Monthly Mean Values Research Articles

On a relation between shrinking of sea ice coverage and climate warming in the marine Arctic

Arctic amplification (AA) of the climate warming is understood as the excess of the surface air temperature rise in the Arctic over the same process in the non-Arctic latitudes, and it is a fundamental characteristic of the climate during periods of warming. The positive feedback between albedo and shrinking of the sea ice coverage has been identified as the first possible cause of AA. The article presents quantitative estimates of the relationship between the summer decrease and autumn-winter restoration of the ice coverage with the rise of the surface air temperature in the marine Arctic based on data of observations. The mean monthly values of the temperature in the marine Arctic and the mean monthly values of areas covered by the sea ice in the Arctic Ocean and the Arctic seas for the period 1989–2020 were used. It has been found that the observed warming and the decrease of the ice coverage are accompanied by a growth of inter-monthly changes in the coverage and the same in the air temperature. Negative trends in increments of the ice coverage in May–July is indicative of a long-term growth in inter-monthly shrinkage of the ice coverage due to increasing melting, while negative trends in increments of the temperature in the same months is suggestive of a slowdown in the temperature rise from month to month, presumably due to the increasing heat consumption for the snow and ice melting and the water heating. The positive correlation confirms the relation between growing negative inter-monthly differences in the ice coverage and decreasing positive differences in the air temperature during these months. Based on that we determined a sensitivity of the inter-monthly increments of the temperature to the increments of the ice coverage, which was used to estimate the weakening of the positive air temperature trend in May–July over the Arctic Ocean and over the seas of the Northern Sea Route (NSR). Estimating of the relationship between month-to-month changes in temperature and ice coverage in the autumn-winter months meet difficulties due to the strong influence of external heat influx. Only in November and January a slowdown in the air temperature drop from October to November and from December to January had been revealed, with a positive trend in the ice coverage increments.

Temporal Variation in and Influence of Environmental Variables on a Lepidopteran Community in a Mediterranean Mid-Mountain Area

The temporal variation in a community of nocturnal and diurnal Lepidoptera was studied in a Mediterranean mid-mountain area of the SW Iberian Peninsula between 2017 and 2019. Monthly samplings that allowed for the identification of 3528 specimens, belonging to 373 species from 40 different families, also provided data on the temporal and seasonal variation in richness and the abundance (dominated by Geometridae and Noctuidae), diet type (mainly oligophagous), voltinism (mostly univoltine) and biogeography of the community, primarily Mediterranean in scope. Richness, abundance and diversity were also found to be highly positively correlated with temperature and solar radiation, and less negatively correlated with precipitation and humidity. Canonical correspondence analyses (CCAs) also indicate that temperature and radiation are the climatic variables with the greatest influence on species occurrence over the different months of the year. The CCAs gave a cumulative variance value of 84.79% when using the monthly mean values of temperature, solar radiation and minimum relative humidity, and 86.4% if only monovoltine species were considered. Guidelines to maintain diversity in the environment of the study area are provided. It is possible that the area may function as a refuge area for Lepidoptera in the face of climate change and deforestation that are occurring in the geographical environment.

Ionospheric responses to the tropical cyclones from different oceanic basins over the globe

