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Epidemiological studies have identified associations between mortality and changes in concentration of particulate matter. These studies have highlighted the public concerns about health effects of particulate air pollution. Modeling fine particulate matter PM2.5 exposure risk and monitoring day-to-day changes in PM2.5 concentration is a critical s...
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... it is possible to build a k-d tree to hold data of any dimension, all of the data stored in a k-d tree must have the same dimension. Figure 2 shows an example of a k-d tree that stores 11 points in a three-dimensional space. The coordinate system in three-dimensional space needs three coordinate axes, the x-, y-and z-axis. ...
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... each level of the k-d tree shown in Figure 2, a certain component of each node has been bolded. Suppose the components are zero-indexed (for example, the x dimension is component zero, the y dimension is component one and the z or t dimension is component two). ...
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... reason that these values are bolded is because each node acts like a binary search tree node that discriminates only along the bolded component. For example, the root of the k-d tree in Figure 2 has the value (3, 1, 4), with the first component, 3, in bold. The first component (x-coordinate) of every node in the k-d tree's left sub-tree is less than or equal to three, while the first component of every node in the right sub-tree is greater than three. ...
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... a circle and a line (or a hyper-sphere and a hyper-plane), how does the algorithm determine whether or not the circle intersects the line? To determine this mathematically, consider the following arbitrary hyper-line and two hyper-circles, one of which crosses the hyper-line and one of which does not, as shown in Figure 12. Figure 12 shows that the distance |y 1 − y 0 | from the center of the left hyper-circle to the hyper-line is greater than the radius of the left hyper-circle, and therefore, the hyper-circle does not cross the hyper-line. ...
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... a circle and a line (or a hyper-sphere and a hyper-plane), how does the algorithm determine whether or not the circle intersects the line? To determine this mathematically, consider the following arbitrary hyper-line and two hyper-circles, one of which crosses the hyper-line and one of which does not, as shown in Figure 12. Figure 12 shows that the distance |y 1 − y 0 | from the center of the left hyper-circle to the hyper-line is greater than the radius of the left hyper-circle, and therefore, the hyper-circle does not cross the hyper-line. However, the distance from the center of the right hyper-circle to the hyper-line is less than the radius of the right hyper-circle, and therefore, some part of that hyper-circle does cross the hyper-line. ...
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... MARE: n = 3 MARE: n = 4 RMSPE: n = 3 RMSPE: n = 4 It is shown from Table 3 that of these nine IDW methods, the IDW methods with the maximum of three or four nearest neighbors and with an exponent of 5.0 (n = 3, 4 and p = 5.0) have the lowest MARE and RMSPE values, according to the leave-one-out cross-validation. Figure 20 illustrates the MARE results and Figure 21 illustrates the RMSPE results in the improved IDW interpolation for the 45 IDW methods that were evaluated in the 10-fold cross-validation, using the PM 2.5 data. As in the leave-one-out cross-validation, the maximum Euclidean distance that was used for the nearest neighbor criteria is 1.4, and the maximum time difference for the nearest neighbor criteria is seven days. ...
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... MARE: n = 3 MARE: n = 4 RMSPE: n = 3 RMSPE: n = 4 It is shown from Table 3 that of these nine IDW methods, the IDW methods with the maximum of three or four nearest neighbors and with an exponent of 5.0 (n = 3, 4 and p = 5.0) have the lowest MARE and RMSPE values, according to the leave-one-out cross-validation. Figure 20 illustrates the MARE results and Figure 21 illustrates the RMSPE results in the improved IDW interpolation for the 45 IDW methods that were evaluated in the 10-fold cross-validation, using the PM 2.5 data. As in the leave-one-out cross-validation, the maximum Euclidean distance that was used for the nearest neighbor criteria is 1.4, and the maximum time difference for the nearest neighbor criteria is seven days. ...
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... the 10-fold cross-validation or the LOOCV function first requires the user to select a data file. If the format of the file is valid and the file is successfully loaded, the user presses the "10 Fold Cross Validation" or the "LOOCV Validation" button, as shown in Figure 22. The user is then presented with a dialog asking to select the exponent, the number of nearest neighbors, the maximum Euclidean distance and the maximum time difference, as shown in Figure 23. ...
