Influence Of Meteorological Factors Research Articles

Revealing the Eco-Environmental Quality of the Yellow River Basin: Trends and Drivers

The Yellow River Basin (YB) acts as a key barrier to ecological security and is an important experimental region for high-quality development in China. There is a growing demand to assess the ecological status in order to promote the sustainable development of the YB. The eco-environmental quality (EEQ) of the YB was assessed at both the regional and provincial scales utilizing the remote sensing-based ecological index (RSEI) with Landsat images from 2000 to 2020. Then, the Theil–Sen (T-S) estimator and Mann–Kendall (M-K) test were utilized to evaluate its variation trend. Next, the optimal parameter-based geodetector (OPGD) model was used to examine the drivers influencing the EEQ in the YB. Finally, the geographically weighted regression (GWR) model was utilized to further explore the responses of the drivers to RSEI changes. The results suggest that (1) a lower RSEI value was found in the north, while a higher RSEI value was found in the south of the YB. Sichuan (SC) and Inner Mongolia (IM) had the highest and the lowest EEQ, respectively, among the YB provinces. (2) Throughout the research period, the EEQ of the YB improved, whereas it deteriorated in both Henan (HA) and Shandong (SD) provinces. (3) The soil-available water content (AWC), annual precipitation (PRE), and distance from impervious surfaces (IMD) were the main factors affecting the spatial differentiation of RSEI in the YB. (4) The influence of meteorological factors (PRE and TMP) on RSEI changes was greater than that of IMD, and the influence of IMD on RSEI changes showed a significant increasing trend. The research results provide valuable information for application in local ecological construction and regional development planning.

Natural Factors of Microplastics Distribution and Migration in Water: A Review

Microplastics are widely present worldwide and are of great concern to scientists and governments due to their toxicity and ability to serve as carriers of other environmental pollutants. The abundance of microplastics in different water bodies varied significantly, mainly attributed to the initial emission concentration of pollutants and the migration ability of pollutants. The migration process of microplastics determines the abundance, fate, and bioavailability of microplastics in water. Previous studies have proved that the physicochemical properties of water bodies and the properties of microplastics themselves are important factors affecting their migration, but the change in external environmental conditions is also one of the main factors controlling the migration of microplastics. In this paper, we focus on the effects of meteorological factors (rainfall, light, and wind) on the distribution and migration of microplastics and conclude that the influence of meteorological factors on microplastics mainly affects the inflow abundance of microplastics, the physical and chemical properties of water, and the dynamics of water. At the same time, we briefly summarized the effects of aquatic organisms, water substrates, and water topography on microplastics. It is believed that aquatic organisms can affect the physical and chemical properties of microplastics through the physical adsorption and in vivo transmission of aquatic plants, through the feeding behavior, swimming, and metabolism of animals, and through the extracellular polymers formed by microorganisms, and can change their original environmental processes in water bodies. A full understanding of the influence and mechanism of external environmental factors on the migration of microplastics is of great theoretical significance for understanding the migration law of microplastics in water and comprehensively assessing the pollution load and safety risk of microplastics in water.

An Evaluation of the Influence of Meteorological Factors and a Pollutant Emission Inventory on PM2.5 Prediction in the Beijing–Tianjin–Hebei Region Based on a Deep Learning Method

In this study, a Long Short-Term Memory (LSTM) network approach is employed to evaluate the prediction performance of PM2.5 in the Beijing–Tianjin–Hebei region (BTH). The proposed method is evaluated using the hourly air quality datasets from the China National Environmental Monitoring Center, European Center for Medium-range Weather Forecasts ERA5 (ECMWF-ERA5), and Multi-resolution Emission Inventory for China (MEIC) for the years 2016 and 2017. The predicted PM2.5 concentrations demonstrate a strong correlation with the observed values (R2 = 0.871–0.940) in the air quality dataset. Furthermore, the model exhibited the best performance in situations of heavy pollution (PM2.5 > 150 μg/m3) and during the winter season, with respective R2 values of 0.689 and 0.915. In addition, the influence of ECMWF-ERA5’s hourly meteorological factors was assessed, and the results revealed regional heterogeneity on a large scale. Further evaluation was conducted by analyzing the chemical components of the MEIC inventory on the prediction performance. We concluded that the same temporal profile may not be suitable for addressing emission inventories in a large area with a deep learning method.

Examining the relationship between atmospheric pollutants and meteorological factors in Asansol city, West Bengal, India, using statistical modelling.

