Variations Of Meteorological Parameters Research Articles

Trend svjetline noćnog neba iznad Zagreba u razdoblju 2012.–2022.

The night sky brightness at the RGN site (near the centre of Zagreb, Croatia) was monitored from January 2012 to August 2022. The first data set, covering the period 2012 to 2017, has already been analysed and the conclusions were published in Andreić (2018). The main conclusions are repeated here for comparison and completeness. The results of the analysis of the second dataset, covering the period from January 2018 to July 2022, are reported here. The first data analysis showed that the average night sky brightness did not change significantly from 2012 to 2017, excluding differences due to annual variations in meteorological parameters. The second data set (2018 to 2022) showed a slight decrease in night sky brightness compared to the first data set. The difference is small, about 0.2 mag/arcsec2 (mean values). In the second data set, there is a trend toward darker nights in later years that began three or four years ago (around 2019 or 2020). It is attributed to the modernisation of the public lighting network, where old, often very poor lighting fixtures are slowly being replaced by modern LED lighting fixtures, and to the effects of climatic changes leading to warmer winters with more clear nights.

Prediction of daily fine particulate matter (PM2.5) concentration in Aksaray, Turkey: Temporal variation, meteorological dependence, and employing artificial neural network

AbstractThis study analyzed the temporal variation and prediction of fine particulate matter (PM2.5) concentrations in Aksaray, Turkey, a city in Central Anatolia. The relationship between PM2.5 and meteorological parameters such as temperature, humidity, wind speed, and wind direction was investigated. An artificial neural network (ANN) model was developed to predict PM2.5 levels based on meteorological data and air pollutant information. Seasonal and diurnal patterns of PM2.5 concentrations were observed, with higher values recorded during the winter and lower values during the summer. Additionally, higher levels were observed in the morning and evening, while lower levels were recorded in the afternoon. The variations in meteorological parameters, especially temperature and wind speed, significantly influenced PM2.5 levels. To predict hourly PM2.5 concentrations, single and multiple data imputation techniques were employed in combination with resilient back‐propagation (RPROP‐ANN). The neural network was applied, consisting of one input layer comprising 11 parameters, one hidden layer with 20 neurons, and an output layer. The results indicate that the best forecasting performance for PM2.5 was demonstrated by the combination of the missForest imputation technique with the RPROP neural network, as assessed by the coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE). The proposed model is characterized by a low RMSE of 5.94 and a high R2 value of 0.88, demonstrating exceptional predictive performance in air quality.

Seasonal spatial variations of urban methane concentrations in a medium-sized city determined by easily measure variables

The concentration of methane (CH4) in the urban atmosphere results from a balance between sources and sinks, as well as meteorological and topographical conditions that affect CH4 dispersion in the environment. Different geophysical, technical and demographic factors will determine the CH4 concentration in each city, and therefore further local research should be undertaken. The present study analyzes the relative contribution of sources on the atmospheric CH4 concentration in the urban core of Tandil city in Argentina. The temporal variation of meteorological parameters and the seasonal spatial variations of CH4 concentrations during a year were analyzed. In 9 of the sampling sites, the variables “distance from the wastewater treatment plant”, “homes connected to the natural gas network” and “distance from compressed natural gas stations” explained 29, 66 and 65% of the spatial variation of the atmospheric CH4 concentrations, respectively. The results highlight that the use of easily measured variables allows explaining a relatively high percentage of the spatial distribution of atmospheric CH4 concentration in the urban area. These demonstrate the need to improve the efficiency of the wastewater treatment systems and natural distribution and consumption to reduce the emissions of this potent greenhouse gas and its resulting impact on the environment.

