Station Data Research Articles

Atmospheric new particle formation identifier using longitudinal global particle number size distribution data.

Atmospheric new particle formation (NPF) is a naturally occurring phenomenon, during which high concentrations of sub-10 nm particles are created through gas to particle conversion. The NPF is observed in multiple environments around the world. Although it has observable influence onto annual total and ultrafine particle number concentrations (PNC and UFP, respectively), only limited epidemiological studies have investigated whether these particles are associated with adverse health effects. One plausible reason for this limitation may be related to the absence of NPF identifiers available in UFP and PNC data sets. Until recently, the regional NPF events were usually identified manually from particle number size distribution contour plots. Identification of NPF across multi-annual and multiple station data sets remained a tedious task. In this work, we introduce a regional NPF identifier, created using an automated, machine learning based algorithm. The regional NPF event tag was created for 65 measurement sites globally, covering the period from 1996 to 2023. The discussed data set can be used in future studies related to regional NPF.

Extreme Precipitation Events During the Wet Season of the South America Monsoon: A Historical Analysis over Three Major Brazilian Watersheds

Most of South America, particularly the region between the southern Amazon and southeastern Brazil, as well as a large part of the La Plata Basin, has its climate regulated by the South American Monsoon System. Extreme weather and climate events in these areas have significant socioeconomic impacts. The Madeira, São Francisco, and Paraná river basins, three major watersheds in Brazil, are especially vulnerable to wet and drought periods due to their importance as freshwater ecosystems and sources of water for consumption, energy generation, and agriculture. The scarcity of surface meteorological stations in these basins makes meteorological studies challenging, often using reanalysis and satellite data. This study aims to identify extreme weather (wet) and climate (wet and drought) events during the extended wet season (October to March) from 1980 to 2022 and evaluate the performance of two gridded datasets (CPC and ERA5) to determine which best captures the observed patterns in the Madeira, São Francisco, and Paraná river basins. Wet weather events were identified using the 95th percentile, and wet and drought periods were identified using the Standardized Precipitation Index (SPI) on a 6-month scale. In general, CPC data showed slightly superior performance compared to ERA5 in reproducing statistical measures. For extreme day precipitation, both datasets captured the time series pattern, but CPC better reproduced extreme values and trends. The results also indicate a decrease in wet periods and an increase in drought events. Both datasets performed well, showing they can be used in the absence of station data.

Full-coverage estimation of CO2 concentrations in China via multisource satellite data and Deep Forest model.

Monitoring China's carbon dioxide (CO2) concentration is essential for formulating effective carbon cycle policies to achieve carbon peaking and neutrality. Despite insufficient satellite observation coverage, this study utilizes high-resolution spatiotemporal data from the Orbiting Carbon Observatory 2 (OCO-2), supplemented with various auxiliary datasets, to estimate full-coverage, monthly, column-averaged carbon dioxide (XCO2) values across China from 2015 to 2022 at a spatial resolution of 0.05° via the deep forest model. The 10-fold cross-validation results indicate a correlation coefficient (R) of 0.95 and a determination coefficient (R²) of 0.90. Validation against ground-based station data yielded R values of 0.93, and R² values reached 0.81. Further validation from the Greenhouse Gases Observing Satellite (GOSAT) and the Copernicus Atmosphere Monitoring Service Reanalysis dataset (CAMS) produced R² values of 0.87 and 0.80, respectively. During the study period, CO2 concentrations in China were higher in spring and winter than in summer and autumn, indicating a clear annual increase. The estimates generated by this study could potentially support CO2 monitoring in China.

