Average Air Temperature Research Articles

Seasonal dynamics of gastroinstestinal helminths in naturally infected horses in Brazil

Helminthiasis in horse breeding stands out due to the damage caused by this infection, which is mainly controlled by anthelmintic drugs. Knowing the population dynamics of helminths in a given region is therefore essential, as environmental conditions have a major impact on the development of these parasites in pastures. The aim of this study was to assess the influence of seasonality on helminths. It was carried out between October and September of the years 2015/2016, 2018/2019 and 2021/2022. A total of 147 animals were used, 64 females and 83 males, all from the same herd. Fecal samples were collected directly from the rectal ampulla of each animal every 28 days for 12 months of each cycle for coproparasitological analysis. Average air temperature, average relative humidity and rainfall were measured daily. The weather records were collected by the Agroclimatological Station in Castilho, SP (21o 05' 12.4" S; 51o 35' 17.2" W), located six kilometers from the experiment site. In the three study cycles, the EPG and rainfall averages showed significant differences between the times of year. In general, the month with the highest EPG in the different cycles was February, with 1626 in 2015/2016, 926.7 in 2018/2019 and 1885 in 2021/2022, and the month with the lowest average EPG was July, with 642 in 2015/2016, 610.5 in 2018/2019 and 804 in 2021/2022. Regarding climatic conditions, rainfall was more concentrated in two seasons, spring and summer, meaning that summer was the season with the highest rainfall in the three cycles evaluated. Autumn, followed by winter were the seasons with the lowest rainfall during the cycles studied. It can be concluded that climatic conditions have a direct influence on the population dynamics of helminths, and that during the drier and colder seasons, horses were less affected, so the suggestion for a possible strategic program would be to concentrate the administration of anthelmintics in spring and summer.

Experimental study on the cooling and electricity-saving effects of radiative cooling coating applied to communication base station

The cooling requirements of communication base stations (CBSs) align with the effects of radiative cooling coatings. However, these effects have not been comprehensively verified by in-situ measurements heretofore. To evaluate the cooling efficacy of radiative cooling coatings on CBSs, in this study, the radiative properties of a radiative cooling coating were tested in the laboratory. The cooling and electricity-saving effects of the radiative cooling coating were assessed using field measurements in Chengdu, China. Based on the experimental data, electricity savings, and carbon emission reductions in summer were evaluated. The experimental results showed that the radiative cooling coating’s shortwave reflectivity and longwave emissivity were both 0.90. The radiative cooling coating effectively lowered the exterior and interior surface temperatures of the CBS roof by 20.8 °C and 3.7°C, respectively, and consequently stabilized fluctuations in the CBS indoor air temperature. The measured daily electricity consumption of the air conditioning system was reduced by approximately 10 %. Additionally, the daily electricity consumption was found to be linearly related to the daily average ambient air temperature, particularly after applying the radiative cooling coating. This relationship was used to predict the electricity-saving effects of radiative cooling coatings on a large scale. If all the 80,000 CBSs in Chengdu used the same radiative cooling coating, the electricity savings in summer could reach 11.54 million kWh every year, corresponding to an annual carbon emission reduction of 1.41 million kgCO2.

Optimal Thermal Output of an Absorption Heat Pump with Steam Heating Integrated in a PT-60/70-130/13 Steam Turbine

