Showing papers in "Environmental Monitoring and Assessment in 2020"
TL;DR: This review shows that the development of modern cities and rapid urbanization are the major causes of heavy metal contamination in the environment.
Abstract: Urban road sediments act as large basins for heavy metal contaminants produced as a result of natural processes and anthropogenic activities. This study is aimed at reviewing research over recent decades on heavy metal contamination in different cities around the world. The study reviews literature from Google Scholar, Web of Science, and Scopus journal publications. Cr, Cu, Pb, Zn, Ni, and Cd levels vary from one city to another. Based on the collected results, the pollution level and geoaccumulation index are estimated in each city. The levels of pollution in these cities range from low to extremely high, depending on the sources of pollution at each site (geogenic and anthropogenic sources, etc.) and factors like the distribution of industrial activities, population, and traffic emissions. This review shows that the development of modern cities and rapid urbanization are the major causes of heavy metal contamination in the environment. The contamination of the urban environment has different sources, both natural and anthropogenic in character. Solving the problem of heavy metal contamination in the urban environment requires the use of different techniques such as urban road control treatment and soil remediation.
88 citations
TL;DR: The research suggested that governmental interference is essential for sustainable development of the country through strict accountability of resources and regulation implemented in the past for generating state-of-the-art climate policy.
Abstract: The devastations and damages caused by climate change are apparent across the globe, specifically in the South Asian region where vulnerabilities to climate change among residents are high and climate change adaptation and mitigation awareness are extremely low. Pakistan's low adaptive capacity due to high poverty rate, limited financial resources and shortage of physical resources, and continual extreme climatic events including varying temperature, continual flooding, melting glaciers, saturation of lakes, earthquakes, hurricanes, storms, avalanches, droughts, scarcity of water, pest diseases, human healthcare issues, and seasonal and lifestyle changes have persistently threatened the ecosystem, biodiversity, human communities, animal habitations, forests, lands, and oceans with a potential to cause further damages in the future. The likely effect of climate change on common residents of Pakistan with comparison to the world and their per capita impact of climate change are terribly high with local animal species such as lions, vultures, dolphins, and tortoise facing extinction regardless of generating and contributing diminutively to global GHG emissions. The findings of the review suggested that GHG emissions cause climate change which has impacted agriculture livestock and forestry, weather trends and patterns, food water and energy security, and society of Pakistan. This review is a sectorial evaluation of climate change mitigation and adaption approaches in Pakistan in the aforementioned sectors and its economic costs which were identified to be between 7 to 14 billion USD per annum. The research suggested that governmental interference is essential for sustainable development of the country through strict accountability of resources and regulation implemented in the past for generating state-of-the-art climate policy.
86 citations
TL;DR: The results showed that the concentrations of Ni and Co elements increased in many organelles depending on traffic density, and that the heavy metal concentrations in fruits could be very high, indicating that fruit and vegetables grown in industrial zones and urban centers, where heavy metal pollution may be high, can be harmful to the public health if consumed as crops.
Abstract: Food scarcity is one of global issues that our world faces today. A significant portion of the world’s population has no access to adequate food, and it is stated that approximately 830 million people suffer from chronic famine. This predicament is estimated to grow even further. Many attempts have been made to solve the food problem. Some examples are using new resources which have not been used for dietary purposes up to this point, planting new areas to produce food products, and increasing the potential harvest per an area unit. One of the solution proposals, which has come up recently within this scope, is the term of “edible landscaping”, which means the use of edible plants in the landscaping works, and thus maximizing the potential for food security. However, edible landscaping poses a considerable risk. Heavy metal accumulation in plants grown in urban centers can reach to high levels, and consuming these plants will allow these heavy metals a direct access into the human body and wreak havoc to the public health. But since this subject has not been sufficiently studied yet, the extent of such a risk is not accurately determined yet. This study aims to determine the changes of Ni, Co and Mn concentrations depending on traffic density in the leaves, branches, barks and fruits of cherry, plum, mulberry and apple trees growing in areas with dense traffic, low-density traffic and no-traffic zones in Kastamonu province. The results showed that the concentrations of Ni and Co elements increased in many organelles depending on traffic density, and that the heavy metal concentrations in fruits could be very high. This situation indicates that fruit and vegetables grown in industrial zones and urban centers, where heavy metal pollution may be high, can be harmful to the public health if consumed as crops.
76 citations
TL;DR: The groundnut husk and corncob based activated carbons were found to possess the maximum adsorption capacities for copper(II, zinc(II), and chromium(VI) removal, when compared with the other plant biomass-based activated carbONS.
