Climatological Factors Research Articles

Temperature and relative humidity in a commercial bread wheat field, recorded by an automated meteorological station and a digital sensor in the Yaqui Valley, Sonora, Mexico

Meteorological parameters determine most agronomic events, with temperature being the climatological factor most closely related to the annual productivity of a crop. The objective of this work was to monitor the temperature and relative humidity at different growth stages of a commercial bread wheat field, during the crop season 2017-2018. The work was carried out with data obtained from December 12, 2017 (sowing date) to April 18, 2018, from a mobile OMEGA OM-EL-USB-2 sensor within the field, sown with the bread wheat cultivar Borlaug 100, located in Block 1020, in the Yaqui Valley, Sonora, Mexico, and from the Block 910-CIANO meteorological station, located 8 km away, which is the closest to the field. Calculations were made for the average temperature, relative humidity and cold units (CU). In addition, the data was filtered by day and night time, considering daytime data from 7:00 to 18:00 and nighttime data from 19:00 to 06:00. The CU recorded by the sensor was higher with 384 than the meteorological station, which accumulated a total of 321. The temperature during the day was on average 2.9 °C higher than that recorded by the meteorological station, and 1.6 °C lower at night. The relative humidity recorded by the sensor was 5 % higher than that recorded by the meteorological station.

Influence of future climate scenarios using CMIP 5 data on malaria transmission in India

BackgroundVector-borne diseases, such as malaria, pose a significant global threat, and climatological factors greatly influence their intensity. Tropical countries, like India, are particularly vulnerable to such diseases, making accurate estimation of malaria risk crucial.MethodsThis study utilized the well-known Vector-borne Disease Community Model, VECTRI, developed by the International Centre for Theoretical Physics in Trieste. The model was implemented to estimate malaria’s Entomological Inoculation Rate (EIR). Future climatic prediction datasets, including CMIP 5 and population data sets, were used as inputs for the analysis. Three RCP scenarios are considered (Representative Concentration Pathways are climate change scenarios that project radiative forcing to 2100 due to future greenhouse gas concentrations). The projections covered the period from 1 Jan, 2020, to 31 Dec, 2029.ResultsThe estimated mean EIR for the years 2020–2029 ranged, and a significant decline in malaria risk was observed with all RCP 2.6, 4.5, and 8.5 scenarios. Each year 0.3 to 2.6 [min–max] EIR/person/day decline is observed with a strong decline in man rainfall ranging from 5 to 17 [min–max] mm/year and associated high temperatures ranging from 0.03 to 0.06 [min–max] °C/year. During the post-monsoon period, August to November were identified as highly prone to malaria transmission. Spatial analysis revealed that the east coast of India faced a higher vulnerability to malaria risk, which kept increasing through RCP scenarios. Thus, it is essential to exercise caution, especially in areas with heavy rainfall.ConclusionThis research provides valuable insights for policy-makers, highlighting the need to implement future strategies to mitigate malaria risk effectively. By utilizing these findings, appropriate measures can be taken to combat the threat posed by malaria and protect public health.

Analysis of radiative heat flux using ASTER thermal images: Climatological and volcanological factors on Java Island, Indonesia

