Influence Of Weather Research Articles

The Influence of Fine Particulate Matter and Cold Weather on Emergency Room Interventions for Childhood Asthma.

(1) Background: Children are the most vulnerable to pollution due to their decreased stature, heightened respiratory rate, and frequent outdoor engagement. PM2.5, nitrogen dioxide (NO2), ozone, and cold weather are associated with pediatric asthma. In this study, we investigated the nexus between air pollution, climate factors, and pediatric asthma emergency room visits (ERVs). (2) Method: Pediatric asthma ERV data for healthcare quality from the Taiwanese National Insurance in the Taipei area were obtained from 2015 to 2019. Air pollution and climate factor data were also collected. Poisson regression was employed to determine the relationships with relative risks (RRs). (3) Results: The incidence of pediatric asthma ERVs decreased, with a crude RR of 0.983 (95% CI: 0.98-0.986, p < 0.001). Fine particulate matter (PM2.5) had an adjusted RR of 1.102 (95% CI: 1.037-1.172, p = 0.002) and a 7.7 µg/m3 increase, and air temperature had an adjusted RR of 0.813 (95% CI: 0.745-0.887, p < 0.001) comparing between the highest and lowest quarter air temperature associated with pediatric asthma ERVs. (4) Conclusions: This inquiry underscores the positive associations of PM2.5 and cold weather with pediatric asthma ERVs. The findings could guide the government to establish policies to reduce air pollution and promote children's health.

CHEMICAL MODIFICATION OF ROAD PETROLEUM BITUMEN WITH MALEIC ANHYDRIDE AND POLYETHYLENE GLYCOLS

A number of problems arise in the production and application of road bitumen. One of them is unsatisfactory operational characteristics, mainly heat resistance, elasticity and adhesion between the binder and aggregate. With an increase in the intensity of traffic, an increase in the volume of heavy goods transportation, the mass of cars, together with the influence of weather and climate factors on the structure, this leads to the destruction of the road surface. The work describes the results of the study of the process of chemical modification of road petroleum bitumen with maleic anhydride and polyethylene glycols (PEG) to obtain bitumen-polymer mixtures, which, in addition to good heat resistance, also have high elasticity. When modifying road bitumen with maleic anhydride, the latter acts as a certain narrowing link, which contains on the one hand a double bond that interacts with bitumen, and on the other hand an anhydride group that can interact with various polymers containing functional groups (hydroxyl, epoxy and other). Further, by adding a polymer to maleinized bitumen (bitumen modified with maleic anhydride), the resulting bitumen-polymer mixtures can be given various properties (heat resistance, elasticity, adhesion, and others). Polyethylene glycol is a cheap polymer capable of interacting with the anhydride group of maleic anhydride in bitumen on the hydroxyl group. It was established that the effect of maleic anhydride and maleic anhydride together with PEG on the heat-resistant characteristics of bitumen is the same regardless of the modification conditions. Compared to the original bitumen, the obtained BPS is characterized by a higher softening temperature, lower penetration and ductility. The temperature of brittleness is not rapid, but it is increasing. The influence of the used modifiers on bitumen elasticity is significant and depends on the modification conditions. It was established that PEG 2000 is the optimal PEG for modifying maleinized bitumen, from the point of view of ensuring good bitumen elasticity.

Study on window-opening behavior of suburban sustainable life-pattern in residential buildings at Donglu town of Hezhou, China

Window opening behavior is a crucial determinant of indoor thermal comfort and air quality. It plays an important role in accurate building performance simulations when it comes to regional contexts of climate. Few studies on window opening behavior have been carried out in suburban regions where window opening, as an adaptative adjustment, is more prevalent than in urban areas. To bridge this gap, this study based on questionnaire surveys and on-site measurements investigates the window opening behavior of suburban residents in bedrooms during the transitional season in Donglu town, China. The results indicate the duration of window opening exhibits a strong correlation with suburban residents' habits and occupations. The suburban residents tend to close their bedrooms' windows when leaving home, while they prefer opening them before going to sleep. A logistic regression model was developed for suburban household to predict the probability of window opening behavior. It has been evidenced that outdoor and indoor humidity, outdoor temperature, time periods and solar radiation intensity are strongly influencing on suburban residents' window opening and closing behaviors. In addition, due to the influence of continuous humid weather in transitional seasons, outdoor humidity is the principal factor that prompts residents to close their windows. Our findings contribute to further achieving novel knowledge on occupants’ interaction with suburban residential buildings.