The perturbations in the ionosphere due to eight tropical cyclones (TCs), namely Iota, Haima, Harold, Willa, Amphan, Gaja, Vadrah and Bulbul, originated and grown in different oceanic basins, are investigated. Total Electron Content (TEC) data, from Global Positioning System (GPS) TEC receiver in operation at Agartala (AGT) or different International GNSS Service (IGS) stations near the cyclone landfall regions, are used in this study. Despite some differences, the ionosphere responds to all tropical cyclones in an almost similar manner. Though the geomagnetic conditions are quiet and there are no perturbations due to any other geophysical phenomena in the active cyclonic storm stage, in all the cases there is a fall in average vertical total electron content (VTEC) deviations below the monthly mean value either on the landfall day or on the following day or even on just previous day. Decrements in Vertical Total Electron Content are found higher for tropical cyclones over North Indian and South Pacific oceanic basins. Recoveries in vertical total electron content values are slower for cyclones over the North Atlantic and North West Pacific basins. Recoveries in vertical total electron content (VTEC) values are slow for tropical cyclones (TCs) over the North Atlantic and North West Pacific basins. But those over other basins are quick. The longer the track of a tropical cyclone (TC), the higher is the reduction in the vertical total electron content (VTEC) value. A negative correlation exists between the maximum sustained surface wind velocities and the total periods of different TCs and also the difference of lowest average differential VTECs with that on the previous day. The observed anomaly in ionospheric responses might be due to the combined effect of TC-inspired gravity waves, ejection of neutral particles from the terminator of a tropical cyclone (TC) and lightning electric fields. To explain the observed results convective activities during TC, with the help of outgoing long wave radiation (OLR) map, are also taken into account. This study provides the primary results regarding regional characteristics and hence a comparative idea for the responses of the ionosphere to different tropical cyclones (TCs) from different geographical positions on the globe, which needs further comprehensive investigation in future.

Suspended particulate matter response to extreme forcings in the Bay of Seine

The latest Intergovernmental Panel on Climate Change report of 2023 alerts about an increase in the occurrence and intensity of extreme hydro-meteorological events such as storms and extreme river flows, i.e. drought and floods. Investigating the occurrence of these extreme events in the past 15 years and their impacts on sediment dynamics will provide crucial knowledge for anticipating future trajectories of coastal ecosystems. Time series from in situ observations are analyzed to identify extreme events of river flows and waves and examine their impact on Suspended Particulate Matter (SPM) dynamics in a highly turbid coastal area equipped with a high frequency in situ monitoring station at the interface between the Seine Estuary and the Bay of Seine (northern coast of France). Extreme river flow and wave orbital velocity events are investigated because high river discharge contributes to deliver large amounts of SPM concentration to the bay and strong wave action within the bay can lead to erosion and resuspension of bottom sediments. An original detection method is proposed, based on high frequency in situ observations combined with satellite and model data from 2006 to 2022. Extreme forcings are examined through their specific characteristics (high intensity, long duration, season of occurrence, succession of events), their impact on SPM concentration in the coastal environment and the comparison to mean seasonal dynamics. A positive relationship exists between SPM concentration and high SPM spatial extent and forcing intensity. Extremes are more intense in winter for both forcings and generate larger SPM concentration anomalies. However, extreme events during late spring/summer, periods or mean low forcing intensity, are demonstrated to generate SPM concentration anomalies up to 4 times larger than the monthly mean value, hence possibly strongly impacting the system during these atypical periods. This is particularly important as analyzing the distribution of extreme river flow events over the last 60 years indicated an increase in their occurrence and more important the progressive occurrence of high intensity extreme events during spring/summer periods.

Unlocking Weather Observations at the End of the World: Late-XIX and Early-XX Century Monthly Mean Temperature Climatology for Southern Patagonia

A climate analysis of the monthly mean temperatures of Southern Patagonia during the late-XIXth and early-XXth centuries was carried out as part of the international data rescue Atmospheric Circulation Reconstructions over the Earth (ACRE) program partnership in Argentina, together with other data sources with regional and global records. The data from these diverse sources were combined to carry out a study in the coastal region of Patagonia, including Tierra del Fuego, between 42° S and 55° S for 11 locations. Furthermore, HadSST monthly/seasonal fields during the period 1880–1920 were also used. Both mean monthly and seasonal temperature values and timeseries variability were considered. Their analysis shows consistent behavior within the study region and compared to Southern Hemisphere mean results, which are characterized by a warm late-XIX century and a cooler early-XX century. This is also in agreement with SST variability along the coasts of Patagonia and hemispheric records. A comparison with present-day observations, where available, also yields consistent behavior. Low-frequency variability, i.e., in periods longer than 3 years, during the study period is consistent with present variability. Trend estimates at Trelew and Rio Gallegos for the period 1901–2020 yield significant trends, consistent with hemispheric warming at their latitudes.

