Original Estimates Research Articles

Pengaruh Kualitas Layanan terhadap Kepuasan Pasien dan Citra Institusi pada Rsud Kabupaten Wakatobi

The objectives of this research are: 1) Testing the influence of service quality on patient satisfaction at the Wakatobi District Hospital; 2) Testing the influence of patient satisfaction on the image of the institution at the Wakatobi District Hospital; 3) Testing the influence of service quality on the image of the institution at the Wakatobi Regency Regional Hospital; 4) Testing the influence of Patient Satisfaction as an intervening variable on Service Quality and Institutional Image at Wakatobi District Hospital. The population in this study were patients and families of patients at the Wakatobi District Hospital. The sample taken was determined to be 68 people. The sampling method used is purposive random sample, namely a sample selection technique based on certain criteria. The data collection technique used was a questionnaire. Data analysis techniques use descriptive analysis, validity testing, reliability testing, hypothesis testing and direct and indirect influence testing using the Structural Equation Modeling (SEM) concept with the Smart PLS (Partial Least Square) program. The result showed that: 1) Service quality has a significant positive effect on patient satisfaction at the Wakatobi district hospital, showing the original sample estimate value between the influence of service quality on satisfaction of 0.742 and a positive value. 2) Patient satisfaction has a significant positive effect on the image of the institution at the Wakatobi District Hospital. 3) Service quality has a significant positive effect on the image of the institution at the Wakatobi District Hospital. The calculated T-value of 1.727 is greater than the T-table of 1.668. 4) Patient Satisfaction as an intervening variable has a dominant influence on Service Quality and Institutional Image at the Wakatobi District Regional Hospital.
 Keywords: Learning Herbal Drink, Community Service, Community Business Opportunities

Geodetic imaging and dynamic modeling of saline mudflat using time-series InSAR in Howz-e-Soltan Salt Lake, Qom, Iran

Due to the physicochemical properties of salt swelling and collapsibility, the potential geological hazards threaten the large-scale infrastructure and residential areas in the saline-soil region. Geodetic imaging and dynamic monitoring of the spatiotemporal evolution process of saline soil are vital to detect the potential geological hazards in a vast area. Therefore, this paper presents a coupled deformation model synthesizing environmental factors and trigonometric function model for estimating the time-series InSAR deformation and interpreting the dynamic evolutionary mechanism of the saline mudflat. To demonstrate the advantages of the proposed model, the sulphate saline mudflat is selected as the study area, which is traversed by the Tehran-Isfahan high-speed railway in Howz-e-Soltan Salt Lake, Qom, Iran. Moreover, 259 interconnecting interferograms were selected for the time-series InSAR estimation, composed of 87 Sentinel-1A images obtained from October 2014 through December 2018. Then, the time-series deformations, including the annual deformation velocity and the accumulated deformation, are extracted by the original estimation, polynomial, trigonometric and environmental models. The comparison results show that the environmental model's RMSEs (root-mean-square errors) are smaller than others. It should be noted that the linear deformation, periodic oscillation, and deformation derived from the environmental factors (i.e., precipitation and temperature) are extracted by the environmental model. Furthermore, the dynamic evolutionary mechanism of the saline mudflat is interpreted according to the physicochemical properties of sulphate/carbonate saline soil. The results derived from the environmental model suggested that the saline-soil deformation is directly related with the environmental changes: rapid collapsibility occurs when the precipitation increases from September to the next May, while uplift due to the salt swelling in the dry and rainless season from May to September every year. Our findings provide an opportunity to detect, prevent and control the potential geological hazards in saline mudflats.

Reducing Bias in Beta Regression Models Using Jackknifed Liu-Type Estimators: Applications to Chemical Data

In the field of chemical data modeling, it is common to encounter response variables that are constrained to the interval (0, 1). In such cases, the beta regression model is often a more suitable choice for modeling. However, like any regression model, collinearity can present a significant challenge. To address this issue, the Liu-type estimator has been used as an alternative to the maximum likelihood estimator, but it suffers from bias. In this paper, we introduce the Jackknifed Liu-type estimator and its modified version, which demonstrate improved bias reduction compared to the original Liu-type estimator. We assess the theoretical and numerical performance of these estimators through Monte Carlo simulations and real-data examples from the field of chemistry. Our findings highlight the significant improvements offered by the proposed estimators in terms of accuracy and reliability.

