Monitoring Stations Research Articles

Spatiotemporal changes in fine particulate matter and ozone in the oasis city of Korla, northeastern Tarim Basin of China

Air pollution is a serious environmental health concern for humans and other living organisms. This study analyzes the spatial and temporal characteristics of air pollutant concentrations, changes in the degree of pollution, and the wavelet coherence of the air quality index (AQI) with pollutants in various monitoring stations. The analysis is based on long-term time series data (January 2016 to December 2023) of air pollutants (PM2.5, PM10, and O3) from Korla, an oasis city in the northeastern part of the Tarim Basin, China. The concentrations of PM2.5, PM10, and O3 in Korla showed a cyclical trend from 2016 to 2023; PM10 concentrations exhibited all-season exceedance and PM2.5 exhibited exceedance only in spring. PM2.5 and PM10 showed a seasonal distribution of spring > winter > fall > summer; O3 concentrations showed a seasonal distribution of summer > spring > fall > winter. Strong positive wavelet coherence between PM and Air Quality Index (AQI) data series suggests that the AQI data series can effectively characterize fluctuating trends in PM concentrations. Moreover, PM10 levels IV and VI were maintained at approximately 10%, indicating that sand and dust have a substantial influence on air quality and pose potential threats to the health of urban inhabitants. Based on the results of this study, future efforts must strengthen relative countermeasures for sand prevention and control, select urban greening species with anti-pollution capabilities, rationally expand urban green spaces, and restrict regulations for reducing particulate matter emissions within city areas.

Applying machine learning algorithms to estimate PM 2.5 using satellite data and metrological data

Air pollution, particularly fine Particulate Matter (PM 2.5), poses significant health risks and environmental challenges worldwide. Therefore, it is essential to monitor air pollution to act on it. In this study, PM 2.5 was estimated using meteorological data and Sentinel-5P air pollution data using machine learning algorithms. The Sentinel-5P data are and the meteorological data utilized are air temperature, Relative Humidity (RH), and Wind Speed (WS). The three Air Quality Monitoring (AQM) stations in Kathmandu, Nepal, were chosen as a study area for this research. The effectiveness of several machine learning methods, such as K-Nearest Neighbors (KNN), Support Vector Machine (SVM), eXtreme Gradient Boosting (XGBoost), and Random Forest (RF), were evaluated. Both RF and XGBoost consistently performed better than SVM and KNN in terms of PM 2.5 estimation accuracy. RF got the highest R2 value of 0.80 and SVM with the lowest R2 value of 0.62 in the Sentinel-5P dataset only. The addition of meteorological data further improved the model's performance. After including metrological data in Sentinel-5P data the RF demonstrated the maximum R2 score of 0.816 and XGBoost with R2 score of 0.814. Hence, this study demonstrated machine learning algorithms can be used to estimate PM 2.5 by utilizing satellite and meteorological data, providing important information for air quality monitoring and management.

An exploration of urban air health navigation system based on dynamic exposure risk forecast of ambient PM2.5

Under international advocacy for a low-carbon and healthy lifestyle, ambient PM2.5 pollution poses a dilemma for urban residents who wish to engage in outdoor exercise and adopt active low-carbon commuting. In this study, an Urban Air Health Navigation System (UAHNS) was designed and proposed to assist users by recommending routes with the least PM2.5 exposure and dynamically issuing early risk warnings based on topologized digital maps, an application programming interface (API), an eXtreme Gradient Boosting (XGBoost) model, and two-step spatial interpolation. A test of the UAHNS’s functions and applications was carried out in Wuhan city. The results showed that, compared with trained random forest (RF), LightGBM, Adaboost models, etc., the XGBoost model performed better, with an R2 exceeding 0.90 and an RMSE of approximately 15.74 μg/m3, based on data from national air and meteorological monitoring stations. Further, the two-step spatial interpolation model was adopted to dynamically generate pollution distribution at a spatial resolution of 300 m*300 m. Then, an exposure comparison was performed under randomly selected commuting routes and times in Wuhan, showing the recommended routes for lower PM2.5 exposure made effectively help. And the route difference ratios of about 14.9 % and 16.9 % for riding and walking, respectively. Finally, the UAHNS platform was integrally realized for Wuhan, consisting of a real-time PM2.5 query, a one-hour PM2.5 prediction function at any location, health navigation on city map, and a personalized health information query.

