Seasonal Variables Research Articles

Assessment and development of air quality forecasting models by the combination of PCA and ANN at metropolis Kolkata, India

PM10, PM2.5, SO2, NO2, Pb, As and Ni were monitored at Jadavpur, Kolkata over the time span of October 2016 to March 2017. The air quality of the study area was assessed in terms of air quality index (AQI) which is calculated on the assessed concentration of the pollutants. The result of the study showed significant seasonal variance and the ambient air of the study area is highly polluted in terms of particulate matters, NO2 and metallic pollutants. AQI study reveals that PM2.5 was found responsible for this degraded air quality of Kolkata. Principal component analysis (PCA) was used to identify possible contributors i.e. i) construction work, windblown dust, road dust and earth crust (variance- 28.98%); ii) automobile exhaust (variance- 20.47%); iii) brake wear source, road dust and tire wear (variance- 18.07%) of air pollution in the study area. Multiple linear regression (MLR) and feedforward backpropagation artificial neural network (FFBPANN) were used in combination with PCA to predict the future PM2.5 concentrations. Several performance indexes were used to assess the degree of accuracy and the magnitude of error in the model predicted values. Overall performance index establishes the fact that PCA-FFBP-ANN model most accurately predicts the future PM2.5 concentrations.

Wheat germination and highly diluted agitated gibberellic acid (10e-30) - a multi researcher study

Grains of winter wheat (Triticum aestivum L., Capo variety) were observed under the influence of highly diluted gibberellic acid (10-30) prepared by stepwise dilution and agitation according to a protocol derived from homeopathy (“G30x”). Adequate control was used (water prepared according to the homeopathic protocol “W30x” and/or untreated water “W0”). Two sets of multicenter experiments were performed, 4 in 2009-2010 and 4 in 2011, involving altogether 6 researchers, 6 laboratories and 4,000 grains per treatment group. Data were found to be homogeneous within the control groups as well as within the verum groups. When the 2009-2010 experiments were pooled, mean germination rates after 24 hours were (85.9 + 2.6) for the control group and (82.1 + 5.7) for G30x (mean + SD at the level of experiments in %) (N = 2,000 per group). Verum germination rate was 4.4% lower than (i.e. equal to 96.6% of) (4.4 + 96.6 = 101) the control germination rate (100%). The difference is statistically significant (p < 0.001) and the effect size (d) is large (> 0.8). Observations at other points in time between 0 and 40 hours of germination yielded similar results. Practically no difference was found between W30x and W0 groups (p > 0.05). When the 2011 experiments were pooled, the mean germination rates after 24 hours were (73 + 12) for the control group and (73 + 14) for G30x (N = 2,000 per group), i.e. there was practically no difference between the groups (p > 0.05). We interpret the data from 2009-2010 on wheat germination within 40 hours as being in line with our previous findings on wheat stalk growth after one week, i.e. as confirmation that gibberellic acid 30x can influence, i.e. slow down, wheat development. Various possible reasons for the absence of any difference between groups in the 2011 experiments, including seasonal variance, are discussed and it is suggested to perform wheat germination experiments in the very beginning of autumn season only.

A study of univariate forecasting methods for crude oil price

PurposeThis paper aims to compare different univariate forecasting methods to provide a more accurate short-term forecasting model on the crude oil price for rendering a reference to manages.Design/methodology/approachSix different univariate methods, namely the classical decomposition model, the trigonometric regression model, the regression model with seasonal dummy variables, the grey forecast, the hybrid grey model and the seasonal autoregressive integrated moving average (SARIMA), have been used.FindingsThe authors found that the grey forecast is a reliable forecasting method for crude oil prices.Originality/valueThe contribution of this research study is using a small size of data and comparing the forecasting results of the six univariate methods. Three commonly used evaluation criteria, mean absolute error (MAE), root mean squared error (RMSE) and mean absolute percent error (MAPE), were adopted to evaluate the model performance. The outcome of this work can help predict the crude oil price.