This study focuses on analysing natural Radiative Heat Flux (RHF) anomalies to map out the heat distribution across the Java Island. Leveraging remote sensing techniques, we calculated natural RHF anomalies using Land Surface Temperature (LST) and Land Surface Emissivity (LSE) data obtained from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) imagery. A key aspect of our approach was distinguishing between natural and anthropogenic heat sources by cross-referencing the LST Map with the Land Use Land Cover (LULC) map of Java Island. The study interprets natural RHF anomalies by examining regional trends in non-volcanic areas and local trends within volcanic regions, considering climatological and volcanological factors. Relation with climatological factors involves assessing soil moisture parameters from Soil Moisture Active Passive (SMAP) data, precipitation from monthly Global Precipitation Measurement (GPM) data, and classifications according to the Köppen-Geiger climate schema. Our regional analysis reveals high natural RHF anomalies in the northern regions of West Java, parts of Central Java, and most of East Java, attributed to low soil moisture and low precipitation in savanna and monsoon climates. On a more localised scale, RHF values are significantly high in volcanic areas, particularly around Central and East Java's volcanoes, such as Mt. Merapi, Mt. Slamet, Mt. Semeru, the Sidoarjo Mud Volcano, and Mt. Ijen. The Natural RHF anomalies at volcanoes in West Java were identified as not being high except at Mt Patuha. These areas exhibit average natural RHF anomalies ranging between 32.22 W/m2 and 115.13 W/m2, indicating strong and intense volcanic activity. The insights obtained from these findings explain the overall thermal characteristics of Java Island and highlight the presence of subsurface thermal zones associated with volcanic activity and geothermal potential.

Enhancing Coffee Agroforestry Systems Suitability Using Geospatial Analysis and Sentinel Satellite Data in Gedeo Zone, Ethiopia.

The Gedeo zone agroforestry systems are the main source of Ethiopia's coffee beans. However, land-use and suitability analyses are not well documented due to complex topography, heterogeneous agroforestry, and lack of information. This research aimed to map the coffee coverage and identify land suitability for coffee plantations using remote sensing, Geographic Information Systems (GIS), and the Analytical Hierarchy Process (AHP) in the Gedeo zone, Southern Ethiopia. Remote sensing classifiers often confuse agroforestry and plantations like coffee cover with forest cover because of their similar spectral signatures. Mapping shaded coffee in Gedeo agroforestry using optical or multispectral remote sensing is challenging. To address this, the study identified and mapped coffee coverage from Sentinel-1 data with a decibel (dB) value matched to actual coffee coverage. The actual field data were overlaid on Sentinel-1, which was used to extract the raster value. Pre-processing, classification, standardization, and reclassification of thematic layers were performed to find potential areas for coffee plantation. Hierarchy levels of the main criteria were formed based on climatological, edaphological, physiographic, and socioeconomic factors. These criteria were divided into 14 sub-criteria, reclassified based on their impact on coffee growing, with their relative weights derived using AHP. From the total study area of 1356.2 km2, the mapped coffee coverage is 583 km2. The outcome of the final computed factor weight indicated that average annual temperature and mean annual rainfall are the primary factors, followed by annual mean maximum temperature, elevation, annual mean minimum temperature, soil pH, Land Use/Land Cover (LULC), soil texture, Cation Exchange Capacity (CEC), slope, Soil Organic Matter (SOM), aspect, distance to roads, and distance to water, respectively. The identified coffee plantation potential land suitability reveals unsuitable (413 km2), sub-suitable (596.1 km2), and suitable (347.1 km2) areas. This study provides comprehensive spatial details for Ethiopian cultivators, government officials, and agricultural extension specialists to select optimal coffee farming locations, enhancing food security and economic prosperity.

Assessment of clear-sky irradiance from 6S affected by local climatology of India

Accurate estimation of solar power generation is crucial for the effective planning and operation of solar energy sector. Solar potential can be estimated at a location from long-term historical irradiance data processed from atmospheric reanalysis datasets. The current study employs a clear-sky radiative transfer model (6S) to simulate the net surface irradiance at four distinct locations in India, comparing our model's output with mean monthly ground-based measurements of surface irradiance. Our findings reveal that the 6S model marginally underestimates solar irradiance, with potential deviations varying depending on the accuracy of input data. This research evaluates the influence of particulate matter concentration and relative humidity on the scattering and absorption of solar radiation. We also state the variability in surface irradiance across the country with the rainfall trends that enhance assessment accuracy. The study reports an annual deviation of 10–15 % in surface irradiance across the country, where rural settlements show lower deviations in simulations. The radiative transfer calculations mentioned in the study are simplistic yet beneficial for a priori evaluation of solar potential. Transmittance estimated from clear-sky models is further implemented in all-sky (with clouds), and thus, model accuracy is an important parameter. Additionally, we explore the trends in aerosol concentrations and the impact of local climatological factors on surface irradiance, providing insights critical for optimizing solar energy utilization.

