Impact assessment of spatial-temporal distribution of riverine dust on air quality using remote sensing data and numerical modeling.

Soil erosion is a severe problem in Taiwan due to the steep terrain, fragile geology, and extreme climatic events resulting from global warming. Due to the rapidly changing hydrological conditions affecting the locations and the amount of transported sand and fine particles, timely impact evaluation and riverine dust control are difficult, particularly when resources are limited. To comprehend the impact of desertification in estuarine areas on the variation of air pollutant concentrations, this study utilized remote sensing technology coupled with an air pollutant dispersion model to determine the unit contribution of potential pollution sources and quantify the effect of riverine dust on air quality. The images of the downstream area of the Beinan River basin captured by Formosat-2 in May 2006 were used to analyze land use and land cover (LULC) composition. Subsequently, the diffusion model ISCST-3 based on Gaussian distribution was utilized to simulate the transport of PM across the study area. Finally, a mixed-integer programming model was developed to optimize resource allocation for dust control. Results reveal that sand deposition in specific river sections significantly influences regional air quality, owing to the unique local topography and wind field conditions. The present optimal plan model for regional air quality control further showed that after implementing engineering measures including water cover, revegetation, armouring cover, and revegetation, total PM concentrations would be reduced by 51%. The contribution equivalent calculation, using the air pollution diffusion model, was effectively integrated into the optimization model to formulate a plan for reducing riverine dust with limited resources based on air quality requirements.

Hyperspectral sensing of heavy metals in soil by integrating AI and UAV technology.

Given the limitation of conventional soil pollution monitoring through mapping which is a costly, time-consuming work, the study aims to establish an image recognition model to identify the source of pollution automatically. The study choses a contaminated land and then use a non-destructive instrument that can quickly and effectively measure the content of heavy metals. A two concentration prediction models of Ni, Cu, Zn, Cr, Pb, As, Cd, and Hg using hyperspectral imaging were developed, Decision Tree and Back Propagation Neural Network, in combination of particle swarm optimization employed for optimization algorithm. As a result, random forest is more accurate than the forecast result of back propagation neural network. This study has established an excellent Cu and Cr model, which can accurately capture the pollution source. In addition, through aerial photographs, we also found that there were also high pollution reactions on the banks of the river. The developed model is beneficial for high pollution areas which can be quickly found, thereby following investigation and remediation work can be carried out with less time and cost consuming comparing with the conventional soil monitoring.

Prostaglandin F2 receptor inhibitor overexpression predicts advanced who grades and adverse prognosis in human glioma tissue.

Prostaglandin F2 receptor inhibitor (PTGFRN) promotes neoplastic cell migration and metastasis in some human cancers. However, the role of PTGFRN in human gliomas is still undetermined. First of all, PTGFRN messenger ribonucleic acid (mRNA) overexpression correlated with some poor prognostic factors of glioma after analyzing The Cancer Genome Atlas and Chinese Glioma Genome Atlas database. In order to detect the effect of PTGFRN expression on tumor characteristics of gliomas, U87MG, LN229, and glioblastoma 8401 glioma cell lines were cultured and prepared for western blot analysis and real-time polymerase chain reaction, respectively. The results revealed the overexpression of PTGFRN in all glioma cell lines as compared to normal brain cells. In addition, PTGFRN immunohistochemical (IHC) staining was performed on two sets of glioma tissue microarrays. Consistent with the results of in vitro studies, cytoplasmic PTGFRN immunostaining scores positively correlated with tumor grades and poor prognosis of gliomas. Therefore, PTGFRN IHC staining may be useful for the evaluation of tumor grades and overall survival time to facilitate the tailoring of appropriate treatment strategy. PTGFRN may serve as a potential pharmacologic target for the suppression of gliomagenesis.

A machine learning model for predicting PM2.5 and nitrate concentrations based on long-term water-soluble inorganic salts datasets at a road site station.