An integrated modeling approach for estimating monthly global rainfall erosivity

Modeling monthly rainfall erosivity is vital to the optimization of measures to control soil erosion. Rain gauge data combined with satellite observations can aid in enhancing rainfall erosivity estimations. Here, we presented a framework which utilized Geographically Weighted Regression approach to model global monthly rainfall erosivity. The framework integrates long-term (2001–2020) mean annual rainfall erosivity estimates from IMERG (Global Precipitation Measurement (GPM) mission’s Integrated Multi-satellitE Retrievals for GPM) with station data from GloREDa (Global Rainfall Erosivity Database, n = 3,286 stations). The merged mean annual rainfall erosivity was disaggregated into mean monthly values based on monthly rainfall erosivity fractions derived from the original IMERG data. Global mean monthly rainfall erosivity was distinctly seasonal; erosivity peaked at ~ 200 MJ mm ha−1 h−1 month−1 in June–August over the Northern Hemisphere and ~ 700 MJ mm ha−1 h−1 month−1 in December–February over the Southern Hemisphere, contributing to over 60% of the annual rainfall erosivity over large areas in each hemisphere. Rainfall erosivity was ~ 4 times higher during the most erosive months than the least erosive months (December–February and June–August in the Northern and Southern Hemisphere, respectively). The latitudinal distributions of monthly and seasonal rainfall erosivity were highly heterogeneous, with the tropics showing the greatest erosivity. The intra-annual variability of monthly rainfall erosivity was particularly high within 10–30° latitude in both hemispheres. The monthly rainfall erosivity maps can be used for improving spatiotemporal modeling of soil erosion and planning of soil conservation measures.

Prospects of Solar Energy Exploration in Nigeria: Assessments, Economic Viability and Hybrid System

The recent happenings in the world such as flood and wild forest fire were as a result of climate effect as being envisaged by scientists. It is urgent now to adopt a source of energy that will eliminate this effect on our universe. Solar energy is the major energy means that is abundant which could be utilized. In this review, the prospects of solar energy exploration were studied in Nigeria which include assessments, economic viability and hybrid systems. Findings show higher potential in the North as compared to the Southern region. Additionally, potential of offshore solar energy system was simulated by considering 2002 to 2021 data sets (20 years) from Era5-land base. Their monthly mean, seasonal changes and annual mean value were estimated. The algebraic annual solar radiance for 2002 and 2003 were highest valued at 34,914.732 kWhm_2 while the least occurred in 2008 as 26,967.168 kWhm-2. Suggestions were made due to the present status of solar energy utilization that will enhance its maximum usage and development. One of these is the establishment of a functioning financial scheme and the database for all renewable energy systems. When all these are put in place, the energy supply will increase, climate effect will be reduced, and the economy will be boosted.

On the Impacts of the Global Sea Level Dynamics

The temporal evolution of the global mean sea level (GMSL) is investigated in the present analysis using the monthly mean values obtained from two sources: a reconstructed dataset and a satellite altimeter dataset. To this end, we use two well-known techniques, detrended fluctuation analysis (DFA) and multifractal DFA (MF-DFA), to study the scaling properties of the time series considered. The main result is that power-law long-range correlations and multifractality apply to both data sets of the global mean sea level. In addition, the analysis revealed nearly identical scaling features for both the 134-year and the last 28-year GMSL-time series, possibly suggesting that the long-range correlations stem more from natural causes. This demonstrates that the relationship between climate change and sea-level anomalies needs more extensive research in the future due to the importance of their indirect processes for ecology and conservation.

