Relative Wind Speed Research Articles

Spatiotemporal variability in human thermal comfort perception in open-air spaces: application to the state of Minas Gerais, Brazil.

The objective of this study was to propose bioclimatic zoning to classify human thermal comfort and discomfort in the state of Minas Gerais, Brazil; both historical and future scenarios are considered. Thus, historical series (1961 to 2017) of the effective temperature index as a function of the wind (ETW) were obtained as a function of the monthly average values of the minimum, mean, and maximum dry-bulb air temperatures (tdb,min, tdb,mean, and tdb,max, respectively), in addition to the mean relative humidity ([Formula: see text], %) and mean wind speed ([Formula: see text], m s -1). The data were obtained from 34 weather stations and subjected to trend analysis by using the nonparametric Mann-Kendall test, thus enabling the simulation of future scenarios (for 2028 and 2038). Then, to define the thermal ranges of the bioclimatic zoning, maps of ETWmin, ETWmean, and ETWmax were created from geostatistical analysis. Overall, the results show warming trends for the upcoming years in Minas Gerais municipalities. All climatic seasons showed an increase in the frequency of new classifications in the upper adjacent classes, which indicates climate warming. Therefore, when considering future scenarios for the autumn and winter seasons, attention should be given to changes in predicted thermal sensation, especially in the Central Minas Gerais, Belo Horizonte Metropolitan, South/Southwest Minas, Campo das Vertentes, and Zona da Mata.

Field and numerical investigation of soil hydro-thermal response to the climatic conditions in southern China considering soil heterogeneity

It is essential to investigate soil hydro-thermal behavior to reduce the potential damage to geotechnical constructions caused by varying climatic conditions. In this work, an in-situ monitoring station was built on a clay slope with high groundwater table, situated in Yixing, China. Ten SMT-100 sensors were instrumented on the site to record the evolutions of soil temperature and volumetric water content at various depths. Meteorological information, including relative humidity, air temperature, rainfall, wind speed and solar radiation, and slope runoff was collected. A numerical investigation was also conducted to analyze the variations of soil hydro-thermal response to the recorded climatic conditions by combing coupled hydro-thermal model and soil-atmosphere interaction model. The profiles of soil temperature and volumetric water content suggest that the diurnal changes are limited to depths <40 cm, and the seasonal changes are usually approaching a location deeper than 120 cm. Soil heterogeneity can affect its hydro-thermal response, e.g., higher volumetric water content was observed at the points with lower dry density, which is mainly related to soil water retention capacity. However, appropriate theoretical models to consider soil heterogeneity in the description of soil hydro-thermal properties need to be further investigated.

Daily reference evapotranspiration estimation utilizing deep learning models with varied combinations of weather data

Effective irrigation planning pivots on the meticulous monitoring of ETo (the reference evapotranspiration), a fundamental variable in diverse studies. The go-to method for approximate ETo, the FAO-56 Penman-Monteith (FAO-56 PM) equation, demands an array of weather data, encompassing relative humidity, temperature, solar radiation, and wind speed. However, this data-intensive requirement presents challenges in situations where such information is limited, and artificial intelligence is being used to address this challenge, come into play to estimate ET0 with a streamlined set of parameters. The study begins with a comprehensive analysis, comparing the performance of Penman-Monteith (FAO-56 PM) and (ASCE_PM) with deep learning models such as artificial neural networks (ANN) and one-dimensional convolutional neural networks (CNN 1d).The principal aim is to estimate daily reference evapotranspiration (ETo) in the region of Morocco, specifically Meknes, employing a minimal set of meteorological variables across various combinations of measured data on the fundamental variables that constitute ETo. These combinations encompass scenarios involving all four variables, different combinations of three, two, and each variable in isolation. Two implementation scenarios are considered: (i) cross-validation across all datasets and (ii) training with one station and validating with another. Across these varied techniques, commendable results emerge, portraying a favourable comparison against empirical models reliant on minimal meteorological data.

A disease predictive model based on epidemiological factors for the management of bacterial leaf blight of rice.