Forecasting the power generation at renewable power plants in Sri Lanka using regression trees

This paper presents the application of regression trees as a versatile alternative to other machine learning and statistical modelling techniques to forecast the power generation at five renewable power plants: one large hydropower plant, two mini hydropower plants, and two wind farms in Sri Lanka. The prediction models for each power station were developed by varying the depth of the regression tree. The regression tree model with the lowest depth that forecasts the output (power) in terms of all the predictor variables was selected for each power station and the accuracy of the models was evaluated by means of the Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE), and the Coefficient of Determination (R2). According to the degree of the above performance indicators, i.e. very low values of MAE, MAPE, and RMSE supplemented by R2 of 0.95 or more, the regression tree method proved to be a convenient forecasting technique to predict the power generation at both hydro and wind power plants. Further, it could be found that a good correlation between the input and output variables paves the way for a smaller depth in the regression tree. Moreover, regression trees presented here could accurately identify the relationship between the power generated and the most influential weather factors, without being affected by potential outliers or missing values while managing collinearity too. Extension of this study would enable to generalize the prediction of renewable power generation based on the regression tree method, leading towards minimizing the use of fossil fuel.

Highlighting Traffic Accidents on Roundabouts Using MRSS-AHP Expert System

The frequency and severity of traffic accidents are causing growing concern. This study aims to develop a tool to improve the traffic safety level on roundabouts and identify the influence of traffic operations, geometric parameters, weather, and time of day on improving roundabout traffic safety. It is the first study to evaluate the performance of the integrated Median Ranked Set Sample (MRSS) and Analytic Hierarchy Process (AHP) with statistical analysis. A hierarchy tree of accident causes has been developed using data gathered from accident reports and relevant authorities. Then, the selected stakeholders’ professionals prioritized the traffic accident causes using a MRSS and AHP. Moreover, traffic microsimulation software VISSIM was also used to extract traffic operation parameters for the analysis. Afterwards, Analysis of Variance (ANOVA) was used to validate the causes of traffic accidents. The results show that geometric design accounts for 36% of accidents at roundabouts, followed by traffic operation (22%). However, conflicting and queuing lengths are responsible for about 20% of traffic accidents. The tools developed, and the causes of accidents determined in this study will help geometric designers and city planners to take the necessary measures to minimize accidents and enhance traffic safety levels in urban areas. Doi: 10.28991/CEJ-2024-010-04-013 Full Text: PDF

Evaluation of the point sampling method and inter-comparison of remote emission sensing systems for screening real-world car emissions

Emissions from internal combustion vehicles are currently not properly monitored throughout their life cycle. Remote emission sensing (RES) is a technology that can measure emissions under real driving conditions without contact. Current light extinction based RES systems are capable of providing emission factors for various gases, but lack accuracy for particulate matter (PM). Point Sampling (PS) is an extraction-based RES technique that can measure gases as well as various particle metrics such as black carbon or particle number. In this work, we evaluated the performance of a recently developed PS system and the state-of-the-art light extinction based remote sensing devices EDAR (HEAT) and ORSD (OPUS RSE) during co-location measurements. Validation measurements with portable emission measurement systems and emissions screening of several thousand cars in three European cities provide detailed insights into system's performance. Meteorological evaluations showed that the PS capture rate is strongly influenced by wind, but no other weather influences were found. Both light extinction based systems are unable to measure during rain. We found that all three systems tested were capable of screening NOx emissions from pre-Euro 6 diesel cars. Measurement results show the ability of the PS system to quantify high and low PM emitters equally well. The open-path RES systems (EDAR, ORSD) are capable of estimating PM emissions from pre-Euro 5 diesel cars. However, deficiencies of open-path RES systems are evident in the quantification of PM emissions from newer engine technologies (diesel Euro 5 and beyond) and from petrol cars. The PS system has a 2 to 5 times lower capture rate than open-path RES systems, but the PS measurement results are more accurate (more than 5 times for PM and more than 1.35 times for NOx). The good accuracy of individual measurements makes PS a powerful tool for reliable high emitter identification.

Association between Weather and Self-Monitored Steps in Individuals with Prediabetes and Type 2 Diabetes in Sweden over Two Years.