The microclimate occupied by the hyperoceanic liverwort Leptoscyphus cuneifolius (Hook.) Mitt. within a temperate rain forest in Wales

ABSTRACT Introduction Because climate change risks serious negative impacts on drought-sensitive species in Britain, in the short as well as the long term, the microclimate occupied by the hyperoceanic liverwort Leptoscyphus cuneifolius within a temperate rain forest in Wales was investigated. Methods At Coed y Rhygen, Gwynedd, Wales, 10 trees occupied by the liverwort were randomly selected, and its abundance was measured on the southerly and northerly aspect of each trunk. Data loggers were installed to measure temperature, relative humidity and illuminance on both aspects, from 1 January 2020 to 31 December 2022. Temperature and relative humidity values were used to calculate vapour pressure deficit (VPD). Bootstrap paired t-tests were used to test for significant differences in the mean number of locations of Leptoscythus cuneifolius on the southerly versus northerly aspects of trunks, and in the monthly mean values of temperature, VPD and illuminance on the southerly versus northerly aspects. Results and conclusions Leptoscyphus cuneifolius is significantly more abundant on the northerly aspect of trunks, and the microclimate that it occupies is dynamic, both spatially and temporally. In early spring, a sharp peak occurs in solar radiation and VPD, especially on the southerly aspect, caused by the altitude of the sun rising from its winter low and the subsequent emergence of the deciduous forest leaf canopy. Further study is required to understand the effects of the macroclimate on the microclimate occupied by hyperoceanic bryophytes in temperate rain forest, and to what extent this habitat may provide a refuge from climate change.

Regional PM2.5 concentration prediction analysis and spatio-temporal mapping incorporating ZWD data

In recent years, air pollution events have been frequent and machine learning models have been widely used in research on long-term and short-term PM2.5 concentration prediction due to their excellent predictive capabilities, but most studies have only dealt with a small number of stations or cities, failing to take into account a spatial plane perspective to demonstrate future PM2.5 concentration changes in the region. Therefore, in this paper, 617 air quality monitoring sites in central and eastern China are used as examples. First, the five meteorological elements of wind speed (WIN), temperature (TEM), precipitation (PRE), pressure (PRS), relative humidity (RHU) and zenith wet delay (ZWD) data were interpolated to the air quality monitoring sites (PM2.5, PM10, SO2, NO2, O3, CO) by the inverse distance interpolation (IDW) method. Secondly, the density distribution of each factor was analyzed, and the monthly mean value of each site was interpolation mapped to observe the overall change trend of PM2.5 in the central and eastern regions, and then Spearman correlation coefficient was used to determine the relevant factors for modelling. Finally, four models of back propagation (BP) neural network, random forest (RF), support vector regression (SVR) and long and short-term memory network (LSTM) were constructed to predict the change of PM2.5 concentration at each air quality monitoring station for the next week (7 days), half month (15 days) and month (30 days), selecting the model with the best applicability at each time scale, and mapping the prediction results of stations with R2 greater than 0.5 in the model through spatial interpolation for spatial and temporal mapping. The results show that: (1) except for the differences between the density distribution trends of TEM, PRS and RHU and PM2.5, the remaining elements are consistent with the PM2.5 change trends, and the PM2.5 concentrations in the central-eastern region are high in winter (January, February and December) and low in summer (June, July and August), with a spatial distribution of high in the central-west and low in the east; (2) through the evaluation of the index results, it can be seen that in the three time scales of PM2.5 concentration prediction, RF has better applicability compared with the three models of BP, SVR and LSTM, among which the model accuracy is the best in predicting the future week; (3) By comparing the model interpolation results with the original data interpolation results, it can be seen that the results obtained from the model interpolation basically fit the spatial and temporal trends of future PM2.5 concentrations, and the best interpolation results in predicting the future week, the highest reliability of the spatial and temporal mapping results, and more accurate description of the future PM2.5 concentration changes.