Space Weather Effects on Heart Rate Variations: Sex Dependence

The effects of solar activity and the accompanying space weather events on human pathological conditions, physiological parameters and other psycho-physiological disturbances have been analyzed in numerous recent investigations. Moreover, many of these studies have particularly focused on the different physical reactions humans have, according to their sex, during variations in the physical environment. In the framework of the above, this work analyses heart rate data obtained from volunteers (687 men and 534 women) from three different regions (Athens, Piraeus and Heraklion) of Greece in relation to the geophysical activity and variations of environmental factors. Dst index and Ap index data, along with cosmic ray intensity data derived from the Athens Neutron Monitor Station (A.Ne.Mo.S.), were used. The study expands from April 2011 to January 2018, covering solar cycle 24. The ANalysis Of Variance (ANOVA) and the superimposed epochs methods were used in order to examine heart rate variations depending on sex. Results revealed that women tend to be more sensitive to physical environmental changes. Statistically significant results are related to the geomagnetic activity but were not obtained for cosmic ray variations.

Pollen and viruses contribute to spatio-temporal variation in asthma-related emergency department visits

BackgroundAsthma exacerbations are an important cause of emergency department visits but much remains unknown about the role of environmental triggers including viruses and allergenic pollen. A better understanding of spatio-temporal variation in exposure and risk posed by viruses and pollen types could help prioritize public health interventions. ObjectiveHere we quantify the effects of regionally important Cupressaceae pollen, tree pollen, other pollen types, rhinovirus, seasonal coronavirus, respiratory syncytial virus, and influenza on asthma-related emergency department visits for people living near eight pollen monitoring stations in Texas. MethodsWe used age stratified Poisson regression analyses to quantify the effects of allergenic pollen and viruses on asthma-related emergency department visits. ResultsYoung children (<5 years of age) had high asthma-related emergency department rates (24.1 visits/1,000,000 person-days), which were mainly attributed to viruses (51.2%). School-aged children also had high rates (20.7 visits/1,000,000 person-days), which were attributed to viruses (57.0%), Cupressaceae pollen (0.7%), and tree pollen (2.8%). Adults had lower rates (8.1 visits/1,000,000 person-days) which were attributed to viruses (25.4%), Cupressaceae pollen (0.8%), and tree pollen (2.3%). This risk was spread unevenly across space and time; for example, during peak Cuppressaceae season, this pollen accounted for 8.2% of adult emergency department visits near Austin where these plants are abundant, but 0.4% in cities like Houston where they are not; results for other age groups were similar. ConclusionsAlthough viruses are a major contributor to asthma-related emergency department visits, airborne pollen can explain a meaningful portion of visits during peak pollen season and this risk varies over both time and space because of differences in plant composition.

Prediction of land surface temperature using spectral indices, air pollutants, and urbanization parameters for Hyderabad city of India using six machine learning approaches

Urban areas face rising land surface temperatures (LST) and increased air pollution due to urbanization and industrialization. The consistent rise in LST impacts lives of the residents. Thus, LST forecasts can help manage activities more comfortably. The present study aims to predict LST for Hyderabad city, India using five-year (2018–2022) data on air pollution and meteorological parameters (from ambient air quality monitoring stations) and MODIS LST data. Six machine learning models i.e., Multiple Linear Regression (MLR), Support Vector Regression (SVR), Random Forest (RF), Artificial Neural Networks (ANN), XGBoost, and Long Short-Term Memory (LSTM), were used for LST prediction. Considerable influence of PM2.5 and CO (during summer) and SO2 (during winter) on LST was observed which demonstrated high sensitivity of these parameters on LST. LST exhibited a weak correlation with individual air pollutants while strong relationship of LST with all the study variables, when considered simultaneously, was observed. ANN method demonstrated better accuracy with lower error metrics, comprising of Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Mean Squared Error (MSE), compared to the other approaches with ranking in the order ANN > RF > SVR > XGBoost > LSTM > MLR. ANN model for Hyderabad city was validated by using it for prediction of LST of another geographical area i.e. Bengaluru city, India. The result of this study can provide insights for policymakers, urban planners, and environmental agencies for targeted interventions for temperature regulations and to mitigate urbanization's impact.

