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Impact of aerosols on health includes both long-term chronic irritation and inflammation of the respiratory tract. Aerosol optical depth (AOD), a crucial optical parameter that assesses the extinction effect of atmospheric aerosols, is frequently used to estimate the extent of air pollution on large scales. So, the better prediction of AOD is crucial for understanding the health impacts of aerosols. The accurate prediction of AOD is difficult due to its nonlinear relationships with other climatic variables, uncertainties, and time series variable characteristics. In this paper, a machine learning (ML) model such as support vector regression (SVR), novel hybrid SVR-GWO model (SVR integrated with gray wolf optimizer (GWO)), and statistical model multi-linear regression (MLR) are used to predict AOD. Also, for SVR-GWO model, SVR hyper-parameters are optimized using meta-heuristic GWO algorithm. Satellite-based data of Pakistan is used for the prediction of AOD on monthly bases. In addition, preprocessing techniques of forward feature selection (FFS) is utilized to select the optimal input features for the SVR-GWO, SVR and MLR models to predict AOD. The performance of the novel hybrid SVR-GWO, SVR, and MLR model is analyzed using RMSE, MAE, RRMSE, R 2 and Taylor diagram, and it is found that hybrid SVR-GWO model (RMSE = 0.07, MAE = 0.06, RRMSE = 0.22 and R 2 = 0.60 ) is better than ordinary SVR model (RMSE = 0.10, MAE = 0.07, RRMSE = 0.29 and R 2 = 0.18 ) and MLR model (RMSE = 0.11, MAE = 0.07, RRMSE = 0.32 and R 2 = 0.03 ). Keynotes: (a) The study demonstrates the potential of ML models such as SVR-GWO for accurate prediction of AOD, which can aid in better understanding of the health impacts of aerosols. (b) The use of preprocessing techniques like FFS and optimization algorithms like GWO can significantly improve the performance of the ML (SVR-GWO) model in predicting AOD. (c) The findings of this study can be useful for policymakers and healthcare professionals in identifying regions and populations at risk of aerosol-induced respiratory health issues and designing effective interventions to mitigate them. [ABSTRACT FROM AUTHOR]

Though the magnitude of future rainfall is important in most water resources applications, many applications require its occurrence/nonoccurrence rather than its magnitude such as in agricultural systems management, drought management systems, regulated deficit irrigation for various crops, short-term municipal water demand modeling and management, and reservoir operation. The occurrence of rainfall is a classification problem that also affects day-to-day human activities and management. However, most of the work on rainfall forecasting is for rainfall magnitude, and very few studies on rainfall occurrence forecasting have been carried out in the past. Also, few artificial intelligence and machine learning techniques have been utilized in rainfall magnitude forecasting but not any work registered so far for forecasting rainfall occurrence using these methods. The proposed novel approach in this paper integrates two machine learning methods, artificial neural network (ANN) and decision tree (DT), which are capable of making rainfall occurrence forecasting comprehensible and accurate. For this purpose, the rules have been extracted by generating a DT using the input-output data obtained from an ANN rainfall occurrence forecasting model. Daily climatic data are employed to illustrate the methodology developed in this study. The obtained results show that during training, ANN models learned a fixed set of rules for rainfall occurrence forecasting. The obtained rules are simple and can be used as a tool for rainfall occurrence forecasting. [ABSTRACT FROM AUTHOR]

Selection of appropriate climatic variables for prediction of electricity demand is critical as it affects the accuracy of the prediction. Different climatic variables may have different impacts on the electricity demand due to the varying geographical conditions. This paper uses multicollinearity and backward elimination processes to select the most appropriate variables and develop a multiple regression model for monthly forecasting of electricity demand. The former process is employed to reduce the collinearity between the explanatory variables by excluding the predictor which has highly linear relationship with the other independent variables in the dataset. In the next step, involving backward elimination regression analysis, the variables with coefficients that have a low level of significance are removed. A case study has been reported in this paper by acquiring the data from the state of New South Wales, Australia. The data analyses have revealed that the climatic variables such as temperature, humidity, and rainy days predominantly affect the electricity demand of the state of New South Wales. A regression model for monthly forecasting of the electricity demand is developed using the climatic variables that are dominant. The model has been trained and validated using the time series data. The monthly forecasted demands obtained using the proposed model are found to be closely matched with the actual electricity demands highlighting the fact that the prediction errors are well within the acceptable limits. [ABSTRACT FROM AUTHOR]

