Minimum Relative Humidity Research Articles

EXPLORING CLIMATIC PATTERNS AMONG BRAZILIAN CAPITALS

This study aims to examine the patterns of climatic variables and delineate climatically homogeneous regions among Brazilian capitals using Principal Component Analysis (PCA) and clustering techniques. Annual climate data from 26 state capitals and the Federal District of Brazil were analyzed, covering the period from 1958 to 2022, representing different regions of the country. The PCA results revealed that the first three principal components accounted for 91.8% of the total data variability. Four climatically homogeneous regions were identified: the East region, the Northeast region, the North region and another region that comprises the rest of the country. Cluster Analysis (CA) also confirmed the formation of four homogeneous groups among municipalities based on climatic variables. The analysis of spatial distribution showed significant variations in annual climate variables between Brazilian capitals, with maximum values observed for maximum and minimum temperatures, precipitation, wind speed, relative humidity, evapotranspiration and soil moisture. These findings contribute to a deeper understanding of climate variability in Brazilian capitals and provide a basis for future studies on climate dynamics and regional impacts. Keywords: Principal Component Analysis, Cluster Analysis, Multivariate Analysis; Biplot

Impact of temperature and humidity on airway obstruction in a cohort of elderly women

Abstract Background Temperature and relative humidity (RH) impact human health. However, little is known about the combined association between temperature and RH on respiratory health in the elderly. In this study, we evaluated the interactive associations of temperature and RH on airway obstruction in elderly German women. Methods Using data from the second follow-up (2007-2010) of the German SALIA cohort study, we assessed the short-term (Lag0-8) combined association between maximum and minimum temperature (Tmax, Tmin) and RH on airway obstruction. Airway obstruction was defined as the ratio of first second of forced expiration (FEV1) / forced vital capacity (FVC) < 0.70. Multivariable logistic regression with a distributed lag non-linear model (DLNM) was constructed for the interaction between temperature and RH and adjusted for a single pollutant, location, socioeconomic status and smoking status. Results 735 elderly women with an average (±SD) age of 73.46 (±3.09) years were included. Around 16.73% of them had airway obstruction. For non-linear antagonistic Tmax and RH interaction models, we found a U-shaped relationship from Lag 0-8, with an immediate adverse association from Lag0-1 and a significant delayed adverse association from Lag7-8. For non-linear antagonistic Tmin and RH interaction models, the relationship was U-shaped and showed an immediate adverse association from Lag0-1, while there was also a significant delayed association from Lag6-8. Conclusions Climate change increases the likelihood of exposure to adverse temperatures and weather conditions. Vulnerable populations like the elderly are more susceptible to the adverse associations of climate change. In this epidemiological study, we found that exposure to Tmax/Tmin and RH had immediate and delayed adverse associations by increasing respiratory obstruction in elderly women. Increasing public awareness of the danger of interactions between non-optimum temperature and RH on respiratory health is crucial. Key messages • Elderly women’s respiratory health is impacted by temperature and humidity interactions, requiring awareness and proactive measures. • Climate change heightens risk for vulnerable groups, emphasising the need to address environmental impacts on health.

Analysis of spatio-temporal variability of temperature, relative humidity and temperature humidity index in Naivasha Sub-county, Kenya

The aim of the study was to understand the environmental conditions impacting the reproductive performance of Sahiwal cattle, focusing on temperature, relative humidity, and relative humidity index (RHI). These factors significantly influence reproductive rates, highlighting the importance of spatio-temporal variability studies for informed herd management decisions. This study analyzed climate variability trends from 1998 to 2019 at the Kenya Agricultural and Livestock Research Organization (KALRO) Dairy Research Institute (DRI)-Naivasha, in Malewa Ward, Naivasha Sub-County, Kenya. Daily minimum and maximum temperature (T) and relative humidity (RH) data sourced from the Kenya Meteorological Department were utilized. The Temperature-Humidity Index (THI) was computed using the THI equation developed by Mader. Time series analyses, including the Mann-Kendall test and Sen's slope estimates, were employed to assess seasonal variability. Results indicated significant decreasing trends in temperature across all the four seasons studied (DJF, MAM, JJA, and SON), with rates of cooling at -0.0625, -0.0175, -0.0125, and -0.0342 °C per year, respectively. In contrast, relative humidity showed statistically significant increases in all seasons, with rates of +0.5977, +0.999, +1.4493, and +1.0499 for DJF, MAM, JJA, and SON, respectively. Seasonal THI exhibited significant decreases in DJF and JJA, potentially impacting livestock reproductive performance. These findings are crucial for policymakers to create adaptation and mitigation methods to address climate change effects on livestock and livelihoods.