The Impact of Climatological Factors on the Multifaceted and Multisystemic Deficiencies of Building Anatomy

The Impact of Climatological Factors on the Multifaceted and Multisystemic Deficiencies of Building Anatomy

Directional connectedness between the electricity prices and natural gas prices: evidence from Alberta’s electricity market

Purpose This study aims to assess volatility spillovers and directional connectedness between electricity (EPs) and natural gas prices (GPs) in the Canadian electricity market, based on a hydrothermal power generation market strongly dependent on exogenous variables such as fossil fuel prices and climatology factors. Design/methodology/approach The methodology is divided into two stages. First, a quantile vector autoregression model is used to evaluate the direction and magnitude of the influence between natural gas and electricity prices through different quantiles of their distributions. Second, a cross-quantilogram is estimated to measure the directional predictability between these prices. The data set consists of daily electricity and natural gas prices between January 2015 and December 2023. Findings The main finding shows that electricity prices are pure shock receivers of volatility from natural gas prices for the different quantiles. In this way, natural gas price fluctuations explain 0.20%, 0.98% and 22.72% of electricity price volatility for the 10th, 50th and 90th quantiles, respectively. On the other hand, a significant and positive correlation is observed in the high quantiles of the electricity prices for any natural gas price value. Originality/value The study described the risk to the electricity market caused by nonrenewable source price fluctuations and provided evidence for designing regulatory policies to reduce its exposure in Alberta, Canada. It also allows us to understand the importance of natural gas in the energy transition process and define it as the fundamental determinant of the electricity market dynamic.

Seasonality Prediction of Meningitis Using Artificial Neural Network (ANN)

Communities are concerned about controlling, preventing, and handling infectious diseases due to recent epidemic outbreaks. Meningitis, an inflammation of the membranes surrounding the brain and spinal cord, is a significant risk in Nigeria. It can cause death within hours of infection, with an average case fatality rate of 10%. To prevent meningitis outbreaks, this paper focuses on using an Artificial Neural Network (ANN) to predict outbreaks based on climatological factors. Previous research has shown that climate plays a major role in these outbreaks. The study found that the Levenberg-Marquaralt ANN algorithm was the best model, with the lowest prediction error and fewer iterations. High temperature and low humidity were identified as major triggers for meningitis outbreaks. It is crucial to address these factors to prevent future outbreaks and protect communities.

A study on spatio-temporal trend of rubber leaf fall phenomenon using planetscope multi-index vegetation imagery in relations to climatological conditions

Background: Rubber plants are one of the most important plantation commodities in Indonesia. However, rubber production has declined due to leaf fall disease caused by the pathogen Pestalotiopsis sp. This study aims to analyze the spatial and temporal distribution of rubber plant leaf fall disease using multi-vegetation indices from PlanetScope imagery, as well as to analyze the influence of climatological conditions on the disease. Methods: The research was conducted at the Sembawa Rubber Research Center Garden, South Sumatra, using PlanetScope imagery data and climatological data in 2017 (before leaf fall) and 2023 (after leaf fall). Finding: Spatially, the 2023 leaf fall occurred in almost the entire garden area with poor to moderate levels. Blocks 2013D, 2012F, and 2009F experienced the most severe levels, with a total defoliated area reaching 396.76 ha. Analysis of monthly variations in vegetation index values revealed a decrease in values during leaf fall due to Pestalotiopsis sp., specifically in February, May, and September 2023. Statistical test results showed significant differences in vegetation index values between 2017 and 2023. Furthermore, based on Spearman's correlation analysis, there was a positive correlation between vegetation index values and humidity, but no significant correlation with rainfall and temperature. Conclusion: This research provides insights into mapping and monitoring rubber leaf fall disease using remote sensing data and climatological factors, which can be used for more effective rubber plantation management. However, the study has some limitations: monthly Planet data for 2017 is not fully available, several Planet image scenes from 2017 still have more than 50% cloud cover, and there may be biases as plants falling into the low health class are included in the high range of vegetation index values. Novelty/Originality of this Study: The study employs multi-vegetation indices derived from high-resolution PlanetScope imagery to detect and assess rubber leaf fall disease (LFD) within a plantation environment. By integrating spatial and temporal analyses with climatological data, the research provides a precise and comprehensive method for monitoring LFD and understanding its environmental determinants, thereby enhancing traditional rubber plantation management practices.

