Meteorological Parameters Research Articles

Chronobiological perspectives and meteorological associations in symptomatic popliteal artery aneurysms

ABSTRACT The potential influence of circadian rhythm, seasonal variations, and alterations in meteorological parameters has been studied across various vascular events. However, there is a lack of evidence on the potential chronobiological impacts on thromboembolic events related to the most common peripheral aneurysm, the popliteal artery aneurysm (PAA). Data was obtained from a German PAA registry and the German Meteorological Service (Deutscher Wetterdienst). In this observational cohort study seasonality and chronobiology as well as associations with meteorological parameters of symptomatic PAA were investigated. In a multivariate logistic regression analysis, it was further analyzed whether meteorological parameters could distinguish asymptomatic from symptomatic patients in the registry. Of 1200 registered PAA, n = 142 PAA presented with acute limb ischemia between February 2011 and September 2022. More symptomatic patients (57.0%) presented to the hospital between January and June than in the second half of the year with a nadir in the fall season. Symptom onset was predominantly in the morning hours (39.5%). Atmospheric pressure and humidity values from the index dates diverged from a normal distribution showing a bimodal (“double-peak”) configuration. Most patients developed symptoms after a reduction in temperatures compared to 1 or 2 d prior to the index date. However, we found evidence for an interaction between age and temperature difference, where the effects of a decreasing temperature fade with increasing age. Facing the complexity of individual-environment interactions, further investigations are needed to determine whether meteorological parameters are true risk modifiers or surrogates for seasonal differences and altered behaviors.

Assessment of Formaldehyde’s Impact on Indoor Environments and Human Health via the Integration of Satellite Tropospheric Total Columns and Outdoor Ground Sensors

Formaldehyde (HCHO) is harmful to human health and an adequate assessment of its concentrations, both in outdoor and indoor environments, is necessary in the context of sustainable policies designed to mitigate health risks. In this research, ground indoor and outdoor HCHO measurements are integrated with the analysis of tropospheric total columns obtained by satellite surveys to assess the concentrations of HCHO in a number of environments, exploiting the proximity of a World Meteorological Organization—Global Atmosphere Watch (WMO/GAW) observation site in Calabria, Southern Italy to a National Institute for Insurance against Accidents at Work (INAIL) department in the municipality of Lamezia Terme. The meteorological parameters used by the WMO station are also used to provide additional data and test new correlations. Using statistical significance tests, this study demonstrates the presence of a correlation between indoor and outdoor HCHO concentrations, thus showing that an exchange between indoor and outdoor formaldehyde does occur. Rooms located in the local INAIL building where indoor measurements took place also demonstrate degrees of susceptibility to HCHO exposure, which are correlated with the orientation of prevailing wind corridors in the area. The new findings constitute an unprecedented characterization of HCHO hazards in Calabria and provide regulators with new tools with which to mitigate formaldehyde-related risks.

Observing super-coarse carbonaceous aerosol particles containing chloride in a tropical savanna climate at an agro-forest site in Thailand.

Coarse aerosol particles containing chloride in tropical forests are significant for understanding biogeochemical cycles and atmospheric processes, with implications for environmental health and climate change mitigation. This study explored the sources of super-coarse carbonaceous aerosol particles containing chloride in a tropical savanna climate. Aerosol samples were collected from an agro-forest site in Thailand during the dry season and analyzed using scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS), and Fourier-transform infrared (FTIR) spectroscopy. By examining the morphology and elemental compositions of individual aerosol particles, along with employing Positive Matrix Factorization (PMF) and backward trajectory analysis, potential sources were identified. The findings revealed two primary sources for the super-coarse aerosol particles: a mixture of biomass burning smoke and inorganic salts (likely from saline soil and sea salt), as well as halophilic fungal spores. FTIR analysis indicated the presence of compounds linked to biomass burning and clay minerals, influenced by prevailing northeast and southeast winds. Recommendations for future research include continued monitoring, correlation with meteorological parameters, and the application of transmission electron microscopy (TEM) for more detailed visualization and confirmation of aerosol sources.

Observational study of ground-level ozone and climatic factors in Craiova, Romania, based on one-year high-resolution data.

