are known.

Spatio-Temporal Variability of Water Quality in the Middle Danube—The Influence of Air Temperature and Discharge

Large watercourses are characterised by significant spatial and temporal changes in water quality due to both natural and anthropogenic impacts. The paper analyses changes in the Danube’s spatial and seasonal water quality in its middle part at five monitoring stations for the period 2018–2022. Examined water quality parameters include oxygen saturation (Os), ammonium (N-NH4), pH, 5-day biochemical oxygen demand (BOD), nitrate nitrogen (N-NO3), orthophosphates (P-PO4), suspended solids (SS), electrical conductivity (EC), and water temperature (WT). Furthermore, the analysis took into account the influence of two additional factors: air temperature (AT) and discharge (D). Throughout the entire period under study, all parameters were characterised by low concentration values, which met the environmental objective of good ecological status. The use of multivariate statistical methods allowed for the identification of EC, N-NH4, WT, Os, SS, and N-NO3 as determining the greatest spatio-seasonal variability of water quality in a selected section of the Danube. Regression models determined WT, EC and nitrogen nitrate changes as depending on AT, flow, and exposure time. Knowing models not only gives a better understanding of the dynamics of changes in water quality in the stretch of the Danube under study but potentially allows the prediction of these parameters based on easily measurable environmental variables.

Satellite-pose estimation using IMU sensor data and Kalman filter with RF-433 Mhz powered communication and helical antenna design for ground station

Space research relies heavily on satellites, and technological developments are critical to the success of these missions. In order to estimate satellite pose, a simulation of an IMU sensor using Matplotlib is presented in this study. A roll, pitch, and yaw value analysis is performed on data obtained from the BNO-055 IMU sensor. A prototype ground station's helical antenna is used to investigate these parameters. For effective communication between the satellite body and ground station setup, a low-cost transceiver module RF-433Mhz module is connected to the ground station. IMUsensors are essential for inertial measurements in spacecraft, which support ground station monitoring. This study makes use of a four-turn helical antenna that is built for deployment and designed using the 4nec2 simulation program for RF-based satellite-ground station communication. The antenna's VSWR is 1.432, and its directivity is 18.581. Matplotlib is utilized to model variations in roll, pitch, and yaw values. Roll, pitch, and yaw ranges that are observed are −0.375 to 0.25, −0.375 to 0.5625, and −0.3125 to 0.1875, in that order. These ranges are useful in effectively visualizing the body pose of the CubeSat when connecting via PySerial to a Matplotlib-developed program.

Sensitivity analysis of a Martian atmospheric column model with data from the Mars Science Laboratory

Abstract. An extensive sensitivity analysis was performed for a horizontally homogeneous and hydrostatic 1-D column model at the Mars Science Laboratory (MSL) location. Model experiments were compared with observations from the Curiosity Rover Environmental Monitoring Station humidity (REMS-H) device and ChemCam. Based on our earlier column model investigations, model surface temperature and pressure, dust optical depth (τ), and column precipitable water content (PWC) were the parameters that we investigated with our sensitivity analysis. Our analysis suggests that the most sensitive parameters for the column model temperature profile are τ and surface temperature. The initial value of PWC does not affect the temperature profile of the model, but it is the most important parameter for the humidity profile. The fixed value of τ also seems to have some effect on the humidity profile of the model. Based on our analysis, variations in surface pressure initialization are negligible for the model's temperature and almost negligible for the model's humidity predictions. The model simulations are generally in good agreement with the observations. Our additional model experiments with a different shape of the model's initial humidity profile yielded better results compared to the well-mixed assumption in the predicted water vapor volume mixing ratios at 1.6 m.

Recent Expansion of the Cascades Volcano Observatory Geophysical Network at Mount Rainier for Improved Volcano and Lahar Monitoring