The effect of seasonal variation, flow conditions and erosion forces on suspended matter fluxes from boreal gypsum-treated agricultural fields

Like many other bodies of water, the Baltic Sea is facing a severe eutrophication problem, the excessive nutrient load currently originating primarily from agriculture. Gypsum treatment has shown promising results in reducing phosphorus and suspended sediment loads, especially in fine-textured soils, but its efficacy has not been investigated on catchment scale to any wide extent. This study examines the functionality of the gypsum treatment in an agriculture-dominated boreal catchment in the Archipelago Sea basin. The relation between turbidity and hydro-climatological factors was analysed during different years and seasons and the effect of stream erosion during different flow stages was investigated by applying 2D hydraulic modeling. A special focus was given to deviating winter conditions and freeze-thaw cycles.The results highlight the dependency of stream water turbidity on the discharge and the potential negative impact of the climate change-altered winter conditions of the hemiboreal climate zone on gypsum treatment effectiveness. The turbidity increased even more during high winter discharges, compared to other seasons suggesting that the more frequent freeze-thaw cycles and precipitation coming as rain during the unvegetated period increase erosion challenging the gypsum efficacy. Modelling results revealed that the increased erosional power during very high discharges caused settled material resuspension or stream bed or bank erosion leading to a cumulative increase in turbidity at downstream, an issue needing further research. Demonstrating the real and long-term effects of nutrient reduction measures requires catchment scale investigations over years, considering climatological factors. Based on this study, local and short-term investigations can potentially lead to over-simplified interpretations.

Assessing Machine Learning and Statistical Methods for Rock Glacier‐Based Permafrost Distribution in Northern Kargil Region

ABSTRACTEstimating permafrost distribution in high‐mountain areas is challenging. In these situations, rock glaciers, provide valuable insights into permafrost distribution and are often used as proxies for identifying permafrost occurrence. Integrating various climatological and topographical conditioning factors with rock glaciers enables inferring the distribution of permafrost in these environments. This study utilized three machine learning models such as random forest (RF), support vector (SVM), and artificial neural network (ANN), and one statistical model, namely, the frequency ratio (FR), to assess the permafrost probability over the northern Kargil region of Indian Himalayas. Among 198 rock glaciers identified through high‐resolution images from Google Earth, 70% are used as training dataset, rest 30% as testing dataset. The study considered eight factors: slope, aspect, elevation, curvature, mean annual land surface temperature (MA‐LST), mean annual normalized difference snow index (MA‐NDSI), mean annual normalized difference water index (MA‐NDWI), and lithology for mapping. Furthermore, the SHapley Additive exPlanations (SHAP) test assessed the variable importance for model performance. The results revealed that the RF model performs best for permafrost probability mapping, followed by the SVM, FR, and ANN models. The study also found that 11% of the total geographic area has a high and very high probability of permafrost occurrence.

The Impact of Climatological Factors on the Incidence of Cutaneous Leishmaniasis (CL) in Colombian Municipalities from 2017 to 2019.

