Monitoring Stations Research Articles

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%.

Increases in Global and East Asian Nitrogen Trifluoride (NF3) Emissions Inferred from Atmospheric Observations.

Nitrogen trifluoride (NF3) is a potent and long-lived greenhouse gas that is widely used in the manufacture of semiconductors, photovoltaic cells, and flat panel displays. Using atmospheric observations from eight monitoring stations from the Advanced Global Atmospheric Gases Experiment (AGAGE) and inverse modeling with a global 3-D atmospheric chemical transport model (GEOS-Chem), we quantify global and regional NF3 emission from 2015 to 2021. We find that global emissions have grown from 1.93 ± 0.58 Gg yr-1 (± one standard deviation) in 2015 to 3.38 ± 0.61 Gg yr-1 in 2021, with an average annual increase of 10% yr-1. The available observations allow us to attribute significant emissions to China (0.93 ± 0.15 Gg yr-1 in 2015 and 1.53 ± 0.20 Gg yr-1 in 2021) and South Korea (0.38 ± 0.07 Gg yr-1 to 0.65 ± 0.10 Gg yr-1). East Asia contributes around 73% of the global NF3 emission increase from 2015 to 2021: approximately 41% of the increase is from emissions from China (with Taiwan included), 19% from South Korea, and 13% from Japan. For Japan, which is the only one of these three countries to submit annual NF3 emissions to UNFCCC, our bottom-up and top-down estimates are higher than reported. With increasing demand for electronics, especially flat panel displays, emissions are expected to further increase in the future.

Exploring PCSWMM for Large Mixed Land Use Watershed by Establishing Monitoring Sites to Evaluate Stream Water Quality

Extensive hydrologic and water quality modeling within a watershed benefits from long-term flow and nutrient data sets for appropriate model calibration and validation. However, due to a lack of local water quality data, simpler water quality modeling techniques are generally adopted. In this study, the monitoring sites were established at two different locations to collect hydraulic data for the hydraulic calibration and validation of the model. In addition, water quality samples were collected at eight monitoring sites and analyzed in the lab for various parameters for calibration. This includes total suspended solids (TSS), soluble phosphorus, five-day biochemical oxygen demand (BOD5), and dissolved oxygen (DO). The Personal Computer Storm Water Management Model (PCSWMM) 7.6 software was used to simulate all the pollutant loads using event mean concentrations (EMCs). The performance of the model for streamflow calibration at the two USGS gauging stations was satisfactory, with Nash–Sutcliffe Efficiency (NSE) values ranging from 0.51 to 0.54 and coefficients of determination (R2) ranging from 0.71 to 0.72. The model was also validated with the help of historical flow data with NSE values ranging from 0.5 to 0.79, and R2 values ranging from 0.6 to 0.95. The hydraulic calibration also showed acceptable results with reasonable NSE and R2 values. The water quality data recorded at the monitoring stations were then compared with the simulated water quality modeling results. The model reasonably simulated the water quality, which was evaluated through visual inspection using a scatter plot. Our analysis showed that the upstream tributaries, particularly from agricultural areas, were contributing more pollutants than the downstream tributaries. Overall, this study demonstrates that the PCSWMM, which was typically used for modeling urban watersheds, could also be used for modeling larger mixed land use watersheds with reasonable accuracy.

Ambient air pollution decreased normal fertilization rate via the mediation of seminal prosaposin