Application of Random Forest in a Predictive Model of PM10 Particles in Mexico City

Over time, predictive models tend to become more accurate but also more complex, thus achieving better predictive accuracy. When the data is improved by increasing its quantity and availability, the models are also better, which implies that the data must be processed to filter and adapt it for initial analysis and then modeling. This work aims to apply the Random Forest model to predict PM10 particles. For this purpose, data were obtained from environmental monitoring stations in Mexico City, which operates 29 stations of which 12 belong to the State of Mexico. The pollutants analyzed were CO carbon monoxide, NO nitrogen oxide, and PM10 particulate matter equal to or less than 10 μg.m-3, NOx nitrogen oxide, NO2 nitrogen dioxide, SO2 sulfur dioxide, O3 ozone, and PM2.5 particulate matter equal to or less than 2.5 μg.m-3. The result was that when calculating the certainty of our model, we have a value of 80.40% when calculating the deviation from the mean, using 15 reference variables.

Environmental Monitoring System using Wireless Multi-Node Sensors based Communication System on Volcano Observations Drones

Indonesia is on the Ring of Fire and has the world's most active volcanoes. Volcanic activity has a significant effect on the landscape and on the people who live there. The difficulty of evacuating and helping victims requires hard work and sometimes even the safety of the rescue team itself. For this reason, high-tech tools are needed. Unmanned aerial vehicles (UAVs), also called drones, have become a hopeful tool for remote environmental monitoring in recent years. The system design has a monitoring platform, gateway, and sensor nodes attached to the UAV, which monitors the content of toxic gas contamination in the air. Using IoT technology, sensor data is sent wirelessly to a central monitoring station for a thorough and accurate volcanic activity study. This system is a flexible and complete way to monitor volcanic activity, learn more about it, and make it easier to respond to disasters. Tests are also done to measure system speed, including latency, and determine network service quality. The results show that data is successfully sent in real-time from the sensor nodes to the monitoring system. The average Round-Trip time for the payload transmission is 446.046226 ms. This shows how well the system works to send data from the sensors connected to the UAV to the monitoring station. The UAV has sensor nodes and a monitoring system platform. These can be used to build and optimize disaster mitigation systems.

Using random forest to improve EMEP4PL model estimates of daily PM2.5 in Poland

Long-term exposure to poor air quality is responsible for many diseases and increased mortality worldwide. European Environmental Agency reports that Poland is one of the most polluted countries in Europe due to high emissions associated with large coal and wood consumption and specific weather conditions. Exceedances of WHO-recommended PM2.5 thresholds are still common in Poland, so further action is needed to protect the health of the population. Atmospheric chemical transport models (CTMs) provide information on air quality to the public and are used to regulate air pollutant emissions. However, uncertainties associated with CTMs, related to e.g. physical/chemical processes and input data quality often lead to underestimation of pollutant concentrations, especially for PM2.5, and limits the applicability of CTMs e.g. for health impact studies.A hybrid approach combining the EMEP4PL chemical transport model for Poland with the Random Forest (RF) machine learning algorithm was applied to address the limitations of CTM and reduce its underestimation. We used EMEP4PL-modelled PM2.5 concentrations for period 2016–2019 as a predictor and measured daily PM2.5 from 71 monitoring stations in Poland as a dependent variable in three RF scenarios, which differed in terms of the selected predictors. The impact of different additional variables for the area was revealed, including population and emission data, dominant type of land use, Weather and Research Forecast (WRF) meteorological parameters, and temporal patterns of PM2.5 concentrations across the years. The models were evaluated in a random 5-fold and spatial leave-one-station-out cross-validations (LOSOCV), as well as for an independent test set. Our final model achieved a test set R2 of 0.71, compared to 0.38 for EMEP4PL, along with a reduction in negative bias (0.25 μg m−3 for the final RF, compared to −11 μg m−3 for EMEP4PL) and an improved ability to detect severe PM2.5 episodes. Enhanced coefficients of determination were observed in all seasons and at all monitoring sites included in the study, for both types of cross-validation and for the test set. We estimated the contribution of each group of variables separately and discovered, that the most impactful predictors for the area of Poland included temporal patterns of PM2.5 calculated based on the averages of EMEP4PL outcome (such as day of the year, week number, etc.) and meteorological modelled factors related to temperature, planetary boundary layer height, wind speed, and atmospheric pressure. The developed approach provides a basis for improved spatiotemporal estimates and forecasting of PM2.5 in the region, which is an important step toward better understanding the impact of air pollution on the local population well-being.