Influence of seasonal and climatic variables on coffee berry borer (Hypothenemus hampei Ferrari) flight activity in Hawaii

Coffee berry borer (CBB, Hypothenemus hampei Ferrari) is the most serious insect pest of coffee worldwide, yet little is known about the effect that weather variables have on CBB flight activity. We sampled flying female CBB adults bi-weekly over a three-year period using red funnel traps baited with an alcohol lure at 14 commercial coffee farms on Hawaii Island to characterize seasonal phenology and the relationship between flight activity and five weather variables. We captured almost 5 million scolytid beetles during the sampling period, with 81–93% of the trap catch comprised of CBB. Of the captured non-target beetles, the majority were tropical nut borer, black twig borer and a species of Cryphalus. Two major flight events were consistent across all three years: an initial emergence from January-April that coincided with early fruit development and a second flight during the harvest season from September-December. A generalized additive mixed model (GAMM) revealed that mean daily air temperature had a highly significant positive correlation with CBB flight; most flight events occurred between 20–26°C. Mean daily solar radiation also had a significant positive relationship with flight. Flight was positively correlated with maximum daily relative humidity at values below ~94%, and cumulative rainfall up to 100 mm; flight was also positively correlated with maximum daily wind speeds up to ~2.5 m/s, after which activity declined. Our findings provide important insight into CBB flight patterns across a highly variable landscape and can serve as a starting point for the development of flight prediction models.

Seasonal variation in serum metabolites of northern European dogs.

BackgroundMetabolic profiling identifies seasonal variance of serum metabolites in humans. Despite the presence of seasonal disease patterns, no studies have assessed whether serum metabolites vary seasonally in dogs.HypothesisThere is seasonal variation in the serum metabolite profiles of healthy dogs.AnimalsEighteen healthy, client‐owned dogs.MethodsA prospective cohort study. Serum metabolomic profiles were assessed monthly in 18 healthy dogs over a 12‐month period. Metabolic profiling was conducted using a canine‐specific proton nuclear magnetic resonance spectroscopy platform, and the effects of seasonality were studied for 98 metabolites using a cosinor model. Seasonal component was calculated, which describes the seasonal variation of each metabolite.ResultsWe found no evidence of seasonal variation in 93 of 98 metabolites. Six metabolites had statistically significant seasonal variance, including cholesterol (mean 249 mg/dL [6.47 mmol/L] with a seasonal component amplitude of 9 mg/dL [0.23 mmol/L]; 95% confidence interval [CI] 6‐13 mg/dL [0.14‐0.33 mmol/L], P < .008), with a peak concentration of 264 mg/dL (6.83 mmol/L) in June and trough concentration of 236 mg/dL (6.12 mmol/L) in December. In contrast, there was a significantly lower concentration of lactate (mean 20 mg/dL [2.27 mmol/L] with a seasonal component amplitude of 4 mg/dL [0.42 mmol/L]; 95% CI 2‐6 mg/dL [0.22‐0.62 mmol/L], P < .001) during the summer months compared to the winter months, with a peak concentration of 26 mg/dL (2.9 mmol/L) in February and trough concentration of 14 mg/dL (1.57 mmol/L) in July.Conclusions and Clinical ImportanceWe found no clear evidence that seasonal reference ranges need to be established for serum metabolites of dogs.

Is stallion epididymal fluid phosphoproteome affected by the equine reproductive season?