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... the format of the file is valid and the file is successfully loaded, the user presses the "10 Fold Cross Validation" or the "LOOCV Validation" button, as shown in Figure 22. The user is then presented with a dialog asking to select the exponent, the number of nearest neighbors, the maximum Euclidean distance and the maximum time difference, as shown in Figure 23. The maximum Euclidean distance is the maximum distance between the nearest neighbors and the point being interpolated. ...
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... file with the locations to be interpolated needs to be selected. Once the location file is selected, the contents will be displayed in the Centroid Locations Data Pane for the user to browse, as shown in Figure 22. The Centroid Locations Data Pane displays the x and y coordinates for the centroid locations of counties or census block groups in the contiguous U.S. that are to be interpolated. ...
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... Centroid Locations Data Pane displays the x and y coordinates for the centroid locations of counties or census block groups in the contiguous U.S. that are to be interpolated. When the user clicks the Interpolate button, the system will ask the user to input the exponent, the number of nearest neighbors, the maximum Euclidean distance and the maximum time difference, as shown in Figure 23, which the user should have decided from the previous validation step. Then, the interpolation is performed, and the the interpolation results are exported to a text file. ...
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... the interpolation is performed, and the the interpolation results are exported to a text file. Figure 22. The web application GUI (graphic user interface) with a location file selected. ...
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... web application GUI (graphic user interface) with a location file selected. Figure 23. The web application GUI dialog where the user selects the exponent, the number of nearest neighbors, the maximum Euclidean distance and the maximum time difference. ...
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... the slider is moved, the interpolated results are updated and displayed on the map. Figures 24 and 25 show the PM 2.5 interpolation results at the centroids of census block groups in the contiguous U.S. at two different time instances of 20 January 2009 and 1 April 2009. Second, clicking on the "Play" button automatically advances the time on the map and animates the daily change in PM 2.5 over the course of the year. ...
Similar publications
Exposure to fine particulate matter (PM2.5) has been associated with a wide range of negative health outcomes. The overwhelming majority of the epidemiological studies that helped establish such associations was conducted in regions with sufficient ground observations and other supporting data, i.e., the data-rich regions. However, air
pollution he...
This paper proposes a regression model using the Eigenvector Spatial Filtering (ESF) method to estimate ground PM2.5 concentrations. Covariates are derived from remotely sensed data including aerosol optical depth, normal differential vegetation index, surface temperature, air pressure, relative humidity, height of planetary boundary layer and digi...
Citations
... The weights are determined based on the inverse distance between the known and unknown points. The IDW algorithm assumes that closer points have a stronger influence on the estimation [6]. In this study, the IDW interpolation method was employed to create air pollution concentration maps. ...
Air pollution is a significant concern for urban areas, particularly in industrial cities like Kanpur located in the state of Uttar Pradesh, India. The proposed study aims to present the findings of an assessment conducted to determine ambient air pollution during summer season for Kanpur City using the Geospatial techniques. In this context, two spatial interpolation techniques namely Inverse Distance Weighting (IDW) and Kriging were used for the analysis. The pollutant selected for the assessment was particulate matter (PM10) since it is responsible for the poor air quality in the city. Monitored Data for the pollutant were collected from different monitoring stations located across the city. Since, these monitoring locations are located at far apart distances, they fail to provide any spatial representation of the monitored pollutants, and it is in this context that spatial interpolation techniques were used for representing the air quality over the entire city. The study period selected varies from March to May 2017, representing the summer season and when dispersion characteristics of air pollutants are high. The selected interpolation techniques were used to generate spatial distribution maps of air pollution concentrations. The study aims that by using these spatial interpolation techniques a comprehensive and accurate representation of the ambient air pollution existing within the Kanpur city perimeter will be represented. Further, the suitability of the two spatial interpolation techniques used will be discussed in greater details. The maps generated through these analyses provide crucial insights into the spatial distribution and potential hotspots of air pollution within Kanpur during the summer season. The findings of this study will have significant implications for urban planning, environmental management and public health in Kanpur. It was inferred from the study that the concentration of PM was significantly higher in areas with heavy traffic and industrial activities.