Comparison of typical meteorological year generation methods for building energy simulation in marine climate of China

The unique marine climate can affect the applicability of typical meteorological year (TMY) methods, which has not been adequately discussed in previous research. Based on meteorological data of five islands of Dongsha, Xisha, Nansha, Meiji, and Yongshu in China, this study selected their respective TMY using four methods including CTYW, Danish, Festa-Ratto and Sandia. Then, the suitability of various methods in marine climate, low-latitude sea area, was evaluated through the deviation between TMY data and measured data, as well as the precision of the building energy loads. The conclusions indicate that TMY chosen through CTYW method exhibits the lowest deviations in predicting the monthly mean value of meteorological elements with an average NRMSE value of 2.98%, and the hourly value with a cumulative Filkenstein-Schafer (FS) value of 20.8. It was followed by Sandia method with NRMSE of 3.44% and FS of 21.7. Compared with the mean building energy loads calculated using long-term measured data, the result calculated based on TMY chosen through CTYW exhibits the lowest error rate, followed by Sandia method, with the average NRMSE values of 2.06% and 2.48%, respectively. In summary, the TMY chosen by CTYW has a reliable representativeness in long-term climate characterization and energy loads simulation for low-latitude sea areas of China.

Spatiotemporal Analysis of Soil Moisture Variability and Its Driving Factor

Soil moisture (SM), as a crucial input variable of land surface processes, plays a pivotal role in the global hydrological cycle. The aim of this paper is to examine the spatiotemporal variability in SM in the Heihe River Basin using all-weather land surface temperature (LST) and reanalysis land surface data. Initially, we downscaled and generated daily 1 km all-weather SM data (2020) for the Heihe River Basin. Subsequently, we investigated the spatial and temporal patterns of SM using geostatistical and time stability methods. The driving forces of the monthly SM were studied using the optimal parameter-based geographical detector (OPGD) model. The results indicate that the monthly mean values of the downscaled SM data range from 0.115 to 0.146, with a consistently lower SM content and suitable temporal stability throughout the year. Geostatistical analysis revealed that months with a higher SM level exhibit larger random errors and higher variability. Driving analysis based on the factor detector demonstrated that in months with a lower SM level, the q values of each driving factor are relatively small, and the primary driving factors are land cover and elevation. Conversely, in months with a higher SM level, the q values for each driving factor are larger, and the primary driving factors are the normalized difference vegetation index and LST. Furthermore, interaction detector analysis suggested that the spatiotemporal variation in SM is not influenced by a single driving factor but is the result of the interaction among multiple driving factors, with most interactions enhancing the combined effect of two factors.

A-086 Vitamin D and its Variation in a Pandemic Context: Pre-pandemic—Intra-pandemic—Post-pandemic Analysis in the Province of Buenos Aires - Argentina

Abstract Background The determination of 25 OH Vitamin D3 is a frequently requested measurement for the purpose of evaluating its deficiency, which would be related to various diseases. The behavior of monthly mean values (X̅) was analyzed in 984 514 patients from Buenos Aires (Argentina) in the period from January 2014 to December 2022, in order to evaluate how seasonality influences and verify the effect of the mandatory isolation due to the SARS-CoV-2 virus pandemic in 2020. Methodology We used the Chemiluminescence Assay (QLIA) ADVIA Centaur Vitamin D Total Assay (Siemens) on an Advia Centaur (2014–2020) and Aliniti I 25 OH VITAMINE D3 (Abbott Diagnostics) on an Aliniti I (2020–2022), reference values are Optimal: >30.0 ng/mL, Insufficiency: 20.0–30.0 ng/mL Deficiency: <20.0 ng/mL. Results Conclusions From the analysis of the data, a decrease in the winter months is observed, as already described, caused by less exposure to sunlight. In 2020, it was also observed that due to confinement, there was a slight increase in mean values (26.40 ng/mL (P < .001)), which was the highest of the years analyzed. One hypothesis is that this could be caused by the increased free time of the population resulting in greater exposure to sunlight. At the same time, the insufficiency of Vitamin D3 in the studied population is evident, as only in 2 out of the 108 months analyzed did it slightly exceed the optimal level of 30.0 ng/mL. We believe that it would be important to evaluate the implementation of a Vitamin D3 supplementation policy for the general population and subsequently conduct further studies of this type to verify the effectiveness of such implementation.