Rice is a widely consumed staple food for a large part of the world's human population. Approximately 90% of the world's rice is grown in Asian continent and constitutes a staple food for 2.7 billion people worldwide. Bacterial leaf blight (BLB) caused by Xanthomonas oryzae pv. oryzae is one of the devastating diseases of rice. A field experiment was conducted during the year 2016 and 2017 to investigate the influence of different meteorological parameters on BLB development as well as the computation of a predictive model to forecast the disease well ahead of its appearance in the field. The seasonal dataset of disease incidence and environmental factors was used to assess five rice varieties/ cultivars (Basmati-2000, KSK-434, KSK-133, Super Basmati, and IRRI-9). The accumulated effect of two year environmental data; maximum and minimum temperature, relative humidity, wind speed, and rainfall, was studied and correlated with disease incidence. Average temperature (maximum & minimum) showed a negative significant correlation with BLB disease and all other variables; relative humidity, rainfall, and wind speed had a positive correlation with BLB disease development on individual varieties. Stepwise regression analysis was performed to indicate potentially useful predictor variables and to rule out incompetent parameters. Environmental data from the growing seasons of July to October 2016 and 2017 revealed that, with the exception of the lowest temperature, all environmental factors contributed to disease development throughout the cropping season. A disease prediction multiple regression model was developed based on two-year data (Y = 214.3-3.691 Max T-0.508 Min T + 0.767 RH + 2.521 RF + 5.740 WS), which explained 95% variability. This disease prediction model will not only help farmers in early detection and timely management of bacterial leaf blight disease of rice but may also help reduce input costs and improve product quality and quantity. The model will be both farmer and environmentally friendly.

A NEW APPROACH TO DETERMINE THE INFLUENCE OF WEATHER CONDITIONS ON FOREST FIRE RISK IN THE MEDITERRANEAN REGION OF TÜRKİYE

The risk of forest fires is a major problem in Türkiye's Mediterranean region and has a significant impact on ecosystems and atmospheric conditions. Throughout the previous century, a significant portion of Türkiye's Mediterranean Region has been destroyed by forest fires. This study aims to determine the meteorological covariates, such as relative humidity, maximum temperature, and wind speed, that affect forest fires. We classified forest fires into two groups. The first group (F1) refers to small forest fires, with burned forest areas of less than 10 hectares. The second group (F2), representing rare events, corresponds to burned areas of more than 10 hectares. The data is composed of binary values (F1=0 and F2=1) taken between the years 2015-2019 from different locations in the Mediterranean Region of Türkiye. For binary data modeling, the ordinary logistic regression (LR) has been frequently used. However, such a method tends to give biased results when using rare event data. Therefore, we employed three different modeling techniques specifically designed for rare event data. According to the results obtained from the best model, Firth's Logistic Regression (FLR), wind speed, and maximum temperature are found to be statistically significant variables in the occurrence of forest fires greater than 10 hectares.

Influence of wind and waves on ambient noise and bubble entrainment depth in the semi-enclosed Baltic Sea

Semi-enclosed, fetch-limited waters create unique conditions for wind wave development and breaking. Parameters of breaking waves influence bubble entrainment depth and associated noise, which is why they differ in semi-enclosed sea compared to open waters. While the established noise-wind speed relationship holds in oceanic conditions, it differs in land-constrained basins like the Baltic Sea. To explore noise level, bubble entrainment depth and wind speed relationships, we conducted noise and sub-surface bubble measurements, coupled with wind observations, in the selected area of the Baltic Sea during two consecutive summers. A novel method was employed to estimate bubble entrainment depth under conditions of strong backscatter. Model data of wave field parameters were employed to assess their influence on noise level and bubble entrainment depth. Results suggest stronger connections between noise level and wind speed, as well as wave height, compared to wave age and wind sea steepness. The same patterns hold true for the correlation between bubble entrainment depth and both wind speed and wave field parameters. The parameterized noise level-wind speed relationship differs from that obtained for oceanic conditions and also varies across measurement periods. Observed differences were shaped by varying wind-wave conditions, notably differences in wind speed, direction, wave height, and the presence of swell. The noise level-bubble entrainment depth relation is reported for the first time for Baltic Sea conditions. For a thorough analysis of the influence of these factors on noise and bubbles, longer measurements under diverse wind-wave conditions are required to account for site-specific wave field characteristics.

