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Human Health, Environmental Informatics and Risk: Managing Hazards in the Information Era

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A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601). This special issue belongs to the section "Environmental Health".

Deadline for manuscript submissions: closed (31 January 2019) | Viewed by 73846

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Prof. Dr. Jason Levy

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Disaster Preparedness and Emergency Management, University of Hawaii, 2540 Dole Street, Honolulu, HI 96822, USA
Interests: epidemiology and prevention of congenital anomalies; psychosis and affective psychosis; cancer epidemiology and prevention; molecular and human genome epidemiology; evidence synthesis related to public health and health services research
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Dear Colleagues,

Environmental informatics is a growing interdisciplinary field that develops methods and software tools for understanding environmental data.

This Special Issue seeks to enhance the integration of environmental information and modeling and statistical tools to help develop management solutions can reduce environmental risk and promote human health. We are particularly interested in inter-disciplinary collaboration that promotes the theoretical and/or applied aspects of environmental information sciences, regardless of disciplinary boundaries. Innovations in public health data analytics and bioinformatics are critically important to take a full advantage of big data and to promote evidence-based public health since public health issues are becoming more urgent and complex. There are large amounts of electronic data dealing with environmental hazards, pollution, public health and other fields. The topics addressed include:

Decision and risk analysis for human health
Mathematical methods for understanding hazards and public health
Biostatistics and its applications
Environmental data, systems modeling and optimization
Control of waste treatment and pollution reduction processes
Environmental systems science, hazards and threat analyses
Environmental, ecological and resources management and planning
Monitoring and analytical techniques of environmental quality
Artificial intelligence and expert systems for understanding environmental hazards
Other areas of and information technology

In summary, this Special Issue examines mathematical and statistical techniques.to further the field of environmental informatics in the era of big and open data. As an interdisciplinary field of science, we encourage environmental and bioinformatics papers that combine computer science, statistics, mathematics, and engineering to analyse and interpret biological data.

Prof. Dr. Jason K. Levy
Guest Editor

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Keywords

Public Health
Environmental Risk
Big and Open Data
Methodological Approaches to Big Data
Data Intensive Computing Theorems and Technologies
Modelling, Simulation and Performance Evaluation

Published Papers (12 papers)

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Research

14 pages, 2468 KiB

Open AccessArticle

A Multi-Pollutant Air Quality Health Index (AQHI) Based on Short-Term Respiratory Effects in Stockholm, Sweden

by Henrik Olstrup, Christer Johansson, Bertil Forsberg, Andreas Tornevi, Agneta Ekebom and Kadri Meister

Int. J. Environ. Res. Public Health 2019, 16(1), 105; https://doi.org/10.3390/ijerph16010105 - 02 Jan 2019

Cited by 35 | Viewed by 9396

Abstract

In this study, an Air Quality Health Index (AQHI) for Stockholm is introduced as a tool to capture the combined effects associated with multi-pollutant exposure. Public information regarding the expected health risks associated with current or forecasted concentrations of pollutants and pollen can be very useful for sensitive persons when planning their outdoor activities. For interventions, it can also be important to know the contribution from pollen and the specific air pollutants, judged to cause the risk. The AQHI is based on an epidemiological analysis of asthma emergency department visits (AEDV) and urban background concentrations of NO_x, O₃, PM₁₀ and birch pollen in Stockholm during 2001–2005. This analysis showed per 10 µg·m^–3 increase in the mean of same day and yesterday an increase in AEDV of 0.5% (95% CI: −1.2–2.2), 0.3% (95% CI: −1.4–2.0) and 2.5% (95% CI: 0.3–4.8) for NO_x, O₃ and PM₁₀, respectively. For birch pollen, the AEDV increased with 0.26% (95% CI: 0.18–0.34) for 10 pollen grains·m^–3. In comparison with the coefficients in a meta-analysis, the mean values of the coefficients obtained in Stockholm are smaller. The mean value of the risk increase associated with PM₁₀ is somewhat smaller than the mean value of the meta-coefficient, while for O₃, it is less than one fifth of the meta-coefficient. We have not found any meta-coefficient using NO_x as an indicator of AEDV, but compared to the mean value associated with NO₂, our value of NO_x is less than half as large. The AQHI is expressed as the predicted percentage increase in AEDV without any threshold level. When comparing the relative contribution of each pollutant to the total AQHI, based on monthly averages concentrations during the period 2015–2017, there is a tangible pattern. The AQHI increase associated with NO_x exhibits a relatively even distribution throughout the year, but with a clear decrease during the summer months due to less traffic. O₃ contributes to an increase in AQHI during the spring. For PM₁₀, there is a significant increase during early spring associated with increased suspension of road dust. For birch pollen, there is a remarkable peak during the late spring and early summer during the flowering period. Based on monthly averages, the total AQHI during 2015–2017 varies between 4 and 9%, but with a peak value of almost 16% during the birch pollen season in the spring 2016. Based on daily mean values, the most important risk contribution during the study period is from PM₁₀ with 3.1%, followed by O₃ with 2.0%. Full article