Abstract The U.S. Geological Survey Cascades Volcano Observatory (CVO) recently expanded its continuous geophysical monitoring at Mount Rainier, an active stratovolcano in Washington state. CVO monitors volcanoes in Oregon, Washington, and Idaho to characterize volcanic systems and detect unrest. Mount Rainier has a history of large lahar occurrences in the Holocene, including at least one that may not have been associated with volcanic activity. Pierce County, Washington, is one of the areas most at risk from large lahars. In the 1990s, CVO collaborated with Pierce County to install the Rainier lahar detection system (RLDS), an automated system designed to detect large lahars in high-risk drainages and mitigate hazards to heavily populated areas. The system was designed to detect lahars within 5–10 min of their occurrence and alert authorities of the need to evacuate populated low-lying areas before lahar arrival. In addition, CVO and the Pacific Northwest Seismic Network (PNSN) maintained and expanded a network of seismic and geodetic monitoring stations on and near the edifice to provide adequate volcano monitoring capabilities. Since 2016, CVO has worked to upgrade the existing RLDS and to expand its capabilities into other drainages around Mount Rainier. This expansion includes installation of 25 new broadband seismic stations with many including infrasound along high-risk drainages, as well as support for equipment upgrades at existing PNSN and CVO volcano monitoring sites. All stations transmit continuous, near-real-time data with dramatically improved spatial coverage for volcano monitoring and lahar hazard mitigation compared to the previous system.

Long-term Exposure to Ambient Air Pollution and its Association with Mental Well-Being, Depression and Anxiety: A Nationally Representative Study.

Exposure to ambient air pollution may play a role in the onset of common mental disorders like depressive and anxiety disorders. The association of long-term exposure to particles smaller than 10 μm (PM10) with these diseases remains unclear. This study aimed to estimate the association of long-term exposure to PM10 with mental well-being and the frequency of probable diagnosis of common mental disorders. A nationally representative cross-sectional study was done in mainland Portugal. Long-term exposure was estimated through one-year average concentrations of PM10, calculated with data from the Portuguese Environment Agency, attributed individually considering individuals' postal codes of residence. The mental well-being and the probable diagnosis of common mental disorders were ascertained through the five-item Mental Health Inventory scale. Linear and Robust Poisson regression models were computed to estimate change percentages, prevalence ratios (PR), and their 95% confidence intervals (95% CI). The median (interquartile range) concentration of PM10 was 18.6 (15.3 - 19.3) μg/m3. The mental well-being score was 72 (56 - 84) points, on a scale from 0 to 100. A probable diagnosis of common mental disorders was found in 22.7% (95% CI: 20.0 to 25.6). Long-term exposure to PM10 was associated with a non-statistically significant decrease in the mental well-being score [for each 10 μg/m³ increment in one-year average PM10 concentrations, there was a 2% (95% CI: -8 to 4) decrease], and with a non-statistically significant increase in the common mental health frequency (PR = 1.012, 95% CI: 0.979 to 1.045). We did not find statistically significant associations between long-term exposure to PM10 and mental well-being or the frequency of probable diagnosis of common mental disorders. These results may be explained by the reduced variability in the exposure values, given the geographical distribution and functioning of the network of air quality monitoring stations. This study contributes with evidence for low levels of air pollutants, being one of the first to adjust for individual and aggregate-level variables. Moreover, to the best of our knowledge, this was the first nationally representative, population-based study conducted on the Portuguese population using real-life data. Maintaining a robust and nationwide air quality monitoring network is essential for obtaining quality exposure data.

Assessing the spatial transferability of calibration models across a low-cost sensors network

Low-cost sensor networks (LCSNs) are expanding worldwide to gather high spatiotemporal resolution data due to their economic feasibility and compact size. The reliability of LCS-recorded data is limited due to their calibration dependencies in the field. Previous studies have focused on the development of LCS calibration models by co-location with the regulatory monitoring stations. However, it is challenging to calibrate LCS in the field for countries with limited infrastructure for air quality monitoring, pointing towards the need for transferable calibration models. Only a few studies have addressed this challenge and provide no information on the factors that may affect the performance of transferable calibration models. Here, we examined the spatial transferability of the calibration models developed using machine learning (ML) algorithms for an LCSN with twenty-two (22) sites in NCT-Delhi. The site-specific calibration models performed well at each site with high R2 and significantly low RMSE values. These models were transferred to the other sites, and the effect of distance between the sites (D), source composition, PM ratios, and particle size distribution (PSD) on the transferability of calibration models was investigated. The models developed at the Mundka (S10) and Punjabi Bagh (S16) sites complied with the evaluation criterion (R2 ≥ 0.70) for each site, irrespective of the distance between the sites. Furthermore, PM ratios reported by the LCSs did not significantly differ across sites, suggesting that the PMS algorithm provides a proxy of the size-resolved mass fractions. Evaluation of the PSD at different sites supported our findings. We also introduced the concept of selecting representative locations for LCS co-location by computing transferability scores using k-means clustering and presented a reference map for NCT-Delhi for developing scalable calibration models.

