Relationship between the variations in glacier features classified on a large scale with climate variables: a case study of Gangotri Glacier

Changes in glacier area, glacial lakes, debris cover, and geomorphological features such as debris fans have a significant impact on glacial dynamics. Therefore, precise and timely observation and tracking of glacier surface changes is a necessity. The availability of high spatial resolution remote sensing images has made it viable to analyse the glacier surface changes at a local level. However, with an increase in spatial resolution, the spectral variability increases, giving rise to additional challenges (such as false changes and misregistration) in the change detection process. These challenges can preferably be dealt with using an object-based change detection (OBCD) approach rather than the conventional pixel-based change detection approach. Therefore, this study has proposed an OBCD methodology using high-spatial-resolution remote sensing images to detect changes in glacier features. Variability in glacier features has been further analysed by associating it with important climate variables, that is, air temperature and precipitation. As a case study, the changes in Gangotri Glacier (Uttarakhand Himalayas in India) features have been studied using high-spatial-resolution WorldView-2 and Linear Imaging Self-Scanning System (LISS)-4 images for a 3-year period 2011-2014. The spectral correspondences between glacier surface and non-glacier surface have been handled by considering brightness temperature and slope as ancillary data to improvise their distinction. A change detection accuracy of ~ 84% has been obtained using the OBCD approach. Results further show that the variations in glacier features are in congruence with the climatic observations.

Micro-scale urbanization-based risk factors for dengue epidemics

Dengue is one of the world's most rapidly spreading mosquito-borne viral diseases. As it is found mostly in urban and semi-urban areas, urbanization and associated human activities that affect the environment and larval habitats could become risk factors (e.g., lane width, conditions of street ditches) for the spread of dengue. However, there are currently no systematic studies of micro-scale urbanization-based risk factors for the spread of dengue epidemics. We describe the study area, two micro-scale environmental risk factors associated with urbanization, and meteorological data. Since the observations involve spatial and temporal correlations, we also use some statistical methods for the analysis of spatial and spatial-temporal data for the relationship between urbanization and dengue. In this study, we analyzed data from Kaohsiung, a densely populated city in southern Taiwan, and found a positive correlation between environmental risk factors associated with urbanization (ditches positive for mosquito larvae and closely packed streets termed "dengue lanes") and clustering effects in dengue cases. The statistical analysis also revealed that the occurrence of positive ditches was significantly associated with that of dengue lanes in the study area. The relationship between climate variables and positive ditches was also analyzed in this paper, indicating a relationship between dengue and both rainfall and temperature, with temperature having a greater effect. Overall, this work is immediately relevant and applicable for policymakers in government, who will need to reduce these favorable habitats for vector-born disease spreaders and implement regulations for new urban constructions to thus reduce dengue spread in future outbreaks.

Spatio-temporal dengue risk modelling in the south of Thailand: a Bayesian approach to dengue vulnerability

Background

More than half of the global population is predicted to be living in areas susceptible to dengue transmission with the vast majority in Asia. Dengue fever is of public health concern, particularly in the southern region of Thailand due to favourable environmental factors for its spread. The risk of dengue infection at the population level varies in time and space among sub-populations thus, it is important to study the risk of infection considering spatio-temporal variation.

Methods

This study presents a joint spatio-temporal epidemiological model in a Bayesian setting using Markov chain Monte Carlo (MCMC) simulation with the CARBayesST package of R software. For this purpose, monthly dengue records by district from 2002 to 2018 from the southern region of Thailand provided by the Ministry of Public Health of Thailand and eight environmental variables were used.

Results

Results show that an increasing level of temperature, number of rainy days and sea level pressure are associated with a higher occurrence of dengue fever and consequently higher incidence risk, while an increasing level of wind speed seems to suggest a protective factor. Likewise, we found that the elevated risks of dengue in the immediate future are in the districts of Phipun, Phrom Kili, Lan Saka, Phra Phrom and Chaloem Phakiat. The resulting estimates provide insights into the effects of covariate risk factors, spatio-temporal trends and dengue-related health inequalities at the district level in southern Thailand.

Conclusion

Possible implications are discussed considering some anthropogenic factors that could inhibit or enhance dengue occurrence. Risk maps indicated which districts are above and below baseline risk, allowing for the identification of local anomalies and high-risk boundaries. In the event of near future, the threat of elevated disease risk needs to be prevented and controlled considering the factors underlying the spread of mosquitoes in the Southeast Asian region.

A successive time-to-event model of phyllochron dynamics for hypothesis testing: application to the analysis of genetic and environmental effects in maize

BACKGROUND

The time between the appearance of successive leaves, or phyllochron, characterizes the vegetative development of annual plants. Hypothesis testing models, which allow the comparison of phyllochrons between genetic groups and/or environmental conditions, are usually based on regression of thermal time on the number of leaves; most of the time a constant leaf appearance rate is assumed. However regression models ignore auto-correlation of the leaf number process and may lead to biased testing procedures. Moreover, the hypothesis of constant leaf appearance rate may be too restrictive.

METHODS

We propose a stochastic process model in which emergence of new leaves is considered to result from successive time-to-events. This model provides a flexible and more accurate modeling as well as unbiased testing procedures. It was applied to an original maize dataset collected in the field over three years on plants originating from two divergent selection experiments for flowering time in two maize inbred lines.

RESULTS AND CONCLUSION

We showed that the main differences in phyllochron were not observed between selection populations but rather between ancestral lines, years of experimentation and leaf ranks. Our results highlight a strong departure from the assumption of a constant leaf appearance rate over a season which could be related to climate variations, even if the impact of individual climate variables could not be clearly determined.