Air pollution affects cities and countries all over the world. One of the most common air pollutants is particulate matter (PM10) which has a direct impact on people´s health (WHO, 2006). The concentration of this pollutant in the troposphere depends principally on weather conditions. This paper seeks to analyse variations in Bahía Blanca (Argentina) PM10 concentration over time by evaluating the relationship between physical and anthropogenic factors that may influence its dynamics. It was observed that variations in PM10 concentrations are related to both natu- ral and anthropogenic factors. Traffic flow and agroindustrial activities, mainly related to grain transport and storage, promote high pollutant concentrations. This is reflected by marked variations of PM10 concentrations throughout the day and between working and nonworking days. PM10 concentrations are affected by Northern, high speed winds, which cause clouds of dust and smoke very often. In addition, results from this investigation revealed that PM10 concentrations may be related to volcanic activity, as the eruption of the Puyehue- Caulle Range. [ABSTRACT FROM AUTHOR]

Enterolobium cyclocarpum is recommended in reforestation and ecological restoration programs. However, before starting large-scale plantations some germination parameter form different seed sources should be identified, which can be used for plant production in each location or use elsewhere. In this paper, the variation of some germination parameters from ten provenances of E. cyclocarpum from the Coast of Oaxaca, Mexico was explored. Four germination parameters: germination capacity, mean daily germination, peak value and germination value were assessed in the laboratory. From information about geographical location and altitude above sea level of each provenance, climatic variables were obtained and afterward correlated with the germination parameters. Significant differences among provenances were detected for all of the germination parameters. There was not significant relationship among germination parameters and geographic variables, but there was a positive relationship between germination capacity and the proportion of precipitation falling during summer; and between mean daily germination and the total precipitation that falls during the growing season. The germinative capacity varied from 28.2 to 90%; it depended on provenance. The low germination of some provenances can cause problems in their permanence and conservation. [ABSTRACT FROM AUTHOR]

The daily reference evapotranspiration (ET0) is generally demanded in the application of agriculture, meteorology, hydrology and other fields. The calculation of daily ET0 based on the data of different time resolutions is always a simplification because the change of ET0 is a continuous process in the time scale. It is valuable to discuss the influence of the simplification with different time resolutions on the accuracy of ET0 estimation. In this paper, based on the observed data of Shouxian National Climate Observatory from 2007 to 2013, the daily average values were calculated using the data with the resolution of 1 minute as the true values. The effects of the 7 different time resolutions (including 10 min, 20 min, 30 min, 40 min, 60 min, 4 times per day and 3 times per day) on the estimation of daily air temperature, wind speed, solar radiation, relative humidity and daily, monthly, yearly reference evapotranspiration (ET0) were analyzed by the error comparison. Results showed that the absolute values of the errors for ET0 and 4 climatic variables increased with the lower time resolution. Wind speed was most sensitive to the change in the time resolution, followed by solar radiation. The mean absolute relative errors (MAPE) of wind speed were 1.35%, 2.20%, 2.79%, 3.54%, 4.48%, 16.01% and 24.29% for the time resolution of 10 min, 20 min, 30 min, 40 min, 60 min, 4 times per day and 3 times per day, respectively. The change in the time resolution showed less influence on daily air temperature and relative humidity than the other 2 factors. The MAPE values of daily ET0 were 0.53%, 1.01%, 1.38%, 1.72%, 2.46%, 4.72% and 6.14% respectively for the 7 time resolutions, indicating that the accuracies of ET0 estimation for 3 and 4 times per day were significantly lower than the other 5 time resolutions. Over 95% of the absolute errors for daily ET0 with the time resolution from 10 to 40 min were in the range of -0.20-0.20 mm/d. These errors were so small and concentrated that the meteorological data with these 4 time resolutions were suitable for daily ET0 estimation. The mean bias errors (MBE) were almost equal to 0 for the time resolutions from 10 to 60 min, and the total deviation degree was very low. The MBE value for 4 times per day was -0.01 and the estimated ET0 was smaller than the true value, while the MBE value for 3 times per day was 0.02 and the estimated ET0 was larger. The change in the time resolution of the solar radiation led to the largest attribution to the error of estimated ET0, followed by wind speed, because these 2 climatic factors were more sensitive to the change in the time resolution and they were the key factors to the radiation item and dynamic item of ET0 respectively. The absolute values of the errors of monthly and yearly ET0 were significantly less than that of daily ET0. The MAPE values of monthly ET0 were 0.13%, 0.21%, 0.27%, 0.40%, 0.50%, 1.18% and 1.48% respectively for the 7 time resolutions. The absolute values of the relative error of yearly ET0 were mostly less than 0.50%. Our study demonstrated that the change in the time resolution of meteorological variables showed less impact on the estimation of the long-term integrated ET0 and it was important to improve observation time resolution for increasing the ET0 estimation accuracy. [ABSTRACT FROM AUTHOR]

