Influence Of Meteorological Parameters Research Articles

Tracing air pollution changes (CO, NO2, SO2, and HCHO) using GEE and Sentinel 5P images in Ahvaz, Iran.

The first case of COVID-19 in Iran was reported on February 25, 2020, leading in the implementation of a government-mandated lockdown as the virus gradually spread to different cities. The objective of this study was to evaluate the impact of the COVID-19 pandemic on air quality in Ahvaz city by utilizing Sentinel 5 images and the Google Earth Engine (GEE) platform. Specifically, the concentrations of air pollutants, including CO, NO2, SO2, and HCHO, during the COVID-19 pandemic from May 10 to June 01, 2021, were examined. Also, they were compared to the same period in 2019. Additionally, the influence of meteorological parameters, such as wind speed and precipitation, on pollutant concentrations during the pandemic and in the pre-pandemic year of 2019 were investigated. The results revealed a significant decrease in the concentrations of NO2 (13.7%), CO (6.1%), SO2 (28%), and HCHO (9.5%) in Ahvaz during the study period in 2021 compared to the same period in 2019. Statistical analyses indicated no significant changes in wind speed and precipitation between the COVID-19 pandemic and the pre-pandemic period in 2019. Therefore, the impact of these parameters on the observed changes in pollutant concentrations can be disregarded. It is clear that the COVID-19 epidemic and the subsequent lockdown measures, including traffic restrictions and business closures, played a crucial role in significantly reducing air pollutant concentrations in Ahvaz.

Cities of Central Asia: New hotspots of air pollution in the world

Central Asian cities are one of the hotspots for air pollution worldwide. There are limited studies and knowledge regarding air quality variation in this region. This study investigated PM2.5 temporal variations and the influence of meteorological parameters on PM2.5 concentrations for six major cities in Central Asia: Almaty and Astana (Kazakhstan), Ashgabat (Turkmenistan), Bishkek (Kyrgyzstan), Dushanbe (Tajikistan), and Tashkent (Uzbekistan). The results show severe air quality deterioration in the cities with annual PM2.5 concentrations up to ten-fold higher than the limits. A clear seasonal pattern with winter peaks was observed in Almaty, Bishkek, and Astana, whereas winter and summer were highly polluted in Tashkent and Dushanbe. Based on the pollution profiles, cities were classified into several clusters. Episodes with high PM2.5 concentrations were evaluated for regional pollutant transportation using the HYSPLIT model. The results of this investigation highlight a significant discrepancy in official emissions inventory studies. While previous studies have suggested that transportation is the primary source of air pollution, the approach to estimate the share of emission sources was based on an outdated methodology that obscures information on the most hazardous pollutants, including PM2.5. This study shows that coal combustion is the primary source of PM2.5 pollution in most cities, offering policymakers critical insights into the sources of air pollution in the region. These findings demonstrate the need for policymakers to take swift action to address coal use and adopt effective measures to mitigate PM2.5 pollution, thereby improving the health and well-being of the population.

Influence of Meteorological Parameters on the Urban Heat Island in Moscow

The urban heat island (UHI) intensity in Moscow and the influence of various meteorological parameters are discussed using weather station data. The maximal and average in-space UHI intensities, i.e., a comparison of air temperature T either in the city centre or in the whole urban area together with rural zone have averaged 1.9 and 0.9 °C, respectively, in recent years. The UHI in Moscow has stabilized over the past decade and is not growing. Under conditions of a strong anticyclone, the maximal UHI intensity in space and time reaches 11–12 °C. Low cloudiness and amplitudes of diurnal air temperature, as well as surface temperature, demonstrate the closest relationship with the UHI intensity among other parameters with the correlation coefficient of up to −0.67 for low cloudiness and the maximal UHI intensity. The effect of wind speed, total cloudiness and relative humidity on the UHI is slightly weaker, but still significant. The relationships of all meteorological parameters with the maximal UHI intensity are closer than those with the average one. The multiple correlation coefficient between the maximal UHI intensity and both parameters (low cloudiness and average daily wind speed) is 0.76–0.82. The UHI intensity function of air temperature has a minimum in the range from −4 to 0 °C; its growth both at lower and higher T is due to the influence of anticyclonic weather. The UHI intensity function of wind speed decreases with wind strength. The threshold value at which this function asymptotically approaches its lower limit is 10 m/s in the 40–200 m air layer. The UHI intensity functions of both total and low cloudiness decrease with increasing cloudiness and the differences between them are significant if the cloud cover is more than 50%.