(This article belongs to the Special Issue Human Health, Environmental Informatics and Risk: Managing Hazards in the Information Era)

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9 pages, 469 KiB

Open AccessArticle

Association Between PM_2.5 and Daily Hospital Admissions for Heart Failure: A Time-Series Analysis in Beijing

by Man Li, Yao Wu, Yao-Hua Tian, Ya-Ying Cao, Jing Song, Zhe Huang, Xiao-Wen Wang and Yong-Hua Hu

Int. J. Environ. Res. Public Health 2018, 15(10), 2217; https://doi.org/10.3390/ijerph15102217 - 11 Oct 2018

Cited by 33 | Viewed by 3734

Abstract

There is little evidence that acute exposure to fine particulate matter (PM_2.5) impacts the rate of hospitalization for congestive heart failure (CHF) in developing countries. The primary purpose of the present retrospective study was to evaluate the short-term association between ambient PM_2.5 and hospitalization for CHF in Beijing, China. A total of 15,256 hospital admissions for CHF from January 2010 to June 2012 were identified from Beijing Medical Claim Data for Employees and a time-series design with generalized additive Poisson model was used to assess the obtained data. We found a clear significant exposure response association between PM_2.5 and the number of hospitalizations for CHF. Increasing PM_2.5 daily concentrations by 10 μg/m³ caused a 0.35% (95% CI, 0.06–0.64%) increase in the number of CHF admissions on the same day. We also found that female and older patients were more susceptible to PM_2.5. These associations remained significant in sensitivity analyses involving changing the degrees of freedom of calendar time, temperature, and relative humidity. PM_2.5 was associated with significantly increased risk of hospitalization for CHF in this citywide study. These findings may contribute to the limited scientific evidence about the acute impacts of PM_2.5 on CHF in China. Full article

(This article belongs to the Special Issue Human Health, Environmental Informatics and Risk: Managing Hazards in the Information Era)

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18 pages, 2847 KiB

Open AccessArticle

Evaluating Economic Growth, Industrial Structure, and Water Quality of the Xiangjiang River Basin in China Based on a Spatial Econometric Approach

by Xiaohong Chen, Guodong Yi, Jia Liu, Xiang Liu and Yang Chen

Int. J. Environ. Res. Public Health 2018, 15(10), 2095; https://doi.org/10.3390/ijerph15102095 - 25 Sep 2018

Cited by 9 | Viewed by 4106

Abstract

This research utilizes the environmental Kuznets curve to demonstrate the interrelationship between economic growth, industrial structure, and water quality of the Xiangjiang river basin in China by employing spatial panel data models. First, it obtains two variables (namely, COD_Mn, which represents the chemical oxygen demand of using KMnO₄ as chemical oxidant, and NH₃-N, which represents the ammonia nitrogen content index of wastewater) by pretreating the data of 42 environmental monitoring stations in the Xiangjiang river basin from 2005 to 2015. Afterward, Moran’s I index is adopted to analyze the spatial autocorrelation of COD_Mn and NH₃-N concentration. Then, a comparative analysis of the nonspatial panel model and spatial panel model is conducted. Finally, this research estimates the intermediate effect of the industrial structure of the Xiangjiang river basin in China. The results show that spatial autocorrelation exists in pollutant concentration and the relationship between economic growth and pollutant concentration shapes as an inverted-N trajectory. Moreover, the turn points of the environmental Kuznets curve for COD_Mn are RMB 83,001 and RMB 108,583 per capita GDP. In contrast, the turn points for NH₃-N are RMB 50,980 and RMB 188,931 per capita GDP. Additionally, the environmental Kuznets curve for COD_Mn can be explained by industrial structure adjustment, while that for NH₃-N cannot. As a consequence, the research suggests that the effect of various pollutants should be taken into account while making industrial policies. Full article