Development and Evaluation of Machine Learning Models for Air-to-Land Temperature Conversion Using the Newly Established Kunlun Mountain Gradient Observation System

Mountainous land types are characterized by a scarcity of observational data, particularly in remote areas such as the Kunlun Mountains, where conventional Automatic Weather Stations (AWSs) typically do not record land surface temperature (LST) data. This study aims to develop and evaluate models for converting air temperature (TA) to LST using newly established meteorological station data from the Kunlun Mountain Gradient Observation System, thereby providing time-continuous LST data for AWSs. We constructed a conceptual model to explore the relationship between 1.5 m TA and LST and instantiated it using three machine learning algorithms: Support Vector Machine (SVR), Convolutional Neural Network (CNN), and CatBoost. The results demonstrated that the CatBoost algorithm outperformed the others under complex terrain and climatic conditions, achieving a coefficient of determination (R2) of 0.997 and the lowest root mean square error (RMSE) of 0.627 °C, indicating superior robustness and accuracy. Consequently, CatBoost was selected as the optimal model. Additionally, this study analyzed the spatiotemporal distribution characteristics of cloud cover in the Kunlun Mountain region using the MOD11A1 product and assessed the uncertainties introduced by the 8-day average compositing method of the MOD11A2 product. The results revealed significant discrepancies between the monthly average LST derived from polar-orbiting satellites and the hourly composite monthly LST measured on-site or under ideal cloud-free conditions. These differences were particularly pronounced in high-altitude regions (4000 m and above), with the greatest differences occurring in winter, reaching up to 10.2 °C. These findings emphasize the importance of hourly LST calculations based on AWSs for accurately assessing the spatiotemporal characteristics of LST in the Kunlun Mountains, thus providing more precise spatiotemporal support for remote sensing applications in high-altitude regions.

Development of Rainfall Intensity Duration Frequency Curves for Debre Tabor Town, Ethiopia Using Non-stationary Method

Stationary rainfall intensity duration frequency curves have historically influenced urban infrastructure designs. In contrast to the stationary model, which takes constant parameters into account throughout the observation periods, the non-stationary method takes into account changes in the extreme parameters that determine the distribution of precipitation over time. The parameters were estimated using maximum likelihood estimator method. The best model were computed using the R-studio software by comparing information criteria then model parameters, return levels, rainfall intensity are computed. The National Meteorological Agency, situated in Addis Ababa, Ethiopia, provided the essential historical rainfall data of the Debre Tabor rainfall station for this study, Tests and trends were looked for in the rainfall data. Due to its ability to produce the lowest Akaike, corrected Akaike information criteria, and diagnosis test of goodness of fitness Model Type-MV was chosen for Debre Tabor stations. The parameters of the best models were used to forecast the return levels for each of the following return periods: 2, 5, 10, 25, 50, and 100 years. Because the non-stationary technique has varied intensity levels over time, the annual maximum rainfall from the best appropriate model was calculated using its exceedance probability. Using the 95% of exceedance of the return level, the highest rainfall in each fit was determined. In comparison to the stationary model, the non-stationary model produced higher rainfall intensity values. Therefore, when developing IDF curves, the non-stationary approach should be taken into consideration.

Estimation of soil loss and sediment yield by using the modified RUSLE model in the Indus River basin, including the quantification of error and uncertainty in remote-sensing images

Context Indus River is the cradle of Pakistani lifeline, and its lower reaches are prone to soil loss owing to bank erosion. Aims The aim was to investigate the sediment yield in the Lower Indus River Basin (LIRB), while addressing challenges related to error or uncertainty in remote-sensing data. Methods We employed a modified revised universal soil loss equation (RUSLE) model, integrating high-resolution digital elevation model (DEM) and calibrated Climate Hazards Group InfraRed Precipitation with station data (CHIRPS). Additional data layers, including land use, soil and cropping data, were also utilised. Key results The extent of actual soil erosion ranges from minimum to maximum erosion; 38.9% area lies in the range >50 Mg ha‒1 year‒1, whereas 23.2% area lies in the range of 0–10 Mg ha‒1 year‒1, and 18.1% area lies in the range of 10–20 Mg ha‒1 year‒1. Conclusions The study identifies critical erosion areas and tackles uncertainties in remote-sensing data. The spatial analysis showed that higher distribution sediment erosion along the channel flow direction from the northern part of LIRB to the Arabian Sea. Implications The findings have provided critical information for policymakers and water managers to implement effective measures to reduce erosion, maintain soil integrity and promote the sustainability of the Indus River system.

