Impact Of Meteorological Variables Research Articles

Impact of meteorological variability on diurnal and seasonal net ecosystem productivity in a desert riparian forest ecosystem.

The desert riparian forests are susceptible to meteorological changes and contribute significantly to the net ecosystem productivity (NEP) variations of arid ecosystems. However, the responsive patterns of their NEP variations to the meteorological variabilities remain inadequately comprehended. To address this gap, we utilized seven years of eddy covariance flux measurements in a representative desert riparian forest to investigate the NEP variations and its response to changing meteorological factors across diverse temporal scales. The results revealed significant periodic variations in half-hourly NEP, with dominant cycles spanning from five hours to one year, with a principal oscillation period of one day. Key meteorological factors including global solar radiation (Rg), relative humidity (RH), air temperature (Ta), soil temperature (Ts), and vapor pressure deficit (VPD) exhibited synchronization with NEP on daily scales. This synchronization, coupled with the observed one-day periodic NEP variations, provides robust evidence supporting the existence of a circadian rhythm in the ecosystem carbon exchange of desert riparian forest regulated by meteorological conditions. Seasonal patterns were significant in the impact of Rg phase, Ta diurnal amplitude, and VPD diurnal amplitude on NEP diurnal amplitude and phase. The NEP diurnal amplitude significantly, directly, and positively affected daily NEP in both the dormant and growing seasons, whereas its phase yielded significant negative effects (P< 0.05). The averages, amplitudes, and phases of diurnal meteorological conditions controlled the daily NEP by regulating NEP diurnal amplitude and phase. These findings provide evidence that the variability in circadian rhythms, caused by the increase in diurnal Ta and VPD, significantly impact the daily NEP at an ecosystem scale. This study enriches our comprehension of the meteorological mechanisms governing diurnal and seasonal carbon uptake dynamics within desert riparian forests, providing fresh insights into the direct and indirect roles of climate change in shaping patterns of ecosystem carbon exchange.

Evaluation of MODIS combined DT and DB AOD retrievals and their association with meteorological variables over Qena, Egypt

The purpose of this study is to validate the daily Terra-MODIS level 2 combined dark target (DT) and deep blue (DB) aerosol optical depth (AOD) retrievals with a spatial resolution of 10 km against the ground-based AERONET AOD data to be used in evaluating the air pollution and impact of meteorological variables over Qena, Egypt, in 2019. The regression analysis demonstrated an accepted agreement between the MODIS and AERONET AOD data with a correlation coefficient (R) of 0.7118 and 74.22% of the collocated points fall within the expected error (EE) limits. Quality flag filtering and spatial and temporal collocation were found to have a significant impact on the regression results. Quality flag filtering increased R by 0.2091 and % within EE by 17.97, spatial collocation increased R by 0.0143 and % within EE by 1.13, and temporal collocation increased R by 0.0089 and % within EE by 4.43. By validating the MODIS AOD data seasonally and analyzing the temporal distribution of the seasonal AOD data to show the retrieval accuracy variations between seasons, it was found that the MODIS AOD observations overestimated the AERONET AOD values in all seasons, and this may be because of underestimating the surface reflectance. Perhaps the main reason for the highest overestimation in summer and autumn is the transportation of aerosols from other regions, which changes the aerosol model in Qena, making accurate aerosol-type assumptions more difficult. Therefore, this study recommends necessary improvements regarding the aerosol model selection and the surface reflectance calculations. Temperature and relative humidity were found to have a strong negative relationship with a correlation of − 0.735, and both have a moderate association with AOD with a correlation of 0.451 and − 0.356, respectively. Because Qena is not a rainy city, precipitation was found to have no correlation with the other variables.