Evaluation of atmospheric particulate matter pollution characteristics in Shanghai based on biomagnetic monitoring technology

Atmospheric particulate matter (PM) pollution is one of the world's most serious environmental challenges, and it poses a significant threat to environmental quality and human health. Biomagnetic monitoring of PM has great potential to improve spatial resolution and provide alternative indicators for large area measurements, with respect and complementary to standard air quality monitoring stations. In this study, 160 samples of evergreen plant leaves were collected from park green spaces within five different functional areas of Shanghai. Magnetic properties were investigated to understand the extent and nature of particulate pollution and the possible sources, and to assess the suitability of various plant leaves for urban particulate pollution monitoring. The results showed that magnetic particles of the plant leaf-adherent PM were predominantly composed of pseudo-single domain (PSD) and multi-domain (MD) ferrimagnetic particles. Magnolia grandiflora, as a large evergreen arbor with robust PM retention capabilities, proved to be a more suitable candidate for monitoring urban particulate pollution compared to Osmanthus fragrans, a small evergreen arbor, and Aucuba japonica Thunb. var. variegata and Photinia serratifolia, evergreen shrubs. Meanwhile, there were significant differences in the spatial distribution of the magnetic particle content and heavy metal enrichment of the samples, mainly showing regional variations of industrial area > traffic area > commercial area > residential area > clean area. Additionally, the combination with the results of scanning electron microscopy, shows that industrial production (metal smelting, coal burning), transport and other activities are the main sources of particulate pollution. Plant leaves can be used as an effective tool for urban particulate pollution monitoring and assessment of atmospheric particulate pollution characteristics, and the technique provided useful information on particle size, mineralogy and possible sources.

Trends of Key Greenhouse Gases as Measured in 2009–2022 at the FTIR Station of St. Petersburg State University

Key long-lived greenhouse gases (CO2, CH4, and N2O) are perhaps among the best-studied components of the Earth’s atmosphere today; however, attempts to predict or explain trends or even shorter-term variations of these trace gases are not always successful. Infrared spectroscopy is a recognized technique for the ground-based long-term monitoring of the gaseous composition of the atmosphere. The current paper is focused on the analysis of new data on CO2, CH4, and N2O total columns (TCs) retrieved from high resolution IR solar spectra acquired during 2009–2022 at the NDACC atmospheric monitoring station of St. Petersburg State University (STP station, 59.88°N, 29.83°E, 20 m asl.). The paper provides information on the FTIR system (Fourier-transform infrared) installed at the STP station, and an overview of techniques used for the CO2, CH4, and N2O retrievals. Trends of key greenhouse gases and their confidence levels were evaluated using an original approach which combines the Lomb–Scargle method with the cross-validation and bootstrapping techniques. As a result, the following fourteen-year (2009–2022) trends of TCs have been revealed: (0.56 ± 0.01) % yr−1 for CO2; (0.46 ± 0.02) % yr−1 for CH4; (0.28 ± 0.01) % yr−1 for N2O. A comparison with trends based on the EMAC numerical modeling data was carried out. The trends of greenhouse gases observed at the STP site are consistent with the results of the in situ monitoring performed at the same geographical location, and with the independent estimates of the global volume mixing ratio growth rates obtained by the GAW network and the NOAA Global Monitoring Laboratory. There is reasonable agreement between the CH4 and N2O TC trends for 2009–2019, which have been derived from FTIR measurements at three locations: the STP site, Izaña Observatory and the University of Toronto Atmospheric Observatory.

