Wet Months Research Articles

The effects of seasonal human mobility and Aedes aegypti habitat suitability on Zika virus epidemic severity in Colombia.

The Zika virus epidemic of 2015-16, which caused over 1 million confirmed or suspected human cases in the Caribbean and Latin America, was driven by a combination of movement of infected humans and availability of suitable habitat for mosquito species that are key disease vectors. Both human mobility and mosquito vector abundances vary seasonally, and the goal of our research was to analyze the interacting effects of disease vector densities and human movement across metapopulations on disease transmission intensity and the probability of super-spreader events. Our research uses the novel approach of combining geographical modeling of mosquito presence with network modeling of human mobility to offer a comprehensive simulation environment for Zika virus epidemics that considers a substantial number of spatial and temporal factors compared to the literature. Specifically, we tested the hypotheses that 1) regions with the highest probability of mosquito presence will have more super-spreader events during dry months, when mosquitoes are predicted to be more abundant, 2) regions reliant on tourism industries will have more super-spreader events during wet months, when they are more likely to contribute to network-level pathogen spread due to increased travel. We used the case study of Colombia, a country with a population of about 50 million people, with an annual calendar that can be partitioned into overlapping cycles of wet and dry seasons and peak tourism and off tourism seasons that drive distinct cyclical patterns of mosquito abundance and human movement. Our results show that whether the first infected human was introduced to the network during the wet versus dry season and during the tourism versus off tourism season profoundly affects the severity and trajectory of the epidemic. For example, Zika virus was first detected in Colombia in October of 2015. Had it originated in January, a dry season month with high rates of tourism, it likely could have infected up to 60% more individuals and up to 40% more super-spreader events may have occurred. In addition, popular tourism destinations such as Barranquilla and Cartagena have the highest risk of super-spreader events during the winter, whereas densely populated areas such as Medellín and Bogotá are at higher risk of sustained transmission during dry months in the summer. Our research demonstrates that public health planning and response to vector-borne disease outbreaks requires a thorough understanding of how vector and host patterns vary due to seasonality in environmental conditions and human mobility dynamics. This research also has strong implications for tourism policy and the potential response strategies in case of an emergent epidemic.

Spatial Distribution Pattern of Aromia bungii Within China and Its Potential Distribution Under Climate Change and Human Activity.

Aromia bungii is a pest that interferes with the health of forests and hinders the development of the fruit tree industry, and its spread is influenced by changes in abiotic factors and human activities. Therefore, exploring their spatial distribution patterns and potential distribution areas under such conditions is crucial for maintaining forest ecosystem security. This study analyzed the spatial differentiation characteristics of the geographic distribution pattern of A. bungii in China using Moran's I and the Getis-Ord General G index. Hot spot distribution areas were identified using Getis-Ord Gi*. An optimized MaxEnt model was used to predict the potential distribution areas of A. bungii within China under four shared economic pathways by combining multivariate environmental data: (1) prediction of natural environmental variables predicted under current climate models; (2) prediction of natural environmental variables + human activities under current climate models; and (3) prediction of natural environmental variables under the future climate models (2050s and 2070s). Meanwhile, MigClim was used to simulate the unoccupied suitable area in the presence of obstacles under future climate change. The results showed that human activities, minimum temperature of the coldest month, and precipitation of the wettest month had positive effects on the distribution of A. bungii. However, in the current period, human activities drastically reduced the survival area of A. bungii, and its suitable distribution area was mainly concentrated in the eastern and central regions of China. Under the influence of climate change in the future, the habitat of A. bungii will gradually increase. Additionally, the MigClim model indicates that the area unoccupied by A. bungii has been on a continuous increasing trend. This study provides a positive reference for the prevention and control of A. bungii and the maintenance of forest health and ecosystem security, and provides important theoretical guidance for researchers, policymakers, and governments.

