Environmental Monitoring Station Research Articles

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.

Prawne podstawy ochrony przed hałasem drogowym (ze szczególnym uwzględnieniem przygotowania i realizacji inwestycji drogowej oraz etapu eksploatacji dróg)

The article discusses the regulations under which information on the state of the environment is collected, which can be used at the stage of planning, designing, constructing and operating roads (state environmental monitoring, programmes of environment protection against noise, strategic noise maps). It also addresses the issue of protection against road traffic noise at the stage of road design, construction and alteration, as well as at the stage of road operation. It also identifies environmental protection authorities that play an important role in the context of supervising compliance with noise protection regulations, and briefly discusses the legal remedies available to people exposed to excessive road traffic noise.

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.

A six-year measurement-based analysis of traffic-related particulate matter pollution in urban areas: the case of Warsaw, Poland (2016-2021)

This study used PM10 and PM2.5 measurements from the State Environmental Monitoring stations in Warsaw and its suburban areas. Analysis of variability characteristics at the traffic and urban background stations was carried out for 2016-2021. A six-year analysis (2016-2021) of air quality in Warsaw, Poland, focusing highlights the persistent impact of transportation on particulate matter concentrations. Comparing a city centre traffic station with urban background locations reveals consistently higher PM10 concentrations at the traffic station throughout the year, with an annual traffic-related increase of 12.6 μg/m³ (32%). PM2.5 concentrations at the traffic station are also consistently about 1.5 μg/m³ (7%) higher. For monthly averages, the highest PM10 concentrations at the traffic station were noted in March, which may be related to the resuspention of sand and salt left over from winter snow removalp rocesses. In the case of PM2.5, the typical annual cycle with maximum concentrations in winter and minimum concentrations in summer was not observed. Diurnal variability patterns show elevated PM10 concentrations at the traffic station from 8:00 a.m. to 10:00 p.m., attributed to the resuspension process. PM2.5 patterns exhibit a smaller amplitude at the traffic station, with nighttime accumulation due to inflow. This study emphasizes the lasting impact of transportation on air quality, providing insights into pollution control strategies in urban areas.

High-Precision Photoacoustic Nitrogen Dioxide Gas Analyzer for Fast Dynamic Measurement.

A high-precision photoacoustic (PA) gas analyzer for fast dynamic measurement of ambient nitrogen dioxide (NO2) was developed. The PA analyzer used a differential PA cell combined with two mufflers to achieve rapid gas flow gas detection. A high-power laser diode (LD) with a center wavelength of 450 nm was used as the PA signal excitation source. To reduce the saturated absorption effect of NO2, ambient air was pumped into the analyzer at a flow rate of 900 sccm. Two mufflers were combined with the differential PA cell to reduce the noise caused by the airflow and pump. The parameters of the mufflers were optimized by using a finite element method. The experimental results showed that the gas flow noise was suppressed by 95%. The response time of the PAS analyzer was 34 s. The detection limits of the analyzer were 0.64 and 0.17 ppb when the integration times were 1 and 15 s, respectively. A 120 h continuous monitoring result was compared with the data from the National Environmental Monitoring Station to demonstrate the high reliability of the analyzer.

A densely connected causal convolutional network separating past and future data for filling missing PM2.5 time series data

Air pollution poses a significant threat to human health and the environment globally. Precise analysis and prediction of pollutant concentrations are essential for monitoring and managing air quality. However, reliable analysis and prediction require comprehensive and high-quality data, which is often compromised due to missing data during collection. Unfortunately, conventional methods for addressing missing data fall short of providing adequate solutions. The missing data for air quality indicators are commonly systematic, with all data points missing for extended periods. This makes it difficult to establish correlations and populate the missing data accurately. To address this problem, we propose a Densely Connected Causal Convolutional Network Separating Past and Future Data (DCCN-SPF), a deep learning-based model that fills in continuous missing PM2.5 concentration data in the original dataset. It extracts features from past and future data separately using densely connected causal convolutional networks and incorporates linear interpolation and deep learning structures to improve prediction accuracy. Using air quality monitoring data from the China Environmental Monitoring Station between 2017 and 2021 in Beijing, we compare our proposed model with baseline models and find that our model outperforms others in predicting PM2.5 concentrations. The evaluation metrics MAE and RMSE are used, revealing significant reductions of 8.7–21.6 % for MAE and 7.1–23.5 % for RMSE in favor of our proposed DCCN-SPF model.