Epidemiological Study of Downy Mildew Disease of Opium Poppy

The downy mildew caused by Peronosporaspp is a major disastrous disease of many plant species. The symptoms of downy mildew disease incidence on opium poppy (Papaver somniferum L.) were collected in three years from 2019-20 to 2021-22 for prediction of weather parameters on the progression of downy mildew disease. The downy mildew disease initiation was recorded in the 52nd SMW (10.0%DMI) when the maximum temperature was 19.430C. At 3rd SMW, the maximum temperature dropped 17.330C and DMI reached up to 20.00 percent depicting the progression and spread of downy mildew disease in opium poppy with the decrease of maximum temperature. The maximum disease incidence was recorded 92.53 percent in 10th SMW when maximum and minimum temperature were 29.030C and 13.530C. Regression analysis between dependent variable downy mildew disease incidence Vs. independent variables (viz., rainfall, maximum and minimum temperature and relative humidity) showed that all the weather parameters contributed more than 85 percent variation (R2 = 0.869, 0.957, 0.859) in the downy mildew incidence of opium poppy. One unit change of maximum temperature, minimum temperature (1.00c), maximum and minimum relative humidity (1.0%) might cause to change 0.128, 0.70, 0.117 0.130 units in downy mildew incidence, respectively.

Augmentation strategy toward superior water absorption performance: An attempt utilizing [EMIm]Ac ionic liquid/water pair

Ionic liquids (ILs) offer significant advantages as alternative absorbents in absorption refrigeration cycles due to their superior physicochemical properties. Assessing the performance of new IL-based working pairs in absorption systems is essential for their rapid application in industrial applications. This study evaluates the water absorption performance of an aqueous solution of 1-ethyl-3-methylimidazolium acetate ([EMIM]Ac) under both saturated atmospheric and vacuum conditions. Additionally, the performance of an absorption chiller utilizing the [EMIM]Ac/H2O working pair was analysed using the Aspen Plus process simulator. A discrepancy was observed between the water vapor pressure in the air and the saturated vapor pressure of the aqueous solution at equilibrium. A theoretical criterion for the minimum relative humidity required for effective water vapor absorption by this absorbent at a given mass fraction is proposed. Under vacuum conditions, lower absolute pressure or higher mass fraction enhances absorption. Surfactants significantly improve absorption performance, with 2-octanol and 1-octanol demonstrating approximately double the absorption capacity compared to [EMIM]Ac. Higher mass fractions of the aqueous solution enhance the absorption rate but reduce the desorption rate. The coefficient of performance (COP) of the system decreases with increasing pressure and mass fraction, reaching a peak of 0.9 at a mass fraction of 20 % and a pressure of 3 kPa. Lower pressure and mass fraction enhance exergy efficiency.

Dynamics of extreme climatic variables for viticulture in the main zones of ampelocenosis of the Krasnodar region

Modern climate changes affect all branches of agriculture. Everywhere there is an increase in air temperature, changes in precipitation, an increase in extreme weather events. Since the productive lifespan of a grape plant is 30-40 years, it is necessary to assess climatic changes in order to create a variety adapted to changes. The purpose of the research is to assess changes in extreme heat supply and relative humidity in the main viticultural areas of the Krasnodar region. The average values of extreme heat supply and relative humidity variables of two climatological periods of 1961–1990 and 1991–2020, their changes over time and the course of variable’s anomalies of 1991–2020 compared with the average values of 1961–1990 are calculated. An increase in the absolute maximum air temperature by 0.2–1.6 °C for the period 1991–2020 was noted compared to the previous period, with the exception of Novorossiysk (decrease by 1.4 °C); an increase in the average absolute maximum air temperature by 1.5–2.5 °C, an increase in the number of days with a maximum air temperature above +35 °C by 1.0–2.3 days; a decrease in the average relative humidity of April–October by 0.7–2.7 % and an increase in the number of days with a minimum relative humidity of less than 30 % over the summer by 0.8–5.4 days. The variability of these variables over time for the period 1991–2020 is consistent with the change in the average. An increase in the absolute maximum was established (by 0.65–0.9 °C/10 years), the number of days with a maximum air temperature above +35 °C (by 0.8–1.1 days/10 years), the number of days with a minimum relative humidity of less than 30% over the summer (by 1.2–7.2 days/10 years); decrease in the average relative humidity of April–October (by 0.5–6.5 %/10 years). These changes indicate an increase in climate extremes and the frequency of unfavorable conditions for grapes in the summer, which requires an adjustment of the assortment.

