Distance Weighting Method Research Articles

Spatial weighting EMD-LSTM based approach for short-term PM2.5 prediction research

Given the significant health and environmental risks posed by atmospheric PM2.5 pollution, accurately predicting its concentration changes is especially important. Current models fall short in researching time-series feature extraction from pollutants and spatial correlations among monitoring stations. In this study, a spatiotemporal prediction model is introduced to address these issues. The model combines spatial weighting, empirical mode decomposition (EMD), and a long short-term memory (LSTM) network. First, weights are allocated to sites using Pearson correlation analysis and distance weighting methods. Next, the pollutant time series is decomposed using the EMD method. The highly correlated intrinsic mode function (IMF) component is selected for signal reconstruction, enhancing denoising. Finally, the model uses an LSTM network to capture nonlinear and dynamic time series traits, which significantly improves the PM2.5 prediction accuracy. The model utilizes data collected from 10 monitoring stations across Hefei city during 2018-2019, employing the previous 24 h of observations to forecast PM2.5 concentrations for the subsequent hour. By comparing with RNN, HPO-RNN, GRU, LSTM, and CBAM-CNN-Bi LSTM, the results show that our model surpasses five benchmark models in terms of prediction accuracy. Relative to the best-performing CBAM-CNN-Bi LSTM model, our model reduces RMSE and MAE by 73.91% and 72.99%, respectively, and improves R2 by 8.15%. In summary, the proposed spatial weighting EMD-LSTM model offers an efficient new approach for predicting atmospheric PM2.5 pollution. It integrates spatial and time series analysis, significantly enhancing the prediction accuracy.

Evaluating the necessity of post-processing techniques on d4PDF data for extreme climate assessment.

The occurrence and severity of extreme precipitation events have been increasing globally. Although numerous projections have been proposed and developed for evaluating the climate change impacts, most models suffer from significant bias error due to the coarse resolution of the climate datasets, which affects the accuracy of the climate change assessment. Therefore, in this study, post-processing techniques (interpolation and bias correction methods) were adopted on the database for Policy Decision Making for Future Climate Change (d4PDF) model for extreme climatic flood events simulation in the Chao Phraya River Basin, Thailand, under + 4-K future climate simulation. Due to the limited number of the rain gages, the gradient plus inverse distance squared interpolation method (combination of multiple linear regression and distance weighting methods) was applied in this study. In the bias correction methods, the additional setting of monthly and seasonal periods was adjusted. The proposed bias correction approach deployed gamma distribution combined with generalized Pareto distribution setting with the seasonal period for the rainy season datasets, whereas only the gamma setting was applied with the monthly period during the dry season. The outcomes revealed that the proposed method could react to extreme rainfall events, expand dry days during dry season, and intensify rainfall amount during rainy season. The post-processing d4PDF trends of six sea surface temperature (SST) patterns (consists of 90 ensemble members) of two periods (near future: 2051-2070 and far future: 2091-2110) recorded the highest and lowest amounts of annual rainfalls of 4,450 mm/year in mid-stream of Nan River and 710 mm/year in the lower CPRB, respectively. Notably, the significant variances noted in the rainfall patterns among ensembles, demanding further investigation in future climate change, impact studies. The findings of the study provided novel insights on the importance of proper post-processing techniques for improving the robustness of d4PDF in climate change impacts assessment.

Temporal climate conditions and spatial drought patterns across rangelands in pastoral areas of West Guji and Borana zones, Southern Ethiopia