Abstract: Metal ion contamination in wastewater is an issue of global concern. The conventional methods of heavy metal removal from wastewater have some drawbacks, ranging from generation of sludge to high cost of removal. Adsorption technique for copper(II), zinc(II), and chromium(VI) using activated carbon has been found efficient. However, it is not economical on a large scale. This, therefore, necessitates the search for economical and readily available plant biomass-based activated carbons for the sequestration of the metal ions. This review presents the state of the art on the adsorption of copper(II), zinc(II), and chromium(VI) from industrial wastewater. Based on the literature review presented, the groundnut husk and corncob based activated carbons were found to possess the maximum adsorption capacities for copper(II), zinc(II), and chromium(VI) removal, when compared with the other plant biomass-based activated carbons. The high values of the adsorption capacities obtained were as a result of the isotherms and pH of the adsorbent as well as the initial concentration of the metal solutions. From the review, the equilibrium data fitted better with Langmuir and Freundlich isotherms than with other isotherms. Research gaps were identified which include a need to investigate the kinetic and the thermodynamic behaviors of the metal ions onto the studied adsorbents. Furthermore, a comparative analysis of the three types of activation of the adsorbents should be investigated using single and multi-metals. The optimization of particle size, contact time, temperature, initial concentration, and adsorbent dosage for adsorption of copper(II), zinc(II), and chromium(VI) onto the studied adsorbents using response surface methodology is equally required.
76 citations
TL;DR: This study investigates a method based on deep neural networks (specifically, long short-term memory) compared with statistical and traditional machine-learning methods that achieves a fault detection rate (recall) of over 92%, thus outperforming traditional methods and enabling timely detection of collective faults.
Abstract: Wastewater treatment plants use many sensors to control energy consumption and discharge quality. These sensors produce a vast amount of data which can be efficiently monitored by automatic systems. Consequently, several different statistical and learning methods are proposed in the literature which can automatically detect faults. While these methods have shown promising results, the nonlinear dynamics and complex interactions of the variables in wastewater data necessitate more powerful methods with higher learning capacities. In response, this study focusses on modelling faults in the oxidation and nitrification process. Specifically, this study investigates a method based on deep neural networks (specifically, long short-term memory) compared with statistical and traditional machine-learning methods. The network is specifically designed to capture temporal behaviour of sensor data. The proposed method is evaluated on a real-life dataset containing over 5.1 million sensor data points. The method achieved a fault detection rate (recall) of over 92%, thus outperforming traditional methods and enabling timely detection of collective faults.
74 citations
TL;DR: The study aims to shed light on the multi-criteria method in an attempt to standardize it in regional planning studies and to inspire similar studies in which different criteria can be used to achieve the maximum efficiency.
Abstract: Population growth, which is the main source of the biggest problems of the world today, combined with migration from rural areas to urban centers, causes the urban centers to be even more concentrated. This necessitates the opening of new residential areas in many city centers, but new residential areas are mostly determined by the decisions of local authorities, who may not base their decisions on scientific data. With the wrong area selection, ordinary natural events can be potentially catastrophic. Such events can result in large numbers of casualties and material damage every year. In this study, an example of applying a method for location selection using various parameters has been realized. The study focuses on Bafra, Turkey (the study area). Risk maps were created in terms of floods and overflows; maps of regions and high-voltage power transmission lines that enjoy a protected area status; and maps of regions in terms of biocomfort suitability. As a result of the evaluation made according to these criteria, it is calculated that only 1.96% of the total working area is suitable for use as a residential area. In relevant literature studies, it was observed that the studies related to the selection of residential areas were carried out only depending on a single standard or criterion. Some suggested biocomfort, and others used vulnerability to risks such as landslide, flood, and earthquakes as their main principle. Studies based on multi-criteria were generally used for purposes such as solid waste site selection and determination of the road routes. The study aims to shed light on the multi-criteria method in an attempt to standardize it in regional planning studies and to inspire similar studies in which different criteria can be used to achieve the maximum efficiency.
66 citations
TL;DR: This investigation suggests higher health risk for children and also recommends that proper management plan should be adopted to improve the drinking water quality in this region in order to avoid major health issues in the near future.
Abstract: Groundwater quality investigations were carried out in one of the urban parts of south India for fluoride and nitrate contaminations, with special focus on human health risk assessment for the rapidly growing and increasingly industrialized Coimbatore City. Twenty-five groundwater samples were collected and analyzed for physico-chemical parameters (EC, pH, TDS, Ca2+, Mg2+, Na+, K+, Cl−, SO42−, HCO3−, PO43−, NO3−, and F−) and the piper diagram characterized 60% of them as Ca-Mg-Cl type. Analysis of fluoride (0.1 to 2.4 mg/l) shows that 32% of the groundwater samples contain F− over the permissible limit, affecting a region of 122.10 km2. Nitrate (0.1 to 148 mg/l) is over the permissible limit in 44% of the groundwater samples spread over an area of 429.43 km2. The total hazard indices (THI) of non-carcinogenic risk for children (0.21 to 4.83), women (0.14 to 3.35), and men (0.12 to 2.90) shows some of the THI values are above the permissible limit of the US Environmental Protection Agency. The THI-based non-carcinogenic risks are 60%, 52%, and 48% for children, women, and men. This investigation suggests higher health risk for children and also recommends that proper management plan should be adopted to improve the drinking water quality in this region in order to avoid major health issues in the near future.
62 citations
TL;DR: Continuous monitoring of antibiotics residues in wastewater, surface water, and other environmental matrices is very important due to their adverse health and environmental effects and is useful in designing strategies for antibiotics pollution control and also in policy formulation.