Climatic Suitability for Outdoor Tourism in Romania’s Big Cities

This research aims to assess the climatic temporal suitability over the year and identify the appropriate season for open-air tourism in ten Romanian cities. It was evaluated using the Enhanced Tourism Climatic Index on a temporal scale of one day and then aggregated to 10 days over 61 years (1961–2021). Daily mean and maximum temperature, mean and minimum relative humidity, wind speed, precipitation, and sunshine hours were employed in the investigation. The Mann–Kendall test and Sen’s slope were used for trend detection in the frequency, season duration, and first/last suitable day during the year for outdoor tourism. Acceptable or better weather conditions usually begin in the last part of April and end in mid-October, with Good or better conditions lasting between 260 and 310 days/year. The trend shows a shift of Good conditions earlier in the year (0.3–9.0 days/decade), resulting in a longer season duration (0.8–13.0 days/decade) for open-air activities. The trend is statistically significant mainly for the extra-Carpathian regions. Big differences in open-air events number during the climatically suitable season have been identified among the cities considered (2–19 events/year). This study is useful for better planning open-air events and activities for tourism and recreation.

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.

Vulnerability assessment of aerosol and climate variability for rice and maize yield using EO datasets for sustainable agriculture over India.

Human-generated aerosol pollution gradually modifies the atmospheric chemical and physical attributes, resulting in significant changes in weather patterns and detrimental effects on agricultural yields. The current study assesses the loss in agricultural productivity due to weather and anthropogenic aerosol variations for rice and maize crops through the analysis of time series data of India spanning from 1998 to 2019. The average values of meteorological variables like maximum temperature (TMAX), minimum temperature (TMIN), rainfall, and relative humidity, as well as aerosol optical depth (AOD), have also shown an increasing tendency, while the average values of soil moisture and fraction of absorbed photosynthetically active radiation (FAPAR) have followed a decreasing trend over that period. This study's primary finding is that unusual variations in weather variables like maximum and minimum temperature, rainfall, relative humidity, soil moisture, and FAPAR resulted in a reduction in rice and maize yield of approximately (2.55%, 2.92%, 2.778%, 4.84%, 2.90%, and 2.82%) and (5.12%, 6.57%, 6.93%, 6.54%, 4.97%, and 5.84%), respectively. However, the increase in aerosol pollution is also responsible for the reduction of rice and maize yield by 7.9% and 8.8%, respectively. In summary, the study presents definitive proof of the detrimental effect of weather, FAPAR, and AOD variability on the yield of rice and maize in India during the study period. Meanwhile, a time series analysis of rice and maize yields revealed an increasing trend, with rates of 0.888 million tons/year and 0.561 million tons/year, respectively, due to the adoption of increasingly advanced agricultural techniques, the best fertilizer and irrigation, climate-resilient varieties, and other factors. Looking ahead, the ongoing challenge is to devise effective long-term strategies to combat air pollution caused by aerosols and to address its adverse effects on agricultural production and food security.