Air pollution is a multifaceted issue affecting people's health, environment, and biodiversity. Gaining comprehension of the interactions between natural and anthropocentric pollutant concentrations and local climate is challenging. This study aims to address the following two questions: (1) What is the influential mechanism of climatic and anthropogenic factors on the ground-level ozone (O3) concentrations in an urban environment during different seasons? (2) Can the ozone weekend effect be observed in a medium-sized city like Craiova, and under which conditions? In order to answer these questions, ozone interactions with meteorological parameters (temperature, pressure, relative humidity) and pollutant concentrations (particulate matter, carbon dioxide, volatile organic compounds, formaldehyde, nitrogen dioxide, nitric oxide and carbon monoxide) is evaluated based on a one-year dataset given by a low-cost sensor and one-year dataset provided by the National Environment Agency. Using two statistical analysis programs, Python and SPSS, a good understanding of the correlations between these variables and ozone concentration is obtained. The SPSS analysis underscores the significant impact of three meteorological factors and nine other pollutants on the ozone level. A positive correlation is noticed in the summer when sunlight is intense and photochemical reactions are elevated. The relationship between temperature and ozone concentration is strong and positive, as confirmed by Spearman's rho correlation coefficient (r = 0.880). A significant negative correlation is found between relative humidity and ozone (r = -0.590). Moreover, the analysis shows that particulate matter concentrations exhibit a significant negative correlation with ozone (r ≈ -0.542), indicating that higher particulate matter concentrations reduce ozone levels. Volatile organic compounds show a significant negative correlation with ozone (r = -0.156). A negative relationship between ozone and carbon dioxide (r = -0.343), indicates that elevated carbon dioxide levels might also suppress ozone concentrations. A significant positive correlation between nitrogen dioxide and ozone (r = 0.060), highlights the role of nitrogen dioxide in the production of ozone through photochemical reactions. However, nitric oxide shows a negative correlation with ozone (r = -0.055) due to its role in ozone formation. Carbon monoxide has no statistically significant effect on ozone concentration. To observe the differences between weekdays and weekends, T-Test was used. Even though significant differences were observed in temperature, humidity, carbon dioxide, volatile organic compounds, nitrogen dioxide, nitric oxide and carbon monoxide levels between weekdays and weekends, the T-Test did not highlight a significant weekend ozone effect in a mid-sized city as Craiova. Using Python, the daily values were calculated and compared with the limit values recommended by the World Health Organization (WHO) and European Environment Agency (EEA). The WHO O3 recommended levels were exceeded for 13 times in one year. This study offers a comprehensive understanding of ozone pollution in a mid-sized city as Craiova, serving as a valuable reference for local decision-makers. It provides critical insights into the seasonal dynamics of ozone levels, emphasizing the significant role of temperature in ozone formation and the complex interactions between various pollutants and meteorological factors.

Evaluation of Rain Shelter Cultivation Mode Effects on Microecological Environment of Mountain Cultivated Ginseng Rhizosphere

Rainfall, particularly in continental climates with a monsoonal tendency, impacts the microbial niches during the growth of mountain cultivated ginseng. With shifts in the microbial community, diseases in ginseng cultivated and protected under rain shelter conditions may ultimately be altered. Such cultivation may influence microflora dynamics through variations in meteorological parameters; however, this is not yet clear. The present study found that rain shelter cultivation affected the distribution of fungal communities within mountain cultivated ginseng. This led to an improved community structure in the ginseng rhizosphere, characterized by the proliferation of antagonistic fungi and a reduction in pathogenic fungi. A correlation analysis of meteorological factors found that soil temperature and humidity were the primary meteorological factors affecting mountain cultivated ginseng. It is evident that rain shelter cultivation regulated the microecological environment of the mountain cultivated ginseng’s rhizosphere and resulted in positive outcomes. A disease investigation supported this finding. The incidence of ginseng root diseases, such as rust and root rot, was reduced by 5–6%. The incidence of ginseng leaf diseases, including gray mold and black spot, was reduced by 5–10%. This research provides evidence to address the dynamics of microbial ecology under rain shelter cultivation and its benefits for sustainable mountain cultivated ginseng management.