Spatial and temporal heterogeneity of air pollution in East Africa

East Africa's air pollution levels are deteriorating due to anthropogenic and biomass burning emissions and unfavorable weather conditions. This study investigates the changes and influencing factors of air pollution in East Africa from 2001 to 2021. The study found that air pollution in the region is heterogeneous, with increasing trends observed in pollution hot spots (PHS) while it decreased in pollution cold spots (PCS). The analysis identified four major pollution periods: High Pollution period 1, Low Pollution period 1, High Pollution period 2, and Low Pollution period 2, which occur during Feb-Mar, Apr-May, Jun-Aug and Oct-Nov, respectively. The study also revealed that long range transport of pollutants to the study area is primarily influenced by distant sources from the eastern, western, southern, and northern part of the continent. The seasonal meteorological conditions, such as high sea level pressure in the upper latitudes, cold air masses from the northern hemisphere, dry vegetation, and a dry and less humid atmosphere from boreal winter, further impact the transport of pollutants. The concentrations of pollutants were found to be influenced by climate factors, such as temperature, precipitation, and wind patterns. The study identified different pollution patterns in different seasons, with some areas having minimal anthropogenic pollution due to high vegetation vigor and moderate precipitation. Using Ordinary Least Square (OLS) regression and Detrended Fluctuation Analysis (DFA), the study quantified the magnitude of spatial variation in air pollution. The OLS trends indicated that 66 % of pixels exhibited decreasing trends while 34 % showed increasing trends, and DFA results indicating that 36 %, 15 %, and 49 % of pixels exhibited anti-persistence, random, and persistence in air pollution, respectively. Areas in the region experiencing increasing or decreasing trends in air pollution, which can be used to prioritize interventions and resources for improving air quality, were also highlighted. It also identifies the driving forces behind air pollution trends, such as anthropogenic or biomass burning, which can inform policy decisions aimed at reducing air pollution emissions from these sources. The findings on the persistence, reversibility, and variability of air pollution can inform the development of long-term policies for improving air quality and protecting public health.

An interpretable machine learning approach based on DNN, SVR, Extra Tree, and XGBoost models for predicting daily pan evaporation

Evaporation is an important hydrological process in the water cycle, especially for water bodies. Machine Learning (ML) models have become accurate and powerful tools in predicting pan evaporation. Meanwhile, the "black-box" character and the consistency with the physical process can decrease the practical implication of ML models. To overcome such limitations, we attempt to develop an interpretable based-ML framework to predict daily pan evaporation using Extra Tree, XGBoost, SVR, and Deep Neural Network (DNN) ML models using hourly climate datasets. To that end, we integrated and employed the Shapely Additive explanations (SHAP), Sobol-based sensitivity analysis, and Local Interpretable Model-agnostic Explanations (LIME) to evaluate the interpretability of the models in predicting daily pan evaporation, at Sidi Mohammed Ben Abdellah (SMBA) weather station, in Morocco. The validation results of the models showed that the developed models are accurate in reproducing the daily pan evaporation with NSE ranging from 0.76 to 0.83 during the validation phase. Furthermore, the interpretability results of the ML models showed that the air temperature (Ta), solar radiation (Rs), followed by relative humidity (H) are the most important climate variables with inflection points of the Ta_median, Ta_mean, Rs_sum, H_mean, and w_std are 17.42 °C, 17.65 °C, 3.8 kw.m^-2, 69.59%, and 1.25 m s^-1, sequentially. Overall, the interpretability of the models showed a good consistency of the ML models with the real hydro-climatic process of evaporation in a semi-arid environment. Hence, the proposed methodology is powerful in enhancing the reliability and transparency of the developed models for predicting daily pan evaporation. Finally, the proposed approach is new insights to reduce the ''Black-Box'' character of ML models in hydrological studies.

The impact of climate variables on marine fish production: an empirical evidence from Bangladesh based on autoregressive distributed lag (ARDL) approach

Several studies have highlighted the significant impact of climate change on agriculture. However, there have been little empirical enquiries into the impact of climate change on marine fish production, particularly in Bangladesh. Hence, this study aims to investigate the impact of climate change on marine fish production in Bangladesh using data from 1961 to 2019. Data were obtained from the Food and Agriculture Organization, Bangladesh Meteorological Department, the World Development Indicators, and the National Oceanic and Atmospheric Administration. The autoregressive distributed lag (ARDL) model was used to describe the dynamic link between CO₂ emissions, average temperature, Sea Surface Temperature (SST), rainfall, sunshine, wind and marine fish production. The ARDL approach to cointegration revealed that SST (β = 0.258), rainfall (β =0.297), and sunshine (β =0.663) significantly influence marine fish production at 1% and 10% levels in the short run and at 1% level in the long run. The results also found that average temperature has a significant negative impact on fish production in both short and long runs. On the other hand, CO₂ emissions have a negative impact on marine fish production in the short run. Specifically, for every 1% rise in CO₂ emissions, marine fish production will decline by 0.11%. The findings of this study suggest that policymakers formulate better policy frameworks for climate change adaptation and sustainable management of marine fisheries at the national level. Research and development in Bangladesh's fisheries sector should also focus on marine fish species that can resist high sea surface temperatures, CO₂ emissions, and average temperatures.

Cumulative effects of air pollution and climate drivers on COVID-19 multiwaves in Bucharest, Romania

Over more than two years of global health crisis due to ongoing COVID-19 pandemic, Romania experienced a five-wave pattern. This study aims to assess the potential impact of environmental drivers on COVID-19 transmission in Bucharest, capital of Romania during the analyzed epidemic period. Through descriptive statistics and cross-correlation tests applied to time series of daily observational and geospatial data of major outdoor inhalable particulate matter with aerodynamic diameter ≤ 2.5 µm (PM2.5) or ≤ 10 µm (PM10), nitrogen dioxide (NO₂), ozone (O₃), sulfur dioxide (SO₂), carbon monoxide (CO), Aerosol Optical Depth at 550 nm (AOD) and radon (²²²Rn), we investigated the COVID-19 waves patterns under different meteorological conditions. This study examined the contribution of individual climate variables on the ground level air pollutants concentrations and COVID-19 disease severity. As compared to the long-term average AOD over Bucharest from 2015 to 2019, for the same year periods, this study revealed major AOD level reduction by ~28 % during the spring lockdown of the first COVID-19 wave (15 March 2020-15 May 2020), and ~16 % during the third COVID-19 wave (1 February 2021-1 June 2021). This study found positive correlations between exposure to air pollutants PM2.5, PM10, NO₂, SO₂, CO and ²²²Rn, and significant negative correlations, especially for spring-summer periods between ground O₃ levels, air temperature, Planetary Boundary Layer height, and surface solar irradiance with COVID-19 incidence and deaths. For the analyzed time period 1 January 2020-1 April 2022, before and during each COVID-19 wave were recorded stagnant synoptic anticyclonic conditions favorable for SARS-CoV-2 virus spreading, with positive Omega surface charts composite average (Pa/s) at 850 mb during fall- winter seasons, clearly evidenced for the second, the fourth and the fifth waves. These findings are relevant for viral infections controls and health safety strategies design in highly polluted urban environments.