ANALYSIS OF THE CALCULATION OF REFERENCE EVAPOTRANSPIRATION ACCORDING TO THE DATA OF THE STATE METEOROLOGICAL STATION

Since direct measurement of reference evapotranspiration (ET0) is a complex, time-consuming and expensive process, the most common procedure is to estimate ET0 from climate data. The purpose of this study was to perform reference evapotranspiration calculations based on the data of the state meteorological station Askania-Nova and compare them with the actual ET0 data obtained using an automatic Internet meteorological station. The data for the study were taken from the state meteorological station Askania-Nova (township Askania-Nova, Kakhovsky district, Kherson region, 46.45°N 33.88°E) and the automatic Internet meteorological station iMetos IMT 300 from the company "Pessl Instruments", which is located at the meteorological site of the Askaniysk DSDS (Tavrychanka village, Kakhovsky district, Kherson region, 46.55°N, 33.83°E). Standard evapotranspiration was calculated using the Penman-Monteith method (FAO56-RM). To assess the accuracy of ET0 calculations, mean absolute percent error (MAPE), root mean square error (RMSE) and Standard Error of Estimate (SEE ) were determined. According to the results of the comparison of indicators from two meteorological stations, it was found that the smallest errors are inherent in the daily average and maximum temperature and relative air humidity (MAPE<10%), for the minimum temperature and relative air humidity, the MAPE errors are 18,1 and 13,7%, respectively. The MAPE error for water vapor pressure deficit and solar radiation is 20,2 and 26,3%, respectively. The largest MAPE error of 40,3% was established for wind speed measurements. The average MAPE error between the calculated ET0, based on the meteorological data of the Askania-Nova station, and the actual ET0 data obtained from the automatic Internet meteorological station iMetos is 16,8%, RMSE – 0,65 mm, SEE – 0,56 mm. Applying a coefficient of 0,92 when calculating ET0 reduces the errors of MAPE, RMSE, and SEE by 3,2%, 0,15 mm, and 0,05 mm, respectively, for all calculation periods. For the May-August period, the MAPE error was 10,7%, which brings the calculations close to high accuracy (MAPE <10%). Based on the results of the calculations, it was established that on average over the years of research, the actual ET0 was 68 mm less than the calculated one. The absolute errors of determination of ETc depended on the crop and the average over the years of research ranged from 33 mm (winter wheat) to 68 mm (early tomatoes). The application of the refined value of ET0 in calculations reduces the absolute errors in the determination of c over the years of research, this error did not exceed 6 mm (early tomato). Research results confirm the possibility of using meteorological indicators obtained from state meteorological stations to calculate ET0. To increase the accuracy of calculations, it is necessary to use a refinement coefficient.

Seasonal Incidence of Major Insect Pest of Chickpea and their Natural Enemies in Vindhaya Region of Madhya Pradesh

Field experiment carried out at the Entomology Instructional Farm, JNKVV-College of Agriculture Rewa, MP during rabi season 2018-19 revealed that incidence of the chickpea pod borer Helicoverpa armigera. Population range of pod borer during different weather weeks varied between 0.30 and 1.89 larvae/mrl (April 2nd week and March 2nd week respectively). Peak larval population of Helicoverpa armigera was recorded in 2nd week of March (11 standard weeks). A weak negative correlation was observed with the maximum relative humidity and minimum relative humidity correlation, -0.172 and -0.595 respectively and a non significant positive correlation with maximum temperature, minimum temperature and rainfall. The respective r was found to be 0.306, 0.391 and 0.269. In district Rewa, MP, India the insect-pests of chickpea and their natural enemies during rabi the total seven species was recorded in Chickpea in which six pests minor in nature and one major pest Chickpea pod borer (Helicoverpa armigera). Termite (Odontotermes obesus Ram.), White grub (Holotrichia consanguinea Bl.), Black aphid (Aphis craccivora Koch), Grasshopper (Chrotogonus trachypterus Blan.), Semilooper (Autographa nigrisigna L.), Cut worm (Agrotis ipsilon Huf.) are minor in nature. Seven natural enemies found during field investigation i.e. lady bird beetle (Chilomenes sexmaculata Fab.), lady bird beetle (Coccinella septempunctata L.), Praying mantis (Mantis religiosa), Dragon fly (Crocothemis servilia Drury), Campoletis parasitoid (Campoletis chlorideae Uch.), Indian mynah (Acridotheris tristis L.), King crow (Dicrurus macrocerus Vie.) in chickpea observed in low to medium population on the crop and to help in reduction of pest population.

Decametric-scale buffering of climate extremes in forest understory within a riparian microrefugia: the key role of microtopography.