The Standard Error/Standard Deviation Mix-Up: Potential Impacts on Meta-Analyses in Sports Medicine

BackgroundA recent review found that 45% of meta-analyses included statistical errors, of which, the most common was the calculation of effect sizes based on standard error (SE) rather than standard deviation (SD) [the SE/SD mix-up].ObjectivesThe first aim of this study was to assess the impact of the SE/SD mix-up on the results of one highly cited meta-analysis. Our second aim was to identify one potential source of the SE/SD mix-up, by assessing how often SE is reported as a measure of sample variability in randomised controlled trials in sports medicine.MethodsWe checked for potential SE/SD mix-ups in a 2015 meta-analysis of randomised controlled trials reporting the effects of recreational football interventions on aerobic fitness in adults. We corrected effect sizes affected by SE/SD mix-ups and re-analysed the data according to the original methodology. We compared pooled estimates of effect sizes from our re-analysis of corrected values with those of the original study. To assess how often SE was reported instead of SD as a measure of sample variance, we text mined results of randomised controlled trials from seven sports medicine journals and reported the proportion reporting of SE versus SD.ResultsWe identified potential SE/SD mix-ups in 9/16 effect sizes included in the meta-analysis describing the effects of football-based interventions versus non-exercise control. The published effect size was standardised mean difference (SMD) = 1.46 (95% confidence interval [CI] 0.91, 2.01). After correcting for SE/SD mix-ups, our re-analysis produced a smaller pooled estimate (SMD = 0.54 [95% CI 0.37, 0.71]). The original pooled estimate for trials comparing football versus running interventions was SMD = 0.68 (95% CI 0.06, 1.4). After correcting for SE/SD mix-ups and re-analysis, the effect was no longer statistically significant (SMD = 0.20 [95% CI − 0.10, 0.49)]). We found that 19.3% of randomised controlled trials reported SE rather than SD to describe sample variability. The relative frequency of the practice ranged from 0 to 25% across the seven journals sampled.ConclusionsWe found the SE/SD mix-up had inflated estimates for the effects of football on aerobic fitness. Meta-analysts should be vigilant to avoid miscalculating effect sizes. Authors, reviewers and editors should avoid and discourage (respectively) the practice of reporting SE as a measure of sample variability in sports medicine research.

Reading in the Digital Age: An Empirical Examination of Digital Literacy's Significance for UIN Suska Riau Students Using SEM Path Analysis

The digital literacy is likely playing a crucial role on the reading interest in this digital age. This study aimed to determine the influence of digital literacy on the reading interest of UIN Suska Riau students. The sample of this study comprised of 82 students of UIN Suska Riau and the data were collected using questionnaires. The collected data were analyzed using Structural Equation Modeling (SEM) Path Analysis 3.0 and SPSS 21. The results showed that digital literacy had a significant influence on the reading interest of UIN Suska Riau students. This showed that the path coefficient value of the original sample estimate (beta) was positive at 0.471, and the direction of the relationship between both variables was in the quite strong category. The effect of digital literacy on the reading interest was also significant in the 2-way test (t table = 1.97) with a t-statistic value of 8.105, which was greater than the t table, and a p-value lower than alpha 5% (0.000 0.05). Overall, the results of this analysis proofed that the digital literacy positively related to the rading interest and it significantly influenced the reading interest.

Groundwater Age and Origin and Its Relation with Anthropogenic and Climatic Factors

Groundwater plays a major role in addressing the worldwide problem of water scarcity and food security. With a growing population and increasing urbanization, there is a rising demand for groundwater to meet agricultural and domestic water needs. A variety of advanced approaches are necessary to sustain groundwater management. This study investigated the age and origin of groundwater, as well as its relationship with anthropogenic and climatic factors. Stable isotopes were used, namely oxygen-18 (18O) and deuterium (2H) for the estimation of groundwater origin and radioactive isotopes of Tritium (3H) for the estimation of its age. The investigation of stable isotopes revealed that the aquifer is predominantly influenced by river water, with a minor contribution from rainwater. Furthermore, the analysis of radioactive isotopes revealed that the groundwater age ranges from 5 to 50 years old in most areas. Older groundwater is predominantly found in urban areas, while younger groundwater is present in agricultural and woodland regions. However, the presence of “old” water in the upper groundwater layers in urban areas is attributed to over-abstraction and limited natural recharge. The primary climatic factor that governs the age and origin of groundwater is rainfall upstream of the study area, which directly contributes to the river flows. The rainfall is high in the east but, due to urbanization, recharge is decreased. Consequently, old and river recharge groundwater is found in this area. These observations underscore the unsustainable and alarming use of groundwater in urban areas.