The impact of meteorological variables on the development of blackgram anthracnose caused by Colletotrichum lindemuthianum

The impact of meteorological variables on the development of blackgram anthracnose caused by Colletotrichum lindemuthianum

Data-driven analysis of weather impacts on urban traffic conditions at the city level

Unhealthy traffic conditions have significant adverse implications for environment-friendly, sustainable, and energy-efficient development. As an real-time environmental factor contributing to travel, comparing the effect mechanisms of weather on traffic conditions in multiple cities will provide a deeper understanding of the weather-traffic relationship. This study conducts a contrastive analysis of travel time index (TTI) variations under weather conditions in four cities. Notably, this study will consider the potential benefits of replacing an absolute index of traffic conditions, like traffic volume and travel speed, with TTI, a relative index. The results identified a strong association between complex weather measurements and traffic conditions. Overall, the impact of meteorological variables on TTI on non-weekdays might result in significantly worse traffic conditions. On weekdays, the impact is relatively weak, due to the flexibility for travel mode decision-making, especially during the commute hour. Weather impacts on traffic conditions are not only interrelated, like higher temperature with less rainfall or slower wind speeds, but they also varied with urban spatial layout and geographical condition. Besides, the traffic congestion is certainly related to extreme weather, but not closely. This study could benefit transit agencies by adding meteorological monitoring into traffic condition real-time analysis.

Characterizing Prediction Uncertainty in Agricultural Modeling via a Coupled Statistical–Physical Framework

Multiple factors, many of them environmental, coalesce to inform agricultural decisions. Farm planning is often done months in advance. These decisions have to be made with the information available at the time, including current trends, historical data, or predictions of what future weather patterns may be. The effort described in this work is geared towards a flexible mathematical and software framework for simulating the impact of meteorological variability on future crop yield. Our framework is data driven and can easily be applied to any location with suitable historical observations. This will enable site-specific studies that are needed for rigorous risk assessments and climate adaptation planning. The framework combines a physics-based model of crop yield with stochastic process models for meteorological inputs. Combined with techniques from uncertainty quantification, global sensitivity analysis, and machine learning, this hybrid statistical–physical framework allows studying the potential impacts of meteorological uncertainty on future agricultural yields and identify the environmental variables that contribute the most to prediction uncertainty. To highlight the utility of our general approach, we studied the predicted yields of multiple crops in multiple scenarios constructed from historical data. Using global sensitivity analysis, we then identified the key environmental factors contributing to uncertainty in these scenarios’ predictions.

Impact of vehicular pollution on carboxyhemoglobin levels and respiratory response in urban cyclists

BACKGROUND AND AIM: The present case-crossover study evaluated changes in carboxyhemoglobin (COHb) levels and spirometry values of 37 volunteers caused by vehicular pollution on a bike path parallel to a road with a high vehicular flow in two exposure scenarios (high exposure on weekdays and low exposure on Sundays) in the city of Bogotá, Colombia METHODS: For each scenario, ultrafine particles (UFP), carbon monoxide (CO), nitrogen dioxide (NO2), sulfur dioxide (SO2), and carbon black concentrations were measured, as well as vehicular flow and speed at two points on the road of interest, to explore the possible associations among these variables and exposure to each pollutant. RESULTS:Significant differences were obtained for UFP, CO, NO2, and SO2 concentrations between the high- and low-exposure scenarios, and between the estimated exposure doses (EED) of UFP, CO, and SO2. A strong correlation was found between the EED of CO and the percentage change in COHb before and after each bike ride (r=0.7571). Concerning UFP, a greater weight of background pollution was found in the high exposure scenario, followed by the impact of meteorological variables, and emission sources along the road under study. CONCLUSIONS:To explain the variability of respiratory parameters, the only statistically significant model was obtained for peak expiratory flow (25-75%) of the forced expiratory maneuver (MMEF25-75%), having EED of CO and average UFP size as explanatory variables. Cyclists arrived at the endpoint of the ride with high COHb levels due to exposure to air pollutants along the route they used to arrive at the study site. KEYWORDS: Air pollution, Mixtures, Biomarkers of exposure, Respiratory outcomes, Short-term exposure

The effect of climate variables on the incidence of cutaneous leishmaniasis in Isfahan, Central Iran.

