Combination Of Meteorological Factors Research Articles

The impact of extreme precipitation on water use efficiency along vertical vegetation belts in Hengduan Mountain during 2001 and 2020

In the context of climate change, extreme precipitation events are continuously increasing and impact the water‑carbon coupling of ecosystems. The vertical vegetation zonation, as a characteristic of mountain ecosystems, reflects the differences in vegetation response to climate change at different elevations. In this study, we used the water use efficiency (WUE) as an indicator to evaluate the water‑carbon relationship. By using MODIS data, we analyzed the spatiotemporal patterns of gross primary productivity (GPP), evapotranspiration (ET), and WUE from 2001 to 2020, as well as the responses of WUE to extreme wetness factor Number of precipitation days (R0.1), extreme dryness factor Consecutive dry days (CDD), and meteorological factors under the vertical vegetation zonation. Our results showed that annual GPP and ET displayed a significant increasing trend between 2001 and 2020, whereas WUE showed a weak decreasing trend. Spatially, GPP and WUE decreased with increasing elevation. Analyzing the WUE of mountainous ecosystems as a unified whole may not precisely capture the reactions of vegetation to severe rainfall occurrences. In fact, across different vegetation belts in mountainous areas, there exists a negative correlation between WUE and R0.1, and a positive correlation with CDD. In terms of meteorological factors, the temporal variation of GPP was primarily associated with vapor pressure deficit (VPD) and temperature (Ta), while those of ET was mainly related to soil water content (SWC). WUE was affected by a combination of meteorological factors and had a certain degree of variation between different altitude intervals. These findings contribute to a better understanding and prediction of the relationship between extreme rainfall climate and water‑carbon coupling in mountainous areas.

Ambient concentrations and dosimetry of inhaled size-segregated particulate matter during periods of low urban mobility in Bragança, Portugal

The restrictive measures in place during the COVID-19 pandemic provided a timely scenario to investigate the effects of human activities on air quality, and the extent to which mobility reduction strategies can impact atmospheric pollutant levels. Real-time concentrations of PM1, PM2.5 and PM10 were measured using a mobile platform in a small city of Portugal, during morning and afternoon rush hours, in two distinct phases of the pandemic: emergency phase (cold period, lockdown) and calamity phase (warm period, less restricted). The Multiple-Path Particle Dosimetry Model (MPPD) was used to calculate the PM deposition for adults. Large spatio-temporal variabilities and pronounced changes in mean PM concentrations were observed, with lower concentrations in the calamity phase: PM1 = 2.33 ± 1.61 μg m−3; PM2.5 = 5.15 ± 2.77 μg m−3; PM10 = 23.30 ± 21.53 μg m−3 than in the emergency phase: PM1 = 16.85 ± 31.80 μg m−3; PM2.5 = 30.92 ± 31.93 μg m−3; PM10 = 111.27 ± 104.53 μg m−3. These changes are explained by a combination of meteorological factors and local emissions, mainly residential firewood burning. Regarding regional deposition, PM1 was the main contributor to deposition in the tracheobronchial (5%) and pulmonary (12%) regions, and PM10 in the head region (92%). In general, total deposition doses were higher for males than for females. This work quantitatively demonstrated that even with a 38% reduction in urban mobility during the lockdown, the use of firewood for residential heating is the main contributor to the high concentrations of PM and the respective inhaled dose.

A geospatial assessment of bio-thermal influence around Çatalagzı power plant region