The problem of determining the optimal thermal power of the absorption bromide-lithium heat pump (AHP) with steam heating, integrated into the thermal scheme of the PT-60/70-130/13 steam turbine when operating in the mode with a slight opening of the rotary regulating diaphragm, was formulated and solved. The turbine plant released steam to consumers and provided heat according to the schedule of 150 / 70 ºС. The characteristics of AHP were modeled using approximate dependencies based on the characteristics of thermotransformer manufacturers. AHP was heated by steam from the production selection of the turbine after the steam screw machine installed for energy saving. The general optimization problem with the objective function of changing the monthly fuel consumption after the integration of AHP, based on the average monthly outdoor air temperature in the heating season in Ukraine, was divided into 6 auxiliary optimization problems. The control parameters of these problems were: thermal capacity of the AHP, steam pressure in the turbine condenser and at the inlet to the heat pump, steam pressure in the turbine headю These problems were solved by the coordinate descent method. Modes with steam consumption in the production selection of the turbine for the consumers were studied: 15, 30 and 45 t/h (with parameters: 1.296 MPa, 280 ºС) and mains water: 1600, 1650 and 1700 m3/h. Their feature is the provision of "useful" generation in volumes corresponding to the work of PT-60/70-130/13 without AHP with a closed rotary diaphragm. For all considered options of the turbine load, the optimal power of the integrated AHP is defined as 20 MW. During the heating period PT-60/70-130/13 with AHP 20 MW when operating in a mode close to the thermal load with the lower of the studied consumptions of production steam and mains water leads to savings of: fuel by ~3.5%, softened water by 8.5%, technical of water by 79.9%, as well as to a noticeable ecological effect due to the reduction of harmful emissions into the atmosphere. The preliminary payback period of AHP is close to 3 years. It is noted that the option of the integrated turbine with a partially open regulating diaphragm at the accepted prices for fuel and electricity loses in terms of economic indicators to the option with a closed diaphragm.

The effect of nitrogen fertilization and the use of biostimulators on the chemical composition of grains of two varieties of corn (Zea mays L.).

The aim of the research was to determine the impact of the use of biostimulators and different nitrogen doses on the yield quality of two varieties of corn grown for grain. The field experiment was carried out in 2015-2017 on an individual farm located in north-eastern Poland (52°30'N and 22°26'E). The following factors were examined in the experiment: group I-two corn varieties: PR38N86 (280 FAO); P8400 (240 FAO) group II-four doses of nitrogen fertilization: control treatment-without nitrogen application (0 kg·ha-1 N) nitrogen doses-80 kg·ha-1 N, 120 kg·ha-1 N, 160 kg·ha-1 N, group III-four types of biostimulators used: (1) control treatment-without the use of a biostimulator, (2) biostimulator containing sodium ortho-nitrophenol, sodium para-nitrophenol, 5-nitroguaiacol sodium, (3) biostimulator containing potassium para-nitrophenolate, potassium ortho-nitrophenolate, potassium 5-nitrovacollate, (4) biostimulator containing molybdenum, zinc. Different doses of nitrogen used in the experiment contributed to an increase in the total protein content in the grain of the tested corn varieties compared to the control treatment. The highest significant increase in protein content by an average of 18.96 g∙kg-1 was obtained after the application of 160 kg N∙ha-1, and after the application of 120 kg N∙ha-1 the value of this feature increased on average by 17.18 kg N∙ha-1. Nitrogen fertilization reduced the starch content and increased the fat content in the tested corn varieties. The lowest starch content was found after the application of 120 kg N∙ha-1 and 160 kg N∙ha-1, while the highest fat content was found in grain tested with 120 kg N∙ha-1. The highest protein content was demonstrated after the use of a biostimulator containing sodium ortho-nitrophenol, sodium para-nitrophenol, and sodium 5-nitroguaiacol. The biostimulators used increased the starch and fat content in the grain of the tested varieties, and the differences between them were statistically insignificant. After using a biostimulator containing sodium ortho-nitrophenol, sodium para-nitrophenol, sodium 5-nitroguaiacol, the protein content increased by an average of 6.0%. The biostimulators used increased the starch and fat content in the grain of the tested varieties, and the differences between them were statistically insignificant. The medium-early variety with FAO 240 was characterized by a higher content of total protein (on average by 7.95 g∙kg-1) and crude fat (on average by 1.5 g∙kg-1) compared to the medium-late variety. All tested varieties were distinguished by the highest concentration of total protein and crude fat in the second, optimal year of the study. The lowest protein content was found in the last year of the study with the highest annual rainfall and the lowest average air temperature, and crude fat content was found in a relatively dry growing season. The opposite tendency was obtained in the case of starch content in the grain of the tested varieties.