Phosphorylation and dephosphorylation of proteins are considered to be the most important processes in sperm maturation during epididymal transit. The main aim of this study was to isolate and identify phosphoproteins from the epididymal milieu obtained from reproductively mature stallions during and out of the breeding season. With the use of 1D-PAGE and nanoLC-MS/MS, we identified phosphoproteins that fulfil various functions: regulatory, transport, motility, ubiquitination, chaperone, antioxidant, apoptotic and enzymatic. Moreover, we characterized tyrosine, serine and threonine phosphorylation patterns, taking into consideration the seasonal and epididymal segment variables. The intensity of phosphorylation and profiles of phosphoproteins varied in subsequent regions of the epididymis. With the use of western and immunoblot tests, we demonstrated that fourteen proteins underwent phosphorylation both during and out of the breeding season. However, significant differences (p≤0.05) in the phosphorylation status were demonstrated in the case of 44 kDa (glutamine synthetase), 38 kDa (malate dehydrogenase), 34 kDa (clusterin/inorganic pyrophosphatase), 31 kDa (clusterin/ /ubiquitin thioesterase), 29 kDa (14-3-3 protein/purine nucleotide phosphorylase) for the season factor and 55 (Rab GDP dissociation inhibitor alpha) and 31 kDa ((clusterin/ubiquitin thioesterase) proteins for the segment factor. The occurrence of the other phosphoproteins was spontaneous among individuals and in both seasons.

Distinct Responses of Leaf Traits to Environment and Phylogeny Between Herbaceous and Woody Angiosperm Species in China.

Leaf traits play key roles in plant resource acquisition and ecosystem processes; however, whether the effects of environment and phylogeny on leaf traits differ between herbaceous and woody species remains unclear. To address this, in this study, we collected data for five key leaf traits from 1,819 angiosperm species across 530 sites in China. The leaf traits included specific leaf area, leaf dry matter content, leaf area, leaf N concentration, and leaf P concentration, all of which are closely related to trade-offs between resource uptake and leaf construction. We quantified the relative contributions of environment variables and phylogeny to leaf trait variation for all species, as well as for herbaceous and woody species separately. We found that environmental factors explained most of the variation (44.4–65.5%) in leaf traits (compared with 3.9–23.3% for phylogeny). Climate variability and seasonality variables, in particular, mean temperature of the warmest and coldest seasons of a year (MTWM/MTWQ and MTCM/MTCQ) and mean precipitation in the wettest and driest seasons of a year (MPWM/MPWQ and MPDM/MPDQ), were more important drivers of leaf trait variation than mean annual temperature (MAT) and mean annual precipitation (MAP). Furthermore, the responses of leaf traits to environment variables and phylogeny differed between herbaceous and woody species. Our study demonstrated the different effects of environment variables and phylogeny on leaf traits among different plant growth forms, which is expected to advance the understanding of plant adaptive strategies and trait evolution under different environmental conditions.

Spatiotemporal Characterization of VIIRS Night Light

The Visible Infrared Imaging Radiometer Suite (VIIRS) Day Night Band (DNB) on board the Suomi NPP satellite now provides almost a decade of daily observations of night light. The temporal frequency of sampling, without the degree of temporal averaging of annual composites, makes it necessary to consider the distinction between apparent temporal changes of night light related to the imaging process and actual changes in the underlying sources of the night light being imaged. The most common approach to night light change detection involves direct attribution of observed changes to the phenomenon of interest. Implicit in this approach is the assumption that other forms of actual and apparent change in the light source are negligible or non-existent. An alternative approach is to characterize the spatiotemporal variability prior to deductive attribution of causation so that the attribution can be made in the context of the full range of spatial and temporal variation. The primary objective of this study is to characterize night light variability over a range of spatial and temporal scales to provide a context for interpretation of night light changes observed on both subannual and interannual time scales. This analysis is based on a combination of temporal moments, spatial correlation and Empirical Orthogonal Function (EOF) analysis. A key result of this study is the pervasive heteroskedasticity of VIIRS monthly mean night light. Specifically, the monotonic decrease of variability with increasing mean brightness. Anthropogenic night light is remarkably stable on subannual time scales while background luminance varies considerably. The variance partition from the eigenvalues of the spatiotemporal covariance matrix are 88, 2 and 2% for spatial, seasonal and interannual variance (respectively) in the most diverse region on Earth (Eurasia). Heteroskedasticity is pervasive in the monthly composites; present in all areas for all months of the year, suggesting that much, if not most, of the month-to-month variability may be related to luminance of otherwise stable sources subjected to multiple aspects of the imaging process varying in time. Given the skewed distribution of all night light arising from radial peripheral dimming of bright sources subject to atmospheric scattering, even aggregate metrics using thresholds must be interpreted in light of the fact that much larger numbers of more variable low luminance pixels may statistically overwhelm smaller numbers of stable higher luminance pixels and cause apparent changes related to the imaging process to be interpreted as actual changes in the light sources.