Comparison of Multiple Methods for Supraglacial Melt-Lake Volume Estimation in Western Greenland During the 2021 Summer Melt Season

Supraglacial melt-lakes form and evolve along the western edge of the Greenland Ice Sheet and have proven to play a significant role in ice sheet surface hydrology and mass balance. Prior methods to quantify melt-lake volume have relied upon Landsat-8 optical imagery, available at 30 m spatial resolution but with temporal resolution limited by satellite overpass times and cloud cover. We propose two novel methods to quantify the volume of meltwater stored in these lakes, including a high-resolution surface DEM (ArcticDEM) and an ablation model using daily averaged automated weather station data. We compare our methods to the depth-reflectance method for five supraglacial melt-lakes during the 2021 summer melt season. We find agreement between the depth-reflectance and DEM lake infilling methods, within +/−15% for most cases, but our ablation model underproduces by 0.5–2 orders of magnitude the volumetric melt needed to match our other methods, and with a significant lag in meltwater onset for routing into the lake basin. Further information regarding energy balance parameters, including insolation and liquid precipitation amounts, is needed for adequate ablation modelling. Despite the differences in melt-lake volume estimates, our approach in combining remote sensing and meteorological methods provides a framework for analysis of seasonal melt-lake evolution at significantly higher spatial and temporal scales, to understand the drivers of meltwater production and its influence on the spatial distribution and extent of meltwater volume stored on the ice sheet surface.

Mapping Climatic Regions of the Cerrado: General Patterns and Future Change

ABSTRACTThe Cerrado biome, renowned for its biodiversity and threatened by rapid land transformation, encompasses a vast savanna ecosystem in Brazil. The region is characterised by a seasonal climate, influenced by a myriad of meteorological systems creating diverse and non‐homogeneous rainfall regimes across the region. To account for this heterogeneity, we propose a novel classification of the Cerrado using rainfall data to delineate three distinct climatic regions: Eastern, Southern and Central‐West Cerrado. The Eastern region exhibited the driest and most seasonal climate, marked by high vapour pressure deficit (VPD) and predominantly open‐canopy vegetation. Conversely, the Southern region, characterised by lower seasonality, boasts a higher proportion of forest cover and lower mean VPD. The Central‐West region, encompassing diverse landscapes, featured areas with higher precipitation levels, particularly along the Amazônia border. Furthermore, we conducted trend analyses on observed station data and used CMIP6 models to evaluate future scenarios under differing emissions trajectories. While observed trends in mean rainfall were marginal, VPD demonstrated a notable upward trend of approximately 1% annually throughout the biome. Climate models indicated a substantial drying close to the Amazônia border, and wetter conditions in the southeast. All Cerrado regions are anticipated to experience amplified seasonality and VPD, with VPD projected to surge by approximately 30% (60%) under low (high) emissions scenarios by the end of the century. Notably, the transition from the dry to wet season was the most affected. Our study provides critical insights on how the climatic heterogeneity of the Cerrado shapes vegetation structure distribution and how future changes will exacerbate water stress throughout the biome. These findings underscore the importance of understanding climate variability for effective conservation and management strategies in the region.

Impact of Tonga volcanic eruption on the ionosphere based on ground-based GNSS and COSMIC occultation data

