Network Of Monitoring Stations Research Articles

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

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

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

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

Optimising the network of air quality monitoring stations

This study explores the application of genetic algorithms (GAs) for optimizing the network of air quality monitoring stations. Recognizing the complexity of accurately assessing air pollution levels across diverse urban and rural landscapes, the research focuses on finding the most effective station placements to maximize coverage and data fidelity while minimizing costs. The methodology entails simulating natural selection processes, including selection, crossover, and mutation, to evolve a population of potential solutions. Each candidate solution in the population represents a unique configuration of monitoring stations. A fitness function evaluates the efficiency of each configuration based on criteria such as population coverage, proximity to pollution sources, and installation and operational expenses. The research employs a genetic algorithm developed in Python, which iteratively refines the population of solutions over thousands of generations. The algorithm's performance is assessed through experimental validation, with an emphasis on the adaptability of the approach to accommodate various environmental, economic, and regulatory constraints. Results indicate that GAs can effectively balance multiple optimization objectives, leading to a network design that is both cost-efficient and comprehensive in its monitoring capabilities. The outcome of the study is an optimized network that significantly improves upon the initial state in terms of coverage and cost-effectiveness. The study concludes that genetic algorithms offer a promising avenue for addressing the challenges of air quality monitoring network design. The flexibility and global search capabilities of GAs make them suitable for the complex, multi-objective nature of the task. Moreover, the findings suggest potential for further improvements and applications of GAs in environmental monitoring and other complex systems optimization scenarios.

Daily Streamflow Forecasting Using Networks of Real-Time Monitoring Stations and Hybrid Machine Learning Methods

Considering the increased risk of urban flooding and drought due to global climate change and rapid urbanization, the imperative for more accurate methods for streamflow forecasting has intensified. This study introduces a pioneering approach leveraging the available network of real-time monitoring stations and advanced machine learning algorithms that can accurately simulate spatial–temporal problems. The Spatio-Temporal Attention Gated Recurrent Unit (STA-GRU) model is renowned for its computational efficacy in forecasting streamflow events with a forecast horizon of 7 days. The novel integration of the groundwater level, precipitation, and river discharge as predictive variables offers a holistic view of the hydrological cycle, enhancing the model’s accuracy. Our findings reveal that for a 7-day forecasting period, the STA-GRU model demonstrates superior performance, with a notable improvement in mean absolute percentage error (MAPE) values and R-square (R2) alongside reductions in the root mean squared error (RMSE) and mean absolute error (MAE) metrics, underscoring the model’s generalizability and reliability. Comparative analysis with seven conventional deep learning models, including the Long Short-Term Memory (LSTM), the Convolutional Neural Network LSTM (CNNLSTM), the Convolutional LSTM (ConvLSTM), the Spatio-Temporal Attention LSTM (STA-LSTM), the Gated Recurrent Unit (GRU), the Convolutional Neural Network GRU (CNNGRU), and the STA-GRU, confirms the superior predictive power of the STA-LSTM and STA-GRU models when faced with long-term prediction. This research marks a significant shift towards an integrated network of real-time monitoring stations with advanced deep-learning algorithms for streamflow forecasting, emphasizing the importance of spatially and temporally encompassing streamflow variability within an urban watershed’s stream network.

Vegetation dieback in the Mississippi River Delta triggered by acute drought and chronic relative sea-level rise

Vegetation dieback and recovery may be dependent on the interplay between infrequent acute disturbances and underlying chronic stresses. Coastal wetlands are vulnerable to the chronic stress of sea-level rise, which may affect their susceptibility to acute disturbance events. Here, we show that a large-scale vegetation dieback in the Mississippi River Delta was precipitated by salt-water incursion during an extreme drought in the summer of 2012 and was most severe in areas exposed to greater flooding. Using 16 years of data (2007–2022) from a coastwide network of monitoring stations, we show that the impacts of the dieback lasted five years and that recovery was only partial in areas exposed to greater inundation. Dieback marshes experienced an increase in percent time flooded from 43% in 2007 to 75% in 2022 and a decline in vegetation cover and species richness over the same period. Thus, while drought-induced high salinities and soil saturation triggered a significant dieback event, the chronic increase in inundation is causing a longer-term decline in cover, more widespread losses, and reduced capacity to recover from acute stressors. Overall, our findings point to the importance of mitigating the underlying stresses to foster resilience to both acute and persistent causes of vegetation loss.