Riparian corridors often act as low-land climate refugia for temperate tree species in their southern distribution range. A plausible mechanism is the buffering of regional climate extremes by local physiographic and biotic factors. We tested this idea using a 3-year-long microclimate dataset collected along the Ciron river, a refugia for European beech (Fagus sylvatica) in southwestern France. Across the whole network, canopy gap fraction was the main predictor for spatial microclimatic variations, together with two other landscape features (elevation above the river and woodland fraction within a 300m radius). However, within the riparian forest only (canopy gap fraction < 25%, distance to the river < 150m), variations of up to -4°C and + 15% in summertime daily maximum air temperature and minimum relative humidity, respectively, were still found from the plateau to the cooler, moister river banks, only ~ 5-10m below. Elevation above the river was then identified as the main predictor, and explained the marked variations from the plateau to the banks much better than canopy gap fraction. The microclimate measured near the river is as cool but moister than the macroclimate encountered at 700-1000m asl further east in F. sylvatica's main distribution range. Indeed, at all locations, we found that air relative humidity was higher than expected from a temperature-only effect, suggesting that extra moisture is brought by the river. Our results explain well why beech trees in this climate refugium are restricted to the river gorges where microtopographic variations are the strongest and canopy gaps are rare.

ANALYSE DE L’INFLUENCE DES PARAMETRES METEOROLOGIQUES SUR LA PLUVIOMETRIE A KINSHASA

The understanding of meteorological parameters influencing rainfall is essential to effectively address environmental challenges and anticipate the consequences of extreme weather events. In this study, we analyze meteorological data provided by the Airways Authority of Kinshasa (RVA), covering the period from 1992 to 2019. This in-depth statistical analysis aims to better understand the factors that influence precipitation in the region and to identify relevant climate trends for anticipating future environmental changes. We used logistic regression to assess the impact of various climatic factors such as temperature (daily extremes), relative humidity, and atmospheric pressure on precipitation. The results indicate that minimum temperature, relative humidity, and atmospheric pressure show a strong correlation with rainfall in Kinshasa, highlighting their significant influence on the likelihood of precipitation. In contrast, maximum temperature has a limited impact, making it a less reliable indicator of rainfall in the city. The conclusions of this study provide valuable tools for local authorities in developing effective strategies for managing climate variations and predicting weather phenomena. These insights enable precise modeling of the impacts of meteorological fluctuations on ecosystems and urban infrastructure. Furthermore, the results of this research can guide local farmers in optimizing their agricultural practices by adjusting planting and harvesting schedules based on detailed weather forecasts, thereby minimizing economic losses and enhancing crop resilience to climate variations. Finally, this study provides climatologists with robust data for in-depth analysis of the effects of climate change on meteorological phenomena in Kinshasa. This knowledge is crucial for refining regional climate models and developing specific adaptation strategies for other urban areas in sub-Saharan Africa.

Spatiotemporal Variation, Meteorological Driving Factors, and Statistical Models Study of Lake Surface Area in the Yellow River Basin

The surface area changes of 151 natural lakes over 37 months in the Yellow River Basin, based on remote sensing data and 21 meteorological indicators, employing spatial distribution feature analysis, principal component analysis (PCA), correlation analysis, and multiple regression analysis, identify key meteorological factors influencing these variations and their interrelationships. During the study period, lake area averages were from 0.009 km2 to 506.497 km2, with standard deviations ranging from 0.003 km2 to 184.372 km2. The coefficient of variation spans from 3.043 to 217.436, indicating considerable variability in lake area stability. Six primary meteorological factors were determined to have a significant impact on lake surface area fluctuations: 24 h precipitation, maximum daily precipitation, hours of sunshine, maximum wind speed, minimum relative humidity, and lakes in the source region of the Yellow River generally showed a significant positive correlation. For maximum wind speed (m/s), 28 lakes showed significant correlations, with five positive and twenty-three negative correlations, correlation coefficients ranging from −0.34 to −0.63, average −0.47, indicating an overall negative correlation between lake surface area and maximum wind speed. For maximum daily precipitation (mm), 36 lakes had 21 showing a positive correlation, indicating a positive correlation between lake surface area and daily precipitation in larger lakes. Furthermore, of the 117 lakes with sufficient data to model, the predictive capabilities of various models for lake surface area changes showcased distinct advantages, with the random forest model outperforming others in a dataset of 65 lakes, Ridge regression is best for 28 lakes, Lasso regression performs best for 20 lakes, Linear model is only best for 4 cases. The random forest model provides the best fit due to its ability to handle a large number of feature variables and consider their interactions, thereby offering the best fitting effect. These insights are crucial for understanding the influence of meteorological factors on lake surface area changes within the Yellow River Basin and are instrumental in developing predictive models based on meteorological data.