Weather integrated malaria prediction system using Bayesian structural time series model for northeast states of India

Malaria is an endemic disease in India and targeted to eliminate by the year 2030. The present study is aimed at understanding the epidemiological patterns of malaria transmission dynamics in Assam and Arunachal Pradesh followed by the development of a malaria prediction model using monthly climate factors. A total of 144,055 cases in Assam during 2011-2018 and 42,970 cases in Arunachal Pradesh were reported during the 2011-2019 period observed, and Plasmodium falciparum (74.5%) was the most predominant parasite in Assam, whereas Plasmodium vivax (66%) in Arunachal Pradesh. Malaria transmission showed a strong seasonal variation where most of the cases were reported during the monsoon period (Assam, 51.9%, and Arunachal Pradesh, 53.6%). Similarly, the malaria incidence was highest in the male population in both states (Asam, 55.75%, and Arunachal Pradesh, 51.43%), and the disease risk is also higher among the > 15 years age group (Assam, 61.7%, and Arunachal Pradesh, 67.9%). To predict the malaria incidence, Bayesian structural time series (BSTS) and Seasonal Auto-Regressive Integrated Moving Average with eXogenous factors (SARIMAX) models were implemented. A statistically significant association between malaria cases and climate variables was observed. The most influencing climate factors are found to be maximum and mean temperature with a 6-month lag, and it showed a negative association with malaria incidence. The BSTS model has shown superior performance on the optimal auto-correlated dataset (OAD) which contains auto-correlated malaria cases, cross-correlated climate variables besides malaria cases in both Assam (RMSE, 0.106; MAE, 0.089; and SMAPE, 19.2%) and Arunachal Pradesh (RMSE, 0.128; MAE, 0.122; and SMAPE, 22.6%) than the SARIMAX model. The findings suggest that the predictive performance of the BSTS model is outperformed, and it may be helpful for ongoing intervention strategies by governmental and nongovernmental agencies in the northeast region to combat the disease effectively.

Predicting the potential distribution of Campsis grandiflora in China under climate change

Because the research on the geographical distribution of species significantly influences people's understanding of species protection and utilization, it is important to study the influence of climate change on plants' geographical distribution patterns. Based on 166 distribution records and 11 climate and terrain variables, we used MaxEnt (Maximum Entropy) model and ArcGIS software to predict the potential distribution of Campsis grandiflora under climate change and then determined the dominant climate variables that significantly affected its geographical distribution. In our study, the area under the curve (AUC) value of the training data was 0.939, proving the accuracy of our prediction. Under current climate conditions, the area of potentially suitable habitat is 238.29 × 10⁴ km², mainly distributed in northern, central, southern, and eastern China. The dominant variables that affect the geographical distribution of C. grandiflora are temperature, precipitation and altitude. In the future climate change scenario, the total area of suitable habitat and highly suitable habitat will increase, whereas the area of moderately suitable habitat and poorly suitable habitat will decrease. In addition, the centroid of the potentially suitable area of C. grandiflora will migrate to higher latitude and higher altitudes areas. The results could give strategic guidance for development, protection, and utilization of C. grandiflora in China.

Impacts of exposure to air pollution, radon and climate drivers on the COVID-19 pandemic in Bucharest, Romania: A time series study

During the ongoing global COVID-19 pandemic disease, like several countries, Romania experienced a multiwaves pattern over more than two years. The spreading pattern of SARS-CoV-2 pathogens in the Bucharest, capital of Romania is a multi-factorial process involving among other factors outdoor environmental variables and viral inactivation. Through descriptive statistics and cross-correlation analysis applied to daily time series of observational and geospatial data, this study aims to evaluate the synergy of COVID-19 incidence and lethality with air pollution and radon under different climate conditions, which may exacerbate the coronavirus' effect on human health. During the entire analyzed period 1 January 2020-21 December 2021, for each of the four COVID-19 waves were recorded different anomalous anticyclonic synoptic meteorological patterns in the mid-troposphere, and favorable stability conditions during fall-early winter seasons for COVID-19 disease fast-spreading, mostly during the second, and the fourth waves. As the temporal pattern of airborne SARS-CoV-2 and its mutagen variants is affected by seasonal variability of the main air pollutants and climate parameters, this paper found: 1) the daily outdoor exposures to air pollutants (particulate matter PM2.5 and PM10, nitrogen dioxide-NO₂, sulfur dioxide-SO₂, carbon monoxide-CO) and radon - ²²²Rn, are directly correlated with the daily COVID-19 incidence and mortality, and may contribute to the spread and the severity of the pandemic; 2) the daily ground ozone-O₃ levels, air temperature, Planetary Boundary Layer height, and surface solar irradiance are anticorrelated with the daily new COVID-19 incidence and deaths, averageingful for spring-summer periods. Outdoor exposure to ambient air pollution associated with radon is a non-negligible driver of COVID-19 transmission in large metropolitan areas, and climate variables are risk factors in spreading the viral infection. The findings of this study provide useful information for public health authorities and decision-makers to develop future pandemic diseases strategies in high polluted metropolitan environments.