The Influence of Meteorological Parameters on PM10: A Statistical Analysis of an Urban and Rural Environment in Izmir/Türkiye

Air pollution is a substantial menace, especially in industrialized urban zones, which affects the balance of the environment, life of vital organisms and human health. Besides the main causes of air pollution such as dense urbanization, poor quality fuels and vehicle emissions, physical environment characteristics play an important role on air quality. Therefore, it is vital to understand the relationship between the characteristics of the natural environment and air quality. This study examines the correlations between the PM10 pollutant data and meteorological parameters such as temperature (Tair), relative humidity (RH), and wind speed (WS) and direction (WD) under the European Union’s Horizon 2020 project. Two different zones (Vilayetler Evi as an urban zone and Sasalı Natural Life Park as a rural zone) of Izmir Province in Türkiye are used as a case study and the PM10 data is evaluated between 1 January 2017 and 31 December 2021. A one-tailed t-test is used in order to statistically determine the relationships between the PM10 pollutant data and meteorological parameters. As a further study, practical significance of the parameters is investigated via the effect size method and the results show that the RH is found to be the most influencing parameter on the PM10 for both zones, while Tair is found to be statistically non-significant.

Influence of meteorological parameters on incidence of mungbean yellow mosaic virus disease in green gram in Assam

Green gram (Vigna radiata) is one of the most important pulse crops grown in India and abroad bean yellow mosaic virus disease caused by Mung bean yellow mosaic virus has become a major threat in greengram cultivation in Assam. An experiment was conducted in the Experimental field, Department of Plant Pathology, Biswanath College of Agriculture, Assam Agricultural University, Biswanath Chariali during 2021-22, where green gram was sown in five dates starting from 5th September 2021to 3rd October 2021 at 7 days interval to study the effect of meteorological parameters in the incidence of green gram yellow mosaic virus disease, effect on growth and yield parameters so as to find out the best sowing time of the crop as a strategy for managing the disease. During the study, symptoms like, appearance of chlorotic specks along the veins, puckering of leaves, typical mosaic pattern, complete yellowing and drying of the leaves were recorded. Highest whitefly population (12.42 no/plant) was recorded in the crop sown on 19.09.2021 and the lowest (6.24no/plant) was recorded in the 05.9.2021 sown crop. Lowest (40.32%) disease incidence with highest seed yield (4.50 q/ha) was recorded in the crop sown on 05.09.2021, whereas highest (88.25%) disease incidence with lowest yield (2.50 q/ha) was recorded in the crop sown in second fortnight of September (19.09.21). Maximum temperature ranging from 29-33 oC, minimum temperature 13-23 oC with 8-9 bright sunshine hours were found to be conducive for incidence and rapid spread of the disease.

Astronomical Seeing and Meteorological Parameters at Maidanak Observatory

In this article, we present the results of astronomical seeing measurements conducted at the Maidanak observatory and the influence of meteorological parameters on astronomical seeing. A differential image motion monitor (DIMM) was used to measure astronomical seeing. Astronomical seeing observations were made between 2018–2022, mainly in the summer and autumn months. A total of 266 night observations were conducted between 2018 and 2022. For the whole time frame, the median seeing value was 0.69 arc seconds. With a median value of 0.65″, 2019 is the year with the best seeing, and 2021 has the worst seeing (0.71″). Analysis of long-term variations of the amount of clear time revealed a small trend of decrease in the amount. The correlation between the meteorological parameters and seeing was analyzed. It was determined that the meteorological parameters with the greatest impact on the seeing are wind speed and nighttime temperature variation. The correlation coefficient was equal to 13% and 21% for both quantities. It was found that wind direction and temperature have a relatively weak correlation with astronomical seeing.