... Some examples are provided in the following. Li et al. [22] utilized IDW for interpolating the PM2.5 series in the United States, whereas Choi and Chong [23] proposed a new version of IDW applied for series from South Korea and proved its increased performance against the classical IDW and kriging [24]. Diggle and Ribeiro [25] proposed model-based geostatistics. ...
Various studies have proved that PM2.5 pollution significantly impacts people’s health and the environment. Reliable models on pollutant levels and trends are essential for policy-makers to decide on pollution reduction. Therefore, this research presents the sensitivity analysis of the Bicubic Spline Smoothing (BSS) and Inverse Distance Weighting (IDW) models built for the PM2.5 monthly series from MERRA-2 Reanalysis collected during January 2010–April 2017 in the region of the Persian Gulf, in the neighborhood of the United Arab Emirates Coast. The models’ performances are assessed using the Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE). RMSE, Mean Bias Error (MBE), and Nash–Sutcliff Efficiency (NSE) were utilized to assess the models’ sensitivity to various parameters. For the IDW, the Mean RMSE decreases as the power parameter increases from 1 to approximately 4 (the optimal beta value) and then stabilizes with a further increase. NSE values close to 1 indicate that the model’s predictions are very efficient in capturing the variance of the observed data. NSE is almost constant as a function of the number of neighbors and the parameter when β > 4. In BSS, the RMSE and NBE plots suggest that incorporating more points into the mean calculation for buffer points leads to a general decrease in model accuracy. Moreover, the MBE plot shows that the mean bias error initially increases with the number of points but then starts to plateau. The increasing trend suggests that the model tends to systematically overestimate the PM2.5 values as more points are included. The leveling-off of the curve indicates that beyond a certain number of points, the bias introduced by including additional points does not significantly increase, suggesting a threshold beyond which further inclusion of points does not markedly change the mean bias. It was also proved that the methods’ generalizability may depend on the dataset’s specific spatial characteristics.
... If multiple in situ measured stations are involved in the matching process, the average value of these stations is taken as the in situ measured wind speed value of the grid. Subsequently, the EC and GL products were interpolated to a resolution of 0.01°/1 h using the inverse distance weighting-based spatio-temporal interpolation method (Li et al., 2014). This allowed for a TCA after the spatio-temporal nearest neighbor matching of the three grid products. ...
The lack of reliable near-surface wind data products in lake district regions significantly limits the efficiency of meteorological models, forecasts, services, and wind resource development. This is primarily due to a shortage of observation data and related studies, as well as insufficient validation and promotion of existing data products, which further hinders the comprehension and monitoring of local wind fields. In this study, comprehensive verification research was conducted on mainstream wind field products, namely, ERA5-Land (EC), GLDAS (GL), and HRCLDAS (HR) in the Dongting Lake area of China. This was achieved by utilizing a large volume of measured data and a triple collocation analysis (TCA) method. Additionally, an exploration into the optimal wind field data fusion method was undertaken. HR products demonstrate superior performance in capturing wind speed at the in situ measured scale, while GL outperforms at the grid scale, and EC products show relatively stable performance with minimal outliers. The long short-term memory (LSTM) neural network model, combined with time-series features, emerges as the most optimal data fusion model. LSTM fusion product is superior to the original product (except for HR products at the in situ measured scale), TCA-based weighted fusion products, and multi-layer fully connected neural network (MFCNN) on various parameters. This study quantifies the performance of mainstream wind products in lake areas and provides a benchmark for further application of these products. Furthermore, the successful implementation of an optimal wind data fusion method can provide valuable insights for related research, and the resulting wind fusion products can offer superior basic data support for local terminal applications.
... Mathematics 2024, 12, 400 2 of 21 such techniques possess enhanced capabilities for handling situations where the temporal component is substantial and can potentially yield higher accuracy, particularly in cases where the spatio-temporal relationships are complex [7]. ...
Research on spatio-temporal geostatistical modeling remains a critical challenge in numerous scientific and engineering disciplines. This paper introduces a novel extension of dual kriging, called spatio-temporal dual kriging (ST-DK), in which drift functions with fixed and adaptive coefficients are established. The approach appears to be effective in modeling complex spatio-temporal dynamics, particularly when relevant auxiliary variables exert substantial influence on the target variable. To illustrate its performance, we compare the ST-DK model with the classical spatio-temporal regression kriging (ST-RK) and geographically and temporally weighted regression (GTWR) models for estimating temperature and air pressure data from Thailand in 2018. Our findings demonstrate that both the ST-DK and ST-RK models when utilizing adaptive coefficients outperform their fixed coefficient counterparts. Furthermore, the ST-DK method consistently exhibits superior performance compared to the ST-RK and GTWR methods.