Monthly streamflow forecasting based on meteorological data from a nearby station

ABSTRACT Monthly streamflow forecasting is critical for improving water resource management. In this study, several base-classifier data-mining algorithms – conjunctive rule (CR), isotonic regression (ISOR), sequential minimal optimization regression (SMOR) – as well as several hybrid data-mining techniques – disjoint aggregating or dagging (DA)-CR, DA-ISOR, and DA-SMOR – that combine dagging with these algorithms were developed and applied to forecasting streamflow 3 and 12 months into the future (i.e., Qt+3 and Qt+12) based on meteorological data from a nearby station. Thirty years of data (from 1988 to 2018) that included precipitation, minimum relative humidity, maximum relative humidity, evaporation, hours of sunshine, maximum temperature, minimum temperature, wind speed, and streamflow were collected at the Kermanshah synoptic station were input to develop and evaluate several models. Varying combinations of the input data were tested to find the optimal set to employ. The models were validated and compared using several quantitative statistical indices. Though all satisfactorily predicted monthly streamflow, the hybrid models (DA-CR, DA-ISOR, and DA-SMOR) outperformed the base-classifier models (CR, ISOR, and SMOR), proving that dagging improved data-mining models significantly. Of the hybrid models, D-SMOR was the best. The models developed in this study are cost-effective tools for quick and accurate monthly streamflow forecasting.

Machine learning ensembles, neural network, hybrid and sparse regression approaches for weather based rainfed cotton yield forecast.

Cotton is a major economic crop predominantly cultivated under rainfed situations. The accurate prediction of cotton yield invariably helps farmers, industries, and policy makers. The final cotton yield is mostly determined by the weather patterns that prevail during the crop growing phase. Crop yield prediction with greater accuracy is possible due to the development of innovative technologies which analyses the bigdata with its high-performance computing abilities. Machine learning technologies can make yield prediction reasonable and faster and with greater flexibility than process based complex crop simulation models. The present study demonstrates the usability of ML algorithms for yield forecasting and facilitates the comparison of different models. The cotton yield was simulated by employing the weekly weather indices as inputs and the model performance was assessed by nRMSE, MAPE and EF values. Results show that stacked generalised ensemble model and artificial neural networks predicted the cotton yield with lower nRMSE, MAPE and higher efficiency compared to other models. Variable importance studies in LASSO and ENET model found minimum temperature and relative humidity as the main determinates of cotton yield in all districts. The models were ranked based these performance metrics in the order of Stacked generalised ensemble > ANN > PCA ANN > SMLR ANN > LASSO> ENET > SVM > PCA SMLR > SMLR SVM > SMLR. This study shows that stacked generalised ensembling and ANN method can be used for reliable yield forecasting at district or county level and helps stakeholders in timely decision-making.

Modeling the Impact of Air Pollution and Meteorological Variables on COVID-19 Transmission in Western Cape, South Africa

Understanding the factors that influence COVID-19 transmission is essential in assessing and mitigating the spread of the pandemic. This study focuses on modeling the impact of air pollution and meteorological parameters on the risk of COVID-19 transmission in Western Cape Province, South Africa. The data used in this study consist of air pollution parameters, meteorological variables, and COVID-19 incidence observed for 262 days from April 26, 2020, to January 12, 2021. Lagged data were prepared for modeling based on a 6-day incubation period for COVID-19 disease. Based on the overdispersion property of the incidence, negative binomial (NB) and generalised Poisson (GP) regression models were fitted. Stepwise regression was used to select the significant predictors in both models based on the Akaike information criterion (AIC). The residuals of both NB and GB regression models were autocorrelated. An autoregressive integrated moving average (ARIMA) model was fitted to the residuals of both models. ARIMA (7, 1, 5) was fitted to the residuals of the NB model while ARIMA (1, 1, 6) was fitted for the residuals of the GP model. NB + ARIMA (7, 1, 5) and GP + ARIMA (1, 1, 6) models were tested for performance using root mean square error (RSME). GP + ARIMA (1, 1, 6) was selected as the optimal model. The results from the optimal model suggest that minimum temperature, ambient relative humidity, ambient wind speed, PM2.5, and NO2 at various lags are positively associated with COVID-19 incidence while maximum relative humidity, minimum relative humidity, solar radiation, maximum temperature, NO, PM load, PM10, SO2, and NOX at various lags have a negative association with COVID-19 incidence. Ambient wind direction and temperature showed a nonsignificant association with COVID-19 at all lags. This study suggests that meteorological and pollution parameters play a vital independent role in the transmission of the SARS-CoV-2 virus.