Climate drivers of phytoplankton production along the Chilean coast

The west coast of South America is known for its high primary productivity. The level of phytoplankton can be measured through satellite images that detect chlorophyll (Chl), which is dependent on several oceanographic and meteorological parameters. Climate drivers such as El Niño Southern Oscillation (ENSO) and the Southeast Pacific Subtropical Anticyclone (SPSA) affect these parameters and, consequently, the phytoplankton. The objective of this study was to identify the impact of ENSO on SPSA, climate variables, and phytoplankton patterns. Composites were created using the years selected with either strongly positive or negative ENSO to understand their influence on different parameters. To create the Chl composite, it was necessary to extend it using Canonical Correlation Analysis (CCA) based on the sea surface temperature (SST) pattern. The study concludes that ENSO has a noticeable impact on Chl, mainly in the Southern Zone during the warm season. This is driven by the expansion of SPSA to the South, which increases the sea level pressure (SLP) in that region. However, predicting the Chl concentration has a high degree of uncertainty due to its complexity.

The Spatiotemporal and Dependency Analysis of selected Meteorological Parameters and Normalized Difference Vegetation Index with Aerosol Optical Depth over East Africa.

The unprecedented rise in atmospheric aerosols, coupled with their intricate interactions with the environment through a wide array of physical, chemical, and biological processes, has profoundly impacted global climate. Their presence in the atmosphere scatters and absorbs solar radiation, thus altering the amount of sunlight reaching the Earth's surface. These direct effects, along with the indirect effects of aerosols, have significantly altered atmospheric temperatures, land surface processes, global surface temperature, hydrological cycle, and ecosystems. Understanding the complex interplay between aerosols and climatic variables necessitates a multidisciplinary approach, such as dependency modeling. Addressing these challenges, the current study conducts a spatiotemporal correlational analysis of selected key meteorological parameters with aerosol optical depth over East Africa (EA) using multisensory data from Moderate-resolution Imaging Spectroradiometer (MODIS), Modern-Era Retrospective analysis for Research and Application (MERRA-2) model, and Tropical Rainfall Measurement Mission (TRMM). Employing a weighted least squares regression (WLS) model, the study quantifies trends in the time series of climatic variables and Normalized Difference Vegetation Index (NDVI), further utilizing a statistical dependency modeling technique for correlational analysis. The trend analysis reveals a significant decreasing trend in surface wind speed (SWS) in most months, with sporadic positive trends attributed to anthropogenic activities, notably biomass burning, observed in January. Spatial trend analysis of Precipitation Rate (PR) displays heterogeneity, with significant negative trends in January and March, and positive trends in February, April, November, and December. Negative trends during May to August are attributed to increased anthropogenic activities, while enhanced positive trends in May correlate with low aerosol optical depth (AOD) during this period. Surface air temperature (SAT) exhibits diverse variations across the region, with dry months recording higher averages and trends than wet months. The study notes heterogeneous correlations in NDVI over the study area, with positive and negative correlations observed in different regions. Specifically, positive correlations are noted along the coastal and Lake Victoria regions, attributed to improved PR enhancing vegetation cover in these areas.

A model predictive control strategy of global optimal dispatch for a combined solar and air source heat pump heating system

Due to low-carbon transformation of heating sources in northern China, the combination of the solar heating system (SHS) and the air source heat pump (ASHP) has attracted widespread attention due to their significant low-carbon and energy-saving characteristics. Nevertheless, different outdoor meteorological parameters simultaneously affect both the SHS and the ASHP. The heat supply of the SHS is primarily influenced by solar radiation intensity, and the efficiency of the ASHP is mainly constrained by outdoor temperature. The dual uncertainty and volatility of the output capacities of these two heat sources pose significant challenges to their coordinated control. Previous research has primarily focused on traditional rule-based control (RBC) and feedback control, prioritizing the utilization of solar energy while employing ASHP to compensate for heating deficiencies. However, these methods ignore the variations in ASHP efficiency due to fluctuations in outdoor temperature, leading to low whole-system operating efficiency and limiting the flexibility and responsiveness of the control. In this paper, a model predictive control strategy of global optimal dispatch for a combined solar and ASHP (SASHP) heating system is proposed, which focuses on the flexibility and adaptability of dual heat source dispatch under different external conditions. A Temporal Convolutional Network (TCN) was used to establish a solar radiation intensity and load prediction model. A solar heat production prediction was realized by combining the mechanism model based on solar radiation intensity prediction; a two-step room temperature prediction model was established by introducing new input parameters in the load prediction. This strategy achieves globally optimal dynamic planning of the heat supply and duration of the SHS and ASHP systems by considering solar radiation, outdoor temperature, room temperature, and energy consumption, ensuring the efficient and stable operation of the system. Compared to RBC, experimental results indicated that under conditions of sufficient solar energy, the heating proportion of the SHS increased by 31.1 %, the average COP of the ASHP improved by 8.7 %, the energy-saving rate was 14.6 %, and the room temperature control was also more effective; whole-season simulation results showed an average energy-saving rate of 8.35 %.