ABSTRACT This paper presents an analysis of winter climate variability based on daily mean temperature and precipitation data since 1970 in the Chic-Chocs Mountain range (located in the Gaspé Peninsula, Eastern Quebec, Canada). Mountain environments are particularly sensitive to rapid climate change and are therefore good indicators of recent global warming. The main goal of this study is to demonstrate how joint probability temperature/precipitation distributions can be used to estimate winter condition changes (trends) for six meteorological stations in the study area (the altitudinal range for the stations is from 5 to 574 m). The presence and persistence of snow cover was also estimated. Previous studies have shown a lack of evidence of significant trends in snow-cover characteristics (density, depth and snow water equivalent (SWE)) from the early 1980s to the present, despite an increase in temperature over the same period. A reanalysis of these data sets in addition to the use of a combination of temperature and precipitation data categorized into four modes (warm/wet, warm/dry, cold/wet and cold/dry) was also performed. Despite this new analysis, no clear evidence of climate change could be found in the study area over the last four decades. The results revealed that patterns and trends are quite different from one station to another, but when the environment is taken into account (valley or plateau, coastal versus inland) some apparent patterns emerge. [ABSTRACT FROM AUTHOR]

This paper investigates the effect of hybrid by sowing date interaction on the 1000 seeds weight in sunflower. The experiment included three sunflower hybrids (Miro, Rimi, and Pobednik) and eight sowing dates. The experimental design was a randomized block design with four replications over three years (2005, 2006 and 2007). For the analysis of hybrid by sowing date interaction linear-bilinear AMMI model (main effects and multiple interaction) was performed. Three-way analysis of variance showed highly significant (P < 0.01) contribution of year (46.2%), hybrid (27.4%) and sowing date (8.4%) on 1000 seed weight. All second and third order interactions were also highly significant. From the hybrid by sowing date interaction highly significant first interaction component (IPC1) was separated which explained 82.3% of the total variance. Based on the graphic presentation the similarity in interaction of hybrids Miro and Rimi was pointed out. With the highest mean value for 1000 seed weight, hybrid Pobednik was different from previous two hybrids, in both main effect and interaction. In the earlier sowing dates (R1, R2) the highest values were achieved, while from sowing date R3 the 1000 seed weight remarkable decreased. Correlations between climatic variables and IPC1 were found which provides agronomic importance in interpretation of the interaction. [ABSTRACT FROM AUTHOR]

Abstract: Stochastically generated stream flow and climatic data may be used as input to water resources simulation models for planning purposes. Dealing with non-concurrent and missing data, variable transformation and parameter uncertainty presents a significant challenge in the development of methods for stochastic data generation. In this paper, a Bayesian method is introduced for multi-site stochastic generation of annual stream flow and climatic data. A contemporaneous autoregressive lag-one model CAR(1) with the Box–Cox transformation is used to capture key statistical structure of multiple annual stream flow and climatic time series while keeping the number of model parameters to a minimum. The posterior joint distribution of the model parameters is formulated, allowing for inputs of historical data series that are not continuous or concurrent, thus avoiding the need to infill or truncate data records and maximising the value of available data. Parameter and uncertainty inference are solved numerically by using Markov Chain Monte Carlo simulations. Subsequent stochastic generation of data fully accounts for parameter uncertainty. In addition, a re-parameterization scheme is used to handle the problem of strong inter-parameter dependence from the Box–Cox transformation. The method was applied to the Melbourne Water supply system to demonstrate its computational feasibility. [Copyright &y& Elsevier]