The eruption of large volcanoes may potentially disturb the upper atmosphere, causing disturbances in ionospheric plasma and triggering irregular changes in the Total Electron Content (TEC). The GNSS ionospheric inversion method has been used to analyze ionospheric anomalies caused by the volcano, while it was limited to the spatial distribution of ground-based GNSS. The joint observation with space-based GNSS can further improve the spatio-temporal distribution of ionospheric monitoring. The Tonga volcano erupted on January 15th, 2022, is the largest volcanic eruption since the beginning of the 21st century and generated intense acoustic, atmospheric gravity waves and ionospheric disturbance. In this paper, we used the GIM products, ground-based GNSS station data around the volcanic eruption point and COSMIC occultation data to analyze the short-term TEC change. The experiments showed that: the average TEC of GIM grid data on eruption day dropped by 7.5 TECu compared to the last day which was substantially larger than other three days. The TEC extracted from nearby tracking stations was smaller than GIM and the difference was inversely proportional to the distance between the station and the volcanic eruption site. The average daily ionospheric TEC obtained from the nearest TONG station was the biggest, as much as 3.44 TECu lower. The peak electron density of COSMIC occultation data gradually diminished and then recovered with TEC about 7.32 TECu lower than GIM products. The experments showed that there was a good consistency in the TEC obtained from space-based and ground-based GNSS, the Tonga volcano produced a huge TEC change over 10 TECu or even bigger. As a global grid product, GIM underestimated ionospheric changes due to its limited spatial resolution, which needs further improvement.

Análise de cluster e regionalização hidrológica para Estados brasileiros

ABSTRACT Streamflow data from gauging stations are essential for effective water resources management. However, some regions in Brazil lack the necessary data. Hydrological regionalization is an alternative technique for obtaining data such regions. However, not all regions in Brazil have defined hydrological regionalization models, including the state of Goiás and the Brazilian Federal District. The objective of this study was to develop a hydrological regionalization methodology based on the separation of hydrologically homogeneous regions and multiple linear regression, using a Geographic Information System (GIS) program. Historical series data were used to calculate reference flows with 90 or 95% duration over time in the watercourse (Q90 and Q95) and the mean flow (Ǭ). Rain gauge station data were used to calculate the mean annual rainfall in each watershed through spatial interpolation by ordinary kriging. Subsequently, the physiographic characteristics of each watershed were calculated. The hydrologically homogeneous regions were delimited based on these data using cluster analysis, which identified seven hydrologically homogeneous regions in Goiás, two of them belonging to the Federal District. Multiple regression allowed the development of seven regionalization models. Models for regions 1, 3, 4, 5, and 7 showed better performance.

APPLICATION OF GLOBAL CIRCULATION MODELS (GCMS) TO PREDICT RAINFALL AND TEMPERATURE VARIABILITIES IN KANO STATE, NIGERIA

The study explored present and future climate scenarios of Kano using Global Circulation Models (GCMs). The metrological data from 12 locations were used for calibration of Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS). The historical record of 10 years was used for both temperatures and rainfall data which represent a period of 2010 to 2019. The future climate projections were made for 2041 to 2060 centered in the 2050s under RCP2.6 and RCP8.5 emission scenarios. The spatial analysis was conducted using the Inverse Distance Weighted (IDW) tool in an ArcGIS environment. The result shows that the temperature of Kano has increased relative to baseline condition by 1.025oC (2.45%) and 4.6 oC (10.98%) under RCP2.6 and RCP8.5 scenarios respectively. However, the annual rainfall was found to decrease from the historical amount by -37.9 mm (5%), and -68.2 mm (9%) under RCP2.6 and RCP8.5 emission scenarios. Moreover, the spatial analysis indicated that there was climate variability across the width and breadth of the state. The study concludes that the climate change effect will cause the temperature of Kano to rise considerably in the future with the consequences of decreased rainfall amount. The findings recommend that policymakers should deploy adaptation strategies to reduce the adverse effects that might be experienced.