Clustering algorithm for the connected model of repeated measurements and survival data: Application to HIV study

The cluster analysis is dividing the individuals into clusters or groups. This job is valuable and helpful to provide facts and information about individuals. Here in this paper we proposed the cluster analysis for special model. It is the connected model for repeated measures called longitudinal and survival for individuals or patients. The statistical analysis of the connected model from the longitudinal and survival datasets becoming popular recently because it comes together in many medical applications. Here in this study, we utilized two statistical methods, cluster analysis and, connect the two models from longitudinal and survival data. It is beneficial since it gather the information from the repeated measurements, and survival responses. The shared random effect term is used joint multivariate Gaussian distribution (longitudinal) and Cox proportional hazard model (survival) for the same patients. Then, the pseudo-likelihood algorithm (clustering methodology) is performed for the joint model to distinguish the clusters or groups of patents. The application is HIV patient’s dataset with CD4+ counts responses and time to death (survival data) with some independent variables as gender and drug treatments. We conducted the clustering for S=2, and estimate the parameters from the longitudinal and survival models with and without clustering, and compare the estimations. Our results showed the generated clusters are different from each other, the estimation parameters be located around the original estimations (without clustering). It is helpful methodology to identify distinct groups or clusters from population. Finally, there is a big need for this type of application in medical elds.

An improved model combining machine learning and Kalman filtering architecture for state of charge estimation of lithium-ion batteries

Accurate state of charge (SOC) estimation of lithium-ion batteries is a fundamental prerequisite for ensuring the normal and safe operation of electric vehicles, and it is also a key technology component in battery management systems. In recent years, lithium-ion battery SOC estimation methods based on data-driven approaches have gained significant popularity. However, these methods commonly face the issue of poor model generalization and limited robustness. To address such issues, this study proposes a closed-loop SOC estimation method based on simulated annealing-optimized support vector regression (SA-SVR) combined with minimum error entropy based extended Kalman filter (MEE-EKF) algorithm. Firstly, a probability-based SA algorithm is employed to optimize the internal parameters of the SVR, thereby enhancing the precision of original SOC estimation. Secondly, utilizing the framework of the Kalman filter, the optimized SVR results are incorporated as the measurement equation and further processed through the MEE-EKF, while the ampere-hour integral physical model serves as the state equation, effectively attenuating the measurement noise, enhancing the estimation accuracy, and improving generalization ability. The proposed method is validated through battery testing experiments conducted under three typical operating conditions and one complex and random operating condition with wide temperature variations under only one condition training. The results demonstrate that the proposed method achieves a mean absolute error below 0.60% and a root mean square error below 0.73% across all operating conditions, showcasing a significant improvement in estimation accuracy compared to the benchmark algorithms. The high precision and generalization capability of the proposed method are evident, ensuring accurate SOC estimation for electric vehicles.

Influence of Accelerometer Calibration on the Estimation of Objectively Measured Physical Activity: The Tromsø Study

Accelerometers are increasingly used to observe human behavior such as physical activity under free-living conditions. An important prerequisite to obtain reliable results is the correct calibration of the sensors. However, accurate calibration is often neglected, leading to potentially biased results. Here, we demonstrate and quantify the effect of accelerometer miscalibration on the estimation of objectively measured physical activity under free-living conditions. The total volume of moderate to vigorous physical activity (MVPA) was significantly reduced after post hoc auto-calibration for uniaxial and triaxial count data, as well as for Euclidean Norm Minus One and mean amplitude deviation raw data. Weekly estimates of MVPA were reduced on average by 5.5, 9.2, 45.8, and 4.8 min, respectively, when compared to the original uncalibrated estimates. Our results indicate a general trend of overestimating physical activity when using factory-calibrated sensors. In particular, the accuracy of estimates derived from the Euclidean Norm Minus One feature suffered from uncalibrated sensors. For all modalities, the more uncalibrated the sensor was, the more MVPA was overestimated. This might especially affect studies with lower sample sizes.