In this study, long-term variations in the concentrations of PM2.5, water-soluble inorganic salts (WIS), and gaseous precursors measured by a roadside air quality monitoring station were investigated from 2017 to February 2021 to examine the formation mechanism of secondary inorganic PM2.5. A new machine learning model using WIS data as input variables was further developed to predict PM2.5 and nitrate concentrations for source tracing and effective control strategy development. The results showed that a reduction in the NOx concentration under VOC-limited O3 formation regime could offset the consumption of OH and O3, causing an increase in secondary NO3- and PM2.5 formation during fall and winter seasons. A good agreement was obtained between the predicted and measured PM2.5 values, with R2, root mean square error (RMSE), and mean absolute error (MAE) values of 0.81, 6.81 µg/m3, and 5.10 µg/m3, respectively. The nitrate ([NO3-]) prediction model could predict ∼59% of the atmospheric nitrate concentration. The sensitivity analysis of the input variables in the present model further revealed that NO3- and VOC were two important pollutants dominating the variation trend of PM2.5. It is recommended that decision makers should focus more on the reduction of VOC and O3 to reduce secondary PM2.5 formation during winter in central Taiwan. Real-time measurements of the chemical composition of PM2.5, taken as the regulatory air quality monitoring items are needed in the future.

Using an unmanned aerial system to monitor and assess irrigation water channels susceptible to sediment deposition.

The irrigation channel of the Qishan River is among the most crucial agricultural water resource facilities in Qishan District, Kaohsiung City, Taiwan. The channel was blocked by debris due to flood events caused by Typhoon Morakot in 2009. This study analyzed images captured by an unmanned aerial system to identify channel areas susceptible to sediment deposition and propose measures for reducing the effects of natural hazards on irrigation water resources. The analysis results revealed that the channel was located downstream of the Qishan River; however, debris flows, riverbank landslides, and natural dam breaches deposited sediment in the downstream section, preventing the flow of water. Furthermore, the sediment and driftwood blocked the channel. The channel was also blocked due to a hyperconcentrated flow. Sediment deposition areas and volumes were estimated. On the basis of these results, we suggest that the damaged riverbed groundsills and river tributary banks be restored to inhibit erosion. In addition, subsurface water collection and transfer structures should be constructed to maintain the flow of water during the dry season. The study findings are expected to increase the efficiency of agricultural irrigation water management and prevent natural hazards from affecting water resources.

Current advances and future challenges of AIoT applications in particulate matters (PM) monitoring and control.

Air pollution is at the center of pollution-control discussion due to the significant adverse health effects on individuals and the environment. Research has shown the association between unsafe environments and different sizes of particulate matter (PM), highlighting the importance of pollutant monitoring to mitigate its detrimental effect. By monitoring air quality with low-cost monitoring devices that collect massive observations, such as Air Box, a comprehensive collection of ground-level PM concentration is plausible due to the simplicity and low-cost, propelling applications in agriculture, aquaculture, and air quality, water resources, and disaster prevention. This paper aims to view IoT-based systems with low-cost microsensors at the sensor, network, and application levels, along with machine learning algorithms that improve sensor networks' precision, providing better resolution. From the analysis at the three levels, we analyze current PM monitoring methods, including the use of sensors when collecting PM concentrations, demonstrate the use of IoT-based systems in PM monitoring and its challenges, and finally present the integration of AI and IoT (AIoT) in PM monitoring, indoor air quality control, and future directions. In addition, the inclusion of Taiwan as a site analysis was illustrated to show an example of AIoT in PM-control policy-making potential directions.

Impact assessment of river dust on regional air quality through integrated remote sensing and air quality modeling.

Sand and dust storms in arid and semiarid regions deteriorate regional air quality and threaten public health security. To quantify the negative effects of river dust on regional air quality, this study selected the estuary areas located in central Taiwan as a case study and proposed an integrated framework to measure the fugitive emission of dust from riverbeds with the aid of satellite remote sensing and wind tunnel test, together with the concentrations of particulate matter with a diameter of <10 µm (PM10) around the river system by using The Air Pollution Model. Additionally, the effects of 25 types of meteorological conditions on the health risk due to exposure to dust were evaluated near the estuary areas. The results reveal landscape changes in the downstream areas of Da'an and Dajia rivers, with an increase of 370,820 m2 and 1,554,850 m2 of bare land areas in the dry season compared with the wet season in Da'an and Dajia rivers, respectively. On the basis of the maximum emission of river dust, PM10 concentration increases considerably during both wet and dry seasons near the two rivers. Among 25 different types of weather conditions, frontal surface transit, outer-region circulation from tropical depression system, weak northeast monsoons, and anticyclonic outflow have considerable influence on PM10 diffusion. In particular, weak northeast monsoons cause the highest health risk in the areas between Da'an and Dajia rivers, which is the densely populated Taichung City. Future studies should attempt to elucidate the environmental impact of dust in different weather conditions and understand the spatial risks to human health due to PM10 concentration. Facing the increasing threat of climate and landscape changes, governments are strongly encouraged to begin multimedia assessments in environmental management and propose a long-term and systematic framework in resources planning.

