Accuracy assessment of inverse distance weighting interpolation of groundwater nitrate concentrations in Bavaria (Germany)

Short-term exposure to fine particulate matter and asthma exacerbation: a large population-based case-crossover study in Southern Thailand

BACKGROUND

Asthma exacerbations remain a significant global health issue despite advances in management. Fine particulate matter (PM2.5, particles ≤ 2.5 μm in diameter) is a known trigger for asthma exacerbations. However, studies on the acute effects of PM2.5, particularly in regions with relatively low pollution levels, are limited. This study examined the time-lagged association between daily PM2.5 exposure and asthma exacerbations in Songkhla province, southern Thailand, where PM2.5 concentrations frequently approach the World Health Organization's (WHO) Global Air Quality Guidelines. Approximately 41% of days during the study period had PM2.5 concentrations below the 2021 Guideline level of 15 µg/m³. Additionally, the province is periodically affected by seasonal transboundary haze from forest fires.

METHODS

A case-crossover study was conducted using daily PM2.5 and meteorological data from January 2010 to December 2023, alongside health records of asthma patients from Songklanagarind Hospital. District-level daily PM2.5 concentrations were estimated through inverse distance weighted interpolation. Conditional logistic regression, incorporating time-lagged models and cubic splines, was applied.

RESULTS

The study included 11,848 case days and 39,810 control days, with a mean daily PM2.5 concentration of 18.2 µg/m³. PM2.5 concentrations > 50 µg/m³ were significantly associated with asthma exacerbations at multiple time lags (lag0, lag2, and lag01 to lag03), with odds ratios ranging from 1.41 to 1.64, compared to the lowest concentration group (PM2.5 0-15 µg/m³). Temperature showed no significant effect, while relative humidity was positively associated with asthma exacerbations at lag3, lag06, and lag07. Subgroup analyses revealed associations between PM2.5 exposure and asthma exacerbations at early lags for both males and females. Additionally, children aged 6-11 years and 12-17 years exhibited greater susceptibility to asthma exacerbations, particularly at PM2.5 concentrations of 15-25 µg/m³.

CONCLUSION

This study underscores the short-term effects of PM2.5 on asthma exacerbations, particularly during high-pollution episodes of transboundary haze in regions that generally experience low levels of air pollution. These findings emphasize the importance of achieving the WHO air quality targets to mitigate the health impacts from PM2.5.

Classification of short-term flood events using stochastic variable selection and Gaussian Naïve Bayes classifier: A case study of Sirajganj district, Bangladesh

Around the world, catastrophes caused by flooding are occurring naturally that cause a great deal of fatalities and financial loss. The loss of life and property can be considerably reduced with precise flood forecasts. The complexity of many flood predicting techniques makes the results difficult to interpret, compromising the process's core goal. This study uses a quick and flexible Gaussian Naïve Bayes (GNB) classifier to categorize eight different years as flooded or non-flooded based on predictor variables obtained via the Mutual Information (MI) technique. During the search, all-sky surface shortwave downward irradiance is identified as the optimum predictor variable out of nineteen stochastic variables, with the highest sensitivity for model accuracy. The model is then validated using four iterations derived from the MAPE of the GNB classification method for Twenty-five percent mean error rates from 4-fold cross-validation indicate that this classification model is suitable for flood forecasting. This high rate of mean error is caused by the short amount of data utilized as training data, as GNB requires huge data records to get effective results. This research could aid in the development and evaluation of hydrological projects in the Sirajganj district.

Comparison of three spatial interpolation methods in predicting time-dependent toxicities of single substances and mixtures