The spatiotemporal and dependency analysis of selected meteorological parameters and normalized difference vegetation index with aerosol optical depth over east Africa

The unprecedented rise in atmospheric aerosols, coupled with their intricate interactions with the environment through a wide array of physical, chemical, and biological processes, has profoundly impacted global climate. Their presence in the atmosphere scatters and absorbs solar radiation, thus altering the amount of sunlight reaching the Earth's surface. These direct effects, along with the indirect effects of aerosols, have significantly altered atmospheric temperatures, land surface processes, global surface temperature, hydrological cycle, and ecosystems. Understanding the complex interplay between aerosols and climatic variables necessitates a multidisciplinary approach, such as dependency modeling. Addressing these challenges, the current study conducts a spatiotemporal correlational analysis of selected key meteorological parameters with aerosol optical depth over East Africa (EA) using multisensory data from Moderate-resolution Imaging Spectroradiometer (MODIS), Modern-Era Retrospective analysis for Research and Application (MERRA-2) model, and Tropical Rainfall Measurement Mission (TRMM). Employing a weighted least squares regression (WLS) model, the study quantifies trends in the time series of climatic variables and Normalized Difference Vegetation Index (NDVI), further utilizing a statistical dependency modeling technique for correlational analysis. The trend analysis reveals a significant decreasing trend in surface wind speed (SWS) in most months, with sporadic positive trends attributed to anthropogenic activities, notably biomass burning, observed in January. Spatial trend analysis of Precipitation Rate (PR) displays heterogeneity, with significant negative trends in January and March, and positive trends in February, April, November, and December. Negative trends during May to August are attributed to increased anthropogenic activities, while enhanced positive trends in May correlate with low aerosol optical depth (AOD) during this period. Surface air temperature (SAT) exhibits diverse variations across the region, with dry months recording higher averages and trends than wet months. The study notes heterogeneous correlations in NDVI over the study area, with positive and negative correlations observed in different regions. Specifically, positive correlations are noted along the coastal and Lake Victoria regions, attributed to improved PR enhancing vegetation cover in these areas.

Intraspecific Variability of Xylem Hydraulic Traits of Calligonum mongolicum Growing in the Desert of Northern Xinjiang, China.

Plant hydraulic traits are essential for understanding and predicting plant drought resistance. Investigations into the mechanisms of the xylem anatomical traits of desert shrubs in response to climate can help us to understand plant survival strategies in extreme environments. This study examined the xylem anatomical traits and related functional traits of the branches of seven Calligonum mongolicum populations along a precipitation gradient, to explore their adaptive responses to climatic factors. We found that (1) the vessel diameter (D), vessel diameter contributing to 95% of hydraulic conductivity (D95), hydraulic weighted vessel diameter (Dh), vessel density (VD), percentage of conductive area (CA), thickness-to-span ratio of vessels ((t/b)2), and theoretical hydraulic conductivity (Kth) varied significantly across sites, while the vessel group index (Vg), wood density (WD), and vulnerability index (VI) showed no significant differences. (2) Principal component analysis revealed that efficiency-related traits (Kth, Dh, D95) and safety-related traits (VI, VD, inter-wall thickness of the vessel (t)) were the primary factors driving trait variation. (3) Precipitation during the wettest month (PWM) had the strongest influence, positively correlating with (t/b)2 and negatively with D, D95, Dh, CA, and Kth. (4) Structural equation modeling confirmed PWM as the main driver of Kth, with indirect effects through CA. These findings indicate that C. mongolicum displays high plasticity in xylem traits, enabling adaptation to changing environments, and providing insight into the hydraulic strategies of desert shrubs under climate change.

Analyzing the distribution patterns and dynamic niche of Magnolia grandiflora L. in the United States and China in response to climate change.

Magnolia grandiflora L. (southern magnolia) is native to the southeastern coastal areas of the United States, from North Carolina to eastern Texas (USDA Cold Hardiness Zone 8). It is currently widely cultivated in Zones 5-10 in the U.S. and in southern Yangtze River regions in China. Limited studies have examined the effects of climate change and human activities on the geographical distribution and adaptability of M. grandiflora during its introduction to China. We selected 127 occurrence points in the U.S. and 87 occurrence points in China, along with 43 environmental variables, to predict suitable habitat areas for M. grandiflora using present climate data (1970-2000) and projected future climate data (2050-2070) based on a complete niche ensemble model (EM) using the Biomod2 package. We also predicted the niche change of M. grandiflora in both countries using the 'ecospat' package in R. The ensemble models demonstrated high reliability, with an AUC of 0.993 and TSS of 0.932. Solar radiation in July, human impact index, and precipitation of the wettest month were identified as the most critical variables influencing M. grandiflora distribution. The species shows a similar trend of distribution expansion under climate change scenarios in both countries, with predicted expansions towards the northwest and northeast, and contractions in southern regions. Our study emphasizes a practical framework for predicting suitable habitats and migration of Magnoliaceae species under climate change scenarios. These findings provide valuable insights. for species conservation, introduction, management strategies, and sustainable utilization of M. grandiflora.