... However, their suitability may be limited in scenarios where the temporal component of the data plays a significant role [5,6]. On the contrary, the extension methods, such as parallel IDW, random forests, and spatio-temporal kriging methods, are more complex and computationally intensive but possess enhanced capabilities for handling situations where the temporal component is substantial and can potentially yield higher accuracy, particularly in cases where the spatio-temporal relationships are complex [7]. ...
Research on spatio-temporal geostatistical modelling remains a critical challenge in numerous scientific and engineering disciplines. This work introduces a novel extension of the dual kriging the spatio-temporal dual kriging (ST-DK) whose drift functions with fixed and adaptive coefficients are established. The approach appears to be effective in modelling complex spatio-temporal dynamics, particularly when relevant auxiliary variables exert substantial influence on the target variable. To illustrate its performance, we compare the ST-DK model with the classical spatio-temporal regression kriging (ST-RK) method for estimating temperature and air pressure data across Thailand in 2018. Our findings demonstrate that both ST-DK and ST-RK models, when utilizing adaptive coefficients, outperform their constant coefficient counterparts. Furthermore, the ST-DK method consistently exhibits superior performance compared to the ST-RK model.
... Many techniques are used in high resolution mapping; two commonly used techniques are Inverse Distance Weighting (IDW) and Kriging (Varentsov et al., 2020). IDW boasts simplicity in modeling and rapid computation (Li et al., 2014;Lu & Wong, 2008). However, this system also has significant shortcomings, such as being more sensitive to outliers, tends to underfit, is unable to handle non-stationarity problems, does not take into account spatiotemporal autocorrelation, and is unable to provide information on prediction uncertainty (Lloyd & Atkinson, 2002). ...
... Due to the stochastic nature of random effects and the inherent properties of hierarchical models, MCMC convergence is typically both slow and unpredictable. INLA emerged as an alternative method to effectively fit Bayesian with random effects components within the latent Gaussian class, in response to this challenge where significantly reducing the computational time required (Li et al., 2014;Lu & Wong, 2008). Specifying the prior is no longer an issue in the INLA method, as it operates under the assumption that all parameters follow a Gaussian prior distribution. ...
The Gaussian Markov Random Field (GMRF) is widely acknowledged for its remarkable flexibility, especially in the realm of high-resolution prediction, when compared to conventional Kriging methods. Rooted in the fundamental principles of Bayesian estimation, this methodology underscores the importance of a meticulous examination of prior and hyperprior distributions, along with their corresponding parameter values. Sensitivity analyses are crucial for evaluating the potential impact of these distributions and parameter values on prediction results. To determine the most effective values for hyperprior parameters, an iterative trial-and-error approach is commonly employed. In our research, we systematically assessed a variety of parameter values through exhaustive cross-validation. Our study is focused on optimizing hyperprior parameter values, with a particular emphasis on Penalized Complexity (PC). We applied our method to conduct spatiotemporal high-resolution predictions of PM2.5 concentrations in Jakarta province, Indonesia. Achieving accurate predictive modeling of PM2.5 concentrations in Jakarta is contingent upon this optimization. We identified that the optimal values for PC hyperprior parameters, with a range of less than 2,000 and a hyperprior standard deviation greater than 1 with a 0.1 probability, yield the most accurate predictions. These parameter values result in the minimum mean absolute percentage error (MAPE) of 19.35393, along with a deviation information criterion (DIC) of -154.23. Our findings highlight that the standard deviation parameter significantly influences model fit compared to the relatively insignificant impact of the range parameter. When coupled with high-resolution mapping, these optimized parameters facilitate a comprehensive understanding of distribution patterns. This process aids in detecting areas particularly susceptible to risks, thereby enhancing decision-making efficacy regarding air quality management.