2019年中国1 km格网河网密度数据集

Drainage density is an outcome of the combined influence of watershed landform, climate, soil, and vegetation. As a comprehensive characterization index, the measurement and monitoring of drainage density is of great significance to environmental protection and water resources allocation in regional watersheds. This paper uses river water surface vector data from the third National Land Survey, extracting the river center line. Then, the kilometer grid network production software is employed to generate the dataset of 1 km grid drainage density in China. Rigorous quality control and quality inspection measures throughout all stages of data processing have been implemented, including river surface vector extraction, river skeleton line extraction, and kilometer grid density calculation, to ensure the correctness, integrity and consistency of the data. This dataset can be used for national and regional hydrogeological analysis, soil erosion estimation, planning and layout of basic monitoring station network, etc.

In the nuclear explosion monitoring context, what is an anomaly?

In the early years of nuclear explosion monitoring, experts used downwind detections with meaningful ratios of radioactive species to identify an explosion. Today’s reality is sparse networks of radionuclide monitoring stations looking for weak signals. Analysts need to discriminate between industrial background radioactivity and nuclear explosion signals, even using the detection of one isotope. Aerosol and xenon measurements potentially related to nuclear tests in 2006 and 2013 announced by the Democratic People’s Republic of Korea and from worldwide civilian background radioactivity are considered when defining radionuclide detection anomalies to objectively guide the use of limited analyst resources and reduce the possibility of not detecting nuclear explosions.

Enhancing Air Pollution Monitoring and Prediction using African Vulture Optimization Algorithm with Machine Learning Model on Internet of Things Environment

An optimal solution for monitoring air pollution, the Internet of Things (IoT)-enabled system delivers real-time data and insights on the air quality within a specific location. Air pollution poses a substantial risk to human health worldwide, with pollutants like nitrogen dioxide, particulate matter, ozone, and sulfur dioxide contributing to a range of cardiovascular and respiratory ailments. Monitoring air pollution levels is critical to understand the effect on public health and the environment. Air Pollution Monitoring includes the systematic analysis and measurement of pollutant concentration in the air, through a network of monitoring stations equipped with instruments and sensors. This station provides real-time data on air quality, allowing authorities to evaluate issue warnings, and pollution levels, and implement strategies to alleviate its negative impact. Machine learning (ML) approaches are becoming more integrated into air pollution monitoring systems for enhancing efficiency and accuracy. By analyzing vast quantities of information gathered from satellite imagery, monitoring stations, and other sources, ML approaches could detect patterns, forecast pollution levels, and pinpoint sources of pollution. This study introduces Air Pollution Monitoring and Prediction using African Vulture Optimization Algorithm with Machine Learning (APMP-AVOAML) model in IoT environment. The drive of the APMP-AVOAML methodology is to recognize and classify the air quality levels in the IoT environment. In the APMP-AVOAML technique, a four stage process is encompassed. Firstly, min-max normalization is applied for scaling the input data. Secondly, a harmony search algorithm (HSA) based feature selection process is executed. Thirdly, the extreme gradient boosting (XGBoost) model is utilized for air pollution prediction. Finally, AVOA based parameter selection process is exploited for the XGBoost model. To illustrate the performance of the APMP-AVOAML algorithm, a brief experimental study is made. The resultant outcomes inferred that the APMP-AVOAML methodology has resulted in effectual outcome.