Evaluation of gridded precipitation datasets in mountainous terrains of Northwestern Mexico

Study regionThe complex mountainous terrains of the Sierra Madre Occidental in northwestern Mexico. Study focusAcquiring high-resolution precipitation data in regions with limited conventional rain gauge coverage poses significant challenges. Gridded precipitation (GP) datasets, including gauge-based, satellite, and reanalysis products, provide a potential solution, but their reliability in areas with complex terrain and intricate precipitation patterns remains uncertain. This study comprehensively evaluates the performance of four GP datasets—AORC, CHIRPS, Daymet, and ERA5—in estimating precipitation. The evaluation was conducted at a daily, monthly, and seasonal timescale, further analyzing extreme precipitation, the influence of elevation, and spatial averaging across hydrologic basins, using as reference the NERN rain gauge data from 2002 to 2004. New hydrological insights for the regionResults indicate that Daymet and AORC are the most accurate GP datasets for daily and monthly timescales, respectively. All datasets improve in accuracy over longer timescales but face challenges during the wet summer monsoon months and extreme events, with Daymet performing relatively better. Terrain elevation had a minimal impact on overall dataset accuracy, though a slight improvement in precipitation detection was noted as elevation increased. This work provides valuable insights into the strengths and limitations of GP datasets in regions with complex terrain and orographically-forced convective precipitation, offering practical outcomes for climate and hydrologic studies in similar regions.

Prediction of Potential Suitability Areas for Ephedra sinica in the Five Northwestern Provinces of China Under Climate Change

Ephedra sinica (E. sinica) holds significant economic and medicinal importance and is predominantly found in arid areas. Due to the limitations of environmental variables, growth habits, and human activities, the production and suitability areas of E. sinica have significantly decreased, especially in the five northwestern provinces of China. In this study, 212 distribution points of E. sinica and 40 environmental variables were obtained to project the habitat suitability of E. sinica under different emission scenarios in the future. It identified precipitation in the wettest month, monthly mean of the diurnal temperature difference, and solar radiation intensity in April and July as the primary environmental factors affecting the suitability of E. sinica in the region. The areas of high, medium, and low suitability in the region cover 103,000 km2, 376,500 km2, and 486,800 km2. Under future scenarios, the suitability areas from 2021 to 2100 will decrease by 20%, with high suitability areas decreasing by 65% to 85% particularly. With comprehensive environmental variables, the suitability areas of E. sinica from 2021 to 2100 are projected, filling the gap in the projection of E. sinica suitability areas in the five northwestern provinces of China over long time period. The suitability areas show a significant decreasing trend. This research provides valuable insights into the suitability areas and crucial environmental factors, offering theoretical support for future protection and management efforts for E. sinica.

Assessment of soil carbon dioxide efflux from contrasting land uses in a semi-arid savannah ecosystem, northeastern Ghana (West Africa)

Soil respiration (SR) emits a vast amount of atmospheric carbon dioxide (CO2) and contributes largely to the global greenhouse gas budget. The study assessed the dynamics of SR rates in the Vea catchment in northeastern Ghana, a sparsely gauged semi-arid savannah ecosystem characterized by distinct patterns of soil CO2 efflux. Through field measurements using soil chambers, the study quantified soil CO2 efflux rates in different land use types (woodland, cropland and grazeland), and assessed the influence of soil moisture, temperature, and soil organic carbon stocks on SR variability. The highest soil CO2 fluxes (12.97 ± 0.89 Mg CO2C ha−1 yr−1) were recorded in woodland, followed by grazeland (9.10 ± 0.42 Mg CO2C ha−1 yr−1) with cropland having the lowest rate (5.61 ± 0.29 Mg CO2C ha−1 yr−1). We recorded mean annual soil CO2 flux of 9.23 ± 0.53 Mg CO2C ha−1 yr−1 across the land use types and also observed significant seasonal and spatial variations in SR rates. The highest SR rate (220 mg CO2C m−2h−1) was recorded in the wet months (Jul-Sept and Mar-May) and the lowest rate (30 mg CO2C m−2h−1) in the dry months (Nov-Jan). For the wet season, the mean weekly soil CO2 fluxes ranged between 140 and 160 mg CO2C m−2 hr−1 as opposed to 60–75 mg CO2C m−2 hr−1 for the dry season. Seasonal and spatial variations in SR rates were largely driven by land use type, soil moisture and the interaction of soil temperature and moisture. The results underscore the importance of understanding the emission patterns from various land uses in West African savanna ecosystems to harnessing their potential for climate change mitigation.

