Association between ambient particulate matter exposure and semen quality in fertile men

Background Several studies have suggested adverse effects of particulate matter (PM) exposure on male reproductive health; few have investigated the association between PM exposure and semen quality in a large population of fertile men. Methods We evaluated 14 parameters of semen quality in 1554 fertile men in Nanjing from 2014 to 2016. Individual exposure to particular matter ≤10 μm in diameter (PM10) and ≤ 2.5 μm in diameter (PM2.5) during key periods of sperm development (0-90, 0-9, 10-14, 15-69, and 70-90 days before semen collection) were estimated by inverse distance weighting interpolation. Associations between PM exposure and semen quality were estimated using multivariable linear regression. Results Higher 90-days average PM2.5 was in association with decreased sperm motility (2.21% for total motility, 1.93% for progressive motility per 10 μg/m3 increase, P < 0.001) and four quantitative aspects of sperm motion (curvilinear velocity (VCL), straight line velocity (VSL), average path velocity (VAP), and amplitude of lateral head displacement (ALH), P < 0.01). The association between PM2.5 exposure and semen quality were generally stronger for the earlier exposure window (70-90 days prior to ejaculation) than for recent exposure (0-9, 10-14, or 15-69 days). In the subgroup of men who had normal sperm parameters (n = 1019), similar results were obtained. Ninety-days PM10 exposure was associated only with decreased VCL and VAP and was not related to sperm concentration. Conclusions Exposure to PM2.5 adversely affects semen quality, specifically lower sperm motility, in fertile men. Graphical abstract

Mapping and distribution of noise using IDW interpolation algorithm in Thuan An city, Binh Duong province

The study is conducted to assess the level of noise pollution from traffic activities in urban areas (in the case of Thuan An city, Binh Duong province) with the specific goal of applying geographic information systems. (GIS) in building noise pollution distribution maps in the study area. The research team collected noise data at peak hours and normal hours on weekdays and weekends using noise meters at 61 survey points. Noise measurement data was then interpolated using IDW (Inverse Distance Weighting) method to assess the spatial distribution of noise in Thuan An city. In addition, the study digitizes traffic routes and special areas (hospitals, schools, churches - pagodas) in the study area to identify areas affected by sound noisy. The results show that the areas near the main road are most affected by noise during rush hour, while areas in the small lane are noise level within the permitted range. In addition, the results also show the difference in noise level and noise time between weekdays and weekends.

Filling and validating rainfall data based on statistical techniques and artificial intelligence

The study of the hydric regime of rainfall helps in management analysis and decision-making in hydrographic basins, but a fundamental condition is the need for continuous time series of data. Therefore, this study compared gap filling methods in precipitation data and validated them using robust statistical techniques. Precipitation data from the municipality of Itirapina, which has four monitoring stations, were used. Four gap filling techniques were used, namely: normal ratio method, inverse distance weighting, multiple regression and artificial neural networks, in the period from 1979 to 1989. For validation and performance evaluation, the coefficient of determination (R²), mean absolute error (MAE), mean squared error (RMSE), Nash-Sutcliffe coefficient (Nash), agreement index (D), confidence index were used (C) and through non-parametric techniques with Mann-Witney and Kruskal-Wallis test. Excellent performances of real data were verified in comparison with estimated data, with values above 0.8 of the coefficient of determination (R²) and of Nash. Kruskal-Wallis and Mann-Whitney tests were not significant in Stations C1 and C2, demonstrating that there is a difference between real and estimated data and between the proposed methods. It was concluded that the multiple regression and neural network methods showed the best performance. From this study, efficient tools were found to fill the gap, thus promoting better management and operation of water resources. Keywords: artificial neural networks, inverse distance weighting, multiple regression, normal ratio method.

Disparities in COPD Hospitalizations: A Spatial Analysis of Proximity to Toxics Release Inventory Facilities in Illinois

Disproportionate distribution of air pollution is a major burden on the health of people living in proximity to toxic facilities. There are over 1000 Toxics Release Inventory (TRI) facilities distributed across the state of Illinois. This study investigates and spatially analyzes the relationship between chronic obstructive pulmonary disease (COPD) hospitalizations and toxic emissions from TRI facilities. In addition, this study investigates the connection between COPD hospitalizations and socioeconomic variables. Accounting for dispersion of air pollution beyond the TRI facilities source was attained using the inverse distance weighting interpolation approach. Multiple statistical methods were used including principal components analysis, linear regression, and bivariate local indicators of spatial association (BiLISA). The results from the linear regression model and BiLISA clustering maps show there is a strong connection between COPD hospitalizations and socioeconomic status along with race. TRI emissions were not statistically significant, but there are three major clusters of high COPD hospitalizations with high TRI emissions. Rural areas also seem to carry a higher burden of pollution-emitting facilities and respiratory hospitalizations.