Meteorological conditions significantly impact ambient air quality in urban environments. This study focuses on Asansol, known as the "Coal City" and the "Industrial Heart of West Bengal," a notable hotspot for air pollution. Despite its significance, limited research has addressed the influence of meteorological factors on key air pollutants in this urban area. From January 2019 to December 2023, this investigation explores the relationships between meteorological parameters (including atmospheric temperature, relative humidity, rainfall, wind speed) and the concentrations of crucial air pollutants (PM2.5, PM10, NO2, SO2). Temporal trends in air pollutant concentrations are also analysed. The Spearman correlation method is used to establish associations between pollutant concentrations and meteorological variables, while multiple linear regression (MLR) models are employed to assess meteorological factors and potential impact on pollutant concentrations. The analysis reveals a decreasing trend in pollutant concentrations in Asansol. Temperature exhibits negative correlations with all pollutants in all seasons except for a positive correlation during the monsoon. Rainfall consistently displays significant negative correlations with pollutants in all seasons. Relative humidity is negatively correlated with pollutants in all seasons, and wind speed, except during the post-monsoon season, shows negative correlations with all pollutants. Linear models excel in predicting particulate matter concentrations but perform poorly in predicting gaseous contaminants. Accounting for seasonal fluctuations and meteorological parameters, this research enhances the accuracy of air pollution forecasting, contributing to a better understanding of air quality dynamics in Asansol and similar urban areas.

The effects of meteorological factors and air pollutants on the incidence of tuberculosis in people living with HIV/AIDS in subtropical Guangxi, China.

Previous studies have shown the association between tuberculosis (TB) and meteorological factors/air pollutants. However, little information is available for people living with HIV/AIDS (PLWHA), who are highly susceptible to TB. Data regarding TB cases in PLWHA from 2014 to2020 were collected from the HIV antiviral therapy cohort in Guangxi, China. Meteorological and air pollutants data for the same period were obtained from the China Meteorological Science Data Sharing Service Network and Department of Ecology and Environment of Guangxi. A distribution lag non-linear model (DLNM) was used to evaluate the effects of meteorological factors and air pollutant exposure on the risk of TB in PLWHA. A total of 2087 new or re-active TB cases were collected, which had a significant seasonal and periodic distribution. Compared with the median values, the maximum cumulative relative risk (RR) for TB in PLWHA was 0.663 (95% confidence interval [CI]: 0.507-0.866, lag 4 weeks) for a 5-unit increase in temperature, and 1.478 (95% CI: 1.116-1.957, lag 4 weeks) for a 2-unit increase in precipitation. However, neither wind speed nor PM10 had a significant cumulative lag effect. Extreme analysis demonstrated that the hot effect (RR = 0.638, 95%CI: 0.425-0.958, lag 4 weeks), the rainy effect (RR = 0.285, 95%CI: 0.135-0.599, lag 4 weeks), and the rainless effect (RR = 0.552, 95%CI: 0.322-0.947, lag 4 weeks) reduced the risk of TB. Furthermore, in the CD4(+) T cells < 200 cells/µL subgroup, temperature, precipitation, and PM10 had a significant hysteretic effect on TB incidence, while temperature and precipitation had a significant cumulative lag effect. However, these effects were not observed in the CD4(+) T cells ≥ 200 cells/µL subgroup. For PLWHA in subtropical Guangxi, temperature and precipitation had a significant cumulative effect on TB incidence among PLWHA, while air pollutants had little effect. Moreover, the influence of meteorological factors on the incidence of TB also depends on the immune status of PLWHA.

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

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

Comprehensive analysis of air pollution and the influence of meteorological factors: a case study of adiyaman province.