Assessment of spatiotemporal variability and trend analysis of reference crop evapotranspiration for the southern region of Peninsular India

Accurate estimation of reference evapotranspiration (ET₀) is an essential requirement for water resource management and scheduling agricultural activities. Several empirical methods have been employed in estimating ET₀ across diverse climate regimes over the past decades. In this study, the Python implementation for estimation of daily and monthly ET₀ values of representative stations of ten agro-climatic zones of Karnataka from 1979 to 2014 using the standard FAO Penman-Monteith method was carried out. The assessment of temporal and spatial variability of monthly ET₀ values across the various agro-climatic zones done by the various statistical measures revealed that the variation in spatial ET₀ values was higher than temporal variation, indicating major difference in ET₀ values was with respect to the stations rather than years under study. The nonparametric Mann-Kendall test conducted at 1% significance level on the annual ET₀ values revealed a statistically significant increasing trend for all the ten stations during the study period. The trend test conducted on the climate variables like mean air temperature, wind speed, relative humidity, and solar radiation signifies their influence on the annual ET₀ values. The magnitude changes in the trends detected by the Theil Sen's slope indicated that increasing values of mean temperature, solar radiation, and decreasing values of relative humidity predominantly contributed to the annual upward trend in ET₀ values for the 10 stations. A trivial impact of wind speed on annual ET₀ values was observed for the stations. Kalburgi and Udupi stations exhibited a positive ET₀ trend with the highest and lowest annual values among ten stations.

Soil carbon stocks and dynamics of different land uses in Italy using the LUCAS soil database

In terrestrial biosphere, soil represents the largest organic carbon pool, and a small change of soil organic carbon (SOC) can significantly affect the global carbon cycle and climate. Land use change (LUC) and soil management practices coupled with climate variables can significantly influence the soil organic carbon stocks (SOC-S) and its dynamics; however, our understanding about the responses of SOC in different LUC's (e.g., cropland, grassland and forest land) to mitigate climate change is quite limited at country level like Italy. Thus, the aims of this study were which factors are affecting SOC dynamics in three LUC's over time across Italy; and their relevance in terms of SOC-S in the superficial layer of soil that significantly contributes to the climate change mitigation, using LUCAS soil database. To calculate the SOC-S, it is necessary to have soil bulk density (BD) which is not present in the LUCAS database. Thus, we estimate the soil BD using the pedotransfer function (PTFs); and results shows that the soil BD obtained from fitting of the PTFs were reasonable to estimate the SOC-S for different land use types (R² ≥ 0.75). Overall, results showed that LUC's and soil management practices can significantly (p < 0.001) influences SOC dynamics and SOC storage from the soil and varied among LUC's but not for over time except grassland. Spatially, the mean SOC-S storage of the different LUC's was in the following order: forest land > grassland > cropland for both years 2009 and 2015. On the other hand, the SOC-S storage increased by 8.33% for cropland, 13.56% for forest land, and 29.79% for grassland during the year of 2009-2015, while SOC-S storage increased significantly (p < 0.001) in grassland over time but not for cropland and forest land which also follow the increasing trend but insignificantly. Our results also reveal that the SOC dynamics negatively correlated with MAT, and positively correlated with MAP for all land uses except forest land. Thus, this research indicates that LUC's and soil management practices coupled with climate variables can significantly influence SOC storage and its dynamics in the superficial layer of soil which have the potential capacity to mitigate climate change.

Assessing the impact of air pollution and climate seasonality on COVID-19 multiwaves in Madrid, Spain

While the COVID-19 pandemic is still in progress, being under the fifth COVID-19 wave in Madrid, over more than one year, Spain experienced a four wave pattern. The transmission of SARS-CoV-2 pathogens in Madrid metropolitan region was investigated from an urban context associated with seasonal variability of climate and air pollution drivers. Based on descriptive statistics and regression methods of in-situ and geospatial daily time series data, this study provides a comparative analysis between COVID-19 waves incidence and mortality cases in Madrid under different air quality and climate conditions. During analyzed period 1 January 2020-1 July 2021, for each of the four COVID-19 waves in Madrid were recorded anomalous anticyclonic synoptic meteorological patterns in the mid-troposphere and favorable stability conditions for COVID-19 disease fast spreading. As airborne microbial temporal pattern is most affected by seasonal changes, this paper found: 1) a significant negative correlation of air temperature, Planetary Boundary Layer height, and surface solar irradiance with daily new COVID-19 incidence and deaths; 2) a similar mutual seasonality with climate variables of the first and the fourth COVID-waves from spring seasons of 2020 and 2021 years. Such information may help the health decision makers and public plan for the future.

Effects of climate variables on the transmission of COVID-19: a systematic review of 62 ecological studies

The new severe acute respiratory syndrome coronavirus 2 was initially discovered at the end of 2019 in Wuhan City in China and has caused one of the most serious global public health crises. A collection and analysis of studies related to the association between COVID-19 (coronavirus disease 2019) transmission and meteorological factors, such as humidity, is vital and indispensable for disease prevention and control. A comprehensive literature search using various databases, including Web of Science, PubMed, and Chinese National Knowledge Infrastructure, was systematically performed to identify eligible studies from Dec 2019 to Feb 1, 2021. We also established six criteria to screen the literature to obtain high-quality literature with consistent research purposes. This systematic review included a total of 62 publications. The study period ranged from 1 to 8 months, with 6 papers considering incubation, and the lag effect of climate factors on COVID-19 activity being taken into account in 22 studies. After quality assessment, no study was found to have a high risk of bias, 30 studies were scored as having moderate risks of bias, and 32 studies were classified as having low risks of bias. The certainty of evidence was also graded as being low. When considering the existing scientific evidence, higher temperatures may slow the progression of the COVID-19 epidemic. However, during the course of the epidemic, these climate variables alone could not account for most of the variability. Therefore, countries should focus more on health policies while also taking into account the influence of weather.