Comparison of multiple machine learning algorithms for urban air quality forecasting

Environmental air pollution has become one of the major threats to human lives nowadays in developed and developing countries. Due to its importance, there exist various air pollution forecasting models, however, machine learning models proved one of the most efficient methods for prediction. In this paper, we assessed the ability of machine learning techniques to forecast NO2, SO2, and PM10 in Amman, Jordan. We compared multiple machine learning methods like artificial neural networks, support vector regression, decision tree regression, and extreme gradient boosting. We also investigated the effect of the pollution station and the meteorological station distance on the prediction result as well as explored the most relevant seasonal variables and the most important minimal set of features required for prediction to improve the prediction time. The experiments showed promising results for predicting air pollution in Amman with artificial neural network outperforming the other algorithms and scoring RMSE of 0.949 ppb, 0.451 ppb, and 5.570 µg/m3 for NO2, SO2, and PM10 respectively. Our results indicated that when the meteorological variables were obtained from the same pollution station the results were better. We were also able to reduce the time by reducing the set of variables required for prediction from 11 down to 3 and achieved major time improvement by about 80% for NO2, 92% for SO2, and 90% for PM10. The most important variables required for predicting NO2 were the previous day values of NO2, humidity and wind direction. While for SO2 they were the previous day values of SO2, temperature, and wind direction values of the previous day. Finally, for PM10 they were the previous day values of PM10, humidity, and day of the year.

Investigation of the formation and effects of upstream trihalomethanes in Tehran's raw and drinking water using the spectrophotometric method

Developing countries, such as Iran, lack the resources and infrastructure to effectively and continually monitor environmental variables related to safe drinking water. The concentration of trihalomethanes (THMs) in Tehran's drinking water was assessed using spectrophotometry as an alternative method rather than the commonly used GC–MS. This method proposes a more economical and available option for developing countries and local water treatment plants in small towns. Three surface water sites, i.e., the Karaj River, the Mamaloo River, the Jajrood River, and eighteen tap water sites, were logistically chosen to test this method. This study evaluates the effect of seasonal variables on trihalomethanes formation potential (THMFP) and its impact on drinking water THMs concentrations. According to the statistical analyses during summer, THMFP and temperature positively correlated with the correlation coefficient of 0.84 and P < 0.05. Also, a positive linear correlation was observed between THMFP and UV254 with a correlation coefficient of 0.73 and P < 0.05. THMFP started to decline during fall as the temperature decreased except in November when THMFP increased due to natural organic matters (NOMs) augmentation in rivers. The highest level of the monthly average concentration of THMs in drinking water was 33.9 ± 5.8 μg/L in September, and the lowest was 22.9 ± 4.2 μg/L in December. The results indicated a linear correlation between THMFP in surface water and THMs concentration in drinking water with a correlation coefficient of 0.81 and P < 0.05. This observation suggests that THMFP in surface water can directly affect THMs' levels in drinking water.