Spatial weighting EMD-LSTM based approach for short-term PM2.5 prediction research

Given the significant health and environmental risks posed by atmospheric PM2.5 pollution, accurately predicting its concentration changes is especially important. Current models fall short in researching time-series feature extraction from pollutants and spatial correlations among monitoring stations. In this study, a spatiotemporal prediction model is introduced to address these issues. The model combines spatial weighting, empirical mode decomposition (EMD), and a long short-term memory (LSTM) network. First, weights are allocated to sites using Pearson correlation analysis and distance weighting methods. Next, the pollutant time series is decomposed using the EMD method. The highly correlated intrinsic mode function (IMF) component is selected for signal reconstruction, enhancing denoising. Finally, the model uses an LSTM network to capture nonlinear and dynamic time series traits, which significantly improves the PM2.5 prediction accuracy. The model utilizes data collected from 10 monitoring stations across Hefei city during 2018-2019, employing the previous 24 h of observations to forecast PM2.5 concentrations for the subsequent hour. By comparing with RNN, HPO-RNN, GRU, LSTM, and CBAM-CNN-Bi LSTM, the results show that our model surpasses five benchmark models in terms of prediction accuracy. Relative to the best-performing CBAM-CNN-Bi LSTM model, our model reduces RMSE and MAE by 73.91% and 72.99%, respectively, and improves R2 by 8.15%. In summary, the proposed spatial weighting EMD-LSTM model offers an efficient new approach for predicting atmospheric PM2.5 pollution. It integrates spatial and time series analysis, significantly enhancing the prediction accuracy.

Enhanced Air Quality Prediction through Spatio-temporal Feature Sxtraction and Fusion: A Self-tuning Hybrid Approach with GCN and GRU

Accurate prediction of air quality change is essential for air pollution control and human daily mobility. Due to the strong spatial and temporal correlation of air quality changes, existing air quality prediction methods often face the problem of low prediction accuracy due to insufficient extraction of spatio-temporal features. In this paper, we proposed a self-tuning spatio-temporal neural network (ST2NN) to enhance air quality prediction. ST2NN model consisted of four modules. First, ST2NN model constructed a temporal feature extraction module and a spatial feature extraction module based on gated recurrent unit (GRU) and graph convolutional neural network (GCN), respectively, and the two feature extraction modules adopted a parallel structure, which could effectively extract the spatio-temporal features in data. Additionally, ST2NN model constructed a feature fusion module based on gating mechanism to delineate the contribution of spatio-temporal features to the predicted values. Further, ST2NN model constructed a Hyperband hyperparameter optimization module based on Hyperband optimization algorithm to automatically adjust the network hyperparameters. The structure of ST2NN model endowed it with excellent spatio-temporal feature extraction and parameter adaptability. ST2NN model was evaluated and compared with existing models, including convolutional long short-term memory neural network (ConvLSTM), GRU, combined convolutional neural network and long short-term memory neural network (CNN-LSTM), and GCN-LSTM for air quality index (AQI) prediction using data from twelve monitoring stations in Beijing, China. Across all four evaluation indexes, ST2NN model outperformed the comparative models, improving prediction accuracy by 0.51%-10.18% (measured using R2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${R}^{2}$$\\end{document}). From the experimental results, it can be seen that ST2NN model constructed from the perspective of spatio-temporal feature extraction has better prediction performance compared with the existing air quality prediction model, which provides a new method for air quality prediction and has certain application value.

Open-source Internet of Things (IoT)-based air pollution monitoring system with protective case for tropical environments

In recent years, the escalation of industrial activities has significantly increased natural resource pollution, with air pollution becoming a major cause of diseases affecting living organisms. To address this critical environmental challenge, this study proposes a comprehensive air pollution monitoring system utilizing advanced technological instruments based on the Internet of Things (IoT). The system’s primary objective is to provide precise, rapid, and efficient measurements, enabling detailed examinations of pollutant behaviors and facilitating data dissemination. The system includes a monitoring station equipped with sensors to measure ambient temperature, relative humidity, and concentrations of pollutants such as carbon monoxide (CO), nitrogen dioxide (NO2), sulfur dioxide (SO2), suspended particles (PM2.5, PM10), and ozone (O3). Additionally, it captures meteorological variables like wind speed, wind direction, and precipitation, allowing a nuanced analysis of their correlation with air pollutants. The collected data are transmitted via the Internet and visualized on a user-friendly platform accessible from any internet-enabled device. A protective case, designed with SolidWorks CAD software and fabricated using 3D printing, was validated through simulations for extreme conditions to ensures the system’s robustness in tropical climates. The cost-effective, low-energy system offers a scalable solution for monitoring air pollution, advancing understanding of pollutant behaviors, and supporting environmental management.