Animal performance over 16 years after implementing a lucerne grazing system on Bog Roy Station - a case study

In 2023, the animal performance data for Bog Roy station show 151 t weaned lamb is being produced from 4272 ewes plus 9.0 t from hogget lambs. The ability to mate merino hoggets has resulted from increased feed supply that means mixed age ewes are now consistently averaging 65.6±1.17 kg/hd at mating (2019-2023). This has enabled an emphasis on two-tooth weight at mating, which has also increased 0.74±0.14 kg/hd/yr (R2 = 0.64) from 52 kg/hd in 2012 to 61 kg/hd in 2023. Consequently, scanning is consistently ~167±1.59% for the mixed age ewes (2012-2023) and increased from 113% (2008) to 154% (2023) in the two-tooths. Lamb wastage for the mixed age ewes has decreased 0.70±0.08%/yr (R2 = 0.88) to 15% in 2023 and, although more variable (R2 = 0.13), the wastage in two-tooths has also reduced from 32% (2014) to 21% (2023). Lamb wastage is negatively related to two-tooth weight at set-stocking, so feeding young stock is a priority. Since 2016, pre-weaning merino lamb growth rates have averaged 286±4.39 g/hd/d with weaning now at 79.3±2.56 days. This consistency coincided with the addition of 210 ha of feed under pivot irrigation that was initially sown in red and white clover and is now in lucerne/grass mixes. The additional feed has meant wool production has remained constant throughout the 15 years of pasture development. Cow numbers have been used to buffer the changes in pasture supply as the dryland and pivot development occurred. These have now increased to their highest level since the development began. The major change in farm income has resulted from the earlier sale of more lambs at a higher average price because they are heavier due to the extra feed. This unique long-term dataset of on-farm performance provides a commercial example of transformational change that has resulted from changes in the farm system, driven by direct feeding of dryland lucerne and irrigation development focused on growing young stock in this 420 mm rainfall environment.

Spatiotemporal wind speed forecasting using conditional local convolution and multidimensional meteorology features

Wind speed prediction is crucial for precisely wind power forecasting and reduced maintenance costs. Highland regions, which possess a considerable wind potential, present complex meteorological conditions, making wind speed prediction challenging. Traditional weather forecasting relies on complex statistical methods and extensive prior knowledge. While recent deep learning models have improved prediction accuracy, they often assume uniform influence weight structure, limiting model effectiveness. This study introduces an enhanced Conditional Local Convolution Recurrent Network (CLCRN) model to improve spatiotemporal wind speed forecasting using multidimensional meteorological inputs such as temperature, pressure, and dew point, alongside wind components. This model addresses uniform influence model weight issue by redesigning convolution kernels to better capture local meteorological features and integrating multiple influencing factors. Our model consistently achieves lower Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) values across various prediction intervals (3, 6, 9, and 12 h) compared to other models, supported by the meteorological station data from 2019 to 2021. Furthermore, the spatial distribution of the local convolution weights aligns with local wind velocity patterns in Inner Mongolia, enhancing model interpretability. These results demonstrate potential for practical applications in renewable energy planning and wind dynamics simulation.

Automatic Segmentation Model and Parameter Extraction Algorithm for Lightning Whistlers

AbstractBased on the magnetic field data recorded by the ZH‐1 electromagnetic satellite, we cerat a training set of 1,300 spectrograms containing the dispersion spectrum of lightning whistlers (LW). The Segment Anything Model (SAM) in the field of image segmentation is trained through the training set to obtain a fine‐tuned SAM model that can be used to detect and segment the dispersion spectrum of LW at pixel level. All track regions of LW are effectively separated from other non‐lightning whistlers regions in the spectrograms after being segmented by the model. The segmentation effect is excellent and detection accuracy is 96.89%, which is better than the previous segmentation model for LW based on ground station data. Then we apply the traditional image processing methods to extract the dispersion spectrum of LW one by one, and develop an algorithm to automatically extract the physical parameters of each LW. The root mean square error between the automatically extracted dispersion parameter and the manually extracted dispersion parameter is only 0.1654 s1/2. The model and algorithm studied in this paper are employed to analyze the dispersion of LW received by the ZH‐1 satellite over China. It is found that the whistlers dispersion received by satellites during summer in the northern hemisphere and summer in the southern hemisphere shows opposite trends with receiving latitude. Both trends can be explained by the relationship between the dispersion and the length of propagation paths of LW.