In recent years, there have been considerable changes in the distribution of diseases that are potentially tied to ongoing climate variability. The aim of this study was to investigate the association between the incidence of cutaneous leishmaniasis (CL) and climatic factors in an Iranian city (Isfahan), which had the highest incidence of CL in the country. CL incidence and meteorological data were acquired from April 2010 to March 2017 (108 months) for Isfahan City. Univariate and multivariate seasonal autoregressive integrated moving average (SARIMA), generalized additive models (GAM), and generalized additive mixed models (GAMM) were used to identify the association between CL cases and meteorological variables, and forecast CL incidence. AIC, BIC, and residual tests were used to test the goodness of fit of SARIMA models; and R2 was used for GAM/GAMM. 6798 CL cases were recorded during this time. The incidence had a seasonal pattern and the highest number of cases was recorded from August to October. In univariate SARIMA, (1,0,1) (0,1,1)12 was the best fit for predicting CL incidence (AIC=8.09, BIC=8.32). Time series regression (1,0,1) (0,1,1)12 showed that monthly mean humidity after 4-month lag was inversely related to CL incidence (AIC=8.53, BIC=8.66). GAMM results showed that average temperature with 2-month lag, average relative humidity with 3-month lag, monthly cumulative rainfall with 1-month lag, and monthly sunshine hours with 1-month lag were related to CL incidence (R2=0.94). The impact of meteorological variables on the incidence of CL is not linear and GAM models that include non-linear structures are a better fit for prediction. In Isfahan, Iran, meteorological variables can greatly predict the incidence of CL, and these variables can be used for predicting outbreaks.

Photovoltaic output power performance assessment and forecasting: Impact of meteorological variables

Meteorological variables have an important effect on the performance of a grid-connected photovoltaic station, in this paper, the impact of meteorological variables on the 6 mWp grid-connected photovoltaic station in the desert of Adrar has been explored through performance assessment and output power forecasting.The impact of the meteorological variables on performance parameters has been investigated by performing interdependence and correlation studies. A clear interdependence between some variables has been observed, but the complete separation between each variable correlation effect has proven to be a difficult task.A combination of random forest method and pre-processing techniques namely feature selection and Principal component analysis has been developed in order to predict power production using meteorological variables as inputs. The forecasting models have been evaluated in terms of computation time, accuracy, and several statistical indicators.

Impact of meteorological factors on hospital admissions for spinal diseases in Shanghai during 2015-2019

ABSTRACT Purpose: To assess the correlation between meteorological factors and spinal disease admissions. Methods: Data was obtained from the electronic medical records of a tertiary general hospital. Meteorological data was collected from China Meteorological Science Data Sharing Service. Distributed lag nonlinear models were used to evaluate the impact of meteorological variables on weekly spinal disease admissions. Results: A total of 2739 spinal cases were documented. Compared with estimates at the 50th, the cumulative relative risk (RR) for extremely high temperatures at the 97.5th over lag week 18 to lag week 20 increased by 75.7%. When the weekly maximum temperature reached 38°C during lag week 20, the maximum RR was 1.96 (95% CI:1.095–3.506). Moreover, the effects of extremely high temperatures on spinal disease admissions were more obvious in females and the age group ≥65 years old compared with males and the age group<65 years old. Conclusions: Extremely high temperatures were significantly associated with higher risks of spinal disease admissions.

Assessment of Particulate Matter (PM\(_{10}\)) using Chemistry Transport Modeling in Agadir City, Morocco

According to the World Health Organization (WHO), the effects of outdoor and indoor air pollution result in approximately 7 million early deaths each year. In Morocco's case, the economic cost of air quality degradation is about 1.62 percent of GDP. Also, several studies reported the significant influence of local meteorological factors on the PM\(_{10}\) daily concentration during the synoptic climate conditions and cyclonic circulation. In this sense, Agadir city is a coastal city, and its proximity to the desert makes it very exposed to particles. Accordingly, this study allows, on one hand, to evaluate the performance and to understand the limitations of the WRF-CHIMERE modeling system used to simulate the maximum concentrations of PM\(_{10}\) in the city of Agadir, based on observations made in spring and summer 2010. On another hand, in order to better understand the situation, the impact of meteorological variables and anthropogenic emissions on PM\(_{10}\) is being investigated. The preliminary results of air quality modeling pointed out some PM\(_{10}\) dispersion issues in the area of Agadir. In particular, the results highlighted the influence of temperature and wind speed, which were correlated with PM\(_{10}\) concentration. Besides, CHIMERE simulations suggested that anthropogenic emissions control is essential to reduce PM\(_{10}\) by up to 15\%. The PM\(_{10}\) underestimation is attributed to the meteorological observation/model discrepancy and the effect of emissions. The results underline the importance of meteorology on PM\(_{10}\) dispersion and have significant policy considerations for Moroccan air quality strategy.