Multiresolution Analysis of HRRR Meteorological Parameters and GOES-R AOD for Hourly PM2.5 Prediction.

High-resolution, comprehensive exposure data are crucial for accurately estimating the human health impact of PM2.5. In recent years, satellite remote sensing data have been increasingly utilized in PM2.5 models to overcome the limited spatial coverage of ground monitoring stations. However, data gaps in satellite-retrieved parameters such as aerosol optical depth (AOD), the sparsity of regulatory air quality monitors for model training, and nonlinear relationships between PM2.5 and meteorological conditions can affect model performance and cause data gaps in most existing PM2.5 models. In this study, spatial gaps in AOD obtained from Geostationary Operational Environmental Satellite-16 are filled using Goddard Earth Observing System Composition Forecasting AOD estimations. Furthermore, to improve model performance, meteorological predictors such as temperature from the High-Resolution Rapid Refresh model are preprocessed using Daubechies wavelet to extract low and high frequency components. The spatially gap-filled AOD, along with meteorological data, are ingested into a machine learning model to predict hourly PM2.5 at a 1 km spatial resolution in California. The model evaluation metrics (OOB (out-of-bag) R2 = 0.86 and RMSE (root-mean-square error) = 9.27 μg/m3 and 10-fold spatial cross-validation R2 = 0.82 and RMSE = 9.82 μg/m3) demonstrate the model's reliability in predicting ambient PM2.5, especially for states like California that experience frequent episodes of wildfires.

Research on urban power load forecasting based on improved LSTM

In this paper, the maximal information coefficient method-variational mode decomposition-bidirectional long short term memory network-adaptive boosting (MIC-VMD-Bi-LSTM-Adaboost) algorithm is used to forecast the power load. Firstly, MIC is used to determine the correlation degree of meteorological parameters influencing power load. Features having a high correlation degree are then chosen to be input vectors. Secondly, the input characteristics are decomposed using VMD, and five distinct IMF components are retrieved in order to remove unnecessary information. Lastly, different assessment indices are computed and the power load is predicted using the Bi-LSTM-Adaboost method. In order to determine the benefit of the approach used in this work, the outcomes of LSTM, Bi-LSTM, and LSTM-Adaboost are compared concurrently.

Temperature and relative humidity in a commercial bread wheat field, recorded by an automated meteorological station and a digital sensor in the Yaqui Valley, Sonora, Mexico

Meteorological parameters determine most agronomic events, with temperature being the climatological factor most closely related to the annual productivity of a crop. The objective of this work was to monitor the temperature and relative humidity at different growth stages of a commercial bread wheat field, during the crop season 2017-2018. The work was carried out with data obtained from December 12, 2017 (sowing date) to April 18, 2018, from a mobile OMEGA OM-EL-USB-2 sensor within the field, sown with the bread wheat cultivar Borlaug 100, located in Block 1020, in the Yaqui Valley, Sonora, Mexico, and from the Block 910-CIANO meteorological station, located 8 km away, which is the closest to the field. Calculations were made for the average temperature, relative humidity and cold units (CU). In addition, the data was filtered by day and night time, considering daytime data from 7:00 to 18:00 and nighttime data from 19:00 to 06:00. The CU recorded by the sensor was higher with 384 than the meteorological station, which accumulated a total of 321. The temperature during the day was on average 2.9 °C higher than that recorded by the meteorological station, and 1.6 °C lower at night. The relative humidity recorded by the sensor was 5 % higher than that recorded by the meteorological station.

Comparative analysis of airborne fungal spore distribution in urban and rural environments of Slovakia.