Variation Characteristics of Actual Evapotranspiration and Uncertainty Analysis of Its Response to Local Climate Change in Arid Inland Region of China

Exploring the variation characteristics of actual evapotranspiration (ETa) and its response to climate change in the arid inland region of China is of great significance for strengthening regional water resources management and maintaining ecological environment security and stability. Taking the Dulan River Basin as the research area, based on the meteorological data from the Wulan Station and hydrological data from the Shanggaba Station from 1981 to 2020, the variation characteristics of ETa at the annual scale were analyzed. The ETa estimation model and joint distribution model of P and potential evapotranspiration (ET0) was constructed based on climate factors, and the uncertainty of ETa response to climate change was explored with the water balance method, multiple linear regression, marginal distribution function, Copula function, and Monte Carlo algorithm. The results showed that the multi-year mean value of ETa in the study area was 261.6 mm, and the interannual process showed an insignificant upward trend, and had no abrupt change during the period. There were two obvious main cycles, which were 19-year periodic changes on the 30-year time scale and 6-year periodic changes on the 9-year time scale. The ETa estimation model based on precipitation (P) and ET0 had good simulation accuracy. The optimal marginal distributions of P and ET0 were Pearson-III (P-III) distribution and Generalized Extreme Value (GEV) distribution, respectively. The Copula joint distribution probability density of P and ET0 was a symmetric saddle-shaped distribution. ETa showed an inverted ‘S’ distribution with the change in joint guarantee rate of P and ET0, ranging from 116.9 mm to 498.6 mm. ETa was an interval range under a certain joint guarantee rate. The research results can provide support for the assessment of ETa, and help to further understand the driving mechanism of climate change on ETa in the arid inland region of China.

Geographically distributed trend and variability analysis of rainfall over the state of Haryana, India, using Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) data dataset

Geographically distributed trend and variability analysis of rainfall over the state of Haryana, India, using Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) data dataset

Spatio‐Temporal Variations and El Niño Modulation of Meteorological Droughts in Malaysia

ABSTRACTMeteorological droughts in Malaysia have significantly impacted critical sectors such as agriculture, water resources, health, the environment, tourism and various socio‐economic sectors, affecting the population's livelihood and well‐being. This study analyses drought characteristics over a 39‐year period, from 1982 to 2021, using the Standardised Precipitation Index (SPI) derived from 5‐km resolution Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS). The droughts were assessed on 3‐, 6‐, 9‐ and 12‐month timescales to investigate spatio‐temporal variations in characteristics such as frequency, duration, peak, severity and intensity. The study also provides an in‐depth analysis of large‐scale drought modulation, particularly associated with the El Niño phenomenon and its teleconnection in the Maritime Continent. Depending on the location and timescale, the number of meteorological drought occurrences varied from 10 to 22 for the 3‐month timescale and 4 to 14 for the 12‐month timescale. Generally, as the timescale of a drought increases, the peak intensity decreases, while the duration and severity increase. Additionally, drought intensity decreases over longer timescales. These characteristics show significant spatial variations. Results indicate that meteorological droughts in Malaysia were almost entirely modulated by the El Niño phenomenon through its induced teleconnection over the Maritime Continent. Drought characteristics exhibit a strong seasonality linked to changes in the Walker circulation and the strengthening and weakening of anticyclonic circulations associated with Rossby waves induced by heating in the Pacific Ocean. Very strong El Niño events had the most significant influence on the droughts. The positive Indian Ocean Dipole (pIOD) strengthened the effects of El Niño but it itself had no significant influence on the droughts. In most regions, there were no significant trends in the characteristics of meteorological droughts. However, in northeast Peninsular Malaysia and some scattered areas along the west coast of the peninsula, significant trends are observed in peak, duration and severity.