Adıyaman, a city recently affected by an earthquake, is facing significant air pollution challenges due to both anthropogenic activities and natural events. The sources of air pollution have been investigated using meteorological variables. Elevated southerly winds, especially prominent in spring and autumn, significantly contribute to dust transport, leading to a decline in local air quality as detected by the HYSPLIT model. Furthermore, using Suomi-NPP Thermal Anomaly satellite product, it is detected and analyzed for crop burning activities. Agricultural practices, including stubble burning, contribute to the exacerbation of PM10 pollution during the summer months, particularly when coupled with winds from all directions except the north. In fall and winter months, heating is identified as the primary cause of pollution. The city center located north of the station is the dominant source of pollution throughout all seasons. The study established the connection between air pollutants and meteorological variables. Furthermore, the Spearman correlation coefficients reveal associations between PM10 and SO2, indicating moderate positive correlations under pressure conditions (r = 0.35, 0.52). Conversely, a negative correlation is observed with windspeed (r = -0.35, -0.50), and temperature also exhibits a negative correlation (r = -0.39, -0.54). During atmospheric conditions with high pressure, PM10 and SO2 concentrations are respectively 41.2% and 117.2% higher. Furthermore, pollutant concentration levels are 29.2% and 53.3% higher on days with low winds. Last, practical strategies for mitigating air pollution have been thoroughly discussed and proposed. It is imperative that decision-makers engaged in city planning and renovation give careful consideration to the profound impact of air pollution on both public health and the environment, particularly in the aftermath of a recent major earthquake.

Influence of meteorological factors on the moisture content of fine forest fuels: responses of fire danger class to different microclimates on the example of European beech (Fagus sylvatica L.) stands

Abstract Due to the increased number of forest fires, a detailed examination and knowledge of the effects of the microclimatic conditions of forests is currently significant. The study carried out in Arboretum Borova hora (Slovak Republik), investigates the influence of meteorological factors (air temperature, relative humidity), the value of the Angström index, and the danger class of the Angström index on the moisture content of fine fuel at the edge of a beech forest stand, but also in its interior. We tested three working hypotheses: a) meteorological conditions differ significantly between the edge and the interior of the beech forest, b) the moisture content of fine fuel is higher in the beech forest interior than at the forest edge, c) the Angström index fire danger class is higher at the edge of the beech forest than in its interior. We created a calibration curve that was also used to measure the humidity of beech leaves with the help of the ME 2000 hygrometer. Our results show that edge beech stands are significantly more susceptible to fires, lower air humidity and fine fuel moisture content, and higher air temperatures than forest interiors. From our point of view, the microclimate is considered the main factor that explains the difference between the vegetation structure of the forest edge and the forest interior.

Maize grain filling characteristics in China: Response to meteorological factors

To investigate the dynamic changes in dry matter accumulation in maize after anthesis, we established a logistic model to describe grain filling characteristics (GFC), and analyzed differences between spring and summer maize, and the influence of meteorological factors. The results showed that the logistic model accurately simulated the dynamic changes in grain growth. For spring maize, the fitted hundred-grain weight at maturity was closely related to the average grain filling rate until maturity, days of the active grain filling period, time of the maximum grain filling rate, and duration of the rapid increase in grain weight. For summer maize, it was closely related to the time of the maximum grain filling rate, days of active grain filling period, duration of gradual grain weight, and the rapidly increasing period. The filling characteristics of spring and summer maize differed because of the different meteorological conditions and biological characteristics. The grain filling duration of spring maize was longer than that of summer maize. The maximum grain filling rate of spring maize occurred later than that of summer maize. Temperature and precipitation were the main meteorological factors affecting the hundred-grain weight of spring maize, whereas temperature was the main factor affecting summer maize. The response of spring maize GFC to meteorological factors was more complex than that of summer maize. These results are important for the development of appropriate strategies for improving maize productivity in China.

Non-linear effects of meteorological factors on COVID-19: An analysis of 440 counties in the americas

BackgroundIn the last three years, COVID-19 has caused significant harm to both human health and economic stability. Analyzing the causes and mechanisms of COVID-19 has significant theoretical and practical implications for its prevention and mitigation. The role of meteorological factors in the transmission of COVID-19 is crucial, yet their relationship remains a subject of intense debate. MethodsTo mitigate the issues arising from short time series, large study units, unrepresentative data and linear research methods in previous studies, this study used counties or districts with populations exceeding 100,000 or 500,000 as the study unit. The commencement of local outbreaks was determined by exceeding 100 cumulative confirmed cases. Pearson correlation analysis, generalized additive model (GAM) and distributed lag nonlinear model (DLNM) were used to analyze the relationship and lag effect between the daily new cases of COVID-19 and meteorological factors (temperature, relative humidity, solar radiation, surface pressure, precipitation, wind speed) across 440 counties or districts in seven countries of the Americas, spanning from January 1, 2020, to December 31, 2021. ResultsThe linear correlations between daily new cases and meteorological indicators such as air temperature, relative humidity and solar radiation were not significant. However, the non-linear correlations were significant. The turning points in the relationship for temperature, relative humidity and solar radiation were 5 °C and 23 °C, 74 % and 750 kJ/m2, respectively. ConclusionThe influence of meteorological factors on COVID-19 is non-linear. There are two thresholds in the relationship with temperature: 5 °C and 23 °C. Below 5 °C and above 23 °C, there is a positive correlation, while between 5 °C and 23 °C, the correlation is negative. Relative humidity and solar radiation show negative correlations, but there is a change in slope at about 74 % and 750 kJ/m2, respectively.

