Fixed Monitoring Stations Research Articles

Smart city air quality management through leveraging drones for precision monitoring

Air pollution stands out as one of the most critical challenges impacting public health, the overall quality of life, and the pursuit of sustainable development in urban environments. Each existing air quality monitoring method carries inherent limitations alongside its advantages. This study addresses the drawbacks of both satellite-based and fixed monitoring stations by proposing a drone-based air quality monitoring solution. The entire urban area was surveyed using a multi-rotor drone equipped with sensors capable of measuring atmospheric components, resulting in the creation of high-resolution air pollution maps. The expansive urban area, exceeding the coverage capacity of a single flight, was systematically subdivided into subregions. Distinct measurements were conducted in each subregion, and a consolidated mapping was generated by combining the individual results obtained. The drone, operating autonomously, conducted precise measurements on a citywide scale, enhancing the consistency of pollutant mapping by eliminating potential errors associated with manual control. Pollution sources were identified throughout the city, and real-time data revealed how transportation-induced air pollution affected different regions. Environmental factors like wind and pressure differences were identified as influential in the varied spread of air pollutants across different parts of the city. The study underscores the pivotal role of transportation in urban air pollution, laying the groundwork for the development of targeted pollution control strategies in urban environments. Furthermore, the research validates the potential of UAVs in advancing the efficient management of smart cities, particularly through the monitoring and analysis of air pollution stemming from transportation.

Integrating land use and traffic to spatial prediction of particulate matter

With degrading air quality and inefficient monitoring systems, spatial prediction of pollutants has become necessary. Air pollution exposure causes significant impacts on human health in urban areas around the world. The current study focuses on developing a deep learning model for spatial prediction of particulate matter at locations where the monitored data is unavailable. Delhi, India, is selected as the study area due to its high air pollution and fewer fixed monitoring stations throughout the city. The parameters used for the prediction modeling included PM2.5 from fixed monitoring stations, PM2.5 from mobile sensors, meteorological parameters (atmospheric temperature and relative humidity), traffic flow data, and land use parameters. The data is taken for six months, from November 2022 to April 2023, at an interval of 15 min. Four buffer sizes are used to find the spatial variability. The ANN model is used for the spatial predictions using the above-mentioned parameters. K-fold cross-validation is used to validate the robustness of the model. The SHAP summary plot is used to identify the feature importance of the features used in the model. It is observed that the integration of the land use data and traffic data increases the prediction capability of the model.

Effect of short-term exposure to air pollution on hospital admission for cardiovascular disease: A time-series study in Xiangyang, China

BackgroundData on the relationship between short-term exposure to air pollution and cardiovascular diseases (CVDs) and the potential modifying factors are limited and inconsistent. ObjectiveTo explore the relationship between short-term exposure to air pollution and CVD risk, and potential modification effect factors. MethodA time series study was conducted on 52,991 hospital admissions for CVD from 2015 to 2019 in Xiangyang City, China. Air pollution data from four national fixed monitoring stations were collected to estimate exposure level in Xiangyang City. A quasi-Poisson generalized additive model incorporating a distributed lag nonlinear model was applied to evaluate the association between air pollution and CVD risk. The potential modification effect of sex, age, and season on the above associations was also evaluated. ResultsCVD risk was positively associated with air pollution. Peak associations in single lag day structures were observed for particulate matter ≤10 μm in aerodynamic (PM10; RR: 1.040, 95 % CI: 0.996–1.087), PM2.5 (1.025, 1.004–1.045), nitrogen dioxide (NO2; 1.074, 1.039–1.111), and sulfur dioxide (SO2; 1.079, 1.019–1.141) at Lag 0 and ozone (O3; 1.018, 1.004–1.031) at Lag 4. In cumulative lag day structures, the highest RRs were 1.225 (1.079,1.392) for PM10 at Lag 06, 1.054 (1.013, 1.098) for PM2.5 at Lag 03, 1.200 (1.119, 1.287) for NO2 at Lag 04, and 1.135 (1.025, 1.257) for SO2 at Lag 02. Moreover, the association between air pollution and CVD risk was modified by sex and age (P < 0.05). Females and individuals aged ≤65 years were more vulnerable to NO2 and had a higher CVD risk. ConclusionShort-term exposure to air pollution was positively associated with CVD risk. Moreover, sex and age could modify the effect of air pollution on CVD risk. Females and individuals aged ≤65 years had a higher NO2 exposure-induced CVD risk.