Influence of seasonal weather variables and habitat type on numbers of colonies of the giant honey bee in Nepal

Influence of seasonal weather variables and habitat type on numbers of colonies of the giant honey bee in Nepal

The Non-stationary Environmental Effects on Spawning Habitat of Fish in Estuaries: A Case Study of Coilia mystus in the Yangtze Estuary

The estuarine areas provide necessary spawning habitat, nursing habitat, and migratory for a variety of fishes and the environmental conditions are of significant heterogeneity. Coilia mystus is the key commercial fish that spawns in the Yangtze Estuary and the yield has declined sharply in the past 30 years. In order to understand the spawning habitat selection mechanism of C. mystus, the geographically weighted regression (GWR) model was applied to explore the non-stationary effect of environmental variables [e.g., the sea surface temperature (SST) and the sea surface salinity (SSS)] and geographical variables [e.g., the distance to the coast (DTC) and the depth (DEP) of water] on the egg density distribution of C. mystus. The data were derived from the spring and summer ichthyoplankton surveys carried out from 2019 to 2020 in the Yangtze Estuary, China. The GWR model results showed that the key variables in different seasons had spatial non-stationary effects on the distribution of spawning habitat of C. mystus, which were mainly caused by regional rise in temperature and saltwater intrusion of the South Branch. In the spring, the SSS, the DTC, and the DEP were the main impact factors and saltwater intrusion in the South Branch might lead to the trend that the spawning habitat moved further upstream to the Changxing Island. The SST was most important in the summer and the relatively lower SST was more crucial in the spawning habitat selection than the DTC and the DEP. The GWR model performed well in the study of C. mystus potential spawning habitat in the Yangtze Estuary. We recommend that more attention should be paid in regional variation of environmental factors to explore fish potential spawning habitat in the estuarine areas.

Evaluation of the safety and efficacy of dihydroartemisinin–piperaquine for intermittent preventive treatment of malaria in HIV-infected pregnant women: protocol of a multicentre, two-arm, randomised, placebo-controlled, superiority clinical trial (MAMAH project)

IntroductionMalaria infection during pregnancy is an important driver of maternal and neonatal health especially among HIV-infected women. Intermittent preventive treatment in pregnancy (IPTp) with sulphadoxine–pyrimethamine is recommended for malaria prevention...

Forecasting the seasonal natural gas consumption in the US using a gray model with dummy variables

To forecast the seasonal fluctuations of US natural gas consumption accurately, a novel gray model based on seasonal dummy variables and its derived model are separately established. Then, the approximate time response formula in the proposed seasonal forecasting model is optimally calculated by using particle swarm optimization algorithm. On this basis, empirical analysis is conducted using data pertaining to natural gas consumption in the US during 2010–2019. The results show that gray model based on seasonal dummy variables and its derived model can recognize seasonal fluctuations in US natural gas consumption, whose prediction performances are much better than that of a traditional gray model, autoregressive integrated moving average (ARIMA), support vector regression (SVR), recurrent neural network (RNN), transformer, and fuzzy time series forecasting models. The mean absolute percentage errors (MAPEs) of the proposed seasonal gray forecasting model and its derived model, the classic gray model, ARIMA, SVR, RNN, Transformer, and Fuzzy time series forecasting models are 3.46%, 2.37%, 12.23%, 3.39%, 2.38%, 3.08%, 3.84%, and 5.02% in the training set, while those are 4.57%, 4.42%, 12.44%, 7.9%, 8.09%, 11.33%, 35.19% and 13.19% in the test set, respectively. The predicted and empirical results obtained by utilizing the proposed gray model implied that natural gas consumption in the US from 2020 to 2022 will maintain its seasonal growth and periodic changes, with the highest and the lowest values in the first and second quarters, respectively.

The influence of weather conditions on breeding dates of the common gull (&lt;i&gt;Larus canus&lt;/i&gt;) in the south of Western Siberia

Aim. The purpose of this research was to find out what local weather factors influence the nesting timing of the common gull (Larus canus). Material and Methods. The time of egg laying by the common gull was determined using data obtained during regular surveys over 8 years (1996‐1998, 2002‐2003, 2006‐2008) on the islands of Lake Bolshie Chany. Weather and climatic factors were assessed using open‐access databases. Results. It was been established that the start of egg‐laying in the colony of the common gull is determined by wind strength, the number of rainy days, the associated atmospheric pressure during the second decade of April, and the air temperature – the transition date at which the average daily air temperature rose above 0°C. In years with unstable spring temperatures, a relationship was revealed between the air temperature and the intensity of egg laying by the common gulls by day. In years when temperatures rise evenly, precipitation and wind speed become the main factors.Conclusion. We conclude that the egg‐laying dates of the common gull is influenced by weather variables during the whole nesting season and not predominantly by early season variables. We also show the importance of large‐scale climatic phenomena such as the EAWR in explaining variability in timing of the nesting of the common gull in Siberia. We suggest that future studies should focus on the effects of extremes in weather variables and global climatic phenomena.