Climate change and its variability adversely impact pastoral livelihoods, and understanding climate variability and its change is important to deduce policy implications for pastoral communities. Accordingly, the aim of this study was to investigate the temporal climate conditions and spatial patterns of drought across the rangelands in pastoral areas of West Guji and Borana Zones, Southern Ethiopia. Temporal climate conditions were performed at the rangeland level, and spatial drought patterns (distributions) were estimated across the sample Kebeles (Kebele is the smallest administrative unit in Ethiopia which works in the Duda rangeland) and Reeras (Reeras refer to the smallest customary administrative unit in the Borana Zonal administration) structures. Station-satellite temperature and rainfall data (38 years from 1981 to 2018) were obtained from the Ethiopian Meteorological Agency. For data analysis, the Mann–Kendall trend test was employed to test rainfall, temperature and drought patterns, while Sen’s slope was used to test their magnitude of change, and coefficient of variation was employed to estimate rainfall and temperature variability. Standardized precipitation index was used to estimate drought event, while inverse distance-weighted method was used to estimate spatial drought patterns. The study reveals very high temporal rainfall variability with notable disparities in the rangelands mainly attributed to climate change. Besides, the study area exhibits high spatial variability of drought signifying agro-ecological characteristics in the rangelands. In the Duda and Gomole rangelands, annual rainfall increased non-significantly by 0.01 and 0.03 mm per annum, respectively. The annual minimum temperature in both Duda and Gomole increased significantly by 0.008 and 0.007 ℃ per year, respectively, albeit the annual maximum temperature in the rangelands decreased non-significantly by 0.02 and 0.009 ℃ per annum. Both rangelands experienced high annual rainfall variability, increasing annual minimum temperature, drought frequency and severity. Therefore, the outcome of the study is believed to be vital for identifying drought hotspot areas in the rangelands and devising strategies that help to reduce drought impacts on pastoral communities in Southern Ethiopia.

Relative Pollen Productivity Estimates for Major Plant Taxa in Middle Subtropical China

Relative pollen productivity (RPP) is a key parameter for quantitative reconstruction of past vegetation cover. However, RPP estimates are rarely obtained in the subtropical and tropical regions. In this study, the extended R-value (ERV) model was used to estimate RPP for major plant taxa in the evergreen broadleaved and mixed forests in middle subtropical China based on soil samples and vegetation data from 23 sites. The best result was obtained with the combinations of ERV sub-model 3 and Prentice’s or 1/d vegetation distance-weighting method. The relevant source area of pollen (RSAP) of the soil samples was estimated to be ca. 500 m. RPP on the basis of ERV sub-model 3 and Prentice’s model was obtained for seven taxa and the ranking is as follows: Castanopsis (1 ± 0), Ilex (0.352 ± 0.031), Mallotus (0.221 ± 0.028), Liquidambar (0.115 ± 0.007), Cyclobalanopsis (0.107 ± 0.006), Camelia (0.033 ± 0.001), Symplocos (0.010 ± 0.002). RPPs for Cyclobalanopsis, Camelia, Ilex, and Symplocos which are dominant elements in the subtropical evergreen broadleaved forests were first obtained. Our result demonstrates a significant effect of pollen dispersal models on the estimates of RPPs. The RPPs obtained in this study provide an important basis for quantitative vegetation reconstruction in the subtropical region of China.

Evaluation and Spatial Variability of Cryogenic Soil Properties (Yamal-Nenets Autonomous District, Russia)

Agricultural development in northern polar areas has potential as a result of global warming. Such expansion requires modern soil surveys and large-scale maps. In this study, the abandoned arable experimental field founded by I.G. Eichfeld one century ago in Salekhard city (Russian Arctic), located in the polar circle, was investigated. Our aims were to assess the nutritional soil properties and their spatial variability. For spatial assessment and mapping, ordinary kriging (OK) and inverse distance-weighted (IDW) methods were employed. We found that due to long-term agriculture use, the soil cover was represented by a unique Plaggic Podzol (Turbic) that is not typical of the region. The soil was characterized by relatively low soil organic carbon (SOC) content, high acidity and a high content of plant-available forms of phosphorus in the humus-accumulative horizon. The results showed that some properties (pH H2O, pH CaCl2) were characterized by large-scale heterogeneity and showed clear spatial dependence. However, some properties (ammonium and nitrate nitrogen, basal respiration) showed a pure-nugget effect, presumably due to experimentation with fertilizer over a long period of time.