This study aims to optimize the time-dependent toxicity assessments for both single substances, particularly those causing hormesis, and mixtures that exhibit toxicological interactions. To achieve this, three time-dependent toxicity prediction methods were developed using geologic interpolation techniques: Inverse distance weighted (IDW), Kriging, and linear interpolation based on Delaunay triangulation (LDT). The toxicity of 7 single substances and 80 mixtures on Vibrio qinghaiensis sp.-Q67, along with 6 single substances and 19 mixtures on Microcystis aeruginosa, were assessed to evaluate the predictive accuracy of these methods. The coefficient of determination (R2), mean absolute error (MAE), and root-mean-square error (RMSE) were employed as performance metrics during cross-validation. The results showed that IDW underperformed LDT and Kriging in terms of both RMSE and MAE, indicating that LDT and Kriging had superior accuracy compared to IDW. Although LDT and Kriging demonstrated comparable predictive capabilities, LDT was identified as the more practical option for time-dependent toxicity prediction due to its simplicity and no requirement for parameter tuning. Consequently, LDT was presented as a new, efficient, and user-friendly tool for assessing the time-dependent toxicity of both individual chemicals and chemical mixtures. LDT will help to better assess the ecological risks of chemicals.

Assessing groundwater quality and its association with child undernutrition in India

BACKGROUND AND OBJECTIVES

Groundwater contamination poses a significant health challenge in India, particularly impacting children. Despite its importance, limited research has explored the nexus between groundwater quality and child nutrition outcomes. This study addresses this gap, examining the association between groundwater quality and child undernutrition, offering pertinent insights for policymakers.

DATA AND METHODS

The study uses data from the fifth round of the National Family Health Survey (NFHS) and the Central Groundwater Board (CGWB) to analyze the association between groundwater quality and child nutritional status. The groundwater quality data were collected by nationwide monitoring stations programmed by CGWB, and the child undernutrition data were obtained from the NFHS-5, 2019-21. The analysis included descriptive and logistic regression model. The study also considers various demographic and socio-economic factors as potential moderators of the relationship between groundwater quality and child undernutrition.

FINDINGS

Significant variation in groundwater quality was observed across India, with numerous regions displaying poor performance. Approximately 26.53 % of geographical areas were deemed unfit for consuming groundwater. Environmental factors such as high temperatures, low precipitation, and arid, alluvial, laterite-type soils are linked to poorer groundwater quality. Unfit-for-consumption groundwater quality increased the odds of undernutrition, revealing a 35 %, 38 %, and 11 % higher likelihood of stunting, underweight, and wasting in children, with higher pH, Magnesium, Sulphate, Nitrate, Total Dissolved Solids, and Arsenic, levels associated with increased odds of stunting, underweight, and wasting. Higher temperatures (>25 °C), high elevations (>1000 m), and proximity to cultivated or industrial areas all contribute to heightened risks of child undernutrition. Children consuming groundwater, lacking access to improved toilets, or living in rural areas are more likely to be undernourished, while females, higher-income households, and those consuming dairy, vegetables, and fruits daily exhibit lower odds of undernutrition.

POLICY IMPLICATIONS

Policy implications highlight the urgent need for investment in piped water supply systems. Additionally, focused efforts are required to monitor and improve groundwater quality in regions with poor water quality. Policies should emphasize safe sanitation practices and enhance public awareness about the critical role of safe drinking water in improving child health.

Assessment of climate change induced rainfall trend and variability with non-parametric and linear approach for Sirajganj district, Bangladesh

The monthly and annual trends and variance of rainfall have been studied for five stations in an economically important Bangladeshi district named Sirajganj since 1965 to 2021. Natural disasters have prevalent in Sirajganj which is indispensable to assess. But, several researchers have been normally focused on river bank management and flood risk assessment. However, no extensive research has been conducted on Sirajganj based on non-normally distributed time series meteorological data such as rainfall time series so the current study is very important. In this study, the non-parametric Mann-Kendall and Sen's methods have been used to determine the statistical significance of a positive or negative trend in rainfall data. Also, cumulative sum charts and bootstrapping, one-way ANOVA, Tukey's range tests, and linear regression have been used to discover the incidence of abrupt changes, compare the significant difference in monthly and annual rainfall data, multiple comparisons amidst mentioned stations to find changes, and to investigate the changeover on dry and rainy days, respectively. The analysis showed a statistically significant decreasing trends in monthly and annual rainfall series. As well, changes from positive to negative direction have been recognized in the February, May, July, September, and annual rainfall time sequence. Besides, ANOVA and Tukey's range tests revealed a statistically substantial difference in all monthly and annual rainfall volume excluding January, March, and June. Additionally, these two tests demonstrated momentous differences in all monthly and annual frequency of rainfall categories excepting January and April. However, Linear regression analysis revealed that the number of dry days gradually reduced at the end of the dry winter, though the number of rainy days decreased during the rainy season. As in, the number of rainy days replaces the number of dry days during the dry season and vice versa during the rainy season. Even though, with very few exceptions, the volume of rainfall decreases throughout the year. The outcomes of this research might helpful for implementing the planning and evaluating hydrological projects on Sirajganj district.