Influence of meteorological variables and air pollutants on measurements from automatic pollen sampling devices

This study examines the influence of meteorological factors and air pollutants on the performance of automatic pollen monitoring devices, as part of the EUMETNET Autopollen COST ADOPT-intercomparison campaign held in Munich, Germany, during the 2021 pollen season. The campaign offered a unique opportunity to compare all automatic monitors available at the time, a Plair Rapid-E, a Hund-Wetzlar BAA500, an OPC Alphasense, a KH-3000 Yamatronics, three Swisens Polenos, a PollenSense APS, a FLIR IBAC2, a DMT WIBS-5, an Aerotape Sextant, to the average of four manual Hirst traps, under the same environmental conditions. The investigation aimed to elucidate how meteorological factors and air pollution impact particle capture and identification efficiency.The analysis showed coherent results for most devices regarding the correlation between environmental conditions and pollen concentrations. This reflects on one hand, a significant correlation between weather and airborne pollen concentration, and on the other hand the capability of devices to provide meaningful data under the conditions under which measurements were taken. However, correlation strength varied among devices, reflecting differences in design, algorithms, or sensors used. Additionally, it was observed that different algorithms applied to the same dataset resulted in different concentration outputs, highlighting the role of algorithm design in these systems (monitor + algorithm).Notably, no significant influence from air pollutants on the pollen concentrations was observed, suggesting that any potential difference in effect on the systems might require higher air pollution concentrations or more complex interactions. However, results from some monitors were affected to a minor degree by specific weather variables.Our findings suggest that the application of real-time devices in urban environments should focus on the associated algorithm that classifies pollen taxa. The impact of air pollution, although not to be excluded, is of secondary concern as long as the pollution levels are similar to a large European city like Munich.

Comprehensive analysis of long-term trends, meteorological influences, and ozone formation sensitivity in the Jakarta Greater Area

Jakarta Greater Area (JGA) has encountered recurrent challenges of air pollution, notably, high ozone levels. We investigate the trends of surface ozone (O3) changes from the air quality monitoring stations and resolve the contribution of meteorological drivers in urban Jakarta (2010–2019) and rural Bogor sites (2017–2019) using stepwise Multi Linear Regression. During 10 years of measurement, 41% of 1-h O3 concentrations exceeded Indonesia’ s national threshold in Jakarta. In Bogor, 0.1% surpassed the threshold during 3 years of available data records. The monthly average of maximum daily 8-h average (MDA8) O3 anomalies exhibited a downward trend at Jakarta sites while increasing at the rural site of Bogor. Meteorological and anthropogenic drivers contribute 30% and 70%, respectively, to the interannual O3 anomalies in Jakarta. Ozone formation sensitivity with satellite demonstrates that a slight decrease in NO2 and an increase in HCHO contributed to declining O3 in Jakarta with 10 years average of HCHO to NO2 ratio (FNR) of 3.7. Conversely, O3 increases in rural areas with a higher FNR of 4.4, likely due to the contribution from the natural emission of O3 precursors and the influence of meteorological factors that magnify the concentration.

Analyzing Air Pollution in Urban Delhi: A Quantitative Assessment of Particulate Matter (PM) Concentrations

Air pollution poses significant challenges to urban environments, with particulate matter (PM) concentrations being a major concern due to their adverse effects on public health and the environment. This research paper aims to quantitatively assess PM concentrations in urban Delhi, focusing on spatial, temporal, and seasonal variations across diverse urban microenvironments. The research design involved mobile air quality monitoring using real-time PM sensors mounted on a vehicle, coupled with Geographic Information System (GIS) analysis to map pollution hotspots and assess correlations with urban features. The key findings of the study reveal significant spatial variability in PM concentrations, with industrial zones and busy intersections emerging as hotspots for pollution. Seasonal variations in PM levels, with elevated concentrations during winter months and lower levels during the monsoon season, underscore the influence of meteorological factors on air quality dynamics. The study also identifies correlations between PM concentrations and respiratory disease cases, highlighting the adverse health effects of air pollution. The implications of these findings extend to air quality management strategies and public health interventions, emphasizing the need for targeted measures to mitigate pollution levels and protect public health in urban environments.