Experience from a light trap Lepidoptera survey in a populated settlement: effects of meteorological factors and changes in lunar phases

In the framework of the research, light trap Lepidoptera collections were carried out in the territory of a settlement (Velyki Berehy, Zakarpatska Oblast, Ukraine), which is bordered by nature reserves, forests, and agricultural areas, as well as by the remains of a large marsh (Szernye bog) that had been drained in the past. A Jermy light trap with a household LED bulb was used to capture the moths and butterflies. In addition to assessing the Lepidoptera fauna of the area, the study aimed to find out which species of Lepidoptera are attracted to energy-saving light bulbs commonly used in households in a settlement located in a habitat-diverse environment. In addition to the artificial light pollution conditions and other anthropogenic influences of the settlement, the direction and magnitude of the effects of temperature, relative humidity, cloud cover, wind speed, and moon phase parameters at the time of collection on the flight trends of nocturnal Lepidoptera species were also investigated. A total of 97 days of collecting between May 2018 and May 2019 resulted in the collection of 122 Lepidoptera species from 16 families. In terms of their ecological requirements, species were found in closed and open forest, forest edge, scrub, meadow, actively cultivated and abandoned agricultural areas, as well as in dry and wet areas. Several species considered being agricultural, forestry, or horticultural pests were identified, but not in abundance. Among the environmental variables, temperature variation showed a strong significant positive correlation with daily species numbers and abundance, while relative humidity, wind speed, cloud coverage and moon phases showed varying degrees of negative correlation, but were generally weak. The effects of the interactions of natural and man-made environmental influences on living organisms are difficult to assess objectively, but their research is of paramount importance for the long-term effective conservation of habitats of particular conservation value and for the preservation of their biodiversity.

Development of a robust daily soil temperature estimation in semi-arid continental climate using meteorological predictors based on computational intelligent paradigms.

Changes in soil temperature (ST) play an important role in the main mechanisms within the soil, including biological and chemical activities. For instance, they affect the microbial community composition, the speed at which soil organic matter breaks down and becomes minerals. Moreover, the growth and physiological activity of plants are directly influenced by the ST. Additionally, ST indirectly affects plant growth by influencing the accessibility of nutrients in the soil. Therefore, designing an efficient tool for ST estimating at different depths is useful for soil studies by considering meteorological parameters as input parameters, maximal air temperature, minimal air temperature, maximal air relative humidity, minimal air relative humidity, precipitation, and wind speed. This investigation employed various statistical metrics to evaluate the efficacy of the implemented models. These metrics encompassed the correlation coefficient (r), root mean square error (RMSE), Nash-Sutcliffe (NS) efficiency, and mean absolute error (MAE). Hence, this study presented several artificial intelligence-based models, MLPANN, SVR, RFR, and GPR for building robust predictive tools for daily scale ST estimation at 05, 10, 20, 30, 50, and 100cm soil depths. The suggested models are evaluated at two meteorological stations (i.e., Sulaimani and Dukan) located in Kurdistan region, Iraq. Based on assessment of outcomes of this study, the suggested models exhibited exceptional predictive capabilities and comparison of the results showed that among the proposed frameworks, GPR yielded the best results for 05, 10, 20, and 100cm soil depths, with RMSE values of 1.814°C, 1.652°C, 1.773°C, and 2.891°C, respectively. Also, for 50cm soil depth, MLPANN performed the best with an RMSE of 2.289°C at Sulaimani station using the RMSE during the validation phase. Furthermore, GPR produced the most superior outcomes for 10cm, 30cm, and 50cm soil depths, with RMSE values of 1.753°C, 2.270°C, and 2.631°C, respectively. In addition, for 05cm soil depth, SVR achieved the highest level of performance with an RMSE of 1.950°C at Dukan station. The results obtained in this research confirmed that the suggested models have the potential to be effectively used as daily predictive tools at different stations and various depths.

Reference evapotranspiration prediction using machine learning models: An empirical study from minimal climate data

AbstractThe scarcity of climatic data is the biggest challenge for developing countries, and the development of models for reference evapotranspiration (ET0) estimation with limited datasets is crucial. Therefore, the current investigation assessed the efficacy of four machine learning (ML) models, namely, linear regression (LR), support vector machine (SVM), random forest (RF), and neural networks (NN), to predict ET0 based on minimal climate data in comparison with the standard FAO‐56 Penman‐Monteith (PM) method. The data on daily climate parameters were collected for the period 2000−2021, including maximum and minimum temperatures (Tmax and Tmin), mean relative humidity (RH), wind speed (WS), and sunshine hours (SSH). The performance of the developed models considering different input combinations was evaluated by using several statistical performance measures. The results showed that the SVM model performed better than the other ML models during training (R2 = 0.985; mean absolute error [MAE] = 0.170 mm/day; mean square error [MSE] = 0.052 mm/day; root mean square error [RMSE] = 0.229 mm/day; mean absolute percentage error [MAPE] = 5.72%) and testing stages (R2 = 0.985; MAE = 0.168 mm/day; MSE = 0.050 mm/day; RMSE = 0.224 mm/day; MAPE = 5.91%) under full dataset scenario. The best performance of the models to estimate was with Tmax, RH, Ws, SSH, and Tmin. The results of the current study are substantial as it offers an approach to estimate ET0 in semi‐arid data‐scarce region.