Adsorption characteristics of trace levels of bromate in drinking water by modified bamboo-based activated carbons.

This study was undertaken to investigate the adsorption kinetics and isotherms of bromate (BrO3-) on bamboo charcoals that are activated with nitrogen and water vapor. Bamboo-based activated carbon (AC) was dipped in acid and oxidized in a mixture of potassium permanganate and sulfuric acid. Oxidation treatment considerably improved the physicochemical properties of AC, including purity, pore structure and surface nature, significantly enhancing BrO3- adsorption capacity. AC with many oxygenated groups and a high mesopore volume exhibited a particularly favorable tendency for BrO3- adsorption. Its adsorption of BrO3- is best fitted using Langmuir isotherm, and forms a monolayer. A kinetic investigation revealed that the adsorption of BrO3- by the ACs involved chemical sorption and was controlled by intra-particle diffusion. The competitive effects of natural organic matter (NOM) on AC were evaluated, and found to reduce the capacity of carbon to adsorb BrO3-. Residual dissolved ozone reacted with AC, reducing its capacity to absorb BrO3-. Proper dosing and staging of the ozonation processes can balance the ozone treatment efficiency, BrO3- formation, and the subsequent removal of BrO3-.

Accumulation of heavy metals and trace elements in fluvial sediments received effluents from traditional and semiconductor industries.

Metal accumulation in sediments threatens adjacent ecosystems due to the potential of metal mobilization and the subsequent uptake into food webs. Here, contents of heavy metals (Cd, Cr, Cu, Ni, Pb, and Zn) and trace elements (Ga, In, Mo, and Se) were determined for river waters and bed sediments that received sewage discharged from traditional and semiconductor industries. We used principal component analysis (PCA) to determine the metal distribution in relation to environmental factors such as pH, EC, and organic matter (OM) contents in the river basin. While water PCA categorized discharged metals into three groups that implied potential origins of contamination, sediment PCA only indicated a correlation between metal accumulation and OM contents. Such discrepancy in metal distribution between river water and bed sediment highlighted the significance of physical-chemical properties of sediment, especially OM, in metal retention. Moreover, we used Se XANES as an example to test the species transformation during metal transportation from effluent outlets to bed sediments and found a portion of Se inventory shifted from less soluble elemental Se to the high soluble and toxic selenite and selenate. The consideration of environmental factors is required to develop pollution managements and assess environmental risks for bed sediments.

Causal relationships among biological toxicity, geochemical conditions and derived DBPs in groundwater.

Groundwater is indispensable water resource in coastal areas of Taiwan and is typically used following simple disinfection. Disinfection by-products (DBP), which are hazardous materials that are biologically toxic, are commonly produced. To elucidate the effect of environmental factors on the formulation of DBPs and arsenic species, and the effect of these factors on the bio-toxicity, data from a one-year monitoring program that was performed in a coastal area of central Taiwan were analyzed using the multivariate statistical method of redundancy analysis (RDA). The results reveal that the dominant DBP for trihalomethanes (THMs) was CHCl3 and for haloacetic acids (HAAs) was CHClBr2COOH (BDCAA). The formation of these compounds was most affected by the concentrations of humic substances and Br(-). As(5+) ions are abundant in the area close to the seashore and are the main source of biological toxicity. Cl(-), Br(-) and As(5+) concentrations were strongly correlated with biological toxicity as they promoted the formation of DBP. A geographic information system (GIS) and the above results revealed that the area near the seashore is rich in Br(-) wherever high As(5+) concentration and bio-toxicity are detected.

Distinct MR Imaging Features of Triple-Negative Breast Cancer with Brain Metastasis.