Land and Seabed Surface Modelling in the Coastal Zone Using UAV/USV-Based Data Integration

The coastal zone is an area that includes the sea coast and adjacent parts of the land and sea, where the mutual interaction of these environments is clearly marked. Hence, the modelling of the land and seabed parts of the coastal zone is crucial and necessary in order to determine the dynamic changes taking place in this area. The accurate determination of the terrain in the coastal zone is now possible thanks to the use of Unmanned Aerial Vehicles (UAVs) and Unmanned Surface Vehicles (USVs). The aim of this article is to present land and seabed surface modelling in the coastal zone using UAV/USV-based data integration. Bathymetric and photogrammetric measurements were carried out on the waterbody adjacent to a public beach in Gdynia (Poland) in 2022 using the DJI Phantom 4 Real Time Kinematic (RTK) UAV and the AutoDron USV. As a result of geospatial data integration, topo-bathymetric models in the coastal zone were developed using the following terrain-modelling methods: Inverse Distance to a Power (IDP), kriging, Modified Shepard's Method (MSM) and Natural Neighbour Interpolation (NNI). Then, the accuracies of the selected models obtained using the different interpolation methods, taking into account the division into land and seabed parts, were analysed. Research has shown that the most accurate method for modelling both the land and seabed surfaces of the coastal zone is the kriging (linear model) method. The differences between the interpolated and measurement values of the R95 measurement are 0.032 m for the land part and 0.034 m for the seabed part. It should also be noted that the data interpolated by the kriging (linear model) method showed a very good fit to the measurement data recorded by the UAVs and USVs.

A new method for estimating ore grade based on sample length weighting

Estimation of ore grade is very important for the value evaluation of ore deposits, and it directly affects the development of mineral resources. To improve the accuracy of the inverse distance weighting (IDW) method in ore grade estimation and reduce the smoothing effect of the IDW method in grade estimation, the weight calculation method involved in the IDW method was improved. The length parameter of the ore sample was used to calculate the weight of the IDW method. The length of the ore samples was used as a new factor of the weighting calculation. A new method of IDW integrated with sample length weighting (IDWW) was proposed. The grade estimation of Li, Al, and Fe in porcelain clay ore was used as a case study. A comparative protocol for grade estimation via the IDWW method was designed and implemented. The number of samples involved in the estimation, sample combination, sample grade distribution, and other factors affecting the grade estimation were considered in the experimental scheme. The grade estimation results of the IDWW and the IDW methods were used for comparative analysis of grades of the original and combined samples. The estimated results of the IDWW method were also compared with those of the IDW method. The deviation analysis of the estimated grade mainly included the minimum, maximum, mean, and coefficient of variation of the ore grade. The estimation effect of IDWW method was verified. The minimum deviations of the estimated grade of Li, Al, and Fe were between 9.129% and 59.554%. The maximum deviations were between 4.210 and 22.375%. The mean deviations were between - 1.068 and 7.187%. The deviations in the coefficient of variation were between 3.076 and 36.186%. The deviations in the maximum, minimum, mean, and coefficients of variation of the IDWW were consistent with those of the IDW, demonstrating the accuracy and stability of the IDWW method. The more the samples involved in the estimation, the greater the estimation deviations of IDW and IDWW methods. The estimated deviations of Li, Al, and Fe were affected by the shape of the grade distribution, when the same estimation parameters were used. The grade distribution pattern of the samples significantly influenced the grade estimation results. The IDWW method offers significant theoretical advantages and addresses the adverse effects of uneven sample lengths on the estimates. The IDWW method can effectively reduce the smoothing effect and improves the utilization efficiency of the original samples.