Performance evaluation of three grid-connected monocrystalline silicon solar photovoltaic systems in Nepal

This study evaluates three grid-connected solar photovoltaic (PV) systems using four criteria: final yield, performance ratio, capacity utilization factor, and system efficiency. The PV systems were installed on the rooftop of the Nepal Telecom office at Sundhara, Pokhara, and Biratnagar. The generation data was collected through the web interface of the data logger installed at the sites. Long-term comparison (four years) of the plant at Sundhara and the same system comparison of plants at Pokhara and Biratnagar were performed. Long-term analysis of the plant at Sundhara found that the final yield decreased from 2.91 kWh/kWp in 2019 to 2.21 kWh/kWp in 2022. Similarly, the performance ratio decreased from 91.0% in 2019 to 67.3% in 2022, capacity utilization factor decreased from 12.1% in 2019 to 9.2% in 2022, and system efficiency decreased from 16.8% in 2019 to 12.5% in 2022. The same system installed at Pokhara and Biratnagar had slightly varied performance owing to the variation in meteorological parameters. The average air temperature at Biratnagar (26.7°C) was higher than at Pokhara (13.1°C) while the amount of solar radiation at Biratnagar was just higher by 4%. This led to the reduction of final yield by 9% at Biratnagar than at Pokhara as higher temperatures at Biratnagar caused more reduction in the power output from the solar panels. Finally, this study provides insights to researchers, PV installers, and panel manufacturers on the actual performance of grid-connected solar PV in Nepal.

From atmospheric water isotopes measurement to firn core interpretation in Adélie Land: a case study for isotope-enabled atmospheric models in Antarctica

Abstract. In a context of global warming and sea level rise acceleration, it is key to estimate the evolution of the atmospheric hydrological cycle and temperature in polar regions, which directly influence the surface mass balance of the Arctic and Antarctic ice sheets. Direct observations are available from satellite data for the last 40 years and a few weather data since the 1950s in Antarctica. One of the best ways to access longer records is to use climate proxies in firn or ice cores. The water isotopic composition in these cores is widely used to reconstruct past temperature variations. We need to progress in our understanding of the influence of the atmospheric hydrological cycle on the water isotopic composition of ice cores. First, we present a 2-year-long time series of vapor and precipitation isotopic composition measurement at Dumont d’Urville Station, in Adélie Land. We characterize diurnal variations of meteorological parameters (temperature, atmospheric water mixing ratio (hereafter humidity) and δ18O) for the different seasons and determine the evolution of key relationships (δ18O versus temperature or humidity) throughout the year: we find that the temperature vs. δ18O relationship is dependent on synoptic events dynamics in winter contrary to summer. Then, this data set is used to evaluate the atmospheric general circulation model ECHAM6-wiso (model version with embedded water stable isotopes) in a coastal region of Adélie Land where local conditions are controlled by strong katabatic winds which directly impact the isotopic signal. We show that a combination of continental (79 %) and oceanic (21 %) grid cells leads model outputs (temperature, humidity and δ18O) to nicely fit the observations, at different timescales (i.e., seasonal to synoptic). Therefore we demonstrate the added value of long-term water vapor isotopic composition records for model evaluation. Then, as a clear link is found between the isotopic composition of water vapor and precipitation, we assess how isotopic models can help interpret short firn cores. In fact, a virtual firn core built from ECHAM-wiso outputs explains much more of the variability observed in S1C1 isotopic record than a virtual firn core built from temperature only. Yet, deposition and post-deposition effects strongly affect the firn isotopic signal and probably account for most of the remaining misfits between archived firn signal and virtual firn core based on atmospheric modeling.

Evaluation of InSAR Tropospheric Delay Correction Methods in the Plateau Monsoon Climate Region Considering Spatial–Temporal Variability