Comparison between Drift Test Bench and Other Techniques in Spray Drift Evaluation of an Eight-Rotor Unmanned Aerial Spraying System: The Influence of Meteorological Parameters and Nozzle Types

In the past decade, an unmanned aerial spraying system (UASS) was applied more and more widely for low-volume aerial pesticides spraying operations in China. However, UASS have a higher drift risk due to more fine droplets sprayed with a higher working height and a faster driving speed than ground sprayers. Study on UASS spray drift is a new hot spot within the field of pesticide application technology. The field test bench was originally designed and applied for the measurement of the spray drift potential of ground sprayers. No methodology using the test bench for UASS drift evaluation was reported. Based on our previous study, field drift measurements of an eight-rotor UASS were conducted using three techniques (test bench, ground petri dish, and airborne collection frame) in this study, and the effects of meteorological parameters and nozzle types were investigated, to explore the applicability and the feasibility of the test bench used in UASS field drift evaluation. The test bench is proven promising for direct drift determination of UASS and the described methodology enabled classification of different UASS configurations. Higher wind speeds and finer droplets produced higher drift values. The faster the wind speed and the lower the humidity, the more the spray drift. The test bench can reduce the site requirements and improve the efficiency of the field drift test.

Influence of meteorological parameters and oxidizing capacity on characteristics of airborne particulate amines in an urban area of the Pearl River Delta, China

Nine amine species in atmospheric particles during haze and low-pollution days with low and high relative humidity (RH) were analyzed in urban Guangzhou, China. The mean concentrations of total measured amines (Ʃamines) in fine particles were 208 ± 127, 63.7 ± 21.3, and 120 ± 20.1 ng m−3 during haze, low pollution-low RH (LP-LRH), and low pollution-high RH (LP-HRH) episodes, respectively. The dominant amine species were methylamine (MA), dimethylamine (DMA), diethylamine (DEA) and dibutylamine (DBA), which in total accounted for 82–91% of the Ʃamines during different pollution episodes. The contributions of Ʃamines-C to water-soluble organic carbon (WSOC) and Ʃamines-N to water-soluble organic nitrogen (WSON) were 1.52% and 2.49% during haze, 1.24% and 1.96% during LP-LRH, and 2.00 and 2.98% during LP-HRH days, respectively. The mass proportion of Ʃamines in fine particles was higher during LP-HRH periods (0.19%) than during haze and LP-LRH periods (0.16%). The mass proportion of DBA in Ʃamines increased from 7% during haze and LP-LRH episodes to 25% during LP-HRH episodes. Compared with other amines, DBA showed a stronger linear relationship with RH (r = 0.867, p < 0.01), which demonstrates its high sensitivity to high RH conditions. Meteorological parameters (including RH, the mixed layer depth, wind speed and temperature), the oxidizing capacity (ozone concentration), and gaseous pollutants (NOx and SO2) correlated with amines under different pollution conditions. Under high RH, acid-base reactions were the dominant pathway for the gas-to-particle distribution of amines in urban areas, while direct dissolution dominated in the background site. To our knowledge, this study is the first attempt to conduct in situ measurements of particulate amines during different pollution conditions in China, and further research is needed to in-depth understanding of the influence of amines on haze formation.

Amalgamation of Geometry Structure, Meteorological and Thermophysical Parameters for Intelligent Prediction of Temperature Fields in 3D Scenes.