Addressing challenges associated with training sample sizes for effective calibration of NWP precipitation forecasts

Abstract Effective calibration of precipitation forecasts produced by numerical weather prediction (NWP) models faces challenges associated with the training sample size. Newly-operationalized NWP models may only accumulate a small number of forecasts and thus may limit robust parameter inference in forecast calibration. It is necessary to investigate how the performance of forecast calibration changes with the amount of training data, to determine an effective training sample size. In this study, we thoroughly investigate the impacts of training sample size on precipitation forecast calibration based on the Seasonally Coherent Calibration (SCC) model across Australia. Overall, the performance of the model tends to stabilize in most parts of Australia when raw forecasts of 10 months or longer are used for parameter inference. Whether the training dataset cover wet months substantially affects forecast calibration. The findings of this study are critical for understanding the impacts of training sample size on forecast calibration, and will provide implications for future forecast calibration and the generation of hindcasts.

Prediction and Analysis of the Global Suitable Habitat of the Oryctes rhinoceros (Linnaeus, 1758) (Coleoptera: Scarabaeidae) Based on the MaxEnt Model.

The Asiatic rhinoceros beetle, Oryctes rhinoceros (Linnaeus, 1758) (Coleoptera: Scarabaeidae), is a destructive invasive species that poses a serious threat to palms, oil palms, and other plants. Defining a suitable area for the distribution of O. rhinoceros is essential for the development of appropriate policies and preventive measures. In this work, the MaxEnt niche model and ArcGIS software were used to predict the potential geographic distribution of O. rhinoceros in the world based on occurrence data and related environmental variables and to speculate on the influence of environmental variables on the distribution of O. rhinoceros. The results showed that the suitable areas of O. rhinoceros beetle were mainly distributed in 30° N-30° S, and the highly suitable areas were concentrated in South Asia, East Asia, Southeast Asia, and northern Oceania. The key environmental variables that determine the distribution location of O. rhinoceros are Precipitation of Wettest Month (bio13), Temperature of July (tmin7), Minimum Temperature of November (tmin11), and Precipitation of September (prec9). The prediction results of the MaxEnt model can reflect the global distribution of O. rhinoceros. This study can provide a theoretical basis for the prevention and control of O. rhinoceros and the development of relevant quarantine measures.

Daytime land surface temperature and its limits as a proxy for surface air temperature in a subtropical, seasonally wet region

Land surface temperatures (LSTs) captured via satellite remote sensing are widely used as a proxy for the surface air temperatures (SATs) experienced outdoors, a key component of human heat exposure. However, LST’s accuracy in capturing SAT can vary through space and time across climate types and geographies and has been less explored in subtropical, seasonally wet regions (where summer precipitation exceeds 570 mm). Utilizing daytime (11 AM/12 PM local time, ET/EST) Landsat 8 remote sensing data, this study derived LST and evaluated its spatiotemporal patterns, as well as its relationship with SAT retrieved from local weather stations, using the case of Miami-Dade County, Florida, USA. Over 2013–2022, a surface urban heat island effect is distinctly present (mean SUHII = 3.43°C)—most intense during spring months rather than summer months (mean spring SUHII = 4.09°C). As such, LST peaks in May/June as opposed to July/August for many other parts of the northern hemisphere. In contrast, Miami-Dade SAT is greatest in August, and the strength of its relationship with LST varies by season. LST and SAT are most correlated in winter (R = 0.91) and spring (R = 0.59) months and least correlated during the wetter fall (R = 0.40) months. The relationship between LST and SAT during the summer is statistically insignificant. In this subtropical region with a seasonally wet climate, LST effectively reflects the spatial heterogeneity of the urban thermal landscape, consistent with the literature across urban regions globally. However, because the strength of the LST-SAT relationship considerably weakens during wet season months, LST data therefore have limits as a proxy for the heat exposure people experience outdoors annually, as they may not accurately represent the magnitude of localized potential heat risks. These findings underscore important considerations in using LST data to identify urban heat exposures and inform potential adaptive responses in seasonally wet, subtropical-to-tropical regions.