Seasonal and aridity influences on the relationships between drought indices and hydrological variables over China

In recent decades multiple drought indices have been developed to quantify drought characteristics and to examine drought effects. However, little is known about regional applicability of drought indices for monitoring seasonal droughts over China. Here we investigated the capacity of three commonly used drought indices (SPI, SPEI and scPDSI) for showing water deficit as compared to independent precipitation, root zone soil moisture (SM) and terrestrial water storage (TWS) estimates collected from satellite observation and model simulation, by using trend and correlation analysis of the drought indices and hydrological variables in different seasons and climatic zones from 2000 to 2019. Our results suggested that areas with a significant increasing trend in precipitation (p < 0.05) occupied 13.4–20% of the study area from spring to autumn. The corresponding area for SM varied from 20 to 26%. A wetting trend was dominant in drylands despite TWS depletion. A drying trend appeared in southern North China Plain and southwest China, where severe droughts in 2009 and 2011 led to substantial declines in SM and TWS. SPI and SPEI worked better than scPDSI in characterizing meteorological drought at the seasonal timescale, given that the inter-annual variation of precipitation was in consistent with that of SPI and SPEI in approximately 70–80% of China. scPDSI was suitable for agricultural drought monitoring in different seasons, and agricultural drought was well characterized by SPI and SPEI only in the summer. SPI, SPEI and scPDSI both showed worse performances in quantifying drought trend and magnitude in drier regions and seasons characterized by a low aridity index. Thus, potential future expansion of global drylands calls for an integration of human impact into hydrological drought modeling.

Trophic groups of soil fauna in semiarid: Impacts of land use change, climatic seasonality and environmental variables

Soil fauna performs several functions in native and anthropized ecosystems, being affected by spatiotemporal variation and by environmental resource availability. However, there is still a gap in knowledge of how land use change, climatic seasonality, and soil and climate variables impact trophic groups of soil fauna in semiarid environments. We investigated the abundance of detritivores, herbivores, microbivores and predators in three land uses (native forest, pasture and irrigated agriculture), during the dry and wet season, using two collection methods (manually separated soil monoliths and pitfalls) and the effects of soil and climate variables on soil fauna. Detritivore and predator activity was predominant in the 0−20 cm layer (monoliths), while herbivores and microbivores abundance were greater on the soil surface (pitfalls). Irrigated agricultural areas had higher abundance of soil fauna and a different community structure from native forest and pasture, due to continuous water availability. Climatic seasonal variables (temperature, air humidity, solar radiation and wind speed), soil characteristics (Macronutrients, C, C:N ratio, pH and CEC) and land use change impacted all trophic groups. Irrigated agriculture in semiarid regions can serve as a refuge for soil fauna, bringing light to a new community structure that can be shaped to shelter the highest possible biodiversity.