BACKGROUND: Patients with triple-negative breast cancer (TNBC) are at increased risk of brain metastases (BMs). In this retrospective single-institutional study, we assessed the radiographic features from a cohort of breast cancer (BC) patients with confirmed BM. METHODS: Women diagnosed with BC with BM from January 1, 1996 to May 31, 2012 were identified through institutional databases. Relevant medical records were reviewed to assess patterns of recurrence, treatment, magnetic resonance imaging (MRI) features of BM, and survival after BM. The MRI finding of BM was classified as solid, necrotic, leptomeningeal spread, or mixed type. We assigned the patient into three groups according to histologic subtype of primary BC. RESULTS: In total, 62 patients, median age 53 years (range 20-78), were identified and specific treatment for BM consisted of radiotherapy, surgical resection, and systemic chemotherapy. The initial stage, post-BM survival and overall survival were not significantly different. However, cystic necrotic BMs appeared on MR images were significantly more associated with the TNBC group. CONCLUSION: Patients with BMs from TNBC have distinct MRI features helping the assessment of newly developed BM. A large confirmatory study with correlated histology in this unique patient population will be required.

Methylmercury accumulation and toxicity to cyanobacteria: implications of extracellural polymeric substances and growth properties.

This investigation examines how extracellular polymeric substances (EPSs) and environmental factors influence the bioaccumulation of monomethylmercury (MMeHg) using a culture of Microcystis aeruginosa, which dominates eutrophic reservoir populations. The identified EPSs were classified as carbohydrates and proteins. Evaluation of the bioaccumulation of MMeHg in cells by multiple regression analysis reveals that the concentration of EPSs in filtrate, the initial concentration of MMeHg in media, and the age of the culture significantly affected the amount of accumulation of MMeHg. Based on the composition profiles, the concentrations of soluble carbohydrates were significantly higher in the cells with bioaccumulated MMeHg than in the control ones. Preliminary results based on SEM-map investigations suggest that most of the MMeHg accumulated in the cytoplasm (intracellular). Additionally, the effective concentrations (EC50) of MMeHg that inhibit the growth of M. aeruginosa were 5.1 to 7.8 microg/L in the logarithmic phase and 2.5 to 4.6 microg/L in the stationary phase.

Effects of extracellular polymeric substances on the bioaccumulation of mercury and its toxicity toward the cyanobacterium Microcystis aeruginosa.

This investigation examines how extracellular polymeric substances (EPSs) and environmental factors affect the bioaccumulation and toxicity of inorganic mercury (+2 oxidation state, Hg(II)) using a culture of Microcystis aeruginosa, which dominates eutrophic reservoir populations. The identified EPSs were classified as carbohydrates and proteins. Evaluation of the bioaccumulation of Hg(II) in cells by multiple regression analysis reveals that the concentration of EPSs in filtrate, the initial concentration of Hg(II) in medium, and the culture age significantly affected the amount of Hg(II) accumulated. Composition profiles revealed that the concentrations of soluble carbohydrates were significantly higher in Hg(II)-accumulated cells than in the control ones. Preliminary results based on scanning electron microscopic (SEM) map investigations suggest that most of the Hg(II) was accumulated in the cytoplasm (intracellular). Additionally, the effective concentrations (EC50) of Hg(II) that inhibit the growth of M. aeruginosa were 38.6 µg L(-1) in the logarithmic phase and 17.5 µg L(-1) in the stationary phase. As expected, the production of more EPSs in the logarithmic phase typically implies higher EC50 values because EPSs may be regarded as a protective barrier of cells against an external Hg(II) load, enabling them to be less influenced by Hg(II).

Biological toxicity of groundwater in a seashore area: causal analysis and its spatial pollutant pattern.

To ensure the safety of groundwater usage in a seashore area where seawater incursion and unexpected leakage are taking place, this paper utilizes the Microtox test to quantify the biological toxicity of groundwater and proposes an integrated data analysis procedure based on hierarchical cluster analysis (HCA) and principal component analysis (PCA) for determining the key environmental factors that may result in the biological toxicity, together with the spatial risk pattern associated with groundwater usage. For these reasons, this study selects the coastal area of Taichung city in Central Taiwan as an example and implements a monitoring program with 40 samples. The results indicate that the concentration of total arsenic in the coastal areas is about 0.23-270.4 µg L(-1), which is obviously higher than the interior of Taichung city. Moreover, the seawater incursion and organic pollution in the study area may be the key factors resulting in the incubation of toxic substances. The results also indicate that As(3+) is the main contributor to biological toxicity compared to other disinfection by-products. With the help of the visualized spatial pollutants pattern of groundwater, an advanced water quality control plan can be made.