An Approach for Design of Air Quality Monitoring Systems

Nowadays, society has realized the dangers of air pollution and is actively implementing air quality monitoring systems. These systems are based either on the deployment of a small number of fixed monitoring stations using high-level sensors, or on many lower-quality sensors integrated into the city infrastructure. However, there is a lack of tools that would allow developing recommendations and techniques for selecting the type of air sensors. In this work this gap is partially filled.

Mobilising Uncertainties in Air Pollution Science in Copenhagen

In this short article we discuss three different ways of measuring air pollution in Copenhagen, Denmark, in relation to the potential for using technoscientific tools and expertise to influence public policymaking meant to curb pollution. Based upon a mix of data ranging from scientific literature and public reports to interviews with scientific and lay stakeholders, we outline how the introduction of Google’s Project Air View, in combination with an increase in citizen engagement in air pollution, has come to play a key role in the re-invigoration of local concerns over air pollution. Previously, expertise on the city’s air pollution has been the domain of established scientists operating fixed monitoring stations, but this recently stable relation between science and policy is currently being replaced by an assemblage of contrasting views on air pollutants. Our analysis suggests that the measurements of emerging pollutants by Google’s project and by citizens themselves have impelled policymakers in Copenhagen to accept, to engage with and act upon new scientific uncertainties. We see this as giving rise to a degree of humility where emerging modes of knowing air pollution are treated as complementary with established ones.

Context matters when using climate model projections for aquaculture

At present, specific guidance on how to choose, assess and interpret climate model projections for the aquaculture sector is scarce. Since many aspects of aquaculture production are influenced by the local farm-level environment, there is a need to consider how climate model projections can be used to predict potential future farming conditions locally. This study compared in-situ measurements of temperature and salinity from Norwegian salmon farms and fixed monitoring stations to simulations from a regional ocean climate model for multiple locations and depths in southern Norway. For locations considered in this study, a similar seasonal cycle in terms of phasing was visible for modelled and measured temperatures. For some depths and times of the year the modelled and measured temperatures were similar, but for others there were differences. The model tended to underestimate temperature. On occasion there were differences between average modelled and measured temperatures of several degrees and aquaculture users would need to consider the implications of using the modelled temperatures. As for salinity, the model does not include localized freshwater inputs, so the model overestimated salinity for locations close to shore and was not able to represent more brackish water conditions in shallower depths. It was not possible to draw a general conclusion as to whether the model was suitable for aquaculture purposes, as the similarities and differences between the modelled and measured values varied by variable, area, depth, and time. These findings made it clear that aquaculture users would have to implement a process to determine whether they could use climate model outputs for their specific purpose. A model vetting framework is presented that can be used to support decisions on the use of climate model projections for aquaculture purposes. The vetting framework describes four stages that can be used to establish the necessary context regarding the aquaculture requirements and model capabilities, and then check how the model is simulating the conditions of interest at farm sites. Although the focus was aquaculture, the findings are relevant for other sectors and the framework can guide use of climate models for more local-scale assessment and management in coastal locations.