Attributions of emission-reduction and meteorological conditions to typical heavy pollution episodes in a cold metropolis, northeast China

Heavy pollution episodes frequently occurred in winter in northeast China due to the multiple anthropogenic emissions coupled with adverse meteorological conditions, which increased the difficulty of environmental pollution control. To better enact strategies for mitigating air pollution in the post-pandemic era, daily pollutant concentration monitoring and meteorological data were used to evaluate the changes and meteorological factors of air pollutants before (2019) and during (2020) the lockdown in Harbin City, northeast China. Moreover, typical pollution episodes under COVID-19 lockdown were identified, and their emission sources, meteorology conditions, and regional pollution transportation were analyzed. The results showed significant decreases in NO2, PM10 and CO, while O3 increased, and no differences in PM2.5 and SO2 during the lockdown compared with non-lockdown periods. It indicated that reduced activities of transportation resulted in reductions of NO2 concentrations by 16%, and stationary emission sources were less affected. Correlation between PM2.5 and O3 tended to change from positive to negative as the threshold of PM2.5 = 90 μg m−3, with the main controlling factor changed from their common gaseous precursors to meteorological conditions (temperature &amp;lt;0°C and wind speed &amp;lt;2 m s−1). Pollution days were concentrated in the COVID-19 lockdown period with PM2.5 as the primary pollutant. SO2 dominant pollution and PM2.5 dominant pollution were distinguished from six sustained heavy pollution events. PM2.5 and SO2 played essential roles in SO2 dominant pollution, which derived from local emissions of coal combustion and firework discharge. PM2.5 dominant pollution might be chemical transformed from coal burning, vehicle exhaust, and other secondary precursors, which was affected and aggravated by CO, NO2, high relative humidity and low wind speed affected by local emission and long-distance transport.

Continuous observations from horizontally pointing lidar, weather parameters and PM2.5: a pre-deployment assessment for monitoring radioactive dust in Fukushima, Japan

Abstract. A horizontally pointing lidar is planned for deployment with other instruments in Fukushima, Japan, to continuously monitor and characterize the optical properties of radioactive aerosols and dust in an uninhabited area. Prior to installation, the performance of the lidar is tested at Chiba University. Data from the continuous operation of the lidar from August 2021 to February 2022 are analyzed for extinction and volume linear depolarization ratio. These are compared with the weather sensor and particulate matter (PM2.5) measurements to quantify the relationships between atmospheric conditions and optical properties of near-ground aerosols. The results show that the lidar data's extinction coefficient and depolarization ratio can have a quantifiable relationship with relative humidity (RH), absolute humidity, rain rate, wind speed, wind direction and PM2.5 concentration. Analysis of the 7-month data shows that the optical properties of aerosol and dust depend on the combined effects of the weather parameters. An increase in RH or PM2.5 concentration does not imply an increase in radioactive aerosols. The average extinction coefficient and depolarization ratio of aerosols and dust, originating from the land and ocean, show different values and opposing trends, which can aid in determining the occurrence of ground-based radioactive dust and aerosols. The information obtained from analyzing the inter-relationship among lidar, weather parameters and PM2.5 concentration is essential in assessing the occurrence of radioactive aerosols and characterizing local aerosol–weather relationships in a radioactive area. This result provides essential information in describing radioactive aerosols in Fukushima.