Atmospheric concentrations and potential sources of dioxin-like contaminants to Acadia National Park

Acadia National Park (ANP) is located on Mt. Desert Island, ME on the U.S. Atlantic coast. ANP is routinely a top-ten most popular National Park with over four million visits in 2022. The overall contribution and negative effects of long-range atmospheric transport and local sources of dioxin-like contaminants endangering natural and wildlife resources is unknown. Dioxin-like (DL) contaminants polychlorinated dibenzo-p-dioxins (∑PCDD) and polychlorinated dibenzofurans (∑PCDF), non-ortho coplanar PCBs (∑CP4), and polychlorinated naphthalenes (∑PCNs) were measured at the McFarland Hill air monitoring station (44.37⁰N, 68.26⁰W). On a mass/volume basis, total PCNs averaged 90.9 % (788 fg/m3) of DL contaminants measured annually, with 92.9 % of the collected total in the vapor-phase. Alternatively, total dioxin/furans (∑PCDD/Fs) represented 71.6 % of the total toxic equivalence (∑TEQ) (1.018 fg-TEQ/m3), with 69.7 % in the particulate-phase. Maximum concentrations measured for individual sampling events for ∑PCDD/F, ∑CP4, and ∑PCN were 159 (winter), 139 (summer), and 2100 (autumn), fg/m3 respectively. Whereas the maximum ∑TEQ concentrations for individual sampling events for ∑PCDD/F, ∑CP4, and ∑PCN were 2.8 (autumn), 0.38 (summer), and 0.71 (autumn), fg-TEQ/m3 respectively. Pearson correlations were calculated for ∑PCDD/Fs and ∑PCN particulate/vapor-phase air concentrations and PM2.5 wood smoke “indicator” species. The most significant correlations were observed in autumn for particulate-phase ∑PCDD/Fs suggesting a relationship between visitation-generated combustion sources (campfires and/or waste burning) or climate-change mediated forest fires. Significant Clausius-Clapeyron (C–C) correlations observed for particulate-phase ∑PCDDs (r2 = 0.567) as ambient temperatures decreased suggests a connection between localized domestic heating sources or visitor-based burning of wood/trash resources. Alternatively, highly significant C–C vapor-phase ∑CP4-PCBs correlations (r2 = 0.815) implies that the majority of ∑CP4-PCB loading to ANP is from long-range atmospheric transport processes. Based on these findings, Acadia National Park should be classified as a remote site with minor depositional impacts from ∑PCDD/Fs, ∑CP4-PCBs, and ∑PCN atmospheric transport or local diffuse sources.

A genetic algorithm's novel rainfall distribution method for optimized hydrological modeling at basin scales

ABSTRACT Rainfall has a dominant role in rainfall-runoff models, with the rendering of these models depending both on the accuracy of the data and on the way that rainfall is spatially allocated. The research proposes a methodological framework where a genetic algorithm (GA)-based method responsible for the spatial distribution of gauge observations at the basin scale is coupled with the HEC-HMS hydrological model to produce simulated discharges of high accuracy. The custom-developed GA is used to divide a 2D space, adhering to specific criteria, into polygonal geometries that represent gauge zones of influences, similar to the Thiessen polygon method concept. Consequently, a collection of vectorial polygonal areas, equivalent in number to the employed monitoring stations, is produced with the areal weights to be used for distributing the rainfall across the case study basin and subsequently to force the hydrological simulations. The generated gauge weights are validated for a different temporal precipitation event. The final outputs expressed through a series of statistical measures, clearly demonstrate the effectiveness of the specific methodology (e.g. R2 and Nash–Sutcliffe are larger than 0.83 and 0.73). The methodology can foster accurate hydrological simulations, especially in cases where there is a limited number of rainfall stations and corresponding observations.

Lora Based Industrial Environment Monitoring System

With the advent of automation, the concept of protecting the business environment has become popular and recognized in the business world. As the factory scale grows and the complexity of the process increases, advanced technology must be used in the device to measure and control many business processes simultaneously. Today, the trend is towards miniaturization of instruments and greater precision and accuracy. Reliable equipment and controls are crucial to the safety and efficiency of any process. The goal of this project is to gain knowledge about the most important aspects of the job to improve safety. The system includes the use of LORA to monitor the business environment. In this project, we use Lora communication to send data, and the receiving end of the monitoring station receives the data from the sending end. In an emergency, when you press the emergency button on the monitoring station, a message is sent to the work unit by LORA. Since LORA is a transceiver, two- way communication is possible in the project. Temperature sensors, gas sensors and fire sensors are used to monitor the environment of business units. Temperature sensors are used in industry to calculate temperature and provide information to microcontrollers. Gas sensors are used in industry to detect gas leaks. Fire sensors are used to detect fire and send the information to the monitoring station. This product is delivered by LORA.