ObjectiveThis study focuses on the association between seminal concentration of prosaposin and ambient air pollutants and whether the association affects the normal fertilization rate in vitro fertilization (IVF) treatment. MethodsThe cohort of 323 couple participants aged 22–46 was recruited from Jan. 2013 to Jun. 2018. At enrollment, resident address information was obtained and semen parameters of male counterparts were evaluated according to WHO criteria. We used inverse distance weighting interpolation to estimate the levels of ambient pollutants (SO2, O3, CO, NO2, PM2.5, and PM10) in the surrounding area. The exposure of each participant was estimated based on the data gathered from air quality monitoring stations and their home address over various periods (0–9, 10–14, and 0–90 days) before semen sampling. The generalized linear regression model (GLM) and the Bayesian kernel machine regression (BKMR) were used to analyze the associations between pollutants, semen parameters, prosaposin, and normal fertilization. Additionally, the mediating effect of prosaposin and semen parameters on the link between pollutants and normal fertilization was investigated. ResultsGLM and BKMR showed exposure to ambient air pollutants was all associated with the concentration of seminal prosaposin, among them, O3 and CO were also associated with normal fertilization (-0.10, 95 %CI: −0.13, −0.06; −26.43, 95 %CI: −33.79, −19.07). Among the semen parameters, only the concentration of prosaposin and total motile sperm count (TMC) was associated with normal fertilization (0.059, 95 %CI: 0.047, 0.071; 0.016, 95 %CI: 0.012, 0.020). Mediation analysis showed that prosaposin played a stronger mediating role than TMC in the relationship between short-term exposure to O3 and fertilization (66.83 %, P<0.001 versus 3.05 %, P>0.05). ConclusionSeminal plasma prosaposin showed a stronger meditating effect reflect the correlation between ambient air pollutants and normal fertilization rate than conventional semen parameters, which may be used as one of the indicators between pollution and fertilization in IVF.

Investigating the impact of urban-environmental factors on air pollutants: a land use regression model approach and health risk assessment.

The presence of pollutants in the earth's atmosphere has a direct impact on human health and the environment. So that pollutants such as carbon monoxide (CO) and particulate matter (PM) cause respiratory diseases, cough headache, etc. Since the amount of pollutants in the air is related to environmental and urban factors, the aim of the current research is to investigate the relationship between the concentration of CO, PM2.5 and PM10 with urban-environmental factors including land use, wind speed and wind direction, topography, traffic, road network, and population through a Land use regression (LUR) model. The concentrations of CO, PM2.5 and PM10 were measured during four seasons from 26th of March 2022 to 16th of March 2023 at 25 monitoring stations and then the information about pollutant measurement points and Land use data were entered into the ArcGIS software. The annual average concentrations of CO, PM2.5 and PM10 were 0.7ppm, 18.94 and 60.76µg/m3, respectively, in which the values of annual average concentration of CO and PMs were outside the air quality guideline standard. The results of the health risk assessment showed that the hazard quotient values for all three investigated pollutants were lower than 1 and therefore, they were not in adverse conditions in terms of health effects. Among the urban-environmental factors affecting air pollution, the traffic variable is the most important factor affecting the annual LUR model of CO, PM2.5 and PM10, and then the topography variable is the second most effective factor on the annual LUR model of the aforementioned pollutants.

Comparative analysis of meteorological parameters and their relationship with NO2, PM10, PM2.5 and O3 concentrations at selected urban air quality monitoring stations in Krakow, Paris, and Milan

Meteorological parameters play a major role in air pollutant concentrations as they create conditions that either hinder or facilitate the reaction and dispersion of pollutants in our environments. This is particularly evident in Europe, where frequent alternation of meteorological parameters has the potential to significantly impact pollutant concentrations. This study applied the R openair package to comparatively analyse the relationship between key meteorological parameters and NO2, O3, PM2.5, and PM10 concentrations measured at selected air quality monitoring stations in Krakow, Milan, and Paris in the year 2021. The study made use of meteorological data acquired from National Aeronautics and Space Administration (NASA) Power data repository, and air pollutants data measured at air quality monitoring stations in each of the three cities. The air pollutants data were retrieved from European Environmental Agency’s Airbase. Concentration and correlation analyses were conducted using the relevant functions of the R openair package. Findings in the study revealed a positive relationship between temperature and O3, wind speed and O3; and a negative relationship between temperature and NO2/PM2.5/PM10. The study further revealed a negative relationship between wind speed and NO2/PM2.5/PM10, as well as a negative relationship between precipitation and NO2/PM2.5/PM10. NO2, PM2.5, and PM10 concentrations were higher in winter periods, weekdays, nights, and evenings, but lower in summer periods, weekends, and midday. Whereas O3 concentration was higher in summer periods, weekends, midday, and lower in winter periods, weekdays, nights, and evenings. NO2, PM10, and PM2.5 concentrations were higher during the periods without precipitation than periods with precipitation. In addition, temperature inversions were found to be linked with higher concentrations of NO2, PM2.5, and PM10, but lower concentrations of O3 in Krakow, Paris and Milan. Accordingly, the study recommends effective monitoring, increased awareness, the use of pollutant removing devices, and further research to enhance adaptation and advance knowledge.