Dynamics and variability of microclimate in an unmanaged mountain forest after a bark beetle outbreak

Forest disturbances alter hydrological, energy, and microclimatic characteristics and their extents vary with the landscape characteristics of the terrain. We evaluated changes in evapotranspiration and latent heat rate in an unmanaged, central European mountain forest (Norway spruce) after a bark beetle-induced mortality of >75 % trees, and the spatial variability in microclimate during its natural regeneration. We observed that (1) the annual mean incident solar radiation to the treeless plots was 8.2 MJ m−2 day−1 (95 W m−2), being reduced by ∼45 % compared to its theoretical flux by high average cloudiness (70.6%) and short daily sunshine (4.3 hour day−1). The observed energy input to the forest floor beneath survived canopies (1.2 MJ m−2 day−1) was an order of magnitude lower than solar radiation to the canopy surface. (2) The mean annual evapotranspiration and the average annual latent heat rate decreased by 155 mm yr−1 and 385 MJ m−2 yr−1 (12 W m−2), respectively, after tree dieback. (3) The soil and air temperatures at individual plots correlated positively with the incident solar radiation and negatively with elevation. Mean soil and air temperatures exhibited similar lapse rates of 5.9–8.0 °C km−1 during the growing season (May-October). The energy input (positive relationship) and elevation (negative relationship) explained 74–83 % of the between-plot variability in mean soil and air temperatures and 51–77 % of their daily amplitudes during the growing season. (4) On sunny days, the observed soil and air temperatures were significantly lower (by ∼50 and 15 %, respectively) than ground surface temperature based on thermal satellite data (Landsat-8). The ground surface temperature explained 80–92 % of the observed variability in the soil and air temperatures. (5) The between-plot differences in daily soil and air temperatures and their amplitudes were from 24 to 70 % explained by solar radiation (positive correlation) and relative air humidity and wind speed (negative correlations).

The Influence of Meteorological Factors and the Time of Day on the Concentration of Ammonia in the Atmosphere Measured Using the Photoacoustic Method near a Cattle Farm—A Case Study

Influences of animals, time of day, air temperature and relative humidity, wind speed and direction on ammonia concentrations were investigated. A case study on a typical summer day from 7:00 to approximately 24:00 CEST (moderate wind speed, variable cloudiness and maximum global radiation higher than 950 W/m2) in west–central Poland is presented. Concentrations of this gas were measured at four heights (0.1–1.5 m), which were changed every 5 min, using a Nitrolux 1000 photoacoustic spectrometer. A micrometeorological station was established to also measure the surface energy budget components. The results presented are the average for each hour and for the entire day. The fine structure of concentration profiles, plume detection and uncertainty of ammonia flux calculation are also presented. The highest NH3 concentrations were at a 0.5 m height between 16:00 and 17:00 h when cows were grazing, but the lowest concentrations were between 23:00 and 24:00 h at the height of 1.5 m. The ammonia concentration increased with increasing air temperature and was the highest with a westerly wind direction and decreased with increasing air relative humidity. The greatest influence on the ammonia concentration was related to the presence of cows and the time of day, while a slightly smaller influence was noted in terms of air temperature and wind direction. A case study is suitable for presenting local effects, inhomogeneities and quantifying uncertainties in the bidirectional ammonia flux calculation.

Temporal Changes in Tourists’ Climate-Based Comfort in the Southeastern Coastal Region of Spain

In the context of climate change, where the average temperature has risen in recent decades on the Mediterranean coast of the Iberian Peninsula, bioclimatic indicators show an increase in thermal discomfort. This is especially relevant in regions with a clear focus on mass and seasonal sun and beach tourism, with a large number of tourists experiencing discomfort in hot and humid summer environments. The research analyses the temporal evolution (1967–2022) of the coasts of the provinces of Alicante and Murcia (Spain) using the Climate Comfort Index (CCI), divided into four different regions. Used are 14 coastal meteorological observatories divided into four regions. Trend analysis was performed using the Mann–Kendall (MKT) and Theil–Sen (TSE) tests. The results revealed a loss of climate comfort during the summer season (−0.3 to −0.4/decade), as well as an expansion of the warm period toward June and early September, with an increase of 38.7 days in “hot” thermal comfort. The increase in thermal discomfort in the summer is influenced by an increase in average temperature (0.5 to 0.7 °C/decade) and a reduction in the average relative humidity (−1.0 to −2.1%/decade) and wind speed (−0.2 to −0.9 km/h/decade). In the last 22 years (2000–2022), decreases (p ≤ 0.05) have been recorded in July and September (−0.2 to −0.4/decade), reaching “excessive heat” climatic comfort thresholds for the first time. Finally, there has been an increase in thermal comfort in winter, especially during December in recent years (2000–2022).