Abstract: The occurrence of 17 antibiotics belonging to sulfonamides, β-lactams, macrolides and aminoglycosides classes, and trimethoprim in raw hospital wastewater, wastewater treatment plant (WWTP), and surface water was determined. Residual antibiotics were quantified by LC/MS/MS. Residues of antibiotics in hospital wastewater were 3–10 times higher than that detected in WWTP and surface water. Trimethoprim, spectinomycin, ampicillin, and oxacillin were detected in all the sampled water. Sulfamethoxazole was detected at the highest concentration of 20.6, 7.8, and 6.8 μg L−1 in hospital wastewater, WWTP and in surface water, respectively. Other detected sulfonamides were sulfamethazine, sulfadiazine, and sulfanilamide at a concentration range of 0.4–15.7 μg L−1. Detected trimethoprim ranged from 0.4–6.6 μg L−1, the rest of the detected antibiotics were up to 1.0 μg L−1. The speciation of the sulfonamides at pH values relevant to sampled water was evaluated by use of pKa values. These compounds existed largely as anionic and neutral species indicating high mobility as these speciation forms are less sorbed in environmental matrices. Continuous monitoring of antibiotics residues in wastewater, surface water, and other environmental matrices is very important due to their adverse health and environmental effects. The information is useful in designing strategies for antibiotics pollution control and also in policy formulation.
61 citations
TL;DR: This study aimed to determine the annual changes of Pb, Co, and Fe elements’ concentrations in these sections by determining the annual rings on the logs taken from the main stem of the cedar tree (Cedrus sp.), which was cut by the end of 2016 in December, 2016, in Kastamonu province.
Abstract: It is important to monitor the heavy metal pollution in order to identify risk zones and to determine the change in the heavy metal concentration of the atmosphere within the process. For this, it is necessary to carry out measurements for many years; however, this is not possible. Especially from past to present, one of the most effective methods to determine the changes of heavy metal concentrations in the atmosphere is to use the annual tree rings as biomonitors. Perennial plants growing in our country create annual rings, and it is possible to gain information regarding the changes of heavy metal concentrations in that region by determining the heavy metal concentrations in these rings. In this study, it was aimed to determine the annual changes of Pb, Co, and Fe elements' concentrations in these sections by determining the annual rings on the logs taken from the main stem of the cedar tree (Cedrus sp.), which was cut by the end of 2016, in December, 2016, in Kastamonu province. Within the scope of the study, the element concentrations were also determined in the inner and outer bark. As a result of the study, it was found that the heavy metal values in the organelles taken from the road-facing part, especially the heavy metal concentrations in the outer bark were higher than the metal concentrations in the inward-facing part, and that the concentrations changed significantly on organelle and year basis.
60 citations
TL;DR: Different sources of sulphate, its distribution and available different remediation techniques for groundwater system reported so far have been discussed in the present paper.
Abstract: Most abundant form of sulphur in the geosphere has been sulphate. Sulphate, with sulphur in the plus six oxidation state is very stable. Sources of sulphate in groundwater include mineral dissolution, atmospheric deposition and other anthropogenic sources (mining, fertilizer, etc.). Gypsum is an important contributor to the high levels of sulphate in many aquifer of the world. Sulphate is not as much as toxic, but it can cause catharsis, dehydration and diarrhoea, and when ingested in higher amount through dietary absorption, the levels of methaemoglobin and sulphaemoglobin are changed in human and animal body. The role of sulphate in aqueous phase and sedimentary phase has been discussed. There is only limited work on sulphate pollution remediation in groundwater at national and international level; therefore, in the light of rising attention in sulphate as a contaminant, different sources of sulphate, its distribution and available different remediation techniques for groundwater system reported so far have been discussed in the present paper. Abiologic processes’ thermochemical sulphate reduction (TSR) also plays significant role in reduction of sulphate.
60 citations
TL;DR: An improved entropy method (IEM) is proposed for the evaluation of the modernization in urban areas, based on the five criteria and 21 indicators for the modernization level of a province or a city in modern China.
Abstract: This paper defines five criteria and identifies 21 indicators for the modernization level of a province or a city in modern China. It collects raw index data from 31 provinces in China, between 2007 and 2017, and introduces formula for its dimensionless processing. An improved entropy method (IEM) is proposed for the evaluation of the modernization in urban areas, based on the five criteria and 21 indicators. A practical study was implemented in Anhui province employing the said IEM. The results demonstrate that the IEM is being effective in evaluating the modernization level in urban areas.
TL;DR: The relationship between bioclimatic comfort and land use in Trabzon is evaluated by using geographical information systems and remote sensing technologies and Physiological equivalent temperature (PET) index, which takes into consideration the physiological characteristics of humans.
Abstract: The aim of this study is to investigate the relationship between bioclimatic comfort and land use in Trabzon by using geographical information systems and remote sensing technologies. We aimed to evaluate the relationship between the bioclimatic conditions of the years 1985, 1994, 2005, and 2018 with the use of land in the same years in the province of Trabzon in seasonal and annual periods. Physiological equivalent temperature (PET) index, which takes into consideration the physiological characteristics of humans, was used when determining bioclimatic comfort zones. The meteorological parameters used in the calculation of this index are as follows: daily average temperature values reduced to sea level, daily average relative humidity, and wind speed. The inverse distance weighting (IDW) method was preferred in the calculation of the spatial distribution of the obtained values at sea level. Using DEM data, height-dependent PET values were obtained and bioclimatic comfort maps were generated. According to the years of the bioclimatic comfort maps produced, land use maps were created by using CORINE land cover data. Then, the relationship between bioclimatic comfort zones and land use was examined.