Ecological monitoring technologies for rational nature use

The article identifies the problems of environmental monitoring at enterprises of the Russian Federation and the level of severity of environmental problems in the regions of Russia. The main objects of environmental monitoring in accordance with the legislation and regulatory legal acts of Russia are presented. The characteristics and functions of the Russian Unified State Environmental Monitoring System are presented. The necessity of monitoring the level of environmental pollution using specially designed automated information systems has been determined. The possibilities and advantages of environmental control and monitoring systems used in the territory of the Russian Federation are revealed. The necessity of implementing the multilevel principle through the information system developed in the Russian Federation – "Ecology. 1C-KSU: Environmental Protection".

On the formation and development of the State environmental monitoring system (State environmental monitoring)

The article talks about scientific and methodological issues of the development of state environmental monitoring. The authors have considered the structure of State environmental monitoring (State environmental monitoring) and the procedure for its implementation. The legal basis of state environmental monitoring systems.
 We present the work within the Framework of the national program of the unified system of state environmental monitoring (State environmental monitoring, SEM). The goals, objectives and procedure for the implementation of the SEM, its main subsystems.
 The scientists examined the role of the Ministry of Natural Resources and Ecology of the Russian Federation (Ministry of Natural Resources of Russia) and the Federal Service for Hydrometeorology and Environmental Monitoring (Roshydromet) in ensuring the functioning of state environmental monitoring and its information resources. The activities of the State Environmental Monitoring Data Fund (State environmental monitoring) and the Unified State Data Fund (USDF) are presented. The researchers have the work of the monitoring system using the state system of observations and control by Roshydromet of indicators of the content of pollutants in atmospheric air, surface waters, marine waters, soil, radiation monitoring, etc.
 The authors have considered the prospects for the development of state environmental monitoring in connection with the adoption by the Government of the Russian Federation of the State program "Environmental Protection" for 2012–2030.
 Ethics. This study did not require the conclusion of the Ethics Committee.

Spatial and temporal urban air pollution patterns based on limited data of monitoring stations

Urban air pollution is one of the serious environmental problems in the development of high-density cities, especially for traffic-related pollution. Effective urban environmental monitoring technologies are helpful to predict and control the air pollution emitted by traffic sectors. However, the complex temporal and spatial characteristics of air pollution in urban areas pose a great challenge for its monitoring, prediction, and control. This work investigated on the spatiotemporal air pollution patterns and its correlation mechanism with urban elements (such as traffic) based on limited data of monitoring stations, which is significant for the accurate prediction and regulation of urban environment. Regional Cluster Monitoring (RCM) methods were proposed through integrating multi-source data, including data from U.S. Geological Survey (USGS) Landsat, OpenStreetMap (OSM) road network, and environmental monitoring stations to categorize different monitoring stations. According to different urban areas (defined as U: ‘urban’ or S: ‘suburban’) and traffic densities (T: ‘traffic’ or B: ‘background’), monitoring stations areas were categorized into U-B, U-T, S-B, and S-T. Then the spatiotemporal characteristics of traffic-related pollutants in different region categories were analyzed. The rapid prediction of air pollution distribution in different categories of urban regions was realized by RCM method-based Long Short-Term Memory (LSTM) neural network. Results showed that the proposed methods could accurately represent the spatiotemporal distribution characteristics of urban air pollution. The mean absolute percentage errors (MAPE) between the predicted results and actual observed values were 20.31% to 28.05% (absolute errors were 3.9-14.3 μg/m³). This work can significantly impact on low-cost & effective monitoring and control strategies for urban area, and further contribute to comprehend the spatial-temporal distribution patterns of urban traffic-related air pollution from limited data of monitoring stations.

A Framework for the Estimation of Air Quality by Applying Meteorological Images: Colours-of-the-Wind (COLD)

This paper presents a new framework, “colours-of-the-wind” (COLD), which is designed to estimate air quality based on images from meteorological cameras, data analytics techniques, and the application of deep learning. Existing air quality estimation systems mainly rely on physical monitoring stations, which are limited by the monitoring stations’ physical constraints. Instead of collecting data from environmental monitoring stations, COLD collects air quality data from meteorological cameras. This approach can collect data from any location where a camera could capture a reliable image, which is otherwise not collectable by the physical environmental monitoring station(s). This approach can also avoid bias due to the location of data collection. The system is evaluated by building a prototype based on the Air Quality Health Index from the Hong Kong Observatory. This is one of the unique data-analytic frameworks based on such air quality measurement. The COLD’s air quality estimation is also based on AQHI, the first machine learning-based estimation framework that generates AQHI as the proposed output. Experimental results suggest that the approach adopted by the COLD prototype is feasible and has some promising outcomes. The results also suggest possible parameters for the CNN model used for the training and analyses of the images.