Navigating Energy Efficiency: A Multifaceted Interpretability of Fuel Oil Consumption Prediction in Cargo Container Vessel Considering the Operational and Environmental Factors

In the maritime industry, optimizing vessel fuel oil consumption is crucial for improving energy efficiency and reducing shipping emissions. However, effectively utilizing operational data to advance performance monitoring and optimization remains a challenge. An XGBoost Regressor model was developed using a comprehensive dataset, delivering strong predictive performance (R2 = 0.95, MAE = 10.78 kg/h). This predictive model considers operational (controllable) and environmental (uncontrollable) variables, offering insights into complex FOC factors. To enhance interpretability, SHAP analysis is employed, revealing ‘Average Draught (Aft and Fore)’ as the key controllable factor and emphasizing ‘Relative Wind Speed’ as the dominant uncontrollable factor impacting vessel FOC. This research extends to further analysis of the extremely high FOC point, identifying patterns in the Strait of Malacca and the South China Sea. These findings provide region-specific insights, guiding energy efficiency improvement, operational strategy refinement, and sea resistance mitigation. In summary, our study introduces a groundbreaking framework leveraging machine learning and SHAP analysis to advance FOC understanding and enhance maritime decision making, contributing significantly to energy efficiency and operational strategies—a substantial contribution to a responsible shipping performance assessment under tightening regulations.

PSXIV-16 Evaluation of Water Intake Equations with an Emphasis on Physiological Status and Evaluation of Current Relevance Behind Environmental Parameters

Abstract Water requirements for beef cattle are often computed according to body weight (BW) and environmental variables. Inherent variation in environmental conditions within geographical locations, in addition to a cascade of physiological changes triggered as consequence of such variability are somewhat fomented by multicollinearity amongst these variables. This conundrum generates instability of current predictive models in reaching optimization equilibrium inflating its overall predictability and robustness. The goals herein involved the identification of physiologically relevant parameters and the generation of robust, data-driven, individualistic models through high frequency data loggers. Further, we evaluate re-parametrized and current proposed equations (e.g.: Hicks et al., 1988 and Arias and Mader, 2011) for the prediction of water intake for beef cattle at different life stages and physiological conditions. The dataset utilized consisted of more than 30,000 observations, including longitudinal data on lactating animals (n = 23), grass finished steers (n = 12), grain finished steers (n = 21), and grain finished implanted steers (n = 9), steers backgrounded on a low (n = 20) or moderate (n = 20) plane of nutrition bulls under negative (n = 13), maintenance (n = 13) and positive energy balance (n = 13). Individual monitoring systems allowed for the daily collection of BW, water and feed intake, and ingestive behavior. Additionally, an on-site data-logging weather station (1-min intervals) collected relative humidity, temperature, wind speed, wind direction, solar radiation, and precipitation. All reported statistics were generated on out-of-bag randomly split portions of the dataset. For identification of relevant predictive parameters of water intake, several feature selection and penalization techniques were examined. When comparing our equations to current, the errors generated for water intake predictions using physiological classification and on-site environmental variables, explained 50% more of the variation in predicting water intake (r2 = 0.38 vs r2 = 0.22-0.27). When evaluating the errors of prediction (root mean squared error, RMSE and mean absolute error, MAE), significant decreases of up to three-fold were observed on the equations generated herein (RMSE = 9.05 vs 28.8 to 35.31, MAE = 7.04 vs 26.9 to 33.9). Accurate monitoring of individual parameters such as feed intake were better predictors of water intake than environmental parameters when evaluating re-parametrization of models. Further, inclusion of a physiological stage (categorical variable) was more significant than any of the other environmental predictors. Overall, the results discussed highlight the importance of appropriately accounting for the water requirements of beef cattle and how far-off current assumptions are. Further, it raises concern on current equations that use environmental monitoring equations and techniques that attempt to describe nutrient requirements of the animals, and potentially brings the approach to requirements rather than environment.

Assessment of meteorological parameters on air pollution variability over Delhi.