Cutaneous leishmaniasis (CL) is a zoonotic disease caused by protozoa of the Leishmania genus, transmitted by vectors from the Phlebotominae subfamily. The interaction between the vector, reservoir, and parasite is susceptible to climate change. This study explores how temperature and rainfall influenced the incidence of CL in 15 Colombian municipalities between 2017 and 2019. Epidemiological data were obtained from Colombia's Instituto Nacional de Salud, while climatological data came from the Instituto de Hidrología, Meteorología y Estudios Ambientales. Using Spearman's rank correlation coefficient, we examined the relationships between monthly climatic variables and the cumulative incidence of CL, considering various lag times. The data were further analyzed using Locally Weighted Scatterplot Smoothing (LOWESS). Our findings reveal both significant positive and negative correlations, depending on locality and climate variables. LOWESS analysis indicates that while rainfall-related incidence remains stable, temperature impacts incidence in a parabolic trend. This study underscores the significant yet complex influence of climatic factors on CL incidence. The insights gained could aid public health efforts by improving predictive models and crafting targeted interventions to mitigate the disease's impact, particularly in regions vulnerable to climate variability.

Resilience-oriented planning for active distribution systems: A probabilistic approach considering regional weather profiles

Recent experiences with increasing climatic variability highlighted the importance of enhanced power distribution system resilience. Conventional methods of power distribution system planning usually focus on persistentinvestments associated with predicted faulty events, aiming to enhance the overall system reliability. Improving distribution system resilience in response to extreme weather events involves reduction of impact of the events having varying probability of occurrence. The probability of an extreme event varies significantly by location due to a combination of geographical, climatological, and meteorological factors. Thus, instead of requiring persistent investments, the solutions for resilience-oriented system upgrades should be guided by the potential effects and the probability of occurrence of extreme weather events in a specific area. To achieve this objective, the present paper proposes a two-stage stochastic mixed-integer linear programming (MILP) model based on regional weather profiles for the long-term resilience planning of distribution systems against extreme weather events. In the planning stage, investment decisions are made regarding overhead distribution line hardening and the distributed generation (DG) installation. During restoration process in the operational stage, dynamic microgrid formation is carried out for advanced distribution grid operations to minimize the cost corresponding to loss of load. The inclusion of regional weather profiles into planning objectives provide added flexibility to the network operators for analysing the trade-off between investment cost and load loss cost in resilient distribution system planning strategies.

Atmospheric corrosion of carbon steel: Results of one-year exposure in an andean tropical atmosphere in Colombia

In this study was examined the response of carbon steel to atmospheric corrosion after one-year exposure in Valle de Aburrá, a subregion located in northwestern Colombia. The study involved the assessment of material mass loss and corrosion rate, the characterization of atmospheric aggressiveness, and the analysis of the morphology and composition of corrosion products in five different sites. Climatological and meteorological factors were assessed by testing for chloride content, sulfur dioxide levels, and time of wetness (TOW). The analysis of corrosion products was conducted using scanning electron microscopy (SEM), X-ray diffraction (XRD), and Raman spectroscopy. Based on corrosion rates, two sites exhibited a more aggressive environment, with a corrosivity category of C3, while the remaining sites were categorized as C2. The study confirmed the presence of lepidocrocite and goethite phases on the surface of carbon steel at all test sites. Data analysis revealed that both the TOW and the industrial activity significantly influence the corrosion of this metal.

Nexus between environmental degradation and climate change during the times of global conflict: Evidence from CS-ARDL model

There has been a substantial amount of research done on air pollution, both in terms of its history and its state at the present time. On the other hand, the ever–changing influence of climatic variables such as sun hour, air pressure, and wind speed has gotten just a small portion of the attention. This divide becomes even more apparent when one considers the ongoing conflicts that are taking place between Russia and Ukraine as well as the current state of the environment's response to climate change. In order to bridge this research gap, the goal of this study is to explore the ways in which climatological factors impact air quality, especially levels of PM2.5 and ozone, in a range of cities situated in India, during 2020–2021. Studies conducted over brief and extended periods of time have made it abundantly evident that sun hour, wind speed, and humidity have an impact on ozone levels. The study also sheds light on prospective policies that may be implemented in the future, such as those relating to carbon emission and particulate pollution such as PM2.5, in order to successfully battle the negative impacts that these variables have on the environment.