Stable state estimation in a network context

Power grid, communications, computer and product reticulation networks are frequently layered or subdivided by design. The OSI seven-layer computer network model and the electrical grid division into generation, transmission, distribution and associated markets are cases in point. The layering divides responsibilities and can be driven by operational, commercial, regulatory and privacy concerns. From a control context, a layer, or part of a layer, in a network isolates the authority to manage, i.e. control, a dynamic system with connections into unknown parts of the network. The topology of these connections is fully prescribed but the interconnecting signals are largely unavailable, through lack of sensing and even prohibition. Accordingly, one is driven to simultaneous input and state estimation methods. This is the province of this paper, guided by the structure of these network problems. We study a class of algorithms for this joint task, which if the system has transmission zeros outside the unit circle leads to an unstable and unimplementable estimator. Two modifications to the algorithm to ameliorate this problem were recently proposed involving (a) replacing the troublesome subsystem with its outer factor from its inner-outer factorization or (b) using the Kalman filter for a high-variance white signal model for the unknown inputs. The outer factor has only stable transmission zeros and so is stably invertible. The Kalman filter is stable by design. Here, we establish the connections between the original estimation problem for state and input signal and the outputs/estimates from the algorithm applied solely to the outer factor. We further show that both fixes coincide.

Robust ISAC Localization in Smart Cities: A Hybrid Network Approach for NLOS Challenges with Uncertain Parameters

Accurate localization holds paramount importance across many applications within the context of smart cities, particularly in vehicular communication systems, the Internet of Things, and Integrated Sensing and Communication (ISAC) technologies. Nonetheless, achieving precise localization remains a persistent challenge, primarily attributed to the prevalence of non-line-of-sight (NLOS) conditions and the presence of uncertainties surrounding key wireless transmission parameters. This paper presents a comprehensive framework tailored to address the intricate task of localizing multiple nodes within ISAC systems significantly impacted by pervasive NLOS conditions and the ambiguity of transmission parameters. The proposed methodology integrates received signal strength (RSS) and time-of-arrival (TOA) measurements as a strategic response to effectively overcome these substantial challenges, even in situations where the precise values of transmitting power and temporal information remain elusive. An approximation approach is judiciously employed to facilitate the inherent non-convex and NP-hard nature of the original estimation problem, resulting in a notable transformation, rendering the problem amenable to a convex optimization paradigm. The comprehensive array of simulations conducted within this study corroborates the efficacy of the proposed hybrid cooperative localization method by underscoring its superior performance relative to conventional approaches relying solely on RSS or TOA measurements. This enhancement in localization accuracy in ISAC systems holds promise in the intricate urban landscape of smart cities, offering substantial contributions to infrastructure optimization and service efficiency.

Convex Combination for Wireless Localization Using Biased RSS Measurements

Received signal strength- (RSS-) based localization in wireless sensor networks (WSNs) has attracted significant attention due to its advantages of low cost and simple implementation. In practice, RSS measurements may suffer from sensor biases, which deteriorates the localization accuracy. However, most of the existing localization methods are designed for bias-free measurements. In this paper, we propose a convex combination method for RSS localization in the presence of sensor biases. The parameter vector composed of unknown location and sensor biases is estimated simultaneously by using a convex combination of some virtual points. These virtual points form a convex hull, into which the parameter vector falls with large probability. By this, the original nonconvex estimation problem is converted to be convex. Numerical examples demonstrate the superiority of the proposed method in terms of localization accuracy, compared to the existing semidefinite programming (SDP) methods.

Machine learning assessment of hydrological model performance under localized water storage changes through downscaling