Aim We analysed body-size variation in relation to latitude, longitude, elevation and environmental variables in Ctenomys (tuco-tucos), subterranean rodents in the Ctenomyidae (Caviomorpha). We tested the existence of inter- and intraspecific size clines to determine if these rodents follow Bergmann's rule, to compare intra- and interspecific size trends and to assess the relevance of the subterranean lifestyle on these trends. Location South America, south of 15° latitude. Methods This paper is based on 719 specimens of tuco-tucos from 133 localities of Argentina, Bolivia, Chile, Paraguay, Peru and Uruguay, representing 47 named species and 32 undescribed forms. Intraspecific analyses were performed for Ctenomys talarum Thomas, 1898 and the Ctenomys perrensi Thomas, 1896 species complex. Head and body length and weight were used for estimating body size. Geographical independent variables included latitude, longitude and altitude. Environmental independent variables were mean minimal and maximal monthly temperature, mean annual temperature, mean minimal and maximal precipitation, and total annual precipitation. To estimate seasonality, the annual variability of the climatic factors was calculated as their coefficients of variation and the difference between maximum and minimum values. Mean annual actual evapotranspiration (AET), and mean annual, January (summer) and July (winter) potential evapotranspiration (PET) values were also calculated for each locality, as well as annual, summer and winter water balance (WB). Statistical analyses consisted of simple and multiple regression and nonparametric correlation. Results Body size of Ctenomys decreases interspecifically from 15°00′ S to 48°15′ S and from 56°33′ W to 71°46′ W, and is positively correlated with ambient temperature and precipitation. The best predictors of body size according to multiple regression analyses were mean annual temperature, the difference between mean maximum and minimum annual temperatures, annual PET, the difference between summer and winter PET, and annual and winter water balance. These patterns are repeated, but not identically, at a smaller geographical scale within the species C. talarum and the superspecies C. perrensi. Main conclusions Tuco-tucos follow the converse to Bergmann's rule at the interspecific level. At the intraspecific level some parallel trends were observed, but the smaller scale of these analyses, involving a very reduced variation of environmental factors, necessitates caution in interpreting results. The subterranean lifestyle probably insulates these rodents from the external temperature. The observed latitudinal body-size gradients are more probably related to seasonality, ambient energy, primary productivity and/or intensity of predation. [ABSTRACT FROM AUTHOR]

Perceptions of the quality of roof harvested rainwater remain an impediment to widespread implementation of rainwater tanks on urban allotments. Previous literature reports on roof water quality have given little consideration to the relative significance of airborne environmental micro-organisms to roof catchment contamination and the issue of tank water quality. This paper outlines the findings of a recent study into the influence of weather on roof water contamination conducted at an urban housing development in Newcastle, on the east coast of Australia. Samples of direct roof run-off were collected during a number of separate rainfall events, and microbial counts were matched to climatic data corresponding to each of the monitored events. Roof run-off contamination was found to be under the strong influence of both wind speed and direction. The preliminary findings of an investigation currently under way into the microbial diversity of rainwater harvesting systems have also been presented. The results indicate that the composition of organisms present varied considerably from source to source and throughout the collection system. In all eases, evidence of faecal contamination was found to be negligible. The implications of these findings to the issues of tank water quality, health risk analysis and monitoring protocols have been discussed. [ABSTRACT FROM AUTHOR]