Spatial Variation of Pollutant (NO2, SO2 & CO) and Its Impact Factors in Jakarta: An Application of Sentinel-5P Products

Abstract This study investigates the impact of transportation, manufacturing, and energy sectors on air quality in Jakarta using Geographically Weighted Regression (GWR) and remote sensing data. Utilizing datasets from Sentinel-5P for NO2, SO2, and CO concentrations, and predictor variables including road density, industrial activity, and energy production, the research aims to quantify the spatial heterogeneity of air pollution. The findings reveal significant spatial variations in pollutant concentrations across Jakarta and its neighbouring provinces, Banten and West Java, during the dry and wet months of 2023. High NO2 levels were primarily linked to dense traffic in central Jakarta, while elevated SO2 and CO concentrations were associated with industrial and energy activities in the peripheral regions. Seasonal analysis indicated that meteorological factors play a crucial role in pollutant dispersion. The integration of remote sensing data with GWR provided a robust framework for environmental monitoring, addressing gaps in traditional air quality observation methods. This research contributes to the broader understanding of urban air pollution dynamics and offers a replicable model for other regions facing similar challenges.

First full-scale application of barium carbonate as an effective dispersed alkaline substrate for sulfate removal from acid mine drainage

A full-scale passive treatment plant using barium carbonate (BaCO3), the mineral witherite, as a Dispersed Alkaline Substrate (DAS) for strongly contaminated acid mine drainage was implemented for the first time globally at Mina Concepción, located in the Iberian Pyrite Belt (SW Spain). The plant was monitored over a 105-day period, covering the wettest months of the hydrological year and with a mean flow around 4 L s−1. The AMD treated in the plant exhibits a high strength, with an average sulfate concentration around 1500 mg L−1 and net acidity close to 1000 mg L−1 as CaCO₃ equivalent. According to the above, the loading rate for SO4 and Fe was around 400 and 80 kg day−1, respectively. The treatment process produced an alkaline effluent with low metal content. Nearly complete removal of most metal(loid)s was achieved, with significant sulfate decrease to below 500 mg L−1 in the alkaline outflow of the barium carbonate tank. Barium carbonate demonstrated superior performance compared to magnesia, particularly in enhancing alkalinity and lowering net acidity and concentrations of sulfate and manganese. The high efficiency attained by the plant after the barium carbonate treatment is evidenced by compliance with environmental water quality standards for most contaminants.

The influence of climate change on the potential distribution of Ageratum conyzoides in China.

Ageratum conyzoides L., an invasive plant originating from South America, is characterized by rapid growth and strong ecological adaptability, posing a threat to China's ecosystems, agricultural industry, and biodiversity. In this study, we optimized the MaxEnt model using the ENMeval package and constructed an ensemble model using the Biomod2 package based on global geospatial distribution data of A. conyzoides and considering climate, soil, and topography factors. We simulated the potential suitable distribution of A. conyzoides in China at present and in the future (2041-2060, 2061-2080). Through multivariate environment similarity surface and most dissimilar variable analysis, we identified the main environmental variables influencing the distribution of A. conyzoides. Additionally, niche analysis elucidated temporal and spatial variations in A. conyzoides' climate niche. Our results demonstrate that the ensemble model, constructed from the top seven single models, outperforms the individual models in predicting the suitable habitat of A. conyzoides. The ensemble model achieved the true skill statistic (TSS) of 0.833 and the area under the subject curve (AUC) of 0.971, indicative of outstanding predictive performance. Presently, the suitable habitat of A. conyzoides in China primarily exists in the region between 18° and 28° N, covering approximately 1.47 million km2. The temperature annual range, precipitation of the wettest month, and mean temperature of the coldest quarter were identified as the primary environmental variables influencing its distribution, while soil and elevation variables had minor roles. Under future climate conditions, the suitable habitat of A. conyzoides is expected to expand northeastward, with the centroid of its habitat shifting northward as the climate warms. The migration speed of A. conyzoides is projected to increase with the degree of warming. Furthermore, the climate niche of A. conyzoides will undergo certain changes and may face both niche expansion and a decrease in niche overlap under different climate conditions.