Introducing intense rainfall and snowmelt variables to implement a process-related non-stationary shallow landslide susceptibility analysis

The study objective was to derive a susceptibility model for shallow landslides that could include process-related non-stationary variables, to be adaptable to climate changes. We selected the territory of the Mont-Emilius and Mont-Cervin Mountain Communities (northern Italy) as the study area. To define summary variables related to landslide predisposing and triggering processes, we investigated the relationships between landslide occurrences and intense rainfall and snowmelt events (period 1991-2020). For landslide susceptibility mapping, we set up a Generalized Additive Model. We defined a reference model through variable penalization (relief, NDVI, land cover and geology predictors). Similarly, we optimized a model including the climate variables, checking their smooth functions to ensure physical plausibility. Finally, we validated the optimized model through a k-fold cross-validation and performed an evaluation based on contingency tables, area under the receiver operating characteristic curve (AUROC) and variable importance (decrease in explained variance). The climate variables that resulted as being statistically and physically significant are the effective annual number of rainfall events with intensity-duration characteristics above a defined threshold (EAT_ean) and the average number of melting events occurring in a hydrological year (ME_n). In the optimized model, EAT_ean and ME_n accounted for 5% of the explained deviance. Compared to the reference model, their introduction led to an increase in true positive rate and AUROC of 2.4% and 0.8%, respectively. Also, their inclusion caused a transition of the vulnerability class in 11.0% of the study area. The k-fold validation confirmed the statistical significance and physical plausibility of the meteorological variables in 74% (EAT_ean) and 93% (ME_n) of the fitted models. Our results demonstrate the validity of the proposed approach to introduce process-related, non-stationary, physically-plausible climate variables within a shallow landslide susceptibility analysis. Not only do the variables improve the model performance, but they make it adaptable to map the future evolution of landslide susceptibility including climate changes.

Eco-epidemiological aspects of Zoonotic cutaneous leishmaniasis in Ouarzazate Province, Morocco

Some epidemiological and ecological aspects of Zoonotic cutaneous leishmaniasis (ZCL) in Ouarzazate province, southern Morocco, were explored with the objective of analyzing ZCL distribution and associated ecological factors. Information on cutaneous leishmaniasis patients attending the local health centers of Ouarzazate during the period 2002-2009 was gathered and compiled. Urban, peri-urban, rural origin, precipitation, wind speed, temperature, water irrigation, dam volume, and altitude were studied. The findings show that the disease affected 5405 person during this period; the major part was found in the municipalities near both oases (desert oasis) and water resources, with a high concentration of cases in the peri-urban area. The highest percentage of cases was recorded mainly in September. Considerable associations were found between relative humidity and wind speed with ZCL occurrence. A large number of cases were recorded in areas with altitude ranging from 800 to 2000 m.a.s.l. and spatial precipitation from 15 to 150 mm. The statistical analysis showed a strong association between water storage volume and water irrigation with the annual ZCL occurrence recorded in the downstream area (Zagora province). The results will lead us to understand ZCL risk areas for effective control. Further work is needed mainly for gathering these variables in one single and simplest model.

Investigating the spatio-temporal variability of soil organic carbon stocks in different ecosystems of China

Soil organic carbon (SOC) significantly influences soil fertility, soil water holding capacity, and plant productivity. In this study, we applied two boosted regression tree (BRT) models to map SOC stocks across China in the 1980s and the 2010s. The models incorporated nine environmental variables (climate, topography, and biology) and 8897 (in the 1980s) and 4534 (in the 2010s) topsoil (0-20 cm) samples. During the two study periods, 20% of the soil samples were randomly selected for model testing, and the remaining samples were used as a training set to construct the models. The verification results showed that incorporating climate environment variables significantly improved the model prediction in both study periods. Mean annual temperature, mean annual precipitation, elevation, and the normalized difference vegetation index were the dominant environmental factors affecting the spatial distribution of SOC stocks. The full-variable model predicted similar spatial distributions of SOC stocks for the 1980s and the 2010s. SOC stocks in China showed an increasing trend over the past 30 years, from 3.9 kg m^-2 in the 1980s to 4.6 kg m^-2 in the 2010s. In both periods, topsoil SOC stocks were mainly stored in agroecosystems, forests, and grasslands in the 1980s, with values of 9.5, 12.0, and 11.4 Pg C, respectively. Our study provides reliable information on Chain's carbon distribution, which can be used by land managers and the national government to formulate relevant land use and carbon sequestration policies.

Effects of climate variables on the COVID-19 outbreak in Spain

An outbreak of the novel COVID-19 virus occurred during February 2020 onwards in almost all the European countries, including Spain. This study covers the correlation found between weather variables (Maximum Temperature, Minimum Temperature, Mean Temperature, Atmospheric Pressure, Daily Rainfall, Daily Sun hours) and the coronavirus propagation in Spain. A strong relationship is found when correlating the virus spread to the mean temperature, minimum temperature, and atmospheric pressure in different Spanish provinces. In this analysis we have used the ratio of the PCR COVID-19 positives with respect to the population size. A linear regression model using the mean temperature is implemented. Moreover, an analysis of variance is used to confirm the influence of mean temperature on the spread of virus. As a second measurement of the COVID-19 outbreak we have used the results of the antibodies tests carried out in Spain that provide an estimation of the heard immunity achieved. Based on this analysis, an estimation of the asymptomatic population is performed. All these results exhibit significant correlation with weather variables. The most affected provinces were Soria, Segovia and Ciudad Real, which are the coldest. On the opposite side, places such as Southern Spain, the Baleares, and Canary Islands showed a lower rate of spread. This might be related to the warmer climate and the insularity of these islands. Besides, the coastal influence and the daily sun hours might also influence the lower rates in the east and west regions in Spain. This analysis provides a deeper insight of the influence of weather variables onto the COVID-19 spread in Spain.