Many studies have identified key factors affecting the rates of engagement in physical activity in older adults with chronic disease. Environmental conditions, such as weather variations, can present challenges for individuals with chronic diseases, such as type 2 diabetes when engaging in physical activity. However, few studies have investigated the influence of weather on daily steps in people with chronic diseases, especially those with prediabetes and type 2 diabetes. This study investigated the association between weather variations and daily self-monitored step counts over two years among individuals with prediabetes and type 2 diabetes in Sweden. The study is a secondary analysis using data from the Sophia Step Study, aimed at promoting physical activity among people with prediabetes and type 2 diabetes, which recruited participants from two urban primary care centers in Stockholm and one rural primary care center in southern Sweden over eight rounds. This study measured physical activity using step counters (Yamax Digiwalker SW200) and collected self-reported daily steps. Environmental factors such as daily average temperature, precipitation, and hours of sunshine were obtained from the Swedish Meteorological and Hydrological Institute. A robust linear mixed-effects model was applied as the analysis method. There was no association found between weather variations and the number of steps taken on a daily basis. The analysis indicated that only 10% of the variation in daily steps could be explained by the average temperature, precipitation, and sunshine hours after controlling for age, gender, and BMI. Conversely, individual factors explained approximately 38% of the variation in the observations. This study revealed that there was no association between weather conditions and the number of daily steps reported by individuals with prediabetes and type 2 diabetes taking part in a physical activity intervention over two years. Despite the weather conditions, women and younger people reported more steps than their male and older counterparts.

Influence of Weather and Air Constituents on Aortic Ruptures in the South of Germany

Influence of Weather and Air Constituents on Aortic Ruptures in the South of Germany

Environment and arthritis.

The present narrative review explores the multifactorial aetiology of rheumatoid arthritis (RA) and other immunemediated inflammatory disorders (IMIDs), emphasising the significant role of various environmental factors in disease development and exacerbation. Key modifiable environmental factors such as cigarette smoking and air pollution are identified as major contributors to RA. We will also focus on the influence of weather, seasonality, and particularly vitamin D levels, on RA activity, suggesting potential for seasonal management and supplementation to mitigate disease severity. The emerging role of diet and the gut microbiome in RA pathogenesis and progression is discussed as well, with dietary interventions and specific nutrients like omega-3 fatty acids offering protective benefits against inflammation. Despite the mounting evidence around these factors, further research is needed, to better understand the clinical impacts on RA, including well-designed randomised clinical trials.

Impacts of emissions and meteorological conditions in three different phases of aerosol pollution during 2013–2022 in Anhui, China

The PM2.5 concentrations in Anhui, which links the Yangtze River Delta region, China's fastest growing economy area, with the Beijing-Tianjin-Hebei (BTH) region, China's most polluted region, are influenced not only by emissions, but also by variation of meteorological conditions. A comprehensive understanding of the relative impacts of meteorology and emissions on heavy pollution in Anhui at three phases (i.e., phase1: from 2013 to 2017; phase2: from 2018 to 2020; phase 3: from 2021 to 2022) from 2013 to 2022, which can provide suggestions for pollution prevention and control in the future. The decrease in pollutant concentrations from 2013 to 2022 is mainly attributed to the continued reduction in emissions, while the year-to-year fluctuations in pollutant concentrations are largely influenced by meteorological conditions. Although emissions are decreasing, the proportions of residential biofuel combustion and cement are increasing. In addition to the effects of prevailing northeasterly and northwesterly winds (i.e., Type1 and Type2), there is also concern about the influences of static weather and neighboring regional transport (i.e., Type5 and Type6), especially in 2016. The contribution of emissions is greater in phase 2 and phase 3, with a 17 % increase compared to phase 1. Overall, approximately 57 % of explosive growth in PM2.5 concentration during the cumulative stage (CS) can be regarded as the feedback effect of the deteriorating meteorological conditions. Therefore, statistical analyses show that limiting PM2.5 concentrations below about 73 μg m−3 would weaken the feedback effects, which in turn would avoid most of the explosive growth processes in the CS of the 60 heavy pollution processes, which can provide a reference for the government to set a target for sustained emission reduction.