TL;DR: Results demonstrated that at low concentrations of CuO NPs, beneficial effects are obtained from seedlings, enhancing plant growth, and the involvement of nitric oxide signaling in the phytotoxic effects induced by high concentration of this formulation.
Abstract: Copper oxide nanoparticles (CuO NPs) have been extensively explored for use in agriculture. Previous studies have indicated that application of CuO NPs might be promising for development and conservation of plants, pest control, and for the recovery of degraded soils. However, depending on the applied concentration copper can cause phytotoxic effects. In this work, biosynthesized CuO NPs (using green tea extract) were evaluated on their effects on lettuce (Lactuca sativa L.) seedling growth, which were exposed at concentrations ranged between 0.2 and 300 μg mL−1. From the biosynthesized were obtained ultra-small CuO NPs (~ 6.6 nm), with high stability in aqueous suspension. Toxicity bioassays have shown that at low concentrations (up to 40 μg mL−1), CuO NPs did not affect or even enhanced the seed germination. At higher concentrations (higher than 40 μg mL−1), inhibition of seed germination and radicle growth ranging from 35 to 75% was observed. With the increase of CuO NPs concentrations, nitrite and S-nitrosothiols levels in radicles increased, whereas superoxide dismutase and total antioxidant activities decreased. The nitrite and S-nitrosothiols levels in lettuce radicles showed a direct dose response to CuO NP application, which may indicate nitric oxide-dependent signaling pathways in the plant responses. Therefore, the results demonstrated that at low concentrations (≤ 20 μg mL−1) of CuO NPs, beneficial effects are obtained from seedlings, enhancing plant growth, and the involvement of nitric oxide signaling in the phytotoxic effects induced by high concentration of this formulation.
TL;DR: Several plant species like Brassica juncea, Pteris vittata and Helianthus annuus are proved to be the most potential candidate for the PTMs removal and the Hg removal from pre-combustion coal is very essential to reduce the possibility of Hg release to the atmosphere.
Abstract: The release of potentially toxic metal(loid)s (PTMs) such as As, Cd, Cr, Pb and Hg has become a serious threat to the environment. The anthropogenic contribution of these PTMs, especially Hg, is increasing continuously, and coal combustion in thermal power plants (TPPs) is considered to be the highest contributor of PTMs. Once entered into the environment, PTMs get deposited on the soil, which is the most important sink of these PTMs. This review centred on the sources of PTMs from coal and flyash and their enrichment in soil, chemical behaviour in soil and plant, bioaccumulation in trees and vegetables, health risk and remediation. Several remediation techniques (physical and chemical) have been used to minimise the PTMs level in soil and water, but the phytoremediation technique is the most commonly used technique for the effective removal of PTMs from contaminated soil and water. Several plant species like Brassica juncea, Pteris vittata and Helianthus annuus are proved to be the most potential candidate for the PTMs removal. Among all the PTMs, the occurrence of Hg in coal is a global concern due to the significant release of Hg into the atmosphere from coal-fired thermal power plants. Therefore, the Hg removal from pre-combustion (coal washing and demercuration techniques) coal is very essential to reduce the possibility of Hg release to the atmosphere.
TL;DR: The paper reviews robustness of Earth Observation data for continuous planning, monitoring, and evaluation of SDGs and the application of big data from earth observation and citizen science data to implement SDGs with a multi-disciplinary approach.
Abstract: It is more than 4 years since the 2030 agenda for sustainable development was adopted by the United Nations and its member states in September 2015. Several efforts are being made by member countries to contribute towards achieving the 17 Sustainable Development Goals (SDGs). The progress which had been made over time in achieving SDGs can be monitored by measuring a set of quantifiable indicators for each of the goals. It has been seen that geospatial information plays a significant role in measuring some of the targets, hence it is relevant in the implementation of SDGs and monitoring of their progress. Synoptic view and repetitive coverage of the Earth's features and phenomenon by different satellites is a powerful and propitious technological advancement. The paper reviews robustness of Earth Observation data for continuous planning, monitoring, and evaluation of SDGs. The scientific world has made commendable progress by providing geospatial data at various spatial, spectral, radiometric, and temporal resolutions enabling usage of the data for various applications. This paper also reviews the application of big data from earth observation and citizen science data to implement SDGs with a multi-disciplinary approach. It covers literature from various academic landscapes utilizing geospatial data for mapping, monitoring, and evaluating the earth's features and phenomena as it establishes the basis of its utilization for the achievement of the SDGs.
TL;DR: Overall, it was found that groundwater in most areas of Jiaodong Peninsula is suitable for domestic use and agricultural irrigation.