Global to USA County Scale Analysis of Weather, Urban Density, Mobility, Homestay, and Mask Use on COVID-19.

Prior evaluations of the relationship between COVID-19 and weather indicate an inconsistent role of meteorology (weather) in the transmission rate. While some effects due to weather may exist, we found possible misconceptions and biases in the analysis that only consider the impact of meteorological variables alone without considering the urban metabolism and environment. This study highlights that COVID-19 assessments can notably benefit by incorporating factors that account for urban dynamics and environmental exposure. We evaluated the role of weather (considering equivalent temperature that combines the effect of humidity and air temperature) with particular consideration of urban density, mobility, homestay, demographic information, and mask use within communities. Our findings highlighted the importance of considering spatial and temporal scales for interpreting the weather/climate impact on the COVID-19 spread and spatiotemporal lags between the causal processes and effects. On global to regional scales, we found contradictory relationships between weather and the transmission rate, confounded by decentralized policies, weather variability, and the onset of screening for COVID-19, highlighting an unlikely impact of weather alone. At a finer spatial scale, the mobility index (with the relative importance of 34.32%) was found to be the highest contributing factor to the COVID-19 pandemic growth, followed by homestay (26.14%), population (23.86%), and urban density (13.03%). The weather by itself was identified as a noninfluential factor (relative importance < 3%). The findings highlight that the relation between COVID-19 and meteorology needs to consider scale, urban density and mobility areas to improve predictions.

A Spatial and Temporal Analysis of Atmospherics Parameters Retrieved by a Neuro-varationnal Method off the West African Coast

In this work, we study spatial and temporal atmospherics parameters evolution retrieved by neuro-variationnal method from SeaWiFS observations measured off the west African coast.&#x0D; The SeaWiFS sensor measures the radiance above the top of atmosphere (TOA) solar irradiance.&#x0D; SeaWiFS use standard algorithm to invert the signal in order to retrieve weakly absorbing aerosol optical thickness (AOT) less than 0.3 whereas the Senegalese coasts are frequently crossed by desert dust plumes from large optical thickness.&#x0D; A neural algorithm, so-called SOM-NV, was developed to deal with absorbing aerosols and to retrieve their optical parameters, off the Senegalese coast, from SeaWiFS observations.&#x0D; The impact of meteorological variables on these restitutions was studied over the entire period of the observations that we analyzed and over the whole studied area, on the one hand, but also in a more thorough way on three "sub-area" located in north, south and center. The results obtained showed that the composition of aerosols in the atmosphere is a function of the seasons. High altitude zonal U winds are correlated with non-desert aerosols of -62.16% in winter and autumn. The correlation is -60.32% between dust aerosols and the zonal wind.

Impact of meteorological variables on water quality parameters of a reservoir and ecological filtration system

Recently, special attention has been given to low-cost technologies for water treatment, with the aim of serving remote communities and thus enabling everyone to have access to drinking water and basic sanitation. In that context, the ecological filter is an alternative for treating water for human consumption. Some studies show that climate change and meteorological parameters can influence the water quality of water bodies and the water supply system. However, most of the papers deal with model projections and therefore do not show real situations. The aim of this study was to determine the impact of meteorological variables (e.g. atmospheric pressure, evaporation, wind speed, radiation, precipitation) on the physico-chemical and biological parameters (e.g. turbidity, apparent colour, pH, total coliforms, Escherichia coli) of Lobo reservoir water treated by 22 ecological filters during a period of 4 months. Statistically significant correlations were found between the water from the Lobo reservoir and the weather variables, and between the water quality parameters of the 22 ecological filters and the average weather variables. The results from the statistical analysis indicate that climatic variations may influence treatment performance.