Monitoring airborne fungal spores is crucial for public health and plant production since they belong to important aeroallergens and phytopathogens. Due to different land use, their concentration can differ significantly between urban and rural areas. We monitored their spectrum and quantity on two geographically close sites with a different degree of urbanisation: Bratislava City and Kaplna Village in Slovakia, located 38km apart. We recorded the spectrum of airborne fungal spores over a year and confirmed the microscopic results by amplicon-based metagenomic analysis. The main spore season of the most frequent genera lasted over a week longer in Kaplna, but its intensity was approximately two-fold higher in Bratislava. This can be possibly connected to the microclimatic conditions of the urban area (especially wind speed and heat island effect) and the lesser use of fungicides. Cladosporium was the dominant genus on both sites, influencing the intensity most significantly. Through statistical analysis of the influence of meteorological parameters on airborne fungal spore levels, we identified a significant relationship with temperature, while the impact of other parameters varied depending on the spore type and release mechanism. Our results show the differences in airborne fungal spore levels between urban and rural areas and highlight the necessity for more monitoring stations in various environments.

New multivariate models for the estimation of earth’s albedo and its variation with meteorological parameters over Kano, Nigeria

The study of what happens to sunlight as it goes through the atmosphere is vital to many fields of science because over time, reflection, scattering, and absorption in the atmosphere have changed the amount of solar radiation that reaches the Earth's surface. This study examined the variation in albedo over Kano, situated in the Sahelian climatic zone of Nigeria. The study's data, which covered the years 1984–2022, totaled thirty-nine, was obtained from the National Aeronautics and Space Administration (NASA). Utilizing monthly average daily measured meteorological data on global solar radiation, wind speed, mean temperature, surface pressure, and relative humidity, eleven models of three categories based on two, three, and four variable regression models were developed. Five validation indicators were used to verify the regression models: the t-test, coefficient of determination (R2), mean percentage error (MPE), root mean square error (RMSE), and mean bias error (MBE). The developed models that were considered most appropriate for estimating surface albedo were ranked; the three-variable regression model ALB7, which included the wind speed, relative humidity, and mean temperature, outperformed the previously employed generalized method from literature in estimating surface albedo over Kano. The variation of measured albedo with meteorological parameters depicts a direct relationship for clearness index and wind speed; an inverse relationship was observed for surface pressure. Mean temperature and surface pressure exhibit an inverse relationship during the wet season and vice versa for the dry season.

Machine learning for air quality index (AQI) forecasting: shallow learning or deep learning?

In this study, several machine learning (ML) models consisting of shallow learning (SL) models (e.g., random forest (RF), K-nearest neighbor (KNN), weighted K-nearest neighbor (WKNN), support vector machine (SVM), artificial neural network (ANN), and deep learning (DL) models (e.g., long short-term memory (LSTM), gated recurrent unit (GRU), recurrent neural network (RNN), and convolutional neural network (CNN)) have been employed for predicting air pollution and its classification. The models were selected based on factors such as prediction accuracy, model generalization, model complexity, and training time. Our study focuses on analyzing and predicting the air quality index (AQI) using daily PM10 concentration as natural pollutants and nine meteorological parameters from March 2013 to February 2022 in Zabol. We also utilized the information gain (IG) method for feature selection. Several measures including accuracy, F1 score, precision, recall, and the area under the curve (AUC), are computed to assess model performance. This study demonstrates the efficacy of DL models, particularly CNN, in predicting the AQI with remarkable accuracy. Our findings reveal that all models effectively classify air quality levels, with an AUC of 0.95 for the good class in both DL and ANN models, significantly outperforming SL models. The AUC values for the hazardous and moderate classes of DL models were also impressive, at 0.90 and 0.83, respectively, underscoring their effectiveness in critical classifications. In terms of performance, CNN achieved an accuracy of 0.60, leading the models, while RF followed closely at 0.58. RNN, GRU, ANN, and SVM each reached an accuracy of 0.57, demonstrating a competitive edge. LSTM and WKNN recorded an accuracy of 0.55, and KNN was slightly lower at 0.53. These results highlight the superior capabilities of DL models in addressing complex air quality classifications, providing invaluable insights for policymakers. By leveraging these advanced techniques, stakeholders can implement more effective strategies to combat air pollution and safeguard public health. It is worth noting that irregular monitoring of air quality data may affect the robustness of our predictions, highlighting the need for more consistent data collection to ensure an accurate representation of pollution levels.