The tropospheric delay caused by the temporal and spatial variation of meteorological parameters is the main error source in interferometric synthetic aperture radar (InSAR) applications for geodesy. To minimize the impact of tropospheric delay errors, it is necessary to select the appropriate tropospheric delay correction method for different regions. In this study, the interferogram results of the InSAR, corrected for tropospheric delay using the Linear, Generic Atmospheric Correction Online Service for InSAR (GACOS) and ERA-5 atmospheric reanalysis dataset (ERA5) methods, are presented for the study area of the junction of the Hengduan Mountains and the Yunnan–Kweichow Plateau, which is significantly influenced by the plateau monsoon climate. Four representative regions, Eryuan, Binchuan, Dali, and Yangbi, are selected for the study and analysis. The phase standard deviation (STD), phase–height correlation, and global navigation satellite system (GNSS) data were used to evaluate the effect of tropospheric delay correction by integrating topographic, seasonal, and meteorological factors. The results show that all three methods can attenuate the tropospheric delay, but the correction effect varies with spatial and temporal characteristics.

Diurnal Variations in Vegetation Activity affecting Shallow Groundwater Flow identified by Microthermal Measurements

Observations of summer microthermal temperature variations suggest, next to hydrological factors, a significant influence of plant activity on groundwater flow in fractured claystone materials. Variations in groundwater microtemperature were compared to variations in meteorological parameters and electrical potential of plants. With an increase in surface temperature, relative air humidity decreases and an increase in tree electrical potential, measured as the difference between the northern and the southern stem exposure (N–S), can be observed. This increase in electrical potential is concomitant with a change in groundwater temperature of approximately 2 mK. This relationship does not always occur. At high temperatures (+30°C) the decrease amounts to just 1 mK. This fact is related to the change in transpiration of plants, decreased or even suspended at high surface temperatures. A frequency analysis of all data showed a daily frequency of high magnitude in all parameters. Possibly changes in the macro weather situation events were observed in the results of atmospheric pressure, southern electric potential and groundwater temperature. The lag time between changes in electric potential and subsurface microtemperature changes amounts to 17 hours, possibly a result of the electrical potential difference between the northern and the southern exposure of the stem (N–S), and 5 hours, the result of the change in electrical potential difference between the southern and the northern stem exposure side (S–N). A comparison between potential changes and the computed change in gravity resulting from earth tidal effects showed that the correlation between the subsurface temperature variation with up to 2 mK and the change in surface temperature variation does not match directly. Other study shows that the impact of earth tides on subsurface microtemperature variation amounts to ca. 1mK. The effect of groundwater abstraction by mature vegetation is determined at the same range. Atmospheric tides can be correlated with the changes in north and south electric potentials.

On the impact of modern climate change on the increase of grain yields in Russia

In recent years, Russia has seen a steady increase in grain crop yields. A positive trend in meteorological conditions is often considered as the reason for this phenomenon, but there is almost no strong scientific evidence for it. The aim of investigation was to analyze changes in the potential (climatic) grain yields in the country for the period from 2000 to 2022. For this purpose, the simulation model of plant growth WOFOST was used, which allows assessing the impact of all meteorological parameters in a comprehensive manner. The assessment was carried out for a network of representative points (42 points) in different regions of the country for grain crops, taking into account daily meteorological parameters and soil type. Modeling was conducted at the level of potential yield, which takes into account the variation of meteorological parameters only, and it is believed that the limiting influence of other factors on the yield is absent. As a result of the analysis, it was found that the trends of potential yields have different direction in different regions of the country. The trend towards more favorable meteorological conditions for grain crops is observed for the North Caucasus, the lower Volga region, Western Siberia and the Far East, while in the Central and Central Black Earth districts, the upper Volga region, as well as in the south of Central Siberia in recent years there is a drop in the potential yields. The growth of grain yields in recent years agrees well with the trend of changes in agrometeorological conditions in most grain-producing regions of the country. Unidirectional trends in meteorological potential and statistical yields are absent only in the regions of central European Russia, where the role of the climatic factor in grain yield fluctuations is less significant, and where one can assume the effect of measures taken by the government of the country to support and develop agriculture.