Results of analysis of temperature monitoring data around the nuclearly hazardous cluster of fuel-containing materials in the sub-reactor room 305/2 of the "Shelter" object, inside and outside the new safe confinement

The results of the analysis of temperature monitoring data were obtained using the information and measuring system (IMS) "Finish" (1991 - 2015) and expert research system, new safe confinement (NSC) integrated control system in the main volume (MV) under the arched space, and the weather station in Chornobyl (1991 - 2023). After the NSC was installed in the design position, a slight increase in average annual MV NSC air temperature was observed before 2021. During the same period, the average annual temperature of concrete around the localization area of nuclearly hazardous clusters of fuel-containing materials (FCM NHC) in room 305/2 of the "Shelter" object increased by 1.3 - 1.4 times and had returned to 0.7 - 0.8 of the value of 1991. From 2021, no next increase in the mean annual temperature of concrete has been noticed. As of 2023, if compared to the monitoring period before Arch pushed into its design position, the calendar shift between the temperature of concrete and the surrounding environment enhanced to 1 month, according to both minimum and maximum average month values.

ОСОБЕННОСТИ РЕЖИМА МЕТЕОРОЛОГИЧЕСКИХ ФАКТОРОВ НА ЛЕСНОМ УЧАСТКЕ КАРБОНОВОГО ПОЛИГОНА «КАРБОН-ПОВОЛЖЬЕ»

The provision of heat and moisture are key factors in the issues of vegetation of forest ecosystems. The purpose of the study is to analyze the behavior of the main meteorological parameters in various local conditions, to identify patterns in their temporal course. The data of observations at the meteorological station of Roshydromet, located in the northern part of the city of Kazan, and the automatic station at the forest site of the carbon polygon “Carbon-Povolzhye” in Zelenodolsk district of the Republic of Tatarstan in the period from 01.12.2022 to 31.01.2024 were compared. The selected meteorological stations are located in different conditions, and the terrain features are reflected in the change of meteorological parameters. During the period under review, the influence of the city proximity on temperature differences was noted - the average annual air temperature at the testing site is 1.05℃ lower than at the meteorological station in Kazan. In May and June the temperature difference reached 1.9℃, the differences in winter are less significant (on average 0.5℃). The study of differences between the regimes of meteorological parameters in forest ecosystems is becoming especially relevant in the context of a changing climate and an increasing risk of forest fires. The forest edge conditions are characterized by a high degree of horizon closure-wind shadow. In the annual cycle at the carbon polygon, days (16 days) with a maximum wind speed of > 10 m/s were identified, the vast majority of them occur in the autumn-winter period. No significant differences in cloudiness were revealed between the meteorological stations. During the period under review, there were no cases of heavy precipitation exceeding 15 mm/12 hours. An assessment of the degree of aridity of the vegetation using the Selyaninov hydrothermal coefficient showed that the growing season of 2023 was characterized by arid conditions.

EVOLUTION OF GLACIAL COMPLEXES IN THE “AKSU-ZHABAGYLY” RESERVE (ARYS RIVER BASIN, SYRDARIA)

The article examines changes in glaciers on the territory of the Aksu-Zhabagyly State Nature Reserve within the Arys River basin (right tributary of the Syrdarya River). Spatial distribution and scale of changes in the boundaries of glaciers under the conditions of current climate change were assessed. The average rate of glaciation degradation for 2000-2023 is estimated. The main factors and trends in the conditions of contemporary climate change are identified. The average air temperature during the ablation period increased by 0.2-0.4 °C, the amount of precipitation for March-May decreased by 10-20 %. Directions for further research of glacial complexes are proposed.