Assessing the coastal hazard of Medicane Ianos through ensemble modelling

Abstract. On 18 September 2020, Medicane Ianos hit the western coast of Greece, resulting in flooding and severe damage at several coastal locations. In this work, we aim at evaluating its impact on sea conditions and the associated uncertainty through the use of an ensemble of numerical simulations. We applied a coupled wave–current model to an unstructured mesh, representing the whole Mediterranean Sea, with a grid resolution increasing in the Ionian Sea along the cyclone path and the landfall area. To investigate the uncertainty in modelling sea levels and waves for such an intense event, we performed an ensemble of ocean simulations using several coarse (10 km) and high-resolution (2 km) meteorological forcings from different mesoscale models. The performance of the ocean and wave models was evaluated against observations retrieved from fixed monitoring stations and satellites. All model runs emphasized the occurrence of severe sea conditions along the cyclone path and at the coast. Due to the rugged and complex coastline, extreme sea levels are localized at specific coastal sites. However, numerical results show a large spread of the simulated sea conditions for both the sea level and waves, highlighting the large uncertainty in simulating this kind of extreme event. The multi-model and multi-physics approach allows us to assess how the uncertainty propagates from meteorological to ocean variables and the subsequent coastal impact. The ensemble mean and standard deviation were combined to prove the hazard scenarios of the potential impact of such an extreme event to be used in a flood risk management plan.

The Representativeness of Outdoor Particulate Matter Concentrations for Estimating Personal Dose and Health Risk Assessment of School Children in Lisbon.

This study investigated the suitability of outdoor particulate matter data obtained from a fixed monitoring station in estimating the personal deposited dose. Outdoor data were retrieved from a station located within the urban area of Lisbon and simulations were performed involving school children. Two scenarios were applied: one where only outdoor data were used assuming an outdoor exposure scenario, and a second one where an actual exposure scenario was adopted using the actual microenvironment during typical school days. Personal PM10 and PM2.5 dose (actual exposure scenario) was 23.4% and 20.2% higher than the ambient (outdoor exposure scenario) PM10 and PM2.5 doses, respectively. The incorporation of the hygroscopic growth in the calculations increased the ambient dose of PM10 and PM2.5 by 8.8% and 21.7%, respectively. Regression analysis between the ambient and personal dose showed no linearity with R2 at 0.07 for PM10 and 0.22 for PM2.5. On the other hand, linear regression between the ambient and school indoor dose showed no linearity (R2 = 0.01) for PM10 but moderate (R2 = 0.48) for PM2.5. These results demonstrate that ambient data must be used with caution for the representativeness of a realistic personal dose of PM2.5 while for PM10 the ambient data cannot be used as a surrogate of a realistic personal dose of school children.

Spatial Distribution of PM10 and NO2 in Ambient Air Quality in Cape Town CBD, South Africa

Fixed air quality monitoring stations generally monitor the air quality in developing countries. However, this practice, in addition to being costly, inherently contains drawbacks associated with the inability to capture the spatial distribution of air pollutants. Against this limitation, it is necessary to employ flexible and dynamic monitoring techniques that are fundamental and influential in comprehending the spatial distribution of pollutants. Because of this, in recent times, the application of GIS as a monitoring technique has proved to be more efficient than using fixed monitoring stations. Therefore, to this end, the current study mapped the spatial distribution of PM10 and NO2 pollutants in Cape Town CBD using the GIS technique. Subsequently, the GIS monitoring technique revealed that both pollutants had high spatial distribution between 2017 and 2018, irrespective of the season. Furthermore, high exposure concentrations of PM10 were generally observed across the CBD in contrast to NO2 exposure levels, which were relatively low. To contextualize the findings, compared with other studies, the current research discovered that spatial distribution of air pollution is associated with meteorological conditions, such as wind speed and temperature, that traditional techniques of monitoring exposure can’t capture.