A novel technique for forecasting the optimal production of a wind generator installed at any geographical point located within a very large area

The need to integrate renewable energy sources into the energy mix is felt because of the many advantages they offer over fossil fuels, notably in terms of environmental protection and more uniformly distributed availability. The intermittent and stochastic ones, such as wind power, present many problems to network operators due to the volatile nature of their output power. This work presents a new technique for optimally forecasting the power output of a wind turbine installed at any geographic point located within a very large area. Once the study area is defined, it is gridded and optimally sampled in order to have a truly representative number of geographical points. The study area is then divided into sub-areas by grouping the samples by similarity of variation of meteorological parameters (wind speed and direction). For each sub-area, the optimal production periods are then identified and used for forecasting the power output. The forecasting technique used combines the LSTM model for forecasting meteorological parameters and the linear model for approximating the power curves of wind turbines. The technique was applied to the Beninese territory on which 90 sub-zones were formed. A 12 h forecasting of wind speed, wind direction and wind power were presented for one of the sub-areas. The clustering results gave a Silhouette score of at least 0.99. The wind speed and direction forecasting gave (0.34 m/s, 7.8 rad) and (93%, 70%) for RMSE and R2, respectively.

Capturing High‐Resolution Air Pollution Features Using the Multi‐Scale Infrastructure for Chemistry and Aerosols Version 0 (MUSICAv0) Global Modeling System

AbstractThe Multi‐Scale Infrastructure for Chemistry and Aerosols (MUSICA) enables the study of atmospheric chemistry and aerosols at global to local scales. MUSICA version 0 (MUSICAv0) is a configuration of the Community Earth System Model (CESM) with regional refinement (RR). We compared the regional scale spatial and temporal variabilities of air pollutants simulated by this newly developed multiscale model and a widely used regional model—WRF‐Chem (the Weather Research and Forecasting model coupled with Chemistry). The O3 and PM2.5 mean biases of the two WRF‐Chem simulations over the U.S. are 5–9 ppb and −3 μg/m3 during August and September 2020. The MUSICAv0 simulation has larger O3 mean bias (11 ppb) but smaller PM2.5 mean bias (−1 μg/m3) compared to the two WRF‐Chem simulations. As indicated by spatial and temporal statistical measures, the variability in meteorological parameters (temperature, precipitation), and ozone and PM2.5 are similar across a range of temporal and spatial scales in MUSICAv0 and WRF‐Chem. We demonstrate the new capability of the global MUSICAv0 at regional scale through two examples—(a) plumes from western U.S. fires that passed through the domain boundary of WRF‐Chem and were transported back into the domain and impacted air quality on the eastern U.S.; and (b) impact of stratospheric intrusion on the troposphere.

Riverine health and the future of dolphins in northeastern India

Abstract The Gangetic Dolphin (Platanista gangetica) was once found in tens of thousands in the rivers of the Indian sub-continent, but the numbers have now dwindled abysmally throughout its geographical distribution. In northeastern India too, dolphins were seen in all the major rivers and also in certain wetlands during monsoon months till the early 1980s. However, the sighting of dolphins is rare now due to natural and human-induced factors. Three factors - adequate water cover, water quality, and abundance of prey food either individually or in combination- relate to River Dolphins’ existence. Variability of meteorological parameters also severely impacts on the limno-biological parameters of riverine habitats. Restoration of Gangetic Dolphins in their traditional habitats can boost ecotourism in the region. An extensive study is necessary to assess the cumulative impact of the proposed hydroelectric projects on the aquatic biodiversity of the entire eastern Himalayan region. The need of the hour is to adopt a holistic and participatory approach for maintaining riverine health and the prey base of the Barak and the Brahmaputra systems.