Анализ сверхдолгосрочных прогнозов метеорологических характеристик речных бассейнов России с помощью модели климата INM-CM5

The analysis of the quality of ultra-long-term forecasts with 1-5 year lead time of the monthly and annual precipitation sums, the average monthly and average annual surface air temperature obtained using the INM-CM5 climate model’s outputs is performed. Series of verification forecasts for the period from 1991 to 2023 were used. Forecasts were checked for 12 river basins located in different parts of Russia. To eliminate systematic forecast errors used a method for correcting, which reduces the error of ultra-long-term precipitation forecasts by almost two times, and air temperatures by almost three times. Forecasts of intra-annual precipitation distribution were satisfactory for 8 out of 12 river basins. Forecasts of the intra-annual distribution of air temperature were good for all 12 river basins. Demonstrated, that the INM-CM5 climate model outputs can be used for ultra-long-term forecasting of monthly and annual precipitation, average monthly and average annual air temperature.

Snow depth sensitivity to mean temperature, precipitation, and elevation in the Austrian and Swiss Alps

Abstract. Snow depth plays an important role in the seasonal climatic and hydrological cycles of alpine regions. Previous studies have shown predominantly decreasing trends in average seasonal snow depth across the European Alps. Additionally, prior work has shown bivariate statistical relationships between average seasonal snow depth and mean air temperature or precipitation. Building upon existing research, our study uses observational records of in situ station data across Austria and Switzerland to better quantify the sensitivity of historical changes in seasonal snow depth through a multivariate framework that depends on elevation, mean temperature, and precipitation. These historical sensitivities, which are obtained over the 1901–1902 to 1970–1971 period, are then used to estimate snow depths over the more recent period of 1971–1972 to 2020–2021. We find that the year-to-year estimates of snow depths, which are derived from an empirical–statistical model (SnowSens), that rely solely on the historical sensitivities are nearly as skillful as the operational SNOWGRID-CL model used by the weather service at GeoSphere Austria. Furthermore, observed long-term changes over the last 50 years are in better agreement with SnowSens than with SNOWGRID-CL. These results indicate that historical sensitivities between snow depth, temperature, and precipitation are quite robust over decadal-length scales of time, and they can be used effectively to translate expected long-term changes in temperature and precipitation into changes in seasonal snow depth.

Predicting Starch Content in Early Potato Varieties Using Neural Networks and Regression Models: A Comparative Study

Starch content serves as a crucial indicator of the quality and palatability of potato tubers. It has become a common practice to evaluate the polysaccharide content directly in tubers freshly harvested from the field. This study aims to develop models that can predict starch content prior to the harvesting of potato tubers. Very early potato varieties were cultivated in the northern and northwestern regions of Poland. The research involved constructing multiple linear regression (MLR) and artificial neural network (ANN-MLP) models, drawing on data from eight years of field trials. The independent variables included factors such as sunshine duration, average daily air temperatures, precipitation, soil nutrient levels, and phytophenological data. The NSM demonstrated a higher accuracy in predicting the dependent variable compared to the RSM, with MAPE errors of 7.258% and 9.825%, respectively. This study confirms that artificial neural networks are an effective tool for predicting starch content in very early potato varieties, making them valuable for monitoring potato quality.

On the use of meteorological parameters and satellite image-based indices for improving solar radiation estimation.

Solar radiation (Rs) is a major renewable energy source and also a crucial factor in designing solar panels, determining water requirement, and irrigation scheduling. In this study, meteorological parameters (air temperature, average air temperature, and relative humidity; Scenario 1), satellite image-based indices (normalized difference vegetation index: NDVI and land surface temperature: LST; Scenario 2), and their combination (Scenario 3) were used as predictors of Rs simulator models in Mashhad watershed (2005-2015). To this end, three different transfer function algorithms of the multi-layer perceptron (MLP), namely Levenberg-Marquardt backpropagation (MLP-LVM), gradient descend backpropagation (MLP-GDB), and batch training with weight and bias learning rules (MLP-BTWB), as well as two other machine learning models, M5 Tree and XGB (eXtreme Gradient Boosting), were employed. In addition, hybrid models obtained through coupling the best MLP transfer function with genetic algorithm (MLP-LVM-GA) and coupling Variational Mode Decomposition with M5 Tree and XGB (VMD-M5 Tree and VMD-XGB, respectively) were also investigated. In the case of MLP-based models, the third scenario proved to be more efficient, with RMSEs equal to 404.2, 424.5, and 423.8J.cm-2.day-1 for the aforementioned transfer functions, respectively. The results showed that GA coupled with the best transfer function algorithm of MLP improved Rs estimates, decreasing estimation error by 14.7, 30.65, and 32.67% in the three defined scenarios, respectively. In training set, VMD-M5 Tree and VMD-XGB hybrid models outperformed corresponding base models under all three scenarios; but in testing set, estimation error was decreased only in the third scenario (by 37 and 42%, respectively). Overall, all models (base and hybrid) had the least Rs estimation errors in the third scenario (application of both meteorological parameters and satellite image-based data).