Water quality evaluation based on water quality index and multiple linear regression: A research on Hanyuan Lake in southern Sichuan Province, China.

This study aims to understand the changes in the water quality of Hanyuan Lake and to show these changes over time. In this study, monthly sampling was conducted at three sampling sites in Hanyuan Lake, and water samples were measured for water quality indicators in the laboratory according to the methods specified in the Environmental Quality Standards for Surface Water (GB3838-2002). Based on the monitoring data from January to December 2019, the WQI comprehensive evaluation method was used to conduct multiple linear stepwise regression analysis, extract key indicators, and establish the WQImin model. The results show that according to the WQI comprehensive evaluation method, the WQI values of Hanyuan Lake are all above 90, and the grade is excellent. The overall water quality of Hanyuan Lake is excellent, and most of the water quality indexes reach the Class I standard in the Environmental Quality Standards for Surface Water (GB3838-2002). WQImin1 (R2 = 0.86, p < 0.001, PE = 4.28) as the best WQImin model. In this study, a model with fewer parameters was established by multiple linear regression method, which is conducive to better monitoring of water quality at monitoring stations while saving costs. PRACTITIONER POINTS: According to the WQI comprehensive evaluation method, the WQI values of Hanyuan Lake are all above 90, the rating is excellent. From January 2019 to September 2020, the monthly change trend of each section is roughly the same, showing a trend of first decreasing, then rising, then decreasing, and finally rising and flattening. The WQImin model was developed to completely describe the change in the water body.

Investigating the differences in driving mechanisms for phytoplankton community composition under various human disturbances in cold regions

In river ecosystems, phytoplankton are essential components, and this study delves into their response to critical environmental indicators. We analyzed the spatiotemporal variations of phytoplankton diversity in cold regions, employing techniques such as Principal Component Analysis (PCA), Spearman correlation coefficient, and Generalized Additive Model (GAM) to unveil the critical environmental factors influencing phytoplankton diversity and habitat suitability under varying degrees of human interference. With 37 monitoring stations across two cold region rivers, phytoplankton and water bodies were monitored in spring, summer, and autumn, resulting in the collection and analysis of 111 phytoplankton samples and 13 environmental indicators at each station. By identifying critical water quality indicators and exploring nonlinear relationships, the study revealed intricate interactions between phytoplankton diversity and environmental factors. Our findings suggest that overall phytoplankton species diversity is lower in rivers with high human interference, with turbidity, NH4+-N, chlorophyll-a, and total phosphorus (TP) deemed critical factors. Conversely, chlorophyll-a, NH4+-N, and electrical conductivity (EC) were identified as important factors in rivers with lower human interference, indicating differences in phytoplankton driving mechanisms between the two rivers. These insights provide valuable references for the sustainable development and effective assessment of cold region river ecosystems in the Anthropocene.

Spatial Temporal Analysis of Air Quality and their Relation to Meteorological Parameters, in India

Air pollution has become a significant global issue, with India ranking 8th in the world for hazardous air quality based on the latest 2022 report. (World Air Quality Report 2023). The growing urban sprawl and human-induced environmental activities have led to a decline in air quality. The Air Quality Index assesses the level of pollution in the air at a particular region. It is a fundamental right for individuals to be informed about the quality of the air they breathe for their well-being. This study aims to examine air pollution levels and trends across various geographical locations in India using data provided by the Central Pollution Control Board under the Ministry of Environment, Forest, and Climate Change in India. The study focuses on analyzing the annual growth of pollutants such as SO2, NO2, PM10, and PM2.5 from 2016 to 2021, collected from monitoring stations in 28 states across 256 locations. The research evaluates the impact of different particulate matter concentrations and showcases the spatial variation of air pollutants between 2016 and 2021. Results indicate a significant decrease in air pollution levels during lockdown periods compared to pre-lockdown periods. On an average (over all cities), more than 24% decrease has been observed for all the AQI of the pollutants. Additionally, a comparative analysis reveals correlations between meteorological factors and air quality, showing that temperature, relative humidity, and wind speed are negatively correlated with AQI, while surface pressure is positively correlated. Temporal analysis was also conducted to examine the relationship between air pollutant concentrations and meteorological parameters.