Modeling the Role of Weather and Pilgrimage Variables on Dengue Fever Incidence in Saudi Arabia.

The first case of dengue fever (DF) in Saudi Arabia appeared in 1993 but by 2022, DF incidence was 11 per 100,000 people. Climatologic and population factors, such as the annual Hajj, likely contribute to DF's epidemiology in Saudi Arabia. In this study, we assess the impact of these variables on the DF burden of disease in Saudi Arabia and we attempt to create robust DF predictive models. Using 10 years of DF, weather, and pilgrimage data, we conducted a bivariate analysis investigating the role of weather and pilgrimage variables on DF incidence. We also compared the abilities of three different predictive models. Amongst weather variables, temperature and humidity had the strongest associations with DF incidence, while rainfall showed little to no significant relationship. Pilgrimage variables did not have strong associations with DF incidence. The random forest model had the highest predictive ability (R2 = 0.62) when previous DF data were withheld, and the ARIMA model was the best (R2 = 0.78) when previous DF data were incorporated. We found that a nonlinear machine-learning model incorporating temperature and humidity variables had the best prediction accuracy for DF, regardless of the availability of previous DF data. This finding can inform DF early warning systems and preparedness in Saudi Arabia.

Contribution of Climatic Factors and Human Activities to Vegetation Changes in Arid Grassland

Clarifying the changing trend in vegetation and its affecting variables is extremely valuable for natural resource management. Vegetation changes in the Yinshanbeilu grassland region, which is situated in the centre of Inner Mongolia in northern China and is part of the arid steppe region, are extremely sensitive to climatic factors. In this study, we investigated the changes in vegetation in the Yinshanbeilu grassland zone from the year 2000 to 2020 using the Normalized Difference Vegetation Index (NDVI) data. The contribution of climatic conditions and human activities to the annual and growing season vegetation changes was quantified. The findings revealed that vegetation cover in the Yinshanbeilu grassland zone increased at a rate of 0.00267/a between 2000 and 2020. Throughout the year and during the growing season, precipitation had a greater influence on the growth of vegetation than other climatological factors. In most places, there was a significant positive correlation between the NDVI and precipitation, which negatively correlated with other climatic factors. The average rates at which precipitation, temperature, cumulative sunshine hours, and potential evapotranspiration contributed to changes in NDVI were 0.00173/a, −0.00027/a, 0.00006/a, and 0.00074/a, respectively, for the entire year, and 0.00180/a, −0.00001/a, 0.00021/a, and 0.00059/a for the growing season. The impact of climate change on vegetation activities was more pronounced, accounting for 84.76% of annual NDVI change and 97.36% of growing season NDVI change. Humans contributed 15.24% of total annual NDVI change and 2.64% of growing season NDVI change. This research’s findings serve as scientific support for preserving the environment in the Yinshanbeilu grassland region, as well as an essential reference for government decision making.

Spatial and temporal classification and prediction of LULC in Brahmani and Baitarni basin using integrated cellular automata models.