Modeling the dioxin emission of a municipal solid waste incinerator using neural networks.

Incineration is considered as an efficient approach in dealing with the increasing demand for municipal and industrial solid waste treatment, especially in areas without sufficient land resources. Facing the concern of health risk, the toxic pollutants emitted from incinerators have attracted much attention from environmentalists, even though this technology is capable of reducing solid waste volume and demand for landfill areas, together with plenty of energy generation. To reduce the negative impacts of toxic chemicals emitted from incinerators, various monitoring and control plans are made not only for use in facilities performance evaluation but also better control of operation for stable effluent quality. How to screen out the key variables from massive observed and control variables for modeling the dioxin emission has become an important issue in incinerator operation and pollution prevention. For these reasons, this study used 4-year monitoring data of an incinerator in Taiwan as a case study, and developed a prediction model based on an artificial neural network (ANN) to forecast the dioxin emission. By doing this, a simplified monitoring strategy for incinerators with regarding to dioxin emission control can be achieved. The result indicated that the prediction model based on a back-propagation neural network is a promising method to deal with complex and non-linear data with the help of statistics in screening out the useful variables for modeling. The suitable architecture of an ANN for using in the dioxin prediction consists of 5 input factors, 3 basic layers with 8 hidden nodes. The R(2) was found to equal 0.99 in both the training and testing steps. In addition, sensitivity analysis can identify the most significant variables for the dioxin emission. From the obtained results, the frequency of activated carbon injection showed as the factor of highest relative importance for the dioxin emission.

Exploring the background features of acidic and basic air pollutants around an industrial complex using data mining approach.

Air pollution data around a monitored site are normally difficult to analyze due to highly inter-related meteorological and topographical factors on top of many complicated atmospheric chemical interactions occurred in local and regional wind fields. The challenge prompts this study to develop a comprehensive data-mining algorithm of cluster analysis followed by meteorological and interspecies correlations to mitigate the inherent data complexity and dissimilarity. This study investigated the background features of acidic and basic air pollutants around a high-tech industrial park in Taiwan. Monthly samplings were taken at 10 sites around the park in a year. The temporal distribution plots show a baseline with two characteristic groups of high and low peaks. Hierarchical cluster analysis confirms that high peaks were primarily associated with low speed south wind in summer for all the chemical species, except for F(-), Cl(-), NH(3) and HF. Crosschecking with the topographical map identifies several major external sources in south and southwest. Further meteorological correlation suggests that HCl is highly positively associated with humidity, while Cl(-) is highly negatively associated with temperature, both for most stations. Interestingly, HNO(3) is highly negatively associated with wind speed for most stations and the hotspot was found in summer and around the foothill of Da-Tu Mountain in the northwest, a stagnant pocket on the study site. However, F(-) is highly positively associated with wind speed at downwind stations to the prevailing north wind in winter, indicating an internal source from the north. The presence of NH(4)(+) stimulates the formation of NO(3)(-), SO(4)(-2) (R=0.7), and HNO(3), H(2)SO(4), NH(3) (R=0.3-0.4). As H(2)SO(4) could be elevated to a level as high as 40% of the regulated standard, species interactions may be a dominate mechanism responsible for the substantial increase in summer from external sources.

Environmental performance evaluation of large-scale municipal solid waste incinerators using data envelopment analysis.

Limited to insufficient land resources, incinerators are considered in many countries such as Japan and Germany as the major technology for a waste management scheme capable of dealing with the increasing demand for municipal and industrial solid waste treatment in urban regions. The evaluation of these municipal incinerators in terms of secondary pollution potential, cost-effectiveness, and operational efficiency has become a new focus in the highly interdisciplinary area of production economics, systems analysis, and waste management. This paper aims to demonstrate the application of data envelopment analysis (DEA)--a production economics tool--to evaluate performance-based efficiencies of 19 large-scale municipal incinerators in Taiwan with different operational conditions. A 4-year operational data set from 2002 to 2005 was collected in support of DEA modeling using Monte Carlo simulation to outline the possibility distributions of operational efficiency of these incinerators. Uncertainty analysis using the Monte Carlo simulation provides a balance between simplifications of our analysis and the soundness of capturing the essential random features that complicate solid waste management systems. To cope with future challenges, efforts in the DEA modeling, systems analysis, and prediction of the performance of large-scale municipal solid waste incinerators under normal operation and special conditions were directed toward generating a compromised assessment procedure. Our research findings will eventually lead to the identification of the optimal management strategies for promoting the quality of solid waste incineration, not only in Taiwan, but also elsewhere in the world.