Combustion of coal in thermal power plants poses significant threat to surrounding ecology, by the release of harmful effluents. This study assesses the bio-thermal impact of emissions from Catalagzı Thermal Power Plant Region (CTPPR). A geospatial analytical framework has been modeled, utilizing satellite-based bio and thermal indicators. Average hazardous bio and thermal influence zones around CTPPR have been found to be 1465 m and 1380 m, respectively. Bio-influence zone extent depends upon a combination of meteorological factors, whereas, the bio-hazard severity depends mainly on atmospheric temperature. Thermal influence extent is inversely related to seasonal temperature, whereas corresponding severity depends upon local heat sinks as well. Collectively, hazardous bio-thermal influence in the surroundings of CTPPR shows spatial constancy over long term. The study concludes usefulness of satellite-based economical alternatives for monitoring area under stress around CTPPR for sustainable environmental management of ecological landscapes, needed to improve the human-environment nexus. Highlights Investigation of Thermal Power Plant (T P P) as source of environmental pollution Dynamic geospatial bio-monitoring of vegetation using satellite-based VIs is done Geospatial analysis reveals details of zones of hazardous bio-influence around T P Ps. Identified factors determining extent and severity of hazard by T P P emissions. Effectiveness of geospatial technology for ecological assessment is demonstrated

Meteorological factors and non-pharmaceutical interventions explain local differences in the spread of SARS-CoV-2 in Austria.

The drivers behind regional differences of SARS-CoV-2 spread on finer spatio-temporal scales are yet to be fully understood. Here we develop a data-driven modelling approach based on an age-structured compartmental model that compares 116 Austrian regions to a suitably chosen control set of regions to explain variations in local transmission rates through a combination of meteorological factors, non-pharmaceutical interventions and mobility. We find that more than 60% of the observed regional variations can be explained by these factors. Decreasing temperature and humidity, increasing cloudiness, precipitation and the absence of mitigation measures for public events are the strongest drivers for increased virus transmission, leading in combination to a doubling of the transmission rates compared to regions with more favourable weather. We conjecture that regions with little mitigation measures for large events that experience shifts toward unfavourable weather conditions are particularly predisposed as nucleation points for the next seasonal SARS-CoV-2 waves.

Research on the Physicochemical Properties of Fine Particulate Matter in Changchun, Northeast China

Objective: Air particulate matter concentrations in Changchun City, Jilin Province, may change around the autumn heating day. The aim of this study was to provide data references for environmental protection, detection and regulation in Changchun. Methods: Atmospheric particulate matter samples were collected using an airborne particulate matter sampler on the roof top of the Civil Engineering Teaching Hall on the campus of Jilin University of Construction; free settling dust of Atmospheric particulate matter was collected using metal trays. Atmospheric particulate matter concentrations were analysed by manual detection methods (weight method), carbonaceous fractions by total organic carbon analyser, and atmospheric fallout material composition and crystal structure by XRD diffractometer. The physicochemical properties of fine particulate matter around the autumn heating day in Changchun were investigated. Conclusions: (1) The daily average concentrations of various types of atmospheric particulate matter PM1, PM2.5 and PM10 generally increased after the start of the heating period. However, air quality is influenced by a combination of meteorological factors, of which emissions of air pollutants from urban heat generating plants during the heating period is only one aspect. So there is a situation where the average daily concentration of atmospheric particulate matter is lower after heating than before. (2) Analysis of the atmospheric its particulate matter PM2.5 samples collected around the heating day showed that the daily average concentrations of organic carbon (OC) and elemental carbon (EC) of atmospheric its particulate matter PM2.5 increased significantly after the heating day. (3) There was no significant difference in the main components of atmospheric dust fall before and after the heating day in Changchun, with the main components being crystalline SiO2 and a small Number of impurities.

Spatiotemporal variations and connections of single and multiple meteorological factors on PM2.5 concentrations in Xi'an, China