Biometric Characteristics and Condition Factor of Tropical Freshwater Atya scabra Leach, 1815 in the Bia River, South-East Region, Côte D’Ivoire

The present study focused on 720 specimens of Atya scabra handcrafted captured in the Bia river, precisely in the localities of Aboisso and Biaka. This study was conducted to assess the adaptation and allometric of the species A. scabra in the Bia river. During the trial, data on the environmental parameters of the water were obtained using a multiparameter. As for the relative growth of the species A. scabra was determined from the individual measurements of the specimens. Then, Student's t-test allowed a comparison of allometric coefficients for the same sex in Aboisso and Biaka. Thus, the average air temperature was 26.92±1.33oC. In terms of rainfall, it fluctuated from 17.78 to 722.9 mm or an annual average of 155.96±191.21 mm. At Aboisso, the annual average temperature of water was 28.50 ± 1oC, 7.54 ± 0.1oC for the pH and 5.03 ± 0.8 for OD. For the locality of Biaka, the mean values recorded were 28.45 ± 1.9 oC, 7.39 ± 0.2, and 4.93 ± 0.9, respectively, for temperature, pH, and OD. The condition factor (k) estimated in Aboisso, ranged from 3.04 ± 0.05 to 3.29 ± 0.26 for males and from 2.85 ± 0.35 to 3.07 ± 0.12 for females of Atya scabra. As for the locality of Biaka, the estimated k values fluctuated between 3.1 ± 0.14 and 3.4 ± 0.31 for males and from 2.75 ± 0.07 to 3.10 ± 0.25 for females. The biometric study revealed a negative allometry in females and a positive allometry in males in both localities for the W/Lt and Lc/Lt relationships. Atya scabra freshwater shrimp has therefore shown a good adaptation in the Bia river. In addition, the results obtained could guide the choice of sex having economic interest in the event of possible aquaculture of this species.

Design and simulation of a simple greenhouse dryer and Peltier thermoelectric module under independent and combined operating conditions

Abstract India is a suitable country for cultivating several food produce, and most of them are seasonal. To make the food produce available in off seasons and to increase their shelf life there is vital need for food processing. Rapid urbanization has imposed a great demand for food due to very less availability of fresh food produce. There is a huge wastage in the food produce as the cultivation land is located far from the end user and time delay due to transportation. There are several food processing methods of which dehydration and cooling play a major role. A simple solar greenhouse dryer and Peltier thermoelectric cooling was designed and studied using COMSOL and EJS tool. The performance study integrating both systems was analysed and reported. The models were considered to work solely on solar energy. Solar thermal energy for dehydration and solar photo voltaic for operating the Peltier device. Studies on performance of the designed systems were carried out along with scope for performance improvement. The simulation studies revealed a 1.439 oC increase in drying chamber temperature by integrating the heat parameter of the Peltier thermoelectric cooler. At 40 K temperature difference the Peltier thermo electric module attained optimal performance. From the analysis using EJS tool, the maximum and average air temperatures inside the dryer were found to be 65 oC and 57 oC, the COP and cooling capacity of the Peltier thermoelectric model were high at 800 W/m2 solar radiation.

New York State Climate Impacts Assessment Chapter 02: New York State's Changing Climate.

Many fundamental aspects of New York State's climate have already begun to change, and the changes are projected to continue-and in some cases, accelerate-throughout the 21st century. This chapter explores observed and projected changes in a variety of physical variables that relate directly to weather and climate, starting with average and extreme air temperature and proceeding to the associated effects on precipitation, extreme events, and core properties of New York's coastal and inland waters. These climate attributes and hazards lead to impacts throughout the eight sectors of this assessment.