Integrated Anomaly Detection and Early Warning System for Forest Fires in the Odisha Region

The present study aims to develop a random forest algorithm-based classifier to predict the occurrence of fire events using observed meteorological parameters a day in advance. We considered the skin temperature, the air temperature close to the surface, the humidity close to the surface level, and soil moisture as important meteorological factors influencing forest fire occurrence. Twenty additional parameters were derived based on these four parameters that account for the energy exchanged in sensible and latent forms and the change in parameters in recent trends. We used the mutual information approach to identify critical meteorological parameters that carry significant information about fire occurrence the next day. The top nine parameters were then fed as input to the random forest algorithm to predict fire/no fire the next day. The weighted data sampling and SMOTE techniques were employed to address the class imbalance in the fire data class. Both techniques correctly classified fire incidents well, given the meteorological input from the previous days. This study also showed that as the class imbalance increases to 1:9, the performance based on the precision, recall, F1 score, and accuracy are maximum, showing the model’s ability to perform with class imbalance. Both techniques helped the random forest algorithm forecast fire instances as the data sample size increased.

Epidemiological characteristics and meteorological factors of acute respiratory infections (ARIs) in hospitalized children in eastern Guangdong, China

Acute respiratory infections (ARIs) are the most common issue in pediatric clinical practice. They pose a significant public threat, with high morbidity and mortality rates worldwide. Aiming at understanding the epidemiological characteristics of respiratory pathogens and their risk factors among children in eastern Guangdong, China. Samples obtained from 15,993 children hospitalized with ARIs in eastern Guangdong Province were tested for 14 pathogens via multiplex polymerase chain reaction (PCR) from May 2019 to July 2023. The number of hospitalizations for ARIs was correlated with pathogens, age, meteorological parameters, and the pandemic of COVID-19. The data were analyzed by different statistical methods. Among all the samples, the positive rate with ARIs accounted for 68.94% (11,026/15,993) in hospitalized patients. Cytomegalovirus (CMV) (24.49%), Streptococcus pneumoniae (SP) (20.54%), and Respiratory Syncytial Virus (RSV) (14.16%) were the top three pathogens with the greatest infection rates. Among hospitalized patients, there were more single infections in pediatric patients (40.91%, P < 0.001). Compared with bacterial infection and mixed infection, the detection rate of virus infection was higher in pediatric (36.04%, P < 0.001). Age-related increases in Mycoplasma pneumoniae (MP) infection (r = 0.729, P < 0.001) and decreases in RSV infection were observed (r = 0.88, P < 0.001). The virus infection peaked at six months, and the bacterial infection and mixed infection peaked at 1–3 years. Viral pathogens are on the rise in the post-pandemic era. The prevalence of SP infection was more influenced by the Air Quality Index (AQI), RSV infections were more clearly influenced by temperature, and Influenza A virus (IAV) infections were more strongly correlated with both the AQI and relative humidity (P < 0.001). This study highlights the need of keeping an eye on monitoring meteorological factors in assessing hospitalizations for pediatric ARIs in eastern Guangdong, China, especially RSV- and SP-associated hospitalizations.

A novel method to estimate the 3D chlorophyll a distribution in the South China Sea surface waters using hydrometeorological parameters

Chlorophyll a (Chl-a) is a key indicator of marine ecosystems, and certain hydro-meteorological parameters (HMPs) are highly correlated with its fluctuations. Here, relevant and accessible HMPs were used as inputs, combined with machine learning (ML) algorithms for estimating 3D Chl-a in the South China Sea (SCS). With the inputs of temperature, salinity, depth, wind speed, wind direction, sea surface pressure, and relative humidity, the LightGBM-based model performed well, achieving high R2 values of 0.985 and 0.789 in validation and testing sets, respectively. Based on a large number of in situ measurements, this model enables the estimation of the 3D distribution of summer Chl-a in the SCS over the past fifteen years using a 3D hydrographic dataset combined with surface meteorological parameters. The results show that the 3D distribution of the model estimated Chl-a is characterized similarly to the previous studies and can capture the effect of hydro-meteorological conditions on Chl-a distribution. The environmental variables affecting Chl-a were considered more comprehensively in this study, and the methodological framework has the potential to be applied to the low-cost monitoring of the remaining water quality parameters.