... The spatial variability of the drought hazard is created using the inverse distance weighted (IDW) interpolation method in the GIS environment. IDW is a modest and easy-to-compute interpolation method extensively used for generating distributed spatial models (Soleimani et al. 2013;Li et al. 2014;Behrang Manesh et al. 2019). Figure 8 represents the drought condition of the basin on the yearly scale for five critical drought years within the study period. ...
Droughts are catastrophic and have become frequent in the last decade. The Upper Kangsabati River Basin (UKRB) in West Bengal state of India is affected by droughts. This research comprehensively evaluates meteorological drought and its spatial variability within the basin. Drought severity levels vary spatially with time, making it difficult for policymakers to prioritize preventive measures for different parts of the UKRB. Droughts were characterized using Standardized Precipitation Index (SPI) for 16 precipitation grid points. SPI-12 showed that the basin experienced two severe and three extreme drought years within the study period. Run theory was applied on SPI-3 to extract drought events, which varied from 34 to 41, spatially in the basin, with the maximum severity being 18.08. After fitting the appropriate marginal distributions to the extracted drought duration and severity, their bivariate relationship was established using copula. Generalized Pareto distribution and Frank copula fitted best for the marginal and bivariate distributions, respectively. Finally, the conditional OR return period of the historical droughts was determined from that relationship. It showed that parts of the basin experienced extreme drought of 50-year return period. The most likely event scenario was determined from the OR return period contours and applied to create the spatially distributed drought hazard maps, which is the uniqueness of the study. The present study suggested that the western and central regions of UKRB would experience more severe droughts when return period increases. Additionally, the modified Mann–Kendall (MMK) test applied to detect drought trends indicated no significant trend for monsoon months.
... Exposure to PM 2.5 has been directly correlated with diseases such as respiratory diseases and even mortality (Li et al., 2018;Xing et al., 2016;Samoli et al., 2005;Ostro et al., 2006;Lewis et al., 2005). The high health impact of PM 2.5 is because of its ability to penetrate deep into the lungs and because its composition is often carcinogenic (Li et al., 2014). The European Study of Cohorts for Air Pollution Effects (ESCAPE) shows that exposure to high PM 2.5 concentrations is linked to a risk of developing lung cancer (Raaschou-Nielsen et al., 2013). ...
Extensive monitoring of particulate matter (PM) smaller than 2.5 µm, i.e., PM2.5, is critical for understanding changes in local air quality due to policy measures. With the emergence of low-cost air quality sensor networks, high spatiotemporal measurements of air quality are now possible. However, the sensitivity, noise, and accuracy of field data from such networks are not fully understood. In this study, we use spectral analysis of a 2-year data record of PM2.5 from both the Environmental Protection Agency (EPA) and PurpleAir (PA), a low-cost sensor network, to identify the contributions of individual periodic sources to local air quality in Chicago. We find that sources with time periods of 4, 8, 12, and 24 h have significant but varying relative contributions to the data for both networks. Further analysis reveals that the 8 and 12 h sources are traffic-related and photochemistry-driven, respectively, and that the contributions of both these sources are significantly lower in the PA data than in the EPA data. The presence of distinct peaks in the power spectrum analysis highlights recurring patterns in the air quality data; however, the underlying factors contributing to these peaks require further investigation and validation. We also use a correction model that accounts for the contribution of relative humidity and temperature, and we observe that the PA temporal components can be made to match those of the EPA over the medium and long term but not over the short term. Thus, standard approaches to improve the accuracy of low-cost sensor network data will not result in unbiased measurements. The strong source dependence of low-cost sensor network measurements demands exceptional care in the analysis of ambient data from these networks, particularly when used to evaluate and drive air quality policies.
... The dispersion of RSIV in seawater was visualized using the inverse distance weighting (IDW) method with eDNA data from 30 sampling points on a fish farm (Fig. 3). IDW is an interpolation technique to estimate values at unmeasured locations based on the values of nearby measured points, which can then be used for visualizing data using contour maps or heatmaps (30). The heatmap images shown The red-filled square with an arrow in the wide-area map indicates the sampling area located on Shikoku Island in Japan. ...