Past, present, and future potential distributions of the African multipurpose tree Detarium senegalense (Fabaceae)

Background and aims – Climate change induces increasing temperatures and drought, with possible profound shifts in species’ presence and distribution. Ecological niche models are widely used to assess plant species responses to climate change. However, such data are scarce for West Africa, particularly for vulnerable multipurpose species. This study focuses on modelling the ecological niche and the conservation status of the multipurpose tree Detarium senegalense to improve insights into its habitat suitability in West Africa under past, present, and future climatic conditions. This will provide an essential basis for setting up global management plans through efficient conservation and ecological restoration policies. Material and methods – The potential distribution of D. senegalense under past, current, and future climate scenarios were assessed using four algorithms including generalized additive models (GAM), generalized linear models (GLM), random forest (RF), and Maximum Entropy (MaxEnt). We also assessed the shift direction of suitable habitats and the conservation status of the species based on IUCN criteria. Overall, 220 occurrences were combined with a set of five bioclimatic variables to run the models. Key results – Models performed well with good values of AUC (0.92) and TSS (0.73). Isothermality (41.10%) and Precipitation of Wettest Month (21.50%) contributed most to the distribution of the species. The distribution of D. senegalense was relatively constant from the past to the present but could decrease in the next decades. In the future, only 17.70% and 13.98% of the areas were predicted to be suitable under respectively ssp245 and ssp585. In protected areas, the suitable areas under ssp245 were estimated at 21.01% with a decrease of 2.50% and 14.60% with a decrease of 8.61% under ssp585 by 2050. The direction of the distribution shifted to the south-east under future climate scenarios. The conservation status assessment of the species is Least Concern (LC). Conclusion – This study improves our understanding of the past, present-day, and future distribution of the species and provides support to better manage the conservation of D. senegalense in West Africa.

Reconstructing the biological invasion of noxious invasive weed Parthenium hysterophorus and invasion risk assessment in China.

Invasive alien plants (IAPs) present a severe threat to native ecosystems and biodiversity. Comprehending the potential distribution patterns of these plant invaders and their responses to climate change is essential. Parthenium hysterophorus, native to the Americas, has become an aggressively invasive species since its introduction to China in the 1930s. This study aims to collect and reconstruct the historical occurrence and invasion of P. hysterophorus. Using the optimal MaxEnt model, the potential geographical distributions of P. hysterophorus were predicted based on screened species occurrences and environmental variables under the current and three future scenarios in the 2030s, 2050s, and 2070s (i.e., SSP1-2.6, SSP2-4.5, and SSP5-8.5), and the invasion risk of P. hysterophorus in Chinese cities, croplands, forests, and grasslands was assessed. The results show that: (1) The species initially invaded highly suitable areas and further spread to regions with non-analogous climate conditions. (2) Under the current climatic conditions, the overall potential distribution of P. hysterophorus is characterized by more in the southeast and less in the northwest. Climate variables, including mean annual temperature (bio1), precipitation in the wettest month (bio13), isothermality (bio3), and temperature seasonality (bio4), are the primary factors influencing its distribution. (3) The potential distribution of P. hysterophorus will expand further under future climate scenarios, particularly toward higher latitudes. (4) Forests and crop lands are the areas with the most serious potential invasion risk of P. hysterophorus. Therefore, we suggest that the government should strengthen the monitoring and management of P. hysterophorus to prevent its spread and protect agro-ecosystems and human habitats. Depending on the potential risk areas, measures such as quarantine, removal, and publicity should be taken to mitigate the threat of P. hysterophorus invasion and to raise awareness of P. hysterophorus invasion prevention.

Assessing changes in intra- and inter-annual precipitation variability using advanced statistical inference techniques

ABSTRACT Aiming to find statistically significant changes on the usual monthly weather conditions in Alentejo, Portugal, in the past 40 years, time series of precipitation from a grid of locations in Alentejo were studied using an unconventional approach. The time series were divided into 4 decades and disposed in contingency tables with two factors, Year and Month. Then, log-linear models were fitted to those tables. The model deviances for Year and Month represent the weight of each factor in explaning precipitation varibility and Year:Month deviance is an estimate of the lack of independence between both factors, representing changes in intra-annual precipitation variability. The set of all the models deviances, per decade and location, were analysed using ANOVA techniques, first to compare the four decades, then to perform a crossed comparison between decades and between four pre-defined zones. Results indicate significant differences between the oldest and recent decades in terms of intra-annual precipitation variability, which could be interpreted as a trend towards softening the differences in precipitation between the wet months of the year. Furthermore, two homogeneous sub-regions could be defined in Alentejo, a large interior region and a smaller one close to the Atlantic Ocean.