As one of the main sources that cause air pollution, the investigation on the change of PM2.5 concentrations and its related factors becomes particularly crucial and important. Previous works have rarely considered the combined effects of multiple meteorological factors on PM2.5 at different scales over time. Based on this consideration, the purpose of this paper is to investigate the spatiotemporal changes of PM2.5 concentrations and the relationship between meteorology and PM2.5 based on hourly ground monitoring data in Xi'an from 2018 to 2021. Specifically, we quantify the overall effect of meteorological factors on PM2.5 by convergent cross-mapping, and then explore the connection between a single or multiple meteorological factors and PM2.5 using wavelet transform coherence and multiple wavelet coherence. The experimental results show that: (1) the PM2.5 concentrations in northern Xi'an is significantly higher than that in other regions, and it is decreasing year by year from 2018 to 2021. Especially in 2021, the reaching national standard rates of daily PM2.5 concentrations achieve 88.77%. (2) Mean temperature, relative humidity and atmospheric pressure are the main single factor resulting in changes of PM2.5 concentrations, while the influences of boundary layer height, wind speed and wind direction are relatively small. Moreover, it exhibits the causality between PM2.5 concentrations and meteorology have obvious seasonal characteristics. (3) The PM2.5 concentrations shows the most consistent and significant correlation with boundary layer height and wind speed, with negative correlations at all scales and periods. Among all combinations of meteorological factors, temperature-humidity, and temperature-wind speed-wind direction present the largest significant coherence area in multiple wavelet coherence experiment and are the best combination factors to explain the changes of PM2.5 concentrations. Furthermore, the coherence values of multiple meteorological factors are obviously higher than those of a single meteorological factor, which indicates that the change of PM2.5 concentrations is determined by multiple variables. This study provides new insights for understanding the long-term spatiotemporal changes of PM2.5 concentrations and the relationship between PM2.5 and meteorology.

Radar Reflectivity and Meteorological Factors Merging‐Based Precipitation Estimation Neural Network

AbstractThe meteorological factors are important determinants of the surface rainfall. However, in studies of quantitative precipitation estimation (QPE) based on Doppler radar data, meteorological elements are usually used as the weighting factors to correct precipitation, and the active role of meteorological factors in determining rainfall is neglected, which limits the improvement of radar QPE accuracy. In this study, the effectiveness of applying one‐dimensional convolutional neural network together with radar data and meteorological factor data to estimate precipitation is explored. Various combinations of meteorological factors were tested for the set of input variables. The proposed model performance was evaluated over the Shijiazhuang area at the spatial resolution of 0.01° and at the 6‐min time scale. The results indicates that the proposed model (RM‐1DCNN) provides more accurate precipitation estimation compared to the Ordinary Kriging interpolation, two Z‐R relationships, and Back Propagation Neural Network. The root mean square error of the RM‐1DCNN with temperature was 0.642 mm per 6 min and the average Threat Score exceed 55%, which was the best among all schemes.

Peculiarities in the Structure of Yield in Common Wheat Accessions from Different Ecological and Geographic Origin

Dobrudzha Agricultural Institute (DAI) is the largest breeding center for cereals in Bulgaria. Among the varieties developed here, 36 genotypes were included in the national list, a large part of them being strong and medium strong wheat cultivars with increased strength. It is a disputable question if a plateau has been reached in the breeding of wheat and what are the further methods for increasing of the production potential. Until recently, the increasing of the spike productivity lay at the basis of the breeding strategy, primarily through a higher number of florets and grains formed per spikelet at the expense of a lower number of productive tillers. The contemporary high level of breeding and the market requirements impose the necessity to search for new approaches to increase productivity. The aim of the investigation was to characterize the structure of the yield in new common winter wheat accessions included in the gene pool of DAI. The investigation was carried out during 2015 – 2019 and encompassed four growth seasons with different combinations of meteorological factors allowing for good differentiation. The trial was designed in two replicates, the size of the harvest plot being 10 m2. Sixty-six accessions from different ecological and geographic origin and with specific combination of the economically important traits were evaluated. The cultivars from France were with the highest coefficient of tillering, followed by the cultivars from Croatia and Germany. The variability with regard to 1000 kernel weight was considerable. The Bulgarian and Serbian accessions were with the highest absolute weight, and the French ones – with the lowest. The differences with regard to the number of grains in spike were significant at a high level, but in the separate group, Athlon (DE), Moison (F), Fani (BG), Fidelius (AT), Iveta (BG) Korona (BG) and Simonida (RS) were with high values of the trait. Within the period, the Bulgarian cultivars, which realized highest yield, were Rada and Dragana, and among the European ones, these were Andalou, Basmati, NS 407 and Sofru.