Temperature field calculation is an important step in infrared image simulation. However, the existing solutions, such as heat conduction modelling and pre-generated lookup tables based on temperature calculation tools, are difficult to meet the requirements of high-performance simulation of infrared images based on three-dimensional scenes under multi-environmental conditions in terms of accuracy, timeliness, and flexibility. In recent years, machine learning-based temperature field prediction methods have been proposed, but these methods only consider the influence of meteorological parameters on the temperature value, while not considering the geometric structure and the thermophysical parameters of the object, which results in the low accuracy. In this paper, a multivariate temperature field prediction network based on heterogeneous data (MTPHNet) is proposed. The network fuses geometry structure, meteorological, and thermophysical parameters to predict temperature. First, a Point Cloud Feature Extraction Module and Environmental Data Mapping Module are used to extract geometric information, thermophysical, and meteorological features. The extracted features are fused by the Data Fusion Module for temperature field prediction. Experiment results show that MTPHNet significantly improves the prediction accuracy of the temperature field. Compared with the v-Support Vector Regression and the combined back-propagation neural network, the mean absolute error and root mean square error of MTPHNet are reduced by at least 23.4% and 27.7%, respectively, while the R-square is increased by at least 5.85%. MTPHNet also achieves good results in multi-target and complex target temperature field prediction tasks. These results validate the effectiveness of the proposed method.

Air quality index prediction with influence of meteorological parameters using machine learning model for IoT application

Air quality index prediction with influence of meteorological parameters using machine learning model for IoT application

Influence of air pollution and meteorological parameters on COVID-19: An overview

The spread of Covid-19 has been rampant across the globe, and studies have indicated a connection between the spike in infection and air pollution. The literature review has shown that the link between the SARS-CoV-2 virus causing the disease and air pollutants is still inconclusive. Current evidence from the studies point out two main contributing mechanisms for the spread of the virus: (1) the weakening of the human natural defence mechanism by the air pollutants facilitates virus entry and replication; (2) particulate matter facilitates the airborne transport of vectors. Meteorological parameters also play a significant role in the transmission of the virus. Ultraviolet radiation was negatively correlated with the number of COVID-19 cases, while wind speed was positively correlated. Temperature and humidity increases were associated with a decrease in the number of infections. Some studies have also shown no relationship between humidity and COVID-19 case numbers. Similarly, rainfall predominantly showed no significant correlation. More studies in this area are suggested to further understand the air pollutants effect on the virus, its interaction and the influence of meteorological parameters.

Influence of meteorological parameters on the incidence of leaf folder and whorl maggot in rice ecosystem under Southern Telangana Agroclimatic Zone of Andhra Pradesh

Influence of meteorological parameters on the incidence of leaf folder and whorl maggot in rice ecosystem under Southern Telangana Agroclimatic Zone of Andhra Pradesh

Daily Meteorological Parameters Influence the Risk of Intracerebral Hemorrhage in a Subtropical Monsoon Basin Climate

Background and PurposeThe correlation between meteorological parameters and intracerebral hemorrhage (ICH) occurrence is controversial. Our research explored the effect of daily meteorological parameters on ICH risk in a subtropical monsoon basin climate.MethodsWe retrospectively analyzed patients with ICH in a teaching hospital. Daily meteorological parameters including temperature (TEM), atmospheric pressure (PRE), relative humidity (RHU), and sunshine duration (SSD) were collected, with the diurnal variation (daily maximum minus minimum) and day-to-day variation (average of the day minus the previous day) calculated to represent their fluctuation. We adopted a time-stratified case-crossover approach and selected conditional logistic regression to explore the effect of meteorological parameters on ICH risk. The influence of monthly mean temperature proceeded via stratified analysis. Air pollutants were gathered as covariates.ResultsOur study included 1052 eligible cases with ICH. In a single-factor model, the risk of ICH decreased by 5.9% (P<0.001) for each 1°C higher of the daily mean TEM, and the risk increased by 2.4% (P=0.002) for each 1hPa higher of the daily mean PRE. Prolongation of daily SSD inhibited the risk of ICH, and OR was 0.959 (P=0.007). The risk was raised by 7.5% (P=0.0496) with a 1°C increment of day-to-day variation of TEM. In a two-factor model, the effect of daily mean TEM or daily SSD on ICH risk was still statistically significant after adjusting another factor. The influence of meteorological parameters on ICH risk continued in cold months but disappeared in warm months after stratified analysis.ConclusionThis research indicates daily TEM and SSD had an inverse correlation to ICH risk in a subtropical monsoon basin climate. They were independent when adjusted by another factor. Daily PRE and day-to-day TEM variation were positively related to ICH risk. The correlation of daily meteorological factors on ICH risk was affected by the monthly thermal background.