Exploring the applicability of the experiment-based ANN and LSTM models for streamflow estimation

AbstractThe Yeşilırmak River Basin in northern Türkiye is crucial for the region’s water supply, agriculture, hydroelectric power generation, and clean drinking water. The primary goal of this study is to determine which modeling approach is most appropriate for various locations within the basin and how well meteorological data can predict river flow rates. Hydrological and meteorological forecasting both depend on the prediction of river flow rates. An artificial neural network (ANN), Univariate and Multivariate Long Short-Term Memory (LSTM) models have been utilized for streamflow forecasting. This research aims to determine the best model for several provinces in the basin area and give decision-makers a tool for reliable river flow rate estimates by combining LSTM and ANN models. According to research findings, the supervised multivariate LSTM model performed better than the unsupervised model in accuracy and precision. The sliding window methodology is suitable for estimating river flow based on meteorological datasets because it offers a primary method for reinterpreting time-series data in a supervised learning style. Compared to LSTM models, the ANN model that has been statistically optimized through experiments (DoE) design performs better in forecasting the river flow rate in the Yeşilırmak River basin (R2 = 0.98, RMSE = 0.18). The study’s findings provided prospective cognitive models for the strategic management of water resources by forecasting future data from flow monitoring stations.

Characteristics of Anthropogenic Pollution in the Atmospheric Air of South-Western Svalbard (Hornsund, Spring 2019)

The character of atmospheric pollution and its impact on surface waters may vary substantially in space, and hence, we add a potentially important location for the studies of atmospheric air pollution to the map of the High Arctic. We have investigated the anthropogenic particle characteristics and selected persistent organic pollutant concentrations, in a priorly unmonitored location in the Arctic (Svalbard), exposed to a climatic gradient. Single-particle analysis of PM indicates that besides the prevailing natural aerosol particles, anthropogenic ones were present. The likely anthropogenic origin of some particles was established for spherical Fe-rich or aluminosilicate particles formed in high-temperature processes or metal-rich particles of the chemical composition corresponding to industrial products and atypical for natural minerals; soot, tar balls, and secondary sulfate were also likely of anthropogenic origin. Some of the observed anthropogenic particles could only come from remote industrial sources. POP concentrations indicated a background of LRAT, consistent with the ΣPCB concentrations and volatility profile. However, the ΣDDX composition indicating aged sources and an order of magnitude higher concentrations of both ΣDDXs and ΣHCHs than at other High Arctic monitoring stations indicate their potential source in two types of re-emission from secondary sources, i.e., from seawater and snowpack, respectively.

Assessment of surface ozone products from downscaled CAMS reanalysis and CAMS daily forecast using urban air quality monitoring stations in Iran

Abstract. Tropospheric ozone time series consist of the effects of various scales of motion, from meso-scales to large timescales, which are often challenging for global models to capture. This study uses two global datasets, namely the reanalysis and the daily forecast of the Copernicus Atmosphere Monitoring Service (CAMS), to assess the capability of these products in presenting ozone's features on regional scales. We obtained 16 relevant meteorological and several pollutant species, such as O3, CO, NOx, etc., from CAMS. Furthermore, we employed a comprehensive set of in situ measurements of ozone at 27 urban stations in Iran for the year 2020. We decomposed the time series into three spectral components, i.e., short (S), medium (M), and long (L) terms. To cope with the scaling issue between the measured data and the CAMS' products, we developed a downscaling approach based on a long short-term memory (LSTM) neural network method which, apart from modeled ozone, also assimilated meteorological quantities as well as lagged O3 observations. Results show the benefit of applying the LSTM method instead of using the original CAMS products for providing O3 over Iran. It is found that lagged O3 observation has a larger contribution than other predictors in improving the LSTM. Compared with the S, the M component shows more associations with observations, e.g., correlation coefficients larger than 0.7 for the S and about 0.95 for the M in both models. The performance of the models varies across cities; for example, the highest error is for areas with high emissions of O3 precursors. The robustness of the results is confirmed by performing an additional downscaling method. This study demonstrates that coarse-scale global model data, such as CAMS, need to be downscaled for regulatory purposes or policy applications at local scales. Our method can be useful not only for the evaluation but also for the prediction of other chemical species, such as aerosols.