Seismic‐electromagnetic signals from two monitoring stations in Southern Italy: Electromagnetic time series release

AbstractThe seismic‐electromagnetic phenomenon entails the generation of transient electromagnetic signals, which can be observed both simultaneously (co‐seismic) and preceding (pre‐seismic) a seismic wave arrival. Following the most accredited hypothesis, these signals are mainly due to electrokinetic effects, generated on microscopic scale in porous media containing electrolytic fluids. Thus, the seismic‐electromagnetic signals are expected to be suitable for the detection and tracking of crustal fluids. Despite the growing interest in this phenomenon, there is a lack of freely available observational database of earthquake‐related electromagnetic signals recorded at co‐located seismic and magnetotelluric stations. To fill this gap, we set up two multicomponent monitoring stations in two seismically active areas of Southern Italy: the Gargano Promontory and the High Agri Valley. This work is both aimed to systematically analyse earthquake‐generated seismic‐electromagnetic recordings and to make the collected database accessible to the scientific community.

Air-surface exchange of halomethoxybenzenes in a Swedish subarctic catchment

Halomethoxybenzenes (HMBs) and related halomethoxyphenols are produced naturally in the marine and terrestrial environment and some also have anthropogenic origins. They are relatively volatile and water soluble and undergo atmospheric exchange with water bodies and soil. Here we report air-surface exchange of HMB compounds brominated anisoles and chlorinated dimethoxybenzenes in a Subarctic lake and catchment in Sweden during September 2022. HMBs were isolated from water on solid-phase extraction cartridges and from ground litter/soil by solvent extraction and determined by capillary gas chromatography - quadrupole mass spectrometry. Identified compounds in lake and stream water in the 10–100 pg L−1 range were 1,2,4,5-tetrachloro-3,6-dimethoxybenzene (DAME) > 2,4-dibromoanisole (DiBA) ≥ 2,4,6-tribromoanisole (TriBA) > 1,2,3,4-tetrachloro-5,6-dimethoxybenzene (tetrachloroveratrole, TeCV). DAME and the related compound 2,3,5,6-tetrachloro-4-methoxyphenol (DA) are reported in Subarctic litter/soil in the range 0.005–1.1 mg kg−1 dry weight (dw), whereas DiBA and TriBA were not detected in any litter/soil sample and TeCV in only one. Exchanges were assessed from concentrations in water and soil, air concentrations from a monitoring station at Pallas, Finland, and the physicochemical properties of the HMBs. Fluxes to and from the lake were estimated using the two-film gas exchange model. Net loadings (deposition minus volatilization) for the month of September were − 23, −15 and − 68 g for DiBA, TriBA and DAME, respectively, which amounted to about 4–7 % of the estimated lake inventory. An exchange assessment for DAME from litter/soil showed significant net volatilization at five sites, net deposition at one site and near-equilibrium at one site. The Torneträsk catchment appeared close to steady state with respect to HMB exchange during September 2022. The situation could be different during the warmer and colder seasons, and extending the study to cover these periods is a suggested next step.

Algorithms and Resources for the Monitoring of Very-Low-Frequency Signal Deviations Due to Solar Activity Using a Web-Based Software-Defined Radio-Distributed Network.

This work presents the development and testing of an experimental web-based SDR (software-defined radio) monitoring system for indirect solar activity detection, which has the ability to estimate and potentially predict various events in space and on earth, including solar flares, coronal mass ejections, and geomagnetic storms. The proposed system can be used to investigate the effect of solar activity on the propagation of very-low-frequency (VLF) signals. The advantages and benefits of the given approach are as follows: increasing measurement accuracy and eventual solar activity identification by combining measurements from multiple spatially distributed SDRs. The verification process involves carrying out several experiments comparing data from the GOES satellite system and the Dunksin SuperSID system with information received by the SDR monitoring system. Then, utilizing Pearson correlation coefficients, the measured data from the SDRs, along with those from the GOES satellite system and the Dunsing monitoring station, are investigated. At the time of a solar flare, the correlation value is above 90% for most of the stations used. Combining the signal-to-noise ratio via summation also shows an improvement in the results, with a correlation above 98%.