Influence of meteorological parameters on population build-up of brinjal shoot and fruit borer, Leucinodes orbonalis Guenee in Assam

Influence of meteorological parameters on population build-up of brinjal shoot and fruit borer, Leucinodes orbonalis Guenee in Assam

Is Meteorology a Factor to COVID-19 Spread in a Tropical Climate?

It was speculated that fewer COVID-19 infections may emerge in tropical countries due to their hot climate, but India emerged as one of the leading hotspot. There is no concrete answer on the influence of meteorological parameters on COVID-19 even after more than a year of outbreak. The present study examines the impacts of Meteorological parameters during the summer and monsoon season of 2020, in different Indian mega cities having distinct climate and geography. The results indicate the sign of association, but it varies from one climatic zone to another. The principal component analysis revealed that humidity is strongly correlated with COVID-19 infections in hillocky city Pune (R = 0.70), dry Delhi (R = 0.50) and coastal Mumbai (R = 0.46), but comparatively weak correlation is found in arid climatic city of Ahmedabad. As against the expectations, no discernible correlation is found with temperature in any of the cities. As the virus in 2020 in India largely travelled with droplets, the association with absolute humidity in the dry regions has serious implications. Clarity in understanding the impact of seasonality will greatly help epidemiological research and in making strategies to control the pandemic in India and other tropical countries around the world.

Association between Meteorological Parameters and Hand, Foot and Mouth Disease in Mainland China: A Systematic Review and Meta-Analysis.

Background:This study reports a systematic review of association between meteorological parameters and hand, foot and mouth disease (HFMD) in mainland China.Methods:Using predefined study eligibility criteria, three electronic databases (PubMed, Web of Science, and Embase) were searched for relevant articles. Using a combination of search terms, including “Hand foot and mouth disease,” “HFMD,” “Meteorological,” “Climate,” and “China,” After removal of duplicates, our initial search generated 2435 studies published from 1990 to December 31, 2019. From this cohort 51 full-text articles were reviewed for eligibility assessment. The meta-analysis was devised in accordance with the published guidelines of the Cochrane Collaboration and Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA). Effect sizes, heterogeneity estimates and publication bias were computed using R software and Review Manager Software.Results:The meta-analysis of 18 eligible studies showed that the meteorological parameters played an important role in the prevalence of HFMD. Lower air pressure may be the main risk factor for the incidence of HFMD in Chinese mainland, and three meteorological parameters (mean temperature, rainfall and relative humidity) have a significant association with the incidence of HFMD in subtropical regions.Conclusion:Lower air pressure might be the main risk factor for the incidence of HFMD in Chinese mainland. The influence of meteorological parameters on the prevalence of HFMD is mainly through changing virus viability in aerosols, which may be different in different climate regions. In an environment with low air pressure, wearing a mask that filters the aerosol outdoors may help prevent HFMD infection.

Concentration of the PM2.5 according to the sampling areas associated with the concentration of heavy metals and anions in Penang

Particulate matter (PM) was identified as the main air pollutants in Malaysia which directly impose a treat to human health and understanding this pollutant is also crucial for its mitigation and control. This study aims to determine and characterize the PM2.5 with the influence of meteorological parameters. The PM2.5 samples were collected at Perai, Nibong Tebal and highway of Penang Bridge represented the industrial, residential, and roadside areas of Mainland Penang, respectively. The descriptive statistical analysis was conducted to understand the variations of PM2.5 concentration on the study areas, while its relationship with meteorological parameters were tested using Pearson correlation. Eleven heavy metals and three anions were determined using mass spectrometry (ICP-MS) and Ion Chromatography, respectively. Results showed that the mean concentration of PM2.5 are 10.88 ?g/m³, 31.96 ?g/m³, and 39.35 g/m³ for residential area, industrial area, and roadside, respectively. The results revealed that mean concentrations of PM2.5 (39.35 ?g/m³) in the roadside are significantly higher than NAAQG 24-h average of 35 ?g/m³. Result also suggested that meteorological parameters were lest significant in controlling the PM2.5 concentrations as only the air pressure was found to be significant with the value of correlation coefficient of 0.476. The major components such as SO42-, K and Na account for about 70-90% of the PM2.5.