Assessing historical climate trends in Dhaka City: A multivariate analysis using Mann-Kendall and Sen's slope method

This study presents a comprehensive assessment of the historical climatology of Dhaka city over a period of fifty years (1971-2021), utilizing five distinct climate parameters: Skin Temperature, Air Temperature, Rainfall, Wind Speed, and Humidity. The study employed various statistical methods, such as Linear regression, the Mann-Kendall test model, and Sen’s slope estimator method, to examine the significant patterns in climate data and quantify the degree of fluctuations in the variables. The results of the Mann-Kendall Test provided evidence that each parameter's values displayed a statistically significant trend. The Sen's Slope estimator revealed a declining trend in the monthly mean value of all climate parameters except for wind speed and humidity. The annual average Skin and Air Temperature in Dhaka City increased at a rate of 0.033°C and 0.065°C respectively over the study period; as a result, the average annual Rainfall and Humidity showed an increasing tendency, which was 1.225 mm and 0.086% respectively. The Monsoon period demonstrated the highest rainfall and humidity levels, while the winter season demonstrated the lowest levels of these parameters. The trend of Wind Speed has exhibited a decrease at a rate of 0.032 m/s over the past five decades. A critical analysis evaluates the trends and patterns observed in the data. This research attempts to improve understanding of the components that have influenced the climate of Dhaka City by evaluating historical data.

Global Gridded Aerosol Models Established for Atmospheric Correction Over Inland and Nearshore Coastal Waters

AbstractThe use of standard aerosol models for atmospheric correction over inland and nearshore coastal waters generates large uncertainties due to their inability to appropriately characterize aerosols in those regions. To address this problem, we developed new aerosol models using data acquired from 1,475 Aerosol Robotic Network (AERONET) sites distributed in inland and coastal regions throughout the globe. We established spatially gridded (5° × 5°) aerosol models on a climatological monthly scale, with a total of 3,207 new aerosol models developed for 310 grid cells worldwide. The monthly aerosol model for each grid was derived using mean monthly values of microphysical aerosol properties from AERONET data. The optical properties (i.e., single scattering albedo and Ångström coefficient) of our global gridded aerosol models demonstrated considerable spatial and seasonal dynamics and deviated substantially from the aerosol models currently utilized by the National Aeronautics and Space Administration (NASA). We built associated lookup tables for all aerosol models and incorporated them into two atmospheric correction algorithms: the combined near‐infrared and shortwave infrared algorithm (NIR‐SWIR) and the Atmospheric Correction algorithm for inLand and Nearshore Coastal waters (ACLANC). For both algorithms, the accuracies of atmospherically corrected satellite remote sensing reflectance (Rrs) improved markedly when the new aerosol models were used, and improvements were noted across all spectral bands and across all six inland and nearshore water bodies used in this study. Our new aerosol models can be easily applied to all ocean color satellite missions to obtain accurate Rrs products for inland and coastal waters worldwide.

A novel statistical model for flood prediction in the Eel River watershed, New Brunswick, Canada