The impact of meteorological parameters on the scrub typhus incidence in Baoshan City, western Yunnan, China.

Scrub typhus has become widespread across various regions in China in recent decades, causing a considerable burden on residents. While meteorological variables significantly impact the spread of scrub typhus, there is insufficient quantitative evidence illustrating this association in known high-endemic areas. A distributed lag non-linear model was applied to explore the relationship between meteorological parameters and scrub typhus incidence from 2010 to 2019 in Baoshan City, western Yunnan Province, China. High monthly mean (20°C) and maximum (30°C) temperatures were associated with a peak risk of scrub typhus in the current month. Higher minimum temperatures and higher relative humidity were followed by increasing cumulative risks over the ensuing 3 months. Higher precipitation was followed by increasing cumulative risk over the ensuing 2-month period, peaking at around 30 cm. The non-linear lag associations between meteorological parameters and scrub typhus incidence suggest that higher monthly minimum temperature and relative humidity could be associated with an increased risk of scrub typhus in the subsequent several months, while warm temperature is more likely to impact the occurrence of scrub typhus in the current month.

Improving the daily pan evaporation estimation of long short-term memory and support vector regression models by using the Wild Horse Optimizer algorithm

ABSTRACT Evaporation is a basic element in the hydrological cycle that plays a vital role in a region's water balance. In this paper, the Wild Horse Optimizer (WHO) algorithm was used to optimize long short-term memory (LSTM) and support vector regression (SVR) to estimate daily pan evaporation (Ep). Primary meteorological variables including minimum temperature (Tmin), maximum temperature (Tmax), sunshine hours (SSH), relative humidity (RH), and wind speed (WS) were collected from two synoptic meteorological stations with different climates which are situated in Fars province, Iran. One of the stations is located in Larestan city with a hot desert climate and the other is in Abadeh city with a cold dry climate. The partial mutual information (PMI) algorithm was utilized to identify the efficient input variables (EIVs) on Ep. The results of the PMI algorithm proved that the Tmax, Tmin, and RH for Larestan station and also the Tmax, Tmin, and SSH for Abadeh station are the EIVs on Ep. The results showed the LSTM–WHO hybrid model for both stations can ameliorate the daily Ep estimation and it can also reduce the estimation error. Therefore, the LSTM–WHO hybrid model was proposed as a powerful model compared to standalone models in estimating daily Ep.

Population dynamics of pod pest complex of pigeonpea

A field experiment was conducted at Main Agricultural Research Station, University of Agricultural Sciences, Dharwad, to study the population dynamics of pod pest complex of pigeonpea. The observations were recorded at fortnightly intervals starting from flower initiation stage upto the harvest of the crop. The peak activity of gram pod borer (3.63 larvae/plant), spotted pod borer (3.63 larvae/plant) and pod bugs (4.62 bugs/plant)was observed during 50th Standard Meteorological Week. The pod damage caused by Helicoverpa armigera (25.02%) and Maruca vitrata (16.77%) was also highest during 50th Standard Meteorological Week. The maximum pod damage by plume moth (12.02%) was noticed at 52nd Standard Meteorological Week. The 52nd Standard Meteorological Week witnessed highest pod fly population (10.80 maggots / 10 pods), per cent pod damage (27.75%), and per cent seed damage (17.51%).The correlation studies of incidence of pod pest to prevalent weather parameters revealed that, gram pod borer and the spotted pod borer were more prevalent as the minimum temperature rose, but pod fly and pod bugs population increased negatively. There was a negative correlation between the population of all pod pest, including gram pod borer, spotted pod borer, pod bugs and pod fly with maximum relative humidity, minimum relative humidity and rainfall.