Dynamical downscaling and data assimilation for a cold-air outbreak in the European Alps during the Year Without a Summer of 1816

Abstract. The “Year Without a Summer” in 1816 was characterized by extraordinarily cold and wet periods in central Europe, and it was associated with severe crop failures, famine, and socio-economic disruptions. From a modern perspective, and beyond its tragic consequences, the summer of 1816 represents a rare opportunity to analyze the adverse weather (and its impacts) after a major volcanic eruption. However, given the distant past, obtaining the high-resolution data needed for such studies is a challenge. In our approach, we use dynamical downscaling, in combination with 3D variational data assimilation of early instrumental observations, for assessing a cold-air outbreak in early June 1816. We find that the cold spell is well represented in the coarse-resolution 20th Century Reanalysis product which is used for initializing the regional Weather Research and Forecasting Model. Our downscaling simulations (including a 19th century land use scheme) reproduce and explain meteorological processes well at regional to local scales, such as a foehn wind situation over the Alps with much lower temperatures on its northern side. Simulated weather variables, such as cloud cover or rainy days, are simulated in good agreement with (eye) observations and (independent) measurements, with small differences between the simulations with and without data assimilation. However, validations with partly independent station data show that simulations with assimilated pressure and temperature measurements are closer to the observations, e.g., regarding temperatures during the coldest night, for which snowfall as low as the Swiss Plateau was reported, followed by a rapid pressure increase thereafter. General improvements from data assimilation are also evident in simple quantitative analyses of temperature and pressure. In turn, data assimilation requires careful selection, preprocessing, and bias-adjustment of the underlying observations. Our findings underline the great value of digitizing efforts of early instrumental data and provide novel opportunities to learn from extreme weather and climate events as far back as 200 years or more.

A Novel Platform and Workflow for Underwater Photogrammetry Surveys

Abstract. Underwater photogrammetry has gained popularity for creating three-dimensional (3D) maps and ortho-images of marine environments as compared to traditional echosounder surveys, they can be more cost-effective at creating high-resolution 3D models, and orthoimages are usually more informative than acoustic backscatter maps. This paper builds on previous work by the authors that developed an underwater image-capturing platform with several additional sensors, including GNSS, IMU, pressure depth sensor and single beam echosounder. This study aims to analyse the impact of calibration and sensor data integration into the photogrammetric processing workflow. The tests were performed using two underwater sites. The low-cost device's pressure sensor and tide station data outperform GNSS PPK-derived heights. Furthermore, it was observed that incorporating IMU motion sensor data did not improve the processing results. Additionally, utilising the echosounder point cloud proves valuable for enhancing the overall quality of the survey. Despite its lower density, it serves a dual purpose by validating the photogrammetry dataset and, more importantly, can be employed for correcting DSM height. This study further underlined the importance of reliable camera calibration for accurate 3D reconstruction.

Augmented Reality for Air Quality Monitoring: Case Study in the Marche Region (Italy)

Abstract. Augmented Reality (AR) allows the real world to be enhanced through the overlapping of digital information generated by an algorithm or software amplifying the user's perception. In this research, the potential of AR is exploited by applying it to the problem of air pollution visualization to raise awareness of the issue. It is well known that the principal sources of air pollution include industry, domestic heating, traffic, production processes and more others. According to European Environment Agency, air pollution is the largest environmental health risk in Europe in 2022 affecting respiratory and cardiovascular diseases and premature deaths. A suitable combination of GIS-based procedures for data analysis and georeferencing, AR systems for the visualization and exploitation of the results, can be a strategic driver. The research presented in this paper has been experimented in the Marche Region, in the central part of Italy, and in particular the city of Ancona. The pollutant data collected come from 17 Agenzia Regionale per la Protezione Ambientale delle Marche (ARPAM) meteorological ground-based stations located in Marche Region. The data processing have been executed using the software ArcGis Pro instead of a 3D City Model has been acquired from the online 3D ArcGIS platform. According to the Dlgs.155/2010 regulation, the AQI index (Air Quality Index) has been provided by the interpolation of the meteorological ground stations data. The visualization phase has been tackled exploiting in a WebGIS platform and an APP by AR. A 3D immersive visualization is made using five AQI level of air quality (excellent, good, fair, poor, bad) in order to increase the urban sensing.