ABSTRACT A strong correlation between the effect of climate change and the increase in flooding frequency and magnitude has been reported in Canada. Consequently, there is a crucial need to examine the effects of future climate change scenarios on flooding conditions. The main objective of this research is to better understand the destructive effects of flood events under historical and future climate change conditions for a small watershed (Eel River watershed) in New Brunswick (NB), Eastern Canada. A practical model had been developed using the modified Artificial Neural Network (ANN) in MATLAB by the authors of this study. The architecture and data structure of ANN is characterized by a back propagation with the Levenberg–Marquardt method. The observed daily total precipitation, daily maximum and minimum air temperatures, daily discharge for the period 1967 to 1983, the simulated monthly maximum and minimum air temperatures, and monthly total precipitation for the period of 1996–2099 from the CanESM2, the second-generation Canadian Earth System Model (CGCM), were used as input of the model. The Representative Concentration Pathways (RCP 4.5 and 8.5), as suitable climate change scenarios, were selected based on the Intergovernmental Panel on Climate Change (IPCC) recommendations for flood studies. Daily values of temperatures, precipitations, and discharges were converted to monthly mean values for better prediction of the output results. In addition, two series of observed discharges were prepared using mean monthly (Qavg) and daily maximum discharges (Qd) as the Target of the model. For more accurate analysis, the time frames of 1996–2012 (for the historical) and 2022–2038, 2039–2055, 2056–2072, 2073–2089, and 2083–2099 (for the future) were considered with a duration of 16 years for each time frame. The output results of ANN were predicted daily maximum (Qd) and mean (Qavg) discharges under the impact of climate change scenarios. As a part of the developed model, Flood Frequency Analysis (FFA) was undertaken using the generalized extreme value (GEV) and the three-parameter lognormal (LN3) distributions based on the predicted and observed discharges. The performance of FFA and ANN were demonstrated using the Anderson–Darling (AD), the Chi-square (CS) tests and coefficient of correlation (R) and mean squared error (MSE), respectively. In conclusion, the three most critical time frames with the highest values of predicted discharges were 2022–2038, 2056–2072, and 2073–2089 for RCP4.5 and 2039–2055, 2073–2089, and 2083–2099 for RCP8.5. Also, based on the FFA, the magnitudes of flood recurrence for the future time period of 100 years will dramatically increase according to the most critical time frames of 2056–2072 and 2039–2055 for RCP 4.5 and 8.5, respectively. Findings indicated that the Eel River watershed will encounter severe floods, and about a 50% increase in mean discharge, especially for the critical time frames. Finally, flood occurrences show increasing trends due to climate change effects in the most critical time frames.

РЕАНАЛИЗ ДЕРЕКТЕРІ БОЙЫНША ИНВЕРСИЯ ҚАБАТЫН ТАЛДАУ МҮМКІНДІКТЕРІ

The study of thermal inversion layers in the atmosphere plays an important role in understanding various atmospheric processes and solving environmental and meteorological problems. This work is devoted to evaluating the potential of thermal inversion layer analysis using ERA5 reanalysis data in the nearground layer as an alternative to the limited availability of aerological stations.The characteristics of inversions calculated using ERA5 reanalysis data and radiosonde data from the Karaganda Aerological Station for the period from 2012 to 2021 were compared. General inversions occur most often in January (15...18 times), while in spring and autumn, the average monthly number of cases is 4...10. In the summertime, surface inversions occur most frequently, with a mean monthly value of up to 25 times. The intensity of different surface inversions varies between -1 °C ... -9 °С. The results of the study showed a close similarity between the ERA5 reanalysis data and the radiosonde data. This indicates the suitability of the reanalysis data for studying thermal inversions. This study contributes to the development of knowledge in atmospheric pollution, weather forecasting, aviation meteorology, and the development of climate models by using reanalysis data to study inversion phenomena.

Evaluation of some metals in sediment, water and snail, from an urban flood reservoir in Benin-city, Edo state, Nigeria

This study was conducted to ascertain the presence of Iron (Fe), Copper (Cu), Manganese (Mn) and Nickel (Ni) in sediment, snail and water at the urban flood reservoir, Useh. Samples were collected in triplicate over a period of six months (November – April, 2023). Sediment samples were collected using a venier caliper at a depth of 10cm using Van-Veen Grab method. Snail samples were collected by randomly hand picking from the sampling site, and the water was collected using labeled water canisters of 50cl properly rinsed with deionized and site water. At the laboratory, samples were identified, prepared and digested according to standard methods. Analyses of the metals in the digest were then analyzed using the Atomic Adsorption Spectroscopy. The presence of Fe, Cu, Mn were confirmed in all the media during the period of study except for Ni that though was present in sediment and snail was only detected in April in water. The mean concentration of the metals in sediment was higher than the values in water and snail. Fe levels varied between 0.11-2.14mg/l in the snail and water, which were not significantly different (p>0.05) but were significantly different (p<0.05) in the sediment (2.24-97.73mg/l). Cu mean value of 0.43mg/l and 0.04mg/l were recorded in the sediment and snail respectively. ANOVA showed that Cu levels was only significantly different in water. The highest Ni levels observed water (0.08mg/l) and sediment (0.05), were not significantly different (p>0.05) in the sediment. The highest and lowest mean monthly values of 1.12mg/l Mn and 0.01mg/Mn were observed in sediment and snail respectively. Ni and Mn levels in these media showed no significant differences (p<0.05). Hence the free inflow of anthropogenic and other waste observed at the site if not properly managed will greatly increase the pollution level at the flood reservoir. A public awareness campaign of the implication of heavy metal accumulation in aquatic flora and fauna is therefore sacrosanct.