A new GA-IDW approach for interpolating the precipitation

This article presents a new algorithm for detecting the Inverse Distance Weighting Algorithm parameter (IDW) using an evolutionary technique. The algorithm was applied to interpolate 51 series of maximum annual precipitation series. Comparisons of its results with those of IDW and the optimized OIDW (a version of IDW optimized with PSO) are provided. The best performances are those of the actual approach.

DISTRIBUSI SUHU DAN SALINITAS PERMUKAAN DI PERAIRAN RANOOHA RAYA KECAMATAN MORAMO SULAWESI TENGGARA

Suhu permukaan laut dan salinitas merupakan parameter kunci oseanografi yang berperan untuk menilai kondisi ekologi lingkungan perairan, terutama di daerah perairan dangkal. Penelitian ini bertujuan untuk mengetahui distribusi spasial suhu dan salinitas permukaan di perairan Ranooha Raya. Metode penelitian menggunakan Hand Refraktometer dan Termometer untuk pengukuran langsung sampel air pada kedalaman 0-2 m. Analisis spasial distribusi suhu dan salinitas permukaan laut menggunakan teknik interpolasi Inverse Distance Weighting (IDW). Hasil penelitian menunjukkan bahwa nilai suhu permukaan laut cenderung homogen berkisar antara 28-30 0C pada saat pasang dan 29-32 0C pada saat surut. Nilai variasi salinitas permukaan laut cukup lebar berkisar antara 22-30 ppt yang diperoleh pada saat pasang dan berkisar antara 15-31 ppt pada saat surut. Distribusi nilai suhu dan salinitas permukaan laut menunjukkan bahwa semakin menjauhi garis pantai nilainya semakin tinggi. Faktor-faktor lingkungan, seperti presipitasi, evaporasi dan masukkan air tawar dari beberapa aliran sungai mempengaruhi perubahan nilai suhu dan salinitas perairan. Kata kunci: Analisis Spasial, Distribusi Spasial, Ranooha Raya, Salinitas Permukaan Laut, Suhu Permukaan Laut

Estimating Baseflow and Baseflow Index in Ungauged Basins Using Spatial Interpolation Techniques: A Case Study of the Southern River Basin of Thailand

This research aims to estimate baseflow (BF) and baseflow index (BFI) in ungauged basins in the southern part of Thailand. Three spatial interpolation methods (namely, inverse distance weighting (IDW), kriging, and spline) were utilized and compared in regard to their performance. Two baseflow separation methods, i.e., the local minimum method (LM) and the Eckhardt filter method (EF), were investigated. Runoff data were collected from 65 runoff stations. These runoff stations were randomly selected and divided into two parts: 75% and 25% for the calibration and validation stages, respectively, with a total of 36 study cases. Four statistical indices including mean absolute error (MAE), root mean squared error (RMSE), correlation coefficient (r), and combined accuracy (CA), were applied for the performance evaluation. The findings revealed that monthly and annual BF and BFI calculated by EF were mostly lower than those calculated by LM. Furthermore, IDW gave the best performance among the three spatial interpolation techniques by providing the highest r-value and the lowest MAE, RMSE, and CA values for both the calibration and validation stages, followed by kriging and spline, respectively. We also provided monthly and annual BF and BFI maps to benefit water resource management.

Aiming for the Optimum: Examining Complex Relationships Between Sampling Regime, Sampling Density and Landscape Complexity to Accurately Model Resource Availability

Context Obtaining accurate maps of landscape features often requires intensive spatial sampling and interpolation. The data required to generate reliable interpolated maps varies with spatial scale and landscape heterogeneity. However, there has been no rigorous examination of sampling density relative to landscape characteristics and interpolation methods.ObjectivesOur objective was to characterize the 3-way relationship among sampling density, interpolation method, and landscape heterogeneity on interpolation accuracy in simulated and in situ landscapes. MethodsWe simulated landscapes of variable heterogeneity and sampled at increasing densities using both systematic and random strategies. We applied each of three local interpolation methods: Inverse Distance Weighting, Universal Kriging, and Nearest Neighbor — to the sampled data and estimated accuracy (R2) between interpolated surfaces and the original surface. Finally, we applied these analyses to in situ data, using a normalized difference vegetation index raster collected from pasture with various resolutions.Results All interpolation methods and sampling strategies resulted in similar accuracy; however, low heterogeneity yielded the highest R2 values at high sampling densities. In situ results showed that Universal Kriging performed best with systematic sampling, and inverse distance weighting with random sampling. Heterogeneity decreased with resolution, which increased accuracy of all interpolation methods. Landscape heterogeneity had the greatest effect on accuracy.ConclusionsHeterogeneity of the original landscape is the most significant factor in determining the accuracy of interpolated maps. There is a need to create structured tools to aid in determining sampling design most appropriate for interpolation methods across landscapes of various heterogeneity.