The Sensitivity of Meteorological Dynamics to the Variability in Catchment Characteristics

Evaluating meteorological dynamics is a challenging task due to the variability in hydro-climatic settings. This study is designed to assess the sensitivity of precipitation and temperature dynamics to catchment variability. The effects of catchment size, land use/cover change, and elevation differences on precipitation and temperature variability were considered to achieve the study objective. The variability in meteorological parameters to the catchment characteristics was determined using the coefficient of variation on the climate data tool (CDT). A land use/cover change and terrain analysis was performed on Google Earth Engine (GEE) and ArcGIS. In addition, a correlation analysis was performed to identify the relative influence of each catchment characteristic on the meteorological dynamics. The results of this study showed that the precipitation dynamics were found to be dominantly influenced by the land use/cover change with a correlation of 0.65, followed by the elevation difference with a correlation of −0.47. The maximum and minimum temperature variations, on the other hand, were found to be most affected by the elevation difference, with Pearson correlation coefficients of −0.53 and −0.57, respectively. However, no significant relationship between catchment size and precipitation variability was observed. In general, it is of great importance to understand the relative and combined effects of catchment characteristics on local meteorological dynamics for sustainable water resource management.

Impact of an annular solar eclipse on trace gases and meteorological parameters over Jaipur, Northwestern India

This study aimed to identify the impact of an annular solar eclipse i.e., 21 June 2020 on the variation of meteorological parameters along with trace gases using statistical analyses. The study site is located at Poornima University, Jaipur (26.7796°N, 75.8771°E), Rajasthan, India. The observational analysis indicates a rapid decrease in solar direct radiation (SDR) which varied between 706 and 79 W/m2 during the eclipse. SDR was reduced to 79 W/m2 at the maximum peak of the solar eclipse at 11:55 a.m. at the study location. The comparative analysis shows the variation of SDR during the solar eclipse day, the previous day, and the day after the event. A strong dip was observed in SDR during the annular eclipse day concerning before (734.31 W/m2) and after (734.375 W/m2) eclipse event. Furthermore, the impact of the solar eclipse on temperature (Ts) and Relative Humidity (RH) was analyzed over Jaipur. The statistical analyses demonstrate an apparent decrease in temperature of about 2°C while RH shows a slight increment (3.45%) during the solar eclipse event. The results show an inverse correlation between the solar eclipse and trace gases variations during the eclipse due to the changes in solar radiation, surface temperature, and variation in winds that might affect the photochemical processes.

Observed Evidence That Subsidence Process Stabilizes the Boundary Layer and Increases the Ground Concentration of Secondary Pollutants

AbstractSubsidence in atmospheric boundary layer (ABL) appears to be rare and short‐lived, so it is very difficult to observe. In this paper, field experimental data from a large tethered balloon (1,900 m3) synchronously measuring meteorological parameters and air pollutants, a ground‐based aerosol lidar, a Doppler wind lidar, and some other ground observations were used to analyze the formation mechanism of subsidence and its influences on the variation of meteorological parameters and pollutant concentrations of the ABL. Results show that the occurrence of subsidence was closely correlated with the cold advection accompanied by cold front. Pollutants were horizontally transported to the observation site driven by southwest low‐level jet. Under the influence of subsidence behind the cold front and turbulent kinetic energy transported toward the surface, the vertical downward transport of pollutants aloft lasted for nearly 5 hr. The observation data of the tethered balloon suspending at 500 m show that the concentrations of PM2.5, NO3−, SO42−, and NH4+ increased simultaneously. O3 concentration in the lower atmosphere also increased. Subsidence had significant effects on the secondary inorganic aerosol concentrations but the concentration of organic aerosols was not affected. Subsidence led to a stronger inversion layer, further suppressing the vertical dispersion of pollutants. Both the stronger subsidence‐induced inversion layer and the downward transport of pollutants have led to an increase in the ground PM2.5 concentration. Our findings demonstrate the close connection between subsidence and air pollution process and may provide the scientific reference for air quality potential forecast.