Variations of the zero degrees isotherm and environmental lapse rate recorded with ADSB and Mode S EHS messages

This study focuses on analyzing the altitude of the zero degrees isotherm and variations in the environmental lapse rate (ELR) using the publicly available data collected from Automatic Dependent Surveillance–Broadcast (ADS-B) and Mode S Enhanced Surveillance (EHS) messages emitted by airplanes over a five-month period in 2021. The data was gathered using a professional receiver stationed in Bucharest (Romania). The aviation messages were decoded and the air temperature and pressure were determined, at the location of the airplane. The method has the advantage of the continuous messages that are emitted by the aircrafts during flight that allow instantaneous determination of the meteorological parameters at no additional costs. It can also be extended to permit almost real time maps of the ELR. When data was analyzed and a standard ELR value of 6.5 K/km is employed it was observed that the mean altitude of the 0 degrees isotherm exhibits a seasonal increase during the summer months, with an average altitude of 2874.2 m. The highest recorded altitude of the 0 degrees isotherm was found to be 5346.8 m, near Alexandria city (Romania), on 22.07.2021. Using a standard Least Mean Square algorithm alongside the International Standard Atmosphere pressure formula, the ELR values were calculated from pressure measurements data. The resulting mean ELR for the five-month period was determined to be 5.1331 K/km, slightly lower than the standard value.

The cloud cover and meteorological parameters at the Lenghu site on the Tibetan Plateau

Abstract The cloud cover and meteorological parameters serve as fundamental criteria for an astronomical observatory working in optical and infrared wavelengths. In this paper, we present a systematic assessment of key meteorological parameters at the Lenghu astronomical observing site on the Tibetan Plateau. The datasets adopted includes the meteorological parameters collected at the local weather stations at the site and in the Lenghu Town, the sky brightness acquired by the Sky Quality Meters and all-sky images from a digital camera, the ERA5 reanalysis database and global climate monitoring data. From 2019 to 2023, the fractional observable time of photometric condition is 69.70%, 74.97%, 70.26%, 74.27% and 65.12%, respectively, which is influenced by a variety of meteorological parameters. Large-scale air-sea interactions affect the climate at Lenghu site, which in fact delivers a clue to understand the irregularity of 2023. Specifically, precipitable water vapor at Lenghu site is correlated to both the westerly wind index and the summer North Atlantic Oscillation index, the yearly average temperature of Lenghu site is observed to increase significantly during the occurrence of a strong El Niño event, and the relative humidity anomaly at Lenghu site is correlated to the Pacific Decadal Oscillation index. The decrease of fractional observing time in 2023 was due to the ongoing strong El Niño event and relevant global climate change. We underscore the substantial role of global climate change in regulating astronomical observing conditions and the necessity for long-term continuous monitoring of the astronomical meteorological parameters at Lenghu site.

Risk assessment and seasonal variation of atmospheric ammonia in Ondo State, Nigeria

The environment is impacted by atmospheric ammonia (NH3) in a number of ways, including the global nitrogen cycle, ecology, and the production of secondary particles. Our findings present an unparalleled depiction of the temporal and geographical properties of atmospheric NH3 in and around this megacity, based on long-term, continuous measurements of NH3 at various locations in Ondo State. The increase in atmospheric ammonia during this anthropogenic era is believed to pose a significant threat to humanity, with scientists linking it to a rise in global warming and climate change. KP-826 multiple gas detector was used to investigate ammonia gas in five locations within Ondo state and its correlation with meteorological parameters was examined in this study. The selection of Dumpsite, Cattle rearing farm and Poultry farm was purposeful because of their involvement in the production of ammonia gas. The mean concentration of NH3 (ppm) during wet season was 3.87 ± 1.60 and for dry season was 5.87 ± 1.60. The mean temperature (°C) in wet conditions is 32.51 ± 1.27, which is a little lower than the mean (33.51 ± 1.25) in dry conditions. The mean wind speed (in m/s) during wet conditions is 1.21 ± 0.32, which is greater than the mean wind speed (in m/s) during dry conditions of 1.11 ± 0.32. The mean value of humidity for wet situations is 72.08 ± 2.31, which is a little lower than the mean value for dry conditions, which is 76.07 ± 2.31. This work evaluated the potential health risks associated with NH3. The total hazard quotient (THQ) for adult was 1.30 × 10–6 for children 1.50 × 10–6. The children's HQ ranged from 2.41 to 4.15. The adult's ranged from 8.70 to 15.13. However, it was discovered that the health risk posed by breathing in atmospheric NH3 was far higher than the USEPA limits, where HQ > 1.