Analysis of the impact of health management measures for entry personnel on imported Dengue fever in Guangdong Province, 2020-2022

Objective: To explore the impact of health management measures for entry personnel (entry management measures) against COVID-19 on the epidemiological characteristics of imported Dengue fever in Guangdong Province from 2020 to 2022. Methods: Data of imported Dengue fever from January 1, 2016 to August 31, 2022, mosquito density surveillance from 2016 to 2021, and international airline passengers and Dengue fever annual reported cases from 2011 to 2021 in Guangdong were collected. Comparative analysis was conducted to explore changes in the epidemic characteristics of imported Dengue fever before the implementation of entry management measures (from January 1, 2016 to March 20, 2020) and after the implementation (from March 21, 2020 to August 31, 2022). Results: From March 21, 2020, to August 31, 2022, a total of 52 cases of imported Dengue fever cases were reported, with an imported risk intensity of 0.12, which were lower than those before implementation of entry management measures (1 828, 5.29). No significant differences were found in the characteristics of imported cases before and after implementation of entry management measures, including seasonality, sex, age, career, and imported countries (all P>0.05). 59.62% (31/52) of cases were found at the centralized isolation sites and 38.46% (20/52) at the entry ports. However, before implementation of entry management measures, 95.08% (1 738/1 828) of cases were found in hospitals. Among 51 cases who had provided entry dates, 82.35% (42/51) and 98.04% (50/51) of cases were found within seven days and fourteen days after entry, slightly higher than before implementation [(72.69%(362/498) and 97.59% (486/498)]. There was significant difference between the monthly mean values of Aedes mosquito larval density (Bretto index) from 2020 to 2021 and those from 2016 to 2019 (Z=2.83, P=0.005). There is a strong positive correlation between the annual international airline passengers volume in Guangdong from 2011 to 2021 and the annual imported Dengue fever cases (r=0.94, P<0.001), and a positive correlation also existed between the international passenger volume and the annual indigenous Dengue fever cases (r=0.72, P=0.013). Conclusions: In Guangdong, the entry management measures of centralized isolation for fourteen days after entry from abroad had been implemented, and most imported Dengue fever cases were found within fourteen days after entry. The risk of local transmission caused by imported cases has reduced significantly.

MORPHOLOGY OF PILA GLOBOSA (GASTROPOD MOLLUSCA: AMPULLARIIDAE) WITHIN ENVIRONMENTAL VARIATION OF SOUTH-WESTERN BANGLADESH

Owing to the community engagement of the gastropod collection in the south western ecosystems in Bangladesh, the gastropod molluscs are getting important attention in terms of edible and non-edible purposes while detailed morphological information about the gastropods is scarce in this area. The present research has investigated the morphological variability of the gastropod molluscs (Pila globosa) in the six localities of south-western coastal Bangladesh (such as, Khulna, Bagerhat, Satkhira, Gopalganj, Pirojpur and Jessore). A total of 2160 gastropod samples (Pila globosa) were collected from July to December monthly over the two consecutive years of 2013 and 2014. The results of the analyses revealed that the maximum monthly mean values of the dry shell mass and the dry body mass were found in November, indicating the complete growth and development of the gastropods in the coastal environment. One-way ANOVA and ANCOVA show that there is a significant morphological variability of Pila globosa among different localities in the south-west coastal Bangladesh, while the covariate of water salinity significantly affects the aperture length of it; the covariate of electrical conductivity of soil significantly affects both the height and aperture length; the covariate of sodium contents of the soil significantly affects the aperture length of the gastropod mollusc. The results also revealed that the morphological variability of the mollusc P. globosa is linked to complex ecological systems and habitat variation.