Abstract: Groundwater is the primary source of water for domestic use and agricultural irrigation in Jiaodong Peninsula. This study collected 80 groundwater samples from Jiaodong Peninsula to characterize groundwater hydrogeochemical processes and the suitability of groundwater for domestic use and agricultural irrigation. The groundwater of Jiaodong Peninsula was categorized as slightly alkaline freshwater, with a Piper diagram classifying most samples as SO4·Cl-Ca·Mg and HCO3-Ca·Mg types. Major ions were Ca2+, Na+, SO42−, and HCO3−. The major processes driving the hydrochemistry of groundwater were identified as water-rock interactions as well as evaporation. The dissolution of silicate and cation exchange were the predominant hydrogeochemical processes responsible for groundwater chemistry. Four water samples showed seawater intrusion and some indicated pollution from anthropogenic activities such as industry, agriculture, and domestic sewage discharge. Overall, it was found that groundwater in most areas of Jiaodong Peninsula is suitable for domestic use and agricultural irrigation.
TL;DR: Based on the spatio-temporal distribution and frequency of droughts in Nigeria, drought monitoring using remote sensing techniques of VCI and NDVI could play an invaluable role in food security and drought preparedness.
Abstract: The existing drought monitoring mechanisms in the sub-Saharan Africa region mostly depend on the conventional methods of drought monitoring. These methods have limitations based on timeliness, objectivity, reliability, and adequacy. This study aims to identify the spread and frequency of drought in Nigeria using Remote Sensing/Geographic Information Systems techniques to determine the areas that are at risk of drought events within the country. The study further develops a web-GIS application platform that provides drought early warning signals. Monthly NOAA-AVHRR Pathfinder NDVI images of 1 km by 1 km spatial resolution and MODIS with a spatial resolution of 500 m by 500 m were used in this study together with rainfall data from 25 synoptic stations covering 32 years. The spatio-temporal variation of drought showed that drought occurred at different times of the year in all parts of the country with the highest drought risk in the north-eastern parts. The map view showed that the high drought risk covered 5.98% (55,312 km2) of the country's landmass, while low drought risk covered 42.4% (391,881 km2) and very low drought risk areas 51.5% (476,578 km2). Results revealed that a strong relationship exists between annual rainfall and season-integrated NDVI (r2 = 0.6). Based on the spatio-temporal distribution and frequency of droughts in Nigeria, drought monitoring using remote sensing techniques of VCI and NDVI could play an invaluable role in food security and drought preparedness. The map view from the web-based drought monitoring system, developed in this study, is accessible through localhost.
TL;DR: The objective of this work was to characterize the spatial and temporal variations of the water quality and to identify the sources of data variability in order to assess the influence and the impact of different natural and anthropogenic processes.
Abstract: Lake Cajititlan is a shallow body of water located in an endorheic basin in western Mexico. This lake receives excess fertilizer runoff from agriculture and approximately 2.3 Hm3 per year of poorly treated wastewater from three municipal treatment plants. Thirteen water quality parameters were monitored at five sampling points within the lake over 9 years. The objective of this work was to characterize the spatial and temporal variations of the water quality and to identify the sources of data variability in order to assess the influence and the impact of different natural and anthropogenic processes. One-way ANOVA tests, principal component analysis (PCA), cluster analysis (CA), and discriminant analysis (DA) were implemented. The one-way ANOVA showed that biochemical oxygen demand and pH present statistically significant spatial variations and that alkalinity, total chloride, conductivity, chemical oxygen demand, total hardness, ammonia, pH, total dissolved solids, and temperature present statistically significant temporal variations. PCA results explained both natural and anthropogenic processes and their relationship with water quality data. The CA results suggested there is no significant spatial variation in the water quality of the lake because of lake mixing caused by wind. The most significant parameters for spatial variations were pH, NO3−, and NO2−, consistent with the configuration of point and nonpoint sources that affect the lake’s water quality. The temporal DA results suggested that conductivity, hardness, NO2−, pH, and temperature were the most significant parameters to discriminate between seasons. The temporal behavior of these parameters was associated with the transport pathways of seasonal contaminants.
TL;DR: Correlation analysis revealed that sensors tended to overestimate PM concentrations in high relative humidity (RH > 75%) and underestimate when RH was below 50%.
Abstract: Integration of low-cost air quality sensors with the internet of things (IoT) has become a feasible approach towards the development of smart cities. Several studies have assessed the performance of low-cost air quality sensors by comparing their measurements with reference instruments. We examined the performance of a low-cost IoT particulate matter (PM10 and PM2.5) sensor in the urban environment of Santiago, Chile. The prototype was assembled from a PM10–PM2.5 sensor (SDS011), a temperature and relative humidity sensor (BME280) and an IoT board (ESP8266/Node MCU). Field tests were conducted at three regulatory monitoring stations during the 2018 austral winter and spring seasons. The sensors at each site were operated in parallel with continuous reference air quality monitors (BAM 1020 and TEOM 1400) and a filter-based sampler (Partisol 2000i). Variability between sensor units (n = 7) and the correlation between the sensor and reference instruments were examined. Moderate inter-unit variability was observed between sensors for PM2.5 (normalized root-mean-square error 9–24%) and PM10 (10–37%). The correlations between the 1-h average concentrations reported by the sensors and continuous monitors were higher for PM2.5 (R2 0.47–0.86) than PM10 (0.24–0.56). The correlations (R2) between the 24-h PM2.5 averages from the sensors and reference instruments were 0.63–0.87 for continuous monitoring and 0.69–0.93 for filter-based samplers. Correlation analysis revealed that sensors tended to overestimate PM concentrations in high relative humidity (RH > 75%) and underestimate when RH was below 50%. Overall, the prototype evaluated exhibited adequate performance and may be potentially suitable for monitoring daily PM2.5 averages after correcting for RH.