Ambient particulate matter (PM2.5) and adverse birth outcomes in Ho Chi Minh City, Vietnam

Exposure to fine particulate matter (PM2.5) has been documented to adversely affect birth outcomes, but the evidence is sparse in developing countries. This study assessed the associations between maternal PM2.5 exposure and adverse birth outcomes, i.e., birth weight (BW) decrease, term low birth weight (LBW), and preterm birth (PTB) in Ho Chi Minh City (HCMC), Vietnam.Between 2016–2019, 163,868 women with singleton pregnancies in HCMC were recruited in the study. The PM2.5 dataset was collected from two available fixed monitoring stations. Five different windows of exposure to PM2.5 were calculated, including the first month of pregnancy, first trimester, second trimester, third trimester, and entire pregnancy. Linear regression was applied to evaluate the association between BW decrease and PM2.5 exposure, and logistic regression was employed to study the risk of term LBW and PTB.The study indicated that prenatal exposure to PM2.5 decreased BW and increased the risk of PTB. Each 10 µg/m3 increase in PM2.5 during the second trimester lowered with 11.771 g the BW (95% confident interval - CI: 5.246 – 18.296), and increased with 23.1% the risk of PTB (Odds ratio – OR = 1.231, 95%CI: 1.136 – 1.336). However, the association between maternal exposure to PM2.5 and the risk of term LBW was not statistically significant.Our study showed that maternal exposure to PM2.5 was associated with lower BW and an increase in the risk of PTB. Reducing exposure to air pollutants, in particular ultrafine particles (PM2.5) for pregnant women will improve infants’ health.

Women with preeclampsia exposed to air pollution during pregnancy: Relationship between oxidative stress and neonatal disease - Pilot study

Oxidative imbalance as a pathophysiological mechanism has been reported as an adverse outcome in pregnant women who develop preeclampsia and in their newborns. Furthermore, emerging evidence suggests the same mechanism by which air pollutants may exert their toxic effects. Therefore, the objective of the study was to evaluate the biomarkers of oxidative stress and their relationship with neonatal disease in premature newborns from mothers with preeclampsia exposed to air pollution during pregnancy. The data of air pollutants (PM2.5, PM10 and ozone) were collected at fixed monitoring stations. Oxidative and antioxidant status markers were obtained through special techniques in women with preeclampsia and in umbilical cord blood of their premature newborns. The oxidative stress markers were significantly higher in women with preeclampsia and their newborns who were exposed to higher levels of ambient air pollutants in the first and second trimester of pregnancy. Neonatal diseases are associated with preeclampsia in pregnancies, specifically intrauterine growth restriction (IUGR) and necrotizing enterocolitis (NEC). A significant correlation was identified in the levels of prooxidant agents and antioxidant enzyme activity in the presence of neonatal diseases associated with preeclampsia. There is increased oxidative damage in both the maternal and fetal circulation in women who develop preeclampsia exposed to air pollution during pregnancy. Therefore, these pregnancies complicated by preeclampsia have a greater adverse outcome as neonatal disease in the preterm infant.

Traffic-Related Air Pollution and Ground-Level Ozone Associated Global DNA Hypomethylation and Bulky DNA Adduct Formation.

Studies have indicated that air pollution, including surface-level ozone (O3), can significantly influence the risk of chronic diseases. To better understand the carcinogenic mechanisms of air pollutants and identify predictive disease biomarkers, we examined the association between traffic-related pollutants with DNA methylation alterations and bulky DNA adducts, two biomarkers of carcinogen exposure and cancer risk, in the peripheral blood of 140 volunteers-95 traffic police officers, and 45 unexposed subjects. The DNA methylation and adduct measurements were performed by bisulfite-PCR and pyrosequencing and 32P-postlabeling assay. Airborne levels of benzo(a)pyrene [B(a)P], carbon monoxide, and tropospheric O3 were determined by personal exposure biomonitoring or by fixed monitoring stations. Overall, air pollution exposure was associated with a significant reduction (1.41 units) in global DNA methylation (95% C.I. -2.65-0.04, p = 0.026). The decrement in ALU repetitive elements was greatest in the policemen working downtown (95% C.I. -3.23--0.49, p = 0.008). The DNA adducts were found to be significantly increased (0.45 units) in the municipal officers with respect to unexposed subjects (95% C.I. 0.02-0.88, p = 0.039), mainly in those who were controlling traffic in downtown areas (95% C.I. 0.39-1.29, p < 0.001). Regression models indicated an increment of ALU methylation at higher B(a)P concentrations (95% C.I. 0.03-0.60, p = 0.032). Moreover, statistical models showed a decrement in ALU methylation and an increment of DNA damage only above the cut-off value of 30 µg/m3 O3. A significant increment of 0.73 units of IL-6 gene methylation was also found in smokers with respect to non-smokers. Our results highlighted the role of air pollution on epigenetic alterations and genotoxic effects, especially above the target value of 30 µg/m3 surface-level O3, supporting the necessity for developing public health strategies aimed to reduce traffic-related air pollution molecular alterations.