(This article belongs to the Special Issue Human Health, Environmental Informatics and Risk: Managing Hazards in the Information Era)

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18 pages, 1292 KiB

Open AccessArticle

Fuzzy Group Consensus Decision Making and Its Use in Selecting Energy-Saving and Low-Carbon Technology Schemes in Star Hotels

by Ping Lu, Xuan Yang and Zhou-Jing Wang

Int. J. Environ. Res. Public Health 2018, 15(9), 2057; https://doi.org/10.3390/ijerph15092057 - 19 Sep 2018

Cited by 7 | Viewed by 3401

Abstract

Energy-saving and low-carbon technologies play important roles in reducing environmental risk and developing green tourism. An energy-saving and low-carbon technology scheme selection may often involve multiple criteria and sub-criteria as well as multiple stakeholders or decision makers, and thus can be structured as a hierarchical multi-criteria group decision making problem. This paper proposes a framework to solve group consensus decision making problems, where decision makers’ preferences between the alternatives considered with respective to each criterion are elicited by the paired comparison method, and expressed as triangular fuzzy preference relations (TFPRs). The paper first simplifies the existing computation formulas used to determine triangular fuzzy weights of TFPRs. A consistency index is then devised to measure the inconsistency degree of a TFPR and is used to check acceptable consistency of TFPRs. By introducing a possibility degree formula of comparing any two triangular fuzzy weights, an index is defined to measure the consensus level between an individual ranking order and the group ranking order for all alternatives. A consensus model is developed in detail for solving group decision making problems with TFPRs. A case study of selecting energy-saving and low-carbon technology schemes in star hotels is provided to illustrate how to apply the proposed group decision making consensus model in practice. Full article

(This article belongs to the Special Issue Human Health, Environmental Informatics and Risk: Managing Hazards in the Information Era)

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14 pages, 1013 KiB

Open AccessArticle

The Oakville Oil Refinery Closure and Its Influence on Local Hospitalizations: A Natural Experiment on Sulfur Dioxide

by Wesley S. Burr, Robert Dales, Ling Liu, Dave Stieb, Marc Smith-Doiron, Branka Jovic, Lisa Marie Kauri and Hwashin Hyun Shin

Int. J. Environ. Res. Public Health 2018, 15(9), 2029; https://doi.org/10.3390/ijerph15092029 - 17 Sep 2018

Cited by 3 | Viewed by 4604

Abstract

Background: An oil refinery in Oakville, Canada, closed over 2004–2005, providing an opportunity for a natural experiment to examine the effects on oil refinery-related air pollution and residents’ health. Methods: Environmental and health data were collected for the 16 years around the refinery closure. Toronto (2.5 million persons) and the Greater Toronto Area (GTA, 6.3 million persons) were used as control and reference populations, respectively, for Oakville (160,000 persons). We compared sulfur dioxide and age- and season-standardized hospitalizations, considering potential factors such as changes in demographics, socio-economics, drug prescriptions, and environmental variables. Results: The closure of the refinery eliminated 6000 tons/year of SO₂ emissions, with an observed reduction of 20% in wind direction-adjusted ambient concentrations in Oakville. After accounting for trends, a decrease in cold-season peak-centered respiratory hospitalizations was observed for Oakville (reduction of 2.2 cases/1000 persons per year,

p = 0.0006

) but not in Toronto (p = 0.856) and the GTA (p = 0.334). The reduction of respiratory hospitalizations in Oakville post closure appeared to have no observed link to known confounders or effect modifiers. Conclusion: The refinery closure allowed an assessment of the change in community health. This natural experiment provides evidence that a reduction in emissions was associated with improvements in population health. This study design addresses the impact of a removed source of air pollution. Full article

(This article belongs to the Special Issue Human Health, Environmental Informatics and Risk: Managing Hazards in the Information Era)

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29 pages, 638 KiB

Open AccessArticle

A Novel Multiattribute Decision-Making Method Based on Point–Choquet Aggregation Operators and Its Application in Supporting the Hierarchical Medical Treatment System in China

by Runtong Zhang, Yuping Xing, Jun Wang, Xiaopu Shang and Xiaomin Zhu

Int. J. Environ. Res. Public Health 2018, 15(8), 1718; https://doi.org/10.3390/ijerph15081718 - 10 Aug 2018