Identifying high risk areas of Zika virus infection by meteorological factors in Colombia

BackgroundSeveral Zika virus (ZIKV) outbreaks have occurred since October 2015. Because there is no effective treatment for ZIKV infection, developing an effective surveillance and warning system is currently a high priority to prevent ZIKV infection. Despite Aedes mosquitos having been known to spread ZIKV, the calculation approach is diverse, and only applied to local areas. This study used meteorological measurements to monitor ZIKV infection due to the high correlation between climate change and Aedes mosquitos and the convenience to obtain meteorological data from weather monitoring stations.MethodsThis study applied the Bayesian structured additive regression modeling approach to include spatial interactive terms with meteorological factors and a geospatial function in a zero-inflated Poisson model. The study area contained 32 administrative departments in Colombia from October 2015 to December 2017. Weekly ZIKV infection cases and daily meteorological measurements were collected. Mapping techniques were adopted to visualize spatial findings. A series of model selections determined the best combinations of meteorological factors in the same model.ResultsWhen multiple meteorological factors are considered in the same model, both total rainfall and average temperature can best assess the geographic disparities of ZIKV infection. Meanwhile, a 1-in. increase in rainfall is associated with an increase in the logarithm of relative risk (logRR) of ZIKV infection of at most 1.66 (95% credible interval [CI] = 1.09, 2.15) as well as a 1 °F increase in average temperature is significantly associated with at most 0.79 (95% CI = 0.12, 1.22) increase in the logRR of ZIKV. Moreover, after controlling rainfall and average temperature, an independent geospatial function in the model results in two departments with an excessive ZIKV risk which may be explained by unobserved factors other than total rainfall and average temperature.ConclusionOur study found that meteorological factors are significantly associated with ZIKV infection across departments. The study determined both total rainfall and average temperature as the best meteorological factors to identify high risk departments of ZIKV infection. These findings can help governmental agencies monitor at risk areas according to meteorological measurements, and develop preventions in those at risk areas in priority.

Using Multiple Linear Regression Method to Evaluate the Impact of Meteorological Conditions and Control Measures on Air Quality in Beijing During APEC 2014

Meteorological conditions have important impact on the diffusion and transport of air pollutants, thus separating and quantifying the impact of meteorological factors is a prerequisite for evaluation of air pollution control measures. Using observation data on SO2, NO, NO2, NOx, CO, PM2.5, PM1, and PM10 as well as meteorological factors at the Chaoyang site, an urban site in Beijing, we evaluated the impact of meteorological conditions and control measures on air quality in Beijing during APEC 2014 (from 15 October to 30 November, 2014) by the multiple linear regression method. The simulation performance of a multivariate linear regression model based on the parameters of meteorological factors for predicting pollutant concentration assuming constant emission conditions were ideal, produced a range of determination coefficient (R2) of 0.494-0.783. Our results suggested that air pollution control measures reduced the concentration of SO2, NO, NO2, NOx, CO, PM2.5, PM1, and PM10 by 48.3%, 53.5%, 18.7%, 40.6%, 3.6%, 34.8%, 28.8%, and 40.6%, while meteorological conditions reduced the concentration of SO2, NO, NO2, NOx, CO, PM2.5, PM1, and PM10 by 1.7%, -2.8%, 18.7%, 4.5%, 18.6%, 27.5%, 30.6%, and 35.6%. The combination of meteorological factors and control measures has significantly improved the air quality in Beijing during the APEC period. Control measures played a leading role in the reduction of SO2 and nitrogen oxides, and meteorological factors played a leading role in the reduction of CO. Meteorological factors and control measures made roughly equal contributions to the reduction of particulate matter. We also used the relative weight method to study the contribution of meteorological factors to the pollutant concentration. The results showed that the decisive meteorological factors on the concentrations of different pollutants were different.