Infection length and host environment influence on Plasmodium falciparum dry season reservoir

Persistence of malaria parasites in asymptomatic hosts is crucial in areas of seasonally-interrupted transmission, where P. falciparum bridges wet seasons months apart. During the dry season, infected erythrocytes exhibit extended circulation with reduced cytoadherence, increasing the risk of splenic clearance of infected cells and hindering parasitaemia increase. However, what determines parasite persistence for long periods of time remains unknown. Here, we investigated whether seasonality affects plasma composition so that P. falciparum can detect and adjust to changing serological cues; or if alternatively, parasite infection length dictates clinical presentation and persistency. Data from Malian children exposed to alternating ~6-month wet and dry seasons show that plasma composition is unrelated to time of year in non-infected children, and that carrying P. falciparum only minimally affects plasma constitution in asymptomatic hosts. Parasites persisting in the blood of asymptomatic children from the dry into the ensuing wet season rarely if ever appeared to cause malaria in their hosts as seasons changed. In vitro culture in the presence of plasma collected in the dry or the wet seasons did not affect parasite development, replication or host-cell remodelling. The absence of a parasite-encoded sensing mechanism was further supported by the observation of similar features in P. falciparum persisting asymptomatically in the dry season and parasites in age- and sex-matched asymptomatic children in the wet season. Conversely, we show that P. falciparum clones transmitted early in the wet season had lower chance of surviving until the end of the following dry season, contrasting with a higher likelihood of survival of clones transmitted towards the end of the wet season, allowing for the re-initiation of transmission. We propose that the decreased virulence observed in persisting parasites during the dry season is not due to the parasites sensing ability, nor is it linked to a decreased capacity for parasite replication but rather a consequence decreased cytoadhesion associated with infection length.

Enhancing Surface Water Monitoring through Multi-Satellite Data-Fusion of Landsat-8/9, Sentinel-2, and Sentinel-1 SAR

Long revisit intervals and cloud susceptibility have restricted the applicability of earth observation satellites in surface water studies. Integrating multiple satellites offers potential for more frequent observations, yet combining different satellite sources, particularly optical and SAR satellites, presents complexities. This research explores the data-fusion potential and limitations of Landsat-8/9 Operational Land Imager (OLI), Sentinel-2 Multispectral Instrument (MSI), and Sentinel-1 Synthetic Aperture (SAR) satellites to enhance surface water monitoring. By focusing on segmented surface water images, we demonstrate that combining optical and SAR data is generally effective and straightforward using a simple statistical thresholding algorithm. Kappa coefficients(κ) ranging from 0.80 to 0.95 indicate very strong harmony for integration across reservoirs, lakes, and river environments. In vegetative environments, integration with S1SAR shows weak harmony, with κ values ranging from 0.27 to 0.45, indicating the need for further studies. Global revisit interval maps reveal significant improvement in median revisit intervals from 15.87 to 22.81 days using L8/9 alone, to 4.51 to 7.77 days after incorporating S2, and further to 3.48 to 4.62 days after adding S1SAR. Even during wet season months, multi-satellite fusion maintained the median revisit intervals to less than a week. Maximizing all available open-source earth observation satellites is integral for advancing studies requiring more frequent surface water observations, such as flood, inundation, and hydrological modeling.

How Local Pollution and Transboundary Air Pollution Impact Air Quality in Jakarta?

Jakarta’s rapid population growth and industrialization have severely impacted air quality, with limited monitoring stations and increasingly complex pollutant sources influenced by meteorological factors. Source apportionment studies are critical for addressing urban air quality issues because knowing the source of pollution is an important step in overcoming urban air quality problems. This study uses the Geographically Weighted Regression (GWR) and Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) models to analyze the distribution and sources of NO2, SO2, and CO in Jakarta and surrounding areas. Data includes spatial information from road networks, industries, and the energy sector during the wet (December) and dry (June) months of 2023. GWR results show that network density significantly contributes to pollutant concentrations, particularly during the dry month, while industrial and energy sectors display seasonal variations. The HYSPLIT model indicates that in December, air masses carrying pollutants mainly originate from the northwest and north, and in June, from the southeast and southwest. These patterns underscore the regional influence of transboundary air pollution from industrial and energy plants. The combined GWR and HYSPLIT findings provide a comprehensive understanding of air pollution dynamics and the critical role of meteorological factors in pollutant distribution.