Association between coronavirus cases and seasonal climatic variables in Mediterranean European Region, evidence by panel data regression

The coronavirus pandemic is one of the most fast-spreading diseases in the history, and the transmission of this virus has crossed rapidly over the whole world. In this study, we intend to detect the effect of temperature, precipitation, and wind speed on the Coronavirus infected cases throughout climate seasons for the whole year of epidemic starting from February 20, 2020 to February 19, 2021 with considering data patterns of each season separately; winter, spring, summer, autumn, in Mediterranean European regions, whereas those are located at the similar temperature zone in southern Europe. We apply the panel data approach by considering the developed robust estimation of clustered standard error which leads to achieving high forecasting accuracy. The main finding supports that temperature and wind speed have significant influence in reducing the Coronavirus cases at the beginning of this epidemic particularly in the first-winter, spring, and early summer, but they have very weak effects in the autumn and second-winter. Therefore, it is important to take into account the changes throughout seasons, and to consider other indirect factors which influence the virus transmission. This finding could lead to significant contributions to policymakers in European Union and European Commission Environment to limit the Coronavirus transmissions. As the Mediterranean region becomes more crowded for tourism purposes particularly in the summer season.

Seasonal climate signals preserved in biochemical varves: insights from novel high-resolution sediment scanning techniques

Abstract. Varved lake sediments are exceptional archives of paleoclimatic information due to their precise chronological control and annual resolution. However, quantitative paleoclimate reconstructions based on the biogeochemical composition of biochemical varves are extremely rare, mainly because the climate–proxy relationships are complex and obtaining biogeochemical proxy data at very high (annual) resolution is difficult. Recent developments in high-resolution hyperspectral imaging (HSI) of sedimentary pigment biomarkers combined with micro X-ray fluorescence (µXRF) elemental mapping make it possible to measure the structure and composition of varves at unprecedented resolution. This provides opportunities to explore seasonal climate signals preserved in biochemical varves and, thus, assess the potential for annual-resolution climate reconstruction from biochemical varves. Here, we present a geochemical dataset including HSI-inferred sedimentary pigments and µXRF-inferred elements at very high spatial resolution (60 µm, i.e. &gt; 100 data points per varve year) in varved sediments of Lake Żabińskie, Poland, over the period 1966–2019 CE. We compare these data with local meteorological observations to explore and quantify how changing seasonal meteorological conditions influenced sediment composition and varve formation processes. Based on the dissimilarity of within-varve multivariate geochemical time series, we classified varves into four types. Multivariate analysis of variance shows that these four varve types were formed in years with significantly different seasonal meteorological conditions. Generalized additive models (GAMs) were used to infer seasonal climate conditions based on sedimentary variables. Spring and summer (MAMJJA) temperatures were predicted using Ti and total C (Radj2=0.55; cross-validated root mean square error (CV-RMSE) = 0.7 ∘C, 14.4 %). Windy days from March to December (mean daily wind speed &gt; 7 m s−1) were predicted using mass accumulation rate (MAR) and Si (Radj2=0.48; CV-RMSE = 19.0 %). This study demonstrates that high-resolution scanning techniques are promising tools to improve our understanding of varve formation processes and climate–proxy relationships in biochemical varves. This knowledge is the basis for quantitative high-resolution paleoclimate reconstructions, and here we provide examples of calibration and validation of annual-resolution seasonal weather inference from varve biogeochemical data.

Spatial and seasonal dynamics of phosphorous and physicochemical variables in the Negro River Estuary (Argentina): a preliminary approach.

Nutrient discharge into rivers and estuaries and the factors that control it need to be further understood to decrease the risk of harmful algae blooms on these ecosystems. Preliminary seasonal physicochemical parameters at six stations along the Negro River Estuary (Argentina) were studied during 2019 (Austral summer, winter, and spring) with high- and low-frequency data. Three of the stations were mainly estuarine-influenced and three were marine-influenced ones. The concentration of phosphate (P), river discharge, meteorological conditions, seasonality, and physicochemical variables were analyzed. Total phosphorus (TP) showed seasonal variations and was higher than previously reported for the upper watershed in all stations in the warmer months, except for the marine control one. Orthophosphate values were also high compared to previous watershed data and changed independently of TP fluctuations. Changing turbidity, water temperature, pH, and conductivity did not appear to have an essential role in phosphorus variations. An unexplained high TP spike in the late spring sample shows the need for further research in the area, while the seawater mixing with P-rich river water could be acting as a dilution agent at the mouth of the river.