In this study, the relationships between meteorological parameters (relative humidity, wind speed, temperature, planetary boundary layer, and rainfall) and air pollutants (particulate matter and gaseous pollutants) have been evaluated during a 3-year period from 2019 to 2021. Diffusion and dispersion of air contaminants were significantly influenced by meteorology over the capital city. The results of correlation matrix and principal component analysis (PCA) suggest a season's specific influence of meteorological parameters on atmospheric pollutants' concentration. Temperature has the strongest negative impact on pollutants' concentration, and all the other studied meteorological parameters negatively (reduced) as well as positively (increased) impacted the air pollutants' concentration. A two-way process was involved during the interaction of pollutants with relative humidity and wind speed. Due to enhanced moisture-holding capacity during non-monsoon summers, particles get larger and settle down on the ground via dry deposition processes. Winter's decreased moisture-holding capacity causes water vapour coupled with air contaminants to remain suspended and further deteriorate the quality of the air. High wind speed helps in the dispersion and dilution but a high wind speed associated with dust particles may increase the pollutants' level downwind side. The PM2.5/PM10 variation revealed that the accumulation effect of relative humidity on PM2.5 was more intense than PM10. Daily average location-specific rainfall data revealed that moderate to high rainfall has a potential wet scavenging impact on both particulate matters and gaseous pollutants.

An empirical technique to predict monthly mean global solar radiation for PV applications in Indian context

AbstractGlobal radiation is a significant input in the design and simulation of solar PV as well as low temperature solar thermal systems. Moreover, meteorological conditions of any location also greatly influence the design, simulation, and output of the solar energy system. The considerable importance of global radiation leads to a dire need for global radiation models in the lack of measurement. Consequently, to enhance the predictability of solar resources over India, models for global radiation simulation through meteorological parameters (temperature, relative humidity, and wind speed) are proposed. Data of nine stations from NASA for 10 years (2008–2017) are used in the study to calibrate the models and performance of developed models is assessed through different statistical indicators (MPE, MBE, RMSE, R2, RMSD, NSE, and TS). The investigation reveals that out of the three proposed models, the model correlating total solar radiation with relative humidity, temperature, and wind speed is found to provide the highest accuracy in global radiation simulation with least statistical errors and is selected as best fit model. Moreover, validation of the proposed models is approved through monthly average daily total solar radiation predication for the remaining four testing cities of India and also through comparison with previously available models in the literature.

Stochastic modelling and forecasting of relative humidity and wind speed for different zones of Kerala

The variations in climatic conditions depend on seasonal changes throughout the year. Modelling and prediction of climatic conditions can help to determine the impacts of seasonal changes in climate over a specific period of time. Climate change can directly and indirectly affect agricultural, industrial, geographical and technological sectors in our society. Agriculture and the allied sector are seriously affected by changes in climate since it leads to complete destruction of cultivated crops. In this study, in order to model and forecast relative humidity and wind speed for northern, central and southern zones of Kerala, stochastic approach using SARIMA (Seasonal Autoregressive Integrated Moving Average) model was employed. The monthly weather data for the northern zone and the central zone of Kerala was taken from the location of RARS Pilicode and RARS Pattambi for a period of 39 years (1982-2020) whereas for southern zone, data was collected from the location of RARS, Vellayani for a period of 36 years (1985-2020) with the help of data access viewer. The model validation was done using MSE (mean square error), RMSE (root mean square error), MAE (mean absolute error) and RMAPE (relative mean absolute percentage error). The RMAPE values of relative humidity and wind speed in different zones of Kerala was less than 10 per cent which indicated that fitted model is showing accurate performance. The best selected SARIMA model is used in attaining anticipated values of relative humidity and wind speed for the next 5 years.

RETRACTED: Seasonal correlation of aerosols with soil moisture, evapotranspiration, and vegetation over Pakistan using remote sensing

Aerosols have a severe impact on the Earth's climate, human health, and ecosystem. To understand the impacts of aerosols on climate, human health, and the ecosystem we must need to understand the variability of aerosols and their optical properties. Therefore, we used Aqua-MODIS retrieved aerosol optical depth (AOD) (550 nm) and Angstrom exponent (AE) (440/870) data to analyze the Spatio-temporal seasonal variability of aerosols and their relationship with different meteorological parameters over Pakistan from 2002 to 2021. High (>0.5) AOD values were observed during the summer season and low (<0.8) in the spring season. AE values were observed to be high (>1) in the northern regions of Pakistan indicating the dominance of fine mode particles during the winter season. Moreover, AOD showed a positive correlation with Relative Humidity (RH), Evapotranspiration, Wind speed (WS), and Temperature. On the other hand, it showed a negative correlation with Soil moisture (SM), Normalized difference vegetation index (NDVI), and precipitation over Pakistan. Therefore, considering the outcomes of this study will help policymakers to understand the spatiotemporal variability of aerosols and their seasonal correlation with different meteorological parameters.