Influence of meteorological factors on atmospheric fungal spores over crop canopy of rice in West Bengal, India with reference to major fungal diseases

Influence of meteorological factors on atmospheric fungal spores over crop canopy of rice in West Bengal, India with reference to major fungal diseases

Aerobiological and clinical study in the semidesertic area of the Southeastern of Spain.

Aerobiological studies constitute a relevant tool to predict the most influential parameters over the pollen seasons with significant clinical relevance in the allergic populations. The aim of this study was to describe the aerobiological behaviour of the most relevant allergenic sources in the semi-arid area of southeast of Spain (Almería) and to investigate the correlation with meteorological factors and clinical symptoms of allergic patients. Daily pollen count and meteorological parameters of Almería, Spain, were compiled for ten years. The clinical symptoms of 248 allergic patients were also recorded. Descriptive statistics and correlations between variables were assessed. Multivariate analyses were performed to predict the influence of meteorological factors on pollen concentration and the risk of suffer respiratory symptoms. Eight pollen families were identified as the most relevant allergenic sources. Temperature correlated with main pollen season evolution of all taxa whereas rainfall and relative humidity only correlated in Oleaceae, Pinaceae, Amaranthaceae, Asteraceae and Urticaceae. Rainfall and relative humidity were the most influential predictors of pollen concentration, except in Amaranthaceaea and Poaceae families, while temperature only influenced on Cupressaceae and Urticaceae pollen concentrations. A significant positive influence was observed between maximum temperature and rainfall with the appearance of allergic symptoms in patients sensitized to grasses, Parietaria sp. and Olea sp. This study, highlight the main aerobiological features in the region and establish a suitable tool for clinical follow-up and management of allergic patients. Further studies are needed to establish an accurate measurement aimed to control and prevent pollinosis in sensitized patients.

The factors affecting microplastic pollution in farmland soil for different agricultural uses: A case study of China

Microplastics (MPs) accumulation in agricultural soil seriously affects soil quality and grain production, and endangers ecological environment and human safety. The factors contributing to the MPs accumulation in various agricultural land-use soils remain unclear. In this study, a meta-analysis was conducted to analyze the influence of meteorological factors. Furthermore, the accumulation process of MPs pollution in different agricultural land-use soils in China was explored. The conclusions are as follows: (1) The MPs abundance varied from 23 to 41,741 n/kg in Chinese agricultural soil. Especially, the moderate and severe pollution (≥4,000 n/kg) were found in the Yunnan-Kweichow Plateau. (2) The order of MPs abundance in soil was as follows: vegetable > orchard > food crops. Compared to non-mulching, MPs abundance in mulching food crops, vegetable, and orchard soil was approximately 1.4-fold, 11.4-fold, and 1.1-fold, respectively. (3) In orchard and food-crop soils, MPs abundance was negatively associated with rain and wind speed, but positively correlated with altitude and temperature. Whereas MPs abundance in vegetable soil was significantly positively affected with temperature, altitude, sunshine hours, wind speed, and rainfall. (4) The MPs pollution accumulation in soil was mainly dominated by mulching agricultural measures under different agricultural land-use. Meteorological factors have an indirect impact by affecting MPs fragmentation and migration. The results provide theoretical support for soil pollution control, agricultural production safety, and watershed management.

High-resolution short-term prediction of the COVID-19 epidemic based on spatial-temporal model modified by historical meteorological data

In the global challenge of Coronavirus disease 2019 (COVID-19) pandemic, accurate prediction of daily new cases is crucial for epidemic prevention and socioeconomic planning. In contrast to traditional local, one-dimensional time-series data-based infection models, the study introduces an innovative approach by formulating the short-term prediction problem of new cases in a region as multidimensional, gridded time series for both input and prediction targets. A spatial-temporal depth prediction model for COVID-19 (ConvLSTM) is presented, and further ConvLSTM by integrating historical meteorological factors (Meteor-ConvLSTM) is refined, considering the influence of meteorological factors on the propagation of COVID-19. The correlation between 10 meteorological factors and the dynamic progression of COVID-19 was evaluated, employing spatial analysis techniques (spatial autocorrelation analysis, trend surface analysis, etc.) to describe the spatial and temporal characteristics of the epidemic. Leveraging the original ConvLSTM, an artificial neural network layer is introduced to learn how meteorological factors impact the infection spread, providing a 5-day forecast at a 0.01° × 0.01° pixel resolution. Simulation results using real dataset from the 3.15 outbreak in Shanghai demonstrate the efficacy of Meteor-ConvLSTM, with reduced RMSE of 0.110 and increased R2 of 0.125 (original ConvLSTM: RMSE = 0.702, R2 = 0.567; Meteor-ConvLSTM: RMSE = 0.592, R2 = 0.692), showcasing its utility for investigating the epidemiological characteristics, transmission dynamics, and epidemic development.