Automobile gross emitter screening with remote sensing data using objective-oriented neural network.

One of the costs of Taiwan's massive economic development has been severe air pollution problems in many parts of the island. Since vehicle emissions are the major source of air pollution in most of Taiwan's urban areas, Taiwan's government has implemented policies to rectify the degrading air quality, especially in areas with high population density. To reduce vehicle pollution emissions an on-road remote sensing and monitoring system is used to check the exhaust emissions from gasoline engine automobiles. By identifying individual vehicles with excessive emissions for follow-up inspection and testing, air quality in the urban environment is expected to improve greatly. Because remote sensing is capable of measuring a large number of moving vehicles in a short period, it has been considered as an assessment technique in place of the stationary emission-sampling techniques. However, inherent measurement uncertainty of remote sensing instrumentation, compounded by the indeterminacy of monitoring site selection, plus the vagaries of weather, causes large errors in pollution discrimination and limits the application of the remote sensing. Many governments are still waiting for a novel data analysis methodology to clamp down on heavily emitting vehicles by using remote sensing data. This paper proposes an artificial neural network (ANN), with vehicle attributes embedded, that can be trained by genetic algorithm (GA) based on different strategies to predict vehicle emission violation. Results show that the accuracy of predicting emission violation is as high as 92%. False determinations tend to occur for vehicles aged 7-13 years, peaking at 10 years of age.

Simultaneously monitoring the particle size distribution, morphology and suspended solids concentration in wastewater applying digital image analysis (DIA).

The efficiency of particle sedimentation in wastewater treatment is seriously affected by particle size distribution and morphology. A laser particle size analyzer is typically to analyze particle size distribution in wastewater. However, using this analyzer for on-line monitoring is difficult. This analyzer cannot measure particle morphology. An on-line digital image analysis (DIA) system was setup in this study to simultaneously measure particle size distribution and morphology in wastewater. The DIA measurement results show that the predominant particle size with an equivalent diameter (ED) was 10-40 microm. The particle size distribution measured by the DIA method similar to that measured with the laser particle size analyzer, indicating that the DIA method precisely measures particle size distribution. In addition, the mean fractal dimension (D (f)) of a set of particles was measured simultaneously, resulting in a good linear relationship between suspended solids (SS) precipitation efficiencies in samples. Finally, by combining the DIA measurement results with an artificial neural network (ANN), the SS concentrations of sample can be precisely predicted and the SS precipitation efficiencies were also evaluated.

Fair fund distribution for a municipal incinerator using GIS-based fuzzy analytic hierarchy process.

Burning municipal solid waste (MSW) can generate energy and reduce the waste volume, which delivers benefits to society through resources conservation. But current practices by society are not sustainable because the associated environmental impacts of waste incineration on urbanized regions have been a long-standing concern in local communities. Public reluctance with regard to accepting the incinerators as typical utilities often results in an intensive debate concerning how much welfare is lost for those residents living in the vicinity of those incinerators. As the measure of welfare change with respect to environmental quality constraints nearby these incinerators remains critical, new arguments related to how to allocate the fair fund among affected communities became a focal point in environmental management. Given the fact that most County fair fund rules allow a great deal of flexibility for redistribution, little is known about what type of methodology may be a good fit to determine the distribution of such a fair fund under uncertainty. This paper purports to demonstrate a system-based approach that helps any fair fund distribution, which is made with respect to residents' possible claim for fair damages due to the installation of a new incinerator. Holding a case study using integrated geographic information system (GIS) and fuzzy analytic hierarchy process (FAHP) for finding out the most appropriate distribution strategy between two neighboring towns in Taipei County, Taiwan demonstrates the application potential. Participants in determining the use of a fair fund also follow a highly democratic procedure where all stakeholders involved eventually express a high level of satisfaction with the results facilitating the final decision making process. It ensures that plans for the distribution of such a fair fund were carefully thought out and justified with a multi-faceted nature that covers political, socio-economic, technical, environmental, public health, and industrial aspects.