Association between cognitive function and dusty weather: a propensity score matching study

BackgroundWith a rapidly aging global population, the health of older adults is a national priority for countries across the world. Dusty weather has been demonstrated to be a potential risk factor of cognitive function among the elderly population. However, there is a paucity of studies exploring the associations between dusty weather and cognitive function among the older in China.MethodsData on individual characteristics were obtained from the China Health and Retirement Longitudinal Survey (CHARLS) 2018, whereas data on air pollution were sourced from environmental monitoring stations in China. Cognitive function, including general cognitive function, episodic memory, and linguistic competence, was assessed by self- or informant-questionnaires. We used propensity score matching and linear regression to investigate the relationship between dusty weather and cognitive function. Sensitivity analyses were conducted to test the robustness of the results.ResultsThis study included 8,604 participants older than 60 years old. After controlling air pollutant weather, dusty weather was demonstrated to be positively associated with a decline in cognitive function (Informant Questionnaire on Cognitive Decline in the Elderly (IQCODE), 4.0, 95% confidence interval (CI): 3.11, 4.89; Mini‐Mental State Exam (MMSE), 0.63, 95% CI: 0.34, 0.92). Results of sensitivity analysis showed that our research findings are robust.ConclusionOlder adults living in dusty weather regions suffered a higher level of cognitive impairment, and such adverse effects were more substantial among females compared with their male counterparts. Targeted health interventions to help older adults living in regions where dusty weather occurs frequently are suggested to be proposed.

Comparison of the Visibility Grading Forecast Method Based on Meteorological Factors and Environmental Factors

The main visibility forecast factors were identified with the support of data from routine meteorological observations from the Mianyang Airport and the Mianyang Environmental Monitoring Station from 2015 to 2018, and a visibility grading forecast model was established and tested by dint of the multiple linear regression and the KNN algorithm based on big data mining technology, and the variation characteristics of visibility in winter at the Mianyang Airport were studied. The results showed that (1) in addition to having a significant positive correlation with wind speed, the visibility in winter at the Mianyang Airport has a significant negative correlation with relative humidity, dew point temperature, AQI, PM2.5 concentration, PM10 concentration, and CO, and it has the strongest correlation with relative humidity, and the correlation coefficient is −0.76. (2) The multivariate linear regression model and the KNN model were adopted for grading forecasting experiments on visibility, and the results revealed that both models could be used for visibility grading forecasts. The multiple regression model secures an accuracy of over 70% for forecasts of level 1–5 visibility. In terms of the KNN model, the forecast accuracy is the best when K = 3 or K = 5, notably for level-2, level-4, and level-5 visibility. The forecast accuracy rate is 100% for level-2 visibility, but the forecast for level-1 visibility is poor. (3) The minimum value of the average daily visibility of the Mianyang Airport in winter appeared at 09 : 00 and the maximum value appeared at 17 : 00. The level-1 visibility occurred and developed before 09 : 00 and faded and vanished between 08 : 00 and 15 : 00.

Short-term ambient ozone exposure increases the risk of hospitalization with depression: a multi-city time-stratified case–crossover study

Background Depression, the most common mental illness worldwide, has been studied and air pollution has been found to increase the risk of depression hospitalization, but research results on ozone (O3) remain limited. In this context, we investigated the relationship between short-term O3 exposure and depression-related hospital admissions (HAs). Methods The 10,459 records of HAs for depression from medical institutions across in 9 cities, China, were collected between 1 January 2017, and 31 December 2018. Air pollutants and meteorological data was obtained from provincial ecological environment monitoring stations in the study area. Conditional Poisson regression was employed to estimate the association between O3 and hospitalizations for depression, with data stratification by sex, age, weather, and economic level. Results Short-term O3 exposure was positively associated with the number of depression-related hospitalizations (Relative risk: 1.04 [95% CI: 1.02, 1.05]). O3 had a significant effect on the risk of depression-related hospitalizations on warm days (P = 0.021, Relative risk: 1.05 [1.03, 1.08]). The high gross domestic product group was more likely to be affected by O3 exposure-associated depression-related hospitalizations (P = 0.005, Relative risk: 1.03 [1.01, 1.05]). Conclusions Short-term changes to O3 exposure may increase the risk of depression related hospitalizations, especially on warm days.