New Graph-Based and Transformer Deep Learning Models for River Dissolved Oxygen Forecasting

Dissolved oxygen (DO) is a key indicator of water quality and the health of an aquatic ecosystem. Aspiring to reach a more accurate forecasting approach for DO levels of natural streams, the present work proposes new graph-based and transformer-based deep learning models. The models were trained and validated using a network of real-time hydrometric and water quality monitoring stations for the Credit River Watershed, Ontario, Canada, and the results were compared with both benchmarking and state-of-the-art approaches. The proposed new Graph Neural Network Sample and Aggregate (GNN-SAGE) model was the best-performing approach, reaching coefficient of determination (R2) and root mean squared error (RMSE) values of 97% and 0.34 mg/L, respectively, when compared with benchmarking models. The findings from the Shapley additive explanations (SHAP) indicated that the GNN-SAGE benefited from spatiotemporal information from the surrounding stations, improving the model’s results. Furthermore, temperature has been found to be a major input attribute for determining future DO levels. The results established that the proposed GNN-SAGE model outperforms the accuracy of existing models for DO forecasting, with great potential for real-time water quality management in urban watersheds.

Beyond technology: reducing environmental information asymmetry by centralizing environmental monitoring

Due to an information gap between the central government and local governments, the principal-agent problem arises when power is devolved to the bureaucracy and local governments. We look at how China’s decision to consolidate its network of air quality monitoring stations has affected the ability of local governments to influence air quality readings. After the switch to centralized monitoring, an increase in PM2.5 and PM10 levels was detected. No increase in aerosol optical depth (AOD) from satellites or emission from nearby polluting firms corroborates the hypothesis that this reported rise in PM2.5 and PM10 is not due to an increase in air pollution. These findings highlight the potential for data manipulation by local governments and the benefits of data centralization in reducing information asymmetry. According to our research results, we suggest that the government should establish a unified, centralized environmental quality monitoring system to reduce information asymmetry and data falsification.

DEVELOPMENT OF AN IOT DEVICE BASED ON A GEIGER COUNTER

The threat of radiation exposure is always present in modern world. In recent years, this threat has become even more pronounced due to Russian aggression on Ukrainian territory. Therefore, the need for remote data collection and analysis of radiation background information has become even more important, and research in the field of creating small, low-cost devices that can monitor radiation levels of a certain area in real-time and provide information to local government agencies responsible for the safety of populated areas can be considered relevant. Radiation background level monitoring is essential to ensuring that people are informed of any potential threats to their health. It is proposed to develop a dense network of monitoring stations that can collect and transmit data about background radiation in real time. Measurement results will be processed and stored on a resource that is available to both local authorities and public. The Message Queue Telemetry Transport (MQTT) protocol is used to transmit information, as it is durable, lightweight, and efficient. A hardware and software system for collecting and transmitting radioactivity values has been developed. The system is based on a module with Geiger counter tube, which is used to measure the level of radiation in the area. The module is connected to a microcontroller, which processes the data and transmits it to a central server. To summarize and process the measurement results, the Majordomo broker is used. It is installed on a remotely accessible network storage. In conclusion, the proposed system can ensure the safety of the public, allowing authorities, experts and trained personnel to quickly and efficiently respond to any potential radiation threats, protecting people from radiation exposure.

A Statistical Approach to Estimate Seismic Monitoring Stations’ Biases and Error Levels

ABSTRACT Magnitudes are common and important measures for the size of seismic events. The International Data Centre (IDC) of the Comprehensive Nuclear-Test Ban Treaty Organization estimates an event magnitude by averaging the magnitudes calculated by individual stations that detected the event, excluding outliers. This approach assumes that all station magnitudes have the same error level and are unbiased, namely, they have no systematic errors. We show that the body-wave and surface-wave magnitudes published in the Reviewed Event Bulletin (REB) of the IDC are inconsistent with these assumptions. We thus consider a model where each station has an unknown bias and error level. Given a large collection of reported event magnitudes by a network of monitoring stations, we propose a novel approach to estimate each individual station’s bias and error level. From a statistical perspective, this is a challenging problem involving a huge number of variables, because in addition to the stations’ biases and error levels, the event magnitudes are also unknown. Our approach is based on analyzing differences between reported magnitude values at pairs of stations, which cancels out the unknown event magnitudes and allows us to derive a simple and computationally efficient algorithm. We use the estimated station biases as station correction terms and the estimated error levels to compute weights for event magnitude estimation. Using a large data set from the REB with millions of reported station magnitudes, we show that our approach yields more consistent station and event magnitudes.