Applying machine learning to a nonlinear spectral mixing model for mapping lunar soils composition using CHANDRAYAAN-1 M3 data

We present a newly developed method which combines the nonlinear spectral mixing model of Shkuratov et al. (1999) with a machine learning algorithm to map the lunar regolith composition using spectral data. The new method performs orders of magnitude faster than the traditionally used numerical optimization approaches, allowing the mapping of regolith properties (including mineralogical composition, average grain size and optical maturity) over large areas of the lunar surface. A new set of basic mineral spectra of the lunar soil for using with spectral mixing models is proposed. Used together with the nonlinear mixing model (Shkuratov et al., 1999), the set is able to describes Chandrayaan-1 M3 instrument spectra collected from test areas which includes the Shapley crater with its surroundings containing mare and highland terrains well. The new set includes a virtual “gray component” with a “flat” (constant) spectrum, accounting for the factors that change general surface albedo, such as spectrally neutral components (e.g., agglutinate glasses), errors in the photometric reduction, uncertainties in estimations of lunar regolith porosity q and the mean grain size S of the basic minerals. The proposed new method takes into account the influence of space weathering and nonlinear correlation between the compositional and spectral parameters of the lunar soils delivering values for the optical properties and mineralogical abundance determination of the lunar regolith which are compatible with the results found from lunar samples measurements in the laboratory. The proposed approach can be used for analyzing spectral observations not only of the lunar surface but also for other surfaces with are covered by regolith.

Regional scale terrace mapping in fragmented mountainous areas using multi-source remote sensing data and sample purification strategy

As a stepped cross section of farmland built along the contour lines, terrace is widely distributed on hill-slopes. It changes the original surface slope and runoff coefficient, reduces soil nutrient loss, and has become the most important soil erosion control measure in China. Accurate terrace mapping at regional scale is crucial for soil conservation, agriculture sustainability and ecological planning. Due to the influence of cloudy and rainy weather, poor data availability makes it difficult to identify terrace distribution only using optical remote sensing images in mountainous areas. In this study, we incorporated multi-spectral optical and SAR data, features of terrain, texture and time sequence information, and proposed a pixel-based supervised classification method based on sample purification strategy to obtain a 10 m resolution terraced map in a plateau mountainous region. With 610 terrace/non-terrace validation sample data, 10-fold cross-validation was used to test the classification results. For identified terrace, the values of Overall Accuracy (OA), Producer's Accuracy (PA) and User's Accuracy (UA) stay stable above 90 %, the F1 score and Kappa coefficient show the smallest fluctuation and is stable in the range of 0.90–0.93 and 0.81–0.87, respectively. The accuracy evaluation of grid units show that the uncertainty of the terrace distribution is mainly concentrated in the north and south of the study area. Slope cultivated land, low-slope terrace and non-agricultural vegetation are easily mixed due to the heterogeneity of terrace features and the spectrum similarity among these land types. It should be noted that the features of time series and texture play a key role in the terrace recognition process, rather than terrain factors, which is different from previous studies. The sample purification strategy proposed provides a more reliable regional scale terrace distribution map compared to the existing product, and is potentially transferable to other mountainous areas as a robust approach for rapid identification of terrace.

Influence of Weather and Climate on Multidecadal Trends in Atlantic Hurricane Genesis and Tracks

Abstract This study investigates the relative roles of sea surface temperature–forced climate changes and weather variability in driving the observed eastward shift of Atlantic hurricane tracks over the period from 1970 to 2021. A 10-member initial condition ensemble with a ∼25-km horizontal resolution tropical cyclone permitting atmospheric model (GFDL AM2.5-C360) with identical sea surface temperature and radiative forcing time series was analyzed in conjunction with historical hurricane track observations. While a frequency increase was recovered by all the simulations, the observed multidecadal eastward shift in tracks was not robust across the ensemble members, indicating that it included a substantial contribution from weather-scale variability. A statistical model was developed to simulate expected storm tracks based on genesis location and steering flow, and it was used to conduct experiments testing the roles of changing genesis location and changing steering flow in producing the multidecadal weather-driven shifts in storm tracks. These experiments indicated that shifts in genesis location were a substantially larger driver of these multidecadal track changes than changes in steering flow. The substantial impact of weather on tracks indicates that there may be limited predictability for multidecadal track changes like those observed, although basinwide frequency has greater potential for prediction. Additionally, understanding changes in genesis location appears essential to understanding changes in track location. Significance Statement From the 1970s to the present, there has been an increase in the frequency of North Atlantic hurricanes, but they have also shifted in location to the east, away from land. We explore whether this shift in hurricanes’ locations was caused by climatic factors or randomness to understand if and how these trends will persist. We also consider whether the shift was due to a change in where hurricanes started or how they moved over their lifespan. Analyzing data from observed and simulated hurricanes, we find that the shift was made more likely by climate factors, but ultimately occurred due to random variability in the hurricanes’ starting locations. These results suggest a higher uncertainty in the future location and impact of hurricanes and highlight the importance of studying why hurricanes originate where they do.