Hourly photovoltaic power prediction based on signal decomposition and deep learning

Accurate photovoltaic (PV) power prediction is important for the utilization of solar energy resources. However, PV power is non-stationary due to the variable influence of meteorological factors, which poses a challenge for accurate forecasting. In this paper, a hybrid method based on signal decomposition and a deep learning model is proposed. The hybrid model integrates complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and the Informer model. The CEEMDAN algorithm is used to separate different modes from the photovoltaic power sequence, enhancing its predictability. The deep learning model, the Informer, is employed to capture the complex relationship between photovoltaic power data and its historical data as well as external meteorological factors, ultimately enabling multi-step forecasting of photovoltaic power data. In hourly PV power forecasting experiments using a public dataset, the model exhibits significant performance improvements when compared to benchmark models such as LSTM, GRU, and Transformer. Specifically, the RMSE reduces by 6.07%-34.74% and the MAE reduces by 7.07%-37.5%. The results demonstrate that the hybrid model exhibits accurate predictive performance in the task of hourly photovoltaic power forecasting.

Comparison of Different Impact Factors and Spatial Scales in PM2.5 Variation

PM2.5 particles with an aerodynamic diameter of less than 2.5 μm are receiving increasing attention in China. Understanding how complex factors affect PM2.5 particles is crucial for the prevention of air pollution. This study investigated the influence of meteorological factors and land use on the dynamics of PM2.5 concentrations in four urban agglomerations of China at different scales from 2010 to 2020, using the Durbin spatial domain model (SDM) at five different grid scales. The results showed that the average annual PM2.5 concentration in four core urban agglomerations in China generally had a downward trend, and the meteorological factors and land use types were closely related to the PM2.5 concentration. The impact of temperature on PM2.5 changed significantly with an increase in grid scale, while other factors did not lead to obvious changes. The direct and spillover effects of different factors on PM2.5 in inland and coastal urban agglomerations were not entirely consistent. The influence of wind speed on coastal urban clusters (the Pearl River urban agglomeration (PRD) and Yangtze River urban agglomeration (YRD)) was not significant among the meteorological factors, but it had a significant impact on inland urban clusters (the Beijing–Tianjin–Hebei urban agglomeration (BTH) and Chengdu–Chongqing urban agglomeration (CC)). The direct effect of land use type factors showed an obvious U-shaped change with an increase in the research scale in the YRD, and the direct effect of land use type factors was almost twice as large as the spillover effect. Among land use type factors, human factors (impermeable surfaces) were found to have a greater impact in inland urban agglomerations, while natural factors (forests) had a greater impact in coastal urban agglomerations. Therefore, targeted policies to alleviate PM2.5 should be formulated in inland and coastal urban agglomerations, combined with local climate measures such as artificial precipitation, and urban land planning should be carried out under the consideration of known impacts.

Влияние на метеорологичните фактори върху добива и евапотранспирацията на суданка за силаж, отглеждана като втора култура при неполивни и поливни условия

The aim of experiment was to establish the influence of meteorological factors during the vegetation period of sudan grass, grown as a second crop on yield and evapotranspiration The experiment was carry out during 2014 – 2017 period, on meadow – cinnamon soil in in the experimental field of the Stara Zagora Agricultural Institute. The data from irrigated (by 75% of FC) and non irrigated Sudan grass have been used. Dependencies between yield of dry biomass and meteorological factors have been established, as well as soil layer water depletion. The value of evapotranspiration and it’s formatting have been calculated (168.3– 183.7 mm without irrigation and 264.3 – 283.8 mm by optimum irrigation). The obtained yields of the sudanka strongly depend on the climatic conditions of the individual years of research, varying from 9800 kg.ha-1 to 11250 kg.ha-1