Characteristics and mechanism of a persistent ozone pollution event in Pearl River Delta induced by typhoon and subtropical high

Ozone (O3) is a significant air pollutant in the Pearl River Delta (PRD) region, with severe pollution events primarily associated with the Western Pacific Subtropical High (WPSH) and typhoon periphery synoptic weather patterns. To investigate the continuity and differences in O3 pollution processes influenced by these patterns, we analyze a severe O3 pollution event in Zhuhai from September 10 to 13, 2022. The study collects vertical profiles of O3 from the differential absorption O3 lidar and wind fields observed by the Doppler wind profile lidar. We utilize ground-level monitoring data from national ambient air quality monitoring network and meteorological stations to analyze diurnal variations of air pollutant concentrations and ground-level meteorological conditions. The study reveals that both the WPSH and the typhoon periphery create favorable meteorological conditions for severe O3 pollution events. The highest temperatures during the WPSH and typhoon periphery stages are 32.1 °C and 37.5 °C, respectively. During the WPSH control stage, the combined effects of the inversion layer and light static wind hinder O3 dispersion. The ground-level O3 concentration reaches a maximum of 106 ppb. Then the typhoon periphery control stage, the ground-level O3 peak increases to 170 ppb, showing a 60% increase compared to the WPSH stage. O3 horizontal and vertical transport processes play a crucial role in the O3 episode during this stage. The northerly winds predominantly transport pollutants from upstream urban clusters to the local area, and intense downdrafts facilitate the downward transport of O3 from the residual layer. The study investigates the mechanisms of three-dimensional O3 transport within the boundary layer influenced by the WPSH and typhoon periphery in the PRD region. The results contribute valuable insights to pollution events in the PRD region under typical synoptic weather patterns.

Simulating spatio-temporal dynamics of surface PM2.5 emitted from Alaskan wildfires

Wildfire is a major disturbance agent in Arctic boreal and tundra ecosystems that emits large quantities of atmospheric pollutants, including PM2.5. Under the substantial Arctic warming which is two to three times of global average, wildfire regimes in the high northern latitude regions are expected to intensify. This imposes a considerable threat to the health of the people residing in the Arctic regions. Alaska, as the northernmost state of the US, has a sizable rural population whose access to healthcare is greatly limited by a lack of transportation and telecommunication infrastructure and low accessibility. Unfortunately, there are only a few air quality monitoring stations across the state of Alaska, and the air quality of most remote Alaskan communities is not being systematically monitored, which hinders our understanding of the relationship between wildfire emissions and human health within these communities. Models simulating the dispersion of pollutants emitted by wildfires can be extremely valuable for providing spatially comprehensive air quality estimates in areas such as Alaska where the monitoring station network is sparse. In this study, we established a methodological framework that is based on an integration of the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model, the Wildland Fire Emissions Inventory System (WFEIS), and the Arctic-Boreal Vulnerability Experiment (ABoVE) Wildfire Date of Burning (WDoB) dataset, an Arctic-oriented fire product. Through our framework, daily gridded surface-level PM2.5 concentrations for the entire state of Alaska between 2001 and 2015 for which wildfires are responsible can be estimated. This product reveals the spatio-temporal patterns of the impacts of wildfires on the regional air quality in Alaska, which, in turn, offers a direct line of evidence indicating that wildfire is the dominant driver of PM2.5 concentrations over Alaska during the fire season. Additionally, it provides critical data inputs for research on understanding how wildfires affect human health which creates the basis for the development of effective and efficient mitigation efforts.