Assessing the relationship between ground levels of ozone (O3) and nitrogen dioxide (NO2) with coronavirus (COVID-19) in Milan, Italy

This paper investigates the correlation between the high level of coronavirus SARS-CoV-2 infection accelerated transmission and lethality, and surface air pollution in Milan metropolitan area, Lombardy region in Italy. For January-April 2020 period, time series of daily average inhalable gaseous pollutants ozone (O3) and nitrogen dioxide (NO2), together climate variables (air temperature, relative humidity, wind speed, precipitation rate, atmospheric pressure field and Planetary Boundary Layer) were analyzed. In spite of being considered primarily transmitted by indoor bioaerosols droplets and infected surfaces or direct human-to-human personal contacts, it seems that high levels of urban air pollution, and climate conditions have a significant impact on SARS-CoV-2 diffusion. Exhibited positive correlations of ambient ozone levels and negative correlations of NO2 with the increased rates of COVID-19 infections (Total number, Daily New positive and Total Deaths cases), can be attributed to airborne bioaerosols distribution. The results show positive correlation of daily averaged O3 with air temperature and inversely correlations with relative humidity and precipitation rates. Viral genome contains distinctive features, including a unique N-terminal fragment within the spike protein, which allows coronavirus attachment on ambient air pollutants. At this moment it is not clear if through airborne diffusion, in the presence of outdoor and indoor aerosols, this protein "spike" of the new COVID-19 is involved in the infectious agent transmission from a reservoir to a susceptible host during the highest nosocomial outbreak in some agglomerated industrialized urban areas like Milan is. Also, in spite of collected data for cold season (winter-early spring) period, when usually ozone levels have lower values than in summer, the findings of this study support possibility as O3 can acts as a COVID-19 virus incubator. Being a novel pandemic coronavirus version, it might be ongoing during summer conditions associated with higher air temperatures, low relative humidity and precipitation levels.

Association between climate variables and global transmission oF SARS-CoV-2

In this study, we aimed at analyzing the associations between transmission of and deaths caused by SARS-CoV-2 and meteorological variables, such as average temperature, minimum temperature, maximum temperature, and precipitation. Two outcome measures were considered, with the first aiming to study SARS-CoV-2 infections and the second aiming to study COVID-19 mortality. Daily data as well as data on SARS-CoV-2 infections and COVID-19 mortality obtained between December 1, 2019 and March 28, 2020 were collected from weather stations around the world. The country's population density and time of exposure to the disease were used as control variables. Finally, a month dummy variable was added. Daily data by country were analyzed using the panel data model. An increase in the average daily temperature by one degree Fahrenheit reduced the number of cases by approximately 6.4 cases/day. There was a negative correlation between the average temperature per country and the number of cases of SARS-CoV-2 infections. This association remained strong even with the incorporation of additional variables and controls (maximum temperature, average temperature, minimum temperature, and precipitation) and fixed country effects. There was a positive correlation between precipitation and SARS-CoV-2 transmission. Countries with higher rainfall measurements showed an increase in disease transmission. For each average inch/day, there was an increase of 56.01 cases/day. COVID-19 mortality showed no significant association with temperature.

The role of climate in the trend and variability of Ethiopia's cereal crop yields

Food security has been and will continue to be a major challenge in Ethiopia. The country's smallholder, rainfed agriculture renders its food production system extremely vulnerable to climate variability and extremes. In this study, we investigate the impact of past climate variability and change on the yields of five major cereal crops in Ethiopia-barley, maize, millet, sorghum, and wheat-during the period 1979-2014 using the Decision Support System for Agrotechnology Transfer (DSSAT) crop model. The model is calibrated at both the site and agroecological-zone scales. At the sites studied, the model results suggest that climate in the past four decades may have contributed to an increasing trend in maize yield, a decreasing trend in wheat yield, and no clear trend in the yields of barley and millet; cereal crop yield is positively correlated with growing season solar radiation and temperature, but negatively correlated with growing season precipitation. For modeled cereal crops across the nation during the study period, yield in western Ethiopia is positively correlated with solar radiation and day time temperature; in the eastern and southeastern Ethiopia where water is a limiting factor for growth, yield is positively correlated with precipitation but negatively correlated with solar radiation and both day time and night time temperature. The national average of simulated yields of most crops (except maize) showed an overall decreasing (although not statistically significant) trend induced by past climate variability and changes. Over a large portion of the highly productive areas where there is a negative correlation between yield and temperature, yield is simulated to have significantly decreased over the past four decades, an indication of adverse climate impact in the past and potential food security concern in the future.

Climate factors and the East Asian summer monsoon may drive large outbreaks of dengue in China

OBJECTIVE

To investigate the relationship between climate variables, East Asian summer monsoon (EASM) and large outbreaks of dengue in China.

METHODS

We constructed ecological niche models (ENMs) to analyse the influence of climate factors on dengue occurrence and predict dengue outbreak areas in China. Furthermore, we formulated a generalised additive model (GAM) to quantify the impact of the EASM on dengue occurrence in mainland China from 1980 to 2016.

RESULTS

Mean Temperature of Coldest Quarter had a 62.6% contribution to dengue outbreaks. Southern China including Guangdong, Guangxi, Fujian and Yunnan provinces are more vulnerable to dengue emergence and resurgence. In addition, we found population density had a 68.7% contribution to dengue widely distribution in China using ENMs. Statistical analysis indicated a dome-shaped association between EASM and dengue outbreak using GAM, with the greatest impact in the South-East of China. Besides, there was a positive nonlinear association between monthly average temperature and dengue occurrence.

CONCLUSION

We demonstrated the influence of climate factors and East Asian summer monsoon on dengue outbreaks, providing a framework for future studies on the association between climate change and vector-borne diseases.

Potential influence of climate and anthropogenic variables on water security using blue and green water scarcity, Falkenmark index, and freshwater provision indicator

Land use change and climate variability have significantly altered the regional water cycle over the last century thereby affecting water security at a local to regional scale. Therefore, it is important to investigate how the climate, land use change, and water demand potentially influence the water security by applying the concept of water footprint. An integrated hydrological modeling framework using SWAT (Soil and Water Assessment Tool) model was developed by considering both anthropogenic (e.g. land use change, water demand) and climatic factors to quantify the spatio-temporal variability of water security indicators such as blue water scarcity, green water scarcity, Falkenmark index, and freshwater provision indicators in Savannah River Basin (SRB). The SRB witnesses a significant change in land use land cover (e.g. forest cover, urban area) as well as water demand (e.g. irrigation, livestock production). Overall our results reveal that, SRB witnessed a significant decrease in blue water due to the climate variability indicating that the precipitation has more control over the blue water resources. Whereas, green water was more sensitive to changes in land use pattern. In addition, the magnitude of various water security indicators are different within each county suggesting that water scarcity are controlled by various factors within a region. An integrated assessment of water footprint, environmental flow, anthropogenic factors, and climatic variables can provide useful information on the rising (how and where) of water related risk to human and ecological health.