Aim The aims of this paper are to examine diversity–variability patterns for species of Aphodiinae (Coleoptera, Scarabaeoidea, Aphodiidae) on the Iberian Peninsula, and to determine the factors that influence their geographic distribution. Location Iberian Peninsula (Spain and Portugal). Methods Data from 30 studies and their bibliographies on species of Iberian Peninsula Aphodiinae were compiled. The reliability of the inventories was evaluated using parametric species richness estimators. In addition, a further 11 variables related to rarity, geographic distribution, or phylogenetic diversity were considered. Diversity variables were analysed using principal components analysis to reduce the number of dependent variables. Subsequently, the effect of differences in locality size among the 30 studies was eliminated by calculating and retaining the residuals of the curvilinear relationship of each diversity variable with the area. Generalized linear models were used to examine the relationships between diversity and 17 environmental variables. The diversity variables and their residuals were also subject to trend surface analysis in order to identify the relevance of spatially structured variables that had not been considered. The contribution of explanatory variables was determined through hierarchical variance analysis. Results Principal components analysis of biodiversity variables revealed that most of the variability could be explained using three biodiversity indexes: BI1, correlated positively with species richness, widely distributed species, frequent species, abundant species, species occurring in North Africa, Europe and the Iberian Peninsula, and phylogenetic diversity; BI2, correlated positively with numbers of infrequent and African–Iberian species; and, BI3, correlated positively with numbers of endemic, non-abundant, European, and Iberian-restricted species. A latitudinal disjunction emerged in BI1, with maximum scores at the north-western and southern corners, while maximum BI2 scores were found throughout the south, and maximum BI3 scores in the north-west. For BI1, it was climate that had the greatest influence, followed by lithology, and livestock presence. Geographic variables were the most significant for BI2, followed by climate and livestock presence. Finally, for BI3, climate variables were the most important, while geography, lithology and livestock presence had some relevance. Main conclusions The relevance of geographic variables indicates that other unaccounted-for factors that are spatially structured could possibly explain additional variation in Aphodiinae diversity. These factors may be historic in nature, relating to the species groups, namely the Ibero-European and the Mediterranean or Afro-Iberian. The northern pattern could reflect the fact that the Iberian Peninsula acted as a colonization route and as a refuge during the glacial/interglacial cycles, while the southern pattern could be a consequence of the connection between the Iberian Peninsula and North Africa during the Messinian crisis, and/or a historic relationship in common, related to human activity. [ABSTRACT FROM AUTHOR]

The electricity industry is significantly affected by weather conditions both in terms of the operation of the network infrastructure and electricity consumption. Following privatization and deregulation, the electricity industry in the U.K. has become fragmented and central planning has largely disappeared. In order to maximize profits, the margin of supply has decreased and the network is being run closer to capacity in certain areas. Careful planning is required to manage future electricity demand within the framework of this leaner electricity network. There is evidence that the climate in the U.K. is changing with a possible 3°C average annual temperature increase by 2080. This paper investigates the impact of weather variables on monthly electricity demand in England and Wales. A multiple regression model is developed to forecast monthly electricity demand based on weather variables, gross domestic product, and population growth. The average mean absolute percentage error (MAPE) for the worst model is approximately 2.60% in fitting the monthly electricity demand from 1989 to 1995 and approximately 2.69% in the forecasting over the period 1996 to 2003. This error may reflect the nonlinear dependence of demand on temperature at the hot and cold temperature extremes; however, the inclusion of degree days, enthalpy latent days, and relative humidity in the model improves the demand forecast during the summer months. [ABSTRACT FROM AUTHOR]

Aim This paper reports the diversity and endemism patterns of African ferns, and explores the potential role of diversity refuges and environmental and historical factors in the shaping of these patterns. Material and locations The extant fern taxa occupying Africa south of the Sahara, Madagascar and some islands of the South Atlantic. Methods The number of taxa in each area or operational geographical unit (OGU) was scored, and the correlation between this number and physical and climatic variables analysed by standard pairwise and stepwise multiple regression analysis (SPR and SMR). The effects of biological factors such as dispersal capacity, reproductive biology, genetic features and certain physiological adaptations were evaluated by comparing the number of species in each OGU. Floral affinities among OGUs were analysed using non-metric multi-dimensional scaling (NMS) and parsimonic analysis of dispersion (PAD), and compared with 13-turnover and inter-OGU distances. Results OGU area, elevation and the distance between refuges determined the composition of local floras, but only greater OGU area and the existence of higher maximum elevations increased species richness. The distance between refuges also affected the number of endemic species, especially on islands. The biological features studied only slightly influenced fern distribution. The main climatic predictor of species number was humidity. SPR and SMR revealed three main groups of ferns with different ecological trends. NMS and PAD analyses separated the four areas of highest diversity in Africa, three of which are inhabited by ferns with distinct ecological requirements. The fourth area was Madagascar, which shows an accumulation of endemic and relict diversity that is not easy to explain. Main conclusions The distribution of ferns in Africa has been influenced by refuges. These probably allowed many species to recolonize the neighbouring areas after the extinctions of the Pleistocene. Three major components were detected in the African flora: Guinea-Congolian thermophilous, cold-tolerant Afro-montane, and Southern drought-tolerant elements. These are related to the three main refuge areas, i.e. the Gulf of Guinea area, the eastern tropical region, and the Cape region. Endemicity in ferns was found to be lower than that of seed plants due to the higher dispersability of fern spores. The distance between OGUs seems to be the main predictor of the number of endemic fern species these areas contain. [ABSTRACT FROM AUTHOR]