Deployment of attractive targeted sugar baits in western Zambia: installation, monitoring, removal, and disposal procedures during a Phase III cluster randomized controlled trial

BackgroundAttractive Targeted Sugar Baits (ATSBs) offer a complementary vector control strategy to interventions targeting blood feeding or larval control by attacking the sugar feeding behaviour of adult mosquitoes using an attract-and-kill approach. Western Zambia was the first location to receive and deploy ATSB Sarabi version 1.2 stations in a Phase III cluster randomized controlled trial. This paper describes ATSB station installation, monitoring, removal, and disposal, quantifies ATSB station coverage, and reports major reasons for ATSB station replacement.MethodsATSB stations were deployed during two annual transmission seasons, through scheduled installation and removal campaigns. During deployment, monitoring was conducted per protocol to maintain high coverage of the ATSB stations in good condition. Routine monitoring visits during the trial captured details on ATSB station damage necessitating replacement following pre-defined replacement criteria. Annual cross-sectional household surveys measured ATSB station coverage during peak malaria transmission.ResultsA total of 67,945 ATSB stations were installed in Year 1 (41,695 initially installed+ 26,250 installed during monitoring) and 69,494 ATSB stations were installed in Year 2 (41,982 initially installed+ 27,512 installed during monitoring) across 35 intervention clusters to maintain high coverage of two ATSB stations in good condition per eligible household structure. The primary reasons for ATSB station replacement due to damage were holes/tears and presence of mold. Cross-sectional household surveys documented high coverage of ATSB stations across Year 1 and Year 2 with 93.1% of eligible structures having ≥ 2 ATSB stations in any condition.DiscussionATSB station deployment and monitoring efforts were conducted in the context of a controlled cRCT to assess potential product efficacy. Damage to ATSB stations during deployment required replacement of a subset of stations. High coverage of eligible structures was maintained over the two-year study despite replacement requirements. Additional research is needed to better understand the impact of damage on ATSB station effectiveness under programmatic conditions, including thresholds of threats to physical integrity and biological deterioration on product efficacy.ConclusionsOptimizing ATSB stations to address causes of damage and conducting implementation research to inform optimal delivery and cost-effective deployment will be important to facilitate scale-up of ATSB interventions.

Integration of ten years of daily weather, traffic, and air pollution data from Norway’s six largest cities

This study integrates ten years of daily weather, traffic, and air pollution data across the six largest Norwegian cities, utilizing data from the Norwegian Public Roads Administration, the Norwegian Institute of Air Research, and the Norwegian Meteorological Institute. The compilation of this dataset involved detailed selection and verification of monitoring stations to ensure consistency and accuracy. Initial data collection focused on the top ten most populous cities in Norway, with the subsequent examination of traffic and air pollution monitoring sites. Weather variables were then matched to the selected sites, resulting in a comprehensive dataset from 2009 to 2018. The resulting dataset encompasses extensive information, including harmful pollutants such as Nitric oxide (NO), Nitrogen dioxide (NO2), Nitrogen oxides (NOx), Particulate Matter less than 2.5 micrometers in diameter (PM2.5), and Particulate Matter less than 10 micrometers in diameter (PM10). The dataset’s potential for further analysis and its utility in informing policy decisions underscore its significance. This integrated dataset is a valuable resource for researchers and policymakers alike, facilitating comprehensive studies on the intersection of weather, traffic, and air pollution in urban environments.

Universal high-frequency monitoring methods of river water quality in China based on machine learning