Short-term association between outdoor air pollution and osteoporotic hip fracture

This study examines the association of the levels of different airborne pollutants on the incidence of osteoporotic hip fracture in a southern European region. Association was detected between SO₂ and NO₂ and hospital admissions due to hip fracture.

INTRODUCTION

To examine the short-term effects of outdoor air pollution on the incidence of osteoporotic hip fracture in a southern European region.

METHODS

This is an ecological retrospective cohort study based on data obtained from three databases. In a time-series analysis, we examined the association between hip fracture incidence and different outdoor air pollutants (sulfur dioxide (SO₂), monoxide (NO), nitrogen dioxide (NO₂), ozone (O₃), and particulate matter in suspension < 2.5 (PM_2.5) and < 10-μm (PM₁₀) conditions by using general additive models (Poisson distribution). The incidence rate ratio (IRR), crude and adjusted by season and different weather conditions, was estimated for all parameters. Hip incidence was later analyzed by sex and age (under or over age 75) subgroups. The main outcome measure was daily hospital admissions due to fracture.

RESULTS

Hip fracture incidence showed association with SO₂ (IRR 1.11 (95% CI 1.04-1.18)), NO (IRR 1.01 (95% CI 1.01-1.02)), and NO₂ (IRR 1.02 (95% CI 1.01-1.04)). For O₃ levels, this association was negative (IRR 0.97 (95% CI 0.95-0.99)). The association persisted for SO₂ and NO₂ when the models were adjusted by season. After adjusting by season and weather conditions, the association persisted for NO₂. When participants were stratified by age and sex, associations persisted only in women older than 75 years.

CONCLUSIONS

A short-term association was observed with several indicators of air pollution on hip fracture incidence. This is the first study that shows these associations.

Reply to the comment on "Synchronizing biological cycles as key to survival under a scenario of global change: The Common quail (Coturnix coturnix) strategy" by Rodriguez-Teijeiro et al

Two methodological criticisms of our paper "Synchronizing biological cycles as key to survival under a scenario of global change: The Common quail (Coturnix coturnix) strategy" (Nadal et al., 2018) were proposed in the comment by Rodriguez-Teijeiro et al. (2018) regarding: 1) our estimates of the mean date of arrival, duration of stay and departure stages in the different regions studied; and 2) the analyses carried out to correlate the phenology of the species with changes in the climate variables. The conceptual model that we presented relates the dynamics of this quail population, which moves between short periods of stays, and the spatio-temporal structure of their geographic distribution data, in order to understand the ecology of these birds and to link their movement and residency patterns with geographical area and climate conditions. The probability that quail are resident in a region on any particular date is a result of their overall ecological strategy. We believe that Rodríguez-Teijeiro et al. (2018) have misunderstood our model, leading to their criticism of the statistical tests that we applied.

Comment on: "Synchronizing biological cycles as key to survival under a scenario of global change: The Common quail (Coturnix coturnix) strategy" by Nadal, J., Ponz, C., Margalida, A

Nadal et al. (2018) recently reported on changes in the phenology of the Common quail (Coturnix coturnix) in seven cloudy regions of Spain in relation to climate change. The authors used a long-term ringing database (1961-2014) and calculated the mean date for three biological stages: arrival at the breeding areas, stay and autumn departure. They observed that some of these phenological variables were associated with the climate variables of temperature and rainfall (Figs. 4 and 6 of their article). They also analysed the yearly variation in temperature and rainfall over the last 86years, reporting an increase in temperature and a decrease in rainfall (Figs. 3 and 5 of their article). Based on these results, the authors suggested that the Common quail phenology has varied as a response to climate change in Spain, thus concluding that "quail movements and breeding attempts are eco-synchronized sequentially in cloudy regions. Our results suggest that quails attempt to overcome the negative impacts of climate change and agricultural intensification by searching for alternative high-quality habitats". We disagree with two methodological aspects of the article by Nadal et al. (2018): (1) the estimation of the mean date of arrival, stay and departure in the different regions studied; and (2) the analyses carried out to correlate the phenology of the species with the changes in climate variables.

Weather variables and the El Niño Southern Oscillation may drive the epidemics of dengue in Guangdong Province, China

OBJECTIVE

To investigate the periodicity of dengue and the relationship between weather variables, El Niño Southern Oscillation (ENSO) and dengue incidence in Guangdong Province, China.

METHODS

Guangdong monthly dengue incidence and weather data and El Niño index information for 1988 to 2015 were collected. Wavelet analysis was used to investigate the periodicity of dengue, and the coherence and time-lag phases between dengue and weather variables and ENSO. The Generalized Additive Model (GAM) approach was further employed to explore the dose-response relationship of those variables on dengue. Finally, random forest analysis was applied to measure the relative importance of the climate predictors.

RESULTS

Dengue in Guangdong has a dominant annual periodicity over the period 1988-2015. Mean minimum temperature, total precipitation, and mean relative humidity are positively related to dengue incidence for 2, 3, and 4months lag, respectively. ENSO in the previous 12months may have driven the dengue epidemics in 1995, 2002, 2006 and 2010 in Guangdong. GAM analysis indicates an approximate linear association for the temperature-dengue relationship, approximate logarithm curve for the humidity-dengue relationship, and an inverted U-shape association for the precipitation-dengue (the threshold of precipitation is 348mm per month) and ENSO-dengue relationships (the threshold of ENSO index is 0.6°C). The monthly mean minimum temperature in the previous two months was identified as the most important climate variable associated with dengue epidemics in Guangdong Province.

CONCLUSION

Our study suggests weather factors and ENSO are important predictors of dengue incidence. These findings provide useful evidence for early warning systems to help to respond to the global expansion of dengue fever.