Aquatic animal viruses are considered to be transmitted via environmental water between fish farms. This study aimed to understand the actual transmission risk of red sea bream iridovirus (RSIV) through environmental water among fish farms. An environmental DNA (eDNA) method using iron-based flocculation coupled with large-pore filtration was used to monitor RSIV DNA copies in seawater from fish farms and from an experimental infection model. RSIV dispersion in seawater from a net pen where the disease outbreak occurred was visualized by the inverse distance weighting method using multiple-sampling data sets from a fish farm. The analysis demonstrated that the center of the net pen had a high viral load, and RSIV seemed to be quickly diluted by the tidal current. To evaluate the transmission risk of RSIV in environmental water, the red sea bream Pagrus major (approximately 10 g) was exposed to RSIV-contained seawater (10 ³ , 10 ⁴ , 10 ⁵ , 10 ⁶ , and 10 ⁷ copies/L) for 3 days, which mimicked field exposure. A probit analysis of the challenge test indicated that the inferred infection rates of seawater containing 10 5.9 copies/L and 10 3.1 copies/L of RSIV were 50% and 0.0001%, respectively. In the surveillance for 3 years at 10 fixed points ( n = 306), there were only seven samples in which the viral load exceeded 10 ⁴ copies/L in seawater. These results suggest that the transmission of RSIV among fish farms via seawater is highly associated with the distance between the net pens, and the environmental water is not always an infection source for the transmission of RSIV between fish farms.
IMPORTANCE
Our surveillance of viral loads for red sea bream iridovirus (RSIV) by monitoring environmental DNA in fish farms suggested that the viral loads in the seawater were low, except for the net pens where RSIV outbreaks occurred. Furthermore, our experimental infection model indicated that the infection risk of RSIV-contained seawater with less than 10 ³ copies/L was extremely low. The limited risk of environmental water for transmission of RSIV gives an insight that RSIV could be partly transmitted between fish farms due to the movement of equipment and/or humans from the fish farm where the disease outbreaks. Since our data suggest that seawater can function as a potential wall to reduce the transmission of RSIV, biosecurity management, such as disinfection of equipment associated with fish farms could be effective, even in the semi-open system aquaculture that the environmental water can be freely transferred, to reduce the risk of RSIV outbreaks.
... After using wind direction and location to identify block groups that potentially could receive smoke from a fire due to congruent wind direction, we used the inverse of the squared distances between centroids of each block group and fire pixel as weights to calculate a daily smoke exposure metric for each block group. Of note, using a distanceweighted approach is common in environmental health, especially to assign pollution impacts from a specific source [27,29,30]. We then generated population-weighted estimates of this potential smoke impact factor (PSIF) at the zip code level from block group level estimates to align with the geographic resolution of the available health data. ...
Background
Prescribed agricultural burning is a common land management practice, but little is known about the health effects from the resulting smoke exposure.
Objective
To examine the association between smoke from prescribed burning and cardiorespiratory outcomes in the U.S. state of Kansas.
Methods
We analyzed a zip code-level, daily time series of primary cardiorespiratory emergency department (ED) visits for February–May (months when prescribed burning is common in Kansas) in the years 2009–2011 (n = 109,220). Given limited monitoring data, we formulated a measure of smoke exposure using non-traditional datasets, including fire radiative power and locational attributes from remote sensing data sources. We then assigned a population-weighted potential smoke impact factor (PSIF) to each zip code, based on fire intensity, smoke transport, and fire proximity. We used Poisson generalized linear models to estimate the association between PSIF on the same day and in the past 3 days and asthma, respiratory including asthma, and cardiovascular ED visits.
Results
During the study period, prescribed burning took place on approximately 8 million acres in Kansas. Same-day PSIF was associated with a 7% increase in the rate of asthma ED visits when adjusting for month, year, zip code, meteorology, day of week, holidays, and correlation within zip codes (rate ratio [RR]: 1.07; 95% confidence interval [CI]: 1.01, 1.13). Same-day PSIF was not associated with a combined outcome of respiratory ED visits (RR [95% CI]: 0.99 [0.97, 1.02]), or cardiovascular ED visits (RR [95% CI]: 1.01 [0.98, 1.04]). There was no consistent association between PSIF during the past 3 days and any of the outcomes.
Significance
These results suggest an association between smoke exposure and asthma ED visits on the same day. Elucidating these associations will help guide public health programs that address population-level exposure to smoke from prescribed burning.