Cited by 11 | Viewed by 3404

Abstract

The hierarchical medical treatment system is an efficient way to solve the problem of insufficient and unbalanced medical resources in China. Essentially, classifying the different degrees of diseases according to the doctor’s diagnosis is a key step in pushing forward the hierarchical medical treatment system. This paper proposes a framework to solve the problem where diagnosis values are given as picture fuzzy numbers (PFNs). Point operators can reduce the uncertainty of doctor’s diagnosis and get intensive information in the process of decision making, and the Choquet integral operator can consider correlations among symptoms. In order to take full advantage of these two kinds of operators, in this paper, we firstly define some point operators under the picture fuzzy environment, and further propose a new class of picture fuzzy point–Choquet integral aggregation operators. Moreover, some desirable properties of these operators are also investigated in detail. Then, a novel approach based on these operators for multiattribute decision-making problems in the picture fuzzy context is introduced. Finally, we give an example to illustrate the applicability of the new approach in assisting hierarchical medical treatment system. This is of great significance for integrating the medical resources of the whole society and improving the service efficiency of the medical service system. Full article

(This article belongs to the Special Issue Human Health, Environmental Informatics and Risk: Managing Hazards in the Information Era)

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20 pages, 3617 KiB

Open AccessArticle

Predicting Infectious Disease Using Deep Learning and Big Data

by Sangwon Chae, Sungjun Kwon and Donghyun Lee

Int. J. Environ. Res. Public Health 2018, 15(8), 1596; https://doi.org/10.3390/ijerph15081596 - 27 Jul 2018

Cited by 222 | Viewed by 17405

Abstract

Infectious disease occurs when a person is infected by a pathogen from another person or an animal. It is a problem that causes harm at both individual and macro scales. The Korea Center for Disease Control (KCDC) operates a surveillance system to minimize infectious disease contagions. However, in this system, it is difficult to immediately act against infectious disease because of missing and delayed reports. Moreover, infectious disease trends are not known, which means prediction is not easy. This study predicts infectious diseases by optimizing the parameters of deep learning algorithms while considering big data including social media data. The performance of the deep neural network (DNN) and long-short term memory (LSTM) learning models were compared with the autoregressive integrated moving average (ARIMA) when predicting three infectious diseases one week into the future. The results show that the DNN and LSTM models perform better than ARIMA. When predicting chickenpox, the top-10 DNN and LSTM models improved average performance by 24% and 19%, respectively. The DNN model performed stably and the LSTM model was more accurate when infectious disease was spreading. We believe that this study’s models can help eliminate reporting delays in existing surveillance systems and, therefore, minimize costs to society. Full article

(This article belongs to the Special Issue Human Health, Environmental Informatics and Risk: Managing Hazards in the Information Era)

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19 pages, 4213 KiB

Open AccessArticle

Comparison of Machine Learning Models for Hazardous Gas Dispersion Prediction in Field Cases

by Rongxiao Wang, Bin Chen, Sihang Qiu, Zhengqiu Zhu, Yiduo Wang, Yiping Wang and Xiaogang Qiu

Int. J. Environ. Res. Public Health 2018, 15(7), 1450; https://doi.org/10.3390/ijerph15071450 - 10 Jul 2018

Cited by 23 | Viewed by 3870

Abstract

Dispersion prediction plays a significant role in the management and emergency response to hazardous gas emissions and accidental leaks. Compared with conventional atmospheric dispersion models, machine leaning (ML) models have both high accuracy and efficiency in terms of prediction, especially in field cases. However, selection of model type and the inputs of the ML model are still essential problems. To address this issue, two ML models (i.e., the back propagation (BP) network and support vector regression (SVR) with different input selections (i.e., original monitoring parameters and integrated Gaussian parameters) are proposed in this paper. To compare the performances of presented ML models in field cases, these models are evaluated using the Prairie Grass and Indianapolis field data sets. The influence of the training set scale on the performances of ML models is analyzed as well. Results demonstrate that the integrated Gaussian parameters indeed improve the prediction accuracy in the Prairie Grass case. However, they do not make much difference in the Indianapolis case due to their inadaptability to the complex terrain conditions. In addition, it can be summarized that the SVR shows better generalization ability with relatively small training sets, but tends to under-fit the training data. In contrast, the BP network has a stronger fitting ability, but sometimes suffers from an over-fitting problem. As a result, the model and input selection presented in this paper will be of great help to environmental and public health protection in real applications. Full article