Climate Change Impacts on Human Health

Our environment provides us with natural resources that are important for our survival and livelihoods. The Earth’s species are dependent on the ecosystem service functions of our planet which is driven by climate stability. Health is a very important aspect and is most essential for life. Health is directly related to the nature of the environment, weather and climate. Changes in weather and climate have influenced the health of human populations in the past and caused several epidemics. Weather and climate can affect the distribution and survival of several arthropods, such as mosquitoes or mammals that are vectors for various diseases. Recently many people in Kerala and some parts of South India were affected by the West Nile virus which is attributed to the changing weather and vector borne influences. This fever is a mosquito-borne zoonotic disease and is caused by the flavivirus — West Nile Virus. The state of Kerala was on high alert as a seven year old boy from Mallapuram died in Kozikode due to the virus. In the same way, the water quality can be impacted giving rise to health problems. We are observing changes in the frequency and severity of weather and climate phenomena. This is seen as heavy rains, droughts, and also extreme cold or hot temperatures in different geographical regions. All this can lead to new climate-related health threats. Therefore, climate variability can pose threats to human health in several ways. The impacts of climate change are already being witnessed and will further increase in the coming decades. It may also lead to several emerging infectious diseases. Scientists study the exposure pathways in humans and animals for understanding the impacts of climate change on health. This involves conducting studies over time and in different locations, among different communities. In this research paper will discuss about the various impacts of climate change on human health.

Influence of Rock Weathering and Saturation on Compressive Strength and Slope Stability: A Uniaxial Test Analysis

Influence of Rock Weathering and Saturation on Compressive Strength and Slope Stability: A Uniaxial Test Analysis

Multi-index comprehensive evaluation model for assessing risk to trainees in an emergency rescue training base for building collapse

Rescues from building collapse accidents present a significant challenge for China’s emergency rescue system. However, there are also many risk factors in a training scenario, which have been summarized in this study. A hierarchical indicator system for personnel safety was established, including 12 first-level indicators and 23s-level indicators. Then, an improved Grey-DEMATEL-ISM-MICMAC evaluation model was constructed to evaluate the level of risk. Influencing factor scores were determined according to the responses from the questionnaire survey. The influencing degree, influenced degree, centrality, and causality were identified, and the importance, relevance, and clustering of the various factors were obtained after making quantitative calculations. The results showed that the order of priority for solving the essential issues was safety education (A2), operating standards and proficiency (A10), equipment inspection (A4), equipment warehousing maintenance and records (A21). The solving of safety education was identified to be the most essential priority. The priority control order of direct causes was Scientific design and construction (A5), Potential fixed hazards in the facility (A12), Physical fitness of personnel (A1), Weather influences (A18), and Initiation efficiency of emergency plans (A20), and direct control measures for these five factors could achieve a relatively significant effect.

Assessing intra-annual growth dynamics in climatically contrasting years, sites, and tree species using dendrometers and wood anatomical data

Detecting the intra-annual dynamics and courses of secondary tree growth enables the accurate identification of crucial steps in the forming of a new tree ring. Furthermore, comparing the high-resolution recordings of tree growth with environmental conditions allows assessment of the influence of weather on wood formation processes. This study investigates the intra-annual growth performance of conifer species and European beech at two high- and two low-elevation sites in Bavaria, southeast Germany. We measured stem circumference changes with electronic band dendrometers and cambial dynamics by collecting microcores at biweekly intervals. We analyzed growth variations between the consecutive years 2020 and 2021, which showed distinct climatic differences during the growing seasons. While warm and dry conditions prevailed in spring and summer in 2020, spring in 2021 was comparatively cold, and summer precipitation was high. Different tree growth patterns were observed in the contrasting years 2020 and 2021. Distinct growth reductions occurred in the drier year 2020 for most of the studied tree species, while trees showed wider tree rings in 2021 despite of low growth rates at the beginning of the growing season. Climate-growth correlations exposed the intraseasonal influence of climatic conditions, particularly available soil water, water vapor pressure deficit, and soil temperature, on short-term tree responses. Wood anatomical analysis and daily stem diameter variations proved to be valid monitoring methods to assess individual wood formation processes and to identify species-specific tree responses to the influence of climatic conditions. However, combining both methods represents the most reliable approach due to the mutual ability to compensate for each other’s deficiencies. While dendrometers provided a very accurate and high-resolution record of intra-annual tree growth, wood anatomical analyses were more reliable in determining the exact onset and cessation of wood formation. For this reason, combining both is recommended for assessing prospective tree growth performance in the context of climate change.