The coarse spatial resolution of the Gravity Recovery and Climate Experiment (GRACE) data has limited its application in the management of local-scale water resources. To address this limitation, we developed a new downscaling approach using predictors from regional and global hydrological models for a 15-year period (2002–2017) and tested it in the northern Great Artesian Basin, Australia. We used four different machine learning algorithms (support vector machine, partial least squares, gaussian process and random forest) to downscale the original GRACE estimate of 0.5° to a spatial grain size of 0.1° (global) and 0.05° (regional). This was based on precipitation, evapotranspiration and runoff estimates from the Famine Early Warning Systems Network Land Data Assimilation System (FLDAS) and Australian Water Outlook (AWO) hydrological models, respectively. The downscaled products were validated using 42 in-situ precipitation observations spread across the test region. We further evaluated which of the downscaled products best mimicked local-scale hydrology using a range of statistical metrics. Our results showed that regional hydrological models best characterized the dynamics of local scale hydrology (rainfall v. downscaled product), and the gaussian process regression algorithm made the best predictions for both models. The correlation coefficients for the raw values varied from 0.45 to 0.49 while that of the standardized values varied from 0.46 to 0.52 with the random forest model providing the best fitting for the regional-based products. The regional downscaling approach employed in this study may be readily integrated into local water resources planning programs.

Common Sense of a Wealth Tax

Thomas Paine’s writing helped spur the American colonies to independence and ensure that the new nation would be a republic, not a monarchy. In light of the renewed interest in wealth taxes, this article provides a close examination of Thomas Paine’s wealth tax proposal in the second volume of The Rights of Man. Unlike Paine’s proposal to tax inheritances, his 1792 proposal to tax wealth on an annual basis is often overlooked. The article identifies Paine’s various design specifications, provides original estimates of the impact of Paine’s wealth tax proposal within his own time period and as applied to billionaires today, and discusses ambiguities in the proposal. The article then places Paine in conversation with the contemporary wealth tax policy debate and demonstrates how Paine informs both the design and evaluation of tax policy. Lastly, the article clarifies the relationship between democratic ideals and taxation, portraying tax policy as a normative expression of republican ideals.

Evaluating Cluster-Based Synthetic Data Generation for Blood-Transfusion Analysis

Synthetic data generation is becoming an increasingly popular approach to making privacy-sensitive data available for analysis. Recently, cluster-based synthetic data generation (CBSDG) has been proposed, which uses explainable and tractable techniques for privacy preservation. Although the algorithm demonstrated promising performance on simulated data, CBSDG has not yet been applied to real, personal data. In this work, a published blood-transfusion analysis is replicated with synthetic data to assess whether CBSDG can reproduce more complex and intricate variable relations than previously evaluated. Data from the Dutch national blood bank, consisting of 250,729 donation records, were used to predict donor hemoglobin (Hb) levels by means of support vector machines (SVMs). Precision scores were equal to the original data results for both male (0.997) and female (0.987) donors, recall was 0.007 higher for male and 0.003 lower for female donors (original estimates 0.739 and 0.637, respectively). The impact of the variables on Hb predictions was similar, as quantified and visualized with Shapley additive explanation values. Opportunities for attribute disclosure were decreased for all but two variables; only the binary variables Deferral Status and Sex could still be inferred. Such inference was also possible for donors who were not used as input for the generator and may result from correlations in the data as opposed to overfitting in the synthetic-data-generation process. The high predictive performance obtained with the synthetic data shows potential of CBSDG for practical implementation.

New Perspectives on Spectroscopic Factor Quenching from Reactions.

The evolution of single-particle strengths as the neutron-to-proton asymmetry changes informs us of the importance of short- and long-range correlations in nuclei and has therefore been extensively studied for the last two decades. Surprisingly, the strong asymmetry dependence of these strengths and their extreme values for highly asymmetric nuclei inferred from knockout reaction measurements on a target nucleus are not consistent with what is extracted from electron-induced, transfer, and quasi-free reaction data, constituting a two-decade old puzzle. This work presents the first consistent analysis of one-nucleon transfer and one-nucleon knockout data, in which theoretical uncertainties associated with the nucleon-nucleus effective interactions considered in the reaction models are quantified using a Bayesian analysis. Our results demonstrate that, taking into account these uncertainties, the spectroscopic strengths of loosely bound nucleons extracted from both probes agree with each other and, although there are still discrepancies for deeply bound nucleons, the slope of the asymmetry dependence of the single-particle strengths inferred from transfer and knockout reactions are consistent within 1σ. Both probes are consistent with a small asymmetry dependence of these strengths. The uncertainties obtained in this work represent a lower bound and are already significantly larger than the original estimates.