(This article belongs to the Special Issue Human Health, Environmental Informatics and Risk: Managing Hazards in the Information Era)

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15 pages, 2351 KiB

Open AccessArticle

Improved Prediction of Harmful Algal Blooms in Four Major South Korea’s Rivers Using Deep Learning Models

by Sangmok Lee and Donghyun Lee

Int. J. Environ. Res. Public Health 2018, 15(7), 1322; https://doi.org/10.3390/ijerph15071322 - 24 Jun 2018

Cited by 105 | Viewed by 8682

Abstract

Harmful algal blooms are an annual phenomenon that cause environmental damage, economic losses, and disease outbreaks. A fundamental solution to this problem is still lacking, thus, the best option for counteracting the effects of algal blooms is to improve advance warnings (predictions). However, existing physical prediction models have difficulties setting a clear coefficient indicating the relationship between each factor when predicting algal blooms, and many variable data sources are required for the analysis. These limitations are accompanied by high time and economic costs. Meanwhile, artificial intelligence and deep learning methods have become increasingly common in scientific research; attempts to apply the long short-term memory (LSTM) model to environmental research problems are increasing because the LSTM model exhibits good performance for time-series data prediction. However, few studies have applied deep learning models or LSTM to algal bloom prediction, especially in South Korea, where algal blooms occur annually. Therefore, we employed the LSTM model for algal bloom prediction in four major rivers of South Korea. We conducted short-term (one week) predictions by employing regression analysis and deep learning techniques on a newly constructed water quality and quantity dataset drawn from 16 dammed pools on the rivers. Three deep learning models (multilayer perceptron, MLP; recurrent neural network, RNN; and long short-term memory, LSTM) were used to predict chlorophyll-a, a recognized proxy for algal activity. The results were compared to those from OLS (ordinary least square) regression analysis and actual data based on the root mean square error (RSME). The LSTM model showed the highest prediction rate for harmful algal blooms and all deep learning models out-performed the OLS regression analysis. Our results reveal the potential for predicting algal blooms using LSTM and deep learning. Full article

(This article belongs to the Special Issue Human Health, Environmental Informatics and Risk: Managing Hazards in the Information Era)

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15 pages, 723 KiB

Open AccessArticle

Application of a Time-Stratified Case-Crossover Design to Explore the Effects of Air Pollution and Season on Childhood Asthma Hospitalization in Cities of Differing Urban Patterns: Big Data Analytics of Government Open Data

by Ching-Yen Kuo, Ren-Hao Pan, Chin-Kan Chan, Chiung-Yi Wu, Dinh-Van Phan and Chien-Lung Chan

Int. J. Environ. Res. Public Health 2018, 15(4), 647; https://doi.org/10.3390/ijerph15040647 - 31 Mar 2018

Cited by 29 | Viewed by 5711

Abstract

Few studies have assessed the lagged effects of levels of different urban city air pollutants and seasons on asthma hospitalization in children. This study used big data analysis to explore the effects of daily changes in air pollution and season on childhood asthma hospitalization from 2001 to 2010 in Taipei and Kaohsiung City, Taiwan. A time-stratified case-crossover study and conditional logistic regression analysis were employed to identify associations between the risk of hospitalization due to asthma in children and the levels of air pollutants (PM_2.5, PM₁₀, O₃, SO₂, and NO₂) in the days preceding hospitalization. During the study period, 2900 children in Taipei and 1337 in Kaohsiung aged ≤15 years were hospitalized due to asthma for the first time. The results indicated that the levels of air pollutants were significantly associated with the risk of asthma hospitalization in children, and seasonal effects were observed. High levels of air pollution in Kaohsiung had greater effects than in Taipei after adjusting for seasonal variation. The most important factor was O₃ in spring in Taipei. In children aged 0–6 years, asthma was associated with O₃ in Taipei and SO₂ in Kaohsiung, after controlling for the daily mean temperature and relative humidity. Full article

(This article belongs to the Special Issue Human Health, Environmental Informatics and Risk: Managing Hazards in the Information Era)