EVALUATION OF GROUNDWATER SUITABILITY FOR IRRIGATION PURPOSE USING GIS AND IRRIGATION WATER QUALITY INDICES

As a major source of water for irrigation, the evaluation and assessment of groundwater to ensure it meets the quality for sustainable agriculture is key. This research presents the findings of the quality of groundwater and its suitability for irrigation purposes. This was carried out using the irrigation water quality indices and the Geographic Information System (GIS). The Inverse Distance Weighted (IDW) method of the GIS was used to study the spatial distribution of these indices. Indices considered include Sodium Absorption Ratio (SAR), Percentage Sodium (%Na), Permeability Index (PI), Kelly Ratio (KR), Magnesium Hazard (MH), Total Hardness (TH), Residual Sodium Bicarbonate (RSBC) and Potential Salinity (PS). The values obtained were SAR (0.00 to 10.99, mean of 3.43), Percentage Sodium (26.00% to 94.42%, mean of 70.33%), Permeability Index (94.14% to 379.47%, mean of 161.45%), Kelly Ratio (0.00 to 8.62, mean of 3.19) and Magnesium Hazard (0.00% to 80.33 %, mean of 39.21%). Total Hardness (12.49 mg/L to 77.50 mg/L, mean of 31.35 mg/L), Residual Sodium Bicarbonate (-0.55 meq/L to 5.46 meq/L, mean of 1.41 meq/L), Potential Salinity (0.88 meq/L to 2.53 meq/L, mean of 1.69 meq/L) and Electrical Conductivity (110µS/cm to 910µS/cm, mean of 277.14µS/cm). The computed water indices when compared with known standards show that the groundwater from the study area is generally fit and can be applied for irrigation purposes.

Characteristics of Ultramafik Rock and Nickel Laterite Distribution in Siuna Area, Pagimana, Banggai, Central Sulawesi

The geology of Sulawesi Island, especially the eastern arm, is known to be composed of extensive ultramafic rocks with mineral deposit potential, including nickel laterite. The research area is located at PT. X Siuna, Pagimana District, Banggai Regency, Central Sulawesi Province. This study purpose to determine the characteristics of ultramafic rocks in the study area and to determine the distribution of laterite nickel and the relationship between ultramafik rock characteristics in the study area. The research methods used are field research methods and laboratory analysis (Petrography and XRF Analysis). Field research was conducted to collect field data in the form of rock samples and soil samples where there were 9 stations for rock samples and 4 stations for soil samples. Furthermore, laboratory analysis in the form of petrographic analysis will be carried out to determine the mineral composition of the rock, then XRF (X-Ray Flourescence) analysis is carried out to determine laterite nickel content which is then processed using ArcGIS 10.5 software, then interpolated using the Inverse Distance Weight (IDW) method. Based on the results of field and laboratory analysis, the rock characteristics in the study area have 3 rock types, namely serpentinized peridotite units, serpentinized dunite units and serpentinite units. The three rocks have been serpentinized with intermediate to strong intensity, the constituent minerals have been dominated by serpentine, opaque mineral, pyroxine, and olivine. Based on IDW interpolation results, the distribution of nickel laterite in the western part of the study area has nickel (Ni) content of 0.50 – 1.2%, while in the southern – eastern part of the research area it has nickel (Ni) content of 0.20 – 0.5%

STUDY OF SNPP-VIIRS SATELLITE IMAGE FOR ESTIMATION SURFACE GILL NETS FISHING GROUND IN SEMARANG WATERS, CENTRAL JAVA, INDONESIA