Ecological and chemical assessment of the state of the Desna river by bottom sediments on the example of the Bryansk region, Non-Chernozem region of the Russian Federation

Studies of the state of bottom soils of the city Desna river (Bryansk) were carried out as part of the recommended studies of state environmental monitoring. The purpose of the work is to identify the current state of the urban watercourse on the basis of ecological and analytical indicators of bottom soils for diagnosing the state of the surface waters of the Desna river. The study of the bottom sediments of the Desna river in the reference areas (cross-sections) showed that the content of heavy metals is within the normal range. The exception is the section of the river in the background region. The excess of concentrations for cobalt and chromium is 8.0 and 1.1 times from the established standards. The highest content of titanium oxide, chromium and cobalt was noted in samples on the right bank of the Desna river in the background area, which indicates that the area is silty. On average, the excess of chromium in spring was 7.5 times, and in summer - 8 times. For cobalt, the excess of the indicator compared to the maximum allowable amounted to 1.1 times throughout the entire spring-summer period. For the first time, the data of three-year studies on the state of bottom sediments are summarized, allowing the development of effective environmental programs and the creation of a constantly updated monitoring base.

The development of mineral resources in the Arctic zone of Russia as a potential environmentally hazardous activity: the legal framework

The current legislation of the Russian Federation lacks a defined term for «environmentally hazardous activity». Existing definitions in scientific literature fall short of capturing its essence comprehensively. To assess the legal framework for ensuring environmental safety during mineral resource development in the Arctic zone of the Russian Federation, this study employs a methodology grounded in general scientific and legal methods, including formal logic, legal interpretation, and legal modeling. According to Russian legislation, the extent of environmentally hazardous economic and other activities is influenced by the facilities where legal entities and entrepreneurs conduct their operations. These facilities are classified based on the following criteria: 1. the extent of the facility’s negative impact on the environment; 2. the facility’s designation as a hazardous production facility; 3. the object of state environmental monitoring, categorized by the risk of damage to values protected by the law, including the universal right to a healthy environment; 4. the geographic location of the object of state environmental monitoring. Taking these criteria into account, this article presents a comprehensive definition and classification of environmentally hazardous economic activities. The development of mineral resources in the Arctic zone of Russia falls under this classification, with varying degrees of environmental risk-ranging from high to significant and average.

Reforming the state environmental monitoring system in Ukraine

Reforming the state environmental monitoring system in Ukraine

Temporal and Spatial Distributions of O3 Concentration and Potential Source Area Analysis of Hexi Corridor Based on Satellite and Ground Monitoring

Based on 2005-2020 O3 column concentration data of OMI remote sensing satellite, combined with air pollutant data from 10 nationally controlled environmental automatic monitoring stations in the Hexi Corridor and global data assimilation system meteorological data, we used Kriging interpolation, correlation analysis, and backward trajectory (HYSPLIT) models to explore the temporal and spatial distribution characteristics, meteorological factors, transmission paths, and potential sources of O3 in the Hexi Corridor. The results showed the following:① in terms of temporal distribution, O3 column concentration showed an upward trend in 2005-2010 and 2014-2020 and downward trend in 2010-2014; the maximum and minimum values were reached in 2010 and 2014 (332.31 DU and 301.00 DU), respectively, and seasonal changes showed that those in spring and winter were significantly higher than those in summer and autumn. ② In terms of spatial distribution, O3 column concentration showed a latitudinal band distribution characteristic of increasing from southwest to northeast; the high-value areas were primarily distributed in urban areas with low terrain, and the median zone was latitudinally striped with the basic alignment of the Qilian foothills. ③ The analysis of meteorological conditions revealed that temperature, wind speed, and sunshine hours were positively correlated with O3, and relative humidity was negatively correlated with O3. ④ By simulating the airflow transportation trajectory of the receiving point in Wuwei City, it was found that the direction of the O3 conveying path was relatively singular; the dominant airflow in each season was primarily in the west and northwest; and the proportions were 71.62%, 66.85%, 61.22%, and 77.78%, respectively. There were certain seasonal differences in the source areas of O3 potential contribution:the high-value areas of O3 potential sources in spring, summer, and autumn were distributed in Baiyin City and Lanzhou City, which were southeast wind sources, and the high-value areas in winter were distributed between the Badain Jaran Desert and the Tengger Desert, which was the north wind source.