Monitoring the dynamics of land use and land cover (LULC) is imperative in the changing climate and evolving urbanization patterns worldwide. The shifts in land use have a significant impact on the hydrological response of watersheds across the globe. Several studies have applied machine learning (ML) algorithms using historical LULC maps along with elevation data and slope for predicting future LULC projections. However, the influence of other driving factors such as socio-economic and climatological factors has not been thoroughly explored. In the present study, a sensitivity analysis approach was adopted to understand the effect of both physical (elevation, slope, aspect, etc.) and socio-economic factors such as population density, distance to built-up, and distance to road and rail, as well as climatic factors (mean precipitation) on the accuracy of LULC prediction in the Brahmani and Baitarni (BB) basin of Eastern India. Additionally, in the absence of the recent LULC maps of the basin, three ML algorithms, i.e., random forest (RF), classified and regression trees (CART), and support vector machine (SVM) were utilized for LULC classification for the years 2007, 2014, and 2021 on Google earth engine (GEE) cloud computing platform. Among the three algorithms, RF performed best for classifying built-up areas along with all the other classes as compared to CART and SVM. The prediction results revealed that the proximity to built-up and population growth dominates in modeling LULC over physical factors such as elevation and slope. The analysis of historical data revealed an increase of 351% in built-up areas over the past years (2007-2021), with a corresponding decline in forest and water areas by 12% and 36% respectively. While the future predictions highlighted an increase in built-up class ranging from 11 to 38% during the years 2028-2070, the forested areas are anticipated to decline by 4 to 16%. The overall findings of the present study suggested that the BB basin, despite being primarily agricultural with a significant forest cover, is undergoing rapid expansion of built-up areas through the encroachment of agricultural and forested lands, which could have far-reaching implications for the region's ecosystem services and sustainability.

Are all bike lanes built equal? Using bike share GPS data to quantify cycling infrastructure investments’ ridership effects in Hamilton, Ontario

Separated cycling infrastructure, together with other investments such as bike share systems, are increasingly seen as important tools for planners and policy makers to promote urban cycling and multi-modal transportation systems. However, determining causality – that the construction of separated cycling infrastructure promotes higher levels of ridership – presents a significant challenge to researchers. This study uses 495,084 Hamilton Bike Share (HBS) trip routes, derived from GPS trajectories and a custom map-matching algorithm, to inform a longitudinal analysis concerning the influence of 10 separated cycling infrastructure investments made by the City of Hamilton between January 2019 and March 2022. Monthly bicycle kilometers traveled (BKT) along the segments, to account for variability in level of segment use per trip, was modeled using fixed effects regression, which controls for time and location-specific effects, climatological, and contextual factors. A suite of variables indicating completion of the various interventions were included, and of the 10 interventions, five were found to have highly positive impacts (71.60% to 441.95% increases) on monthly BKT. Heterogeneity across the investments was explored. Concrete-barrier separated cycle tracks were the most common interventions with significant and positive coefficients in the model, as were segments more connected with other separated cycling infrastructure, and more accessible to system ridership. This study provides an empirical, longitudinal, city-wide examination of the effects of cycling infrastructure on bike share ridership, setting a framework for further study of private cycling and the nature of the increases in bike share traffic across the interventions of interest.

Spatial model of Dengue Hemorrhagic Fever (DHF) risk: scoping review

BackgroundCreating a spatial model of dengue fever risk is challenging duet to many interrelated factors that could affect dengue. Therefore, it is crucial to understand how these critical factors interact and to create reliable predictive models that can be used to mitigate and control the spread of dengue.MethodsThis scoping review aims to provide a comprehensive overview of the important predictors, and spatial modelling tools capable of producing Dengue Haemorrhagic Fever (DHF) risk maps. We conducted a methodical exploration utilizing diverse sources, i.e., PubMed, Scopus, Science Direct, and Google Scholar. The following data were extracted from articles published between January 2011 to August 2022: country, region, administrative level, type of scale, spatial model, dengue data use, and categories of predictors. Applying the eligibility criteria, 45 out of 1,349 articles were selected.ResultsA variety of models and techniques were used to identify DHF risk areas with an arrangement of various multiple-criteria decision-making, statistical, and machine learning technique. We found that there was no pattern of predictor use associated with particular approaches. Instead, a wide range of predictors was used to create the DHF risk maps. These predictors may include climatology factors (e.g., temperature, rainfall, humidity), epidemiological factors (population, demographics, socio-economic, previous DHF cases), environmental factors (land-use, elevation), and relevant factors.ConclusionsDHF risk spatial models are useful tools for detecting high-risk locations and driving proactive public health initiatives. Relying on geographical and environmental elements, these models ignored the impact of human behaviour and social dynamics. To improve the prediction accuracy, there is a need for a more comprehensive approach to understand DHF transmission dynamics.