Gillnet fishermen in Semarang often have difficulty determining the fishing grounds. The aims of the study were to analysed the spatial and temporal distribution of sea surface temperature (SST) and chlorophyll-a, to analysed the relationship between variables and to study the estimation of fishing grounds. The data collection method used purposive sampling. Map processing using the Inverse Distance Weighted (IDW) method. The data processing method uses normality test, classical assumption test and linear regression test. Analysis of image data spatially (4 miles from the shoreline) and temporally (west, east and transition monsoons). SST and chlorophyll-a tend to be high near the shore. The lowest SST (28.82OC) and the highest chlorophyll-a (5.11 mg/L) occurred in the east monsoon. The highest SST (31.74OC) and the lowest chlorophyll-a occurred in the west monsoon (1.31 mg/L). SST and chlorophyll-a have an erratic distribution pattern during the transitional monsoon. The value of SST and chlorophyll-a correlation partially to catch was 14.3% (weak) and 74% (very strong). The correlation value of SST to chlorophyll-a is 30.2% (medium). The correlation value of SST and chlorophyll-a simultaneously with catch is 75.3% (very strong). Determination of fishing areas based on high chlorophyll-a1 values and low SST. The coordinates for determining the fishing ground include 6° 53' 24,858" South Latitude 110° 26' 0.071" east west monsoon, 6° 55' 43,941" LS 110° 24' 47,903" east longitude transitional monsoon I, 6° 55' 29,439" latitude 110° 25' 54,790" east longitude, and 6° 55' 11,394" latitude 110° 26' 15,904" east longitude of transitional monsoon II.

A Modified Local Distance-weighted (MLD) Method of Interpolation and Its Numerical Performances for Large Scattered Datasets

The purpose of this study was to propose a new interpolation scheme that was designed to remedy the shortcomings encountered in two popular interpolation methods; the triangle-based blending (TBB) method and the inverse distance weighted (IDW) method. At the same time, the proposed method combines their desirable aspects, which are the local nature and non-use of quadratic surface construction, making it comparatively less time-consuming and more independent of the global effect. Because of these properties, the new scheme was named the ‘Modified Local Distance-Weighted (MLD)’ method, and it was tested in detail with different sizes of datasets. The datasets involved in this investigation were of two types; uniformly and non-uniformly distributed. The performances were carefully monitored and assessed via several criteria; accuracy, sensitivity to parameters, CPU-time, storage requirement and ease of implementation. For comparative purposes, three alternative interpolation methods were simultaneously carried out, i.e. TBB, IDW and Radial Basis Function-Based (RBF) method. The investigation clearly revealed promising aspects of the proposed scheme where a good quality of results was anticipated, and CPU-time and storage were seen to have been significantly reduced. The research strongly indicates the benefits of the proposed method for larger sized datasets for real practical scientific and engineering uses in the future.

Future Climate Change Impact on the Nyabugogo Catchment Water Balance in Rwanda

Droughts and floods are common in tropical regions, including Rwanda, and are likely to be aggravated by climate change. Consequently, assessing the effects of climate change on hydrological systems has become critical. The goal of this study is to analyze the impact of climate change on the water balance in the Nyabugogo catchment by downscaling 10 global climate models (GCMs) from CMIP6 using the inverse distance weighting (IDW) method. To apply climate change signals under the Shared Socioeconomic Pathways (SSPs) (low and high emission) scenarios, the Soil and Water Assessment Tool (SWAT) model was used. For the baseline scenario, the period 1950–2014 was employed, whereas the periods 2020–2050 and 2050–2100 were used for future scenario analysis. The streamflow was projected to decrease by 7.2 and 3.49% under SSP126 in the 2020–2050 and 2050–2100 periods, respectively; under SSP585, it showed a 3.26% increase in 2020–2050 and a 4.53% decrease in 2050–2100. The average annual surface runoff was projected to decrease by 11.66 (4.40)% under SSP126 in the 2020–2050 (2050–2100) period, while an increase of 3.25% in 2020–2050 and a decline of 5.42% in 2050–2100 were expected under SSP585. Climate change is expected to have an impact on the components of the hydrological cycle (such as streamflow and surface runoff). This situation may, therefore, lead to an increase in water stress, calling for the integrated management of available water resources in order to match the increasing water demand in the study area. This study’s findings could be useful for the establishment of adaptation plans to climate change, managing water resources, and water engineering.