Influence of Meteorological Parameters on the Dynamics of Ozone and Aerosol Particles Near a Road Transport Street

Influence of Meteorological Parameters on the Dynamics of Ozone and Aerosol Particles Near a Road Transport Street

Determination of vulnerable regions of SARS-CoV-2 in Malaysia using meteorology and air quality data.

This study aims to explore the state-wise assessment of SARS-CoV-2 (COVID-19) pandemic spread in Malaysia with focus on influence of meteorological parameters and air quality. In this study, state-wise COVID-19 data, meteorological parameters and air quality index (AQI) were collected from March 13 to April 30, 2020, which encompass three movement control order (MCO) periods in the country. Overall, total infected cases were observed to be higher in MCO phase 1 and 2 and significantly reduced in MCO phase 3. Due to the variation in the spatial interval of population density and individual immunity, the relationship of these parameters to pandemic spread could not be achieved. The study infers that temperature (T) between 23 and 25 °C and relative humidity (RH) (70–80%) triggered the pandemic spread by increase in the infected cases in northern and central Peninsular Malaysia. Selangor, WP Kuala Lumpur and WP Putrajaya show significantly high infected cases and a definite trend was not observed with respect to a particular meteorological factor. It is identified that high precipitation (PPT), RH and good air quality have reduced the spread in East Malaysia. A negative correlation of T and AQI and positive correlation of RH with total infected cases were found during MCO phase 3. Principal component analysis (PCA) indicated that T, RH, PPT, dew point (DP) and AQI are the main controlling factors for the spread across the country apart from social distancing. Vulnerability zones were identified based on the spatial analysis of T, RH, PPT and AQI with reference to total infected cases. Based on time series analysis, it was determined that higher RH and T in Peninsular Malaysia and high amount of PPT, RH and good air quality in East Malaysia have controlled the spreading during MCO phase 3. The predominance of D614 mutant was observed prior to March and decreases at the end of March, coinciding with the fluctuation of meteorological factors and air quality. The outcome of this study gives a general awareness to the public on COVID-19 and the influence of meteorological factors. It will also help the policymakers to enhance the management plans against the pandemic spreading apart from social distancing in the next wave of COVID-19.Supplementary InformationThe online version contains supplementary material available at 10.1007/s10668-021-01719-z.

Influence of Meteorological Parameters on Explosive Charge and Stemming Length Predictions in Clay Soil during Blasting Using Artificial Neural Networks

The influence of the meteorological parameters (precipitation and air temperature) during blasting in clay has a direct impact on the success of blasting. In the case of large amounts of precipitation (rain and snow) recorded in the subject area, blasting in clays cannot be carried out due to the grain of the clay and the inability to access the subject area. Moreover, the air temperature in the subject area affects the blasting performance. The most ideal temperature for blasting in clays is between 15 and 25 °C because then the clay has the best geotechnical characteristics. The research was conducted on the exploitation field Cukavec II, which is located near the city of Varaždin in the Republic of Croatia. Amount of precipitation and air temperature were considered to obtain the best blasting effect. Influence of meteorological parameters on the amount of the explosive charge and stemming length when blasting in clays was demonstrated via models based on Artificial Neural Networks (ANN). The ANN model network consists of a Long Short-term Memory (LSTM) part to process time dependent meteorological data, and fully connected layers to process blasting input data. Two types of explosive charges were compared, Pakaex and Permonex V19.