Evaluation of effects of climate change on climate variable extremes is a key topic in civil and structural engineering, strongly affecting adaptation strategy for resilience. Appropriate procedures to assess the evolution over time of climatic actions are needed to deal with the inherent uncertainty of climate projections, also in view of providing more sound and robust predictions at the local scale. In this paper, an ad hoc weather generator is presented that is able to provide a quantification of climate model inherent uncertainties. Similar to other weather generators, the proposed algorithm allows the virtualization of the climatic data projection process, overcoming the usual limitations due to the restricted number of available climate model runs, requiring huge computational time. However, differently from other weather generation procedures, this new tool directly samples from the output of Regional Climate Models (RCMs), avoiding the introduction of additional hypotheses about the stochastic properties of the distributions of climate variables. Analyzing the ensemble of so-generated series, future changes of climatic actions can be assessed, and the associated uncertainties duly estimated, as a function of considered greenhouse gases emission scenarios. The efficiency of the proposed weather generator is discussed evaluating performance metrics and referring to a relevant case study: the evaluation of extremes of minimum and maximum temperature, precipitation, and ground snow load in a central Eastern region of Italy, which is part of the Mediterranean climatic zone. Starting from the model ensemble of six RCMs, factors of change uncertainty maps for the investigated region are derived concerning extreme daily temperatures, daily precipitation, and ground snow loads, underlying the potentialities of the proposed approach. [ABSTRACT FROM AUTHOR]

On March 11, 2020 the World Health Organization announced that the COVID-19 disease developed into a global pandemic. In the present paper, we aimed at analysing how the implementation of Non-Pharmaceutical Interventions (NPI) as well as climatic, social, and demographic variables affected the initial growth rate of COVID-19. In more detail, we aimed at identifying and assessing all the predictors in a whole picture of the COVID-19 outbreak and the effectiveness of the response of the countries to the pandemic. It can be expected, indeed, that there is a subtle and complex interplay among the various parameters. As such, we estimated the initial growth rate of COVID-19 for countries across the globe, and used a multiple linear regression model to study the association between the initial growth rate and NPI as well as pre-existing country characteristics (climatic, social and demographic variables measured before the current epidemic began). We obtained a mean initial growth rate of 0.120 (SD 0.076), in the range 0.023–0.315. Ten (8 pre-existing country characteristics and 2 NPI) out of 29 factors considered (21 pre-existing country characteristics and 8 NPI) were associated with the initial growth of COVID-19. Population in urban agglomerations of more than 1 million, PM2.5 air pollution mean annual exposure, life expectancy, hospital beds available, urban population, Global Health Security detection index and restrictions on international movement had the most significant effects on the initial growth of COVID-19. Based on available data and the results we obtained, NPI put in place by governments around the world alone may not have had a significant impact on the initial growth of COVID-19. Only restrictions on international movements had a relative significance with respect to the initial growth rate, whereas demographic, climatic, and social variables seemed to play a greater role in the initial growth rate of COVID-19. Unlabelled Image • A multiple regression model was developed to explore the drivers of the initial growth rate of the COVID-19. • Non-pharmaceutical interventions (NPI) as well as climatic, social, and demographic variables were considered in the model. • NPI are not strongly associated to the initial growth rate of COVID-19. • Social-demographic variables are strongly associated to the initial growth rate of COVID-19. • Climatic variables, like pollution but not temperature, are strongly associated to the initial growth rate of COVID-19. [ABSTRACT FROM AUTHOR]