Description of BTEX concentrations with wind roses in Tehran city: the study of monitoring, GIS-zoning maps and risk assessment

In the present study, the emission of BTEX compounds has been investigated in a one-year period (2016–2017) in Tehran city. According to, 26 sampling stations were selected in different parts of the city and had been sampled in four seasons of year to determine the concentration of BTEX as well as other meteorological parameters such as temperature, humidity and wind speed. Spatial distribution analysis of BTEX was introduced by using the Inverse Distance Weighting (IDW) method in Arc GIS 10.3. Also, to determine the carcinogenic and non-carcinogenic risk of target compounds, Monte Carlo simulation was used. The results showed that the annual mean of benzene, toluene, ethylbenzene, m-xylene and o-xylene were 30.08 ± 21.97, 54.17 ± 40.29, 25.81 ± 15.21, 27.83 ± 19.1 and 23.16 ± 12.28 μg/m3, respectively. According to the result, the maximum and minimum concentrations of BTEX were in the center and north of the city, respectively. Temporally, the maximum value of BTEX was 232.61 ± 123.93 µg/m3 in the autumn and the minimum value was 57.88 ± 24.93 µg/m3 in the spring. The result obtains from the inhalation lifetime cancer risk (LTCR) indicative the carcinogenic potential of benzene. Noncarcinogenic risk index, namely the hazard quotient (HQ) for all BTEX compound except benzene concentration in the autumn (1.55), were lower than 1 degree so they were not harmful to human health.

Forecast of Thunderstorm Cloud Trend Based on Monitoring Data of Thunder Mobile Positioning System

As a natural phenomenon, thunder and lightning have a major impact on human production and life. As an important part of lightning protection technology, the main task of the lightning mobile positioning system is to detect and determine the location of lightning and, at the same time, provide more accurate lightning discharge parameters for lightning research. It is a new technology that serves the entire society and is in urgent need of development. This paper aims to study the trend prediction of thunderstorm cloud based on the monitoring data of the thunder and lightning mobile positioning system. In this thesis, the inverse distance-weighted interpolation method can be used to determine the lightning area and the principle of lightning monitoring and positioning, and the classification of lightning and the practical significance of lightning mobile positioning system monitoring are also studied. Finally, the Hurst index of this paper can reveal the trend elements in the time series well through the experiment, so as to judge the lightning strikes. At the same time, it also introduces everyone’s satisfaction survey on the lightning mobile positioning system. The results of this paper show that the lightning mobile positioning system has been widely used in our country’s meteorological monitoring stations, and it plays a very important role in our national defense lightning strikes and effectively realizes the lightning strike prediction in the monitoring process, which can better enable the competent department to take timely and accurate measures to prevent lightning strikes. Experimental analysis shows that the accuracy of the lightning mobile positioning system has reached 92%, and the practicability has reached 88%.

The relationship between ambient temperature and fasting plasma glucose, temperature-adjusted type 2 diabetes prevalence and control rate: a series of cross-sectional studies in Guangdong Province, China