Dispersion of SO2 emissions in a gas refinery by AERMOD modeling and human health risk: a case study in the Middle East

ABSTRACT This study aimed to model the dispersion of emitted SO2 from stacks and flares in one of the largest Gas Refinery Companies in the Middle East . Pollutant emission coefficients and air pollution’s various sources contributions were determined based on the collected data after measuring SO2 concentrations in a fixed monitoring station (stack) and across different distances from it for a year. The SO2 release pattern was simulated, and annual pollutant concentrations in average periods of 1-hr and 24-hr were predicted using AERMOD 8.9.0. The maximum simulated ambient SO2 were 27,447 and 4592 µg/m3 in average sampling times of 1-hr and 24-hr, respectively. The hazard quotient of 95% percentile for children, teenagers, and adults due to inhalation of SO2 was more than one.The maximum concentration of SO2 in the 1-hour and 24-hour period in the study area was higher than the amount introduced by Iran’s clean air standard and the WHO standard.

A hydrodynamic model–based approach to assess sampling approaches for dissolved oxygen criteria in the Chesapeake Bay

Technological advances in water quality measurement systems have provided the potential to expand high-frequency observations into coastal monitoring programs. However, with limited resources for monitoring budgets in natural waters that exhibit high temporal and spatial variability in water quality, there is a need to identify the locations and time periods where these new technologies can be deployed for maximum efficacy. To advance the capacity to make quantitative and objective decisions on the selection of monitoring locations and sampling frequency, we combined high-resolution numerical model simulations and multi-frequency water quality measurements to conduct a power analysis comparing alternative sampling designs in the assessment of water quality in the Chesapeake Bay. Specifically, we evaluated candidate monitoring networks that deployed both conventional long-term fixed station monitoring in deep channel areas and short-term continuous monitoring technologies in near-shore, shallow areas to assess 30-day dissolved oxygen criteria in two Bay tributaries. We conducted a cumulative frequency diagrams analysis to quantify the accuracy of each monitoring scheme in evaluating compliance with respect to the model. We used a Monte Carlo simulation to incorporate the spatial and temporal uncertainty of criteria failure. We found that additional long-term biweekly channel and short-term continuous shallow sampling efforts can lead to statistically unbiased and improved assessments at local spatial extents (less than 0.2 proportion of the assessed water body), especially when additional sampling is added at stations representing hypoxic water areas. Stations that represented seaward regions of the tributaries were more valuable in maintaining unbiased assessments of dissolved oxygen criteria attainment. This analysis highlights the importance of statistical evaluation of ongoing monitoring programs and suggests an approach to identify efficient deployments of monitoring resources and to improve assessment of other water quality metrics in estuarine ecosystems.