Relationship between rice yield and climate variables in southwest Nigeria using multiple linear regression and support vector machine analysis

This study examines the variations of climate variables and rice yield and quantifies the relationships among them using multiple linear regression, principal component analysis, and support vector machine (SVM) analysis in southwest Nigeria. The climate and yield data used was for a period of 36 years between 1980 and 2015. Similar to the observed decrease (P < 0.001) in rice yield, pan evaporation, solar radiation, and wind speed declined significantly. Eight principal components exhibited an eigenvalue > 1 and explained 83.1% of the total variance of predictor variables. The SVM regression function using the scores of the first principal component explained about 75% of the variance in rice yield data and linear regression about 64%. SVM regression between annual solar radiation values and yield explained 67% of the variance. Only the first component of the principal component analysis (PCA) exhibited a clear long-term trend and sometimes short-term variance similar to that of rice yield. Short-term fluctuations of the scores of the PC1 are closely coupled to those of rice yield during the 1986-1993 and the 2006-2013 periods thereby revealing the inter-annual sensitivity of rice production to climate variability. Solar radiation stands out as the climate variable of highest influence on rice yield, and the influence was especially strong during monsoon and post-monsoon periods, which correspond to the vegetative, booting, flowering, and grain filling stages in the study area. The outcome is expected to provide more in-depth regional-specific climate-rice linkage for screening of better cultivars that can positively respond to future climate fluctuations as well as providing information that may help optimized planting dates for improved radiation use efficiency in the study area.

Niche differences may explain the geographic distribution of cytotypes in Erysimum mediohispanicum

Polyploidisation has played an important role in plant diversification, and variation in ploidy level may be found not only between species of the same genus, but also within a single species. Although establishing the adaptive significance of polyploidy to explain the geographic distribution of cytotypes is challenging, the occurrence of different cytotypes in different ecological niches may suggest an adaptive role of genome duplication. We studied the adaptive significance of the geographic distribution of cytotypes across the entire distribution range of the endemic Erysimum mediohispanicum (Brassicaceae). For that, we have used climate variables, population elevation and soil properties to model ecological niches for the different cytotypes. In addition, we analysed the effect that ploidy level has on the floral phenotype. We found a clear geographic pattern in the distribution of cytotypes, with diploid individuals occurring in the southernmost part of the distribution range, while tetraploids were found in the northern area. A contact (mosaic) zone between both cytotypes was identified, but diploids and tetraploids occur in sympatry in only one population (although in a highly unbalanced proportion). Gene flow between different cytotypes seems to be negligible, as evident from an almost complete absence of triploids and other minority cytotypes. Niches occupied by both cytotypes showed subtle, but significant differences, even in the contact zone. Precipitation was higher in regions occupied by tetraploid individuals, which present wider corolla tubes and thinner but taller stalks than diploids. Our findings highlight the potential role of polyploidy in the ecological adaptation of E. mediohispanicum to both abiotic factors and biotic interactions.

Characterizing and explaining spatio-temporal variation of water quality in a highly disturbed river by multi-statistical techniques

Assessing the spatio-temporal variations of surface water quality is important for water environment management. In this study, surface water samples are collected from 2008 to 2015 at 17 stations in the Ying River basin in China. The two pollutants i.e. chemical oxygen demand (COD) and ammonia nitrogen (NH3-N) are analyzed to characterize the river water quality. Cluster analysis and the seasonal Kendall test are used to detect the seasonal and inter-annual variations in the dataset, while the Moran's index is utilized to understand the spatial autocorrelation of the variables. The influence of natural factors such as hydrological regime, water temperature and etc., and anthropogenic activities with respect to land use and pollutant load are considered as driving factors to understand the water quality evolution. The results of cluster analysis present three groups according to the similarity in seasonal pattern of water quality. The trend analysis indicates an improvement in water quality during the dry seasons at most of the stations. Further, the spatial autocorrelation of water quality shows great difference between the dry and wet seasons due to sluices and dams regulation and local nonpoint source pollution. The seasonal variation in water quality is found associated with the climatic factors (hydrological and biochemical processes) and flow regulation. The analysis of land use indicates a good explanation for spatial distribution and seasonality of COD at the sub-catchment scale. Our results suggest that an integrated water quality measures including city sewage treatment, agricultural diffuse pollution control as well as joint scientific operations of river projects is needed for an effective water quality management in the Ying River basin.

Phylogeography of Rhipicephalus sanguineus sensu lato and its relationships with climatic factors

Brown dog ticks morphologically identifiable as Rhipicephalus sanguineus sensu lato, are distributed world-wide and their systematics is controversial. Results of genetic and reproductive compatibility studies of geographically distinct populations of R. sanguineus s.l. indicate that the R. sanguineus complex is paraphyletic. To further elucidate systematic relationships within R. sanguineus s.l. and geographic boundaries of its lineages, we conducted a phylogeographical study of 136 tick specimens from 23 countries. Voucher specimens were morphologically identified. A phylogenetic tree was constructed using concatenated partial mitochondrial 12S and 16S rDNA gene sequences and analyzed by the Neighbor-Joining method. A set of 19 bioclimatic variables within the WorldClim dataset were extracted and analyzed to assess correlations between distribution of R. sanguineus s.l. lineages and climatic variables. The following four branches are clearly recognized on the phylogenetic tree: R. sanguineus s.l.-tropical and temperate clades, R. leporis, and R. turanicus. DNA sequences of Rhipicephalus ticks from Israel differ from those of other groups. Strong association between geographical locations of major clades of R. sanguineus s.l. and temperature was identified. The tropical clade of R. sanguineus s.l. occupies areas with the annual mean temperature >20 °C, whereas the temperate clade is present in areas with the annual mean temperature <20 °C. Our results indicate that ticks in two closely related phylogenetic clades are adapted to different environmental conditions and support proposals for re-classification of R. sanguineus complex. Differences in R. sanguineus s.l. ecology and human/animal pathogens transmitted by different taxa of brown dog tick need to be studied.