BackgroundThere existed evidence that type 2 diabetes mellitus (T2DM) prevalence and control rate have seasonal variation. Our study aimed to examine the ambient temperature and fasting plasma glucose (FPG) association and estimate temperature-adjusted T2DM prevalence and control rate.MethodsFour cross-sectional health surveys with 26,350 respondents were conducted in Guangdong Province from 2007 to 2015. Multistage cluster sampling was used to recruit study participants. The data of demographic characteristics, lifestyle factors, diet and use of hypoglycemic medicine, height, weight, FPG and meteorological information were collected. And an inverse distance-weighted method was employed to estimate daily temperature exposures at the individual’ s residential district/county. Base on World Health Organization 2006 criteria, participants were divided into normal fasting glucose (NFG) participants (n = 23,877), known T2DM patients (n = 916) and newly detected T2DM patients (n = 1557). Generalized additive mixed model was employed to evaluate the nonlinear associations between temperature and FPG among different T2DM subgroups. The T2DM prevalence and control rate were estimated based on temperature-FPG association.ResultsThe curves of temperature and FPG were downward parabola for total, NFG and known T2DM groups, while it was “U”-shaped for newly detected T2DM patients. When temperature decreased from 30 °C to 4 °C, the FPG significantly increased 0.24 (95%CI: 0.15, 0.33) mmol/L, 0.10 (95%CI: 0.06, 0.14) mmol/L and 1.34 (95%CI: 0.56, 2.12) mmol/L in total, NFG and known T2DM groups, respectively. Compared to 19 °C, newly detected T2DM patients’ FPGs were increased 0.73 (95%CI: 0.13, 1.30) mmol/L at 4 °C and 0.53 (0.00, 1.07) mmol/L at 30 °C. The model-estimated temperature-adjusted T2DM prevalence had a down and up trend, with 9.7% at 5 °C, 8.9% at 20 °C and 9.4% at 30 °C, respectively. At 5, 10, 15, 20, 25 and 30 °C, the model-estimated temperature-adjusted T2DM control rates were 33.2, 35.4, 38.2, 43.6, 49.1 and 55.2%.ConclusionTemperature was negatively associated with FPG for NFG and known T2DM subgroups, while their association was U-shape for newly detected T2DM patients. Hence, the temperature-adjusted T2DM prevalence show a dip/peak pattern and T2DM control rate display a rising trend when temperature increase. Our findings suggest temperature should be considered in T2DM clinic management and epidemiological survey.

An Investigation of Takagi-Sugeno Fuzzy Modeling for Spatial Prediction with Sparsely Distributed Geospatial Data

Fuzzy set theory has shown potential for reducing uncertainty as a result of data sparsity and also provides advantages for quantifying gradational changes like those of pollutant concentrations through fuzzy clustering based approaches. The ability to lower the sampling frequency and perform laboratory analyses on fewer samples, yet still produce an adequate pollutant distribution map, would reduce the initial cost of new remediation projects. To assess the ability of fuzzy modeling to make spatial predictions using fewer sample points, its predictive ability was compared with the ordinary kriging (OK) and inverse distance weighting (IDW) methods under increasingly sparse data conditions. This research used a Takagi–Sugeno (TS) fuzzy modelling approach with fuzzy c-means (FCM) clustering to make spatial predictions of the lead concentrations in soil. The performance of the TS model was very dependent on the number of outliers in the respective validation set. For modeling under sparse data conditions, the TS fuzzy modeling approach using FCM clustering and constant width Gaussian shaped membership functions did not show any advantages over IDW and OK for the type of data tested. Therefore, it was not possible to speculate on a possible reduction in sampling frequency for delineating the extent of contamination for new remediation projects.

A robust parametrization method of photovoltaic modules for enhancing one-diode model accuracy under varying operating conditions

Modeling the current-voltage (I–V) characteristics of photovoltaic (PV) modules is of importance for condition monitoring and fault diagnosis of PV arrays. The practicality of the one-diode model (ODM) is relying on the behaviors of parameter extraction and translation techniques. In this paper, a simple and robust dual-iteration algorithm is presented to extract the ODM parameters using only three points under limited conditions. These points can be easily obtained from both the datasheet and measurements, making the method universal and practical. Parameters under other unknown conditions are translated from the nearest acquired condition to make the proposed method applicable for various conditions. The modeling I–V curve obtained by the proposed method is in a close agreement with the field measurement, demonstrating its effectiveness to characterize the PV modules with different technologies. Moreover, three existing models are used to benchmark the accuracy of the proposed method. The comparison further confirms that the accuracy of the ODM has been enhanced especially at low irradiance using the proposed parameter translation method. Furthermore, a distance-weighted method is presented to reliably translate the parameters, whose superiority has been validated against measurements and the other four methods, thus being an effective output assessment for monitoring and diagnosis.