Understanding Agricultural Water Consumption Trends in Henan Province: A Spatio-Temporal and Determinant Analysis Using Geospatial Models

In the context of water scarcity, understanding the mechanisms influencing and altering agricultural water consumption can offer valuable insights into the scientific management of limited water resources. Using Henan Province as a case study, this research applies the Mann–Kendall test method, the spatial Markov transfer chain model, the optimal parameter geo-detector model, and the Logarithmic Mean Divisia Index (LMDI) decomposition method to investigate the evolution characteristics of agricultural water consumption in Henan Province and its key influencing factors. The findings revealed the following: (1) Agricultural water consumption has shown a significant decline from 1999 to 2022. (2) According to observations, the stability of agricultural water consumption exceeds the spillover effect, and cross-border grade transfer is challenging. Moreover, this phenomenon is influenced by the neighboring regions. (3) The key influencing factors of added agricultural value are the sown area of food crops, total sown area, irrigated area, and average annual air temperature. (4) Among the decomposition effects on agricultural water consumption, the contribution of each decomposition effect to changes in agricultural water consumption and the role of spatial distribution exhibit notable differences. Overall, these findings provide theoretical references for the efficient use of agricultural water resources and sustainable development in the region.

Prediction Model of the Temporal Dynamics of Severe Pest Cashew Anacampsis phytomiella Using Artificial Neural Networks

ABSTRACTThe cashew tree (Anacardium occidentale) is a tropical plant that yields the world's most consumed nut. Anacampsis phytomiella (Lepidoptera: Gelechiidae) is the primary pest that infests cashew nuts, leading to losses of up to 80%. The comprehension of the spatiotemporal dynamics of pests enables enhanced planning of sampling and control methods. Artificial neural networks (ANNs) are computational models in machine learning with high predictive capability. Thus, the objective of this study was to determine a seasonal dynamic model of A. phytomiella using ANNs. Over 3 years, the pest attack intensity and climatic elements were monitored in two cashew orchards. A total of 1716 ANN models were determined. The model predictors included the time of plant fruiting, average air temperature, dew point, atmospheric pressure and rainfall. The temperature, atmospheric pressure and time of plant fruiting had a positive effect on pest infestation, while the opposite occurred with rainfall. The pest infestation curve in relation to the dew point exhibited a point of maximum. The model successfully predicted the intensity of A. phytomiella infestation across different years, plant fruiting stages and pest densities. Therefore, the ANN model determined in this study is promising for predicting the intensity of A. phytomiella infestation in cashew orchards.

Predicting the seasonal dynamics of Dalbulus maidis (Hemiptera: Cicadellidae) in corn using artificial neural networks.

This study addresses the challenge of predicting Dalbulus maidis (DeLong & Wolcott) (Hemiptera: Cicadellidae) density in cornfields by developing an artificial neural network (ANN). Over two years, we collected data on meteorological variables (atmospheric pressure, air temperature, dew point, rainfall, relative humidity, solar irradiance, and wind speed), plant age, and density of D. maidis in cornfields located in two Brazilian biomes (Atlantic Forest and Brazilian Tropical Savannah). Out of 1056 ANNs tested, the neural network featuring a 30-day time lag, six neurons, logistic activation, and resilient propagation demonstrated the lowest root mean squared error (0.057) and a high correlation (0.919) with observed D. maidis densities. This ANN exhibited an goodness of fit in low-density (Atlantic Forest) and high-density (Brazilian Tropical Savannah) scenarios for D. maidis. Critical factors influencing D. maidis seasonal dynamics, including corn plant age, rainfall, average air temperature, and relative humidity, were identified. This study highlights the potential of the ANN as a promising tool for precise predictions of pest seasonal dynamics, positioning it as a valuable asset for integrated pest management programs targeting D. maidis.