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17 pages, 7294 KiB

Open AccessArticle

Spatial Distribution, Chemical Fraction and Fuzzy Comprehensive Risk Assessment of Heavy Metals in Surface Sediments from the Honghu Lake, China

by Fei Li, Minsi Xiao, Jingdong Zhang, Chaoyang Liu, Zhenzhen Qiu and Ying Cai

Int. J. Environ. Res. Public Health 2018, 15(2), 207; https://doi.org/10.3390/ijerph15020207 - 26 Jan 2018

Cited by 23 | Viewed by 4359

Abstract

Spatial concentrations and chemical fractions of heavy metals (Cr, Cu, Pb, Zn and Cd) in 16 sampling sites from the Honghu Lake were investigated using an atomic absorption spectrophotometer and optimized BCR (the European Community Bureau of Reference) three-stage extraction procedure. Compared with the corresponding probable effect levels (PELs), adverse biological effects of the studied five sediment metals decreased in the sequence of Cr > Cu > Zn > Pb > Cd. Geo-accumulation index (I_geo) values for Cr, Cu, Pb and Zn in each sampling site were at un-contamination level, while the values for Cd varied from un-contamination level to moderate contamination level. Spatially, the enrichment degree of Cd in lower part of the South Lake, the west part of the North Lake and the outlet were higher than the other parts of Honghu Lake. For metal chemical fractions, the proportions of the acid-extractable fraction of five metal contents were in the descending order: Cd, Cu, Zn, Pb and Cr. Cd had the highest bioaccessibility. Being the above indexes focused always on heavy metals’ total content or chemical fraction in deterministic assessment system, which may confuse decision makers, the fuzzy comprehensive risk assessment method was established based on PEI (Potential ecological risk index), RAC (Risk assessment code) and fuzzy theory. Average comprehensive risks of heavy metals in sediments revealed the following orders: Cd (considerable risk) > Cu (moderate risk) > Zn (low risk) > Pb > Cr. Thus, Cd and Cu were determined as the pollutants of most concern. The central part of South Honghu Lake (S4, S5, S6, S9, S12 and S14), east part of the North Honghu Lake (S1) and outlet of outlet of the Honghu Lake (S10) were recommended as the priority control areas. Specifically, it is necessary to pay more attention to S1, S4, S5, S6, S9 and S16 when decision making for their calculated membership values (probabilities) of adjacent risk levels quite close. Full article

(This article belongs to the Special Issue Human Health, Environmental Informatics and Risk: Managing Hazards in the Information Era)

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803 KiB

Open AccessArticle

Differences in Spontaneous Intracerebral Hemorrhage Cases between Urban and Rural Regions of Taiwan: Big Data Analytics of Government Open Data

by Hsien-Wei Ting, Ting-Ying Chien, K. Robert Lai, Ren-Hao Pan, Kuan-Hsien Wu, Jun-Min Chen and Chien-Lung Chan

Int. J. Environ. Res. Public Health 2017, 14(12), 1548; https://doi.org/10.3390/ijerph14121548 - 10 Dec 2017

Cited by 14 | Viewed by 4238

Abstract

This study evaluated the differences in spontaneous intracerebral hemorrhage (sICH) between rural and urban areas of Taiwan with big data analysis. We used big data analytics and visualization tools to examine government open data, which included the residents’ health medical administrative data, economic status, educational status, and relevant information. The study subjects included sICH patients of Taipei region (29,741 cases) and Eastern Taiwan (4565 cases). The incidence of sICH per 100,000 population per year in Eastern Taiwan (71.3 cases) was significantly higher than that of the Taipei region (42.3 cases). The mean coverage area per hospital in Eastern Taiwan (452.4 km²) was significantly larger than the Taipei region (24 km²). The residents educational level in the Taipei region was significantly higher than that in Eastern Taiwan. The mean hospital length of stay in the Taipei region (17.9 days) was significantly greater than that in Eastern Taiwan (16.3 days) (p < 0.001). There were no significant differences in other medical profiles between two areas. Distance and educational barriers were two possible reasons for the higher incidence of sICH in the rural area of Eastern Taiwan. Further studies are necessary in order to understand these phenomena in greater depth. Full article

(This article belongs to the Special Issue Human Health, Environmental Informatics and Risk: Managing Hazards in the Information Era)

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