Spatial distribution of soil salinity: a GIS-based assessment at the Hardap irrigation scheme, Namibia

Salinization is a worldwide problem that affects the physical and chemical properties of soils. It is the most common and widely recognized form of land degradation, particularly in arid and semi-arid regions, where evaporation surpasses precipitation. Namibia aims to grow the agricultural sector for food independence, economic growth and stability. As a result, it is of great importance to manage and protect crop farms from salinization in the country. This study assessed the spatial distribution of soil salinity at the Hardap Irrigation Scheme, one of the two largest and oldest schemes situated in a hot desert climate in Namibia. The grab sampling method was used to collect 59 surface soil samples from randomly selected sites in the study area. The sampling sites included both irrigated and non-irrigated areas. Samples were subsequently analysed in the laboratory for electrical conductivity (EC), using saturated paste EC1:5 method. Resultant measurements were then grouped into five salinity classes. The point data were interpolated using the Inverse Distance Weighted (IDW) method in ArcGIS. Salinity levels in the study area ranged between 0.004 and 1.97 dS/m ± 0.557 dS/m. Although high salinity levels were recorded, non- and slightly saline classes cover just over two-thirds of the study area. Soil salinity in the study area is thus generally low, but the hazard exists. The low salinity level in the study area may be due to hydropedological factors, the prevailing management system or both. Regular monitoring of soil salinity is recommended, especially when new management system is introduced.

Temperature and hand, foot and mouth disease in California, 2005-2013, an exploratory analysis

Background: Researchers have found that temperature has been associated with hand, foot and mouth disease (HFMD) in Asia. However, few have explored the relationship in the United States, particularly California where HFMD cases have increased recently.Methods: We collected weekly counts of HFMD emergency department (ED) visits in California from 2005 to 2013 and calculated weekly temperature for each of the state’s sixteen climate zones using an inverse distance-weighting method for weekly lags 0 to 2. For each season, we conducted a time-series analysis using Poisson regression, adjusting for weekly average relative humidity, average number of HFMD cases in previous weeks and long-term temporal trends. We combined season-specific climate zone estimates to produce an overall estimate for California.Results: We found an increased risk of ED visits for HFMD per 1°F increase in temperature during the same week by 2.00% (95% confidence intervals: 1.15, 2.86) and 2.35% (1.38, 3.33) during the warm and cold season, respectively. The coastal region showed a higher association during the cold season [3.18% (1.99, 4.39)] than the warm season [1.64% (0.47, 2.82)] for weekly lag 0, which may be attributed to the relatively mild winter in the coastal region. Additionally, during the warm season, only weekly lag 0 showed a positive association while weekly lags 0, 1 and 2 remained elevated during the cold season in the all California analysis.Conclusions: Our findings indicated an association between temperature and ED visits for HFMD in California, which varied both seasonally and regionally. The elevated associations for later lags observed in the cold season could indicate a more favorable environment for HFMD pathogens to survive on surfaces and in the air. Because HFMD is a disease that mainly affects children, a group that is already vulnerable to higher temperature, health care providers should be especially aware.

Application of Finite Difference Method in Multifractal Techniques to Identify Pb–Zn Anomalies: a Case Study from Khomein, Iran

Separation of geochemical anomalies from background is an important and essential issue in the exploration of mineral deposits. Statistical methods are used to separation of anomaly from background. Fractal/multifractal and singularity methods are very suitable for the separation of weak anomalies. For interpolation, various methods could be used such as most distance-weighting methods, kriging, spline interpolation, interpolating polynomials, and finite difference methods. This paper addressed application of multifractal techniques together with finite difference procedure to detect the anomalous regions. Fractal models such as concentration–area, spectrum–area, and singularity index model were selected to identify Pb–Zn anomalies. In this research, 170 samples from 33 boreholes were taken in the Haft-Savaran area and analyzed for determination of Pb–Zn anomaly. Singularity method was a suitable method for separating Pb and Zn anomalies. Also, the results showed that the geochemical anomaly has occurred along NE–SW trend and is consistent with the sulfide mineralization process and the faults of the region.