TL;DR: The results of this study showed that ‘built-up area’ increased with 7.2% of all the classes during 1988 to 2017 in the Multan district, and the LCLUC indicate that rice and sugarcane had less volatility of change in comparison with both cotton and wheat.
Abstract: Water and land both are limited resources. Current management strategies are facing multiple challenges to meet food security of an increasing population in numerous South Asian countries, including Pakistan. The study of land cover/land use changes (LCLUC) and land surface temperature (LST) is important as both provide critical information for policymaking of natural resources. We spatially examined LCLU and LST changes in district Multan, Pakistan, and its impacts on vegetation cover and water during 1988 to 2017. The LCLUC indicate that rice and sugarcane had less volatility of change in comparison with both cotton and wheat. Producer's accuracy (PA) is the map accuracy (the producer of map), but user's accuracy (UA) is the accuracy from the point of view of a map user, not the map maker. Average overall producer's and user's accuracy for the region was 85.7% and 87.7% for Rabi (winter) and Kharif (summer) seasons, respectively. The results of this study showed that 'built-up area' increased with 7.2% of all the classes during 1988 to 2017 in the Multan district. Anthropogenic activities decreased the vegetation, leading to an increase in LST in study area. Changes on LCLU and LST during the last 30 years have shown that vegetation pattern has changed and temperature has increased in the Multan district.
TL;DR: The proposed hybrid SVR-WOA model was found to be more appropriate and efficient in comparison to Svr-MVO and SVR -ALO models for estimating monthly ETo in the study region.
Abstract: For effective planning of irrigation scheduling, water budgeting, crop simulation, and water resources management, the accurate estimation of reference evapotranspiration (ETo) is essential. In the current study, the hybrid support vector regression (SVR) coupled with Whale Optimization Algorithm (SVR-WOA) was employed to estimate the monthly ETo at Algiers and Tlemcen meteorological stations positioned in the north of Algeria under three different optimal input scenarios. Monthly climatic parameters, i.e., solar radiation (Rs), wind speed (Us), relative humidity (RH), and maximum and minimum air temperatures (Tmax and Tmin) of 14 years (2000–2013), were obtained from both stations. The accuracy of the hybrid SVR-WOA model was appraised against hybrid SVR-MVO (Multi-Verse Optimizer), and SVR-ALO (Ant Lion Optimizer) models through performance measures, i.e., mean absolute error (MAE), root-mean-square error (RMSE), index of scattering (IOS), index of agreement (IOA), Pearson correlation coefficient (PCC), Nash-Sutcliffe efficiency (NSE), and graphical interpretation (time-variation and scatter plots, radar chart, and Taylor diagram). The results showed that the SVR-WOA model performed superior to the SVR-MVO and SVR-ALO models at both stations in all scenarios. The SVR-WOA-1 model with five inputs (i.e., Tmin, Tmax, RH, Us, Rs: scenario-1) had the lowest value of MAE = 0.0658/0.0489 mm/month, RMSE = 0.0808/0.0617 mm/month, IOS = 0.0259/0.0165, and the highest value of NSE = 0.9949/0.9989, PCC = 0.9975/0.9995, and IOA = 0.9987/0.9997 for testing period at both stations, respectively. The proposed hybrid SVR-WOA model was found to be more appropriate and efficient in comparison to SVR-MVO and SVR-ALO models for estimating monthly ETo in the study region.
TL;DR: The work in this paper can provide a well-organized and summative knowledge reference for further study on the dynamic mechanism between the changes of water quality factors and the fish body characteristics and behavior and can also provide valuable reference for promoting the smart, ecological, and efficient development of aquaculture.
Abstract: Aquaculture is an important part of agricultural economy. In the past, major farming accidents often occurred due to subjective experience. There are many factors affecting the water quality of aquaculture. Maintaining an ecological environment with good water quality is the most critical link to ensure the production efficiency and quality of aquaculture. With the continuous development of science and technology, intelligence and informatization in aquaculture has become a new trend. Smart aquaculture cannot only realize real-time monitoring, prediction, warning, and risk control of the physical and chemical factors of the aquaculture environment but can also conduct real-time monitoring of the characteristics and behaviors of the fish, which infers the changes of the aquaculture ecological environment. In this paper, the research achievements over past two decades both are summarized from four aspects: water quality factor acquisition and pre-processing, water quality factor prediction, morphological characteristics, and behavioral characteristic recognition of fish and the mechanism between fish behavior and water quality factors. The advantages and disadvantages of existing research routes, algorithm models, and research methods in smart aquaculture are summarized. The work in this paper can provide a well-organized and summative knowledge reference for further study on the dynamic mechanism between the changes of water quality factors and the fish body characteristics and behavior. Meanwhile, the work can also provide valuable reference for promoting the smart, ecological, and efficient development of aquaculture.
TL;DR: The drinking water quality of Ikem rural agricultural area (southeastern Nigeria) was assessed using chemometrics and multiple indexical methods and revealed that all the hand-dug wells were in excellent condition, and hence safe for drinking purposes.