Spatiotemporal analysis of fine particulate matter for India (1980–2021) from MERRA-2 using ensemble machine learning

Particle exposure affects more humans globally than any other air pollutant. However, due to expensive instruments and infrastructural deficiency, a high spatiotemporal network of monitoring stations is not possible, leading to data-scarce regions. Satellite and reanalysis datasets can be implemented to estimate particulate matter, but they do not provide surface concentration and needs to be reconstructed from the components. In this study, a machine learning (ML) framework is implemented to reconstruct PM2.5 from MERRA-2 data components, namely black carbon (BC), organic carbon (OC), dust (DUST), sea salt (SS), and sulfate (SO4) mass concentration. The ground-level data were collected from India's 335 continuous ambient air quality monitoring stations (CAAQMS) and respective MERRA-2 data for 2017–2021 at hourly resolution. Random forest (RF) performs better with train and test scores (R2) of 0.84 and 0.73, respectively, while the empirical equation provides an R2 of only 0.26 on test data. The estimated PM2.5 for Indian states from 1980 to 2021 indicates a significant increase in most cases. However, states in the Indo-Gangetic plain such as Delhi, Punjab, Haryana, and Uttar Pradesh, are the most polluted regions of India. The major shift in concentration is from 2000 onwards, which can be seen as a direct result of the economic liberalization policies implemented in 1991. The results provide evidence for the limitations of the broad application of the empirical equation and the feasibility of ML algorithms as a potential reconstruction technique for developing robust and accurate region-specific models from MERRA-2 data.

Multi-Tier Validation of a Macroscale Nitrogen Model for Groundwater Management in Watersheds Using Data from Different Monitoring Networks

For the Hessian river basins, an area-differentiated modeling of the nitrogen input to the groundwater and surface waters was carried out for six diffuse input pathways and six point source input pathways on the basis of the geodata available at the state level. In this context, extensive plausibility checks of the model results were carried out using the data from several official monitoring networks at the state level. These include the comparison of modeled runoff components and input pathways for nitrogen using the data from the network of discharge monitoring stations. For the validation of the modeled nitrate concentrations in the leachate, the data from groundwater monitoring wells for controlling the chemical status of groundwater were used. The validation of the modeled nitrate inputs to the groundwater and denitrification in the groundwater was carried out using the data from a special monitoring network of groundwater monitoring wells that include N2/Ar measurements. The data from the Surface Water Quality Monitoring Network were used to verify the plausibility of the modeled total N inputs to the surface waters from diffuse sources and from point sources. All of the model results evaluated by the plausibility checks prove that the nitrate pollution situation in Hesse is adequately represented by the model. This is a prerequisite for accepting the model results at the state level as a basis for developing and implementing regionally appropriate mitigation measures. The Hessian State Agency for Nature Conservation, Environment and Geology uses the model results in the broader context of the work on implementing the EU Water Framework Directive and the EU Nitrate Directive.

Design of a Low-Cost System for the Measurement of Variables Associated With Air Quality

Poor air quality is a global problem that affects human health and ecosystems all over the world. For that reason, many countries invest in networks of reference monitoring stations that measure air quality in real-time. However, the high installation and maintenance costs of these stations mean that smaller cities or underdeveloped regions are less covered by such stations. In these regions, alternative detection technologies such as low-cost monitoring systems can be used instead. Unfortunately, the results of such monitoring devices are often questioned by academics. This contribution proposes the design of an air quality monitoring system where cost and reliability have been optimized from a hardware point of view. In addition, the device has been coupled to an IoT platform so that inexperienced users can view and share the collected information The design of affordable systems increases the ability to perform ad hoc analysis at the level of neighborhoods or cities by citizen science organizations or governmental entities.