IoT-based air pollution monitoring system

Air pollution seriously damages the ecological environment and affects the health of humans, animals and plants. In order to prevent and control air pollution, air quality monitoring has been proposed to accurately monitor the concentration of pollutants and abnormal conditions in a region, which can purify the air. However, conventional fixed monitoring stations are complex, expensive and power hungry, these characteristics lead to monitoring stations being unsuitable for most areas. Here an internet of things (IoT)-based air pollution monitoring system is reported including architecture, principles and monitoring objects, providing detailed information for effective environmental management. It manages the current status and trends of regional air pollution in a simple, portable and cost-effective way. The technologies and applications involved in IoT-based air pollution monitoring systems are also presented, contributing to the development of IoT technologies, cloud computing technologies and smart city. The rapid spread of such monitoring systems would add greenhouse gases, photochemical smog and light detection and ranging (LIDAR) gases to the group of objects to be monitored in the future.

Space-Time Prediction of PM2.5 Concentrations in Santiago de Chile Using LSTM Networks

Currently, air pollution is a highly important issue in society due to its harmful effects on human health and the environment. The prediction of pollutant concentrations in Santiago de Chile is typically based on statistical methods or classical neural networks. Existing methods often assume that historical values are known at a fixed geographic point, such that air pollution can be predicted at a future hour using time series analysis. However, these methods are inapplicable when it is necessary to know the pollutant concentrations at every point of the space. This work proposes a method that addresses the space-time prediction of PM2.5 concentration in Santiago de Chile at any spatial points through the use of the LSTM recurrent network model. In particular, by considering historical values of air pollutants (PM2.5, PM10 and nitrogen dioxide) and meteorological variables (temperature, wind speed and direction and relative humidity), measured at fixed monitoring stations, the proposed model can predict PM2.5 concentrations for the next 24 h in a new location where measurements are not available. This work describes the experiments carried out, with particular emphasis on the pre-processing step, which constitutes an important factor for obtaining relatively good results. The proposed multilayer LSTM model obtained R2 values equal to 0.74 and 0.38 in seven stations when considering forecasts of 1 and 24 h, respectively. As future work, we plan to include more input variables in the proposed model and to use attention-based networks.

Body Mass Index Modulates the Impact of Short-Term Exposure to Air Particulate Matter on High-Density Lipoprotein Function.

Air particulate matter (PM) exposure has been associated with increased cardiovascular risk, especially in obesity. By triggering inflammation and oxidative stress, PM could impact atheroprotection by high-density lipoproteins (HDL). The aim of the study was to assess the relationship between short-term exposure to PM and HDL function, and the modifying effect of body mass index (BMI). Daily exposures to PM10 and PM2.5 of 50 subjects with overweight/obesity and 41 healthy volunteers with BMI < 30 kg/m2 were obtained from fixed monitoring stations. HDL function was assessed as promotion of nitric oxide (NO) release by endothelial cells and reduction in cholesterol in macrophages. HDL-induced NO release progressively declined with the increase in BMI. No association was found between HDL function and PM exposure, but a modifying effect of BMI was observed. The positive association between PM10 exposure at day −1 and NO production found at normal BMI values was lost in participants with higher BMI. Similar results were obtained for the reduction in macrophage cholesterol. The loss of the compensatory response of HDL function to PM exposure at increasing BMI levels could contribute to the endothelial dysfunction induced by PM and help to explain the susceptibility of subjects with obesity to air pollution.

Short-term ambient air pollution exposure and risk of atrial fibrillation in patients with intracardiac devices