Impact of meteorological variables on physical activity in a sub-tropical city

TPS 664: Climate change: temperature effects 2, Exhibition Hall, Ground floor, August 27, 2019, 3:00 PM - 4:30 PM Background/Aim: Physical activity (PA) maintains health and well-being. Patterns and associations between meteorological variables and PA, however, have rarely been examined in subtropical climate contexts. The study objective was to assess the relationship between meteorological variables and objectively-assessed PA levels in the humid sub-tropical city of Hong Kong. Methods: Daily step counts from anonymised Hong Kong users of mobile app WeChat’s in-app function WeRun were collected from December 6, 2017 to November 30, 2018 and aggregated to produce daily mean step counts for the city. Daily meteorological and air pollutant data were obtained from the Hong Kong Observatory and Environmental Protection Department. Generalised additive models were used to examine the association of meteorological variables with daily mean step count, adjusting for air pollutants, months, day-of-week, public holidays and typhoons. Results: The final study sample included a daily average of 419,849 persons and reported an average of 9,034 steps per day. Temperature and relative humidity demonstrated a highly significant inverse U-shaped relationship with daily mean step count, peaking at approximately 22-23°C and 60% relative humidity. Highly significant decreasing trends were also found for rainfall and wind speed. Conclusions: Weather variables were found associated with objectively-assessed PA levels in the humid subtropical city of Hong Kong. These findings suggest that higher temperatures and increased rainfall due to climate change may lead to a net decrease in PA levels.

Rainfall partitioning after thinning in two low-biomass semiarid forests: Impact of meteorological variables and forest structure on the effectiveness of water-oriented treatments

Water-oriented forest management is an urgent need in semiarid catchments. In the case of low-biomass forests and shrublands, the magnitude, efficiency and temporal duration of thinning effects on rainfall partitioning needs further attention. This work studies the effects of juvenile thinning and shrub clearing on stemflow (Stf), throughfall (Thr) and interception (It) in two low-biomass forests (CAL: post-fire Aleppo pine saplings with 74% of basal area, BA, removed; and HU: evergreen oak coppice with 41% of BA removed), as well as the relative contribution of the event meteorology. The effects are compared with a control plot during the first 3–4 years. Stf rate (%) decreased with density and, on a tree scale, it was enhanced by the treatment only in the bigger oaks. Event Thr increased from 55 to 81% and from 68 to 86% of gross rainfall (Pg) for CAL and HU respectively after thinning, resulting in about 15% less intercepted Pg. High evaporative conditions and an open (ventilated) forest structure led to high It rates in the controls when comparing with other studies, thus making the treatments more efficient in net precipitation (Pn) gain (Pg intercepted decreased 17% or 2.3% per unit of LAI or BA removed respectively). In general, depths (mm) were mostly explained (>75%) by the rainfall characteristics of the event (e.g. amount, duration, intensity), with a limited contribution from forest structure (e.g. cover, LAI) and event meteorology (e.g. temperature, wind speed, vapor pressure deficit). On the contrary, when expressed as rates (% of Pg), forest structure and event-meteorology gained importance (explaining 25–65%), especially in the drier site (CAL). In this site, the low gain in Pn (∼25 mm per year on average) was offset with no temporal dampening during the span of this study, as observed in the wetter site (HU), where plant growth tended to mitigate the effect of the treatment by the end of the study. The results presented here make a contribution to a better understanding of the effects of water-oriented forest management in low-biomass semiarid forests.

Assessment of metro ridership fluctuation caused by weather conditions in Asian context: Using archived weather and ridership data in Nanjing

This paper aims to assess metro ridership fluctuation caused by weather conditions in an Asian context (Nanjing, China) on metro single line and network levels. The daily ridership data utilized in the study is obtained from Nanjing Metro Agency, while meteorological data is from the Nanjing Meteorology Bureau. To address the main research questions of this paper, time-serials moving average method is extended and refined to define benchmark and to model ridership fluctuation (expressed as ridership residual). On the other hand, analyses of variance (ANOVAs) are introduced to examine meteorological events and temporal effects on ridership residuals. The data and result demonstrates that the impact of meteorological variables on daily ridership fluctuations generally results in a decrease in passengers on single lines and the travel network, especially on weekends. Precipitation related events produce more significant ridership fluctuations than temperature related events. Snowfall related events and large temperature deviations in winter account for the most dramatic changes in ridership. However, seasonality is not a significant factor in meteorological events-ridership residual relationship. This paper contributes to extending research on the weather-ridership relationship around the world especially to refining and extending the nine-term moving average method in this field. This could benefit metro operators by enabling them to add meteorological effects into their ridership prediction and budgeting work.