Using loyalty card data to understand the impact of weather on click & collect behaviours in UK retailing

ABSTRACT This study assesses the influence of weather on Click & Collect (C&C) usage in England, examining differences between the day of order and day of collection, and how the weather influences the time taken to collect an order. Using sales and loyalty card data from a national retailer, we employ a series of gradient boosting models to analyse the influence of weather on daily order/collection totals, totals stratified by demographic group, and the time taken to collect an order. Weather is found to be consistently more influential on the day of order than the day of collection, however store location and thetiming of purchase have a higher relative influence than weather overall. The findings also evidence significant differences in influence between male and female customers and those aged above and below 65. Temperature and humidity are typically the most influential weather conditions, demonstrating a wide variation in effect on order totals. Temperature was also found to be significantly more influential than other weather conditions on the time taken to collect an order, with the highest temperatures leading to the longest collection times. Understanding the influences of different weather conditions on online consumption behaviours is of significant value to retailers, policy makers and associated stakeholders. However, it is an area that has been significantly under-researched compared to physical retailing, often due to a lack of reliable data. This study provides key insights into how different types of consumers behave on these platforms.

Analysis of methods of increasing the productivity of grain crops to reduce the interannual variation of their yield

Scientific observations of the growth and productivity of grain crops have been carried out in the world for more than 100 years. It is established that the productivity of plants primarily depends on the amount of nutrients in the soil, the effectiveness of which depends on the varieties used, the agrotechnics of their cultivation and weather conditions. The ever-increasing need for food has led to the accelerated use of nutrients and the creation of plant varieties that are more demanding to the conditions of development. At the same time, the industrial development of the planet has led to climate changes that reduce many previous achievements of agricultural science and practice by their impact on yields. The effect of nutrition elements on yield from 50–60% on an annual scale is reduced to 20–30% with long-term consideration. The dependence of the yield of modern varieties on weather conditions is 30–80%. It is possible to reduce its dependence on weather conditions by increasing soil fertility, while understanding under fertility not so much the saturation of the soil with nutrients, as a set of measures, including agrophysical ones, aimed at a controlled effect on the soil in order to create an optimal water-air regime, since one of the main directions of the influence of weather on plant productivity there is a change in the moisture supply of plants. Separately, each of the traditional factors of increasing yields to reduce dependence on weather conditions is ineffective.

Hydrogeochemical characterization and non-carcinogenic health risk assessment of fluoride and nitrate-affected groundwater in northern parts of Tumkur district, Karnataka, India

Groundwater contamination from geogenic and anthropogenic sources is a significant concern for the public health across the globe. In India, fluoride and nitrate contamination are the most prevalent contaminants in the groundwater. This study evaluated groundwater quality in four over-exploited sub-districts of Tumkur district in Karnataka by collecting 143 samples from post and pre-monsoon seasons and using different hydrogeochemical, statistical and bivariate graphical approaches. Comparison with Drinking Water Standard guidelines revealed elevated levels of fluoride, nitrate, and total dissolved solids (TDS), rendering the groundwater unsuitable for drinking. According to Chadha’s diagram, the predominant groundwater type is Na-Cl, and Gibbs diagram suggested the influence of rock dissolution and weathering. Bivariate plots indicated that hydrogeochemical characteristics were primarily influenced by silicate weathering of minerals, cation ion exchange processes, and anthropogenic factors. A non-carcinogenic health risk assessment was conducted using two different hazard index (HI) approaches from United States Environmental Protection Agency (USEPA) and Agency for Toxic Substances and Disease Registry (ATSDR). It was found that health risk associated with fluoride is more significant than the health risk from nitrate, with children being the most affected, followed by adolescents and adults. A holistic approach to assess cumulative health risks from fluoride and nitrate contamination in groundwater, as demonstrated in this study, promotes the synergistic advancement of SDG 3: Good health and well-being and SDG 6: Clean water and sanitation.