Associations of maternal ambient temperature exposures during pregnancy with the placental weight, volume and PFR: A birth cohort study in Guangzhou, China

BackgroundThe placenta performs crucial functions to ensure normal fetal development. Experimental studies have indicated associations between exposure to elevated temperatures during pregnancy and reduction in placental weight and volume. However, epidemiological studies in humans are lacking. ObjectiveTo estimate the associations between prenatal exposure to ambient temperature with placental weight, volume, and the placental weight to birth weight ratio (PFR). MethodsWe conducted a prospective birth cohort study using the Prenatal Environment and Offspring Health Cohort (PEOH Cohort) beginning in 2016 in Guangzhou, China. Women in early pregnancy were recruited and followed up during their hospitalization for childbirth. An inverse distance-weighted method was employed to estimate the average temperature exposure of every 4 weeks as well as the trimester-specific average temperature exposure at the individual’s residential address. A generalized linear model was applied to estimate the effects of temperature exposure during pregnancy on the placental weight, volume, and PFR. ResultsA total of 4051 pregnant women were enrolled. Compared with the reference temperature of 20 °C, maternal exposure to 29 °C (95th centile) during late pregnancy was associated with an average of −6.03 g (95% confidence interval [CI]: −11.28 g, −0.78 g) in placental weight, −16.15 cm3 (95% CI: −26.24 cm3, −6.07 cm3) in placental volume, and 0.26 (95% CI: 0.07, 0.45) in PFR. The peak effects of high temperatures on placental weight, volume, and PFR were found from 29 to 32 weeks (β = –3.79 g, 95% CI: −8.39 g, 0.82 g), 37 to 40 weeks (β = −19.34 cm3, 95% CI: −30.99 cm3, −7.69 cm3), and 25 to 28 weeks (β = 0.35, 95% CI: 0.04, 0.66), respectively. ConclusionsMaternal exposure to elevated temperatures was associated with a decrease in placental weight and volume and an increase in PFR. The associations were stronger when exposures occurred during late pregnancy.

Temperature and hand, foot and mouth disease in California: An exploratory analysis of emergency department visits by season, 2005–2013

BackgroundFor the past decade, hand, foot and mouth disease (HFMD), caused by entero and coxsackie viruses, has been spreading in Asia, particularly among children, overloading healthcare settings and creating economic hardships for parents. Recent studies have found meteorological factors, such as temperature, are associated with HFMD in Asia. However, few studies have explored the relationship in the United States, although HFMD cases have steadily increased recently. As concerns of climate change grow, we explored the association between temperature and HFMD admissions to the Emergency Department (ED) in California. MethodsWeekly counts of HFMD for 16 California climate zones were collected from 2005 to 2013. We calculated weekly temperature for each climate zone using an inverse distance-weighting method. For each climate zone stratified by season, we conducted a time-series using Poisson regression models. We adjusted models for weekly averaged relative humidity, average number of HFMD cases in previous weeks and long-term temporal trends. Climate zone estimates were combined to obtain an overall seasonal estimate. We attempted stratified analyses by region, race/ethnicity, and sex to identify sensitive subpopulations. ResultsRisk of ED visits for HFMD per 1 °F increase in mean temperature during the same week increased 2.00% (95% confidence intervals 1.15, 2.86%) and 2.35% (1.38, 3.33%) during the warm and cold seasons, respectively. The coastal region showed a higher, though not statistically different, association during the cold season [3.18% (1.99, 4.39)] than the warm season [1.64% (0.47, 2.82)]. ConclusionsOur findings indicated an association between temperature and ED visits for HFMD, with variation by season and region. Thus, the causative pathogen's ability to persist in the atmosphere may vary by season. Furthermore, the mild and wet winter in the coastal region of California may contribute to different results than studies in Asia. With the onset of climate change, HFMD cases will likely grow in California, warranting further investigation on this relationship, including new populations at-risk.