Modelling of intra-urban variability of prevailing ambient noise at different temporal resolution

Abstract The impact of temporal aspects of noise data on model development and intra-urban variability on environmental noise levels are often ignored in the development of models used to predict its spatiotemporal variation within a city. Using a Land Use Regression approach, this study develops a framework which uses routine noise monitors to model the prevailing ambient noise, and to develop a noise variability map showing the variation within a city caused by land-use setting. The impact of data resolution on model development and the impact of meteorological variables on the noise level which are often ignored were also assessed. Six models were developed based on monthly, daily and hourly resolutions of both the noise and predictor data. Cross validation highlighted that only the hourly resolution model having 59%explanatory power of the observed data (adjusted R2) and a potential of explaining at least 0.47% variation of any independent dataset (cross validation R2), was a suitable candidate among all the developed models for explaining intraurban variability of noise. In the hourly model, regions with roads of high traffic volumes, with higher concentrations of heavy goods vehicles, and being close to activity centreswere found to have more impact on the prevailing ambient noise. Road lengthswere found to be the most influential predictors and identified as having an impact on the ambient noise monitors.

Meteorological influence on the 2009 influenza a (H1N1) pandemic in mainland China

Since May 2009, a novel influenza A (H1N1) pandemic has spread rapidly in mainland China from Mexico. Although there has been substantial analysis of this influenza, a reliable work estimating its spatial dynamics and determinants remain scarce. The survival and transmission of this pandemic virus not only depends on its biological properties, but also a correlation with external environmental factors. In this study, we collected daily influenza A (H1N1) cases and corresponding annual meteorological factors in mainland China from May 2009 to April 2010. By analyzing these data at county-level, a similarity index, which considered the spatio-temporal characteristics of the disease, was proposed to evaluate the role and lag time of meteorological factors in the influenza transmission. The results indicated that the influenza spanned a large geographical area, following an overall trend from east to west across the country. The spatio-temporal transmission of the disease was affected by a series of meteorological variables, especially absolute humidity with a 3-week lag. These findings confirmed that the absolute humidity and other meteorological variables contributed to the local occurrence and dispersal of influenza A (H1N1). The impact of meteorological variables and their lag effects could be involved in the improvement of effective strategies to control and prevent the disease outbreaks.

Association of Meteorological Variables and Coronary Blood Flow.

We aimed to assess the impact of meteorological variables on coronary blood flow (CBF). Coronary blood flow was evaluated using the thrombolysis in myocardial infarction frame count (TFC). The association of CBF with meteorological parameters such as temperature, relative humidity, total solar radiation, atmospheric pressure, wind velocity, and total sunshine duration were investigated as well as demographic, clinical, and laboratory characteristics. Assessment of 1206 patients (median age = 53 years, 723 females) revealed the presence of slow coronary flow (SCF) in 196 patients. Daily maximum temperature [odds ratio = 0.951, 95% confidence interval = 0.916-0.986, P = .007] was the only independent predictor of the presence of SCF, whereas systolic blood pressure (β = -0.139, P = .026), hematocrit level (β = 0.128, P = .044), and daily maximum temperature (β = -1.479, P = .049) were independent predictors of log10 (mean TFC). Findings of the present study suggest a role of meteorological parameters in CBF regulation.

Occurrence and distribution of tomato seed-borne mycoflora in Saudi Arabia and its correlation with the climatic variables.

One hundred samples of tomato seeds were collected in 2011 and 2012 from tomato-cultivated fields in Saudi Arabia and screened for their seed-borne mycoflora. A total of 30 genera and 57 species of fungi were recovered from the collected seed samples using agar plate and deep-freezing blotter methods. The two methods differed as regards the frequency of recovered seed-borne fungi. Seven fungi among those recovered from tomato seeds, which are known as plant pathogens, were tested for their pathogenicity and transmission on tomato seedlings. The recovery rate of these pathogens gradually decreased from root up to the upper stem, and did not reach to the stem apex. The distribution of tomato seed-borne fungi was also investigated throughout Saudi Arabia. In this concern, Al-Madena governorate recorded the highest incidence of fungal flora associated with tomato seeds. The impact of meteorological variables on the distribution of tomato seed-borne mycoflora was explored using the ordination technique (canonical correspondence analysis). Among all climatic factors, relative humidity was the most influential variable in this regard. Our findings may provide a valuable contribution to our understanding of future global disease change and may be used also to predict disease occurrence and fungal transfer to new uninfected areas.