Composition of carbohydrates, surfactants, major elements and anions in PM2.5 during the 2013 Southeast Asia high pollution episode in Malaysia

Haze episodes have become a major concern in Malaysia over the past few decades and have an increasingly important impact on the country each and every year. During haze episodes from biomass burning in Southeast Asia, particularly from Sumatra, Indonesia, particulate matter PM2.5 is found to be one of the dangerous sources of airborne pollution and is known to seriously affect human health. This study determines the composition of carbohydrates (as levoglucosan), surfactants, major elements, and anions in PM2.5 during a 2013 haze episode. PM2.5 samples were collected from Universiti Kebangsaan Malaysia, Bangi, using a high volume sampler during a seven-day monitoring campaign during the peak of that year’s haze episode. PM2.5 concentrations ranged between 14.52 and 160.93μg/m3, exceeding the 2005 WHO air quality guidelines for PM2.5 (25μg/m3 for 24-h mean). The patterns for levoglucosan, surfactants, major elements, and anionic compositions were proportional to the PM2.5 concentrations. Changes in PM2.5 observed on days 3 and 4 were influenced by a combination of meteorological factors, which substantiate the theory that such factors play a pivotal role in haze episodes.

The climatic rhythm and blooms of cyanobacteria in a tropical reservoir in São Paulo, Brazil.

The present study sought to develop a methodology to analyse water quality based on the concepts and methods of climate and climatology. Accordingly, we attempted to relate hydro- and limnometeorological techniques and methodologies to a rhythmic analysis technique developed within the context of the Brazilian geographical climatology. Our goal was to assess and analyse cyanobacterial blooms, the main index of water quality for the reservoirs of the "Alto Tietê" Basin and, consequently, the Metropolitan Region of São Paulo, an area of high environmental complexity due to its high degree of development and high population density. The meteorological data used were collected by the Institute of Astronomy, Geophysics and Atmospheric Sciences at the University of São Paulo meteorological station, and the limnological data were collected through the Hydrological Monitoring System implemented by SABESP in the Billings and Guarapiranga Reservoirs and the laboratory of the same entity. The rhythmic and integrated analysis showed that the process of cyanobacterial blooms is dependent on a combination of meteorological factors as temperature and wind intensity that may disrupt the stability of the reservoir, providing the conditions necessary for the development of cyanobacteria during the stabilisation process. The pace of the Atlantic Polar Front Entrance during the winter in São Paulo is a limiting factor for the growth of cyanobacteria because of their high frequency, thus maintaining the balance of the reservoir throughout this period. The weather types those could cause a instability in the water column were: Cold Front entrance (66.67%), conflict between masses (22.22%) and the Tropical Instability Line (11.11%). The possibility for prevention and forecasting periods advise when these reservoirs should not be used, mainly with regard to recreational activities.

Advance in earthquake prediction by physical simulation on the baikal ice cover

The main challenge in the prediction of tectonic earthquakes and their control is still insufficient awareness of seismotectonic processes in the lithosphere and upper mantle during the preparation of strong earthquakes. This is associated in many respects with not quite appropriate equipment for researchers. Among relevant problems is also a lack of adequate models of preparation of earthquake sources at different stages, and this retards the development of earthquake prediction methods. The paper discusses long-term research on deformation and destruction of the Baikal ice cover in the context of physical mesomechanics. With certain combinations of meteorological factors (wind, temperature, precipitation, undercurrents, etc.) responsible for deformation, major cracks of many kilometers arise in the Baikal ice cover. Their spontaneous growth often involves seismic phenomena as ice quakes whose energy reaches Emax = 104–107 J. The nucleation of major cracks is similar to that of rock bursts of moderate strength or weak earthquakes. It is found that ice quakes and earthquakes are both preceded by foreshocks, seismic calm for tens of minutes, aftershocks and other events against the background of accelerated creep in fractures and increased seismoacoustic activity. Research data make it possible to put forward two genetically interrelated criteria among basic factors for ice quake prediction: variations in deformation modes at convergent boundaries of ice sheets and a specific intensification mode-generation of strong foreshocks in a segment in which ice sheets are prepared for dynamic motion. We substantiate the conclusion that simpler and clearer scenarios of preparation of strong seismic events in the Baikal ice cover allow successful physical simulation of preparation of tectonic earthquakes and rock bursts and advances in their prediction. We also consider and substantiate the feasibility of techniques for more efficient seismic risk reduction.