Abstract: The continuous deterioration of drinking water quality supplies by several anthropogenic activities is a serious global challenge in recent times. In this current study, the drinking water quality of Ikem rural agricultural area (southeastern Nigeria) was assessed using chemometrics and multiple indexical methods. Twenty-five groundwater samples were collected from hand-dug wells and analyzed for physicochemical parameters such as pH, major ions, and heavy metals. The pH of the samples (which ranged between 5.2 and 6.7) indicated that waters were slightly acidic. Cations and anions (except for phosphate) were within their respective standard limits. Except for Mn, heavy metals were also found to be below their maximum allowable limits. Factor analysis identified both geogenic processes and anthropogenic inputs as possible origins of the analyzed physicochemical parameters. Modified heavy metal index, geoaccumulation index, and overall index of pollution revealed that all the hand-dug wells were in excellent condition, and hence safe for drinking purposes. However, pollution load index, water quality index (WQI), and entropy-weighted water quality index (EWQI) revealed that some wells (about 8–12%) were slightly contaminated, and hence are placed in good water category. A hierarchical cluster analysis (HCA) was performed based on the integration of the WQI and EWQI results. The HCA revealed two major quality categories of the samples. While the first cluster comprises of samples classified as excellent drinking water by both WQI and EWQI models, the second cluster comprises of about 12% samples which were identified as good water by either the WQI or EWQI.
TL;DR: ANFIS model with Gaussian membership function and fuzzy set combination of [4 5 2 3] was found to be the best method, with accuracy of 90.24% and 87.06% for training and testing dataset, respectively.
Abstract: Presence of copper within water bodies deteriorates human health and degrades natural environment. This heavy metal in water is treated using a promising biochar derived from rambutan (Nephelium lappaceum) peel through slow pyrolysis. This research compares the efficacies of artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), and multiple linear regression (MLR) models and evaluates their capability in estimating the adsorption efficiency of biochar for the removal of Cu (II) ions based on 480 experimental sets obtained in a laboratory batch study. The effects of operational parameters such as contact time, operating temperature, biochar dosage, and initial Cu (II) ion concentration on removing Cu (II) ions were investigated. Eleven different training algorithms in ANN and 8 different membership functions in ANFIS were compared statistically and evaluated in terms of estimation errors, which are root mean squared error (RMSE), mean absolute error (MAE), and accuracy. The effects of number of hidden neuron in ANN model and fuzzy set combination in ANFIS were studied. In this study, ANFIS model with Gaussian membership function and fuzzy set combination of [4 5 2 3] was found to be the best method, with accuracy of 90.24% and 87.06% for training and testing dataset, respectively. Contribution of this study is that ANN, ANFIS, and MLR modeling techniques were used for the first time to study the adsorption of Cu (II) ions from aqueous solutions using rambutan peel biochar.
TL;DR: Three widely used machine learning models, namely, artificial neural networks (ANN), support vector machines (SVM), and extreme gradient boosting (XGB), were evaluated for their efficacy in predicting contamination levels using sparse data with non-linear relationships.
Abstract: Contamination from pesticides and nitrate in groundwater is a significant threat to water quality in general and agriculturally intensive regions in particular. Three widely used machine learning models, namely, artificial neural networks (ANN), support vector machines (SVM), and extreme gradient boosting (XGB), were evaluated for their efficacy in predicting contamination levels using sparse data with non-linear relationships. The predictive ability of the models was assessed using a dataset consisting of 303 wells across 12 Midwestern states in the USA. Multiple hydrogeologic, water quality, and land use features were chosen as the independent variables, and classes were based on measured concentration ranges of nitrate and pesticide. This study evaluates the classification performance of the models for two, three, and four class scenarios and compares them with the corresponding regression models. The study also examines the issue of class imbalance and tests the efficacy of three class imbalance mitigation techniques: oversampling, weighting, and oversampling and weighting, for all the scenarios. The models' performance is reported using multiple metrics, both insensitive to class imbalance (accuracy) and sensitive to class imbalance (F1 score and MCC). Finally, the study assesses the importance of features using game-theoretic Shapley values to rank features consistently and offer model interpretability.
TL;DR: The results indicated green space has a significant mitigating effect on air pollution and mortality of respiratory diseases and also air pollution has a meaningful increasing effect on mortality due to respiratory diseases in Tehran.
Abstract: Green space and its spatial formation are important elements of public welfare in urban environments and green ecosystems in big cities largely contribute to the mental and physical health of citizens. Tehran is Iran's biggest and most polluted city and air pollution in this city causes loss of human lives due to respiratory diseases. The effect of green area has been less studied in former researches in Tehran, and the reducing effects of green landscape on the mortality of respiratory diseases have not yet been evaluated. To measure the effects of green area landscape patterns (fragmentation, area-edge, shape, and aggregation) on public health, the current study evaluated the pathways and effects of green space on air pollution and the mortality of respiratory diseases using structural equation modeling approach and the partial least squares method. The results of the study indicated green space has a significant mitigating effect on air pollution and mortality of respiratory diseases and also air pollution has a meaningful increasing effect on mortality due to respiratory diseases in Tehran. The most important latent variable in green space is class area that indicates more area of green space is correlated with less mortality of respiratory diseases. The most important indicator of air pollution was the PM2.5 that needs to be considered and controlled by urban policymakers. Accordingly, maximizing the green area and its cohesion and minimizing fragmentation and green patch edge can contribute to a reduction in air pollution and consequently lower mortality of citizens.