The in-situ high-frequency monitoring of total nitrogen (TN) and total phosphorus (TP) in rivers is a challenge and key to instant water quality judgment and early warning. Based on the physical and chemical association between TN/TP and sensor-measurable predictors, we proposed a novel “indirect” measurement method for TN and TP in rivers. This method combines the timeliness of multi-sensor and the accuracy of intelligent algorithms, utilizing 188,629 data sets from 131 water monitoring stations across China. Under 5 algorithms and 4 predictor group scenarios, the results showed that: (1) extra tree regression (ETR) with 6 predictors exhibited the best precision, and the mean determination coefficient (R2) of TN and TP inversion across 131 stations reached 0.78 ± 0.25 and 0.79 ± 0.22 respectively; (2) among 6 potential predictors, the importance degrees of temperature, electrical conductivity, NH4-N, and turbidity were greater than that of pH and DO, and >80 % of stations exhibited acceptable prediction accuracy (R2 > 0.6) when the number of predictors (P) ranged from 4 to 6, which showed good tolerability to predictor variations; (3) the accurate classification rates of water quality standard (ACRws) of all stations based on TN and TP reached 90.41 ± 6.96 % and 92.33 ± 6.41 %; (4) in 9 regions/basins of China, this method showed universal application potential with no significant prediction difference. Compared with laboratory test, water quality automatic monitoring station, and remote sensing inversion, the proposed method offers high-frequency, high-precision, regional adaptability, low cost, and stable operation under rainy, cloudy, and nighttime conditions. The new method may provide important technological support for timely pollutant tracing, pre-warning, and emergency control for river pollution.

High-Resolution PM10 Estimation Using Satellite Data and Model-Agnostic Meta-Learning

Characterizing the spatial distribution of particles smaller than 10 μm (PM10) is of great importance for air quality management yet is very challenging because of the sparseness of air quality monitoring stations. In this study, we use a model-agnostic meta-learning-trained artificial neural network (MAML-ANN) to estimate the concentrations of PM10 at 60 m × 60 m spatial resolution by combining satellite-derived aerosol optical depth (AOD) with meteorological data. The network is designed to regress from the predictors at a specific time to the ground-level PM10 concentration. We utilize the ANN model to capture the time-specific nonlinearity among aerosols, meteorological conditions, and PM10, and apply MAML to enable the model to learn the nonlinearity across time from only a small number of data samples. MAML is also employed to transfer the knowledge learned from coarse spatial resolution to high spatial resolution. The MAML-ANN model is shown to accurately estimate high-resolution PM10 in Beijing, with coefficient of determination of 0.75. MAML improves the PM10 estimation performance of the ANN model compared with the baseline using pre-trained initial weights. Thus, MAML-ANN has the potential to estimate particulate matter estimation at high spatial resolution over other data-sparse, heavily polluted, and small regions.

Age and sex differences in the effects of short- and long-term exposure to air pollution on endothelial dysfunction

BackgroundThe effects of air pollution on endothelial function remain unclear across populations. We aimed to use brachial artery flow-mediated dilatation (FMD) to identify demographic differences in the effects of air pollution exposure on endothelial dysfunction.MethodsWe measured FMD in 850 participants from October 2016 to January 2020. Location-specific concentrations of fine particulate matter < 2.5 μm aerodynamic diameter (PM2.5), inhalable particulate matter < 10 μm aerodynamic diameter (PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), and ozone (O3) measured by fixed ambient air monitoring stations were collected for short- and long-term exposure assessment. Multiple linear regression models and restricted cubic splines were used to assess the associations before and after stratification by age and sex.ResultsThis study eventually included 828 participants [551 (66.5%) younger than 65 years and 553 (66.8%) men]. Each 10 µg/m3 increase in 7-day exposure to PM2.5 and PM10 was significantly linearly associated with a 0.07% (β = -0.07, 95% CI: -0.13 to -0.004) and 0.05% (β = -0.05, 95% CI: -0.10 to -0.004) decrease in FMD in the fully adjusted model. After full adjustment, long-term exposure to all air pollutants was significantly associated with impaired FMD. Each 10 µg/m3 increase in long-term exposure to PM2.5 and PM10 was significantly associated with a -0.18% (95% CI: -0.34 to -0.03) and − 0.23% (95% CI: -0.40 to -0.06) change in FMD, respectively. After stratification, the associations of lower FMD with long-term exposure to PM2.5, PM10, SO2, NO2, and CO significantly persisted in men and participants younger than 65 years instead of women or older participants. For short-term exposure, we observed differences consistent with long-term exposure and a stronger effect of 7-day exposure to SO2 in men due to a significant interaction effect.ConclusionShort- and long-term exposure to different air pollutants are strongly associated with decreased endothelial function, and susceptibility to air pollution varies significantly with age and sex.