An experimental technique based on globe thermometers for the measurement of mean radiant temperature in urban settings

Different variables concur to the outdoor comfort assessment and, among them, the Mean Radiant Temperature (Tmr) represents the most challenging one to experimentally evaluate. According to the scientific literature, the most accurate method for in-field Tmr calculation is integral radiation measurements with three net radiometers. Nevertheless, net radiometers are expensive sensors and their implementation on large scale may be hindered by their price. To provide a cost-effective alternative some researchers proposed the use of globe thermometers. Globe thermometers are affordable sensors but typically exploited indoors, and their accuracy in urban settings is still under investigation.The scope of this work is to provide information regarding the extent to which globe thermometers can substitute net radiometers in the evaluation of the Tmr in outdoor urban settings. To this purpose, an experimental comparison has been performed between the Tmr calculated using net radiometers, assumed as a reference, and using two different globe thermometers: a standard 150 mm black globe and a grey 50 mm globe.The results revealed that the black globe tends to overestimate the Tmr while the grey globe mainly underestimates it. Moreover, the analyses evidenced a wide fluctuation in the profile of the Tmr calculated using globe thermometers, due to the rapid variation of meteorological parameters and the globes' long response time. To improve the fit between globe thermometers and net radiometers' response, regressive functions have been proposed, resulting in a significant enhancement of the adherence between Tmr calculated using the standard black globe's measurements and the reference Tmr.

Multi-Parameter Observations of Seismogenic Phenomena Related to the Tokyo Earthquake (M = 5.9) on 7 October 2021

Multi-parameter observations, powerful for the study of lithosphere–atmosphere–ionosphere coupling (LAIC), have been performed for a recent Tokyo earthquake (EQ) with a moderate magnitude (M = 5.9) and rather larger depth (~70 km) on 7 October 2021, in the hope of predicting the next Kanto (Tokyo) huge EQ, such as the 1923 Great Kanto EQ (with a magnitude greater than 7). Various possible precursors have been searched during the two-month period of 1 September to 31 October 2021, based on different kinds of data sets: (i) ULF (ultra-low frequency) magnetic data from Kakioka, Japan, (ii) ULF/ELF (extremely low frequency) magnetic field data from the Chubu University network, (iii) meteorological data (temperature and humidity) from the Japan Meteorological Agency (JMA), (iv) AGW (atmospheric gravity wave) ERA5 data provided by the European Centre for Medium-Range Weather Forecast (ECMWF), (v) subionospheric VLF/LF (very low frequency/low frequency) data from Russia and Japan, (vi) ionosonde Japanese data, and (vii) GIM (global ionosphere map) TEC (total electron content) data. After extensive analyses of all of the above data, we have found that there are a few obvious precursors: (i) ULF/ELF electromagnetic radiation in the atmosphere, and (ii) lower ionospheric perturbations (with two independent tools from the ULF depression and subionospheric VLF anomaly) which took place just two days before the EQ. Further, ULF/ELF atmospheric electromagnetic radiation has been observed from approximately one week before the EQ until a few days after the EQ, which seems to be approximately synchronous in time to the anomalous variation in meteorological parameters (a combination of temperature and humidity, atmospheric chemical potential). On the other hand, there have been no clear anomalies detected in the stratospheric AGW activity, and in the NmF2 and TEC data for the upper F region ionosphere. So, it seems that the lithospheric origin is not strong enough to perturb the upper F region. Finally, we discuss the possible hypothesis for the LAIC process, and we can conclude that the AGW hypothesis might be ruled out, but other possible channels such as the chemical channel (radon emanation) and the associated effects might be in operation, at least, for this Tokyo EQ.

Proposing a combined method for the estimation of spatial and temporal variation of crop water productivity under deficit irrigation scenarios based on the AquaCrop model