Introduction: Atrial fibrillation (AF) is the most common cardiac arrhythmia and is associated with substantial morbidity and mortality. Short term exposure to fine particulate matter (PM2.5) air pollution has been causally linked to higher risk of cardiovascular disease, but the association with atrial fibrillation (AF) is less clear. Methods: We conducted a time-stratified case-crossover study to estimate the association between short-term air pollution levels and risk of AF episodes. The episodes were identified among patients with paroxysmal AF and an intracardiac devices able to register and store AF episodes. We obtained air pollution and temperature data from fixed monitoring stations and used conditional logistic regression to quantify the association of fine particulate matter (PM2.5) and ozone (O3) with onset of AF episodes, adjusting for temperature and public holidays. Results: We analyzed 584 episodes of AF from 91 participants and observed increased risk of AF episodes with PM2.5 levels for the 48-72 hours lag (OR 1.05 CI [1.01,1.09] per IQR)] and 72-96 hour (OR 1.05 CI [1.00,1.10] per IQR). Our results were suggestive of an association between O3 levels and AF episodes during the warm season. We did not observe any statistically significant associations for PM10 nor NO2. Conclusion: Short-term increases in PM2.5 in a low-pollution level environment were associated with increased risk of AF episodes in a population with intracardiac devices. Our findings add to the evidence of a potential triggering of AF by short term increases in air pollution levels, well below the new WHO air quality guidelines.

Short-term ambient air pollution exposure and risk of atrial fibrillation in patients with intracardiac devices.

Introduction:Atrial fibrillation (AF) is the most common cardiac arrhythmia and is associated with substantial morbidity and mortality. Short-term exposure to fine particulate matter (PM2.5) has been causally linked to higher risk of cardiovascular disease, but the association with atrial fibrillation (AF) is less clear.Methods:We conducted a time-stratified case-crossover study to estimate the association between short-term air pollution levels and risk of AF episodes. The episodes were identified among patients with paroxysmal AF and an intracardiac devices able to register and store AF episodes. We obtained air pollution and temperature data from fixed monitoring stations and used conditional logistic regression to quantify the association of PM2.5, particulate matter (PM10), nitrogen dioxide (NO2) and ozone (O3) with onset of AF episodes, adjusting for temperature and public holidays.”.Results:We analyzed 584 episodes of AF from 91 participants and observed increased risk of AF episodes with PM2.5 levels for the 48–72 hours lag (OR 1.05; CI [1.01,1.09] per IQR)] and 72–96 hours (OR 1.05 CI [1.00,1.10] per IQR). Our results were suggestive of an association between O3 levels and AF episodes during the warm season. We did not observe any statistically significant associations for PM10 nor NO2.Conclusion:Short-term increases in PM2.5 in a low-pollution level environment were associated with increased risk of AF episodes in a population with intracardiac devices. Our findings add to the evidence of a potential triggering of AF by short-term increases in air pollution levels, well below the new WHO air quality guidelines.

Gulf of Naples Advanced Model (GNAM): A Multiannual Comparison with Coastal HF Radar Data and Hydrological Measurements in a Coastal Tyrrhenian Basin

High-resolution modelling systems have increasingly become an essential requirement to investigate ocean dynamics over a wide range of spatial and temporal scales, and to integrate the punctual ocean observations. When applied in coastal areas, they also have the potential to provide a detailed representation of transport and exchange processes at the sub-basin scale. This paper presents a validation exercise between the surface fields generated by the regional ocean modeling system (ROMS), developed for the Tyrrhenian Sea and downscaled for the Gulf of Naples (GNAM Gulf of Naples advanced model), and a 4 year-long (2009–2012) record of high-frequency radar (HFR) data. The comparison between hourly and seasonal model results and HFR surface fields is focused on the Gulf of Naples (GoN), where an observational network of three HFR sites has been operational since 2004, and on a specific subdomain characterized by the presence of the Sarno river, a long-term ecological research station (LTER-MC) and one important canyon area. An evaluation on a transect delimiting inshore–offshore zones in the GoN is also presented. The GNAM model was also compared with in situ hydrological parameters of temperatures and salinities retrieved at the LTER-MC fixed monitoring station. According to the skill metrics, basic circulation features are accurately reproduced by the circulation model, despite some model drawbacks in terms of increment of energy content in the surface current field occurring during specific seasonal events. The results allow us to identify potential model errors and to suggest useful improvements, the outcome also confirms the unique capability of HF radar systems to provide fine-scale measurements for the validation of numerical models and to counterbalance the lack of high-resolution measurements in coastal areas.