Outdoor thermal comfort in face of the urban heat island effect: innovative solutions for cooler cities; a case study in Souk Ahras city

This study investigates the relationship between the urban heat island effect and outdoor thermal comfort through a bibliometric analysis of relevant keywords, highlighting their strong connection. The analysis highlights how trees improve urban microclimates and enhance outdoor comfort by integrating them into public space design. The research in Souk Ahras city investigates two public spaces, using in situ measurements and the Rayman model to analyze thermal indices (SVF, Tmrt, and PET) based on climatic data and urban geometry collected on a hot summer day. The findings show that trees at each intervention site reduce average daily air temperature by 0.43°C to 1.043°C, lower mean radiant temperature (Tmrt) by 1°C to 1.2°C, and decrease physiological equivalent temperature (PET) by 0.16°C to 1.58°C. Trees also reduce the sky view factor, highlighting their role in improving outdoor thermal comfort through shading and evapotranspiration. These results confirm that integrating trees into public space design effectively enhances outdoor thermal comfort and urban microclimates during warmer seasons.

Assessing climate-growth relationships with daily and monthly observational and gridded meteorological data

We compared climate-growth relationships by correlating tree-ring variation with daily and monthly meteorological data obtained from the stations of the Slovenian Environment Agency (ARSO) and modelled data from the SLOCLIM database. Tree-ring width series for analyses were obtained from previously collected European beech (Fagus sylvatica) tree-ring data from 30 sites all over Slovenia. Climate-growth correlations were calculated to evaluate whether daily meteorological data exhibits stronger correlations than monthly data. We also compared the maximum correlation coefficients using meteorological station data and gridded SLOCLIM data. The analysis was conducted using the dendroTools R package, incorporating data on daily and monthly average air temperatures and precipitation sums from the period 1960–2018. Our findings revealed significantly higher maximum correlation coefficients for daily data compared to monthly data, underscoring the importance of using daily data, particularly for precipitation. However, no significant difference was observed between maximum correlation coefficients using the meteorological station and modelled data, and the difference did not change significantly with increasing altitude.

Modeling the lagged and nonlinear effects of weather conditions on abundance of Culex tarsalis mosquitoes in Saskatchewan, Western Canada using a bi-dimensional distributed lag nonlinear model.

The establishment of West Nile Virus (WNV) competent vectors continues to pose a major public health challenge in Canada, especially in the south. While studies have examined the association between weather conditions and the abundance of mosquitoes over trap weeks, there is limited research on the effects of weather conditions on the abundance of Culex tarsalis (Cx. tarsalis) mosquitoes for a lapse of time beyond the trap week in Saskatchewan, Western Canada. To address this gap, we analyzed provincially available weekly mosquito trap and co-incident meteorological station data in Saskatchewan from 2010 to 2021 using a bi-dimensional distributed lag and nonlinear model. Data indicate that 171,141 Cx. tarsalis mosquitoes were trapped across much of Saskatchewan, from 2010 to 2021. Cx. tarsalis were found to be most abundant between weeks 26 and 35 (July and August) and peaked in weeks 30 and 31. Based on the WNV-positive pools, mosquito infection rates increased from week 23 to 36. While weekly average maximum air temperatures between 20 °Cand 30 °C were associated with more Cx. tarsalis across all lags (0 - 8 weeks), higher weekly average minimum air temperatures had a strong and immediate effect that diminished over longer lags. Higher weekly average rainfall amounts (> 20 mm) were associated with fewer Cx. tarsalis mosquitoes across all lags, while average weekly rainfall between 8 and 20 mm was strongly associated with a high abundance of Cx. tarsalis mosquitoes over longer lags (5 -7 weeks). Additionally, increasing wind speed was associated with lower abundance of Cx. tarsalis across all lags. Findings identified nonlinear lag associations for weekly average maximum air temperature and rainfall, but linear associations for weekly average minimum air temperature and wind speed. Identified lags and thresholds for temperature, rainfall, and wind speed at which mosquito abundance peaked could help to inform public health authorities in timing of vector control measures to prevent WNV transmission.