A gridded air quality forecast through fusing site-available machine learning predictions from RFSML v1.0 and chemical transport model results from GEOS-Chem v13.1.0 using the ensemble Kalman filter

Abstract. Statistical methods, particularly machine learning models, have gained significant popularity in air quality predictions. These prediction models are commonly trained using the historical measurement datasets independently collected at the environmental monitoring stations and their operational forecasts in advance using inputs of the real-time ambient pollutant observations. Therefore, these high-quality machine learning models only provide site-available predictions and cannot solely be used as the operational forecast. In contrast, deterministic chemical transport models (CTMs), which simulate the full life cycles of air pollutants, provide predictions that are continuous in the 3D field. Despite their benefits, CTM predictions are typically biased, particularly on a fine scale, owing to the complex error sources due to the emission, transport, and removal of pollutants. In this study, we proposed a fusion of site-available machine learning prediction, which is from our regional feature selection-based machine learning model (RFSML v1.0), and a CTM prediction. Compared to the normal pure machine learning model, the fusion system provides a gridded prediction with relatively high accuracy. The prediction fusion was conducted using the Bayesian-theory-based ensemble Kalman filter (EnKF). Background error covariance was an essential part in the assimilation process. Ensemble CTM predictions driven by the perturbed emission inventories were initially used for representing their spatial covariance statistics, which could resolve the main part of the CTM error. In addition, a covariance inflation algorithm was designed to amplify the ensemble perturbations to account for other model errors next to the uncertainty in emission inputs. Model evaluation tests were conducted based on independent measurements. Our EnKF-based prediction fusion presented superior performance compared to the pure CTM. Moreover, covariance inflation further enhanced the fused prediction, particularly in cases of severe underestimation.

Assessment of Spatial and Temporal Variation in NO2 Levels over Tourist Reception Areas in Poland

Air quality in tourist reception areas can be a significant health concern. It also plays an increasingly important role when it comes to choosing tourist destinations. NO2 is a harmful gas that can cause an increased number of cancer or respiratory diseases. The development of satellite remote sensing techniques now enables a much broader spectrum of air quality analysis than mere point measurements at environment monitoring stations. In the study, the spatial diversity of nitrogen dioxide air pollution over tourist reception areas in Poland was assessed. The lowest pollution was found in national parks and tourist regions. The most polluted air was found in tourist reception areas located near industrial regions and large urban agglomerations. Temporal variation—annual and monthly—and spatial variation were determined (for the period 2019–2021). The highest concentrations, exceeding the WHO recommended value (40 μmol/m2), occurred in the winter and autumn. Low pollution was found in most reception areas in the summer (except cities). In 2020, due to restrictions related to the SARS-CoV-2 epidemic, the NO2 pollution decreased (10–20%). In the cold half of the year (October–April), NO2 concentrations greater than 40 μmol/m2 occurred for about 20% of national parks, 50% of health resorts, 30% of tourist regions, and 100% of provincial capitals.

Retrieval of nighttime aerosol optical depth by simultaneous consideration of artificial and natural light sources

Aerosol Optical Depth (AOD) is a critical optical parameter that quantifies the degree of light attenuation by aerosols and serves as a fundamental indicator of atmospheric quality. Therefore, accurate quantification and retrieval of AOD is crucial for relevant studies. However, current satellite-based AOD retrieval algorithms suffer from inapplicability under low-light conditions, limiting the development of nighttime AOD retrieval. Under this context, we proposed a novel algorithm, namely Simultaneous Consideration of Artificial and Natural light Sources (SCANS), to obtain nighttime AOD. The core of the SCANS algorithm is considering the synergy of both the natural and artificial light sources to obtain nighttime AOD by integrating atmospheric radiative transfer simulation into an extinction method and performing multiple iterations. SCANS was applied to the Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) and the retrieved nighttime AOD was validated with in-situ measurements from five AERONET sites. Results indicate that the Mean Bias Errors (MBEs) of the retrieved nighttime AOD range from 0.0 to 0.08 and the corresponding Root Mean Square Errors (RMSEs) range from 0.11 to 0.17, which exhibit better accuracy than that of the nighttime MERRA-2 AOD. We further compared the retrieved nighttime AOD with the corresponding Air Quality Index (AQI) measurements at six environment monitoring stations and obtained high correlation coefficients (i.e., ranging from 0.733 to 0.940), indicating SCANS's reliability and high accuracy. The proposed SCANS algorithm can effectively obtain nighttime AOD with high quality, thereby advancing research on the diurnal variation of crucial Earth's key elements.