Deficit irrigation is a management strategy to improve crop water productivity, especially in arid and semi-arid regions. Soil characteristics and weather parameters are among the factors affecting crop water productivity in water stress conditions. Due to spatial changes in soil characteristics and temporal and spatial variations in meteorological parameters, it can be expected that crop water productivity will also have temporal and spatial variations. In this study, by combining the Geographic Information System (GIS) with the grid weather generation tools from the Crop Growth Monitoring System (CGMS) and the plug-in version of the AquaCrop, a combined method was developed to investigate the spatial and temporal variation of crop yield, seasonal crop evapotranspiration, and water productivity of maize under various irrigation scenarios. The proposed model was implemented in a case study in the west of Iran. The study area was divided into 37 grid weather with 5 * 5 km and 19 soil units. By overlaying soil units and grid weathers, 94 homogeneous units were created. The model was executed for 94 homogeneous areas, using calibrated crop file of grain maize under four irrigation scenarios of 40, 60, 80, and 100% of potential irrigation requirement (S40, S60, S80, and S100, respectively) for 28 years (1988–2015) of weather data (10,528 runs). The results showed that by increasing water stress, the percentage of spatial and temporal variation of the studied parameters (crop yield, seasonal crop water requirement, and water productivity) would be increased. The percentage of spatial changes in crop yield and crop water productivity was more significant than temporal changes. The average of crop water productivity in the scenarios of S100, S80, S60, and S40 was determined as 1.5, 1.4, 1.2, and 0.5 kg m−3, respectively.

Comprehensive analysis of ambient air quality during second lockdown in national capital territory of Delhi

The lockdown imposed in Delhi, due to the second wave of the COVID-19 pandemic has led to significant gains in air quality. Under the lockdown, restrictions were imposed on movement of people, operation of industrial establishments and hospitality sector amongst others. In the study, Air Quality Index and concentration trends of six pollutants, i.e. PM2.5, PM10, NO2, SO2, CO, and O3 were analysed for National Capital Territory of Delhi, India for three periods in 2021 (pre-lockdown: 15 March to 16 April 2021, lockdown: 17 April to 31 May 2021 and post-lockdown: 01 June to 30 June). Data for corresponding periods in 2018–2020 was also analysed. Lockdown period saw 6 days in satisfactory AQI category as against 0 days in the same category during the pre-lockdown period. Average PM2.5, PM10, NO2 and SO2 concentrations reduced by 22%, 31%, 25% and 28% respectively during lockdown phase as compared to pre-lockdown phase, while O3 was seen to increase. Variation in meteorological parameters and correlation of pollutants has also been examined. The significant improvement arising due to curtailment of certain activities in the lockdown period indicates the importance of local emission control, and helps improve the understanding of the dynamics of air pollution, thus highlighting policy areas to regulatory bodies for effective control of air pollution.

Human Discomfort Index as a measure of biometeorological parameters influencing population: A study over selected cities of Andhra Pradesh

The climate of a place influences human lives. The variations of meteorological parameters occur in space and time. While humans adapt to a particular environment, the variations in local weather to a greater degree of intensity can have far-reaching implications on the health and comfort of a specific place’s habitat. These variations that have a bearing on human health and comfort are assayed using specific indices known as Human Discomfort Indices (HDI). In this study, the authors attempted to analyze the biometeorological influences over the cities of Andhra Pradesh during the recent years 2011-2019 using the well-known Thom’s Human Discomfort Index (HDI).

Impact and Variability of Meteorological Parameters on Rice Crop at Siddharthnagar, Uttar Pradesh, India

The present study explores the impacts and variability of meteorological parameter on the rice yields at Siddharthnagar of Uttar Pradesh, India. This study that the maximum and minimum temperatures during the period 2011 to 2020 show increasing trends. Whereas the duration of sunlight increased in June and July. Whereas the rainfall data shows decreasing trends for the period August-September. Furthermore, this study suggests that there was a decrease in rainfall trends during the paddy growing period. A negative correlation was observed with rainfall, particularly during the duff stage, indicating that wetting during the flowering to maturity period may be decisive. This study suggests taking into account the variability of annual or seasonal temperatures and precipitation within the region. The warming trend and irregular precipitation over time may have a significant impact on paddy farming. As a result, plans must be developed to manage the impact of the current variability of meteorological parameters on the paddy yields in this district of Uttar Pradesh by developing appropriate alternatives for increasing paddy crop production.