ШЫҒЫС ҚАЗАҚСТАН ОБЛЫСЫ АУМАҒЫНЫҢ СУ БАСУЫН ТАЛДАУ

It is known that the state and development of both the biosphere and human society directly depends on the state of water resources. Among the problems facing humanity in recent decades, experts and politicians put the problem of water in the first place. One of these phenomena is undoubtedly flooding. Floods can sometimes be a source of prosperity, but they can also pose a serious threat to many large settlements. Today, in the course of scientific and technological development, humanity has learned to deal with river disasters to a certain extent, regulate the flow of important rivers, control changes in the water level in the river, etc. b. An important issue today is a comprehensive assessment of possible risks of river flooding. In particular, this applies to the territory of developing countries, where the network of monitoring stations is not yet sufficiently developed, and river systems are poorly studied. The territory of the Republic of Kazakhstan cannot boast of a large number of large rivers and lakes. However, every spring in some areas of the republic there are often problems with flooding. The article considers the consequences of floods typical for some areas of the East Kazakhstan region. The characteristic of the state of the river basin in the spring-summer period and the general river network is given. A brief overview of floods in the study area that have occurred over the past 10 years was conducted. The analysis of the flooding of the river basin with the provision of satellite images is given. Based on the generalization of foreign and domestic experience, engineering and non-engineering measures to reduce the risk of flooding were proposed.

Plugging the ambient air monitoring gaps in India's national clean air programme (NCAP) airsheds

Building an effective clean air action plan for a city's air pollution problem requires an extensive network of monitoring stations to represent spatial and temporal patterns and an understanding of the sources contributing to the problem. In India, the National Clean Air Programme (NCAP) was launched in 2019 to develop clean air action plans for 131 non-attainment cities, which includes conducting source apportionment studies and establishing emission baselines. As of February 2023, only 39 cities have completed the apportionment studies. In this paper, we present the essential resources needed to strengthen the ambient air monitoring networks, for designing a representative airshed size, sampling size, and sampling frequency, to effectively track the progress made in the cities for better air quality. The NCAP cities were grouped into 104 airsheds (5.3% of the national area), collectively representing a total of 164 cities and a total population of 295 million (21% of the national total). Of these airsheds, 73 contain only one city; 18 contain two cities, and nine contain three cities. Four airsheds – Delhi, Mumbai, Indore, and Chandigarh contain 10, 8, 5, and 5 cities respectively. To measure and analyse particulate matter pollution, a total of 2118 sampling sites are recommended for the 104 airsheds. Cities could consider hybrid monitoring networks by complementing existing regulatory monitoring network with a high-density network of low-cost/sensor-grade monitors. An airshed level air quality management plan, an enhanced monitoring network, and consolidation of information on emission sources, are crucial for optimizing the clean air efforts under NCAP.

Deployment and Evaluation of a Network of Open Low-Cost Air Quality Sensor Systems

Low-cost air quality sensors have the potential to complement the regulatory network of air quality monitoring stations, with respect to increased spatial density of observations, however, their data quality continues to be of concern. Here we report on our experience with a small network of open low-cost sensor systems for air quality, which was deployed in the region of Stavanger, Norway, under Nordic winter conditions. The network consisted of AirSensEUR sensor systems, equipped with sensors for, among others, nitrogen dioxide and fine particulate matter. The systems were co-located at an air quality monitoring station, for a period of approximately six weeks. A subset of the systems was subsequently deployed at various roadside locations for half a year, and finally co-located at the same air quality monitoring station again, for a post-deployment evaluation. For fine particulate matter, the co-location results indicate a good inter-unit consistency, but poor average out-of-the-box performance (R2 = 0.25, RMSE = 9.6 μg m−3). While Köhler correction did not significantly improve the accuracy in our study, filtering for high relative humidity conditions improved the results (R2 = 0.63, RMSE = 7.09 μg m−3). For nitrogen dioxide, the inter-unit consistency was found to be excellent, and calibration models were developed which showed good performance during the testing period (on average R2 = 0.98, RMSE = 5.73 μg m−3), however, due to the short training period, the calibration models are likely not able to capture the full annual variability in environmental conditions. A post-deployment co-location showed, respectively, a slight and significant decrease in inter-sensor consistency for fine particulate matter and nitrogen dioxide. We further demonstrate, how observations from even such a small network can be exploited by assimilation in a high-resolution air quality model, thus adding value to both the observations and the model, and ultimately providing a more comprehensive perspective of air quality than is possible from either of the two input datasets alone. Our study provides valuable insights on the operation and performance of an open sensor system for air quality, particularly under challenging Nordic environmental conditions.