Reproduction number projection for the COVID-19 pandemic

The recently derived Hybrid-Incidence Susceptible-Transmissible-Removed (HI-STR) prototype is a deterministic compartment model for epidemics and an alternative to the Susceptible-Infected-Removed (SIR) model. The HI-STR predicts that pathogen transmission depends on host population characteristics including population size, population density and social behaviour common within that population.The HI-STR prototype is applied to the ancestral Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV2) to show that the original estimates of the Coronavirus Disease 2019 (COVID-19) basic reproduction number mathcal{R}_{0} for the United Kingdom (UK) could have been projected onto the individual states of the United States of America (USA) prior to being detected in the USA.The Imperial College London (ICL) group’s estimate of mathcal{R}_{0} for the UK is projected onto each USA state. The difference between these projections and the ICL’s estimates for USA states is either not statistically significant on the paired Student t-test or not epidemiologically significant.The SARS-CoV2 Delta variant’s mathcal{R}_{0} is also projected from the UK to the USA to prove that projection can be applied to a Variant of Concern (VOC). Projection provides both a localised baseline for evaluating the implementation of an intervention policy and a mechanism for anticipating the impact of a VOC before local manifestation.

Collaborative Uncertainty Benefits Multi-Agent Multi-Modal Trajectory Forecasting.

In multi-modal multi-agent trajectory forecasting, two major challenges have not been fully tackled: 1) how to measure the uncertainty brought by the interaction module that causes correlations among the predicted trajectories of multiple agents; 2) how to rank the multiple predictions and select the optimal predicted trajectory. In order to handle the aforementioned challenges, this work first proposes a novel concept, collaborative uncertainty (CU), which models the uncertainty resulting from interaction modules. Then we build a general CU-aware regression framework with an original permutation-equivariant uncertainty estimator to do both tasks of regression and uncertainty estimation. Furthermore, we apply the proposed framework to current SOTA multi-agent multi-modal forecasting systems as a plugin module, which enables the SOTA systems to 1) estimate the uncertainty in the multi-agent multi-modal trajectory forecasting task; 2) rank the multiple predictions and select the optimal one based on the estimated uncertainty. We conduct extensive experiments on a synthetic dataset and two public large-scale multi-agent trajectory forecasting benchmarks. Experiments show that: 1) on the synthetic dataset, the CU-aware regression framework allows the model to appropriately approximate the ground-truth Laplace distribution; 2) on the multi-agent trajectory forecasting benchmarks, the CU-aware regression framework steadily helps SOTA systems improve their performances. Especially, the proposed framework helps VectorNet improve by 262 cm regarding the Final Displacement Error of the chosen optimal prediction on the nuScenes dataset; 3) in multi-agent multi-modal trajectory forecasting, prediction uncertainty is proportional to future stochasticity; 4) the estimated CU values are highly related to the interactive information among agents. The proposed framework can guide the development of more reliable and safer forecasting systems in the future. Our code is available at https://github.com/MediaBrain-SJTU/Collaborative-Uncertainty.

Natural resource wealth in sub-Saharan Africa: A boon for public investment in renewable energy?

This paper examines the role of natural resource wealth in financing public investment in renewable energy in 25 sub-Saharan African countries over the period 1996 to 2019. Results from two step system Generalized Method of Moments (GMM) estimates reveal that natural resource wealth negatively affects public investment in renewable energy. A distinction between the types of natural resources allowed to establish that this negative effect comes exclusively from the rents of point natural resources. Furthermore, the results show that institutional quality and human capital favor public investment in renewable energy. These results are robust when using the Driscoll-Kraay estimator. In terms of policy implications, it is imperative to promote good governance of natural resource rents, especially those from point source resources. It will also be necessary to redirect these rents towards the financing of structuring projects such as renewable energies in order to promote sustainable development.

Methodology for the estimation of an oil spill origin: Analysis of the 2019 Brazilian coast oil spill

This research presents a procedure for determining the origin of marine pollution through the use of a time-direct trajectory modeling, associated with a Kriging metamodel technique and Monte Carlo random sampling. These methods were applied to a real case, specifically the oil spill that affected the Brazilian coast in the second half of 2019 and early 2020. A total of 140 trajectories, defined by the geographical coordinates of the origin and the spill date, were generated through Latin Hypercube Sampling and simulated using the PyGNOME model to construct the Kriging metamodel. The metamodel demonstrated cost-effectiveness by efficiently simulating numerous input data combinations which were compared and optimized based on available real data regarding temporal and spatial pollution distribution.