Extending the historical in situ gauge network over Indian river reaches utilizing the Surface Water and Ocean Topography (SWOT) mission

ABSTRACT We present a preliminary study on using the Surface Water and Ocean Topography (SWOT) mission to extend water surface elevation (WSE) measurements from a sparsely distributed in situ gauge network along a one-dimensional (1D) study reach within the Mahanadi River basin in India. We use the Centre National d’Etudes Spatiales (CNES) simulator to simulate 3 years of synthetic measurements of SWOT WSE, which are assimilated into a 1D stochastic model based on six independent configurations. For each configuration, the model is derived using historical measurements of in situ WSE to simulate daily WSE estimates every 200 m along the study reach. Assimilated outputs showed high Nash-Sutcliffe efficiency (0.697–0.811), high correlation (0.838–0.901) and low root mean square error (0.242–0.513 m) with respect to in situ validation data. Results indicate consistent performance of the SWOT-assimilated model framework and are relevant considering the declining availability of in situ monitoring stations.

Prediction of Deformation in Expansive Soil Landslides Utilizing AMPSO-SVR

A non-periodic “step-like” variation in displacement is exhibited owing to the repeated instability of expansive soil landslides. The dynamic prediction of deformation for expansive soil landslides has become a challenge in actual engineering for disaster prevention and mitigation. Therefore, a support vector regression prediction (AMPSO-SVR) model based on adaptive mutation particle swarm optimization is proposed, which is suitable for small samples of data. The shallow displacement is decomposed into a trend component and fluctuating component by complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), and the trend displacement is predicted by cubic polynomial fitting. In this paper, the multiple disaster-inducing factors of expansive landslides and the time hysteresis effect between displacement and its influencing factors are fully considered, and the crucial influencing factors which eliminate the time lag effect and state factors are input into the model to predict the fluctuation displacement. Monitoring data in the Ningming area of China are employed for the model validation. The predicted results are compared with those of the traditional model. The model performance is evaluated through indicators such as the goodness of fit R2 and root mean square error RMSE. The results show that the prediction RMSE of the new model for three monitoring stations can reach 2.6 mm, 6.6 mm, and 2.5 mm, respectively. Compared with the common Grid search support vector regression (GS-SVR), the Particle Swarm Optimization Support Vector Regression (PSO-SVR) and Back Propagation Neural Network (BPNN) models have average improvements of 58.4%, 38.1%, and 25.2% respectively. The goodness of fit R2 is superior to 0.99 in the new method. The proposed model can effectively be deployed for the displacement prediction of non-periodic stepped expansive soil landslides driven by multiple influencing factors, providing a reference idea for the deformation prediction of expansive soil landslides.

Long-term effects of air quality on hospital readmission for heart failure in patients with acute myocardial infarction

BackgroundCardiovascular disease (CVD) is the leading cause of death worldwide, with air pollution posing significant risks to cardiovascular health. The effect of air quality on heart failure (HF) readmission in acute myocardial infarction (AMI) patients is unclear.The aim of this study was to evaluate the role of a single measure of air pollution exposure collected on the day of first hospitalization. MethodsWe retrospectively analyzed data from 12,857 acute coronary syndrome (ACS) patients (January 2015–March 2023). After multiple screenings, 4023 AMI patients were included. The air pollution data is updated by the automatic monitoring data of the national urban air quality monitoring stations in real time and synchronized to the China Environmental Monitoring Station. Cox proportional hazards regression assessed the impact of air quality indicators on admission and outcomes in 4013 AMI patients. A decision tree model identified the most susceptible groups. ResultsAfter adjusting for confounders, NO2 (HR 1.009, 95% CI 1.004–1.015, P = 0.00066) and PM10 (HR 1.006, 95% CI 1.002–1.011, P = 0.00751) increased the risk of HF readmission in ST-segment elevation myocardial infarction (STEMI) patients. No significant effect was observed in non-STEMI (NSTEMI) patients (P > 0.05). STEMI patients had a 2.8-fold higher risk of HF readmission with NO2 > 13 μg/m3 (HR 2.857, 95% CI 1.439–5.670, P = 0.00269) and a 1.65-fold higher risk with PM10 > 55 μg/m3 (HR 1.654, 95% CI 1.124–2.434, P = 0.01064). ConclusionNO2 and PM10 are linked to increased HF readmission risk in STEMI patients, particularly when NO2 exceeds 13 μg/m3 and PM10 exceeds 55 μg/m3. Younger, less symptomatic male STEMI patients with fewer underlying conditions are more vulnerable to these pollutants.