Copula-Based Security Assessment of Power Insulators Influenced by the Storm

In this paper the model of Copula-based security assessment of power insulators under the impact of the storm is proposed. The model firstly analysis the daily average winds speed, the historical daily rainfall data to get the probability of wind speed and daily rainfall distribution. Then presents the model of security assessment of power insulators under the impact of wind and rainfall by considering not only the probability of relevance between the daily average wind speed and daily rainfall by using Copula join function, but also the combination of meteorological factors on the impact of power insulators security. This work was supported by the Key Scientific and Technological Project of Hunan Province of China (2008FJ1006).

Air Quality as a Meteorological Hazard

Meteorological hazards are usually considered to be tropical cyclones, droughts, hail-storms, severe storms and their effects such as tsunamis, storm surges, wildfire, and floods. Urban air pollution is not normally considered to be a meteorological hazard. This view has arisen because the causes of urban air pollution – industrial and motor vehicleemissions – are not meteorological in nature. Air pollution episodes, however, are sporadic in nature and their occurrence depends on a particular combination of meteorological factors. This is true even in megacities such as Mexico City, Manila, and Los Angeles that have acquired a reputation for polluted air. Analyses of air pollution episodes and hospital admissions from many countries indicate that thereis a significant increase in morbidity and mortality as a result of such episodes.Time-series studies undertaken in Sydney have shown that particulate matter, ozone and nitrogen dioxide are the pollutants that are primarily responsible for adverse health effects in that city. Air pollution, and in particular particulate matter, is believed to be responsible for just under 400 premature deaths per year in Sydney alone. This death rate is over twenty times larger than deaths due to other meteorological hazards. Part of the reason for the low death rate for the more traditional meteorological hazards is that the provision of high quality numerical weather prediction, coupled with modern communications technology, has enabled emergency service personnel to take appropriate action. Air quality forecasting systems can play an important role in mitigating the adverse effects of air pollution. The forecasts will affect the behaviour of susceptible individuals, and thus reduce adverse health effects. The outputs from forecasting systems can also be used to provide improved estimates of the total exposure to air pollutants of the inhabitants who areat risk. Such improved estimates can then be used in conjunction with longitudinal studies ofhealth effects to obtain better understanding of the complex interaction between air quality and health.

Deposition and drift of ulv and vlv insecticide sprays applied to cotton by hand applicator in northern nigeria

AbstractExperiments have been carried out in northern Nigeria to study the deposition and drift of the small spray droplets (70–130 μ v.m.d.), utilised in the application of carbaryl plus an indicating dye, to cotton, at very low volume rates (6–12 litre/ha) using water‐based formulations and at ultralow volume rates (3 litre/ha) using waterless formulations. The character of the deposition and extent of drift have been shown to relate to spray droplet size, formulation and a combination of meteorological factors. Practical recommendations have been evolved for very low volume and ultralow volume techniques in which the time of application is restricted to those periods of the day which favour high recovery.

Prediction of the Severity of Atmospheric Corrosion by Discriminant Analysis of Local Meteorological Factors

AbstractDiscriminant function analysis has been used to enable the corrosiveness of an environment to be classified into one of several broad categories, knowing the values of specified atmospheric factors. Of various combinations of meteorological factors that were tried, the following combination proved to be the most discriminating: average of daily maximum temperature in °c, average daily maximum relative humidity, number of rainy days in a month, total rainfall in mm in a month, and average pollution (SO2 + NaCl in mg/cm2 per day over a month). These criteria were used to classify stations at Delhi, Bombay and Tezpur, giving results that agreed well with those obtainedin exposure trials.

Effects of weather conditions on thrips activity

Analysis has been made of possible relationships between various weather factors and thrips activity, and tables illustrating these relationships are presented. It was found, as broadly expected, that settled, sunny and warm weather was favourable for flight, and that unstable thundery weather was adverse. It proved possible to formulate some rules to foretell whether particular combinations of meteorological factors would give rise to above average thrips activity. These included an indication of temperature and humidity, sunshine, weather and stability of the lower air.