TL;DR: The implementation of a smart water measurement consumption system under an architecture design, with high decoupling and integration of various technologies, which allows real-time visualizing the consumptions, and a leak detection algorithm that has 100% Accuracy, Recall, Precision, and F1 score to detect leaks is presented.
Abstract: Presently, in several parts of the world, water consumption is not measured or visualized in real time, in addition, water leaks are not detected in time and with high precision, generating unnecessary waste of water. That is why this article presents the implementation of a smart water measurement consumption system under an architecture design, with high decoupling and integration of various technologies, which allows real-time visualizing the consumptions, in addition, a leak detection algorithm is proposed based on rules, historical context, and user location that manages to cover 10 possible water consumption scenarios between normal and anomalous consumption. The system allows data to be collected by a smart meter, which is preprocessed by a local server (Gateway) and sent to the Cloud from time to time to be analyzed by the leak detection algorithm and, simultaneously, be viewed on a web interface. The results show that the algorithm has 100% Accuracy, Recall, Precision, and F1 score to detect leaks, far better than other procedures, and a margin of error of 4.63% recorded by the amount of water consumed.
TL;DR: Comparing available chlorophyll-a data from in situ and satellite imagery measures at the national scale and performing a cost analysis of these different monitoring approaches underscores the importance of continued support for both field-based in situ monitoring and satellite sensor programs that provide complementary information to water quality managers.
Abstract: Assessment of chlorophyll-a, an algal pigment, typically measured by field and laboratory in situ analyses, is used to estimate algal abundance and trophic status in lakes and reservoirs. In situ-based monitoring programs can be expensive, may not be spatially, and temporally comprehensive and results may not be available in the timeframe needed to make some management decisions, but can be more accurate, precise, and specific than remotely sensed measures. Satellite remotely sensed chlorophyll-a offers the potential for more geographically and temporally dense data collection to support estimates when used to augment or substitute for in situ measures. In this study, we compare available chlorophyll-a data from in situ and satellite imagery measures at the national scale and perform a cost analysis of these different monitoring approaches. The annual potential avoided costs associated with increasing the availability of remotely sensed chlorophyll-a values were estimated to range between $5.7 and $316 million depending upon the satellite program used and the timeframe considered. We also compared sociodemographic characteristics of the regions (both public and private lands) covered by both remote sensing and in situ data to check for any systematic differences across areas that have monitoring data. This analysis underscores the importance of continued support for both field-based in situ monitoring and satellite sensor programs that provide complementary information to water quality managers, given increased challenges associated with eutrophication, nuisance, and harmful algal bloom events.
TL;DR: The proposed ES-WQI describes the changes in water quality over the year well and correlates with the capability of the lake to support aquatic life, as the lowest estimated values coincide with the biggest events of massive fish mortality in the lake.
Abstract: A Water Quality Index (WQI) is a formulation that enables the estimation of the overall quality of a water body based on significant parameters. One example of this is the well-known and widely accepted NSF-WQI, which is frequently used to assess chemical, physical, and microbiologic features of waterbodies in temperate latitudes. In this work, a well-structured method, completely based on multivariate statistical methods and historical data distributions, was used to develop an ecosystem specific water quality index (ES-WQI). Lake Cajititlan, a subtropical Mexican lake located in Tlajomulco de Zuniga, was selected as a case of study because it is an endorheic shallow lake that shows signs of high levels of eutrophication due to anthropogenic contamination. As a result of the contamination, and its sensibility to changes in the water level, it undergoes important changes in its water features, such as turbidity and intense green color, and experiences massive events of fish mortality. The proposed ES-WQI describes the changes in water quality over the year well and correlates with the capability of the lake to support aquatic life, as the lowest estimated values coincide with the biggest events of massive fish mortality in the lake. Furthermore, the ES-WQI clearly differentiates between typical cyclic behaviors and actual deteriorating trends and is capable of tracking incremental changes all over the range of the possible concentration values of the water quality parameters.
TL;DR: A comprehensive overview of the basic PSO algorithm search strategy and PSO’s applications and performance analysis in water resources engineering optimization problems is presented.
Abstract: Particle swarm optimization (PSO) is a stochastic population-based optimization algorithm inspired by the interactions of individuals in a social world. This algorithm is widely applied in different fields of water resources problems. This paper presents a comprehensive overview of the basic PSO algorithm search strategy and PSO’s applications and performance analysis in water resources engineering optimization problems. Our literature review revealed 22 different varieties of the PSO algorithm. The characteristics of each PSO variety together with their applications in different fields of water resources engineering (e.g., reservoir operation, rainfall–runoff modeling, water quality modeling, and groundwater modeling) are highlighted. The performances of different PSO variants were compared with other evolutionary algorithms (EAs) and mathematical optimization methods. The review evaluates the capability and comparative performance of PSO variants over conventional EAs (e.g., simulated annealing, differential evolution, genetic algorithm, and shark algorithm) and mathematical methods (e.g., support vector machine and differential dynamic programming) in terms of proper convergence to optimal Pareto fronts, faster convergence rate, and diversity of computed solutions.